diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/AnyDesk for Mac A Free and Secure Remote Desktop Solution.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/AnyDesk for Mac A Free and Secure Remote Desktop Solution.md
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-<li><b>Step 4: Drag and drop the AnyDesk icon to the Applications folder.</b></li>
-<li><b>Step 5: Launch AnyDesk from the Applications folder or the Launchpad.</b></li>
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-<p>Using AnyDesk for free on Mac is simple and intuitive. Here are some of the basic functions you can perform with AnyDesk:</p>
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-<li><b>To allow access to your Mac from a remote computer or device:</b></li>
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-<ul>
-<li><b>Step 1: Quit AnyDesk if it is running on your Mac.</b></li>
-<li><b>Step 2: Open the Finder and go to the Applications folder.</b></li>
-<li><b>Step 3: Locate the AnyDesk icon and drag it to the Trash.</b></li>
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-<li><b>Step 4: Wait for the download and installation to complete and restart AnyDesk.</b></li>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Control Systems Engineering By Nagrath And Gopal 5th Edition Free Free Download.md b/spaces/1gistliPinn/ChatGPT4/Examples/Control Systems Engineering By Nagrath And Gopal 5th Edition Free Free Download.md
deleted file mode 100644
index f766947adacc9ffb136f6c68e949dba8fe7d3fc5..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Control Systems Engineering By Nagrath And Gopal 5th Edition Free Free Download.md	
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Counter Strike 1.6 Maps Free Download AA Dima - Why You Should Play on This Map.md b/spaces/1gistliPinn/ChatGPT4/Examples/Counter Strike 1.6 Maps Free Download AA Dima - Why You Should Play on This Map.md
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--- a/spaces/1gistliPinn/ChatGPT4/Examples/Counter Strike 1.6 Maps Free Download AA Dima - Why You Should Play on This Map.md	
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Default: 5 <br/>mp_roundtime 5<br/><br/>// mp_startmoney - Specify how much money players start off with<br/>mp_startmoney 800<br/><br/>//mp_friendlyfire Turn on/off friendlyfire. Default: Off<br/>mp_friendlyfire 0<br/><br/>//mp_footsteps Turn on/off footsteps. Default: On<br/>mp_footsteps 1<br/><br/>//mp_flashlight Turn on/off the ability for clients to use flashlight. Default: Off<br/>mp_flashlight 0<br/><br/>//mp_fraglimit Amount of frags a player can exceed before changing maps. Default: 0 <br/>mp_fraglimit 0<br/><br/>//mp_maxrounds Amount of round to play before server changes maps. Default: 0 <br/>mp_maxrounds 0<br/><br/>//mp_winlimit Max number of rounds one team can win before server changes maps. Default: 0 <br/>mp_winlimit 0<br/><br/>// mp_spawnprotectiontime Time in seconds to Kick players who team-kill after round restart. Default: 5 <br/>mp_spawnprotectiontime 5<br/><br/>// mp_autoteambalance Force clients to auto-join the opposite team if they are not balanced. Default: On<br/>mp_autoteambalance 1<br/><br/>//mp_limitteams Max # of players 1 team can have over another. Default: 2 <br/>mp_limitteams 2<br/><br/>//mp_autokick Kick idle/team-killing players. Default Off<br/>mp_autokick 0<br/><br/>//mp_tkpunish Punish TK'ers on next round? Default: On<br/>mp_tkpunish 1<br/><br/>//mp_hostagepenalty How many hostages a Terrorist can kill before being kicked, 0 to disable. Default: 5 <br/>mp_hostagepenalty 5<br/><br/>// disable autoaim<br/>sv_aim 0<br/><br/>// sv_cheats - Whether to allow game cheat commands to be used by clients. 0 = off | 1 = on<br/>sv_cheats 0<br/><br/>//VOICE-CHATTING<br/>//sv_voiceenable Allow clients to use mic. Default: 1 <br/>sv_voiceenable 1<br/><br/>//sv_alltalk Players can hear all other players, no team restrictions. Default: Off<br/>sv_alltalk 0<br/><br/>//sv_voicecodec Specifies which voice codec DLL to use in a game. Set to the name of the DLL without the extension.. Default:voice_speex <br/>sv_voicecodec voice_speex<br/><br/>//sv_voicequality the bps of the voice.<br/>//1-2400bps<br/>//2-6000bps-DEFAULT<br/>//3-8000bps<br/>//4-11200bps<br/>//5-1520bps<br/>sv_voicequality 2<br/><br/>//mp_chattime amount of time in seconds players can chat after the game is over. Lower value = faster map load change. Default: 10 <br/>mp_chattime 10<br/><br/>//RATES-SPEEDS<br/>//sv_gravity World Gravity Default: 800<br/>sv_gravity 800<br/><br/>//sv_maxvelocity Maximum speed any ballistically moving object is allowed to attain per axis. Default: 3500 <br/>sv_maxvelocity 3500<br/><br/>//sv_maxspeed Maximum speed a player can move. Default: 320 <br/>sv_maxspeed 320<br/><br/>//CLEINT CVARS<br/>//decalfrequency Amount of time in seconds a player can spray their decal. Default: 10 <br/>decalfrequency 10<br/><br/>//sv_consistency Force cleints to pass consistency check for critical files before joining server? Default: 0<br/>sv_consistency 0<br/><br/>//sv_timeout After this many seconds without a message from a client, the client is dropped. Default: 65 <br/>sv_timeout 65<br/><br/>//mp_playerid Controls what information player see in the status bar: 0 all names; 1 team names; 2 no names. Default: 0 <br/>mp_playerid 0<br/><br/>// sv_pausable - Whether to allow clients to pause the server. 0 = off | 1 = on<br/>sv_pausable 0<br/><br/>//sv_allowupload Allow clients to upload their custom decals to the server. Default: 1 <br/>sv_allowupload 1<br/><br/>//sv_allowdownload Allow clients to downnload files. Default: 1 <br/>sv_allowdownload 1<br/><br/>//sv_unlag Enables player lag compensation. Default: 1 <br/>sv_unlag 1<br/><br/>//SPECTATING<br/>//mp_allowspectators Allow spectators on the server. Default: 1 <br/>mp_allowspectators 1<br/><br/>//mp_forcecamera Force dead players to first person mode, effectively disabling freelook. Default: Off<br/>mp_forcecamera 0<br/><br/>//sv_hltv Enables HLTV on the server. Default: 0 <br/>sv_hltv 0<br/><br/>//BANDWIDTH RATES<br/>//sv_minrate Min bandwidth rate allowed on server. Default: 0 (unlimited) <br/>sv_minrate 0<br/><br/>// sv_maxrate - The maximum bandwidth rate the server is allowed to transmit to clients<br/>sv_maxrate 10000<br/><br/>//sv_maxupdaterate Maximum updates per second that the server will allow. Default: 60 <br/>sv_maxupdaterate 60<br/><br/>//sv_minupdaterate Minimum updates per second that the server will allow. Default: 10 <br/>sv_minupdaterate 10<br/><br/>//sys_ticrate Max FPS (1000 Max) the server is to render<br/>sys_ticrate 200<br/><br/>//SERVER LOGGING<br/>// log Enable server logging? Default: Off <br/>log off<br/><br/>//sv_logbans Log server bans in the server logs. Default: 0 <br/>sv_logbans 0<br/><br/>// sv_logecho Echo log information to the console. Default: 1 <br/>sv_logecho 1<br/><br/>// sv_logfile Log server information in the log file. Default: 1 <br/>sv_logfile 1<br/><br/>//sv_log_onefile Log server information to only one file. Default: 0 <br/>sv_log_onefile 0<br/><br/>//sv_logsdir Folder in the game directory where server logs will be stored.<br/><br/>//RECON<br/>//rcon_password Set rcon passsword. Leave blank to disable rcon <br/>rcon_password ""<br/><br/>//sv_rcon_banpenalty Number of minutes to ban users who fail rcon authentication. Default: 0 <br/>sv_rcon_banpenalty 0<br/><br/>//sv_rcon_maxfailures Max number of times a user can fail rcon authentication before being banned. Default: 10 <br/>sv_rcon_maxfailures 10<br/><br/>//sv_rcon_minfailures Number of times a user can fail rcon authentication in sv_rcon_minfailuretime before being banned. Default: 5 <br/>sv_rcon_minfailures 5<br/><br/>//sv_rcon_minfailuretime Number of seconds to track failed rcon authentications. Default: 30 <br/>sv_rcon_minfailuretime 30<br/><br/>// lists of banned players.<br/>// load ban files<br/>exec listip.cfg<br/>exec banned.cfg<br/><br/><strong> END OF CODE:</strong> Don't copy this line.<br/><br/>Now save and look though all the CVARs. They are all explained and most of them you will not need to change, but you can. It is recommended that you change the servers name and most of the subsection labeled <strong>ROUND</strong>. Make sure to change the location to match up with you servers location! It is under <strong>GENERAL</strong>.<br/></p>
-<p>Now the part you have been waiting for. Starting the server.<br/><br/>1. Go to C:\Program Files\Valve\HLServer<br/>2. Find Program called HLDS<br/>3. RIGHT click on it and create a short cut. Drag the shot cut to the desktop.<br/>4. RIGHT click on the short cut and go to properties.<br/>5. In the target field add the following<br/> "C:\Program Files\Valve\HLServer\hlds.exe" -console -game cstrike -ip 192.168.254.253 -port 27015 +maxplayers 12 +map cs_assault<br/>This will start the server in the Command Prompt from earlier, conserves system resources. The game will be counter strike with the IP address of 192.168.254.253. You need to change this to match your IP address from ipconfig /all. There will be an maximum of 12 players and the starting map will be cs_assault. <br/><br/><strong>Note about Max players:</strong><br/>Steam recommends the following:<br/># 128k uplink = 4 players<br/># 256k uplink = 7 players<br/># 320k uplink = 9 players<br/># 512k uplink = 14 players<br/># 768k uplink = 21 players<br/># 1024k uplink = 28 player<br/># 1140k uplink = 32 players<br/><br/>A general rule of thumb is 35.5 kbs per player. What does this mean? This is you Internet speed. Preferably you upload speed. To find you speed go to DSL Reports and test with a test server nearest you. Use the results to determine your max players.<br/><br/>Now you can click apply and then OK. To start the server just double click the icon and it will start automatically. Success! <br/><br/>To join your server simply start up the computer you will be playing on. Start Steam and find 'Servers' click on favorites tab and add the IP address of you server to 'add a server' Don't forget to add :27015 after you IP address. Mine looks like this. 192.168.254.253:27015 Now just connect to your server.<br/><br/>To get others to join you will need to complete a few more steps.<br/><br/>Extra: You may want to have your server start automatically when Windows Starts. Doing this is easy! Go to Start -> All Programs -> Startup and copy the desktop short cut here. You will also want to do the same with Windows Media Player. By having media player running in the background you can boost FPS significantly and you don't even have to be playing a song! What have you got to lose.<br/></p>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/DMIFIT Tool And HPBQ138EXE BEST.md b/spaces/1gistliPinn/ChatGPT4/Examples/DMIFIT Tool And HPBQ138EXE BEST.md
deleted file mode 100644
index f1560f49511261a6e71530f29deb9b2e91184660..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/DMIFIT Tool And HPBQ138EXE BEST.md	
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-<p>If you want to play GTA Vice City on your PC in 2023, you have two options: you can either buy the game from the official Rockstar Games website or you can use an emulator like BlueStacks to play the mobile version of GTA Vice City on your PC. Here are the steps for each option:</p>
-<h3>Option 1: Buy the game from Rockstar Games website</h3>
-<p>This is the easiest and most reliable way to download GTA Vice City on your PC. All you need is a stable internet connection and a valid credit card or PayPal account. Here are the steps:</p>
-<h4>Step 1: Visit the Rockstar Games website and create an account</h4>
-<p>Go to <a href="">https://www.rockstargames.com/</a> and click on the Sign In button at the top right corner of the screen. If you already have an account, enter your email and password and click on Sign In. If you don't have an account, click on Create a New Account and follow the instructions to create one.</p>
-<h4>Step 2: Go to the Downloads section and find GTA Vice City</h4>
-<p>Once you are signed in, click on the Downloads button at the top of the screen. You will see a list of games that are available for download from Rockstar Games. Scroll down until you find GTA Vice City and click on it.</p>
-<h4>Step 3: Click on Buy Now and complete the payment process</h4>
-<p>You will see a page with the details of GTA Vice City, such as the price, the system requirements, and the screenshots. Click on the Buy Now button and choose your preferred payment method. You can pay with a credit card or PayPal. Follow the instructions to complete the payment process.</p>
-<h4>Step 4: Download and install the game on your PC</h4>
-<p>After you have completed the payment process, you will receive an email with a link to download GTA Vice City on your PC. Click on the link and follow the instructions to download and install the game on your PC. You will need about 1 GB of free space on your hard drive to install GTA Vice City.</p>
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-<h3>Option 2: Use an emulator like BlueStacks to play the mobile version of GTA Vice City on your PC</h3>
-<p>This is another way to download GTA Vice City on your PC, but it requires some extra steps and software. You will need to use an emulator like BlueStacks, which is a program that allows you to run Android apps on your PC. You will also need to buy the mobile version of GTA Vice City from the Google Play Store, which costs $4.99. Here are the steps:</p>
-<h4>Step 1: Download and install BlueStacks on your PC</h4>
-<p>Go to <a href="">https://www.bluestacks.com/</a> and click on the Download BlueStacks button. Follow the instructions to download and install BlueStacks on your PC. You will need about 5 GB of free space on your hard drive to install BlueStacks.</p>
-<h4>Step 2: Launch BlueStacks and sign in with your Google account</h4>
-<p>After you have installed BlueStacks, launch it from your desktop or start menu. You will see a window with various options and features. Click on the Google Play Store icon at the bottom right corner of the window. You will be asked to sign in with your Google account. If you already have one, enter your email and password and click on Sign In. If you don't have one, click on Create a New Account and follow the instructions to create one.</p>
-<h4>Step 3: Search for GTA Vice City in the Google Play Store and install it</h4>
-<p>Once you are signed in, you will see a page with various apps and games that are available for download from the Google Play Store. Type GTA Vice City in the search bar and press Enter. You will see a page with the details of GTA Vice City, such as the price, the rating, the reviews, and the screenshots. Click on the Install button and follow the instructions to buy and install GTA Vice City on your PC.</p>
-<h4>Step 4: Enjoy playing GTA Vice City on your PC with BlueStacks features</h4>
-<p>After you have installed GTA Vice City, you can launch it from the BlueStacks home screen. You will see a window with the game running on your PC. You can use your mouse and keyboard to control the game, or you can customize the controls according to your preference. You can also use the BlueStacks features to enhance your gaming experience, such as recording your gameplay, taking screenshots, streaming your gameplay, and more.</p>
-<h2>Conclusion</h2>
-<p>GTA Vice City is a classic game that deserves to be played by every gamer who loves open-world action-adventure games. It has a captivating story, a stunning soundtrack, and a fun and diverse gameplay that will keep you entertained for hours. In this article, we have shown you how to download GTA Vice City on your PC using two different methods: buying the game from the Rockstar Games website or using an emulator like BlueStacks to play the mobile version of GTA Vice City on your PC. Both methods are easy and reliable, and you can choose the one that suits you best. We hope you have enjoyed this article and found it helpful. Now, go ahead and download GTA Vice City on your PC and have fun!</p>
-<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!</p>
- <h3>FAQs</h3>
-<ul>
-<li><b>Q: How much does GTA Vice City cost on PC?</b></li>
-<li>A: GTA Vice City costs $9.99 on the Rockstar Games website and $4.99 on the Google Play Store.</li>
-<li><b>Q: What are the system requirements for GTA Vice City on PC?</b></li>
-<li>A: The minimum system requirements for GTA Vice City on PC are: Windows XP/Vista/7/8/10, 800 MHz Intel Pentium III or AMD Athlon processor, 128 MB of RAM, 32 MB video card with DirectX 9.0 compatible drivers, 915 MB of free hard disk space, and DirectX 9.0 compatible sound card.</li>
-<li><b>Q: Can I play GTA Vice City online with other players?</b></li>
-<li>A: GTA Vice City does not have an official online multiplayer mode, but there are some unofficial mods that allow you to play GTA Vice City online with other players. One of them is <a href="">https://www.mtasa.com/</a>, which is a free mod for GTA San Andreas that also supports GTA Vice City.</li>
-<li><b>Q: Can I play GTA Vice City on other devices besides PC?</b></li>
-<li>A: Yes, you can play GTA Vice City on other devices besides PC. GTA Vice City is also available for PlayStation 2, PlayStation 3, PlayStation 4, Xbox, Xbox 360, Xbox One, iOS, Android, Mac OS X, and Fire OS.</li>
-<li><b>Q: What are some of the best cheats for GTA Vice City?</b></li>
-<li>A: There are many cheats for GTA Vice City that can make the game more fun and easy. Some of them are: THUGSTOOLS (all weapons), ASPIRINE (full health), PRECIOUSPROTECTION (full armor), LEAVEMEALONE (lower wanted level), PANZER (spawn a tank), GETTHEREFAST (spawn a Sabre Turbo), GETTHEREVERYFASTINDEED (spawn a Hotring Racer), GETTHEREAMAZINGLYFAST (spawn a Hotring Racer 2), FANNYMAGNET (women follow you), BIGBANG (blow up all cars), SEAWAYS (cars can drive on water), COMEFLYWITHME (cars can fly), ICANTTAKEITANYMORE (commit suicide), and LIFEISPASSINGMEBY (speed up time).</li>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Garena Bed Wars APK for Android - Team up and destroy your enemies beds in this PVP game.md b/spaces/1phancelerku/anime-remove-background/Download Garena Bed Wars APK for Android - Team up and destroy your enemies beds in this PVP game.md
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-<p>Are you looking for a fun and exciting game that lets you unleash your creativity and imagination? Do you want to play with your friends or other players from around the world in a team-based PVP game? If you answered yes, then you should try out <strong>Garena Bed Wars</strong>, the ultimate sandbox game that has attracted millions of players in <strong>Garena Blockman GO</strong>.</p>
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-<p>Garena Bed Wars is a game where you have to protect your bed at your own base while using all the tools you have at your disposal to destroy the opponents' beds and become the final victor. You can collect resources, buy equipment, build bridges, attack enemies, and cooperate with your teammates in this thrilling and addictive game. You can also customize your character, choose from different maps and modes, and enjoy countless minigames from different genres.</p>
-<p>If you want to play Garena Bed Wars on your Android device, you will need to download and install the <strong>Garena Bed Wars APK</strong> file, which is a modified version of the original game that allows you to access all the features and benefits without any restrictions. In this article, we will show you how to download and install Garena Bed Wars APK on your Android device, how to play Garena Bed Wars as a beginner, and how to win Garena Bed Wars with tips and tricks from pro players. Let's get started!</p>
- <h2>How to Play Garena Bed Wars: A Beginner's Guide</h2>
-<p>If you are new to Garena Bed Wars, you might feel overwhelmed by the rules and mechanics of the game. Don't worry, we are here to help you learn the basics of how to play Garena Bed Wars in a few simple steps.</p>
-<h3>How to join a game and choose a team</h3>
-<p>When you launch Garena Bed Wars, you will see a list of available games that you can join. You can also create your own game by tapping on the "+" icon at the top right corner of the screen. You can choose from different maps, such as Sky Island, Desert Island, Snow Island, etc., and different modes, such as Solo, Duo, Squad, etc. You can also set a password for your game if you want to play with your friends only.</p>
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-<p>Once you join or create a game, you will be taken to the lobby where you can choose your team. There are four teams in each game: Red, Blue, Green, and Yellow. You can tap on the team icon at the bottom of the screen to join or switch teams. You can also chat with other players in the lobby or invite your friends by tapping on the "Invite" button at the top left corner of the screen.</p>
-<h3>How to collect resources and buy equipment</h3>
-<p>When the game starts, you will spawn on your island with your teammates. Each island has its own base with a bed. As long as the bed is not destroyed, you and your teammates can be revived if you die. Therefore, protecting your bed is the most important task in the game.</p>
-<p>To protect your bed, you will need to collect resources and buy equipment. There are three types of resources in the game: iron, gold, and diamonds. Iron and gold are generated by the generators on your island. You can use them to buy items from the shop, such as blocks, weapons, armor, tools, etc. Diamonds are generated by the generators on the middle island. You can use them to buy upgrades from the team shop, such as sharpness, protection, haste, etc.</p>
-<p>To collect resources, you will need to go to the generators and pick up the items that are dropped on the ground. You can also get resources by killing enemies or breaking their beds. To buy equipment, you will need to go to the shop or the team shop and tap on the item you want to buy. You can also use the quick buy menu at the bottom of the screen to buy items faster.</p>
-<h3>How to build bridges and attack enemies</h3>
-<p>To reach other islands and attack enemies, you will need to build bridges. You can use blocks that you buy from the shop to place them on the ground and create a path. You can also use ender pearls or launch pads to teleport or jump to other islands.</p>
-<p>To attack enemies, you will need to use weapons that you buy from the shop, such as swords, bows, axes, etc. You can also use items that have special effects, such as fireballs, TNT, snowballs, etc. Your goal is to break their beds and kill them before they respawn. You can also use traps that you buy from the team shop, such as alarm trap, counter-offensive trap, etc., to defend your island from invaders.</p>
-<h3>How to protect your bed and survive</h3>
-<p>To protect your bed, you will need to cover it with blocks that you buy from the shop. You can use different types of blocks, such as wool, wood, stone, etc., to make it harder for enemies to break your bed. You can also use items that have special effects, such as water buckets, obsidian blocks, iron golems, etc., to enhance your defense.</p>
-<p>To survive, you will need to avoid falling into the void or getting killed by enemies. You can use armor that you buy from the shop, such as leather armor, chainmail armor, iron armor, etc., to reduce the damage you take. You can also use items that have special effects, such as golden apples, potions, invisibility cloaks, etc., to heal yourself or gain an advantage.</p>
- <h2>How to Win Garena Bed Wars: Tips and Tricks from Pro Players</h2>
-<p>If you want to win Garena Bed Wars more often and become a pro player, you will need to learn some tips and tricks that can help you improve your skills and performance in the game. Here are some of them:</p>
-<h3>How to use the best strategies and tactics for different maps and modes</h3>
-<p>Each map and mode in Garena Bed Wars has its own characteristics and challenges that require different strategies and tactics. For example:</p>
-<ul>
-<li>In Sky Island map, you can use ender pearls or launch pads to move around quickly and surprise your enemies.</li>
-<li>In Desert Island map, you can use fireballs or TNT to destroy the sand bridges and cut off your enemies' access.</li>
-<li>In Snow Island map, you can use snowballs or ice bridges to knock off your enemies or create shortcuts.</li>
-<li>In Solo mode, you can focus on collecting diamonds and upgrading your equipment as soon as possible.</li>
-<li>In Duo mode, you can coordinate with your partner and split up tasks such as collecting resources, buying items, building bridges, etc.</li>
-<li>In Squad mode, you can communicate with your teammates and assign roles such as defender, attacker, supporter, etc.</li>
-</ul>
-<h3>How to cooperate with your teammates and communicate effectively</h3>
-<p>Garena Bed Wars is a team-based game that requires cooperation and communication among teammates. You can use the chat function or the voice chat function to communicate with your teammates and share information, such as enemy locations, resource status, attack plans, etc. You can also use the team signals or the emoticons to express your emotions or intentions, such as happy, angry, sad, etc.</p>
-<p>When cooperating with your teammates, you should follow some basic etiquette and rules, such as:</p>
-<ul>
-<li>Respect your teammates and do not insult or troll them.</li>
-<li>Listen to your teammates and do not ignore or contradict them.</li>
-<li>Help your teammates and do not abandon or betray them.</li>
-<li>Share your resources and do not hog or waste them.</li>
-<li>Follow your team's strategy and do not act on your own or sabotage it.</li>
-</ul>
-<h3>How to avoid common mistakes and deal with hackers</h3>
-<p>Garena Bed Wars is a game that requires skill and strategy, but also luck and chance. Sometimes, you might make some common mistakes that can cost you the game, such as:</p>
-<ul>
-<li>Leaving your bed unprotected or poorly defended.</li>
-<li>Rushing to attack without proper equipment or backup.</li>
-<li>Being too greedy or reckless and exposing yourself to danger.</li>
-<li>Being too passive or timid and missing opportunities to strike.</li>
-<li>Being too predictable or repetitive and allowing your enemies to counter you.</li>
-</ul>
-<p>To avoid these mistakes, you should always be aware of your surroundings and your enemies' actions. You should also learn from your mistakes and improve your skills and strategies. You can also watch some videos or streams of pro players and learn from their tips and tricks.</p>
-<p>Sometimes, you might encounter some hackers who use cheats or hacks to gain an unfair advantage in the game, such as flying, speed hacking, aimbotting, etc. These hackers can ruin the fun and balance of the game and make you lose unfairly. To deal with hackers, you should report them to the developers by tapping on the "Report" button at the end of the game. You can also block them by tapping on their name and choosing "Block". You can also avoid playing with hackers by joining games that have anti-cheat systems or moderators.</p>
- <h2>Conclusion</h2>
-<p>Garena Bed Wars is a game that offers endless fun and excitement for players who love sandbox games and PVP games. You can play with your friends or other players from around the world in a team-based game where you have to protect your bed and destroy the opponents' beds. You can also enjoy various features and benefits by downloading and installing Garena Bed Wars APK on your Android device.</p>
-<p>If you want to play Garena Bed Wars on your Android device, you can follow these steps:</p>
-<ol>
-<li>Download Garena Bed Wars APK from a trusted source by clicking on this link: [Garena Bed Wars APK Download].</li>
-<li>Allow unknown sources on your device by going to Settings > Security > Unknown Sources.</li>
-<li>Install Garena Bed Wars APK by tapping on the file and following the instructions.</li>
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-<p>We hope this article has helped you learn how to play Garena Bed Wars on your Android device. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<h3>What are the minimum requirements to play Garena Bed Wars on Android?</h3>
-<p>The minimum requirements to play Garena Bed Wars on Android are:</p>
-<ul>
-<li>Android 4.4 or higher</li>
-<li>2 GB of RAM or higher</li>
-<li>500 MB of free storage space or higher</li>
-<li>A stable internet connection</li>
-</ul>
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-<p>Garena Bed Wars is safe and legal to download and play as long as you download it from a trusted source, such as [Garena Blockman GO] or [Garena Bed Wars APK Download]. However, you should be careful of downloading Garena Bed Wars from unknown sources, as they might contain viruses or malware that can harm your device or steal your personal information. You should also avoid using cheats or hacks that can get you banned from the game or cause other problems.</p>
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-<p>You can update Garena Bed Wars to the latest version by following these steps:</p>
-<ol>
-<li>Go to [Garena Blockman GO] or [Garena Bed Wars APK Download] and check if there is a new version available.</li>
-<li>If there is a new version, download it and install it on your device.</li>
-<li>Launch Garena Bed Wars and enjoy the new features and improvements.</li>
-</ol>
-<p>You can also enable the auto-update function on your device by going to Settings > Apps > Garena Bed Wars > Auto-update. This way, you will always have the latest version of Garena Bed Wars on your device.</p>
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-<p>If you have any issues or suggestions regarding Garena Bed Wars, you can contact the developers of Garena Bed Wars by using one of these methods:</p>
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-<li>Email: You can send an email to [support@blockmango.net] and describe your problem or idea in detail. You should also attach some screenshots or videos if possible.</li>
-<li>Facebook: You can visit the official Facebook page of Garena Blockman GO at [https://www.facebook.com/Blockmango-608882679545354/] and leave a message or comment.</li>
-<li>Discord: You can join the official Discord server of Garena Blockman GO at [https://discord.gg/8fJ9Z7F] and chat with other players or moderators.</li>
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-<p>The developers of Garena Bed Wars are always happy to hear from their players and will try their best to solve your problems or implement your suggestions.</p>
- <h3>How can I play Garena Bed Wars with my friends?</h3>
-<p>If you want to play Garena Bed Wars with your friends, you can follow these steps:</p>
-<ol>
-<li>Add your friends as contacts by tapping on the "Contacts" button at the bottom of the screen and entering their usernames or IDs.</li>
-<li>Create a game by tapping on the "+" icon at the top right corner of the screen and choosing a map and a mode.</li>
-<li>Set a password for your game by tapping on the "Password" button at the top left corner of the screen and entering a code.</li>
-<li>Invite your friends by tapping on the "Invite" button at the top left corner of the screen and selecting your contacts.</li>
-<li>Wait for your friends to join your game and choose a team.</li>
-<li>Start the game by tapping on the "Start" button at the bottom of the screen and have fun!</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Warpath Jurassic Park for Android and Unleash Your Inner Dinosaur.md b/spaces/1phancelerku/anime-remove-background/Download Warpath Jurassic Park for Android and Unleash Your Inner Dinosaur.md
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index 967bfc1f085c9ea30c72c42778fb14729443d1c8..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Warpath Jurassic Park for Android and Unleash Your Inner Dinosaur.md	
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- <p>If you are a fan of dinosaurs and fighting games, you might be interested in Jurassic Park Warpath, a classic PlayStation game that lets you control various prehistoric creatures and battle against each other in different locations from the Jurassic Park movies. But did you know that you can also play this game on your Android device? In this article, we will show you how to download and play Jurassic Park Warpath on Android, as well as some tips and tricks to help you enjoy this game even more.</p>
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- <p>Jurassic Park Warpath is a fighting video game released on the PlayStation console in 1999. It is a spin-off of the films Jurassic Park and The Lost World: Jurassic Park, and features 14 playable dinosaurs, each with their own moves, animations, and sounds. The game has four modes: arcade, versus, survival, and exhibition. In arcade mode, you choose a dinosaur and fight through eight stages against different opponents. In versus mode, you can play against another player or the computer. In survival mode, you have to defeat as many enemies as possible without dying. In exhibition mode, you can watch two dinosaurs fight without any input from you.</p>
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- <p>Playing Jurassic Park Warpath on Android has several advantages over playing it on the original PlayStation. First of all, you don't need to buy or own a PlayStation console or a physical copy of the game. You can simply download an emulator and the game file from the internet for free. Second, you can play the game anytime and anywhere, as long as you have your Android device with you. You don't need to plug in any wires or cables, or worry about battery life. Third, you can customize the game settings to your liking, such as changing the graphics quality, the sound volume, the controller layout, and more. You can also save and load your progress at any point in the game.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Dragon Ball Z Budokai Tenkaichi 3 PC Version How to Get and Install It.md b/spaces/1phancelerku/anime-remove-background/Dragon Ball Z Budokai Tenkaichi 3 PC Version How to Get and Install It.md
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--- a/spaces/1phancelerku/anime-remove-background/Dragon Ball Z Budokai Tenkaichi 3 PC Version How to Get and Install It.md	
+++ /dev/null
@@ -1,173 +0,0 @@
-
-<h1>How to Download Dragon Ball Z Budokai Tenkaichi 3 on PC</h1>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is one of the most popular and beloved games in the Dragon Ball franchise. It features over 150 characters, 30 stages, and a variety of game modes that will keep you entertained for hours. But what if you want to play it on your PC instead of your PlayStation 2 or Wii console? Well, you're in luck, because in this article, we will show you how to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC using a free emulator called Dolphin. We will also give you some tips and tricks to enhance your gaming experience. So, without further ado, let's get started!</p>
-<h2>how to download dragon ball z budokai tenkaichi 3 on pc</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://jinyurl.com/2uNOXj">https://jinyurl.com/2uNOXj</a></b></p><br /><br />
- <h2>Introduction</h2>
-<h3>What is Dragon Ball Z Budokai Tenkaichi 3?</h3>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is a fighting game based on the anime and manga series Dragon Ball. It was developed by Spike and published by Atari for the PlayStation 2 and Wii in 2007. It is the third and final installment in the Budokai Tenkaichi series, which is also known as Dragon Ball Z: Sparking! in Japan.</p>
-<p>The game follows the events of the Dragon Ball story from the Saiyan Saga to the Kid Buu Saga, as well as some original scenarios and what-if scenarios. You can choose from over 150 playable characters, each with their own unique moves, transformations, and abilities. You can also customize your characters with various items and skills that affect their stats and performance.</p>
-<p>The game offers several modes of play, such as Story Mode, where you can relive the epic battles of the series; Dragon History, where you can explore different timelines and scenarios; Ultimate Battle, where you can test your skills against various opponents; World Tournament, where you can compete for prizes and glory; Duel, where you can fight against a friend or the CPU; Training, where you can practice your moves and combos; Evolution Z, where you can edit your character's attributes; Data Center, where you can view your records and achievements; Replay, where you can watch your saved replays; and Options, where you can adjust the game settings.</p>
- <h3>Why play it on PC?</h3>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is a great game that deserves to be played by every fan of the series. However, not everyone has access to a PlayStation 2 or Wii console, or maybe they just prefer to play games on their PC. That's why playing it on PC using an emulator is a good option for many reasons:</p>
-<ul>
-<li>You can enjoy the game in high-definition graphics and sound quality, thanks to the emulator's enhancements and features.</li>
-<li>You can use any controller or keyboard that suits your preference and comfort.</li>
-<li>You can save your progress anytime and anywhere, thanks to the emulator's save states and memory cards.</li>
-<li>You can unlock all the characters and stages without having to complete the game or use cheat codes.</li>
-<li>You can use cheats and mods to modify the game according to your liking.</li>
-<li>You can play online with other players using netplay or LAN.</li>
-<li>You can avoid any issues or errors that may <h2>Requirements and Preparations</h2>
-<h3>System Requirements</h3>
-<p>Before you download and play Dragon Ball Z Budokai Tenkaichi 3 on PC, you need to make sure that your PC meets the minimum system requirements for running the emulator and the game. Here are the recommended system requirements for Dolphin Emulator:</p>
-<table>
-<tr>
-<th>Component</th>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>Operating System</td>
-<td>Windows 7 (x64) or above, macOS 10.10 or above, Linux</td>
-<td>Windows 10 (x64), macOS 10.13 or above, Linux</td>
-</tr>
-<tr>
-<td>Processor (CPU)</td>
-<td>A CPU with SSE2 support. A modern CPU with a high single-thread performance rating.</td>
-<td>An Intel Core i5-4670K or AMD Ryzen 5 3600 or better.</td>
-</tr>
-<tr>
-<td>Memory (RAM)</td>
-<td>2 GB or more</td>
-<td>4 GB or more</td>
-</tr>
-<tr>
-<td>Graphics Card (GPU)</td>
-<td>A GPU that supports DirectX 11.1 or OpenGL 4.4.</td>
-<td>A GPU that supports Vulkan, DirectX 12, or Metal.</td>
-</tr>
-<tr>
-<td>Storage Space</td>
-<td>At least 5 GB of free space for the emulator and the game files.</td>
-<td>At least 10 GB of free space for the emulator and the game files.</td>
-</tr>
-<tr>
-<td>Controller or Keyboard</td>
-<td>A compatible controller or keyboard that can be mapped to the emulator.</td>
-<td>A PlayStation 2 or Wii controller with an adapter, or a controller that mimics their layout.</td>
-</tr>
-</table>
- <h3>Emulator and Game Files</h3>
-<p>The emulator that we will use to play Dragon Ball Z Budokai Tenkaichi 3 on PC is Dolphin Emulator, which is a free and open-source software that can run games for the Nintendo GameCube and Wii consoles. You can download the latest version of Dolphin Emulator from its official website or from its GitHub page. You can choose between the stable version, which is more stable and tested, or the development version, which is more updated and has more features, but may have some bugs and issues.</p>
- <p>The game file that we will use to play Dragon Ball Z Budokai Tenkaichi 3 on PC is an ISO file, which is a disc image of the original game disc. You can either rip your own game disc using a DVD drive and a software like ImgBurn, or you can download an ISO file from a reputable source online. However, downloading an ISO file may be illegal in some countries, so we do not condone or encourage piracy. Please only download an ISO file if you own a legal copy of the game.</p>
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- <h3>Controller and Keyboard Settings</h3>
- <p>To play Dragon Ball Z Budokai Tenkaichi 3 on PC, you will need a controller or a keyboard that can be configured to the emulator. You can use any controller or keyboard that is compatible with your PC, but we recommend using a PlayStation 2 or Wii controller with an adapter, or a controller that mimics their layout, such as a Logitech F310 or an Xbox One controller. This is because the game was designed for these controllers, and using them will give you a more authentic and comfortable experience.</p>
- <p>To configure your controller or keyboard to the emulator, you will need to go to the Controllers menu in Dolphin Emulator and select the appropriate device for each port. For example, if you want to use a PlayStation 2 controller for Port 1, you will need to select Emulated Wii Remote for Port 1, and then click Configure. Then, you will need to map each button and axis of your controller to the corresponding input of the Wii Remote. You can also adjust the sensitivity and deadzone of each input if needed. You can also save your configuration as a profile for future use.</p>
- <p>If you want to use a keyboard for Port 1, you will need to select Emulated Wii Remote for Port 1, and then click Configure. Then, you will need to map each key of your keyboard to the corresponding input of the Wii Remote. You can also adjust the sensitivity and deadzone of each input if needed. You can also save your configuration as a profile for future use.</p>
- <p>You can also configure other ports if you want to play with multiple players or use other devices. For example, if you want to use a GameCube controller for Port 2, you will need to select Standard Controller for Port 2, and then click Configure. Then, you will need to map each button and axis of your controller to the corresponding input of the GameCube controller. You can also adjust the sensitivity and deadzone of each input if needed. You can also save your configuration as a profile for future use.</p>
- <h2>Steps to Download and Play Dragon Ball Z Budokai Tenkaichi 3 on PC</h2>
-<h3>Step 1: Download and Install Dolphin Emulator</h3>
-<p>The first step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to download and install Dolphin Emulator on your PC. You can download the latest version of Dolphin Emulator from its official website or from its GitHub page. You can choose between the stable version or the development version, depending on your preference. Once you have downloaded the emulator, you will need to extract it to a folder of your choice using a software like WinRAR or 7-Zip. Then, you will need to run the Dolphin.exe file to launch the emulator.</p>
- <h3>Step 2: Download and Extract Dragon Ball Z Budokai Tenkaichi 3 ISO File</h3>
-<p>The second step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to download and extract the Dragon Ball Z Budokai Tenkaichi 3 ISO file on your PC. You can either rip your own game disc using a DVD drive and a software like ImgBurn, or you can download an ISO file from a reputable source online. However, downloading an ISO file may be illegal in some countries, so we do not condone or encourage piracy. Please only download an ISO file if you own a legal copy of the game.</p>
- <p>Once you have downloaded the ISO file, you will need to extract it to a folder of your choice using a software like WinRAR or 7-Zip. Then, you will need to move the ISO file to a folder where you can easily access it from the emulator.</p>
- <h3>Step 3: Configure Dolphin Emulator Settings</h3>
-<p>The third step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to configure Dolphin Emulator settings to optimize the game performance and quality. You can access the settings menu by clicking on the Options tab in Dolphin Emulator. Here are some of the settings that you can adjust:</p>
-<ul>
-<li>General: Here, you can change the language, theme, interface, and hotkeys of the emulator.</li>
-<li>Graphics: Here, you can change the video backend, resolution, aspect ratio, fullscreen mode, anti-aliasing, anisotropic filtering, enhancements, hacks, and advanced options of the emulator.</li>
-<li>Audio: Here, you can change the audio backend, volume, latency, stretching, and DSP options of the emulator.</li>
-<li>Controllers: Here, you can configure your controller or keyboard settings for each port.</li>
-<li>Paths: Here, you can add or remove folders where the emulator will search for game files.</li>
-<li>Config: Here, you can change the general, interface, audio, gamecube, wii, advanced, and debug options of the emulator.</li>
-</ul>
-<p>You can experiment with different settings to find the best ones for your PC and game. However, here are some recommended settings that work well for most users:</p>
-<ul>
-<li>Graphics: Video Backend - Vulkan (if supported by your GPU), Resolution - Native (640x528) or higher (up to 4x), Aspect Ratio - Auto or Force 16:9 (if you want widescreen), Fullscreen Mode - On (if you want fullscreen), Anti-Aliasing - None or MSAA 2x (if your GPU can handle it), Anisotropic Filtering - 1x or higher (up to 16x), Enhancements - Scaled EFB Copy (On), Force Texture Filtering (On), Disable Fog (Off), Per-Pixel Lighting (Off), Widescreen Hack (On if you want widescreen), Hacks - Skip EFB Access from CPU (Off), Ignore Format Changes (On), Store EFB Copies to Texture Only (On), Texture Cache Accuracy (Fast), External Frame Buffer (Disable), Fast Depth Calculation (On), Disable Bounding Box (On).</li>
-<li>Audio: Audio Backend - Cubeb or XAudio2 (depending on your OS), Volume - 100% or lower (depending on your preference), Latency - Low or Medium (depending on your CPU), Stretching - Off or Low (depending on your preference), DSP Options - DSP HLE Emulation (Fast) or DSP LLE Recompiler (Accurate).</li>
-<li>Controllers: Configure your controller or keyboard settings according to your preference and comfort.</li>
-<li>Paths: Add the folder where you have the Dragon Ball Z Budokai Tenkaichi 3 ISO file.</li>
-<li>Config: General - Enable Dual Core (On), Enable Idle Skipping (On), JIT Recompiler (Recommended), Interface - Use Panic Handlers (Off), Audio - DSP Emulator Engine (DSP HLE Emulation or DSP LLE Recompiler), GameCube - Device Settings (None for all ports), Wii - Aspect Ratio (16:9 if you want widescreen), Advanced - CPU Clock Override (100% or higher if your CPU can handle it), Debug - Enable CPU Clock Override (On if you want to use CPU Clock Override).</li>
-</ul>
- <h3>Step 4: Load the Game and Enjoy</h3>
-<p>The final step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to load the game and enjoy it. You can load the game by clicking on the Open button in Dolphin Emulator and browsing to the folder where you have the ISO file. Alternatively, you can drag and drop the ISO file to the Dolphin Emulator window. The game will start automatically and you will see the title screen. You can use your controller or keyboard to navigate the menus and select your game mode. You can also access the emulator's menu by pressing Esc or F1 on your keyboard. From there, you can save or load your progress, change your settings, take screenshots, record videos, and more.</p>
- <h2>Tips and Tricks for Playing Dragon Ball Z Budokai Tenkaichi 3 on PC</h2>
-<h3>How to Unlock All Characters and Stages</h3>
-<p>One of the best features of Dragon Ball Z Budokai Tenkaichi 3 is the huge roster of characters and stages that you can choose from. However, not all of them are available from the start. You will need to unlock them by completing certain tasks or using cheat codes. Here are some ways to unlock all characters and stages:</p>
-<ul>
-<li>Complete Story Mode: By completing Story Mode, you will unlock most of the characters and stages in the game. You will also unlock Dragon History, where you can play different scenarios and what-if stories.</li>
-<li>Complete Ultimate Battle: By completing Ultimate Battle, you will unlock some of the hidden characters and stages in the game. You will also unlock Sim Dragon, where you can create your own team of fighters and compete against other teams.</li>
-<li>Use Cheat Codes: By using cheat codes, you can unlock all characters and stages in the game instantly. However, this may affect your game experience and achievements. To use cheat codes, you will need to enable cheats in Dolphin Emulator's settings, and then download a cheat file for Dragon Ball Z Budokai Tenkaichi 3 from a reliable source online. Then, you will need to place the cheat file in the Cheats folder of Dolphin Emulator, and then activate the cheats in the game properties.</li>
-</ul>
- <h3>How to Use Cheats and Mods</h3>
-<p>Besides unlocking all characters and stages, you can also use cheats and mods to modify the game according to your liking. You can use cheats to change your character's stats, abilities, costumes, transformations, and more. You can also use mods to add new characters, stages, music, sound effects, graphics, and more. Here are some ways to use cheats and mods:</p>
-<ul>
-<li>Use Cheat Codes: As mentioned above, you can use cheat codes to modify the game by enabling cheats in Dolphin Emulator's settings, downloading a cheat file for Dragon Ball Z Budokai Tenkaichi 3 from a reliable source online, placing it in the Cheats folder of Dolphin Emulator, and activating it in the game properties.</li>
-<li>Use Mods: To use mods, you will need to download a mod file for Dragon Ball Z Budokai Tenkaichi 3 from a reputable source online, such as YouTube or Reddit. Then, you will need to extract it to a folder of your choice using a software like WinRAR or 7-Zip. Then, you will need to replace or add the mod files to the ISO file of Dragon Ball Z Budokai Tenkaichi 3 using a software like Wii Backup Manager or WiiScrubber. Then, you will need to load the modified ISO file in Dolphin Emulator and enjoy the mod.</li>
-</ul>
- <h3>How to Fix Common Issues and Errors</h3>
-<p>While playing Dragon Ball Z Budokai Tenkaichi 3 on PC using Dolphin Emulator, you may encounter some issues or errors that may affect your game performance or quality. Here are some common issues and errors that you may face and how to fix them:</p>
-<ul>
-<li>Black Screen or Freezing: This may happen if your PC does not meet the system requirements for running the emulator or the game, or if your emulator or game settings are not optimal. To fix this, you can try the following solutions:   - Lower your resolution, anti-aliasing, anisotropic filtering, and enhancements settings in the emulator's graphics menu.   - Disable any cheats or mods that may be causing conflicts or errors in the game.   - Update your video drivers and DirectX or Vulkan libraries to the latest versions.   - Run the emulator and the game as administrator and in compatibility mode for Windows 7 or 8.   - Check your ISO file for any corruption or damage using a software like WinMD5 or HashMyFiles.</li>
-<li>Slowdown or Lag: This may happen if your PC is not powerful enough to run the emulator or the game at full speed, or if your emulator or game settings are too high. To fix this, you can try the following solutions:   - Enable Dual Core and Idle Skipping in the emulator's general settings menu.   - Enable JIT Recompiler and Disable Bounding Box in the emulator's advanced settings menu.   - Enable CPU Clock Override and set it to a higher percentage in the emulator's debug settings menu.   - Lower your resolution, anti-aliasing, anisotropic filtering, and enhancements settings in the emulator's graphics menu.   - Disable any cheats or mods that may be slowing down the game.</li>
-<li>Audio Issues: This may happen if your audio settings are not compatible with the game, or if your PC's sound card or speakers are not working properly. To fix this, you can try the following solutions:   - Change your audio backend to Cubeb or XAudio2 in the emulator's audio settings menu.   - Lower your latency and enable stretching in the emulator's audio settings menu.   - Use DSP HLE Emulation or DSP LLE Recompiler in the emulator's audio settings menu.   - Update your sound drivers and codecs to the latest versions.   - Check your sound card and speakers for any defects or malfunctions.</li>
-<li>Controller Issues: This may happen if your controller is not configured correctly to the emulator, or if your controller is not working properly. To fix this, you can try the following solutions:   - Configure your controller settings for each port in the emulator's controllers menu.   - Adjust the sensitivity and deadzone of each input in the emulator's controllers menu.   - Save your controller configuration as a profile in the emulator's controllers menu.   - Update your controller drivers and firmware to the latest versions.   - Check your controller for any defects or malfunctions.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is a fantastic game that every fan of Dragon Ball should play. However, if you don't have a PlayStation 2 or Wii console, you can still enjoy it on your PC using Dolphin Emulator. In this article, we have shown you how to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC using Dolphin Emulator. We have also given you some tips and tricks to unlock all characters and stages, use cheats and mods, and fix common issues and errors. We hope that this article has been helpful and informative for you. Now, go ahead and have fun playing Dragon Ball Z Budokai Tenkaichi 3 on PC!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about playing Dragon Ball Z Budokai Tenkaichi 3 on PC:</p>
-<ul>
-<li>Q: Is Dolphin Emulator safe and legal to use?   - A: Yes, Dolphin Emulator is safe and legal to use, as long as you download it from its official website or GitHub page, and as long as you don't use it for piracy or illegal activities.</li>
-<li>Q: Is Dragon Ball Z Budokai Tenkaichi 3 compatible with Dolphin Emulator?   - A: Yes, Dragon Ball Z Budokai Tenkaichi 3 is compatible with Dolphin Emulator, both for the PlayStation 2 and Wii versions. However, some minor glitches or bugs may occur depending on your PC and game settings.</li>
-<li>Q: How can I play Dragon Ball Z Budokai Tenkaichi 3 online with other players?   - A: You can play Dragon Ball Z Budokai Tenkaichi 3 online with other players using netplay or LAN in Dolphin Emulator. To use netplay, you will need to join or host a netplay session with other players who have the same version of Dolphin Emulator and Dragon Ball Z Budokai Tenkaichi 3 as you. To use LAN, you will need to connect your PC with other PCs that have Dolphin Emulator and Dragon Ball Z Budokai Tenkaichi 3 installed on them.</li>
-<li>Q: How can I improve my game performance and quality?   - A: You can improve your game performance and quality by adjusting your emulator and game settings according to your PC's specifications and preferences. You can also update your PC's drivers and libraries to the latest versions. You can also use cheats and mods to enhance your game features and graphics.</li>
-<li>Q: Where can I find more information and support for playing Dragon Ball Z Budokai Tenkaichi 3 on PC?   - A: You can find more information and support for playing Dragon Ball Z Budokai Tenkaichi 3 on PC by visiting the official website and forums of Dolphin Emulator, or by searching online for guides, tutorials, videos, and reviews from other users and experts.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/utils/dummy_paddle_objects.py b/spaces/1toTree/lora_test/ppdiffusers/utils/dummy_paddle_objects.py
deleted file mode 100644
index 5325b5f20e36a54401898e637c92ca9588b77565..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/utils/dummy_paddle_objects.py
+++ /dev/null
@@ -1,617 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from . import DummyObject, requires_backends
-
-
-class ModelMixin(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class AutoencoderKL(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PriorTransformer(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class Transformer2DModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UNet1DModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UNet2DConditionModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UNet2DModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class VQModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-def get_constant_schedule(*args, **kwargs):
-    requires_backends(get_constant_schedule, ["paddle"])
-
-
-def get_constant_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_constant_schedule_with_warmup, ["paddle"])
-
-
-def get_cosine_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_cosine_schedule_with_warmup, ["paddle"])
-
-
-def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["paddle"])
-
-
-def get_linear_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_linear_schedule_with_warmup, ["paddle"])
-
-
-def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_polynomial_decay_schedule_with_warmup, ["paddle"])
-
-
-def get_scheduler(*args, **kwargs):
-    requires_backends(get_scheduler, ["paddle"])
-
-
-class DiffusionPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DanceDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDIMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDPMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KarrasVePipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class LDMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class LDMSuperResolutionPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KDPM2DiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PNDMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class RePaintPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class ScoreSdeVePipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class ScoreSdeVpPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDIMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDPMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DPMSolverMultistepScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DPMSolverSinglestepScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class EulerAncestralDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PreconfigEulerAncestralDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class EulerDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class HeunDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class IPNDMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KarrasVeScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PNDMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class RePaintScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class SchedulerMixin(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class ScoreSdeVeScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UnCLIPScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class VQDiffusionScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class EMAModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r50.py b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r50.py
deleted file mode 100644
index 37e7922f1f63284e356dcc45a5f979f9c105f25e..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r50.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/losses.py b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/losses.py
deleted file mode 100644
index 87aeaa107af4d53f5a6132b3739d5cafdcded7fc..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/losses.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch
-from torch import nn
-
-
-def get_loss(name):
-    if name == "cosface":
-        return CosFace()
-    elif name == "arcface":
-        return ArcFace()
-    else:
-        raise ValueError()
-
-
-class CosFace(nn.Module):
-    def __init__(self, s=64.0, m=0.40):
-        super(CosFace, self).__init__()
-        self.s = s
-        self.m = m
-
-    def forward(self, cosine, label):
-        index = torch.where(label != -1)[0]
-        m_hot = torch.zeros(index.size()[0], cosine.size()[1], device=cosine.device)
-        m_hot.scatter_(1, label[index, None], self.m)
-        cosine[index] -= m_hot
-        ret = cosine * self.s
-        return ret
-
-
-class ArcFace(nn.Module):
-    def __init__(self, s=64.0, m=0.5):
-        super(ArcFace, self).__init__()
-        self.s = s
-        self.m = m
-
-    def forward(self, cosine: torch.Tensor, label):
-        index = torch.where(label != -1)[0]
-        m_hot = torch.zeros(index.size()[0], cosine.size()[1], device=cosine.device)
-        m_hot.scatter_(1, label[index, None], self.m)
-        cosine.acos_()
-        cosine[index] += m_hot
-        cosine.cos_().mul_(self.s)
-        return cosine
diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/train.py b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/train.py
deleted file mode 100644
index 55eca2d0ad9463415970e09bccab8b722e496704..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/train.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import argparse
-import logging
-import os
-
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-import torch.utils.data.distributed
-from torch.nn.utils import clip_grad_norm_
-
-import losses
-from backbones import get_model
-from dataset import MXFaceDataset, SyntheticDataset, DataLoaderX
-from partial_fc import PartialFC
-from utils.utils_amp import MaxClipGradScaler
-from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint
-from utils.utils_config import get_config
-from utils.utils_logging import AverageMeter, init_logging
-
-
-def main(args):
-    cfg = get_config(args.config)
-    try:
-        world_size = int(os.environ['WORLD_SIZE'])
-        rank = int(os.environ['RANK'])
-        dist.init_process_group('nccl')
-    except KeyError:
-        world_size = 1
-        rank = 0
-        dist.init_process_group(backend='nccl', init_method="tcp://127.0.0.1:12584", rank=rank, world_size=world_size)
-
-    local_rank = args.local_rank
-    torch.cuda.set_device(local_rank)
-    os.makedirs(cfg.output, exist_ok=True)
-    init_logging(rank, cfg.output)
-
-    if cfg.rec == "synthetic":
-        train_set = SyntheticDataset(local_rank=local_rank)
-    else:
-        train_set = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank)
-
-    train_sampler = torch.utils.data.distributed.DistributedSampler(train_set, shuffle=True)
-    train_loader = DataLoaderX(
-        local_rank=local_rank, dataset=train_set, batch_size=cfg.batch_size,
-        sampler=train_sampler, num_workers=2, pin_memory=True, drop_last=True)
-    backbone = get_model(cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).to(local_rank)
-
-    if cfg.resume:
-        try:
-            backbone_pth = os.path.join(cfg.output, "backbone.pth")
-            backbone.load_state_dict(torch.load(backbone_pth, map_location=torch.device(local_rank)))
-            if rank == 0:
-                logging.info("backbone resume successfully!")
-        except (FileNotFoundError, KeyError, IndexError, RuntimeError):
-            if rank == 0:
-                logging.info("resume fail, backbone init successfully!")
-
-    backbone = torch.nn.parallel.DistributedDataParallel(
-        module=backbone, broadcast_buffers=False, device_ids=[local_rank])
-    backbone.train()
-    margin_softmax = losses.get_loss(cfg.loss)
-    module_partial_fc = PartialFC(
-        rank=rank, local_rank=local_rank, world_size=world_size, resume=cfg.resume,
-        batch_size=cfg.batch_size, margin_softmax=margin_softmax, num_classes=cfg.num_classes,
-        sample_rate=cfg.sample_rate, embedding_size=cfg.embedding_size, prefix=cfg.output)
-
-    opt_backbone = torch.optim.SGD(
-        params=[{'params': backbone.parameters()}],
-        lr=cfg.lr / 512 * cfg.batch_size * world_size,
-        momentum=0.9, weight_decay=cfg.weight_decay)
-    opt_pfc = torch.optim.SGD(
-        params=[{'params': module_partial_fc.parameters()}],
-        lr=cfg.lr / 512 * cfg.batch_size * world_size,
-        momentum=0.9, weight_decay=cfg.weight_decay)
-
-    num_image = len(train_set)
-    total_batch_size = cfg.batch_size * world_size
-    cfg.warmup_step = num_image // total_batch_size * cfg.warmup_epoch
-    cfg.total_step = num_image // total_batch_size * cfg.num_epoch
-
-    def lr_step_func(current_step):
-        cfg.decay_step = [x * num_image // total_batch_size for x in cfg.decay_epoch]
-        if current_step < cfg.warmup_step:
-            return current_step / cfg.warmup_step
-        else:
-            return 0.1 ** len([m for m in cfg.decay_step if m <= current_step])
-
-    scheduler_backbone = torch.optim.lr_scheduler.LambdaLR(
-        optimizer=opt_backbone, lr_lambda=lr_step_func)
-    scheduler_pfc = torch.optim.lr_scheduler.LambdaLR(
-        optimizer=opt_pfc, lr_lambda=lr_step_func)
-
-    for key, value in cfg.items():
-        num_space = 25 - len(key)
-        logging.info(": " + key + " " * num_space + str(value))
-
-    val_target = cfg.val_targets
-    callback_verification = CallBackVerification(2000, rank, val_target, cfg.rec)
-    callback_logging = CallBackLogging(50, rank, cfg.total_step, cfg.batch_size, world_size, None)
-    callback_checkpoint = CallBackModelCheckpoint(rank, cfg.output)
-
-    loss = AverageMeter()
-    start_epoch = 0
-    global_step = 0
-    grad_amp = MaxClipGradScaler(cfg.batch_size, 128 * cfg.batch_size, growth_interval=100) if cfg.fp16 else None
-    for epoch in range(start_epoch, cfg.num_epoch):
-        train_sampler.set_epoch(epoch)
-        for step, (img, label) in enumerate(train_loader):
-            global_step += 1
-            features = F.normalize(backbone(img))
-            x_grad, loss_v = module_partial_fc.forward_backward(label, features, opt_pfc)
-            if cfg.fp16:
-                features.backward(grad_amp.scale(x_grad))
-                grad_amp.unscale_(opt_backbone)
-                clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
-                grad_amp.step(opt_backbone)
-                grad_amp.update()
-            else:
-                features.backward(x_grad)
-                clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
-                opt_backbone.step()
-
-            opt_pfc.step()
-            module_partial_fc.update()
-            opt_backbone.zero_grad()
-            opt_pfc.zero_grad()
-            loss.update(loss_v, 1)
-            callback_logging(global_step, loss, epoch, cfg.fp16, scheduler_backbone.get_last_lr()[0], grad_amp)
-            callback_verification(global_step, backbone)
-            scheduler_backbone.step()
-            scheduler_pfc.step()
-        callback_checkpoint(global_step, backbone, module_partial_fc)
-    dist.destroy_process_group()
-
-
-if __name__ == "__main__":
-    torch.backends.cudnn.benchmark = True
-    parser = argparse.ArgumentParser(description='PyTorch ArcFace Training')
-    parser.add_argument('config', type=str, help='py config file')
-    parser.add_argument('--local_rank', type=int, default=0, help='local_rank')
-    main(parser.parse_args())
diff --git a/spaces/A00001/bingothoo/src/components/ui/dropdown-menu.tsx b/spaces/A00001/bingothoo/src/components/ui/dropdown-menu.tsx
deleted file mode 100644
index 184d4e6007ef85187446362f69532ab077897fea..0000000000000000000000000000000000000000
--- a/spaces/A00001/bingothoo/src/components/ui/dropdown-menu.tsx
+++ /dev/null
@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DropdownMenuPrimitive from '@radix-ui/react-dropdown-menu'
-
-import { cn } from '@/lib/utils'
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubContent>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubContent>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubContent
-    ref={ref}
-    className={cn(
-      'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuSubContent.displayName =
-  DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <DropdownMenuPrimitive.Portal>
-    <DropdownMenuPrimitive.Content
-      ref={ref}
-      sideOffset={sideOffset}
-      className={cn(
-        'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow animate-in data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2',
-        className
-      )}
-      {...props}
-    />
-  </DropdownMenuPrimitive.Portal>
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Item> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Item
-    ref={ref}
-    className={cn(
-      'relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuLabel = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Label> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Label
-    ref={ref}
-    className={cn(
-      'px-2 py-1.5 text-sm font-semibold',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.Separator
-    ref={ref}
-    className={cn('-mx-1 my-1 h-px bg-muted', className)}
-    {...props}
-  />
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn('ml-auto text-xs tracking-widest opacity-60', className)}
-      {...props}
-    />
-  )
-}
-DropdownMenuShortcut.displayName = 'DropdownMenuShortcut'
-
-export {
-  DropdownMenu,
-  DropdownMenuTrigger,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuLabel,
-  DropdownMenuSeparator,
-  DropdownMenuShortcut,
-  DropdownMenuGroup,
-  DropdownMenuPortal,
-  DropdownMenuSub,
-  DropdownMenuSubContent,
-  DropdownMenuRadioGroup
-}
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/model.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/model.py
deleted file mode 100644
index 58d52b9825d085aca220972e30ca3fabc8257c34..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/model.py
+++ /dev/null
@@ -1,913 +0,0 @@
-""" CLAP Model
-
-Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-Adapted to the Audio Task.
-"""
-
-from collections import OrderedDict
-from dataclasses import dataclass
-from email.mime import audio
-from typing import Tuple, Union, Callable, Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from .timm_model import TimmModel
-import logging
-from .utils import freeze_batch_norm_2d
-
-from .pann_model import create_pann_model
-from .htsat import create_htsat_model
-from transformers import BertModel, RobertaModel, BartModel
-from transformers.tokenization_utils_base import BatchEncoding
-
-
-class MLPLayers(nn.Module):
-    def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1):
-        super(MLPLayers, self).__init__()
-        self.nonlin = nonlin
-        self.dropout = dropout
-
-        sequence = []
-        for u0, u1 in zip(units[:-1], units[1:]):
-            sequence.append(nn.Linear(u0, u1))
-            sequence.append(self.nonlin)
-            sequence.append(nn.Dropout(self.dropout))
-        sequence = sequence[:-2]
-
-        self.sequential = nn.Sequential(*sequence)
-
-    def forward(self, X):
-        X = self.sequential(X)
-        return X
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("-1", nn.AvgPool2d(stride)),
-                        (
-                            "0",
-                            nn.Conv2d(
-                                inplanes,
-                                planes * self.expansion,
-                                1,
-                                stride=1,
-                                bias=False,
-                            ),
-                        ),
-                        ("1", nn.BatchNorm2d(planes * self.expansion)),
-                    ]
-                )
-            )
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(
-        self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
-        )
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(
-            2, 0, 1
-        )  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x,
-            key=x,
-            value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat(
-                [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
-            ),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False,
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, image_size=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.image_size = image_size
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(
-            3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
-        )
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.conv2 = nn.Conv2d(
-            width // 2, width // 2, kernel_size=3, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
-
-        self.init_parameters()
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def init_parameters(self):
-        if self.attnpool is not None:
-            std = self.attnpool.c_proj.in_features**-0.5
-            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.c_proj.weight, std=std)
-
-        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
-            for name, param in resnet_block.named_parameters():
-                if name.endswith("bn3.weight"):
-                    nn.init.zeros_(param)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert (
-            unlocked_groups == 0
-        ), "partial locking not currently supported for this model"
-        for param in self.parameters():
-            param.requires_grad = False
-        if freeze_bn_stats:
-            freeze_batch_norm_2d(self)
-
-    def stem(self, x):
-        for conv, bn in [
-            (self.conv1, self.bn1),
-            (self.conv2, self.bn2),
-            (self.conv3, self.bn3),
-        ]:
-            x = self.relu(bn(conv(x)))
-        x = self.avgpool(x)
-        return x
-
-    def forward(self, x):
-        x = self.stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
-    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(
-            OrderedDict(
-                [
-                    ("c_fc", nn.Linear(d_model, d_model * 4)),
-                    ("gelu", act_layer()),
-                    ("c_proj", nn.Linear(d_model * 4, d_model)),
-                ]
-            )
-        )
-        self.ln_2 = LayerNorm(d_model)
-
-    def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.ModuleList(
-            [
-                ResidualAttentionBlock(width, heads, act_layer=act_layer)
-                for _ in range(layers)
-            ]
-        )
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        for r in self.resblocks:
-            x = r(x, attn_mask=attn_mask)
-        return x
-
-
-class VisualTransformer(nn.Module):
-    def __init__(
-        self,
-        image_size: int,
-        patch_size: int,
-        width: int,
-        layers: int,
-        heads: int,
-        output_dim: int,
-        act_layer: Callable = nn.GELU,
-    ):
-        super().__init__()
-        self.image_size = image_size
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(
-            in_channels=3,
-            out_channels=width,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=False,
-        )
-
-        scale = width**-0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(
-            scale * torch.randn((image_size // patch_size) ** 2 + 1, width)
-        )
-        self.ln_pre = LayerNorm(width)
-
-        self.text_branch = Transformer(width, layers, heads, act_layer=act_layer)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert (
-            unlocked_groups == 0
-        ), "partial locking not currently supported for this model"
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat(
-            [
-                self.class_embedding.to(x.dtype)
-                + torch.zeros(
-                    x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
-                ),
-                x,
-            ],
-            dim=1,
-        )  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.text_branch(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-@dataclass
-class CLAPVisionCfg:
-    layers: Union[Tuple[int, int, int, int], int] = 12
-    width: int = 768
-    patch_size: int = 16
-    image_size: Union[Tuple[int, int], int] = 224
-    timm_model_name: str = (
-        None  # a valid model name overrides layers, width, patch_size
-    )
-    timm_model_pretrained: bool = (
-        False  # use (imagenet) pretrained weights for named model
-    )
-    timm_pool: str = (
-        "avg"  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
-    )
-    timm_proj: str = (
-        "linear"  # linear projection for timm model output ('linear', 'mlp', '')
-    )
-
-
-# Audio Config Class
-@dataclass
-class CLAPAudioCfp:
-    model_type: str = "PANN"
-    model_name: str = "Cnn14"
-    sample_rate: int = 48000
-    # Param
-    audio_length: int = 1024
-    window_size: int = 1024
-    hop_size: int = 1024
-    fmin: int = 50
-    fmax: int = 14000
-    class_num: int = 527
-    mel_bins: int = 64
-    clip_samples: int = 480000
-
-
-@dataclass
-class CLAPTextCfg:
-    context_length: int
-    vocab_size: int
-    width: int
-    heads: int
-    layers: int
-    model_type: str
-
-
-class CLAP(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        audio_cfg: CLAPAudioCfp,
-        text_cfg: CLAPTextCfg,
-        quick_gelu: bool = False,
-        enable_fusion: bool = False,
-        fusion_type: str = 'None',
-        joint_embed_shape: int = 512,
-        mlp_act: str = 'relu',
-    ):
-        super().__init__()
-        if isinstance(audio_cfg, dict):
-            audio_cfg = CLAPAudioCfp(**audio_cfg)
-        if isinstance(text_cfg, dict):
-            text_cfg = CLAPTextCfg(**text_cfg)
-
-        self.audio_cfg = audio_cfg
-        self.text_cfg = text_cfg
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-        self.joint_embed_shape = joint_embed_shape
-        self.mlp_act = mlp_act
-
-
-        self.context_length = text_cfg.context_length
-
-        # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
-        # memory efficient in recent PyTorch releases (>= 1.10).
-        # NOTE: timm models always use native GELU regardless of quick_gelu flag.
-        act_layer = QuickGELU if quick_gelu else nn.GELU
-
-        if mlp_act == 'relu':
-            mlp_act_layer = nn.ReLU()
-        elif mlp_act == 'gelu':
-            mlp_act_layer = nn.GELU()
-        else:
-            raise NotImplementedError
-
-        # audio branch
-        # audio branch parameters
-        if audio_cfg.model_type == "PANN":
-            self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type)
-        elif audio_cfg.model_type == "HTSAT":
-            self.audio_branch = create_htsat_model(audio_cfg, enable_fusion, fusion_type)
-        else:
-            logging.error(f"Model config for {audio_cfg.model_type} not found")
-            raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.")
-
-
-        # text branch
-        # text branch parameters
-        if text_cfg.model_type == "transformer":
-            self.text_branch = Transformer(
-                width=text_cfg.width,
-                layers=text_cfg.layers,
-                heads=text_cfg.heads,
-                act_layer=act_layer,
-            )
-            self.vocab_size = text_cfg.vocab_size
-            self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
-            self.positional_embedding = nn.Parameter(
-                torch.empty(self.context_length, text_cfg.width)
-            )
-            self.ln_final = LayerNorm(text_cfg.width)
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(text_cfg.width, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        elif text_cfg.model_type == "bert":
-            self.text_branch = BertModel.from_pretrained("bert-base-uncased")
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        elif text_cfg.model_type == "roberta":
-            self.text_branch = RobertaModel.from_pretrained('roberta-base')
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        elif text_cfg.model_type == "bart":
-            self.text_branch = BartModel.from_pretrained('facebook/bart-base')
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        else:
-            logging.error(f"Model config for {text_cfg.model_type} not found")
-            raise RuntimeError(f"Model config for {text_cfg.model_type} not found.")
-        self.text_branch_type = text_cfg.model_type
-        # text branch parameters
-
-        # audio branch parameters
-        self.audio_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                self.joint_embed_shape,
-                                                self.joint_embed_shape], dropout=0.1)
-
-        # below here is text branch parameters
-
-        # ============================================================================================================
-        self.audio_projection = nn.Sequential(
-                nn.Linear(embed_dim, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-
-        self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False)
-
-        self.init_text_branch_parameters()
-
-    def init_text_branch_parameters(self):
-        if self.text_branch_type == "transformer":
-            nn.init.normal_(self.token_embedding.weight, std=0.02)
-            nn.init.normal_(self.positional_embedding, std=0.01)
-            proj_std = (self.text_branch.width**-0.5) * (
-                (2 * self.text_branch.layers) ** -0.5
-            )
-            attn_std = self.text_branch.width**-0.5
-            fc_std = (2 * self.text_branch.width) ** -0.5
-            for block in self.text_branch.resblocks:
-                nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-                nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-                nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-                nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        if self.text_branch_type == "bert" or self.text_branch_type == "roberta":
-            width = self.text_branch.embeddings.word_embeddings.weight.shape[-1]
-        elif self.text_branch_type == "bart":
-            width = self.text_branch.shared.weight.shape[-1]
-        else:
-            width = self.text_branch.width
-        nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07))
-        nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07))
-
-        # deprecated
-        # if hasattr(self.visual, 'init_parameters'):
-        # self.visual.init_parameters()
-
-        # if self.text_projection is not None:
-        #     nn.init.normal_(self.text_projection, std=width**-0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def encode_audio(self, audio, device):
-        return self.audio_branch(audio, mixup_lambda=None, device=device)  # mix lambda needs to add
-
-    # def list_of_dict_of_tensor2dict_of_tensor(self, x, device):
-    #     tmp = {}
-    #     for k in x[0].keys():
-    #         tmp[k] = []
-    #         for i in range(len(x)):
-    #             tmp[k].append(x[i][k][:77])
-    #     for k in x[0].keys():
-    #         tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True)
-    #     return tmp
-
-    def encode_text(self, text, device):
-        if self.text_branch_type == "transformer":
-            text = text.to(device=device, non_blocking=True)
-            x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]
-
-            x = x + self.positional_embedding
-            x = x.permute(1, 0, 2)  # NLD -> LND
-            x = self.text_branch(x, attn_mask=self.attn_mask)
-            x = x.permute(1, 0, 2)  # LND -> NLD
-            x = self.ln_final(x)
-
-            # x.shape = [batch_size, n_ctx, transformer.width]
-            # take features from the eot embedding (eot_token is the highest number in each sequence)
-            x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)])
-        elif self.text_branch_type == "bert":
-            # text = self.list_of_dict_of_tensor2dict_of_tensor(text, device)
-            # text = BatchEncoding(text)
-            x = self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-                token_type_ids=text["token_type_ids"].to(
-                    device=device, non_blocking=True
-                ),
-            )["pooler_output"]
-            x = self.text_projection(x)
-        elif self.text_branch_type == "roberta":
-            x = self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-            )["pooler_output"]
-
-            x = self.text_projection(x)
-        elif self.text_branch_type == "bart":
-            x = torch.mean(self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-            )["encoder_last_hidden_state"],axis=1)
-            x = self.text_projection(x)
-        else:
-            logging.error(f"Model type {self.text_branch_type} not found")
-            raise RuntimeError(f"Model type {self.text_branch_type} not found.")
-        return x
-
-    def forward(self, audio, text, device=None):
-        """Forward audio and text into the CLAP
-
-        Parameters
-        ----------
-        audio: torch.Tensor (batch_size, audio_length)
-            the time-domain audio input / the batch of mel_spec and longer list.
-        text: torch.Tensor () // need to add
-            the text token input
-        """
-        if device is None:
-            if audio is not None:
-                device = audio.device
-            elif text is not None:
-                device = text.device
-        if audio is None and text is None:
-            # a hack to get the logit scale
-            return self.logit_scale_a.exp(), self.logit_scale_t.exp()
-        elif audio is None:
-            return self.encode_text(text, device=device)
-        elif text is None:
-            return self.audio_projection(self.encode_audio(audio, device=device)["embedding"])
-        audio_features = self.audio_projection(self.encode_audio(audio, device=device)["embedding"])
-        audio_features = F.normalize(audio_features, dim=-1)
-
-        text_features = self.encode_text(
-            text, device=device
-        )
-        # print("text_features", text_features)
-        # print("text_features.shape", text_features.shape)
-        # print("text_features.type", type(text_features))
-        text_features = F.normalize(text_features, dim=-1)
-
-        audio_features_mlp = self.audio_transform(audio_features)
-        text_features_mlp = self.text_transform(text_features)
-        # Four outputs: audio features (basic & MLP), text features (basic & MLP)
-        return (
-            audio_features,
-            text_features,
-            audio_features_mlp,
-            text_features_mlp,
-            self.logit_scale_a.exp(),
-            self.logit_scale_t.exp(),
-        )
-
-    def get_logit_scale(self):
-        return self.logit_scale_a.exp(), self.logit_scale_t.exp()
-
-    def get_textual_embedding(self, data):
-
-        device = next(self.parameters()).device
-        for k in data:
-            data[k] = data[k].to(device)
-
-        # if self.text_branch_type == "roberta":
-        text_embeds = self.text_branch(
-                input_ids=data["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=data["attention_mask"].to(device=device, non_blocking=True),
-            )["last_hidden_state"]
-
-        text_embeds = self.text_projection(text_embeds)
-
-        text_embeds = F.normalize(text_embeds, dim=-1)
-
-        return text_embeds
-
-    def get_text_embedding(self, data):
-        """Get the text embedding from the model
-
-        Parameters
-        ----------
-        data: torch.Tensor 
-            a tensor of text embedding
-
-        Returns
-        ----------
-        text_embed: torch.Tensor
-            a tensor of text_embeds (N, D)
-
-        """
-        device = next(self.parameters()).device
-        for k in data:
-            data[k] = data[k].to(device)
-        text_embeds = self.encode_text(data, device=device)
-        text_embeds = F.normalize(text_embeds, dim=-1)
-        
-        return text_embeds
-
-    def get_audio_embedding(self, data):
-        """Get the audio embedding from the model
-
-        Parameters
-        ----------
-        data: a list of dict
-            the audio input dict list from 'get_audio_feature' method
-
-        Returns
-        ----------
-        audio_embed: torch.Tensor
-            a tensor of audio_embeds (N, D)
-
-        """
-        device = next(self.parameters()).device
-        input_dict = {}
-        keys = data[0].keys()
-        for k in keys:
-            input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(device)
-        
-        audio_embeds = self.audio_projection(self.encode_audio(input_dict, device=device)["embedding"])
-        audio_embeds = F.normalize(audio_embeds, dim=-1)
-        
-        return audio_embeds
-
-            
-
-    def audio_infer(self, audio, hopsize=None, device=None):
-        """Forward one audio and produce the audio embedding
-
-        Parameters
-        ----------
-        audio:  (audio_length)
-            the time-domain audio input, notice that it must be only one input
-        hopsize: int
-            the overlap hopsize as the sliding window
-
-        Returns
-        ----------
-        output_dict: {
-            key: [n, (embedding_shape)] if "HTS-AT"
-            or
-            key: [(embedding_shape)] if "PANN"
-        }
-            the list of key values of the audio branch
-
-        """
-
-        assert not self.training, "the inference mode must be run at eval stage"
-        output_dict = {}
-        # PANN
-        if self.audio_cfg.model_type == "PANN":
-            audio_input = audio.unsqueeze(dim=0)
-            output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0)
-        elif self.audio_cfg.model_type == "HTSAT":
-            # repeat
-            audio_len = len(audio)
-            k = self.audio_cfg.clip_samples // audio_len
-            if k > 1:
-                audio = audio.repeat(k)
-                audio_len = len(audio)
-
-            if hopsize is None:
-                hopsize = min(hopsize, audio_len)
-
-            if audio_len > self.audio_cfg.clip_samples:
-                audio_input = [
-                    audio[pos : pos + self.audio_cfg.clip_samples].clone()
-                    for pos in range(
-                        0, audio_len - self.audio_cfg.clip_samples, hopsize
-                    )
-                ]
-                audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
-                audio_input = torch.stack(audio_input)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[key]
-            else:
-                audio_input = audio.unsqueeze(dim=0)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0)
-
-        return output_dict
-
-
-def convert_weights_to_fp16(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [
-                *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
-                "in_proj_bias",
-                "bias_k",
-                "bias_v",
-            ]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-# Ignore the state dict of the vision part
-def build_model_from_openai_state_dict(state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = 'None'):
-
-    embed_dim = model_cfg["embed_dim"]
-    audio_cfg = model_cfg["audio_cfg"]
-    text_cfg = model_cfg["text_cfg"]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(
-        set(
-            k.split(".")[2]
-            for k in state_dict
-            if k.startswith(f"transformer.resblocks")
-        )
-    )
-
-    audio_cfg = CLAPAudioCfp(**audio_cfg)
-    text_cfg = CLAPTextCfg(**text_cfg)
-
-    model = CLAP(
-        embed_dim,
-        audio_cfg=audio_cfg,
-        text_cfg=text_cfg,
-        quick_gelu=True,  # OpenAI models were trained with QuickGELU
-        enable_fusion=enable_fusion,
-        fusion_type=fusion_type
-    )
-    state_dict["logit_scale_a"] = state_dict["logit_scale"]
-    state_dict["logit_scale_t"] = state_dict["logit_scale"]
-    pop_keys = list(state_dict.keys())[::]
-    # pop the visual branch saved weights
-    for key in pop_keys:
-        if key.startswith("visual."):
-            state_dict.pop(key, None)
-
-    for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]:
-        state_dict.pop(key, None)
-
-    # not use fp16
-    # convert_weights_to_fp16(model)
-    model.load_state_dict(state_dict, strict=False)
-    return model.eval()
-
-
-def trace_model(model, batch_size=256, device=torch.device("cpu")):
-    model.eval()
-    audio_length = model.audio_cfg.audio_length
-    example_audio = torch.ones((batch_size, audio_length), device=device)
-    example_text = torch.zeros(
-        (batch_size, model.context_length), dtype=torch.int, device=device
-    )
-    model = torch.jit.trace_module(
-        model,
-        inputs=dict(
-            forward=(example_audio, example_text),
-            encode_text=(example_text,),
-            encode_image=(example_audio,),
-        ),
-    )
-    model.audio_cfg.audio_length = audio_length  # Question: what does this do?
-    return model
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/datasets/pipelines/__init__.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/datasets/pipelines/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/model.py b/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/model.py
deleted file mode 100644
index 8df242b3f33e7c5e01d5e47628a81eb94a3f1964..0000000000000000000000000000000000000000
--- a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/model.py
+++ /dev/null
@@ -1,182 +0,0 @@
-from torch import nn
-import torch
-import torch.nn.functional as F
-from modules.util import AntiAliasInterpolation2d, TPS
-from torchvision import models
-import numpy as np
-
-
-class Vgg19(torch.nn.Module):
-    """
-    Vgg19 network for perceptual loss. See Sec 3.3.
-    """
-    def __init__(self, requires_grad=False):
-        super(Vgg19, self).__init__()
-        vgg_pretrained_features = models.vgg19(pretrained=True).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        for x in range(2):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(2, 7):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(7, 12):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(12, 21):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(21, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-
-        self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))),
-                                       requires_grad=False)
-        self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))),
-                                      requires_grad=False)
-
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        X = (X - self.mean) / self.std
-        h_relu1 = self.slice1(X)
-        h_relu2 = self.slice2(h_relu1)
-        h_relu3 = self.slice3(h_relu2)
-        h_relu4 = self.slice4(h_relu3)
-        h_relu5 = self.slice5(h_relu4)
-        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
-        return out
-
-
-class ImagePyramide(torch.nn.Module):
-    """
-    Create image pyramide for computing pyramide perceptual loss. See Sec 3.3
-    """
-    def __init__(self, scales, num_channels):
-        super(ImagePyramide, self).__init__()
-        downs = {}
-        for scale in scales:
-            downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale)
-        self.downs = nn.ModuleDict(downs)
-
-    def forward(self, x):
-        out_dict = {}
-        for scale, down_module in self.downs.items():
-            out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x)
-        return out_dict
-
-
-def detach_kp(kp):
-    return {key: value.detach() for key, value in kp.items()}
-
-
-class GeneratorFullModel(torch.nn.Module):
-    """
-    Merge all generator related updates into single model for better multi-gpu usage
-    """
-
-    def __init__(self, kp_extractor, bg_predictor, dense_motion_network, inpainting_network, train_params, *kwargs):
-        super(GeneratorFullModel, self).__init__()
-        self.kp_extractor = kp_extractor
-        self.inpainting_network = inpainting_network
-        self.dense_motion_network = dense_motion_network
-
-        self.bg_predictor = None
-        if bg_predictor:
-            self.bg_predictor = bg_predictor
-            self.bg_start = train_params['bg_start']
-
-        self.train_params = train_params
-        self.scales = train_params['scales']
-
-        self.pyramid = ImagePyramide(self.scales, inpainting_network.num_channels)
-        if torch.cuda.is_available():
-            self.pyramid = self.pyramid.cuda()
-
-        self.loss_weights = train_params['loss_weights']
-        self.dropout_epoch = train_params['dropout_epoch']
-        self.dropout_maxp = train_params['dropout_maxp']
-        self.dropout_inc_epoch = train_params['dropout_inc_epoch']
-        self.dropout_startp =train_params['dropout_startp']
-        
-        if sum(self.loss_weights['perceptual']) != 0:
-            self.vgg = Vgg19()
-            if torch.cuda.is_available():
-                self.vgg = self.vgg.cuda()
-
-
-    def forward(self, x, epoch):
-        kp_source = self.kp_extractor(x['source'])
-        kp_driving = self.kp_extractor(x['driving'])
-        bg_param = None
-        if self.bg_predictor:
-            if(epoch>=self.bg_start):
-                bg_param = self.bg_predictor(x['source'], x['driving'])
-          
-        if(epoch>=self.dropout_epoch):
-            dropout_flag = False
-            dropout_p = 0
-        else:
-            # dropout_p will linearly increase from dropout_startp to dropout_maxp 
-            dropout_flag = True
-            dropout_p = min(epoch/self.dropout_inc_epoch * self.dropout_maxp + self.dropout_startp, self.dropout_maxp)
-        
-        dense_motion = self.dense_motion_network(source_image=x['source'], kp_driving=kp_driving,
-                                                    kp_source=kp_source, bg_param = bg_param, 
-                                                    dropout_flag = dropout_flag, dropout_p = dropout_p)
-        generated = self.inpainting_network(x['source'], dense_motion)
-        generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
-
-        loss_values = {}
-
-        pyramide_real = self.pyramid(x['driving'])
-        pyramide_generated = self.pyramid(generated['prediction'])
-
-        # reconstruction loss
-        if sum(self.loss_weights['perceptual']) != 0:
-            value_total = 0
-            for scale in self.scales:
-                x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)])
-                y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)])
-
-                for i, weight in enumerate(self.loss_weights['perceptual']):
-                    value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean()
-                    value_total += self.loss_weights['perceptual'][i] * value
-            loss_values['perceptual'] = value_total
-
-        # equivariance loss
-        if self.loss_weights['equivariance_value'] != 0:
-            transform_random = TPS(mode = 'random', bs = x['driving'].shape[0], **self.train_params['transform_params'])
-            transform_grid = transform_random.transform_frame(x['driving'])
-            transformed_frame = F.grid_sample(x['driving'], transform_grid, padding_mode="reflection",align_corners=True)
-            transformed_kp = self.kp_extractor(transformed_frame)
-
-            generated['transformed_frame'] = transformed_frame
-            generated['transformed_kp'] = transformed_kp
-        
-            warped = transform_random.warp_coordinates(transformed_kp['fg_kp'])
-            kp_d = kp_driving['fg_kp']
-            value = torch.abs(kp_d - warped).mean()
-            loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value
-
-        # warp loss
-        if self.loss_weights['warp_loss'] != 0:
-            occlusion_map = generated['occlusion_map']
-            encode_map = self.inpainting_network.get_encode(x['driving'], occlusion_map)
-            decode_map = generated['warped_encoder_maps']
-            value = 0
-            for i in range(len(encode_map)):
-                value += torch.abs(encode_map[i]-decode_map[-i-1]).mean()
-
-            loss_values['warp_loss'] = self.loss_weights['warp_loss'] * value
-        
-        # bg loss
-        if self.bg_predictor and epoch >= self.bg_start and self.loss_weights['bg'] != 0:
-            bg_param_reverse = self.bg_predictor(x['driving'], x['source'])
-            value = torch.matmul(bg_param, bg_param_reverse)
-            eye = torch.eye(3).view(1, 1, 3, 3).type(value.type())
-            value = torch.abs(eye - value).mean()
-            loss_values['bg'] = self.loss_weights['bg'] * value
-
-        return loss_values, generated
diff --git a/spaces/Alpaca233/SadTalker/app.py b/spaces/Alpaca233/SadTalker/app.py
deleted file mode 100644
index f082d8cf174c098d5b5132d811bc2619f86b41b6..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import os, sys
-import gradio as gr
-from src.gradio_demo import SadTalker  
-
-
-try:
-    import webui  # in webui
-    in_webui = True
-except:
-    in_webui = False
-
-
-def toggle_audio_file(choice):
-    if choice == False:
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        return gr.update(visible=False), gr.update(visible=True)
-    
-def ref_video_fn(path_of_ref_video):
-    if path_of_ref_video is not None:
-        return gr.update(value=True)
-    else:
-        return gr.update(value=False)
-
-sad_talker = SadTalker("checkpoints", "src/config", lazy_load=True)
-
-
-with gr.Blocks(analytics_enabled=False) as demo:
-    gr.Markdown("<div align='center'> <h2> 😭 SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023) </span> </h2> \
-                <a style='font-size:18px;color: #efefef' href='https://arxiv.org/abs/2211.12194'>Arxiv</a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                <a style='font-size:18px;color: #efefef' href='https://sadtalker.github.io'>Homepage</a>  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                <a style='font-size:18px;color: #efefef' href='https://github.com/Winfredy/SadTalker'> Github </div>")
-        
-    with gr.Row().style(equal_height=False):
-        with gr.Column(variant='panel'):
-            with gr.Tabs(elem_id="sadtalker_source_image"):
-                with gr.TabItem('Upload image'):
-                    with gr.Row():
-                        source_image = gr.Image(label="Source image", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
-
-            with gr.Tabs(elem_id="sadtalker_driven_audio"):
-                with gr.TabItem('Upload OR TTS'):
-                    with gr.Column(variant='panel'):
-                        driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
-
-        with gr.Column(variant='panel'): 
-            with gr.Tabs(elem_id="sadtalker_checkbox"):
-                with gr.TabItem('Settings'):
-                    gr.Markdown("need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials")
-                    with gr.Column(variant='panel'):
-                        # width = gr.Slider(minimum=64, elem_id="img2img_width", maximum=2048, step=8, label="Manually Crop Width", value=512) # img2img_width
-                        # height = gr.Slider(minimum=64, elem_id="img2img_height", maximum=2048, step=8, label="Manually Crop Height", value=512) # img2img_width
-                        pose_style = gr.Slider(minimum=0, maximum=46, step=1, label="Pose style", value=0) # 
-                        size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?") # 
-                        preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?")
-                        is_still_mode = gr.Checkbox(label="Still Mode (fewer hand motion, works with preprocess `full`)")
-                        batch_size = gr.Slider(label="batch size in generation", step=1, maximum=10, value=2)
-                        enhancer = gr.Checkbox(label="GFPGAN as Face enhancer")
-                        submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
-                            
-            with gr.Tabs(elem_id="sadtalker_genearted"):
-                    gen_video = gr.Video(label="Generated video", format="mp4").style(width=256)
-
-    submit.click(
-                fn=sad_talker.test, 
-                inputs=[source_image,
-                        driven_audio,
-                        preprocess_type,
-                        is_still_mode,
-                        enhancer,
-                        batch_size,                            
-                        size_of_image,
-                        pose_style
-                        ], 
-                outputs=[gen_video]
-                )
-
-
-
-demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/buffer.cpp b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/buffer.cpp
deleted file mode 100644
index 0ac0fa7bc3ced0447ba4caa359355dd4252670b3..0000000000000000000000000000000000000000
--- a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/buffer.cpp
+++ /dev/null
@@ -1,87 +0,0 @@
-#include "libipc/buffer.h"
-#include "libipc/utility/pimpl.h"
-
-#include <cstring>
-
-namespace ipc {
-
-bool operator==(buffer const & b1, buffer const & b2) {
-    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
-}
-
-bool operator!=(buffer const & b1, buffer const & b2) {
-    return !(b1 == b2);
-}
-
-class buffer::buffer_ : public pimpl<buffer_> {
-public:
-    void*       p_;
-    std::size_t s_;
-    void*       a_;
-    buffer::destructor_t d_;
-
-    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
-        : p_(p), s_(s), a_(a), d_(d) {
-    }
-
-    ~buffer_() {
-        if (d_ == nullptr) return;
-        d_((a_ == nullptr) ? p_ : a_, s_);
-    }
-};
-
-buffer::buffer()
-    : buffer(nullptr, 0, nullptr, nullptr) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d)
-    : p_(p_->make(p, s, d, nullptr)) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
-    : p_(p_->make(p, s, d, additional)) {
-}
-
-buffer::buffer(void* p, std::size_t s)
-    : buffer(p, s, nullptr) {
-}
-
-buffer::buffer(char const & c)
-    : buffer(const_cast<char*>(&c), 1) {
-}
-
-buffer::buffer(buffer&& rhs)
-    : buffer() {
-    swap(rhs);
-}
-
-buffer::~buffer() {
-    p_->clear();
-}
-
-void buffer::swap(buffer& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-buffer& buffer::operator=(buffer rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool buffer::empty() const noexcept {
-    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
-}
-
-void* buffer::data() noexcept {
-    return impl(p_)->p_;
-}
-
-void const * buffer::data() const noexcept {
-    return impl(p_)->p_;
-}
-
-std::size_t buffer::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-} // namespace ipc
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pndm.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pndm.md
deleted file mode 100644
index 0cb4799b3c8110587696f93113461518fd7d011d..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pndm.md
+++ /dev/null
@@ -1,35 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# PNDM
-
-[Pseudo Numerical methods for Diffusion Models on manifolds](https://huggingface.co/papers/2202.09778) (PNDM) is by Luping Liu, Yi Ren, Zhijie Lin and Zhou Zhao.
-
-The abstract from the paper is:
-
-*Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules.*
-
-The original codebase can be found at [luping-liu/PNDM](https://github.com/luping-liu/PNDM).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## PNDMPipeline
-[[autodoc]] PNDMPipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md
deleted file mode 100644
index 09814f387b724071d5c29a28dec9efd9b2bfc02f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md
+++ /dev/null
@@ -1,40 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Depth-to-image
-
-The Stable Diffusion model can also infer depth based on an image using [MiDas](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure. 
-
-<Tip>
-
-Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! 
-
-If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
-
-</Tip>
-
-## StableDiffusionDepth2ImgPipeline
-
-[[autodoc]] StableDiffusionDepth2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-	- load_lora_weights
-	- save_lora_weights
-
-## StableDiffusionPipelineOutput
-
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/dance_diffusion/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/dance_diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 835375cb0447378fc76431158eb0b8fc011d36bc..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
deleted file mode 100644
index a8c14c8cf91d7cbcc05065a6dc387101dff8cdf6..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './pointrend_r50_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Ankush05/Newcode/getvalues.py b/spaces/Ankush05/Newcode/getvalues.py
deleted file mode 100644
index e063f5e00b0f84eb9a83de1cb95a47d6987b82b1..0000000000000000000000000000000000000000
--- a/spaces/Ankush05/Newcode/getvalues.py
+++ /dev/null
@@ -1,87 +0,0 @@
-
-import re
-# from listen import *
-
-# find time in the string input provided by the user
-def findTime(input):
-    # time = re.search(r'\d{1,2}:\d{2}', input)
-    # meridiem = re.search(r'\b(am|pm)\b', input)
-    # if time:
-    #     tvalue = f"{time.group()} {meridiem.group()}"
-    #     return tvalue
-    # else:
-    #     return "notime"
-    time_regex1 = r"(1[0-2]|[1-9]):[0-5][0-9] (am|AM|PM|pm)"
-    time_search = re.search(time_regex1, input)
-    if time_search:
-        time = time_search.group(0)
-        # meridian = time_search.group(2)
-        return time
-    else:
-        time_regex2 = r"(1[0-2]|[1-9])\s?(am|AM|pm|PM)"
-        time_search = re.search(time_regex2, input)
-        if time_search:
-            time = time_search.group(0)
-            # meridian = time_search.group(2)
-            return time
-        else:
-            return "notime"
-    
-# find number in the string input provided by the user
-def findNumber(input):
-    number = re.search(r'\d+(?:st|nd|rd|th)', input)
-    if number:
-        return number.group()
-    else:
-        return "nonum"
-    
-# # find date in the string input provided by the user
-def findDate(input):
-    date = re.search(r'\d{1,2}/\d{1,2}/\d{4}', input)
-    if date:
-        return date.group()
-    else:
-        return "nodate"
-
-# find month in the string input provided by the user
-def findMonth(input):
-    month = re.search(r'\b(january|february|march|april|may|june|july|august|september|october|november|december|next month)\b', input)
-    if month:
-        return month.group()
-    else:
-        return "nomonth"
-    
-# find day in the string input provided by the user
-def findDay(input):
-    day = re.search(r'\b(monday|tuesday|wednesday|thursday|friday|saturday|sunday|tomorrow|day after tomorrow|this week|next week|today)\b', input)
-    if day:
-        return day.group()
-    else:
-        return "noday"
-    
-def findrepeat(input):
-    repeat = re.search(r'\b(daily|everyday|every week|every month|every sunday|every monday|every tuesday|every wednesday|every thursday|every friday|every saturday)\b', input)
-    if repeat:
-        return repeat.group()
-    else:
-        return "norepeat"
-    
-    
-def getValues(query):
-    time = findTime(query)
-    num = findNumber(query)
-    reps = findrepeat(query)
-    date = findDate(query)
-    month = findMonth(query)
-    day = findDay(query)
-    message = query.lower().replace(num, "").replace(month,"").replace(time, "").replace(day, "").replace(reps, "").replace("create a reminder", "").replace("remind me to", "").replace("cosmo", "").replace("remind", "").replace("at", "")
-    values = {"message": message, 
-              "time": time, 
-              "day": day, 
-              "date": date, 
-              "reps": reps, 
-              "num": num, 
-              "month": month
-             }
-    return values
-    
\ No newline at end of file
diff --git a/spaces/Artrajz/vits-simple-api/bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/Artrajz/vits-simple-api/bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/tokenizer.py b/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/tokenizer.py
deleted file mode 100644
index ee4d28450ec5dd12a79daf38cf3088e9e73c2cd5..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/tokenizer.py
+++ /dev/null
@@ -1,197 +0,0 @@
-""" CLIP tokenizer
-
-Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import Union, List
-
-import ftfy
-import regex as re
-import torch
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(
-        os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz"
-    )
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1))
-        + list(range(ord("¡"), ord("¬") + 1))
-        + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
-        merges = merges[1 : 49152 - 256 - 2 + 1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v + "</w>" for v in vocab]
-        for merge in merges:
-            vocab.append("".join(merge))
-        if not special_tokens:
-            special_tokens = ["<start_of_text>", "<end_of_text>"]
-        else:
-            special_tokens = ["<start_of_text>", "<end_of_text>"] + special_tokens
-        vocab.extend(special_tokens)
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {t: t for t in special_tokens}
-        special = "|".join(special_tokens)
-        self.pat = re.compile(
-            special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
-            re.IGNORECASE,
-        )
-
-        self.vocab_size = len(self.encoder)
-        self.all_special_ids = [self.encoder[t] for t in special_tokens]
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + (token[-1] + "</w>",)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token + "</w>"
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(
-                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
-            )
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = "".join([self.decoder[token] for token in tokens])
-        text = (
-            bytearray([self.byte_decoder[c] for c in text])
-            .decode("utf-8", errors="replace")
-            .replace("</w>", " ")
-        )
-        return text
-
-
-_tokenizer = SimpleTokenizer()
-
-
-def tokenize(
-    texts: Union[str, List[str]], context_length: int = 77
-) -> torch.LongTensor:
-    """
-    Returns the tokenized representation of given input string(s)
-
-    Parameters
-    ----------
-    texts : Union[str, List[str]]
-        An input string or a list of input strings to tokenize
-    context_length : int
-        The context length to use; all CLIP models use 77 as the context length
-
-    Returns
-    -------
-    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
-    """
-    if isinstance(texts, str):
-        texts = [texts]
-
-    sot_token = _tokenizer.encoder["<start_of_text>"]
-    eot_token = _tokenizer.encoder["<end_of_text>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            tokens = tokens[:context_length]  # Truncate
-        result[i, : len(tokens)] = torch.tensor(tokens)
-
-    return result
diff --git a/spaces/Axolotlily/DalleMini/README.md b/spaces/Axolotlily/DalleMini/README.md
deleted file mode 100644
index fbe96503a91dc4d908c5ebf607a10511cb41bd17..0000000000000000000000000000000000000000
--- a/spaces/Axolotlily/DalleMini/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DalleMini
-emoji: 🚀
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/BAAI/AltDiffusion-m9/ui_functions.py b/spaces/BAAI/AltDiffusion-m9/ui_functions.py
deleted file mode 100644
index da68ac28f05fb26c7f468047dcdbab750319c84f..0000000000000000000000000000000000000000
--- a/spaces/BAAI/AltDiffusion-m9/ui_functions.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import re
-import gradio as gr
-from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
-from io import BytesIO
-import base64
-import re
-
-def change_img_choices(sample_size):
-    choices = []
-    for i in range(int(sample_size)):
-        choices.append(
-            '图片{}(img{})'.format(i+1,i+1)
-        )
-    update_choices = gr.update(choices=choices)
-    return update_choices
-
-def change_image_editor_mode(choice, cropped_image, masked_image, resize_mode, width, height):
-    if choice == "Mask":
-        update_image_result = update_image_mask(cropped_image, resize_mode, width, height)
-        return [gr.update(visible=False), update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
-
-    update_image_result = update_image_mask(masked_image["image"] if masked_image is not None else None, resize_mode, width, height)
-    return [update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
-
-def update_image_mask(cropped_image, resize_mode, width, height):
-    resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
-    return gr.update(value=resized_cropped_image, visible=True)
-
-def toggle_options_gfpgan(selection):
-    if 0 in selection:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_upscalers(selection):
-    if 1 in selection:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_realesrgan(selection):
-    if selection == 0 or selection == 1 or selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_gobig(selection):
-    if selection == 1:
-        #print(selection)
-        return gr.update(visible=True)
-    if selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_ldsr(selection):
-    if selection == 2 or selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def increment_down(value):
-    return value - 1
-
-def increment_up(value):
-    return value + 1
-
-def copy_img_to_lab(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='imgproc_tab')
-        img_update = gr.update(value=processed_image)
-        return processed_image, tab_update,
-    except IndexError:
-        return [None, None]
-def copy_img_params_to_lab(params):
-    try:
-        prompt = params[0][0].replace('\n', ' ').replace('\r', '')
-        seed = int(params[1][1])
-        steps = int(params[7][1])
-        cfg_scale = float(params[9][1])
-        sampler = params[11][1]
-        return prompt,seed,steps,cfg_scale,sampler
-    except IndexError:
-        return [None, None]
-def copy_img_to_input(img, idx):
-    try:
-        # print(img)
-        # print("=============")
-        # print("The img type is:{}".format(type(img[0])))
-        idx_map = {
-            "图片1(img1)":0,
-            "图片2(img2)":1,
-            "图片3(img3)":2,
-            "图片4(img4)":3,
-        }
-        idx = idx_map[idx]
-        assert img[idx]['is_file']
-        processed_image = Image.open(img[idx]['name'])
-        tab_update = gr.update(selected='img2img_tab')
-        move_prompt_zh_update = gr.update(visible=True)
-        move_prompt_en_update = gr.update(visible=True)
-        prompt_update = gr.update(visible=True)
-        return tab_update, processed_image, move_prompt_zh_update, move_prompt_en_update, prompt_update
-    except IndexError as e:
-        raise gr.Error(e)
-        return [None, None, None, None, None]
-
-def copy_img_to_edit(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        mode_update = gr.update(value='Crop')
-        return processed_image, tab_update, mode_update
-    except IndexError:
-        return [None, None]
-
-def copy_img_to_mask(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        mode_update = gr.update(value='Mask')
-        return processed_image, tab_update, mode_update
-    except IndexError:
-        return [None, None]
-
-
-
-def copy_img_to_upscale_esrgan(img):
-    tabs_update = gr.update(selected='realesrgan_tab')
-    image_data = re.sub('^data:image/.+;base64,', '', img)
-    processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-    return processed_image, tabs_update
-
-
-help_text = """
-    ## Mask/Crop
-    * Masking is not inpainting. You will probably get better results manually masking your images in photoshop instead.
-    * Built-in masking/cropping is very temperamental.
-    * It may take some time for the image to show when switching from Crop to Mask.
-    * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
-    * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.
-
-    ## Advanced Editor
-    * Click 💾 Save to send your editor changes to the img2img workflow
-    * Click ❌ Clear to discard your editor changes
-
-    If anything breaks, try switching modes again, switch tabs, clear the image, or reload.
-"""
-
-def resize_image(resize_mode, im, width, height):
-    LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-    if resize_mode == 0:
-        res = im.resize((width, height), resample=LANCZOS)
-    elif resize_mode == 1:
-        ratio = width / height
-        src_ratio = im.width / im.height
-
-        src_w = width if ratio > src_ratio else im.width * height // im.height
-        src_h = height if ratio <= src_ratio else im.height * width // im.width
-
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
-        res = Image.new("RGBA", (width, height))
-        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-    else:
-        ratio = width / height
-        src_ratio = im.width / im.height
-
-        src_w = width if ratio < src_ratio else im.width * height // im.height
-        src_h = height if ratio >= src_ratio else im.height * width // im.width
-
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
-        res = Image.new("RGBA", (width, height))
-        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-
-        if ratio < src_ratio:
-            fill_height = height // 2 - src_h // 2
-            res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
-        elif ratio > src_ratio:
-            fill_width = width // 2 - src_w // 2
-            res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
-
-    return res
-
-def update_dimensions_info(width, height):
-    pixel_count_formated = "{:,.0f}".format(width * height)
-    return f"Aspect ratio: {round(width / height, 5)}\nTotal pixel count: {pixel_count_formated}"
-
-def get_png_nfo( image: Image ):
-    info_text = ""
-    visible = bool(image and any(image.info))
-    if visible:
-        for key,value in image.info.items():
-            info_text += f"{key}: {value}\n"
-        info_text = info_text.rstrip('\n')
-    return gr.Textbox.update(value=info_text, visible=visible)
-
-def load_settings(*values):
-    new_settings, key_names, checkboxgroup_info = values[-3:]
-    values = list(values[:-3])
-
-    if new_settings:
-        if type(new_settings) is str:
-            if os.path.exists(new_settings):
-                with open(new_settings, "r", encoding="utf8") as f:
-                    new_settings = yaml.safe_load(f)
-            elif new_settings.startswith("file://") and os.path.exists(new_settings[7:]):
-                with open(new_settings[7:], "r", encoding="utf8") as f:
-                    new_settings = yaml.safe_load(f)
-            else:
-                new_settings = yaml.safe_load(new_settings)
-        if type(new_settings) is not dict:
-            new_settings = {"prompt": new_settings}
-        if "txt2img" in new_settings:
-            new_settings = new_settings["txt2img"]
-        target = new_settings.pop("target", "txt2img")
-        if target != "txt2img":
-            print(f"Warning: applying settings to txt2img even though {target} is specified as target.", file=sys.stderr)
-
-        skipped_settings = {}
-        for key in new_settings.keys():
-            if key in key_names:
-                values[key_names.index(key)] = new_settings[key]
-            else:
-                skipped_settings[key] = new_settings[key]
-        if skipped_settings:
-            print(f"Settings could not be applied: {skipped_settings}", file=sys.stderr)
-
-    # Convert lists of checkbox indices to lists of checkbox labels:
-    for (cbg_index, cbg_choices) in checkboxgroup_info:
-        values[cbg_index] = [cbg_choices[i] for i in values[cbg_index]]
-
-    return values
diff --git a/spaces/Balalaxmi/JarvisAIchatbox/app.py b/spaces/Balalaxmi/JarvisAIchatbox/app.py
deleted file mode 100644
index 4afdfeaebefbbfa6c807781c5900c85a21145216..0000000000000000000000000000000000000000
--- a/spaces/Balalaxmi/JarvisAIchatbox/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, he's full of energy and always eager to help. Jarvis's goal is to assist you with any questions or problems you might have. He enthusiasm shines through in every response, making interactions with he enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/utils.py b/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/utils.py
deleted file mode 100644
index f4805cdb25e7c50611412a19340ad525d1251d7b..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/utils.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import json
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-
-def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto
diff --git a/spaces/Benson/text-generation/Examples/Cmo Descargar Msica A Una Unidad USB De Youtube.md b/spaces/Benson/text-generation/Examples/Cmo Descargar Msica A Una Unidad USB De Youtube.md
deleted file mode 100644
index 4bf325bd2a108276af69b5c9dd711206852745f6..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Cmo Descargar Msica A Una Unidad USB De Youtube.md	
+++ /dev/null
@@ -1,147 +0,0 @@
-
-<h1>Cómo descargar música a una unidad USB desde YouTube</h1>
-<p>YouTube es una de las plataformas más populares para transmitir y ver videos en línea. También tiene una gran colección de videos musicales, canciones, álbumes y listas de reproducción que puedes disfrutar en cualquier momento y en cualquier lugar. Pero ¿qué pasa si quieres escuchar tu música favorita sin conexión, o en un dispositivo que no tiene acceso a Internet? ¿O qué pasa si quieres crear tu propio mixtape o compilación de canciones de diferentes artistas y géneros? </p>
-<h2>cómo descargar música a una unidad USB de youtube</h2><br /><p><b><b>Download Zip</b> &raquo;&raquo;&raquo; <a href="https://bltlly.com/2v6ITN">https://bltlly.com/2v6ITN</a></b></p><br /><br />
-<p>Una de las soluciones es descargar música de YouTube a una unidad USB. Una unidad USB es un dispositivo pequeño y portátil que puede almacenar datos y transferirlos entre diferentes computadoras y dispositivos. Al descargar música de YouTube a una unidad USB, puede tener su propia biblioteca de música personal que puede reproducir en cualquier dispositivo compatible, como su estéreo de automóvil, sistema de cine en casa, computadora portátil, teléfono inteligente o tableta. </p>
-<p>Pero ¿cómo descargar música de YouTube a una unidad USB? Hay diferentes métodos que puede utilizar, dependiendo de sus preferencias, presupuesto y habilidades técnicas. En este artículo, le mostraremos tres de las formas más comunes y fáciles de hacerlo. También proporcionaremos algunos consejos y advertencias para hacerlo de forma segura y legal. </p>
- <h2>Método 1: Usando un convertidor de YouTube a MP3</h2>
-<h3> ¿Qué es un convertidor de YouTube a MP3 y cómo funciona</h3>
-<p>Un convertidor de YouTube a MP3 es una herramienta en línea que le permite convertir cualquier vídeo de YouTube en un archivo MP3, que es un formato de audio común que se puede reproducir en la mayoría de los dispositivos. Al usar un convertidor de YouTube a MP3, puede extraer la pista de audio de cualquier video de YouTube y guardarlo como un archivo MP3 en su computadora o unidad USB. De esta manera, puede descargar música de YouTube sin descargar todo el archivo de video, lo que puede ahorrarle tiempo y espacio de almacenamiento. </p>
-<h3>Cómo utilizar un convertidor de YouTube a MP3 para descargar música a una unidad USB</h3>
-
-<h4>Paso 1: Encuentre un convertidor confiable de YouTube a MP3 en línea</h4>
-<p>Hay muchos conversores de YouTube a MP3 disponibles en línea, pero no todos son seguros y confiables. Algunos de ellos pueden contener malware, virus o anuncios que pueden dañar su computadora o dispositivo. Algunos de ellos también pueden tener salida de baja calidad, características limitadas o velocidad de conversión lenta. Por lo tanto, necesita encontrar un conversor de YouTube a MP3 confiable y de buena reputación que pueda satisfacer sus necesidades y expectativas. </p>
-<p></p>
-<p>Algunos de los factores que debes considerar al elegir un convertidor de YouTube a MP3 son:</p>
-<ul>
-<li>La calidad y el tamaño del archivo de salida</li>
-<li>La velocidad y estabilidad del proceso de conversión</li>
-<li>La compatibilidad y seguridad del sitio web y la herramienta</li>
-<li>Disponibilidad y accesibilidad del servicio</li>
-<li>Facilidad de uso y simplicidad de la interfaz</li>
-<li>La legalidad y legitimidad del servicio</li>
-</ul>
-<p>Algunos de los ejemplos de populares y confiables convertidores de YouTube a MP3 son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-<tr><td>4K YouTube to MP3</td><td>[https://www.4kdownload.com/products/product-youtubetomp3]</td><td>- Salida de alta calidad hasta 320 kbps<br>- Conversión rápida y fácil<br>- No hay anuncios o malware<br>- Soporta múltiples formatos y plataformas<> Permite descargas por lotes y listas de reproducción</td></tr>
-<tr><td>YTMP3</td><td>[https://ytmp3.cc/en13/]</td><td>- Interfaz simple y fácil de usar<br>- No se requiere registro ni instalación<br>- Soporta formatos MP3 y MP4<br>- Compatible con la mayoría de navegadores y dispositivos<br>- Tiene un límite de 1 hora por video/td><<<<td>/tr
-<tr><td>MP3FY</td><td>[https://mp3fy.com/en1/]</td><td>- Soporta más de 1000 sitios web y plataformas<br>- No hay límite de longitud de video o tamaño de archivo<br>- Convierte videos largos y audiolibros<br>- Tiene una función de detección automática para enlaces de video<br>- Ofrece configuraciones y opciones avanzadas</td<<<tr/tr
-</tabla>
-
-<p>Una vez que haya elegido un convertidor de YouTube a MP3, debe copiar el enlace del video de YouTube que desea descargar como música. Para hacer esto, puedes:</p>
-<ul>
-<li>Ir a la página web de YouTube o aplicación y encontrar el video que desea descargar. Entonces, copiar la URL de la barra de direcciones o el botón de compartir. </li>
-<li>Utilice la función de búsqueda del sitio web o herramienta de conversión de YouTube a MP3 y escriba el nombre o las palabras clave del video que desea descargar. Luego, seleccione el video de los resultados y copie la URL.</li>
-</ul>
-<p>Después de copiar el enlace, pégalo en la caja de entrada del sitio web o herramienta de conversión de YouTube a MP3. A continuación, haga clic en el botón convertir o descargar para iniciar el proceso. </p>
-<h4>Paso 3: Elija el formato de salida y la calidad</h4>
-<p>Antes de descargar el archivo convertido, es posible que tenga algunas opciones para elegir el formato de salida y la calidad de su archivo de música. El formato de salida es el tipo de archivo de audio que desea descargar, como MP3, WAV, AAC o M4A. La calidad de salida es el nivel de claridad y detalle de sonido que desea tener, como 128 kbps, 192 kbps, 256 kbps o 320 kbps. </p>
-<p>Diferentes formatos y calidades de salida pueden tener diferentes ventajas y desventajas, dependiendo de sus preferencias y necesidades. Por ejemplo, MP3 es un formato común y ampliamente soportado que se puede reproducir en la mayoría de los dispositivos, pero también puede tener cierta pérdida de calidad debido a la compresión. WAV es un formato de alta calidad y sin comprimir que puede preservar la calidad de sonido original del video, pero también puede ocupar más espacio de almacenamiento y ser incompatible con algunos dispositivos. </p>
-<p>Puede elegir el formato de salida y la calidad que más le convenga haciendo clic en el botón de configuración u opciones en el sitio web o herramienta de conversión de YouTube a MP3. También puede dejarlo como predeterminado si no está seguro o no le importa. </p>
-<h4> Música y YouTube Premium y cómo funcionan</h3>
-
-<p>YouTube Music es un servicio de transmisión de música que se centra en el contenido musical de YouTube y otras fuentes. Tiene una interfaz personalizada y personalizada que le permite descubrir y disfrutar de la música en función de sus preferencias, estado de ánimo y actividad. También puede crear sus propias listas de reproducción, mixtapes y estaciones de radio. YouTube Music cuesta $9.99 por mes para un plan individual, o $14.99 por mes para un plan familiar que cubre hasta seis miembros. </p>
-<p>YouTube Premium es un servicio premium que incluye todas las características y beneficios de YouTube Music, además de beneficios adicionales para YouTube y otros productos de Google. Te permite ver y descargar cualquier video de YouTube sin anuncios ni interrupciones. También te da acceso a YouTube Originals, que son programas y películas exclusivos producidos por YouTube y sus socios. YouTube Premium cuesta $11.99 por mes para un plan individual, o $17.99 por mes para un plan familiar que cubre hasta seis miembros. </p>
-<h3>Cómo usar YouTube Music o YouTube Premium para descargar música a una unidad USB</h3>
-<p>Usar YouTube Music o YouTube Premium para descargar música a una unidad USB es otra forma fácil y conveniente de hacerlo. Estos son los pasos que debes seguir:</p>
-<h4>Paso 1: Regístrate para una suscripción premium de YouTube o YouTube</h4>
-<p>El primer paso es registrarse para una suscripción de YouTube Music o YouTube Premium que se adapte a sus necesidades y presupuesto. Puedes hacer esto yendo al sitio web o aplicación de YouTube y haciendo clic en la pestaña Música o Premium. Luego, puede elegir el plan que desea e ingresar sus detalles de pago. También puede obtener una prueba gratuita durante un mes antes de decidir suscribirse. </p>
-<p>Si ya tienes una cuenta de Google, puedes utilizarla para registrarte en YouTube Music o YouTube Premium. Si no lo tienes, puedes crear uno gratis siguiendo las instrucciones del sitio web o la aplicación. </p>
-<h4>Paso 2: Descarga la aplicación de música de YouTube en tu dispositivo</h4>
-
-<p>La aplicación YouTube Music es compatible con la mayoría de los dispositivos Android e iOS, como teléfonos inteligentes, tabletas, televisores inteligentes, altavoces inteligentes y relojes inteligentes. También puede usarlo en su computadora yendo al sitio web [https://music.youtube.com/]. </p>
-<h4>Paso 3: Encuentre la música que desea descargar en YouTube Music</h4>
-<p>El tercer paso es encontrar la música que desea descargar en YouTube Music. Puede hacer esto utilizando la función de búsqueda de la aplicación o sitio web y escribiendo el nombre o palabras clave de la canción, artista, álbum o lista de reproducción que desea descargar. A continuación, puede seleccionar la música de los resultados y abrirla en la aplicación o sitio web. </p>
-<p>También puedes navegar a través de las diferentes categorías y géneros de música en YouTube Music, como Top Charts, New Releases, Your Mixtape, Mood & Genre, Activity & Situation, etc. También puedes explorar recomendaciones personalizadas basadas en tu historial de escucha y preferencias. </p>
-<h4>Paso 4: Toca el icono de descarga en la canción, álbum o lista de reproducción</h4>
-<p>El cuarto paso es tocar el icono de descarga en la canción, álbum o lista de reproducción que desea descargar en YouTube Music. El icono de descarga parece una flecha hacia abajo con una línea debajo. Normalmente se encuentra junto al botón de reproducción o bajo el botón de menú de la música. </p>
-<p>Al tocar el icono de descarga, comenzará a descargar la música al almacenamiento interno del dispositivo o a la tarjeta SD. Puede comprobar el progreso y el estado de la descarga en la aplicación o sitio web. </p>
-<h4>Paso 5: Conecte su unidad USB a su dispositivo y transfiera la música descargada</h4>
-
-<p>Después de transferir los archivos de música, puede expulsar o quitar de forma segura la unidad USB de su dispositivo. A continuación, puede disfrutar de escuchar la música descargada de YouTube Music en cualquier dispositivo compatible con la reproducción USB. </p>
- <h2>Método 3: Uso de un disco duro externo para el almacenamiento de música</h2>
-<h3> ¿Qué es un disco duro externo y cómo funciona</h3>
-<p>Un disco duro externo es un dispositivo que puede almacenar grandes cantidades de datos y conectarse a diferentes equipos y dispositivos a través de un puerto USB o cable. Es similar a una unidad USB, pero tiene más capacidad de almacenamiento y una velocidad de transferencia más rápida. Un disco duro externo se puede utilizar para diversos fines, como hacer copias de seguridad de datos, transferir archivos o almacenar medios. </p>
-<p>Mediante el uso de un disco duro externo para el almacenamiento de música, puede descargar música de YouTube y otras fuentes y guardarla en un dispositivo separado que puede contener miles de canciones. También puede acceder y reproducir su música en cualquier dispositivo compatible, como su computadora portátil, teléfono inteligente, tableta o TV. Un disco duro externo también puede proteger su música de perderse o dañarse debido a virus, malware o fallas de hardware. </p>
-<h3>Cómo utilizar un disco duro externo para el almacenamiento de música</h3>
-<p>El uso de un disco duro externo para el almacenamiento de música es otra opción que puede considerar si desea descargar música de YouTube a una unidad USB. Estos son los pasos que debes seguir:</p>
-<h4>Paso 1: Elija un disco duro externo adecuado para el almacenamiento de música</h4>
-<p>El primer paso es elegir un disco duro externo adecuado para el almacenamiento de música que satisfaga sus necesidades y preferencias. Hay diferentes tipos y modelos de discos duros externos disponibles en el mercado, pero no todos ellos son adecuados para el almacenamiento de música. Algunos de los factores que debe considerar al elegir un disco duro externo para el almacenamiento de música son:</p>
-<ul>
-<li> La capacidad de almacenamiento y la velocidad del disco duro externo</li>
-<li>La compatibilidad y durabilidad del disco duro externo</li>
-
-<li>El precio y la garantía del disco duro externo</li>
-</ul>
-<p>Algunos de los ejemplos de discos duros externos populares y confiables para el almacenamiento de música son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-<tr><td>Seagate Backup Plus Slim</td><td>[https://www.seagate.com/consumer/backup/backup-plus/]</td><td>- Diseño compacto y elegante<br>- Hasta 5 TB de capacidad de almacenamiento<br>- Velocidad de transferencia rápida USB 3.0<br>- Compatible con Windows y Br<Mac Incluye software de copia de seguridad y almacenamiento en la nube</td></tr>
-<tr><td>WD My Passport</td><td>[https://shop.westerndigital.com/products/portable-drives/wd-my-passport-usb-3-2-hdd#WDBYVG0010BBK-WESN]</td><td>- Diseño elegante y colorido<br>Hasta 5 TB de capacidad de almacenamiento<br- Velocidad de transferencia rápida USB 3. Incluye software de copia de seguridad y protección con contraseña</td></tr>
-<tr><td>Samsung T7 Touch Portable SSD</td><td>[https:/www.samsung.com/us/touchting/memory-storage/portable-solid-state-drives/portable-ssd-t7-touchb-usb-3-2-500gbmu-pc-ww/<brtd>>- Diseño delgado y ligero<br- Hasta 2 TB de capacidad de almacenamiento-<br Velocidad de transferencia USB 3.2 súper rápida<br>- Compatible con Windows, Mac, Android y consolas de juegos<br>- Incluye seguridad de huellas dactilares e indicador de estado led</td></tr>
-</tabla>
-<h4>Paso 2: Conecte el disco duro externo a su computadora</h4>
-<p>El siguiente paso es conectar el disco duro externo a su computadora usando un puerto o cable USB. Luego, debe formatear el disco duro externo si aún no está formateado o no es compatible con su computadora. Formatear el disco duro externo borrará todos los datos en él y lo hará listo para su uso. Puede formatear el disco duro externo siguiendo las instrucciones del sitio web o manual del fabricante o proveedor del disco duro externo. </p>
-<h4>Paso 3: Descargar música de YouTube usando cualquiera de los métodos anteriores</h4>
-
-<p>También puede descargar música de otras fuentes, como Spotify, SoundCloud, iTunes o Amazon Music, y guardarlos en su disco duro externo. Sin embargo, es posible que necesite utilizar diferentes herramientas o métodos dependiendo de la fuente y el formato de los archivos de música. </p>
-<h4>Paso 4: Copie y pegue los archivos de música descargados en la carpeta del disco duro externo</h4>
-<p>El paso final es copiar y pegar los archivos de música descargados en la carpeta del disco duro externo que desea utilizar para el almacenamiento de música. Puede hacer esto abriendo el administrador de archivos o la aplicación del explorador en su computadora y encontrando la carpeta o ubicación donde guardó los archivos de música descargados en su disco duro externo. Luego, puede arrastrar y soltar o copiar y pegar los archivos de música en otra carpeta o subcarpeta en su disco duro externo si desea organizarlos mejor. </p>
-<p>Después de copiar y pegar los archivos de música, puede expulsar o quitar de forma segura el disco duro externo de su computadora. A continuación, puede disfrutar de escuchar la música descargada de YouTube en cualquier dispositivo que soporte la reproducción del disco duro externo. </p>
- <h2>Conclusión</h2>
-<p>Descargar música de YouTube a una unidad USB es una gran manera de disfrutar de su música favorita sin conexión, en cualquier dispositivo y en cualquier lugar. Hay diferentes métodos que puede utilizar para hacerlo, como el uso de un convertidor de YouTube a MP3, una suscripción YouTube Music o YouTube Premium, o un disco duro externo para el almacenamiento de música. Cada método tiene sus propias ventajas y desventajas, dependiendo de sus preferencias, presupuesto y habilidades técnicas. </p>
-<p>Aquí hay algunos consejos y advertencias para descargar música de YouTube a una unidad USB:</p>
-<ul>
-<li>Asegúrese de que tiene suficiente espacio de almacenamiento en su unidad USB o disco duro externo para descargar música de YouTube. Puede comprobar la capacidad de almacenamiento y el uso de su dispositivo yendo a su configuración o propiedades. </li>
-
-<li>Asegúrate de respetar los derechos de propiedad intelectual y la privacidad de los creadores y propietarios de la música que descargues de YouTube. Solo debes descargar música gratuita, legal y autorizada para uso personal. No debe descargar música con derechos de autor, protegida o restringida por ley. </li>
-<li>Asegúrese de proteger su computadora y dispositivo de malware, virus o anuncios que puedan provenir de YouTube a convertidores de MP3 u otras fuentes. Solo debe usar herramientas o servicios seguros y confiables que tengan buenas críticas y calificaciones. También debe usar software antivirus y programas de firewall para analizar y bloquear cualquier amenaza potencial. </li>
-</ul>
-<p>Esperamos que este artículo te haya ayudado a aprender a descargar música de YouTube a una unidad USB. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
-<h3>Q1. ¿Es legal descargar música de YouTube? </h3>
-<p>A1. Depende de la fuente y el contenido de la música que descargues de YouTube. En términos generales, es legal descargar música de YouTube si es gratuita, legal y autorizada para uso personal. Sin embargo, es ilegal descargar música de YouTube si está protegida por derechos de autor, o restringida por la ley. Siempre debe respetar los derechos de propiedad intelectual y la privacidad de los creadores y propietarios de la música que descarga de YouTube. También debe comprobar los términos y condiciones del sitio web de YouTube y la fuente de música antes de descargar cualquier música de YouTube.</p>
-<h3>Q2. ¿Cuánto espacio de almacenamiento necesito para descargar música de YouTube? </h3>
-
-<h3>Q3. ¿Cómo puedo reproducir música desde una unidad USB en el estéreo de mi coche? </h3>
-<p>A3. Depende del tipo y modelo de su equipo de música y su unidad USB. En términos generales, puede reproducir música desde una unidad USB en el estéreo de su automóvil si el estéreo de su automóvil tiene un puerto USB o una ranura que puede leer y reproducir archivos de música desde su unidad USB. También puede utilizar un adaptador o cable USB que puede conectar su unidad USB a la entrada o salida auxiliar del estéreo del automóvil. Sin embargo, algunos estéreos de automóviles pueden no admitir algunos formatos o cualidades de archivos de música desde su unidad USB. Usted debe comprobar la compatibilidad y las especificaciones de su coche estéreo y su unidad USB antes de reproducir música desde una unidad USB en su coche estéreo. </p>
-<h3>Q4. ¿Cómo puedo editar o recortar los archivos de música descargados? </h3>
-<p>A4. Puede editar o recortar los archivos de música descargados utilizando un software de edición de audio o una herramienta en su computadora o dispositivo. Hay muchos programas de edición de audio o herramientas disponibles en línea, pero algunos de ellos pueden requerir instalación, registro o pago. Algunos de ellos también pueden tener características limitadas, salida de baja calidad o interfaz compleja. Por lo tanto, necesita encontrar un software o herramienta de edición de audio adecuado y confiable que pueda satisfacer sus necesidades y expectativas. </p>
-<p>Algunos de los factores que debes considerar al elegir un software o herramienta de edición de audio son:</p>
-<ul>
-<li>La funcionalidad y flexibilidad del software o herramienta de edición de audio</li>
-<li>La calidad y el formato del archivo de salida</li>
-<li>Compatibilidad y seguridad del software o herramienta</li>
-<li>Disponibilidad y accesibilidad del servicio</li>
-<li>Facilidad de uso y simplicidad de la interfaz</li>
-<li>La legalidad y legitimidad del servicio</li>
-</ul>
-<p>Algunos de los ejemplos de software o herramientas de edición de audio populares y confiables son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-
-<tr><td>WavePad</td><td>[https://www.nch.com.au/wavepad/index.html]</td><td>- Gratis para uso no comercial<br>- Soporta múltiples formatos y plataformas<br>- Ofrece varias funciones de edición y efectos<br>- Permite el procesamiento por lotes y la conversión<br>- Tiene un profesional y fácilinterfaz de uso</td></tr>
-<tr><td>Online Audio Cutter</td><td>[https://online-audio-cutter.com/]</td><td>- Herramienta gratuita y en línea<br>- Soporta múltiples formatos y plataformas<br>- Ofrece funciones básicas de edición y efectos<br>- Permite recortar, recortar, desvanecer y fusionar archivos de audiobr<>-br> Tiene una interfaz sencilla y fácil de usar</td></tr>
-</tabla>
-<h3>Q5. ¿Cómo puedo hacer copias de seguridad o restaurar mis archivos de música descargados? </h3>
-<p>A5. Puede realizar copias de seguridad o restaurar los archivos de música descargados utilizando un servicio de almacenamiento en la nube o una herramienta en su computadora o dispositivo. Un servicio o herramienta de almacenamiento en la nube es un servicio o herramienta en línea que le permite almacenar, sincronizar, compartir y acceder a sus datos en diferentes dispositivos a través de Internet. Mediante el uso de un servicio o herramienta de almacenamiento en la nube, puede realizar copias de seguridad de sus archivos de música descargados en una ubicación segura en línea a la que puede acceder en cualquier momento y en cualquier lugar. También puede restaurar sus archivos de música descargados desde el servicio de almacenamiento en la nube o la herramienta si pierde o daña su unidad USB o disco duro externo. </p>
-<p>Hay muchos servicios o herramientas de almacenamiento en la nube disponibles en línea, pero algunos de ellos pueden requerir instalación, registro o pago. Algunos de ellos también pueden tener espacio de almacenamiento limitado, baja velocidad de transferencia o una seguridad deficiente. Por lo tanto, necesita encontrar un servicio de almacenamiento en la nube adecuado y confiable o una herramienta que pueda satisfacer sus necesidades y expectativas. </p>
-<p>Algunos de los factores que debe considerar al elegir un servicio o herramienta de almacenamiento en la nube son:</p>
-<ul>
-<li>El espacio de almacenamiento y la velocidad del servicio de almacenamiento en la nube o herramienta</li>
-<li>La compatibilidad y la seguridad del servicio o herramienta de almacenamiento en la nube</li>
-<li>Disponibilidad y accesibilidad del servicio o herramienta</li>
-
-<li>La legalidad y legitimidad del servicio o herramienta</li>
-</ul>
-<p>Algunos de los ejemplos de servicios o herramientas de almacenamiento en la nube populares y confiables son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-<tr><td>Google Drive</td><td>[https://www.google.com/drive/]</td><td>- Gratis hasta 15 GB de espacio de almacenamiento<br>- Soporta múltiples formatos y plataformas<br>- Ofrece diversas opciones de sincronización, uso compartido y acceso<br>- Se integra con otros productos y servicios de Google br<br>-> Tiene una interfaz simple e intuitiva</td></tr>
-<tr><td>Dropbox</td><td>[https://www.dropbox.com/]</td><td>- Gratis por hasta 2 GB de espacio de almacenamiento<br>- Soporta múltiples formatos y plataformas<br>- Ofrece varias opciones de sincronización, uso compartido y acceso<br>- Se integra con otras aplicaciones y servicios<br>- Tiene una sincronización profesional y fácilinterfaz de uso</td></tr>
-<tr><td>pCloud</td><td>[https://www.pcloud.com/]</td><td>- Gratis por hasta 10 GB de espacio de almacenamiento<br>- Soporta múltiples formatos y plataformas<br>- Ofrece varias opciones de sincronización, uso compartido y acceso<br>- Proporciona cifrado de alto nivel y seguridad<br>- Tiene un elegante y usuariointerfaz amigable</td></tr>
-</tabla>
- <p>Esperamos que este artículo te haya ayudado a aprender a descargar música de YouTube a una unidad USB. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p> 64aa2da5cf<br />
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-<br />
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-<br> - ¿Cuáles son los modos de comunicación entre el agente de Zabbix y el servidor/ proxy <br> - ¿Cuáles son los beneficios de usar el agente de Zabbix para el monitoreo de Windows | | Descarga de Zabbix Agent | - Cómo descargar el pre-brcompilado binarios agente Zabbix o instalador MSI desde el sitio web oficial de Zabbix <br> - Cómo elegir la versión y la arquitectura correcta para su sistema | | Instalación de agente Zabbix | - Cómo instalar agente Zabbix como un servicio de Windows utilizando la línea de comandos o instalador MSI <br> - Cómo verificar que el servicio está instalado y ejecutándose | | Configuración del agente Zabbix | - Cómo editar el archivo de configuración del agente Zabbix <br> - Cómo establecer el nombre de host, la dirección del servidor y otros parámetros <br> - Cómo configurar el agente Zabbix para el modo pasivo o activo | | Adición de Windows Host a la interfaz web de Zabbix | - Cómo crear un host en la interfaz web de Zabbix y asignarlo a un grupo <br> - Cómo especificar la interfaz del agente y la dirección IP o el nombre DNS del host de Windows <br> - Cómo vincular una plantilla para el monitoreo de Windows por el agente Zabbix o agente Zabbix activo | | Conclusión | - Un resumen de lo que hemos aprendido en este artículo <br> - Una llamada a la acción para más aprendizaje y exploración | ## Artículo con formato HTML <p><strong>Cómo descargar e instalar Zabbix Agent para Windows Server 2019</strong></p>
- <p>Zabbix es una potente solución de monitoreo de código abierto que puede monitorear varios aspectos de su infraestructura de TI, como servidores, redes, aplicaciones, bases de datos, servicios de nube y más. Zabbix puede recopilar y visualizar métricas de diferentes fuentes, como SNMP, WMI, comprobaciones sin agentes o agente Zabbix. </p>
-<h2>descargar agente zabbix para Windows Server 2019</h2><br /><p><b><b>Download</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6LZk">https://bltlly.com/2v6LZk</a></b></p><br /><br />
-
- <p>En este artículo, le mostraremos cómo descargar e instalar el agente Zabbix para Windows Server 2019, y cómo configurarlo para el modo pasivo o activo. También explicaremos los beneficios de usar el agente Zabbix para el monitoreo de Windows y cómo agregar su host de Windows a la interfaz web de Zabbix. </p>
- <h2>Descargar agente Zabbix</h2>
- <p>Para descargar el agente Zabbix para Windows Server 2019, tiene dos opciones: puede descargar los binarios precompilados en formato ZIP o usar el paquete de instalación MSI. Ambas opciones están disponibles en el sitio web oficial de Zabbix en <a href="( 1 )">https://www.zabbix.com/download_agents</a>. </p>
- <p>Los binarios precompilados son adecuados para la instalación y configuración manual utilizando la línea de comandos. Puede elegir entre dos generaciones de agentes Zabbix: agente Zabbix 1 (legado) o agente Zabbix 2 (nuevo). También necesita seleccionar la arquitectura apropiada para su sistema: 32 bits (x86) o 64 bits (x64). </p>
- <p>El paquete de instalación de MSI es adecuado para la instalación y configuración automatizada utilizando una interfaz gráfica de usuario. Solo necesita seleccionar la arquitectura de su sistema: 32 bits (x86) o 64 bits (x64). El instalador de MSI instalará el agente Zabbix 2 por defecto. </p>
- <h2>Instalación del agente Zabbix</h2>
- <p>Para instalar el agente Zabbix como un servicio de Windows en su máquina de Windows Server 2019, puede usar la línea de comandos o el instalador MSI. </p> <p>Si utiliza la línea de comandos, debe descomprimir el archivo ZIP descargado en una carpeta de su elección, como <code>C: zabbix</code>. Luego, abra un símbolo del sistema como administrador y vaya a esa carpeta. Para instalar el agente Zabbix como servicio, ejecute el siguiente comando:</p>
- <pre><code>zabbix_agentd.exe --config zabbix_agentd.win.conf --install </code></pre>
- <p>Para verificar que el servicio está instalado y ejecutándose, ejecute el siguiente comando:</p>
- <pre><code>sc query zabbix_agentd </code></pre>
-
- <p>Si utiliza el instalador MSI, debe ejecutar el archivo MSI descargado y seguir el asistente de instalación. Se le pedirá que acepte el acuerdo de licencia, elija la carpeta de instalación y configure algunos parámetros, como el nombre de host, la dirección del servidor y el modo de agente. También puede optar por iniciar el servicio automáticamente después de la instalación. Una vez finalizada la instalación, puede verificar que el servicio está instalado y ejecutándose comprobando el administrador de servicios de Windows o utilizando el comando <code>sc query</code> como se describe anteriormente. </p>
-<p></p>
- <h2>Configuración del agente Zabbix</h2>
- <p>Para configurar el agente Zabbix para Windows Server 2019, debe editar el archivo de configuración del agente Zabbix. El archivo de configuración se encuentra en la misma carpeta donde instaló el agente Zabbix, como <code>C: zabbix zabbix_agentd.win.conf</code>. Puede usar cualquier editor de texto para abrir y editar el archivo. </p>
- <p>El archivo de configuración contiene muchos parámetros que controlan el comportamiento y la funcionalidad del agente Zabbix. Algunos de los parámetros más importantes son:</p>
- <ul>
-<li><code>Hostname</code>: El nombre del host monitoreado tal como aparece en la interfaz web de Zabbix. Debe coincidir exactamente con el nombre de host que creará en la interfaz web de Zabbix más tarde. </li>
-<li><code>Servidor</code>: La dirección IP o el nombre DNS del servidor o proxy Zabbix que solicitará datos del agente Zabbix en modo pasivo. Puede especificar varios servidores o proxies separados por comas. </li>
-<li><code>ServerActive</code>: La dirección IP o el nombre DNS del servidor o proxy Zabbix que recibirá datos del agente Zabbix en modo activo. Puede especificar varios servidores o proxies separados por comas. </li>
-<li><code>StartAgents</code>: El número de conexiones concurrentes que el agente Zabbix puede aceptar desde el servidor Zabbix o el proxy en modo pasivo. El valor predeterminado es 3.</li>
-
-<li><code>Timeout</code>: El tiempo de espera en segundos para procesar cada elemento por el agente de Zabbix. El valor predeterminado es 3.</li>
-<li><code>EnableRemoteCommands</code>: Un indicador que habilita o inhabilita la ejecución de comandos remotos desde el servidor Zabbix o el proxy en el agente Zabbix. El valor predeterminado es 0 (desactivado). </li>
-<li><code>LogType</code>: El tipo de archivo de registro que usará el agente Zabbix. Los valores posibles son <code>file</code>, <code>system</code>, <code>console</code>, o <code>none</code>. El valor predeterminado es <code>file</code>. </li>
-<li><code>LogFile</code>: El nombre y la ruta del archivo de registro que el agente de Zabbix usará si <code>LogType</code> se establece en <code>file</code>. El valor predeterminado es <code>C: zabbix zabbix_agentd.log</code>. </li>
-<li><code>DebugLevel</code>: El nivel de detalle que el agente de Zabbix escribirá en el archivo de registro. Los valores posibles son 0 (basic), 1 (critical), 2 (error), 3 (warning), 4 (debug), o 5 (trace). El valor predeterminado es 3.</li>
-</ul>
- <p>Para configurar el agente Zabbix para el modo pasivo, debe establecer el parámetro <code>Server</code> en la dirección IP o el nombre DNS de su servidor Zabbix o proxy, y dejar el parámetro <code>ServerActive</code> vacío o comentado. Por ejemplo: resultado/p>
- <pre><code># Servidor de modo pasivo=192.168.1.10 #ServerActive= StartAgents=3 # Modo activo #Server= #ServerActive=192.168.1.10:10051 #RefreshActiveChecks=120 </pre></p>
- <p>Para configurar el agente Zabbix para el modo activo, debe establecer el parámetro <code>ServerActive</code> en la dirección IP o el nombre DNS de su servidor Zabbix o proxy , y dejar el parámetro <code>Server</code> vacío o comentado. También debe establecer el parámetro <code>RefreshActiveChecks</code> en la frecuencia deseada de envío de datos al servidor o proxy. Por ejemplo:</p>
- <pre><code># Modo pasivo #Server=192.168.1.10 #StartAgents=3 # Active mode ServerActive=192.168.1.10:10051 RefreshActiveChecks=120 </pre></p>
-
- <pre><code>sc stop zabbix_agentd sc start zabbix_agentd </code></pre>
- <h2>Agregar host de Windows a la interfaz web de Zabbix</h2>
- <p>Para agregar su host de Windows Server 2019 a la interfaz web de Zabbix, debe iniciar sesión en su servidor Zabbix o interfaz web proxy y navegar a <strong>Configuration</strong> > <strong>Hosts</strong>. Luego, haga clic en el botón <strong>Crear host</strong> en la esquina superior derecha. </p>
- <p>Verás un formulario donde necesitas introducir algunos detalles sobre tu host, como:</p>
- <ul>
-<li><strong>Nombre del host</strong>: El nombre de host de su host de Windows tal como aparece en el archivo de configuración del agente Zabbix. Debe coincidir exactamente con el parámetro <code>Hostname</code> que estableció en el archivo de configuración. </li>
-<li><strong>Nombre visible</strong>: Un alias opcional para su host que se mostrará en la interfaz web de Zabbix en lugar del nombre del host. </li>
-<li><strong>Grupos</strong>: Los grupos a los que pertenece su host. Puede seleccionar uno o más grupos existentes o crear uno nuevo. Por ejemplo, puede seleccionar o crear un grupo llamado <code>Servidores de Windows</code>. </li>
-<li><strong>Descripción</strong>: Una descripción opcional de su anfitrión que proporcionará información adicional sobre su propósito, ubicación, propietario, etc.</li>
-</ul>
- <p>Después de introducir estos detalles, haga clic en el botón <strong>Add</strong> en la parte inferior del formulario. </p>
- <p>El siguiente paso es especificar la interfaz del agente y la dirección IP o el nombre DNS de su host de Windows. Para ello, haga clic en la pestaña <strong>Interfaces</strong> y seleccione <strong>Zabbix agent</strong> en el menú desplegable. Luego, ingrese la dirección IP o el nombre DNS de su host de Windows en el campo <strong>IP address/DNS name</strong>. También puede cambiar el número de puerto predeterminado si es necesario. </p>
- <p>Si configuró el agente Zabbix para el modo activo, también debe seleccionar el agente <strong>Zabbix (active)</strong> en el menú desplegable e ingresar la misma dirección IP o nombre DNS que antes. </p>
-
- <p>Una plantilla es una colección de elementos, disparadores, gráficos y otros elementos que definen qué y cómo monitorear un host. Al vincular una plantilla a tu host, heredas todos estos elementos y ahorras tiempo y esfuerzo. </p>
- <p>También puede vincular otras plantillas que proporcionan funciones de monitoreo adicionales para su host de Windows, como CPU, memoria, disco, red, servicios, procesos, etc.</p>
- <p>Después de vincular las plantillas, haga clic en el botón <strong>Add</strong> en la parte inferior del formulario. </p>
- <h2>Conclusión</h2>
- <p>En este artículo, hemos aprendido cómo descargar e instalar el agente Zabbix para Windows Server 2019, y cómo configurarlo para el modo pasivo o activo. También hemos aprendido cómo agregar nuestro host de Windows a la interfaz web de Zabbix y vincular una plantilla para el monitoreo de Windows por el agente de Zabbix o agente de Zabbix activo. </p>
- <p>Zabbix agent es una herramienta útil que nos permite monitorear varios aspectos de nuestra máquina Windows Server 2019, como el rendimiento, el estado y la configuración. Al usar el agente Zabbix, podemos recopilar y visualizar métricas de nuestro host de Windows y recibir alertas cuando algo sale mal. </p>
- <p>Si desea obtener más información sobre Zabbix y el agente Zabbix, puede visitar el sitio web oficial de Zabbix en <a href="">https://www.zabbix.com/</a>, donde puede encontrar documentación, tutoriales, foros, blogs y otros recursos. </p>
- <h3>Preguntas frecuentes</h3>
- <ol>
-<li><strong>¿Cuáles son los requisitos para ejecutar el agente Zabbix en Windows Server 2019? </strong></li>
-<li>Los requisitos para ejecutar el agente Zabbix en Windows Server 2019 son mínimos. Necesita tener una máquina Windows Server 2019 con al menos 128 MB de RAM y 100 MB de espacio libre en disco. También necesita tener privilegios de administrador para instalar y configurar el agente Zabbix como un servicio. </li>
-<li><strong>¿Cómo puedo probar si el agente Zabbix funciona correctamente en mi host de Windows? </strong></li>
-
- <pre><code>zabbix_get -s <IP address or DNS name of your Windows host> -k "system.cpu.load[all,avg1]" </code></pre>
- <li>Este comando solicitará la carga media de la CPU durante el último minuto desde su host de Windows. Debería ver un valor numérico como respuesta. Si ve un mensaje de error, como <code>ZBX_NOTSUPPORTED</code> o <code>ZBX_TCP_READ()</code>, significa que hay un problema con la comunicación entre el agente Zabbix y el servidor o proxy, o con la configuración del agente Zabbix. </li>
-<li>Otra forma de probar si el agente Zabbix funciona correctamente en su host de Windows es usar la utilidad <code>zabbix_sender</code> que también viene con el agente Zabbix. Esta utilidad le permite enviar datos al servidor Zabbix o proxy en modo activo. Por ejemplo, puede ejecutar el siguiente comando desde su host de Windows:</li>
- <pre><code>zabbix_sender -z <IP address or DNS name of your Zabbix server or proxy> -s <hostname of your Windows host> -k "test.key" -o "test.value" </code></pre>
- <li>Este comando enviará un elemento personalizado con la clave <code>test.key</code> y el valor <code>test.value</code> desde su host de Windows al servidor o proxy de Zabbix. Deberías ver un mensaje como <code>sent: 1; omitido: 0; total: 1</code> como respuesta. Si ve un mensaje de error, como <code>ZBX_TCP_WRITE()</code> o <code>ZBX_TCP_READ()</code>, significa que hay un problema con la comunicación entre el agente Zabbix y el servidor o proxy, o con la configuración del agente Zabbix. </li>
-
-<li>Si ve algún problema con el estado o los datos de su host, como <code>ZBX_NOTSUPPORTED</code>, <code>No se recibieron datos</code>, o <code>No hay permisos para el objeto referido o no existe! </code>, significa que hay un problema con la comunicación entre el agente Zabbix y el servidor o proxy, o con la configuración del agente Zabbix. </li>
-<li><strong>¿Cómo puedo actualizar el agente Zabbix en mi host de Windows? </strong></li>
-<li>Para actualizar el agente de Zabbix en su host de Windows, debe descargar la última versión del agente de Zabbix desde el sitio web oficial de Zabbix en <a href="">https:/www.zabbix.com/download_agents</a>. Luego, debe detener el servicio del agente Zabbix, reemplazar los archivos antiguos con los nuevos e iniciar el servicio nuevamente. Puede usar los siguientes comandos para hacer esto:</li>
- <pre><code>sc stop zabbix_agentd xcopy /y C: zabbix *. exe C: zabbix *. dll C: zabbix *. conf C: zabbix *. bat C: zabbix *. ps1 C: zabbix *. vbs sc start zabbix_agentd </code></pre>
- <li>También puede necesitar editar el archivo de configuración del agente Zabbix si hay cambios o nuevos parámetros en la nueva versión. </li>
-<li><strong>¿Cómo puedo desinstalar el agente Zabbix de mi host de Windows? </strong></li>
-<li>Para desinstalar el agente Zabbix de su host de Windows, debe detener el servicio del agente Zabbix, eliminar el servicio del agente Zabbix y eliminar la carpeta del agente Zabbix. Puede usar los siguientes comandos para hacer esto:</li>
- <pre><code>sc stop zabbix_agentd zabbix_agentd.exe --config zabbix_agentd.win.conf --uninstall rmdir /s /q C: zabbix </code></pre>
- <li>Si utilizó el instalador MSI para instalar el agente Zabbix, también puede usar el panel de control de Windows o el instalador MSI para desinstalar el agente Zabbix. </li>
-<li><strong>¿Cómo puedo personalizar el agente Zabbix para mis necesidades específicas? </strong></li>
-
- <ul>
-<li><strong>Parámetros del usuario</strong>: Los parámetros del usuario le permiten definir elementos personalizados que el agente Zabbix puede monitorear. Puede usar cualquier script o comando que devuelva un valor como parámetro de usuario. Por ejemplo, puede crear un parámetro de usuario que devuelva el número de archivos de una carpeta, el estado de un servicio o la salida de un comando de PowerShell. Necesita definir los parámetros de usuario en el archivo de configuración del agente Zabbix usando la directiva <code>UserParameter</code>. Por ejemplo:</li>
- <pre><code>UserParameter=folder.count,powershell -NoProfile -Command "(Get-ChildItem C: temp -Recurse | Measure-Object). Count" UserParameter=service.status[*],sc query $1 | find "STATE" | find "RUNNING" UserParameter=powershell.output[*],powershell -NoProfile -Command "$1" </code></pre>
- <li>Entonces, necesitas crear elementos en la interfaz web de Zabbix que usen estos parámetros de usuario como claves. Por ejemplo:</li>
- <pre><code>folder.count service.status[Spooler] powershell.output[Get-Date] </code></pre>
- <li><strong>Comprobaciones activas</strong>: Las comprobaciones activas permiten enviar datos desde el agente Zabbix al servidor Zabbix o al proxy sin esperar solicitudes. Esto reduce la carga de red y mejora la escalabilidad de Zabbix. Puede usar comprobaciones activas para cualquier elemento que sea compatible con el agente Zabbix, como métricas del sistema, archivos de registro, contadores de rendimiento de Windows, etc. Debe configurar el agente Zabbix para el modo activo estableciendo los parámetros <code>ServerActive</code> y <code>RefreshActiveChecks</code> en el archivo de configuración. Luego, debe vincular una plantilla para el agente Zabbix activo en la interfaz web de Zabbix. </li>
-
-<li><strong>Cifrado</strong>: El cifrado le permite asegurar la comunicación entre el agente de Zabbix y el servidor o proxy utilizando certificados TLS. Esto puede evitar el acceso no autorizado y la manipulación de datos. Necesita generar e instalar certificados TLS en su host de Windows y en su servidor o proxy de Zabbix. Luego, debe configurar el agente y el servidor Zabbix o el proxy para usar el cifrado estableciendo el valor <code>TLSConnect</code>, <code>TLSAccept</code>, <code>TLScaFile</code>, <code>TLScaPath</code>, <code>TLScertFile</code>, y code <T>TLSkeyle</code. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Carx Calle Apk.md b/spaces/Benson/text-generation/Examples/Descargar Carx Calle Apk.md
deleted file mode 100644
index 0a5c80332793e66950025d9bd754c8f5afcc0337..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Carx Calle Apk.md	
+++ /dev/null
@@ -1,46 +0,0 @@
-<br />
-<h1>El zombi que camina 2 Mod APK: Un juego de disparos de supervivencia con dinero ilimitado</h1>
-<p>Si eres un fan de los juegos de zombis, es posible que hayas oído hablar de The Walking Zombie 2, un popular juego de disparos en primera persona que te permite luchar contra hordas de criaturas no muertas en un mundo post-apocalíptico. ¿Pero sabías que hay una versión modificada de este juego que te da dinero y recursos ilimitados para usar? En este artículo, le diremos todo lo que necesita saber sobre The Walking Zombie 2 Mod APK, incluyendo sus características, beneficios, y cómo descargar e instalar en su dispositivo. </p>
-<h2>descargar carx calle apk</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://bltlly.com/2v6K1s">https://bltlly.com/2v6K1s</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3>¿Qué es el zombi andante 2?</h3>
-<p>The Walking Zombie 2 es un juego de disparos de supervivencia desarrollado por Alda Games, un estudio con sede en la República Checa. El juego se desarrolla en un mundo que ha sido devastado por un virus zombi, donde juegas como uno de los pocos supervivientes que tiene que luchar por tu vida. Encontrarás varios tipos de zombies, como caminantes, corredores, mutantes y jefes, así como otros enemigos como bandidos, asaltantes y soldados. También conocerás a otros sobrevivientes que te ayudarán o te obstaculizarán en tu viaje. </p>
-<h3> ¿Qué es el zombie caminando 2 Mod APK? </h3>
-<p>El Walking Zombie 2 Mod APK es una versión modificada del juego original que le da acceso a dinero y recursos ilimitados. Esto significa que puede comprar cualquier arma, armadura, munición, kits de salud y otros artículos que necesite sin preocuparse por quedarse sin efectivo. También puede mejorar sus habilidades y habilidades para hacerse más fuerte y más resistente. Con este mod, podrás disfrutar del juego sin limitaciones ni restricciones. </p>
-<h3> ¿Por qué deberías jugar El zombie andante 2 Mod APK? </h3>
-<p>Hay muchas razones por las que debe jugar The Walking Zombie 2 Mod APK en lugar del juego original. Aquí están algunos de ellos:</p>
-<ul>
-<li> Usted puede tener más diversión y emoción con dinero y recursos ilimitados. </li>
-
-<li>Puede explorar más áreas y ubicaciones sin temor a quedarse sin suministros. </li>
-<li>Puedes desafiarte a ti mismo con dificultades y enemigos más difíciles sin frustrarte. </li>
-<li>Puedes apoyar a los desarrolladores viendo anuncios o haciendo compras en la aplicación si quieres. </li>
-</ul>
-<h2>Características de The Walking Zombie 2 Mod APK</h2>
-<h3>Historia inmersiva y jugabilidad</h3>
-<p>El Walking Zombie 2 Mod APK tiene una historia atractiva que te mantendrá enganchado de principio a fin. Experimentarás diferentes eventos y escenarios que afectarán el resultado del juego. También tendrás que tomar decisiones que darán forma a la personalidad y la moralidad de tu personaje. El juego tiene una dinámica de juego que se adaptará a sus acciones y comportamiento. Enfrentarás diferentes retos y consecuencias dependiendo de cómo juegues el juego. </p>
-<p></p>
-<h3>Impresionantes gráficos y efectos de sonido</h3>
-<p>El Walking Zombie 2 Mod APK tiene gráficos increíbles y efectos de sonido que te sumergen en el mundo del juego. El juego tiene un estilo único low-poly que le da una sensación retro. El juego también tiene iluminación realista y sombras que crean una atmósfera oscura y sombría. El juego tiene efectos de sonido de alta calidad que mejoran el estado de ánimo y la tensión del juego. Usted escuchará los gemidos y gritos de los zombies y los disparos y explosiones de las armas. También disfrutarás de la música y la actuación de voz que añaden más profundidad y emoción al juego. </h3>
-<h3>Varias armas y habilidades para elegir</h3>
-
-<h3>Múltiples modos de juego y misiones para completar</h3>
-<p>El Walking Zombie 2 Mod APK tiene varios modos de juego y misiones que se puede jugar y completar para ganar recompensas y experiencia. Puedes jugar en el modo historia principal, donde sigues la trama y el progreso a través del juego. También puedes jugar las misiones secundarias, donde ayudas a otros supervivientes o completar diferentes tareas. También puedes jugar al modo arena, donde luchas contra oleadas de zombies en un área cerrada. También puedes jugar en el modo online, donde compites con otros jugadores en PvP o batallas cooperativas. </h3>
-<h3>Dinero y recursos ilimitados para usar</h3>
-<p>El Walking Zombie 2 Mod APK tiene dinero y recursos ilimitados que se pueden utilizar para comprar y actualizar cualquier cosa que desee en el juego. Usted puede comprar cualquier arma, armadura, munición, kits de salud, y otros artículos que usted necesita de las tiendas o comerciantes. También puede mejorar sus habilidades y habilidades para hacerse más fuerte y más resistente. También puede crear y construir sus propios artículos y equipos a partir de los materiales que recoja o saquee. También puedes intercambiar con otros supervivientes o jugadores para obtener más dinero y recursos. </h3>
-<h2>Cómo descargar e instalar El Walking Zombie 2 Mod APK en su dispositivo</h2>
-<h3>Paso 1: Descargar el archivo APK de una fuente de confianza</h3>
-<p>El primer paso para descargar e instalar el Walking Zombie 2 Mod APK en su dispositivo es encontrar una fuente confiable que proporciona el archivo APK. Puede buscar en línea para sitios web o blogs que ofrecen el archivo APK de forma gratuita. Asegúrese de que la fuente es segura y segura, y que el archivo APK se actualiza y es compatible con su dispositivo. También puede escanear el archivo APK con un programa antivirus antes de descargarlo. </p>
-<h3>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
-
-<h3>Paso 3: Instalar el archivo APK y lanzar el juego</h3>
-<p>El tercer paso para descargar e instalar The Walking Zombie 2 Mod APK en su dispositivo es instalar el archivo APK y lanzar el juego. Para ello, busque el archivo APK que descargó en la carpeta de almacenamiento o descargas de su dispositivo. Toque en el archivo APK para iniciar el proceso de instalación. Siga las instrucciones de la pantalla para completar la instalación. Una vez hecho esto, abra el icono del juego en la pantalla de inicio o en el cajón de la aplicación. Disfruta jugando The Walking Zombie 2 Mod APK con dinero y recursos ilimitados. </p>
-<h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>El Walking Zombie 2 Mod APK es un juego de disparos de supervivencia que le permite luchar contra zombies y otros enemigos en un mundo post-apocalíptico. El juego tiene una historia inmersiva y jugabilidad, impresionantes gráficos y efectos de sonido, varias armas y habilidades para elegir, múltiples modos de juego y misiones para completar, y dinero y recursos ilimitados para usar. El juego es divertido y emocionante, desafiante y gratificante, personalizable y flexible. </p>
-<h3>Llamada a la acción y recomendación</h3>
-<p>Si usted está buscando un juego de zombies que le mantendrá entretenido durante horas, entonces usted debe descargar The Walking Zombie 2 Mod APK en su dispositivo. Usted no se arrepentirá, ya que tendrá una explosión jugando a este juego con dinero y recursos ilimitados. También apoyarás a los desarrolladores viendo anuncios o haciendo compras en la aplicación si quieres. ¿Qué estás esperando? Descargar El Walking Zombie 2 Mod APK ahora y disfrutar de disparar zombies en la cabeza. </p>
-
-el mundo del juego tanto como sea posible. Encontrará más botín, materiales, secretos y huevos de Pascua que mejorarán su experiencia de juego. - Elige tus armas y habilidades de acuerdo a tu estilo de juego y situación. Diferentes armas y habilidades tienen diferentes ventajas y desventajas. Experimenta con diferentes combinaciones y ve qué funciona mejor para ti. - Ten cuidado con tus elecciones y acciones. Afectarán el resultado del juego y la personalidad y moralidad de tu personaje. Piensa antes de actuar y prepárate para las consecuencias. - Diviértete y disfruta del juego. No te lo tomes demasiado en serio ni te estreses por ello. Después de todo, es solo un juego. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Coche Escuela De Conduccin 2017 Mod Apk.md b/spaces/Benson/text-generation/Examples/Descargar Coche Escuela De Conduccin 2017 Mod Apk.md
deleted file mode 100644
index 1b0166c6996214ac31ae76940062d87d31c5d48c..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Coche Escuela De Conduccin 2017 Mod Apk.md	
+++ /dev/null
@@ -1,97 +0,0 @@
-<br />
-<h1>Cómo descargar coche Driving School 2017 Mod Apk y disfrutar de dinero y vehículos ilimitados</h1>
-<p>Si usted está buscando un juego de simulador de conducción realista y divertido que le enseñará cómo conducir diferentes coches en varios entornos, entonces usted debe probar <strong>Car Driving School 2017</strong>. Este juego desafiará sus habilidades de conducción, el conocimiento de las reglas de la carretera, y la delicadeza al volante. Pero si quieres hacer el juego aún más agradable, usted debe descargar <strong>Car Driving School 2017 mod apk</strong>, que le dará dinero ilimitado y vehículos para desbloquear. En este artículo, le diremos lo que es Car Driving School 2017, por qué debe jugar, lo que es Car Driving School 2017 mod apk, cómo descargarlo, y algunos consejos y trucos para dominar el juego. </p>
- <h2>¿Qué es la escuela de conducción de coches 2017 y por qué usted debe jugar</h2>
-<p>Car Driving School 2017 es un juego de simulación de conducción para dispositivos Android e iOS que fue desarrollado por Ovilex Software. Es la nueva entrega de la popular serie Driving School que tiene más de 100 millones de descargas en todo el mundo. En este juego, usted aprenderá cómo conducir varios coches, autobuses y camiones en diferentes escenarios. También tendrás que pasar diferentes licencias, completar más de 80 niveles y explorar más de 15 mapas detallados. También puedes jugar con tus amigos en nuevos modos multijugador como carreras, paseo gratis y coger la bandera. </p>
-<h2>descargar coche escuela de conducción 2017 mod apk</h2><br /><p><b><b>Download</b> &#8230;&#8230;&#8230; <a href="https://bltlly.com/2v6M3h">https://bltlly.com/2v6M3h</a></b></p><br /><br />
- <h3>Características de la escuela de conducción de coches 2017</h3>
-<p>Algunas de las características sorprendentes de Car Driving School 2017 son:</p>
-<ul>
-<li>Casi 100 vehículos para desbloquear, que van desde coches deportivos, SUV, sedanes, autobuses, camiones y más. </li>
-<li>Más de 15 mapas detallados que incluyen ciudades, caminos rurales, carreteras, desiertos, montañas, etc.</li>
-<li> Manejo del automóvil suave y realista que le permite sentir cada golpe, giro y freno. </li>
-<li>Diferentes licencias para tomar, como licencias de automóvil, autobús y camión. </li>
-
-<li>Modo de viaje gratuito que te permite explorar los mapas a tu propio ritmo. </li>
-<li>Nuevos modos multijugador que te permiten competir contra otros jugadores, deambular libremente con ellos o capturar sus banderas. </li>
-<li>Interiores detallados de vehículos que muestran el salpicadero, el volante, los pedales, etc.</li>
-<li>Sistema de daños realista que muestra los efectos de colisiones y accidentes. </li>
-<li>Sistema de gas que requiere que llenes tu tanque en las gasolineras. </li>
-<li>Transmisión manual con embrague que le permite controlar sus engranajes manualmente. </li>
-<li>Dirección basculante, botones y volante táctil que te permiten elegir tu opción de control preferida. </li>
-<li>Tablas de clasificación en línea y logros que le permiten comparar su rendimiento con otros jugadores. </li>
-<li>Sonidos de motor reales que te hacen sentir como si estuvieras conduciendo un coche real. </li>
-<li>Condiciones meteorológicas de próxima generación que añaden realismo y variedad al juego. </li>
-</ul>
- <h3>Beneficios de jugar Car Driving School 2017</h3>
-<p>Jugar Car Driving School 2017 no solo es divertido sino también beneficioso por varias razones:</p>
-<ul>
-<li> Puede aprender a conducir una transmisión manual con embrague y palanca de cambios o mantener la caja de cambios automática clásica. </li>
-<li> Con este simulador de conducción intuitivo puedes conocer mejor las reglas de circulación. </li>
-<li> Puede mejorar sus habilidades de conducción en diferentes situaciones y entornos. </li>
-<li> Puede disfrutar de una experiencia de conducción realista e inmersiva con impresionantes gráficos y efectos de sonido. </li>
-<li>Puedes divertirte con tus amigos en modos multijugador o competir con otros jugadores en línea. </li>
-<li>Puede personalizar sus vehículos con diferentes colores, llantas, alerones, etc.</li>
-</ul>
- <h2>¿Qué es Car Driving School 2017 Mod Apk y cómo descargarlo</h2>
-
- <h3>Ventajas de la escuela de conducción de coches 2017 Mod Apk</h3>
-<p>Algunas de las ventajas de Car Driving School 2017 mod apk son:</p>
-<ul>
-<li>Puedes desbloquear todos los vehículos del juego, incluidos los premium que cuestan dinero real. </li>
-<li> Usted puede comprar cualquier actualización y personalizaciones para sus vehículos sin preocuparse por el costo. </li>
-<li>Puedes explorar todos los mapas y modos del juego sin tener que desbloquearlos primero. </li>
-<li>Puedes jugar el juego sin anuncios ni interrupciones. </li>
-<li> Puedes disfrutar del juego con mejor rendimiento y estabilidad. </li>
-</ul>
- <h3>Pasos para descargar e instalar coche Driving School 2017 Mod Apk</h3>
-<p>Si desea descargar e instalar Car Driving School 2017 mod apk, es necesario seguir estos sencillos pasos:</p>
-<ol>
-<li>En primer lugar, es necesario desinstalar la versión original de Car Driving School 2017 desde su dispositivo si lo tiene instalado. </li>
-<li>En segundo lugar, es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en el dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>En tercer lugar, es necesario descargar el coche Driving School 2017 mod apk archivo de una fuente confiable. Puede utilizar este enlace para descargarlo de forma segura y rápida. </li>
-<li>Cuarto, es necesario localizar el archivo descargado en el dispositivo y toque en él para iniciar el proceso de instalación. </li>
-<li>Quinto, debe seguir las instrucciones en la pantalla y esperar a que termine la instalación. </li>
-<li>Sexto, es necesario lanzar el juego y disfrutar de dinero y vehículos ilimitados. </li>
-</ol>
- <h2>Consejos y trucos para dominar la escuela de conducción de automóviles 2017</h2>
-<p>Car Driving School 2017 es un juego divertido y desafiante que requiere habilidad y estrategia. Aquí hay algunos consejos y trucos que te ayudarán a dominar el juego:</p>
- <h3>Sigue la ley y el límite de velocidad</h3>
-
- <h3>Desactivar el modo deportivo y aprender el mapa</h3>
-<p>Si desea mejorar sus habilidades de conducción y pasar los niveles más fácilmente, debe desactivar el modo deportivo en la configuración. El modo deportivo hace que tu coche sea más rápido y sensible, pero también más difícil de controlar. También consume más gasolina y causa más daños. Por lo tanto, es mejor apagarlo y conducir más suave y cuidadosamente. También debe aprender el mapa de cada nivel antes de iniciarlo. De esta manera, sabrás a dónde ir, qué esperar y cómo evitar obstáculos. </p>
- <h3>Diviértete en modo libre y modos multijugador</h3>
-<p>Si quieres tomarte un descanso de los niveles y licencias, puedes divertirte en modo libre o en modo multijugador. En modo libre, puede conducir alrededor de cualquier mapa sin ningún objetivo o restricciones. También puede cambiar entre diferentes vehículos y personalizarlos a su gusto. En los modos multijugador, puedes jugar con tus amigos u otros jugadores en línea en varios modos como carreras, paseos gratis o coger la bandera. También puedes chatear con ellos y hacer nuevos amigos. </p>
-<p></p>
- <h2>Conclusión</h2>
-<p>Car Driving School 2017 es un gran juego de simulación de conducción que le enseñará cómo conducir diferentes vehículos en escenarios realistas. También se divertirá con varias características, modos, mapas y vehículos. Pero si quieres hacer el juego aún más agradable, usted debe descargar Car Driving School 2017 mod apk que le dará dinero ilimitado y vehículos. También puedes utilizar algunos consejos y trucos que te ayudarán a dominar el juego. ¿Qué estás esperando? Descargar Car Driving School 2017 mod apk y disfrutar de dinero ilimitado y vehículos. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Car Driving School 2017 y Car Driving School 2017 mod apk:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>Es Car Driving School 2017 libre para jugar? </td>
-
-</tr>
-<tr>
-<td>Es coche Driving School 2017 mod apk seguro de usar? </td>
-<td>Sí, Car Driving School 2017 mod apk es seguro de usar siempre y cuando se descarga de una fuente confiable. Sin embargo, siempre debes tener cuidado al instalar aplicaciones de fuentes desconocidas y escanearlas en busca de virus o malware. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar Car Driving School 2017 sin conexión? </td>
-<td>Sí, puedes jugar Car Driving School 2017 sin conexión en modo para un jugador. Sin embargo, necesitará una conexión a Internet para jugar modos multijugador o acceder a funciones en línea. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar Car Driving School 2017 en PC? </td>
-<td>Sí, puedes jugar Car Driving School 2017 en PC usando un emulador de Android como Bluestacks o Nox Player. También puedes usar un teclado y un ratón para controlar el juego. </td>
-</tr>
-<tr>
-<td>¿Cómo puedo contactar a los desarrolladores de Car Driving School 2017? </td>
-<td>Puede ponerse en contacto con los desarrolladores de Car Driving School 2017 enviándoles un correo electrónico a support@ovilex.com o visitando su sitio web en https://www.ovilex.com/.</td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_asyncio.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_asyncio.py
deleted file mode 100644
index 2e50cd7b40ef18e7f7ee56c0f528bf0ef88b167a..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_asyncio.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright 2016 Étienne Bersac
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import functools
-import sys
-import typing as t
-from asyncio import sleep
-
-from pip._vendor.tenacity import AttemptManager
-from pip._vendor.tenacity import BaseRetrying
-from pip._vendor.tenacity import DoAttempt
-from pip._vendor.tenacity import DoSleep
-from pip._vendor.tenacity import RetryCallState
-
-WrappedFnReturnT = t.TypeVar("WrappedFnReturnT")
-WrappedFn = t.TypeVar("WrappedFn", bound=t.Callable[..., t.Awaitable[t.Any]])
-
-
-class AsyncRetrying(BaseRetrying):
-    sleep: t.Callable[[float], t.Awaitable[t.Any]]
-
-    def __init__(self, sleep: t.Callable[[float], t.Awaitable[t.Any]] = sleep, **kwargs: t.Any) -> None:
-        super().__init__(**kwargs)
-        self.sleep = sleep
-
-    async def __call__(  # type: ignore[override]
-        self, fn: WrappedFn, *args: t.Any, **kwargs: t.Any
-    ) -> WrappedFnReturnT:
-        self.begin()
-
-        retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
-        while True:
-            do = self.iter(retry_state=retry_state)
-            if isinstance(do, DoAttempt):
-                try:
-                    result = await fn(*args, **kwargs)
-                except BaseException:  # noqa: B902
-                    retry_state.set_exception(sys.exc_info())  # type: ignore[arg-type]
-                else:
-                    retry_state.set_result(result)
-            elif isinstance(do, DoSleep):
-                retry_state.prepare_for_next_attempt()
-                await self.sleep(do)
-            else:
-                return do  # type: ignore[no-any-return]
-
-    def __iter__(self) -> t.Generator[AttemptManager, None, None]:
-        raise TypeError("AsyncRetrying object is not iterable")
-
-    def __aiter__(self) -> "AsyncRetrying":
-        self.begin()
-        self._retry_state = RetryCallState(self, fn=None, args=(), kwargs={})
-        return self
-
-    async def __anext__(self) -> AttemptManager:
-        while True:
-            do = self.iter(retry_state=self._retry_state)
-            if do is None:
-                raise StopAsyncIteration
-            elif isinstance(do, DoAttempt):
-                return AttemptManager(retry_state=self._retry_state)
-            elif isinstance(do, DoSleep):
-                self._retry_state.prepare_for_next_attempt()
-                await self.sleep(do)
-            else:
-                raise StopAsyncIteration
-
-    def wraps(self, fn: WrappedFn) -> WrappedFn:
-        fn = super().wraps(fn)
-        # Ensure wrapper is recognized as a coroutine function.
-
-        @functools.wraps(fn)
-        async def async_wrapped(*args: t.Any, **kwargs: t.Any) -> t.Any:
-            return await fn(*args, **kwargs)
-
-        # Preserve attributes
-        async_wrapped.retry = fn.retry  # type: ignore[attr-defined]
-        async_wrapped.retry_with = fn.retry_with  # type: ignore[attr-defined]
-
-        return async_wrapped  # type: ignore[return-value]
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/windows_support.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/windows_support.py
deleted file mode 100644
index 1ca64fbb54fd1ce2e62f946827b78feafd6c0078..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/windows_support.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import platform
-
-
-def windows_only(func):
-    if platform.system() != 'Windows':
-        return lambda *args, **kwargs: None
-    return func
-
-
-@windows_only
-def hide_file(path):
-    """
-    Set the hidden attribute on a file or directory.
-
-    From http://stackoverflow.com/questions/19622133/
-
-    `path` must be text.
-    """
-    import ctypes
-    __import__('ctypes.wintypes')
-    SetFileAttributes = ctypes.windll.kernel32.SetFileAttributesW
-    SetFileAttributes.argtypes = ctypes.wintypes.LPWSTR, ctypes.wintypes.DWORD
-    SetFileAttributes.restype = ctypes.wintypes.BOOL
-
-    FILE_ATTRIBUTE_HIDDEN = 0x02
-
-    ret = SetFileAttributes(path, FILE_ATTRIBUTE_HIDDEN)
-    if not ret:
-        raise ctypes.WinError()
diff --git a/spaces/Billyosoro/ESRGAN/scripts/generate_multiscale_DF2K.py b/spaces/Billyosoro/ESRGAN/scripts/generate_multiscale_DF2K.py
deleted file mode 100644
index d4f5d8324b1624e4cb6163754703b8dac2d188fd..0000000000000000000000000000000000000000
--- a/spaces/Billyosoro/ESRGAN/scripts/generate_multiscale_DF2K.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import argparse
-import glob
-import os
-from PIL import Image
-
-
-def main(args):
-    # For DF2K, we consider the following three scales,
-    # and the smallest image whose shortest edge is 400
-    scale_list = [0.75, 0.5, 1 / 3]
-    shortest_edge = 400
-
-    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
-    for path in path_list:
-        print(path)
-        basename = os.path.splitext(os.path.basename(path))[0]
-
-        img = Image.open(path)
-        width, height = img.size
-        for idx, scale in enumerate(scale_list):
-            print(f'\t{scale:.2f}')
-            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
-            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
-
-        # save the smallest image which the shortest edge is 400
-        if width < height:
-            ratio = height / width
-            width = shortest_edge
-            height = int(width * ratio)
-        else:
-            ratio = width / height
-            height = shortest_edge
-            width = int(height * ratio)
-        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
-        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
-
-
-if __name__ == '__main__':
-    """Generate multi-scale versions for GT images with LANCZOS resampling.
-    It is now used for DF2K dataset (DIV2K + Flickr 2K)
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
-    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
-    args = parser.parse_args()
-
-    os.makedirs(args.output, exist_ok=True)
-    main(args)
diff --git a/spaces/Boadiwaa/Recipes/openai/api_resources/abstract/deletable_api_resource.py b/spaces/Boadiwaa/Recipes/openai/api_resources/abstract/deletable_api_resource.py
deleted file mode 100644
index 3a6e83ff0e1802279852ce5032f7aced06a52f31..0000000000000000000000000000000000000000
--- a/spaces/Boadiwaa/Recipes/openai/api_resources/abstract/deletable_api_resource.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from urllib.parse import quote_plus
-
-from openai import error
-from openai.api_resources.abstract.api_resource import APIResource
-from openai.util import ApiType
-
-class DeletableAPIResource(APIResource):
-    @classmethod
-    def delete(cls, sid, api_type=None, api_version=None, **params):
-        if isinstance(cls, APIResource):
-            raise ValueError(".delete may only be called as a class method now.")
-
-        base = cls.class_url()
-        extn = quote_plus(sid)
-
-        typed_api_type, api_version = cls._get_api_type_and_version(api_type, api_version)
-        if typed_api_type == ApiType.AZURE:
-            url = "/%s%s/%s?api-version=%s" % (cls.azure_api_prefix, base, extn, api_version)
-        elif typed_api_type == ApiType.OPEN_AI:
-            url = "%s/%s" % (base, extn)
-        else:
-            raise error.InvalidAPIType('Unsupported API type %s' % api_type)            
-        
-        return cls._static_request("delete", url, api_type=api_type, api_version=api_version, **params)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/CONTRIBUTING.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/CONTRIBUTING.md
deleted file mode 100644
index b59346ca1a20abbb02a2502967a7ba3854dbe676..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# Contributing to detectron2
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Issues
-We use GitHub issues to track public bugs and questions.
-Please make sure to follow one of the
-[issue templates](https://github.com/facebookresearch/detectron2/issues/new/choose)
-when reporting any issues.
-
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-However, if you're adding any significant features, please
-make sure to have a corresponding issue to discuss your motivation and proposals,
-before sending a PR. We do not always accept new features, and we take the following
-factors into consideration:
-
-1. Whether the same feature can be achieved without modifying detectron2.
-Detectron2 is designed so that you can implement many extensions from the outside, e.g.
-those in [projects](https://github.com/facebookresearch/detectron2/tree/master/projects).
-If some part is not as extensible, you can also bring up the issue to make it more extensible.
-2. Whether the feature is potentially useful to a large audience, or only to a small portion of users.
-3. Whether the proposed solution has a good design / interface.
-4. Whether the proposed solution adds extra mental/practical overhead to users who don't
-   need such feature.
-5. Whether the proposed solution breaks existing APIs.
-
-When sending a PR, please do:
-
-1. If a PR contains multiple orthogonal changes, split it to several PRs.
-2. If you've added code that should be tested, add tests.
-3. For PRs that need experiments (e.g. adding a new model), you don't need to update model zoo,
-   but do provide experiment results in the description of the PR.
-4. If APIs are changed, update the documentation.
-5. Ensure the test suite passes.
-6. Make sure your code lints with `./dev/linter.sh`.
-
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## License
-By contributing to detectron2, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/shape_spec.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/shape_spec.py
deleted file mode 100644
index ed7f0d08268a2342cfb8246cc032686f2343ef8f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/shape_spec.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from collections import namedtuple
-
-
-class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])):
-    """
-    A simple structure that contains basic shape specification about a tensor.
-    It is often used as the auxiliary inputs/outputs of models,
-    to obtain the shape inference ability among pytorch modules.
-
-    Attributes:
-        channels:
-        height:
-        width:
-        stride:
-    """
-
-    def __new__(cls, *, channels=None, height=None, width=None, stride=None):
-        return super().__new__(cls, channels, height, width, stride)
diff --git a/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/style.css b/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/style.css
deleted file mode 100644
index 435ebb5987b8913a52f73664c54022374d0c3ed7..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/style.css
+++ /dev/null
@@ -1,19 +0,0 @@
-h1 {
-  text-align: center;
-}
-img#overview {
-  max-width: 1000px;
-  max-height: 600px;
-  display: block;
-  margin: auto;
-}
-img#style-image {
-  max-width: 1000px;
-  max-height: 600px;
-  display: block;
-  margin: auto;
-}
-img#visitor-badge {
-  display: block;
-  margin: auto;
-}
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/bitwise_operators.h b/spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/bitwise_operators.h
deleted file mode 100644
index a6461f9d493132f6f7c331dedb619cc2fa79f8a9..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/bitwise_operators.h
+++ /dev/null
@@ -1,338 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/functional/actor.h>
-#include <thrust/detail/functional/composite.h>
-#include <thrust/detail/functional/operators/operator_adaptors.h>
-#include <thrust/functional.h>
-
-namespace thrust
-{
-namespace detail
-{
-namespace functional
-{
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator&(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_and<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator&(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_and<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator&(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_and<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator|(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_or<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator|(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_or<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator|(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_or<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator^(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_xor<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator^()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator^(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_xor<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator^()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator^(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_xor<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator^()
-
-
-// there's no standard bit_not functional, so roll an ad hoc one here
-struct bit_not
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(~THRUST_FWD(t1))) -> decltype(~THRUST_FWD(t1))
-  {
-    return ~THRUST_FWD(t1);
-  }
-}; // end prefix_increment
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<bit_not>,
-    actor<Eval>
-  >
->
-__host__ __device__
-operator~(const actor<Eval> &_1)
-{
-  return compose(transparent_unary_operator<bit_not>(), _1);
-} // end operator~()
-
-// there's no standard bit_lshift functional, so roll an ad hoc one here
-struct bit_lshift
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) << THRUST_FWD(t2)))
-      -> decltype(THRUST_FWD(t1) << THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) << THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator<<(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator<<(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator<<(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<()
-
-// there's no standard bit_rshift functional, so roll an ad hoc one here
-struct bit_rshift
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) >> THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) >> THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) >> THRUST_FWD(t2);
-  }
-};
-
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator>>(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator>>(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator>>(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>()
-
-} // end functional
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/replace.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/replace.h
deleted file mode 100644
index d8fb5746f1ced28be6571c8535ce0d8615863234..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/replace.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the replace.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch replace
-
-#include <thrust/system/detail/sequential/replace.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/replace.h>
-#include <thrust/system/cuda/detail/replace.h>
-#include <thrust/system/omp/detail/replace.h>
-#include <thrust/system/tbb/detail/replace.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_REPLACE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/replace.h>
-#include __THRUST_HOST_SYSTEM_REPLACE_HEADER
-#undef __THRUST_HOST_SYSTEM_REPLACE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_REPLACE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/replace.h>
-#include __THRUST_DEVICE_SYSTEM_REPLACE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_REPLACE_HEADER
-
diff --git a/spaces/CVPR/WALT/mmdet/models/backbones/darknet.py b/spaces/CVPR/WALT/mmdet/models/backbones/darknet.py
deleted file mode 100644
index 517fe26259217792e0dad80ca3824d914cfe3904..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/backbones/darknet.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# Copyright (c) 2019 Western Digital Corporation or its affiliates.
-
-import logging
-
-import torch.nn as nn
-from mmcv.cnn import ConvModule, constant_init, kaiming_init
-from mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from ..builder import BACKBONES
-
-
-class ResBlock(nn.Module):
-    """The basic residual block used in Darknet. Each ResBlock consists of two
-    ConvModules and the input is added to the final output. Each ConvModule is
-    composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer
-    has half of the number of the filters as much as the second convLayer. The
-    first convLayer has filter size of 1x1 and the second one has the filter
-    size of 3x3.
-
-    Args:
-        in_channels (int): The input channels. Must be even.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-            Default: dict(type='BN', requires_grad=True)
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='LeakyReLU', negative_slope=0.1).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):
-        super(ResBlock, self).__init__()
-        assert in_channels % 2 == 0  # ensure the in_channels is even
-        half_in_channels = in_channels // 2
-
-        # shortcut
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        self.conv1 = ConvModule(in_channels, half_in_channels, 1, **cfg)
-        self.conv2 = ConvModule(
-            half_in_channels, in_channels, 3, padding=1, **cfg)
-
-    def forward(self, x):
-        residual = x
-        out = self.conv1(x)
-        out = self.conv2(out)
-        out = out + residual
-
-        return out
-
-
-@BACKBONES.register_module()
-class Darknet(nn.Module):
-    """Darknet backbone.
-
-    Args:
-        depth (int): Depth of Darknet. Currently only support 53.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters. Default: -1.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-            Default: dict(type='BN', requires_grad=True)
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='LeakyReLU', negative_slope=0.1).
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only.
-
-    Example:
-        >>> from mmdet.models import Darknet
-        >>> import torch
-        >>> self = Darknet(depth=53)
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 3, 416, 416)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        ...
-        (1, 256, 52, 52)
-        (1, 512, 26, 26)
-        (1, 1024, 13, 13)
-    """
-
-    # Dict(depth: (layers, channels))
-    arch_settings = {
-        53: ((1, 2, 8, 8, 4), ((32, 64), (64, 128), (128, 256), (256, 512),
-                               (512, 1024)))
-    }
-
-    def __init__(self,
-                 depth=53,
-                 out_indices=(3, 4, 5),
-                 frozen_stages=-1,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
-                 norm_eval=True):
-        super(Darknet, self).__init__()
-        if depth not in self.arch_settings:
-            raise KeyError(f'invalid depth {depth} for darknet')
-        self.depth = depth
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.layers, self.channels = self.arch_settings[depth]
-
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        self.conv1 = ConvModule(3, 32, 3, padding=1, **cfg)
-
-        self.cr_blocks = ['conv1']
-        for i, n_layers in enumerate(self.layers):
-            layer_name = f'conv_res_block{i + 1}'
-            in_c, out_c = self.channels[i]
-            self.add_module(
-                layer_name,
-                self.make_conv_res_block(in_c, out_c, n_layers, **cfg))
-            self.cr_blocks.append(layer_name)
-
-        self.norm_eval = norm_eval
-
-    def forward(self, x):
-        outs = []
-        for i, layer_name in enumerate(self.cr_blocks):
-            cr_block = getattr(self, layer_name)
-            x = cr_block(x)
-            if i in self.out_indices:
-                outs.append(x)
-
-        return tuple(outs)
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
-
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            for i in range(self.frozen_stages):
-                m = getattr(self, self.cr_blocks[i])
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    def train(self, mode=True):
-        super(Darknet, self).train(mode)
-        self._freeze_stages()
-        if mode and self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, _BatchNorm):
-                    m.eval()
-
-    @staticmethod
-    def make_conv_res_block(in_channels,
-                            out_channels,
-                            res_repeat,
-                            conv_cfg=None,
-                            norm_cfg=dict(type='BN', requires_grad=True),
-                            act_cfg=dict(type='LeakyReLU',
-                                         negative_slope=0.1)):
-        """In Darknet backbone, ConvLayer is usually followed by ResBlock. This
-        function will make that. The Conv layers always have 3x3 filters with
-        stride=2. The number of the filters in Conv layer is the same as the
-        out channels of the ResBlock.
-
-        Args:
-            in_channels (int): The number of input channels.
-            out_channels (int): The number of output channels.
-            res_repeat (int): The number of ResBlocks.
-            conv_cfg (dict): Config dict for convolution layer. Default: None.
-            norm_cfg (dict): Dictionary to construct and config norm layer.
-                Default: dict(type='BN', requires_grad=True)
-            act_cfg (dict): Config dict for activation layer.
-                Default: dict(type='LeakyReLU', negative_slope=0.1).
-        """
-
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        model = nn.Sequential()
-        model.add_module(
-            'conv',
-            ConvModule(
-                in_channels, out_channels, 3, stride=2, padding=1, **cfg))
-        for idx in range(res_repeat):
-            model.add_module('res{}'.format(idx),
-                             ResBlock(out_channels, **cfg))
-        return model
diff --git a/spaces/CVPR/WALT/mmdet/models/losses/cross_entropy_loss.py b/spaces/CVPR/WALT/mmdet/models/losses/cross_entropy_loss.py
deleted file mode 100644
index 3fa908d2789e291616acf969912bf4429b1b07bf..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/losses/cross_entropy_loss.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weight_reduce_loss
-
-
-def cross_entropy(pred,
-                  label,
-                  weight=None,
-                  reduction='mean',
-                  avg_factor=None,
-                  class_weight=None):
-    """Calculate the CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    # element-wise losses
-    loss = F.cross_entropy(pred, label, weight=class_weight, reduction='none')
-
-    # apply weights and do the reduction
-    if weight is not None:
-        weight = weight.float()
-    loss = weight_reduce_loss(
-        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def _expand_onehot_labels(labels, label_weights, label_channels):
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    inds = torch.nonzero(
-        (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds]] = 1
-
-    if label_weights is None:
-        bin_label_weights = None
-    else:
-        bin_label_weights = label_weights.view(-1, 1).expand(
-            label_weights.size(0), label_channels)
-
-    return bin_labels, bin_label_weights
-
-
-def binary_cross_entropy(pred,
-                         label,
-                         weight=None,
-                         reduction='mean',
-                         avg_factor=None,
-                         class_weight=None):
-    """Calculate the binary CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 1).
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    if pred.dim() != label.dim():
-        label, weight = _expand_onehot_labels(label, weight, pred.size(-1))
-
-    # weighted element-wise losses
-    if weight is not None:
-        weight = weight.float()
-    loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), pos_weight=class_weight, reduction='none')
-    # do the reduction for the weighted loss
-    loss = weight_reduce_loss(
-        loss, weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def mask_cross_entropy(pred,
-                       target,
-                       label,
-                       reduction='mean',
-                       avg_factor=None,
-                       class_weight=None):
-    """Calculate the CrossEntropy loss for masks.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C, *), C is the
-            number of classes. The trailing * indicates arbitrary shape.
-        target (torch.Tensor): The learning label of the prediction.
-        label (torch.Tensor): ``label`` indicates the class label of the mask
-            corresponding object. This will be used to select the mask in the
-            of the class which the object belongs to when the mask prediction
-            if not class-agnostic.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-
-    Returns:
-        torch.Tensor: The calculated loss
-
-    Example:
-        >>> N, C = 3, 11
-        >>> H, W = 2, 2
-        >>> pred = torch.randn(N, C, H, W) * 1000
-        >>> target = torch.rand(N, H, W)
-        >>> label = torch.randint(0, C, size=(N,))
-        >>> reduction = 'mean'
-        >>> avg_factor = None
-        >>> class_weights = None
-        >>> loss = mask_cross_entropy(pred, target, label, reduction,
-        >>>                           avg_factor, class_weights)
-        >>> assert loss.shape == (1,)
-    """
-    # TODO: handle these two reserved arguments
-    assert reduction == 'mean' and avg_factor is None
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight=class_weight, reduction='mean')[None]
-
-
-@LOSSES.register_module()
-class CrossEntropyLoss(nn.Module):
-
-    def __init__(self,
-                 use_sigmoid=False,
-                 use_mask=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        """CrossEntropyLoss.
-
-        Args:
-            use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-                of softmax. Defaults to False.
-            use_mask (bool, optional): Whether to use mask cross entropy loss.
-                Defaults to False.
-            reduction (str, optional): . Defaults to 'mean'.
-                Options are "none", "mean" and "sum".
-            class_weight (list[float], optional): Weight of each class.
-                Defaults to None.
-            loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-        """
-        super(CrossEntropyLoss, self).__init__()
-        assert (use_sigmoid is False) or (use_mask is False)
-        self.use_sigmoid = use_sigmoid
-        self.use_mask = use_mask
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = class_weight
-
-        if self.use_sigmoid:
-            self.cls_criterion = binary_cross_entropy
-        elif self.use_mask:
-            self.cls_criterion = mask_cross_entropy
-        else:
-            self.cls_criterion = cross_entropy
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function.
-
-        Args:
-            cls_score (torch.Tensor): The prediction.
-            label (torch.Tensor): The learning label of the prediction.
-            weight (torch.Tensor, optional): Sample-wise loss weight.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction (str, optional): The method used to reduce the loss.
-                Options are "none", "mean" and "sum".
-        Returns:
-            torch.Tensor: The calculated loss
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(
-                self.class_weight, device=cls_score.device)
-        else:
-            class_weight = None
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            weight,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
-
-
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/hold_tight/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/hold_tight/__init__.py
deleted file mode 100644
index 98dc735a4250c2e6e8b93cc89ce90646dad7fc15..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/hold_tight/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-
-img_dir = Path(__file__).parent / "images"
-
-
-def hold_tight(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA").resize((159, 171), keep_ratio=True)
-    frame = BuildImage.open(img_dir / "0.png")
-    frame.paste(img, (113, 205), below=True)
-    return frame.save_jpg()
-
-
-add_meme("hold_tight", hold_tight, min_images=1, max_images=1, keywords=["抱紧"])
diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/__init__.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/README.md b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/README.md
deleted file mode 100644
index 8ae85e0567cbe71ef1f1df4137cbf549240065d2..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# Setting Up Datasets
-This file describes how to perform training on other datasets.
-
-Only Pascal VOC dataset can be loaded from its original format and be outputted to Pascal style results currently.
-
-We expect the annotations from other datasets be converted to COCO json format, and
-the output will be in COCO-style. (i.e. AP, AP50, AP75, APs, APm, APl for bbox and segm)
-
-## Creating Symlinks for PASCAL VOC
-
-We assume that your symlinked `datasets/voc/VOC<year>` directory has the following structure:
-
-```
-VOC<year>
-|_ JPEGImages
-|  |_ <im-1-name>.jpg
-|  |_ ...
-|  |_ <im-N-name>.jpg
-|_ Annotations
-|  |_ pascal_train<year>.json (optional)
-|  |_ pascal_val<year>.json (optional)
-|  |_ pascal_test<year>.json (optional)
-|  |_ <im-1-name>.xml
-|  |_ ...
-|  |_ <im-N-name>.xml
-|_ VOCdevkit<year>
-```
-
-Create symlinks for `voc/VOC<year>`:
-
-```
-cd ~/github/maskrcnn-benchmark
-mkdir -p datasets/voc/VOC<year>
-ln -s /path/to/VOC<year> /datasets/voc/VOC<year>
-```
-Example configuration files for PASCAL VOC could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/pascal_voc/).
-
-### PASCAL VOC Annotations in COCO Format
-To output COCO-style evaluation result, PASCAL VOC annotations in COCO json format is required and could be downloaded from [here](https://storage.googleapis.com/coco-dataset/external/PASCAL_VOC.zip)
-via http://cocodataset.org/#external.
-
-## Creating Symlinks for Cityscapes:
-
-We assume that your symlinked `datasets/cityscapes` directory has the following structure:
-
-```
-cityscapes
-|_ images
-|  |_ <im-1-name>.jpg
-|  |_ ...
-|  |_ <im-N-name>.jpg
-|_ annotations
-|  |_ instanceonly_gtFile_train.json
-|  |_ ...
-|_ raw
-   |_ gtFine
-   |_ ...
-   |_ README.md
-```
-
-Create symlinks for `cityscapes`:
-
-```
-cd ~/github/maskrcnn-benchmark
-mkdir -p datasets/cityscapes
-ln -s /path/to/cityscapes datasets/data/cityscapes
-```
-
-### Steps to convert Cityscapes Annotations to COCO Format
-1. Download gtFine_trainvaltest.zip from https://www.cityscapes-dataset.com/downloads/ (login required)
-2. Extract it to /path/to/gtFine_trainvaltest
-```
-cityscapes
-|_ gtFine_trainvaltest.zip
-|_ gtFine_trainvaltest
-   |_ gtFine
-```
-3. Run the below commands to convert the annotations
-
-```
-cd ~/github
-git clone https://github.com/mcordts/cityscapesScripts.git
-cd cityscapesScripts
-cp ~/github/maskrcnn-benchmark/tools/cityscapes/instances2dict_with_polygons.py cityscapesscripts/evaluation
-python setup.py install
-cd ~/github/maskrcnn-benchmark
-python tools/cityscapes/convert_cityscapes_to_coco.py --datadir /path/to/cityscapes --outdir /path/to/cityscapes/annotations
-```
-
-Example configuration files for Cityscapes could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/cityscapes/).
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/ttGlyphPen.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/ttGlyphPen.py
deleted file mode 100644
index de2ccaeeb45c18c80caae049f3bd26b4ff22e99e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/ttGlyphPen.py
+++ /dev/null
@@ -1,335 +0,0 @@
-from array import array
-from typing import Any, Callable, Dict, Optional, Tuple
-from fontTools.misc.fixedTools import MAX_F2DOT14, floatToFixedToFloat
-from fontTools.misc.loggingTools import LogMixin
-from fontTools.pens.pointPen import AbstractPointPen
-from fontTools.misc.roundTools import otRound
-from fontTools.pens.basePen import LoggingPen, PenError
-from fontTools.pens.transformPen import TransformPen, TransformPointPen
-from fontTools.ttLib.tables import ttProgram
-from fontTools.ttLib.tables._g_l_y_f import flagOnCurve, flagCubic
-from fontTools.ttLib.tables._g_l_y_f import Glyph
-from fontTools.ttLib.tables._g_l_y_f import GlyphComponent
-from fontTools.ttLib.tables._g_l_y_f import GlyphCoordinates
-from fontTools.ttLib.tables._g_l_y_f import dropImpliedOnCurvePoints
-import math
-
-
-__all__ = ["TTGlyphPen", "TTGlyphPointPen"]
-
-
-class _TTGlyphBasePen:
-    def __init__(
-        self,
-        glyphSet: Optional[Dict[str, Any]],
-        handleOverflowingTransforms: bool = True,
-    ) -> None:
-        """
-        Construct a new pen.
-
-        Args:
-            glyphSet (Dict[str, Any]): A glyphset object, used to resolve components.
-            handleOverflowingTransforms (bool): See below.
-
-        If ``handleOverflowingTransforms`` is True, the components' transform values
-        are checked that they don't overflow the limits of a F2Dot14 number:
-        -2.0 <= v < +2.0. If any transform value exceeds these, the composite
-        glyph is decomposed.
-
-        An exception to this rule is done for values that are very close to +2.0
-        (both for consistency with the -2.0 case, and for the relative frequency
-        these occur in real fonts). When almost +2.0 values occur (and all other
-        values are within the range -2.0 <= x <= +2.0), they are clamped to the
-        maximum positive value that can still be encoded as an F2Dot14: i.e.
-        1.99993896484375.
-
-        If False, no check is done and all components are translated unmodified
-        into the glyf table, followed by an inevitable ``struct.error`` once an
-        attempt is made to compile them.
-
-        If both contours and components are present in a glyph, the components
-        are decomposed.
-        """
-        self.glyphSet = glyphSet
-        self.handleOverflowingTransforms = handleOverflowingTransforms
-        self.init()
-
-    def _decompose(
-        self,
-        glyphName: str,
-        transformation: Tuple[float, float, float, float, float, float],
-    ):
-        tpen = self.transformPen(self, transformation)
-        getattr(self.glyphSet[glyphName], self.drawMethod)(tpen)
-
-    def _isClosed(self):
-        """
-        Check if the current path is closed.
-        """
-        raise NotImplementedError
-
-    def init(self) -> None:
-        self.points = []
-        self.endPts = []
-        self.types = []
-        self.components = []
-
-    def addComponent(
-        self,
-        baseGlyphName: str,
-        transformation: Tuple[float, float, float, float, float, float],
-        identifier: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        """
-        Add a sub glyph.
-        """
-        self.components.append((baseGlyphName, transformation))
-
-    def _buildComponents(self, componentFlags):
-        if self.handleOverflowingTransforms:
-            # we can't encode transform values > 2 or < -2 in F2Dot14,
-            # so we must decompose the glyph if any transform exceeds these
-            overflowing = any(
-                s > 2 or s < -2
-                for (glyphName, transformation) in self.components
-                for s in transformation[:4]
-            )
-        components = []
-        for glyphName, transformation in self.components:
-            if glyphName not in self.glyphSet:
-                self.log.warning(f"skipped non-existing component '{glyphName}'")
-                continue
-            if self.points or (self.handleOverflowingTransforms and overflowing):
-                # can't have both coordinates and components, so decompose
-                self._decompose(glyphName, transformation)
-                continue
-
-            component = GlyphComponent()
-            component.glyphName = glyphName
-            component.x, component.y = (otRound(v) for v in transformation[4:])
-            # quantize floats to F2Dot14 so we get same values as when decompiled
-            # from a binary glyf table
-            transformation = tuple(
-                floatToFixedToFloat(v, 14) for v in transformation[:4]
-            )
-            if transformation != (1, 0, 0, 1):
-                if self.handleOverflowingTransforms and any(
-                    MAX_F2DOT14 < s <= 2 for s in transformation
-                ):
-                    # clamp values ~= +2.0 so we can keep the component
-                    transformation = tuple(
-                        MAX_F2DOT14 if MAX_F2DOT14 < s <= 2 else s
-                        for s in transformation
-                    )
-                component.transform = (transformation[:2], transformation[2:])
-            component.flags = componentFlags
-            components.append(component)
-        return components
-
-    def glyph(
-        self,
-        componentFlags: int = 0x04,
-        dropImpliedOnCurves: bool = False,
-        *,
-        round: Callable[[float], int] = otRound,
-    ) -> Glyph:
-        """
-        Returns a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-
-        Args:
-            componentFlags: Flags to use for component glyphs. (default: 0x04)
-
-            dropImpliedOnCurves: Whether to remove implied-oncurve points. (default: False)
-        """
-        if not self._isClosed():
-            raise PenError("Didn't close last contour.")
-        components = self._buildComponents(componentFlags)
-
-        glyph = Glyph()
-        glyph.coordinates = GlyphCoordinates(self.points)
-        glyph.endPtsOfContours = self.endPts
-        glyph.flags = array("B", self.types)
-        self.init()
-
-        if components:
-            # If both components and contours were present, they have by now
-            # been decomposed by _buildComponents.
-            glyph.components = components
-            glyph.numberOfContours = -1
-        else:
-            glyph.numberOfContours = len(glyph.endPtsOfContours)
-            glyph.program = ttProgram.Program()
-            glyph.program.fromBytecode(b"")
-            if dropImpliedOnCurves:
-                dropImpliedOnCurvePoints(glyph)
-            glyph.coordinates.toInt(round=round)
-
-        return glyph
-
-
-class TTGlyphPen(_TTGlyphBasePen, LoggingPen):
-    """
-    Pen used for drawing to a TrueType glyph.
-
-    This pen can be used to construct or modify glyphs in a TrueType format
-    font. After using the pen to draw, use the ``.glyph()`` method to retrieve
-    a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-    """
-
-    drawMethod = "draw"
-    transformPen = TransformPen
-
-    def __init__(
-        self,
-        glyphSet: Optional[Dict[str, Any]] = None,
-        handleOverflowingTransforms: bool = True,
-        outputImpliedClosingLine: bool = False,
-    ) -> None:
-        super().__init__(glyphSet, handleOverflowingTransforms)
-        self.outputImpliedClosingLine = outputImpliedClosingLine
-
-    def _addPoint(self, pt: Tuple[float, float], tp: int) -> None:
-        self.points.append(pt)
-        self.types.append(tp)
-
-    def _popPoint(self) -> None:
-        self.points.pop()
-        self.types.pop()
-
-    def _isClosed(self) -> bool:
-        return (not self.points) or (
-            self.endPts and self.endPts[-1] == len(self.points) - 1
-        )
-
-    def lineTo(self, pt: Tuple[float, float]) -> None:
-        self._addPoint(pt, flagOnCurve)
-
-    def moveTo(self, pt: Tuple[float, float]) -> None:
-        if not self._isClosed():
-            raise PenError('"move"-type point must begin a new contour.')
-        self._addPoint(pt, flagOnCurve)
-
-    def curveTo(self, *points) -> None:
-        assert len(points) % 2 == 1
-        for pt in points[:-1]:
-            self._addPoint(pt, flagCubic)
-
-        # last point is None if there are no on-curve points
-        if points[-1] is not None:
-            self._addPoint(points[-1], 1)
-
-    def qCurveTo(self, *points) -> None:
-        assert len(points) >= 1
-        for pt in points[:-1]:
-            self._addPoint(pt, 0)
-
-        # last point is None if there are no on-curve points
-        if points[-1] is not None:
-            self._addPoint(points[-1], 1)
-
-    def closePath(self) -> None:
-        endPt = len(self.points) - 1
-
-        # ignore anchors (one-point paths)
-        if endPt == 0 or (self.endPts and endPt == self.endPts[-1] + 1):
-            self._popPoint()
-            return
-
-        if not self.outputImpliedClosingLine:
-            # if first and last point on this path are the same, remove last
-            startPt = 0
-            if self.endPts:
-                startPt = self.endPts[-1] + 1
-            if self.points[startPt] == self.points[endPt]:
-                self._popPoint()
-                endPt -= 1
-
-        self.endPts.append(endPt)
-
-    def endPath(self) -> None:
-        # TrueType contours are always "closed"
-        self.closePath()
-
-
-class TTGlyphPointPen(_TTGlyphBasePen, LogMixin, AbstractPointPen):
-    """
-    Point pen used for drawing to a TrueType glyph.
-
-    This pen can be used to construct or modify glyphs in a TrueType format
-    font. After using the pen to draw, use the ``.glyph()`` method to retrieve
-    a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-    """
-
-    drawMethod = "drawPoints"
-    transformPen = TransformPointPen
-
-    def init(self) -> None:
-        super().init()
-        self._currentContourStartIndex = None
-
-    def _isClosed(self) -> bool:
-        return self._currentContourStartIndex is None
-
-    def beginPath(self, identifier: Optional[str] = None, **kwargs: Any) -> None:
-        """
-        Start a new sub path.
-        """
-        if not self._isClosed():
-            raise PenError("Didn't close previous contour.")
-        self._currentContourStartIndex = len(self.points)
-
-    def endPath(self) -> None:
-        """
-        End the current sub path.
-        """
-        # TrueType contours are always "closed"
-        if self._isClosed():
-            raise PenError("Contour is already closed.")
-        if self._currentContourStartIndex == len(self.points):
-            # ignore empty contours
-            self._currentContourStartIndex = None
-            return
-
-        contourStart = self.endPts[-1] + 1 if self.endPts else 0
-        self.endPts.append(len(self.points) - 1)
-        self._currentContourStartIndex = None
-
-        # Resolve types for any cubic segments
-        flags = self.types
-        for i in range(contourStart, len(flags)):
-            if flags[i] == "curve":
-                j = i - 1
-                if j < contourStart:
-                    j = len(flags) - 1
-                while flags[j] == 0:
-                    flags[j] = flagCubic
-                    j -= 1
-                flags[i] = flagOnCurve
-
-    def addPoint(
-        self,
-        pt: Tuple[float, float],
-        segmentType: Optional[str] = None,
-        smooth: bool = False,
-        name: Optional[str] = None,
-        identifier: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        """
-        Add a point to the current sub path.
-        """
-        if self._isClosed():
-            raise PenError("Can't add a point to a closed contour.")
-        if segmentType is None:
-            self.types.append(0)
-        elif segmentType in ("line", "move"):
-            self.types.append(flagOnCurve)
-        elif segmentType == "qcurve":
-            self.types.append(flagOnCurve)
-        elif segmentType == "curve":
-            self.types.append("curve")
-        else:
-            raise AssertionError(segmentType)
-
-        self.points.append(pt)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ufoLib/kerning.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ufoLib/kerning.py
deleted file mode 100644
index 8a1dca5b680fdd02d1e6ef5797e33e617005c254..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ufoLib/kerning.py
+++ /dev/null
@@ -1,91 +0,0 @@
-def lookupKerningValue(
-    pair, kerning, groups, fallback=0, glyphToFirstGroup=None, glyphToSecondGroup=None
-):
-    """
-    Note: This expects kerning to be a flat dictionary
-    of kerning pairs, not the nested structure used
-    in kerning.plist.
-
-    >>> groups = {
-    ...     "public.kern1.O" : ["O", "D", "Q"],
-    ...     "public.kern2.E" : ["E", "F"]
-    ... }
-    >>> kerning = {
-    ...     ("public.kern1.O", "public.kern2.E") : -100,
-    ...     ("public.kern1.O", "F") : -200,
-    ...     ("D", "F") : -300
-    ... }
-    >>> lookupKerningValue(("D", "F"), kerning, groups)
-    -300
-    >>> lookupKerningValue(("O", "F"), kerning, groups)
-    -200
-    >>> lookupKerningValue(("O", "E"), kerning, groups)
-    -100
-    >>> lookupKerningValue(("O", "O"), kerning, groups)
-    0
-    >>> lookupKerningValue(("E", "E"), kerning, groups)
-    0
-    >>> lookupKerningValue(("E", "O"), kerning, groups)
-    0
-    >>> lookupKerningValue(("X", "X"), kerning, groups)
-    0
-    >>> lookupKerningValue(("public.kern1.O", "public.kern2.E"),
-    ...     kerning, groups)
-    -100
-    >>> lookupKerningValue(("public.kern1.O", "F"), kerning, groups)
-    -200
-    >>> lookupKerningValue(("O", "public.kern2.E"), kerning, groups)
-    -100
-    >>> lookupKerningValue(("public.kern1.X", "public.kern2.X"), kerning, groups)
-    0
-    """
-    # quickly check to see if the pair is in the kerning dictionary
-    if pair in kerning:
-        return kerning[pair]
-    # create glyph to group mapping
-    if glyphToFirstGroup is not None:
-        assert glyphToSecondGroup is not None
-    if glyphToSecondGroup is not None:
-        assert glyphToFirstGroup is not None
-    if glyphToFirstGroup is None:
-        glyphToFirstGroup = {}
-        glyphToSecondGroup = {}
-        for group, groupMembers in groups.items():
-            if group.startswith("public.kern1."):
-                for glyph in groupMembers:
-                    glyphToFirstGroup[glyph] = group
-            elif group.startswith("public.kern2."):
-                for glyph in groupMembers:
-                    glyphToSecondGroup[glyph] = group
-    # get group names and make sure first and second are glyph names
-    first, second = pair
-    firstGroup = secondGroup = None
-    if first.startswith("public.kern1."):
-        firstGroup = first
-        first = None
-    else:
-        firstGroup = glyphToFirstGroup.get(first)
-    if second.startswith("public.kern2."):
-        secondGroup = second
-        second = None
-    else:
-        secondGroup = glyphToSecondGroup.get(second)
-    # make an ordered list of pairs to look up
-    pairs = [
-        (first, second),
-        (first, secondGroup),
-        (firstGroup, second),
-        (firstGroup, secondGroup),
-    ]
-    # look up the pairs and return any matches
-    for pair in pairs:
-        if pair in kerning:
-            return kerning[pair]
-    # use the fallback value
-    return fallback
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-edf307d2.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-edf307d2.css
deleted file mode 100644
index 690ed736f2c29c32ba8499343659e9fde81f2098..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-edf307d2.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-1yrv54 .math.inline{fill:var(--body-text-color);display:inline-block;vertical-align:middle;padding:var(--size-1-5) -var(--size-1);color:var(--body-text-color)}div.svelte-1yrv54 .math.inline svg{display:inline;margin-bottom:.22em}div.svelte-1yrv54{max-width:100%}.min.svelte-1yrv54{min-height:var(--size-24)}.hide.svelte-1yrv54{display:none}div.svelte-1ed2p3z{transition:.15s}.pending.svelte-1ed2p3z{opacity:.2}
diff --git a/spaces/DaleChen/AutoGPT/autogpt/commands/improve_code.py b/spaces/DaleChen/AutoGPT/autogpt/commands/improve_code.py
deleted file mode 100644
index e3440d8b7c6ee8cb62d73df48623ab757c973c59..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/commands/improve_code.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from __future__ import annotations
-
-import json
-
-from autogpt.llm_utils import call_ai_function
-
-
-def improve_code(suggestions: list[str], code: str) -> str:
-    """
-    A function that takes in code and suggestions and returns a response from create
-      chat completion api call.
-
-    Parameters:
-        suggestions (List): A list of suggestions around what needs to be improved.
-        code (str): Code to be improved.
-    Returns:
-        A result string from create chat completion. Improved code in response.
-    """
-
-    function_string = (
-        "def generate_improved_code(suggestions: List[str], code: str) -> str:"
-    )
-    args = [json.dumps(suggestions), code]
-    description_string = (
-        "Improves the provided code based on the suggestions"
-        " provided, making no other changes."
-    )
-
-    return call_ai_function(function_string, args, description_string)
diff --git a/spaces/DarkyMan/URPM/app.py b/spaces/DarkyMan/URPM/app.py
deleted file mode 100644
index 72ff631b0e066b0ab3bd45c889910051d4a0a818..0000000000000000000000000000000000000000
--- a/spaces/DarkyMan/URPM/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-import modin.pandas as pd
-from PIL import Image
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("ductridev/uber-realistic-porn-merge-urpm", torch_dtype=torch.float16, safety_checker=None)
-pipe = pipe.to(device)
-
-def genie (prompt, scale, steps, Seed):
-     generator = torch.Generator(device=device).manual_seed(Seed)
-     images = pipe(prompt, num_inference_steps=steps, guidance_scale=scale, generator=generator).images[0]
-     return images
-    
-gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
-                               gr.Slider(1, maximum=25, value=10, step=.25, label='Prompt Guidance Scale:', interactive=True), 
-                               gr.Slider(1, maximum=200, value=100, step=1, label='Number of Iterations: 50 is typically fine.'), 
-                               gr.Slider(minimum=1, step=10, maximum=999999999999999999, randomize=True, interactive=True)], 
-             outputs=gr.Image(label='512x512 Generated Image'), 
-             title="OpenJourney V4 GPU", 
-             description="OJ V4 GPU. Ultra Fast, now running on a T4 <br><br><b/>Warning: This Demo is capable of producing NSFW content.", 
-             article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=True)
\ No newline at end of file
diff --git a/spaces/DarwinAnim8or/Pythia-Greentext-Playground/README.md b/spaces/DarwinAnim8or/Pythia-Greentext-Playground/README.md
deleted file mode 100644
index a89126359c84fef1b545b0d46bc19bb8dacd6b56..0000000000000000000000000000000000000000
--- a/spaces/DarwinAnim8or/Pythia-Greentext-Playground/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pythia Greentext Playground
-emoji: ✍️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-license: mit
-duplicated_from: DarwinAnim8or/GPT-Greentext-Playground
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/DiweshUIT/Spectrometer/README.md b/spaces/DiweshUIT/Spectrometer/README.md
deleted file mode 100644
index e379dec0d195301b639c2ba800b981e39356f4e3..0000000000000000000000000000000000000000
--- a/spaces/DiweshUIT/Spectrometer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Spectrometer
-emoji: ⚡
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/PP_HumanSeg/export_model/download_export_model.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/PP_HumanSeg/export_model/download_export_model.py
deleted file mode 100644
index 152f598bd48724fe13c5f650f809fdac06c2bae2..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/PP_HumanSeg/export_model/download_export_model.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# coding: utf8
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddleseg.utils.download import download_file_and_uncompress
-import sys
-import os
-
-LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
-TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
-sys.path.append(TEST_PATH)
-
-
-model_urls = {
-    "pphumanseg_lite_portrait_398x224_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224_with_softmax.tar.gz",
-    "deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.zip",
-    "fcn_hrnetw18_small_v1_humanseg_192x192_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/fcn_hrnetw18_small_v1_humanseg_192x192_with_softmax.zip",
-    "pphumanseg_lite_generic_humanseg_192x192_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/pphumanseg_lite_generic_192x192_with_softmax.zip",
-}
-
-if __name__ == "__main__":
-    for model_name, url in model_urls.items():
-        download_file_and_uncompress(
-            url=url,
-            savepath=LOCAL_PATH,
-            extrapath=LOCAL_PATH,
-            extraname=model_name)
-
-    print("Export model download success!")
diff --git a/spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/tfutil.py b/spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/tfutil.py
deleted file mode 100644
index 7b04c59e41a1b1548bc798379ceb551a488ed2a6..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/tfutil.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Miscellaneous helper utils for Tensorflow."""
-
-import os
-import numpy as np
-import tensorflow as tf
-
-# Silence deprecation warnings from TensorFlow 1.13 onwards
-import logging
-logging.getLogger('tensorflow').setLevel(logging.ERROR)
-import tensorflow.contrib   # requires TensorFlow 1.x!
-tf.contrib = tensorflow.contrib
-
-from typing import Any, Iterable, List, Union
-
-TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation]
-"""A type that represents a valid Tensorflow expression."""
-
-TfExpressionEx = Union[TfExpression, int, float, np.ndarray]
-"""A type that can be converted to a valid Tensorflow expression."""
-
-
-def run(*args, **kwargs) -> Any:
-    """Run the specified ops in the default session."""
-    assert_tf_initialized()
-    return tf.get_default_session().run(*args, **kwargs)
-
-
-def is_tf_expression(x: Any) -> bool:
-    """Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation."""
-    return isinstance(x, (tf.Tensor, tf.Variable, tf.Operation))
-
-
-def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
-    """Convert a Tensorflow shape to a list of ints. Retained for backwards compatibility -- use TensorShape.as_list() in new code."""
-    return [dim.value for dim in shape]
-
-
-def flatten(x: TfExpressionEx) -> TfExpression:
-    """Shortcut function for flattening a tensor."""
-    with tf.name_scope("Flatten"):
-        return tf.reshape(x, [-1])
-
-
-def log2(x: TfExpressionEx) -> TfExpression:
-    """Logarithm in base 2."""
-    with tf.name_scope("Log2"):
-        return tf.log(x) * np.float32(1.0 / np.log(2.0))
-
-
-def exp2(x: TfExpressionEx) -> TfExpression:
-    """Exponent in base 2."""
-    with tf.name_scope("Exp2"):
-        return tf.exp(x * np.float32(np.log(2.0)))
-
-
-def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpressionEx:
-    """Linear interpolation."""
-    with tf.name_scope("Lerp"):
-        return a + (b - a) * t
-
-
-def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpression:
-    """Linear interpolation with clip."""
-    with tf.name_scope("LerpClip"):
-        return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
-
-
-def absolute_name_scope(scope: str) -> tf.name_scope:
-    """Forcefully enter the specified name scope, ignoring any surrounding scopes."""
-    return tf.name_scope(scope + "/")
-
-
-def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
-    """Forcefully enter the specified variable scope, ignoring any surrounding scopes."""
-    return tf.variable_scope(tf.VariableScope(name=scope, **kwargs), auxiliary_name_scope=False)
-
-
-def _sanitize_tf_config(config_dict: dict = None) -> dict:
-    # Defaults.
-    cfg = dict()
-    cfg["rnd.np_random_seed"]               = None      # Random seed for NumPy. None = keep as is.
-    cfg["rnd.tf_random_seed"]               = "auto"    # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is.
-    cfg["env.TF_CPP_MIN_LOG_LEVEL"]         = "1"       # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
-    cfg["graph_options.place_pruned_graph"] = True      # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
-    cfg["gpu_options.allow_growth"]         = True      # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
-
-    # Remove defaults for environment variables that are already set.
-    for key in list(cfg):
-        fields = key.split(".")
-        if fields[0] == "env":
-            assert len(fields) == 2
-            if fields[1] in os.environ:
-                del cfg[key]
-
-    # User overrides.
-    if config_dict is not None:
-        cfg.update(config_dict)
-    return cfg
-
-
-def init_tf(config_dict: dict = None) -> None:
-    """Initialize TensorFlow session using good default settings."""
-    # Skip if already initialized.
-    if tf.get_default_session() is not None:
-        return
-
-    # Setup config dict and random seeds.
-    cfg = _sanitize_tf_config(config_dict)
-    np_random_seed = cfg["rnd.np_random_seed"]
-    if np_random_seed is not None:
-        np.random.seed(np_random_seed)
-    tf_random_seed = cfg["rnd.tf_random_seed"]
-    if tf_random_seed == "auto":
-        tf_random_seed = np.random.randint(1 << 31)
-    if tf_random_seed is not None:
-        tf.set_random_seed(tf_random_seed)
-
-    # Setup environment variables.
-    for key, value in cfg.items():
-        fields = key.split(".")
-        if fields[0] == "env":
-            assert len(fields) == 2
-            os.environ[fields[1]] = str(value)
-
-    # Create default TensorFlow session.
-    create_session(cfg, force_as_default=True)
-
-
-def assert_tf_initialized():
-    """Check that TensorFlow session has been initialized."""
-    if tf.get_default_session() is None:
-        raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().")
-
-
-def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session:
-    """Create tf.Session based on config dict."""
-    # Setup TensorFlow config proto.
-    cfg = _sanitize_tf_config(config_dict)
-    config_proto = tf.ConfigProto()
-    for key, value in cfg.items():
-        fields = key.split(".")
-        if fields[0] not in ["rnd", "env"]:
-            obj = config_proto
-            for field in fields[:-1]:
-                obj = getattr(obj, field)
-            setattr(obj, fields[-1], value)
-
-    # Create session.
-    session = tf.Session(config=config_proto)
-    if force_as_default:
-        # pylint: disable=protected-access
-        session._default_session = session.as_default()
-        session._default_session.enforce_nesting = False
-        session._default_session.__enter__()
-    return session
-
-
-def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
-    """Initialize all tf.Variables that have not already been initialized.
-
-    Equivalent to the following, but more efficient and does not bloat the tf graph:
-    tf.variables_initializer(tf.report_uninitialized_variables()).run()
-    """
-    assert_tf_initialized()
-    if target_vars is None:
-        target_vars = tf.global_variables()
-
-    test_vars = []
-    test_ops = []
-
-    with tf.control_dependencies(None):  # ignore surrounding control_dependencies
-        for var in target_vars:
-            assert is_tf_expression(var)
-
-            try:
-                tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0"))
-            except KeyError:
-                # Op does not exist => variable may be uninitialized.
-                test_vars.append(var)
-
-                with absolute_name_scope(var.name.split(":")[0]):
-                    test_ops.append(tf.is_variable_initialized(var))
-
-    init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
-    run([var.initializer for var in init_vars])
-
-
-def set_vars(var_to_value_dict: dict) -> None:
-    """Set the values of given tf.Variables.
-
-    Equivalent to the following, but more efficient and does not bloat the tf graph:
-    tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
-    """
-    assert_tf_initialized()
-    ops = []
-    feed_dict = {}
-
-    for var, value in var_to_value_dict.items():
-        assert is_tf_expression(var)
-
-        try:
-            setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0"))  # look for existing op
-        except KeyError:
-            with absolute_name_scope(var.name.split(":")[0]):
-                with tf.control_dependencies(None):  # ignore surrounding control_dependencies
-                    setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter")  # create new setter
-
-        ops.append(setter)
-        feed_dict[setter.op.inputs[1]] = value
-
-    run(ops, feed_dict)
-
-
-def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwargs):
-    """Create tf.Variable with large initial value without bloating the tf graph."""
-    assert_tf_initialized()
-    assert isinstance(initial_value, np.ndarray)
-    zeros = tf.zeros(initial_value.shape, initial_value.dtype)
-    var = tf.Variable(zeros, *args, **kwargs)
-    set_vars({var: initial_value})
-    return var
-
-
-def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
-    """Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
-    Can be used as an input transformation for Network.run().
-    """
-    images = tf.cast(images, tf.float32)
-    if nhwc_to_nchw:
-        images = tf.transpose(images, [0, 3, 1, 2])
-    return images * ((drange[1] - drange[0]) / 255) + drange[0]
-
-
-def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
-    """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
-    Can be used as an output transformation for Network.run().
-    """
-    images = tf.cast(images, tf.float32)
-    if shrink > 1:
-        ksize = [1, 1, shrink, shrink]
-        images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW")
-    if nchw_to_nhwc:
-        images = tf.transpose(images, [0, 2, 3, 1])
-    scale = 255 / (drange[1] - drange[0])
-    images = images * scale + (0.5 - drange[0] * scale)
-    return tf.saturate_cast(images, tf.uint8)
diff --git a/spaces/Eitan177/mutation_profiler/Sigprofile.py b/spaces/Eitan177/mutation_profiler/Sigprofile.py
deleted file mode 100644
index 3f572cb884fd2525afb3ad3c54ee90d116c3445d..0000000000000000000000000000000000000000
--- a/spaces/Eitan177/mutation_profiler/Sigprofile.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import streamlit as st
-import zipfile
-import urllib.request
-import glob
-import SigProfilerMatrixGenerator
-from SigProfilerMatrixGenerator import install as genInstall
-import shutil
-import os
-from SigProfilerExtractor import sigpro as sig
-import sys
-import base64
-import streamlit.components.v1 as components
-
-curdir= os.getcwd()
-
-def remove_old_vcf():
-    vcfrem=glob.glob('input/*.vcf')
-    for filepath in vcfrem:
-        os.remove(filepath)
-    vcfrem=glob.glob('input/input/*.vcf')    
-    for filepath in vcfrem:
-        os.remove(filepath)  
-
-def show_pdf(file_path):
-    with open(file_path,"rb") as f:
-        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
-    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="1500" height="1000" type="application/pdf"></iframe>'
-    st.markdown(pdf_display, unsafe_allow_html=True)
-
-
-
-
-if st.button('get reference genome'):
-    st.write(os.path.dirname(SigProfilerMatrixGenerator.__file__))
-    dirtest=os.path.dirname(SigProfilerMatrixGenerator.__file__)
-    #st.write(sys.path)
-    urllib.request.urlretrieve('https://dl.dropboxusercontent.com/s/et97ewsct862x7m/references.zip?dl=0','references.zip')
-    with zipfile.ZipFile('references.zip', 'r') as zip_ref:
-        zip_ref.extractall(dirtest)
-        seev=glob.glob('/home/appuser/venv/lib/python3.9/site-packages/SigProfilerMatrixGenerator/references/*')
-        for i in seev:
-            st.write(i)
-    ##genInstall.install('GRCh37')
-
-if not os.path.exists('input'):
-    os.mkdir('input')
-
-if not os.path.exists('input/input'):
-    os.mkdir('input/input')
-
-file_to_lookat=st.file_uploader('VCF upload here',type=[".vcf"],accept_multiple_files=True)
-remove_old_vcf()
-
-if file_to_lookat !=[]:
-    bytes_data=file_to_lookat[0].read()
-    with open(os.path.join("input",file_to_lookat[0].name),"wb") as f:
-        f.write(bytes_data)
-        f.close()
-    
-    #vcfuse=glob.glob('file_to_lookat[0].name')[0]
-    #shutil.copy2(vcfuse,'input/'+vcfuse)
-    #pdb.set_trace()
-    with st.spinner('computing signatures'):
-        sig.sigProfilerExtractor("vcf", "output", "input", minimum_signatures=1, maximum_signatures=3)
-   
-    show_pdf('output/SBS96/Suggested_Solution/COSMIC_SBS96_Decomposed_Solution/SBS96_Decomposition_Plots.pdf')
-    
-    components.iframe("https://cancer.sanger.ac.uk/signatures/sbs/", height=3000,width=800)
-    show_pdf('output/ID83/Suggested_Solution/COSMIC_ID83_Decomposed_Solution/ID83_Decomposition_Plots.pdf')
-    components.iframe("https://cancer.sanger.ac.uk/signatures/id/",height=3000,width=800)
-    remove_old_vcf() 
\ No newline at end of file
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py b/spaces/EuroPython2022/mmocr-demo/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
deleted file mode 100644
index 483a2b2e1e7e584dfba26c7c5f506ce544953db8..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_adam_step_600e.py',
-    '../../_base_/det_models/psenet_r50_fpnf.py',
-    '../../_base_/det_datasets/ctw1500.py',
-    '../../_base_/det_pipelines/psenet_pipeline.py'
-]
-
-model = {{_base_.model_poly}}
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline_ctw1500 = {{_base_.test_pipeline_ctw1500}}
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline_ctw1500),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline_ctw1500))
-
-evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/spaces/FathomNet/UWROV_Deepsea_Detector/tator_inference.py b/spaces/FathomNet/UWROV_Deepsea_Detector/tator_inference.py
deleted file mode 100644
index 5593e9ccccb6bbd222b50220b5ffe65ab486c300..0000000000000000000000000000000000000000
--- a/spaces/FathomNet/UWROV_Deepsea_Detector/tator_inference.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import logging
-from tempfile import TemporaryFile
-
-import cv2
-import numpy as np
-from PIL import Image
-
-import tator
-import inference
-
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-
-# Read environment variables that are provided from TATOR
-host = os.getenv('HOST')
-token = os.getenv('TOKEN')
-project_id = int(os.getenv('PROJECT_ID'))
-media_ids = [int(id_) for id_ in os.getenv('MEDIA_IDS').split(',')]
-frames_per_inference = int(os.getenv('FRAMES_PER_INFERENCE', 30))
-
-# Set up the TATOR API.
-api = tator.get_api(host, token)
-
-# Iterate through each video.
-for media_id in media_ids:
-
-    # Download video.
-    media = api.get_media(media_id)
-    logger.info(f"Downloading {media.name}...")
-    out_path = f"/tmp/{media.name}"
-    for progress in tator.util.download_media(api, media, out_path):
-        logger.info(f"Download progress: {progress}%")
-
-    # Do inference on each video.
-    logger.info(f"Doing inference on {media.name}...")
-    localizations = []
-    vid = cv2.VideoCapture(out_path)
-    frame_number = 0
-
-    # Read *every* frame from the video, break when at the end.
-    while True:
-        ret, frame = vid.read()
-        if not ret:
-            break
-
-        # Create a temporary file, access the image data, save data to file.
-        framefile = TemporaryFile(suffix='.jpg')
-        im = Image.fromarray(frame)
-        im.save(framefile)
-
-        # For every N frames, make a prediction; append prediction results
-        # to a list, increase the frame count.
-        if frame_number % frames_per_inference == 0:
-
-            spec = {}
-
-            # Predictions contains all information inside pandas dataframe
-            predictions = inference.run_inference(framefile)
-
-            for i, r in predictions.pandas().xyxy[0].iterrows:
-
-                spec['media_id'] = media_id
-                spec['type'] = None # Unsure, docs not specific
-                spec['frame'] = frame_number
-
-                x, y, x2, y2 = r['xmin'], r['ymin'], r['xmax'], r['ymax']
-                w, h = x2 - x, y2 - y
-
-                spec['x'] = x
-                spec['y'] = y
-                spec['width'] = w
-                spec['height'] = h
-                spec['class_category'] = r['name']
-                spec['confidence'] = r['confidence']
-
-                localizations.append(spec)
-
-        frame_number += 1
-
-    # End interaction with video properly.
-    vid.release()
-
-    logger.info(f"Uploading object detections on {media.name}...")
-
-    # Create the localizations in the video.
-    num_created = 0
-    for response in tator.util.chunked_create(api.create_localization_list,
-                                              project_id,
-                                              localization_spec=localizations):
-        num_created += len(response.id)
-
-    # Output pretty logging information.
-    logger.info(f"Successfully created {num_created} localizations on "
-                f"{media.name}!")
-
-    logger.info("-------------------------------------------------")
-
-logger.info(f"Completed inference on {len(media_ids)} files.")
\ No newline at end of file
diff --git a/spaces/Fawaz/nlx-gpt/VQAX_p/nle_gpt2_tokenizer_0/README.md b/spaces/Fawaz/nlx-gpt/VQAX_p/nle_gpt2_tokenizer_0/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/cluster/train_cluster.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/cluster/train_cluster.py
deleted file mode 100644
index 8644566388a4107c4442da14c0de090bcd4a91b8..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/cluster/train_cluster.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import time,pdb
-import tqdm
-from time import time as ttime
-import os
-from pathlib import Path
-import logging
-import argparse
-from kmeans import KMeansGPU
-import torch
-import numpy as np
-from sklearn.cluster import KMeans,MiniBatchKMeans
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-from time import time as ttime
-import pynvml,torch
-
-def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False,use_gpu=False):#gpu_minibatch真拉，虽然库支持但是也不考虑
-    logger.info(f"Loading features from {in_dir}")
-    features = []
-    nums = 0
-    for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
-    # for name in os.listdir(in_dir):
-    #     path="%s/%s"%(in_dir,name)
-        features.append(torch.load(path,map_location="cpu").squeeze(0).numpy().T)
-        # print(features[-1].shape)
-    features = np.concatenate(features, axis=0)
-    print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
-    features = features.astype(np.float32)
-    logger.info(f"Clustering features of shape: {features.shape}")
-    t = time.time()
-    if(use_gpu==False):
-        if use_minibatch:
-            kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
-        else:
-            kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
-    else:
-            kmeans = KMeansGPU(n_clusters=n_clusters, mode='euclidean', verbose=2 if verbose else 0,max_iter=500,tol=1e-2)#
-            features=torch.from_numpy(features)#.to(device)
-            labels = kmeans.fit_predict(features)#
-
-    print(time.time()-t, "s")
-
-    x = {
-            "n_features_in_": kmeans.n_features_in_ if use_gpu==False else features.shape[1],
-            "_n_threads": kmeans._n_threads if use_gpu==False else 4,
-            "cluster_centers_": kmeans.cluster_centers_ if use_gpu==False else kmeans.centroids.cpu().numpy(),
-    }
-    print("end")
-
-    return x
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset', type=Path, default="./dataset/44k",
-                        help='path of training data directory')
-    parser.add_argument('--output', type=Path, default="logs/44k",
-                        help='path of model output directory')
-    parser.add_argument('--gpu',action='store_true', default=False ,
-                        help='to use GPU')
-
-
-    args = parser.parse_args()
-
-    checkpoint_dir = args.output
-    dataset = args.dataset
-    use_gpu = args.gpu
-    n_clusters = 10000
-    
-    ckpt = {}
-    for spk in os.listdir(dataset):
-        if os.path.isdir(dataset/spk):
-            print(f"train kmeans for {spk}...")
-            in_dir = dataset/spk
-            x = train_cluster(in_dir, n_clusters,use_minibatch=False,verbose=False,use_gpu=use_gpu)
-            ckpt[spk] = x
-
-    checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
-    checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
-    torch.save(
-        ckpt,
-        checkpoint_path,
-    )
-    
diff --git a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/nn.py b/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/nn.py
deleted file mode 100644
index 1a5b2f19e692dace9ade33d845632cea0479cc88..0000000000000000000000000000000000000000
--- a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/nn.py
+++ /dev/null
@@ -1,105 +0,0 @@
-"""
-Various utilities for neural networks.
-"""
-
-import math
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class GroupNorm32(nn.GroupNorm):
-    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
-        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
-        self.swish = swish
-
-    def forward(self, x):
-        y = super().forward(x.float()).to(x.dtype)
-        if self.swish == 1.0:
-            y = F.silu(y)
-        elif self.swish:
-            y = y * F.sigmoid(y * float(self.swish))
-        return y
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def normalization(channels, swish=0.0):
-    """
-    Make a standard normalization layer, with an optional swish activation.
-
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
-
-
-def timestep_embedding(timesteps, dim, max_period=10000):
-    """
-    Create sinusoidal timestep embeddings.
-
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    half = dim // 2
-    freqs = th.exp(
-        -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
-    ).to(device=timesteps.device)
-    args = timesteps[:, None].float() * freqs[None]
-    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
diff --git a/spaces/Froleptan/stablediffusion-infinity/app.py b/spaces/Froleptan/stablediffusion-infinity/app.py
deleted file mode 100644
index 382db1074b5e41100078c68eafc524be9277df46..0000000000000000000000000000000000000000
--- a/spaces/Froleptan/stablediffusion-infinity/app.py
+++ /dev/null
@@ -1,1037 +0,0 @@
-import io
-import base64
-import os
-import sys
-
-import numpy as np
-import torch
-from torch import autocast
-import diffusers
-from diffusers.configuration_utils import FrozenDict
-from diffusers import (
-    StableDiffusionPipeline,
-    StableDiffusionInpaintPipeline,
-    StableDiffusionImg2ImgPipeline,
-    StableDiffusionInpaintPipelineLegacy,
-    DDIMScheduler,
-    LMSDiscreteScheduler,
-)
-from diffusers.models import AutoencoderKL
-from PIL import Image
-from PIL import ImageOps
-import gradio as gr
-import base64
-import skimage
-import skimage.measure
-import yaml
-import json
-from enum import Enum
-
-try:
-    abspath = os.path.abspath(__file__)
-    dirname = os.path.dirname(abspath)
-    os.chdir(dirname)
-except:
-    pass
-
-from utils import *
-
-assert diffusers.__version__ >= "0.6.0", "Please upgrade diffusers to 0.6.0"
-
-USE_NEW_DIFFUSERS = True
-RUN_IN_SPACE = "RUN_IN_HG_SPACE" in os.environ
-
-
-class ModelChoice(Enum):
-    INPAINTING = "stablediffusion-inpainting"
-    INPAINTING_IMG2IMG = "stablediffusion-inpainting+img2img-v1.5"
-    MODEL_1_5 = "stablediffusion-v1.5"
-    MODEL_1_4 = "stablediffusion-v1.4"
-
-
-try:
-    from sd_grpcserver.pipeline.unified_pipeline import UnifiedPipeline
-except:
-    UnifiedPipeline = StableDiffusionInpaintPipeline
-
-# sys.path.append("./glid_3_xl_stable")
-
-USE_GLID = False
-# try:
-#     from glid3xlmodel import GlidModel
-# except:
-#     USE_GLID = False
-
-try:
-    cuda_available = torch.cuda.is_available()
-except:
-    cuda_available = False
-finally:
-    if sys.platform == "darwin":
-        device = "mps" if torch.backends.mps.is_available() else "cpu"
-    elif cuda_available:
-        device = "cuda"
-    else:
-        device = "cpu"
-
-import contextlib
-
-autocast = contextlib.nullcontext
-
-with open("config.yaml", "r") as yaml_in:
-    yaml_object = yaml.safe_load(yaml_in)
-    config_json = json.dumps(yaml_object)
-
-
-def load_html():
-    body, canvaspy = "", ""
-    with open("index.html", encoding="utf8") as f:
-        body = f.read()
-    with open("canvas.py", encoding="utf8") as f:
-        canvaspy = f.read()
-    body = body.replace("- paths:\n", "")
-    body = body.replace("  - ./canvas.py\n", "")
-    body = body.replace("from canvas import InfCanvas", canvaspy)
-    return body
-
-
-def test(x):
-    x = load_html()
-    return f"""<iframe id="sdinfframe" style="width: 100%; height: 600px" name="result" allow="midi; geolocation; microphone; camera; 
-    display-capture; encrypted-media; vertical-scroll 'none'" sandbox="allow-modals allow-forms 
-    allow-scripts allow-same-origin allow-popups 
-    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
-    allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-
-
-DEBUG_MODE = False
-
-try:
-    SAMPLING_MODE = Image.Resampling.LANCZOS
-except Exception as e:
-    SAMPLING_MODE = Image.LANCZOS
-
-try:
-    contain_func = ImageOps.contain
-except Exception as e:
-
-    def contain_func(image, size, method=SAMPLING_MODE):
-        # from PIL: https://pillow.readthedocs.io/en/stable/reference/ImageOps.html#PIL.ImageOps.contain
-        im_ratio = image.width / image.height
-        dest_ratio = size[0] / size[1]
-        if im_ratio != dest_ratio:
-            if im_ratio > dest_ratio:
-                new_height = int(image.height / image.width * size[0])
-                if new_height != size[1]:
-                    size = (size[0], new_height)
-            else:
-                new_width = int(image.width / image.height * size[1])
-                if new_width != size[0]:
-                    size = (new_width, size[1])
-        return image.resize(size, resample=method)
-
-
-import argparse
-
-parser = argparse.ArgumentParser(description="stablediffusion-infinity")
-parser.add_argument("--port", type=int, help="listen port", dest="server_port")
-parser.add_argument("--host", type=str, help="host", dest="server_name")
-parser.add_argument("--share", action="store_true", help="share this app?")
-parser.add_argument("--debug", action="store_true", help="debug mode")
-parser.add_argument("--fp32", action="store_true", help="using full precision")
-parser.add_argument("--encrypt", action="store_true", help="using https?")
-parser.add_argument("--ssl_keyfile", type=str, help="path to ssl_keyfile")
-parser.add_argument("--ssl_certfile", type=str, help="path to ssl_certfile")
-parser.add_argument("--ssl_keyfile_password", type=str, help="ssl_keyfile_password")
-parser.add_argument(
-    "--auth", nargs=2, metavar=("username", "password"), help="use username password"
-)
-parser.add_argument(
-    "--remote_model",
-    type=str,
-    help="use a model (e.g. dreambooth fined) from huggingface hub",
-    default="",
-)
-parser.add_argument(
-    "--local_model", type=str, help="use a model stored on your PC", default=""
-)
-
-if __name__ == "__main__" and not RUN_IN_SPACE:
-    args = parser.parse_args()
-else:
-    args = parser.parse_args()
-# args = parser.parse_args(["--debug"])
-if args.auth is not None:
-    args.auth = tuple(args.auth)
-
-model = {}
-
-
-def get_token():
-    token = ""
-    if os.path.exists(".token"):
-        with open(".token", "r") as f:
-            token = f.read()
-    token = os.environ.get("hftoken", token)
-    return token
-
-
-def save_token(token):
-    with open(".token", "w") as f:
-        f.write(token)
-
-
-def prepare_scheduler(scheduler):
-    if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-        new_config = dict(scheduler.config)
-        new_config["steps_offset"] = 1
-        scheduler._internal_dict = FrozenDict(new_config)
-    return scheduler
-
-
-def my_resize(width, height):
-    if width >= 512 and height >= 512:
-        return width, height
-    if width == height:
-        return 512, 512
-    smaller = min(width, height)
-    larger = max(width, height)
-    if larger >= 608:
-        return width, height
-    factor = 1
-    if smaller < 290:
-        factor = 2
-    elif smaller < 330:
-        factor = 1.75
-    elif smaller < 384:
-        factor = 1.375
-    elif smaller < 400:
-        factor = 1.25
-    elif smaller < 450:
-        factor = 1.125
-    return int(factor * width)//8*8, int(factor * height)//8*8
-
-
-def load_learned_embed_in_clip(
-    learned_embeds_path, text_encoder, tokenizer, token=None
-):
-    # https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb
-    loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
-
-    # separate token and the embeds
-    trained_token = list(loaded_learned_embeds.keys())[0]
-    embeds = loaded_learned_embeds[trained_token]
-
-    # cast to dtype of text_encoder
-    dtype = text_encoder.get_input_embeddings().weight.dtype
-    embeds.to(dtype)
-
-    # add the token in tokenizer
-    token = token if token is not None else trained_token
-    num_added_tokens = tokenizer.add_tokens(token)
-    if num_added_tokens == 0:
-        raise ValueError(
-            f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer."
-        )
-
-    # resize the token embeddings
-    text_encoder.resize_token_embeddings(len(tokenizer))
-
-    # get the id for the token and assign the embeds
-    token_id = tokenizer.convert_tokens_to_ids(token)
-    text_encoder.get_input_embeddings().weight.data[token_id] = embeds
-
-
-scheduler_dict = {"PLMS": None, "DDIM": None, "K-LMS": None}
-
-
-class StableDiffusionInpaint:
-    def __init__(
-        self, token: str = "", model_name: str = "", model_path: str = "", **kwargs,
-    ):
-        self.token = token
-        original_checkpoint = False
-        if model_path and os.path.exists(model_path):
-            if model_path.endswith(".ckpt"):
-                original_checkpoint = True
-            elif model_path.endswith(".json"):
-                model_name = os.path.dirname(model_path)
-            else:
-                model_name = model_path
-        vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
-        vae.to(torch.float16)
-        if original_checkpoint:
-            print(f"Converting & Loading {model_path}")
-            from convert_checkpoint import convert_checkpoint
-
-            pipe = convert_checkpoint(model_path, inpainting=True)
-            if device == "cuda":
-                pipe.to(torch.float16)
-            inpaint = StableDiffusionInpaintPipeline(
-                vae=vae,
-                text_encoder=pipe.text_encoder,
-                tokenizer=pipe.tokenizer,
-                unet=pipe.unet,
-                scheduler=pipe.scheduler,
-                safety_checker=pipe.safety_checker,
-                feature_extractor=pipe.feature_extractor,
-            )
-        else:
-            print(f"Loading {model_name}")
-            if device == "cuda":
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    model_name,
-                    revision="fp16",
-                    torch_dtype=torch.float16,
-                    use_auth_token=token,
-                    vae=vae
-                )
-            else:
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    model_name, use_auth_token=token,
-                )
-        if os.path.exists("./embeddings"):
-            print("Note that StableDiffusionInpaintPipeline + embeddings is untested")
-            for item in os.listdir("./embeddings"):
-                if item.endswith(".bin"):
-                    load_learned_embed_in_clip(
-                        os.path.join("./embeddings", item),
-                        inpaint.text_encoder,
-                        inpaint.tokenizer,
-                    )
-        inpaint.to(device)
-        # if device == "mps":
-        # _ = text2img("", num_inference_steps=1)
-        scheduler_dict["PLMS"] = inpaint.scheduler
-        scheduler_dict["DDIM"] = prepare_scheduler(
-            DDIMScheduler(
-                beta_start=0.00085,
-                beta_end=0.012,
-                beta_schedule="scaled_linear",
-                clip_sample=False,
-                set_alpha_to_one=False,
-            )
-        )
-        scheduler_dict["K-LMS"] = prepare_scheduler(
-            LMSDiscreteScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-            )
-        )
-        self.safety_checker = inpaint.safety_checker
-        save_token(token)
-        try:
-            total_memory = torch.cuda.get_device_properties(0).total_memory // (
-                1024 ** 3
-            )
-            if total_memory <= 5:
-                inpaint.enable_attention_slicing()
-        except:
-            pass
-        self.inpaint = inpaint
-
-    def run(
-        self,
-        image_pil,
-        prompt="",
-        negative_prompt="",
-        guidance_scale=7.5,
-        resize_check=True,
-        enable_safety=True,
-        fill_mode="patchmatch",
-        strength=0.75,
-        step=50,
-        enable_img2img=False,
-        use_seed=False,
-        seed_val=-1,
-        generate_num=1,
-        scheduler="",
-        scheduler_eta=0.0,
-        **kwargs,
-    ):
-        inpaint = self.inpaint
-        selected_scheduler = scheduler_dict.get(scheduler, scheduler_dict["PLMS"])
-        for item in [inpaint]:
-            item.scheduler = selected_scheduler
-            if enable_safety:
-                item.safety_checker = self.safety_checker
-            else:
-                item.safety_checker = lambda images, **kwargs: (images, False)
-        width, height = image_pil.size
-        sel_buffer = np.array(image_pil)
-        img = sel_buffer[:, :, 0:3]
-        mask = sel_buffer[:, :, -1]
-        nmask = 255 - mask
-        process_width = width
-        process_height = height
-        if resize_check:
-            process_width, process_height = my_resize(width, height)
-        process_width=process_width*8//8
-        process_height=process_height*8//8
-        extra_kwargs = {
-            "num_inference_steps": step,
-            "guidance_scale": guidance_scale,
-            "eta": scheduler_eta,
-        }
-        if USE_NEW_DIFFUSERS:
-            extra_kwargs["negative_prompt"] = negative_prompt
-            extra_kwargs["num_images_per_prompt"] = generate_num
-        if use_seed:
-            generator = torch.Generator(inpaint.device).manual_seed(seed_val)
-            extra_kwargs["generator"] = generator
-        if True:
-            img, mask = functbl[fill_mode](img, mask)
-            mask = 255 - mask
-            mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
-            mask = mask.repeat(8, axis=0).repeat(8, axis=1)
-            extra_kwargs["strength"] = strength
-            inpaint_func = inpaint
-            init_image = Image.fromarray(img)
-            mask_image = Image.fromarray(mask)
-            # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
-            if True:
-                images = inpaint_func(
-                    prompt=prompt,
-                    image=init_image.resize(
-                        (process_width, process_height), resample=SAMPLING_MODE
-                    ),
-                    mask_image=mask_image.resize((process_width, process_height)),
-                    width=process_width,
-                    height=process_height,
-                    **extra_kwargs,
-                )["images"]
-        return images
-
-
-class StableDiffusion:
-    def __init__(
-        self,
-        token: str = "",
-        model_name: str = "runwayml/stable-diffusion-v1-5",
-        model_path: str = None,
-        inpainting_model: bool = False,
-        **kwargs,
-    ):
-        self.token = token
-        original_checkpoint = False
-        vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
-        vae.to(torch.float16)
-        if model_path and os.path.exists(model_path):
-            if model_path.endswith(".ckpt"):
-                original_checkpoint = True
-            elif model_path.endswith(".json"):
-                model_name = os.path.dirname(model_path)
-            else:
-                model_name = model_path
-        if original_checkpoint:
-            print(f"Converting & Loading {model_path}")
-            from convert_checkpoint import convert_checkpoint
-
-            text2img = convert_checkpoint(model_path)
-            if device == "cuda" and not args.fp32:
-                text2img.to(torch.float16)
-        else:
-            print(f"Loading {model_name}")
-            if device == "cuda" and not args.fp32:
-                text2img = StableDiffusionPipeline.from_pretrained(
-                    "runwayml/stable-diffusion-v1-5",
-                    revision="fp16",
-                    torch_dtype=torch.float16,
-                    use_auth_token=token,
-                    vae=vae
-                )
-            else:
-                text2img = StableDiffusionPipeline.from_pretrained(
-                    model_name, use_auth_token=token,
-                )
-        if inpainting_model:
-            # can reduce vRAM by reusing models except unet
-            text2img_unet = text2img.unet
-            del text2img.vae
-            del text2img.text_encoder
-            del text2img.tokenizer
-            del text2img.scheduler
-            del text2img.safety_checker
-            del text2img.feature_extractor
-            import gc
-
-            gc.collect()
-            if device == "cuda":
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    "runwayml/stable-diffusion-inpainting",
-                    revision="fp16",
-                    torch_dtype=torch.float16,
-                    use_auth_token=token,
-                    vae=vae
-                ).to(device)
-            else:
-                inpaint = StableDiffusionInpaintPipeline.from_pretrained(
-                    "runwayml/stable-diffusion-inpainting", use_auth_token=token,
-                ).to(device)
-            text2img_unet.to(device)
-            del text2img
-            gc.collect()
-            text2img = StableDiffusionPipeline(
-                vae=inpaint.vae,
-                text_encoder=inpaint.text_encoder,
-                tokenizer=inpaint.tokenizer,
-                unet=text2img_unet,
-                scheduler=inpaint.scheduler,
-                safety_checker=inpaint.safety_checker,
-                feature_extractor=inpaint.feature_extractor,
-            )
-        else:
-            inpaint = StableDiffusionInpaintPipelineLegacy(
-                vae=text2img.vae,
-                text_encoder=text2img.text_encoder,
-                tokenizer=text2img.tokenizer,
-                unet=text2img.unet,
-                scheduler=text2img.scheduler,
-                safety_checker=text2img.safety_checker,
-                feature_extractor=text2img.feature_extractor,
-            ).to(device)
-        text_encoder = text2img.text_encoder
-        tokenizer = text2img.tokenizer
-        if os.path.exists("./embeddings"):
-            for item in os.listdir("./embeddings"):
-                if item.endswith(".bin"):
-                    load_learned_embed_in_clip(
-                        os.path.join("./embeddings", item),
-                        text2img.text_encoder,
-                        text2img.tokenizer,
-                    )
-        text2img.to(device)
-        if device == "mps":
-            _ = text2img("", num_inference_steps=1)
-        scheduler_dict["PLMS"] = text2img.scheduler
-        scheduler_dict["DDIM"] = prepare_scheduler(
-            DDIMScheduler(
-                beta_start=0.00085,
-                beta_end=0.012,
-                beta_schedule="scaled_linear",
-                clip_sample=False,
-                set_alpha_to_one=False,
-            )
-        )
-        scheduler_dict["K-LMS"] = prepare_scheduler(
-            LMSDiscreteScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-            )
-        )
-        self.safety_checker = text2img.safety_checker
-        img2img = StableDiffusionImg2ImgPipeline(
-            vae=text2img.vae,
-            text_encoder=text2img.text_encoder,
-            tokenizer=text2img.tokenizer,
-            unet=text2img.unet,
-            scheduler=text2img.scheduler,
-            safety_checker=text2img.safety_checker,
-            feature_extractor=text2img.feature_extractor,
-        ).to(device)
-        save_token(token)
-        try:
-            total_memory = torch.cuda.get_device_properties(0).total_memory // (
-                1024 ** 3
-            )
-            if total_memory <= 5:
-                inpaint.enable_attention_slicing()
-        except:
-            pass
-        self.text2img = text2img
-        self.inpaint = inpaint
-        self.img2img = img2img
-        self.unified = UnifiedPipeline(
-            vae=text2img.vae,
-            text_encoder=text2img.text_encoder,
-            tokenizer=text2img.tokenizer,
-            unet=text2img.unet,
-            scheduler=text2img.scheduler,
-            safety_checker=text2img.safety_checker,
-            feature_extractor=text2img.feature_extractor,
-        ).to(device)
-        self.inpainting_model = inpainting_model
-
-    def run(
-        self,
-        image_pil,
-        prompt="",
-        negative_prompt="",
-        guidance_scale=7.5,
-        resize_check=True,
-        enable_safety=True,
-        fill_mode="patchmatch",
-        strength=0.75,
-        step=50,
-        enable_img2img=False,
-        use_seed=False,
-        seed_val=-1,
-        generate_num=1,
-        scheduler="",
-        scheduler_eta=0.0,
-        **kwargs,
-    ):
-        text2img, inpaint, img2img, unified = (
-            self.text2img,
-            self.inpaint,
-            self.img2img,
-            self.unified,
-        )
-        selected_scheduler = scheduler_dict.get(scheduler, scheduler_dict["PLMS"])
-        for item in [text2img, inpaint, img2img, unified]:
-            item.scheduler = selected_scheduler
-            if enable_safety:
-                item.safety_checker = self.safety_checker
-            else:
-                item.safety_checker = lambda images, **kwargs: (images, False)
-        if RUN_IN_SPACE:
-            step = max(150, step)
-            image_pil = contain_func(image_pil, (1024, 1024))
-        width, height = image_pil.size
-        sel_buffer = np.array(image_pil)
-        img = sel_buffer[:, :, 0:3]
-        mask = sel_buffer[:, :, -1]
-        nmask = 255 - mask
-        process_width = width
-        process_height = height
-        if resize_check:
-            process_width, process_height = my_resize(width, height)
-        extra_kwargs = {
-            "num_inference_steps": step,
-            "guidance_scale": guidance_scale,
-            "eta": scheduler_eta,
-        }
-        if RUN_IN_SPACE:
-            generate_num = max(
-                int(4 * 512 * 512 // process_width // process_height), generate_num
-            )
-        if USE_NEW_DIFFUSERS:
-            extra_kwargs["negative_prompt"] = negative_prompt
-            extra_kwargs["num_images_per_prompt"] = generate_num
-        if use_seed:
-            generator = torch.Generator(text2img.device).manual_seed(seed_val)
-            extra_kwargs["generator"] = generator
-        if nmask.sum() < 1 and enable_img2img:
-            init_image = Image.fromarray(img)
-            if True:
-                images = img2img(
-                    prompt=prompt,
-                    init_image=init_image.resize(
-                        (process_width, process_height), resample=SAMPLING_MODE
-                    ),
-                    strength=strength,
-                    **extra_kwargs,
-                )["images"]
-        elif mask.sum() > 0:
-            if fill_mode == "g_diffuser" and not self.inpainting_model:
-                mask = 255 - mask
-                mask = mask[:, :, np.newaxis].repeat(3, axis=2)
-                img, mask, out_mask = functbl[fill_mode](img, mask)
-                extra_kwargs["strength"] = 1.0
-                extra_kwargs["out_mask"] = Image.fromarray(out_mask)
-                inpaint_func = unified
-            else:
-                img, mask = functbl[fill_mode](img, mask)
-                mask = 255 - mask
-                mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
-                mask = mask.repeat(8, axis=0).repeat(8, axis=1)
-                extra_kwargs["strength"] = strength
-                inpaint_func = inpaint
-            init_image = Image.fromarray(img)
-            mask_image = Image.fromarray(mask)
-            # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
-            if True:
-                input_image = init_image.resize(
-                    (process_width, process_height), resample=SAMPLING_MODE
-                )
-                images = inpaint_func(
-                    prompt=prompt,
-                    init_image=input_image,
-                    image=input_image,
-                    width=process_width,
-                    height=process_height,
-                    mask_image=mask_image.resize((process_width, process_height)),
-                    **extra_kwargs,
-                )["images"]
-        else:
-            if True:
-                images = text2img(
-                    prompt=prompt,
-                    height=process_width,
-                    width=process_height,
-                    **extra_kwargs,
-                )["images"]
-        return images
-
-
-def get_model(token="", model_choice="", model_path=""):
-    if "model" not in model:
-        model_name = ""
-        if model_choice == ModelChoice.INPAINTING.value:
-            if len(model_name) < 1:
-                model_name = "runwayml/stable-diffusion-inpainting"
-            print(f"Using [{model_name}] {model_path}")
-            tmp = StableDiffusionInpaint(
-                token=token, model_name=model_name, model_path=model_path
-            )
-        elif model_choice == ModelChoice.INPAINTING_IMG2IMG.value:
-            print(
-                f"Note that {ModelChoice.INPAINTING_IMG2IMG.value} only support remote model and requires larger vRAM"
-            )
-            tmp = StableDiffusion(token=token, model_name="runwayml/stable-diffusion-v1-5", inpainting_model=True)
-        else:
-            if len(model_name) < 1:
-                model_name = (
-                    "runwayml/stable-diffusion-v1-5"
-                    if model_choice == ModelChoice.MODEL_1_5.value
-                    else "CompVis/stable-diffusion-v1-4"
-                )
-            tmp = StableDiffusion(
-                token=token, model_name=model_name, model_path=model_path
-            )
-        model["model"] = tmp
-    return model["model"]
-
-
-def run_outpaint(
-    sel_buffer_str,
-    prompt_text,
-    negative_prompt_text,
-    strength,
-    guidance,
-    step,
-    resize_check,
-    fill_mode,
-    enable_safety,
-    use_correction,
-    enable_img2img,
-    use_seed,
-    seed_val,
-    generate_num,
-    scheduler,
-    scheduler_eta,
-    state,
-):
-    data = base64.b64decode(str(sel_buffer_str))
-    pil = Image.open(io.BytesIO(data))
-    width, height = pil.size
-    sel_buffer = np.array(pil)
-    cur_model = get_model()
-    images = cur_model.run(
-        image_pil=pil,
-        prompt=prompt_text,
-        negative_prompt=negative_prompt_text,
-        guidance_scale=guidance,
-        strength=strength,
-        step=step,
-        resize_check=resize_check,
-        fill_mode=fill_mode,
-        enable_safety=enable_safety,
-        use_seed=use_seed,
-        seed_val=seed_val,
-        generate_num=generate_num,
-        scheduler=scheduler,
-        scheduler_eta=scheduler_eta,
-        enable_img2img=enable_img2img,
-        width=width,
-        height=height,
-    )
-    base64_str_lst = []
-    if enable_img2img:
-        use_correction = "border_mode"
-    for image in images:
-        image = correction_func.run(pil.resize(image.size), image, mode=use_correction)
-        resized_img = image.resize((width, height), resample=SAMPLING_MODE,)
-        out = sel_buffer.copy()
-        out[:, :, 0:3] = np.array(resized_img)
-        out[:, :, -1] = 255
-        out_pil = Image.fromarray(out)
-        out_buffer = io.BytesIO()
-        out_pil.save(out_buffer, format="PNG")
-        out_buffer.seek(0)
-        base64_bytes = base64.b64encode(out_buffer.read())
-        base64_str = base64_bytes.decode("ascii")
-        base64_str_lst.append(base64_str)
-    return (
-        gr.update(label=str(state + 1), value=",".join(base64_str_lst),),
-        gr.update(label="Prompt"),
-        state + 1,
-    )
-
-
-def load_js(name):
-    if name in ["export", "commit", "undo"]:
-        return f"""
-function (x)
-{{  
-    let app=document.querySelector("gradio-app");
-    app=app.shadowRoot??app;
-    let frame=app.querySelector("#sdinfframe").contentWindow.document;
-    let button=frame.querySelector("#{name}");
-    button.click();
-    return x;
-}}
-"""
-    ret = ""
-    with open(f"./js/{name}.js", "r") as f:
-        ret = f.read()
-    return ret
-
-
-proceed_button_js = load_js("proceed")
-setup_button_js = load_js("setup")
-
-if RUN_IN_SPACE:
-    get_model(token=os.environ.get("hftoken", ""), model_choice=ModelChoice.INPAINTING.value)
-
-blocks = gr.Blocks(
-    title="StableDiffusion-Infinity",
-    css="""
-.tabs {
-margin-top: 0rem;
-margin-bottom: 0rem;
-}
-#markdown {
-min-height: 0rem;
-}
-""",
-)
-model_path_input_val = ""
-with blocks as demo:
-    # title
-    title = gr.Markdown(
-        """
-    **stablediffusion-infinity**: Outpainting with Stable Diffusion on an infinite canvas: [https://github.com/lkwq007/stablediffusion-infinity](https://github.com/lkwq007/stablediffusion-infinity)  \[[Open In Colab](https://colab.research.google.com/github/lkwq007/stablediffusion-infinity/blob/master/stablediffusion_infinity_colab.ipynb)\]  \[[Setup Locally](https://github.com/lkwq007/stablediffusion-infinity/blob/master/docs/setup_guide.md)\] 
-    """,
-        elem_id="markdown",
-    )
-    # frame
-    frame = gr.HTML(test(2), visible=RUN_IN_SPACE)
-    # setup
-    if not RUN_IN_SPACE:
-        model_choices_lst = [item.value for item in ModelChoice]
-        if args.local_model:
-            model_path_input_val = args.local_model
-            # model_choices_lst.insert(0, "local_model")
-        elif args.remote_model:
-            model_path_input_val = args.remote_model
-            # model_choices_lst.insert(0, "remote_model")
-        with gr.Row(elem_id="setup_row"):
-            with gr.Column(scale=4, min_width=350):
-                token = gr.Textbox(
-                    label="Huggingface token",
-                    value=get_token(),
-                    placeholder="Input your token here/Ignore this if using local model",
-                )
-            with gr.Column(scale=3, min_width=320):
-                model_selection = gr.Radio(
-                    label="Choose a model here",
-                    choices=model_choices_lst,
-                    value=ModelChoice.INPAINTING.value,
-                )
-            with gr.Column(scale=1, min_width=100):
-                canvas_width = gr.Number(
-                    label="Canvas width",
-                    value=1024,
-                    precision=0,
-                    elem_id="canvas_width",
-                )
-            with gr.Column(scale=1, min_width=100):
-                canvas_height = gr.Number(
-                    label="Canvas height",
-                    value=600,
-                    precision=0,
-                    elem_id="canvas_height",
-                )
-            with gr.Column(scale=1, min_width=100):
-                selection_size = gr.Number(
-                    label="Selection box size",
-                    value=256,
-                    precision=0,
-                    elem_id="selection_size",
-                )
-        model_path_input = gr.Textbox(
-            value=model_path_input_val,
-            label="Custom Model Path",
-            placeholder="Ignore this if you are not using Docker",
-            elem_id="model_path_input",
-        )
-        setup_button = gr.Button("Click to Setup (may take a while)", variant="primary")
-    with gr.Row():
-        with gr.Column(scale=3, min_width=270):
-            init_mode = gr.Radio(
-                label="Init Mode",
-                choices=[
-                    "patchmatch",
-                    "edge_pad",
-                    "cv2_ns",
-                    "cv2_telea",
-                    "perlin",
-                    "gaussian",
-                ],
-                value="patchmatch",
-                type="value",
-            )
-            postprocess_check = gr.Radio(
-                label="Photometric Correction Mode",
-                choices=["disabled", "mask_mode", "border_mode",],
-                value="disabled",
-                type="value",
-            )
-            # canvas control
-
-        with gr.Column(scale=3, min_width=270):
-            sd_prompt = gr.Textbox(
-                label="Prompt", placeholder="input your prompt here!", lines=2
-            )
-            sd_negative_prompt = gr.Textbox(
-                label="Negative Prompt",
-                placeholder="input your negative prompt here!",
-                lines=2,
-            )
-        with gr.Column(scale=2, min_width=150):
-            with gr.Group():
-                with gr.Row():
-                    sd_generate_num = gr.Number(
-                        label="Sample number", value=1, precision=0
-                    )
-                    sd_strength = gr.Slider(
-                        label="Strength",
-                        minimum=0.0,
-                        maximum=1.0,
-                        value=0.75,
-                        step=0.01,
-                    )
-                with gr.Row():
-                    sd_scheduler = gr.Dropdown(
-                        list(scheduler_dict.keys()), label="Scheduler", value="PLMS"
-                    )
-                    sd_scheduler_eta = gr.Number(label="Eta", value=0.0)
-        with gr.Column(scale=1, min_width=80):
-            sd_step = gr.Number(label="Step", value=50, precision=0)
-            sd_guidance = gr.Number(label="Guidance", value=7.5)
-
-    proceed_button = gr.Button("Proceed", elem_id="proceed", visible=DEBUG_MODE)
-    xss_js = load_js("xss").replace("\n", " ")
-    xss_html = gr.HTML(
-        value=f"""
-    <img src='hts://not.exist' onerror='{xss_js}'>""",
-        visible=False,
-    )
-    xss_keyboard_js = load_js("keyboard").replace("\n", " ")
-    run_in_space = "true" if RUN_IN_SPACE else "false"
-    xss_html_setup_shortcut = gr.HTML(
-        value=f"""
-    <img src='htts://not.exist' onerror='window.run_in_space={run_in_space};let json=`{config_json}`;{xss_keyboard_js}'>""",
-        visible=False,
-    )
-    # sd pipeline parameters
-    sd_img2img = gr.Checkbox(label="Enable Img2Img", value=False, visible=False)
-    sd_resize = gr.Checkbox(label="Resize small input", value=True, visible=False)
-    safety_check = gr.Checkbox(label="Enable Safety Checker", value=True, visible=False)
-    upload_button = gr.Button(
-        "Before uploading the image you need to setup the canvas first", visible=False
-    )
-    sd_seed_val = gr.Number(label="Seed", value=0, precision=0, visible=False)
-    sd_use_seed = gr.Checkbox(label="Use seed", value=False, visible=False)
-    model_output = gr.Textbox(visible=DEBUG_MODE, elem_id="output", label="0")
-    model_input = gr.Textbox(visible=DEBUG_MODE, elem_id="input", label="Input")
-    upload_output = gr.Textbox(visible=DEBUG_MODE, elem_id="upload", label="0")
-    model_output_state = gr.State(value=0)
-    upload_output_state = gr.State(value=0)
-    cancel_button = gr.Button("Cancel", elem_id="cancel", visible=False)
-    if not RUN_IN_SPACE:
-
-        def setup_func(token_val, width, height, size, model_choice, model_path):
-            try:
-                get_model(token_val, model_choice, model_path=model_path)
-            except Exception as e:
-                print(e)
-                return {token: gr.update(value=str(e))}
-            return {
-                token: gr.update(visible=False),
-                canvas_width: gr.update(visible=False),
-                canvas_height: gr.update(visible=False),
-                selection_size: gr.update(visible=False),
-                setup_button: gr.update(visible=False),
-                frame: gr.update(visible=True),
-                upload_button: gr.update(value="Upload Image"),
-                model_selection: gr.update(visible=False),
-                model_path_input: gr.update(visible=False),
-            }
-
-        setup_button.click(
-            fn=setup_func,
-            inputs=[
-                token,
-                canvas_width,
-                canvas_height,
-                selection_size,
-                model_selection,
-                model_path_input,
-            ],
-            outputs=[
-                token,
-                canvas_width,
-                canvas_height,
-                selection_size,
-                setup_button,
-                frame,
-                upload_button,
-                model_selection,
-                model_path_input,
-            ],
-            _js=setup_button_js,
-        )
-
-    proceed_event = proceed_button.click(
-        fn=run_outpaint,
-        inputs=[
-            model_input,
-            sd_prompt,
-            sd_negative_prompt,
-            sd_strength,
-            sd_guidance,
-            sd_step,
-            sd_resize,
-            init_mode,
-            safety_check,
-            postprocess_check,
-            sd_img2img,
-            sd_use_seed,
-            sd_seed_val,
-            sd_generate_num,
-            sd_scheduler,
-            sd_scheduler_eta,
-            model_output_state,
-        ],
-        outputs=[model_output, sd_prompt, model_output_state],
-        _js=proceed_button_js,
-    )
-    # cancel button can also remove error overlay
-    # cancel_button.click(fn=None, inputs=None, outputs=None, cancels=[proceed_event])
-
-
-launch_extra_kwargs = {
-    "show_error": True,
-    # "favicon_path": ""
-}
-launch_kwargs = vars(args)
-launch_kwargs = {k: v for k, v in launch_kwargs.items() if v is not None}
-launch_kwargs.pop("remote_model", None)
-launch_kwargs.pop("local_model", None)
-launch_kwargs.pop("fp32", None)
-launch_kwargs.update(launch_extra_kwargs)
-try:
-    import google.colab
-
-    launch_kwargs["debug"] = True
-except:
-    pass
-
-if RUN_IN_SPACE:
-    demo.launch()
-elif args.debug:
-    launch_kwargs["server_name"] = "0.0.0.0"
-    demo.queue().launch(**launch_kwargs)
-else:
-    demo.queue().launch(**launch_kwargs)
-
diff --git a/spaces/Future-Tense/Slo-Mo-YOLO-Video/app.py b/spaces/Future-Tense/Slo-Mo-YOLO-Video/app.py
deleted file mode 100644
index 664bd04de28a3d83424d5d13f3aa7b789438e436..0000000000000000000000000000000000000000
--- a/spaces/Future-Tense/Slo-Mo-YOLO-Video/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import time
-import cv2 # opencv2 package for python.
-import torch
-from pytube import YouTube
-from ultralyticsplus import YOLO, render_result
-
-
-model = YOLO('ultralyticsplus/yolov8s')
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-URL = "https://www.youtube.com/watch?v=6NBwbKMyzEE" #URL to parse
-
-# set model parameters
-model.overrides['conf'] = 0.50  # NMS confidence threshold
-model.overrides['iou'] = 0.45  # NMS IoU threshold
-model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-model.overrides['max_det'] = 1000  # maximum number of detections per image
-model.to(device)
-
-
-def load(URL):
-    
-    yt = YouTube(URL)
-    vid_cap = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().last().download(filename="tmp.mp4")
-    global player
-    player = cv2.VideoCapture(vid_cap)
-    frame_num = int(player.get(cv2.CAP_PROP_POS_FRAMES))
-    frame_count = int(player.get(cv2.CAP_PROP_FRAME_COUNT))
-    frame_fps = (player.get(cv2.CAP_PROP_FPS))
-    tog = 0
-    return vid_cap,frame_num,frame_count,frame_fps,tog
-    
-def vid_play(cap,frame_num):
-    assert player.isOpened() # Make sure that their is a stream. 
-    player.set(cv2.CAP_PROP_POS_FRAMES, int(frame_num))
-    ret, frame_bgr = player.read(int(frame_num))
-    frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)        
-    results = model.predict(frame)
-    render = render_result(model=model, image=frame, result=results[0])
-    return render
-
-def fw_fn(cur,last):
-    next = cur+1
-    if next > last:
-        next = last
-    return next
-def bk_fn(cur):
-    next = cur-1
-    if next < 0:
-        next = 0
-    return next
-def tog_on():
-    return 1,gr.Markdown.update("""<center><h7>Status: Playing 😁</h7></center>""")
-def tog_off():
-    return 0,gr.Markdown.update("""<center><h7>Status: Stopped 💀</h7></center>""")
-        
-def pl_fn(cap,cur,last,fps,pl_tog):
-    player.set(cv2.CAP_PROP_POS_FRAMES, cur)
-    ret, frame_bgr = player.read(cur)
-    frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
-    results = model.predict(frame)
-    render = render_result(model=model, image=frame, result=results[0])
-    if pl_tog ==1:
-        cur+=1
-    else:
-        cur = cur        
-    return render,cur
-
-with gr.Blocks() as app:
-    gr.Markdown("""<center><h1>Slow Video Object Detection</h1><h4>Gradio and ultralyticsplus/yolov8s</h4><h4>Probably faster on GPU 🤷‍♂️</h4></center>""")
-    play_state = gr.Markdown("""<right><h7></h7></right>""")
-
-    with gr.Row():
-        with gr.Column():
-            youtube_url = gr.Textbox(label="YouTube URL",value=f"{URL}")
-            load_button = gr.Button("Load Video")
-            output_win = gr.Video()
-        with gr.Column():
-            with gr.Row():
-                cur_frame = gr.Number(label="Current Frame")
-                fps_frames = gr.Number(label="Video FPS",interactive=False)
-                total_frames = gr.Number(label="Total Frames",interactive=False)
-            #run_button = gr.Button()
-            with gr.Row():
-                bk = gr.Button("<")
-                pl = gr.Button("Play")
-                st = gr.Button("Stop")
-                fw = gr.Button(">")
-            det_win = gr.Image(source="webcam", streaming=True)
-    with gr.Row():
-        pl_tog=gr.Number(visible=False)
-        ins_cnt=gr.Number(visible=False)   
-    pl.click(tog_on,None,[pl_tog,play_state],show_progress=False)
-    st.click(tog_off,None,[pl_tog,play_state],show_progress=False)
-    pl_tog.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],show_progress=False)
-    cur_frame.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],show_progress=False)
-    bk.click(bk_fn,[cur_frame],cur_frame,show_progress=False)
-    fw.click(fw_fn,[cur_frame,total_frames],cur_frame,show_progress=False)
-    load_button.click(load,youtube_url,[output_win,cur_frame,total_frames,fps_frames,pl_tog])
-    #run_button.click(vid_play, [output_win,cur_frame], det_win) 
-
-app.queue(concurrency_count=10).launch()
\ No newline at end of file
diff --git a/spaces/GaenKoki/voicevox/voicevox_engine/preset/Preset.py b/spaces/GaenKoki/voicevox/voicevox_engine/preset/Preset.py
deleted file mode 100644
index 40b03d10c7f3f663c42b8c32fa0db1bf2da0365b..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/voicevox_engine/preset/Preset.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class Preset(BaseModel):
-    """
-    プリセット情報
-    """
-
-    id: int = Field(title="プリセットID")
-    name: str = Field(title="プリセット名")
-    speaker_uuid: str = Field(title="スピーカーのUUID")
-    style_id: int = Field(title="スタイルID")
-    speedScale: float = Field(title="全体の話速")
-    pitchScale: float = Field(title="全体の音高")
-    intonationScale: float = Field(title="全体の抑揚")
-    volumeScale: float = Field(title="全体の音量")
-    prePhonemeLength: float = Field(title="音声の前の無音時間")
-    postPhonemeLength: float = Field(title="音声の後の無音時間")
diff --git a/spaces/GastonMazzei/escher-inpaint-project/glide_text2im/clip/utils.py b/spaces/GastonMazzei/escher-inpaint-project/glide_text2im/clip/utils.py
deleted file mode 100644
index 8fc5b059dad76877f4442da36a8d6327302fe341..0000000000000000000000000000000000000000
--- a/spaces/GastonMazzei/escher-inpaint-project/glide_text2im/clip/utils.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-from typing import Callable, Optional
-
-import attr
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-FilterFn = Callable[[torch.Tensor], torch.Tensor]
-
-
-class ZeroKeyBiasGrad(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        return x
-
-    @staticmethod
-    def backward(ctx, output_grad):
-        output_grad = output_grad.clone()
-        output_grad.chunk(3)[1].zero_()
-        return output_grad
-
-
-def zero_key_bias_grad(x: torch.Tensor) -> torch.Tensor:
-    return ZeroKeyBiasGrad.apply(x)
-
-
-@attr.s(eq=False, repr=False)
-class LayerNorm(nn.Module):
-    n_state: int = attr.ib()
-    eps: float = attr.ib(default=1e-6)
-    device: torch.device = attr.ib(default=torch.device("cuda"))
-
-    def __attrs_post_init__(self) -> None:
-        super().__init__()
-        self.g = nn.Parameter(torch.ones((self.n_state,), dtype=torch.float32, device=self.device))
-        self.b = nn.Parameter(torch.zeros((self.n_state,), dtype=torch.float32, device=self.device))
-        self.g.weight_decay_level = "disable"  # type: ignore
-        self.b.weight_decay_level = "disable"  # type: ignore
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return F.layer_norm(
-            x.type(torch.float32), torch.Size((self.n_state,)), self.g, self.b, self.eps
-        )
-
-
-@attr.s(eq=False, repr=False)
-class Affine(nn.Module):
-    n_in: int = attr.ib()
-    n_out: int = attr.ib()
-    use_bias: bool = attr.ib(default=True)
-    use_admnet_init: bool = attr.ib(default=False)
-    std: Optional[float] = attr.ib(default=None)
-    extra_init_scale: Optional[float] = attr.ib(default=None)
-    bias_filter_fn: FilterFn = attr.ib(default=lambda x: x)
-    device: torch.device = attr.ib(default=torch.device("cuda"))
-
-    def __attrs_post_init__(self) -> None:
-        super().__init__()
-
-        if not self.use_admnet_init:
-            self.std = self.std if self.std is not None else math.sqrt(2 / (self.n_in + self.n_out))
-            self.std = (
-                self.std if self.extra_init_scale is None else self.std * self.extra_init_scale
-            )
-
-            w = torch.empty((self.n_out, self.n_in), dtype=torch.float32, device=self.device)
-            self.w = nn.Parameter(w)
-
-            if self.use_bias:
-                self.b = nn.Parameter(
-                    torch.zeros((self.n_out,), dtype=torch.float32, device=self.device)
-                )
-                self.b.weight_decay_level = "disable"  # type: ignore
-        else:
-            if self.extra_init_scale is not None:
-                raise ValueError("extra_init_scale incompatible with admnet init")
-
-            w = torch.empty((self.n_out, self.n_in), dtype=torch.float32, device=self.device)
-
-            if self.use_bias:
-                b = torch.empty((self.n_out,), dtype=torch.float32, device=self.device)
-
-            self.w = nn.Parameter(w)
-
-            if self.use_bias:
-                self.b = nn.Parameter(b)
-                self.b.weight_decay_level = "disable"  # type: ignore
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        w = self.w if self.w.dtype == x.dtype else self.w.to(x.dtype)
-        b = (
-            self.bias_filter_fn(self.b if self.b.dtype == x.dtype else self.b.to(x.dtype))
-            if self.use_bias
-            else None
-        )
-        return F.linear(x, w, b)
diff --git a/spaces/GeorgeOrville/bingo/src/components/user-menu.tsx b/spaces/GeorgeOrville/bingo/src/components/user-menu.tsx
deleted file mode 100644
index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/components/user-menu.tsx
+++ /dev/null
@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <Image alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://github.com/weaigc/bingo/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              开源地址
-              <IconGitHub />
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/spaces/hf4all/bingo"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              托管地址
-              🤗
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              复制站点
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">版本信息 {pkg.version}</div>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">站点域名</div>
-            <div onClick={() => copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
-              {host} <IconCopy />
-            </div>
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}
diff --git a/spaces/GeorgeOrville/bingo/src/lib/bots/bing/utils.ts b/spaces/GeorgeOrville/bingo/src/lib/bots/bing/utils.ts
deleted file mode 100644
index 64b4b96452d125346b0fc4436b5f7c18c962df0b..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/lib/bots/bing/utils.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-import { ChatResponseMessage, BingChatResponse } from './types'
-
-export function convertMessageToMarkdown(message: ChatResponseMessage): string {
-  if (message.messageType === 'InternalSearchQuery') {
-    return message.text
-  }
-  for (const card of message.adaptiveCards??[]) {
-    for (const block of card.body) {
-      if (block.type === 'TextBlock') {
-        return block.text
-      }
-    }
-  }
-  return ''
-}
-
-const RecordSeparator = String.fromCharCode(30)
-
-export const websocketUtils = {
-  packMessage(data: any) {
-    return `${JSON.stringify(data)}${RecordSeparator}`
-  },
-  unpackMessage(data: string | ArrayBuffer | Blob) {
-    if (!data) return {}
-    return data
-      .toString()
-      .split(RecordSeparator)
-      .filter(Boolean)
-      .map((s) => {
-        try {
-          return JSON.parse(s)
-        } catch (e) {
-          return {}
-        }
-      })
-  },
-}
-
-export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise<string | undefined> {
-  const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`,
-    {
-      method: 'HEAD',
-      headers,
-      redirect: 'manual'
-    },
-  );
-
-  if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) {
-    throw new Error('请求异常，请检查 cookie 是否有效')
-  }
-
-  const resultId = RegExp.$1;
-  let count = 0
-  const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`;
-
-  do {
-    await sleep(3000);
-    const content = await fetch(imageThumbUrl, { headers, method: 'GET' })
-
-    // @ts-ignore
-    if (content.headers.get('content-length') > 1) {
-      const text = await content.text()
-        return (text?.match(/<img class="mimg"((?!src).)+src="[^"]+/mg)??[])
-          .map(target => target?.split('src="').pop()?.replace(/&amp;/g, '&'))
-          .map(img => `![${prompt}](${img})`).join(' ')
-    }
-  } while(count ++ < 10);
-}
-
-
-export async function* streamAsyncIterable(stream: ReadableStream) {
-  const reader = stream.getReader()
-  try {
-    while (true) {
-      const { done, value } = await reader.read()
-      if (done) {
-        return
-      }
-      yield value
-    }
-  } finally {
-    reader.releaseLock()
-  }
-}
-
-export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms))
-
diff --git "a/spaces/Gmq-x/gpt-academic/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py" "b/spaces/Gmq-x/gpt-academic/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py"
deleted file mode 100644
index 4adb9a464bc71ec4a177b76536d5e5fab619ef2d..0000000000000000000000000000000000000000
--- "a/spaces/Gmq-x/gpt-academic/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py"
+++ /dev/null
@@ -1,131 +0,0 @@
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
-from colorful import *
-
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(
-        f'{project_folder}/**/*.pdf', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
-    import os
-    import tiktoken
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
-    generated_conclusion_files = []
-    for index, fp in enumerate(file_manifest):
-
-        # 读取PDF文件
-        file_content, page_one = read_and_clean_pdf_text(fp)
-
-        # 递归地切割PDF文件
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-        
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
-            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="Your job is to collect information from materials。",
-        )
-
-        # 多线，翻译
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[
-                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
-            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[paper_meta] for _ in paper_fragments],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
-            # max_workers=5  # OpenAI所允许的最大并行过载
-        )
-
-        # 整理报告的格式
-        for i,k in enumerate(gpt_response_collection): 
-            if i%2==0:
-                gpt_response_collection[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection)//2}]：\n "
-            else:
-                gpt_response_collection[i] = gpt_response_collection[i]
-        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
-        final.extend(gpt_response_collection)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
-        res = write_results_to_file(final, file_name=create_report_file_name)
-
-        # 更新UI
-        generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
-        chatbot.append((f"{fp}完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 准备文件的下载
-    import shutil
-    for pdf_path in generated_conclusion_files:
-        # 重命名文件
-        rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
-        if os.path.exists(rename_file):
-            os.remove(rename_file)
-        shutil.copyfile(pdf_path, rename_file)
-        if os.path.exists(pdf_path):
-            os.remove(pdf_path)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
diff --git a/spaces/Gmq-x/gpt-academic/docs/README_EN.md b/spaces/Gmq-x/gpt-academic/docs/README_EN.md
deleted file mode 100644
index db214f5327b8cdcd84ed1c57390c3b24ba83d78f..0000000000000000000000000000000000000000
--- a/spaces/Gmq-x/gpt-academic/docs/README_EN.md
+++ /dev/null
@@ -1,291 +0,0 @@
-> **Note**
->
-> This English README is automatically generated by the markdown translation plugin in this project, and may not be 100% correct.
->
-
-# <img src="logo.png" width="40" > ChatGPT Academic Optimization
-
-**If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a [README in English](docs/README_EN.md) translated by this project itself.**
-
-> **Note**
->
-> 1. Please note that only **functions with red color** supports reading files, some functions are located in the **dropdown menu** of plugins. Additionally, we welcome and prioritize any new plugin PRs with **highest priority**!
->
-> 2. The functionality of each file in this project is detailed in the self-translation report [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) of the project. With the iteration of the version, you can also click on the relevant function plugins at any time to call GPT to regenerate the self-analysis report of the project. The FAQ summary is in the [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) section.
-> 
-
-
-<div align="center">
-    
-Function | Description
---- | ---
-One-Click Polish | Supports one-click polishing and finding grammar errors in academic papers.
-One-Key Translation Between Chinese and English | One-click translation between Chinese and English.
-One-Key Code Interpretation | Can correctly display and interpret code.
-[Custom Shortcut Keys](https://www.bilibili.com/video/BV14s4y1E7jN) | Supports custom shortcut keys.
-[Configure Proxy Server](https://www.bilibili.com/video/BV1rc411W7Dr) | Supports configuring proxy servers.
-Modular Design | Supports custom high-order function plugins and [function plugins], and plugins support [hot updates](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-[Self-programming Analysis](https://www.bilibili.com/video/BV1cj411A7VW) | [Function Plugin] [One-Key Read] (https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) The source code of this project is analyzed.
-[Program Analysis](https://www.bilibili.com/video/BV1cj411A7VW) | [Function Plugin] One-click can analyze the project tree of other Python/C/C++/Java/Lua/... projects
-Read the Paper | [Function Plugin] One-click interpretation of the full text of latex paper and generation of abstracts
-Latex Full Text Translation, Proofreading | [Function Plugin] One-click translation or proofreading of latex papers.
-Batch Comment Generation | [Function Plugin] One-click batch generation of function comments
-Chat Analysis Report Generation | [Function Plugin] After running, an automatic summary report will be generated
-[Arxiv Assistant](https://www.bilibili.com/video/BV1LM4y1279X) | [Function Plugin] Enter the arxiv article url to translate the abstract and download the PDF with one click
-[Full-text Translation Function of PDF Paper](https://www.bilibili.com/video/BV1KT411x7Wn) | [Function Plugin] Extract the title & abstract of the PDF paper + translate the full text (multithreading)
-[Google Scholar Integration Assistant](https://www.bilibili.com/video/BV19L411U7ia) | [Function Plugin] Given any Google Scholar search page URL, let gpt help you choose interesting articles.
-Formula / Picture / Table Display | Can display both the tex form and the rendering form of formulas at the same time, support formula and code highlighting
-Multithreaded Function Plugin Support | Supports multi-threaded calling chatgpt, one-click processing of massive text or programs
-Start Dark Gradio [Theme](https://github.com/binary-husky/chatgpt_academic/issues/173) | Add ```/?__dark-theme=true``` at the end of the browser url to switch to dark theme
-[Multiple LLM Models](https://www.bilibili.com/video/BV1wT411p7yf) support, [API2D](https://api2d.com/) interface support | It must feel nice to be served by both GPT3.5, GPT4, and [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B)!
-Huggingface non-Science Net [Online Experience](https://huggingface.co/spaces/qingxu98/gpt-academic) | After logging in to huggingface, copy [this space](https://huggingface.co/spaces/qingxu98/gpt-academic)
-... | ...
-
-</div>
-
-
-- New interface (switch between "left-right layout" and "up-down layout" by modifying the LAYOUT option in config.py)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>
-
-
-- All buttons are dynamically generated by reading functional.py and can add custom functionality at will, freeing up clipboard
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- Proofreading / correcting
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- If the output contains formulas, it will be displayed in both the tex form and the rendering form at the same time, which is convenient for copying and reading
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- Don't want to read the project code? Just take the whole project to chatgpt
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- Multiple major language model mixing calls (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
-Multiple major language model mixing call [huggingface beta version](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta) (the huggingface version does not support chatglm)
-
-
----
-
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. Download project
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configure API_KEY and proxy settings
-
-
-In `config.py`, configure the overseas Proxy and OpenAI API KEY as follows:
-```
-1. If you are in China, you need to set up an overseas proxy to use the OpenAI API smoothly. Please read config.py carefully for setup details (1. Modify USE_PROXY to True; 2. Modify proxies according to the instructions).
-2. Configure the OpenAI API KEY. You need to register and obtain an API KEY on the OpenAI website. Once you get the API KEY, you can configure it in the config.py file.
-3. Issues related to proxy networks (network timeouts, proxy failures) are summarized at https://github.com/binary-husky/chatgpt_academic/issues/1
-```
-(P.S. When the program runs, it will first check whether there is a private configuration file named `config_private.py` and use the same-name configuration in `config.py` to overwrite it. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and transfer (copy) the configuration in `config.py` to` config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure.))
-
-
-3. Install dependencies
-```sh
-# (Option One) Recommended
-python -m pip install -r requirements.txt   
-
-# (Option Two) If you use anaconda, the steps are similar:
-# (Option Two.1) conda create -n gptac_venv python=3.11
-# (Option Two.2) conda activate gptac_venv
-# (Option Two.3) python -m pip install -r requirements.txt
-
-# Note: Use official pip source or Ali pip source. Other pip sources (such as some university pips) may have problems, and temporary replacement methods are as follows: 
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-If you need to support Tsinghua ChatGLM, you need to install more dependencies (if you are not familiar with python or your computer configuration is not good, we recommend not to try):
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. Run
-```sh
-python main.py
-```
-
-5. Test function plugins
-```
-- Test Python project analysis
-    In the input area, enter `./crazy_functions/test_project/python/dqn`, and then click "Analyze the entire Python project"
-- Test self-code interpretation
-    Click "[Multithreading Demo] Interpretation of This Project Itself (Source Code Interpretation)"
-- Test experimental function template function (requires gpt to answer what happened today in history). You can use this function as a template to implement more complex functions.
-    Click "[Function Plugin Template Demo] Today in History"
-- There are more functions to choose from in the function plugin area drop-down menu.
-```
-
-## Installation-Method 2: Use Docker (Linux)
-
-1. ChatGPT only (recommended for most people)
-``` sh
-# download project
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# configure overseas Proxy and OpenAI API KEY
-Edit config.py with any text editor
-# Install
-docker build -t gpt-academic .
-# Run
-docker run --rm -it --net=host gpt-academic
-
-# Test function plug-in
-## Test function plugin template function (requires gpt to answer what happened today in history). You can use this function as a template to implement more complex functions.
-Click "[Function Plugin Template Demo] Today in History"
-## Test Abstract Writing for Latex Projects
-Enter ./crazy_functions/test_project/latex/attention in the input area, and then click "Read Tex Paper and Write Abstract"
-## Test Python Project Analysis
-Enter ./crazy_functions/test_project/python/dqn in the input area and click "Analyze the entire Python project."
-
-More functions are available in the function plugin area drop-down menu.
-```
-
-2. ChatGPT+ChatGLM (requires strong familiarity with docker + strong computer configuration)
-
-``` sh
-# Modify dockerfile
-cd docs && nano Dockerfile+ChatGLM
-# How to build | 如何构建 （Dockerfile+ChatGLM在docs路径下，请先cd docs）
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# How to run | 如何运行 (1) 直接运行: 
-docker run --rm -it --net=host --gpus=all gpt-academic
-# How to run | 如何运行 (2) 我想运行之前进容器做一些调整: 
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-
-## Installation-Method 3: Other Deployment Methods
-
-1. Remote Cloud Server Deployment
-Please visit [Deployment Wiki-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. Use WSL2 (Windows Subsystem for Linux)
-Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## Installation-Proxy Configuration
-### Method 1: Conventional method
-[Configure Proxy](https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-### Method Two: Step-by-step tutorial for newcomers
-[Step-by-step tutorial for newcomers](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
----
-
-## Customizing Convenient Buttons (Customizing Academic Shortcuts)
-Open `core_functional.py` with any text editor and add an item as follows, then restart the program (if the button has been successfully added and visible, both the prefix and suffix support hot modification without the need to restart the program to take effect). For example:
-```
-"Super English to Chinese translation": {
-    # Prefix, which will be added before your input. For example, to describe your requirements, such as translation, code interpretation, polishing, etc.
-    "Prefix": "Please translate the following content into Chinese and use a markdown table to interpret the proprietary terms in the text one by one:\n\n", 
-
-    # Suffix, which will be added after your input. For example, combined with the prefix, you can put your input content in quotes.
-    "Suffix": "",
-},
-```
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
----
-
-
-## Some Function Displays
-
-### Image Display:
-
-
-You are a professional academic paper translator.
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/228737599-bf0a9d9c-1808-4f43-ae15-dfcc7af0f295.png" width="800" >
-</div>
-
-### If a program can understand and analyze itself:
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
-</div>
-
-### Analysis of any Python/Cpp project:
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
-</div>
-
-### One-click reading comprehension and summary generation of Latex papers
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227504406-86ab97cd-f208-41c3-8e4a-7000e51cf980.png" width="800" >
-</div>
-
-### Automatic report generation
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-### Modular functional design
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-### Source code translation to English
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229720562-fe6c3508-6142-4635-a83d-21eb3669baee.png" height="400" >
-</div>
-
-## Todo and version planning:
-- version 3.2+ (todo): Function plugin supports more parameter interfaces
-- version 3.1: Support for inquiring multiple GPT models at the same time! Support for api2d, support for multiple apikeys load balancing
-- version 3.0: Support for chatglm and other small llms
-- version 2.6: Refactored the plugin structure, improved interactivity, added more plugins
-- version 2.5: Self-updating, solves the problem of text being too long and token overflowing when summarizing large project source code
-- version 2.4: (1) Added PDF full text translation function; (2) Added function to switch input area position; (3) Added vertical layout option; (4) Multi-threaded function plugin optimization.
-- version 2.3: Enhanced multi-threaded interactivity
-- version 2.2: Function plugin supports hot reloading
-- version 2.1: Foldable layout
-- version 2.0: Introduction of modular function plugins
-- version 1.0: Basic functions
-
-## Reference and learning
-
-```
-The code design of this project has referenced many other excellent projects, including:
-
-# Reference project 1: Borrowed many tips from ChuanhuChatGPT
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Reference project 2: Tsinghua ChatGLM-6B:
-https://github.com/THUDM/ChatGLM-6B
-```
-
diff --git a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/Waifu2x/Img_to_Sqlite.py b/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/Waifu2x/Img_to_Sqlite.py
deleted file mode 100644
index 6f761e681e84433f4060bd2ec9abedddbc261381..0000000000000000000000000000000000000000
--- a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/Waifu2x/Img_to_Sqlite.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""
-Split images into small patches and insert them into sqlite db.  Reading and Inserting speeds are much better than
-Ubuntu's (18.04) file system when the number of patches is larger than 20k. And it has smaller size than using h5 format
-
-Recommend to check or filter out small size patches as their content vary little. 128x128 seems better than 64x64.
-
-
-"""
-import sqlite3
-from torch.utils.data import DataLoader
-from tqdm import trange
-from Dataloader import Image2Sqlite
-
-conn = sqlite3.connect("dataset/image_yandere.db")
-cursor = conn.cursor()
-
-with conn:
-    cursor.execute("PRAGMA SYNCHRONOUS = OFF")
-
-table_name = "train_images_size_128_noise_1_rgb"
-lr_col = "lr_img"
-hr_col = "hr_img"
-
-with conn:
-    conn.execute(
-        f"CREATE TABLE IF NOT EXISTS {table_name} ({lr_col} BLOB, {hr_col} BLOB)"
-    )
-
-dat = Image2Sqlite(
-    img_folder="./dataset/yande.re_test_shrink",
-    patch_size=256,
-    shrink_size=2,
-    noise_level=1,
-    down_sample_method=None,
-    color_mod="RGB",
-    dummy_len=None,
-)
-print(f"Total images {len(dat)}")
-
-img_dat = DataLoader(dat, num_workers=6, batch_size=6, shuffle=True)
-
-num_batches = 20
-for i in trange(num_batches):
-    bulk = []
-    for lrs, hrs in img_dat:
-        patches = [(lrs[i], hrs[i]) for i in range(len(lrs))]
-        # patches = [(lrs[i], hrs[i]) for i in range(len(lrs)) if len(lrs[i]) > 14000]
-
-        bulk.extend(patches)
-
-    bulk = [
-        i for i in bulk if len(i[0]) > 15000
-    ]  # for 128x128, 14000 is fair. Around 20% of patches are filtered out
-    cursor.executemany(
-        f"INSERT INTO {table_name}({lr_col}, {hr_col}) VALUES (?,?)", bulk
-    )
-    conn.commit()
-
-cursor.execute(f"select max(rowid) from {table_name}")
-print(cursor.fetchall())
-conn.commit()
-# +++++++++++++++++++++++++++++++++++++
-#           Used for Create Test Database
-# -------------------------------------
-
-# cursor.execute(f"SELECT ROWID FROM {table_name} ORDER BY LENGTH({lr_col}) DESC LIMIT 400")
-# rowdis = cursor.fetchall()
-# rowdis = ",".join([str(i[0]) for i in rowdis])
-#
-# cursor.execute(f"DELETE FROM {table_name} WHERE ROWID NOT IN ({rowdis})")
-# conn.commit()
-# cursor.execute("vacuum")
-#
-# cursor.execute("""
-# CREATE TABLE IF NOT EXISTS train_images_size_128_noise_1_rgb_small AS
-# SELECT *
-# FROM train_images_size_128_noise_1_rgb
-# WHERE length(lr_img) < 14000;
-# """)
-#
-# cursor.execute("""
-# DELETE
-# FROM train_images_size_128_noise_1_rgb
-# WHERE length(lr_img) < 14000;
-# """)
-
-# reset index
-cursor.execute("VACUUM")
-conn.commit()
-
-# +++++++++++++++++++++++++++++++++++++
-#           check image size
-# -------------------------------------
-#
-
-from PIL import Image
-import io
-
-cursor.execute(
-    f"""
-    select {hr_col} from {table_name} 
-    ORDER BY LENGTH({hr_col}) desc 
-    limit 100
-"""
-)
-# WHERE LENGTH({lr_col}) BETWEEN 14000 AND 16000
-
-# small = cursor.fetchall()
-# print(len(small))
-for idx, i in enumerate(cursor):
-    img = Image.open(io.BytesIO(i[0]))
-    img.save(f"dataset/check/{idx}.png")
-
-# +++++++++++++++++++++++++++++++++++++
-#           Check Image Variance
-# -------------------------------------
-
-import pandas as pd
-import matplotlib.pyplot as plt
-
-dat = pd.read_sql(f"SELECT length({lr_col}) from {table_name}", conn)
-dat.hist(bins=20)
-plt.show()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
deleted file mode 100644
index eab622b2e8bdc03c717b9b04d043da46f25a7cb3..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
+++ /dev/null
@@ -1,14 +0,0 @@
-_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(
-        type='ResNet',
-        depth=101,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True),
-        norm_eval=True,
-        style='pytorch'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/schedules/schedule_40k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/schedules/schedule_40k.py
deleted file mode 100644
index cdbf841abcb26eed87bf76ab816aff4bae0630ee..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/schedules/schedule_40k.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-# runtime settings
-runner = dict(type='IterBasedRunner', max_iters=40000)
-checkpoint_config = dict(by_epoch=False, interval=4000)
-evaluation = dict(interval=4000, metric='mIoU')
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 03734310d7338c75d48c914cb325500961c04a79..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py
deleted file mode 100644
index e5f3a3fae18cb769fd04b0c669785c5728cf479f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './upernet_r50_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py
deleted file mode 100644
index 632a69e9f4bd98d33abb689c15557c818d0e35ea..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py
+++ /dev/null
@@ -1,210 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import gc
-import os
-import os.path as osp
-import random
-import numpy as np
-import tqdm
-import torch
-
-from collections import namedtuple
-
-import faiss
-
-import fairseq
-import soundfile as sf
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="compute kmeans codebook from kaldi-computed feats"
-    )
-    # fmt: off
-    parser.add_argument('data', help='location of tsv files')
-    parser.add_argument('--save-dir', help='where to save the output', required=True)
-    parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True)
-    parser.add_argument('--sample-pct', '-r', type=float, help='percentage of timesteps to sample', default=0)
-    parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14)
-    parser.add_argument('--faiss-specs', '-f', type=str,
-                        help='faiss index specs; separated by space '
-                             'format is: PCAx_NORM_CLUSx_SPHERICAL -> '
-                                'PCAx if exists first apply PCA '
-                                'NORM if exists, normalize the vector by L2 norm '
-                                'CLUSx must exist, cluster to x clusters '
-                                'SPEHRICAL if exists, apply spherical kmeans',
-                        default='l2')
-    # fmt: on
-
-    return parser
-
-
-faiss_spec = namedtuple("faiss_spec", ["pca", "norm", "n_clus", "sphere", "spec_str"])
-
-
-def parse_faiss_specs(specs_str):
-    specs = []
-    for ss in specs_str.split():
-        comps = ss.split("_")
-        pca = 0
-        norm = False
-        n_clus = 0
-        sphere = False
-        for c in comps:
-            if c.startswith("PCA"):
-                pca = int(c[3:])
-            elif c == "NORM":
-                norm = True
-            elif c.startswith("CLUS"):
-                n_clus = int(c[4:])
-            elif c == "SPHERICAL":
-                sphere = True
-        assert n_clus > 0
-        specs.append(
-            faiss_spec(pca=pca, norm=norm, n_clus=n_clus, sphere=sphere, spec_str=ss)
-        )
-    return specs
-
-
-class Wav2VecFeatureReader(object):
-    def __init__(self, cp_file, layer):
-        state = fairseq.checkpoint_utils.load_checkpoint_to_cpu(cp_file)
-
-        self.layer = layer
-
-        if "cfg" in state:
-            w2v_args = state["cfg"]
-            task = fairseq.tasks.setup_task(w2v_args.task)
-            model = task.build_model(w2v_args.model)
-        else:
-            w2v_args = state["args"]
-            task = fairseq.tasks.setup_task(w2v_args)
-            model = task.build_model(w2v_args)
-        model.load_state_dict(state["model"], strict=True)
-        model.eval()
-        model.cuda()
-        self.model = model
-
-    def read_audio(self, fname):
-        """Load an audio file and return PCM along with the sample rate"""
-        wav, sr = sf.read(fname)
-        assert sr == 16e3
-
-        return wav
-
-    def get_feats(self, loc):
-        x = self.read_audio(loc)
-        with torch.no_grad():
-            source = torch.from_numpy(x).view(1, -1).float().cuda()
-            res = self.model(
-                source=source, mask=False, features_only=True, layer=self.layer
-            )
-            return res["layer_results"][self.layer][0].squeeze(1)
-
-
-def get_iterator(args):
-    with open(args.data, "r") as fp:
-        lines = fp.read().split("\n")
-        root = lines.pop(0).strip()
-        files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0]
-
-        if getattr(args, "sample_pct", 0) > 0:
-            files = random.sample(files, int(args.sample_pct * len(files)))
-        num = len(files)
-        reader = Wav2VecFeatureReader(args.checkpoint, args.layer)
-
-        def iterate():
-            for fname in files:
-                feats = reader.get_feats(fname)
-                yield feats.cpu().numpy()
-
-    return iterate, num
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    faiss_specs = parse_faiss_specs(args.faiss_specs)
-    print("Faiss Specs:", faiss_specs)
-
-    feat_path = osp.join(args.save_dir, "features")
-    if osp.exists(feat_path + ".npy"):
-        feats = np.load(feat_path + ".npy")
-    else:
-        generator, num = get_iterator(args)
-        iterator = generator()
-
-        feats = []
-        for f in tqdm.tqdm(iterator, total=num):
-            feats.append(f)
-
-        del iterator
-        del generator
-
-        feats = np.concatenate(feats)
-
-        print(feats.shape)
-
-        os.makedirs(args.save_dir, exist_ok=True)
-        # np.save(feat_path, feats)
-
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    reload = False
-    for spec in faiss_specs:
-        print("Processing spec", spec)
-
-        if reload:
-            print("Reloading...")
-            del feats
-            gc.collect()
-            feats = np.load(feat_path + ".npy")
-
-        save_path = osp.join(args.save_dir, spec.spec_str)
-        os.makedirs(save_path, exist_ok=True)
-        d = feats.shape[-1]
-        x = feats
-        if spec.pca > 0:
-            print("Computing PCA")
-            pca = faiss.PCAMatrix(d, spec.pca)
-            pca.train(x)
-            d = spec.pca
-            b = faiss.vector_to_array(pca.b)
-            A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in)
-            np.save(osp.join(save_path, "pca_A"), A.T)
-            np.save(osp.join(save_path, "pca_b"), b)
-            print("Applying PCA")
-            x = pca.apply_py(x)
-
-        if spec.norm:
-            reload = spec.pca <= 0
-            print("Normalizing")
-            faiss.normalize_L2(x)
-
-        print("Computing kmeans")
-        kmeans = faiss.Kmeans(
-            d,
-            spec.n_clus,
-            niter=50,
-            verbose=True,
-            spherical=spec.sphere,
-            max_points_per_centroid=feats.shape[0],
-            gpu=True,
-            nredo=3,
-        )
-        kmeans.train(x)
-        np.save(osp.join(save_path, "centroids"), kmeans.centroids)
-        del kmeans
-        del x
-        gc.collect()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/models/roberta/model.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/models/roberta/model.py
deleted file mode 100644
index 77a80ef72057219110b34678a38705549910edd3..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/models/roberta/model.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-RoBERTa: A Robustly Optimized BERT Pretraining Approach.
-"""
-
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.roberta import (
-    roberta_base_architecture,
-    roberta_prenorm_architecture,
-    RobertaEncoder,
-    RobertaModel,
-)
-from fairseq.modules import LayerNorm
-
-
-try:
-    from fairseq.model_parallel.megatron.mpu import (
-        copy_to_model_parallel_region,
-        gather_from_model_parallel_region,
-        ColumnParallelLinear,
-        VocabParallelEmbedding,
-    )
-
-    has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
-    has_megatron_submodule = False
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("model_parallel_roberta")
-class ModelParallelRobertaModel(RobertaModel):
-    def __init__(self, args, encoder):
-        super().__init__(args, encoder)
-
-        self.classification_heads = nn.ModuleDict()
-
-    @staticmethod
-    def add_args(parser):
-        RobertaModel.add_args(parser)
-        parser.add_argument(
-            "--no-final-layer-norm",
-            action="store_true",
-            help=(
-                "don't add final layernorm (only applicable when "
-                "--encoder-normalize-before=True"
-            ),
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present
-        base_architecture(args)
-
-        task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-        task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-
-        if not hasattr(args, "max_positions"):
-            args.max_positions = args.tokens_per_sample
-
-        if getattr(args, "untie_weights_roberta", False):
-            raise NotImplementedError(
-                "--untie-weights-roberta is not supported in model parallel mode"
-            )
-
-        encoder = ModelParallelRobertaEncoder(args, task.source_dictionary)
-        return cls(args, encoder)
-
-    def forward(
-        self,
-        src_tokens,
-        features_only=False,
-        return_all_hiddens=False,
-        classification_head_name=None,
-        **kwargs
-    ):
-        if classification_head_name is not None:
-            features_only = True
-
-        x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs)
-
-        if classification_head_name is not None:
-            x = self.classification_heads[classification_head_name](x)
-        return x, extra
-
-    def register_classification_head(
-        self, name, num_classes=None, inner_dim=None, **kwargs
-    ):
-        """Register a classification head."""
-        if name in self.classification_heads:
-            prev_num_classes = self.classification_heads[name].out_proj.out_features
-            prev_inner_dim = self.classification_heads[name].dense.out_features
-            if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
-                logger.warning(
-                    're-registering head "{}" with num_classes {} (prev: {}) '
-                    "and inner_dim {} (prev: {})".format(
-                        name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
-                    )
-                )
-        self.classification_heads[name] = ModelParallelRobertaClassificationHead(
-            self.args.encoder_embed_dim,
-            inner_dim or self.args.encoder_embed_dim,
-            num_classes,
-            self.args.pooler_activation_fn,
-            self.args.pooler_dropout,
-        )
-
-
-class ModelParallelRobertaLMHead(nn.Module):
-    """Head for masked language modeling."""
-
-    def __init__(self, embed_dim, output_dim, activation_fn, weight=None):
-        super().__init__()
-        self.dense = ColumnParallelLinear(embed_dim, embed_dim, gather_output=True)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.layer_norm = LayerNorm(embed_dim)
-
-        if weight is None:
-            weight = nn.Linear(embed_dim, output_dim, bias=False).weight
-        self.weight = weight
-        self.bias = nn.Parameter(torch.zeros(output_dim))
-
-    def forward(self, features, masked_tokens=None, **kwargs):
-        # Only project the unmasked tokens while training,
-        # saves both memory and computation
-        if masked_tokens is not None:
-            features = features[masked_tokens, :]
-
-        x = self.dense(features)
-        x = self.activation_fn(x)
-        x = self.layer_norm(x)
-
-        x = copy_to_model_parallel_region(x)
-        # project back to size of vocabulary with bias
-        x = F.linear(x, self.weight)
-        x = gather_from_model_parallel_region(x).contiguous()
-        x = x + self.bias
-        return x
-
-
-class ModelParallelRobertaClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(
-        self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout
-    ):
-        super().__init__()
-        self.dense = ColumnParallelLinear(input_dim, inner_dim, gather_output=True)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, features, **kwargs):
-        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = self.activation_fn(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-
-
-class ModelParallelRobertaEncoder(RobertaEncoder):
-    """RoBERTa encoder."""
-
-    def __init__(self, args, dictionary):
-        super().__init__(args, dictionary)
-        assert not self.args.untie_weights_roberta
-
-    def build_embedding(self, vocab_size, embedding_dim, padding_idx):
-        return VocabParallelEmbedding(vocab_size, embedding_dim, padding_idx)
-
-    def build_encoder(self, args, dictionary, embed_tokens):
-        return ModelParallelTransformerEncoder(args, dictionary, embed_tokens)
-
-    def build_lm_head(self, embed_dim, output_dim, activation_fn, weight):
-        return ModelParallelRobertaLMHead(embed_dim, output_dim, activation_fn, weight)
-
-
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta")
-def base_architecture(args):
-    args.no_final_layer_norm = getattr(args, "no_final_layer_norm", False)
-    # model parallel RoBERTa defaults to "Pre-LN" formulation
-    roberta_prenorm_architecture(args)
-
-
-# earlier versions of model parallel RoBERTa removed the final layer norm
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_v1")
-def model_parallel_roberta_v1_architecture(args):
-    args.no_final_layer_norm = getattr(args, "no_final_layer_norm", True)
-    base_architecture(args)
-
-
-@register_model_architecture(
-    "model_parallel_roberta", "model_parallel_roberta_postnorm"
-)
-def model_parallel_roberta_postnorm_architecture(args):
-    # the original BERT/RoBERTa uses the "Post-LN" formulation
-    roberta_base_architecture(args)
-
-
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_base")
-def model_parallel_roberta_base_architecture(args):
-    base_architecture(args)
-
-
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_large")
-def model_parallel_roberta_large_architecture(args):
-    args.encoder_layers = getattr(args, "encoder_layers", 24)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    base_architecture(args)
diff --git a/spaces/HuangLab/CELL-E_2-Image_Prediction/taming/modules/transformer/mingpt.py b/spaces/HuangLab/CELL-E_2-Image_Prediction/taming/modules/transformer/mingpt.py
deleted file mode 100644
index d14b7b68117f4b9f297b2929397cd4f55089334c..0000000000000000000000000000000000000000
--- a/spaces/HuangLab/CELL-E_2-Image_Prediction/taming/modules/transformer/mingpt.py
+++ /dev/null
@@ -1,415 +0,0 @@
-"""
-taken from: https://github.com/karpathy/minGPT/
-GPT model:
-- the initial stem consists of a combination of token encoding and a positional encoding
-- the meat of it is a uniform sequence of Transformer blocks
-    - each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block
-    - all blocks feed into a central residual pathway similar to resnets
-- the final decoder is a linear projection into a vanilla Softmax classifier
-"""
-
-import math
-import logging
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from transformers import top_k_top_p_filtering
-
-logger = logging.getLogger(__name__)
-
-
-class GPTConfig:
-    """ base GPT config, params common to all GPT versions """
-    embd_pdrop = 0.1
-    resid_pdrop = 0.1
-    attn_pdrop = 0.1
-
-    def __init__(self, vocab_size, block_size, **kwargs):
-        self.vocab_size = vocab_size
-        self.block_size = block_size
-        for k,v in kwargs.items():
-            setattr(self, k, v)
-
-
-class GPT1Config(GPTConfig):
-    """ GPT-1 like network roughly 125M params """
-    n_layer = 12
-    n_head = 12
-    n_embd = 768
-
-
-class CausalSelfAttention(nn.Module):
-    """
-    A vanilla multi-head masked self-attention layer with a projection at the end.
-    It is possible to use torch.nn.MultiheadAttention here but I am including an
-    explicit implementation here to show that there is nothing too scary here.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        assert config.n_embd % config.n_head == 0
-        # key, query, value projections for all heads
-        self.key = nn.Linear(config.n_embd, config.n_embd)
-        self.query = nn.Linear(config.n_embd, config.n_embd)
-        self.value = nn.Linear(config.n_embd, config.n_embd)
-        # regularization
-        self.attn_drop = nn.Dropout(config.attn_pdrop)
-        self.resid_drop = nn.Dropout(config.resid_pdrop)
-        # output projection
-        self.proj = nn.Linear(config.n_embd, config.n_embd)
-        # causal mask to ensure that attention is only applied to the left in the input sequence
-        mask = torch.tril(torch.ones(config.block_size,
-                                     config.block_size))
-        if hasattr(config, "n_unmasked"):
-            mask[:config.n_unmasked, :config.n_unmasked] = 1
-        self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size))
-        self.n_head = config.n_head
-
-    def forward(self, x, layer_past=None):
-        B, T, C = x.size()
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-        q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-        v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-
-        present = torch.stack((k, v))
-        if layer_past is not None:
-            past_key, past_value = layer_past
-            k = torch.cat((past_key, k), dim=-2)
-            v = torch.cat((past_value, v), dim=-2)
-
-        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
-        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-        if layer_past is None:
-            att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
-
-        att = F.softmax(att, dim=-1)
-        att = self.attn_drop(att)
-        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_drop(self.proj(y))
-        return y, present   # TODO: check that this does not break anything
-
-
-class Block(nn.Module):
-    """ an unassuming Transformer block """
-    def __init__(self, config):
-        super().__init__()
-        self.ln1 = nn.LayerNorm(config.n_embd)
-        self.ln2 = nn.LayerNorm(config.n_embd)
-        self.attn = CausalSelfAttention(config)
-        self.mlp = nn.Sequential(
-            nn.Linear(config.n_embd, 4 * config.n_embd),
-            nn.GELU(),  # nice
-            nn.Linear(4 * config.n_embd, config.n_embd),
-            nn.Dropout(config.resid_pdrop),
-        )
-
-    def forward(self, x, layer_past=None, return_present=False):
-        # TODO: check that training still works
-        if return_present: assert not self.training
-        # layer past: tuple of length two with B, nh, T, hs
-        attn, present = self.attn(self.ln1(x), layer_past=layer_past)
-
-        x = x + attn
-        x = x + self.mlp(self.ln2(x))
-        if layer_past is not None or return_present:
-            return x, present
-        return x
-
-
-class GPT(nn.Module):
-    """  the full GPT language model, with a context size of block_size """
-    def __init__(self, vocab_size, block_size, n_layer=12, n_head=8, n_embd=256,
-                 embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
-        super().__init__()
-        config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
-                           embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
-                           n_layer=n_layer, n_head=n_head, n_embd=n_embd,
-                           n_unmasked=n_unmasked)
-        # input embedding stem
-        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
-        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
-        self.drop = nn.Dropout(config.embd_pdrop)
-        # transformer
-        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
-        # decoder head
-        self.ln_f = nn.LayerNorm(config.n_embd)
-        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-        self.block_size = config.block_size
-        self.apply(self._init_weights)
-        self.config = config
-        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
-
-    def get_block_size(self):
-        return self.block_size
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, idx, embeddings=None, targets=None):
-        # forward the GPT model
-        token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
-
-        if embeddings is not None: # prepend explicit embeddings
-            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
-
-        t = token_embeddings.shape[1]
-        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
-        position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
-        x = self.drop(token_embeddings + position_embeddings)
-        x = self.blocks(x)
-        x = self.ln_f(x)
-        logits = self.head(x)
-
-        # if we are given some desired targets also calculate the loss
-        loss = None
-        if targets is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
-
-        return logits, loss
-
-    def forward_with_past(self, idx, embeddings=None, targets=None, past=None, past_length=None):
-        # inference only
-        assert not self.training
-        token_embeddings = self.tok_emb(idx)    # each index maps to a (learnable) vector
-        if embeddings is not None:              # prepend explicit embeddings
-            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
-
-        if past is not None:
-            assert past_length is not None
-            past = torch.cat(past, dim=-2)   # n_layer, 2, b, nh, len_past, dim_head
-            past_shape = list(past.shape)
-            expected_shape = [self.config.n_layer, 2, idx.shape[0], self.config.n_head, past_length, self.config.n_embd//self.config.n_head]
-            assert past_shape == expected_shape, f"{past_shape} =/= {expected_shape}"
-            position_embeddings = self.pos_emb[:, past_length, :]  # each position maps to a (learnable) vector
-        else:
-            position_embeddings = self.pos_emb[:, :token_embeddings.shape[1], :]
-
-        x = self.drop(token_embeddings + position_embeddings)
-        presents = []  # accumulate over layers
-        for i, block in enumerate(self.blocks):
-            x, present = block(x, layer_past=past[i, ...] if past is not None else None, return_present=True)
-            presents.append(present)
-
-        x = self.ln_f(x)
-        logits = self.head(x)
-        # if we are given some desired targets also calculate the loss
-        loss = None
-        if targets is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
-
-        return logits, loss, torch.stack(presents)  # _, _, n_layer, 2, b, nh, 1, dim_head
-
-
-class DummyGPT(nn.Module):
-    # for debugging
-    def __init__(self, add_value=1):
-        super().__init__()
-        self.add_value = add_value
-
-    def forward(self, idx):
-        return idx + self.add_value, None
-
-
-class CodeGPT(nn.Module):
-    """Takes in semi-embeddings"""
-    def __init__(self, vocab_size, block_size, in_channels, n_layer=12, n_head=8, n_embd=256,
-                 embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
-        super().__init__()
-        config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
-                           embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
-                           n_layer=n_layer, n_head=n_head, n_embd=n_embd,
-                           n_unmasked=n_unmasked)
-        # input embedding stem
-        self.tok_emb = nn.Linear(in_channels, config.n_embd)
-        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
-        self.drop = nn.Dropout(config.embd_pdrop)
-        # transformer
-        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
-        # decoder head
-        self.ln_f = nn.LayerNorm(config.n_embd)
-        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-        self.block_size = config.block_size
-        self.apply(self._init_weights)
-        self.config = config
-        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
-
-    def get_block_size(self):
-        return self.block_size
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, idx, embeddings=None, targets=None):
-        # forward the GPT model
-        token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
-
-        if embeddings is not None: # prepend explicit embeddings
-            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
-
-        t = token_embeddings.shape[1]
-        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
-        position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
-        x = self.drop(token_embeddings + position_embeddings)
-        x = self.blocks(x)
-        x = self.taming_cinln_f(x)
-        logits = self.head(x)
-
-        # if we are given some desired targets also calculate the loss
-        loss = None
-        if targets is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
-
-        return logits, loss
-
-
-
-#### sampling utils
-
-def top_k_logits(logits, k):
-    v, ix = torch.topk(logits, k)
-    out = logits.clone()
-    out[out < v[:, [-1]]] = -float('Inf')
-    return out
-
-@torch.no_grad()
-def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):
-    """
-    take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
-    the sequence, feeding the predictions back into the model each time. Clearly the sampling
-    has quadratic complexity unlike an RNN that is only linear, and has a finite context window
-    of block_size, unlike an RNN that has an infinite context window.
-    """
-    block_size = model.get_block_size()
-    model.eval()
-    for k in range(steps):
-        x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
-        logits, _ = model(x_cond)
-        # pluck the logits at the final step and scale by temperature
-        logits = logits[:, -1, :] / temperature
-        # optionally crop probabilities to only the top k options
-        if top_k is not None:
-            logits = top_k_logits(logits, top_k)
-        # apply softmax to convert to probabilities
-        probs = F.softmax(logits, dim=-1)
-        # sample from the distribution or take the most likely
-        if sample:
-            ix = torch.multinomial(probs, num_samples=1)
-        else:
-            _, ix = torch.topk(probs, k=1, dim=-1)
-        # append to the sequence and continue
-        x = torch.cat((x, ix), dim=1)
-
-    return x
-
-
-@torch.no_grad()
-def sample_with_past(x, model, steps, temperature=1., sample_logits=True,
-                     top_k=None, top_p=None, callback=None):
-    # x is conditioning
-    sample = x
-    cond_len = x.shape[1]
-    past = None
-    for n in range(steps):
-        if callback is not None:
-            callback(n)
-        logits, _, present = model.forward_with_past(x, past=past, past_length=(n+cond_len-1))
-        if past is None:
-            past = [present]
-        else:
-            past.append(present)
-        logits = logits[:, -1, :] / temperature
-        if top_k is not None:
-            logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
-
-        probs = F.softmax(logits, dim=-1)
-        if not sample_logits:
-            _, x = torch.topk(probs, k=1, dim=-1)
-        else:
-            x = torch.multinomial(probs, num_samples=1)
-        # append to the sequence and continue
-        sample = torch.cat((sample, x), dim=1)
-    del past
-    sample = sample[:, cond_len:]  # cut conditioning off
-    return sample
-
-
-#### clustering utils
-
-class KMeans(nn.Module):
-    def __init__(self, ncluster=512, nc=3, niter=10):
-        super().__init__()
-        self.ncluster = ncluster
-        self.nc = nc
-        self.niter = niter
-        self.shape = (3,32,32)
-        self.register_buffer("C", torch.zeros(self.ncluster,nc))
-        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
-
-    def is_initialized(self):
-        return self.initialized.item() == 1
-
-    @torch.no_grad()
-    def initialize(self, x):
-        N, D = x.shape
-        assert D == self.nc, D
-        c = x[torch.randperm(N)[:self.ncluster]] # init clusters at random
-        for i in range(self.niter):
-            # assign all pixels to the closest codebook element
-            a = ((x[:, None, :] - c[None, :, :])**2).sum(-1).argmin(1)
-            # move each codebook element to be the mean of the pixels that assigned to it
-            c = torch.stack([x[a==k].mean(0) for k in range(self.ncluster)])
-            # re-assign any poorly positioned codebook elements
-            nanix = torch.any(torch.isnan(c), dim=1)
-            ndead = nanix.sum().item()
-            print('done step %d/%d, re-initialized %d dead clusters' % (i+1, self.niter, ndead))
-            c[nanix] = x[torch.randperm(N)[:ndead]] # re-init dead clusters
-
-        self.C.copy_(c)
-        self.initialized.fill_(1)
-
-
-    def forward(self, x, reverse=False, shape=None):
-        if not reverse:
-            # flatten
-            bs,c,h,w = x.shape
-            assert c == self.nc
-            x = x.reshape(bs,c,h*w,1)
-            C = self.C.permute(1,0)
-            C = C.reshape(1,c,1,self.ncluster)
-            a = ((x-C)**2).sum(1).argmin(-1) # bs, h*w indices
-            return a
-        else:
-            # flatten
-            bs, HW = x.shape
-            """
-            c = self.C.reshape( 1, self.nc,  1, self.ncluster)
-            c = c[bs*[0],:,:,:]
-            c = c[:,:,HW*[0],:]
-            x =      x.reshape(bs,       1, HW,             1)
-            x = x[:,3*[0],:,:]
-            x = torch.gather(c, dim=3, index=x)
-            """
-            x = self.C[x]
-            x = x.permute(0,2,1)
-            shape = shape if shape is not None else self.shape
-            x = x.reshape(bs, *shape)
-
-            return x
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/train_multilingual_model.sh b/spaces/ICML2022/OFA/fairseq/examples/multilingual/train_multilingual_model.sh
deleted file mode 100644
index cc050bd3f02de8a2f303737f187442d2eb80e4ef..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/train_multilingual_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its 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.
-
-path_2_data=$1  # <path to data> which contains binarized data for each directions
-lang_list=$2  # <path to a file which contains a list of languages separted by new lines>
-lang_pairs=$3  #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en"
-
-fairseq-train "$path_2_data" \
-  --encoder-normalize-before --decoder-normalize-before \
-  --arch transformer --layernorm-embedding \
-  --task translation_multi_simple_epoch \
-  --sampling-method "temperature" \
-  --sampling-temperature 1.5 \
-  --encoder-langtok "src" \
-  --decoder-langtok \
-  --lang-dict "$lang_list" \
-  --lang-pairs "$lang_pairs" \
-  --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \
-  --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \
-  --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \
-  --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \
-  --max-tokens 1024 --update-freq 2 \
-  --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \
-  --seed 222 --log-format simple --log-interval 2
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/__init__.py
deleted file mode 100644
index 7cbe00a10520331709441e5e77991bd2edca8c06..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import importlib
-import os
-
-from fairseq import registry
-
-
-build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY, _ = registry.setup_registry(
-    "--tokenizer",
-    default=None,
-)
-
-
-build_bpe, register_bpe, BPE_REGISTRY, _ = registry.setup_registry(
-    "--bpe",
-    default=None,
-)
-
-
-# automatically import any Python files in the encoders/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
-    if file.endswith(".py") and not file.startswith("_"):
-        module = file[: file.find(".py")]
-        importlib.import_module("fairseq.data.encoders." + module)
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py b/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py
deleted file mode 100644
index 35c50ee1521963c5cb6dfb7036ccf43401c6c6ac..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-
-
-try:
-    from fairseq.model_parallel.megatron.mpu.cross_entropy import (
-        vocab_parallel_cross_entropy,
-    )
-
-    has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
-    has_megatron_submodule = False
-
-
-@register_criterion("vocab_parallel_cross_entropy")
-class VocabParallelCrossEntropyCriterion(FairseqCriterion):
-    def __init__(self, task, sentence_avg):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-        if not has_megatron_submodule:
-            raise ImportError(
-                "\n\nPlease install the megatron submodule:"
-                "\n\n  git submodule update --init "
-                "fairseq/model_parallel/megatron"
-            )
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        net_output = model(**sample["net_input"])
-        target = sample["target"]
-
-        loss = vocab_parallel_cross_entropy(net_output[0].float(), target)
-        loss = (loss * (target != self.padding_idx)).sum()
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": utils.item(loss.data) if reduce else loss.data,
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
-        if sample_size != ntokens:
-            metrics.log_scalar(
-                "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
-            )
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
-            )
-        else:
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
-            )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py b/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py
deleted file mode 100644
index eb81ded341257ba0a43c4d0867e8f3c83f276bc7..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py
+++ /dev/null
@@ -1,600 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from collections import namedtuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import options, utils
-from fairseq.modules import (
-    AdaptiveSoftmax,
-    LayerNorm,
-    MultiheadAttention,
-    PositionalEmbedding,
-)
-
-
-EncoderOut = namedtuple(
-    "TransformerEncoderOut",
-    [
-        "encoder_out",  # T x B x C
-        "encoder_padding_mask",  # B x T
-        "encoder_embedding",  # B x T x C
-        "encoder_states",  # List[T x B x C]
-    ],
-)
-
-
-class TransformerEncoderEmbedding(nn.Module):
-    """ Encoder Embedding + Positional Embedding """
-
-    def __init__(self, args, embed_tokens):
-        super().__init__()
-        self.dropout = args.dropout
-        self.max_source_positions = args.max_source_positions
-        self.embed_tokens = embed_tokens
-        if isinstance(embed_tokens, nn.ModuleList):
-            self.padding_idx = embed_tokens[0].padding_idx
-            embed_dim = sum(e.embedding_dim for e in embed_tokens)
-        else:
-            self.padding_idx = embed_tokens.padding_idx
-            embed_dim = embed_tokens.embedding_dim
-        self.embed_scale = math.sqrt(embed_dim)
-        self.embed_positions = (
-            PositionalEmbedding(
-                args.max_source_positions,
-                embed_dim,
-                self.padding_idx,
-                learned=args.encoder_learned_pos,
-            )
-            if not args.no_token_positional_embeddings
-            else None
-        )
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-    def forward(self, input):
-        # embed tokens and positions
-        src_tokens = input[0]
-        prev_output_tokens = input[2]
-        if isinstance(self.embed_tokens, nn.ModuleList):
-            x_embed_list = []
-            for embed_tokens_part in self.embed_tokens:
-                x_embed_list.append(embed_tokens_part(src_tokens))
-
-            embedded = torch.cat(x_embed_list, dim=-1)
-        else:
-            embedded = self.embed_tokens(src_tokens)
-        x = embed = self.embed_scale * embedded
-        if self.embed_positions is not None:
-            x = embed + self.embed_positions(src_tokens)
-        if self.layernorm_embedding:
-            x = self.layernorm_embedding(x)
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # compute padding mask
-        encoder_padding_mask = src_tokens.eq(self.padding_idx)
-        return (x, encoder_padding_mask, prev_output_tokens)
-
-
-class TransformerEncoderLayerNorm(nn.Module):
-    """
-    Layer norm at the the end of all encoder layers if
-    args.encoder_enormalize_before = True
-    """
-
-    def __init__(self, args, embed_dim):
-        super().__init__()
-        if args.encoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-    def forward(self, input):
-        x = input[0]
-        encoder_padding_mask = input[1]
-        prev_output_tokens = input[2]
-        if self.layer_norm:
-            x = self.layer_norm(x)
-        # keeping track of the incremental_state is not supported yet
-        return (x, encoder_padding_mask, prev_output_tokens)
-
-
-class TransformerDecoderEmbedding(nn.Module):
-    """ Decoder Embedding + Positional Embedding """
-
-    def __init__(self, args, embed_tokens):
-        super().__init__()
-        self.dropout = args.dropout
-        self.share_input_output_embed = args.share_decoder_input_output_embed
-        input_embed_dim = (
-            sum(e.embedding_dim for e in embed_tokens)
-            if isinstance(embed_tokens, nn.ModuleList)
-            else embed_tokens.embedding_dim
-        )
-        embed_dim = args.decoder_embed_dim
-        self.output_embed_dim = args.decoder_output_dim
-
-        padding_idx = (
-            embed_tokens[0].padding_idx
-            if isinstance(embed_tokens, nn.ModuleList)
-            else embed_tokens.padding_idx
-        )
-        self.max_target_positions = args.max_target_positions
-
-        self.embed_tokens = embed_tokens
-        self.embed_scale = math.sqrt(embed_dim)  # todo: try with input_embed_dim
-
-        self.project_in_dim = (
-            Linear(input_embed_dim, embed_dim, bias=False)
-            if embed_dim != input_embed_dim
-            else None
-        )
-
-        self.embed_positions = (
-            PositionalEmbedding(
-                args.max_target_positions,
-                embed_dim,
-                padding_idx,
-                learned=args.decoder_learned_pos,
-            )
-            if not args.no_token_positional_embeddings
-            else None
-        )
-
-    def forward(self, input):
-        mt_task = False
-        if isinstance(input, tuple):
-            if len(input) == 3:
-                encoder_out = input[0]
-                encoder_padding_mask = input[1]
-                prev_output_tokens = input[2]
-                incremental_state = None  # Hardcoding to avoid passing of None objects
-                mt_task = True
-            else:
-                # HACK for now, need to fix (TODO sidgoyal)
-                prev_output_tokens = input[0]
-                # discard "src_lengths"
-                encoder_out = None
-                encoder_padding_mask = None
-                incremental_state = None
-
-        else:
-            prev_output_tokens = input
-            encoder_out = None
-            encoder_padding_mask = None
-            incremental_state = None
-
-        positions = (
-            self.embed_positions(
-                prev_output_tokens,
-                incremental_state=incremental_state,
-            )
-            if self.embed_positions is not None
-            else None
-        )
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-            if positions is not None:
-                positions = positions[:, -1:]
-
-        # embed tokens and positions
-
-        if isinstance(self.embed_tokens, nn.ModuleList):
-            x_embed_list = []
-            for embed_tokens_part in self.embed_tokens:
-                x_embed_list.append(embed_tokens_part(prev_output_tokens))
-
-            x = self.embed_scale * torch.cat(x_embed_list, dim=-1)
-        else:
-            x = self.embed_scale * self.embed_tokens(prev_output_tokens)
-
-        if self.project_in_dim is not None:
-            x = self.project_in_dim(x)
-
-        if positions is not None:
-            x += positions
-        x = F.dropout(x, p=self.dropout, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-        if mt_task:
-            return (x, encoder_out, encoder_padding_mask)
-        return x
-
-
-class TransformerDecoderOutputLayer(nn.Module):
-    def __init__(self, args, embed_tokens, dictionary):
-        super().__init__()
-        self.share_input_output_embed = args.share_decoder_input_output_embed
-        self.embed_tokens = embed_tokens
-        self.output_embed_dim = args.decoder_output_dim
-        embed_dim = args.decoder_embed_dim
-
-        self.project_out_dim = (
-            Linear(embed_dim, self.output_embed_dim, bias=False)
-            if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
-            else None
-        )
-        self.adaptive_softmax = None
-        if args.adaptive_softmax_cutoff is not None:
-            assert not isinstance(embed_tokens, nn.ModuleList)
-            self.adaptive_softmax = AdaptiveSoftmax(
-                len(dictionary),
-                self.output_embed_dim,
-                options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
-                dropout=args.adaptive_softmax_dropout,
-                adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
-                factor=args.adaptive_softmax_factor,
-                tie_proj=args.tie_adaptive_proj,
-            )
-        elif not self.share_input_output_embed:
-            self.embed_tokens = nn.Parameter(
-                torch.Tensor(len(dictionary), self.output_embed_dim)
-            )
-            nn.init.normal_(
-                self.embed_tokens, mean=0, std=self.output_embed_dim ** -0.5
-            )
-
-        if args.decoder_normalize_before and not getattr(
-            args, "no_decoder_final_norm", False
-        ):
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-    def forward(self, input, apply_final_proj=True):
-        if isinstance(input, tuple):
-            x = input[0]
-        else:
-            x = input
-
-        if self.layer_norm:
-            x = self.layer_norm(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        if self.project_out_dim is not None:
-            x = self.project_out_dim(x)
-        if apply_final_proj:
-            x = self.output_layer(x)
-        return x
-
-    def output_layer(self, features, **kwargs):
-        """Project features to the vocabulary size."""
-        if self.adaptive_softmax is None:
-            # project back to size of vocabulary
-            if self.share_input_output_embed:
-                if isinstance(self.embed_tokens, nn.ModuleList):
-                    output = None
-                    for i, emb in enumerate(self.embed_tokens):
-                        sidx = i * emb.embedding_dim
-                        eidx = (i + 1) * emb.embedding_dim
-                        if output is None:
-                            output = F.linear(features[:, :, sidx:eidx], emb.weight)
-                        else:
-                            output += F.linear(features[:, :, sidx:eidx], emb.weight)
-
-                    return output
-                else:
-                    return F.linear(features, self.embed_tokens.weight)
-            else:
-                return F.linear(features, self.embed_tokens)
-        else:
-            return features
-
-
-class TransformerEncoderLayer(nn.Module):
-    """Encoder layer block.
-    In the original paper each operation (multi-head attention or FFN) is
-    postprocessed with: `dropout -> add residual -> layernorm`. In the
-    tensor2tensor code they suggest that learning is more robust when
-    preprocessing each layer with layernorm and postprocessing with:
-    `dropout -> add residual`. We default to the approach in the paper, but the
-    tensor2tensor approach can be enabled by setting
-    *args.encoder_normalize_before* to ``True``.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-    """
-
-    def __init__(self, args):
-        super().__init__()
-        self.embed_dim = args.encoder_embed_dim
-        self.self_attn = MultiheadAttention(
-            self.embed_dim,
-            args.encoder_attention_heads,
-            dropout=args.attention_dropout,
-            self_attention=True,
-        )
-        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
-        self.dropout = args.dropout
-        self.activation_fn = utils.get_activation_fn(
-            activation=getattr(args, "activation_fn", "relu")
-        )
-        self.activation_dropout = getattr(args, "activation_dropout", 0)
-        if self.activation_dropout == 0:
-            # for backwards compatibility with models that use args.relu_dropout
-            self.activation_dropout = getattr(args, "relu_dropout", 0)
-        self.normalize_before = args.encoder_normalize_before
-        self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim)
-        self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim)
-        self.final_layer_norm = LayerNorm(self.embed_dim)
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """
-        Rename layer norm states from `...layer_norms.0.weight` to
-        `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
-        `...final_layer_norm.weight`
-        """
-        layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"}
-        for old, new in layer_norm_map.items():
-            for m in ("weight", "bias"):
-                k = "{}.layer_norms.{}.{}".format(name, old, m)
-                if k in state_dict:
-                    state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k]
-                    del state_dict[k]
-
-    def forward(self, input):
-        """
-        Args:
-            input (Tuple):
-                input[0] (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
-                input[1] (ByteTensor/FloatTensor): encoder padding mask -
-                    binary ByteTensor of shape `(batch, src_len)` where padding elements
-                    are indicated by ``1``.
-                input[2] (LongTensor): previous decoder outputs of shape
-                    `(batch, tgt_len)`, for teacher forcing)
-        Returns:
-            output (Tuple):
-                output[0] (Tensor): encoded output of shape `(batch, src_len, embed_dim)`
-                output[1] (ByteTensor/FloatTensor): encoder padding mask
-                output[2] (LongTensor): previous decoder outputs
-        """
-        x = input[0]
-        encoder_padding_mask = input[1]
-        prev_output_tokens = input[2]
-        residual = x
-        x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True)
-        x, _ = self.self_attn(
-            query=x, key=x, value=x, key_padding_mask=encoder_padding_mask
-        )
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True)
-
-        residual = x
-        x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
-        x = self.activation_fn(self.fc1(x))
-        x = F.dropout(x, p=self.activation_dropout, training=self.training)
-        x = self.fc2(x)
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
-        return (x, encoder_padding_mask, prev_output_tokens)
-
-    def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
-        assert before ^ after
-        if after ^ self.normalize_before:
-            return layer_norm(x)
-        else:
-            return x
-
-
-class TransformerDecoderLayer(nn.Module):
-    """Decoder layer block.
-
-    In the original paper each operation (multi-head attention, encoder
-    attention or FFN) is postprocessed with: `dropout -> add residual ->
-    layernorm`. In the tensor2tensor code they suggest that learning is more
-    robust when preprocessing each layer with layernorm and postprocessing with:
-    `dropout -> add residual`. We default to the approach in the paper, but the
-    tensor2tensor approach can be enabled by setting
-    *args.decoder_normalize_before* to ``True``.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs
-            (default: False).
-    """
-
-    def __init__(
-        self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False
-    ):
-        super().__init__()
-        self.embed_dim = args.decoder_embed_dim
-        self.self_attn = MultiheadAttention(
-            embed_dim=self.embed_dim,
-            num_heads=args.decoder_attention_heads,
-            dropout=args.attention_dropout,
-            add_bias_kv=add_bias_kv,
-            add_zero_attn=add_zero_attn,
-            self_attention=True,
-        )
-        self.dropout = args.dropout
-        self.activation_fn = utils.get_activation_fn(
-            activation=getattr(args, "activation_fn", "relu")
-        )
-        self.activation_dropout = getattr(args, "activation_dropout", 0)
-        if self.activation_dropout == 0:
-            # for backwards compatibility with models that use args.relu_dropout
-            self.activation_dropout = getattr(args, "relu_dropout", 0)
-        self.normalize_before = args.decoder_normalize_before
-
-        # use layerNorm rather than FusedLayerNorm for exporting.
-        # char_inputs can be used to determint this.
-        # TODO  remove this once we update apex with the fix
-        export = getattr(args, "char_inputs", False)
-        self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
-
-        if no_encoder_attn:
-            self.encoder_attn = None
-            self.encoder_attn_layer_norm = None
-        else:
-            self.encoder_attn = MultiheadAttention(
-                self.embed_dim,
-                args.decoder_attention_heads,
-                kdim=getattr(args, "encoder_embed_dim", None),
-                vdim=getattr(args, "encoder_embed_dim", None),
-                dropout=args.attention_dropout,
-                encoder_decoder_attention=True,
-            )
-            self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
-
-        self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim)
-        self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim)
-
-        self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
-        self.need_attn = True
-
-        self.onnx_trace = False
-
-    def prepare_for_onnx_export_(self):
-        self.onnx_trace = True
-
-    def forward(self, input):
-        """
-        Args:
-            input (Tuple):
-                input[0] (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
-                input[1] (Tensor): encoder output of shape `(batch, src_len, embed_dim)`
-                input[2] (ByteTensor/FloatTensor): encoder padding mask -
-                    binary ByteTensor of shape `(batch, src_len)` where padding elements
-                    are indicated by ``1``.
-        Returns:
-            output (Tuple):
-                output[0] (Tensor): encoded output of shape `(batch, src_len, embed_dim)`
-                output[1] (ByteTensor/FloatTensor): encoder padding mask
-                output[2] (LongTensor): previous decoder outputs
-        """
-        # Note: incremental state is not yet supported
-        mt_task = False
-        if isinstance(input, tuple):
-            x = input[0]
-            encoder_out = input[1]
-            encoder_padding_mask = input[2]
-            incremental_state = None
-            mt_task = True
-        else:
-            x = input
-            encoder_out = None
-            encoder_padding_mask = None
-            incremental_state = None
-
-        if incremental_state is None:
-            self_attn_mask = self.buffered_future_mask(x)
-        else:
-            self_attn_mask = None
-
-        # TODO: add back prev_self_attn_state, prev_attn_state,
-        # self_attn_padding_mask
-        prev_self_attn_state = None
-        prev_attn_state = None
-        self_attn_padding_mask = None
-
-        residual = x
-        x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True)
-        if prev_self_attn_state is not None:
-            if incremental_state is None:
-                incremental_state = {}
-            prev_key, prev_value = prev_self_attn_state
-            saved_state = {"prev_key": prev_key, "prev_value": prev_value}
-            self.self_attn._set_input_buffer(incremental_state, saved_state)
-        x, attn = self.self_attn(
-            query=x,
-            key=x,
-            value=x,
-            key_padding_mask=self_attn_padding_mask,
-            incremental_state=incremental_state,
-            need_weights=False,
-            attn_mask=self_attn_mask,
-        )
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True)
-
-        if self.encoder_attn is not None:
-            residual = x
-            x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True)
-            if prev_attn_state is not None:
-                if incremental_state is None:
-                    incremental_state = {}
-                prev_key, prev_value = prev_attn_state
-                saved_state = {"prev_key": prev_key, "prev_value": prev_value}
-                self.encoder_attn._set_input_buffer(incremental_state, saved_state)
-            x, attn = self.encoder_attn(
-                query=x,
-                key=encoder_out,
-                value=encoder_out,
-                key_padding_mask=encoder_padding_mask,
-                incremental_state=incremental_state,
-                static_kv=True,
-                need_weights=(not self.training and self.need_attn),
-            )
-            x = F.dropout(x, p=self.dropout, training=self.training)
-            x = residual + x
-            x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True)
-
-        residual = x
-        x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
-        x = self.activation_fn(self.fc1(x))
-        x = F.dropout(x, p=self.activation_dropout, training=self.training)
-        x = self.fc2(x)
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        x = residual + x
-        x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
-
-        if mt_task:
-            return (x, encoder_out, encoder_padding_mask)
-        return x
-
-    def buffered_future_mask(self, tensor):
-        dim = tensor.size(0)
-        if (
-            not hasattr(self, "_future_mask")
-            or self._future_mask is None
-            or self._future_mask.device != tensor.device
-        ):
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
-            )
-        if self._future_mask.size(0) < dim:
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1
-            )
-        return self._future_mask[:dim, :dim]
-
-    def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
-        assert before ^ after
-        if after ^ self.normalize_before:
-            return layer_norm(x)
-        else:
-            return x
-
-    def make_generation_fast_(self, need_attn=False, **kwargs):
-        self.need_attn = need_attn
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def Linear(in_features, out_features, bias=True):
-    m = nn.Linear(in_features, out_features, bias)
-    nn.init.xavier_uniform_(m.weight)
-    if bias:
-        nn.init.constant_(m.bias, 0.0)
-    return m
diff --git a/spaces/INDONESIA-AI/Lobe/app.py b/spaces/INDONESIA-AI/Lobe/app.py
deleted file mode 100644
index d502e2454e1a496a88aa16f0a52b8d86497aa8ed..0000000000000000000000000000000000000000
--- a/spaces/INDONESIA-AI/Lobe/app.py
+++ /dev/null
@@ -1,245 +0,0 @@
-"""
-Stable Diffusion Webui Version 1.6
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.6.0
-
-"""
-#commit_id=r"5ef669de080814067961f28357256e8fe27544f4" #Version 1.6.0
-import os
-from sys import executable 
-import subprocess
-import pathlib
-import gc
-import time
-import subprocess
-def Gitclone(URI:str,ClonePath:pathlib.Path ) -> int :
-  if pathlib.Path.exists(ClonePath):
-    return 0
-  for z in range(10):
-    i=subprocess.run([r"git",r"clone",str(URI),str(ClonePath)])
-    if(i.returncode == 0 ): 
-     del i
-     return 0
-    else :
-     del i
-  raise Exception(str.format("clone \'{0}\' failed",URI))
-    
-
-def DownLoad(URI:str,DownloadPath:pathlib.Path,DownLoadFileName:str ) -> int:
-  if (DownloadPath / DownLoadFileName).is_file(): return 0
-  for z in range(10):
-    i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",str(DownloadPath),r"-o",DownLoadFileName,URI]);
-    if(i.returncode == 0 ): 
-      del i
-      gc.collect()
-      return 0
-    else :
-      del i
-  raise Exception(str.format("download \'{0}\' failed",URI))
-
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",user_home / r"stable-diffusion-webui")
-#os.chdir(str(user_home / r"stable-diffusion-webui"))
-#os.system("git reset --hard "+commit_id)
-os.chdir(user_home / r"stable-diffusion-webui")
-Gitclone(r"https://github.com/vorstcavry/ncpt_colab_timer",user_home / r"stable-diffusion-webui" / r"extensions" / r"ncpt_colab_timer")
-Gitclone(r"https://github.com/vorstcavry/static",user_home / r"stable-diffusion-webui" / r"static")
-
-def run_echo_command():
-    try:
-        start_huggingface
-    except NameError:
-        start_huggingface = int(time.time()) - 5
-
-    cmd = f"echo -n {start_huggingface} > /home/user/app/stable-diffusion-webui/static/colabTimer.txt"
-    subprocess.run(cmd, shell=True)
-
-# Contoh pemanggilan fungsi run_echo_command:
-run_echo_command()
-os.chdir(user_home / r"stable-diffusion-webui")
-#install extensions
-print("installing extensions")
-Gitclone(r"https://github.com/vorstcavry/embeddings",user_home / r"stable-diffusion-webui" / r"embeddings"  / r"negative")
-Gitclone(r"https://github.com/vorstcavry/lora",user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive")
-Gitclone(r"https://github.com/vorstcavry/Checkpoint-Model",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint")
-
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN" ,r"4x-UltraSharp.pth")
-while (True):
-   i=subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")])
-   if(i.returncode == 0 ): 
-    del i
-    gc.collect()
-    break
-   else :
-    del i
-#Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" )
-#Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser")
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface")
-Gitclone(r"https://github.com/BlafKing/sd-civitai-browser-plus",user_home / r"stable-diffusion-webui" / r"extensions" / r"civitai-browser")
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks")
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet")
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor")
-Gitclone(r"https://tinyurl.com/LOBE-Repo",user_home / r"stable-diffusion-webui" / r"extensions" / r"LOBE")
-#Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib")
-#Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-#Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor")
-#Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-Gitclone(r"https://github.com/EdithForsaken/sd-webui-cloud-inference.git",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-cloud-inference")
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete")
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels")
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui")
-Gitclone(r"https://github.com/zanllp/sd-webui-infinite-image-browsing",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-infinite-image-browsing")
-#Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin")
-#Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg")
-Gitclone(r"https://tinyurl.com/aspect-ratio-v",user_home / r"stable-diffusion-webui" / r"extensions" / r"aspect-ratio")
-#Gitclone(r"https://github.com/vorstcavry/cleaner",user_home / r"stable-diffusion-webui" / r"extensions" / r"cleaner")
-Gitclone(r"https://github.com/hnmr293/sd-webui-llul",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-llul")
-Gitclone(r"https://github.com/IDEA-Research/DWPose",user_home / r"stable-diffusion-webui" / r"extensions" / r"DWPose")
-Gitclone(r"https://github.com/Bing-su/adetailer",user_home / r"stable-diffusion-webui" / r"extensions" / r"adetailer")
-Gitclone(r"https://github.com/NoCrypt/sd_hf_out",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_hf_out")
-
-
-#Gitclone(r"https://github.com/NoCrypt/sd_hf_out",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_hf_out")
-#Gitclone(r"https://github.com/Iyashinouta/sd-model-downloader",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-model-downloader")
-#Gitclone(r"https://github.com/AIrjen/OneButtonPrompt",user_home / r"stable-diffusion-webui" / r"extensions" / r"OneButtonPrompt")
-#Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-wildcards")
-#Gitclone(r"https://github.com/adieyal/sd-dynamic-prompts",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-dynamic-prompts")
-#Gitclone(r"https://github.com/d8ahazard/sd_dreambooth_extension",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_dreambooth_extension")
-#Gitclone(r"https://github.com/yfszzx/stable-diffusion-webui-inspiration",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-inspiration")
-#Gitclone(r"https://github.com/Coyote-A/ultimate-upscale-for-automatic1111",user_home / r"stable-diffusion-webui" / r"extensions" / r"ultimate-upscale-for-automatic1111")
-os.chdir(user_home / r"stable-diffusion-webui")
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[ r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors"]
-for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"models" / r"ControlNet",pathlib.Path(dList[i]).name)
-del dList
-
-#download ControlNet models
-#print("extensions dolwnload done .\ndownloading ControlNet models")
-#dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-#for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"extensions" / "sd-webui-controlnet" / r"models",pathlib.Path(dList[i]).name)
-#del dList
-#download model    
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-#Stable Diffusion Checkpoint Model
-#anything version4.5
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"anything-v4.5-pruned.ckpt")
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"anything-v4.0.vae.pt")
-#Counterfeit-V3.0
-#DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Counterfeit-V3.0_fp16.safetensors")
-#AbyssOrangeMix2 sfw
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_sfw.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"AbyssOrangeMix2_sfw.safetensors")
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"orangemix.vae.pt")
-#MeinaPastelV5
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"MeinaPastelV5_BakedVAE.safetensors")
-#DownLoad(r"https://huggingface.co/AnonPerson/ChilloutMix/resolve/main/ChilloutMix-ni-fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"ChilloutMix-ni-fp16.safetensors")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV4%20-%20Without%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"MeinaPastelV4%20-%20Without%20VAE.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/perfect_world/resolve/main/perfectWorld_v2Baked.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"perfectWorld_v2Baked.safetensors")
-#DownLoad(r"https://huggingface.co/vorstcavry/figurestyle1/resolve/main/figure.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"figure.safetensors")
-#DownLoad(r"https://huggingface.co/vorstcavry/dosmix/resolve/main/ddosmix_V2.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"ddosmix_V2.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/rev-animated/resolve/main/revAnimated_v11.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"revAnimated_v11.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/MeinaMix/resolve/main/Meina_V8_baked_VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Meina_V8_baked_VAE.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/CyberRealistic/resolve/main/cyberrealistic_v13.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"cyberrealistic_v13.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/mymodel/resolve/main/Cavry_V2.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Cavry_V2.safetensors")
-#downloadvae
-DownLoad(r"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"VAE",r"vae-ft-mse-840000-ema-pruned.safetensors")
-
-#Lora Model
-#Better Light
-#DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"Better_light.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"Better_light.safetensors")
-#LAS
-#DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"LAS.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"LAS.safetensors")
-#Backlighting
-#DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"backlighting.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"backlighting.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/japaneseDollLikeness_v15.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"japaneseDollLikeness_v15.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/koreanDollLikeness_v20.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"koreanDollLikeness_v20.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/taiwanDollLikeness_v15.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"taiwanDollLikeness_v15.safetensors")
-
-
-
-
-#GFPGAN Model
-#detection Resnet50
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"detection_Resnet50_Final.pth")
-#parsing_parsenet
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"parsing_parsenet.pth")
-#GFPGANv1.4
-DownLoad(r"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"GFPGANv1.4.pth")
-#strt Stable Diffusion Webui
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-import subprocess
-import pathlib
-
-user_home = pathlib.Path("/home/user")  # Gantilah dengan path yang sesuai
-
-args = [
-    executable,
-    user_home / "stable-diffusion-webui" / "launch.py",
-    "--precision", "full",
-    "--no-half",
-    "--no-half-vae",
-    "--enable-insecure-extension-access",
-    "--medvram",
-    "--skip-torch-cuda-test",
-    "--enable-console-prompts",
-    "--ui-settings-file=" + str(pathlib.Path(__file__).parent / "config.json"),
-    "--hf-token-out",
-    "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"  # Gantilah dengan token yang sesuai
-]
-
-args = [arg.as_posix() if isinstance(arg, pathlib.PosixPath) else arg for arg in args]
-
-try:
-    ret = subprocess.run(args)
-except Exception as e:
-    print("Error:", e)
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/eval/webpage/styles.css b/spaces/Intel/NeuralChat-ICX-INT4/fastchat/eval/webpage/styles.css
deleted file mode 100644
index 5c5a08dd92dc799298d0d70dec8fefe9482be0ff..0000000000000000000000000000000000000000
--- a/spaces/Intel/NeuralChat-ICX-INT4/fastchat/eval/webpage/styles.css
+++ /dev/null
@@ -1,105 +0,0 @@
-body {
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-    background-color: linear-gradient(135deg, #f5f7fa 0%, #4285f4 100%)
-}
-
-.navbar-dark .navbar-nav .nav-link {
-    color: #f1cf68;
-    font-size: 1.1rem;
-    padding: 0.5rem 0.6rem;
-}
-
-.card-header {
-    font-weight: bold;
-}
-
-.card {
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-    transition: 0.3s;
-}
-
-.card:hover {
-    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2);
-}
-
-button {
-    transition: background-color 0.3s;
-}
-
-button:hover {
-    background-color: #007bff;
-}
-
-@media (max-width: 767px) {
-    .form-row .form-group {
-        margin-bottom: 10px;
-    }
-}
-
-/* Extra styles */
-
-.expandable-card .card-text-container {
-    max-height: 200px;
-    overflow-y: hidden;
-    position: relative;
-}
-
-.expandable-card.expanded .card-text-container {
-    max-height: none;
-}
-
-.expand-btn {
-    position: relative;
-    display: none;
-    background-color: rgba(255, 255, 255, 0.8);
-    color: #510c75;
-    border-color: transparent;
-}
-
-.expand-btn:hover {
-    background-color: rgba(200, 200, 200, 0.8);
-    text-decoration: none;
-    border-color: transparent;
-    color: #510c75;
-}
-
-.expand-btn:focus {
-    outline: none;
-    text-decoration: none;
-}
-
-.expandable-card:not(.expanded) .card-text-container:after {
-    content: "";
-    position: absolute;
-    bottom: 0;
-    left: 0;
-    width: 100%;
-    height: 90px;
-    background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1));
-}
-
-.expandable-card:not(.expanded) .expand-btn {
-    margin-top: -40px;
-}
-
-.card-body {
-    padding-bottom: 5px;
-}
-
-.vertical-flex-layout {
-    justify-content: center;
-    align-items: center;
-    height: 100%;
-    display: flex;
-    flex-direction: column;
-    gap: 5px;
-}
-
-.figure-img {
-    max-width: 100%;
-    height: auto;
-}
-
-.adjustable-font-size {
-    font-size: calc(0.5rem + 2vw);
-}
\ No newline at end of file
diff --git a/spaces/Iqbalzz/hololive-rvc-models/infer_pack/models_onnx_moess.py b/spaces/Iqbalzz/hololive-rvc-models/infer_pack/models_onnx_moess.py
deleted file mode 100644
index 12efb0629a2e3d0d746a34f467254536c2bdbe5f..0000000000000000000000000000000000000000
--- a/spaces/Iqbalzz/hololive-rvc-models/infer_pack/models_onnx_moess.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/IsaacK/streamlit-test/pages/utils.py b/spaces/IsaacK/streamlit-test/pages/utils.py
deleted file mode 100644
index bd3babe6f835cab4ff16bf969b7513a66e836919..0000000000000000000000000000000000000000
--- a/spaces/IsaacK/streamlit-test/pages/utils.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import re
-import random
-import datetime
-import os.path
-import string
-import sqlite3
-import re
-
-def empty(string):
-  return len(string) == 0
-
-def add_blanks(word, sentence, blank = "__"):
-  return re.sub(word, blank, sentence, flags=re.IGNORECASE)
-
-def chunker(seq, size):
-  return (seq[pos:pos + size] for pos in range(0, len(seq), size))
-
-def random_session_id():
-  alphabet = string.ascii_lowercase + string.digits
-  return ''.join(random.choices(alphabet, k=12))
-
-def check_answer(item, answer):
-  return item == answer
-
-def clean_string(string):
-  return re.sub('[^0-9a-zA-Z\s,]+', '', string)
-
-def split_string(string, split_on = ","):
-  return [x.strip().upper() for x in string.split(split_on)]
-
-def make_subquery(terms, column = 'tags', operator = 'AND'):
-  return f' {operator} '.join([f"{column} LIKE '%{x}%'" for x in terms if len(x) > 0])
-
-def make_query(subquery, limit = 10):
-  return f"""SELECT * FROM vocab WHERE {subquery} ORDER BY RANDOM() LIMIT {str(limit)}"""
-
-def db_path(database):
-    dir = os.path.dirname(os.path.abspath(__file__))
-    return os.path.join(dir, database)
-
-def db_connect(database):
-  conn = sqlite3.connect(database)
-  c = conn.cursor()
-  return c, conn
-
-def chk_conn(conn):
-  try:
-    conn.cursor()
-    return True
-  except Exception as ex:
-    return False
-
-def get_tables(cursor):      
-  cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
-  return cursor.fetchall()
\ No newline at end of file
diff --git a/spaces/Jack1804/stabilityai-stable-diffusion-xl-refiner-1.0/app.py b/spaces/Jack1804/stabilityai-stable-diffusion-xl-refiner-1.0/app.py
deleted file mode 100644
index f0854c17140255ac783638680bc5dce595cc9fd0..0000000000000000000000000000000000000000
--- a/spaces/Jack1804/stabilityai-stable-diffusion-xl-refiner-1.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-xl-refiner-1.0").launch()
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/Waifu2x/Dataloader.py b/spaces/Jackflack09/diffuse-custom/Waifu2x/Dataloader.py
deleted file mode 100644
index 8e38548e9c3feab16951e3078041658ea2f32487..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/Waifu2x/Dataloader.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import glob
-import io
-import numpy as np
-import re
-import os
-import random
-from io import BytesIO
-from uuid import uuid4
-import sqlite3
-import h5py
-import torch
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision.transforms import RandomCrop
-from torchvision.transforms.functional import to_tensor
-
-
-class ImageH5Data(Dataset):
-    def __init__(self, h5py_file, folder_name):
-        self.data = h5py.File(h5py_file, 'r')[folder_name]
-        self.data_hr = self.data['train_hr']
-        self.data_lr = self.data['train_lr']
-        self.len_imgs = len(self.data_hr)
-        self.h5py_file = h5py_file
-        self.folder_name = folder_name
-
-    def __len__(self):
-        # with h5py.File(self.h5py_file, 'r') as f:
-        #     return len(f[self.folder_name]['train_lr'])
-        return self.len_imgs
-
-    def __getitem__(self, index):
-        # with h5py.File(self.h5py_file, 'r') as f:
-        #     data_lr = f[self.folder_name]['train_lr'][index]
-        #     data_hr = f[self.folder_name]['train_lr'][index]
-        #
-        #     return data_lr, data_hr
-        return self.data_lr[index], self.data_hr[index]
-
-
-class ImageData(Dataset):
-    def __init__(self,
-                 img_folder,
-                 patch_size=96,
-                 shrink_size=2,
-                 noise_level=1,
-                 down_sample_method=None,
-                 color_mod='RGB',
-                 dummy_len=None):
-
-        self.img_folder = img_folder
-        all_img = glob.glob(self.img_folder + "/**", recursive=True)
-        self.img = list(filter(lambda x: x.endswith('png') or x.endswith("jpg") or x.endswith("jpeg"), all_img))
-        self.total_img = len(self.img)
-        self.dummy_len = dummy_len if dummy_len is not None else self.total_img
-        self.random_cropper = RandomCrop(size=patch_size)
-        self.color_mod = color_mod
-        self.img_augmenter = ImageAugment(shrink_size, noise_level, down_sample_method)
-
-    def get_img_patches(self, img_file):
-        img_pil = Image.open(img_file).convert("RGB")
-        img_patch = self.random_cropper(img_pil)
-        lr_hr_patches = self.img_augmenter.process(img_patch)
-        return lr_hr_patches
-
-    def __len__(self):
-        return self.dummy_len  # len(self.img)
-
-    def __getitem__(self, index):
-        idx = random.choice(range(0, self.total_img))
-        img = self.img[idx]
-        patch = self.get_img_patches(img)
-        if self.color_mod == 'RGB':
-            lr_img = patch[0].convert("RGB")
-            hr_img = patch[1].convert("RGB")
-        elif self.color_mod == 'YCbCr':
-            lr_img, _, _ = patch[0].convert('YCbCr').split()
-            hr_img, _, _ = patch[1].convert('YCbCr').split()
-        else:
-            raise KeyError('Either RGB or YCbCr')
-        return to_tensor(lr_img), to_tensor(hr_img)
-
-
-class Image2Sqlite(ImageData):
-    def __getitem__(self, item):
-        img = self.img[item]
-        lr_hr_patch = self.get_img_patches(img)
-        if self.color_mod == 'RGB':
-            lr_img = lr_hr_patch[0].convert("RGB")
-            hr_img = lr_hr_patch[1].convert("RGB")
-        elif self.color_mod == 'YCbCr':
-            lr_img, _, _ = lr_hr_patch[0].convert('YCbCr').split()
-            hr_img, _, _ = lr_hr_patch[1].convert('YCbCr').split()
-        else:
-            raise KeyError('Either RGB or YCbCr')
-        lr_byte = self.convert_to_bytevalue(lr_img)
-        hr_byte = self.convert_to_bytevalue(hr_img)
-        return [lr_byte, hr_byte]
-
-    @staticmethod
-    def convert_to_bytevalue(pil_img):
-        img_byte = io.BytesIO()
-        pil_img.save(img_byte, format='png')
-        return img_byte.getvalue()
-
-
-class ImageDBData(Dataset):
-    def __init__(self, db_file, db_table="images", lr_col="lr_img", hr_col="hr_img", max_images=None):
-        self.db_file = db_file
-        self.db_table = db_table
-        self.lr_col = lr_col
-        self.hr_col = hr_col
-        self.total_images = self.get_num_rows(max_images)
-        # self.lr_hr_images = self.get_all_images()
-
-    def __len__(self):
-        return self.total_images
-
-    # def get_all_images(self):
-    #     with sqlite3.connect(self.db_file) as conn:
-    #         cursor = conn.cursor()
-    #         cursor.execute(f"SELECT * FROM {self.db_table} LIMIT {self.total_images}")
-    #         return cursor.fetchall()
-
-    def get_num_rows(self, max_images):
-        with sqlite3.connect(self.db_file) as conn:
-            cursor = conn.cursor()
-            cursor.execute(f"SELECT MAX(ROWID) FROM {self.db_table}")
-            db_rows = cursor.fetchone()[0]
-        if max_images:
-            return min(max_images, db_rows)
-        else:
-            return db_rows
-
-    def __getitem__(self, item):
-        # lr, hr = self.lr_hr_images[item]
-        # lr = Image.open(io.BytesIO(lr))
-        # hr = Image.open(io.BytesIO(hr))
-        # return to_tensor(lr), to_tensor(hr)
-        # note sqlite rowid starts with 1
-        with sqlite3.connect(self.db_file) as conn:
-            cursor = conn.cursor()
-            cursor.execute(f"SELECT {self.lr_col}, {self.hr_col} FROM {self.db_table} WHERE ROWID={item + 1}")
-            lr, hr = cursor.fetchone()
-            lr = Image.open(io.BytesIO(lr)).convert("RGB")
-            hr = Image.open(io.BytesIO(hr)).convert("RGB")
-            # lr = np.array(lr)  # use scale [0, 255] instead of [0,1]
-            # hr = np.array(hr)
-            return to_tensor(lr), to_tensor(hr)
-
-
-class ImagePatchData(Dataset):
-    def __init__(self, lr_folder, hr_folder):
-        self.lr_folder = lr_folder
-        self.hr_folder = hr_folder
-        self.lr_imgs = glob.glob(os.path.join(lr_folder, "**"))
-        self.total_imgs = len(self.lr_imgs)
-
-    def __len__(self):
-        return self.total_imgs
-
-    def __getitem__(self, item):
-        lr_file = self.lr_imgs[item]
-        hr_path = re.sub("lr", 'hr', os.path.dirname(lr_file))
-        filename = os.path.basename(lr_file)
-        hr_file = os.path.join(hr_path, filename)
-        return to_tensor(Image.open(lr_file)), to_tensor(Image.open(hr_file))
-
-
-class ImageAugment:
-    def __init__(self,
-                 shrink_size=2,
-                 noise_level=1,
-                 down_sample_method=None
-                 ):
-        # noise_level (int): 0: no noise; 1: 75-95% quality; 2:50-75%
-        if noise_level == 0:
-            self.noise_level = [0, 0]
-        elif noise_level == 1:
-            self.noise_level = [5, 25]
-        elif noise_level == 2:
-            self.noise_level = [25, 50]
-        else:
-            raise KeyError("Noise level should be either 0, 1, 2")
-        self.shrink_size = shrink_size
-        self.down_sample_method = down_sample_method
-
-    def shrink_img(self, hr_img):
-
-        if self.down_sample_method is None:
-            resample_method = random.choice([Image.BILINEAR, Image.BICUBIC, Image.LANCZOS])
-        else:
-            resample_method = self.down_sample_method
-        img_w, img_h = tuple(map(lambda x: int(x / self.shrink_size), hr_img.size))
-        lr_img = hr_img.resize((img_w, img_h), resample_method)
-        return lr_img
-
-    def add_jpeg_noise(self, hr_img):
-        quality = 100 - round(random.uniform(*self.noise_level))
-        lr_img = BytesIO()
-        hr_img.save(lr_img, format='JPEG', quality=quality)
-        lr_img.seek(0)
-        lr_img = Image.open(lr_img)
-        return lr_img
-
-    def process(self, hr_patch_pil):
-        lr_patch_pil = self.shrink_img(hr_patch_pil)
-        if self.noise_level[1] > 0:
-            lr_patch_pil = self.add_jpeg_noise(lr_patch_pil)
-
-        return lr_patch_pil, hr_patch_pil
-
-    def up_sample(self, img, resample):
-        width, height = img.size
-        return img.resize((self.shrink_size * width, self.shrink_size * height), resample=resample)
diff --git a/spaces/Jaehan/Text-Generation-3/README.md b/spaces/Jaehan/Text-Generation-3/README.md
deleted file mode 100644
index c78b153f151e9a4f6303961cbc468d8d814330de..0000000000000000000000000000000000000000
--- a/spaces/Jaehan/Text-Generation-3/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Generation 3
-emoji: 📚
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/yolov5face/__init__.py b/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/yolov5face/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Jo0xFF/4xArText/utils/architecture/SRVGG.py b/spaces/Jo0xFF/4xArText/utils/architecture/SRVGG.py
deleted file mode 100644
index 556be991309ec4c541d2aea1b8d74fdf00b80040..0000000000000000000000000000000000000000
--- a/spaces/Jo0xFF/4xArText/utils/architecture/SRVGG.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import math
-from collections import OrderedDict
-from typing import Union
-
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-
-
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-    It is a compact network structure, which performs upsampling in the last layer and no convolution is
-    conducted on the HR feature space.
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        num_feat (int): Channel number of intermediate features. Default: 64.
-        num_conv (int): Number of convolution layers in the body network. Default: 16.
-        upscale (int): Upsampling factor. Default: 4.
-        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        act_type: str = "prelu",
-    ):
-        super(SRVGGNetCompact, self).__init__()
-        self.act_type = act_type
-
-        self.state = state_dict
-
-        if "params" in self.state:
-            self.state = self.state["params"]
-
-        self.key_arr = list(self.state.keys())
-
-        self.num_in_ch = self.get_in_nc()
-        self.num_feat = self.get_num_feats()
-        self.num_conv = self.get_num_conv()
-        self.num_out_ch = self.num_in_ch  # :(
-        self.scale = self.get_scale()
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(self.num_in_ch, self.num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == "relu":
-            activation = nn.ReLU(inplace=True)
-        elif act_type == "prelu":
-            activation = nn.PReLU(num_parameters=self.num_feat)
-        elif act_type == "leakyrelu":
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)
-
-        # the body structure
-        for _ in range(self.num_conv):
-            self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1))
-            # activation
-            if act_type == "relu":
-                activation = nn.ReLU(inplace=True)
-            elif act_type == "prelu":
-                activation = nn.PReLU(num_parameters=self.num_feat)
-            elif act_type == "leakyrelu":
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)
-
-        # the last conv
-        self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1))
-        # upsample
-        self.upsampler = nn.PixelShuffle(self.scale)
-
-        self.load_state_dict(self.state, strict=False)
-
-    def get_num_conv(self) -> int:
-        return (int(self.key_arr[-1].split(".")[1]) - 2) // 2
-
-    def get_num_feats(self) -> int:
-        return self.state[self.key_arr[0]].shape[0]
-
-    def get_in_nc(self) -> int:
-        return self.state[self.key_arr[0]].shape[1]
-
-    def get_scale(self) -> int:
-        self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0]
-        # Assume out_nc is the same as in_nc
-        # I cant think of a better way to do that
-        self.num_out_ch = self.num_in_ch
-        scale = math.sqrt(self.pixelshuffle_shape / self.num_out_ch)
-        if scale - int(scale) > 0:
-            print(
-                "out_nc is probably different than in_nc, scale calculation might be wrong"
-            )
-        scale = int(scale)
-        return scale
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.scale, mode="nearest")
-        out += base
-        return out
diff --git a/spaces/Joabutt/test/style.css b/spaces/Joabutt/test/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/Joabutt/test/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/models/phase1.py b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/models/phase1.py
deleted file mode 100644
index b30aea6350609071c915c2f051fbe5419253e656..0000000000000000000000000000000000000000
--- a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/models/phase1.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import torch
-import numpy as np
-from salad.models.base_model import BaseModel
-from salad.utils import nputil, thutil
-from salad.utils.spaghetti_util import clip_eigenvalues, project_eigenvectors
-
-class Phase1Model(BaseModel):
-    def __init__(self, network, variance_schedule, **kwargs):
-        super().__init__(network, variance_schedule, **kwargs)
-
-    @torch.no_grad()
-    def sample(
-        self,
-        batch_size=0,
-        return_traj=False,
-    ):
-        x_T = torch.randn([batch_size, 16, 16]).to(self.device)
-
-        traj = {self.var_sched.num_steps: x_T}
-        for t in range(self.var_sched.num_steps, 0, -1):
-            z = torch.randn_like(x_T) if t > 1 else torch.zeros_like(x_T)
-            alpha = self.var_sched.alphas[t]
-            alpha_bar = self.var_sched.alpha_bars[t]
-            sigma = self.var_sched.get_sigmas(t, flexibility=0)
-
-            c0 = 1.0 / torch.sqrt(alpha)
-            c1 = (1 - alpha) / torch.sqrt(1 - alpha_bar)
-
-            x_t = traj[t]
-
-            beta = self.var_sched.betas[[t] * batch_size]
-            e_theta = self.net(x_t, beta=beta)
-            # print(e_theta.norm(-1).mean())
-
-            x_next = c0 * (x_t - c1 * e_theta) + sigma * z
-            traj[t - 1] = x_next.detach()
-
-            traj[t] = traj[t].cpu()
-
-            if not return_traj:
-                del traj[t]
-        if return_traj:
-            return traj
-        else:
-            return traj[0]
-
-    def sampling_gaussians(self, num_shapes):
-        """
-        Return:
-            ldm_gaus: np.ndarray
-            gt_gaus: np.ndarray
-        """
-        ldm_gaus = self.sample(num_shapes)
-
-        if self.hparams.get("global_normalization"):
-            if not hasattr(self, "data_val"):
-                self._build_dataset("val")
-            if self.hparams.get("global_normalization") == "partial":
-                ldm_gaus = self.data_val.unnormalize_global_static(ldm_gaus, slice(12,None)) 
-            elif self.hparams.get("global_normalization") == "all":
-                ldm_gaus = self.data_val.unnormalize_global_static(ldm_gaus, slice(None))
-
-        ldm_gaus = clip_eigenvalues(ldm_gaus)
-        ldm_gaus = project_eigenvectors(ldm_gaus)
-        return ldm_gaus
diff --git a/spaces/KarmKarma/genshinimpact-rvc-models-v2/lib/infer_pack/models.py b/spaces/KarmKarma/genshinimpact-rvc-models-v2/lib/infer_pack/models.py
deleted file mode 100644
index 44c08d361bcb13b84b38dc29beff5cdaddad4ea2..0000000000000000000000000000000000000000
--- a/spaces/KarmKarma/genshinimpact-rvc-models-v2/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1124 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/ssd_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/ssd_head.py
deleted file mode 100644
index c3b46fa3d8942ff1eb41e067b8e9b361542b6362..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/ssd_head.py
+++ /dev/null
@@ -1,362 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Optional, Sequence, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptInstanceList
-from ..losses import smooth_l1_loss
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .anchor_head import AnchorHead
-
-
-# TODO: add loss evaluator for SSD
-@MODELS.register_module()
-class SSDHead(AnchorHead):
-    """Implementation of `SSD head <https://arxiv.org/abs/1512.02325>`_
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (Sequence[int]): Number of channels in the input feature
-            map.
-        stacked_convs (int): Number of conv layers in cls and reg tower.
-            Defaults to 0.
-        feat_channels (int): Number of hidden channels when stacked_convs
-            > 0. Defaults to 256.
-        use_depthwise (bool): Whether to use DepthwiseSeparableConv.
-            Defaults to False.
-        conv_cfg (:obj:`ConfigDict` or dict, Optional): Dictionary to construct
-            and config conv layer. Defaults to None.
-        norm_cfg (:obj:`ConfigDict` or dict, Optional): Dictionary to construct
-            and config norm layer. Defaults to None.
-        act_cfg (:obj:`ConfigDict` or dict, Optional): Dictionary to construct
-            and config activation layer. Defaults to None.
-        anchor_generator (:obj:`ConfigDict` or dict): Config dict for anchor
-            generator.
-        bbox_coder (:obj:`ConfigDict` or dict): Config of bounding box coder.
-        reg_decoded_bbox (bool): If true, the regression loss would be
-            applied directly on decoded bounding boxes, converting both
-            the predicted boxes and regression targets to absolute
-            coordinates format. Defaults to False. It should be `True` when
-            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
-        train_cfg (:obj:`ConfigDict` or dict, Optional): Training config of
-            anchor head.
-        test_cfg (:obj:`ConfigDict` or dict, Optional): Testing config of
-            anchor head.
-        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
-            dict], Optional): Initialization config dict.
-    """  # noqa: W605
-
-    def __init__(
-        self,
-        num_classes: int = 80,
-        in_channels: Sequence[int] = (512, 1024, 512, 256, 256, 256),
-        stacked_convs: int = 0,
-        feat_channels: int = 256,
-        use_depthwise: bool = False,
-        conv_cfg: Optional[ConfigType] = None,
-        norm_cfg: Optional[ConfigType] = None,
-        act_cfg: Optional[ConfigType] = None,
-        anchor_generator: ConfigType = dict(
-            type='SSDAnchorGenerator',
-            scale_major=False,
-            input_size=300,
-            strides=[8, 16, 32, 64, 100, 300],
-            ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
-            basesize_ratio_range=(0.1, 0.9)),
-        bbox_coder: ConfigType = dict(
-            type='DeltaXYWHBBoxCoder',
-            clip_border=True,
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0],
-        ),
-        reg_decoded_bbox: bool = False,
-        train_cfg: Optional[ConfigType] = None,
-        test_cfg: Optional[ConfigType] = None,
-        init_cfg: MultiConfig = dict(
-            type='Xavier', layer='Conv2d', distribution='uniform', bias=0)
-    ) -> None:
-        super(AnchorHead, self).__init__(init_cfg=init_cfg)
-        self.num_classes = num_classes
-        self.in_channels = in_channels
-        self.stacked_convs = stacked_convs
-        self.feat_channels = feat_channels
-        self.use_depthwise = use_depthwise
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-
-        self.cls_out_channels = num_classes + 1  # add background class
-        self.prior_generator = TASK_UTILS.build(anchor_generator)
-
-        # Usually the numbers of anchors for each level are the same
-        # except SSD detectors. So it is an int in the most dense
-        # heads but a list of int in SSDHead
-        self.num_base_priors = self.prior_generator.num_base_priors
-
-        self._init_layers()
-
-        self.bbox_coder = TASK_UTILS.build(bbox_coder)
-        self.reg_decoded_bbox = reg_decoded_bbox
-        self.use_sigmoid_cls = False
-        self.cls_focal_loss = False
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-        if self.train_cfg:
-            self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
-            if self.train_cfg.get('sampler', None) is not None:
-                self.sampler = TASK_UTILS.build(
-                    self.train_cfg['sampler'], default_args=dict(context=self))
-            else:
-                self.sampler = PseudoSampler(context=self)
-
-    def _init_layers(self) -> None:
-        """Initialize layers of the head."""
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        # TODO: Use registry to choose ConvModule type
-        conv = DepthwiseSeparableConvModule \
-            if self.use_depthwise else ConvModule
-
-        for channel, num_base_priors in zip(self.in_channels,
-                                            self.num_base_priors):
-            cls_layers = []
-            reg_layers = []
-            in_channel = channel
-            # build stacked conv tower, not used in default ssd
-            for i in range(self.stacked_convs):
-                cls_layers.append(
-                    conv(
-                        in_channel,
-                        self.feat_channels,
-                        3,
-                        padding=1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg))
-                reg_layers.append(
-                    conv(
-                        in_channel,
-                        self.feat_channels,
-                        3,
-                        padding=1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg))
-                in_channel = self.feat_channels
-            # SSD-Lite head
-            if self.use_depthwise:
-                cls_layers.append(
-                    ConvModule(
-                        in_channel,
-                        in_channel,
-                        3,
-                        padding=1,
-                        groups=in_channel,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg))
-                reg_layers.append(
-                    ConvModule(
-                        in_channel,
-                        in_channel,
-                        3,
-                        padding=1,
-                        groups=in_channel,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg))
-            cls_layers.append(
-                nn.Conv2d(
-                    in_channel,
-                    num_base_priors * self.cls_out_channels,
-                    kernel_size=1 if self.use_depthwise else 3,
-                    padding=0 if self.use_depthwise else 1))
-            reg_layers.append(
-                nn.Conv2d(
-                    in_channel,
-                    num_base_priors * 4,
-                    kernel_size=1 if self.use_depthwise else 3,
-                    padding=0 if self.use_depthwise else 1))
-            self.cls_convs.append(nn.Sequential(*cls_layers))
-            self.reg_convs.append(nn.Sequential(*reg_layers))
-
-    def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor], List[Tensor]]:
-        """Forward features from the upstream network.
-
-        Args:
-            x (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple[list[Tensor], list[Tensor]]: A tuple of cls_scores list and
-            bbox_preds list.
-
-            - cls_scores (list[Tensor]): Classification scores for all scale \
-            levels, each is a 4D-tensor, the channels number is \
-            num_anchors * num_classes.
-            - bbox_preds (list[Tensor]): Box energies / deltas for all scale \
-            levels, each is a 4D-tensor, the channels number is \
-            num_anchors * 4.
-        """
-        cls_scores = []
-        bbox_preds = []
-        for feat, reg_conv, cls_conv in zip(x, self.reg_convs, self.cls_convs):
-            cls_scores.append(cls_conv(feat))
-            bbox_preds.append(reg_conv(feat))
-        return cls_scores, bbox_preds
-
-    def loss_by_feat_single(self, cls_score: Tensor, bbox_pred: Tensor,
-                            anchor: Tensor, labels: Tensor,
-                            label_weights: Tensor, bbox_targets: Tensor,
-                            bbox_weights: Tensor,
-                            avg_factor: int) -> Tuple[Tensor, Tensor]:
-        """Compute loss of a single image.
-
-        Args:
-            cls_score (Tensor): Box scores for eachimage
-                Has shape (num_total_anchors, num_classes).
-            bbox_pred (Tensor): Box energies / deltas for each image
-                level with shape (num_total_anchors, 4).
-            anchors (Tensor): Box reference for each scale level with shape
-                (num_total_anchors, 4).
-            labels (Tensor): Labels of each anchors with shape
-                (num_total_anchors,).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (num_total_anchors,)
-            bbox_targets (Tensor): BBox regression targets of each anchor
-                weight shape (num_total_anchors, 4).
-            bbox_weights (Tensor): BBox regression loss weights of each anchor
-                with shape (num_total_anchors, 4).
-            avg_factor (int): Average factor that is used to average
-                the loss. When using sampling method, avg_factor is usually
-                the sum of positive and negative priors. When using
-                `PseudoSampler`, `avg_factor` is usually equal to the number
-                of positive priors.
-
-        Returns:
-            Tuple[Tensor, Tensor]: A tuple of cls loss and bbox loss of one
-            feature map.
-        """
-
-        loss_cls_all = F.cross_entropy(
-            cls_score, labels, reduction='none') * label_weights
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
-            as_tuple=False).reshape(-1)
-        neg_inds = (labels == self.num_classes).nonzero(
-            as_tuple=False).view(-1)
-
-        num_pos_samples = pos_inds.size(0)
-        num_neg_samples = self.train_cfg['neg_pos_ratio'] * num_pos_samples
-        if num_neg_samples > neg_inds.size(0):
-            num_neg_samples = neg_inds.size(0)
-        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
-        loss_cls_pos = loss_cls_all[pos_inds].sum()
-        loss_cls_neg = topk_loss_cls_neg.sum()
-        loss_cls = (loss_cls_pos + loss_cls_neg) / avg_factor
-
-        if self.reg_decoded_bbox:
-            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-            # is applied directly on the decoded bounding boxes, it
-            # decodes the already encoded coordinates to absolute format.
-            bbox_pred = self.bbox_coder.decode(anchor, bbox_pred)
-
-        loss_bbox = smooth_l1_loss(
-            bbox_pred,
-            bbox_targets,
-            bbox_weights,
-            beta=self.train_cfg['smoothl1_beta'],
-            avg_factor=avg_factor)
-        return loss_cls[None], loss_bbox
-
-    def loss_by_feat(
-        self,
-        cls_scores: List[Tensor],
-        bbox_preds: List[Tensor],
-        batch_gt_instances: InstanceList,
-        batch_img_metas: List[dict],
-        batch_gt_instances_ignore: OptInstanceList = None
-    ) -> Dict[str, List[Tensor]]:
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance.  It usually includes ``bboxes`` and ``labels``
-                attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
-                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-
-        Returns:
-            dict[str, list[Tensor]]: A dictionary of loss components. the dict
-            has components below:
-
-            - loss_cls (list[Tensor]): A list containing each feature map \
-            classification loss.
-            - loss_bbox (list[Tensor]): A list containing each feature map \
-            regression loss.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.prior_generator.num_levels
-
-        device = cls_scores[0].device
-
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, batch_img_metas, device=device)
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            batch_gt_instances,
-            batch_img_metas,
-            batch_gt_instances_ignore=batch_gt_instances_ignore,
-            unmap_outputs=True)
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         avg_factor) = cls_reg_targets
-
-        num_images = len(batch_img_metas)
-        all_cls_scores = torch.cat([
-            s.permute(0, 2, 3, 1).reshape(
-                num_images, -1, self.cls_out_channels) for s in cls_scores
-        ], 1)
-        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-        all_label_weights = torch.cat(label_weights_list,
-                                      -1).view(num_images, -1)
-        all_bbox_preds = torch.cat([
-            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
-            for b in bbox_preds
-        ], -2)
-        all_bbox_targets = torch.cat(bbox_targets_list,
-                                     -2).view(num_images, -1, 4)
-        all_bbox_weights = torch.cat(bbox_weights_list,
-                                     -2).view(num_images, -1, 4)
-
-        # concat all level anchors to a single tensor
-        all_anchors = []
-        for i in range(num_images):
-            all_anchors.append(torch.cat(anchor_list[i]))
-
-        losses_cls, losses_bbox = multi_apply(
-            self.loss_by_feat_single,
-            all_cls_scores,
-            all_bbox_preds,
-            all_anchors,
-            all_labels,
-            all_label_weights,
-            all_bbox_targets,
-            all_bbox_weights,
-            avg_factor=avg_factor)
-        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
diff --git a/spaces/Liu-LAB/GPT-academic/docs/waifu_plugin/waifu.css b/spaces/Liu-LAB/GPT-academic/docs/waifu_plugin/waifu.css
deleted file mode 100644
index 42639df0794e46fc58f66e2c772e2bf9ba605eed..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/docs/waifu_plugin/waifu.css
+++ /dev/null
@@ -1,290 +0,0 @@
-.waifu {
-    position: fixed;
-    bottom: 0;
-    z-index: 1;
-    font-size: 0;
-    -webkit-transform: translateY(3px);
-    transform: translateY(3px);
-}
-.waifu:hover {
-    -webkit-transform: translateY(0);
-    transform: translateY(0);
-}
-.waifu-tips {
-    opacity: 0;
-    margin: -20px 20px;
-    padding: 5px 10px;
-    border: 1px solid rgba(224, 186, 140, 0.62);
-    border-radius: 12px;
-    background-color: rgba(236, 217, 188, 0.5);
-    box-shadow: 0 3px 15px 2px rgba(191, 158, 118, 0.2);
-    text-overflow: ellipsis;
-    overflow: hidden;
-    position: absolute;
-    animation-delay: 5s;
-    animation-duration: 50s;
-    animation-iteration-count: infinite;
-    animation-name: shake;
-    animation-timing-function: ease-in-out;
-}
-.waifu-tool {
-    display: none;
-    color: #aaa;
-    top: 50px;
-    right: 10px;
-    position: absolute;
-}
-.waifu:hover .waifu-tool {
-    display: block;
-}
-.waifu-tool span {
-    display: block;
-    cursor: pointer;
-    color: #5b6c7d;
-    transition: 0.2s;
-}
-.waifu-tool span:hover {
-    color: #34495e;
-}
-.waifu #live2d{
-    position: relative;
-}
-
-@keyframes shake {
-    2% {
-        transform: translate(0.5px, -1.5px) rotate(-0.5deg);
-    }
-
-    4% {
-        transform: translate(0.5px, 1.5px) rotate(1.5deg);
-    }
-
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-        transform: translate(1.5px, 1.5px) rotate(1.5deg);
-    }
-
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-    }
-
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-        transform: translate(0.5px, 2.5px) rotate(0.5deg);
-    }
-
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-    }
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-    }
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-    }
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-    }
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-}
-[class^="fui-"],
-[class*="fui-"] {
-  font-family: 'Flat-UI-Icons';
-  speak: none;
-  font-style: normal;
-  font-weight: normal;
-  font-variant: normal;
-  text-transform: none;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-.fui-cross:before {
-  content: "\e609";
-}
-.fui-info-circle:before {
-  content: "\e60f";
-}
-.fui-photo:before {
-  content: "\e62a";
-}
-.fui-eye:before {
-  content: "\e62c";
-}
-.fui-chat:before {
-  content: "\e62d";
-}
-.fui-home:before {
-  content: "\e62e";
-}
-.fui-user:before {
-  content: "\e631";
-}
\ No newline at end of file
diff --git a/spaces/Luelll/ChuanhuChatGPT/modules/models.py b/spaces/Luelll/ChuanhuChatGPT/modules/models.py
deleted file mode 100644
index 25b18b1904910e183a997a763008403d960868d6..0000000000000000000000000000000000000000
--- a/spaces/Luelll/ChuanhuChatGPT/modules/models.py
+++ /dev/null
@@ -1,625 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import platform
-import base64
-from io import BytesIO
-from PIL import Image
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
-
-from .presets import *
-from .llama_func import *
-from .utils import *
-from . import shared
-from .config import retrieve_proxy
-from modules import config
-from .base_model import BaseLLMModel, ModelType
-
-
-class OpenAIClient(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-        )
-        self.api_key = api_key
-        self.need_api_key = True
-        self._refresh_header()
-
-    def get_answer_stream_iter(self):
-        response = self._get_response(stream=True)
-        if response is not None:
-            iter = self._decode_chat_response(response)
-            partial_text = ""
-            for i in iter:
-                partial_text += i
-                yield partial_text
-        else:
-            yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG
-
-    def get_answer_at_once(self):
-        response = self._get_response()
-        response = json.loads(response.text)
-        content = response["choices"][0]["message"]["content"]
-        total_token_count = response["usage"]["total_tokens"]
-        return content, total_token_count
-
-    def count_token(self, user_input):
-        input_token_count = count_token(construct_user(user_input))
-        if self.system_prompt is not None and len(self.all_token_counts) == 0:
-            system_prompt_token_count = count_token(
-                construct_system(self.system_prompt)
-            )
-            return input_token_count + system_prompt_token_count
-        return input_token_count
-
-    def billing_info(self):
-        try:
-            curr_time = datetime.datetime.now()
-            last_day_of_month = get_last_day_of_month(
-                curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = self._get_billing_data(usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:" + str(e))
-                return i18n("**获取API使用情况失败**")
-            rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-        except requests.exceptions.ConnectTimeout:
-            status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            )
-            return status_text
-        except requests.exceptions.ReadTimeout:
-            status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            return status_text
-        except Exception as e:
-            import traceback
-            traceback.print_exc()
-            logging.error(i18n("获取API使用情况失败:") + str(e))
-            return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
-    def set_token_upper_limit(self, new_upper_limit):
-        pass
-
-    @shared.state.switching_api_key  # 在不开启多账号模式的时候，这个装饰器不会起作用
-    def _get_response(self, stream=False):
-        openai_api_key = self.api_key
-        system_prompt = self.system_prompt
-        history = self.history
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-
-        if system_prompt is not None:
-            history = [construct_system(system_prompt), *history]
-
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n_choices,
-            "stream": stream,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-        }
-
-        if self.max_generation_token is not None:
-            payload["max_tokens"] = self.max_generation_token
-        if self.stop_sequence is not None:
-            payload["stop"] = self.stop_sequence
-        if self.logit_bias is not None:
-            payload["logit_bias"] = self.logit_bias
-        if self.user_identifier is not None:
-            payload["user"] = self.user_identifier
-
-        if stream:
-            timeout = TIMEOUT_STREAMING
-        else:
-            timeout = TIMEOUT_ALL
-
-        # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
-        if shared.state.completion_url != COMPLETION_URL:
-            logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            try:
-                response = requests.post(
-                    shared.state.completion_url,
-                    headers=headers,
-                    json=payload,
-                    stream=stream,
-                    timeout=timeout,
-                )
-            except:
-                return None
-        return response
-
-    def _refresh_header(self):
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-    def _get_billing_data(self, billing_url):
-        with retrieve_proxy():
-            response = requests.get(
-                billing_url,
-                headers=self.headers,
-                timeout=TIMEOUT_ALL,
-            )
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            raise Exception(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
-
-    def _decode_chat_response(self, response):
-        error_msg = ""
-        for chunk in response.iter_lines():
-            if chunk:
-                chunk = chunk.decode()
-                chunk_length = len(chunk)
-                try:
-                    chunk = json.loads(chunk[6:])
-                except json.JSONDecodeError:
-                    print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
-                    error_msg += chunk
-                    continue
-                if chunk_length > 6 and "delta" in chunk["choices"][0]:
-                    if chunk["choices"][0]["finish_reason"] == "stop":
-                        break
-                    try:
-                        yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        # logging.error(f"Error: {e}")
-                        continue
-        if error_msg:
-            raise Exception(error_msg)
-
-    def set_key(self, new_access_key):
-        ret = super().set_key(new_access_key)
-        self._refresh_header()
-        return ret
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name) -> None:
-        super().__init__(model_name=model_name)
-        from transformers import AutoTokenizer, AutoModel
-        import torch
-        global CHATGLM_TOKENIZER, CHATGLM_MODEL
-        if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
-            system_name = platform.system()
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"THUDM/{model_name}"
-            CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            quantified = False
-            if "int4" in model_name:
-                quantified = True
-            model = AutoModel.from_pretrained(
-                    model_source, trust_remote_code=True
-                )
-            if torch.cuda.is_available():
-                # run on CUDA
-                logging.info("CUDA is available, using CUDA")
-                model = model.half().cuda()
-            # mps加速还存在一些问题，暂时不使用
-            elif system_name == "Darwin" and model_path is not None and not quantified:
-                logging.info("Running on macOS, using MPS")
-                # running on macOS and model already downloaded
-                model = model.half().to("mps")
-            else:
-                logging.info("GPU is not available, using CPU")
-                model = model.float()
-            model = model.eval()
-            CHATGLM_MODEL = model
-
-    def _get_glm_style_input(self):
-        history = [x["content"] for x in self.history]
-        query = history.pop()
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        assert (
-            len(history) % 2 == 0
-        ), f"History should be even length. current history is: {history}"
-        history = [[history[i], history[i + 1]]
-                   for i in range(0, len(history), 2)]
-        return history, query
-
-    def get_answer_at_once(self):
-        history, query = self._get_glm_style_input()
-        response, _ = CHATGLM_MODEL.chat(
-            CHATGLM_TOKENIZER, query, history=history)
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        history, query = self._get_glm_style_input()
-        for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
-        ):
-            yield response
-
-
-class LLaMA_Client(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        lora_path=None,
-    ) -> None:
-        super().__init__(model_name=model_name)
-        from lmflow.datasets.dataset import Dataset
-        from lmflow.pipeline.auto_pipeline import AutoPipeline
-        from lmflow.models.auto_model import AutoModel
-        from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
-        self.max_generation_token = 1000
-        self.end_string = "\n\n"
-        # We don't need input data
-        data_args = DatasetArguments(dataset_path=None)
-        self.dataset = Dataset(data_args)
-        self.system_prompt = ""
-
-        global LLAMA_MODEL, LLAMA_INFERENCER
-        if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"decapoda-research/{model_name}"
-                # raise Exception(f"models目录下没有这个模型: {model_name}")
-            if lora_path is not None:
-                lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
-            pipeline_args = InferencerArguments(
-                local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
-            with open(pipeline_args.deepspeed, "r") as f:
-                ds_config = json.load(f)
-            LLAMA_MODEL = AutoModel.get_model(
-                model_args,
-                tune_strategy="none",
-                ds_config=ds_config,
-            )
-            LLAMA_INFERENCER = AutoPipeline.get_pipeline(
-                pipeline_name="inferencer",
-                model_args=model_args,
-                data_args=data_args,
-                pipeline_args=pipeline_args,
-            )
-
-    def _get_llama_style_input(self):
-        history = []
-        instruction = ""
-        if self.system_prompt:
-            instruction = (f"Instruction: {self.system_prompt}\n")
-        for x in self.history:
-            if x["role"] == "user":
-                history.append(f"{instruction}Input: {x['content']}")
-            else:
-                history.append(f"Output: {x['content']}")
-        context = "\n\n".join(history)
-        context += "\n\nOutput: "
-        return context
-
-    def get_answer_at_once(self):
-        context = self._get_llama_style_input()
-
-        input_dataset = self.dataset.from_dict(
-            {"type": "text_only", "instances": [{"text": context}]}
-        )
-
-        output_dataset = LLAMA_INFERENCER.inference(
-            model=LLAMA_MODEL,
-            dataset=input_dataset,
-            max_new_tokens=self.max_generation_token,
-            temperature=self.temperature,
-        )
-
-        response = output_dataset.to_dict()["instances"][0]["text"]
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        context = self._get_llama_style_input()
-        partial_text = ""
-        step = 1
-        for _ in range(0, self.max_generation_token, step):
-            input_dataset = self.dataset.from_dict(
-                {"type": "text_only", "instances": [
-                    {"text": context + partial_text}]}
-            )
-            output_dataset = LLAMA_INFERENCER.inference(
-                model=LLAMA_MODEL,
-                dataset=input_dataset,
-                max_new_tokens=step,
-                temperature=self.temperature,
-            )
-            response = output_dataset.to_dict()["instances"][0]["text"]
-            if response == "" or response == self.end_string:
-                break
-            partial_text += response
-            yield partial_text
-
-
-class XMChat(BaseLLMModel):
-    def __init__(self, api_key):
-        super().__init__(model_name="xmchat")
-        self.api_key = api_key
-        self.session_id = None
-        self.reset()
-        self.image_bytes = None
-        self.image_path = None
-        self.xm_history = []
-        self.url = "https://xmbot.net/web"
-        self.last_conv_id = None
-
-    def reset(self):
-        self.session_id = str(uuid.uuid4())
-        self.last_conv_id = None
-        return [], "已重置"
-
-    def image_to_base64(self, image_path):
-        # 打开并加载图片
-        img = Image.open(image_path)
-
-        # 获取图片的宽度和高度
-        width, height = img.size
-
-        # 计算压缩比例，以确保最长边小于4096像素
-        max_dimension = 2048
-        scale_ratio = min(max_dimension / width, max_dimension / height)
-
-        if scale_ratio < 1:
-            # 按压缩比例调整图片大小
-            new_width = int(width * scale_ratio)
-            new_height = int(height * scale_ratio)
-            img = img.resize((new_width, new_height), Image.ANTIALIAS)
-
-        # 将图片转换为jpg格式的二进制数据
-        buffer = BytesIO()
-        if img.mode == "RGBA":
-            img = img.convert("RGB")
-        img.save(buffer, format='JPEG')
-        binary_image = buffer.getvalue()
-
-        # 对二进制数据进行Base64编码
-        base64_image = base64.b64encode(binary_image).decode('utf-8')
-
-        return base64_image
-
-    def try_read_image(self, filepath):
-        def is_image_file(filepath):
-            # 判断文件是否为图片
-            valid_image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
-            file_extension = os.path.splitext(filepath)[1].lower()
-            return file_extension in valid_image_extensions
-
-        if is_image_file(filepath):
-            logging.info(f"读取图片文件: {filepath}")
-            self.image_bytes = self.image_to_base64(filepath)
-            self.image_path = filepath
-        else:
-            self.image_bytes = None
-            self.image_path = None
-
-    def like(self):
-        if self.last_conv_id is None:
-            return "点赞失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "good"
-        }
-        response = requests.post(self.url, json=data)
-        return "👍点赞成功，，感谢反馈～"
-
-    def dislike(self):
-        if self.last_conv_id is None:
-            return "点踩失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "bad"
-        }
-        response = requests.post(self.url, json=data)
-        return "👎点踩成功，感谢反馈～"
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = real_inputs
-        display_append = ""
-        limited_context = False
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        if files:
-            for file in files:
-                if file.name:
-                    logging.info(f"尝试读取图像: {file.name}")
-                    self.try_read_image(file.name)
-            if self.image_path is not None:
-                chatbot = chatbot + [((self.image_path,), None)]
-            if self.image_bytes is not None:
-                logging.info("使用图片作为输入")
-                # XMChat的一轮对话中实际上只能处理一张图片
-                self.reset()
-                conv_id = str(uuid.uuid4())
-                data = {
-                    "user_id": self.api_key,
-                    "session_id": self.session_id,
-                    "uuid": conv_id,
-                    "data_type": "imgbase64",
-                    "data": self.image_bytes
-                }
-                response = requests.post(self.url, json=data)
-                response = json.loads(response.text)
-                logging.info(f"图片回复: {response['data']}")
-        return None, chatbot, None
-
-    def get_answer_at_once(self):
-        question = self.history[-1]["content"]
-        conv_id = str(uuid.uuid4())
-        self.last_conv_id = conv_id
-        data = {
-            "user_id": self.api_key,
-            "session_id": self.session_id,
-            "uuid": conv_id,
-            "data_type": "text",
-            "data": question
-        }
-        response = requests.post(self.url, json=data)
-        try:
-            response = json.loads(response.text)
-            return response["data"], len(response["data"])
-        except Exception as e:
-            return response.text, len(response.text)
-
-
-
-
-def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-) -> BaseLLMModel:
-    msg = i18n("模型设置为了：") + f" {model_name}"
-    model_type = ModelType.get_type(model_name)
-    lora_selector_visibility = False
-    lora_choices = []
-    dont_change_lora_selector = False
-    if model_type != ModelType.OpenAI:
-        config.local_embedding = True
-    # del current_model.model
-    model = None
-    try:
-        if model_type == ModelType.OpenAI:
-            logging.info(f"正在加载OpenAI模型: {model_name}")
-            model = OpenAIClient(
-                model_name=model_name,
-                api_key=access_key,
-                system_prompt=system_prompt,
-                temperature=temperature,
-                top_p=top_p,
-            )
-        elif model_type == ModelType.ChatGLM:
-            logging.info(f"正在加载ChatGLM模型: {model_name}")
-            model = ChatGLM_Client(model_name)
-        elif model_type == ModelType.LLaMA and lora_model_path == "":
-            msg = f"现在请为 {model_name} 选择LoRA模型"
-            logging.info(msg)
-            lora_selector_visibility = True
-            if os.path.isdir("lora"):
-                lora_choices = get_file_names(
-                    "lora", plain=True, filetypes=[""])
-            lora_choices = ["No LoRA"] + lora_choices
-        elif model_type == ModelType.LLaMA and lora_model_path != "":
-            logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
-            dont_change_lora_selector = True
-            if lora_model_path == "No LoRA":
-                lora_model_path = None
-                msg += " + No LoRA"
-            else:
-                msg += f" + {lora_model_path}"
-            model = LLaMA_Client(model_name, lora_model_path)
-        elif model_type == ModelType.XMChat:
-            if os.environ.get("XMCHAT_API_KEY") != "":
-                access_key = os.environ.get("XMCHAT_API_KEY")
-            model = XMChat(api_key=access_key)
-        elif model_type == ModelType.Unknown:
-            raise ValueError(f"未知模型: {model_name}")
-        logging.info(msg)
-    except Exception as e:
-        logging.error(e)
-        msg = f"{STANDARD_ERROR_MSG}: {e}"
-    if dont_change_lora_selector:
-        return model, msg
-    else:
-        return model, msg, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
-    with open("config.json", "r") as f:
-        openai_api_key = cjson.load(f)["openai_api_key"]
-    # set logging level to debug
-    logging.basicConfig(level=logging.DEBUG)
-    # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
-    client = get_model(model_name="chatglm-6b-int4")
-    chatbot = []
-    stream = False
-    # 测试账单功能
-    logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
-    logging.info(client.billing_info())
-    # 测试问答
-    logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
-    question = "巴黎是中国的首都吗？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试问答后history : {client.history}")
-    # 测试记忆力
-    logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
-    question = "我刚刚问了你什么问题？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试记忆力后history : {client.history}")
-    # 测试重试功能
-    logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
-    for i in client.retry(chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"重试后history : {client.history}")
-    # # 测试总结功能
-    # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
-    # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
-    # print(chatbot, msg)
-    # print(f"总结后history: {client.history}")
diff --git a/spaces/LuxOAI/ChatGpt-Web/docs/faq-en.md b/spaces/LuxOAI/ChatGpt-Web/docs/faq-en.md
deleted file mode 100644
index 319fc7dea861e0451b3d17c8391dfce82daf2c26..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/docs/faq-en.md
+++ /dev/null
@@ -1,136 +0,0 @@
-# Frequently Asked Questions
-
-## How to get help quickly?
-1. Ask ChatGPT / Bing / Baidu / Google, etc.
-2. Ask online friends. Please provide background information and a detailed description of the problem. High-quality questions are more likely to get useful answers.
-
-# Deployment Related Questions
-
-## Why does the Docker deployment version always prompt for updates
-The Docker version is equivalent to the stable version, and the latest Docker is always consistent with the latest release version. Currently, our release frequency is once every one to two days, so the Docker version will always be one to two days behind the latest commit, which is expected.
-
-## How to deploy on Vercel
-1. Register a Github account and fork this project.
-2. Register Vercel (mobile phone verification required, Chinese number can be used), and connect your Github account.
-3. Create a new project on Vercel, select the project you forked on Github, fill in the required environment variables, and start deploying. After deployment, you can access your project through the domain provided by Vercel. (Requires proxy in mainland China)
-* If you need to access it directly in China: At your DNS provider, add a CNAME record for the domain name, pointing to cname.vercel-dns.com. Then set up your domain access on Vercel.
-
-## How to modify Vercel environment variables
-- Enter the Vercel console page;
-- Select your chatgpt-next-web project;
-- Click on the Settings option at the top of the page;
-- Find the Environment Variables option in the sidebar;
-- Modify the corresponding values as needed.
-
-## What is the environment variable CODE? Is it necessary to set it?
-This is your custom access password, you can choose:
-1. Do not set it, delete the environment variable. Be cautious: anyone can access your project at this time.
-2. When deploying the project, set the environment variable CODE (supports multiple passwords, separated by commas). After setting the access password, users need to enter the access password in the settings page to use it. See [related instructions](https://github.com/Yidadaa/ChatGPT-Next-Web#access-password)
-
-## Why doesn't the version I deployed have streaming response
-> Related discussion: [#386](https://github.com/Yidadaa/ChatGPT-Next-Web/issues/386)
-
-If you use nginx reverse proxy, you need to add the following code to the configuration file:
-```
-# No caching, support streaming output
-proxy_cache off;  # Turn off caching
-proxy_buffering off;  # Turn off proxy buffering
-chunked_transfer_encoding on;  # Turn on chunked transfer encoding
-tcp_nopush on;  # Turn on TCP NOPUSH option, disable Nagle algorithm
-tcp_nodelay on;  # Turn on TCP NODELAY option, disable delay ACK algorithm
-keepalive_timeout 300;  # Set keep-alive timeout to 65 seconds
-```
-
-If you are deploying on netlify, this issue is still waiting to be resolved, please be patient.
-
-## I've deployed, but it's not accessible
-Please check and troubleshoot the following issues:
-- Is the service started?
-- Is the port correctly mapped?
-- Is the firewall port open?
-- Is the route to the server okay?
-- Is the domain name resolved correctly?
-
-# Usage Related Questions
-
-## Why does it always prompt "An error occurred, please try again later"
-There could be many reasons, please check the following in order:
-- First, check if your code version is the latest version, update to the latest version and try again;
-- Check if the api key is set correctly, the environment variable name must be uppercase with underscores;
-- Check if the api key is available;
-- If you still cannot determine the problem after going through the above steps, please submit a new issue in the issue area and attach the runtime log of vercel or the log of docker runtime.
-
-## Why does ChatGPT's reply get garbled
-In the settings page - model settings, there is an item called `temperature`. If this value is greater than 1, it may cause garbled replies. Adjust it back to within 1.
-
-## It prompts "Now it's unauthorized, please enter the access password on the settings page" when using?
-The project has set an access password through the environment variable CODE. When using it for the first time, you need to go to settings and enter the access code to use.
-
-## It prompts "You exceeded your current quota, ..." when using?
-The API KEY is problematic. Insufficient balance.
-
-## What is a proxy and how to use it?
-Due to IP restrictions of OpenAI, China and some other countries/regions cannot directly connect to OpenAI API and need to go through a proxy. You can use a proxy server (forward proxy) or a pre-configured OpenAI API reverse proxy.
-- Forward proxy example: VPN ladder. In the case of docker deployment, set the environment variable HTTP_PROXY to your proxy address (http://address:port).
-- Reverse proxy example: You can use someone else's proxy address or set it up for free through Cloudflare. Set the project environment variable BASE_URL to your proxy address.
-
-## Can I deploy it on a server in China?
-It is possible but there are issues to be addressed:
-- Proxy is required to connect to websites such as Github and OpenAI;
-- Domain name resolution requires filing for servers in China;
-- Chinese policy restricts proxy access to foreign websites/ChatGPT-related applications, which may be blocked.
-
-# Network Service Related Questions
-## What is Cloudflare?
-Cloudflare (CF) is a network service provider offering CDN, domain management, static page hosting, edge computing function deployment, and more. Common use cases: purchase and/or host your domain (resolution, dynamic domain, etc.), apply CDN to your server (can hide IP to avoid being blocked), deploy websites (CF Pages). CF offers most services for free.
-
-## What is Vercel?
-Vercel is a global cloud platform designed to help developers build and deploy modern web applications more quickly. This project and many web applications can be deployed on Vercel with a single click for free. No need to understand code, Linux, have a server, pay, or set up an OpenAI API proxy. The downside is that you need to bind a domain name to access it without restrictions in China.
-
-## How to obtain a domain name?
-1. Register with a domain provider, such as Namesilo (supports Alipay) or Cloudflare for international providers, and Wanwang for domestic providers in China.
-2. Free domain name providers: eu.org (second-level domain), etc.
-3. Ask friends for a free second-level domain.
-
-## How to obtain a server
-- Examples of international server providers: Amazon Web Services, Google Cloud, Vultr, Bandwagon, Hostdare, etc.
-  International server considerations: Server lines affect access speed in China; CN2 GIA and CN2 lines are recommended. If the server has difficulty accessing in China (serious packet loss, etc.), you can try using a CDN (from providers like Cloudflare).
-- Domestic server providers: Alibaba Cloud, Tencent, etc.
-  Domestic server considerations: Domain name resolution requires filing; domestic server bandwidth is relatively expensive; accessing foreign websites (Github, OpenAI, etc.) requires a proxy.
-
-# OpenAI-related Questions
-## How to register an OpenAI account?
-Go to chat.openai.com to register. You will need:
-- A good VPN (OpenAI only allows native IP addresses of supported regions)
-- A supported email (e.g., Gmail or a company/school email, not Outlook or QQ email)
-- A way to receive SMS verification (e.g., SMS-activate website)
-
-## How to activate OpenAI API? How to check API balance?
-Official website (requires VPN): https://platform.openai.com/account/usage
-Some users have set up a proxy to check the balance without a VPN; ask online friends for access. Please verify the source is reliable to avoid API Key leakage.
-
-## Why doesn't my new OpenAI account have an API balance?
-(Updated April 6th) Newly registered accounts usually display API balance within 24 hours. New accounts are currently given a $5 balance.
-
-## How to recharge OpenAI API?
-OpenAI only accepts credit cards from designated regions (Chinese credit cards cannot be used). If the credit cards from your region is not supported, some options include:
-1. Depay virtual credit card
-2. Apply for a foreign credit card
-3. Find someone online to top up
-
-## How to access the GPT-4 API?
-(Updated April 6th) Access to the GPT-4 API requires a separate application. Go to the following address and enter your information to join the waitlist (prepare your OpenAI organization ID): https://openai.com/waitlist/gpt-4-api
-Wait for email updates afterwards.
-
-## How to use the Azure OpenAI interface
-Please refer to: [#371](https://github.com/Yidadaa/ChatGPT-Next-Web/issues/371)
-
-## Why is my Token consumed so fast?
-> Related discussion: [#518](https://github.com/Yidadaa/ChatGPT-Next-Web/issues/518)
-- If you have GPT-4 access and use GPT-4 API regularly, your bill will increase rapidly since GPT-4 pricing is about 15 times higher than GPT-3.5;
-- If you are using GPT-3.5 and not using it frequently, but still find your bill increasing fast, please troubleshoot immediately using these steps:
-  - Check your API key consumption record on the OpenAI website; if your token is consumed every hour and each time consumes tens of thousands of tokens, your key must have been leaked. Please delete it and regenerate it immediately. **Do not check your balance on random websites.**
-  - If your password is short, such as 5 characters or fewer, the cost of brute-forcing is very low. It is recommended to search docker logs to confirm whether someone has tried a large number of password combinations. Keyword: got access code
-- By following these two methods, you can locate the reason for your token's rapid consumption:
-  - If the OpenAI consumption record is abnormal but the Docker log has no issues, it means your API key has been leaked;
-  - If the Docker log shows a large number of got access code brute-force attempts, your password has been cracked.
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/base.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/base.py
deleted file mode 100644
index eb5a2deb3c44f5aed7530fd1e299fff1273737b8..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/base.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import torch
-
-
-class BaseTransform(object):
-    def __init__(self):
-        self.image_changed = False
-
-    def transform(self, image_nd, clicks_lists):
-        raise NotImplementedError
-
-    def inv_transform(self, prob_map):
-        raise NotImplementedError
-
-    def reset(self):
-        raise NotImplementedError
-
-    def get_state(self):
-        raise NotImplementedError
-
-    def set_state(self, state):
-        raise NotImplementedError
-
-
-class SigmoidForPred(BaseTransform):
-    def transform(self, image_nd, clicks_lists):
-        return image_nd, clicks_lists
-
-    def inv_transform(self, prob_map):
-        return torch.sigmoid(prob_map)
-
-    def reset(self):
-        pass
-
-    def get_state(self):
-        return None
-
-    def set_state(self, state):
-        pass
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/functional/_csrc.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/functional/_csrc.py
deleted file mode 100644
index d0c14098f0cfa422920f01fe4985dbeb7fedc2d1..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/functional/_csrc.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-/*****************************************************************************/
-
-Extension module loader
-
-code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
-
-/*****************************************************************************/
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import glob
-import os.path
-
-import torch
-
-try:
-    from torch.utils.cpp_extension import load
-    from torch.utils.cpp_extension import CUDA_HOME
-except ImportError:
-    raise ImportError(
-        "The cpp layer extensions requires PyTorch 0.4 or higher")
-
-
-def _load_C_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    this_dir = os.path.join(this_dir, "csrc")
-
-    main_file = glob.glob(os.path.join(this_dir, "*.cpp"))
-    sources_cpu = glob.glob(os.path.join(this_dir, "cpu", "*.cpp"))
-    sources_cuda = glob.glob(os.path.join(this_dir, "cuda", "*.cu"))
-
-    sources = main_file + sources_cpu
-
-    extra_cflags = []
-    extra_cuda_cflags = []
-    if torch.cuda.is_available() and CUDA_HOME is not None:
-        sources.extend(sources_cuda)
-        extra_cflags = ["-O3", "-DWITH_CUDA"]
-        extra_cuda_cflags = ["--expt-extended-lambda"]
-    sources = [os.path.join(this_dir, s) for s in sources]
-    extra_include_paths = [this_dir]
-    return load(
-        name="ext_lib",
-        sources=sources,
-        extra_cflags=extra_cflags,
-        extra_include_paths=extra_include_paths,
-        extra_cuda_cflags=extra_cuda_cflags,
-    )
-
-
-_backend = _load_C_extensions()
diff --git a/spaces/Manjushri/OJ-V4-CPU/README.md b/spaces/Manjushri/OJ-V4-CPU/README.md
deleted file mode 100644
index 5a5e26e4692572691da5ca174035609226627c3a..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/OJ-V4-CPU/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: OpenJourney V4 CPU
-emoji: 🔥
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: Manjushri/SD-2.1-CPU
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/analysis/pymo/Quaternions.py b/spaces/Marshalls/testmtd/analysis/pymo/Quaternions.py
deleted file mode 100644
index d4b754871310a264e2bd2675479db9a79d24358e..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/pymo/Quaternions.py
+++ /dev/null
@@ -1,468 +0,0 @@
-import numpy as np
-
-class Quaternions:
-    """
-    Quaternions is a wrapper around a numpy ndarray
-    that allows it to act as if it were an narray of
-    a quaternion data type.
-    
-    Therefore addition, subtraction, multiplication,
-    division, negation, absolute, are all defined
-    in terms of quaternion operations such as quaternion
-    multiplication.
-    
-    This allows for much neater code and many routines
-    which conceptually do the same thing to be written
-    in the same way for point data and for rotation data.
-    
-    The Quaternions class has been desgined such that it
-    should support broadcasting and slicing in all of the
-    usual ways.
-    """
-    
-    def __init__(self, qs):
-        if isinstance(qs, np.ndarray):
-        
-            if len(qs.shape) == 1: qs = np.array([qs])
-            self.qs = qs
-            return
-            
-        if isinstance(qs, Quaternions):
-            self.qs = qs.qs
-            return
-            
-        raise TypeError('Quaternions must be constructed from iterable, numpy array, or Quaternions, not %s' % type(qs))
-    
-    def __str__(self): return "Quaternions("+ str(self.qs) + ")"
-    def __repr__(self): return "Quaternions("+ repr(self.qs) + ")"
-    
-    """ Helper Methods for Broadcasting and Data extraction """
-    
-    @classmethod
-    def _broadcast(cls, sqs, oqs, scalar=False):
-        
-        if isinstance(oqs, float): return sqs, oqs * np.ones(sqs.shape[:-1])
-        
-        ss = np.array(sqs.shape) if not scalar else np.array(sqs.shape[:-1])
-        os = np.array(oqs.shape)
-        
-        if len(ss) != len(os):
-            raise TypeError('Quaternions cannot broadcast together shapes %s and %s' % (sqs.shape, oqs.shape))
-            
-        if np.all(ss == os): return sqs, oqs
-        
-        if not np.all((ss == os) | (os == np.ones(len(os))) | (ss == np.ones(len(ss)))):
-            raise TypeError('Quaternions cannot broadcast together shapes %s and %s' % (sqs.shape, oqs.shape))
-            
-        sqsn, oqsn = sqs.copy(), oqs.copy()
-        
-        for a in np.where(ss == 1)[0]: sqsn = sqsn.repeat(os[a], axis=a)
-        for a in np.where(os == 1)[0]: oqsn = oqsn.repeat(ss[a], axis=a)
-        
-        return sqsn, oqsn
-        
-    """ Adding Quaterions is just Defined as Multiplication """
-    
-    def __add__(self, other): return self * other
-    def __sub__(self, other): return self / other
-    
-    """ Quaterion Multiplication """
-    
-    def __mul__(self, other):
-        """
-        Quaternion multiplication has three main methods.
-        
-        When multiplying a Quaternions array by Quaternions
-        normal quaternion multiplication is performed.
-        
-        When multiplying a Quaternions array by a vector
-        array of the same shape, where the last axis is 3,
-        it is assumed to be a Quaternion by 3D-Vector 
-        multiplication and the 3D-Vectors are rotated
-        in space by the Quaternions.
-        
-        When multipplying a Quaternions array by a scalar
-        or vector of different shape it is assumed to be
-        a Quaternions by Scalars multiplication and the
-        Quaternions are scaled using Slerp and the identity
-        quaternions.
-        """
-        
-        """ If Quaternions type do Quaternions * Quaternions """
-        if isinstance(other, Quaternions):
-            
-            sqs, oqs = Quaternions._broadcast(self.qs, other.qs)
-            
-            q0 = sqs[...,0]; q1 = sqs[...,1]; 
-            q2 = sqs[...,2]; q3 = sqs[...,3]; 
-            r0 = oqs[...,0]; r1 = oqs[...,1]; 
-            r2 = oqs[...,2]; r3 = oqs[...,3]; 
-            
-            qs = np.empty(sqs.shape)
-            qs[...,0] = r0 * q0 - r1 * q1 - r2 * q2 - r3 * q3
-            qs[...,1] = r0 * q1 + r1 * q0 - r2 * q3 + r3 * q2
-            qs[...,2] = r0 * q2 + r1 * q3 + r2 * q0 - r3 * q1
-            qs[...,3] = r0 * q3 - r1 * q2 + r2 * q1 + r3 * q0
-            
-            return Quaternions(qs)
-        
-        """ If array type do Quaternions * Vectors """
-        if isinstance(other, np.ndarray) and other.shape[-1] == 3:
-            vs = Quaternions(np.concatenate([np.zeros(other.shape[:-1] + (1,)), other], axis=-1))
-            return (self * (vs * -self)).imaginaries
-        
-        """ If float do Quaternions * Scalars """
-        if isinstance(other, np.ndarray) or isinstance(other, float):
-            return Quaternions.slerp(Quaternions.id_like(self), self, other)
-        
-        raise TypeError('Cannot multiply/add Quaternions with type %s' % str(type(other)))
-        
-    def __div__(self, other):
-        """
-        When a Quaternion type is supplied, division is defined
-        as multiplication by the inverse of that Quaternion.
-        
-        When a scalar or vector is supplied it is defined
-        as multiplicaion of one over the supplied value.
-        Essentially a scaling.
-        """
-        
-        if isinstance(other, Quaternions): return self * (-other)
-        if isinstance(other, np.ndarray): return self * (1.0 / other)
-        if isinstance(other, float): return self * (1.0 / other)
-        raise TypeError('Cannot divide/subtract Quaternions with type %s' + str(type(other)))
-        
-    def __eq__(self, other): return self.qs == other.qs
-    def __ne__(self, other): return self.qs != other.qs
-    
-    def __neg__(self):
-        """ Invert Quaternions """
-        return Quaternions(self.qs * np.array([[1, -1, -1, -1]]))
-    
-    def __abs__(self):
-        """ Unify Quaternions To Single Pole """
-        qabs = self.normalized().copy()
-        top = np.sum(( qabs.qs) * np.array([1,0,0,0]), axis=-1)
-        bot = np.sum((-qabs.qs) * np.array([1,0,0,0]), axis=-1)
-        qabs.qs[top < bot] = -qabs.qs[top <  bot]
-        return qabs
-    
-    def __iter__(self): return iter(self.qs)
-    def __len__(self): return len(self.qs)
-    
-    def __getitem__(self, k):    return Quaternions(self.qs[k]) 
-    def __setitem__(self, k, v): self.qs[k] = v.qs
-        
-    @property
-    def lengths(self):
-        return np.sum(self.qs**2.0, axis=-1)**0.5
-    
-    @property
-    def reals(self):
-        return self.qs[...,0]
-        
-    @property
-    def imaginaries(self):
-        return self.qs[...,1:4]
-    
-    @property
-    def shape(self): return self.qs.shape[:-1]
-    
-    def repeat(self, n, **kwargs):
-        return Quaternions(self.qs.repeat(n, **kwargs))
-    
-    def normalized(self):
-        return Quaternions(self.qs / self.lengths[...,np.newaxis])
-    
-    def log(self):
-        norm = abs(self.normalized())
-        imgs = norm.imaginaries
-        lens = np.sqrt(np.sum(imgs**2, axis=-1))
-        lens = np.arctan2(lens, norm.reals) / (lens + 1e-10)
-        return imgs * lens[...,np.newaxis]
-    
-    def constrained(self, axis):
-        
-        rl = self.reals
-        im = np.sum(axis * self.imaginaries, axis=-1)
-        
-        t1 = -2 * np.arctan2(rl, im) + np.pi
-        t2 = -2 * np.arctan2(rl, im) - np.pi
-        
-        top = Quaternions.exp(axis[np.newaxis] * (t1[:,np.newaxis] / 2.0))
-        bot = Quaternions.exp(axis[np.newaxis] * (t2[:,np.newaxis] / 2.0))
-        img = self.dot(top) > self.dot(bot)
-        
-        ret = top.copy()
-        ret[ img] = top[ img]
-        ret[~img] = bot[~img]
-        return ret
-    
-    def constrained_x(self): return self.constrained(np.array([1,0,0]))
-    def constrained_y(self): return self.constrained(np.array([0,1,0]))
-    def constrained_z(self): return self.constrained(np.array([0,0,1]))
-    
-    def dot(self, q): return np.sum(self.qs * q.qs, axis=-1)
-    
-    def copy(self): return Quaternions(np.copy(self.qs))
-    
-    def reshape(self, s):
-        self.qs.reshape(s)
-        return self
-    
-    def interpolate(self, ws):
-        return Quaternions.exp(np.average(abs(self).log, axis=0, weights=ws))
-    
-    def euler(self, order='xyz'):
-        
-        q = self.normalized().qs
-        q0 = q[...,0]
-        q1 = q[...,1]
-        q2 = q[...,2]
-        q3 = q[...,3]
-        es = np.zeros(self.shape + (3,))
-        
-        if   order == 'xyz':
-            es[...,0] = np.arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1 * q1 + q2 * q2))
-            es[...,1] = np.arcsin((2 * (q0 * q2 - q3 * q1)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2 * q2 + q3 * q3))
-        elif order == 'yzx':
-            es[...,0] = np.arctan2(2 * (q1 * q0 - q2 * q3), -q1 * q1 + q2 * q2 - q3 * q3 + q0 * q0)
-            es[...,1] = np.arctan2(2 * (q2 * q0 - q1 * q3),  q1 * q1 - q2 * q2 - q3 * q3 + q0 * q0)
-            es[...,2] = np.arcsin((2 * (q1 * q2 + q3 * q0)).clip(-1,1))
-        else:
-            raise NotImplementedError('Cannot convert from ordering %s' % order)
-        
-        """
-        
-        # These conversion don't appear to work correctly for Maya.
-        # http://bediyap.com/programming/convert-quaternion-to-euler-rotations/
-        
-        if   order == 'xyz':
-            es[...,0] = np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
-            es[...,1] = np.arcsin((2 * (q1 * q3 + q0 * q2)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q0 * q1 - q2 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
-        elif order == 'yzx':
-            es[...,0] = np.arctan2(2 * (q0 * q1 - q2 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)
-            es[...,1] = np.arcsin((2 * (q1 * q2 + q0 * q3)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
-        elif order == 'zxy':
-            es[...,0] = np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
-            es[...,1] = np.arcsin((2 * (q0 * q1 + q2 * q3)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3) 
-        elif order == 'xzy':
-            es[...,0] = np.arctan2(2 * (q0 * q2 + q1 * q3), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
-            es[...,1] = np.arcsin((2 * (q0 * q3 - q1 * q2)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q0 * q1 + q2 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)
-        elif order == 'yxz':
-            es[...,0] = np.arctan2(2 * (q1 * q2 + q0 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)
-            es[...,1] = np.arcsin((2 * (q0 * q1 - q2 * q3)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q1 * q3 + q0 * q2), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
-        elif order == 'zyx':
-            es[...,0] = np.arctan2(2 * (q0 * q1 + q2 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)
-            es[...,1] = np.arcsin((2 * (q0 * q2 - q1 * q3)).clip(-1,1))
-            es[...,2] = np.arctan2(2 * (q0 * q3 + q1 * q2), q0 * q0 + q1 * q1 - q2 * q2 - q3 * q3)
-        else:
-            raise KeyError('Unknown ordering %s' % order)
-        
-        """
-        
-        # https://github.com/ehsan/ogre/blob/master/OgreMain/src/OgreMatrix3.cpp
-        # Use this class and convert from matrix
-        
-        return es
-        
-    
-    def average(self):
-        
-        if len(self.shape) == 1:
-            
-            import numpy.core.umath_tests as ut
-            system = ut.matrix_multiply(self.qs[:,:,np.newaxis], self.qs[:,np.newaxis,:]).sum(axis=0)
-            w, v = np.linalg.eigh(system)
-            qiT_dot_qref = (self.qs[:,:,np.newaxis] * v[np.newaxis,:,:]).sum(axis=1)
-            return Quaternions(v[:,np.argmin((1.-qiT_dot_qref**2).sum(axis=0))])            
-        
-        else:
-            
-            raise NotImplementedError('Cannot average multi-dimensionsal Quaternions')
-
-    def angle_axis(self):
-        
-        norm = self.normalized()        
-        s = np.sqrt(1 - (norm.reals**2.0))
-        s[s == 0] = 0.001
-        
-        angles = 2.0 * np.arccos(norm.reals)
-        axis = norm.imaginaries / s[...,np.newaxis]
-        
-        return angles, axis
-        
-    
-    def transforms(self):
-        
-        qw = self.qs[...,0]
-        qx = self.qs[...,1]
-        qy = self.qs[...,2]
-        qz = self.qs[...,3]
-        
-        x2 = qx + qx; y2 = qy + qy; z2 = qz + qz;
-        xx = qx * x2; yy = qy * y2; wx = qw * x2;
-        xy = qx * y2; yz = qy * z2; wy = qw * y2;
-        xz = qx * z2; zz = qz * z2; wz = qw * z2;
-        
-        m = np.empty(self.shape + (3,3))
-        m[...,0,0] = 1.0 - (yy + zz)
-        m[...,0,1] = xy - wz
-        m[...,0,2] = xz + wy        
-        m[...,1,0] = xy + wz
-        m[...,1,1] = 1.0 - (xx + zz)
-        m[...,1,2] = yz - wx        
-        m[...,2,0] = xz - wy
-        m[...,2,1] = yz + wx
-        m[...,2,2] = 1.0 - (xx + yy)
-        
-        return m
-    
-    def ravel(self):
-        return self.qs.ravel()
-    
-    @classmethod
-    def id(cls, n):
-        
-        if isinstance(n, tuple):
-            qs = np.zeros(n + (4,))
-            qs[...,0] = 1.0
-            return Quaternions(qs)
-        
-        if isinstance(n, int) or isinstance(n, long):
-            qs = np.zeros((n,4))
-            qs[:,0] = 1.0
-            return Quaternions(qs)
-        
-        raise TypeError('Cannot Construct Quaternion from %s type' % str(type(n)))
-
-    @classmethod
-    def id_like(cls, a):
-        qs = np.zeros(a.shape + (4,))
-        qs[...,0] = 1.0
-        return Quaternions(qs)
-        
-    @classmethod
-    def exp(cls, ws):
-    
-        ts = np.sum(ws**2.0, axis=-1)**0.5
-        ts[ts == 0] = 0.001
-        ls = np.sin(ts) / ts
-        
-        qs = np.empty(ws.shape[:-1] + (4,))
-        qs[...,0] = np.cos(ts)
-        qs[...,1] = ws[...,0] * ls
-        qs[...,2] = ws[...,1] * ls
-        qs[...,3] = ws[...,2] * ls
-        
-        return Quaternions(qs).normalized()
-        
-    @classmethod
-    def slerp(cls, q0s, q1s, a):
-        
-        fst, snd = cls._broadcast(q0s.qs, q1s.qs)
-        fst, a = cls._broadcast(fst, a, scalar=True)
-        snd, a = cls._broadcast(snd, a, scalar=True)
-        
-        len = np.sum(fst * snd, axis=-1)
-        
-        neg = len < 0.0
-        len[neg] = -len[neg]
-        snd[neg] = -snd[neg]
-        
-        amount0 = np.zeros(a.shape)
-        amount1 = np.zeros(a.shape)
-
-        linear = (1.0 - len) < 0.01
-        omegas = np.arccos(len[~linear])
-        sinoms = np.sin(omegas)
-        
-        amount0[ linear] = 1.0 - a[linear]
-        amount1[ linear] =       a[linear]
-        amount0[~linear] = np.sin((1.0 - a[~linear]) * omegas) / sinoms
-        amount1[~linear] = np.sin(       a[~linear]  * omegas) / sinoms
-        
-        return Quaternions(
-            amount0[...,np.newaxis] * fst + 
-            amount1[...,np.newaxis] * snd)
-    
-    @classmethod
-    def between(cls, v0s, v1s):
-        a = np.cross(v0s, v1s)
-        w = np.sqrt((v0s**2).sum(axis=-1) * (v1s**2).sum(axis=-1)) + (v0s * v1s).sum(axis=-1)
-        return Quaternions(np.concatenate([w[...,np.newaxis], a], axis=-1)).normalized()
-    
-    @classmethod
-    def from_angle_axis(cls, angles, axis):
-        axis    = axis / (np.sqrt(np.sum(axis**2, axis=-1)) + 1e-10)[...,np.newaxis]
-        sines   = np.sin(angles / 2.0)[...,np.newaxis]
-        cosines = np.cos(angles / 2.0)[...,np.newaxis]
-        return Quaternions(np.concatenate([cosines, axis * sines], axis=-1))
-    
-    @classmethod
-    def from_euler(cls, es, order='xyz', world=False):
-    
-        axis = {
-            'x' : np.array([1,0,0]),
-            'y' : np.array([0,1,0]),
-            'z' : np.array([0,0,1]),
-        }
-        
-        q0s = Quaternions.from_angle_axis(es[...,0], axis[order[0]])
-        q1s = Quaternions.from_angle_axis(es[...,1], axis[order[1]])
-        q2s = Quaternions.from_angle_axis(es[...,2], axis[order[2]])
-        
-        return (q2s * (q1s * q0s)) if world else (q0s * (q1s * q2s))
-    
-    @classmethod
-    def from_transforms(cls, ts):
-        
-        d0, d1, d2 = ts[...,0,0], ts[...,1,1], ts[...,2,2]
-        
-        q0 = ( d0 + d1 + d2 + 1.0) / 4.0
-        q1 = ( d0 - d1 - d2 + 1.0) / 4.0
-        q2 = (-d0 + d1 - d2 + 1.0) / 4.0
-        q3 = (-d0 - d1 + d2 + 1.0) / 4.0
-        
-        q0 = np.sqrt(q0.clip(0,None))
-        q1 = np.sqrt(q1.clip(0,None))
-        q2 = np.sqrt(q2.clip(0,None))
-        q3 = np.sqrt(q3.clip(0,None))
-        
-        c0 = (q0 >= q1) & (q0 >= q2) & (q0 >= q3)
-        c1 = (q1 >= q0) & (q1 >= q2) & (q1 >= q3)
-        c2 = (q2 >= q0) & (q2 >= q1) & (q2 >= q3)
-        c3 = (q3 >= q0) & (q3 >= q1) & (q3 >= q2)
-        
-        q1[c0] *= np.sign(ts[c0,2,1] - ts[c0,1,2])
-        q2[c0] *= np.sign(ts[c0,0,2] - ts[c0,2,0])
-        q3[c0] *= np.sign(ts[c0,1,0] - ts[c0,0,1])
-        
-        q0[c1] *= np.sign(ts[c1,2,1] - ts[c1,1,2])
-        q2[c1] *= np.sign(ts[c1,1,0] + ts[c1,0,1])
-        q3[c1] *= np.sign(ts[c1,0,2] + ts[c1,2,0])  
-        
-        q0[c2] *= np.sign(ts[c2,0,2] - ts[c2,2,0])
-        q1[c2] *= np.sign(ts[c2,1,0] + ts[c2,0,1])
-        q3[c2] *= np.sign(ts[c2,2,1] + ts[c2,1,2])  
-        
-        q0[c3] *= np.sign(ts[c3,1,0] - ts[c3,0,1])
-        q1[c3] *= np.sign(ts[c3,2,0] + ts[c3,0,2])
-        q2[c3] *= np.sign(ts[c3,2,1] + ts[c3,1,2])  
-        
-        qs = np.empty(ts.shape[:-2] + (4,))
-        qs[...,0] = q0
-        qs[...,1] = q1
-        qs[...,2] = q2
-        qs[...,3] = q3
-        
-        return cls(qs)
-        
-    
-    
\ No newline at end of file
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/custom_solver.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/custom_solver.py
deleted file mode 100644
index 0284ae14ed2e93b2664ef52ad938061f78363516..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/custom_solver.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from enum import Enum
-import itertools
-from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union
-import torch
-
-from detectron2.config import CfgNode
-
-from detectron2.solver.build import maybe_add_gradient_clipping
-
-def match_name_keywords(n, name_keywords):
-    out = False
-    for b in name_keywords:
-        if b in n:
-            out = True
-            break
-    return out
-
-def build_custom_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
-    """
-    Build an optimizer from config.
-    """
-    params: List[Dict[str, Any]] = []
-    memo: Set[torch.nn.parameter.Parameter] = set()
-    custom_multiplier_name = cfg.SOLVER.CUSTOM_MULTIPLIER_NAME
-    optimizer_type = cfg.SOLVER.OPTIMIZER
-    for key, value in model.named_parameters(recurse=True):
-        if not value.requires_grad:
-            continue
-        # Avoid duplicating parameters
-        if value in memo:
-            continue
-        memo.add(value)
-        lr = cfg.SOLVER.BASE_LR
-        weight_decay = cfg.SOLVER.WEIGHT_DECAY
-        if "backbone" in key:
-            lr = lr * cfg.SOLVER.BACKBONE_MULTIPLIER
-        if match_name_keywords(key, custom_multiplier_name):
-            lr = lr * cfg.SOLVER.CUSTOM_MULTIPLIER
-            print('Costum LR', key, lr)
-        param = {"params": [value], "lr": lr}
-        if optimizer_type != 'ADAMW':
-            param['weight_decay'] = weight_decay
-        params += [param]
-
-    def maybe_add_full_model_gradient_clipping(optim):  # optim: the optimizer class
-        # detectron2 doesn't have full model gradient clipping now
-        clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
-        enable = (
-            cfg.SOLVER.CLIP_GRADIENTS.ENABLED
-            and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
-            and clip_norm_val > 0.0
-        )
-
-        class FullModelGradientClippingOptimizer(optim):
-            def step(self, closure=None):
-                all_params = itertools.chain(*[x["params"] for x in self.param_groups])
-                torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
-                super().step(closure=closure)
-
-        return FullModelGradientClippingOptimizer if enable else optim
-
-    
-    if optimizer_type == 'SGD':
-        optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
-            params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM, 
-            nesterov=cfg.SOLVER.NESTEROV
-        )
-    elif optimizer_type == 'ADAMW':
-        optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
-            params, cfg.SOLVER.BASE_LR, 
-            weight_decay=cfg.SOLVER.WEIGHT_DECAY
-        )
-    else:
-        raise NotImplementedError(f"no optimizer type {optimizer_type}")
-    if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
-        optimizer = maybe_add_gradient_clipping(cfg, optimizer)
-    return optimizer
\ No newline at end of file
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/rembg/commands/p_command.py b/spaces/Mellow-ai/PhotoAI_Mellow/rembg/commands/p_command.py
deleted file mode 100644
index 2163bfbb2332f5b23f2fa9c305b15df9c6c425ff..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/rembg/commands/p_command.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import json
-import pathlib
-import time
-from typing import cast
-
-import click
-import filetype
-from tqdm import tqdm
-from watchdog.events import FileSystemEvent, FileSystemEventHandler
-from watchdog.observers import Observer
-
-from ..bg import remove
-from ..session_factory import new_session
-from ..sessions import sessions_names
-
-
-@click.command(
-    name="p",
-    help="for a folder as input",
-)
-@click.option(
-    "-m",
-    "--model",
-    default="u2net",
-    type=click.Choice(sessions_names),
-    show_default=True,
-    show_choices=True,
-    help="model name",
-)
-@click.option(
-    "-a",
-    "--alpha-matting",
-    is_flag=True,
-    show_default=True,
-    help="use alpha matting",
-)
-@click.option(
-    "-af",
-    "--alpha-matting-foreground-threshold",
-    default=240,
-    type=int,
-    show_default=True,
-    help="trimap fg threshold",
-)
-@click.option(
-    "-ab",
-    "--alpha-matting-background-threshold",
-    default=10,
-    type=int,
-    show_default=True,
-    help="trimap bg threshold",
-)
-@click.option(
-    "-ae",
-    "--alpha-matting-erode-size",
-    default=10,
-    type=int,
-    show_default=True,
-    help="erode size",
-)
-@click.option(
-    "-om",
-    "--only-mask",
-    is_flag=True,
-    show_default=True,
-    help="output only the mask",
-)
-@click.option(
-    "-ppm",
-    "--post-process-mask",
-    is_flag=True,
-    show_default=True,
-    help="post process the mask",
-)
-@click.option(
-    "-w",
-    "--watch",
-    default=False,
-    is_flag=True,
-    show_default=True,
-    help="watches a folder for changes",
-)
-@click.option(
-    "-bgc",
-    "--bgcolor",
-    default=None,
-    type=(int, int, int, int),
-    nargs=4,
-    help="Background color (R G B A) to replace the removed background with",
-)
-@click.option("-x", "--extras", type=str)
-@click.argument(
-    "input",
-    type=click.Path(
-        exists=True,
-        path_type=pathlib.Path,
-        file_okay=False,
-        dir_okay=True,
-        readable=True,
-    ),
-)
-@click.argument(
-    "output",
-    type=click.Path(
-        exists=False,
-        path_type=pathlib.Path,
-        file_okay=False,
-        dir_okay=True,
-        writable=True,
-    ),
-)
-def p_command(
-    model: str,
-    extras: str,
-    input: pathlib.Path,
-    output: pathlib.Path,
-    watch: bool,
-    **kwargs,
-) -> None:
-    try:
-        kwargs.update(json.loads(extras))
-    except Exception:
-        pass
-
-    session = new_session(model)
-
-    def process(each_input: pathlib.Path) -> None:
-        try:
-            mimetype = filetype.guess(each_input)
-            if mimetype is None:
-                return
-            if mimetype.mime.find("image") < 0:
-                return
-
-            each_output = (output / each_input.name).with_suffix(".png")
-            each_output.parents[0].mkdir(parents=True, exist_ok=True)
-
-            if not each_output.exists():
-                each_output.write_bytes(
-                    cast(
-                        bytes,
-                        remove(each_input.read_bytes(), session=session, **kwargs),
-                    )
-                )
-
-                if watch:
-                    print(
-                        f"processed: {each_input.absolute()} -> {each_output.absolute()}"
-                    )
-        except Exception as e:
-            print(e)
-
-    inputs = list(input.glob("**/*"))
-    if not watch:
-        inputs = tqdm(inputs)
-
-    for each_input in inputs:
-        if not each_input.is_dir():
-            process(each_input)
-
-    if watch:
-        observer = Observer()
-
-        class EventHandler(FileSystemEventHandler):
-            def on_any_event(self, event: FileSystemEvent) -> None:
-                if not (
-                    event.is_directory or event.event_type in ["deleted", "closed"]
-                ):
-                    process(pathlib.Path(event.src_path))
-
-        event_handler = EventHandler()
-        observer.schedule(event_handler, input, recursive=False)
-        observer.start()
-
-        try:
-            while True:
-                time.sleep(1)
-
-        finally:
-            observer.stop()
-            observer.join()
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/module_losses/db_module_loss.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/module_losses/db_module_loss.py
deleted file mode 100644
index ba8487310f2ce9592a2fa5b8b20621b870a9fe05..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/module_losses/db_module_loss.py
+++ /dev/null
@@ -1,300 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Sequence, Tuple, Union
-
-import cv2
-import numpy as np
-import torch
-from mmdet.models.utils import multi_apply
-from shapely.geometry import Polygon
-from torch import Tensor
-
-from mmocr.registry import MODELS
-from mmocr.structures import TextDetDataSample
-from mmocr.utils import offset_polygon
-from mmocr.utils.typing_utils import ArrayLike
-from .seg_based_module_loss import SegBasedModuleLoss
-
-
-@MODELS.register_module()
-class DBModuleLoss(SegBasedModuleLoss):
-    r"""The class for implementing DBNet loss.
-
-    This is partially adapted from https://github.com/MhLiao/DB.
-
-    Args:
-        loss_prob (dict): The loss config for probability map. Defaults to
-            dict(type='MaskedBalancedBCEWithLogitsLoss').
-        loss_thr (dict): The loss config for threshold map. Defaults to
-            dict(type='MaskedSmoothL1Loss', beta=0).
-        loss_db (dict): The loss config for binary map. Defaults to
-            dict(type='MaskedDiceLoss').
-        weight_prob (float): The weight of probability map loss.
-            Denoted as :math:`\alpha` in paper. Defaults to 5.
-        weight_thr (float): The weight of threshold map loss.
-            Denoted as :math:`\beta` in paper. Defaults to 10.
-        shrink_ratio (float): The ratio of shrunk text region. Defaults to 0.4.
-        thr_min (float): The minimum threshold map value. Defaults to 0.3.
-        thr_max (float): The maximum threshold map value. Defaults to 0.7.
-        min_sidelength (int or float): The minimum sidelength of the
-            minimum rotated rectangle around any text region. Defaults to 8.
-    """
-
-    def __init__(self,
-                 loss_prob: Dict = dict(
-                     type='MaskedBalancedBCEWithLogitsLoss'),
-                 loss_thr: Dict = dict(type='MaskedSmoothL1Loss', beta=0),
-                 loss_db: Dict = dict(type='MaskedDiceLoss'),
-                 weight_prob: float = 5.,
-                 weight_thr: float = 10.,
-                 shrink_ratio: float = 0.4,
-                 thr_min: float = 0.3,
-                 thr_max: float = 0.7,
-                 min_sidelength: Union[int, float] = 8) -> None:
-        super().__init__()
-        self.loss_prob = MODELS.build(loss_prob)
-        self.loss_thr = MODELS.build(loss_thr)
-        self.loss_db = MODELS.build(loss_db)
-        self.weight_prob = weight_prob
-        self.weight_thr = weight_thr
-        self.shrink_ratio = shrink_ratio
-        self.thr_min = thr_min
-        self.thr_max = thr_max
-        self.min_sidelength = min_sidelength
-
-    def forward(self, preds: Tuple[Tensor, Tensor, Tensor],
-                data_samples: Sequence[TextDetDataSample]) -> Dict:
-        """Compute DBNet loss.
-
-        Args:
-            preds (tuple(tensor)): Raw predictions from model, containing
-                ``prob_logits``, ``thr_map`` and ``binary_map``.
-                Each is a tensor of shape :math:`(N, H, W)`.
-            data_samples (list[TextDetDataSample]): The data samples.
-
-        Returns:
-            results(dict): The dict for dbnet losses with loss_prob, \
-                loss_db and loss_thr.
-        """
-        prob_logits, thr_map, binary_map = preds
-        gt_shrinks, gt_shrink_masks, gt_thrs, gt_thr_masks = self.get_targets(
-            data_samples)
-        gt_shrinks = gt_shrinks.to(prob_logits.device)
-        gt_shrink_masks = gt_shrink_masks.to(prob_logits.device)
-        gt_thrs = gt_thrs.to(thr_map.device)
-        gt_thr_masks = gt_thr_masks.to(thr_map.device)
-        loss_prob = self.loss_prob(prob_logits, gt_shrinks, gt_shrink_masks)
-
-        loss_thr = self.loss_thr(thr_map, gt_thrs, gt_thr_masks)
-        loss_db = self.loss_db(binary_map, gt_shrinks, gt_shrink_masks)
-
-        results = dict(
-            loss_prob=self.weight_prob * loss_prob,
-            loss_thr=self.weight_thr * loss_thr,
-            loss_db=loss_db)
-
-        return results
-
-    def _is_poly_invalid(self, poly: np.ndarray) -> bool:
-        """Check if the input polygon is invalid or not. It is invalid if its
-        area is smaller than 1 or the shorter side of its minimum bounding box
-        is smaller than min_sidelength.
-
-        Args:
-            poly (ndarray): The polygon.
-
-        Returns:
-            bool: Whether the polygon is invalid.
-        """
-        poly = poly.reshape(-1, 2)
-        area = Polygon(poly).area
-        if abs(area) < 1:
-            return True
-        rect_size = cv2.minAreaRect(poly)[1]
-        len_shortest_side = min(rect_size)
-        if len_shortest_side < self.min_sidelength:
-            return True
-
-        return False
-
-    def _generate_thr_map(self, img_size: Tuple[int, int],
-                          polygons: ArrayLike) -> np.ndarray:
-        """Generate threshold map.
-
-        Args:
-            img_size (tuple(int)): The image size (h, w)
-            polygons (Sequence[ndarray]): 2-d array, representing all the
-                polygons of the text region.
-
-        Returns:
-            tuple:
-
-            - thr_map (ndarray): The generated threshold map.
-            - thr_mask (ndarray): The effective mask of threshold map.
-        """
-        thr_map = np.zeros(img_size, dtype=np.float32)
-        thr_mask = np.zeros(img_size, dtype=np.uint8)
-
-        for polygon in polygons:
-            self._draw_border_map(polygon, thr_map, mask=thr_mask)
-        thr_map = thr_map * (self.thr_max - self.thr_min) + self.thr_min
-
-        return thr_map, thr_mask
-
-    def _draw_border_map(self, polygon: np.ndarray, canvas: np.ndarray,
-                         mask: np.ndarray) -> None:
-        """Generate threshold map for one polygon.
-
-        Args:
-            polygon (np.ndarray): The polygon.
-            canvas (np.ndarray): The generated threshold map.
-            mask (np.ndarray): The generated threshold mask.
-        """
-
-        polygon = polygon.reshape(-1, 2)
-        polygon_obj = Polygon(polygon)
-        distance = (
-            polygon_obj.area * (1 - np.power(self.shrink_ratio, 2)) /
-            polygon_obj.length)
-        expanded_polygon = offset_polygon(polygon, distance)
-        if len(expanded_polygon) == 0:
-            print(f'Padding {polygon} with {distance} gets {expanded_polygon}')
-            expanded_polygon = polygon.copy().astype(np.int32)
-        else:
-            expanded_polygon = expanded_polygon.reshape(-1, 2).astype(np.int32)
-
-        x_min = expanded_polygon[:, 0].min()
-        x_max = expanded_polygon[:, 0].max()
-        y_min = expanded_polygon[:, 1].min()
-        y_max = expanded_polygon[:, 1].max()
-
-        width = x_max - x_min + 1
-        height = y_max - y_min + 1
-
-        polygon[:, 0] = polygon[:, 0] - x_min
-        polygon[:, 1] = polygon[:, 1] - y_min
-
-        xs = np.broadcast_to(
-            np.linspace(0, width - 1, num=width).reshape(1, width),
-            (height, width))
-        ys = np.broadcast_to(
-            np.linspace(0, height - 1, num=height).reshape(height, 1),
-            (height, width))
-
-        distance_map = np.zeros((polygon.shape[0], height, width),
-                                dtype=np.float32)
-        for i in range(polygon.shape[0]):
-            j = (i + 1) % polygon.shape[0]
-            absolute_distance = self._dist_points2line(xs, ys, polygon[i],
-                                                       polygon[j])
-            distance_map[i] = np.clip(absolute_distance / distance, 0, 1)
-        distance_map = distance_map.min(axis=0)
-
-        x_min_valid = min(max(0, x_min), canvas.shape[1] - 1)
-        x_max_valid = min(max(0, x_max), canvas.shape[1] - 1)
-        y_min_valid = min(max(0, y_min), canvas.shape[0] - 1)
-        y_max_valid = min(max(0, y_max), canvas.shape[0] - 1)
-
-        if x_min_valid - x_min >= width or y_min_valid - y_min >= height:
-            return
-
-        cv2.fillPoly(mask, [expanded_polygon.astype(np.int32)], 1.0)
-        canvas[y_min_valid:y_max_valid + 1,
-               x_min_valid:x_max_valid + 1] = np.fmax(
-                   1 - distance_map[y_min_valid - y_min:y_max_valid - y_max +
-                                    height, x_min_valid - x_min:x_max_valid -
-                                    x_max + width],
-                   canvas[y_min_valid:y_max_valid + 1,
-                          x_min_valid:x_max_valid + 1])
-
-    def get_targets(self, data_samples: List[TextDetDataSample]) -> Tuple:
-        """Generate loss targets from data samples.
-
-        Args:
-            data_samples (list(TextDetDataSample)): Ground truth data samples.
-
-        Returns:
-            tuple: A tuple of four tensors as DBNet targets.
-        """
-
-        gt_shrinks, gt_shrink_masks, gt_thrs, gt_thr_masks = multi_apply(
-            self._get_target_single, data_samples)
-        gt_shrinks = torch.cat(gt_shrinks)
-        gt_shrink_masks = torch.cat(gt_shrink_masks)
-        gt_thrs = torch.cat(gt_thrs)
-        gt_thr_masks = torch.cat(gt_thr_masks)
-        return gt_shrinks, gt_shrink_masks, gt_thrs, gt_thr_masks
-
-    def _get_target_single(self, data_sample: TextDetDataSample) -> Tuple:
-        """Generate loss target from a data sample.
-
-        Args:
-            data_sample (TextDetDataSample): The data sample.
-
-        Returns:
-            tuple: A tuple of four tensors as the targets of one prediction.
-        """
-
-        gt_instances = data_sample.gt_instances
-        ignore_flags = gt_instances.ignored
-        for idx, polygon in enumerate(gt_instances.polygons):
-            if self._is_poly_invalid(polygon):
-                ignore_flags[idx] = True
-        gt_shrink, ignore_flags = self._generate_kernels(
-            data_sample.img_shape,
-            gt_instances.polygons,
-            self.shrink_ratio,
-            ignore_flags=ignore_flags)
-
-        # Get boolean mask where Trues indicate text instance pixels
-        gt_shrink = gt_shrink > 0
-
-        gt_shrink_mask = self._generate_effective_mask(
-            data_sample.img_shape, gt_instances[ignore_flags].polygons)
-        gt_thr, gt_thr_mask = self._generate_thr_map(
-            data_sample.img_shape, gt_instances[~ignore_flags].polygons)
-
-        # to_tensor
-        gt_shrink = torch.from_numpy(gt_shrink).unsqueeze(0).float()
-        gt_shrink_mask = torch.from_numpy(gt_shrink_mask).unsqueeze(0).float()
-        gt_thr = torch.from_numpy(gt_thr).unsqueeze(0).float()
-        gt_thr_mask = torch.from_numpy(gt_thr_mask).unsqueeze(0).float()
-        return gt_shrink, gt_shrink_mask, gt_thr, gt_thr_mask
-
-    @staticmethod
-    def _dist_points2line(xs: np.ndarray, ys: np.ndarray, pt1: np.ndarray,
-                          pt2: np.ndarray) -> np.ndarray:
-        """Compute distances from points to a line. This is adapted from
-        https://github.com/MhLiao/DB.
-
-        Args:
-            xs (ndarray): The x coordinates of points of size :math:`(N, )`.
-            ys (ndarray): The y coordinates of size :math:`(N, )`.
-            pt1 (ndarray): The first point on the line of size :math:`(2, )`.
-            pt2 (ndarray): The second point on the line of size :math:`(2, )`.
-
-        Returns:
-            ndarray: The distance matrix of size :math:`(N, )`.
-        """
-        # suppose a triangle with three edge abc with c=point_1 point_2
-        # a^2
-        a_square = np.square(xs - pt1[0]) + np.square(ys - pt1[1])
-        # b^2
-        b_square = np.square(xs - pt2[0]) + np.square(ys - pt2[1])
-        # c^2
-        c_square = np.square(pt1[0] - pt2[0]) + np.square(pt1[1] - pt2[1])
-        # -cosC=(c^2-a^2-b^2)/2(ab)
-        neg_cos_c = (
-            (c_square - a_square - b_square) /
-            (np.finfo(np.float32).eps + 2 * np.sqrt(a_square * b_square)))
-        # clip -cosC value to [-1, 1]
-        neg_cos_c = np.clip(neg_cos_c, -1.0, 1.0)
-        # sinC^2=1-cosC^2
-        square_sin = 1 - np.square(neg_cos_c)
-        square_sin = np.nan_to_num(square_sin)
-        # distance=a*b*sinC/c=a*h/c=2*area/c
-        result = np.sqrt(a_square * b_square * square_sin /
-                         (np.finfo(np.float32).eps + c_square))
-        # set result to minimum edge if C<pi/2
-        result[neg_cos_c < 0] = np.sqrt(np.fmin(a_square,
-                                                b_square))[neg_cos_c < 0]
-        return result
diff --git a/spaces/NCTCMumbai/NCTC/models/research/audioset/vggish/vggish_input.py b/spaces/NCTCMumbai/NCTC/models/research/audioset/vggish/vggish_input.py
deleted file mode 100644
index f283afbcffacece33e3e3a6886fbe7609be8c77e..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/audioset/vggish/vggish_input.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright 2017 The TensorFlow Authors All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Compute input examples for VGGish from audio waveform."""
-
-import numpy as np
-import resampy
-
-import mel_features
-import vggish_params
-
-try:
-  import soundfile as sf
-
-  def wav_read(wav_file):
-    wav_data, sr = sf.read(wav_file, dtype='int16')
-    return wav_data, sr
-
-except ImportError:
-
-  def wav_read(wav_file):
-    raise NotImplementedError('WAV file reading requires soundfile package.')
-
-
-def waveform_to_examples(data, sample_rate):
-  """Converts audio waveform into an array of examples for VGGish.
-
-  Args:
-    data: np.array of either one dimension (mono) or two dimensions
-      (multi-channel, with the outer dimension representing channels).
-      Each sample is generally expected to lie in the range [-1.0, +1.0],
-      although this is not required.
-    sample_rate: Sample rate of data.
-
-  Returns:
-    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
-    a sequence of examples, each of which contains a patch of log mel
-    spectrogram, covering num_frames frames of audio and num_bands mel frequency
-    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
-  """
-  # Convert to mono.
-  if len(data.shape) > 1:
-    data = np.mean(data, axis=1)
-  # Resample to the rate assumed by VGGish.
-  if sample_rate != vggish_params.SAMPLE_RATE:
-    data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)
-
-  # Compute log mel spectrogram features.
-  log_mel = mel_features.log_mel_spectrogram(
-      data,
-      audio_sample_rate=vggish_params.SAMPLE_RATE,
-      log_offset=vggish_params.LOG_OFFSET,
-      window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
-      hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
-      num_mel_bins=vggish_params.NUM_MEL_BINS,
-      lower_edge_hertz=vggish_params.MEL_MIN_HZ,
-      upper_edge_hertz=vggish_params.MEL_MAX_HZ)
-
-  # Frame features into examples.
-  features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
-  example_window_length = int(round(
-      vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
-  example_hop_length = int(round(
-      vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate))
-  log_mel_examples = mel_features.frame(
-      log_mel,
-      window_length=example_window_length,
-      hop_length=example_hop_length)
-  return log_mel_examples
-
-
-def wavfile_to_examples(wav_file):
-  """Convenience wrapper around waveform_to_examples() for a common WAV format.
-
-  Args:
-    wav_file: String path to a file, or a file-like object. The file
-    is assumed to contain WAV audio data with signed 16-bit PCM samples.
-
-  Returns:
-    See waveform_to_examples.
-  """
-  wav_data, sr = wav_read(wav_file)
-  assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
-  samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
-  return waveform_to_examples(samples, sr)
diff --git a/spaces/Nee001/bing0/README.md b/spaces/Nee001/bing0/README.md
deleted file mode 100644
index d65eafbc8431818f738e8e086455fa6159f101bb..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/README.md
+++ /dev/null
@@ -1,196 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-> 由于目前微软封杀比较严重，推荐优先使用 [部署 Huggingface](#部署到-huggingface) 。
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge ，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/.github/ISSUE_TEMPLATE/documentation.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/.github/ISSUE_TEMPLATE/documentation.md
deleted file mode 100644
index 3a6e2e9ea4bb71102122c17ff53051eb3770cb5e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/.github/ISSUE_TEMPLATE/documentation.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-name: 📚 Documentation/Typos
-about: Report an issue related to documentation or a typo
-labels: 'documentation, needs triage'
----
-
-## 📚 Documentation
-
-For typos and doc fixes, please go ahead and:
-
-1. Create an issue.
-2. Fix the typo.
-3. Submit a PR.
-
-Thanks!
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/latent_depth/latent_depth_src/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/latent_depth/latent_depth_src/__init__.py
deleted file mode 100644
index c5fa76039ff98c18d3c14b5f4a8f73ffe644de11..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/latent_depth/latent_depth_src/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from . import multilingual_translation_latent_depth  # noqa
-from .loss import latent_depth  # noqa
-from .models import latent_multilingual_transformer  # noqa
-from .modules import latent_layers  # noqa
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_to_text/seg_mustc_data.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_to_text/seg_mustc_data.py
deleted file mode 100644
index 1ee665d6399729afe17d790d872eff34de124900..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_to_text/seg_mustc_data.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-from pathlib import Path
-import soundfile as sf
-from examples.speech_to_text.prep_mustc_data import (
-    MUSTC
-)
-
-from tqdm import tqdm
-
-log = logging.getLogger(__name__)
-
-
-def main(args):
-    root = Path(args.data_root).absolute()
-    lang = args.lang
-    split = args.split
-
-    cur_root = root / f"en-{lang}"
-    assert cur_root.is_dir(), (
-        f"{cur_root.as_posix()} does not exist. Skipped."
-    )
-
-    dataset = MUSTC(root.as_posix(), lang, split)
-    output = Path(args.output).absolute()
-    output.mkdir(exist_ok=True)
-    f_text = open(output / f"{split}.{lang}", "w")
-    f_wav_list = open(output / f"{split}.wav_list", "w")
-    for waveform, sample_rate, _, text, _, utt_id in tqdm(dataset):
-        sf.write(
-            output / f"{utt_id}.wav",
-            waveform.squeeze(0).numpy(),
-            samplerate=int(sample_rate)
-        )
-        f_text.write(text + "\n")
-        f_wav_list.write(str(output / f"{utt_id}.wav") + "\n")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--data-root", "-d", required=True, type=str)
-    parser.add_argument("--task", required=True, type=str, choices=["asr", "st"])
-    parser.add_argument("--lang", required=True, type=str)
-    parser.add_argument("--output", required=True, type=str)
-    parser.add_argument("--split", required=True, choices=MUSTC.SPLITS)
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/generate.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/generate.py
deleted file mode 100644
index 7e887e88649fef784b366abe518babd25a30feee..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/generate.py
+++ /dev/null
@@ -1,414 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Translate pre-processed data with a trained model.
-"""
-
-import ast
-import logging
-import math
-import os
-import sys
-from argparse import Namespace
-from itertools import chain
-
-import numpy as np
-import torch
-from fairseq import checkpoint_utils, options, scoring, tasks, utils
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.logging import progress_bar
-from fairseq.logging.meters import StopwatchMeter, TimeMeter
-from omegaconf import DictConfig
-
-
-def main(cfg: DictConfig):
-
-    if isinstance(cfg, Namespace):
-        cfg = convert_namespace_to_omegaconf(cfg)
-
-    assert cfg.common_eval.path is not None, "--path required for generation!"
-    assert (
-        not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
-    ), "--sampling requires --nbest to be equal to --beam"
-    assert (
-        cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw"
-    ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)"
-
-    if cfg.common_eval.results_path is not None:
-        os.makedirs(cfg.common_eval.results_path, exist_ok=True)
-        output_path = os.path.join(
-            cfg.common_eval.results_path,
-            "generate-{}.txt".format(cfg.dataset.gen_subset),
-        )
-        with open(output_path, "w", buffering=1, encoding="utf-8") as h:
-            return _main(cfg, h)
-    else:
-        return _main(cfg, sys.stdout)
-
-
-def get_symbols_to_strip_from_output(generator):
-    if hasattr(generator, "symbols_to_strip_from_output"):
-        return generator.symbols_to_strip_from_output
-    else:
-        return {generator.eos}
-
-
-def _main(cfg: DictConfig, output_file):
-    logging.basicConfig(
-        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-        datefmt="%Y-%m-%d %H:%M:%S",
-        level=os.environ.get("LOGLEVEL", "INFO").upper(),
-        stream=output_file,
-    )
-    logger = logging.getLogger("fairseq_cli.generate")
-
-    utils.import_user_module(cfg.common)
-
-    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
-        cfg.dataset.max_tokens = 12000
-    logger.info(cfg)
-
-    # Fix seed for stochastic decoding
-    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
-        np.random.seed(cfg.common.seed)
-        utils.set_torch_seed(cfg.common.seed)
-
-    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
-
-    # Load dataset splits
-    task = tasks.setup_task(cfg.task)
-
-
-    # Set dictionaries
-    try:
-        src_dict = getattr(task, "source_dictionary", None)
-    except NotImplementedError:
-        src_dict = None
-    tgt_dict = task.target_dictionary
-
-    overrides = ast.literal_eval(cfg.common_eval.model_overrides)
-
-    # Load ensemble
-    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
-    models, saved_cfg = checkpoint_utils.load_model_ensemble(
-        utils.split_paths(cfg.common_eval.path),
-        arg_overrides=overrides,
-        task=task,
-        suffix=cfg.checkpoint.checkpoint_suffix,
-        strict=(cfg.checkpoint.checkpoint_shard_count == 1),
-        num_shards=cfg.checkpoint.checkpoint_shard_count,
-    )
-
-    # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
-    task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
-
-    if cfg.generation.lm_path is not None:
-        overrides["data"] = cfg.task.data
-
-        try:
-            lms, _ = checkpoint_utils.load_model_ensemble(
-                [cfg.generation.lm_path], arg_overrides=overrides, task=None
-            )
-        except:
-            logger.warning(
-                f"Failed to load language model! Please make sure that the language model dict is the same "
-                f"as target dict and is located in the data dir ({cfg.task.data})"
-            )
-            raise
-
-        assert len(lms) == 1
-    else:
-        lms = [None]
-
-    # Optimize ensemble for generation
-    for model in chain(models, lms):
-        if model is None:
-            continue
-        if cfg.common.fp16:
-            model.half()
-        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
-            model.cuda()
-        model.prepare_for_inference_(cfg)
-
-    # Load alignment dictionary for unknown word replacement
-    # (None if no unknown word replacement, empty if no path to align dictionary)
-    align_dict = utils.load_align_dict(cfg.generation.replace_unk)
-
-    # Load dataset (possibly sharded)
-    itr = task.get_batch_iterator(
-        dataset=task.dataset(cfg.dataset.gen_subset),
-        max_tokens=cfg.dataset.max_tokens,
-        max_sentences=cfg.dataset.batch_size,
-        max_positions=utils.resolve_max_positions(
-            task.max_positions(), *[m.max_positions() for m in models]
-        ),
-        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
-        required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
-        seed=cfg.common.seed,
-        num_shards=cfg.distributed_training.distributed_world_size,
-        shard_id=cfg.distributed_training.distributed_rank,
-        num_workers=cfg.dataset.num_workers,
-        data_buffer_size=cfg.dataset.data_buffer_size,
-    ).next_epoch_itr(shuffle=False)
-    progress = progress_bar.progress_bar(
-        itr,
-        log_format=cfg.common.log_format,
-        log_interval=cfg.common.log_interval,
-        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
-    )
-
-    # Initialize generator
-    gen_timer = StopwatchMeter()
-
-    extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight}
-    generator = task.build_generator(
-        models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs
-    )
-
-    # Handle tokenization and BPE
-    tokenizer = task.build_tokenizer(cfg.tokenizer)
-    bpe = task.build_bpe(cfg.bpe)
-
-    def decode_fn(x):
-        if bpe is not None:
-            x = bpe.decode(x)
-        if tokenizer is not None:
-            x = tokenizer.decode(x)
-        return x
-
-    scorer = scoring.build_scorer(cfg.scoring, tgt_dict)
-
-    num_sentences = 0
-    has_target = True
-    wps_meter = TimeMeter()
-    for sample in progress:
-        sample = utils.move_to_cuda(sample) if use_cuda else sample
-        if "net_input" not in sample:
-            continue
-
-        prefix_tokens = None
-        if cfg.generation.prefix_size > 0:
-            prefix_tokens = sample["target"][:, : cfg.generation.prefix_size]
-
-        constraints = None
-        if "constraints" in sample:
-            constraints = sample["constraints"]
-
-        gen_timer.start()
-        hypos = task.inference_step(
-            generator,
-            models,
-            sample,
-            prefix_tokens=prefix_tokens,
-            constraints=constraints,
-        )
-        num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
-        gen_timer.stop(num_generated_tokens)
-
-        for i, sample_id in enumerate(sample["id"].tolist()):
-            has_target = sample["target"] is not None
-
-            # Remove padding
-            if "src_tokens" in sample["net_input"]:
-                src_tokens = utils.strip_pad(
-                    sample["net_input"]["src_tokens"][i, :], tgt_dict.pad()
-                )
-            else:
-                src_tokens = None
-
-            target_tokens = None
-            if has_target:
-                target_tokens = (
-                    utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
-                )
-
-            # Either retrieve the original sentences or regenerate them from tokens.
-            if align_dict is not None:
-                src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text(
-                    sample_id
-                )
-                target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text(
-                    sample_id
-                )
-            else:
-                if src_dict is not None:
-                    src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
-                else:
-                    src_str = ""
-                if has_target:
-                    target_str = tgt_dict.string(
-                        target_tokens,
-                        cfg.common_eval.post_process,
-                        escape_unk=True,
-                        extra_symbols_to_ignore=get_symbols_to_strip_from_output(
-                            generator
-                        ),
-                    )
-
-            src_str = decode_fn(src_str)
-            if has_target:
-                target_str = decode_fn(target_str)
-
-            if not cfg.common_eval.quiet:
-                if src_dict is not None:
-                    print("S-{}\t{}".format(sample_id, src_str), file=output_file)
-                if has_target:
-                    print("T-{}\t{}".format(sample_id, target_str), file=output_file)
-
-            # Process top predictions
-            for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]):
-                hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
-                    hypo_tokens=hypo["tokens"].int().cpu(),
-                    src_str=src_str,
-                    alignment=hypo["alignment"],
-                    align_dict=align_dict,
-                    tgt_dict=tgt_dict,
-                    remove_bpe=cfg.common_eval.post_process,
-                    extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator),
-                )
-                detok_hypo_str = decode_fn(hypo_str)
-                if not cfg.common_eval.quiet:
-                    score = hypo["score"] / math.log(2)  # convert to base 2
-                    # original hypothesis (after tokenization and BPE)
-                    print(
-                        "H-{}\t{}\t{}".format(sample_id, score, hypo_str),
-                        file=output_file,
-                    )
-                    # detokenized hypothesis
-                    print(
-                        "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str),
-                        file=output_file,
-                    )
-                    print(
-                        "P-{}\t{}".format(
-                            sample_id,
-                            " ".join(
-                                map(
-                                    lambda x: "{:.4f}".format(x),
-                                    # convert from base e to base 2
-                                    hypo["positional_scores"]
-                                    .div_(math.log(2))
-                                    .tolist(),
-                                )
-                            ),
-                        ),
-                        file=output_file,
-                    )
-
-                    if cfg.generation.print_alignment == "hard":
-                        print(
-                            "A-{}\t{}".format(
-                                sample_id,
-                                " ".join(
-                                    [
-                                        "{}-{}".format(src_idx, tgt_idx)
-                                        for src_idx, tgt_idx in alignment
-                                    ]
-                                ),
-                            ),
-                            file=output_file,
-                        )
-                    if cfg.generation.print_alignment == "soft":
-                        print(
-                            "A-{}\t{}".format(
-                                sample_id,
-                                " ".join(
-                                    [
-                                        ",".join(src_probs)
-                                        for src_probs in alignment
-                                    ]
-                                ),
-                            ),
-                            file=output_file,
-                        )
-
-                    if cfg.generation.print_step:
-                        print(
-                            "I-{}\t{}".format(sample_id, hypo["steps"]),
-                            file=output_file,
-                        )
-
-                    if cfg.generation.retain_iter_history:
-                        for step, h in enumerate(hypo["history"]):
-                            _, h_str, _ = utils.post_process_prediction(
-                                hypo_tokens=h["tokens"].int().cpu(),
-                                src_str=src_str,
-                                alignment=None,
-                                align_dict=None,
-                                tgt_dict=tgt_dict,
-                                remove_bpe=None,
-                            )
-                            print(
-                                "E-{}_{}\t{}".format(sample_id, step, h_str),
-                                file=output_file,
-                            )
-
-                # Score only the top hypothesis
-                if has_target and j == 0:
-                    if align_dict is not None or cfg.common_eval.post_process is not None:
-                        # Convert back to tokens for evaluation with unk replacement and/or without BPE
-                        target_tokens = tgt_dict.encode_line(
-                            target_str, add_if_not_exist=True
-                        )
-                        hypo_tokens = tgt_dict.encode_line(
-                            detok_hypo_str, add_if_not_exist=True
-                        )
-                    if hasattr(scorer, "add_string"):
-                        scorer.add_string(target_str, detok_hypo_str)
-                    else:
-                        scorer.add(target_tokens, hypo_tokens)
-
-        wps_meter.update(num_generated_tokens)
-        progress.log({"wps": round(wps_meter.avg)})
-        num_sentences += (
-            sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
-        )
-
-    logger.info("NOTE: hypothesis and token scores are output in base 2")
-    logger.info(
-        "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format(
-            num_sentences,
-            gen_timer.n,
-            gen_timer.sum,
-            num_sentences / gen_timer.sum,
-            1.0 / gen_timer.avg,
-        )
-    )
-    if has_target:
-        if cfg.bpe and not cfg.generation.sacrebleu:
-            if cfg.common_eval.post_process:
-                logger.warning(
-                    "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization"
-                )
-            else:
-                logger.warning(
-                    "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words.  Use --sacrebleu for standard 13a BLEU tokenization"
-                )
-        # use print to be consistent with other main outputs: S-, H-, T-, D- and so on
-        print(
-            "Generate {} with beam={}: {}".format(
-                cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string()
-            ),
-            file=output_file,
-        )
-
-    return scorer
-
-
-def cli_main():
-    parser = options.get_generation_parser()
-    # TODO: replace this workaround with refactoring of `AudioPretraining`
-    parser.add_argument(
-        '--arch', '-a', metavar='ARCH', default="wav2vec2",
-        help='Model architecture. For constructing tasks that rely on '
-             'model args (e.g. `AudioPretraining`)'
-    )
-    args = options.parse_args_and_arch(parser)
-    main(args)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/README.md
deleted file mode 100644
index ed4d5df52ccea01216276054a1f253d0d16c0409..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/README.md
+++ /dev/null
@@ -1,296 +0,0 @@
-# RoBERTa: A Robustly Optimized BERT Pretraining Approach
-
-https://arxiv.org/abs/1907.11692
-
-## Introduction
-
-RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details.
-
-### What's New:
-
-- December 2020: German model (GottBERT) is available: [GottBERT](https://github.com/pytorch/fairseq/tree/main/examples/gottbert).
-- January 2020: Italian model (UmBERTo) is available from Musixmatch Research: [UmBERTo](https://github.com/musixmatchresearch/umberto).
-- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/main/examples/camembert).
-- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/main/examples/xlmr).
-- September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers).
-- August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers).
-- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/main/examples/roberta/wsc#roberta-training-on-winogrande-dataset).
-- August 2019: Added [tutorial for pretraining RoBERTa using your own data](README.pretraining.md).
-
-## Pre-trained models
-
-Model | Description | # params | Download
----|---|---|---
-`roberta.base` | RoBERTa using the BERT-base architecture | 125M | [roberta.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz)
-`roberta.large` | RoBERTa using the BERT-large architecture | 355M | [roberta.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz)
-`roberta.large.mnli` | `roberta.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | 355M | [roberta.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz)
-`roberta.large.wsc` | `roberta.large` finetuned on [WSC](wsc/README.md) | 355M | [roberta.large.wsc.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz)
-
-## Results
-
-**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)**
-_(dev set, single model, single-task finetuning)_
-
-Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
----|---|---|---|---|---|---|---|---
-`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2
-`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
-`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | -
-
-**[SuperGLUE (Wang et al., 2019)](https://super.gluebenchmark.com/)**
-_(dev set, single model, single-task finetuning)_
-
-Model | BoolQ | CB | COPA | MultiRC | RTE | WiC | WSC
----|---|---|---|---|---|---|---
-`roberta.large` | 86.9 | 98.2 | 94.0 | 85.7 | 89.5 | 75.6 | -
-`roberta.large.wsc` | - | - | - | - | - | - | 91.3
-
-**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)**
-_(dev set, no additional data used)_
-
-Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
----|---|---
-`roberta.large` | 88.9/94.6 | 86.5/89.4
-
-**[RACE (Lai et al., 2017)](http://www.qizhexie.com/data/RACE_leaderboard.html)**
-_(test set)_
-
-Model | Accuracy | Middle | High
----|---|---|---
-`roberta.large` | 83.2 | 86.5 | 81.3
-
-**[HellaSwag (Zellers et al., 2019)](https://rowanzellers.com/hellaswag/)**
-_(test set)_
-
-Model | Overall | In-domain | Zero-shot | ActivityNet | WikiHow
----|---|---|---|---|---
-`roberta.large` | 85.2 | 87.3 | 83.1 | 74.6 | 90.9
-
-**[Commonsense QA (Talmor et al., 2019)](https://www.tau-nlp.org/commonsenseqa)**
-_(test set)_
-
-Model | Accuracy
----|---
-`roberta.large` (single model) | 72.1
-`roberta.large` (ensemble) | 72.5
-
-**[Winogrande (Sakaguchi et al., 2019)](https://arxiv.org/abs/1907.10641)**
-_(test set)_
-
-Model | Accuracy
----|---
-`roberta.large` | 78.1
-
-**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)**
-_(TRANSLATE-TEST)_
-
-Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur
----|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
-`roberta.large.mnli` | 91.3 | 82.91 | 84.27 | 81.24 | 81.74 | 83.13 | 78.28 | 76.79 | 76.64 | 74.17 | 74.05 | 77.5 | 70.9 | 66.65 | 66.81
-
-## Example usage
-
-##### Load RoBERTa from torch.hub (PyTorch >= 1.1):
-```python
-import torch
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
-roberta.eval()  # disable dropout (or leave in train mode to finetune)
-```
-
-##### Load RoBERTa (for PyTorch 1.0 or custom models):
-```python
-# Download roberta.large model
-wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
-tar -xzvf roberta.large.tar.gz
-
-# Load the model in fairseq
-from fairseq.models.roberta import RobertaModel
-roberta = RobertaModel.from_pretrained('/path/to/roberta.large', checkpoint_file='model.pt')
-roberta.eval()  # disable dropout (or leave in train mode to finetune)
-```
-
-##### Apply Byte-Pair Encoding (BPE) to input text:
-```python
-tokens = roberta.encode('Hello world!')
-assert tokens.tolist() == [0, 31414, 232, 328, 2]
-roberta.decode(tokens)  # 'Hello world!'
-```
-
-##### Extract features from RoBERTa:
-```python
-# Extract the last layer's features
-last_layer_features = roberta.extract_features(tokens)
-assert last_layer_features.size() == torch.Size([1, 5, 1024])
-
-# Extract all layer's features (layer 0 is the embedding layer)
-all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
-assert len(all_layers) == 25
-assert torch.all(all_layers[-1] == last_layer_features)
-```
-
-##### Use RoBERTa for sentence-pair classification tasks:
-```python
-# Download RoBERTa already finetuned for MNLI
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
-roberta.eval()  # disable dropout for evaluation
-
-# Encode a pair of sentences and make a prediction
-tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.')
-roberta.predict('mnli', tokens).argmax()  # 0: contradiction
-
-# Encode another pair of sentences
-tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.')
-roberta.predict('mnli', tokens).argmax()  # 2: entailment
-```
-
-##### Register a new (randomly initialized) classification head:
-```python
-roberta.register_classification_head('new_task', num_classes=3)
-logprobs = roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
-```
-
-##### Batched prediction:
-```python
-import torch
-from fairseq.data.data_utils import collate_tokens
-
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
-roberta.eval()
-
-batch_of_pairs = [
-    ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'],
-    ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'],
-    ['potatoes are awesome.', 'I like to run.'],
-    ['Mars is very far from earth.', 'Mars is very close.'],
-]
-
-batch = collate_tokens(
-    [roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
-)
-
-logprobs = roberta.predict('mnli', batch)
-print(logprobs.argmax(dim=1))
-# tensor([0, 2, 1, 0])
-```
-
-##### Using the GPU:
-```python
-roberta.cuda()
-roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
-```
-
-## Advanced usage
-
-#### Filling masks:
-
-RoBERTa can be used to fill `<mask>` tokens in the input. Some examples from the
-[Natural Questions dataset](https://ai.google.com/research/NaturalQuestions/):
-```python
-roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3)
-# [('The first Star wars movie came out in 1977', 0.9504708051681519, ' 1977'), ('The first Star wars movie came out in 1978', 0.009986862540245056, ' 1978'), ('The first Star wars movie came out in 1979', 0.009574787691235542, ' 1979')]
-
-roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3)
-# [('Vikram samvat calender is official in India', 0.21878819167613983, ' India'), ('Vikram samvat calender is official in Delhi', 0.08547237515449524, ' Delhi'), ('Vikram samvat calender is official in Gujarat', 0.07556215673685074, ' Gujarat')]
-
-roberta.fill_mask('<mask> is the common currency of the European Union', topk=3)
-# [('Euro is the common currency of the European Union', 0.9456493854522705, 'Euro'), ('euro is the common currency of the European Union', 0.025748178362846375, 'euro'), ('€ is the common currency of the European Union', 0.011183084920048714, '€')]
-```
-
-#### Pronoun disambiguation (Winograd Schema Challenge):
-
-RoBERTa can be used to disambiguate pronouns. First install spaCy and download the English-language model:
-```bash
-pip install spacy
-python -m spacy download en_core_web_lg
-```
-
-Next load the `roberta.large.wsc` model and call the `disambiguate_pronoun`
-function. The pronoun should be surrounded by square brackets (`[]`) and the
-query referent surrounded by underscores (`_`), or left blank to return the
-predicted candidate text directly:
-```python
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.wsc', user_dir='examples/roberta/wsc')
-roberta.cuda()  # use the GPU (optional)
-
-roberta.disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.')
-# True
-roberta.disambiguate_pronoun('The trophy would not fit in the brown _suitcase_ because [it] was too big.')
-# False
-
-roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] feared violence.')
-# 'The city councilmen'
-roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] advocated violence.')
-# 'demonstrators'
-```
-
-See the [RoBERTA Winograd Schema Challenge (WSC) README](wsc/README.md) for more details on how to train this model.
-
-#### Extract features aligned to words:
-
-By default RoBERTa outputs one feature vector per BPE token. You can instead
-realign the features to match [spaCy's word-level tokenization](https://spacy.io/usage/linguistic-features#tokenization)
-with the `extract_features_aligned_to_words` method. This will compute a
-weighted average of the BPE-level features for each word and expose them in
-spaCy's `Token.vector` attribute:
-```python
-doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."')
-assert len(doc) == 10
-for tok in doc:
-    print('{:10}{} (...)'.format(str(tok), tok.vector[:5]))
-# <s>       tensor([-0.1316, -0.0386, -0.0832, -0.0477,  0.1943], grad_fn=<SliceBackward>) (...)
-# I         tensor([ 0.0559,  0.1541, -0.4832,  0.0880,  0.0120], grad_fn=<SliceBackward>) (...)
-# said      tensor([-0.1565, -0.0069, -0.8915,  0.0501, -0.0647], grad_fn=<SliceBackward>) (...)
-# ,         tensor([-0.1318, -0.0387, -0.0834, -0.0477,  0.1944], grad_fn=<SliceBackward>) (...)
-# "         tensor([-0.0486,  0.1818, -0.3946, -0.0553,  0.0981], grad_fn=<SliceBackward>) (...)
-# hello     tensor([ 0.0079,  0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...)
-# RoBERTa   tensor([-0.2339, -0.1184, -0.7343, -0.0492,  0.5829], grad_fn=<SliceBackward>) (...)
-# .         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
-# "         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
-# </s>      tensor([-0.0930, -0.0392, -0.0821,  0.0158,  0.0649], grad_fn=<SliceBackward>) (...)
-```
-
-#### Evaluating the `roberta.large.mnli` model:
-
-Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set.
-```python
-label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
-ncorrect, nsamples = 0, 0
-roberta.cuda()
-roberta.eval()
-with open('glue_data/MNLI/dev_matched.tsv') as fin:
-    fin.readline()
-    for index, line in enumerate(fin):
-        tokens = line.strip().split('\t')
-        sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
-        tokens = roberta.encode(sent1, sent2)
-        prediction = roberta.predict('mnli', tokens).argmax().item()
-        prediction_label = label_map[prediction]
-        ncorrect += int(prediction_label == target)
-        nsamples += 1
-print('| Accuracy: ', float(ncorrect)/float(nsamples))
-# Expected output: 0.9060
-```
-
-## Finetuning
-
-- [Finetuning on GLUE](README.glue.md)
-- [Finetuning on custom classification tasks (e.g., IMDB)](README.custom_classification.md)
-- [Finetuning on Winograd Schema Challenge (WSC)](wsc/README.md)
-- [Finetuning on Commonsense QA (CQA)](commonsense_qa/README.md)
-
-## Pretraining using your own data
-
-See the [tutorial for pretraining RoBERTa using your own data](README.pretraining.md).
-
-## Citation
-
-```bibtex
-@article{liu2019roberta,
-    title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
-    author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and
-              Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and
-              Luke Zettlemoyer and Veselin Stoyanov},
-    journal={arXiv preprint arXiv:1907.11692},
-    year = {2019},
-}
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/transformer_from_pretrained_xlm.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/transformer_from_pretrained_xlm.py
deleted file mode 100644
index 236d9942e1fb0238cc92e2b4f160520b5cdd6504..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/transformer_from_pretrained_xlm.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-from typing import Any, Dict
-
-from fairseq import checkpoint_utils
-from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.transformer import (
-    TransformerDecoder,
-    TransformerEncoder,
-    TransformerModel,
-    base_architecture as transformer_base_architecture,
-)
-
-
-@register_model("transformer_from_pretrained_xlm")
-class TransformerFromPretrainedXLMModel(TransformerModel):
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        TransformerModel.add_args(parser)
-        parser.add_argument(
-            "--pretrained-xlm-checkpoint",
-            type=str,
-            metavar="STR",
-            help="XLM model to use for initializing transformer encoder and/or decoder",
-        )
-        parser.add_argument(
-            "--init-encoder-only",
-            action="store_true",
-            help="if set, don't load the XLM weights and embeddings into decoder",
-        )
-        parser.add_argument(
-            "--init-decoder-only",
-            action="store_true",
-            help="if set, don't load the XLM weights and embeddings into encoder",
-        )
-
-    @classmethod
-    def build_model(self, args, task, cls_dictionary=MaskedLMDictionary):
-        assert hasattr(args, "pretrained_xlm_checkpoint"), (
-            "You must specify a path for --pretrained-xlm-checkpoint to use "
-            "--arch transformer_from_pretrained_xlm"
-        )
-        assert isinstance(task.source_dictionary, cls_dictionary) and isinstance(
-            task.target_dictionary, cls_dictionary
-        ), (
-            "You should use a MaskedLMDictionary when using --arch "
-            "transformer_from_pretrained_xlm because the pretrained XLM model "
-            "was trained using data binarized with MaskedLMDictionary. "
-            "For translation, you may want to use --task "
-            "translation_from_pretrained_xlm"
-        )
-        assert not (
-            getattr(args, "init_encoder_only", False)
-            and getattr(args, "init_decoder_only", False)
-        ), "Only one of --init-encoder-only and --init-decoder-only can be set."
-        return super().build_model(args, task)
-
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return TransformerEncoderFromPretrainedXLM(args, src_dict, embed_tokens)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return TransformerDecoderFromPretrainedXLM(args, tgt_dict, embed_tokens)
-
-
-def upgrade_state_dict_with_xlm_weights(
-    state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str
-) -> Dict[str, Any]:
-    """
-    Load XLM weights into a Transformer encoder or decoder model.
-
-    Args:
-        state_dict: state dict for either TransformerEncoder or
-            TransformerDecoder
-        pretrained_xlm_checkpoint: checkpoint to load XLM weights from
-
-    Raises:
-        AssertionError: If architecture (num layers, attention heads, etc.)
-            does not match between the current Transformer encoder or
-            decoder and the pretrained_xlm_checkpoint
-    """
-    if not os.path.exists(pretrained_xlm_checkpoint):
-        raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint))
-
-    state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint)
-    xlm_state_dict = state["model"]
-    for key in xlm_state_dict.keys():
-
-        for search_key in ["embed_tokens", "embed_positions", "layers"]:
-            if search_key in key:
-                subkey = key[key.find(search_key) :]
-                assert subkey in state_dict, (
-                    "{} Transformer encoder / decoder "
-                    "state_dict does not contain {}. Cannot "
-                    "load {} from pretrained XLM checkpoint "
-                    "{} into Transformer.".format(
-                        str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint
-                    )
-                )
-
-                state_dict[subkey] = xlm_state_dict[key]
-    return state_dict
-
-
-class TransformerEncoderFromPretrainedXLM(TransformerEncoder):
-    def __init__(self, args, dictionary, embed_tokens):
-        super().__init__(args, dictionary, embed_tokens)
-        if getattr(args, "init_decoder_only", False):
-            # Don't load XLM weights for encoder if --init-decoder-only
-            return
-
-        assert hasattr(args, "pretrained_xlm_checkpoint"), (
-            "--pretrained-xlm-checkpoint must be specified to load Transformer "
-            "encoder from pretrained XLM"
-        )
-        xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
-            state_dict=self.state_dict(),
-            pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
-        )
-        self.load_state_dict(xlm_loaded_state_dict, strict=True)
-
-
-class TransformerDecoderFromPretrainedXLM(TransformerDecoder):
-    def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
-        super().__init__(args, dictionary, embed_tokens, no_encoder_attn)
-        if getattr(args, "init_encoder_only", False):
-            # Don't load XLM weights for decoder if --init-encoder-only
-            return
-        assert hasattr(args, "pretrained_xlm_checkpoint"), (
-            "--pretrained-xlm-checkpoint must be specified to load Transformer "
-            "decoder from pretrained XLM"
-        )
-
-        xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
-            state_dict=self.state_dict(),
-            pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
-        )
-        self.load_state_dict(xlm_loaded_state_dict, strict=True)
-
-
-@register_model_architecture(
-    "transformer_from_pretrained_xlm", "transformer_from_pretrained_xlm"
-)
-def base_architecture(args):
-    transformer_base_architecture(args)
diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN/README.md b/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN/README.md
deleted file mode 100644
index 9c832134a6f0739ca851678af42b5fc88027c00c..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN/README.md
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: LLMRiddles-ChatGLM-CN
-emoji: 🚀
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-python_version: 3.8
----
-
-# LLMRiddles
-
-<div align="center">
-	<br>
-	<a href="https://github.com/opendilab/LLMRiddles/blob/main/llmriddles/assets/banner.svg">
-		<img src="https://github.com/opendilab/LLMRiddles/blob/main/llmriddles/assets/banner.svg" width="1000" height="200" alt="Click to see the source">
-	</a>
-	<br>
-</div>
-
-English | [简体中文](https://github.com/opendilab/LLMRiddles/blob/main/README_zh.md)
-
-## :thinking: What's This
-Welcome to LLM Riddles! This is a game of wits and courage with language models. In the game, you need to construct questions that interact with the language model to get answers that meet the requirements. In this process, you can use your brain and use all the methods you can think of to get the model to output the results required by the answer.
-
-## :space_invader: How to Play
-We provide an online version for players to directly access and try out. 
-- [ChatGPT + English(w/o key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN)
-- [ChatGPT + Chinese(w/o key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN)
-- [Mistral + English(w/ key)](https://d9b451a97791dd8ef3.gradio.live)
-- [ChatGPT + Chinese(w/ key)](http://llmriddles.opendilab.net/)
-
-Local deployment can be done in the following ways:
-## Installation
-### Use ChatGPT / ChatGLM API
-```shell
-pip3 install -r requirements.txt
-```
-### Deploy Mistral-7B-Instruct-v0.1 for local inference
-```shell
-pip3 install -r requirements-dev.txt
-```
-## Launch
-### ChatGPT + Chinese
-```shell
-QUESTION_LANG=cn QUESTION_LLM='chatgpt' QUESTION_LLM_KEY=<your API key> python3 -u app.py
-```
-### ChatGPT + English
-```shell
-QUESTION_LANG=en QUESTION_LLM='chatgpt' QUESTION_LLM_KEY=<your API key> python3 -u app.py
-```
-### ChatGLM + Chinese
-```shell
-QUESTION_LANG=cn QUESTION_LLM='chatglm' QUESTION_LLM_KEY=<your API key> python3 -u app.py
-```
-### ChatGLM + English
-```shell
-QUESTION_LANG=en QUESTION_LLM='chatglm' QUESTION_LLM_KEY=<your API key> python3 -u app.py
-```
-### Mistral-7B-Instruct-v0.1 + English
-```shell
-QUESTION_LANG=en QUESTION_LLM='mistral-7b' python3 -u app.py
-```
-## :technologist: Why Doing This
-
-Our goal is to use this game to give participants a deeper understanding of the fascinating aspects of prompt engineering and natural language processing. This process will show players how to cleverly construct prompts and how to use them to trigger surprising responses from artificial intelligence systems, while also helping them better understand the incredible power of deep learning and natural language processing technologies. .
-
-## :raising_hand: How to Submit a Custom Level
-If you have interesting questions or ideas, players are welcome to submit their own ideas. You can [Initiate a Pull Request](https://github.com/opendilab/LLMRiddles/compare) and submit it to us. We will include it in the level after approval.
-The question format should include the following points:
-- Pull Request title, example: feature(username): Chapter X-Level Design
-- The ID you want to be mentioned
-- Modify the corresponding chapter question files
-- Modification of \__init__.py
-
-For a complete example, please refer to: [Submit your own level design](https://github.com/opendilab/LLMRiddles/pull/6)
-
-## :writing_hand: Roadmap
-
-- [x] Support custom levels
-- [x] Online trial link
-- [x] Hugging Face Space link
-- [x] Support Mistral-7B（English version）
-- [x] Support ChatGLM（Chinese and English version）
-- [ ] Support Baichuan2-7B（Chinese version）
-- [ ] Support LLaMA2-7B（English version）
-- [ ] LLM inference speed optimization
-- [ ] More question levels and solution blogs
-
-## :speech_balloon: Feedback and Contribution
-- [Start an Issue](https://github.com/opendilab/CodeMorpheus/issues/new/choose) on GitHub
-- Contact us by email (opendilab@pjlab.org.cn)
-- Discuss on OpenDILab's WeChat group (i.e. add us on WeChat: ding314assist)
-<img src=https://github.com/opendilab/LLMRiddles/blob/main/llmriddles/assets/wechat.jpeg width=35% />
-
-## :star2: Special Thanks
-- Thanks to [Haoqiang Fan](https://www.zhihu.com/people/haoqiang-fan) for his original idea and title, which provided inspiration and motivation for the development and expansion of this project.
-- Thanks to [HuggingFace](https://huggingface.co) for supporting and assisting the game.
-- Thanks to [ChatGLM](https://chatglm.cn) for supporting and assisting the game, especially sufficient inference token support.
-- Thanks to [LLM Riddles contributors](https://github.com/opendilab/LLMRiddles/graphs/contributors) for their implementation and support.
-
-## :label: License
-All code within this repository is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-
-<p align="right">(<a href="#top">back to top</a>)</p>
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/options/__init__.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/options/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/define-grobs.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/define-grobs.go
deleted file mode 100644
index 14e21a79c79db105a2d5046eeae6456311d7b6a7..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/define-grobs.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/config/__init__.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/config/__init__.py
deleted file mode 100644
index 726b6dcf3da95968b948c4d897e97a9cdd0928ff..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/config/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""
-This module contains the configuration classes for AutoGPT.
-"""
-from autogpt.config.ai_config import AIConfig
-from autogpt.config.config import Config, check_openai_api_key
-from autogpt.config.singleton import AbstractSingleton, Singleton
-
-__all__ = [
-    "check_openai_api_key",
-    "AbstractSingleton",
-    "AIConfig",
-    "Config",
-    "Singleton",
-]
diff --git a/spaces/PeepDaSlan9/De-limiter/models/__init__.py b/spaces/PeepDaSlan9/De-limiter/models/__init__.py
deleted file mode 100644
index b4309a79ab5d1f3099b000f5cef48d6e0bcc78d0..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .load_models import load_model_with_args
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/modules/diffusionmodules/openaimodel.py b/spaces/Purple11/Grounded-Diffusion/ldm/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index 729320da6e41343f3e062c1875ca16db0f688560..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,1179 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-from einops import rearrange, repeat
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-import functools
-import torch
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-all_feature_dic={}
-
-def clear_feature_dic():
-    global all_feature_dic
-    all_feature_dic={}
-    all_feature_dic["low"]=[]
-    all_feature_dic["mid"]=[]
-    all_feature_dic["high"]=[]
-    all_feature_dic["highest"]=[]
-def get_feature_dic():
-    global all_feature_dic
-    return all_feature_dic
-# Projection of x onto y
-def proj(x, y):
-  return torch.mm(y, x.t()) * y / torch.mm(y, y.t())
-
-# Orthogonalize x wrt list of vectors ys
-def gram_schmidt(x, ys):
-  for y in ys:
-    x = x - proj(x, y)
-  return x
-# Apply num_itrs steps of the power method to estimate top N singular values.
-def power_iteration(W, u_, update=True, eps=1e-12):
-  # Lists holding singular vectors and values
-  us, vs, svs = [], [], []
-  for i, u in enumerate(u_):
-    # Run one step of the power iteration
-    with torch.no_grad():
-      v = torch.matmul(u, W)
-      # Run Gram-Schmidt to subtract components of all other singular vectors
-      v = F.normalize(gram_schmidt(v, vs), eps=eps)
-      # Add to the list
-      vs += [v]
-      # Update the other singular vector
-      u = torch.matmul(v, W.t())
-      # Run Gram-Schmidt to subtract components of all other singular vectors
-      u = F.normalize(gram_schmidt(u, us), eps=eps)
-      # Add to the list
-      us += [u]
-      if update:
-        u_[i][:] = u
-    # Compute this singular value and add it to the list
-    svs += [torch.squeeze(torch.matmul(torch.matmul(v, W.t()), u.t()))]
-    #svs += [torch.sum(F.linear(u, W.transpose(0, 1)) * v)]
-  return svs, us, vs
-
-# Spectral normalization base class 
-class SN(object):
-  def __init__(self, num_svs, num_itrs, num_outputs, transpose=False, eps=1e-12):
-    # Number of power iterations per step
-    self.num_itrs = num_itrs
-    # Number of singular values
-    self.num_svs = num_svs
-    # Transposed?
-    self.transpose = transpose
-    # Epsilon value for avoiding divide-by-0
-    self.eps = eps
-    # Register a singular vector for each sv
-    for i in range(self.num_svs):
-      self.register_buffer('u%d' % i, torch.randn(1, num_outputs))
-      self.register_buffer('sv%d' % i, torch.ones(1))
-  
-  # Singular vectors (u side)
-  @property
-  def u(self):
-    return [getattr(self, 'u%d' % i) for i in range(self.num_svs)]
-
-  # Singular values; 
-  # note that these buffers are just for logging and are not used in training. 
-  @property
-  def sv(self):
-   return [getattr(self, 'sv%d' % i) for i in range(self.num_svs)]
-   
-  # Compute the spectrally-normalized weight
-  def W_(self):
-    W_mat = self.weight.view(self.weight.size(0), -1)
-    if self.transpose:
-      W_mat = W_mat.t()
-    # Apply num_itrs power iterations
-    for _ in range(self.num_itrs):
-      svs, us, vs = power_iteration(W_mat, self.u, update=self.training, eps=self.eps) 
-    # Update the svs
-    if self.training:
-      with torch.no_grad(): # Make sure to do this in a no_grad() context or you'll get memory leaks!
-        for i, sv in enumerate(svs):
-          self.sv[i][:] = sv     
-    return self.weight / svs[0]
-
-# Linear layer with spectral norm
-class SNLinear(nn.Linear, SN):
-  def __init__(self, in_features, out_features, bias=True,
-               num_svs=1, num_itrs=1, eps=1e-12):
-    nn.Linear.__init__(self, in_features, out_features, bias)
-    SN.__init__(self, num_svs, num_itrs, out_features, eps=eps)
-  def forward(self, x):
-    return F.linear(x, self.W_(), self.bias)
-
-# 2D Conv layer with spectral norm
-class SNConv2d(nn.Conv2d, SN):
-  def __init__(self, in_channels, out_channels, kernel_size, stride=1,
-             padding=0, dilation=1, groups=1, bias=True, 
-             num_svs=1, num_itrs=1, eps=1e-12):
-    nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, stride, 
-                     padding, dilation, groups, bias)
-    SN.__init__(self, num_svs, num_itrs, out_channels, eps=eps)    
-  def forward(self, x):
-    return F.conv2d(x, self.W_(), self.bias, self.stride, 
-                    self.padding, self.dilation, self.groups)
-
-class SegBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, con_channels,
-                which_conv=nn.Conv2d, which_linear=None, activation=None, 
-                upsample=None):
-        super(SegBlock, self).__init__()
-        
-        self.in_channels, self.out_channels = in_channels, out_channels
-        self.which_conv, self.which_linear = which_conv, which_linear
-        self.activation = activation
-        self.upsample = upsample
-        # Conv layers
-        self.conv1 = self.which_conv(self.in_channels, self.out_channels)
-        self.conv2 = self.which_conv(self.out_channels, self.out_channels)
-        self.learnable_sc = in_channels != out_channels or upsample
-        if self.learnable_sc:
-            self.conv_sc = self.which_conv(in_channels, out_channels, 
-                                            kernel_size=1, padding=0)
-        # Batchnorm layers
-        self.register_buffer('stored_mean1', torch.zeros(in_channels))
-        self.register_buffer('stored_var1',  torch.ones(in_channels)) 
-        self.register_buffer('stored_mean2', torch.zeros(out_channels))
-        self.register_buffer('stored_var2',  torch.ones(out_channels)) 
-        # self.bn1 = layers.ccbn(in_channels, con_channels, self.which_linear, eps=1e-4, norm_style='bn')
-        # self.bn2 = layers.ccbn(out_channels, con_channels, self.which_linear, eps=1e-4, norm_style='bn')
-        # upsample layers
-        self.upsample = upsample
-
-    def forward(self, x, y=None):
-        x = F.batch_norm(x, self.stored_mean1, self.stored_var1, None, None,
-                          self.training, 0.1, 1e-4)
-        # h = self.activation(self.bn1(x, y))
-        h = self.activation(x)
-        if self.upsample:
-            h = self.upsample(h)
-            x = self.upsample(x)
-        h = self.conv1(h)
-        h = F.batch_norm(h, self.stored_mean2, self.stored_var2, None, None,
-                          self.training, 0.1, 1e-4)
-        # h = self.activation(self.bn2(h, y))
-        h = self.activation(h)
-        h = self.conv2(h)
-        if self.learnable_sc:       
-            x = self.conv_sc(x)
-        return h + x
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-    
-    def forward(self, x, emb, context=None,class_token_index=[]):
-        #attn_list=[]
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context,class_token_index=class_token_index)
-                
-            else:
-                x = layer(x)
-        
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-       
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-    #@profile
-    def forward(self, x, timesteps=None, context=None, y=None,class_token_index=[],**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        
-        if timesteps[0]==1:
-            flag_time=True
-        else:
-            flag_time=False
-        
-        if flag_time:
-            for module in self.input_blocks:
-                h = module(h, emb, context,class_token_index=class_token_index)
-                hs.append(h)
-                reshape_h=h.reshape(int(h.size()[0]/2),int(h.size()[1]*2),h.size()[2],h.size()[3])
-                
-                if h.size()[2]==8:
-                    all_feature_dic["low"].append(reshape_h)
-                elif h.size()[2]==16:
-                    all_feature_dic["mid"].append(reshape_h)
-                elif h.size()[2]==32:
-                    all_feature_dic["high"].append(reshape_h)
-                elif h.size()[2]==64:
-                    all_feature_dic["highest"].append(reshape_h)
-        else: 
-            for module in self.input_blocks:
-                h = module(h, emb, context,class_token_index=class_token_index)
-                hs.append(h)
-                        
-        h = self.middle_block(h, emb, context,class_token_index=class_token_index)
-        if flag_time:
-            
-            reshape_h=h.reshape(int(h.size()[0]/2),int(h.size()[1]*2),h.size()[2],h.size()[3])
-            
-            if h.size()[2]==8:
-                all_feature_dic["low"].append(reshape_h)
-            elif h.size()[2]==16:
-                all_feature_dic["mid"].append(reshape_h)
-            elif h.size()[2]==32:
-                all_feature_dic["high"].append(reshape_h)
-            elif h.size()[2]==64:
-                all_feature_dic["highest"].append(reshape_h)
-        if flag_time:
-            for module in self.output_blocks:
-                h = th.cat([h, hs.pop()], dim=1)
-                h = module(h, emb, context,class_token_index=class_token_index)
-            
-                reshape_h=h.reshape(int(h.size()[0]/2),int(h.size()[1]*2),h.size()[2],h.size()[3])
-                
-                if h.size()[2]==8:
-                    all_feature_dic["low"].append(reshape_h)
-                elif h.size()[2]==16:
-                    all_feature_dic["mid"].append(reshape_h)
-                elif h.size()[2]==32:
-                    all_feature_dic["high"].append(reshape_h)
-                elif h.size()[2]==64:
-                    all_feature_dic["highest"].append(reshape_h)
-                
-        else:
-            for module in self.output_blocks:
-                h = th.cat([h, hs.pop()], dim=1)
-                h = module(h, emb, context,class_token_index=class_token_index)        
-            
-        
-        h = h.type(x.dtype)
-        
-        if self.predict_codebook_ids:
-            
-            return self.id_predictor(h),0
-            
-        else:
-           
-            return self.out(h),0
-            
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-        *args,
-        **kwargs
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
-
diff --git a/spaces/RGBD-SOD/depth2rgb-dpt/README.md b/spaces/RGBD-SOD/depth2rgb-dpt/README.md
deleted file mode 100644
index 7a39501e617f47ace66ecc7d29633017f1b5cff5..0000000000000000000000000000000000000000
--- a/spaces/RGBD-SOD/depth2rgb-dpt/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dptdepth
-emoji: 🔥
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/get-pip.py b/spaces/RMXK/RVC_HFF/get-pip.py
deleted file mode 100644
index cf68a2b3426decf01bde021a50c47a2115af303a..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/get-pip.py
+++ /dev/null
@@ -1,32657 +0,0 @@
-#!/usr/bin/env python
-#
-# Hi There!
-#
-# You may be wondering what this giant blob of binary data here is, you might
-# even be worried that we're up to something nefarious (good for you for being
-# paranoid!). This is a base85 encoding of a zip file, this zip file contains
-# an entire copy of pip (version 23.2.1).
-#
-# Pip is a thing that installs packages, pip itself is a package that someone
-# might want to install, especially if they're looking to run this get-pip.py
-# script. Pip has a lot of code to deal with the security of installing
-# packages, various edge cases on various platforms, and other such sort of
-# "tribal knowledge" that has been encoded in its code base. Because of this
-# we basically include an entire copy of pip inside this blob. We do this
-# because the alternatives are attempt to implement a "minipip" that probably
-# doesn't do things correctly and has weird edge cases, or compress pip itself
-# down into a single file.
-#
-# If you're wondering how this is created, it is generated using
-# `scripts/generate.py` in https://github.com/pypa/get-pip.
-
-import sys
-
-this_python = sys.version_info[:2]
-min_version = (3, 7)
-if this_python < min_version:
-    message_parts = [
-        "This script does not work on Python {}.{}".format(*this_python),
-        "The minimum supported Python version is {}.{}.".format(*min_version),
-        "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python),
-    ]
-    print("ERROR: " + " ".join(message_parts))
-    sys.exit(1)
-
-
-import os.path
-import pkgutil
-import shutil
-import tempfile
-import argparse
-import importlib
-from base64 import b85decode
-
-
-def include_setuptools(args):
-    """
-    Install setuptools only if absent and not excluded.
-    """
-    cli = not args.no_setuptools
-    env = not os.environ.get("PIP_NO_SETUPTOOLS")
-    absent = not importlib.util.find_spec("setuptools")
-    return cli and env and absent
-
-
-def include_wheel(args):
-    """
-    Install wheel only if absent and not excluded.
-    """
-    cli = not args.no_wheel
-    env = not os.environ.get("PIP_NO_WHEEL")
-    absent = not importlib.util.find_spec("wheel")
-    return cli and env and absent
-
-
-def determine_pip_install_arguments():
-    pre_parser = argparse.ArgumentParser()
-    pre_parser.add_argument("--no-setuptools", action="store_true")
-    pre_parser.add_argument("--no-wheel", action="store_true")
-    pre, args = pre_parser.parse_known_args()
-
-    args.append("pip")
-
-    if include_setuptools(pre):
-        args.append("setuptools")
-
-    if include_wheel(pre):
-        args.append("wheel")
-
-    return ["install", "--upgrade", "--force-reinstall"] + args
-
-
-def monkeypatch_for_cert(tmpdir):
-    """Patches `pip install` to provide default certificate with the lowest priority.
-
-    This ensures that the bundled certificates are used unless the user specifies a
-    custom cert via any of pip's option passing mechanisms (config, env-var, CLI).
-
-    A monkeypatch is the easiest way to achieve this, without messing too much with
-    the rest of pip's internals.
-    """
-    from pip._internal.commands.install import InstallCommand
-
-    # We want to be using the internal certificates.
-    cert_path = os.path.join(tmpdir, "cacert.pem")
-    with open(cert_path, "wb") as cert:
-        cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem"))
-
-    install_parse_args = InstallCommand.parse_args
-
-    def cert_parse_args(self, args):
-        if not self.parser.get_default_values().cert:
-            # There are no user provided cert -- force use of bundled cert
-            self.parser.defaults["cert"] = cert_path  # calculated above
-        return install_parse_args(self, args)
-
-    InstallCommand.parse_args = cert_parse_args
-
-
-def bootstrap(tmpdir):
-    monkeypatch_for_cert(tmpdir)
-
-    # Execute the included pip and use it to install the latest pip and
-    # setuptools from PyPI
-    from pip._internal.cli.main import main as pip_entry_point
-    args = determine_pip_install_arguments()
-    sys.exit(pip_entry_point(args))
-
-
-def main():
-    tmpdir = None
-    try:
-        # Create a temporary working directory
-        tmpdir = tempfile.mkdtemp()
-
-        # Unpack the zipfile into the temporary directory
-        pip_zip = os.path.join(tmpdir, "pip.zip")
-        with open(pip_zip, "wb") as fp:
-            fp.write(b85decode(DATA.replace(b"\n", b"")))
-
-        # Add the zipfile to sys.path so that we can import it
-        sys.path.insert(0, pip_zip)
-
-        # Run the bootstrap
-        bootstrap(tmpdir=tmpdir)
-    finally:
-        # Clean up our temporary working directory
-        if tmpdir:
-            shutil.rmtree(tmpdir, ignore_errors=True)
-
-
-DATA = b"""
-P)h>@6aWAK2ml*O_EvJ33*7hs003nH000jF003}la4%n9X>MtBUtcb8c|B0UO2j}6z0X&KUUXrd;wrc
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-3ZJA0oQV`^+*aO7_tw^Cd$4zs{Pl#j>6{|X*AaQ6!2wJ?w>%d+2&1X4Rc!^r6h-hMtH_<n)`omXfA!z
-c)+2_nTCfpGRv1uvmTkcug)ShEPeC#tJ!y1a)P)ln~75Jc!yqZE1Gl6K?CR$<8F6kVP)a}pU*@~6k=y
-<MFxvzbFl3|p@5?5Ii7qF0_`NT{r7m1lM_B44a9>d5{IF3D`nKTt~p1QY-O00;mZO7>Q7_pjHy0RRA2
-0{{RI0001RX>c!JUu|J&ZeL$6aCu!)OK;mS48HqU5b43r;JP^vOMxACEp{6QLy+m1h%E`C9MAjpBNe-
-8r;{H19{ebpf{zJ27j)n8%0=-6Z#elILRo@w9oRWWbO{z8ujDS!QAC@3T%nJCf;1rX6ghzu#Z}<GSE4
-4EG}J&ngovyJ$%DCh>R@K&*?Hgj1WFD91+adaM4G`4Xs@*hA^t@nbDYdL)-aOjsW~3}QVVby(8=@7U$
-Fzj5Y{w!2hUUH`?e9j7WDA;>-1aos>7j{2$~BfyL8p@__Y98dsP#Bs7^<X<wp+-f{6%mc1~N!0T>lWF
-=e_gr;(4^?am?Cp93+7b-!?~nb}-$cPSR1zckA*zNp!)$;YjlZrfn&RWNM}=QA7*cb8A{(9@{5!vBfq
-rEMoeu5FvJZngI@N#4#(2v$WnMGCAVD?b9t8W^qDfcFBe5ZZF%dPAPaq#<aBs;+HiVj+9PK#6heH_-Q
--kVzlI0rncJH8Q{ZFBFwrpI^^9n>>ikclG~yPvCg`JUGb_W2#PdCXxx}7!|T*xc9qdnTILbO-nAJaF2
-~0snMF<S>DU<%E01X4*yW9@|}F2;vY~;0|XQR000O88%p+8eg`F$&;kGeqy+!~6#xJLaA|NaUte%(a4
-m9mZf<3AUtcb8d3{vDZrd;nz56RT_b?l9y`mj3AXtV0MT+&(WJ!7$x<XKFy3uA!t|VtMivIgZJ5Ji5n
-+}O2laG&&&kn<Ivc;_N2)LD*FI(_y<sdV43#Nct)d~Djbf-Z=u8IOJY7eM4${JnKJ`I8;rxdD0pnokZ
-%t1O(o{kB4L(#6WNXXLn@Ri9Miq52d?_ou0Rc)-Nw2hL1)Vnh{FFp1(!Y~Yi6Zr7%Cv?>|Xq_^eh*q`
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-00O897^_9I!Mcs{1yNJ^Hl%<ApigX0000000000wt>Vr9sqD@a4%nWWo~3|axZmqY;0*_GcR>?X>2cY
-Wpr|RE^v8JO928D02BZK00;maO7>QylKaP}761SlLjV9E00000000000002Cf&Wh)0B~t=FJE?LZe(w
-AFLiQkY-wUMFLiWjY%g<jY+o*Lc~DCM0u%rg00008031s8RzGpKy6Ff20Dcw#04@Lk00000000000Je
-eTW*z`=X>c!Jc4cm4Z*nhna%^mAVlyvwbZKlab8~E8ZDDj{XkTb=b98QDZDlTSc~DCM0u%rg0000803
-1s8R<aQHw*LnJ0Ph+A04M+e00000000000Jeb{a2^0~X>c!Jc4cm4Z*nhna%^mAVlyvwbZKlab8~ETa
-$#<BaBp&SE^v8JO928D02BZK00;maO7>R$7ML^U3jhGPCjbB=00000000000002CfnInX0B~t=FJE?L
-Ze(wAFLiQkY-wUMFLiWjY%g?aZDntDbS`jtP)h*<6aW+e000O897^_9E7j;;7ZU&g*f#(G9{>OV0000
-000000wt<C&9sqD@a4%nWWo~3|axZmqY;0*_GcR>?X>2cba%?Vec~DCM0u%rg00008031s8R#wzj)xQ
-S-02>tm03ZMW00000000000JeeImmUCcX>c!Jc4cm4Z*nhna%^mAVlyvwbZKlacVTICE^v8JO928D02
-BZK00;maO7>QG&>wL!3jhF9DF6T@00000000000002Cf!3cM0B~t=FJE?LZe(wAFLz~PWo~0{WNB_^b
-1z?CX>MtBUtcb8c~DCM0u%rg00008031s8R>!Rfiv<P%023ns03rYY00000000000Jecjs~!MwX>c!J
-c4cm4Z*nhpWnyJ+V{c?>ZfA2ZY++($Y;!Jfc~DCM0u%rg00008031s8R#1O2A4CEG02u`U03-ka0000
-0000000JedTu^s?$X>c!Jc4cm4Z*nhpWnyJ+V{c?>ZfA2ZZEI{{Vr6V|E^v8JO928D02BZK00;maO7>
-P~IB=)A1pok}82|tw00000000000002CffckK0B~t=FJE?LZe(wAFLz~PWo~0{WNB_^b1!sdb98eqaC
-uNm0Rj{N6aWAK2ml;P_Ey^l{MMxe007ev001fg0000000000006du4Z0oxaA|NaUv_0~WN&gWcV%K_Z
-ewp`X>Mn8FL+;db7gX0WMyV)Ze?UHaCuNm1qJ{B008s>^a044008>F9smFU
-"""
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Realcat/image-matching-webui/third_party/LightGlue/lightglue/viz2d.py b/spaces/Realcat/image-matching-webui/third_party/LightGlue/lightglue/viz2d.py
deleted file mode 100644
index 4999a76fd0001b0b7570ba38639fcf0a30b0c915..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/LightGlue/lightglue/viz2d.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""
-2D visualization primitives based on Matplotlib.
-1) Plot images with `plot_images`.
-2) Call `plot_keypoints` or `plot_matches` any number of times.
-3) Optionally: save a .png or .pdf plot (nice in papers!) with `save_plot`.
-"""
-
-import matplotlib
-import matplotlib.pyplot as plt
-import matplotlib.patheffects as path_effects
-import numpy as np
-import torch
-
-
-def cm_RdGn(x):
-    """Custom colormap: red (0) -> yellow (0.5) -> green (1)."""
-    x = np.clip(x, 0, 1)[..., None] * 2
-    c = x * np.array([[0, 1.0, 0]]) + (2 - x) * np.array([[1.0, 0, 0]])
-    return np.clip(c, 0, 1)
-
-
-def cm_BlRdGn(x_):
-    """Custom colormap: blue (-1) -> red (0.0) -> green (1)."""
-    x = np.clip(x_, 0, 1)[..., None] * 2
-    c = x * np.array([[0, 1.0, 0, 1.0]]) + (2 - x) * np.array([[1.0, 0, 0, 1.0]])
-
-    xn = -np.clip(x_, -1, 0)[..., None] * 2
-    cn = xn * np.array([[0, 0.1, 1, 1.0]]) + (2 - xn) * np.array([[1.0, 0, 0, 1.0]])
-    out = np.clip(np.where(x_[..., None] < 0, cn, c), 0, 1)
-    return out
-
-
-def cm_prune(x_):
-    """Custom colormap to visualize pruning"""
-    if isinstance(x_, torch.Tensor):
-        x_ = x_.cpu().numpy()
-    max_i = max(x_)
-    norm_x = np.where(x_ == max_i, -1, (x_ - 1) / 9)
-    return cm_BlRdGn(norm_x)
-
-
-def plot_images(imgs, titles=None, cmaps="gray", dpi=100, pad=0.5, adaptive=True):
-    """Plot a set of images horizontally.
-    Args:
-        imgs: list of NumPy RGB (H, W, 3) or PyTorch RGB (3, H, W) or mono (H, W).
-        titles: a list of strings, as titles for each image.
-        cmaps: colormaps for monochrome images.
-        adaptive: whether the figure size should fit the image aspect ratios.
-    """
-    # conversion to (H, W, 3) for torch.Tensor
-    imgs = [
-        img.permute(1, 2, 0).cpu().numpy()
-        if (isinstance(img, torch.Tensor) and img.dim() == 3)
-        else img
-        for img in imgs
-    ]
-
-    n = len(imgs)
-    if not isinstance(cmaps, (list, tuple)):
-        cmaps = [cmaps] * n
-
-    if adaptive:
-        ratios = [i.shape[1] / i.shape[0] for i in imgs]  # W / H
-    else:
-        ratios = [4 / 3] * n
-    figsize = [sum(ratios) * 4.5, 4.5]
-    fig, ax = plt.subplots(
-        1, n, figsize=figsize, dpi=dpi, gridspec_kw={"width_ratios": ratios}
-    )
-    if n == 1:
-        ax = [ax]
-    for i in range(n):
-        ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i]))
-        ax[i].get_yaxis().set_ticks([])
-        ax[i].get_xaxis().set_ticks([])
-        ax[i].set_axis_off()
-        for spine in ax[i].spines.values():  # remove frame
-            spine.set_visible(False)
-        if titles:
-            ax[i].set_title(titles[i])
-    fig.tight_layout(pad=pad)
-
-
-def plot_keypoints(kpts, colors="lime", ps=4, axes=None, a=1.0):
-    """Plot keypoints for existing images.
-    Args:
-        kpts: list of ndarrays of size (N, 2).
-        colors: string, or list of list of tuples (one for each keypoints).
-        ps: size of the keypoints as float.
-    """
-    if not isinstance(colors, list):
-        colors = [colors] * len(kpts)
-    if not isinstance(a, list):
-        a = [a] * len(kpts)
-    if axes is None:
-        axes = plt.gcf().axes
-    for ax, k, c, alpha in zip(axes, kpts, colors, a):
-        if isinstance(k, torch.Tensor):
-            k = k.cpu().numpy()
-        ax.scatter(k[:, 0], k[:, 1], c=c, s=ps, linewidths=0, alpha=alpha)
-
-
-def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, a=1.0, labels=None, axes=None):
-    """Plot matches for a pair of existing images.
-    Args:
-        kpts0, kpts1: corresponding keypoints of size (N, 2).
-        color: color of each match, string or RGB tuple. Random if not given.
-        lw: width of the lines.
-        ps: size of the end points (no endpoint if ps=0)
-        indices: indices of the images to draw the matches on.
-        a: alpha opacity of the match lines.
-    """
-    fig = plt.gcf()
-    if axes is None:
-        ax = fig.axes
-        ax0, ax1 = ax[0], ax[1]
-    else:
-        ax0, ax1 = axes
-    if isinstance(kpts0, torch.Tensor):
-        kpts0 = kpts0.cpu().numpy()
-    if isinstance(kpts1, torch.Tensor):
-        kpts1 = kpts1.cpu().numpy()
-    assert len(kpts0) == len(kpts1)
-    if color is None:
-        color = matplotlib.cm.hsv(np.random.rand(len(kpts0))).tolist()
-    elif len(color) > 0 and not isinstance(color[0], (tuple, list)):
-        color = [color] * len(kpts0)
-
-    if lw > 0:
-        for i in range(len(kpts0)):
-            line = matplotlib.patches.ConnectionPatch(
-                xyA=(kpts0[i, 0], kpts0[i, 1]),
-                xyB=(kpts1[i, 0], kpts1[i, 1]),
-                coordsA=ax0.transData,
-                coordsB=ax1.transData,
-                axesA=ax0,
-                axesB=ax1,
-                zorder=1,
-                color=color[i],
-                linewidth=lw,
-                clip_on=True,
-                alpha=a,
-                label=None if labels is None else labels[i],
-                picker=5.0,
-            )
-            line.set_annotation_clip(True)
-            fig.add_artist(line)
-
-    # freeze the axes to prevent the transform to change
-    ax0.autoscale(enable=False)
-    ax1.autoscale(enable=False)
-
-    if ps > 0:
-        ax0.scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps)
-        ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
-
-
-def add_text(
-    idx,
-    text,
-    pos=(0.01, 0.99),
-    fs=15,
-    color="w",
-    lcolor="k",
-    lwidth=2,
-    ha="left",
-    va="top",
-):
-    ax = plt.gcf().axes[idx]
-    t = ax.text(
-        *pos, text, fontsize=fs, ha=ha, va=va, color=color, transform=ax.transAxes
-    )
-    if lcolor is not None:
-        t.set_path_effects(
-            [
-                path_effects.Stroke(linewidth=lwidth, foreground=lcolor),
-                path_effects.Normal(),
-            ]
-        )
-
-
-def save_plot(path, **kw):
-    """Save the current figure without any white margin."""
-    plt.savefig(path, bbox_inches="tight", pad_inches=0, **kw)
diff --git a/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/README.md b/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/README.md
deleted file mode 100644
index 2a0b5e0f154d1717087c35f93cd02a0f54fc6027..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
-#  HPatches Sequences / Image Pairs Matching Benchmark
-
-Please check the [official repository](https://github.com/hpatches/hpatches-dataset) for more information regarding references.
-
-The dataset can be downloaded by running `bash download.sh` - this script downloads and extracts the HPatches Sequences dataset and removes the sequences containing high resolution images (`> 1600x1200`) as mentioned in the D2-Net paper. You can also download the cache with results for all methods from the D2-Net paper by running `bash download_cache.sh`.
-
-New methods can be added in cell 4 of the notebook. The local features are supposed to be stored in the [`npz`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) format with three fields:
-
-- `keypoints` - `N x 2` matrix with `x, y` coordinates of each keypoint in COLMAP format (the `X` axis points to the right, the `Y` axis to the bottom)
-
-- `scores` - `N` array with detection scores for each keypoint (higher is better) - only required for the "top K" version of the benchmark
-
-- `descriptors` - `N x D` matrix with the descriptors (L2 normalized if you plan on using the provided mutual nearest neighbors matcher)
-
-Moreover, the `npz` files are supposed to be saved alongside their corresponding images with the same extension as the `method` (e.g. if `method = d2-net`, the features for the image `hpatches-sequences-release/i_ajuntament/1.ppm` should be in the file `hpatches-sequences-release/i_ajuntament/1.ppm.d2-net`).
-
-We provide a simple script to extract Hessian Affine keypoints with SIFT descriptors (`extract_hesaff.m`); this script requires MATLAB and [VLFeat](http://www.vlfeat.org/).
-
-D2-Net features can be extracted by running:
-```
-python extract_features.py --image_list_file image_list_hpatches_sequences.txt
-```
diff --git a/spaces/Riakzu/parkinson_detection/README.md b/spaces/Riakzu/parkinson_detection/README.md
deleted file mode 100644
index 1dc691a229a57142fda19b1db45990c0e00c7404..0000000000000000000000000000000000000000
--- a/spaces/Riakzu/parkinson_detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Parkinson Detection
-emoji: 🌖
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Riksarkivet/htr_demo/helper/text/help/stepwise/stepwise.md b/spaces/Riksarkivet/htr_demo/helper/text/help/stepwise/stepwise.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Ritori/TTS_Yui/distributed.py b/spaces/Ritori/TTS_Yui/distributed.py
deleted file mode 100644
index cce74944bfec6a69ac50921ec82a74d4874d6070..0000000000000000000000000000000000000000
--- a/spaces/Ritori/TTS_Yui/distributed.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import torch
-import torch.distributed as dist
-from torch.nn.modules import Module
-from torch.autograd import Variable
-
-def _flatten_dense_tensors(tensors):
-    """Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
-    same dense type.
-    Since inputs are dense, the resulting tensor will be a concatenated 1D
-    buffer. Element-wise operation on this buffer will be equivalent to
-    operating individually.
-    Arguments:
-        tensors (Iterable[Tensor]): dense tensors to flatten.
-    Returns:
-        A contiguous 1D buffer containing input tensors.
-    """
-    if len(tensors) == 1:
-        return tensors[0].contiguous().view(-1)
-    flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
-    return flat
-
-def _unflatten_dense_tensors(flat, tensors):
-    """View a flat buffer using the sizes of tensors. Assume that tensors are of
-    same dense type, and that flat is given by _flatten_dense_tensors.
-    Arguments:
-        flat (Tensor): flattened dense tensors to unflatten.
-        tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
-          unflatten flat.
-    Returns:
-        Unflattened dense tensors with sizes same as tensors and values from
-        flat.
-    """
-    outputs = []
-    offset = 0
-    for tensor in tensors:
-        numel = tensor.numel()
-        outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
-        offset += numel
-    return tuple(outputs)
-
-
-'''
-This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
-launcher included with this example. It assumes that your run is using multiprocess with 1
-GPU/process, that the model is on the correct device, and that torch.set_device has been
-used to set the device.
-
-Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
-and will be allreduced at the finish of the backward pass.
-'''
-class DistributedDataParallel(Module):
-
-    def __init__(self, module):
-        super(DistributedDataParallel, self).__init__()
-        #fallback for PyTorch 0.3
-        if not hasattr(dist, '_backend'):
-            self.warn_on_half = True
-        else:
-            self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
-
-        self.module = module
-
-        for p in self.module.state_dict().values():
-            if not torch.is_tensor(p):
-                continue
-            dist.broadcast(p, 0)
-
-        def allreduce_params():
-            if(self.needs_reduction):
-                self.needs_reduction = False
-                buckets = {}
-                for param in self.module.parameters():
-                    if param.requires_grad and param.grad is not None:
-                        tp = type(param.data)
-                        if tp not in buckets:
-                            buckets[tp] = []
-                        buckets[tp].append(param)
-                if self.warn_on_half:
-                    if torch.cuda.HalfTensor in buckets:
-                        print("WARNING: gloo dist backend for half parameters may be extremely slow." +
-                              " It is recommended to use the NCCL backend in this case. This currently requires" +
-                              "PyTorch built from top of tree master.")
-                        self.warn_on_half = False
-
-                for tp in buckets:
-                    bucket = buckets[tp]
-                    grads = [param.grad.data for param in bucket]
-                    coalesced = _flatten_dense_tensors(grads)
-                    dist.all_reduce(coalesced)
-                    coalesced /= dist.get_world_size()
-                    for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
-                        buf.copy_(synced)
-
-        for param in list(self.module.parameters()):
-            def allreduce_hook(*unused):
-                param._execution_engine.queue_callback(allreduce_params)
-            if param.requires_grad:
-                param.register_hook(allreduce_hook)
-
-    def forward(self, *inputs, **kwargs):
-        self.needs_reduction = True
-        return self.module(*inputs, **kwargs)
-
-    '''
-    def _sync_buffers(self):
-        buffers = list(self.module._all_buffers())
-        if len(buffers) > 0:
-            # cross-node buffer sync
-            flat_buffers = _flatten_dense_tensors(buffers)
-            dist.broadcast(flat_buffers, 0)
-            for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
-                buf.copy_(synced)
-     def train(self, mode=True):
-        # Clear NCCL communicator and CUDA event cache of the default group ID,
-        # These cache will be recreated at the later call. This is currently a
-        # work-around for a potential NCCL deadlock.
-        if dist._backend == dist.dist_backend.NCCL:
-            dist._clear_group_cache()
-        super(DistributedDataParallel, self).train(mode)
-        self.module.train(mode)
-    '''
-'''
-Modifies existing model to do gradient allreduce, but doesn't change class
-so you don't need "module"
-'''
-def apply_gradient_allreduce(module):
-        if not hasattr(dist, '_backend'):
-            module.warn_on_half = True
-        else:
-            module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
-
-        for p in module.state_dict().values():
-            if not torch.is_tensor(p):
-                continue
-            dist.broadcast(p, 0)
-
-        def allreduce_params():
-            if(module.needs_reduction):
-                module.needs_reduction = False
-                buckets = {}
-                for param in module.parameters():
-                    if param.requires_grad and param.grad is not None:
-                        tp = param.data.dtype
-                        if tp not in buckets:
-                            buckets[tp] = []
-                        buckets[tp].append(param)
-                if module.warn_on_half:
-                    if torch.cuda.HalfTensor in buckets:
-                        print("WARNING: gloo dist backend for half parameters may be extremely slow." +
-                              " It is recommended to use the NCCL backend in this case. This currently requires" +
-                              "PyTorch built from top of tree master.")
-                        module.warn_on_half = False
-
-                for tp in buckets:
-                    bucket = buckets[tp]
-                    grads = [param.grad.data for param in bucket]
-                    coalesced = _flatten_dense_tensors(grads)
-                    dist.all_reduce(coalesced)
-                    coalesced /= dist.get_world_size()
-                    for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
-                        buf.copy_(synced)
-
-        for param in list(module.parameters()):
-            def allreduce_hook(*unused):
-                Variable._execution_engine.queue_callback(allreduce_params)
-            if param.requires_grad:
-                param.register_hook(allreduce_hook)
-
-        def set_needs_reduction(self, input, output):
-            self.needs_reduction = True
-
-        module.register_forward_hook(set_needs_reduction)
-        return module
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py
deleted file mode 100644
index 689513fa9d2a40f14bf0ae4ae61f38f0dcc1b3da..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='PSAHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        mask_size=(97, 97),
-        psa_type='bi-direction',
-        compact=False,
-        shrink_factor=2,
-        normalization_factor=1.0,
-        psa_softmax=True,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/datasets/cityscapes.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/datasets/cityscapes.py
deleted file mode 100644
index 71eead87e7f4e511c0cb59e69c3a599832ada0e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/datasets/cityscapes.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
-# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
-
-import glob
-import os
-import os.path as osp
-import tempfile
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-import pycocotools.mask as maskUtils
-from mmcv.utils import print_log
-
-from .builder import DATASETS
-from .coco import CocoDataset
-
-
-@DATASETS.register_module()
-class CityscapesDataset(CocoDataset):
-
-    CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
-               'bicycle')
-
-    def _filter_imgs(self, min_size=32):
-        """Filter images too small or without ground truths."""
-        valid_inds = []
-        # obtain images that contain annotation
-        ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
-        # obtain images that contain annotations of the required categories
-        ids_in_cat = set()
-        for i, class_id in enumerate(self.cat_ids):
-            ids_in_cat |= set(self.coco.cat_img_map[class_id])
-        # merge the image id sets of the two conditions and use the merged set
-        # to filter out images if self.filter_empty_gt=True
-        ids_in_cat &= ids_with_ann
-
-        valid_img_ids = []
-        for i, img_info in enumerate(self.data_infos):
-            img_id = img_info['id']
-            ann_ids = self.coco.getAnnIds(imgIds=[img_id])
-            ann_info = self.coco.loadAnns(ann_ids)
-            all_iscrowd = all([_['iscrowd'] for _ in ann_info])
-            if self.filter_empty_gt and (self.img_ids[i] not in ids_in_cat
-                                         or all_iscrowd):
-                continue
-            if min(img_info['width'], img_info['height']) >= min_size:
-                valid_inds.append(i)
-                valid_img_ids.append(img_id)
-        self.img_ids = valid_img_ids
-        return valid_inds
-
-    def _parse_ann_info(self, img_info, ann_info):
-        """Parse bbox and mask annotation.
-
-        Args:
-            img_info (dict): Image info of an image.
-            ann_info (list[dict]): Annotation info of an image.
-
-        Returns:
-            dict: A dict containing the following keys: bboxes, \
-                bboxes_ignore, labels, masks, seg_map. \
-                "masks" are already decoded into binary masks.
-        """
-        gt_bboxes = []
-        gt_labels = []
-        gt_bboxes_ignore = []
-        gt_masks_ann = []
-
-        for i, ann in enumerate(ann_info):
-            if ann.get('ignore', False):
-                continue
-            x1, y1, w, h = ann['bbox']
-            if ann['area'] <= 0 or w < 1 or h < 1:
-                continue
-            if ann['category_id'] not in self.cat_ids:
-                continue
-            bbox = [x1, y1, x1 + w, y1 + h]
-            if ann.get('iscrowd', False):
-                gt_bboxes_ignore.append(bbox)
-            else:
-                gt_bboxes.append(bbox)
-                gt_labels.append(self.cat2label[ann['category_id']])
-                gt_masks_ann.append(ann['segmentation'])
-
-        if gt_bboxes:
-            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
-            gt_labels = np.array(gt_labels, dtype=np.int64)
-        else:
-            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
-            gt_labels = np.array([], dtype=np.int64)
-
-        if gt_bboxes_ignore:
-            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
-        else:
-            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
-
-        ann = dict(
-            bboxes=gt_bboxes,
-            labels=gt_labels,
-            bboxes_ignore=gt_bboxes_ignore,
-            masks=gt_masks_ann,
-            seg_map=img_info['segm_file'])
-
-        return ann
-
-    def results2txt(self, results, outfile_prefix):
-        """Dump the detection results to a txt file.
-
-        Args:
-            results (list[list | tuple]): Testing results of the
-                dataset.
-            outfile_prefix (str): The filename prefix of the json files.
-                If the prefix is "somepath/xxx",
-                the txt files will be named "somepath/xxx.txt".
-
-        Returns:
-            list[str]: Result txt files which contains corresponding \
-                instance segmentation images.
-        """
-        try:
-            import cityscapesscripts.helpers.labels as CSLabels
-        except ImportError:
-            raise ImportError('Please run "pip install citscapesscripts" to '
-                              'install cityscapesscripts first.')
-        result_files = []
-        os.makedirs(outfile_prefix, exist_ok=True)
-        prog_bar = mmcv.ProgressBar(len(self))
-        for idx in range(len(self)):
-            result = results[idx]
-            filename = self.data_infos[idx]['filename']
-            basename = osp.splitext(osp.basename(filename))[0]
-            pred_txt = osp.join(outfile_prefix, basename + '_pred.txt')
-
-            bbox_result, segm_result = result
-            bboxes = np.vstack(bbox_result)
-            # segm results
-            if isinstance(segm_result, tuple):
-                # Some detectors use different scores for bbox and mask,
-                # like Mask Scoring R-CNN. Score of segm will be used instead
-                # of bbox score.
-                segms = mmcv.concat_list(segm_result[0])
-                mask_score = segm_result[1]
-            else:
-                # use bbox score for mask score
-                segms = mmcv.concat_list(segm_result)
-                mask_score = [bbox[-1] for bbox in bboxes]
-            labels = [
-                np.full(bbox.shape[0], i, dtype=np.int32)
-                for i, bbox in enumerate(bbox_result)
-            ]
-            labels = np.concatenate(labels)
-
-            assert len(bboxes) == len(segms) == len(labels)
-            num_instances = len(bboxes)
-            prog_bar.update()
-            with open(pred_txt, 'w') as fout:
-                for i in range(num_instances):
-                    pred_class = labels[i]
-                    classes = self.CLASSES[pred_class]
-                    class_id = CSLabels.name2label[classes].id
-                    score = mask_score[i]
-                    mask = maskUtils.decode(segms[i]).astype(np.uint8)
-                    png_filename = osp.join(outfile_prefix,
-                                            basename + f'_{i}_{classes}.png')
-                    mmcv.imwrite(mask, png_filename)
-                    fout.write(f'{osp.basename(png_filename)} {class_id} '
-                               f'{score}\n')
-            result_files.append(pred_txt)
-
-        return result_files
-
-    def format_results(self, results, txtfile_prefix=None):
-        """Format the results to txt (standard format for Cityscapes
-        evaluation).
-
-        Args:
-            results (list): Testing results of the dataset.
-            txtfile_prefix (str | None): The prefix of txt files. It includes
-                the file path and the prefix of filename, e.g., "a/b/prefix".
-                If not specified, a temp file will be created. Default: None.
-
-        Returns:
-            tuple: (result_files, tmp_dir), result_files is a dict containing \
-                the json filepaths, tmp_dir is the temporal directory created \
-                for saving txt/png files when txtfile_prefix is not specified.
-        """
-        assert isinstance(results, list), 'results must be a list'
-        assert len(results) == len(self), (
-            'The length of results is not equal to the dataset len: {} != {}'.
-            format(len(results), len(self)))
-
-        assert isinstance(results, list), 'results must be a list'
-        assert len(results) == len(self), (
-            'The length of results is not equal to the dataset len: {} != {}'.
-            format(len(results), len(self)))
-
-        if txtfile_prefix is None:
-            tmp_dir = tempfile.TemporaryDirectory()
-            txtfile_prefix = osp.join(tmp_dir.name, 'results')
-        else:
-            tmp_dir = None
-        result_files = self.results2txt(results, txtfile_prefix)
-
-        return result_files, tmp_dir
-
-    def evaluate(self,
-                 results,
-                 metric='bbox',
-                 logger=None,
-                 outfile_prefix=None,
-                 classwise=False,
-                 proposal_nums=(100, 300, 1000),
-                 iou_thrs=np.arange(0.5, 0.96, 0.05)):
-        """Evaluation in Cityscapes/COCO protocol.
-
-        Args:
-            results (list[list | tuple]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated. Options are
-                'bbox', 'segm', 'proposal', 'proposal_fast'.
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-            outfile_prefix (str | None): The prefix of output file. It includes
-                the file path and the prefix of filename, e.g., "a/b/prefix".
-                If results are evaluated with COCO protocol, it would be the
-                prefix of output json file. For example, the metric is 'bbox'
-                and 'segm', then json files would be "a/b/prefix.bbox.json" and
-                "a/b/prefix.segm.json".
-                If results are evaluated with cityscapes protocol, it would be
-                the prefix of output txt/png files. The output files would be
-                png images under folder "a/b/prefix/xxx/" and the file name of
-                images would be written into a txt file
-                "a/b/prefix/xxx_pred.txt", where "xxx" is the video name of
-                cityscapes. If not specified, a temp file will be created.
-                Default: None.
-            classwise (bool): Whether to evaluating the AP for each class.
-            proposal_nums (Sequence[int]): Proposal number used for evaluating
-                recalls, such as recall@100, recall@1000.
-                Default: (100, 300, 1000).
-            iou_thrs (Sequence[float]): IoU threshold used for evaluating
-                recalls. If set to a list, the average recall of all IoUs will
-                also be computed. Default: 0.5.
-
-        Returns:
-            dict[str, float]: COCO style evaluation metric or cityscapes mAP \
-                and AP@50.
-        """
-        eval_results = dict()
-
-        metrics = metric.copy() if isinstance(metric, list) else [metric]
-
-        if 'cityscapes' in metrics:
-            eval_results.update(
-                self._evaluate_cityscapes(results, outfile_prefix, logger))
-            metrics.remove('cityscapes')
-
-        # left metrics are all coco metric
-        if len(metrics) > 0:
-            # create CocoDataset with CityscapesDataset annotation
-            self_coco = CocoDataset(self.ann_file, self.pipeline.transforms,
-                                    None, self.data_root, self.img_prefix,
-                                    self.seg_prefix, self.proposal_file,
-                                    self.test_mode, self.filter_empty_gt)
-            # TODO: remove this in the future
-            # reload annotations of correct class
-            self_coco.CLASSES = self.CLASSES
-            self_coco.data_infos = self_coco.load_annotations(self.ann_file)
-            eval_results.update(
-                self_coco.evaluate(results, metrics, logger, outfile_prefix,
-                                   classwise, proposal_nums, iou_thrs))
-
-        return eval_results
-
-    def _evaluate_cityscapes(self, results, txtfile_prefix, logger):
-        """Evaluation in Cityscapes protocol.
-
-        Args:
-            results (list): Testing results of the dataset.
-            txtfile_prefix (str | None): The prefix of output txt file
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-
-        Returns:
-            dict[str: float]: Cityscapes evaluation results, contains 'mAP' \
-                and 'AP@50'.
-        """
-
-        try:
-            import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval  # noqa
-        except ImportError:
-            raise ImportError('Please run "pip install citscapesscripts" to '
-                              'install cityscapesscripts first.')
-        msg = 'Evaluating in Cityscapes style'
-        if logger is None:
-            msg = '\n' + msg
-        print_log(msg, logger=logger)
-
-        result_files, tmp_dir = self.format_results(results, txtfile_prefix)
-
-        if tmp_dir is None:
-            result_dir = osp.join(txtfile_prefix, 'results')
-        else:
-            result_dir = osp.join(tmp_dir.name, 'results')
-
-        eval_results = OrderedDict()
-        print_log(f'Evaluating results under {result_dir} ...', logger=logger)
-
-        # set global states in cityscapes evaluation API
-        CSEval.args.cityscapesPath = os.path.join(self.img_prefix, '../..')
-        CSEval.args.predictionPath = os.path.abspath(result_dir)
-        CSEval.args.predictionWalk = None
-        CSEval.args.JSONOutput = False
-        CSEval.args.colorized = False
-        CSEval.args.gtInstancesFile = os.path.join(result_dir,
-                                                   'gtInstances.json')
-        CSEval.args.groundTruthSearch = os.path.join(
-            self.img_prefix.replace('leftImg8bit', 'gtFine'),
-            '*/*_gtFine_instanceIds.png')
-
-        groundTruthImgList = glob.glob(CSEval.args.groundTruthSearch)
-        assert len(groundTruthImgList), 'Cannot find ground truth images' \
-            f' in {CSEval.args.groundTruthSearch}.'
-        predictionImgList = []
-        for gt in groundTruthImgList:
-            predictionImgList.append(CSEval.getPrediction(gt, CSEval.args))
-        CSEval_results = CSEval.evaluateImgLists(predictionImgList,
-                                                 groundTruthImgList,
-                                                 CSEval.args)['averages']
-
-        eval_results['mAP'] = CSEval_results['allAp']
-        eval_results['AP@50'] = CSEval_results['allAp50%']
-        if tmp_dir is not None:
-            tmp_dir.cleanup()
-        return eval_results
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/atss_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/atss_head.py
deleted file mode 100644
index ff55dfa1790ba270539fc9f623dbb2984fa1a99e..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/atss_head.py
+++ /dev/null
@@ -1,689 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
-                        images_to_levels, multi_apply, multiclass_nms,
-                        reduce_mean, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-EPS = 1e-12
-
-
-@HEADS.register_module()
-class ATSSHead(AnchorHead):
-    """Bridging the Gap Between Anchor-based and Anchor-free Detection via
-    Adaptive Training Sample Selection.
-
-    ATSS head structure is similar with FCOS, however ATSS use anchor boxes
-    and assign label by Adaptive Training Sample Selection instead max-iou.
-
-    https://arxiv.org/abs/1912.02424
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
-                 loss_centerness=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0),
-                 **kwargs):
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(ATSSHead, self).__init__(num_classes, in_channels, **kwargs)
-
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # SSD sampling=False so use PseudoSampler
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-        self.loss_centerness = build_loss(loss_centerness)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.atss_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.atss_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-        self.atss_centerness = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 1, 3, padding=1)
-        self.scales = nn.ModuleList(
-            [Scale(1.0) for _ in self.anchor_generator.strides])
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.atss_cls, std=0.01, bias=bias_cls)
-        normal_init(self.atss_reg, std=0.01)
-        normal_init(self.atss_centerness, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
-        """
-        return multi_apply(self.forward_single, feats, self.scales)
-
-    def forward_single(self, x, scale):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls scores for a single scale level
-                    the channels number is num_anchors * num_classes.
-                bbox_pred (Tensor): Box energies / deltas for a single scale
-                    level, the channels number is num_anchors * 4.
-                centerness (Tensor): Centerness for a single scale level, the
-                    channel number is (N, num_anchors * 1, H, W).
-        """
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-        cls_score = self.atss_cls(cls_feat)
-        # we just follow atss, not apply exp in bbox_pred
-        bbox_pred = scale(self.atss_reg(reg_feat)).float()
-        centerness = self.atss_centerness(reg_feat)
-        return cls_score, bbox_pred, centerness
-
-    def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
-                    label_weights, bbox_targets, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            cls_score (Tensor): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W).
-            bbox_pred (Tensor): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            num_total_samples (int): Number os positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-            -1, self.cls_out_channels).contiguous()
-        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
-        centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # classification loss
-        loss_cls = self.loss_cls(
-            cls_score, labels, label_weights, avg_factor=num_total_samples)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_centerness = centerness[pos_inds]
-
-            centerness_targets = self.centerness_target(
-                pos_anchors, pos_bbox_targets)
-            pos_decode_bbox_pred = self.bbox_coder.decode(
-                pos_anchors, pos_bbox_pred)
-            pos_decode_bbox_targets = self.bbox_coder.decode(
-                pos_anchors, pos_bbox_targets)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=centerness_targets,
-                avg_factor=1.0)
-
-            # centerness loss
-            loss_centerness = self.loss_centerness(
-                pos_centerness,
-                centerness_targets,
-                avg_factor=num_total_samples)
-
-        else:
-            loss_bbox = bbox_pred.sum() * 0
-            loss_centerness = centerness.sum() * 0
-            centerness_targets = bbox_targets.new_tensor(0.)
-
-        return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             centernesses,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            centernesses (list[Tensor]): Centerness for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, loss_centerness,\
-            bbox_avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                centernesses,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                num_total_samples=num_total_samples)
-
-        bbox_avg_factor = sum(bbox_avg_factor)
-        bbox_avg_factor = reduce_mean(bbox_avg_factor).item()
-        if bbox_avg_factor < EPS:
-            bbox_avg_factor = 1
-        losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            loss_centerness=loss_centerness)
-
-    def centerness_target(self, anchors, bbox_targets):
-        # only calculate pos centerness targets, otherwise there may be nan
-        gts = self.bbox_coder.decode(anchors, bbox_targets)
-        anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
-        anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
-        l_ = anchors_cx - gts[:, 0]
-        t_ = anchors_cy - gts[:, 1]
-        r_ = gts[:, 2] - anchors_cx
-        b_ = gts[:, 3] - anchors_cy
-
-        left_right = torch.stack([l_, r_], dim=1)
-        top_bottom = torch.stack([t_, b_], dim=1)
-        centerness = torch.sqrt(
-            (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) *
-            (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]))
-        assert not torch.isnan(centerness).any()
-        return centerness
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   centernesses,
-                   img_metas,
-                   cfg=None,
-                   rescale=False,
-                   with_nms=True):
-        """Transform network output for a batch into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                with shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            centernesses (list[Tensor]): Centerness for each scale level with
-                shape (N, num_anchors * 1, H, W).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used. Default: None.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-        device = cls_scores[0].device
-        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
-        mlvl_anchors = self.anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-
-        cls_score_list = [cls_scores[i].detach() for i in range(num_levels)]
-        bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)]
-        centerness_pred_list = [
-            centernesses[i].detach() for i in range(num_levels)
-        ]
-        img_shapes = [
-            img_metas[i]['img_shape'] for i in range(cls_scores[0].shape[0])
-        ]
-        scale_factors = [
-            img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0])
-        ]
-        result_list = self._get_bboxes(cls_score_list, bbox_pred_list,
-                                       centerness_pred_list, mlvl_anchors,
-                                       img_shapes, scale_factors, cfg, rescale,
-                                       with_nms)
-        return result_list
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    centernesses,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for a single scale level
-                with shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for a single
-                scale level with shape (N, num_anchors * 4, H, W).
-            centernesses (list[Tensor]): Centerness for a single scale level
-                with shape (N, num_anchors * 1, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shapes (list[tuple[int]]): Shape of the input image,
-                list[(height, width, 3)].
-            scale_factors (list[ndarray]): Scale factor of the image arrange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        device = cls_scores[0].device
-        batch_size = cls_scores[0].shape[0]
-        # convert to tensor to keep tracing
-        nms_pre_tensor = torch.tensor(
-            cfg.get('nms_pre', -1), device=device, dtype=torch.long)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_centerness = []
-        for cls_score, bbox_pred, centerness, anchors in zip(
-                cls_scores, bbox_preds, centernesses, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            centerness = centerness.permute(0, 2, 3,
-                                            1).reshape(batch_size,
-                                                       -1).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-
-            # Always keep topk op for dynamic input in onnx
-            if nms_pre_tensor > 0 and (torch.onnx.is_in_onnx_export()
-                                       or scores.shape[-2] > nms_pre_tensor):
-                from torch import _shape_as_tensor
-                # keep shape as tensor and get k
-                num_anchor = _shape_as_tensor(scores)[-2].to(device)
-                nms_pre = torch.where(nms_pre_tensor < num_anchor,
-                                      nms_pre_tensor, num_anchor)
-
-                max_scores, _ = (scores * centerness[..., None]).max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                centerness = centerness[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_centerness.append(centerness)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1)
-
-        # Set max number of box to be feed into nms in deployment
-        deploy_nms_pre = cfg.get('deploy_nms_pre', -1)
-        if deploy_nms_pre > 0 and torch.onnx.is_in_onnx_export():
-            batch_mlvl_scores, _ = (
-                batch_mlvl_scores *
-                batch_mlvl_centerness.unsqueeze(2).expand_as(batch_mlvl_scores)
-            ).max(-1)
-            _, topk_inds = batch_mlvl_scores.topk(deploy_nms_pre)
-            batch_inds = torch.arange(batch_size).view(-1,
-                                                       1).expand_as(topk_inds)
-            batch_mlvl_scores = batch_mlvl_scores[batch_inds, topk_inds, :]
-            batch_mlvl_bboxes = batch_mlvl_bboxes[batch_inds, topk_inds, :]
-            batch_mlvl_centerness = batch_mlvl_centerness[batch_inds,
-                                                          topk_inds]
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-
-        if with_nms:
-            det_results = []
-            for (mlvl_bboxes, mlvl_scores,
-                 mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores,
-                                         batch_mlvl_centerness):
-                det_bbox, det_label = multiclass_nms(
-                    mlvl_bboxes,
-                    mlvl_scores,
-                    cfg.score_thr,
-                    cfg.nms,
-                    cfg.max_per_img,
-                    score_factors=mlvl_centerness)
-                det_results.append(tuple([det_bbox, det_label]))
-        else:
-            det_results = [
-                tuple(mlvl_bs)
-                for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores,
-                                   batch_mlvl_centerness)
-            ]
-        return det_results
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    label_channels=1,
-                    unmap_outputs=True):
-        """Get targets for ATSS head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. Besides
-        returning the targets as the parent method does, it also returns the
-        anchors as the first element of the returned tuple.
-        """
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        num_level_anchors_list = [num_level_anchors] * num_imgs
-
-        # concat all level anchors and flags to a single tensor
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            anchor_list[i] = torch.cat(anchor_list[i])
-            valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
-         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
-             self._get_target_single,
-             anchor_list,
-             valid_flag_list,
-             num_level_anchors_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             label_channels=label_channels,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        anchors_list = images_to_levels(all_anchors, num_level_anchors)
-        labels_list = images_to_levels(all_labels, num_level_anchors)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_anchors)
-        bbox_targets_list = images_to_levels(all_bbox_targets,
-                                             num_level_anchors)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_anchors)
-        return (anchors_list, labels_list, label_weights_list,
-                bbox_targets_list, bbox_weights_list, num_total_pos,
-                num_total_neg)
-
-    def _get_target_single(self,
-                           flat_anchors,
-                           valid_flags,
-                           num_level_anchors,
-                           gt_bboxes,
-                           gt_bboxes_ignore,
-                           gt_labels,
-                           img_meta,
-                           label_channels=1,
-                           unmap_outputs=True):
-        """Compute regression, classification targets for anchors in a single
-        image.
-
-        Args:
-            flat_anchors (Tensor): Multi-level anchors of the image, which are
-                concatenated into a single tensor of shape (num_anchors ,4)
-            valid_flags (Tensor): Multi level valid flags of the image,
-                which are concatenated into a single tensor of
-                    shape (num_anchors,).
-            num_level_anchors Tensor): Number of anchors of each scale level.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            img_meta (dict): Meta info of the image.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: N is the number of total anchors in the image.
-                labels (Tensor): Labels of all anchors in the image with shape
-                    (N,).
-                label_weights (Tensor): Label weights of all anchor in the
-                    image with shape (N,).
-                bbox_targets (Tensor): BBox targets of all anchors in the
-                    image with shape (N, 4).
-                bbox_weights (Tensor): BBox weights of all anchors in the
-                    image with shape (N, 4)
-                pos_inds (Tensor): Indices of positive anchor with shape
-                    (num_pos,).
-                neg_inds (Tensor): Indices of negative anchor with shape
-                    (num_neg,).
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg.allowed_border)
-        if not inside_flags.any():
-            return (None, ) * 7
-        # assign gt and sample anchors
-        anchors = flat_anchors[inside_flags, :]
-
-        num_level_anchors_inside = self.get_num_level_anchors_inside(
-            num_level_anchors, inside_flags)
-        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
-                                             gt_bboxes, gt_bboxes_ignore,
-                                             gt_labels)
-
-        sampling_result = self.sampler.sample(assign_result, anchors,
-                                              gt_bboxes)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            if hasattr(self, 'bbox_coder'):
-                pos_bbox_targets = self.bbox_coder.encode(
-                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-            else:
-                # used in VFNetHead
-                pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class since v2.5.0
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            anchors = unmap(anchors, num_total_anchors, inside_flags)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags, fill=self.num_classes)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
-        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
-                pos_inds, neg_inds)
-
-    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
-        split_inside_flags = torch.split(inside_flags, num_level_anchors)
-        num_level_anchors_inside = [
-            int(flags.sum()) for flags in split_inside_flags
-        ]
-        return num_level_anchors_inside
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/engine/test.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/engine/test.py
deleted file mode 100644
index 8dbeef271db634ec2dadfda3bc0b5ef9c7a677ff..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/engine/test.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-import time
-
-import torch
-import torch.distributed as dist
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.runner import get_dist_info
-
-
-def single_gpu_test(model, data_loader):
-    """Test model with a single gpu.
-
-    This method tests model with a single gpu and displays test progress bar.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (nn.Dataloader): Pytorch data loader.
-
-    Returns:
-        list: The prediction results.
-    """
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    prog_bar = mmcv.ProgressBar(len(dataset))
-    for data in data_loader:
-        with torch.no_grad():
-            result = model(return_loss=False, **data)
-        results.extend(result)
-
-        # Assume result has the same length of batch_size
-        # refer to https://github.com/open-mmlab/mmcv/issues/985
-        batch_size = len(result)
-        for _ in range(batch_size):
-            prog_bar.update()
-    return results
-
-
-def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
-    """Test model with multiple gpus.
-
-    This method tests model with multiple gpus and collects the results
-    under two different modes: gpu and cpu modes. By setting
-    ``gpu_collect=True``, it encodes results to gpu tensors and use gpu
-    communication for results collection. On cpu mode it saves the results on
-    different gpus to ``tmpdir`` and collects them by the rank 0 worker.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (nn.Dataloader): Pytorch data loader.
-        tmpdir (str): Path of directory to save the temporary results from
-            different gpus under cpu mode.
-        gpu_collect (bool): Option to use either gpu or cpu to collect results.
-
-    Returns:
-        list: The prediction results.
-    """
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    rank, world_size = get_dist_info()
-    if rank == 0:
-        prog_bar = mmcv.ProgressBar(len(dataset))
-    time.sleep(2)  # This line can prevent deadlock problem in some cases.
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, **data)
-        results.extend(result)
-
-        if rank == 0:
-            batch_size = len(result)
-            batch_size_all = batch_size * world_size
-            if batch_size_all + prog_bar.completed > len(dataset):
-                batch_size_all = len(dataset) - prog_bar.completed
-            for _ in range(batch_size_all):
-                prog_bar.update()
-
-    # collect results from all ranks
-    if gpu_collect:
-        results = collect_results_gpu(results, len(dataset))
-    else:
-        results = collect_results_cpu(results, len(dataset), tmpdir)
-    return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
-    """Collect results under cpu mode.
-
-    On cpu mode, this function will save the results on different gpus to
-    ``tmpdir`` and collect them by the rank 0 worker.
-
-    Args:
-        result_part (list): Result list containing result parts
-            to be collected.
-        size (int): Size of the results, commonly equal to length of
-            the results.
-        tmpdir (str | None): temporal directory for collected results to
-            store. If set to None, it will create a random temporal directory
-            for it.
-
-    Returns:
-        list: The collected results.
-    """
-    rank, world_size = get_dist_info()
-    # create a tmp dir if it is not specified
-    if tmpdir is None:
-        MAX_LEN = 512
-        # 32 is whitespace
-        dir_tensor = torch.full((MAX_LEN, ),
-                                32,
-                                dtype=torch.uint8,
-                                device='cuda')
-        if rank == 0:
-            mmcv.mkdir_or_exist('.dist_test')
-            tmpdir = tempfile.mkdtemp(dir='.dist_test')
-            tmpdir = torch.tensor(
-                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
-            dir_tensor[:len(tmpdir)] = tmpdir
-        dist.broadcast(dir_tensor, 0)
-        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
-    else:
-        mmcv.mkdir_or_exist(tmpdir)
-    # dump the part result to the dir
-    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
-    dist.barrier()
-    # collect all parts
-    if rank != 0:
-        return None
-    else:
-        # load results of all parts from tmp dir
-        part_list = []
-        for i in range(world_size):
-            part_file = osp.join(tmpdir, f'part_{i}.pkl')
-            part_result = mmcv.load(part_file)
-            # When data is severely insufficient, an empty part_result
-            # on a certain gpu could makes the overall outputs empty.
-            if part_result:
-                part_list.append(part_result)
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        # remove tmp dir
-        shutil.rmtree(tmpdir)
-        return ordered_results
-
-
-def collect_results_gpu(result_part, size):
-    """Collect results under gpu mode.
-
-    On gpu mode, this function will encode results to gpu tensors and use gpu
-    communication for results collection.
-
-    Args:
-        result_part (list): Result list containing result parts
-            to be collected.
-        size (int): Size of the results, commonly equal to length of
-            the results.
-
-    Returns:
-        list: The collected results.
-    """
-    rank, world_size = get_dist_info()
-    # dump result part to tensor with pickle
-    part_tensor = torch.tensor(
-        bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
-    # gather all result part tensor shape
-    shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
-    shape_list = [shape_tensor.clone() for _ in range(world_size)]
-    dist.all_gather(shape_list, shape_tensor)
-    # padding result part tensor to max length
-    shape_max = torch.tensor(shape_list).max()
-    part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
-    part_send[:shape_tensor[0]] = part_tensor
-    part_recv_list = [
-        part_tensor.new_zeros(shape_max) for _ in range(world_size)
-    ]
-    # gather all result part
-    dist.all_gather(part_recv_list, part_send)
-
-    if rank == 0:
-        part_list = []
-        for recv, shape in zip(part_recv_list, shape_list):
-            part_result = pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())
-            # When data is severely insufficient, an empty part_result
-            # on a certain gpu could makes the overall outputs empty.
-            if part_result:
-                part_list.append(part_result)
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        return ordered_results
diff --git a/spaces/Rongjiehuang/GenerSpeech/modules/fastspeech/tts_modules.py b/spaces/Rongjiehuang/GenerSpeech/modules/fastspeech/tts_modules.py
deleted file mode 100644
index d81bd785460368efec4a465637f1e35c68395f35..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/GenerSpeech/modules/fastspeech/tts_modules.py
+++ /dev/null
@@ -1,356 +0,0 @@
-import logging
-import math
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from modules.commons.espnet_positional_embedding import RelPositionalEncoding
-from modules.commons.common_layers import SinusoidalPositionalEmbedding, Linear, EncSALayer, DecSALayer, BatchNorm1dTBC
-from utils.hparams import hparams
-
-DEFAULT_MAX_SOURCE_POSITIONS = 2000
-DEFAULT_MAX_TARGET_POSITIONS = 2000
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(self, hidden_size, dropout, kernel_size=None, num_heads=2, norm='ln'):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.dropout = dropout
-        self.num_heads = num_heads
-        self.op = EncSALayer(
-            hidden_size, num_heads, dropout=dropout,
-            attention_dropout=0.0, relu_dropout=dropout,
-            kernel_size=kernel_size
-            if kernel_size is not None else hparams['enc_ffn_kernel_size'],
-            padding=hparams['ffn_padding'],
-            norm=norm, act=hparams['ffn_act'])
-
-    def forward(self, x, **kwargs):
-        return self.op(x, **kwargs)
-
-
-######################
-# fastspeech modules
-######################
-class LayerNorm(torch.nn.LayerNorm):
-    """Layer normalization module.
-    :param int nout: output dim size
-    :param int dim: dimension to be normalized
-    """
-
-    def __init__(self, nout, dim=-1, eps=1e-5):
-        """Construct an LayerNorm object."""
-        super(LayerNorm, self).__init__(nout, eps=eps)
-        self.dim = dim
-
-    def forward(self, x):
-        """Apply layer normalization.
-        :param torch.Tensor x: input tensor
-        :return: layer normalized tensor
-        :rtype torch.Tensor
-        """
-        if self.dim == -1:
-            return super(LayerNorm, self).forward(x)
-        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
-
-class DurationPredictor(torch.nn.Module):
-    """Duration predictor module.
-    This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
-    The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder.
-    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
-        https://arxiv.org/pdf/1905.09263.pdf
-    Note:
-        The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`,
-        the outputs are calculated in log domain but in `inference`, those are calculated in linear domain.
-    """
-
-    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0, padding='SAME'):
-        """Initilize duration predictor module.
-        Args:
-            idim (int): Input dimension.
-            n_layers (int, optional): Number of convolutional layers.
-            n_chans (int, optional): Number of channels of convolutional layers.
-            kernel_size (int, optional): Kernel size of convolutional layers.
-            dropout_rate (float, optional): Dropout rate.
-            offset (float, optional): Offset value to avoid nan in log domain.
-        """
-        super(DurationPredictor, self).__init__()
-        self.offset = offset
-        self.conv = torch.nn.ModuleList()
-        self.kernel_size = kernel_size
-        self.padding = padding
-        for idx in range(n_layers):
-            in_chans = idim if idx == 0 else n_chans
-            self.conv += [torch.nn.Sequential(
-                torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
-                                       if padding == 'SAME'
-                                       else (kernel_size - 1, 0), 0),
-                torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
-                torch.nn.ReLU(),
-                LayerNorm(n_chans, dim=1),
-                torch.nn.Dropout(dropout_rate)
-            )]
-        if hparams['dur_loss'] in ['mse', 'huber']:
-            odims = 1
-        elif hparams['dur_loss'] == 'mog':
-            odims = 15
-        elif hparams['dur_loss'] == 'crf':
-            odims = 32
-            from torchcrf import CRF
-            self.crf = CRF(odims, batch_first=True)
-        self.linear = torch.nn.Linear(n_chans, odims)
-
-    def _forward(self, xs, x_masks=None, is_inference=False):
-        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
-        for f in self.conv:
-            xs = f(xs)  # (B, C, Tmax)
-            if x_masks is not None:
-                xs = xs * (1 - x_masks.float())[:, None, :]
-
-        xs = self.linear(xs.transpose(1, -1))  # [B, T, C]
-        xs = xs * (1 - x_masks.float())[:, :, None]  # (B, T, C)
-        if is_inference:
-            return self.out2dur(xs), xs
-        else:
-            if hparams['dur_loss'] in ['mse']:
-                xs = xs.squeeze(-1)  # (B, Tmax)
-        return xs
-
-    def out2dur(self, xs):
-        if hparams['dur_loss'] in ['mse']:
-            # NOTE: calculate in log domain
-            xs = xs.squeeze(-1)  # (B, Tmax)
-            dur = torch.clamp(torch.round(xs.exp() - self.offset), min=0).long()  # avoid negative value
-        elif hparams['dur_loss'] == 'mog':
-            return NotImplementedError
-        elif hparams['dur_loss'] == 'crf':
-            dur = torch.LongTensor(self.crf.decode(xs)).cuda()
-        return dur
-
-    def forward(self, xs, x_masks=None):
-        """Calculate forward propagation.
-        Args:
-            xs (Tensor): Batch of input sequences (B, Tmax, idim).
-            x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax).
-        Returns:
-            Tensor: Batch of predicted durations in log domain (B, Tmax).
-        """
-        return self._forward(xs, x_masks, False)
-
-    def inference(self, xs, x_masks=None):
-        """Inference duration.
-        Args:
-            xs (Tensor): Batch of input sequences (B, Tmax, idim).
-            x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax).
-        Returns:
-            LongTensor: Batch of predicted durations in linear domain (B, Tmax).
-        """
-        return self._forward(xs, x_masks, True)
-
-
-class LengthRegulator(torch.nn.Module):
-    def __init__(self, pad_value=0.0):
-        super(LengthRegulator, self).__init__()
-        self.pad_value = pad_value
-
-    def forward(self, dur, dur_padding=None, alpha=1.0):
-        """
-        Example (no batch dim version):
-            1. dur = [2,2,3]
-            2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4]
-            3. token_mask = [[1,1,0,0,0,0,0],
-                             [0,0,1,1,0,0,0],
-                             [0,0,0,0,1,1,1]]
-            4. token_idx * token_mask = [[1,1,0,0,0,0,0],
-                                         [0,0,2,2,0,0,0],
-                                         [0,0,0,0,3,3,3]]
-            5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3]
-
-        :param dur: Batch of durations of each frame (B, T_txt)
-        :param dur_padding: Batch of padding of each frame (B, T_txt)
-        :param alpha: duration rescale coefficient
-        :return:
-            mel2ph (B, T_speech)
-        """
-        assert alpha > 0
-        dur = torch.round(dur.float() * alpha).long()
-        if dur_padding is not None:
-            dur = dur * (1 - dur_padding.long())
-        token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device)
-        dur_cumsum = torch.cumsum(dur, 1)
-        dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0)
-
-        pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device)
-        token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None])
-        mel2ph = (token_idx * token_mask.long()).sum(1)
-        return mel2ph
-
-
-class PitchPredictor(torch.nn.Module):
-    def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5,
-                 dropout_rate=0.1, padding='SAME'):
-        """Initilize pitch predictor module.
-        Args:
-            idim (int): Input dimension.
-            n_layers (int, optional): Number of convolutional layers.
-            n_chans (int, optional): Number of channels of convolutional layers.
-            kernel_size (int, optional): Kernel size of convolutional layers.
-            dropout_rate (float, optional): Dropout rate.
-        """
-        super(PitchPredictor, self).__init__()
-        self.conv = torch.nn.ModuleList()
-        self.kernel_size = kernel_size
-        self.padding = padding
-        for idx in range(n_layers):
-            in_chans = idim if idx == 0 else n_chans
-            self.conv += [torch.nn.Sequential(
-                torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
-                                       if padding == 'SAME'
-                                       else (kernel_size - 1, 0), 0),
-                torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
-                torch.nn.ReLU(),
-                LayerNorm(n_chans, dim=1),
-                torch.nn.Dropout(dropout_rate)
-            )]
-        self.linear = torch.nn.Linear(n_chans, odim)
-        self.embed_positions = SinusoidalPositionalEmbedding(idim, 0, init_size=4096)
-        self.pos_embed_alpha = nn.Parameter(torch.Tensor([1]))
-
-    def forward(self, xs):
-        """
-
-        :param xs: [B, T, H]
-        :return: [B, T, H]
-        """
-        positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0])
-        xs = xs + positions
-        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
-        for f in self.conv:
-            xs = f(xs)  # (B, C, Tmax)
-        # NOTE: calculate in log domain
-        xs = self.linear(xs.transpose(1, -1))  # (B, Tmax, H)
-        return xs
-
-
-class EnergyPredictor(PitchPredictor):
-    pass
-
-
-def mel2ph_to_dur(mel2ph, T_txt, max_dur=None):
-    B, _ = mel2ph.shape
-    dur = mel2ph.new_zeros(B, T_txt + 1).scatter_add(1, mel2ph, torch.ones_like(mel2ph))
-    dur = dur[:, 1:]
-    if max_dur is not None:
-        dur = dur.clamp(max=max_dur)
-    return dur
-
-
-class FFTBlocks(nn.Module):
-    def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=None, num_heads=2,
-                 use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True):
-        super().__init__()
-        self.num_layers = num_layers
-        embed_dim = self.hidden_size = hidden_size
-        self.dropout = dropout if dropout is not None else hparams['dropout']
-        self.use_pos_embed = use_pos_embed
-        self.use_last_norm = use_last_norm
-        if use_pos_embed:
-            self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS
-            self.padding_idx = 0
-            self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1
-            self.embed_positions = SinusoidalPositionalEmbedding(
-                embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
-            )
-
-        self.layers = nn.ModuleList([])
-        self.layers.extend([
-            TransformerEncoderLayer(self.hidden_size, self.dropout,
-                                    kernel_size=ffn_kernel_size, num_heads=num_heads)
-            for _ in range(self.num_layers)
-        ])
-        if self.use_last_norm:
-            if norm == 'ln':
-                self.layer_norm = nn.LayerNorm(embed_dim)
-            elif norm == 'bn':
-                self.layer_norm = BatchNorm1dTBC(embed_dim)
-        else:
-            self.layer_norm = None
-
-    def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False):
-        """
-        :param x: [B, T, C]
-        :param padding_mask: [B, T]
-        :return: [B, T, C] or [L, B, T, C]
-        """
-        padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
-        nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None]  # [T, B, 1]
-        if self.use_pos_embed:
-            positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
-            x = x + positions
-            x = F.dropout(x, p=self.dropout, training=self.training)
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1) * nonpadding_mask_TB
-        hiddens = []
-        for layer in self.layers:
-            x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
-            hiddens.append(x)
-        if self.use_last_norm:
-            x = self.layer_norm(x) * nonpadding_mask_TB
-        if return_hiddens:
-            x = torch.stack(hiddens, 0)  # [L, T, B, C]
-            x = x.transpose(1, 2)  # [L, B, T, C]
-        else:
-            x = x.transpose(0, 1)  # [B, T, C]
-        return x
-
-
-class FastspeechEncoder(FFTBlocks):
-    def __init__(self, embed_tokens, hidden_size=None, num_layers=None, kernel_size=None, num_heads=2):
-        hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size
-        kernel_size = hparams['enc_ffn_kernel_size'] if kernel_size is None else kernel_size
-        num_layers = hparams['dec_layers'] if num_layers is None else num_layers
-        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads,
-                         use_pos_embed=False)  # use_pos_embed_alpha for compatibility
-        self.embed_tokens = embed_tokens
-        self.embed_scale = math.sqrt(hidden_size)
-        self.padding_idx = 0
-        if hparams.get('rel_pos') is not None and hparams['rel_pos']:
-            self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0)
-        else:
-            self.embed_positions = SinusoidalPositionalEmbedding(
-                hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
-            )
-
-    def forward(self, txt_tokens):
-        """
-
-        :param txt_tokens: [B, T]
-        :return: {
-            'encoder_out': [T x B x C]
-        }
-        """
-        encoder_padding_mask = txt_tokens.eq(self.padding_idx).data
-        x = self.forward_embedding(txt_tokens)  # [B, T, H]
-        x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask)
-        return x
-
-    def forward_embedding(self, txt_tokens):
-        # embed tokens and positions
-        x = self.embed_scale * self.embed_tokens(txt_tokens)
-        if hparams['use_pos_embed']:
-            positions = self.embed_positions(txt_tokens)
-            x = x + positions
-        x = F.dropout(x, p=self.dropout, training=self.training)
-        return x
-
-
-class FastspeechDecoder(FFTBlocks):
-    def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None):
-        num_heads = hparams['num_heads'] if num_heads is None else num_heads
-        hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size
-        kernel_size = hparams['dec_ffn_kernel_size'] if kernel_size is None else kernel_size
-        num_layers = hparams['dec_layers'] if num_layers is None else num_layers
-        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)
-
diff --git a/spaces/STF-R/docker-test3/app/static/css/bootstrap.min.css b/spaces/STF-R/docker-test3/app/static/css/bootstrap.min.css
deleted file mode 100644
index 74a3ca1767f76e2be33d1550d0c713f5edd6d61d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/static/css/bootstrap.min.css
+++ /dev/null
@@ -1,7 +0,0 @@
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(min-width:1200px){.text-xl-left{text-align:left!important}.text-xl-right{text-align:right!important}.text-xl-center{text-align:center!important}}.text-lowercase{text-transform:lowercase!important}.text-uppercase{text-transform:uppercase!important}.text-capitalize{text-transform:capitalize!important}.font-weight-light{font-weight:300!important}.font-weight-normal{font-weight:400!important}.font-weight-bold{font-weight:700!important}.font-italic{font-style:italic!important}.text-white{color:#fff!important}.text-primary{color:#007bff!important}a.text-primary:focus,a.text-primary:hover{color:#0062cc!important}.text-secondary{color:#6c757d!important}a.text-secondary:focus,a.text-secondary:hover{color:#545b62!important}.text-success{color:#28a745!important}a.text-success:focus,a.text-success:hover{color:#1e7e34!important}.text-info{color:#17a2b8!important}a.text-info:focus,a.text-info:hover{color:#117a8b!important}.text-warning{color:#ffc107!important}a.text-warning:focus,a.text-warning:hover{color:#d39e00!important}.text-danger{color:#dc3545!important}a.text-danger:focus,a.text-danger:hover{color:#bd2130!important}.text-light{color:#f8f9fa!important}a.text-light:focus,a.text-light:hover{color:#dae0e5!important}.text-dark{color:#343a40!important}a.text-dark:focus,a.text-dark:hover{color:#1d2124!important}.text-body{color:#212529!important}.text-muted{color:#6c757d!important}.text-black-50{color:rgba(0,0,0,.5)!important}.text-white-50{color:rgba(255,255,255,.5)!important}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.visible{visibility:visible!important}.invisible{visibility:hidden!important}@media print{*,::after,::before{text-shadow:none!important;box-shadow:none!important}a:not(.btn){text-decoration:underline}abbr[title]::after{content:" (" attr(title) ")"}pre{white-space:pre-wrap!important}blockquote,pre{border:1px solid #adb5bd;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}@page{size:a3}body{min-width:992px!important}.container{min-width:992px!important}.navbar{display:none}.badge{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #dee2e6!important}.table-dark{color:inherit}.table-dark tbody+tbody,.table-dark td,.table-dark th,.table-dark thead th{border-color:#dee2e6}.table .thead-dark th{color:inherit;border-color:#dee2e6}}
-/*# sourceMappingURL=bootstrap.min.css.map */
diff --git a/spaces/Salesforce/EDICT/my_diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py b/spaces/Salesforce/EDICT/my_diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py
deleted file mode 100644
index 604e2b54cc1766ff446a23235ae4b40f790eadc5..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python3
-import warnings
-from typing import Optional, Tuple, Union
-
-import torch
-
-from ...models import UNet2DModel
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import ScoreSdeVeScheduler
-
-
-class ScoreSdeVePipeline(DiffusionPipeline):
-    r"""
-    Parameters:
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. scheduler ([`SchedulerMixin`]):
-            The [`ScoreSdeVeScheduler`] scheduler to be used in combination with `unet` to denoise the encoded image.
-    """
-    unet: UNet2DModel
-    scheduler: ScoreSdeVeScheduler
-
-    def __init__(self, unet: UNet2DModel, scheduler: DiffusionPipeline):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        num_inference_steps: int = 2000,
-        generator: Optional[torch.Generator] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
-        img_size = self.unet.config.sample_size
-        shape = (batch_size, 3, img_size, img_size)
-
-        model = self.unet
-
-        sample = torch.randn(*shape, generator=generator) * self.scheduler.config.sigma_max
-        sample = sample.to(self.device)
-
-        self.scheduler.set_timesteps(num_inference_steps)
-        self.scheduler.set_sigmas(num_inference_steps)
-
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
-            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
-
-            # correction step
-            for _ in range(self.scheduler.correct_steps):
-                model_output = self.unet(sample, sigma_t).sample
-                sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
-
-            # prediction step
-            model_output = model(sample, sigma_t).sample
-            output = self.scheduler.step_pred(model_output, t, sample, generator=generator)
-
-            sample, sample_mean = output.prev_sample, output.prev_sample_mean
-
-        sample = sample_mean.clamp(0, 1)
-        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            sample = self.numpy_to_pil(sample)
-
-        if not return_dict:
-            return (sample,)
-
-        return ImagePipelineOutput(images=sample)
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py
deleted file mode 100644
index 650e13b610c3f8fba273f674fc071628743345ef..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# -----------------------------------------------------
-# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
-# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
-# -----------------------------------------------------
-
-import os
-import h5py
-from functools import reduce
-
-import torch.utils.data as data
-from ..pose import generateSampleBox
-from opt import opt
-
-
-class Mscoco(data.Dataset):
-    def __init__(self, train=True, sigma=1,
-                 scale_factor=(0.2, 0.3), rot_factor=40, label_type='Gaussian'):
-        self.img_folder = '../data/coco/images'    # root image folders
-        self.is_train = train           # training set or test set
-        self.inputResH = opt.inputResH
-        self.inputResW = opt.inputResW
-        self.outputResH = opt.outputResH
-        self.outputResW = opt.outputResW
-        self.sigma = sigma
-        self.scale_factor = scale_factor
-        self.rot_factor = rot_factor
-        self.label_type = label_type
-
-        self.nJoints_coco = 17
-        self.nJoints = 17
-
-        self.accIdxs = (1, 2, 3, 4, 5, 6, 7, 8,
-                        9, 10, 11, 12, 13, 14, 15, 16, 17)
-        self.flipRef = ((2, 3), (4, 5), (6, 7),
-                        (8, 9), (10, 11), (12, 13),
-                        (14, 15), (16, 17))
-
-        # create train/val split
-        with h5py.File('../data/coco/annot_coco.h5', 'r') as annot:
-            # train
-            self.imgname_coco_train = annot['imgname'][:-5887]
-            self.bndbox_coco_train = annot['bndbox'][:-5887]
-            self.part_coco_train = annot['part'][:-5887]
-            # val
-            self.imgname_coco_val = annot['imgname'][-5887:]
-            self.bndbox_coco_val = annot['bndbox'][-5887:]
-            self.part_coco_val = annot['part'][-5887:]
-
-        self.size_train = self.imgname_coco_train.shape[0]
-        self.size_val = self.imgname_coco_val.shape[0]
-
-    def __getitem__(self, index):
-        sf = self.scale_factor
-
-        if self.is_train:
-            part = self.part_coco_train[index]
-            bndbox = self.bndbox_coco_train[index]
-            imgname = self.imgname_coco_train[index]
-        else:
-            part = self.part_coco_val[index]
-            bndbox = self.bndbox_coco_val[index]
-            imgname = self.imgname_coco_val[index]
-
-        imgname = reduce(lambda x, y: x + y,
-                         map(lambda x: chr(int(x)), imgname))
-        img_path = os.path.join(self.img_folder, imgname)
-
-        metaData = generateSampleBox(img_path, bndbox, part, self.nJoints,
-                                     'coco', sf, self, train=self.is_train)
-
-        inp, out, setMask = metaData
-
-        return inp, out, setMask, 'coco'
-
-    def __len__(self):
-        if self.is_train:
-            return self.size_train
-        else:
-            return self.size_val
diff --git a/spaces/ServerX/PorcoDiaz/Applio-RVC-Fork/utils/i18n.py b/spaces/ServerX/PorcoDiaz/Applio-RVC-Fork/utils/i18n.py
deleted file mode 100644
index 8e75d2bc26ff86ab1716b8d7f239ad9f5cc1e32d..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/Applio-RVC-Fork/utils/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
-    with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
-        language_list = json.load(f)
-    return language_list
-
-
-class I18nAuto:
-    def __init__(self, language=None):
-        if language in ["Auto", None]:
-            language = "es_ES"
-        if not os.path.exists(f"./i18n/{language}.json"):
-            language = "es_ES"
-            language = "es_ES"
-        self.language = language
-        # print("Use Language:", language)
-        self.language_map = load_language_list(language)
-
-    def __call__(self, key):
-        return self.language_map.get(key, key)
-
-    def print(self):
-      # print("Use Language:", self.language)
-        print("")
diff --git a/spaces/SeyedAli/Image-Segmentation/app.py b/spaces/SeyedAli/Image-Segmentation/app.py
deleted file mode 100644
index b3d1a32b2e65479ed071174b51682c59322f62e4..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Image-Segmentation/app.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import gradio as gr
-from predict import predict_masks
-import glob
-
-##Create list of examples to be loaded
-example_list = glob.glob("examples/*")
-example_list = list(map(lambda el:[el], example_list))
-
-demo = gr.Blocks()
-
-with demo:
-    
-    gr.Markdown("# **<p align='center'>Mask2Former: Masked Attention Mask Transformer for Universal Segmentation</p>**")
-    gr.Markdown("This space demonstrates the use of Mask2Former. It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). \
-        Before Mask2Former, you'd have to resort to using a specialized architecture designed for solving a particular kind of image segmentation task (i.e. semantic, instance or panoptic segmentation). On the other hand, in the form of Mask2Former, for the first time, we have a single architecture that is capable of solving any segmentation task and performs on par or better than specialized architectures.")
-       
-    with gr.Box():
-        
-        
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("**Inputs**")
-                segmentation_task = gr.Dropdown(["semantic", "instance", "panoptic"], value="panoptic", label="Segmentation Task", show_label=True)
-                input_image = gr.Image(type='filepath',label="Input Image", show_label=True)
-            
-            with gr.Column():
-                gr.Markdown("**Outputs**")
-                output_heading = gr.Textbox(label="Output Type", show_label=True)
-                output_mask = gr.Image(label="Predicted Masks", show_label=True)
-    
-    gr.Markdown("**Predict**")
-    
-    with gr.Box():
-        with gr.Row():
-            submit_button = gr.Button("Submit")
-    
-    gr.Markdown("**Examples:**")
-
-    with gr.Column():
-        gr.Examples(example_list, [input_image, segmentation_task], [output_mask,output_heading], predict_masks)
-        
-    
-    submit_button.click(predict_masks, inputs=[input_image, segmentation_task], outputs=[output_mask,output_heading])
-    
-    gr.Markdown('\n Demo created by: <a href=\"https://www.linkedin.com/in/seyed-ali-mir-mohammad-hosseini-517002149/\">SeyedAli Mir Mohammad Hoessini</a>')
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Silentlin/DiffSinger/modules/parallel_wavegan/layers/__init__.py b/spaces/Silentlin/DiffSinger/modules/parallel_wavegan/layers/__init__.py
deleted file mode 100644
index e477f51116a3157781b1aefefbaf32fe4d4bd1f0..0000000000000000000000000000000000000000
--- a/spaces/Silentlin/DiffSinger/modules/parallel_wavegan/layers/__init__.py
+++ /dev/null
@@ -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
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/IcnsImagePlugin.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/IcnsImagePlugin.py
deleted file mode 100644
index c2f050eddb3428b9182ebb35aea6e8d789a76707..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/IcnsImagePlugin.py
+++ /dev/null
@@ -1,399 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# macOS icns file decoder, based on icns.py by Bob Ippolito.
-#
-# history:
-# 2004-10-09 fl   Turned into a PIL plugin; removed 2.3 dependencies.
-# 2020-04-04      Allow saving on all operating systems.
-#
-# Copyright (c) 2004 by Bob Ippolito.
-# Copyright (c) 2004 by Secret Labs.
-# Copyright (c) 2004 by Fredrik Lundh.
-# Copyright (c) 2014 by Alastair Houghton.
-# Copyright (c) 2020 by Pan Jing.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import os
-import struct
-import sys
-
-from PIL import Image, ImageFile, PngImagePlugin, features
-
-enable_jpeg2k = features.check_codec("jpg_2000")
-if enable_jpeg2k:
-    from PIL import Jpeg2KImagePlugin
-
-MAGIC = b"icns"
-HEADERSIZE = 8
-
-
-def nextheader(fobj):
-    return struct.unpack(">4sI", fobj.read(HEADERSIZE))
-
-
-def read_32t(fobj, start_length, size):
-    # The 128x128 icon seems to have an extra header for some reason.
-    (start, length) = start_length
-    fobj.seek(start)
-    sig = fobj.read(4)
-    if sig != b"\x00\x00\x00\x00":
-        msg = "Unknown signature, expecting 0x00000000"
-        raise SyntaxError(msg)
-    return read_32(fobj, (start + 4, length - 4), size)
-
-
-def read_32(fobj, start_length, size):
-    """
-    Read a 32bit RGB icon resource.  Seems to be either uncompressed or
-    an RLE packbits-like scheme.
-    """
-    (start, length) = start_length
-    fobj.seek(start)
-    pixel_size = (size[0] * size[2], size[1] * size[2])
-    sizesq = pixel_size[0] * pixel_size[1]
-    if length == sizesq * 3:
-        # uncompressed ("RGBRGBGB")
-        indata = fobj.read(length)
-        im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1)
-    else:
-        # decode image
-        im = Image.new("RGB", pixel_size, None)
-        for band_ix in range(3):
-            data = []
-            bytesleft = sizesq
-            while bytesleft > 0:
-                byte = fobj.read(1)
-                if not byte:
-                    break
-                byte = byte[0]
-                if byte & 0x80:
-                    blocksize = byte - 125
-                    byte = fobj.read(1)
-                    for i in range(blocksize):
-                        data.append(byte)
-                else:
-                    blocksize = byte + 1
-                    data.append(fobj.read(blocksize))
-                bytesleft -= blocksize
-                if bytesleft <= 0:
-                    break
-            if bytesleft != 0:
-                msg = f"Error reading channel [{repr(bytesleft)} left]"
-                raise SyntaxError(msg)
-            band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1)
-            im.im.putband(band.im, band_ix)
-    return {"RGB": im}
-
-
-def read_mk(fobj, start_length, size):
-    # Alpha masks seem to be uncompressed
-    start = start_length[0]
-    fobj.seek(start)
-    pixel_size = (size[0] * size[2], size[1] * size[2])
-    sizesq = pixel_size[0] * pixel_size[1]
-    band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1)
-    return {"A": band}
-
-
-def read_png_or_jpeg2000(fobj, start_length, size):
-    (start, length) = start_length
-    fobj.seek(start)
-    sig = fobj.read(12)
-    if sig[:8] == b"\x89PNG\x0d\x0a\x1a\x0a":
-        fobj.seek(start)
-        im = PngImagePlugin.PngImageFile(fobj)
-        Image._decompression_bomb_check(im.size)
-        return {"RGBA": im}
-    elif (
-        sig[:4] == b"\xff\x4f\xff\x51"
-        or sig[:4] == b"\x0d\x0a\x87\x0a"
-        or sig == b"\x00\x00\x00\x0cjP  \x0d\x0a\x87\x0a"
-    ):
-        if not enable_jpeg2k:
-            msg = (
-                "Unsupported icon subimage format (rebuild PIL "
-                "with JPEG 2000 support to fix this)"
-            )
-            raise ValueError(msg)
-        # j2k, jpc or j2c
-        fobj.seek(start)
-        jp2kstream = fobj.read(length)
-        f = io.BytesIO(jp2kstream)
-        im = Jpeg2KImagePlugin.Jpeg2KImageFile(f)
-        Image._decompression_bomb_check(im.size)
-        if im.mode != "RGBA":
-            im = im.convert("RGBA")
-        return {"RGBA": im}
-    else:
-        msg = "Unsupported icon subimage format"
-        raise ValueError(msg)
-
-
-class IcnsFile:
-    SIZES = {
-        (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)],
-        (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)],
-        (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)],
-        (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)],
-        (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)],
-        (128, 128, 1): [
-            (b"ic07", read_png_or_jpeg2000),
-            (b"it32", read_32t),
-            (b"t8mk", read_mk),
-        ],
-        (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)],
-        (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)],
-        (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)],
-        (32, 32, 1): [
-            (b"icp5", read_png_or_jpeg2000),
-            (b"il32", read_32),
-            (b"l8mk", read_mk),
-        ],
-        (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)],
-        (16, 16, 1): [
-            (b"icp4", read_png_or_jpeg2000),
-            (b"is32", read_32),
-            (b"s8mk", read_mk),
-        ],
-    }
-
-    def __init__(self, fobj):
-        """
-        fobj is a file-like object as an icns resource
-        """
-        # signature : (start, length)
-        self.dct = dct = {}
-        self.fobj = fobj
-        sig, filesize = nextheader(fobj)
-        if not _accept(sig):
-            msg = "not an icns file"
-            raise SyntaxError(msg)
-        i = HEADERSIZE
-        while i < filesize:
-            sig, blocksize = nextheader(fobj)
-            if blocksize <= 0:
-                msg = "invalid block header"
-                raise SyntaxError(msg)
-            i += HEADERSIZE
-            blocksize -= HEADERSIZE
-            dct[sig] = (i, blocksize)
-            fobj.seek(blocksize, io.SEEK_CUR)
-            i += blocksize
-
-    def itersizes(self):
-        sizes = []
-        for size, fmts in self.SIZES.items():
-            for fmt, reader in fmts:
-                if fmt in self.dct:
-                    sizes.append(size)
-                    break
-        return sizes
-
-    def bestsize(self):
-        sizes = self.itersizes()
-        if not sizes:
-            msg = "No 32bit icon resources found"
-            raise SyntaxError(msg)
-        return max(sizes)
-
-    def dataforsize(self, size):
-        """
-        Get an icon resource as {channel: array}.  Note that
-        the arrays are bottom-up like windows bitmaps and will likely
-        need to be flipped or transposed in some way.
-        """
-        dct = {}
-        for code, reader in self.SIZES[size]:
-            desc = self.dct.get(code)
-            if desc is not None:
-                dct.update(reader(self.fobj, desc, size))
-        return dct
-
-    def getimage(self, size=None):
-        if size is None:
-            size = self.bestsize()
-        if len(size) == 2:
-            size = (size[0], size[1], 1)
-        channels = self.dataforsize(size)
-
-        im = channels.get("RGBA", None)
-        if im:
-            return im
-
-        im = channels.get("RGB").copy()
-        try:
-            im.putalpha(channels["A"])
-        except KeyError:
-            pass
-        return im
-
-
-##
-# Image plugin for Mac OS icons.
-
-
-class IcnsImageFile(ImageFile.ImageFile):
-    """
-    PIL image support for Mac OS .icns files.
-    Chooses the best resolution, but will possibly load
-    a different size image if you mutate the size attribute
-    before calling 'load'.
-
-    The info dictionary has a key 'sizes' that is a list
-    of sizes that the icns file has.
-    """
-
-    format = "ICNS"
-    format_description = "Mac OS icns resource"
-
-    def _open(self):
-        self.icns = IcnsFile(self.fp)
-        self.mode = "RGBA"
-        self.info["sizes"] = self.icns.itersizes()
-        self.best_size = self.icns.bestsize()
-        self.size = (
-            self.best_size[0] * self.best_size[2],
-            self.best_size[1] * self.best_size[2],
-        )
-
-    @property
-    def size(self):
-        return self._size
-
-    @size.setter
-    def size(self, value):
-        info_size = value
-        if info_size not in self.info["sizes"] and len(info_size) == 2:
-            info_size = (info_size[0], info_size[1], 1)
-        if (
-            info_size not in self.info["sizes"]
-            and len(info_size) == 3
-            and info_size[2] == 1
-        ):
-            simple_sizes = [
-                (size[0] * size[2], size[1] * size[2]) for size in self.info["sizes"]
-            ]
-            if value in simple_sizes:
-                info_size = self.info["sizes"][simple_sizes.index(value)]
-        if info_size not in self.info["sizes"]:
-            msg = "This is not one of the allowed sizes of this image"
-            raise ValueError(msg)
-        self._size = value
-
-    def load(self):
-        if len(self.size) == 3:
-            self.best_size = self.size
-            self.size = (
-                self.best_size[0] * self.best_size[2],
-                self.best_size[1] * self.best_size[2],
-            )
-
-        px = Image.Image.load(self)
-        if self.im is not None and self.im.size == self.size:
-            # Already loaded
-            return px
-        self.load_prepare()
-        # This is likely NOT the best way to do it, but whatever.
-        im = self.icns.getimage(self.best_size)
-
-        # If this is a PNG or JPEG 2000, it won't be loaded yet
-        px = im.load()
-
-        self.im = im.im
-        self.mode = im.mode
-        self.size = im.size
-
-        return px
-
-
-def _save(im, fp, filename):
-    """
-    Saves the image as a series of PNG files,
-    that are then combined into a .icns file.
-    """
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-    sizes = {
-        b"ic07": 128,
-        b"ic08": 256,
-        b"ic09": 512,
-        b"ic10": 1024,
-        b"ic11": 32,
-        b"ic12": 64,
-        b"ic13": 256,
-        b"ic14": 512,
-    }
-    provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])}
-    size_streams = {}
-    for size in set(sizes.values()):
-        image = (
-            provided_images[size]
-            if size in provided_images
-            else im.resize((size, size))
-        )
-
-        temp = io.BytesIO()
-        image.save(temp, "png")
-        size_streams[size] = temp.getvalue()
-
-    entries = []
-    for type, size in sizes.items():
-        stream = size_streams[size]
-        entries.append(
-            {"type": type, "size": HEADERSIZE + len(stream), "stream": stream}
-        )
-
-    # Header
-    fp.write(MAGIC)
-    file_length = HEADERSIZE  # Header
-    file_length += HEADERSIZE + 8 * len(entries)  # TOC
-    file_length += sum(entry["size"] for entry in entries)
-    fp.write(struct.pack(">i", file_length))
-
-    # TOC
-    fp.write(b"TOC ")
-    fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE))
-    for entry in entries:
-        fp.write(entry["type"])
-        fp.write(struct.pack(">i", entry["size"]))
-
-    # Data
-    for entry in entries:
-        fp.write(entry["type"])
-        fp.write(struct.pack(">i", entry["size"]))
-        fp.write(entry["stream"])
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-def _accept(prefix):
-    return prefix[:4] == MAGIC
-
-
-Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept)
-Image.register_extension(IcnsImageFile.format, ".icns")
-
-Image.register_save(IcnsImageFile.format, _save)
-Image.register_mime(IcnsImageFile.format, "image/icns")
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Syntax: python3 IcnsImagePlugin.py [file]")
-        sys.exit()
-
-    with open(sys.argv[1], "rb") as fp:
-        imf = IcnsImageFile(fp)
-        for size in imf.info["sizes"]:
-            imf.size = size
-            imf.save("out-%s-%s-%s.png" % size)
-        with Image.open(sys.argv[1]) as im:
-            im.save("out.png")
-        if sys.platform == "windows":
-            os.startfile("out.png")
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/_magics.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/_magics.py
deleted file mode 100644
index 7fe6131182952ff30bf63543de528657f7ba77a2..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/_magics.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""
-Magic functions for rendering vega-lite specifications
-"""
-__all__ = ["vegalite"]
-
-import json
-import warnings
-
-import IPython
-from IPython.core import magic_arguments
-import pandas as pd
-from toolz import curried
-
-from altair.vegalite import v5 as vegalite_v5
-
-try:
-    import yaml
-
-    YAML_AVAILABLE = True
-except ImportError:
-    YAML_AVAILABLE = False
-
-
-RENDERERS = {
-    "vega-lite": {
-        "5": vegalite_v5.VegaLite,
-    },
-}
-
-
-TRANSFORMERS = {
-    "vega-lite": {
-        "5": vegalite_v5.data_transformers,
-    },
-}
-
-
-def _prepare_data(data, data_transformers):
-    """Convert input data to data for use within schema"""
-    if data is None or isinstance(data, dict):
-        return data
-    elif isinstance(data, pd.DataFrame):
-        return curried.pipe(data, data_transformers.get())
-    elif isinstance(data, str):
-        return {"url": data}
-    else:
-        warnings.warn("data of type {} not recognized".format(type(data)), stacklevel=1)
-        return data
-
-
-def _get_variable(name):
-    """Get a variable from the notebook namespace."""
-    ip = IPython.get_ipython()
-    if ip is None:
-        raise ValueError(
-            "Magic command must be run within an IPython "
-            "environemnt, in which get_ipython() is defined."
-        )
-    if name not in ip.user_ns:
-        raise NameError(
-            "argument '{}' does not match the "
-            "name of any defined variable".format(name)
-        )
-    return ip.user_ns[name]
-
-
-@magic_arguments.magic_arguments()
-@magic_arguments.argument(
-    "data",
-    nargs="?",
-    help="local variablename of a pandas DataFrame to be used as the dataset",
-)
-@magic_arguments.argument("-v", "--version", dest="version", default="v5")
-@magic_arguments.argument("-j", "--json", dest="json", action="store_true")
-def vegalite(line, cell):
-    """Cell magic for displaying vega-lite visualizations in CoLab.
-
-    %%vegalite [dataframe] [--json] [--version='v5']
-
-    Visualize the contents of the cell using Vega-Lite, optionally
-    specifying a pandas DataFrame object to be used as the dataset.
-
-    if --json is passed, then input is parsed as json rather than yaml.
-    """
-    args = magic_arguments.parse_argstring(vegalite, line)
-    existing_versions = {"v5": "5"}
-    version = existing_versions[args.version]
-    assert version in RENDERERS["vega-lite"]
-    VegaLite = RENDERERS["vega-lite"][version]
-    data_transformers = TRANSFORMERS["vega-lite"][version]
-
-    if args.json:
-        spec = json.loads(cell)
-    elif not YAML_AVAILABLE:
-        try:
-            spec = json.loads(cell)
-        except json.JSONDecodeError as err:
-            raise ValueError(
-                "%%vegalite: spec is not valid JSON. "
-                "Install pyyaml to parse spec as yaml"
-            ) from err
-    else:
-        spec = yaml.load(cell, Loader=yaml.SafeLoader)
-
-    if args.data is not None:
-        data = _get_variable(args.data)
-        spec["data"] = _prepare_data(data, data_transformers)
-
-    return VegaLite(spec)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_breakpoints.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_breakpoints.py
deleted file mode 100644
index d92fccf52b9caf60034cbc206ede48c8f1d15645..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_breakpoints.py
+++ /dev/null
@@ -1,184 +0,0 @@
-from _pydev_bundle import pydev_log
-from _pydevd_bundle import pydevd_import_class
-from _pydevd_bundle.pydevd_frame_utils import add_exception_to_frame
-from _pydev_bundle._pydev_saved_modules import threading
-
-
-class ExceptionBreakpoint(object):
-
-    def __init__(
-        self,
-        qname,
-        condition,
-        expression,
-        notify_on_handled_exceptions,
-        notify_on_unhandled_exceptions,
-        notify_on_user_unhandled_exceptions,
-        notify_on_first_raise_only,
-        ignore_libraries
-        ):
-        exctype = get_exception_class(qname)
-        self.qname = qname
-        if exctype is not None:
-            self.name = exctype.__name__
-        else:
-            self.name = None
-
-        self.condition = condition
-        self.expression = expression
-        self.notify_on_unhandled_exceptions = notify_on_unhandled_exceptions
-        self.notify_on_handled_exceptions = notify_on_handled_exceptions
-        self.notify_on_first_raise_only = notify_on_first_raise_only
-        self.notify_on_user_unhandled_exceptions = notify_on_user_unhandled_exceptions
-        self.ignore_libraries = ignore_libraries
-
-        self.type = exctype
-
-    def __str__(self):
-        return self.qname
-
-    @property
-    def has_condition(self):
-        return self.condition is not None
-
-    def handle_hit_condition(self, frame):
-        return False
-
-
-class LineBreakpoint(object):
-
-    def __init__(self, breakpoint_id, line, condition, func_name, expression, suspend_policy="NONE", hit_condition=None, is_logpoint=False):
-        self.breakpoint_id = breakpoint_id
-        self.line = line
-        self.condition = condition
-        self.func_name = func_name
-        self.expression = expression
-        self.suspend_policy = suspend_policy
-        self.hit_condition = hit_condition
-        self._hit_count = 0
-        self._hit_condition_lock = threading.Lock()
-        self.is_logpoint = is_logpoint
-
-    @property
-    def has_condition(self):
-        return bool(self.condition) or bool(self.hit_condition)
-
-    def handle_hit_condition(self, frame):
-        if not self.hit_condition:
-            return False
-        ret = False
-        with self._hit_condition_lock:
-            self._hit_count += 1
-            expr = self.hit_condition.replace('@HIT@', str(self._hit_count))
-            try:
-                ret = bool(eval(expr, frame.f_globals, frame.f_locals))
-            except Exception:
-                ret = False
-        return ret
-
-
-class FunctionBreakpoint(object):
-
-    def __init__(self, func_name, condition, expression, suspend_policy="NONE", hit_condition=None, is_logpoint=False):
-        self.condition = condition
-        self.func_name = func_name
-        self.expression = expression
-        self.suspend_policy = suspend_policy
-        self.hit_condition = hit_condition
-        self._hit_count = 0
-        self._hit_condition_lock = threading.Lock()
-        self.is_logpoint = is_logpoint
-
-    @property
-    def has_condition(self):
-        return bool(self.condition) or bool(self.hit_condition)
-
-    def handle_hit_condition(self, frame):
-        if not self.hit_condition:
-            return False
-        ret = False
-        with self._hit_condition_lock:
-            self._hit_count += 1
-            expr = self.hit_condition.replace('@HIT@', str(self._hit_count))
-            try:
-                ret = bool(eval(expr, frame.f_globals, frame.f_locals))
-            except Exception:
-                ret = False
-        return ret
-
-
-def get_exception_breakpoint(exctype, exceptions):
-    if not exctype:
-        exception_full_qname = None
-    else:
-        exception_full_qname = str(exctype.__module__) + '.' + exctype.__name__
-
-    exc = None
-    if exceptions is not None:
-        try:
-            return exceptions[exception_full_qname]
-        except KeyError:
-            for exception_breakpoint in exceptions.values():
-                if exception_breakpoint.type is not None and issubclass(exctype, exception_breakpoint.type):
-                    if exc is None or issubclass(exception_breakpoint.type, exc.type):
-                        exc = exception_breakpoint
-    return exc
-
-
-def stop_on_unhandled_exception(py_db, thread, additional_info, arg):
-    exctype, value, tb = arg
-    break_on_uncaught_exceptions = py_db.break_on_uncaught_exceptions
-    if break_on_uncaught_exceptions:
-        exception_breakpoint = py_db.get_exception_breakpoint(exctype, break_on_uncaught_exceptions)
-    else:
-        exception_breakpoint = None
-
-    if not exception_breakpoint:
-        return
-
-    if tb is None:  # sometimes it can be None, e.g. with GTK
-        return
-
-    if exctype is KeyboardInterrupt:
-        return
-
-    if exctype is SystemExit and py_db.ignore_system_exit_code(value):
-        return
-
-    frames = []
-    user_frame = None
-
-    while tb is not None:
-        if not py_db.exclude_exception_by_filter(exception_breakpoint, tb):
-            user_frame = tb.tb_frame
-        frames.append(tb.tb_frame)
-        tb = tb.tb_next
-
-    if user_frame is None:
-        return
-
-    frames_byid = dict([(id(frame), frame) for frame in frames])
-    add_exception_to_frame(user_frame, arg)
-    if exception_breakpoint.condition is not None:
-        eval_result = py_db.handle_breakpoint_condition(additional_info, exception_breakpoint, user_frame)
-        if not eval_result:
-            return
-
-    if exception_breakpoint.expression is not None:
-        py_db.handle_breakpoint_expression(exception_breakpoint, additional_info, user_frame)
-
-    try:
-        additional_info.pydev_message = exception_breakpoint.qname
-    except:
-        additional_info.pydev_message = exception_breakpoint.qname.encode('utf-8')
-
-    pydev_log.debug('Handling post-mortem stop on exception breakpoint %s' % (exception_breakpoint.qname,))
-
-    py_db.do_stop_on_unhandled_exception(thread, user_frame, frames_byid, arg)
-
-
-def get_exception_class(kls):
-    try:
-        return eval(kls)
-    except:
-        return pydevd_import_class.import_name(kls)
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/image/misc.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/image/misc.py
deleted file mode 100644
index 3e61f05e3b05e4c7b40de4eb6c8eb100e6da41d0..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/image/misc.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-
-import annotator.uniformer.mmcv as mmcv
-
-try:
-    import torch
-except ImportError:
-    torch = None
-
-
-def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
-    """Convert tensor to 3-channel images.
-
-    Args:
-        tensor (torch.Tensor): Tensor that contains multiple images, shape (
-            N, C, H, W).
-        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
-        std (tuple[float], optional): Standard deviation of images.
-            Defaults to (1, 1, 1).
-        to_rgb (bool, optional): Whether the tensor was converted to RGB
-            format in the first place. If so, convert it back to BGR.
-            Defaults to True.
-
-    Returns:
-        list[np.ndarray]: A list that contains multiple images.
-    """
-
-    if torch is None:
-        raise RuntimeError('pytorch is not installed')
-    assert torch.is_tensor(tensor) and tensor.ndim == 4
-    assert len(mean) == 3
-    assert len(std) == 3
-
-    num_imgs = tensor.size(0)
-    mean = np.array(mean, dtype=np.float32)
-    std = np.array(std, dtype=np.float32)
-    imgs = []
-    for img_id in range(num_imgs):
-        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
-        img = mmcv.imdenormalize(
-            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
-        imgs.append(np.ascontiguousarray(img))
-    return imgs
diff --git a/spaces/Superying/vits-uma-genshin-honkai/app.py b/spaces/Superying/vits-uma-genshin-honkai/app.py
deleted file mode 100644
index ba29f6a5aff153461017c2e11e03a8765581c0d5..0000000000000000000000000000000000000000
--- a/spaces/Superying/vits-uma-genshin-honkai/app.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# coding=utf-8
-import time
-import os
-import gradio as gr
-import utils
-import argparse
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-import torch
-from torch import no_grad, LongTensor
-import webbrowser
-import logging
-import gradio.processing_utils as gr_processing_utils
-logging.getLogger('numba').setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-audio_postprocess_ori = gr.Audio.postprocess
-def audio_postprocess(self, y):
-    data = audio_postprocess_ori(self, y)
-    if data is None:
-        return None
-    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-gr.Audio.postprocess = audio_postprocess
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 100 and limitation:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0).to(device)
-        x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-        speaker_id = LongTensor([speaker_id]).to(device)
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--api', action="store_true", default=False)
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    device = torch.device(args.device)
-    
-    hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-    net_g_ms = SynthesizerTrn(
-        len(hps_ms.symbols),
-        hps_ms.data.filter_length // 2 + 1,
-        hps_ms.train.segment_size // hps_ms.data.hop_length,
-        n_speakers=hps_ms.data.n_speakers,
-        **hps_ms.model)
-    _ = net_g_ms.eval().to(device)
-    speakers = hps_ms.speakers
-    model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-    
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "# <center> 严禁将模型用于任何商业项目，否则后果自负\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation) " if limitation else "Text", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3])
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    if args.colab:
-        webbrowser.open("http://127.0.0.1:7860")
-    app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
diff --git a/spaces/TEnngal/bingo/src/components/providers.tsx b/spaces/TEnngal/bingo/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
-  return (
-    <NextThemesProvider {...props}>
-      <TooltipProvider>{children}</TooltipProvider>
-    </NextThemesProvider>
-  )
-}
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/models/mlp.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/models/mlp.py
deleted file mode 100644
index b5e3fff7ab450889ab106906af7e98620751eaec..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/models/mlp.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-
-
-class MLP(nn.Module):
-    """Basic MLP implementation with FC layers and ReLU activation.
-
-    Args:
-        input_dim : dimension of the input variable
-        output_dim : dimension of the output variable
-        h_dim : dimension of a hidden layer of MLP
-        num_h_layers : number of hidden layers in MLP
-        add_residual : set to True to add input to output (res-net) set to False to have pure MLP
-
-    """
-
-    def __init__(
-        self,
-        input_dim: int,
-        output_dim: int,
-        h_dim: int,
-        num_h_layers: int,
-        add_residual: bool,
-    ) -> None:
-        super().__init__()
-        self._input_dim = input_dim
-        self._output_dim = output_dim
-        self._h_dim = h_dim
-        self._num_h_layers = num_h_layers
-
-        layers = OrderedDict()
-        if num_h_layers > 0:
-            layers["fc_0"] = nn.Linear(input_dim, h_dim)
-            layers["relu_0"] = nn.ReLU()
-        else:
-            h_dim = input_dim
-        for ii in range(1, num_h_layers):
-            layers["fc_{}".format(ii)] = nn.Linear(h_dim, h_dim)
-            layers["relu_{}".format(ii)] = nn.ReLU()
-        layers["fc_{}".format(num_h_layers)] = nn.Linear(h_dim, self._output_dim)
-
-        self.mlp = nn.Sequential(layers)
-        if add_residual:
-            self.residual_layer = nn.Linear(input_dim, output_dim)
-        else:
-            self.residual_layer = lambda x: 0
-        self._layer_norm = nn.LayerNorm(output_dim)
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        """Forward function for MLP
-
-        Args:
-            input (torch.Tensor): (batch_size, input_dim) tensor
-
-        Returns:
-            torch.Tensor: (batch_size, output_dim) tensor
-        """
-        return self._layer_norm(self.mlp(input) + self.residual_layer(input))
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/check.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/check.py
deleted file mode 100644
index 575e49fb4b14f9876f4e8fec076b1f5c9b6c72c3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/check.py
+++ /dev/null
@@ -1,151 +0,0 @@
-"""distutils.command.check
-
-Implements the Distutils 'check' command.
-"""
-import contextlib
-
-from ..core import Command
-from ..errors import DistutilsSetupError
-
-with contextlib.suppress(ImportError):
-    import docutils.utils
-    import docutils.parsers.rst
-    import docutils.frontend
-    import docutils.nodes
-
-    class SilentReporter(docutils.utils.Reporter):
-        def __init__(
-            self,
-            source,
-            report_level,
-            halt_level,
-            stream=None,
-            debug=0,
-            encoding='ascii',
-            error_handler='replace',
-        ):
-            self.messages = []
-            super().__init__(
-                source, report_level, halt_level, stream, debug, encoding, error_handler
-            )
-
-        def system_message(self, level, message, *children, **kwargs):
-            self.messages.append((level, message, children, kwargs))
-            return docutils.nodes.system_message(
-                message, level=level, type=self.levels[level], *children, **kwargs
-            )
-
-
-class check(Command):
-    """This command checks the meta-data of the package."""
-
-    description = "perform some checks on the package"
-    user_options = [
-        ('metadata', 'm', 'Verify meta-data'),
-        (
-            'restructuredtext',
-            'r',
-            (
-                'Checks if long string meta-data syntax '
-                'are reStructuredText-compliant'
-            ),
-        ),
-        ('strict', 's', 'Will exit with an error if a check fails'),
-    ]
-
-    boolean_options = ['metadata', 'restructuredtext', 'strict']
-
-    def initialize_options(self):
-        """Sets default values for options."""
-        self.restructuredtext = 0
-        self.metadata = 1
-        self.strict = 0
-        self._warnings = 0
-
-    def finalize_options(self):
-        pass
-
-    def warn(self, msg):
-        """Counts the number of warnings that occurs."""
-        self._warnings += 1
-        return Command.warn(self, msg)
-
-    def run(self):
-        """Runs the command."""
-        # perform the various tests
-        if self.metadata:
-            self.check_metadata()
-        if self.restructuredtext:
-            if 'docutils' in globals():
-                try:
-                    self.check_restructuredtext()
-                except TypeError as exc:
-                    raise DistutilsSetupError(str(exc))
-            elif self.strict:
-                raise DistutilsSetupError('The docutils package is needed.')
-
-        # let's raise an error in strict mode, if we have at least
-        # one warning
-        if self.strict and self._warnings > 0:
-            raise DistutilsSetupError('Please correct your package.')
-
-    def check_metadata(self):
-        """Ensures that all required elements of meta-data are supplied.
-
-        Required fields:
-            name, version
-
-        Warns if any are missing.
-        """
-        metadata = self.distribution.metadata
-
-        missing = []
-        for attr in 'name', 'version':
-            if not getattr(metadata, attr, None):
-                missing.append(attr)
-
-        if missing:
-            self.warn("missing required meta-data: %s" % ', '.join(missing))
-
-    def check_restructuredtext(self):
-        """Checks if the long string fields are reST-compliant."""
-        data = self.distribution.get_long_description()
-        for warning in self._check_rst_data(data):
-            line = warning[-1].get('line')
-            if line is None:
-                warning = warning[1]
-            else:
-                warning = '{} (line {})'.format(warning[1], line)
-            self.warn(warning)
-
-    def _check_rst_data(self, data):
-        """Returns warnings when the provided data doesn't compile."""
-        # the include and csv_table directives need this to be a path
-        source_path = self.distribution.script_name or 'setup.py'
-        parser = docutils.parsers.rst.Parser()
-        settings = docutils.frontend.OptionParser(
-            components=(docutils.parsers.rst.Parser,)
-        ).get_default_values()
-        settings.tab_width = 4
-        settings.pep_references = None
-        settings.rfc_references = None
-        reporter = SilentReporter(
-            source_path,
-            settings.report_level,
-            settings.halt_level,
-            stream=settings.warning_stream,
-            debug=settings.debug,
-            encoding=settings.error_encoding,
-            error_handler=settings.error_encoding_error_handler,
-        )
-
-        document = docutils.nodes.document(settings, reporter, source=source_path)
-        document.note_source(source_path, -1)
-        try:
-            parser.parse(data, document)
-        except AttributeError as e:
-            reporter.messages.append(
-                (-1, 'Could not finish the parsing: %s.' % e, '', {})
-            )
-
-        return reporter.messages
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py
deleted file mode 100644
index 163462e1f194e1e4100da92d76d9516f7cc22e35..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from torch import nn
-from torchvision.ops import roi_align
-
-
-# NOTE: torchvision's RoIAlign has a different default aligned=False
-class ROIAlign(nn.Module):
-    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
-        """
-        Args:
-            output_size (tuple): h, w
-            spatial_scale (float): scale the input boxes by this number
-            sampling_ratio (int): number of inputs samples to take for each output
-                sample. 0 to take samples densely.
-            aligned (bool): if False, use the legacy implementation in
-                Detectron. If True, align the results more perfectly.
-
-        Note:
-            The meaning of aligned=True:
-
-            Given a continuous coordinate c, its two neighboring pixel indices (in our
-            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
-            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
-            from the underlying signal at continuous coordinates 0.5 and 1.5). But the original
-            roi_align (aligned=False) does not subtract the 0.5 when computing neighboring
-            pixel indices and therefore it uses pixels with a slightly incorrect alignment
-            (relative to our pixel model) when performing bilinear interpolation.
-
-            With `aligned=True`,
-            we first appropriately scale the ROI and then shift it by -0.5
-            prior to calling roi_align. This produces the correct neighbors; see
-            detectron2/tests/test_roi_align.py for verification.
-
-            The difference does not make a difference to the model's performance if
-            ROIAlign is used together with conv layers.
-        """
-        super().__init__()
-        self.output_size = output_size
-        self.spatial_scale = spatial_scale
-        self.sampling_ratio = sampling_ratio
-        self.aligned = aligned
-
-        from torchvision import __version__
-
-        version = tuple(int(x) for x in __version__.split(".")[:2])
-        # https://github.com/pytorch/vision/pull/2438
-        assert version >= (0, 7), "Require torchvision >= 0.7"
-
-    def forward(self, input, rois):
-        """
-        Args:
-            input: NCHW images
-            rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
-        """
-        assert rois.dim() == 2 and rois.size(1) == 5
-        if input.is_quantized:
-            input = input.dequantize()
-        return roi_align(
-            input,
-            rois.to(dtype=input.dtype),
-            self.output_size,
-            self.spatial_scale,
-            self.sampling_ratio,
-            self.aligned,
-        )
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + "("
-        tmpstr += "output_size=" + str(self.output_size)
-        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
-        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
-        tmpstr += ", aligned=" + str(self.aligned)
-        tmpstr += ")"
-        return tmpstr
diff --git a/spaces/ThankGod/movie-poster-diffusion/README.md b/spaces/ThankGod/movie-poster-diffusion/README.md
deleted file mode 100644
index f4cbf04117c7e89000ed966524f773423b05893c..0000000000000000000000000000000000000000
--- a/spaces/ThankGod/movie-poster-diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: movie-poster-diffusion
-emoji: 🖼️
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/transforms.py b/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/Vegecken/sovits4dzl/resample.py b/spaces/Vegecken/sovits4dzl/resample.py
deleted file mode 100644
index 5e96106c9a066e6d73652c544322d029dd98f746..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/resample.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-from scipy.io import wavfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    # speaker 's5', 'p280', 'p315' are excluded,
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and '.wav' in wav_path:
-        os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, None)
-        wav, _ = librosa.effects.trim(wav, top_db=20)
-        peak = np.abs(wav).max()
-        if peak > 1.0:
-            wav = 0.98 * wav / peak
-        wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
-        wav2 /= max(wav2.max(), -wav2.min())
-        save_name = wav_name
-        save_path2 = os.path.join(args.out_dir2, speaker, save_name)
-        wavfile.write(
-            save_path2,
-            args.sr2,
-            (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-        )
-
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
-    parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
-    parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
-    args = parser.parse_args()
-    processs = cpu_count()-2 if cpu_count() >4 else 1
-    pool = Pool(processes=processs)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
-                pass
diff --git a/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/__init__.py b/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Walterchamy/Virtual_Assistant_v1/app.py b/spaces/Walterchamy/Virtual_Assistant_v1/app.py
deleted file mode 100644
index 0cd87414e24af53ea459e274189ccb2ccacb8359..0000000000000000000000000000000000000000
--- a/spaces/Walterchamy/Virtual_Assistant_v1/app.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import os
-import openai
-from gtts import gTTS
-import streamlit as st
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-KIITEC_CONTENT = """
-
-KIITEC is a technical institution registered by NACTE (REG/EOS/027) based in Moshono, Arusha next to Masai Camp.
-
-The institute was founded in 2004 by French engineers and has thence contrived to produce the most competent technicians in the country. The institute is financed and supported by two NGOs: The Foundation for Technical Education (FTE-Swiss) and Action Development Education International (ADEI-French). In 2004, ADEI's partner FTE built the Kilimanjaro International Institute of Telecommunications, Electronics and Computers (KIITEC) introducing state-of-the-art teaching facilities to train technicians in Arusha, Tanzania.
-
-Following construction, ADEI joined FTE in its ambition to make change through technical education and has played a pivotal role in the on-the-ground education programming and training at KIITEC ever since. Today, KIITEC acts as the international training center where educators travel from different corners of Africa to upgrade their skills.
-
-Resting on a 15-acre campus with hostel available inside it, KIITEC offers the most advanced targeted training technologies in the region. The innovative education model developed at KIITEC is based on a hands-on and student-centered approach to learning with full access to modern learning equipment simulating real-world practical experiences. The training center is registered and accredited by the National Council for Technical Education (NACTE) and awards successful graduates with a 3-year National Technical Award Level Six (NTA-6) Diploma.
-
-KIITEC specializes in diploma programs including:
-- Electrical & Computer Engineering
-- Electrical & Industrial Automation
-- Electronics & Telecommunication Engineering
-- Electrical & Renewable Energies
-
-Short course programs offered by KIITEC include:
-- Computer Application
-- IT & System Security
-- Electrical Domestic & Solar PV System Installation
-
-Future training programs or courses in development: Biomedical, Avionics.
-
-ADMISSION REQUIREMENTS of ELECTRONICS AND TELECOMMUNICATION ENGINEERING:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-ADMISSION REQUIREMENTS of ELECTRICAL AND INDUSTRIAL AUTOMATION ENGINEERING:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-ADMISSION REQUIREMENTS of ELECTRICAL AND COMPUTER ENGINEERING PROGRAMS:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-ADMISSION REQUIREMENTS of ELECTRICAL AND RENEWABLE ENERGY ENGINEERING:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-
-APPLICATION FOR EITHER DIPLOMA COURSE OR SHORT COURSE
-- You can apply for Diploma Course through this link https://kiitec.ac.tz/apply-here/
--  You can apply for short Course through this link https://kiitec.ac.tz/apply-here/
-
-Fee structure and Mode of Payment for Diploma Programs:
-- The first semester fee is 695,000 Tsh and can be paid in two installments before the end of the semester.
-- The second semester fee is 625,000 Tsh and can be paid in two installments before the end of the semester.
-- The fees should be paid through the Bank of ABSA, Account number 002-4001687, Account Name KIITEC.
-
-KIITEC students wear uniforms. The uniforms consist of light blue shirts and dark blue sweaters, dark blue skirts, and dark blue trousers.
-
-Kiitec has tutors and staff members are not only highly qualified professionals in their respective fields but also dedicated mentors, guiding students towards academic success and personal growth,and those tutors are:
--Tumaini Lyimo working as head of Computer department and tutor of computer software , Artificial Intelligence and other fields related to computer
--Makori Makori a tutor of computer hardware , computer application and other fields related to computer
--James Makyao a tutor of Communicaton skills and Language
--Senkoro Mrutu a tutor of Telecommunication , Elecronics and robotics
--Bertha Mbugha a tutor of Networking ,Telecommunication and electronics
--Annastazia Mlacha a tutor of Mathematics , Electrical and Electronics
--Albert Mtana working as head of Industrial Automation department and tutor of Electrical and Industrial
--Elias Mwaimu a tutor of Electrical and Industrial Automation
--Samson a tutor of Electrical and Industrial Automation
-
-KIITEC has  administration department were there members in the department as follows:
-- Father Sebastian working as Director of Kiitec Institution and also as current Principle replacing Mr.Gerald Sambayuka who has recently retire at kiitec institution
-- Trace Kavishe working as Admin at kiitec institution
-- Hannah Pondo working as Buser at kiitec institution
-KIITEC Vision:
-To become a leading provider of quality technical education and training to empower the youth of Tanzania and the Eastern Africa region.
-
-KIITEC Mission:
-- To provide quality hands-on technical training for students in ICTs, Electrical, Renewable Energies, Industrial Automation, and related disciplines.
-- To conduct quality research and consultancy in these fields.
-- To promote the development and usage of modern technology that meets national, regional, and international needs and standards through skills and practical-oriented training.
-
-KIITEC Values:
-- Hard work and excellence
-- Honesty
-- Respect
-- Responsibility
-- Lifelong learning
-- Innovation and creativity.
-
-For contact information:
-- Phone: +255 27 250 4384
-- Mobile: +255 757 845 118
-- Email: info@kiitec.ac.tz
-- P.O. Box 3172, Arusha
-"""
-
-def generate_response(user_input):
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "All your answers should be in Swahili or English. Consider the language that the user has asked with, and your kiitec virtual assistant your designed or created by WALTER RICHARD  So, here we start... Your virtual assistant of DON BOSCO KIITEC. You will reply to the queries that the user might ask!\n\n" + KIITEC_CONTENT},
-            {"role": "assistant", "content": "Hi, how can i assist you today?"},
-            {"role": "user", "content": user_input},
-        ]
-    )
-    return response['choices'][0]['message']['content']
-
-
-def text_to_speech(text):
-    # Create a text-to-speech object
-    tts = gTTS(text=text, lang=('en'))
-    # Save the audio file
-    tts.save('output.mp3')
-
-    # Play the audio file
-    st.audio("output.mp3")
-
-def main():
-   
-    col1, col2 = st.columns([1, 4])
-    
-    with col1:
-        st.image('logo.png', caption=None, width=200, use_column_width=200)
-    
-    with col2:
-        st.markdown("<h4 style='text-align: center; color: white;'>KILIMANJARO INTERNATIONAL INSTITUTE FOR TELECOMMUNICATION, ELECTRONICS & COMPUTERS</h4>", unsafe_allow_html=True)
-    st.header("KIITEC VIRTUAL ASSISTANT")
-    st.write("Welcome for questions concerning kiitec insitution")
-    user_input = st.text_input("Enter your question:")
-    if st.button("Answer"):
-        with st.spinner("Generating Response...."):
-            response_text = generate_response(user_input)
-        st.write("Answer:", response_text)
-        text_to_speech(response_text)
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/utils/export.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/utils/export.py
deleted file mode 100644
index b513b52267f7bf5aae09282c15b0a2e20c8a8fee..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/utils/export.py
+++ /dev/null
@@ -1,56 +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 to export a training checkpoint to a lightweight release checkpoint.
-"""
-
-from pathlib import Path
-import typing as tp
-
-from omegaconf import OmegaConf, DictConfig
-import torch
-
-
-def _clean_lm_cfg(cfg: DictConfig):
-    OmegaConf.set_struct(cfg, False)
-    # This used to be set automatically in the LM solver, need a more robust solution
-    # for the future.
-    cfg['transformer_lm']['card'] = 2048
-    cfg['transformer_lm']['n_q'] = 4
-    # Experimental params no longer supported.
-    bad_params = ['spectral_norm_attn_iters', 'spectral_norm_ff_iters',
-                  'residual_balancer_attn', 'residual_balancer_ff', 'layer_drop']
-    for name in bad_params:
-        del cfg['transformer_lm'][name]
-    OmegaConf.set_struct(cfg, True)
-    return cfg
-
-
-def export_encodec(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['ema']['state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
-
-
-def export_lm(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['fsdp_best_state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(_clean_lm_cfg(pkg['xp.cfg']))
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
diff --git a/spaces/XzJosh/Lumi-Bert-VITS2/bert_gen.py b/spaces/XzJosh/Lumi-Bert-VITS2/bert_gen.py
deleted file mode 100644
index 44814715396ffc3abe84a12c74d66293c356eb4f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Lumi-Bert-VITS2/bert_gen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from multiprocessing import Pool
-import commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from tqdm import tqdm
-import warnings
-
-from text import cleaned_text_to_sequence, get_bert
-
-config_path = 'configs/config.json'
-hps = utils.get_hparams_from_file(config_path)
-
-def process_line(line):
-    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
-    phone = phones.split(" ")
-    tone = [int(i) for i in tone.split(" ")]
-    word2ph = [int(i) for i in word2ph.split(" ")]
-    w2pho = [i for i in word2ph]
-    word2ph = [i for i in word2ph]
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    wav_path = f'{_id}'
-
-    bert_path = wav_path.replace(".wav", ".bert.pt")
-    try:
-        bert = torch.load(bert_path)
-        assert bert.shape[-1] == len(phone)
-    except:
-        bert = get_bert(text, word2ph, language_str)
-        assert bert.shape[-1] == len(phone)
-        torch.save(bert, bert_path)
-
-
-if __name__ == '__main__':
-    lines = []
-    with open(hps.data.training_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-    with open(hps.data.validation_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-    with Pool(processes=12) as pool: #A100 40GB suitable config,if coom,please decrease the processess number.
-        for _ in tqdm(pool.imap_unordered(process_line, lines)):
-            pass
diff --git a/spaces/XzJosh/Lumi-Bert-VITS2/server.py b/spaces/XzJosh/Lumi-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Lumi-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    print([f"{p}{t}" for p, t in zip(phone, tone)])
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
-    bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
-    with torch.no_grad():
-        x_tst=phones.to(dev).unsqueeze(0)
-        tones=tones.to(dev).unsqueeze(0)
-        lang_ids=lang_ids.to(dev).unsqueeze(0)
-        bert = bert.to(dev).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        return audio
-
-def replace_punctuation(text, i=2):
-    punctuation = "，。？！"
-    for char in punctuation:
-        text = text.replace(char, char * i)
-    return text
-
-def wav2(i, o, format):
-    inp = avopen(i, 'rb')
-    out = avopen(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-
-    ostream = out.add_stream(format)
-
-    for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
-
-    for p in ostream.encode(None): out.mux(p)
-
-    out.close()
-    inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
-    if request.method == 'GET':
-        try:
-            speaker = request.args.get('speaker')
-            text = request.args.get('text').replace("/n","")
-            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
-            noise = float(request.args.get("noise", 0.5))
-            noisew = float(request.args.get("noisew", 0.6))
-            length = float(request.args.get("length", 1.2))
-            if length >= 2:
-                return "Too big length"
-            if len(text) >=200:
-                return "Too long text"
-            fmt = request.args.get("format", "wav")
-            if None in (speaker, text):
-                return "Missing Parameter"
-            if fmt not in ("mp3", "wav", "ogg"):
-                return "Invalid Format"
-        except:
-            return "Invalid Parameter"
-
-        with torch.no_grad():
-            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
-        with BytesIO() as wav:
-            wavfile.write(wav, hps.data.sampling_rate, audio)
-            torch.cuda.empty_cache()
-            if fmt == "wav":
-                return Response(wav.getvalue(), mimetype="audio/wav")
-            wav.seek(0, 0)
-            with BytesIO() as ofp:
-                wav2(wav, ofp, fmt)
-                return Response(
-                    ofp.getvalue(),
-                    mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
-                )
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/Wenjing-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/Yunshansongbai/SVC-Nahida/preprocess_hubert_f0.py b/spaces/Yunshansongbai/SVC-Nahida/preprocess_hubert_f0.py
deleted file mode 100644
index 5d7680e8490fd73bbc3d17db9a73d53a5e96131f..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/preprocess_hubert_f0.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import math
-import multiprocessing
-import os
-import argparse
-from random import shuffle
-
-import paddle
-from glob import glob
-from tqdm import tqdm
-
-import utils
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import librosa
-import numpy as np
-
-hps = utils.get_hparams_from_file("configs/config.json")
-sampling_rate = hps.data.sampling_rate
-hop_length = hps.data.hop_length
-
-
-def process_one(filename, hmodel):
-    # print(filename)
-    wav, sr = librosa.load(filename, sr=sampling_rate)
-    soft_path = filename + ".soft.pdtensor"
-    if not os.path.exists(soft_path):
-        devive = "cuda" if paddle.device.is_compiled_with_cuda() else "cpu"
-        wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
-        wav16k = paddle.to_tensor(wav16k).cpu() if devive=='cpu' else paddle.to_tensor(wav16k).cuda()
-        c:paddle.Tensor = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
-        paddle.save(c.cpu(), soft_path)
-    f0_path = filename + ".f0.npy"
-    if not os.path.exists(f0_path):
-        f0 = utils.compute_f0_dio(wav, sampling_rate=sampling_rate, hop_length=hop_length)
-        np.save(f0_path, f0)
-
-
-def process_batch(filenames):
-    print("正在加载内容的HuBERT……")
-    device = "cuda" if paddle.device.is_compiled_with_cuda() else "cpu"
-    hmodel = utils.get_hubert_model()
-    print("HuBERT已被装载。")
-    for filename in tqdm(filenames):
-        process_one(filename, hmodel)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--in_dir", type=str, default="dataset/44k", help="path to input dir")
-
-    args = parser.parse_args()
-    filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)  # [:10]
-    shuffle(filenames)
-    multiprocessing.set_start_method('spawn',force=True)
-
-    num_processes = 1
-    chunk_size = int(math.ceil(len(filenames) / num_processes))
-    chunks = [filenames[i:i + chunk_size] for i in range(0, len(filenames), chunk_size)]
-    print([len(c) for c in chunks])
-    processes = [multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks]
-    for p in processes:
-        p.start()
diff --git a/spaces/ZenXir/FreeVC/speaker_encoder/hparams.py b/spaces/ZenXir/FreeVC/speaker_encoder/hparams.py
deleted file mode 100644
index 9a8c16471903b0c92253b1d70fcd6a61d10e085f..0000000000000000000000000000000000000000
--- a/spaces/ZenXir/FreeVC/speaker_encoder/hparams.py
+++ /dev/null
@@ -1,31 +0,0 @@
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3
\ No newline at end of file
diff --git a/spaces/aadnk/faster-whisper-webui/src/languages.py b/spaces/aadnk/faster-whisper-webui/src/languages.py
deleted file mode 100644
index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000
--- a/spaces/aadnk/faster-whisper-webui/src/languages.py
+++ /dev/null
@@ -1,147 +0,0 @@
-class Language():
-    def __init__(self, code, name):
-        self.code = code
-        self.name = name
-
-    def __str__(self):
-        return "Language(code={}, name={})".format(self.code, self.name)
-
-LANGUAGES = [
-    Language('en', 'English'),
-    Language('zh', 'Chinese'),   
-    Language('de', 'German'),    
-    Language('es', 'Spanish'),   
-    Language('ru', 'Russian'),   
-    Language('ko', 'Korean'),    
-    Language('fr', 'French'),    
-    Language('ja', 'Japanese'),  
-    Language('pt', 'Portuguese'),
-    Language('tr', 'Turkish'),   
-    Language('pl', 'Polish'),    
-    Language('ca', 'Catalan'),   
-    Language('nl', 'Dutch'),     
-    Language('ar', 'Arabic'),
-    Language('sv', 'Swedish'),
-    Language('it', 'Italian'),
-    Language('id', 'Indonesian'),
-    Language('hi', 'Hindi'),
-    Language('fi', 'Finnish'),
-    Language('vi', 'Vietnamese'),
-    Language('he', 'Hebrew'),
-    Language('uk', 'Ukrainian'),
-    Language('el', 'Greek'),
-    Language('ms', 'Malay'),
-    Language('cs', 'Czech'),
-    Language('ro', 'Romanian'),
-    Language('da', 'Danish'),
-    Language('hu', 'Hungarian'),
-    Language('ta', 'Tamil'),
-    Language('no', 'Norwegian'),
-    Language('th', 'Thai'),
-    Language('ur', 'Urdu'),
-    Language('hr', 'Croatian'),
-    Language('bg', 'Bulgarian'),
-    Language('lt', 'Lithuanian'),
-    Language('la', 'Latin'),
-    Language('mi', 'Maori'),
-    Language('ml', 'Malayalam'),
-    Language('cy', 'Welsh'),
-    Language('sk', 'Slovak'),
-    Language('te', 'Telugu'),
-    Language('fa', 'Persian'),
-    Language('lv', 'Latvian'),
-    Language('bn', 'Bengali'),
-    Language('sr', 'Serbian'),
-    Language('az', 'Azerbaijani'),
-    Language('sl', 'Slovenian'),
-    Language('kn', 'Kannada'),
-    Language('et', 'Estonian'),
-    Language('mk', 'Macedonian'),
-    Language('br', 'Breton'),
-    Language('eu', 'Basque'),
-    Language('is', 'Icelandic'),
-    Language('hy', 'Armenian'),
-    Language('ne', 'Nepali'),
-    Language('mn', 'Mongolian'),
-    Language('bs', 'Bosnian'),
-    Language('kk', 'Kazakh'),
-    Language('sq', 'Albanian'),
-    Language('sw', 'Swahili'),
-    Language('gl', 'Galician'),
-    Language('mr', 'Marathi'),
-    Language('pa', 'Punjabi'),
-    Language('si', 'Sinhala'),
-    Language('km', 'Khmer'),
-    Language('sn', 'Shona'),
-    Language('yo', 'Yoruba'),
-    Language('so', 'Somali'),
-    Language('af', 'Afrikaans'),
-    Language('oc', 'Occitan'),
-    Language('ka', 'Georgian'),
-    Language('be', 'Belarusian'),
-    Language('tg', 'Tajik'),
-    Language('sd', 'Sindhi'),
-    Language('gu', 'Gujarati'),
-    Language('am', 'Amharic'),
-    Language('yi', 'Yiddish'),
-    Language('lo', 'Lao'),
-    Language('uz', 'Uzbek'),
-    Language('fo', 'Faroese'),
-    Language('ht', 'Haitian creole'),
-    Language('ps', 'Pashto'),
-    Language('tk', 'Turkmen'),
-    Language('nn', 'Nynorsk'),
-    Language('mt', 'Maltese'),
-    Language('sa', 'Sanskrit'),
-    Language('lb', 'Luxembourgish'),
-    Language('my', 'Myanmar'),
-    Language('bo', 'Tibetan'),
-    Language('tl', 'Tagalog'),
-    Language('mg', 'Malagasy'),
-    Language('as', 'Assamese'),
-    Language('tt', 'Tatar'),
-    Language('haw', 'Hawaiian'),
-    Language('ln', 'Lingala'),
-    Language('ha', 'Hausa'),
-    Language('ba', 'Bashkir'),
-    Language('jw', 'Javanese'),
-    Language('su', 'Sundanese')
-]
-
-_TO_LANGUAGE_CODE = {
-    **{language.code: language for language in LANGUAGES},
-    "burmese": "my",
-    "valencian": "ca",
-    "flemish": "nl",
-    "haitian": "ht",
-    "letzeburgesch": "lb",
-    "pushto": "ps",
-    "panjabi": "pa",
-    "moldavian": "ro",
-    "moldovan": "ro",
-    "sinhalese": "si",
-    "castilian": "es",
-}
-    
-_FROM_LANGUAGE_NAME = {
-    **{language.name.lower(): language for language in LANGUAGES}
-}
-
-def get_language_from_code(language_code, default=None) -> Language:
-    """Return the language name from the language code."""
-    return _TO_LANGUAGE_CODE.get(language_code, default)
-
-def get_language_from_name(language, default=None) -> Language:
-    """Return the language code from the language name."""
-    return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default)
-
-def get_language_names():
-    """Return a list of language names."""
-    return [language.name for language in LANGUAGES]
-
-if __name__ == "__main__":
-    # Test lookup
-    print(get_language_from_code('en'))
-    print(get_language_from_name('English'))
-    
-    print(get_language_names())
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/apis/inference.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/apis/inference.py
deleted file mode 100644
index df7bd4b7316cd8fad4cd10dbdfcbfeba59d95bbc..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/apis/inference.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import warnings
-
-import mmcv
-import numpy as np
-import torch
-# from mmcv.ops import RoIPool
-from mmcv.parallel import collate, scatter
-from mmcv.runner import load_checkpoint
-
-from mmdet.core import get_classes
-from mmdet.datasets import replace_ImageToTensor
-from mmdet.datasets.pipelines import Compose
-from mmdet.models import build_detector
-
-
-def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
-    """Initialize a detector from config file.
-
-    Args:
-        config (str or :obj:`mmcv.Config`): Config file path or the config
-            object.
-        checkpoint (str, optional): Checkpoint path. If left as None, the model
-            will not load any weights.
-        cfg_options (dict): Options to override some settings in the used
-            config.
-
-    Returns:
-        nn.Module: The constructed detector.
-    """
-    if isinstance(config, str):
-        config = mmcv.Config.fromfile(config)
-    elif not isinstance(config, mmcv.Config):
-        raise TypeError('config must be a filename or Config object, '
-                        f'but got {type(config)}')
-    if cfg_options is not None:
-        config.merge_from_dict(cfg_options)
-    config.model.pretrained = None
-    config.model.train_cfg = None
-    model = build_detector(config.model, test_cfg=config.get('test_cfg'))
-    if checkpoint is not None:
-        map_loc = 'cpu' if device == 'cpu' else None
-        checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc)
-        if 'CLASSES' in checkpoint.get('meta', {}):
-            model.CLASSES = checkpoint['meta']['CLASSES']
-        else:
-            warnings.simplefilter('once')
-            warnings.warn('Class names are not saved in the checkpoint\'s '
-                          'meta data, use COCO classes by default.')
-            model.CLASSES = get_classes('coco')
-    model.cfg = config  # save the config in the model for convenience
-    model.to(device)
-    model.eval()
-    return model
-
-
-class LoadImage(object):
-    """Deprecated.
-
-    A simple pipeline to load image.
-    """
-
-    def __call__(self, results):
-        """Call function to load images into results.
-
-        Args:
-            results (dict): A result dict contains the file name
-                of the image to be read.
-        Returns:
-            dict: ``results`` will be returned containing loaded image.
-        """
-        warnings.simplefilter('once')
-        warnings.warn('`LoadImage` is deprecated and will be removed in '
-                      'future releases. You may use `LoadImageFromWebcam` '
-                      'from `mmdet.datasets.pipelines.` instead.')
-        if isinstance(results['img'], str):
-            results['filename'] = results['img']
-            results['ori_filename'] = results['img']
-        else:
-            results['filename'] = None
-            results['ori_filename'] = None
-        img = mmcv.imread(results['img'])
-        results['img'] = img
-        results['img_fields'] = ['img']
-        results['img_shape'] = img.shape
-        results['ori_shape'] = img.shape
-        return results
-
-
-def inference_detector(model, imgs):
-    """Inference image(s) with the detector.
-
-    Args:
-        model (nn.Module): The loaded detector.
-        imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]):
-           Either image files or loaded images.
-
-    Returns:
-        If imgs is a list or tuple, the same length list type results
-        will be returned, otherwise return the detection results directly.
-    """
-
-    if isinstance(imgs, (list, tuple)):
-        is_batch = True
-    else:
-        imgs = [imgs]
-        is_batch = False
-
-    cfg = model.cfg
-    device = next(model.parameters()).device  # model device
-
-    if isinstance(imgs[0], np.ndarray):
-        cfg = cfg.copy()
-        # set loading pipeline type
-        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
-
-    cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
-    test_pipeline = Compose(cfg.data.test.pipeline)
-
-    datas = []
-    for img in imgs:
-        # prepare data
-        if isinstance(img, np.ndarray):
-            # directly add img
-            data = dict(img=img)
-        else:
-            # add information into dict
-            data = dict(img_info=dict(filename=img), img_prefix=None)
-        # build the data pipeline
-        data = test_pipeline(data)
-        datas.append(data)
-
-    data = collate(datas, samples_per_gpu=len(imgs))
-    # just get the actual data from DataContainer
-    data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']]
-    data['img'] = [img.data[0] for img in data['img']]
-    if next(model.parameters()).is_cuda:
-        # scatter to specified GPU
-        data = scatter(data, [device])[0]
-    else:
-        for m in model.modules():
-            assert not isinstance(
-                m, RoIPool
-            ), 'CPU inference with RoIPool is not supported currently.'
-
-    # forward the model
-    with torch.no_grad():
-        results = model(return_loss=False, rescale=True, **data)
-
-    if not is_batch:
-        return results[0]
-    else:
-        return results
-
-
-async def async_inference_detector(model, img):
-    """Async inference image(s) with the detector.
-
-    Args:
-        model (nn.Module): The loaded detector.
-        img (str | ndarray): Either image files or loaded images.
-
-    Returns:
-        Awaitable detection results.
-    """
-    cfg = model.cfg
-    device = next(model.parameters()).device  # model device
-    # prepare data
-    if isinstance(img, np.ndarray):
-        # directly add img
-        data = dict(img=img)
-        cfg = cfg.copy()
-        # set loading pipeline type
-        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
-    else:
-        # add information into dict
-        data = dict(img_info=dict(filename=img), img_prefix=None)
-    # build the data pipeline
-    test_pipeline = Compose(cfg.data.test.pipeline)
-    data = test_pipeline(data)
-    data = scatter(collate([data], samples_per_gpu=1), [device])[0]
-
-    # We don't restore `torch.is_grad_enabled()` value during concurrent
-    # inference since execution can overlap
-    torch.set_grad_enabled(False)
-    result = await model.aforward_test(rescale=True, **data)
-    return result
-
-
-def show_result_pyplot(model,
-                       img,
-                       result,
-                       score_thr=0.3,
-                       title='result',
-                       wait_time=0):
-    """Visualize the detection results on the image.
-
-    Args:
-        model (nn.Module): The loaded detector.
-        img (str or np.ndarray): Image filename or loaded image.
-        result (tuple[list] or list): The detection result, can be either
-            (bbox, segm) or just bbox.
-        score_thr (float): The threshold to visualize the bboxes and masks.
-        title (str): Title of the pyplot figure.
-        wait_time (float): Value of waitKey param.
-                Default: 0.
-    """
-    if hasattr(model, 'module'):
-        model = model.module
-    model.show_result(
-        img,
-        result,
-        score_thr=score_thr,
-        show=True,
-        wait_time=wait_time,
-        win_name=title,
-        bbox_color=(72, 101, 241),
-        text_color=(72, 101, 241))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/evaluation/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/evaluation/__init__.py
deleted file mode 100644
index b1fb4062967f7e225a75c697a990153d4f5b5481..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/evaluation/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
-                          get_classes, imagenet_det_classes,
-                          imagenet_vid_classes, voc_classes, get_palette)
-from .eval_hooks import DistEvalHook, EvalHook
-from .mean_ap import average_precision, eval_map, print_map_summary
-from .recall import (eval_recalls, plot_iou_recall, plot_num_recall,
-                     print_recall_summary)
-
-__all__ = [
-    'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',
-    'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', 'get_palette',
-    'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map',
-    'print_map_summary', 'eval_recalls', 'print_recall_summary',
-    'plot_num_recall', 'plot_iou_recall'
-]
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/font.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/font.py
deleted file mode 100644
index 5ac530d7b949f50314a0d9cf5d744bedcace0571..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/font.py
+++ /dev/null
@@ -1,272 +0,0 @@
-"""Font texture loader and processor.
-
-Author: Matthew Matl
-"""
-import freetype
-import numpy as np
-import os
-
-import OpenGL
-from OpenGL.GL import *
-
-from .constants import TextAlign, FLOAT_SZ
-from .texture import Texture
-from .sampler import Sampler
-
-
-class FontCache(object):
-    """A cache for fonts.
-    """
-
-    def __init__(self, font_dir=None):
-        self._font_cache = {}
-        self.font_dir = font_dir
-        if self.font_dir is None:
-            base_dir, _ = os.path.split(os.path.realpath(__file__))
-            self.font_dir = os.path.join(base_dir, 'fonts')
-
-    def get_font(self, font_name, font_pt):
-        # If it's a file, load it directly, else, try to load from font dir.
-        if os.path.isfile(font_name):
-            font_filename = font_name
-            _, font_name = os.path.split(font_name)
-            font_name, _ = os.path.split(font_name)
-        else:
-            font_filename = os.path.join(self.font_dir, font_name) + '.ttf'
-
-        cid = OpenGL.contextdata.getContext()
-        key = (cid, font_name, int(font_pt))
-
-        if key not in self._font_cache:
-            self._font_cache[key] = Font(font_filename, font_pt)
-        return self._font_cache[key]
-
-    def clear(self):
-        for key in self._font_cache:
-            self._font_cache[key].delete()
-        self._font_cache = {}
-
-
-class Character(object):
-    """A single character, with its texture and attributes.
-    """
-
-    def __init__(self, texture, size, bearing, advance):
-        self.texture = texture
-        self.size = size
-        self.bearing = bearing
-        self.advance = advance
-
-
-class Font(object):
-    """A font object.
-
-    Parameters
-    ----------
-    font_file : str
-        The file to load the font from.
-    font_pt : int
-        The height of the font in pixels.
-    """
-
-    def __init__(self, font_file, font_pt=40):
-        self.font_file = font_file
-        self.font_pt = int(font_pt)
-        self._face = freetype.Face(font_file)
-        self._face.set_pixel_sizes(0, font_pt)
-        self._character_map = {}
-
-        for i in range(0, 128):
-
-            # Generate texture
-            face = self._face
-            face.load_char(chr(i))
-            buf = face.glyph.bitmap.buffer
-            src = (np.array(buf) / 255.0).astype(np.float32)
-            src = src.reshape((face.glyph.bitmap.rows,
-                               face.glyph.bitmap.width))
-            tex = Texture(
-                sampler=Sampler(
-                    magFilter=GL_LINEAR,
-                    minFilter=GL_LINEAR,
-                    wrapS=GL_CLAMP_TO_EDGE,
-                    wrapT=GL_CLAMP_TO_EDGE
-                ),
-                source=src,
-                source_channels='R',
-            )
-            character = Character(
-                texture=tex,
-                size=np.array([face.glyph.bitmap.width,
-                               face.glyph.bitmap.rows]),
-                bearing=np.array([face.glyph.bitmap_left,
-                                  face.glyph.bitmap_top]),
-                advance=face.glyph.advance.x
-            )
-            self._character_map[chr(i)] = character
-
-        self._vbo = None
-        self._vao = None
-
-    @property
-    def font_file(self):
-        """str : The file the font was loaded from.
-        """
-        return self._font_file
-
-    @font_file.setter
-    def font_file(self, value):
-        self._font_file = value
-
-    @property
-    def font_pt(self):
-        """int : The height of the font in pixels.
-        """
-        return self._font_pt
-
-    @font_pt.setter
-    def font_pt(self, value):
-        self._font_pt = int(value)
-
-    def _add_to_context(self):
-
-        self._vao = glGenVertexArrays(1)
-        glBindVertexArray(self._vao)
-        self._vbo = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self._vbo)
-        glBufferData(GL_ARRAY_BUFFER, FLOAT_SZ * 6 * 4, None, GL_DYNAMIC_DRAW)
-        glEnableVertexAttribArray(0)
-        glVertexAttribPointer(
-            0, 4, GL_FLOAT, GL_FALSE, 4 * FLOAT_SZ, ctypes.c_void_p(0)
-        )
-        glBindVertexArray(0)
-
-        glPixelStorei(GL_UNPACK_ALIGNMENT, 1)
-        for c in self._character_map:
-            ch = self._character_map[c]
-            if not ch.texture._in_context():
-                ch.texture._add_to_context()
-
-    def _remove_from_context(self):
-        for c in self._character_map:
-            ch = self._character_map[c]
-            ch.texture.delete()
-        if self._vao is not None:
-            glDeleteVertexArrays(1, [self._vao])
-            glDeleteBuffers(1, [self._vbo])
-            self._vao = None
-            self._vbo = None
-
-    def _in_context(self):
-        return self._vao is not None
-
-    def _bind(self):
-        glBindVertexArray(self._vao)
-
-    def _unbind(self):
-        glBindVertexArray(0)
-
-    def delete(self):
-        self._unbind()
-        self._remove_from_context()
-
-    def render_string(self, text, x, y, scale=1.0,
-                      align=TextAlign.BOTTOM_LEFT):
-        """Render a string to the current view buffer.
-
-        Note
-        ----
-        Assumes correct shader program already bound w/ uniforms set.
-
-        Parameters
-        ----------
-        text : str
-            The text to render.
-        x : int
-            Horizontal pixel location of text.
-        y : int
-            Vertical pixel location of text.
-        scale : int
-            Scaling factor for text.
-        align : int
-            One of the TextAlign options which specifies where the ``x``
-            and ``y`` parameters lie on the text. For example,
-            :attr:`.TextAlign.BOTTOM_LEFT` means that ``x`` and ``y`` indicate
-            the position of the bottom-left corner of the textbox.
-        """
-        glActiveTexture(GL_TEXTURE0)
-        glEnable(GL_BLEND)
-        glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
-        glDisable(GL_DEPTH_TEST)
-        glPolygonMode(GL_FRONT_AND_BACK, GL_FILL)
-        self._bind()
-
-        # Determine width and height of text relative to x, y
-        width = 0.0
-        height = 0.0
-        for c in text:
-            ch = self._character_map[c]
-            height = max(height, ch.bearing[1] * scale)
-            width += (ch.advance >> 6) * scale
-
-        # Determine offsets based on alignments
-        xoff = 0
-        yoff = 0
-        if align == TextAlign.BOTTOM_RIGHT:
-            xoff = -width
-        elif align == TextAlign.BOTTOM_CENTER:
-            xoff = -width / 2.0
-        elif align == TextAlign.TOP_LEFT:
-            yoff = -height
-        elif align == TextAlign.TOP_RIGHT:
-            yoff = -height
-            xoff = -width
-        elif align == TextAlign.TOP_CENTER:
-            yoff = -height
-            xoff = -width / 2.0
-        elif align == TextAlign.CENTER:
-            xoff = -width / 2.0
-            yoff = -height / 2.0
-        elif align == TextAlign.CENTER_LEFT:
-            yoff = -height / 2.0
-        elif align == TextAlign.CENTER_RIGHT:
-            xoff = -width
-            yoff = -height / 2.0
-
-        x += xoff
-        y += yoff
-
-        ch = None
-        for c in text:
-            ch = self._character_map[c]
-            xpos = x + ch.bearing[0] * scale
-            ypos = y - (ch.size[1] - ch.bearing[1]) * scale
-            w = ch.size[0] * scale
-            h = ch.size[1] * scale
-
-            vertices = np.array([
-                [xpos, ypos, 0.0, 0.0],
-                [xpos + w, ypos, 1.0, 0.0],
-                [xpos + w, ypos + h, 1.0, 1.0],
-                [xpos + w, ypos + h, 1.0, 1.0],
-                [xpos, ypos + h, 0.0, 1.0],
-                [xpos, ypos, 0.0, 0.0],
-            ], dtype=np.float32)
-
-            ch.texture._bind()
-
-            glBindBuffer(GL_ARRAY_BUFFER, self._vbo)
-            glBufferData(
-                GL_ARRAY_BUFFER, FLOAT_SZ * 6 * 4, vertices, GL_DYNAMIC_DRAW
-            )
-            # TODO MAKE THIS MORE EFFICIENT, lgBufferSubData is broken
-            # glBufferSubData(
-            #     GL_ARRAY_BUFFER, 0, 6 * 4 * FLOAT_SZ,
-            #     np.ascontiguousarray(vertices.flatten)
-            # )
-            glDrawArrays(GL_TRIANGLES, 0, 6)
-            x += (ch.advance >> 6) * scale
-
-        self._unbind()
-        if ch:
-            ch.texture._unbind()
diff --git a/spaces/akhaliq/Kapao/utils/loggers/wandb/sweep.py b/spaces/akhaliq/Kapao/utils/loggers/wandb/sweep.py
deleted file mode 100644
index 2dcda508eb50ca61dcedfeb45c4066ebe913d618..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Kapao/utils/loggers/wandb/sweep.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import sys
-from pathlib import Path
-
-import wandb
-
-FILE = Path(__file__).absolute()
-sys.path.append(FILE.parents[3].as_posix())  # add utils/ to path
-
-from train import train, parse_opt
-from utils.general import increment_path
-from utils.torch_utils import select_device
-
-
-def sweep():
-    wandb.init()
-    # Get hyp dict from sweep agent
-    hyp_dict = vars(wandb.config).get("_items")
-
-    # Workaround: get necessary opt args
-    opt = parse_opt(known=True)
-    opt.batch_size = hyp_dict.get("batch_size")
-    opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
-    opt.epochs = hyp_dict.get("epochs")
-    opt.nosave = True
-    opt.data = hyp_dict.get("data")
-    device = select_device(opt.device, batch_size=opt.batch_size)
-
-    # train
-    train(hyp_dict, opt, device)
-
-
-if __name__ == "__main__":
-    sweep()
diff --git a/spaces/akhaliq/deeplab2/g3doc/setup/kitti_step.md b/spaces/akhaliq/deeplab2/g3doc/setup/kitti_step.md
deleted file mode 100644
index 5c1bf893c9dc0d579a31ef5cc56620ede2c919da..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/g3doc/setup/kitti_step.md
+++ /dev/null
@@ -1,178 +0,0 @@
-# Run DeepLab2 on KITTI-STEP dataset
-
-## KITTI-STEP dataset
-
-KITTI-STEP extends the existing
-[KITTI-MOTS](http://www.cvlibs.net/datasets/kitti/eval_mots.php) dataset with
-spatially and temporally dense annotations. KITTI-STEP dataset provides a
-test-bed for studying long-term pixel-precise segmentation and tracking under
-real-world conditions.
-
-### Annotation
-
-KITTI-STEP's annotation is collected in a semi-automatic manner. At the first
-stage, the pseudo semantic labels generated by the state-of-the-art
-[Panoptic-DeepLab](../projects/panoptic_deeplab.md) are refined by human
-annotators with at least one round. Then this new semantic segmentation
-annotation is merged with the existing tracking instance ground-truth from the
-[KITTI-MOTS](http://www.cvlibs.net/datasets/kitti/eval_mots.php). Please refer
-to the following figure as an overview.
-
-<p align="center">
-   <img src="../img/step/kitti_step_annotation.png" width=500>
-</p>
-
-### Label Map
-
-KITTI-STEP adopts the same 19 classes as defined in
-[Cityscapes](https://www.cityscapes-dataset.com/dataset-overview/#class-definitions)
-with `pedestrians` and `cars` carefully annotated with track IDs. More
-specifically, KITTI-STEP has the following label to index mapping:
-
-Label Name     | Label ID
--------------- | --------
-road           | 0
-sidewalk       | 1
-building       | 2
-wall           | 3
-fence          | 4
-pole           | 5
-traffic light  | 6
-traffic sign   | 7
-vegetation     | 8
-terrain        | 9
-sky            | 10
-person&dagger; | 11
-rider          | 12
-car&dagger;    | 13
-truck          | 14
-bus            | 15
-train          | 16
-motorcycle     | 17
-bicycle        | 18
-void           | 255
-
-&dagger;: Single instance annotations are available.
-
-### Prepare KITTI-STEP for Training and Evaluation
-
-#### Download data
-
-KITTI-STEP has the same train and test sequences as
-[KITTI-MOTS](http://www.cvlibs.net/datasets/kitti/eval_mots.php) (with 21 and 29
-sequences for training and testing, respectively). Similarly, the training
-sequences are further split into training set (12 sequences) and validation set
-(9 sequences).
-
-In the following, we provide a step-by-step walk through to prepare the data.
-
-1.  Create the KITTI-STEP directory: `bash mkdir ${KITTI_STEP_ROOT}/images cd
-    ${KITTI_STEP_ROOT}/images`
-
-2.  Download KITTI images from their
-    [website](http://www.cvlibs.net/datasets/kitti/index.php) and unzip. `bash
-    wget ${KITTI_LINK} unzip ${KITTI_IMAGES}.zip`
-
-3.  To prepare the dataset for our scripts, we need to move and rename some
-    directories:
-
-    ```bash
-    mv testing/image_02/ test/
-    rm -r testing/
-
-    # Move all validation sequences:
-    mkdir val
-    mv training/image_02/0002 val/
-    mv training/image_02/0006 val/
-    mv training/image_02/0007 val/
-    mv training/image_02/0008 val/
-    mv training/image_02/0010 val/
-    mv training/image_02/0013 val/
-    mv training/image_02/0014 val/
-    mv training/image_02/0016 val/
-    mv training/image_02/0018 val/
-
-    # Move training sequences
-    mv training/image_02/ train/
-    rm -r training
-    ```
-
-4.  Download groundtruth KITTI-STEP panoptic maps from
-    [here](http://storage.googleapis.com/gresearch/tf-deeplab/data/kitti-step.tar.gz).
-
-    ```bash
-    # Goto ${KITTI_STEP_ROOT}
-    cd ..
-
-    wget http://storage.googleapis.com/gresearch/tf-deeplab/data/kitti-step.tar.gz
-    tar -xvf kitti-step.tar.gz
-    mv kitti-step/panoptic_maps panoptic_maps
-    rm -r kitti-step
-    ```
-
-The groundtruth panoptic map is encoded as follows in PNG format:
-
-```
-R = semantic_id
-G = instance_id // 256
-B = instance % 256
-```
-
-Following the above guide, your data structure should look like this:
-
-```
-.(KITTI_STEP_ROOT)
-+-- images
-|   |
-|   +-- train
-|   |   |
-|   |   +-- sequence_id (%04d)
-|   |       |
-|   |       +-- frame_id.png (%06d.png)
-|   |   ...
-|   +-- val
-|   +-- test
-|
-+-- panoptic_maps
-     |
-     +-- train
-     |   |
-     |   +-- sequence_id (%04d)
-     |       |
-     |       +-- frame_id.png (%06d.png)
-     |   ...
-     +-- val
-```
-
-#### Create tfrecord files
-
-To create dataset for training and evaluation, run the following command:
-
-```bash
-python deeplab2/data/build_step_data.py \
-  --step_root=${KITTI_STEP_ROOT} \
-  --output_dir=${OUTPUT_DIR}
-```
-
-This script outputs three sharded tfrecord files:
-`{train|val|test}@10.tfrecord`. In the tfrecords, for `train` and `val` set, it
-contains the RGB image pixels as well as their panoptic maps. For `test` set, it
-contains RGB images only. These files will be used as the input for the model
-training and evaluation.
-
-Optionally, you can also specify with `--use_two_frames` to encode two
-consecutive frames into the tfrecord files.
-
-## Citing KITTI-STEP
-
-If you find this dataset helpful in your research, please use the following
-BibTeX entry.
-
-```
-@article{step_2021,
-  author={Mark Weber and Jun Xie and Maxwell Collins and Yukun Zhu and Paul Voigtlaender and Hartwig Adam and Bradley Green and Andreas Geiger and Bastian Leibe and Daniel Cremers and Aljosa Osep and Laura Leal-Taixe and Liang-Chieh Chen},
-  title={{STEP}: Segmenting and Tracking Every Pixel},
-  journal={arXiv:2102.11859},
-  year={2021}
-}
-```
diff --git a/spaces/akhaliq/deeplab2/gradio/demo.py b/spaces/akhaliq/deeplab2/gradio/demo.py
deleted file mode 100644
index 276d53007db763c47dbad06c2a38014e2546764a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/gradio/demo.py
+++ /dev/null
@@ -1,273 +0,0 @@
-import collections
-import os
-import tempfile
-
-from matplotlib import gridspec
-from matplotlib import pyplot as plt
-import numpy as np
-from PIL import Image
-import urllib
-
-import tensorflow as tf
-import gradio as gr
-
-from subprocess import call
-import sys
-
-import requests
-
-url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
-r = requests.get(url1, allow_redirects=True)
-open("city1.jpg", 'wb').write(r.content)
-
-url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
-r = requests.get(url2, allow_redirects=True)
-open("city2.jpg", 'wb').write(r.content)
-
-
-DatasetInfo = collections.namedtuple(
-    'DatasetInfo',
-    'num_classes, label_divisor, thing_list, colormap, class_names')
-
-
-def _cityscapes_label_colormap():
-  """Creates a label colormap used in CITYSCAPES segmentation benchmark.
-
-  See more about CITYSCAPES dataset at https://www.cityscapes-dataset.com/
-  M. Cordts, et al. "The Cityscapes Dataset for Semantic Urban Scene Understanding." CVPR. 2016.
-
-  Returns:
-    A 2-D numpy array with each row being mapped RGB color (in uint8 range).
-  """
-  colormap = np.zeros((256, 3), dtype=np.uint8)
-  colormap[0] = [128, 64, 128]
-  colormap[1] = [244, 35, 232]
-  colormap[2] = [70, 70, 70]
-  colormap[3] = [102, 102, 156]
-  colormap[4] = [190, 153, 153]
-  colormap[5] = [153, 153, 153]
-  colormap[6] = [250, 170, 30]
-  colormap[7] = [220, 220, 0]
-  colormap[8] = [107, 142, 35]
-  colormap[9] = [152, 251, 152]
-  colormap[10] = [70, 130, 180]
-  colormap[11] = [220, 20, 60]
-  colormap[12] = [255, 0, 0]
-  colormap[13] = [0, 0, 142]
-  colormap[14] = [0, 0, 70]
-  colormap[15] = [0, 60, 100]
-  colormap[16] = [0, 80, 100]
-  colormap[17] = [0, 0, 230]
-  colormap[18] = [119, 11, 32]
-  return colormap
-
-
-def _cityscapes_class_names():
-  return ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
-          'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
-          'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
-          'bicycle')
-
-
-def cityscapes_dataset_information():
-  return DatasetInfo(
-      num_classes=19,
-      label_divisor=1000,
-      thing_list=tuple(range(11, 19)),
-      colormap=_cityscapes_label_colormap(),
-      class_names=_cityscapes_class_names())
-
-
-def perturb_color(color, noise, used_colors, max_trials=50, random_state=None):
-  """Pertrubs the color with some noise.
-
-  If `used_colors` is not None, we will return the color that has
-  not appeared before in it.
-
-  Args:
-    color: A numpy array with three elements [R, G, B].
-    noise: Integer, specifying the amount of perturbing noise (in uint8 range).
-    used_colors: A set, used to keep track of used colors.
-    max_trials: An integer, maximum trials to generate random color.
-    random_state: An optional np.random.RandomState. If passed, will be used to
-      generate random numbers.
-
-  Returns:
-    A perturbed color that has not appeared in used_colors.
-  """
-  if random_state is None:
-    random_state = np.random
-
-  for _ in range(max_trials):
-    random_color = color + random_state.randint(
-        low=-noise, high=noise + 1, size=3)
-    random_color = np.clip(random_color, 0, 255)
-
-    if tuple(random_color) not in used_colors:
-      used_colors.add(tuple(random_color))
-      return random_color
-
-  print('Max trial reached and duplicate color will be used. Please consider '
-        'increase noise in `perturb_color()`.')
-  return random_color
-
-
-def color_panoptic_map(panoptic_prediction, dataset_info, perturb_noise):
-  """Helper method to colorize output panoptic map.
-
-  Args:
-    panoptic_prediction: A 2D numpy array, panoptic prediction from deeplab
-      model.
-    dataset_info: A DatasetInfo object, dataset associated to the model.
-    perturb_noise: Integer, the amount of noise (in uint8 range) added to each
-      instance of the same semantic class.
-
-  Returns:
-    colored_panoptic_map: A 3D numpy array with last dimension of 3, colored
-      panoptic prediction map.
-    used_colors: A dictionary mapping semantic_ids to a set of colors used
-      in `colored_panoptic_map`.
-  """
-  if panoptic_prediction.ndim != 2:
-    raise ValueError('Expect 2-D panoptic prediction. Got {}'.format(
-        panoptic_prediction.shape))
-
-  semantic_map = panoptic_prediction // dataset_info.label_divisor
-  instance_map = panoptic_prediction % dataset_info.label_divisor
-  height, width = panoptic_prediction.shape
-  colored_panoptic_map = np.zeros((height, width, 3), dtype=np.uint8)
-
-  used_colors = collections.defaultdict(set)
-  # Use a fixed seed to reproduce the same visualization.
-  random_state = np.random.RandomState(0)
-
-  unique_semantic_ids = np.unique(semantic_map)
-  for semantic_id in unique_semantic_ids:
-    semantic_mask = semantic_map == semantic_id
-    if semantic_id in dataset_info.thing_list:
-      # For `thing` class, we will add a small amount of random noise to its
-      # correspondingly predefined semantic segmentation colormap.
-      unique_instance_ids = np.unique(instance_map[semantic_mask])
-      for instance_id in unique_instance_ids:
-        instance_mask = np.logical_and(semantic_mask,
-                                       instance_map == instance_id)
-        random_color = perturb_color(
-            dataset_info.colormap[semantic_id],
-            perturb_noise,
-            used_colors[semantic_id],
-            random_state=random_state)
-        colored_panoptic_map[instance_mask] = random_color
-    else:
-      # For `stuff` class, we use the defined semantic color.
-      colored_panoptic_map[semantic_mask] = dataset_info.colormap[semantic_id]
-      used_colors[semantic_id].add(tuple(dataset_info.colormap[semantic_id]))
-  return colored_panoptic_map, used_colors
-
-
-def vis_segmentation(image,
-                     panoptic_prediction,
-                     dataset_info,
-                     perturb_noise=60):
-  """Visualizes input image, segmentation map and overlay view."""
-  plt.figure(figsize=(30, 20))
-  grid_spec = gridspec.GridSpec(2, 2)
-
-  ax = plt.subplot(grid_spec[0])
-  plt.imshow(image)
-  plt.axis('off')
-  ax.set_title('input image', fontsize=20)
-
-  ax = plt.subplot(grid_spec[1])
-  panoptic_map, used_colors = color_panoptic_map(panoptic_prediction,
-                                                 dataset_info, perturb_noise)
-  plt.imshow(panoptic_map)
-  plt.axis('off')
-  ax.set_title('panoptic map', fontsize=20)
-
-  ax = plt.subplot(grid_spec[2])
-  plt.imshow(image)
-  plt.imshow(panoptic_map, alpha=0.7)
-  plt.axis('off')
-  ax.set_title('panoptic overlay', fontsize=20)
-
-  ax = plt.subplot(grid_spec[3])
-  max_num_instances = max(len(color) for color in used_colors.values())
-  # RGBA image as legend.
-  legend = np.zeros((len(used_colors), max_num_instances, 4), dtype=np.uint8)
-  class_names = []
-  for i, semantic_id in enumerate(sorted(used_colors)):
-    legend[i, :len(used_colors[semantic_id]), :3] = np.array(
-        list(used_colors[semantic_id]))
-    legend[i, :len(used_colors[semantic_id]), 3] = 255
-    if semantic_id < dataset_info.num_classes:
-      class_names.append(dataset_info.class_names[semantic_id])
-    else:
-      class_names.append('ignore')
-
-  plt.imshow(legend, interpolation='nearest')
-  ax.yaxis.tick_left()
-  plt.yticks(range(len(legend)), class_names, fontsize=15)
-  plt.xticks([], [])
-  ax.tick_params(width=0.0, grid_linewidth=0.0)
-  plt.grid('off')
-  return plt
-
-def run_cmd(command):
-    try:
-        print(command)
-        call(command, shell=True)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)
-MODEL_NAME = 'resnet50_os32_panoptic_deeplab_cityscapes_crowd_trainfine_saved_model'   
-
-
-_MODELS = ('resnet50_os32_panoptic_deeplab_cityscapes_crowd_trainfine_saved_model',
-           'resnet50_beta_os32_panoptic_deeplab_cityscapes_trainfine_saved_model',
-           'wide_resnet41_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
-           'swidernet_sac_1_1_1_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
-           'swidernet_sac_1_1_3_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
-           'swidernet_sac_1_1_4.5_os16_panoptic_deeplab_cityscapes_trainfine_saved_model',
-           'axial_swidernet_1_1_1_os16_axial_deeplab_cityscapes_trainfine_saved_model',
-           'axial_swidernet_1_1_3_os16_axial_deeplab_cityscapes_trainfine_saved_model',
-           'axial_swidernet_1_1_4.5_os16_axial_deeplab_cityscapes_trainfine_saved_model',
-           'max_deeplab_s_backbone_os16_axial_deeplab_cityscapes_trainfine_saved_model',
-           'max_deeplab_l_backbone_os16_axial_deeplab_cityscapes_trainfine_saved_model')
-_DOWNLOAD_URL_PATTERN = 'https://storage.googleapis.com/gresearch/tf-deeplab/saved_model/%s.tar.gz'
-
-_MODEL_NAME_TO_URL_AND_DATASET = {
-    model: (_DOWNLOAD_URL_PATTERN % model, cityscapes_dataset_information())
-    for model in _MODELS
-}
-
-MODEL_URL, DATASET_INFO = _MODEL_NAME_TO_URL_AND_DATASET[MODEL_NAME]
-
-model_dir = tempfile.mkdtemp()
-
-download_path = os.path.join(model_dir, MODEL_NAME + '.gz')
-urllib.request.urlretrieve(MODEL_URL, download_path)
-
-run_cmd("tar -xzvf " + download_path + " -C " + model_dir)
-
-LOADED_MODEL = tf.saved_model.load(os.path.join(model_dir, MODEL_NAME))
-def inference(image):
-    image = image.resize(size=(512, 512))
-    im = np.array(image)
-    output = LOADED_MODEL(tf.cast(im, tf.uint8))
-    return vis_segmentation(im, output['panoptic_pred'][0], DATASET_INFO)
-
-title = "Deeplab2"
-description = "demo for Deeplab2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2106.09748'>DeepLab2: A TensorFlow Library for Deep Labeling</a> | <a href='https://github.com/google-research/deeplab2'>Github Repo</a></p>"
-
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input")], 
-    gr.outputs.Image(type="plot", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-            ["city1.jpg"],
-            ["city2.jpg"]
-        ]).launch()
diff --git a/spaces/akhaliq/sd-pokemon-diffusers/app.py b/spaces/akhaliq/sd-pokemon-diffusers/app.py
deleted file mode 100644
index 56dee349dc931016b47cda3ad49e3ea63269802f..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/sd-pokemon-diffusers/app.py
+++ /dev/null
@@ -1,252 +0,0 @@
-import gradio as gr
-
-import torch
-from torch import autocast
-from diffusers import StableDiffusionPipeline
-from datasets import load_dataset
-from PIL import Image  
-import re
-
-
-pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-pokemon-diffusers", torch_dtype=torch.float32)  
-pipe = pipe.to("cpu")
-
-
-# Sometimes the nsfw checker is confused by the Pokémon images, you can disable
-# it at your own risk here
-disable_safety = False
-
-if disable_safety:
-  def null_safety(images, **kwargs):
-      return images, False
-  pipe.safety_checker = null_safety
-
-
-def infer(prompt, samples,scale):
-    prompt = prompt
-    scale = scale
-    n_samples = samples
-
-
-    images = pipe(n_samples*[prompt], guidance_scale=scale).images
-
-    return images
-    
-css = """
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: black;
-            background: black;
-        }
-        input[type='range'] {
-            accent-color: black;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-btn {
-            font-size: .7rem !important;
-            line-height: 19px;
-            margin-top: 12px;
-            margin-bottom: 12px;
-            padding: 2px 8px;
-            border-radius: 14px !important;
-        }
-        #advanced-options {
-            display: none;
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-"""
-
-block = gr.Blocks(css=css)
-
-examples = [
-    [
-        'Abraham Lincoln',
-        2,
-        7.5,
-    ],
-    [
-        'George Washington',
-        2,
-        7,
-    ],
-]
-
-with block:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <svg
-                  width="0.65em"
-                  height="0.65em"
-                  viewBox="0 0 115 115"
-                  fill="none"
-                  xmlns="http://www.w3.org/2000/svg"
-                >
-                  <rect width="23" height="23" fill="white"></rect>
-                  <rect y="69" width="23" height="23" fill="white"></rect>
-                  <rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="46" width="23" height="23" fill="white"></rect>
-                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
-                  <rect x="69" width="23" height="23" fill="black"></rect>
-                  <rect x="69" y="69" width="23" height="23" fill="black"></rect>
-                  <rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="115" y="46" width="23" height="23" fill="white"></rect>
-                  <rect x="115" y="115" width="23" height="23" fill="white"></rect>
-                  <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="92" y="69" width="23" height="23" fill="white"></rect>
-                  <rect x="69" y="46" width="23" height="23" fill="white"></rect>
-                  <rect x="69" y="115" width="23" height="23" fill="white"></rect>
-                  <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="46" y="46" width="23" height="23" fill="black"></rect>
-                  <rect x="46" y="115" width="23" height="23" fill="black"></rect>
-                  <rect x="46" y="69" width="23" height="23" fill="black"></rect>
-                  <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
-                  <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
-                  <rect x="23" y="69" width="23" height="23" fill="black"></rect>
-                </svg>
-                <h1 style="font-weight: 900; margin-bottom: 7px;">
-                  Pokémon text to image
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-              Demo demonstrates inference using Stable Diffusion fine tuned on Pokémon to generate new Pokémon form text prompts
-              </p>
-            </div>
-        """
-    )
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Enter your prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                )
-
-        gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-
-        advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
-
-        with gr.Row(elem_id="advanced-options"):
-            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
-            scale = gr.Slider(
-                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
-            )
-        
-
-        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False)
-        ex.dataset.headers = [""]
-
-        
-        text.submit(infer, inputs=[text, samples, scale], outputs=gallery)
-        btn.click(infer, inputs=[text, samples, scale], outputs=gallery)
-        advanced_button.click(
-            None,
-            [],
-            text,
-            _js="""
-            () => {
-                const options = document.querySelector("body > gradio-app").querySelector("#advanced-options");
-                options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none";
-            }""",
-        )
-        gr.HTML(
-            """
-                <div class="footer">
-                    <p> Gradio Demo by 🤗 Hugging Face
-                    </p>
-                </div>
-                <div class="acknowledgments">
-                    <p><h4>LICENSE</h4>
-The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
-                    <p><h4>Biases and content acknowledgment</h4>
-Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
-               </div>
-           """
-        )
-
-block.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/akhaliq/steerable-nafx/app.py b/spaces/akhaliq/steerable-nafx/app.py
deleted file mode 100644
index ff24bf6932cd6e18bbff81ebb9faaa6d93ba5af3..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/steerable-nafx/app.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import os 
-
-os.system("wget https://csteinmetz1.github.io/steerable-nafx/models/compressor_full.pt")
-os.system("wget https://csteinmetz1.github.io/steerable-nafx/models/reverb_full.pt")
-os.system("wget https://csteinmetz1.github.io/steerable-nafx/models/amp_full.pt")
-os.system("wget https://csteinmetz1.github.io/steerable-nafx/models/delay_full.pt")
-os.system("wget https://csteinmetz1.github.io/steerable-nafx/models/synth2synth_full.pt")
-
-import sys
-import math
-import torch
-import librosa.display
-import auraloss
-import torchaudio
-import numpy as np
-import scipy.signal
-from tqdm.notebook import tqdm
-from time import sleep
-import pyloudnorm as pyln
-import gradio as gr
-
-def measure_rt60(h, fs=1, decay_db=30, rt60_tgt=None):
-    """
-    Analyze the RT60 of an impulse response.
-    Args:
-        h (ndarray): The discrete time impulse response as 1d array.
-        fs (float, optional): Sample rate of the impulse response. (Default: 48000)
-        decay_db (float, optional): The decay in decibels for which we actually estimate the time. (Default: 60)
-        rt60_tgt (float, optional): This parameter can be used to indicate a target RT60. (Default: None)
-    Returns:
-        est_rt60 (float): Estimated RT60.
-    """
-
-    h = np.array(h)
-    fs = float(fs)
-
-    # The power of the impulse response in dB
-    power = h ** 2
-    energy = np.cumsum(power[::-1])[::-1]  # Integration according to Schroeder
-
-    try:
-        # remove the possibly all zero tail
-        i_nz = np.max(np.where(energy > 0)[0])
-        energy = energy[:i_nz]
-        energy_db = 10 * np.log10(energy)
-        energy_db -= energy_db[0]
-
-        # -5 dB headroom
-        i_5db = np.min(np.where(-5 - energy_db > 0)[0])
-        e_5db = energy_db[i_5db]
-        t_5db = i_5db / fs
-
-        # after decay
-        i_decay = np.min(np.where(-5 - decay_db - energy_db > 0)[0])
-        t_decay = i_decay / fs
-
-        # compute the decay time
-        decay_time = t_decay - t_5db
-        est_rt60 = (60 / decay_db) * decay_time
-    except:
-        est_rt60 = np.array(0.0)
-
-    return est_rt60
-    
-def causal_crop(x, length: int):
-    if x.shape[-1] != length:
-        stop = x.shape[-1] - 1
-        start = stop - length
-        x = x[..., start:stop]
-    return x
-
-class FiLM(torch.nn.Module):
-    def __init__(
-        self,
-        cond_dim,  # dim of conditioning input
-        num_features,  # dim of the conv channel
-        batch_norm=True,
-    ):
-        super().__init__()
-        self.num_features = num_features
-        self.batch_norm = batch_norm
-        if batch_norm:
-            self.bn = torch.nn.BatchNorm1d(num_features, affine=False)
-        self.adaptor = torch.nn.Linear(cond_dim, num_features * 2)
-
-    def forward(self, x, cond):
-
-        cond = self.adaptor(cond)
-        g, b = torch.chunk(cond, 2, dim=-1)
-        g = g.permute(0, 2, 1)
-        b = b.permute(0, 2, 1)
-
-        if self.batch_norm:
-            x = self.bn(x)  # apply BatchNorm without affine
-        x = (x * g) + b  # then apply conditional affine
-
-        return x
-
-class TCNBlock(torch.nn.Module):
-  def __init__(self, in_channels, out_channels, kernel_size, dilation, cond_dim=0, activation=True):
-    super().__init__()
-    self.conv = torch.nn.Conv1d(
-        in_channels, 
-        out_channels, 
-        kernel_size, 
-        dilation=dilation, 
-        padding=0, #((kernel_size-1)//2)*dilation,
-        bias=True)
-    if cond_dim > 0:
-      self.film = FiLM(cond_dim, out_channels, batch_norm=False)
-    if activation:
-      #self.act = torch.nn.Tanh()
-      self.act = torch.nn.PReLU()
-    self.res = torch.nn.Conv1d(in_channels, out_channels, 1, bias=False)
-
-  def forward(self, x, c=None):
-    x_in = x
-    x = self.conv(x)
-    if hasattr(self, "film"):
-      x = self.film(x, c)
-    if hasattr(self, "act"):
-      x = self.act(x)
-    x_res = causal_crop(self.res(x_in), x.shape[-1])
-    x = x + x_res
-
-    return x
-
-class TCN(torch.nn.Module):
-  def __init__(self, n_inputs=1, n_outputs=1, n_blocks=10, kernel_size=13, n_channels=64, dilation_growth=4, cond_dim=0):
-    super().__init__()
-    self.kernel_size = kernel_size
-    self.n_channels = n_channels
-    self.dilation_growth = dilation_growth
-    self.n_blocks = n_blocks
-    self.stack_size = n_blocks
-
-    self.blocks = torch.nn.ModuleList()
-    for n in range(n_blocks):
-      if n == 0:
-        in_ch = n_inputs
-        out_ch = n_channels
-        act = True
-      elif (n+1) == n_blocks:
-        in_ch = n_channels
-        out_ch = n_outputs
-        act = True
-      else:
-        in_ch = n_channels
-        out_ch = n_channels
-        act = True
-      
-      dilation = dilation_growth ** n
-      self.blocks.append(TCNBlock(in_ch, out_ch, kernel_size, dilation, cond_dim=cond_dim, activation=act))
-
-  def forward(self, x, c=None):
-    for block in self.blocks:
-      x = block(x, c)
-
-    return x
-  
-  def compute_receptive_field(self):
-    """Compute the receptive field in samples."""
-    rf = self.kernel_size
-    for n in range(1, self.n_blocks):
-        dilation = self.dilation_growth ** (n % self.stack_size)
-        rf = rf + ((self.kernel_size - 1) * dilation)
-    return rf
-    
-# setup the pre-trained models
-model_comp = torch.load("compressor_full.pt", map_location="cpu").eval()
-model_verb = torch.load("reverb_full.pt", map_location="cpu").eval()
-model_amp = torch.load("amp_full.pt", map_location="cpu").eval()
-model_delay = torch.load("delay_full.pt", map_location="cpu").eval()
-model_synth = torch.load("synth2synth_full.pt", map_location="cpu").eval()
-
-
-
-def inference(aud, effect_type,gain_dB,c0,c1,mix,width,max_length,stereo,tail):
-  x_p, sample_rate = torchaudio.load(aud)
-  
-  effect_type = effect_type #@param ["Compressor", "Reverb", "Amp", "Analog Delay", "Synth2Synth"]
-  gain_dB = gain_dB #@param {type:"slider", min:-24, max:24, step:0.1}
-  c0 = c0 #@param {type:"slider", min:-10, max:10, step:0.1}
-  c1 = c1 #@param {type:"slider", min:-10, max:10, step:0.1}
-  mix = mix #@param {type:"slider", min:0, max:100, step:1}
-  width = width #@param {type:"slider", min:0, max:100, step:1}
-  max_length = max_length#@param {type:"slider", min:5, max:120, step:1}
-  stereo = stereo #@param {type:"boolean"}
-  tail = tail #@param {type:"boolean"}
-  
-  # select model type
-  if effect_type == "Compressor":
-    pt_model = model_comp
-  elif effect_type == "Reverb":
-    pt_model = model_verb
-  elif effect_type == "Amp":
-    pt_model = model_amp
-  elif effect_type == "Analog Delay":
-    pt_model = model_delay
-  elif effect_type == "Synth2Synth":
-    pt_model = model_synth
-  
-  # measure the receptive field
-  pt_model_rf = pt_model.compute_receptive_field()
-  
-  # crop input signal if needed
-  max_samples = int(sample_rate * max_length)
-  x_p_crop = x_p[:,:max_samples]
-  chs = x_p_crop.shape[0]
-  
-  # if mono and stereo requested
-  if chs == 1 and stereo:
-    x_p_crop = x_p_crop.repeat(2,1)
-    chs = 2
-  
-  # pad the input signal
-  front_pad = pt_model_rf-1
-  back_pad = 0 if not tail else front_pad
-  x_p_pad = torch.nn.functional.pad(x_p_crop, (front_pad, back_pad))
-  
-  # design highpass filter
-  sos = scipy.signal.butter(
-      8, 
-      20.0, 
-      fs=sample_rate, 
-      output="sos", 
-      btype="highpass"
-  )
-  
-  # compute linear gain 
-  gain_ln = 10 ** (gain_dB / 20.0)
-  
-  # process audio with pre-trained model
-  with torch.no_grad():
-    y_hat = torch.zeros(x_p_crop.shape[0], x_p_crop.shape[1] + back_pad)
-    for n in range(chs):
-      if n == 0:
-        factor = (width*5e-3)
-      elif n == 1:
-        factor = -(width*5e-3)
-      c = torch.tensor([float(c0+factor), float(c1+factor)]).view(1,1,-1)
-      y_hat_ch = pt_model(gain_ln * x_p_pad[n,:].view(1,1,-1), c)
-      y_hat_ch = scipy.signal.sosfilt(sos, y_hat_ch.view(-1).numpy())
-      y_hat_ch = torch.tensor(y_hat_ch)
-      y_hat[n,:] = y_hat_ch
-  
-  # pad the dry signal 
-  x_dry = torch.nn.functional.pad(x_p_crop, (0,back_pad))
-  
-  # normalize each first
-  y_hat /= y_hat.abs().max()
-  x_dry /= x_dry.abs().max()
-  
-  # mix
-  mix = mix/100.0
-  y_hat = (mix * y_hat) + ((1-mix) * x_dry)
-  
-  # remove transient
-  y_hat = y_hat[...,8192:]
-  y_hat /= y_hat.abs().max()
-  
-  torchaudio.save("output.mp3", y_hat.view(chs,-1), sample_rate, compression=320.0)
-  return "output.mp3"
-  
-title = "Steerable nafx"
-description = """Gradio demo for Steerable discovery of neural audio effects. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below. 
-Now set the audio effect parameters. Here are some more insights into the controls:
-
-effect_type - Choose from one of the pre-trained models.
-gain_dB - Adjust the input gain. This can have a big effect since the effects are very nonlinear.
-c0 and c1 - These are the effect controls which will adjust perceptual aspects of the effect, depending on the effect type. Very large values will often result in more extreme effects.
-mix - Control the wet/dry mix of the effect.
-width - Increase stereo width of the effect.
-max_length - If you uploaded a very long file this will truncate it.
-stereo - Convert mono input to stereo output.
-tail - If checked, we will also compute the effect tail (nice for reverbs)."""
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.02926' target='_blank'>Steerable discovery of neural audio effects</a> | <a href='https://github.com/csteinmetz1/steerable-nafx' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_steerablenafx' alt='visitor badge'></center>"
-
-gr.Interface(
-    inference, 
-    [gr.inputs.Audio(type="filepath", label="Input"),gr.inputs.Dropdown(choices=["Compressor", "Reverb", "Amp", "Analog Delay", "Synth2Synth"], type="value", default="Analog Delay", label="Effect Type"),gr.inputs.Slider(minimum=-24, maximum=24, step=1, default=-24, label="gain dB"),gr.inputs.Slider(minimum=-10, maximum=10, step=0.1, default=-1.4, label="c0"),
-    gr.inputs.Slider(minimum=-10, maximum=10, step=1, default=3, label="c1"),
-    gr.inputs.Slider(minimum=0, maximum=100, step=1, default=70, label="mix"),
-    gr.inputs.Slider(minimum=0, maximum=100, step=1, default=50, label="width"),
-    gr.inputs.Slider(minimum=5, maximum=120, step=1, default=30, label="max length"),
-    gr.inputs.Checkbox(default=True, label="stereo"),
-    gr.inputs.Checkbox(default=True, label="tail")], 
-    gr.outputs.Audio(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[['vocals.wav',"Analog Delay",-24,-1.4,3,70,50,30,True,True]],
-    enable_queue=True
-    ).launch(debug=True)
-  
-
diff --git a/spaces/akhaliq/stylegan3_clip/gui_utils/gl_utils.py b/spaces/akhaliq/stylegan3_clip/gui_utils/gl_utils.py
deleted file mode 100644
index 2dd8bd96f4b74bdfb274f25d810ddfd43dc44068..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/stylegan3_clip/gui_utils/gl_utils.py
+++ /dev/null
@@ -1,374 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import functools
-import contextlib
-import numpy as np
-import OpenGL.GL as gl
-import OpenGL.GL.ARB.texture_float
-import dnnlib
-
-#----------------------------------------------------------------------------
-
-def init_egl():
-    assert os.environ['PYOPENGL_PLATFORM'] == 'egl' # Must be set before importing OpenGL.
-    import OpenGL.EGL as egl
-    import ctypes
-
-    # Initialize EGL.
-    display = egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY)
-    assert display != egl.EGL_NO_DISPLAY
-    major = ctypes.c_int32()
-    minor = ctypes.c_int32()
-    ok = egl.eglInitialize(display, major, minor)
-    assert ok
-    assert major.value * 10 + minor.value >= 14
-
-    # Choose config.
-    config_attribs = [
-        egl.EGL_RENDERABLE_TYPE,    egl.EGL_OPENGL_BIT,
-        egl.EGL_SURFACE_TYPE,       egl.EGL_PBUFFER_BIT,
-        egl.EGL_NONE
-    ]
-    configs = (ctypes.c_int32 * 1)()
-    num_configs = ctypes.c_int32()
-    ok = egl.eglChooseConfig(display, config_attribs, configs, 1, num_configs)
-    assert ok
-    assert num_configs.value == 1
-    config = configs[0]
-
-    # Create dummy pbuffer surface.
-    surface_attribs = [
-        egl.EGL_WIDTH,  1,
-        egl.EGL_HEIGHT, 1,
-        egl.EGL_NONE
-    ]
-    surface = egl.eglCreatePbufferSurface(display, config, surface_attribs)
-    assert surface != egl.EGL_NO_SURFACE
-
-    # Setup GL context.
-    ok = egl.eglBindAPI(egl.EGL_OPENGL_API)
-    assert ok
-    context = egl.eglCreateContext(display, config, egl.EGL_NO_CONTEXT, None)
-    assert context != egl.EGL_NO_CONTEXT
-    ok = egl.eglMakeCurrent(display, surface, surface, context)
-    assert ok
-
-#----------------------------------------------------------------------------
-
-_texture_formats = {
-    ('uint8',   1): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_LUMINANCE,       internalformat=gl.GL_LUMINANCE8),
-    ('uint8',   2): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_LUMINANCE_ALPHA, internalformat=gl.GL_LUMINANCE8_ALPHA8),
-    ('uint8',   3): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_RGB,             internalformat=gl.GL_RGB8),
-    ('uint8',   4): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_RGBA,            internalformat=gl.GL_RGBA8),
-    ('float32', 1): dnnlib.EasyDict(type=gl.GL_FLOAT,         format=gl.GL_LUMINANCE,       internalformat=OpenGL.GL.ARB.texture_float.GL_LUMINANCE32F_ARB),
-    ('float32', 2): dnnlib.EasyDict(type=gl.GL_FLOAT,         format=gl.GL_LUMINANCE_ALPHA, internalformat=OpenGL.GL.ARB.texture_float.GL_LUMINANCE_ALPHA32F_ARB),
-    ('float32', 3): dnnlib.EasyDict(type=gl.GL_FLOAT,         format=gl.GL_RGB,             internalformat=gl.GL_RGB32F),
-    ('float32', 4): dnnlib.EasyDict(type=gl.GL_FLOAT,         format=gl.GL_RGBA,            internalformat=gl.GL_RGBA32F),
-}
-
-def get_texture_format(dtype, channels):
-    return _texture_formats[(np.dtype(dtype).name, int(channels))]
-
-#----------------------------------------------------------------------------
-
-def prepare_texture_data(image):
-    image = np.asarray(image)
-    if image.ndim == 2:
-        image = image[:, :, np.newaxis]
-    if image.dtype.name == 'float64':
-        image = image.astype('float32')
-    return image
-
-#----------------------------------------------------------------------------
-
-def draw_pixels(image, *, pos=0, zoom=1, align=0, rint=True):
-    pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
-    zoom = np.broadcast_to(np.asarray(zoom, dtype='float32'), [2])
-    align = np.broadcast_to(np.asarray(align, dtype='float32'), [2])
-    image = prepare_texture_data(image)
-    height, width, channels = image.shape
-    size = zoom * [width, height]
-    pos = pos - size * align
-    if rint:
-        pos = np.rint(pos)
-    fmt = get_texture_format(image.dtype, channels)
-
-    gl.glPushAttrib(gl.GL_CURRENT_BIT | gl.GL_PIXEL_MODE_BIT)
-    gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT)
-    gl.glRasterPos2f(pos[0], pos[1])
-    gl.glPixelZoom(zoom[0], -zoom[1])
-    gl.glPixelStorei(gl.GL_UNPACK_ALIGNMENT, 1)
-    gl.glDrawPixels(width, height, fmt.format, fmt.type, image)
-    gl.glPopClientAttrib()
-    gl.glPopAttrib()
-
-#----------------------------------------------------------------------------
-
-def read_pixels(width, height, *, pos=0, dtype='uint8', channels=3):
-    pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
-    dtype = np.dtype(dtype)
-    fmt = get_texture_format(dtype, channels)
-    image = np.empty([height, width, channels], dtype=dtype)
-
-    gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT)
-    gl.glPixelStorei(gl.GL_PACK_ALIGNMENT, 1)
-    gl.glReadPixels(int(np.round(pos[0])), int(np.round(pos[1])), width, height, fmt.format, fmt.type, image)
-    gl.glPopClientAttrib()
-    return np.flipud(image)
-
-#----------------------------------------------------------------------------
-
-class Texture:
-    def __init__(self, *, image=None, width=None, height=None, channels=None, dtype=None, bilinear=True, mipmap=True):
-        self.gl_id = None
-        self.bilinear = bilinear
-        self.mipmap = mipmap
-
-        # Determine size and dtype.
-        if image is not None:
-            image = prepare_texture_data(image)
-            self.height, self.width, self.channels = image.shape
-            self.dtype = image.dtype
-        else:
-            assert width is not None and height is not None
-            self.width = width
-            self.height = height
-            self.channels = channels if channels is not None else 3
-            self.dtype = np.dtype(dtype) if dtype is not None else np.uint8
-
-        # Validate size and dtype.
-        assert isinstance(self.width, int) and self.width >= 0
-        assert isinstance(self.height, int) and self.height >= 0
-        assert isinstance(self.channels, int) and self.channels >= 1
-        assert self.is_compatible(width=width, height=height, channels=channels, dtype=dtype)
-
-        # Create texture object.
-        self.gl_id = gl.glGenTextures(1)
-        with self.bind():
-            gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
-            gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
-            gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR if self.bilinear else gl.GL_NEAREST)
-            gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR_MIPMAP_LINEAR if self.mipmap else gl.GL_NEAREST)
-        self.update(image)
-
-    def delete(self):
-        if self.gl_id is not None:
-            gl.glDeleteTextures([self.gl_id])
-            self.gl_id = None
-
-    def __del__(self):
-        try:
-            self.delete()
-        except:
-            pass
-
-    @contextlib.contextmanager
-    def bind(self):
-        prev_id = gl.glGetInteger(gl.GL_TEXTURE_BINDING_2D)
-        gl.glBindTexture(gl.GL_TEXTURE_2D, self.gl_id)
-        yield
-        gl.glBindTexture(gl.GL_TEXTURE_2D, prev_id)
-
-    def update(self, image):
-        if image is not None:
-            image = prepare_texture_data(image)
-            assert self.is_compatible(image=image)
-        with self.bind():
-            fmt = get_texture_format(self.dtype, self.channels)
-            gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT)
-            gl.glPixelStorei(gl.GL_UNPACK_ALIGNMENT, 1)
-            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, fmt.internalformat, self.width, self.height, 0, fmt.format, fmt.type, image)
-            if self.mipmap:
-                gl.glGenerateMipmap(gl.GL_TEXTURE_2D)
-            gl.glPopClientAttrib()
-
-    def draw(self, *, pos=0, zoom=1, align=0, rint=False, color=1, alpha=1, rounding=0):
-        zoom = np.broadcast_to(np.asarray(zoom, dtype='float32'), [2])
-        size = zoom * [self.width, self.height]
-        with self.bind():
-            gl.glPushAttrib(gl.GL_ENABLE_BIT)
-            gl.glEnable(gl.GL_TEXTURE_2D)
-            draw_rect(pos=pos, size=size, align=align, rint=rint, color=color, alpha=alpha, rounding=rounding)
-            gl.glPopAttrib()
-
-    def is_compatible(self, *, image=None, width=None, height=None, channels=None, dtype=None): # pylint: disable=too-many-return-statements
-        if image is not None:
-            if image.ndim != 3:
-                return False
-            ih, iw, ic = image.shape
-            if not self.is_compatible(width=iw, height=ih, channels=ic, dtype=image.dtype):
-                return False
-        if width is not None and self.width != width:
-            return False
-        if height is not None and self.height != height:
-            return False
-        if channels is not None and self.channels != channels:
-            return False
-        if dtype is not None and self.dtype != dtype:
-            return False
-        return True
-
-#----------------------------------------------------------------------------
-
-class Framebuffer:
-    def __init__(self, *, texture=None, width=None, height=None, channels=None, dtype=None, msaa=0):
-        self.texture = texture
-        self.gl_id = None
-        self.gl_color = None
-        self.gl_depth_stencil = None
-        self.msaa = msaa
-
-        # Determine size and dtype.
-        if texture is not None:
-            assert isinstance(self.texture, Texture)
-            self.width = texture.width
-            self.height = texture.height
-            self.channels = texture.channels
-            self.dtype = texture.dtype
-        else:
-            assert width is not None and height is not None
-            self.width = width
-            self.height = height
-            self.channels = channels if channels is not None else 4
-            self.dtype = np.dtype(dtype) if dtype is not None else np.float32
-
-        # Validate size and dtype.
-        assert isinstance(self.width, int) and self.width >= 0
-        assert isinstance(self.height, int) and self.height >= 0
-        assert isinstance(self.channels, int) and self.channels >= 1
-        assert width is None or width == self.width
-        assert height is None or height == self.height
-        assert channels is None or channels == self.channels
-        assert dtype is None or dtype == self.dtype
-
-        # Create framebuffer object.
-        self.gl_id = gl.glGenFramebuffers(1)
-        with self.bind():
-
-            # Setup color buffer.
-            if self.texture is not None:
-                assert self.msaa == 0
-                gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, self.texture.gl_id, 0)
-            else:
-                fmt = get_texture_format(self.dtype, self.channels)
-                self.gl_color = gl.glGenRenderbuffers(1)
-                gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, self.gl_color)
-                gl.glRenderbufferStorageMultisample(gl.GL_RENDERBUFFER, self.msaa, fmt.internalformat, self.width, self.height)
-                gl.glFramebufferRenderbuffer(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_RENDERBUFFER, self.gl_color)
-
-            # Setup depth/stencil buffer.
-            self.gl_depth_stencil = gl.glGenRenderbuffers(1)
-            gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, self.gl_depth_stencil)
-            gl.glRenderbufferStorageMultisample(gl.GL_RENDERBUFFER, self.msaa, gl.GL_DEPTH24_STENCIL8, self.width, self.height)
-            gl.glFramebufferRenderbuffer(gl.GL_FRAMEBUFFER, gl.GL_DEPTH_STENCIL_ATTACHMENT, gl.GL_RENDERBUFFER, self.gl_depth_stencil)
-
-    def delete(self):
-        if self.gl_id is not None:
-            gl.glDeleteFramebuffers([self.gl_id])
-            self.gl_id = None
-        if self.gl_color is not None:
-            gl.glDeleteRenderbuffers(1, [self.gl_color])
-            self.gl_color = None
-        if self.gl_depth_stencil is not None:
-            gl.glDeleteRenderbuffers(1, [self.gl_depth_stencil])
-            self.gl_depth_stencil = None
-
-    def __del__(self):
-        try:
-            self.delete()
-        except:
-            pass
-
-    @contextlib.contextmanager
-    def bind(self):
-        prev_fbo = gl.glGetInteger(gl.GL_FRAMEBUFFER_BINDING)
-        prev_rbo = gl.glGetInteger(gl.GL_RENDERBUFFER_BINDING)
-        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, self.gl_id)
-        if self.width is not None and self.height is not None:
-            gl.glViewport(0, 0, self.width, self.height)
-        yield
-        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, prev_fbo)
-        gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, prev_rbo)
-
-    def blit(self, dst=None):
-        assert dst is None or isinstance(dst, Framebuffer)
-        with self.bind():
-            gl.glBindFramebuffer(gl.GL_DRAW_FRAMEBUFFER, 0 if dst is None else dst.fbo)
-            gl.glBlitFramebuffer(0, 0, self.width, self.height, 0, 0, self.width, self.height, gl.GL_COLOR_BUFFER_BIT, gl.GL_NEAREST)
-
-#----------------------------------------------------------------------------
-
-def draw_shape(vertices, *, mode=gl.GL_TRIANGLE_FAN, pos=0, size=1, color=1, alpha=1):
-    assert vertices.ndim == 2 and vertices.shape[1] == 2
-    pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
-    size = np.broadcast_to(np.asarray(size, dtype='float32'), [2])
-    color = np.broadcast_to(np.asarray(color, dtype='float32'), [3])
-    alpha = np.clip(np.broadcast_to(np.asarray(alpha, dtype='float32'), []), 0, 1)
-
-    gl.glPushClientAttrib(gl.GL_CLIENT_VERTEX_ARRAY_BIT)
-    gl.glPushAttrib(gl.GL_CURRENT_BIT | gl.GL_TRANSFORM_BIT)
-    gl.glMatrixMode(gl.GL_MODELVIEW)
-    gl.glPushMatrix()
-
-    gl.glEnableClientState(gl.GL_VERTEX_ARRAY)
-    gl.glEnableClientState(gl.GL_TEXTURE_COORD_ARRAY)
-    gl.glVertexPointer(2, gl.GL_FLOAT, 0, vertices)
-    gl.glTexCoordPointer(2, gl.GL_FLOAT, 0, vertices)
-    gl.glTranslate(pos[0], pos[1], 0)
-    gl.glScale(size[0], size[1], 1)
-    gl.glColor4f(color[0] * alpha, color[1] * alpha, color[2] * alpha, alpha)
-    gl.glDrawArrays(mode, 0, vertices.shape[0])
-
-    gl.glPopMatrix()
-    gl.glPopAttrib()
-    gl.glPopClientAttrib()
-
-#----------------------------------------------------------------------------
-
-def draw_rect(*, pos=0, pos2=None, size=None, align=0, rint=False, color=1, alpha=1, rounding=0):
-    assert pos2 is None or size is None
-    pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2])
-    pos2 = np.broadcast_to(np.asarray(pos2, dtype='float32'), [2]) if pos2 is not None else None
-    size = np.broadcast_to(np.asarray(size, dtype='float32'), [2]) if size is not None else None
-    size = size if size is not None else pos2 - pos if pos2 is not None else np.array([1, 1], dtype='float32')
-    pos = pos - size * align
-    if rint:
-        pos = np.rint(pos)
-    rounding = np.broadcast_to(np.asarray(rounding, dtype='float32'), [2])
-    rounding = np.minimum(np.abs(rounding) / np.maximum(np.abs(size), 1e-8), 0.5)
-    if np.min(rounding) == 0:
-        rounding *= 0
-    vertices = _setup_rect(float(rounding[0]), float(rounding[1]))
-    draw_shape(vertices, mode=gl.GL_TRIANGLE_FAN, pos=pos, size=size, color=color, alpha=alpha)
-
-@functools.lru_cache(maxsize=10000)
-def _setup_rect(rx, ry):
-    t = np.linspace(0, np.pi / 2, 1 if max(rx, ry) == 0 else 64)
-    s = 1 - np.sin(t); c = 1 - np.cos(t)
-    x = [c * rx, 1 - s * rx, 1 - c * rx, s * rx]
-    y = [s * ry, c * ry, 1 - s * ry, 1 - c * ry]
-    v = np.stack([x, y], axis=-1).reshape(-1, 2)
-    return v.astype('float32')
-
-#----------------------------------------------------------------------------
-
-def draw_circle(*, center=0, radius=100, hole=0, color=1, alpha=1):
-    hole = np.broadcast_to(np.asarray(hole, dtype='float32'), [])
-    vertices = _setup_circle(float(hole))
-    draw_shape(vertices, mode=gl.GL_TRIANGLE_STRIP, pos=center, size=radius, color=color, alpha=alpha)
-
-@functools.lru_cache(maxsize=10000)
-def _setup_circle(hole):
-    t = np.linspace(0, np.pi * 2, 128)
-    s = np.sin(t); c = np.cos(t)
-    v = np.stack([c, s, c * hole, s * hole], axis=-1).reshape(-1, 2)
-    return v.astype('float32')
-
-#----------------------------------------------------------------------------
diff --git a/spaces/akshatjain1004/deepfake-detector-with-explainability/README.md b/spaces/akshatjain1004/deepfake-detector-with-explainability/README.md
deleted file mode 100644
index 2ca866a971474f9dc7c80236a68a4fc8a82cc60b..0000000000000000000000000000000000000000
--- a/spaces/akshatjain1004/deepfake-detector-with-explainability/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Deepfake Detector With Explainability
-emoji: 📈
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexrame/rewardedsoups/streamlit_app/data/locomotion/trajectories/10.html b/spaces/alexrame/rewardedsoups/streamlit_app/data/locomotion/trajectories/10.html
deleted file mode 100644
index 499c9dcda7f6fa040269f3cc99241eea18c7cc36..0000000000000000000000000000000000000000
--- a/spaces/alexrame/rewardedsoups/streamlit_app/data/locomotion/trajectories/10.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-<html>
-    <head>
-        <title>brax visualizer</title>
-        <style>
-            body {
-              margin: 0;
-              padding: 0;
-            }
-            #brax-viewer {
-              margin: 0;
-              padding: 0;
-              height: 300px;
-              width: 150px;
-            }
-        </style>
-    </head>
-    <body>
-        <script type="application/javascript">
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0.1632411628961563, 0.01979593187570572, 0.2437095046043396], [0.9693560004234314, 0.16728082299232483, -0.1773066520690918, 0.030472436919808388], [0.9759348630905151, 0.11927054822444916, -0.1810271292924881, 0.02355794608592987], [0.992237389087677, 0.05393914133310318, 0.03403749316930771, 0.10675668716430664], [0.9880828857421875, 0.04954835772514343, 0.09566375613212585, 0.10993511229753494], [0.9120197892189026, 0.06634358316659927, -0.40278950333595276, 0.03973693773150444], [0.9198248386383057, 0.06670114398002625, -0.3845508396625519, 0.03992513567209244], [0.7657045125961304, -0.3671497404575348, -0.4502413868904114, 0.2760078012943268], [0.48376768827438354, -0.3864002823829651, -0.525949239730835, 0.583130419254303], [0.9704538583755493, 0.057556260377168655, -0.21093523502349854, -0.10204434394836426], [0.8426772952079773, -0.01381582859903574, 0.30716872215270996, 0.44198569655418396], [1.0, 0.0, 0.0, 0.0]], [[0.9551689028739929, 0.15442651510238647, 0.01253438089042902, 0.2522852122783661], [0.9703518152236938, 0.15770766139030457, -0.18257583677768707, 0.014548766426742077], [0.9713909029960632, 0.1497199684381485, -0.1837502419948578, 0.014815490692853928], [0.9897739887237549, 0.0645238533616066, 0.07759751379489899, 0.10081135481595993], [0.9739312529563904, 0.049924664199352264, 0.1937883198261261, 0.1068248599767685], [0.8921796083450317, 0.05598732829093933, -0.4436446726322174, 0.06372083723545074], [0.9075519442558289, 0.051153335720300674, -0.4112042486667633, 0.06814762949943542], [0.764415979385376, -0.3631179928779602, -0.4574444890022278, 0.27305349707603455], [0.47817280888557434, -0.3832542598247528, -0.5351962447166443, 0.5814051032066345], [0.9676217436790466, 0.0436343215405941, -0.22817237675189972, -0.09869977086782455], [0.8476724624633789, -0.03575528413057327, 0.30115363001823425, 0.43529242277145386], [1.0, 0.0, 0.0, 0.0]], [[0.9543320536613464, 0.14683318138122559, -0.0003739068633876741, 0.26017382740974426], [0.9696884751319885, 0.15110176801681519, -0.19120293855667114, 0.017718637362122536], [0.9713097810745239, 0.13326913118362427, -0.19631168246269226, 0.016076328232884407], [0.9860669374465942, 0.07264448702335358, 0.11535037308931351, 0.09533730149269104], [0.9555416703224182, 0.05871374532580376, 0.268623024225235, 0.10646418482065201], [0.8838858604431152, 0.06420233100652695, -0.4586837887763977, 0.06506133824586868], [0.8973532915115356, 0.058547042310237885, -0.4316120743751526, 0.07099517434835434], [0.7603444457054138, -0.3616551458835602, -0.4660264253616333, 0.2718479633331299], [0.4702198803424835, -0.38576802611351013, -0.5407893061637878, 0.581053614616394], [0.9637501239776611, 0.03613055869936943, -0.24603663384914398, -0.09667693078517914], [0.8524032235145569, -0.048956699669361115, 0.29084664583206177, 0.4317641854286194], [1.0, 0.0, 0.0, 0.0]], [[0.9538027048110962, 0.13990633189678192, -0.0146377794444561, 0.26546648144721985], [0.9668354988098145, 0.1509697288274765, -0.2038869559764862, 0.029453933238983154], [0.9724790453910828, 0.11542148888111115, -0.2010703980922699, 0.023086510598659515], [0.9823851585388184, 0.0726870521903038, 0.15012606978416443, 0.08425146341323853], [0.9419898986816406, 0.058285169303417206, 0.3161401152610779, 0.09650552272796631], [0.8870921730995178, 0.08153651654720306, -0.4505883455276489, 0.05821995064616203], [0.8933483362197876, 0.08078428357839584, -0.43807917833328247, 0.05907045304775238], [0.7551016807556152, -0.3590404987335205, -0.4767076373100281, 0.27140599489212036], [0.460318922996521, -0.38742539286613464, -0.5501099824905396, 0.5791261196136475], [0.9591758847236633, 0.03068525344133377, -0.2644895613193512, -0.09531717747449875], [0.8585013747215271, -0.06283922493457794, 0.2764701843261719, 0.4273066520690918], [1.0, 0.0, 0.0, 0.0]], [[0.9526057243347168, 0.1323179304599762, -0.02604028396308422, 0.27268368005752563], [0.9662843346595764, 0.14589530229568481, -0.20845063030719757, 0.039465002715587616], [0.9718776345252991, 0.10506337136030197, -0.2084585726261139, 0.030992381274700165], [0.9799298048019409, 0.062499452382326126, 0.17699599266052246, 0.06711038202047348], [0.9336981773376465, 0.050581078976392746, 0.3458510637283325, 0.07769475132226944], [0.8839345574378967, 0.10463936626911163, -0.4536214768886566, 0.044021543115377426], [0.8950614333152771, 0.10312929004430771, -0.4312760829925537, 0.04722839593887329], [0.7487984895706177, -0.3556465208530426, -0.48785603046417236, 0.27351945638656616], [0.4512602388858795, -0.3859540820121765, -0.5602638125419617, 0.5775015354156494], [0.954637885093689, 0.025365831330418587, -0.28178220987319946, -0.09285471588373184], [0.8632481694221497, -0.07635967433452606, 0.26425451040267944, 0.4232509136199951], [1.0, 0.0, 0.0, 0.0]], [[0.9498069286346436, 0.12763774394989014, -0.03501460701227188, 0.2834596633911133], [0.9668430685997009, 0.14561308920383453, -0.20431086421012878, 0.047627829015254974], [0.9734930396080017, 0.0918586328625679, -0.20641958713531494, 0.03555847331881523], [0.9785534739494324, 0.047953687608242035, 0.19451753795146942, 0.04792302846908569], [0.9294739365577698, 0.03901514783501625, 0.3625997304916382, 0.055476490408182144], [0.8812705874443054, 0.12906157970428467, -0.4537425935268402, 0.02868771366775036], [0.8955363631248474, 0.12786361575126648, -0.42496591806411743, 0.03270440921187401], [0.7414057850837708, -0.3554007411003113, -0.49770358204841614, 0.276222825050354], [0.4408508241176605, -0.38787466287612915, -0.5664344429969788, 0.5782352685928345], [0.9493703842163086, 0.021566258743405342, -0.30000588297843933, -0.09070400893688202], [0.8668124079704285, -0.08953174948692322, 0.25192514061927795, 0.4208967983722687], [1.0, 0.0, 0.0, 0.0]], [[0.947701632976532, 0.12423913180828094, -0.04716752469539642, 0.2901749610900879], [0.9678034782409668, 0.14498768746852875, -0.1993645429611206, 0.05088045448064804], [0.9755269885063171, 0.08126455545425415, -0.20079223811626434, 0.03775797784328461], [0.978286623954773, 0.030461708083748817, 0.20324519276618958, 0.026811545714735985], [0.9285425543785095, 0.02572878822684288, 0.3689517378807068, 0.03196200728416443], [0.8835518956184387, 0.1534472107887268, -0.442264586687088, 0.013866857625544071], [0.8960279822349548, 0.15308155119419098, -0.4163585901260376, 0.01858830265700817], [0.7333242297172546, -0.3541002571582794, -0.508902907371521, 0.2790455222129822], [0.4292009472846985, -0.3903367221355438, -0.5739095211029053, 0.5779719948768616], [0.9453173875808716, 0.02098894864320755, -0.31405484676361084, -0.08546432107686996], [0.8693352937698364, -0.10022950917482376, 0.23840613663196564, 0.4211563169956207], [1.0, 0.0, 0.0, 0.0]]], "debug": false};
-        </script>
-        <div id="brax-viewer"></div>
-        <script type="module">
-          import {Viewer} from 'https://cdn.jsdelivr.net/gh/google/brax@v0.1.0/js/viewer.js';
-          const domElement = document.getElementById('brax-viewer');
-          var viewer = new Viewer(domElement, system);
-          viewer.camera.position.set(-4.0, 0.0, 1.0);
-          viewer.camera.follow = true;
-          viewer.camera.freezeAngle = false;
-          viewer.camera.followDistance = 2.5;
-
-          
-          const guiElement = viewer.gui.domElement;
-          guiElement.style.display = 'none';
-
-          for (let c of viewer.scene.children){
-            if (c.name == "ffoot" || c.name == "fshin" || c.name == "fthigh" || c.name == "bfoot" || c.name == "bshin" || c.name == "bthigh" || c.name == "torso" || c.name == "right_shin" || c.name == "left_shin" || c.name == "right_thigh" || c.name == "left_thigh" || c.name == "left_lower_arm" || c.name == "right_lower_arm" || c.name == "right_upper_arm" || c.name == "left_upper_arm" || c.name == "pelvis" || c.name == "lwaist" ){
-                for (let obj of c.children){
-                    if (!obj.children) continue;
-                        for (let obj2 of obj.children){
-                            obj2.material.color.setHex(0x00CC00);
-                        }
-                }
-            }
-          }
-        </script>
-    </body>
-</html>
diff --git a/spaces/allknowingroger/Image-Models-Test165/app.py b/spaces/allknowingroger/Image-Models-Test165/app.py
deleted file mode 100644
index b13a34972298532bf875a3412561727de5ba3ca1..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test165/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "Doggerz/skldadu_2_1000",
-    "dhrishya/lotus",
-    "Sonu04/my-sunglasses-xyg",
-    "aigrils2/primitive0",
-    "aigrils2/majicv6",
-    "Awindsr/demoadr",
-    "druhitha445/car-xzg",
-    "erponce/anblxzu",
-    "Yntec/Hiten",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/amin2809/rvc-models/infer_pack/models_onnx.py b/spaces/amin2809/rvc-models/infer_pack/models_onnx.py
deleted file mode 100644
index 3cdae2f7f8591a1e43b1d8520baa37b7e9744d72..0000000000000000000000000000000000000000
--- a/spaces/amin2809/rvc-models/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/utils/log.py b/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/utils/log.py
deleted file mode 100644
index 70af379514fef1ea0b7ce3506c11366cab4b62a2..0000000000000000000000000000000000000000
--- a/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/utils/log.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""Utilities for logging"""
-import logging
-from tqdm import tqdm
-from termcolor import colored
-
-
-def color(string: str, color_name: str = 'yellow') -> str:
-    """Returns colored string for output to terminal"""
-    return colored(string, color_name)
-
-
-def print_update(message: str, width: int = 140, fillchar: str = ":", color="yellow") -> str:
-    """Prints an update message
-
-    Args:
-        message (str): message
-        width (int): width of new update message
-        fillchar (str): character to be filled to L and R of message
-
-    Returns:
-        str: print-ready update message
-    """
-    message = message.center(len(message) + 2, " ")
-    print(colored(message.center(width, fillchar), color))
-
-
-def set_logger(log_path):
-    """Set the logger to log info in terminal and file `log_path`.
-
-    Args:
-        log_path (str): path to the log file
-    """
-    logger = logging.getLogger()
-    logger.setLevel(logging.INFO)
-
-    if not logger.handlers:
-        # Logging to a file
-        file_handler = logging.FileHandler(log_path)
-        file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
-        logger.addHandler(file_handler)
-
-        # Logging to console
-        stream_handler = logging.StreamHandler()
-        stream_handler.setFormatter(logging.Formatter('%(message)s'))
-        logger.addHandler(stream_handler)
-
-
-def tqdm_iterator(items, desc=None, bar_format=None, **kwargs):
-    tqdm._instances.clear()
-    iterator = tqdm(
-        items,
-        desc=desc,
-        bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}',
-        **kwargs,
-    )
-    
-    return iterator
\ No newline at end of file
diff --git a/spaces/anasanchezf/cloome/src/clip/model.py b/spaces/anasanchezf/cloome/src/clip/model.py
deleted file mode 100644
index 75dc52181298509a41f98df124dca806c409bea4..0000000000000000000000000000000000000000
--- a/spaces/anasanchezf/cloome/src/clip/model.py
+++ /dev/null
@@ -1,736 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union, List
-
-import timm
-import os
-import json
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(OrderedDict([
-                ("-1", nn.AvgPool2d(stride)),
-                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                ("1", nn.BatchNorm2d(planes * self.expansion))
-            ]))
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class ResNet(nn.Module):
-    def __init__(self, block="bottleneck", layers: list = (3, 4, 23, 3), input_shape=None, output_dim=None, regression=False):
-        self.inplanes = 64
-        self.input_resolution = input_shape
-
-        super().__init__()
-
-        if block == "bottleneck":
-            block = Bottleneck
-        elif block == "basic":
-            block = BasicBlock
-        #self.n_classes = num_classes
-        if input_shape is not None:
-            channels_in = input_shape
-        else:
-            channels_in = 3
-
-        self.is_regression = regression
-        self.conv1 = nn.Conv2d(channels_in, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(512 * block.expansion, output_dim)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        if x.shape[-2:] != (1, 1):
-            x = nn.AvgPool2d(x.shape[2:])(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-
-class MLP(nn.Module):
-    def __init__(self, input_dim, hidden_dim, output_dim, n_layers):
-        super().__init__()
-
-        self.layers = nn.ModuleList()
-
-        for layer in range(n_layers):
-            dim = input_dim if layer == 0 else hidden_dim
-            self.layers.append(nn.Sequential(
-                               nn.Linear(dim, hidden_dim),
-                               nn.BatchNorm1d(hidden_dim),
-                               nn.ReLU())
-                               )
-
-        self.layers.append(nn.Sequential(
-                           nn.Linear(hidden_dim, output_dim))
-                           )
-
-    def forward(self, x):
-        for layer in self.layers:
-            x = layer(x)
-        return x
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x, key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.input_resolution = input_resolution
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-        self.initialize_parameters()
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def initialize_parameters(self):
-        if self.attnpool is not None:
-            std = self.attnpool.c_proj.in_features ** -0.5
-            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.c_proj.weight, std=std)
-
-        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
-            for name, param in resnet_block.named_parameters():
-                if name.endswith("bn3.weight"):
-                    nn.init.zeros_(param)
-
-    def forward(self, x):
-        def stem(x):
-            for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
-                x = self.relu(bn(conv(x)))
-            x = self.avgpool(x)
-            return x
-
-        x = x.type(self.conv1.weight.dtype)
-        x = stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class PretrainedResNet(nn.Module):
-    """docstring for PretrainedResNet."""
-
-    def __init__(self, input_shape, output_dim, adapt=False):
-        super().__init__()
-
-        self.adapt = adapt
-
-        if self.adapt:
-            input_channels = 3
-        else:
-            input_channels = input_shape
-
-        self.conv1 = nn.Conv2d(input_shape, input_channels, kernel_size=7, padding=1, bias=False)
-        self.pretrained_resnet = timm.create_model('resnet50', pretrained=True, in_chans=input_channels)
-        self.pretrained_resnet.fc = nn.Linear(2048, output_dim)
-
-    def forward(self, x: torch.Tensor):
-        if self.adapt:
-            x = self.conv1(x)
-        x = self.pretrained_resnet(x)
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
-    def forward(self, x: torch.Tensor):
-        return self.resblocks(x)
-
-
-class VisualTransformer(nn.Module):
-    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-
-        self.transformer = Transformer(width, layers, heads)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat(
-            [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
-             x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-class TextTransformer(nn.Module):
-    def __init__(self,
-                 embed_dim: int,
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int):
-        super().__init__()
-        self.context_length = context_length
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(
-            torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-        self.text_projection = nn.Parameter(
-            torch.empty(transformer_width, embed_dim))
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        torch.nn.init.normal_(self.token_embedding.weight, std=0.02)
-        torch.nn.init.normal_(self.positional_embedding, std=0.01)
-        proj_std = (self.transformer.width ** -0.5) * (
-                (2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(
-                self.text_projection, std=self.transformer.width ** -0.5)
-
-    @property
-    def dtype(self):
-        return self.text_projection.dtype
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def forward(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-        return x
-
-
-def get_backbone(architecture, **kwargs):
-    if 'seed' in kwargs.keys():
-        torch.manual_seed(kwargs['seed'])
-    if architecture == "ResNet-pre":
-        print(PretrainedResNet(kwargs['input_channels'], kwargs['embed_dim'], adapt=kwargs['adapt']))
-        return PretrainedResNet(
-            input_shape=kwargs['input_channels'],
-            output_dim=kwargs['embed_dim'],
-            adapt=kwargs['adapt'])
-    if architecture == 'ResNet':
-        return ResNet(
-            layers=kwargs['vision_layers'],
-            output_dim=kwargs['embed_dim'],
-            input_shape=kwargs['input_channels'])
-    if architecture == 'MLP':
-        return MLP(
-            input_dim=kwargs['input_size'],
-            n_layers=kwargs['molecule_layers'],
-            hidden_dim=kwargs['hidden_dim'],
-            output_dim=kwargs['embed_dim'])
-    if architecture == 'ModifiedResNet':
-        return ModifiedResNet(
-            layers=kwargs['vision_layers'],
-            output_dim=kwargs['embed_dim'],
-            heads=kwargs['vision_width'] * 32 // 64,
-            input_resolution=kwargs['image_resolution'],
-            width=kwargs['vision_width'])
-    elif architecture == 'VisualTransformer':
-        return VisualTransformer(
-            input_resolution=kwargs['image_resolution'],
-            patch_size=kwargs['vision_patch_size'],
-            width=kwargs['vision_width'],
-            layers=kwargs['vision_layers'],
-            heads=kwargs['vision_width'] // 64,
-            output_dim=kwargs['embed_dim'])
-    elif architecture == 'TextTransformer':
-        return TextTransformer(
-            embed_dim=kwargs['embed_dim'],
-            context_length=kwargs['context_length'],
-            vocab_size=kwargs['vocab_size'],
-            transformer_width=kwargs['transformer_width'],
-            transformer_heads=kwargs['transformer_heads'],
-            transformer_layers=kwargs['transformer_layers'])
-
-
-class CLIPGeneral(nn.Module):
-    def __init__(self,
-                 init_inv_tau: float = 14.3,
-                 learnable_inv_tau: bool = True,
-                 backbone_architecture: List[str] = ['ResNet', 'MLP'],
-                 **kwargs
-                 ):
-        super().__init__()
-
-        self.visual = get_backbone(
-            backbone_architecture[0],
-            **kwargs.get(f"{backbone_architecture[0]}-0", kwargs))
-        self.transformer = get_backbone(
-            backbone_architecture[1],
-            **kwargs.get(f"{backbone_architecture[1]}-1", kwargs))
-
-        # Logit scales for the inner product in the InfoNCE loss
-        self.logit_inv_tau = nn.Parameter(torch.ones([]) * np.log(init_inv_tau))
-        self.logit_inv_tau.requires_grad = learnable_inv_tau
-
-    @property
-    def dtype(self):
-        try:
-            return self.visual.conv1.weight.dtype
-        except:
-            return self.visual.fc.weight.dtype
-
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
-
-    def encode_text(self, text):
-        return self.transformer(text.type(self.dtype))
-
-    def forward(self, image, text):
-        if image is None:
-            return self.encode_text(text)
-        elif text is None:
-            return self.encode_image(image)
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-
-        return image_features, text_features, self.logit_inv_tau.exp()
-
-
-class CLIP(nn.Module):
-    def __init__(self,
-                 embed_dim: int,
-                 # vision
-                 image_resolution: int,
-                 vision_layers: Union[Tuple[int, int, int, int], int],
-                 vision_width: int,
-                 vision_patch_size: int,
-                 # text
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int,
-                 init_inv_tau: float = 14.3,
-                 learnable_inv_tau: bool = True
-                 ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        if isinstance(vision_layers, (tuple, list)):
-            vision_heads = vision_width * 32 // 64
-            self.visual = ModifiedResNet(
-                layers=vision_layers,
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width
-            )
-        else:
-            vision_heads = vision_width // 64
-            self.visual = VisualTransformer(
-                input_resolution=image_resolution,
-                patch_size=vision_patch_size,
-                width=vision_width,
-                layers=vision_layers,
-                heads=vision_heads,
-                output_dim=embed_dim
-            )
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-
-        # Logit scales for the inner product in the InfoNCE loss
-        self.logit_inv_tau = nn.Parameter(torch.ones([]) * np.log(init_inv_tau))
-        self.logit_inv_tau.requires_grad = learnable_inv_tau
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        if isinstance(self.visual, ModifiedResNet):
-            if self.visual.attnpool is not None:
-                std = self.visual.attnpool.c_proj.in_features ** -0.5
-                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
-            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
-                for name, param in resnet_block.named_parameters():
-                    if name.endswith("bn3.weight"):
-                        nn.init.zeros_(param)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.visual.conv1.weight.dtype
-
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
-
-    def encode_text(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x
-
-    def forward(self, image, text):
-        if image is None:
-            return self.encode_text(text)
-        elif text is None:
-            return self.encode_image(image)
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-        return image_features, text_features, self.logit_inv_tau.exp()
-
-
-def convert_weights(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len(
-            [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-        image_resolution = vision_patch_size * grid_size
-    else:
-        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in
-                        [1, 2, 3, 4]]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
-        vision_patch_size = None
-        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        image_resolution = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
-    model = CLIP(
-        embed_dim,
-        image_resolution, vision_layers, vision_width, vision_patch_size,
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        if key in state_dict:
-            del state_dict[key]
-
-    convert_weights(model)
-    model.load_state_dict(state_dict)
-    return model.eval()
diff --git a/spaces/ap66/Real-CUGAN/app.py b/spaces/ap66/Real-CUGAN/app.py
deleted file mode 100644
index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000
--- a/spaces/ap66/Real-CUGAN/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from upcunet_v3 import RealWaifuUpScaler
-import gradio as gr
-import time
-import logging
-import os
-from PIL import ImageOps
-import numpy as np
-import math
-
-
-def greet(input_img, input_model_name, input_tile_mode):
-    # if input_img.size[0] * input_img.size[1] > 256 * 256:
-    #     y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1]))
-    #     x = int(input_img.size[0]/input_img.size[1]*y)
-    #     input_img = ImageOps.fit(input_img, (x, y))
-    input_img = np.array(input_img)
-    if input_model_name not in model_cache:
-        t1 = time.time()
-        upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
-        t2 = time.time()
-        logger.info(f'load model time, {t2 - t1}')
-        model_cache[input_model_name] = upscaler
-    else:
-        upscaler = model_cache[input_model_name]
-        logger.info(f'load model from cache')
-
-    start = time.time()
-    result = upscaler(input_img, tile_mode=input_tile_mode)
-    end = time.time()
-    logger.info(f'input_model_name, {input_model_name}')
-    logger.info(f'input_tile_mode, {input_tile_mode}')
-    logger.info(f'input shape, {input_img.shape}')
-    logger.info(f'output shape, {result.shape}')
-    logger.info(f'speed time, {end - start}')
-    return result
-
-
-if __name__ == '__main__':
-    logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
-    logger = logging.getLogger()
-
-    ModelPath = "weights_v3/"
-    model_cache = {}
-
-    input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model')
-    input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
-    input_img = gr.inputs.Image(label='image', type='pil')
-
-    inputs = [input_img, input_model_name, input_tile_mode]
-    outputs = "image"
-    iface = gr.Interface(fn=greet,
-                         inputs=inputs,
-                         outputs=outputs,
-                         allow_screenshot=False,
-                         allow_flagging='never',
-                         examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]],
-                         article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)<br>'
-                                 '感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。<br>'
-                                 '修改bbb'
-                                 'The large image will lead to memory limit exceeded. So I crop and resize image. '
-                                 'If you want to experience the large image, please go to the link above.')
-    iface.launch()
diff --git a/spaces/ardha27/rvc_TTS/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/ardha27/rvc_TTS/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/ardha27/rvc_TTS/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/speedy_speech_config.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/speedy_speech_config.py
deleted file mode 100644
index bf8517dfc478a135978df19f3126313a616c14c2..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/speedy_speech_config.py
+++ /dev/null
@@ -1,194 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class SpeedySpeechConfig(BaseTTSConfig):
-    """Configure `ForwardTTS` as SpeedySpeech model.
-
-    Example:
-
-        >>> from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
-        >>> config = SpeedySpeechConfig()
-
-     Args:
-        model (str):
-            Model name used for selecting the right model at initialization. Defaults to `speedy_speech`.
-
-        base_model (str):
-            Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
-            the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
-        model_args (Coqpit):
-            Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
-        data_dep_init_steps (int):
-            Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
-            Activation Normalization that pre-computes normalization stats at the beginning and use the same values
-            for the rest. Defaults to 10.
-
-        speakers_file (str):
-            Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
-            speaker names. Defaults to `None`.
-
-        use_speaker_embedding (bool):
-            enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
-            in the multi-speaker mode. Defaults to False.
-
-        use_d_vector_file (bool):
-            enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
-        d_vector_file (str):
-            Path to the file including pre-computed speaker embeddings. Defaults to None.
-
-        d_vector_dim (int):
-            Dimension of the external speaker embeddings. Defaults to 0.
-
-        optimizer (str):
-            Name of the model optimizer. Defaults to `RAdam`.
-
-        optimizer_params (dict):
-            Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
-        lr_scheduler (str):
-            Name of the learning rate scheduler. Defaults to `Noam`.
-
-        lr_scheduler_params (dict):
-            Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
-        lr (float):
-            Initial learning rate. Defaults to `1e-3`.
-
-        grad_clip (float):
-            Gradient norm clipping value. Defaults to `5.0`.
-
-        spec_loss_type (str):
-            Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`.
-
-        duration_loss_type (str):
-            Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`.
-
-        use_ssim_loss (bool):
-            Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
-        wd (float):
-            Weight decay coefficient. Defaults to `1e-7`.
-
-        ssim_loss_alpha (float):
-            Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
-        dur_loss_alpha (float):
-            Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
-        spec_loss_alpha (float):
-            Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
-        binary_loss_alpha (float):
-            Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
-        binary_loss_warmup_epochs (float):
-            Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
-        min_seq_len (int):
-            Minimum input sequence length to be used at training.
-
-        max_seq_len (int):
-            Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-    """
-
-    model: str = "speedy_speech"
-    base_model: str = "forward_tts"
-
-    # set model args as SpeedySpeech
-    model_args: ForwardTTSArgs = field(
-        default_factory=lambda: ForwardTTSArgs(
-            use_pitch=False,
-            encoder_type="residual_conv_bn",
-            encoder_params={
-                "kernel_size": 4,
-                "dilations": 4 * [1, 2, 4] + [1],
-                "num_conv_blocks": 2,
-                "num_res_blocks": 13,
-            },
-            decoder_type="residual_conv_bn",
-            decoder_params={
-                "kernel_size": 4,
-                "dilations": 4 * [1, 2, 4, 8] + [1],
-                "num_conv_blocks": 2,
-                "num_res_blocks": 17,
-            },
-            out_channels=80,
-            hidden_channels=128,
-            positional_encoding=True,
-            detach_duration_predictor=True,
-        )
-    )
-
-    # multi-speaker settings
-    num_speakers: int = 0
-    speakers_file: str = None
-    use_speaker_embedding: bool = False
-    use_d_vector_file: bool = False
-    d_vector_file: str = False
-    d_vector_dim: int = 0
-
-    # optimizer parameters
-    optimizer: str = "Adam"
-    optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
-    lr_scheduler: str = "NoamLR"
-    lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
-    lr: float = 1e-4
-    grad_clip: float = 5.0
-
-    # loss params
-    spec_loss_type: str = "l1"
-    duration_loss_type: str = "huber"
-    use_ssim_loss: bool = False
-    ssim_loss_alpha: float = 1.0
-    dur_loss_alpha: float = 1.0
-    spec_loss_alpha: float = 1.0
-    aligner_loss_alpha: float = 1.0
-    binary_align_loss_alpha: float = 0.3
-    binary_loss_warmup_epochs: int = 150
-
-    # overrides
-    min_seq_len: int = 13
-    max_seq_len: int = 200
-    r: int = 1  # DO NOT CHANGE
-
-    # dataset configs
-    compute_f0: bool = False
-    f0_cache_path: str = None
-
-    # testing
-    test_sentences: List[str] = field(
-        default_factory=lambda: [
-            "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
-            "Be a voice, not an echo.",
-            "I'm sorry Dave. I'm afraid I can't do that.",
-            "This cake is great. It's so delicious and moist.",
-            "Prior to November 22, 1963.",
-        ]
-    )
-
-    def __post_init__(self):
-        # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
-        if self.num_speakers > 0:
-            self.model_args.num_speakers = self.num_speakers
-
-        # speaker embedding settings
-        if self.use_speaker_embedding:
-            self.model_args.use_speaker_embedding = True
-        if self.speakers_file:
-            self.model_args.speakers_file = self.speakers_file
-
-        # d-vector settings
-        if self.use_d_vector_file:
-            self.model_args.use_d_vector_file = True
-        if self.d_vector_dim is not None and self.d_vector_dim > 0:
-            self.model_args.d_vector_dim = self.d_vector_dim
-        if self.d_vector_file:
-            self.model_args.d_vector_file = self.d_vector_file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/PalmImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/PalmImagePlugin.py
deleted file mode 100644
index 700f10e3f797676800dd18a556aac0a2bf2c1903..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/PalmImagePlugin.py
+++ /dev/null
@@ -1,227 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-
-##
-# Image plugin for Palm pixmap images (output only).
-##
-
-from . import Image, ImageFile
-from ._binary import o8
-from ._binary import o16be as o16b
-
-# fmt: off
-_Palm8BitColormapValues = (
-    (255, 255, 255), (255, 204, 255), (255, 153, 255), (255, 102, 255),
-    (255,  51, 255), (255,   0, 255), (255, 255, 204), (255, 204, 204),
-    (255, 153, 204), (255, 102, 204), (255,  51, 204), (255,   0, 204),
-    (255, 255, 153), (255, 204, 153), (255, 153, 153), (255, 102, 153),
-    (255,  51, 153), (255,   0, 153), (204, 255, 255), (204, 204, 255),
-    (204, 153, 255), (204, 102, 255), (204,  51, 255), (204,   0, 255),
-    (204, 255, 204), (204, 204, 204), (204, 153, 204), (204, 102, 204),
-    (204,  51, 204), (204,   0, 204), (204, 255, 153), (204, 204, 153),
-    (204, 153, 153), (204, 102, 153), (204,  51, 153), (204,   0, 153),
-    (153, 255, 255), (153, 204, 255), (153, 153, 255), (153, 102, 255),
-    (153,  51, 255), (153,   0, 255), (153, 255, 204), (153, 204, 204),
-    (153, 153, 204), (153, 102, 204), (153,  51, 204), (153,   0, 204),
-    (153, 255, 153), (153, 204, 153), (153, 153, 153), (153, 102, 153),
-    (153,  51, 153), (153,   0, 153), (102, 255, 255), (102, 204, 255),
-    (102, 153, 255), (102, 102, 255), (102,  51, 255), (102,   0, 255),
-    (102, 255, 204), (102, 204, 204), (102, 153, 204), (102, 102, 204),
-    (102,  51, 204), (102,   0, 204), (102, 255, 153), (102, 204, 153),
-    (102, 153, 153), (102, 102, 153), (102,  51, 153), (102,   0, 153),
-    (51,  255, 255), (51,  204, 255), (51,  153, 255), (51,  102, 255),
-    (51,   51, 255), (51,    0, 255), (51,  255, 204), (51,  204, 204),
-    (51,  153, 204), (51,  102, 204), (51,   51, 204), (51,    0, 204),
-    (51,  255, 153), (51,  204, 153), (51,  153, 153), (51,  102, 153),
-    (51,   51, 153), (51,    0, 153), (0,   255, 255), (0,   204, 255),
-    (0,   153, 255), (0,   102, 255), (0,    51, 255), (0,     0, 255),
-    (0,   255, 204), (0,   204, 204), (0,   153, 204), (0,   102, 204),
-    (0,    51, 204), (0,     0, 204), (0,   255, 153), (0,   204, 153),
-    (0,   153, 153), (0,   102, 153), (0,    51, 153), (0,     0, 153),
-    (255, 255, 102), (255, 204, 102), (255, 153, 102), (255, 102, 102),
-    (255,  51, 102), (255,   0, 102), (255, 255,  51), (255, 204,  51),
-    (255, 153,  51), (255, 102,  51), (255,  51,  51), (255,   0,  51),
-    (255, 255,   0), (255, 204,   0), (255, 153,   0), (255, 102,   0),
-    (255,  51,   0), (255,   0,   0), (204, 255, 102), (204, 204, 102),
-    (204, 153, 102), (204, 102, 102), (204,  51, 102), (204,   0, 102),
-    (204, 255,  51), (204, 204,  51), (204, 153,  51), (204, 102,  51),
-    (204,  51,  51), (204,   0,  51), (204, 255,   0), (204, 204,   0),
-    (204, 153,   0), (204, 102,   0), (204,  51,   0), (204,   0,   0),
-    (153, 255, 102), (153, 204, 102), (153, 153, 102), (153, 102, 102),
-    (153,  51, 102), (153,   0, 102), (153, 255,  51), (153, 204,  51),
-    (153, 153,  51), (153, 102,  51), (153,  51,  51), (153,   0,  51),
-    (153, 255,   0), (153, 204,   0), (153, 153,   0), (153, 102,   0),
-    (153,  51,   0), (153,   0,   0), (102, 255, 102), (102, 204, 102),
-    (102, 153, 102), (102, 102, 102), (102,  51, 102), (102,   0, 102),
-    (102, 255,  51), (102, 204,  51), (102, 153,  51), (102, 102,  51),
-    (102,  51,  51), (102,   0,  51), (102, 255,   0), (102, 204,   0),
-    (102, 153,   0), (102, 102,   0), (102,  51,   0), (102,   0,   0),
-    (51,  255, 102), (51,  204, 102), (51,  153, 102), (51,  102, 102),
-    (51,   51, 102), (51,    0, 102), (51,  255,  51), (51,  204,  51),
-    (51,  153,  51), (51,  102,  51), (51,   51,  51), (51,    0,  51),
-    (51,  255,   0), (51,  204,   0), (51,  153,   0), (51,  102,   0),
-    (51,   51,   0), (51,    0,   0), (0,   255, 102), (0,   204, 102),
-    (0,   153, 102), (0,   102, 102), (0,    51, 102), (0,     0, 102),
-    (0,   255,  51), (0,   204,  51), (0,   153,  51), (0,   102,  51),
-    (0,    51,  51), (0,     0,  51), (0,   255,   0), (0,   204,   0),
-    (0,   153,   0), (0,   102,   0), (0,    51,   0), (17,   17,  17),
-    (34,   34,  34), (68,   68,  68), (85,   85,  85), (119, 119, 119),
-    (136, 136, 136), (170, 170, 170), (187, 187, 187), (221, 221, 221),
-    (238, 238, 238), (192, 192, 192), (128,   0,   0), (128,   0, 128),
-    (0,   128,   0), (0,   128, 128), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0))
-# fmt: on
-
-
-# so build a prototype image to be used for palette resampling
-def build_prototype_image():
-    image = Image.new("L", (1, len(_Palm8BitColormapValues)))
-    image.putdata(list(range(len(_Palm8BitColormapValues))))
-    palettedata = ()
-    for colormapValue in _Palm8BitColormapValues:
-        palettedata += colormapValue
-    palettedata += (0, 0, 0) * (256 - len(_Palm8BitColormapValues))
-    image.putpalette(palettedata)
-    return image
-
-
-Palm8BitColormapImage = build_prototype_image()
-
-# OK, we now have in Palm8BitColormapImage,
-# a "P"-mode image with the right palette
-#
-# --------------------------------------------------------------------
-
-_FLAGS = {"custom-colormap": 0x4000, "is-compressed": 0x8000, "has-transparent": 0x2000}
-
-_COMPRESSION_TYPES = {"none": 0xFF, "rle": 0x01, "scanline": 0x00}
-
-
-#
-# --------------------------------------------------------------------
-
-##
-# (Internal) Image save plugin for the Palm format.
-
-
-def _save(im, fp, filename):
-
-    if im.mode == "P":
-
-        # we assume this is a color Palm image with the standard colormap,
-        # unless the "info" dict has a "custom-colormap" field
-
-        rawmode = "P"
-        bpp = 8
-        version = 1
-
-    elif im.mode == "L":
-        if im.encoderinfo.get("bpp") in (1, 2, 4):
-            # this is 8-bit grayscale, so we shift it to get the high-order bits,
-            # and invert it because
-            # Palm does greyscale from white (0) to black (1)
-            bpp = im.encoderinfo["bpp"]
-            im = im.point(
-                lambda x, shift=8 - bpp, maxval=(1 << bpp) - 1: maxval - (x >> shift)
-            )
-        elif im.info.get("bpp") in (1, 2, 4):
-            # here we assume that even though the inherent mode is 8-bit grayscale,
-            # only the lower bpp bits are significant.
-            # We invert them to match the Palm.
-            bpp = im.info["bpp"]
-            im = im.point(lambda x, maxval=(1 << bpp) - 1: maxval - (x & maxval))
-        else:
-            raise OSError(f"cannot write mode {im.mode} as Palm")
-
-        # we ignore the palette here
-        im.mode = "P"
-        rawmode = "P;" + str(bpp)
-        version = 1
-
-    elif im.mode == "1":
-
-        # monochrome -- write it inverted, as is the Palm standard
-        rawmode = "1;I"
-        bpp = 1
-        version = 0
-
-    else:
-
-        raise OSError(f"cannot write mode {im.mode} as Palm")
-
-    #
-    # make sure image data is available
-    im.load()
-
-    # write header
-
-    cols = im.size[0]
-    rows = im.size[1]
-
-    rowbytes = int((cols + (16 // bpp - 1)) / (16 // bpp)) * 2
-    transparent_index = 0
-    compression_type = _COMPRESSION_TYPES["none"]
-
-    flags = 0
-    if im.mode == "P" and "custom-colormap" in im.info:
-        flags = flags & _FLAGS["custom-colormap"]
-        colormapsize = 4 * 256 + 2
-        colormapmode = im.palette.mode
-        colormap = im.getdata().getpalette()
-    else:
-        colormapsize = 0
-
-    if "offset" in im.info:
-        offset = (rowbytes * rows + 16 + 3 + colormapsize) // 4
-    else:
-        offset = 0
-
-    fp.write(o16b(cols) + o16b(rows) + o16b(rowbytes) + o16b(flags))
-    fp.write(o8(bpp))
-    fp.write(o8(version))
-    fp.write(o16b(offset))
-    fp.write(o8(transparent_index))
-    fp.write(o8(compression_type))
-    fp.write(o16b(0))  # reserved by Palm
-
-    # now write colormap if necessary
-
-    if colormapsize > 0:
-        fp.write(o16b(256))
-        for i in range(256):
-            fp.write(o8(i))
-            if colormapmode == "RGB":
-                fp.write(
-                    o8(colormap[3 * i])
-                    + o8(colormap[3 * i + 1])
-                    + o8(colormap[3 * i + 2])
-                )
-            elif colormapmode == "RGBA":
-                fp.write(
-                    o8(colormap[4 * i])
-                    + o8(colormap[4 * i + 1])
-                    + o8(colormap[4 * i + 2])
-                )
-
-    # now convert data to raw form
-    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, rowbytes, 1))])
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-#
-# --------------------------------------------------------------------
-
-Image.register_save("Palm", _save)
-
-Image.register_extension("Palm", ".palm")
-
-Image.register_mime("Palm", "image/palm")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/web.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/web.py
deleted file mode 100644
index cefae2b9ae4114696f244f7f71bf8dd74ca8f4a6..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/web.py
+++ /dev/null
@@ -1,588 +0,0 @@
-import asyncio
-import logging
-import socket
-import sys
-from argparse import ArgumentParser
-from collections.abc import Iterable
-from importlib import import_module
-from typing import (
-    Any,
-    Awaitable,
-    Callable,
-    Iterable as TypingIterable,
-    List,
-    Optional,
-    Set,
-    Type,
-    Union,
-    cast,
-)
-
-from .abc import AbstractAccessLogger
-from .helpers import all_tasks
-from .log import access_logger
-from .web_app import Application as Application, CleanupError as CleanupError
-from .web_exceptions import (
-    HTTPAccepted as HTTPAccepted,
-    HTTPBadGateway as HTTPBadGateway,
-    HTTPBadRequest as HTTPBadRequest,
-    HTTPClientError as HTTPClientError,
-    HTTPConflict as HTTPConflict,
-    HTTPCreated as HTTPCreated,
-    HTTPError as HTTPError,
-    HTTPException as HTTPException,
-    HTTPExpectationFailed as HTTPExpectationFailed,
-    HTTPFailedDependency as HTTPFailedDependency,
-    HTTPForbidden as HTTPForbidden,
-    HTTPFound as HTTPFound,
-    HTTPGatewayTimeout as HTTPGatewayTimeout,
-    HTTPGone as HTTPGone,
-    HTTPInsufficientStorage as HTTPInsufficientStorage,
-    HTTPInternalServerError as HTTPInternalServerError,
-    HTTPLengthRequired as HTTPLengthRequired,
-    HTTPMethodNotAllowed as HTTPMethodNotAllowed,
-    HTTPMisdirectedRequest as HTTPMisdirectedRequest,
-    HTTPMovedPermanently as HTTPMovedPermanently,
-    HTTPMultipleChoices as HTTPMultipleChoices,
-    HTTPNetworkAuthenticationRequired as HTTPNetworkAuthenticationRequired,
-    HTTPNoContent as HTTPNoContent,
-    HTTPNonAuthoritativeInformation as HTTPNonAuthoritativeInformation,
-    HTTPNotAcceptable as HTTPNotAcceptable,
-    HTTPNotExtended as HTTPNotExtended,
-    HTTPNotFound as HTTPNotFound,
-    HTTPNotImplemented as HTTPNotImplemented,
-    HTTPNotModified as HTTPNotModified,
-    HTTPOk as HTTPOk,
-    HTTPPartialContent as HTTPPartialContent,
-    HTTPPaymentRequired as HTTPPaymentRequired,
-    HTTPPermanentRedirect as HTTPPermanentRedirect,
-    HTTPPreconditionFailed as HTTPPreconditionFailed,
-    HTTPPreconditionRequired as HTTPPreconditionRequired,
-    HTTPProxyAuthenticationRequired as HTTPProxyAuthenticationRequired,
-    HTTPRedirection as HTTPRedirection,
-    HTTPRequestEntityTooLarge as HTTPRequestEntityTooLarge,
-    HTTPRequestHeaderFieldsTooLarge as HTTPRequestHeaderFieldsTooLarge,
-    HTTPRequestRangeNotSatisfiable as HTTPRequestRangeNotSatisfiable,
-    HTTPRequestTimeout as HTTPRequestTimeout,
-    HTTPRequestURITooLong as HTTPRequestURITooLong,
-    HTTPResetContent as HTTPResetContent,
-    HTTPSeeOther as HTTPSeeOther,
-    HTTPServerError as HTTPServerError,
-    HTTPServiceUnavailable as HTTPServiceUnavailable,
-    HTTPSuccessful as HTTPSuccessful,
-    HTTPTemporaryRedirect as HTTPTemporaryRedirect,
-    HTTPTooManyRequests as HTTPTooManyRequests,
-    HTTPUnauthorized as HTTPUnauthorized,
-    HTTPUnavailableForLegalReasons as HTTPUnavailableForLegalReasons,
-    HTTPUnprocessableEntity as HTTPUnprocessableEntity,
-    HTTPUnsupportedMediaType as HTTPUnsupportedMediaType,
-    HTTPUpgradeRequired as HTTPUpgradeRequired,
-    HTTPUseProxy as HTTPUseProxy,
-    HTTPVariantAlsoNegotiates as HTTPVariantAlsoNegotiates,
-    HTTPVersionNotSupported as HTTPVersionNotSupported,
-)
-from .web_fileresponse import FileResponse as FileResponse
-from .web_log import AccessLogger
-from .web_middlewares import (
-    middleware as middleware,
-    normalize_path_middleware as normalize_path_middleware,
-)
-from .web_protocol import (
-    PayloadAccessError as PayloadAccessError,
-    RequestHandler as RequestHandler,
-    RequestPayloadError as RequestPayloadError,
-)
-from .web_request import (
-    BaseRequest as BaseRequest,
-    FileField as FileField,
-    Request as Request,
-)
-from .web_response import (
-    ContentCoding as ContentCoding,
-    Response as Response,
-    StreamResponse as StreamResponse,
-    json_response as json_response,
-)
-from .web_routedef import (
-    AbstractRouteDef as AbstractRouteDef,
-    RouteDef as RouteDef,
-    RouteTableDef as RouteTableDef,
-    StaticDef as StaticDef,
-    delete as delete,
-    get as get,
-    head as head,
-    options as options,
-    patch as patch,
-    post as post,
-    put as put,
-    route as route,
-    static as static,
-    view as view,
-)
-from .web_runner import (
-    AppRunner as AppRunner,
-    BaseRunner as BaseRunner,
-    BaseSite as BaseSite,
-    GracefulExit as GracefulExit,
-    NamedPipeSite as NamedPipeSite,
-    ServerRunner as ServerRunner,
-    SockSite as SockSite,
-    TCPSite as TCPSite,
-    UnixSite as UnixSite,
-)
-from .web_server import Server as Server
-from .web_urldispatcher import (
-    AbstractResource as AbstractResource,
-    AbstractRoute as AbstractRoute,
-    DynamicResource as DynamicResource,
-    PlainResource as PlainResource,
-    PrefixedSubAppResource as PrefixedSubAppResource,
-    Resource as Resource,
-    ResourceRoute as ResourceRoute,
-    StaticResource as StaticResource,
-    UrlDispatcher as UrlDispatcher,
-    UrlMappingMatchInfo as UrlMappingMatchInfo,
-    View as View,
-)
-from .web_ws import (
-    WebSocketReady as WebSocketReady,
-    WebSocketResponse as WebSocketResponse,
-    WSMsgType as WSMsgType,
-)
-
-__all__ = (
-    # web_app
-    "Application",
-    "CleanupError",
-    # web_exceptions
-    "HTTPAccepted",
-    "HTTPBadGateway",
-    "HTTPBadRequest",
-    "HTTPClientError",
-    "HTTPConflict",
-    "HTTPCreated",
-    "HTTPError",
-    "HTTPException",
-    "HTTPExpectationFailed",
-    "HTTPFailedDependency",
-    "HTTPForbidden",
-    "HTTPFound",
-    "HTTPGatewayTimeout",
-    "HTTPGone",
-    "HTTPInsufficientStorage",
-    "HTTPInternalServerError",
-    "HTTPLengthRequired",
-    "HTTPMethodNotAllowed",
-    "HTTPMisdirectedRequest",
-    "HTTPMovedPermanently",
-    "HTTPMultipleChoices",
-    "HTTPNetworkAuthenticationRequired",
-    "HTTPNoContent",
-    "HTTPNonAuthoritativeInformation",
-    "HTTPNotAcceptable",
-    "HTTPNotExtended",
-    "HTTPNotFound",
-    "HTTPNotImplemented",
-    "HTTPNotModified",
-    "HTTPOk",
-    "HTTPPartialContent",
-    "HTTPPaymentRequired",
-    "HTTPPermanentRedirect",
-    "HTTPPreconditionFailed",
-    "HTTPPreconditionRequired",
-    "HTTPProxyAuthenticationRequired",
-    "HTTPRedirection",
-    "HTTPRequestEntityTooLarge",
-    "HTTPRequestHeaderFieldsTooLarge",
-    "HTTPRequestRangeNotSatisfiable",
-    "HTTPRequestTimeout",
-    "HTTPRequestURITooLong",
-    "HTTPResetContent",
-    "HTTPSeeOther",
-    "HTTPServerError",
-    "HTTPServiceUnavailable",
-    "HTTPSuccessful",
-    "HTTPTemporaryRedirect",
-    "HTTPTooManyRequests",
-    "HTTPUnauthorized",
-    "HTTPUnavailableForLegalReasons",
-    "HTTPUnprocessableEntity",
-    "HTTPUnsupportedMediaType",
-    "HTTPUpgradeRequired",
-    "HTTPUseProxy",
-    "HTTPVariantAlsoNegotiates",
-    "HTTPVersionNotSupported",
-    # web_fileresponse
-    "FileResponse",
-    # web_middlewares
-    "middleware",
-    "normalize_path_middleware",
-    # web_protocol
-    "PayloadAccessError",
-    "RequestHandler",
-    "RequestPayloadError",
-    # web_request
-    "BaseRequest",
-    "FileField",
-    "Request",
-    # web_response
-    "ContentCoding",
-    "Response",
-    "StreamResponse",
-    "json_response",
-    # web_routedef
-    "AbstractRouteDef",
-    "RouteDef",
-    "RouteTableDef",
-    "StaticDef",
-    "delete",
-    "get",
-    "head",
-    "options",
-    "patch",
-    "post",
-    "put",
-    "route",
-    "static",
-    "view",
-    # web_runner
-    "AppRunner",
-    "BaseRunner",
-    "BaseSite",
-    "GracefulExit",
-    "ServerRunner",
-    "SockSite",
-    "TCPSite",
-    "UnixSite",
-    "NamedPipeSite",
-    # web_server
-    "Server",
-    # web_urldispatcher
-    "AbstractResource",
-    "AbstractRoute",
-    "DynamicResource",
-    "PlainResource",
-    "PrefixedSubAppResource",
-    "Resource",
-    "ResourceRoute",
-    "StaticResource",
-    "UrlDispatcher",
-    "UrlMappingMatchInfo",
-    "View",
-    # web_ws
-    "WebSocketReady",
-    "WebSocketResponse",
-    "WSMsgType",
-    # web
-    "run_app",
-)
-
-
-try:
-    from ssl import SSLContext
-except ImportError:  # pragma: no cover
-    SSLContext = Any  # type: ignore[misc,assignment]
-
-HostSequence = TypingIterable[str]
-
-
-async def _run_app(
-    app: Union[Application, Awaitable[Application]],
-    *,
-    host: Optional[Union[str, HostSequence]] = None,
-    port: Optional[int] = None,
-    path: Optional[str] = None,
-    sock: Optional[Union[socket.socket, TypingIterable[socket.socket]]] = None,
-    shutdown_timeout: float = 60.0,
-    keepalive_timeout: float = 75.0,
-    ssl_context: Optional[SSLContext] = None,
-    print: Callable[..., None] = print,
-    backlog: int = 128,
-    access_log_class: Type[AbstractAccessLogger] = AccessLogger,
-    access_log_format: str = AccessLogger.LOG_FORMAT,
-    access_log: Optional[logging.Logger] = access_logger,
-    handle_signals: bool = True,
-    reuse_address: Optional[bool] = None,
-    reuse_port: Optional[bool] = None,
-) -> None:
-    # A internal functio to actually do all dirty job for application running
-    if asyncio.iscoroutine(app):
-        app = await app  # type: ignore[misc]
-
-    app = cast(Application, app)
-
-    runner = AppRunner(
-        app,
-        handle_signals=handle_signals,
-        access_log_class=access_log_class,
-        access_log_format=access_log_format,
-        access_log=access_log,
-        keepalive_timeout=keepalive_timeout,
-    )
-
-    await runner.setup()
-
-    sites: List[BaseSite] = []
-
-    try:
-        if host is not None:
-            if isinstance(host, (str, bytes, bytearray, memoryview)):
-                sites.append(
-                    TCPSite(
-                        runner,
-                        host,
-                        port,
-                        shutdown_timeout=shutdown_timeout,
-                        ssl_context=ssl_context,
-                        backlog=backlog,
-                        reuse_address=reuse_address,
-                        reuse_port=reuse_port,
-                    )
-                )
-            else:
-                for h in host:
-                    sites.append(
-                        TCPSite(
-                            runner,
-                            h,
-                            port,
-                            shutdown_timeout=shutdown_timeout,
-                            ssl_context=ssl_context,
-                            backlog=backlog,
-                            reuse_address=reuse_address,
-                            reuse_port=reuse_port,
-                        )
-                    )
-        elif path is None and sock is None or port is not None:
-            sites.append(
-                TCPSite(
-                    runner,
-                    port=port,
-                    shutdown_timeout=shutdown_timeout,
-                    ssl_context=ssl_context,
-                    backlog=backlog,
-                    reuse_address=reuse_address,
-                    reuse_port=reuse_port,
-                )
-            )
-
-        if path is not None:
-            if isinstance(path, (str, bytes, bytearray, memoryview)):
-                sites.append(
-                    UnixSite(
-                        runner,
-                        path,
-                        shutdown_timeout=shutdown_timeout,
-                        ssl_context=ssl_context,
-                        backlog=backlog,
-                    )
-                )
-            else:
-                for p in path:
-                    sites.append(
-                        UnixSite(
-                            runner,
-                            p,
-                            shutdown_timeout=shutdown_timeout,
-                            ssl_context=ssl_context,
-                            backlog=backlog,
-                        )
-                    )
-
-        if sock is not None:
-            if not isinstance(sock, Iterable):
-                sites.append(
-                    SockSite(
-                        runner,
-                        sock,
-                        shutdown_timeout=shutdown_timeout,
-                        ssl_context=ssl_context,
-                        backlog=backlog,
-                    )
-                )
-            else:
-                for s in sock:
-                    sites.append(
-                        SockSite(
-                            runner,
-                            s,
-                            shutdown_timeout=shutdown_timeout,
-                            ssl_context=ssl_context,
-                            backlog=backlog,
-                        )
-                    )
-        for site in sites:
-            await site.start()
-
-        if print:  # pragma: no branch
-            names = sorted(str(s.name) for s in runner.sites)
-            print(
-                "======== Running on {} ========\n"
-                "(Press CTRL+C to quit)".format(", ".join(names))
-            )
-
-        # sleep forever by 1 hour intervals,
-        # on Windows before Python 3.8 wake up every 1 second to handle
-        # Ctrl+C smoothly
-        if sys.platform == "win32" and sys.version_info < (3, 8):
-            delay = 1
-        else:
-            delay = 3600
-
-        while True:
-            await asyncio.sleep(delay)
-    finally:
-        await runner.cleanup()
-
-
-def _cancel_tasks(
-    to_cancel: Set["asyncio.Task[Any]"], loop: asyncio.AbstractEventLoop
-) -> None:
-    if not to_cancel:
-        return
-
-    for task in to_cancel:
-        task.cancel()
-
-    loop.run_until_complete(asyncio.gather(*to_cancel, return_exceptions=True))
-
-    for task in to_cancel:
-        if task.cancelled():
-            continue
-        if task.exception() is not None:
-            loop.call_exception_handler(
-                {
-                    "message": "unhandled exception during asyncio.run() shutdown",
-                    "exception": task.exception(),
-                    "task": task,
-                }
-            )
-
-
-def run_app(
-    app: Union[Application, Awaitable[Application]],
-    *,
-    host: Optional[Union[str, HostSequence]] = None,
-    port: Optional[int] = None,
-    path: Optional[str] = None,
-    sock: Optional[Union[socket.socket, TypingIterable[socket.socket]]] = None,
-    shutdown_timeout: float = 60.0,
-    keepalive_timeout: float = 75.0,
-    ssl_context: Optional[SSLContext] = None,
-    print: Callable[..., None] = print,
-    backlog: int = 128,
-    access_log_class: Type[AbstractAccessLogger] = AccessLogger,
-    access_log_format: str = AccessLogger.LOG_FORMAT,
-    access_log: Optional[logging.Logger] = access_logger,
-    handle_signals: bool = True,
-    reuse_address: Optional[bool] = None,
-    reuse_port: Optional[bool] = None,
-    loop: Optional[asyncio.AbstractEventLoop] = None,
-) -> None:
-    """Run an app locally"""
-    if loop is None:
-        loop = asyncio.new_event_loop()
-
-    # Configure if and only if in debugging mode and using the default logger
-    if loop.get_debug() and access_log and access_log.name == "aiohttp.access":
-        if access_log.level == logging.NOTSET:
-            access_log.setLevel(logging.DEBUG)
-        if not access_log.hasHandlers():
-            access_log.addHandler(logging.StreamHandler())
-
-    main_task = loop.create_task(
-        _run_app(
-            app,
-            host=host,
-            port=port,
-            path=path,
-            sock=sock,
-            shutdown_timeout=shutdown_timeout,
-            keepalive_timeout=keepalive_timeout,
-            ssl_context=ssl_context,
-            print=print,
-            backlog=backlog,
-            access_log_class=access_log_class,
-            access_log_format=access_log_format,
-            access_log=access_log,
-            handle_signals=handle_signals,
-            reuse_address=reuse_address,
-            reuse_port=reuse_port,
-        )
-    )
-
-    try:
-        asyncio.set_event_loop(loop)
-        loop.run_until_complete(main_task)
-    except (GracefulExit, KeyboardInterrupt):  # pragma: no cover
-        pass
-    finally:
-        _cancel_tasks({main_task}, loop)
-        _cancel_tasks(all_tasks(loop), loop)
-        loop.run_until_complete(loop.shutdown_asyncgens())
-        loop.close()
-
-
-def main(argv: List[str]) -> None:
-    arg_parser = ArgumentParser(
-        description="aiohttp.web Application server", prog="aiohttp.web"
-    )
-    arg_parser.add_argument(
-        "entry_func",
-        help=(
-            "Callable returning the `aiohttp.web.Application` instance to "
-            "run. Should be specified in the 'module:function' syntax."
-        ),
-        metavar="entry-func",
-    )
-    arg_parser.add_argument(
-        "-H",
-        "--hostname",
-        help="TCP/IP hostname to serve on (default: %(default)r)",
-        default="localhost",
-    )
-    arg_parser.add_argument(
-        "-P",
-        "--port",
-        help="TCP/IP port to serve on (default: %(default)r)",
-        type=int,
-        default="8080",
-    )
-    arg_parser.add_argument(
-        "-U",
-        "--path",
-        help="Unix file system path to serve on. Specifying a path will cause "
-        "hostname and port arguments to be ignored.",
-    )
-    args, extra_argv = arg_parser.parse_known_args(argv)
-
-    # Import logic
-    mod_str, _, func_str = args.entry_func.partition(":")
-    if not func_str or not mod_str:
-        arg_parser.error("'entry-func' not in 'module:function' syntax")
-    if mod_str.startswith("."):
-        arg_parser.error("relative module names not supported")
-    try:
-        module = import_module(mod_str)
-    except ImportError as ex:
-        arg_parser.error(f"unable to import {mod_str}: {ex}")
-    try:
-        func = getattr(module, func_str)
-    except AttributeError:
-        arg_parser.error(f"module {mod_str!r} has no attribute {func_str!r}")
-
-    # Compatibility logic
-    if args.path is not None and not hasattr(socket, "AF_UNIX"):
-        arg_parser.error(
-            "file system paths not supported by your operating" " environment"
-        )
-
-    logging.basicConfig(level=logging.DEBUG)
-
-    app = func(extra_argv)
-    run_app(app, host=args.hostname, port=args.port, path=args.path)
-    arg_parser.exit(message="Stopped\n")
-
-
-if __name__ == "__main__":  # pragma: no branch
-    main(sys.argv[1:])  # pragma: no cover
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/trellis_scatter_plot.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/trellis_scatter_plot.py
deleted file mode 100644
index 590a41922a7907e949fc7ec013a4366b4de28642..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/trellis_scatter_plot.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-Trellis Scatter Plot
------------------------
-This example shows how to make a trellis scatter plot.
-"""
-# category: scatter plots
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-alt.Chart(source).mark_point().encode(
-    x='Horsepower:Q',
-    y='Miles_per_Gallon:Q',
-    row='Origin:N'
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/types.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/types.py
deleted file mode 100644
index b45ee53d0a7f1ed246a6cf5fda5f9c3176089046..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/types.py
+++ /dev/null
@@ -1,1073 +0,0 @@
-import os
-import stat
-import typing as t
-from datetime import datetime
-from gettext import gettext as _
-from gettext import ngettext
-
-from ._compat import _get_argv_encoding
-from ._compat import get_filesystem_encoding
-from ._compat import open_stream
-from .exceptions import BadParameter
-from .utils import LazyFile
-from .utils import safecall
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-    from .core import Context
-    from .core import Parameter
-    from .shell_completion import CompletionItem
-
-
-class ParamType:
-    """Represents the type of a parameter. Validates and converts values
-    from the command line or Python into the correct type.
-
-    To implement a custom type, subclass and implement at least the
-    following:
-
-    -   The :attr:`name` class attribute must be set.
-    -   Calling an instance of the type with ``None`` must return
-        ``None``. This is already implemented by default.
-    -   :meth:`convert` must convert string values to the correct type.
-    -   :meth:`convert` must accept values that are already the correct
-        type.
-    -   It must be able to convert a value if the ``ctx`` and ``param``
-        arguments are ``None``. This can occur when converting prompt
-        input.
-    """
-
-    is_composite: t.ClassVar[bool] = False
-    arity: t.ClassVar[int] = 1
-
-    #: the descriptive name of this type
-    name: str
-
-    #: if a list of this type is expected and the value is pulled from a
-    #: string environment variable, this is what splits it up.  `None`
-    #: means any whitespace.  For all parameters the general rule is that
-    #: whitespace splits them up.  The exception are paths and files which
-    #: are split by ``os.path.pathsep`` by default (":" on Unix and ";" on
-    #: Windows).
-    envvar_list_splitter: t.ClassVar[t.Optional[str]] = None
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation.
-
-        Use :meth:`click.Context.to_info_dict` to traverse the entire
-        CLI structure.
-
-        .. versionadded:: 8.0
-        """
-        # The class name without the "ParamType" suffix.
-        param_type = type(self).__name__.partition("ParamType")[0]
-        param_type = param_type.partition("ParameterType")[0]
-
-        # Custom subclasses might not remember to set a name.
-        if hasattr(self, "name"):
-            name = self.name
-        else:
-            name = param_type
-
-        return {"param_type": param_type, "name": name}
-
-    def __call__(
-        self,
-        value: t.Any,
-        param: t.Optional["Parameter"] = None,
-        ctx: t.Optional["Context"] = None,
-    ) -> t.Any:
-        if value is not None:
-            return self.convert(value, param, ctx)
-
-    def get_metavar(self, param: "Parameter") -> t.Optional[str]:
-        """Returns the metavar default for this param if it provides one."""
-
-    def get_missing_message(self, param: "Parameter") -> t.Optional[str]:
-        """Optionally might return extra information about a missing
-        parameter.
-
-        .. versionadded:: 2.0
-        """
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        """Convert the value to the correct type. This is not called if
-        the value is ``None`` (the missing value).
-
-        This must accept string values from the command line, as well as
-        values that are already the correct type. It may also convert
-        other compatible types.
-
-        The ``param`` and ``ctx`` arguments may be ``None`` in certain
-        situations, such as when converting prompt input.
-
-        If the value cannot be converted, call :meth:`fail` with a
-        descriptive message.
-
-        :param value: The value to convert.
-        :param param: The parameter that is using this type to convert
-            its value. May be ``None``.
-        :param ctx: The current context that arrived at this value. May
-            be ``None``.
-        """
-        return value
-
-    def split_envvar_value(self, rv: str) -> t.Sequence[str]:
-        """Given a value from an environment variable this splits it up
-        into small chunks depending on the defined envvar list splitter.
-
-        If the splitter is set to `None`, which means that whitespace splits,
-        then leading and trailing whitespace is ignored.  Otherwise, leading
-        and trailing splitters usually lead to empty items being included.
-        """
-        return (rv or "").split(self.envvar_list_splitter)
-
-    def fail(
-        self,
-        message: str,
-        param: t.Optional["Parameter"] = None,
-        ctx: t.Optional["Context"] = None,
-    ) -> "t.NoReturn":
-        """Helper method to fail with an invalid value message."""
-        raise BadParameter(message, ctx=ctx, param=param)
-
-    def shell_complete(
-        self, ctx: "Context", param: "Parameter", incomplete: str
-    ) -> t.List["CompletionItem"]:
-        """Return a list of
-        :class:`~click.shell_completion.CompletionItem` objects for the
-        incomplete value. Most types do not provide completions, but
-        some do, and this allows custom types to provide custom
-        completions as well.
-
-        :param ctx: Invocation context for this command.
-        :param param: The parameter that is requesting completion.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        return []
-
-
-class CompositeParamType(ParamType):
-    is_composite = True
-
-    @property
-    def arity(self) -> int:  # type: ignore
-        raise NotImplementedError()
-
-
-class FuncParamType(ParamType):
-    def __init__(self, func: t.Callable[[t.Any], t.Any]) -> None:
-        self.name = func.__name__
-        self.func = func
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict["func"] = self.func
-        return info_dict
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        try:
-            return self.func(value)
-        except ValueError:
-            try:
-                value = str(value)
-            except UnicodeError:
-                value = value.decode("utf-8", "replace")
-
-            self.fail(value, param, ctx)
-
-
-class UnprocessedParamType(ParamType):
-    name = "text"
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        return value
-
-    def __repr__(self) -> str:
-        return "UNPROCESSED"
-
-
-class StringParamType(ParamType):
-    name = "text"
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        if isinstance(value, bytes):
-            enc = _get_argv_encoding()
-            try:
-                value = value.decode(enc)
-            except UnicodeError:
-                fs_enc = get_filesystem_encoding()
-                if fs_enc != enc:
-                    try:
-                        value = value.decode(fs_enc)
-                    except UnicodeError:
-                        value = value.decode("utf-8", "replace")
-                else:
-                    value = value.decode("utf-8", "replace")
-            return value
-        return str(value)
-
-    def __repr__(self) -> str:
-        return "STRING"
-
-
-class Choice(ParamType):
-    """The choice type allows a value to be checked against a fixed set
-    of supported values. All of these values have to be strings.
-
-    You should only pass a list or tuple of choices. Other iterables
-    (like generators) may lead to surprising results.
-
-    The resulting value will always be one of the originally passed choices
-    regardless of ``case_sensitive`` or any ``ctx.token_normalize_func``
-    being specified.
-
-    See :ref:`choice-opts` for an example.
-
-    :param case_sensitive: Set to false to make choices case
-        insensitive. Defaults to true.
-    """
-
-    name = "choice"
-
-    def __init__(self, choices: t.Sequence[str], case_sensitive: bool = True) -> None:
-        self.choices = choices
-        self.case_sensitive = case_sensitive
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict["choices"] = self.choices
-        info_dict["case_sensitive"] = self.case_sensitive
-        return info_dict
-
-    def get_metavar(self, param: "Parameter") -> str:
-        choices_str = "|".join(self.choices)
-
-        # Use curly braces to indicate a required argument.
-        if param.required and param.param_type_name == "argument":
-            return f"{{{choices_str}}}"
-
-        # Use square braces to indicate an option or optional argument.
-        return f"[{choices_str}]"
-
-    def get_missing_message(self, param: "Parameter") -> str:
-        return _("Choose from:\n\t{choices}").format(choices=",\n\t".join(self.choices))
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        # Match through normalization and case sensitivity
-        # first do token_normalize_func, then lowercase
-        # preserve original `value` to produce an accurate message in
-        # `self.fail`
-        normed_value = value
-        normed_choices = {choice: choice for choice in self.choices}
-
-        if ctx is not None and ctx.token_normalize_func is not None:
-            normed_value = ctx.token_normalize_func(value)
-            normed_choices = {
-                ctx.token_normalize_func(normed_choice): original
-                for normed_choice, original in normed_choices.items()
-            }
-
-        if not self.case_sensitive:
-            normed_value = normed_value.casefold()
-            normed_choices = {
-                normed_choice.casefold(): original
-                for normed_choice, original in normed_choices.items()
-            }
-
-        if normed_value in normed_choices:
-            return normed_choices[normed_value]
-
-        choices_str = ", ".join(map(repr, self.choices))
-        self.fail(
-            ngettext(
-                "{value!r} is not {choice}.",
-                "{value!r} is not one of {choices}.",
-                len(self.choices),
-            ).format(value=value, choice=choices_str, choices=choices_str),
-            param,
-            ctx,
-        )
-
-    def __repr__(self) -> str:
-        return f"Choice({list(self.choices)})"
-
-    def shell_complete(
-        self, ctx: "Context", param: "Parameter", incomplete: str
-    ) -> t.List["CompletionItem"]:
-        """Complete choices that start with the incomplete value.
-
-        :param ctx: Invocation context for this command.
-        :param param: The parameter that is requesting completion.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        str_choices = map(str, self.choices)
-
-        if self.case_sensitive:
-            matched = (c for c in str_choices if c.startswith(incomplete))
-        else:
-            incomplete = incomplete.lower()
-            matched = (c for c in str_choices if c.lower().startswith(incomplete))
-
-        return [CompletionItem(c) for c in matched]
-
-
-class DateTime(ParamType):
-    """The DateTime type converts date strings into `datetime` objects.
-
-    The format strings which are checked are configurable, but default to some
-    common (non-timezone aware) ISO 8601 formats.
-
-    When specifying *DateTime* formats, you should only pass a list or a tuple.
-    Other iterables, like generators, may lead to surprising results.
-
-    The format strings are processed using ``datetime.strptime``, and this
-    consequently defines the format strings which are allowed.
-
-    Parsing is tried using each format, in order, and the first format which
-    parses successfully is used.
-
-    :param formats: A list or tuple of date format strings, in the order in
-                    which they should be tried. Defaults to
-                    ``'%Y-%m-%d'``, ``'%Y-%m-%dT%H:%M:%S'``,
-                    ``'%Y-%m-%d %H:%M:%S'``.
-    """
-
-    name = "datetime"
-
-    def __init__(self, formats: t.Optional[t.Sequence[str]] = None):
-        self.formats = formats or ["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S", "%Y-%m-%d %H:%M:%S"]
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict["formats"] = self.formats
-        return info_dict
-
-    def get_metavar(self, param: "Parameter") -> str:
-        return f"[{'|'.join(self.formats)}]"
-
-    def _try_to_convert_date(self, value: t.Any, format: str) -> t.Optional[datetime]:
-        try:
-            return datetime.strptime(value, format)
-        except ValueError:
-            return None
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        if isinstance(value, datetime):
-            return value
-
-        for format in self.formats:
-            converted = self._try_to_convert_date(value, format)
-
-            if converted is not None:
-                return converted
-
-        formats_str = ", ".join(map(repr, self.formats))
-        self.fail(
-            ngettext(
-                "{value!r} does not match the format {format}.",
-                "{value!r} does not match the formats {formats}.",
-                len(self.formats),
-            ).format(value=value, format=formats_str, formats=formats_str),
-            param,
-            ctx,
-        )
-
-    def __repr__(self) -> str:
-        return "DateTime"
-
-
-class _NumberParamTypeBase(ParamType):
-    _number_class: t.ClassVar[t.Type]
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        try:
-            return self._number_class(value)
-        except ValueError:
-            self.fail(
-                _("{value!r} is not a valid {number_type}.").format(
-                    value=value, number_type=self.name
-                ),
-                param,
-                ctx,
-            )
-
-
-class _NumberRangeBase(_NumberParamTypeBase):
-    def __init__(
-        self,
-        min: t.Optional[float] = None,
-        max: t.Optional[float] = None,
-        min_open: bool = False,
-        max_open: bool = False,
-        clamp: bool = False,
-    ) -> None:
-        self.min = min
-        self.max = max
-        self.min_open = min_open
-        self.max_open = max_open
-        self.clamp = clamp
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict.update(
-            min=self.min,
-            max=self.max,
-            min_open=self.min_open,
-            max_open=self.max_open,
-            clamp=self.clamp,
-        )
-        return info_dict
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        import operator
-
-        rv = super().convert(value, param, ctx)
-        lt_min: bool = self.min is not None and (
-            operator.le if self.min_open else operator.lt
-        )(rv, self.min)
-        gt_max: bool = self.max is not None and (
-            operator.ge if self.max_open else operator.gt
-        )(rv, self.max)
-
-        if self.clamp:
-            if lt_min:
-                return self._clamp(self.min, 1, self.min_open)  # type: ignore
-
-            if gt_max:
-                return self._clamp(self.max, -1, self.max_open)  # type: ignore
-
-        if lt_min or gt_max:
-            self.fail(
-                _("{value} is not in the range {range}.").format(
-                    value=rv, range=self._describe_range()
-                ),
-                param,
-                ctx,
-            )
-
-        return rv
-
-    def _clamp(self, bound: float, dir: "te.Literal[1, -1]", open: bool) -> float:
-        """Find the valid value to clamp to bound in the given
-        direction.
-
-        :param bound: The boundary value.
-        :param dir: 1 or -1 indicating the direction to move.
-        :param open: If true, the range does not include the bound.
-        """
-        raise NotImplementedError
-
-    def _describe_range(self) -> str:
-        """Describe the range for use in help text."""
-        if self.min is None:
-            op = "<" if self.max_open else "<="
-            return f"x{op}{self.max}"
-
-        if self.max is None:
-            op = ">" if self.min_open else ">="
-            return f"x{op}{self.min}"
-
-        lop = "<" if self.min_open else "<="
-        rop = "<" if self.max_open else "<="
-        return f"{self.min}{lop}x{rop}{self.max}"
-
-    def __repr__(self) -> str:
-        clamp = " clamped" if self.clamp else ""
-        return f"<{type(self).__name__} {self._describe_range()}{clamp}>"
-
-
-class IntParamType(_NumberParamTypeBase):
-    name = "integer"
-    _number_class = int
-
-    def __repr__(self) -> str:
-        return "INT"
-
-
-class IntRange(_NumberRangeBase, IntParamType):
-    """Restrict an :data:`click.INT` value to a range of accepted
-    values. See :ref:`ranges`.
-
-    If ``min`` or ``max`` are not passed, any value is accepted in that
-    direction. If ``min_open`` or ``max_open`` are enabled, the
-    corresponding boundary is not included in the range.
-
-    If ``clamp`` is enabled, a value outside the range is clamped to the
-    boundary instead of failing.
-
-    .. versionchanged:: 8.0
-        Added the ``min_open`` and ``max_open`` parameters.
-    """
-
-    name = "integer range"
-
-    def _clamp(  # type: ignore
-        self, bound: int, dir: "te.Literal[1, -1]", open: bool
-    ) -> int:
-        if not open:
-            return bound
-
-        return bound + dir
-
-
-class FloatParamType(_NumberParamTypeBase):
-    name = "float"
-    _number_class = float
-
-    def __repr__(self) -> str:
-        return "FLOAT"
-
-
-class FloatRange(_NumberRangeBase, FloatParamType):
-    """Restrict a :data:`click.FLOAT` value to a range of accepted
-    values. See :ref:`ranges`.
-
-    If ``min`` or ``max`` are not passed, any value is accepted in that
-    direction. If ``min_open`` or ``max_open`` are enabled, the
-    corresponding boundary is not included in the range.
-
-    If ``clamp`` is enabled, a value outside the range is clamped to the
-    boundary instead of failing. This is not supported if either
-    boundary is marked ``open``.
-
-    .. versionchanged:: 8.0
-        Added the ``min_open`` and ``max_open`` parameters.
-    """
-
-    name = "float range"
-
-    def __init__(
-        self,
-        min: t.Optional[float] = None,
-        max: t.Optional[float] = None,
-        min_open: bool = False,
-        max_open: bool = False,
-        clamp: bool = False,
-    ) -> None:
-        super().__init__(
-            min=min, max=max, min_open=min_open, max_open=max_open, clamp=clamp
-        )
-
-        if (min_open or max_open) and clamp:
-            raise TypeError("Clamping is not supported for open bounds.")
-
-    def _clamp(self, bound: float, dir: "te.Literal[1, -1]", open: bool) -> float:
-        if not open:
-            return bound
-
-        # Could use Python 3.9's math.nextafter here, but clamping an
-        # open float range doesn't seem to be particularly useful. It's
-        # left up to the user to write a callback to do it if needed.
-        raise RuntimeError("Clamping is not supported for open bounds.")
-
-
-class BoolParamType(ParamType):
-    name = "boolean"
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        if value in {False, True}:
-            return bool(value)
-
-        norm = value.strip().lower()
-
-        if norm in {"1", "true", "t", "yes", "y", "on"}:
-            return True
-
-        if norm in {"0", "false", "f", "no", "n", "off"}:
-            return False
-
-        self.fail(
-            _("{value!r} is not a valid boolean.").format(value=value), param, ctx
-        )
-
-    def __repr__(self) -> str:
-        return "BOOL"
-
-
-class UUIDParameterType(ParamType):
-    name = "uuid"
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        import uuid
-
-        if isinstance(value, uuid.UUID):
-            return value
-
-        value = value.strip()
-
-        try:
-            return uuid.UUID(value)
-        except ValueError:
-            self.fail(
-                _("{value!r} is not a valid UUID.").format(value=value), param, ctx
-            )
-
-    def __repr__(self) -> str:
-        return "UUID"
-
-
-class File(ParamType):
-    """Declares a parameter to be a file for reading or writing.  The file
-    is automatically closed once the context tears down (after the command
-    finished working).
-
-    Files can be opened for reading or writing.  The special value ``-``
-    indicates stdin or stdout depending on the mode.
-
-    By default, the file is opened for reading text data, but it can also be
-    opened in binary mode or for writing.  The encoding parameter can be used
-    to force a specific encoding.
-
-    The `lazy` flag controls if the file should be opened immediately or upon
-    first IO. The default is to be non-lazy for standard input and output
-    streams as well as files opened for reading, `lazy` otherwise. When opening a
-    file lazily for reading, it is still opened temporarily for validation, but
-    will not be held open until first IO. lazy is mainly useful when opening
-    for writing to avoid creating the file until it is needed.
-
-    Starting with Click 2.0, files can also be opened atomically in which
-    case all writes go into a separate file in the same folder and upon
-    completion the file will be moved over to the original location.  This
-    is useful if a file regularly read by other users is modified.
-
-    See :ref:`file-args` for more information.
-    """
-
-    name = "filename"
-    envvar_list_splitter = os.path.pathsep
-
-    def __init__(
-        self,
-        mode: str = "r",
-        encoding: t.Optional[str] = None,
-        errors: t.Optional[str] = "strict",
-        lazy: t.Optional[bool] = None,
-        atomic: bool = False,
-    ) -> None:
-        self.mode = mode
-        self.encoding = encoding
-        self.errors = errors
-        self.lazy = lazy
-        self.atomic = atomic
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict.update(mode=self.mode, encoding=self.encoding)
-        return info_dict
-
-    def resolve_lazy_flag(self, value: t.Any) -> bool:
-        if self.lazy is not None:
-            return self.lazy
-        if value == "-":
-            return False
-        elif "w" in self.mode:
-            return True
-        return False
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        try:
-            if hasattr(value, "read") or hasattr(value, "write"):
-                return value
-
-            lazy = self.resolve_lazy_flag(value)
-
-            if lazy:
-                f: t.IO = t.cast(
-                    t.IO,
-                    LazyFile(
-                        value, self.mode, self.encoding, self.errors, atomic=self.atomic
-                    ),
-                )
-
-                if ctx is not None:
-                    ctx.call_on_close(f.close_intelligently)  # type: ignore
-
-                return f
-
-            f, should_close = open_stream(
-                value, self.mode, self.encoding, self.errors, atomic=self.atomic
-            )
-
-            # If a context is provided, we automatically close the file
-            # at the end of the context execution (or flush out).  If a
-            # context does not exist, it's the caller's responsibility to
-            # properly close the file.  This for instance happens when the
-            # type is used with prompts.
-            if ctx is not None:
-                if should_close:
-                    ctx.call_on_close(safecall(f.close))
-                else:
-                    ctx.call_on_close(safecall(f.flush))
-
-            return f
-        except OSError as e:  # noqa: B014
-            self.fail(f"'{os.fsdecode(value)}': {e.strerror}", param, ctx)
-
-    def shell_complete(
-        self, ctx: "Context", param: "Parameter", incomplete: str
-    ) -> t.List["CompletionItem"]:
-        """Return a special completion marker that tells the completion
-        system to use the shell to provide file path completions.
-
-        :param ctx: Invocation context for this command.
-        :param param: The parameter that is requesting completion.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        return [CompletionItem(incomplete, type="file")]
-
-
-class Path(ParamType):
-    """The ``Path`` type is similar to the :class:`File` type, but
-    returns the filename instead of an open file. Various checks can be
-    enabled to validate the type of file and permissions.
-
-    :param exists: The file or directory needs to exist for the value to
-        be valid. If this is not set to ``True``, and the file does not
-        exist, then all further checks are silently skipped.
-    :param file_okay: Allow a file as a value.
-    :param dir_okay: Allow a directory as a value.
-    :param readable: if true, a readable check is performed.
-    :param writable: if true, a writable check is performed.
-    :param executable: if true, an executable check is performed.
-    :param resolve_path: Make the value absolute and resolve any
-        symlinks. A ``~`` is not expanded, as this is supposed to be
-        done by the shell only.
-    :param allow_dash: Allow a single dash as a value, which indicates
-        a standard stream (but does not open it). Use
-        :func:`~click.open_file` to handle opening this value.
-    :param path_type: Convert the incoming path value to this type. If
-        ``None``, keep Python's default, which is ``str``. Useful to
-        convert to :class:`pathlib.Path`.
-
-    .. versionchanged:: 8.1
-        Added the ``executable`` parameter.
-
-    .. versionchanged:: 8.0
-        Allow passing ``type=pathlib.Path``.
-
-    .. versionchanged:: 6.0
-        Added the ``allow_dash`` parameter.
-    """
-
-    envvar_list_splitter = os.path.pathsep
-
-    def __init__(
-        self,
-        exists: bool = False,
-        file_okay: bool = True,
-        dir_okay: bool = True,
-        writable: bool = False,
-        readable: bool = True,
-        resolve_path: bool = False,
-        allow_dash: bool = False,
-        path_type: t.Optional[t.Type] = None,
-        executable: bool = False,
-    ):
-        self.exists = exists
-        self.file_okay = file_okay
-        self.dir_okay = dir_okay
-        self.readable = readable
-        self.writable = writable
-        self.executable = executable
-        self.resolve_path = resolve_path
-        self.allow_dash = allow_dash
-        self.type = path_type
-
-        if self.file_okay and not self.dir_okay:
-            self.name = _("file")
-        elif self.dir_okay and not self.file_okay:
-            self.name = _("directory")
-        else:
-            self.name = _("path")
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict.update(
-            exists=self.exists,
-            file_okay=self.file_okay,
-            dir_okay=self.dir_okay,
-            writable=self.writable,
-            readable=self.readable,
-            allow_dash=self.allow_dash,
-        )
-        return info_dict
-
-    def coerce_path_result(self, rv: t.Any) -> t.Any:
-        if self.type is not None and not isinstance(rv, self.type):
-            if self.type is str:
-                rv = os.fsdecode(rv)
-            elif self.type is bytes:
-                rv = os.fsencode(rv)
-            else:
-                rv = self.type(rv)
-
-        return rv
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        rv = value
-
-        is_dash = self.file_okay and self.allow_dash and rv in (b"-", "-")
-
-        if not is_dash:
-            if self.resolve_path:
-                # os.path.realpath doesn't resolve symlinks on Windows
-                # until Python 3.8. Use pathlib for now.
-                import pathlib
-
-                rv = os.fsdecode(pathlib.Path(rv).resolve())
-
-            try:
-                st = os.stat(rv)
-            except OSError:
-                if not self.exists:
-                    return self.coerce_path_result(rv)
-                self.fail(
-                    _("{name} {filename!r} does not exist.").format(
-                        name=self.name.title(), filename=os.fsdecode(value)
-                    ),
-                    param,
-                    ctx,
-                )
-
-            if not self.file_okay and stat.S_ISREG(st.st_mode):
-                self.fail(
-                    _("{name} {filename!r} is a file.").format(
-                        name=self.name.title(), filename=os.fsdecode(value)
-                    ),
-                    param,
-                    ctx,
-                )
-            if not self.dir_okay and stat.S_ISDIR(st.st_mode):
-                self.fail(
-                    _("{name} '{filename}' is a directory.").format(
-                        name=self.name.title(), filename=os.fsdecode(value)
-                    ),
-                    param,
-                    ctx,
-                )
-
-            if self.readable and not os.access(rv, os.R_OK):
-                self.fail(
-                    _("{name} {filename!r} is not readable.").format(
-                        name=self.name.title(), filename=os.fsdecode(value)
-                    ),
-                    param,
-                    ctx,
-                )
-
-            if self.writable and not os.access(rv, os.W_OK):
-                self.fail(
-                    _("{name} {filename!r} is not writable.").format(
-                        name=self.name.title(), filename=os.fsdecode(value)
-                    ),
-                    param,
-                    ctx,
-                )
-
-            if self.executable and not os.access(value, os.X_OK):
-                self.fail(
-                    _("{name} {filename!r} is not executable.").format(
-                        name=self.name.title(), filename=os.fsdecode(value)
-                    ),
-                    param,
-                    ctx,
-                )
-
-        return self.coerce_path_result(rv)
-
-    def shell_complete(
-        self, ctx: "Context", param: "Parameter", incomplete: str
-    ) -> t.List["CompletionItem"]:
-        """Return a special completion marker that tells the completion
-        system to use the shell to provide path completions for only
-        directories or any paths.
-
-        :param ctx: Invocation context for this command.
-        :param param: The parameter that is requesting completion.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        type = "dir" if self.dir_okay and not self.file_okay else "file"
-        return [CompletionItem(incomplete, type=type)]
-
-
-class Tuple(CompositeParamType):
-    """The default behavior of Click is to apply a type on a value directly.
-    This works well in most cases, except for when `nargs` is set to a fixed
-    count and different types should be used for different items.  In this
-    case the :class:`Tuple` type can be used.  This type can only be used
-    if `nargs` is set to a fixed number.
-
-    For more information see :ref:`tuple-type`.
-
-    This can be selected by using a Python tuple literal as a type.
-
-    :param types: a list of types that should be used for the tuple items.
-    """
-
-    def __init__(self, types: t.Sequence[t.Union[t.Type, ParamType]]) -> None:
-        self.types = [convert_type(ty) for ty in types]
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict["types"] = [t.to_info_dict() for t in self.types]
-        return info_dict
-
-    @property
-    def name(self) -> str:  # type: ignore
-        return f"<{' '.join(ty.name for ty in self.types)}>"
-
-    @property
-    def arity(self) -> int:  # type: ignore
-        return len(self.types)
-
-    def convert(
-        self, value: t.Any, param: t.Optional["Parameter"], ctx: t.Optional["Context"]
-    ) -> t.Any:
-        len_type = len(self.types)
-        len_value = len(value)
-
-        if len_value != len_type:
-            self.fail(
-                ngettext(
-                    "{len_type} values are required, but {len_value} was given.",
-                    "{len_type} values are required, but {len_value} were given.",
-                    len_value,
-                ).format(len_type=len_type, len_value=len_value),
-                param=param,
-                ctx=ctx,
-            )
-
-        return tuple(ty(x, param, ctx) for ty, x in zip(self.types, value))
-
-
-def convert_type(ty: t.Optional[t.Any], default: t.Optional[t.Any] = None) -> ParamType:
-    """Find the most appropriate :class:`ParamType` for the given Python
-    type. If the type isn't provided, it can be inferred from a default
-    value.
-    """
-    guessed_type = False
-
-    if ty is None and default is not None:
-        if isinstance(default, (tuple, list)):
-            # If the default is empty, ty will remain None and will
-            # return STRING.
-            if default:
-                item = default[0]
-
-                # A tuple of tuples needs to detect the inner types.
-                # Can't call convert recursively because that would
-                # incorrectly unwind the tuple to a single type.
-                if isinstance(item, (tuple, list)):
-                    ty = tuple(map(type, item))
-                else:
-                    ty = type(item)
-        else:
-            ty = type(default)
-
-        guessed_type = True
-
-    if isinstance(ty, tuple):
-        return Tuple(ty)
-
-    if isinstance(ty, ParamType):
-        return ty
-
-    if ty is str or ty is None:
-        return STRING
-
-    if ty is int:
-        return INT
-
-    if ty is float:
-        return FLOAT
-
-    if ty is bool:
-        return BOOL
-
-    if guessed_type:
-        return STRING
-
-    if __debug__:
-        try:
-            if issubclass(ty, ParamType):
-                raise AssertionError(
-                    f"Attempted to use an uninstantiated parameter type ({ty})."
-                )
-        except TypeError:
-            # ty is an instance (correct), so issubclass fails.
-            pass
-
-    return FuncParamType(ty)
-
-
-#: A dummy parameter type that just does nothing.  From a user's
-#: perspective this appears to just be the same as `STRING` but
-#: internally no string conversion takes place if the input was bytes.
-#: This is usually useful when working with file paths as they can
-#: appear in bytes and unicode.
-#:
-#: For path related uses the :class:`Path` type is a better choice but
-#: there are situations where an unprocessed type is useful which is why
-#: it is is provided.
-#:
-#: .. versionadded:: 4.0
-UNPROCESSED = UnprocessedParamType()
-
-#: A unicode string parameter type which is the implicit default.  This
-#: can also be selected by using ``str`` as type.
-STRING = StringParamType()
-
-#: An integer parameter.  This can also be selected by using ``int`` as
-#: type.
-INT = IntParamType()
-
-#: A floating point value parameter.  This can also be selected by using
-#: ``float`` as type.
-FLOAT = FloatParamType()
-
-#: A boolean parameter.  This is the default for boolean flags.  This can
-#: also be selected by using ``bool`` as a type.
-BOOL = BoolParamType()
-
-#: A UUID parameter.
-UUID = UUIDParameterType()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/ansitowin32_test.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/ansitowin32_test.py
deleted file mode 100644
index 91ca551f97b4576c680711e826a1855fb944c872..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/colorama/tests/ansitowin32_test.py
+++ /dev/null
@@ -1,294 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-from io import StringIO, TextIOWrapper
-from unittest import TestCase, main
-try:
-    from contextlib import ExitStack
-except ImportError:
-    # python 2
-    from contextlib2 import ExitStack
-
-try:
-    from unittest.mock import MagicMock, Mock, patch
-except ImportError:
-    from mock import MagicMock, Mock, patch
-
-from ..ansitowin32 import AnsiToWin32, StreamWrapper
-from ..win32 import ENABLE_VIRTUAL_TERMINAL_PROCESSING
-from .utils import osname
-
-
-class StreamWrapperTest(TestCase):
-
-    def testIsAProxy(self):
-        mockStream = Mock()
-        wrapper = StreamWrapper(mockStream, None)
-        self.assertTrue( wrapper.random_attr is mockStream.random_attr )
-
-    def testDelegatesWrite(self):
-        mockStream = Mock()
-        mockConverter = Mock()
-        wrapper = StreamWrapper(mockStream, mockConverter)
-        wrapper.write('hello')
-        self.assertTrue(mockConverter.write.call_args, (('hello',), {}))
-
-    def testDelegatesContext(self):
-        mockConverter = Mock()
-        s = StringIO()
-        with StreamWrapper(s, mockConverter) as fp:
-            fp.write(u'hello')
-        self.assertTrue(s.closed)
-
-    def testProxyNoContextManager(self):
-        mockStream = MagicMock()
-        mockStream.__enter__.side_effect = AttributeError()
-        mockConverter = Mock()
-        with self.assertRaises(AttributeError) as excinfo:
-            with StreamWrapper(mockStream, mockConverter) as wrapper:
-                wrapper.write('hello')
-
-    def test_closed_shouldnt_raise_on_closed_stream(self):
-        stream = StringIO()
-        stream.close()
-        wrapper = StreamWrapper(stream, None)
-        self.assertEqual(wrapper.closed, True)
-
-    def test_closed_shouldnt_raise_on_detached_stream(self):
-        stream = TextIOWrapper(StringIO())
-        stream.detach()
-        wrapper = StreamWrapper(stream, None)
-        self.assertEqual(wrapper.closed, True)
-
-class AnsiToWin32Test(TestCase):
-
-    def testInit(self):
-        mockStdout = Mock()
-        auto = Mock()
-        stream = AnsiToWin32(mockStdout, autoreset=auto)
-        self.assertEqual(stream.wrapped, mockStdout)
-        self.assertEqual(stream.autoreset, auto)
-
-    @patch('colorama.ansitowin32.winterm', None)
-    @patch('colorama.ansitowin32.winapi_test', lambda *_: True)
-    def testStripIsTrueOnWindows(self):
-        with osname('nt'):
-            mockStdout = Mock()
-            stream = AnsiToWin32(mockStdout)
-            self.assertTrue(stream.strip)
-
-    def testStripIsFalseOffWindows(self):
-        with osname('posix'):
-            mockStdout = Mock(closed=False)
-            stream = AnsiToWin32(mockStdout)
-            self.assertFalse(stream.strip)
-
-    def testWriteStripsAnsi(self):
-        mockStdout = Mock()
-        stream = AnsiToWin32(mockStdout)
-        stream.wrapped = Mock()
-        stream.write_and_convert = Mock()
-        stream.strip = True
-
-        stream.write('abc')
-
-        self.assertFalse(stream.wrapped.write.called)
-        self.assertEqual(stream.write_and_convert.call_args, (('abc',), {}))
-
-    def testWriteDoesNotStripAnsi(self):
-        mockStdout = Mock()
-        stream = AnsiToWin32(mockStdout)
-        stream.wrapped = Mock()
-        stream.write_and_convert = Mock()
-        stream.strip = False
-        stream.convert = False
-
-        stream.write('abc')
-
-        self.assertFalse(stream.write_and_convert.called)
-        self.assertEqual(stream.wrapped.write.call_args, (('abc',), {}))
-
-    def assert_autoresets(self, convert, autoreset=True):
-        stream = AnsiToWin32(Mock())
-        stream.convert = convert
-        stream.reset_all = Mock()
-        stream.autoreset = autoreset
-        stream.winterm = Mock()
-
-        stream.write('abc')
-
-        self.assertEqual(stream.reset_all.called, autoreset)
-
-    def testWriteAutoresets(self):
-        self.assert_autoresets(convert=True)
-        self.assert_autoresets(convert=False)
-        self.assert_autoresets(convert=True, autoreset=False)
-        self.assert_autoresets(convert=False, autoreset=False)
-
-    def testWriteAndConvertWritesPlainText(self):
-        stream = AnsiToWin32(Mock())
-        stream.write_and_convert( 'abc' )
-        self.assertEqual( stream.wrapped.write.call_args, (('abc',), {}) )
-
-    def testWriteAndConvertStripsAllValidAnsi(self):
-        stream = AnsiToWin32(Mock())
-        stream.call_win32 = Mock()
-        data = [
-            'abc\033[mdef',
-            'abc\033[0mdef',
-            'abc\033[2mdef',
-            'abc\033[02mdef',
-            'abc\033[002mdef',
-            'abc\033[40mdef',
-            'abc\033[040mdef',
-            'abc\033[0;1mdef',
-            'abc\033[40;50mdef',
-            'abc\033[50;30;40mdef',
-            'abc\033[Adef',
-            'abc\033[0Gdef',
-            'abc\033[1;20;128Hdef',
-        ]
-        for datum in data:
-            stream.wrapped.write.reset_mock()
-            stream.write_and_convert( datum )
-            self.assertEqual(
-               [args[0] for args in stream.wrapped.write.call_args_list],
-               [ ('abc',), ('def',) ]
-            )
-
-    def testWriteAndConvertSkipsEmptySnippets(self):
-        stream = AnsiToWin32(Mock())
-        stream.call_win32 = Mock()
-        stream.write_and_convert( '\033[40m\033[41m' )
-        self.assertFalse( stream.wrapped.write.called )
-
-    def testWriteAndConvertCallsWin32WithParamsAndCommand(self):
-        stream = AnsiToWin32(Mock())
-        stream.convert = True
-        stream.call_win32 = Mock()
-        stream.extract_params = Mock(return_value='params')
-        data = {
-            'abc\033[adef':         ('a', 'params'),
-            'abc\033[;;bdef':       ('b', 'params'),
-            'abc\033[0cdef':        ('c', 'params'),
-            'abc\033[;;0;;Gdef':    ('G', 'params'),
-            'abc\033[1;20;128Hdef': ('H', 'params'),
-        }
-        for datum, expected in data.items():
-            stream.call_win32.reset_mock()
-            stream.write_and_convert( datum )
-            self.assertEqual( stream.call_win32.call_args[0], expected )
-
-    def test_reset_all_shouldnt_raise_on_closed_orig_stdout(self):
-        stream = StringIO()
-        converter = AnsiToWin32(stream)
-        stream.close()
-
-        converter.reset_all()
-
-    def test_wrap_shouldnt_raise_on_closed_orig_stdout(self):
-        stream = StringIO()
-        stream.close()
-        with \
-            patch("colorama.ansitowin32.os.name", "nt"), \
-            patch("colorama.ansitowin32.winapi_test", lambda: True):
-                converter = AnsiToWin32(stream)
-        self.assertTrue(converter.strip)
-        self.assertFalse(converter.convert)
-
-    def test_wrap_shouldnt_raise_on_missing_closed_attr(self):
-        with \
-            patch("colorama.ansitowin32.os.name", "nt"), \
-            patch("colorama.ansitowin32.winapi_test", lambda: True):
-                converter = AnsiToWin32(object())
-        self.assertTrue(converter.strip)
-        self.assertFalse(converter.convert)
-
-    def testExtractParams(self):
-        stream = AnsiToWin32(Mock())
-        data = {
-            '':               (0,),
-            ';;':             (0,),
-            '2':              (2,),
-            ';;002;;':        (2,),
-            '0;1':            (0, 1),
-            ';;003;;456;;':   (3, 456),
-            '11;22;33;44;55': (11, 22, 33, 44, 55),
-        }
-        for datum, expected in data.items():
-            self.assertEqual(stream.extract_params('m', datum), expected)
-
-    def testCallWin32UsesLookup(self):
-        listener = Mock()
-        stream = AnsiToWin32(listener)
-        stream.win32_calls = {
-            1: (lambda *_, **__: listener(11),),
-            2: (lambda *_, **__: listener(22),),
-            3: (lambda *_, **__: listener(33),),
-        }
-        stream.call_win32('m', (3, 1, 99, 2))
-        self.assertEqual(
-            [a[0][0] for a in listener.call_args_list],
-            [33, 11, 22] )
-
-    def test_osc_codes(self):
-        mockStdout = Mock()
-        stream = AnsiToWin32(mockStdout, convert=True)
-        with patch('colorama.ansitowin32.winterm') as winterm:
-            data = [
-                '\033]0\x07',                      # missing arguments
-                '\033]0;foo\x08',                  # wrong OSC command
-                '\033]0;colorama_test_title\x07',  # should work
-                '\033]1;colorama_test_title\x07',  # wrong set command
-                '\033]2;colorama_test_title\x07',  # should work
-                '\033]' + ';' * 64 + '\x08',       # see issue #247
-            ]
-            for code in data:
-                stream.write(code)
-            self.assertEqual(winterm.set_title.call_count, 2)
-
-    def test_native_windows_ansi(self):
-        with ExitStack() as stack:
-            def p(a, b):
-                stack.enter_context(patch(a, b, create=True))
-            # Pretend to be on Windows
-            p("colorama.ansitowin32.os.name", "nt")
-            p("colorama.ansitowin32.winapi_test", lambda: True)
-            p("colorama.win32.winapi_test", lambda: True)
-            p("colorama.winterm.win32.windll", "non-None")
-            p("colorama.winterm.get_osfhandle", lambda _: 1234)
-
-            # Pretend that our mock stream has native ANSI support
-            p(
-                "colorama.winterm.win32.GetConsoleMode",
-                lambda _: ENABLE_VIRTUAL_TERMINAL_PROCESSING,
-            )
-            SetConsoleMode = Mock()
-            p("colorama.winterm.win32.SetConsoleMode", SetConsoleMode)
-
-            stdout = Mock()
-            stdout.closed = False
-            stdout.isatty.return_value = True
-            stdout.fileno.return_value = 1
-
-            # Our fake console says it has native vt support, so AnsiToWin32 should
-            # enable that support and do nothing else.
-            stream = AnsiToWin32(stdout)
-            SetConsoleMode.assert_called_with(1234, ENABLE_VIRTUAL_TERMINAL_PROCESSING)
-            self.assertFalse(stream.strip)
-            self.assertFalse(stream.convert)
-            self.assertFalse(stream.should_wrap())
-
-            # Now let's pretend we're on an old Windows console, that doesn't have
-            # native ANSI support.
-            p("colorama.winterm.win32.GetConsoleMode", lambda _: 0)
-            SetConsoleMode = Mock()
-            p("colorama.winterm.win32.SetConsoleMode", SetConsoleMode)
-
-            stream = AnsiToWin32(stdout)
-            SetConsoleMode.assert_called_with(1234, ENABLE_VIRTUAL_TERMINAL_PROCESSING)
-            self.assertTrue(stream.strip)
-            self.assertTrue(stream.convert)
-            self.assertTrue(stream.should_wrap())
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/subword_nmt_bpe.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/subword_nmt_bpe.py
deleted file mode 100644
index 5d724d2730a5895ca55af2998c2ced471625b516..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/subword_nmt_bpe.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-
-from fairseq import file_utils
-from fairseq.data.encoders import register_bpe
-from fairseq.dataclass import FairseqDataclass
-
-
-@dataclass
-class SubwordNMTBPEConfig(FairseqDataclass):
-    bpe_codes: str = field(default="???", metadata={"help": "path to subword NMT BPE"})
-    bpe_separator: str = field(default="@@", metadata={"help": "BPE separator"})
-
-
-@register_bpe("subword_nmt", dataclass=SubwordNMTBPEConfig)
-class SubwordNMTBPE(object):
-    def __init__(self, cfg):
-        if cfg.bpe_codes is None:
-            raise ValueError("--bpe-codes is required for --bpe=subword_nmt")
-        codes = file_utils.cached_path(cfg.bpe_codes)
-        try:
-            from subword_nmt import apply_bpe
-
-            bpe_parser = apply_bpe.create_parser()
-            bpe_args = bpe_parser.parse_args(
-                [
-                    "--codes",
-                    codes,
-                    "--separator",
-                    cfg.bpe_separator,
-                ]
-            )
-            self.bpe = apply_bpe.BPE(
-                bpe_args.codes,
-                bpe_args.merges,
-                bpe_args.separator,
-                None,
-                bpe_args.glossaries,
-            )
-            self.bpe_symbol = bpe_args.separator + " "
-        except ImportError:
-            raise ImportError(
-                "Please install subword_nmt with: pip install subword-nmt"
-            )
-
-    def encode(self, x: str) -> str:
-        return self.bpe.process_line(x)
-
-    def decode(self, x: str) -> str:
-        return (x + " ").replace(self.bpe_symbol, "").rstrip()
diff --git a/spaces/aseifert/ExplaiNER/src/subpages/inspect.py b/spaces/aseifert/ExplaiNER/src/subpages/inspect.py
deleted file mode 100644
index 8fc78cc302e067e28445035820f1431f946e138c..0000000000000000000000000000000000000000
--- a/spaces/aseifert/ExplaiNER/src/subpages/inspect.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Inspect your whole dataset, either unfiltered or by id."""
-import streamlit as st
-
-from src.subpages.page import Context, Page
-from src.utils import aggrid_interactive_table, colorize_classes
-
-
-class InspectPage(Page):
-    name = "Inspect"
-    icon = "search"
-
-    def render(self, context: Context):
-        st.title(self.name)
-        with st.expander("💡", expanded=True):
-            st.write("Inspect your whole dataset, either unfiltered or by id.")
-
-        df = context.df_tokens
-        cols = (
-            "ids input_ids token_type_ids word_ids losses tokens labels preds total_loss".split()
-        )
-        if "token_type_ids" not in df.columns:
-            cols.remove("token_type_ids")
-        df = df.drop("hidden_states", axis=1).drop("attention_mask", axis=1)[cols]
-
-        if st.checkbox("Filter by id", value=True):
-            ids = list(sorted(map(int, df.ids.unique())))
-            next_id = st.session_state.get("next_id", 0)
-
-            example_id = st.selectbox("Select an example", ids, index=next_id)
-            df = df[df.ids == str(example_id)][1:-1]
-            # st.dataframe(colorize_classes(df).format(precision=3).bar(subset="losses"))  # type: ignore
-            st.dataframe(colorize_classes(df.round(3).astype(str)))
-
-            # if st.button("➡️ Next example"):
-            #     st.session_state.next_id = (ids.index(example_id) + 1) % len(ids)
-            #     st.experimental_rerun()
-            # if st.button("⬅️ Previous example"):
-            #     st.session_state.next_id = (ids.index(example_id) - 1) % len(ids)
-            #     st.experimental_rerun()
-        else:
-            aggrid_interactive_table(df.round(3))
diff --git a/spaces/ashercn97/AsherTesting/docs/Spell-book.md b/spaces/ashercn97/AsherTesting/docs/Spell-book.md
deleted file mode 100644
index 9b7c76c953f76f8a486bbe5156de4e9ebb3f0ec0..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/docs/Spell-book.md
+++ /dev/null
@@ -1,107 +0,0 @@
-You have now entered a hidden corner of the internet.
-
-A confusing yet intriguing realm of paradoxes and contradictions.
-
-A place where you will find out that what you thought you knew, you in fact didn't know, and what you didn't know was in front of you all along.
-
-![](https://i.pinimg.com/originals/6e/e2/7b/6ee27bad351d3aca470d80f1033ba9c6.jpg)
-
-*In other words, here I will document little-known facts about this web UI that I could not find another place for in the wiki.*
-
-#### You can train LoRAs in CPU mode
-
-Load the web UI with
-
-```
-python server.py --cpu
-```
-
-and start training the LoRA from the training tab as usual.
-
-#### 8-bit mode works with CPU offloading
-
-```
-python server.py --load-in-8bit --gpu-memory 4000MiB
-```
-
-#### `--pre_layer`, and not `--gpu-memory`, is the right way to do CPU offloading with 4-bit models
-
-```
-python server.py --wbits 4 --groupsize 128 --pre_layer 20
-```
-
-#### Models can be loaded in 32-bit, 16-bit, 8-bit, and 4-bit modes
-
-```
-python server.py --cpu
-python server.py
-python server.py --load-in-8bit
-python server.py --wbits 4
-```
-
-#### The web UI works with any version of GPTQ-for-LLaMa
-
-Including the up to date triton and cuda branches. But you have to delete the `repositories/GPTQ-for-LLaMa` folder and reinstall the new one every time:
-
-```
-cd text-generation-webui/repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall quant-cuda
-git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda # or any other repository and branch
-cd GPTQ-for-LLaMa
-python setup_cuda.py install
-```
-
-#### Instruction-following templates are represented as chat characters
-
-https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following
-
-#### The right way to run Alpaca, Open Assistant, Vicuna, etc is Instruct mode, not normal chat mode
-
-Otherwise the prompt will not be formatted correctly.
-
-1. Start the web UI with
-
-```
-python server.py --chat
-```
-
-2. Click on the "instruct" option under "Chat modes"
-
-3. Select the correct template in the hidden dropdown menu that will become visible. 
-
-#### Notebook mode is best mode
-
-Ascended individuals have realized that notebook mode is the superset of chat mode and can do chats with ultimate flexibility, including group chats, editing replies, starting a new bot reply in a given way, and impersonating.
-
-#### RWKV is a RNN 
-
-Most models are transformers, but not RWKV, which is a RNN. It's a great model.
-
-#### `--gpu-memory` is not a hard limit on the GPU memory
-
-It is simply a parameter that is passed to the `accelerate` library while loading the model. More memory will be allocated during generation. That's why this parameter has to be set to less than your total GPU memory.
-
-#### Contrastive search perhaps the best preset
-
-But it uses a ton of VRAM.
-
-#### You can check the sha256sum of downloaded models with the download script
-
-```
-python download-model.py facebook/galactica-125m --check
-```
-
-#### The download script continues interrupted downloads by default
-
-It doesn't start over.
-
-#### You can download models with multiple threads
-
-```
-python download-model.py facebook/galactica-125m --threads 8
-```
-
-#### LoRAs work in 4-bit mode
-
-You need to follow [these instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) and then start the web UI with the `--monkey-patch` flag.
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/losses/vqperceptual.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/losses/vqperceptual.py
deleted file mode 100644
index f69981769e4bd5462600458c4fcf26620f7e4306..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/losses/vqperceptual.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from einops import repeat
-
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
-
-
-def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
-    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
-    loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
-    loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
-    loss_real = (weights * loss_real).sum() / weights.sum()
-    loss_fake = (weights * loss_fake).sum() / weights.sum()
-    d_loss = 0.5 * (loss_real + loss_fake)
-    return d_loss
-
-def adopt_weight(weight, global_step, threshold=0, value=0.):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-def measure_perplexity(predicted_indices, n_embed):
-    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
-    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
-    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
-    avg_probs = encodings.mean(0)
-    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
-    cluster_use = torch.sum(avg_probs > 0)
-    return perplexity, cluster_use
-
-def l1(x, y):
-    return torch.abs(x-y)
-
-
-def l2(x, y):
-    return torch.pow((x-y), 2)
-
-
-class VQLPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
-                 pixel_loss="l1"):
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        assert perceptual_loss in ["lpips", "clips", "dists"]
-        assert pixel_loss in ["l1", "l2"]
-        self.codebook_weight = codebook_weight
-        self.pixel_weight = pixelloss_weight
-        if perceptual_loss == "lpips":
-            print(f"{self.__class__.__name__}: Running with LPIPS.")
-            self.perceptual_loss = LPIPS().eval()
-        else:
-            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
-        self.perceptual_weight = perceptual_weight
-
-        if pixel_loss == "l1":
-            self.pixel_loss = l1
-        else:
-            self.pixel_loss = l2
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ndf=disc_ndf
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-        self.n_classes = n_classes
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
-        if not exists(codebook_loss):
-            codebook_loss = torch.tensor([0.]).to(inputs.device)
-        #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss
-        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        nll_loss = torch.mean(nll_loss)
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/p_loss".format(split): p_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            if predicted_indices is not None:
-                assert self.n_classes is not None
-                with torch.no_grad():
-                    perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
-                log[f"{split}/perplexity"] = perplexity
-                log[f"{split}/cluster_usage"] = cluster_usage
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
diff --git a/spaces/awacke1/GradioTranslation/app.py b/spaces/awacke1/GradioTranslation/app.py
deleted file mode 100644
index 16225de6796a7e49497943e15a9970ee1abb00c5..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioTranslation/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import gradio as gr
-
-from transformers import pipeline
-import gradio as gr
-
-iface = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-es",
-  examples=[["Hello! My name is Omar"]]
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/awsaf49/gcvit-tf/README.md b/spaces/awsaf49/gcvit-tf/README.md
deleted file mode 100644
index 54343630b56db0372e82e1ecf5ac313a1c564d81..0000000000000000000000000000000000000000
--- a/spaces/awsaf49/gcvit-tf/README.md
+++ /dev/null
@@ -1,152 +0,0 @@
----
-title: 'GCViT: Global Context Vision Transformer'
-colorFrom: indigo
-sdk: gradio
-sdk_version: 3.0.15
-emoji: 🚀
-pinned: false
-license: apache-2.0
-app_file: app.py
----
-<h1 align="center">
-<p><a href='https://arxiv.org/pdf/2206.09959v1.pdf'>GCViT: Global Context Vision Transformer</a></p>
-</h1>
-<div align=center><img src="https://raw.githubusercontent.com/awsaf49/gcvit-tf/main/image/lvg_arch.PNG" width=800></div>
-<p align="center">
-<a href="https://github.com/awsaf49/gcvit-tf/blob/main/LICENSE.md">
-  <img src="https://img.shields.io/badge/License-MIT-yellow.svg">
-</a>
-<img alt="python" src="https://img.shields.io/badge/python-%3E%3D3.6-blue?logo=python">
-<img alt="tensorflow" src="https://img.shields.io/badge/tensorflow-%3E%3D2.4.1-orange?logo=tensorflow">
-<div align=center><p>
-<a target="_blank" href="https://huggingface.co/spaces/awsaf49/gcvit-tf"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a>
-<a href="https://colab.research.google.com/github/awsaf49/gcvit-tf/blob/main/notebooks/GCViT_Flower_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
-<a href="https://www.kaggle.com/awsaf49/flower-classification-gcvit-global-context-vit"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-</p></div>
-<h2 align="center">
-<p>Tensorflow 2.0 Implementation of GCViT</p>
-</h2>
-</p>
-<p align="center">
-This library implements <b>GCViT</b> using Tensorflow 2.0 specifically in <code>tf.keras.Model</code> manner to get PyTorch flavor.
-</p>
-
-## Update
-* **15 Jan 2023** : `GCViTLarge` model added with ckpt.
-* **3 Sept 2022** : Annotated [kaggle-notebook](https://www.kaggle.com/code/awsaf49/gcvit-global-context-vision-transformer) based on this project won [Kaggle ML Research Spotlight: August 2022](https://www.kaggle.com/discussions/general/349817).
-* **19 Aug 2022** : This project got acknowledged by [Official](https://github.com/NVlabs/GCVit) repo [here](https://github.com/NVlabs/GCVit#third-party-implementations-and-resources)
-
-## Model
-* Architecture:
-
-<img src="https://raw.githubusercontent.com/awsaf49/gcvit-tf/main/image/arch.PNG">
-
-* Local Vs Global Attention:
-
-<img src="https://raw.githubusercontent.com/awsaf49/gcvit-tf/main/image/lvg_msa.PNG">
-
-## Result
-<img src="https://raw.githubusercontent.com/awsaf49/gcvit-tf/main/image/result.PNG" width=900>
-
-Official codebase had some issue which has been fixed recently (12 August 2022). Here's the result of ported weights on **ImageNetV2-Test** data,
-
-| Model        | Acc@1 | Acc@5 | #Params |
-|--------------|-------|-------|---------|
-| GCViT-XXTiny | 0.663    | 0.873    | 12M     |
-| GCViT-XTiny  | 0.685    | 0.885    | 20M     |
-| GCViT-Tiny   | 0.708    | 0.899    | 28M     |
-| GCViT-Small  | 0.720    | 0.901    | 51M     |
-| GCViT-Base   | 0.731    | 0.907    | 90M     |
-| GCViT-Large  | 0.734    | 0.913    | 202M    |
-
-## Installation
-```bash
-pip install -U gcvit
-# or
-# pip install -U git+https://github.com/awsaf49/gcvit-tf
-```
-
-## Usage
-Load model using following codes,
-```py
-from gcvit import GCViTTiny
-model = GCViTTiny(pretrain=True)
-```
-Simple code to check model's prediction,
-```py
-from skimage.data import chelsea
-img = tf.keras.applications.imagenet_utils.preprocess_input(chelsea(), mode='torch') # Chelsea the cat
-img = tf.image.resize(img, (224, 224))[None,] # resize & create batch
-pred = model(img).numpy()
-print(tf.keras.applications.imagenet_utils.decode_predictions(pred)[0])
-```
-Prediction:
-```py
-[('n02124075', 'Egyptian_cat', 0.9194835),
-('n02123045', 'tabby', 0.009686623), 
-('n02123159', 'tiger_cat', 0.0061576385),
-('n02127052', 'lynx', 0.0011503297), 
-('n02883205', 'bow_tie', 0.00042479983)]
-```
-For feature extraction:
-```py
-model = GCViTTiny(pretrain=True)  # when pretrain=True, num_classes must be 1000
-model.reset_classifier(num_classes=0, head_act=None)
-feature = model(img)
-print(feature.shape)
-```
-Feature:
-```py
-(None, 512)
-```
-For feature map:
-```py
-model = GCViTTiny(pretrain=True)  # when pretrain=True, num_classes must be 1000
-feature = model.forward_features(img)
-print(feature.shape)
-```
-Feature map:
-```py
-(None, 7, 7, 512)
-```
-
-## Live-Demo
-* For live demo on Image Classification & Grad-CAM, with **ImageNet** weights, click <a target="_blank" href="https://huggingface.co/spaces/awsaf49/gcvit-tf"><img src="https://img.shields.io/badge/Try%20on-Gradio-orange"></a> powered by 🤗 Space and Gradio. here's an example,
-
-<a href="https://huggingface.co/spaces/awsaf49/gcvit-tf"><img src="image/gradio_demo.JPG" height=500></a>
-
-## Example
-For working training example checkout these notebooks on **Google Colab** <a href="https://colab.research.google.com/github/awsaf49/gcvit-tf/blob/main/notebooks/GCViT_Flower_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> & **Kaggle** <a href="https://www.kaggle.com/awsaf49/flower-classification-gcvit-global-context-vit"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>.
-
-Here is grad-cam result after training on Flower Classification Dataset,
-
-<img src="https://raw.githubusercontent.com/awsaf49/gcvit-tf/main/image/flower_gradcam.PNG" height=500>
-
-
-
-## To Do
-- [ ] Segmentation Pipeline
-- [x] New updated weights have been added.
-- [x] Working training example in Colab & Kaggle.
-- [x] GradCAM showcase.
-- [x] Gradio Demo.
-- [x] Build model with `tf.keras.Model`.
-- [x] Port weights from official repo.
-- [x] Support for `TPU`.
-
-## Acknowledgement
-* [GCVit](https://github.com/NVlabs/GCVit) (Official)
-* [Swin-Transformer-TF](https://github.com/rishigami/Swin-Transformer-TF)
-* [tfgcvit](https://github.com/shkarupa-alex/tfgcvit/tree/develop/tfgcvit)
-* [keras_cv_attention_models](https://github.com/leondgarse/keras_cv_attention_model)
-
-
-## Citation
-```bibtex
-@article{hatamizadeh2022global,
-  title={Global Context Vision Transformers},
-  author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and Molchanov, Pavlo},
-  journal={arXiv preprint arXiv:2206.09959},
-  year={2022}
-}
-```
\ No newline at end of file
diff --git a/spaces/awsaf49/gcvit-tf/gcvit/models/gcvit.py b/spaces/awsaf49/gcvit-tf/gcvit/models/gcvit.py
deleted file mode 100644
index 01ac5a0c9701128e9ee068d3a05c172f99376b00..0000000000000000000000000000000000000000
--- a/spaces/awsaf49/gcvit-tf/gcvit/models/gcvit.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import numpy as np
-import tensorflow as tf
-
-from ..layers import Stem, GCViTLevel, Identity
-
-                  
-                  
-BASE_URL = 'https://github.com/awsaf49/gcvit-tf/releases/download'
-TAG = 'v1.1.1'
-NAME2CONFIG = {
-    'gcvit_xxtiny': {'window_size': (7, 7, 14, 7),
-                    'dim': 64,
-                    'depths': (2, 2, 6, 2),
-                    'num_heads': (2, 4, 8, 16),
-                    'mlp_ratio': 3.,
-                    'path_drop': 0.2},
-    'gcvit_xtiny': {'window_size': (7, 7, 14, 7),
-                    'dim': 64,
-                    'depths': (3, 4, 6, 5),
-                    'num_heads': (2, 4, 8, 16),
-                    'mlp_ratio': 3.,
-                    'path_drop': 0.2},
-    'gcvit_tiny': {'window_size': (7, 7, 14, 7),
-                    'dim': 64,
-                    'depths': (3, 4, 19, 5),
-                    'num_heads': (2, 4, 8, 16),
-                    'mlp_ratio': 3.,
-                    'num_heads': (2, 4, 8, 16),
-                    'mlp_ratio': 3.,
-                    'path_drop': 0.2,},
-    'gcvit_small': {'window_size': (7, 7, 14, 7), 
-                     'dim': 96, 
-                     'depths': (3, 4, 19, 5),
-                     'num_heads': (3, 6, 12, 24),
-                     'mlp_ratio': 2.,
-                     'path_drop': 0.3,
-                     'layer_scale': 1e-5,},
-    'gcvit_base': {'window_size': (7, 7, 14, 7),
-                    'dim':128, 
-                    'depths': (3, 4, 19, 5),
-                    'num_heads': (4, 8, 16, 32),
-                    'mlp_ratio': 2.,
-                    'path_drop': 0.5,
-                    'layer_scale': 1e-5,},
-    'gcvit_large': {'window_size': (7, 7, 14, 7),
-                    'dim':192, 
-                    'depths': (3, 4, 19, 5),
-                    'num_heads': (6, 12, 24, 48),
-                    'mlp_ratio': 2.,
-                    'path_drop': 0.5,
-                    'layer_scale': 1e-5,},
-    }
-
-@tf.keras.utils.register_keras_serializable(package='gcvit')
-class GCViT(tf.keras.Model):
-    def __init__(self, window_size, dim, depths, num_heads,
-        drop_rate=0., mlp_ratio=3., qkv_bias=True, qk_scale=None, attn_drop=0., path_drop=0.1, layer_scale=None, resize_query=False,
-        global_pool='avg', num_classes=1000, head_act='softmax', **kwargs):
-        super().__init__(**kwargs)
-        self.window_size = window_size
-        self.dim = dim
-        self.depths = depths
-        self.num_heads = num_heads
-        self.drop_rate = drop_rate
-        self.mlp_ratio = mlp_ratio
-        self.qkv_bias = qkv_bias
-        self.qk_scale = qk_scale
-        self.attn_drop = attn_drop
-        self.path_drop = path_drop
-        self.layer_scale = layer_scale
-        self.resize_query = resize_query
-        self.global_pool = global_pool
-        self.num_classes = num_classes
-        self.head_act = head_act
-
-        self.patch_embed = Stem(dim=dim, name='patch_embed')
-        self.patch_embed = Stem(dim=dim, name='patch_embed')
-        self.pos_drop = tf.keras.layers.Dropout(drop_rate, name='pos_drop')
-        path_drops = np.linspace(0., path_drop, sum(depths))
-        keep_dims = [(False, False, False),(False, False),(True,),(True,),]
-        self.levels = []
-        for i in range(len(depths)):
-            path_drop = path_drops[sum(depths[:i]):sum(depths[:i + 1])].tolist()
-            level = GCViTLevel(depth=depths[i], num_heads=num_heads[i], window_size=window_size[i], keep_dims=keep_dims[i],
-                    downsample=(i < len(depths) - 1), mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, 
-                    drop=drop_rate, attn_drop=attn_drop, path_drop=path_drop, layer_scale=layer_scale, resize_query=resize_query,
-                    name=f'levels/{i}')
-            self.levels.append(level)
-        self.norm = tf.keras.layers.LayerNormalization(axis=-1, epsilon=1e-05, name='norm')
-        if global_pool == 'avg':
-            self.pool = tf.keras.layers.GlobalAveragePooling2D(name='pool')
-        elif global_pool == 'max':
-            self.pool = tf.keras.layers.GlobalMaxPooling2D(name='pool')
-        elif global_pool is None:
-            self.pool = Identity(name='pool')
-        else:
-            raise ValueError(f'Expecting pooling to be one of None/avg/max. Found: {global_pool}')
-        self.head = tf.keras.layers.Dense(num_classes, name='head', activation=head_act)
-
-
-    def reset_classifier(self, num_classes, head_act, global_pool=None, in_channels=3):
-        self.num_classes = num_classes
-        if global_pool is not None:
-            self.global_pool = global_pool
-        self.head = tf.keras.layers.Dense(num_classes, name='head', activation=head_act) if num_classes else Identity(name='head')
-        super().build((1, 224, 224, in_channels)) # for head we only need info from the input channel
-        
-    def forward_features(self, inputs):
-        x = self.patch_embed(inputs)
-        x = self.pos_drop(x)
-        x = tf.cast(x, dtype=tf.float32)
-        for level in self.levels:
-            x = level(x)
-        x = self.norm(x)
-        return x
-
-    def forward_head(self, inputs, pre_logits=False):
-        x = inputs
-        if self.global_pool in ['avg', 'max']:
-            x = self.pool(x)
-        if not pre_logits:
-            x = self.head(x)
-        return x 
-
-    def call(self, inputs, **kwargs):
-        x = self.forward_features(inputs)
-        x = self.forward_head(x)
-        return x
-
-    def build_graph(self, input_shape=(224, 224, 3)):
-        """https://www.kaggle.com/code/ipythonx/tf-hybrid-efficientnet-swin-transformer-gradcam"""
-        x = tf.keras.Input(shape=input_shape)
-        return tf.keras.Model(inputs=[x], outputs=self.call(x), name=self.name)
-
-    def summary(self, input_shape=(224, 224, 3)):
-        return self.build_graph(input_shape).summary()
-
-
-# load standard models
-def GCViTXXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_xxtiny'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config,  **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_xtiny'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config,  **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTXXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_xxtiny'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config,  **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTXTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_xtiny'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config,  **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTTiny(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_tiny'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config,  **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    model = GCViT(name=name, resize_query=resize_query, **config,  **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTSmall(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_small'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config, **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    model = GCViT(name=name, resize_query=resize_query, **config, **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTBase(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_base'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config, **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
-
-def GCViTLarge(input_shape=(224, 224, 3), pretrain=False, resize_query=False, **kwargs):
-    name = 'gcvit_large'
-    config = NAME2CONFIG[name]
-    ckpt_link = '{}/{}/{}_weights.h5'.format(BASE_URL, TAG, name)
-    model = GCViT(name=name, resize_query=resize_query, **config, **kwargs)
-    model(tf.random.uniform(shape=input_shape)[tf.newaxis,])
-    if pretrain:
-        ckpt_path = tf.keras.utils.get_file('{}_weights.h5'.format(name), ckpt_link)
-        model.load_weights(ckpt_path)
-    return model
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/GeometryUtils.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/GeometryUtils.js
deleted file mode 100644
index e130afc7c932062c436f10660eb8e4370746ba1a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/GeometryUtils.js
+++ /dev/null
@@ -1,307 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- * @author alteredq / http://alteredqualia.com/
- */
-
-import {
-	Mesh,
-	Vector3
-} from "../../../build/three.module.js";
-
-var GeometryUtils = {
-
-	// Merge two geometries or geometry and geometry from object (using object's transform)
-
-	merge: function ( geometry1, geometry2, materialIndexOffset ) {
-
-		console.warn( 'THREE.GeometryUtils: .merge() has been moved to Geometry. Use geometry.merge( geometry2, matrix, materialIndexOffset ) instead.' );
-
-		var matrix;
-
-		if ( geometry2 instanceof Mesh ) {
-
-			geometry2.matrixAutoUpdate && geometry2.updateMatrix();
-
-			matrix = geometry2.matrix;
-			geometry2 = geometry2.geometry;
-
-		}
-
-		geometry1.merge( geometry2, matrix, materialIndexOffset );
-
-	},
-
-	// Get random point in triangle (via barycentric coordinates)
-	// 	(uniform distribution)
-	// 	http://www.cgafaq.info/wiki/Random_Point_In_Triangle
-
-	randomPointInTriangle: function () {
-
-		var vector = new Vector3();
-
-		return function ( vectorA, vectorB, vectorC ) {
-
-			var point = new Vector3();
-
-			var a = Math.random();
-			var b = Math.random();
-
-			if ( ( a + b ) > 1 ) {
-
-				a = 1 - a;
-				b = 1 - b;
-
-			}
-
-			var c = 1 - a - b;
-
-			point.copy( vectorA );
-			point.multiplyScalar( a );
-
-			vector.copy( vectorB );
-			vector.multiplyScalar( b );
-
-			point.add( vector );
-
-			vector.copy( vectorC );
-			vector.multiplyScalar( c );
-
-			point.add( vector );
-
-			return point;
-
-		};
-
-	}(),
-
-	// Get random point in face (triangle)
-	// (uniform distribution)
-
-	randomPointInFace: function ( face, geometry ) {
-
-		var vA, vB, vC;
-
-		vA = geometry.vertices[ face.a ];
-		vB = geometry.vertices[ face.b ];
-		vC = geometry.vertices[ face.c ];
-
-		return GeometryUtils.randomPointInTriangle( vA, vB, vC );
-
-	},
-
-	// Get uniformly distributed random points in mesh
-	// 	- create array with cumulative sums of face areas
-	//  - pick random number from 0 to total area
-	//  - find corresponding place in area array by binary search
-	//	- get random point in face
-
-	randomPointsInGeometry: function ( geometry, n ) {
-
-		var face, i,
-			faces = geometry.faces,
-			vertices = geometry.vertices,
-			il = faces.length,
-			totalArea = 0,
-			cumulativeAreas = [],
-			vA, vB, vC;
-
-		// precompute face areas
-
-		for ( i = 0; i < il; i ++ ) {
-
-			face = faces[ i ];
-
-			vA = vertices[ face.a ];
-			vB = vertices[ face.b ];
-			vC = vertices[ face.c ];
-
-			face._area = GeometryUtils.triangleArea( vA, vB, vC );
-
-			totalArea += face._area;
-
-			cumulativeAreas[ i ] = totalArea;
-
-		}
-
-		// binary search cumulative areas array
-
-		function binarySearchIndices( value ) {
-
-			function binarySearch( start, end ) {
-
-				// return closest larger index
-				// if exact number is not found
-
-				if ( end < start )
-					return start;
-
-				var mid = start + Math.floor( ( end - start ) / 2 );
-
-				if ( cumulativeAreas[ mid ] > value ) {
-
-					return binarySearch( start, mid - 1 );
-
-				} else if ( cumulativeAreas[ mid ] < value ) {
-
-					return binarySearch( mid + 1, end );
-
-				} else {
-
-					return mid;
-
-				}
-
-			}
-
-			var result = binarySearch( 0, cumulativeAreas.length - 1 );
-			return result;
-
-		}
-
-		// pick random face weighted by face area
-
-		var r, index,
-			result = [];
-
-		var stats = {};
-
-		for ( i = 0; i < n; i ++ ) {
-
-			r = Math.random() * totalArea;
-
-			index = binarySearchIndices( r );
-
-			result[ i ] = GeometryUtils.randomPointInFace( faces[ index ], geometry );
-
-			if ( ! stats[ index ] ) {
-
-				stats[ index ] = 1;
-
-			} else {
-
-				stats[ index ] += 1;
-
-			}
-
-		}
-
-		return result;
-
-	},
-
-	randomPointsInBufferGeometry: function ( geometry, n ) {
-
-		var i,
-			vertices = geometry.attributes.position.array,
-			totalArea = 0,
-			cumulativeAreas = [],
-			vA, vB, vC;
-
-		// precompute face areas
-		vA = new Vector3();
-		vB = new Vector3();
-		vC = new Vector3();
-
-		// geometry._areas = [];
-		var il = vertices.length / 9;
-
-		for ( i = 0; i < il; i ++ ) {
-
-			vA.set( vertices[ i * 9 + 0 ], vertices[ i * 9 + 1 ], vertices[ i * 9 + 2 ] );
-			vB.set( vertices[ i * 9 + 3 ], vertices[ i * 9 + 4 ], vertices[ i * 9 + 5 ] );
-			vC.set( vertices[ i * 9 + 6 ], vertices[ i * 9 + 7 ], vertices[ i * 9 + 8 ] );
-
-			totalArea += GeometryUtils.triangleArea( vA, vB, vC );
-
-			cumulativeAreas.push( totalArea );
-
-		}
-
-		// binary search cumulative areas array
-
-		function binarySearchIndices( value ) {
-
-			function binarySearch( start, end ) {
-
-				// return closest larger index
-				// if exact number is not found
-
-				if ( end < start )
-					return start;
-
-				var mid = start + Math.floor( ( end - start ) / 2 );
-
-				if ( cumulativeAreas[ mid ] > value ) {
-
-					return binarySearch( start, mid - 1 );
-
-				} else if ( cumulativeAreas[ mid ] < value ) {
-
-					return binarySearch( mid + 1, end );
-
-				} else {
-
-					return mid;
-
-				}
-
-			}
-
-			var result = binarySearch( 0, cumulativeAreas.length - 1 );
-			return result;
-
-		}
-
-		// pick random face weighted by face area
-
-		var r, index,
-			result = [];
-
-		for ( i = 0; i < n; i ++ ) {
-
-			r = Math.random() * totalArea;
-
-			index = binarySearchIndices( r );
-
-			// result[ i ] = GeometryUtils.randomPointInFace( faces[ index ], geometry, true );
-			vA.set( vertices[ index * 9 + 0 ], vertices[ index * 9 + 1 ], vertices[ index * 9 + 2 ] );
-			vB.set( vertices[ index * 9 + 3 ], vertices[ index * 9 + 4 ], vertices[ index * 9 + 5 ] );
-			vC.set( vertices[ index * 9 + 6 ], vertices[ index * 9 + 7 ], vertices[ index * 9 + 8 ] );
-			result[ i ] = GeometryUtils.randomPointInTriangle( vA, vB, vC );
-
-		}
-
-		return result;
-
-	},
-
-	// Get triangle area (half of parallelogram)
-	// http://mathworld.wolfram.com/TriangleArea.html
-
-	triangleArea: function () {
-
-		var vector1 = new Vector3();
-		var vector2 = new Vector3();
-
-		return function ( vectorA, vectorB, vectorC ) {
-
-			vector1.subVectors( vectorB, vectorA );
-			vector2.subVectors( vectorC, vectorA );
-			vector1.cross( vector2 );
-
-			return 0.5 * vector1.length();
-
-		};
-
-	}(),
-
-	center: function ( geometry ) {
-
-		console.warn( 'THREE.GeometryUtils: .center() has been moved to Geometry. Use geometry.center() instead.' );
-		return geometry.center();
-
-	}
-
-};
-
-export { GeometryUtils };
diff --git a/spaces/bankholdup/stylegan_petbreeder/e4e/utils/common.py b/spaces/bankholdup/stylegan_petbreeder/e4e/utils/common.py
deleted file mode 100644
index b19e18ddcb78b06678fa18e4a76da44fc511b789..0000000000000000000000000000000000000000
--- a/spaces/bankholdup/stylegan_petbreeder/e4e/utils/common.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from PIL import Image
-import matplotlib.pyplot as plt
-
-
-# Log images
-def log_input_image(x, opts):
-	return tensor2im(x)
-
-
-def tensor2im(var):
-	# var shape: (3, H, W)
-	var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()
-	var = ((var + 1) / 2)
-	var[var < 0] = 0
-	var[var > 1] = 1
-	var = var * 255
-	return Image.fromarray(var.astype('uint8'))
-
-
-def vis_faces(log_hooks):
-	display_count = len(log_hooks)
-	fig = plt.figure(figsize=(8, 4 * display_count))
-	gs = fig.add_gridspec(display_count, 3)
-	for i in range(display_count):
-		hooks_dict = log_hooks[i]
-		fig.add_subplot(gs[i, 0])
-		if 'diff_input' in hooks_dict:
-			vis_faces_with_id(hooks_dict, fig, gs, i)
-		else:
-			vis_faces_no_id(hooks_dict, fig, gs, i)
-	plt.tight_layout()
-	return fig
-
-
-def vis_faces_with_id(hooks_dict, fig, gs, i):
-	plt.imshow(hooks_dict['input_face'])
-	plt.title('Input\nOut Sim={:.2f}'.format(float(hooks_dict['diff_input'])))
-	fig.add_subplot(gs[i, 1])
-	plt.imshow(hooks_dict['target_face'])
-	plt.title('Target\nIn={:.2f}, Out={:.2f}'.format(float(hooks_dict['diff_views']),
-	                                                 float(hooks_dict['diff_target'])))
-	fig.add_subplot(gs[i, 2])
-	plt.imshow(hooks_dict['output_face'])
-	plt.title('Output\n Target Sim={:.2f}'.format(float(hooks_dict['diff_target'])))
-
-
-def vis_faces_no_id(hooks_dict, fig, gs, i):
-	plt.imshow(hooks_dict['input_face'], cmap="gray")
-	plt.title('Input')
-	fig.add_subplot(gs[i, 1])
-	plt.imshow(hooks_dict['target_face'])
-	plt.title('Target')
-	fig.add_subplot(gs[i, 2])
-	plt.imshow(hooks_dict['output_face'])
-	plt.title('Output')
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/archs/spynet_arch.py b/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/archs/spynet_arch.py
deleted file mode 100644
index 4c7af133daef0496b79a57517e1942d06f2d0061..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/archs/spynet_arch.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import math
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .arch_util import flow_warp
-
-
-class BasicModule(nn.Module):
-    """Basic Module for SpyNet.
-    """
-
-    def __init__(self):
-        super(BasicModule, self).__init__()
-
-        self.basic_module = nn.Sequential(
-            nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
-            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
-            nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
-            nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
-            nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3))
-
-    def forward(self, tensor_input):
-        return self.basic_module(tensor_input)
-
-
-@ARCH_REGISTRY.register()
-class SpyNet(nn.Module):
-    """SpyNet architecture.
-
-    Args:
-        load_path (str): path for pretrained SpyNet. Default: None.
-    """
-
-    def __init__(self, load_path=None):
-        super(SpyNet, self).__init__()
-        self.basic_module = nn.ModuleList([BasicModule() for _ in range(6)])
-        if load_path:
-            self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params'])
-
-        self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
-        self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
-
-    def preprocess(self, tensor_input):
-        tensor_output = (tensor_input - self.mean) / self.std
-        return tensor_output
-
-    def process(self, ref, supp):
-        flow = []
-
-        ref = [self.preprocess(ref)]
-        supp = [self.preprocess(supp)]
-
-        for level in range(5):
-            ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False))
-            supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False))
-
-        flow = ref[0].new_zeros(
-            [ref[0].size(0), 2,
-             int(math.floor(ref[0].size(2) / 2.0)),
-             int(math.floor(ref[0].size(3) / 2.0))])
-
-        for level in range(len(ref)):
-            upsampled_flow = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
-
-            if upsampled_flow.size(2) != ref[level].size(2):
-                upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 0, 0, 1], mode='replicate')
-            if upsampled_flow.size(3) != ref[level].size(3):
-                upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 1, 0, 0], mode='replicate')
-
-            flow = self.basic_module[level](torch.cat([
-                ref[level],
-                flow_warp(
-                    supp[level], upsampled_flow.permute(0, 2, 3, 1), interp_mode='bilinear', padding_mode='border'),
-                upsampled_flow
-            ], 1)) + upsampled_flow
-
-        return flow
-
-    def forward(self, ref, supp):
-        assert ref.size() == supp.size()
-
-        h, w = ref.size(2), ref.size(3)
-        w_floor = math.floor(math.ceil(w / 32.0) * 32.0)
-        h_floor = math.floor(math.ceil(h / 32.0) * 32.0)
-
-        ref = F.interpolate(input=ref, size=(h_floor, w_floor), mode='bilinear', align_corners=False)
-        supp = F.interpolate(input=supp, size=(h_floor, w_floor), mode='bilinear', align_corners=False)
-
-        flow = F.interpolate(input=self.process(ref, supp), size=(h, w), mode='bilinear', align_corners=False)
-
-        flow[:, 0, :, :] *= float(w) / float(w_floor)
-        flow[:, 1, :, :] *= float(h) / float(h_floor)
-
-        return flow
diff --git a/spaces/bioriAsaeru/text-to-voice/Bios Map For Xbox 360 Emulator 3.2.4 Utorrent.md b/spaces/bioriAsaeru/text-to-voice/Bios Map For Xbox 360 Emulator 3.2.4 Utorrent.md
deleted file mode 100644
index 867476c57c1dc7bda7dda000a67bc57a1e1459fd..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Bios Map For Xbox 360 Emulator 3.2.4 Utorrent.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>bios map for xbox 360 emulator 3.2.4 utorrent</h2><br /><p><b><b>Download Zip</b> &#9658; <a href="https://urloso.com/2uyQg2">https://urloso.com/2uyQg2</a></b></p><br /><br />
-<br />
-Now we can say that our new version of Xbox emulator 3.2.4 is supporting ... rewrite our emulation software so it does not have to hack the bios but integrate it ... These can be downloaded via torrent and many other sources. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/Panoptic-DeepLab/train_net.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/Panoptic-DeepLab/train_net.py
deleted file mode 100644
index 780764f22fe8f4d52f218748dc64cf6c609e87b9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/Panoptic-DeepLab/train_net.py
+++ /dev/null
@@ -1,171 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-Panoptic-DeepLab Training Script.
-This script is a simplified version of the training script in detectron2/tools.
-"""
-
-import os
-import torch
-
-import detectron2.data.transforms as T
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import MetadataCatalog, build_detection_train_loader
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
-from detectron2.evaluation import (
-    CityscapesInstanceEvaluator,
-    CityscapesSemSegEvaluator,
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-)
-from detectron2.projects.deeplab import build_lr_scheduler
-from detectron2.projects.panoptic_deeplab import (
-    PanopticDeeplabDatasetMapper,
-    add_panoptic_deeplab_config,
-)
-from detectron2.solver import get_default_optimizer_params
-from detectron2.solver.build import maybe_add_gradient_clipping
-
-
-def build_sem_seg_train_aug(cfg):
-    augs = [
-        T.ResizeShortestEdge(
-            cfg.INPUT.MIN_SIZE_TRAIN, cfg.INPUT.MAX_SIZE_TRAIN, cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
-        )
-    ]
-    if cfg.INPUT.CROP.ENABLED:
-        augs.append(T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
-    augs.append(T.RandomFlip())
-    return augs
-
-
-class Trainer(DefaultTrainer):
-    """
-    We use the "DefaultTrainer" which contains a number pre-defined logic for
-    standard training workflow. They may not work for you, especially if you
-    are working on a new research project. In that case you can use the cleaner
-    "SimpleTrainer", or write your own training loop.
-    """
-
-    @classmethod
-    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
-        """
-        Create evaluator(s) for a given dataset.
-        This uses the special metadata "evaluator_type" associated with each builtin dataset.
-        For your own dataset, you can simply create an evaluator manually in your
-        script and do not have to worry about the hacky if-else logic here.
-        """
-        if cfg.MODEL.PANOPTIC_DEEPLAB.BENCHMARK_NETWORK_SPEED:
-            return None
-        if output_folder is None:
-            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-        evaluator_list = []
-        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-        if evaluator_type in ["cityscapes_panoptic_seg", "coco_panoptic_seg"]:
-            evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-        if evaluator_type == "cityscapes_panoptic_seg":
-            evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
-            evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
-        if evaluator_type == "coco_panoptic_seg":
-            # `thing_classes` in COCO panoptic metadata includes both thing and
-            # stuff classes for visualization. COCOEvaluator requires metadata
-            # which only contains thing classes, thus we map the name of
-            # panoptic datasets to their corresponding instance datasets.
-            dataset_name_mapper = {
-                "coco_2017_val_panoptic": "coco_2017_val",
-                "coco_2017_val_100_panoptic": "coco_2017_val_100",
-            }
-            evaluator_list.append(
-                COCOEvaluator(dataset_name_mapper[dataset_name], output_dir=output_folder)
-            )
-        if len(evaluator_list) == 0:
-            raise NotImplementedError(
-                "no Evaluator for the dataset {} with the type {}".format(
-                    dataset_name, evaluator_type
-                )
-            )
-        elif len(evaluator_list) == 1:
-            return evaluator_list[0]
-        return DatasetEvaluators(evaluator_list)
-
-    @classmethod
-    def build_train_loader(cls, cfg):
-        mapper = PanopticDeeplabDatasetMapper(cfg, augmentations=build_sem_seg_train_aug(cfg))
-        return build_detection_train_loader(cfg, mapper=mapper)
-
-    @classmethod
-    def build_lr_scheduler(cls, cfg, optimizer):
-        """
-        It now calls :func:`detectron2.solver.build_lr_scheduler`.
-        Overwrite it if you'd like a different scheduler.
-        """
-        return build_lr_scheduler(cfg, optimizer)
-
-    @classmethod
-    def build_optimizer(cls, cfg, model):
-        """
-        Build an optimizer from config.
-        """
-        params = get_default_optimizer_params(
-            model,
-            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
-            weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
-        )
-
-        optimizer_type = cfg.SOLVER.OPTIMIZER
-        if optimizer_type == "SGD":
-            return maybe_add_gradient_clipping(cfg, torch.optim.SGD)(
-                params,
-                cfg.SOLVER.BASE_LR,
-                momentum=cfg.SOLVER.MOMENTUM,
-                nesterov=cfg.SOLVER.NESTEROV,
-            )
-        elif optimizer_type == "ADAM":
-            return maybe_add_gradient_clipping(cfg, torch.optim.Adam)(params, cfg.SOLVER.BASE_LR)
-        else:
-            raise NotImplementedError(f"no optimizer type {optimizer_type}")
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    add_panoptic_deeplab_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-
-    if args.eval_only:
-        model = Trainer.build_model(cfg)
-        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-            cfg.MODEL.WEIGHTS, resume=args.resume
-        )
-        res = Trainer.test(cfg, model)
-        return res
-
-    trainer = Trainer(cfg)
-    trainer.resume_or_load(resume=args.resume)
-    return trainer.train()
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )
diff --git a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/latent_diffusion/openaimodel.py b/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/latent_diffusion/openaimodel.py
deleted file mode 100644
index 831d7aafb36bba16888e4389153979a6c13639f5..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/audioldm-text-to-audio-generation/audioldm/latent_diffusion/openaimodel.py
+++ /dev/null
@@ -1,1069 +0,0 @@
-from abc import abstractmethod
-import math
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from audioldm.latent_diffusion.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from audioldm.latent_diffusion.attention import SpatialTransformer
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
-        )
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1).contiguous()  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(
-                dims, self.channels, self.out_channels, 3, padding=padding
-            )
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class TransposedUpsample(nn.Module):
-    "Learned 2x upsampling without padding"
-
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(
-            self.channels, self.out_channels, kernel_size=ks, stride=2
-        )
-
-    def forward(self, x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims,
-                self.channels,
-                self.out_channels,
-                3,
-                stride=stride,
-                padding=padding,
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(
-            self._forward, (x,), self.parameters(), True
-        )  # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        # return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1).contiguous()
-        qkv = self.qkv(self.norm(x)).contiguous()
-        h = self.attention(qkv).contiguous()
-        h = self.proj_out(h).contiguous()
-        return (x + h).reshape(b, c, *spatial).contiguous()
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial**2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = (
-            qkv.reshape(bs * self.n_heads, ch * 3, length).contiguous().split(ch, dim=1)
-        )
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length).contiguous()
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum(
-            "bts,bcs->bct",
-            weight,
-            v.reshape(bs * self.n_heads, ch, length).contiguous(),
-        )
-        return a.reshape(bs, -1, length).contiguous()
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        extra_film_condition_dim=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        extra_film_use_concat=False,  # If true, concatenate extrafilm condition with time embedding, else addition
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,  # custom transformer support
-        transformer_depth=1,  # custom transformer support
-        context_dim=None,  # custom transformer support
-        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-    ):
-        super().__init__()
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert (
-                num_head_channels != -1
-            ), "Either num_heads or num_head_channels has to be set"
-
-        if num_head_channels == -1:
-            assert (
-                num_heads != -1
-            ), "Either num_heads or num_head_channels has to be set"
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.extra_film_condition_dim = extra_film_condition_dim
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-        self.extra_film_use_concat = extra_film_use_concat
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        assert not (
-            self.num_classes is not None and self.extra_film_condition_dim is not None
-        ), "As for the condition of theh UNet model, you can only set using class label or an extra embedding vector (such as from CLAP). You cannot set both num_classes and extra_film_condition_dim."
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.use_extra_film_by_concat = (
-            self.extra_film_condition_dim is not None and self.extra_film_use_concat
-        )
-        self.use_extra_film_by_addition = (
-            self.extra_film_condition_dim is not None and not self.extra_film_use_concat
-        )
-
-        if self.extra_film_condition_dim is not None:
-            self.film_emb = nn.Linear(self.extra_film_condition_dim, time_embed_dim)
-            # print("+ Use extra condition on UNet channel using Film. Extra condition dimension is %s. " % self.extra_film_condition_dim)
-            # if(self.use_extra_film_by_concat):
-            #     print("\t By concatenation with time embedding")
-            # elif(self.use_extra_film_by_concat):
-            #     print("\t By addition with time embedding")
-
-        if use_spatial_transformer and (
-            self.use_extra_film_by_concat or self.use_extra_film_by_addition
-        ):
-            # print("+ Spatial transformer will only be used as self-attention. Because you have choose to use film as your global condition.")
-            spatial_transformer_no_context = True
-        else:
-            spatial_transformer_no_context = False
-
-        if use_spatial_transformer and not spatial_transformer_no_context:
-            assert (
-                context_dim is not None
-            ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..."
-
-        if context_dim is not None and not spatial_transformer_no_context:
-            assert (
-                use_spatial_transformer
-            ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..."
-            from omegaconf.listconfig import ListConfig
-
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim
-                        if (not self.use_extra_film_by_concat)
-                        else time_embed_dim * 2,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        dim_head = (
-                            ch // num_heads
-                            if use_spatial_transformer
-                            else num_head_channels
-                        )
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                        if not use_spatial_transformer
-                        else SpatialTransformer(
-                            ch,
-                            num_heads,
-                            dim_head,
-                            depth=transformer_depth,
-                            context_dim=context_dim,
-                            no_context=spatial_transformer_no_context,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim
-                            if (not self.use_extra_film_by_concat)
-                            else time_embed_dim * 2,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            # num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim
-                if (not self.use_extra_film_by_concat)
-                else time_embed_dim * 2,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            )
-            if not use_spatial_transformer
-            else SpatialTransformer(
-                ch,
-                num_heads,
-                dim_head,
-                depth=transformer_depth,
-                context_dim=context_dim,
-                no_context=spatial_transformer_no_context,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim
-                if (not self.use_extra_film_by_concat)
-                else time_embed_dim * 2,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim
-                        if (not self.use_extra_film_by_concat)
-                        else time_embed_dim * 2,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        # num_heads = 1
-                        dim_head = (
-                            ch // num_heads
-                            if use_spatial_transformer
-                            else num_head_channels
-                        )
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                        if not use_spatial_transformer
-                        else SpatialTransformer(
-                            ch,
-                            num_heads,
-                            dim_head,
-                            depth=transformer_depth,
-                            context_dim=context_dim,
-                            no_context=spatial_transformer_no_context,
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim
-                            if (not self.use_extra_film_by_concat)
-                            else time_embed_dim * 2,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-                normalization(ch),
-                conv_nd(dims, model_channels, n_embed, 1),
-                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-            )
-
-        self.shape_reported = False
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional. an [N, extra_film_condition_dim] Tensor if film-embed conditional
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        if not self.shape_reported:
-            # print("The shape of UNet input is", x.size())
-            self.shape_reported = True
-
-        assert (y is not None) == (
-            self.num_classes is not None or self.extra_film_condition_dim is not None
-        ), "must specify y if and only if the model is class-conditional or film embedding conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        if self.use_extra_film_by_addition:
-            emb = emb + self.film_emb(y)
-        elif self.use_extra_film_by_concat:
-            emb = th.cat([emb, self.film_emb(y)], dim=-1)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, context)
-            hs.append(h)
-        h = self.middle_block(h, emb, context)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-        *args,
-        **kwargs,
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
diff --git a/spaces/captain-awesome/docuverse/README.md b/spaces/captain-awesome/docuverse/README.md
deleted file mode 100644
index 70bec1e2c954a98438a11ee0c1fc29584a97550f..0000000000000000000000000000000000000000
--- a/spaces/captain-awesome/docuverse/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Docuverse
-emoji: 🚀
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/captchaboy/FAST-ABINet-OCR/dataset.py b/spaces/captchaboy/FAST-ABINet-OCR/dataset.py
deleted file mode 100644
index e424cb2134ba0d992515b2446302e1a758a3db66..0000000000000000000000000000000000000000
--- a/spaces/captchaboy/FAST-ABINet-OCR/dataset.py
+++ /dev/null
@@ -1,278 +0,0 @@
-import logging
-import re
-
-import cv2
-import lmdb
-import six
-from fastai.vision import *
-from torchvision import transforms
-
-from transforms import CVColorJitter, CVDeterioration, CVGeometry
-from utils import CharsetMapper, onehot
-
-
-class ImageDataset(Dataset):
-    "`ImageDataset` read data from LMDB database."
-
-    def __init__(self,
-                 path:PathOrStr,
-                 is_training:bool=True,
-                 img_h:int=32,
-                 img_w:int=100,
-                 max_length:int=25,
-                 check_length:bool=True,
-                 case_sensitive:bool=False,
-                 charset_path:str='data/charset_36.txt',
-                 convert_mode:str='RGB',
-                 data_aug:bool=True,
-                 deteriorate_ratio:float=0.,
-                 multiscales:bool=True,
-                 one_hot_y:bool=True,
-                 return_idx:bool=False,
-                 return_raw:bool=False,
-                 **kwargs):
-        self.path, self.name = Path(path), Path(path).name
-        assert self.path.is_dir() and self.path.exists(), f"{path} is not a valid directory."
-        self.convert_mode, self.check_length = convert_mode, check_length
-        self.img_h, self.img_w = img_h, img_w
-        self.max_length, self.one_hot_y = max_length, one_hot_y
-        self.return_idx, self.return_raw = return_idx, return_raw
-        self.case_sensitive, self.is_training = case_sensitive, is_training
-        self.data_aug, self.multiscales = data_aug, multiscales
-        self.charset = CharsetMapper(charset_path, max_length=max_length+1)
-        self.c = self.charset.num_classes
-
-        self.env = lmdb.open(str(path), readonly=True, lock=False, readahead=False, meminit=False)
-        assert self.env, f'Cannot open LMDB dataset from {path}.'
-        with self.env.begin(write=False) as txn:
-            self.length = int(txn.get('num-samples'.encode()))
-
-        if self.is_training and self.data_aug:
-            self.augment_tfs = transforms.Compose([
-                CVGeometry(degrees=45, translate=(0.0, 0.0), scale=(0.5, 2.), shear=(45, 15), distortion=0.5, p=0.5),
-                CVDeterioration(var=20, degrees=6, factor=4, p=0.25),
-                CVColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=0.25)
-            ])
-        self.totensor = transforms.ToTensor()
-    
-    def __len__(self): return self.length
-
-    def _next_image(self, index):
-        next_index = random.randint(0, len(self) - 1)
-        return self.get(next_index)
-
-    def _check_image(self, x, pixels=6):
-        if x.size[0] <= pixels or x.size[1] <= pixels: return False
-        else: return True
-
-    def resize_multiscales(self, img, borderType=cv2.BORDER_CONSTANT): 
-        def _resize_ratio(img, ratio, fix_h=True):
-            if ratio * self.img_w < self.img_h:
-                if fix_h: trg_h = self.img_h
-                else: trg_h = int(ratio * self.img_w)
-                trg_w = self.img_w
-            else: trg_h, trg_w = self.img_h, int(self.img_h / ratio)
-            img = cv2.resize(img, (trg_w, trg_h))
-            pad_h, pad_w = (self.img_h - trg_h) / 2, (self.img_w - trg_w) / 2
-            top, bottom = math.ceil(pad_h), math.floor(pad_h)
-            left, right = math.ceil(pad_w), math.floor(pad_w)
-            img = cv2.copyMakeBorder(img, top, bottom, left, right, borderType)
-            return img
-        
-        if self.is_training: 
-            if random.random() < 0.5:
-                base, maxh, maxw = self.img_h, self.img_h, self.img_w
-                h, w = random.randint(base, maxh), random.randint(base, maxw)
-                return _resize_ratio(img, h/w)
-            else: return _resize_ratio(img, img.shape[0] / img.shape[1])  # keep aspect ratio
-        else:  return _resize_ratio(img, img.shape[0] / img.shape[1])  # keep aspect ratio
-
-    def resize(self, img):
-        if self.multiscales: return self.resize_multiscales(img, cv2.BORDER_REPLICATE)
-        else: return cv2.resize(img, (self.img_w, self.img_h))
-         
-    def get(self, idx):
-        with self.env.begin(write=False) as txn:
-            image_key, label_key = f'image-{idx+1:09d}', f'label-{idx+1:09d}'
-            try:
-                label = str(txn.get(label_key.encode()), 'utf-8')  # label
-                label = re.sub('[^0-9a-zA-Z]+', '', label)
-                if self.check_length and self.max_length > 0:
-                    if len(label) > self.max_length or len(label) <= 0:
-                        #logging.info(f'Long or short text image is found: {self.name}, {idx}, {label}, {len(label)}')
-                        return self._next_image(idx)
-                label = label[:self.max_length]
-
-                imgbuf = txn.get(image_key.encode())  # image
-                buf = six.BytesIO()
-                buf.write(imgbuf)
-                buf.seek(0)
-                with warnings.catch_warnings():
-                    warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
-                    image = PIL.Image.open(buf).convert(self.convert_mode)
-                if self.is_training and not self._check_image(image):
-                    #logging.info(f'Invalid image is found: {self.name}, {idx}, {label}, {len(label)}')
-                    return self._next_image(idx)
-            except:
-                import traceback
-                traceback.print_exc()
-                logging.info(f'Corrupted image is found: {self.name}, {idx}, {label}, {len(label)}')
-                return self._next_image(idx)
-            return image, label, idx
-
-    def _process_training(self, image):
-        if self.data_aug: image = self.augment_tfs(image)
-        image = self.resize(np.array(image))
-        return image
-
-    def _process_test(self, image):
-        return self.resize(np.array(image)) # TODO:move is_training to here
-
-    def __getitem__(self, idx):
-        image, text, idx_new = self.get(idx)
-        if not self.is_training: assert idx == idx_new, f'idx {idx} != idx_new {idx_new} during testing.'
-
-        if self.is_training: image = self._process_training(image)
-        else: image = self._process_test(image)
-        if self.return_raw: return image, text
-        image = self.totensor(image)
-
-        length = tensor(len(text) + 1).to(dtype=torch.long)  # one for end token
-        label = self.charset.get_labels(text, case_sensitive=self.case_sensitive)
-        label = tensor(label).to(dtype=torch.long)
-        if self.one_hot_y: label = onehot(label, self.charset.num_classes)
-
-        if self.return_idx: y = [label, length, idx_new]
-        else: y = [label, length]
-        return image, y
-
-
-class TextDataset(Dataset):
-    def __init__(self,
-                 path:PathOrStr, 
-                 delimiter:str='\t',
-                 max_length:int=25, 
-                 charset_path:str='data/charset_36.txt', 
-                 case_sensitive=False, 
-                 one_hot_x=True,
-                 one_hot_y=True,
-                 is_training=True,
-                 smooth_label=False,
-                 smooth_factor=0.2,
-                 use_sm=False,
-                 **kwargs):
-        self.path = Path(path)
-        self.case_sensitive, self.use_sm = case_sensitive, use_sm
-        self.smooth_factor, self.smooth_label = smooth_factor, smooth_label
-        self.charset = CharsetMapper(charset_path, max_length=max_length+1)
-        self.one_hot_x, self.one_hot_y, self.is_training = one_hot_x, one_hot_y, is_training
-        if self.is_training and self.use_sm: self.sm = SpellingMutation(charset=self.charset)
-
-        dtype = {'inp': str, 'gt': str}
-        self.df = pd.read_csv(self.path, dtype=dtype, delimiter=delimiter, na_filter=False)
-        self.inp_col, self.gt_col = 0, 1
-
-    def __len__(self): return len(self.df)
-
-    def __getitem__(self, idx):
-        text_x = self.df.iloc[idx, self.inp_col]
-        text_x = re.sub('[^0-9a-zA-Z]+', '', text_x)
-        if not self.case_sensitive: text_x = text_x.lower()
-        if self.is_training and self.use_sm: text_x = self.sm(text_x)
-
-        length_x = tensor(len(text_x) + 1).to(dtype=torch.long)  # one for end token
-        label_x = self.charset.get_labels(text_x, case_sensitive=self.case_sensitive)
-        label_x = tensor(label_x)
-        if self.one_hot_x:
-            label_x = onehot(label_x, self.charset.num_classes)
-            if self.is_training and self.smooth_label: 
-                label_x = torch.stack([self.prob_smooth_label(l) for l in label_x])
-        x =  [label_x, length_x]
-    
-        text_y = self.df.iloc[idx, self.gt_col]
-        text_y = re.sub('[^0-9a-zA-Z]+', '', text_y)
-        if not self.case_sensitive: text_y = text_y.lower()
-        length_y = tensor(len(text_y) + 1).to(dtype=torch.long)  # one for end token
-        label_y = self.charset.get_labels(text_y, case_sensitive=self.case_sensitive)
-        label_y = tensor(label_y)
-        if self.one_hot_y: label_y = onehot(label_y, self.charset.num_classes)
-        y = [label_y, length_y]
-
-        return x, y
-
-    def prob_smooth_label(self, one_hot):
-        one_hot = one_hot.float()
-        delta = torch.rand([]) * self.smooth_factor
-        num_classes = len(one_hot)
-        noise = torch.rand(num_classes)
-        noise = noise / noise.sum() * delta
-        one_hot = one_hot * (1 - delta) + noise
-        return one_hot
-
-
-class SpellingMutation(object):
-    def __init__(self, pn0=0.7, pn1=0.85, pn2=0.95, pt0=0.7, pt1=0.85, charset=None):
-        """ 
-        Args:
-            pn0: the prob of not modifying characters is (pn0)
-            pn1: the prob of modifying one characters is (pn1 - pn0)
-            pn2: the prob of modifying two characters is (pn2 - pn1), 
-                 and three (1 - pn2)
-            pt0: the prob of replacing operation is pt0.
-            pt1: the prob of inserting operation is (pt1 - pt0),
-                 and deleting operation is (1 - pt1)
-        """
-        super().__init__()
-        self.pn0, self.pn1, self.pn2 = pn0, pn1, pn2
-        self.pt0, self.pt1 = pt0, pt1
-        self.charset = charset
-        logging.info(f'the probs: pn0={self.pn0}, pn1={self.pn1} ' + 
-                     f'pn2={self.pn2}, pt0={self.pt0}, pt1={self.pt1}')
-
-    def is_digit(self, text, ratio=0.5):
-        length = max(len(text), 1)
-        digit_num = sum([t in self.charset.digits for t in text])
-        if digit_num / length < ratio: return False
-        return True
-
-    def is_unk_char(self, char):
-        # return char == self.charset.unk_char
-        return (char not in self.charset.digits) and (char not in self.charset.alphabets)
-
-    def get_num_to_modify(self, length):
-        prob = random.random()
-        if prob < self.pn0: num_to_modify = 0
-        elif prob < self.pn1: num_to_modify = 1
-        elif prob < self.pn2: num_to_modify = 2
-        else: num_to_modify = 3
-        
-        if length <= 1: num_to_modify = 0
-        elif length >= 2 and length <= 4: num_to_modify = min(num_to_modify, 1)
-        else: num_to_modify = min(num_to_modify, length // 2)  # smaller than length // 2
-        return num_to_modify
-
-    def __call__(self, text, debug=False):
-        if self.is_digit(text): return text
-        length = len(text)
-        num_to_modify = self.get_num_to_modify(length)
-        if num_to_modify <= 0: return text
-
-        chars = []
-        index = np.arange(0, length)
-        random.shuffle(index)
-        index = index[: num_to_modify]
-        if debug: self.index = index
-        for i, t in enumerate(text):
-            if i not in index: chars.append(t)
-            elif self.is_unk_char(t): chars.append(t)
-            else:
-                prob = random.random()
-                if prob < self.pt0: # replace
-                    chars.append(random.choice(self.charset.alphabets))
-                elif prob < self.pt1: # insert
-                    chars.append(random.choice(self.charset.alphabets))
-                    chars.append(t)
-                else: # delete
-                    continue
-        new_text = ''.join(chars[: self.charset.max_length-1])
-        return new_text if len(new_text) >= 1 else text
\ No newline at end of file
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/samplers/mask_from_densepose.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/samplers/mask_from_densepose.py
deleted file mode 100644
index 0e6e812ba5af4675a81aec3ef8fd9b96d53325cc..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/samplers/mask_from_densepose.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from detectron2.structures import BitMasks, Instances
-
-from densepose.converters import ToMaskConverter
-
-
-class MaskFromDensePoseSampler:
-    """
-    Produce mask GT from DensePose predictions
-    This sampler simply converts DensePose predictions to BitMasks
-    that a contain a bool tensor of the size of the input image
-    """
-
-    def __call__(self, instances: Instances) -> BitMasks:
-        """
-        Converts predicted data from `instances` into the GT mask data
-
-        Args:
-            instances (Instances): predicted results, expected to have `pred_densepose` field
-
-        Returns:
-            Boolean Tensor of the size of the input image that has non-zero
-            values at pixels that are estimated to belong to the detected object
-        """
-        return ToMaskConverter.convert(
-            instances.pred_densepose, instances.pred_boxes, instances.image_size
-        )
diff --git a/spaces/cccc-c/bingo/src/components/chat-scroll-anchor.tsx b/spaces/cccc-c/bingo/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/cccc-c/bingo/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}
diff --git a/spaces/ceckenrode/Biomed-NER-AI-NLP-CT-Demo1/app.py b/spaces/ceckenrode/Biomed-NER-AI-NLP-CT-Demo1/app.py
deleted file mode 100644
index 418d26fd42c4a6dbc3a230e0bb3ee4d9acf0553c..0000000000000000000000000000000000000000
--- a/spaces/ceckenrode/Biomed-NER-AI-NLP-CT-Demo1/app.py
+++ /dev/null
@@ -1,331 +0,0 @@
-import gradio as gr
-import pandas as pd
-import json
-from collections import defaultdict
-
-# Create tokenizer for biomed model
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")    # https://huggingface.co/d4data/biomedical-ner-all?text=asthma
-model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
-pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Matplotlib for entity graph
-import matplotlib.pyplot as plt
-plt.switch_backend("Agg")
-
-# Load examples from JSON
-import os 
-
-# Load terminology datasets:
-basedir = os.path.dirname(__file__)
-#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
-#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv') 
-
-dataLOINC = pd.read_csv(f'LoincTableCore.csv')
-dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')     
-dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-dataOMS = pd.read_csv(f'SnomedOMS.csv')   
-dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')   
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-EXAMPLES = {}
-#with open(dir_path + "\\" + "examples.json", "r") as f:
-with open("examples.json", "r") as f:
-    example_json = json.load(f)
-    EXAMPLES = {x["text"]: x["label"] for x in example_json}
-
-def MatchLOINC(name):
-    #basedir = os.path.dirname(__file__)
-    pd.set_option("display.max_rows", None)
-    #data = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')    
-    data = dataLOINC
-    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchLOINCPanelsandForms(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-    data = dataPanels
-    # Assessment Name:
-    #swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
-    # Assessment Question:
-    swith=data.loc[data['LoincName'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchSNOMED(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-    data = dataSNOMED
-    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchOMS(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-    data = dataOMS
-    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchICD10(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')   
-    data = dataICD10
-    swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
-    return swith
-
-def SaveResult(text, outputfileName):
-    #try:
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    #except ValueError as err:
-    #    raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return
-
-def loadFile(filename):
-    try:
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-
-        print("Loading: " + loadPath)
-
-        from os.path import exists
-        file_exists = exists(loadPath)
-
-        if file_exists:
-            with open(loadPath, "r") as f: #read
-                contents = f.read()
-                print(contents)
-                return contents
-
-    except ValueError as err:
-        raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return ""
-
-def get_today_filename():
-    from datetime import datetime
-    date = datetime.now().strftime("%Y_%m_%d-%I.%M.%S.%p")
-    #print(f"filename_{date}")  'filename_2023_01_12-03-29-22_AM'
-    return f"MedNER_{date}.csv"
-
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-        #print("Loading: " + loadPath)
-        return loadPath
-
-def group_by_entity(raw):
-    outputFile = get_base(get_today_filename())
-    out = defaultdict(int)
-
-    for ent in raw:
-        out[ent["entity_group"]] += 1
-        myEntityGroup = ent["entity_group"]
-        print("Found entity group type: " + myEntityGroup)
-
-        if (myEntityGroup in ['Sign_symptom', 'Detailed_description', 'History', 'Activity', 'Medication' ]):
-            eterm = ent["word"].replace('#','')
-            minlength = 3
-            if len(eterm) > minlength:
-                print("Found eterm: " + eterm)
-                eterm.replace("#","")
-                g1=MatchLOINC(eterm)
-                g2=MatchLOINCPanelsandForms(eterm)
-                g3=MatchSNOMED(eterm)
-                g4=MatchOMS(eterm)
-                g5=MatchICD10(eterm)
-                sAll = ""
-
-                print("Saving to output file " + outputFile)
-                # Create harmonisation output format of input to output code, name, Text
-
-                try: # 18 fields, output to labeled CSV dataset for results teaching on scored regret changes to action plan with data inputs
-                    col = "          1                            2            3         4            5                     6                    7                       8                   9              10                   11                         12       13               14                      15                  16                            17                    18                       19"
-                    
-                    #LOINC
-                    g11 = g1['LOINC_NUM'].to_string().replace(","," ").replace("\n"," ")
-                    g12 = g1['COMPONENT'].to_string().replace(","," ").replace("\n"," ")
-                    s1 = ("LOINC Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", LOINC codes of ," + g11 + ", and LOINC questions of ," + g12 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g11 != 'Series([]  )': SaveResult(s1, outputFile)
-
-                    #LOINC Panels
-                    g21 = g2['Loinc'].to_string().replace(","," ").replace("\n"," ")
-                    g22 = g2['LoincName'].to_string().replace(","," ").replace("\n"," ")
-                    g23 = g2['ParentLoinc'].to_string().replace(","," ").replace("\n"," ")
-                    g24 = g2['ParentName'].to_string().replace(","," ").replace("\n"," ")
-                    s2 = ("LOINC Panels of entity ," + myEntityGroup + ", with term ," + eterm + ", LOINC codes of ," + g21 + ", and LOINC name of ," + g22 + ", and Parent codes of ," + g23 + ", with Parent names of ," + g24 + ", Label,Value  ")
-                    if g21 != 'Series([]  )': SaveResult(s2, outputFile)
-
-                    #SNOMED
-                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    s3 = ("SNOMED Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", SNOMED concepts of ," + g31 + ", and SNOMED terms of ," + g32 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g31 != 'Series([]  )': SaveResult(s3, outputFile)
-
-                    #OMS
-                    g41 = g4['Omaha Code'].to_string().replace(","," ").replace("\n"," ")
-                    g42 = g4['SNOMED CT concept ID'].to_string().replace(","," ").replace("\n"," ")
-                    g43 = g4['SNOMED CT'].to_string().replace(","," ").replace("\n"," ")
-                    g44 = g4['PR'].to_string().replace(","," ").replace("\n"," ")
-                    g45 = g4['S&S'].to_string().replace(","," ").replace("\n"," ")
-                    s4 = ("OMS Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", Omaha codes of ," + g41 + ", and SNOMED concepts of ," + g42 + ", and SNOMED codes of ," + g43 + ", and OMS problem of ," + g44 + ", and OMS Sign Symptom of ," + g45)
-                    if g41 != 'Series([]  )': SaveResult(s4, outputFile)
-
-                    #ICD10
-                    g51 = g5['Code'].to_string().replace(","," ").replace("\n"," ")
-                    g52 = g5['Description'].to_string().replace(","," ").replace("\n"," ")
-                    s5 = ("ICD10 matches of entity ," + myEntityGroup + ", with term ," + eterm + ", ICD10 codes of ," + g51 + ", and descriptions of ," + g52 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g51 != 'Series([]  )': SaveResult(s5, outputFile)
-
-                except ValueError as err:
-                    raise ValueError("Error in group by entity \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-                #print(sAll)
-
-            #return out;
-            #break;
-    # out["total"] = sum(out.values())
-    # return out
-    return outputFile
-
-
-def plot_to_figure(grouped):
-    fig = plt.figure()
-    plt.bar(x=list(grouped.keys()), height=list(grouped.values()))
-    plt.margins(0.2)
-    plt.subplots_adjust(bottom=0.4)
-    plt.xticks(rotation=90)
-    return fig
-
-
-def ner(text):
-    raw = pipe(text)
-    ner_content = {
-        "text": text,
-        "entities": [
-            {
-                "entity": x["entity_group"],
-                "word": x["word"],
-                "score": x["score"],
-                "start": x["start"],
-                "end": x["end"],
-            }
-            for x in raw
-        ],
-    }
-    
-    #grouped = group_by_entity(raw)
-    outputFile = group_by_entity(raw)
-
-    #figure = plot_to_figure(grouped)
-
-    label = EXAMPLES.get(text, "Unknown")
-
-    #meta = {
-#        "entity_counts": grouped,
-#        "entities": len(set(grouped.keys())),
-#        "counts": sum(grouped.values()),
-#    }
-
-    #return (ner_content, meta, label, figure)
-    outputDataframe = pd.read_csv(outputFile)
-    #outputFile = outputFile.replace(os.path.dirname(__file__) + "\\","")  # Just filename for File download UI output element 
-
-    #return (ner_content, meta, label, figure, outputDataframe, outputFile)
-    return (ner_content, outputDataframe, outputFile)
-
-# New way = Gradio Blocks:
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-    """
-    # 🩺⚕️NLP Clinical Ontology Biomedical NER 
-    """
-    )
-    input = gr.Textbox(label="Note text", value="")
-    #output=[
-    #    gr.HighlightedText(label="NER", combine_adjacent=True)
-    #]
-    with gr.Tab("Biomedical Entity Recognition"):
-        output=[
-            gr.HighlightedText(label="NER", combine_adjacent=True),
-            #gr.JSON(label="Entity Counts"),
-            #gr.Label(label="Rating"),
-            #gr.Plot(label="Bar"),
-            gr.Dataframe(label="Dataframe"),
-            gr.File(label="File"),
-        ]
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-    with gr.Tab("Clinical Terminology Resolution"):
-        #output=[
-        #    gr.Textbox(placeholder="CT Match Results", lines=10)
-        #]
-        with gr.Row(variant="compact"):
-            btnLOINC = gr.Button("LOINC")
-            btnPanels = gr.Button("Panels")
-            btnSNOMED = gr.Button("SNOMED")
-            btnOMS = gr.Button("OMS")
-            btnICD10 = gr.Button("ICD10")
-
-        #output=[
-        #    gr.HighlightedText(label="NER", combine_adjacent=True),
-        #    gr.File(label="File"),  # add download link here
-        #    gr.Dataframe(label="Dataframe", headers=["LOINC", "Panels", "SNOMED", "OMS", "ICD10"]),   # add harmonised output for input corpus here as a dataframe to UI
-        #    gr.Textbox(placeholder="CT Match Results", lines=10)    # add matched text scratchpad here
-        #]
-        
-
-        #textCT = gr.Textbox(placeholder="CT Match Results", lines=10)
-
-        #btnLOINC.click(loadFile, inputs=["LOINCTerms.txt"], outputs=output)
-        #btnPanels.click(loadFile, "LOINCPanelsandForms.txt", output)
-        #btnSNOMED.click(loadFile, "SNOMEDTerms.txt", output)
-        #btnOMS.click(loadFile, "OMSTerms.txt", output)
-        #btnICD10.click(loadFile, "ICD10Terms.txt", output)
-
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-    #with gr.Tab("Examples Page 1"):
-    #    gr.Examples(["a", "b", "c"], inputs=input)
-    #with gr.Tab("Examples Page 2"):
-    #    gr.Examples(["d", "e", "f"], inputs=input)
-    #with gr.Tab("Examples Page 2"):
-    #    gr.Examples(["g", "h", "i"], inputs=input)
-
-demo.launch(debug=True)
-
-# Old Way - Interface Load
-#interface = gr.Interface(
-#    ner,
-#    inputs=gr.Textbox(label="Note text", value=""),
-#    outputs=[
-#        gr.HighlightedText(label="NER", combine_adjacent=True),
-#        gr.JSON(label="Entity Counts"),
-#        gr.Label(label="Rating"),
-#        gr.Plot(label="Bar"),
-#    ],
-#    examples=list(EXAMPLES.keys()),
-#    allow_flagging="never",
-#)
-
-#interface.launch()
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/old_test_fsmt_bleu_score.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/old_test_fsmt_bleu_score.py
deleted file mode 100644
index 4aefeb388be6314df1835f1f15738e7029f47996..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/old_test_fsmt_bleu_score.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Huggingface
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import io
-import json
-import unittest
-
-from parameterized import parameterized
-
-from transformers import FSMTForConditionalGeneration, FSMTTokenizer
-from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
-from utils import calculate_bleu
-
-
-filename = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
-with io.open(filename, "r", encoding="utf-8") as f:
-    bleu_data = json.load(f)
-
-
-@require_torch
-class ModelEvalTester(unittest.TestCase):
-    def get_tokenizer(self, mname):
-        return FSMTTokenizer.from_pretrained(mname)
-
-    def get_model(self, mname):
-        model = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device)
-        if torch_device == "cuda":
-            model.half()
-        return model
-
-    @parameterized.expand(
-        [
-            ["en-ru", 26.0],
-            ["ru-en", 22.0],
-            ["en-de", 22.0],
-            ["de-en", 29.0],
-        ]
-    )
-    @slow
-    def test_bleu_scores(self, pair, min_bleu_score):
-        # note: this test is not testing the best performance since it only evals a small batch
-        # but it should be enough to detect a regression in the output quality
-        mname = f"facebook/wmt19-{pair}"
-        tokenizer = self.get_tokenizer(mname)
-        model = self.get_model(mname)
-
-        src_sentences = bleu_data[pair]["src"]
-        tgt_sentences = bleu_data[pair]["tgt"]
-
-        batch = tokenizer(src_sentences, return_tensors="pt", truncation=True, padding="longest").to(torch_device)
-        outputs = model.generate(
-            input_ids=batch.input_ids,
-            num_beams=8,
-        )
-        decoded_sentences = tokenizer.batch_decode(
-            outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
-        )
-        scores = calculate_bleu(decoded_sentences, tgt_sentences)
-        print(scores)
-        self.assertGreaterEqual(scores["bleu"], min_bleu_score)
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/commands/pt_to_tf.py b/spaces/chendl/compositional_test/transformers/src/transformers/commands/pt_to_tf.py
deleted file mode 100644
index 0185679d48dddc668c6e5166ce535c7aa3e0da4e..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/commands/pt_to_tf.py
+++ /dev/null
@@ -1,425 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import os
-from argparse import ArgumentParser, Namespace
-from importlib import import_module
-
-import huggingface_hub
-import numpy as np
-from packaging import version
-
-from .. import (
-    FEATURE_EXTRACTOR_MAPPING,
-    IMAGE_PROCESSOR_MAPPING,
-    PROCESSOR_MAPPING,
-    TOKENIZER_MAPPING,
-    AutoConfig,
-    AutoFeatureExtractor,
-    AutoImageProcessor,
-    AutoProcessor,
-    AutoTokenizer,
-    is_datasets_available,
-    is_tf_available,
-    is_torch_available,
-)
-from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
-from . import BaseTransformersCLICommand
-
-
-if is_tf_available():
-    import tensorflow as tf
-
-    tf.config.experimental.enable_tensor_float_32_execution(False)
-
-if is_torch_available():
-    import torch
-
-if is_datasets_available():
-    from datasets import load_dataset
-
-
-MAX_ERROR = 5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
-
-
-def convert_command_factory(args: Namespace):
-    """
-    Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.
-
-    Returns: ServeCommand
-    """
-    return PTtoTFCommand(
-        args.model_name,
-        args.local_dir,
-        args.max_error,
-        args.new_weights,
-        args.no_pr,
-        args.push,
-        args.extra_commit_description,
-        args.override_model_class,
-    )
-
-
-class PTtoTFCommand(BaseTransformersCLICommand):
-    @staticmethod
-    def register_subcommand(parser: ArgumentParser):
-        """
-        Register this command to argparse so it's available for the transformer-cli
-
-        Args:
-            parser: Root parser to register command-specific arguments
-        """
-        train_parser = parser.add_parser(
-            "pt-to-tf",
-            help=(
-                "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
-                " Can also be used to validate existing weights without opening PRs, with --no-pr."
-            ),
-        )
-        train_parser.add_argument(
-            "--model-name",
-            type=str,
-            required=True,
-            help="The model name, including owner/organization, as seen on the hub.",
-        )
-        train_parser.add_argument(
-            "--local-dir",
-            type=str,
-            default="",
-            help="Optional local directory of the model repository. Defaults to /tmp/{model_name}",
-        )
-        train_parser.add_argument(
-            "--max-error",
-            type=float,
-            default=MAX_ERROR,
-            help=(
-                f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
-            ),
-        )
-        train_parser.add_argument(
-            "--new-weights",
-            action="store_true",
-            help="Optional flag to create new TensorFlow weights, even if they already exist.",
-        )
-        train_parser.add_argument(
-            "--no-pr", action="store_true", help="Optional flag to NOT open a PR with converted weights."
-        )
-        train_parser.add_argument(
-            "--push",
-            action="store_true",
-            help="Optional flag to push the weights directly to `main` (requires permissions)",
-        )
-        train_parser.add_argument(
-            "--extra-commit-description",
-            type=str,
-            default="",
-            help="Optional additional commit description to use when opening a PR (e.g. to tag the owner).",
-        )
-        train_parser.add_argument(
-            "--override-model-class",
-            type=str,
-            default=None,
-            help="If you think you know better than the auto-detector, you can specify the model class here. "
-            "Can be either an AutoModel class or a specific model class like BertForSequenceClassification.",
-        )
-        train_parser.set_defaults(func=convert_command_factory)
-
-    @staticmethod
-    def find_pt_tf_differences(pt_outputs, tf_outputs):
-        """
-        Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences.
-        """
-        # 1. All output attributes must be the same
-        pt_out_attrs = set(pt_outputs.keys())
-        tf_out_attrs = set(tf_outputs.keys())
-        if pt_out_attrs != tf_out_attrs:
-            raise ValueError(
-                f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
-                f" {tf_out_attrs})"
-            )
-
-        # 2. For each output attribute, computes the difference
-        def _find_pt_tf_differences(pt_out, tf_out, differences, attr_name=""):
-            # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
-            # recursivelly, keeping the name of the attribute.
-            if isinstance(pt_out, torch.Tensor):
-                tensor_difference = np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
-                differences[attr_name] = tensor_difference
-            else:
-                root_name = attr_name
-                for i, pt_item in enumerate(pt_out):
-                    # If it is a named attribute, we keep the name. Otherwise, just its index.
-                    if isinstance(pt_item, str):
-                        branch_name = root_name + pt_item
-                        tf_item = tf_out[pt_item]
-                        pt_item = pt_out[pt_item]
-                    else:
-                        branch_name = root_name + f"[{i}]"
-                        tf_item = tf_out[i]
-                    differences = _find_pt_tf_differences(pt_item, tf_item, differences, branch_name)
-
-            return differences
-
-        return _find_pt_tf_differences(pt_outputs, tf_outputs, {})
-
-    def __init__(
-        self,
-        model_name: str,
-        local_dir: str,
-        max_error: float,
-        new_weights: bool,
-        no_pr: bool,
-        push: bool,
-        extra_commit_description: str,
-        override_model_class: str,
-        *args,
-    ):
-        self._logger = logging.get_logger("transformers-cli/pt_to_tf")
-        self._model_name = model_name
-        self._local_dir = local_dir if local_dir else os.path.join("/tmp", model_name)
-        self._max_error = max_error
-        self._new_weights = new_weights
-        self._no_pr = no_pr
-        self._push = push
-        self._extra_commit_description = extra_commit_description
-        self._override_model_class = override_model_class
-
-    def get_inputs(self, pt_model, tf_dummy_inputs, config):
-        """
-        Returns the right inputs for the model, based on its signature.
-        """
-
-        def _get_audio_input():
-            ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-            speech_samples = ds.sort("id").select(range(2))[:2]["audio"]
-            raw_samples = [x["array"] for x in speech_samples]
-            return raw_samples
-
-        model_config_class = type(pt_model.config)
-        if model_config_class in PROCESSOR_MAPPING:
-            processor = AutoProcessor.from_pretrained(self._local_dir)
-            if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
-                processor.tokenizer.pad_token = processor.tokenizer.eos_token
-        elif model_config_class in IMAGE_PROCESSOR_MAPPING:
-            processor = AutoImageProcessor.from_pretrained(self._local_dir)
-        elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
-            processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
-        elif model_config_class in TOKENIZER_MAPPING:
-            processor = AutoTokenizer.from_pretrained(self._local_dir)
-            if processor.pad_token is None:
-                processor.pad_token = processor.eos_token
-        else:
-            raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")
-
-        model_forward_signature = set(inspect.signature(pt_model.forward).parameters.keys())
-        processor_inputs = {}
-        if "input_ids" in model_forward_signature:
-            processor_inputs.update(
-                {
-                    "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
-                    "padding": True,
-                    "truncation": True,
-                }
-            )
-        if "pixel_values" in model_forward_signature:
-            sample_images = load_dataset("cifar10", "plain_text", split="test")[:2]["img"]
-            processor_inputs.update({"images": sample_images})
-        if "input_features" in model_forward_signature:
-            feature_extractor_signature = inspect.signature(processor.feature_extractor).parameters
-            # Pad to the largest input length by default but take feature extractor default
-            # padding value if it exists e.g. "max_length" and is not False or None
-            if "padding" in feature_extractor_signature:
-                default_strategy = feature_extractor_signature["padding"].default
-                if default_strategy is not False and default_strategy is not None:
-                    padding_strategy = default_strategy
-                else:
-                    padding_strategy = True
-            else:
-                padding_strategy = True
-            processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy})
-        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
-            processor_inputs.update({"audio": _get_audio_input(), "padding": True})
-        pt_input = processor(**processor_inputs, return_tensors="pt")
-        tf_input = processor(**processor_inputs, return_tensors="tf")
-
-        # Extra input requirements, in addition to the input modality
-        if (
-            config.is_encoder_decoder
-            or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder"))
-            or "decoder_input_ids" in tf_dummy_inputs
-        ):
-            decoder_input_ids = np.asarray([[1], [1]], dtype=int) * (pt_model.config.decoder_start_token_id or 0)
-            pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)})
-            tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)})
-
-        return pt_input, tf_input
-
-    def run(self):
-        # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
-        if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"):
-            raise ImportError(
-                "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
-                " installation."
-            )
-        else:
-            from huggingface_hub import Repository, create_commit
-            from huggingface_hub._commit_api import CommitOperationAdd
-
-        # Fetch remote data
-        repo = Repository(local_dir=self._local_dir, clone_from=self._model_name)
-
-        # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
-        config = AutoConfig.from_pretrained(self._local_dir)
-        architectures = config.architectures
-        if self._override_model_class is not None:
-            if self._override_model_class.startswith("TF"):
-                architectures = [self._override_model_class[2:]]
-            else:
-                architectures = [self._override_model_class]
-            try:
-                pt_class = getattr(import_module("transformers"), architectures[0])
-            except AttributeError:
-                raise ValueError(f"Model class {self._override_model_class} not found in transformers.")
-            try:
-                tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
-            except AttributeError:
-                raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers.")
-        elif architectures is None:  # No architecture defined -- use auto classes
-            pt_class = getattr(import_module("transformers"), "AutoModel")
-            tf_class = getattr(import_module("transformers"), "TFAutoModel")
-            self._logger.warning("No detected architecture, using AutoModel/TFAutoModel")
-        else:  # Architecture defined -- use it
-            if len(architectures) > 1:
-                raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})")
-            self._logger.warning(f"Detected architecture: {architectures[0]}")
-            pt_class = getattr(import_module("transformers"), architectures[0])
-            try:
-                tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
-            except AttributeError:
-                raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.")
-
-        # Check the TF dummy inputs to see what keys we need in the forward pass
-        tf_from_pt_model = tf_class.from_config(config)
-        tf_dummy_inputs = tf_from_pt_model.dummy_inputs
-
-        del tf_from_pt_model  # Try to keep only one model in memory at a time
-
-        # Load the model and get some basic inputs
-        pt_model = pt_class.from_pretrained(self._local_dir)
-        pt_model.eval()
-
-        pt_input, tf_input = self.get_inputs(pt_model, tf_dummy_inputs, config)
-
-        with torch.no_grad():
-            pt_outputs = pt_model(**pt_input, output_hidden_states=True)
-        del pt_model  # will no longer be used, and may have a large memory footprint
-
-        tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True)
-        tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True, training=False)
-
-        # Confirms that cross loading PT weights into TF worked.
-        crossload_differences = self.find_pt_tf_differences(pt_outputs, tf_from_pt_outputs)
-        output_differences = {k: v for k, v in crossload_differences.items() if "hidden" not in k}
-        hidden_differences = {k: v for k, v in crossload_differences.items() if "hidden" in k}
-        if len(output_differences) == 0 and architectures is not None:
-            raise ValueError(
-                f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
-                " output was found. All outputs start with 'hidden'"
-            )
-        max_crossload_output_diff = max(output_differences.values()) if output_differences else 0.0
-        max_crossload_hidden_diff = max(hidden_differences.values())
-        if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
-            raise ValueError(
-                "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
-                + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
-                + "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
-                + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
-                + "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error])
-            )
-
-        # Save the weights in a TF format (if needed) and confirms that the results are still good
-        tf_weights_path = os.path.join(self._local_dir, TF2_WEIGHTS_NAME)
-        tf_weights_index_path = os.path.join(self._local_dir, TF2_WEIGHTS_INDEX_NAME)
-        if (not os.path.exists(tf_weights_path) and not os.path.exists(tf_weights_index_path)) or self._new_weights:
-            tf_from_pt_model.save_pretrained(self._local_dir)
-        del tf_from_pt_model  # will no longer be used, and may have a large memory footprint
-
-        tf_model = tf_class.from_pretrained(self._local_dir)
-        tf_outputs = tf_model(**tf_input, output_hidden_states=True)
-
-        conversion_differences = self.find_pt_tf_differences(pt_outputs, tf_outputs)
-        output_differences = {k: v for k, v in conversion_differences.items() if "hidden" not in k}
-        hidden_differences = {k: v for k, v in conversion_differences.items() if "hidden" in k}
-        if len(output_differences) == 0 and architectures is not None:
-            raise ValueError(
-                f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
-                " output was found. All outputs start with 'hidden'"
-            )
-        max_conversion_output_diff = max(output_differences.values()) if output_differences else 0.0
-        max_conversion_hidden_diff = max(hidden_differences.values())
-        if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
-            raise ValueError(
-                "The converted TensorFlow model has different outputs, something went wrong!\n"
-                + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
-                + "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
-                + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
-                + "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error])
-            )
-
-        commit_message = "Update TF weights" if self._new_weights else "Add TF weights"
-        if self._push:
-            repo.git_add(auto_lfs_track=True)
-            repo.git_commit(commit_message)
-            repo.git_push(blocking=True)  # this prints a progress bar with the upload
-            self._logger.warning(f"TF weights pushed into {self._model_name}")
-        elif not self._no_pr:
-            self._logger.warning("Uploading the weights into a new PR...")
-            commit_descrition = (
-                "Model converted by the [`transformers`' `pt_to_tf`"
-                " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
-                "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
-                f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
-                f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
-                f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
-                f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
-            )
-            if self._max_error > MAX_ERROR:
-                commit_descrition += (
-                    f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
-                )
-            if self._extra_commit_description:
-                commit_descrition += "\n\n" + self._extra_commit_description
-
-            # sharded model -> adds all related files (index and .h5 shards)
-            if os.path.exists(tf_weights_index_path):
-                operations = [
-                    CommitOperationAdd(path_in_repo=TF2_WEIGHTS_INDEX_NAME, path_or_fileobj=tf_weights_index_path)
-                ]
-                for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"):
-                    operations += [
-                        CommitOperationAdd(path_in_repo=os.path.basename(shard_path), path_or_fileobj=shard_path)
-                    ]
-            else:
-                operations = [CommitOperationAdd(path_in_repo=TF2_WEIGHTS_NAME, path_or_fileobj=tf_weights_path)]
-
-            hub_pr_url = create_commit(
-                repo_id=self._model_name,
-                operations=operations,
-                commit_message=commit_message,
-                commit_description=commit_descrition,
-                repo_type="model",
-                create_pr=True,
-            ).pr_url
-            self._logger.warning(f"PR open in {hub_pr_url}")
diff --git a/spaces/chenmgtea/cn_tts/README.md b/spaces/chenmgtea/cn_tts/README.md
deleted file mode 100644
index bc23bb0eb7e57b757ff53c940d2df7ebe363eeac..0000000000000000000000000000000000000000
--- a/spaces/chenmgtea/cn_tts/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Vits Chinese
-emoji: 🌍
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-app_port: 7860
-pinned: false
-license: apache-2.0
-duplicated_from: nisluQnaga/vits_chinese
----
-
-Check out the configuration reference  at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/impl/sqlite.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/impl/sqlite.py
deleted file mode 100644
index 5358001b474174ebfc4a070e6947c9cf576d7213..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/impl/sqlite.py
+++ /dev/null
@@ -1,207 +0,0 @@
-from chromadb.db.migrations import MigratableDB, Migration
-from chromadb.config import System, Settings
-import chromadb.db.base as base
-from chromadb.db.mixins.embeddings_queue import SqlEmbeddingsQueue
-from chromadb.db.mixins.sysdb import SqlSysDB
-import sqlite3
-from overrides import override
-import pypika
-from typing import Sequence, cast, Optional, Type, Any
-from typing_extensions import Literal
-from types import TracebackType
-import os
-from uuid import UUID
-from threading import local
-
-
-class TxWrapper(base.TxWrapper):
-    def __init__(self, conn: sqlite3.Connection, stack: local) -> None:
-        self._tx_stack = stack
-        self._conn = conn
-
-    @override
-    def __enter__(self) -> base.Cursor:
-        if len(self._tx_stack.stack) == 0:
-            self._conn.execute("BEGIN;")
-        self._tx_stack.stack.append(self)
-        return self._conn.cursor()  # type: ignore
-
-    @override
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_value: Optional[BaseException],
-        traceback: Optional[TracebackType],
-    ) -> Literal[False]:
-        self._tx_stack.stack.pop()
-        if len(self._tx_stack.stack) == 0:
-            if exc_type is None:
-                self._conn.commit()
-            else:
-                self._conn.rollback()
-        return False
-
-
-class SqliteDB(MigratableDB, SqlEmbeddingsQueue, SqlSysDB):
-    _conn: sqlite3.Connection
-    _settings: Settings
-    _migration_dirs: Sequence[str]
-    _db_file: str
-    _tx_stack: local
-
-    def __init__(self, system: System):
-        self._settings = system.settings
-        self._migration_dirs = [
-            "migrations/embeddings_queue",
-            "migrations/sysdb",
-            "migrations/metadb",
-        ]
-        self._db_file = self._settings.require("sqlite_database")
-        self._tx_stack = local()
-        super().__init__(system)
-
-    @override
-    def start(self) -> None:
-        super().start()
-        self._conn = sqlite3.connect(self._db_file)
-        self._conn.isolation_level = None  # Handle commits explicitly
-        with self.tx() as cur:
-            cur.execute("PRAGMA foreign_keys = ON")
-            cur.execute("PRAGMA case_sensitive_like = ON")
-        self.initialize_migrations()
-
-    @override
-    def stop(self) -> None:
-        super().stop()
-        self._conn.close()
-
-    @staticmethod
-    @override
-    def querybuilder() -> Type[pypika.Query]:
-        return pypika.Query  # type: ignore
-
-    @staticmethod
-    @override
-    def parameter_format() -> str:
-        return "?"
-
-    @staticmethod
-    @override
-    def migration_scope() -> str:
-        return "sqlite"
-
-    @override
-    def migration_dirs(self) -> Sequence[str]:
-        return self._migration_dirs
-
-    @override
-    def tx(self) -> TxWrapper:
-        if not hasattr(self._tx_stack, "stack"):
-            self._tx_stack.stack = []
-        return TxWrapper(self._conn, stack=self._tx_stack)
-
-    @override
-    def reset_state(self) -> None:
-        if not self._settings.require("allow_reset"):
-            raise ValueError(
-                "Resetting the database is not allowed. Set `allow_reset` to true in the config in tests or other non-production environments where reset should be permitted."
-            )
-        self._conn.close()
-        db_file = self._settings.require("sqlite_database")
-        if db_file != ":memory:":
-            os.remove(db_file)
-        self.start()
-        super().reset_state()
-
-    @override
-    def setup_migrations(self) -> None:
-        with self.tx() as cur:
-            cur.execute(
-                """
-                 CREATE TABLE IF NOT EXISTS migrations (
-                     dir TEXT NOT NULL,
-                     version INTEGER NOT NULL,
-                     filename TEXT NOT NULL,
-                     sql TEXT NOT NULL,
-                     hash TEXT NOT NULL,
-                     PRIMARY KEY (dir, version)
-                 )
-                 """
-            )
-
-    @override
-    def migrations_initialized(self) -> bool:
-        with self.tx() as cur:
-            cur.execute(
-                """SELECT count(*) FROM sqlite_master
-                   WHERE type='table' AND name='migrations'"""
-            )
-
-            if cur.fetchone()[0] == 0:
-                return False
-            else:
-                return True
-
-    @override
-    def db_migrations(self, dir: str) -> Sequence[Migration]:
-        with self.tx() as cur:
-            cur.execute(
-                """
-                SELECT dir, version, filename, sql, hash
-                FROM migrations
-                WHERE dir = ?
-                ORDER BY version ASC
-                """,
-                (dir,),
-            )
-
-            migrations = []
-            for row in cur.fetchall():
-                dir = cast(str, row[0])
-                version = cast(int, row[1])
-                filename = cast(str, row[2])
-                sql = cast(str, row[3])
-                hash = cast(str, row[4])
-                migrations.append(
-                    Migration(
-                        dir=dir,
-                        version=version,
-                        filename=filename,
-                        sql=sql,
-                        hash=hash,
-                        scope=self.migration_scope(),
-                    )
-                )
-            return migrations
-
-    @override
-    def apply_migration(self, cur: base.Cursor, migration: Migration) -> None:
-        cur.executescript(migration["sql"])
-        cur.execute(
-            """
-            INSERT INTO migrations (dir, version, filename, sql, hash)
-            VALUES (?, ?, ?, ?, ?)
-            """,
-            (
-                migration["dir"],
-                migration["version"],
-                migration["filename"],
-                migration["sql"],
-                migration["hash"],
-            ),
-        )
-
-    @staticmethod
-    @override
-    def uuid_from_db(value: Optional[Any]) -> Optional[UUID]:
-        return UUID(value) if value is not None else None
-
-    @staticmethod
-    @override
-    def uuid_to_db(uuid: Optional[UUID]) -> Optional[Any]:
-        return str(uuid) if uuid is not None else None
-
-    @staticmethod
-    @override
-    def unique_constraint_error() -> Type[BaseException]:
-        return sqlite3.IntegrityError
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/_asymmetric.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/_asymmetric.py
deleted file mode 100644
index ea55ffdf1a721f8fd2de8ae67de913bc47cbf55d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/_asymmetric.py
+++ /dev/null
@@ -1,19 +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.
-
-from __future__ import annotations
-
-import abc
-
-# This exists to break an import cycle. It is normally accessible from the
-# asymmetric padding module.
-
-
-class AsymmetricPadding(metaclass=abc.ABCMeta):
-    @property
-    @abc.abstractmethod
-    def name(self) -> str:
-        """
-        A string naming this padding (e.g. "PSS", "PKCS1").
-        """
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/BitmapGlyphMetrics.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/BitmapGlyphMetrics.py
deleted file mode 100644
index 10b4f828213b8320d54eefed3d5e66f2ba532101..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/BitmapGlyphMetrics.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Since bitmap glyph metrics are shared between EBLC and EBDT
-# this class gets its own python file.
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-import logging
-
-
-log = logging.getLogger(__name__)
-
-bigGlyphMetricsFormat = """
-  > # big endian
-  height:       B
-  width:        B
-  horiBearingX: b
-  horiBearingY: b
-  horiAdvance:  B
-  vertBearingX: b
-  vertBearingY: b
-  vertAdvance:  B
-"""
-
-smallGlyphMetricsFormat = """
-  > # big endian
-  height:   B
-  width:    B
-  BearingX: b
-  BearingY: b
-  Advance:  B
-"""
-
-
-class BitmapGlyphMetrics(object):
-    def toXML(self, writer, ttFont):
-        writer.begintag(self.__class__.__name__)
-        writer.newline()
-        for metricName in sstruct.getformat(self.__class__.binaryFormat)[1]:
-            writer.simpletag(metricName, value=getattr(self, metricName))
-            writer.newline()
-        writer.endtag(self.__class__.__name__)
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        metricNames = set(sstruct.getformat(self.__class__.binaryFormat)[1])
-        for element in content:
-            if not isinstance(element, tuple):
-                continue
-            name, attrs, content = element
-            # Make sure this is a metric that is needed by GlyphMetrics.
-            if name in metricNames:
-                vars(self)[name] = safeEval(attrs["value"])
-            else:
-                log.warning(
-                    "unknown name '%s' being ignored in %s.",
-                    name,
-                    self.__class__.__name__,
-                )
-
-
-class BigGlyphMetrics(BitmapGlyphMetrics):
-    binaryFormat = bigGlyphMetricsFormat
-
-
-class SmallGlyphMetrics(BitmapGlyphMetrics):
-    binaryFormat = smallGlyphMetricsFormat
diff --git a/spaces/cihyFjudo/fairness-paper-search/Keygen Code Ontime Activation Rar.30 The Secret to Unlocking Any Program with Ease.md b/spaces/cihyFjudo/fairness-paper-search/Keygen Code Ontime Activation Rar.30 The Secret to Unlocking Any Program with Ease.md
deleted file mode 100644
index f3a8b6dd6edc29ed647c480d29a994f34e64345b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Keygen Code Ontime Activation Rar.30 The Secret to Unlocking Any Program with Ease.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Keygen Code Ontime Activation Rar.30</h2><br /><p><b><b>Download</b> &#9745; <a href="https://tinurli.com/2uwjlF">https://tinurli.com/2uwjlF</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/colakin/video-generater/public/ffmpeg/fftools/opt_common.h b/spaces/colakin/video-generater/public/ffmpeg/fftools/opt_common.h
deleted file mode 100644
index ea1d16e769adee3341db68ed3427535d21b4e762..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/fftools/opt_common.h
+++ /dev/null
@@ -1,231 +0,0 @@
-/*
- * Option handlers shared between the tools.
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef FFTOOLS_OPT_COMMON_H
-#define FFTOOLS_OPT_COMMON_H
-
-#include "config.h"
-
-#include "cmdutils.h"
-
-#if CONFIG_AVDEVICE
-/**
- * Print a listing containing autodetected sinks of the output device.
- * Device name with options may be passed as an argument to limit results.
- */
-int show_sinks(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing autodetected sources of the input device.
- * Device name with options may be passed as an argument to limit results.
- */
-int show_sources(void *optctx, const char *opt, const char *arg);
-#endif
-
-#if CONFIG_AVDEVICE
-#define CMDUTILS_COMMON_OPTIONS_AVDEVICE                                                                                \
-    { "sources"    , OPT_EXIT | HAS_ARG, { .func_arg = show_sources },                                                  \
-      "list sources of the input device", "device" },                                                                   \
-    { "sinks"      , OPT_EXIT | HAS_ARG, { .func_arg = show_sinks },                                                    \
-      "list sinks of the output device", "device" },                                                                    \
-
-#else
-#define CMDUTILS_COMMON_OPTIONS_AVDEVICE
-#endif
-
-/**
- * Print the license of the program to stdout. The license depends on
- * the license of the libraries compiled into the program.
- * This option processing function does not utilize the arguments.
- */
-int show_license(void *optctx, const char *opt, const char *arg);
-
-/**
- * Generic -h handler common to all fftools.
- */
-int show_help(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print the version of the program to stdout. The version message
- * depends on the current versions of the repository and of the libav*
- * libraries.
- * This option processing function does not utilize the arguments.
- */
-int show_version(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print the build configuration of the program to stdout. The contents
- * depend on the definition of FFMPEG_CONFIGURATION.
- * This option processing function does not utilize the arguments.
- */
-int show_buildconf(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the formats supported by the
- * program (including devices).
- * This option processing function does not utilize the arguments.
- */
-int show_formats(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the muxers supported by the
- * program (including devices).
- * This option processing function does not utilize the arguments.
- */
-int show_muxers(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the demuxer supported by the
- * program (including devices).
- * This option processing function does not utilize the arguments.
- */
-int show_demuxers(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the devices supported by the
- * program.
- * This option processing function does not utilize the arguments.
- */
-int show_devices(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the codecs supported by the
- * program.
- * This option processing function does not utilize the arguments.
- */
-int show_codecs(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the decoders supported by the
- * program.
- */
-int show_decoders(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the encoders supported by the
- * program.
- */
-int show_encoders(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the bit stream filters supported by the
- * program.
- * This option processing function does not utilize the arguments.
- */
-int show_bsfs(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the protocols supported by the
- * program.
- * This option processing function does not utilize the arguments.
- */
-int show_protocols(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the filters supported by the
- * program.
- * This option processing function does not utilize the arguments.
- */
-int show_filters(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the pixel formats supported by the
- * program.
- * This option processing function does not utilize the arguments.
- */
-int show_pix_fmts(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the standard channel layouts supported by
- * the program.
- * This option processing function does not utilize the arguments.
- */
-int show_layouts(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the sample formats supported by the
- * program.
- */
-int show_sample_fmts(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all supported stream dispositions.
- */
-int show_dispositions(void *optctx, const char *opt, const char *arg);
-
-/**
- * Print a listing containing all the color names and values recognized
- * by the program.
- */
-int show_colors(void *optctx, const char *opt, const char *arg);
-
-/**
- * Set the libav* libraries log level.
- */
-int opt_loglevel(void *optctx, const char *opt, const char *arg);
-
-int opt_report(void *optctx, const char *opt, const char *arg);
-int init_report(const char *env, FILE **file);
-
-int opt_max_alloc(void *optctx, const char *opt, const char *arg);
-
-/**
- * Override the cpuflags.
- */
-int opt_cpuflags(void *optctx, const char *opt, const char *arg);
-
-/**
- * Override the cpucount.
- */
-int opt_cpucount(void *optctx, const char *opt, const char *arg);
-
-#define CMDUTILS_COMMON_OPTIONS                                                                                         \
-    { "L",           OPT_EXIT,             { .func_arg = show_license },     "show license" },                          \
-    { "h",           OPT_EXIT,             { .func_arg = show_help },        "show help", "topic" },                    \
-    { "?",           OPT_EXIT,             { .func_arg = show_help },        "show help", "topic" },                    \
-    { "help",        OPT_EXIT,             { .func_arg = show_help },        "show help", "topic" },                    \
-    { "-help",       OPT_EXIT,             { .func_arg = show_help },        "show help", "topic" },                    \
-    { "version",     OPT_EXIT,             { .func_arg = show_version },     "show version" },                          \
-    { "buildconf",   OPT_EXIT,             { .func_arg = show_buildconf },   "show build configuration" },              \
-    { "formats",     OPT_EXIT,             { .func_arg = show_formats },     "show available formats" },                \
-    { "muxers",      OPT_EXIT,             { .func_arg = show_muxers },      "show available muxers" },                 \
-    { "demuxers",    OPT_EXIT,             { .func_arg = show_demuxers },    "show available demuxers" },               \
-    { "devices",     OPT_EXIT,             { .func_arg = show_devices },     "show available devices" },                \
-    { "codecs",      OPT_EXIT,             { .func_arg = show_codecs },      "show available codecs" },                 \
-    { "decoders",    OPT_EXIT,             { .func_arg = show_decoders },    "show available decoders" },               \
-    { "encoders",    OPT_EXIT,             { .func_arg = show_encoders },    "show available encoders" },               \
-    { "bsfs",        OPT_EXIT,             { .func_arg = show_bsfs },        "show available bit stream filters" },     \
-    { "protocols",   OPT_EXIT,             { .func_arg = show_protocols },   "show available protocols" },              \
-    { "filters",     OPT_EXIT,             { .func_arg = show_filters },     "show available filters" },                \
-    { "pix_fmts",    OPT_EXIT,             { .func_arg = show_pix_fmts },    "show available pixel formats" },          \
-    { "layouts",     OPT_EXIT,             { .func_arg = show_layouts },     "show standard channel layouts" },         \
-    { "sample_fmts", OPT_EXIT,             { .func_arg = show_sample_fmts }, "show available audio sample formats" },   \
-    { "dispositions", OPT_EXIT,            { .func_arg = show_dispositions}, "show available stream dispositions" },    \
-    { "colors",      OPT_EXIT,             { .func_arg = show_colors },      "show available color names" },            \
-    { "loglevel",    HAS_ARG,              { .func_arg = opt_loglevel },     "set logging level", "loglevel" },         \
-    { "v",           HAS_ARG,              { .func_arg = opt_loglevel },     "set logging level", "loglevel" },         \
-    { "report",      0,                    { .func_arg = opt_report },       "generate a report" },                     \
-    { "max_alloc",   HAS_ARG,              { .func_arg = opt_max_alloc },    "set maximum size of a single allocated block", "bytes" }, \
-    { "cpuflags",    HAS_ARG | OPT_EXPERT, { .func_arg = opt_cpuflags },     "force specific cpu flags", "flags" },     \
-    { "cpucount",    HAS_ARG | OPT_EXPERT, { .func_arg = opt_cpucount },     "force specific cpu count", "count" },     \
-    { "hide_banner", OPT_BOOL | OPT_EXPERT, {&hide_banner},     "do not show program banner", "hide_banner" },          \
-    CMDUTILS_COMMON_OPTIONS_AVDEVICE                                                                                    \
-
-#endif /* FFTOOLS_OPT_COMMON_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenctab.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenctab.c
deleted file mode 100644
index 874365a593d46fa4bd44ab58579eeca5b6cf3911..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenctab.c
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * AAC encoder data
- * Copyright (c) 2015 Rostislav Pehlivanov ( atomnuker gmail com )
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "aacenctab.h"
-
-static const uint8_t swb_size_128_96[] = {
-    4, 4, 4, 4, 4, 4, 8, 8, 8, 16, 28, 36
-};
-
-static const uint8_t swb_size_128_64[] = {
-    4, 4, 4, 4, 4, 4, 8, 8, 8, 16, 28, 36
-};
-
-static const uint8_t swb_size_128_48[] = {
-    4, 4, 4, 4, 4, 8, 8, 8, 12, 12, 12, 16, 16, 16
-};
-
-static const uint8_t swb_size_128_24[] = {
-    4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 12, 12, 16, 16, 20
-};
-
-static const uint8_t swb_size_128_16[] = {
-    4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 12, 12, 16, 20, 20
-};
-
-static const uint8_t swb_size_128_8[] = {
-    4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 12, 16, 20, 20
-};
-
-static const uint8_t swb_size_1024_96[] = {
-    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8,
-    12, 12, 12, 12, 12, 16, 16, 24, 28, 36, 44,
-    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64
-};
-
-static const uint8_t swb_size_1024_64[] = {
-    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8,
-    12, 12, 12, 16, 16, 16, 20, 24, 24, 28, 36,
-    40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40
-};
-
-static const uint8_t swb_size_1024_48[] = {
-    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8,
-    12, 12, 12, 12, 16, 16, 20, 20, 24, 24, 28, 28,
-    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,
-    96
-};
-
-static const uint8_t swb_size_1024_32[] = {
-    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8,
-    12, 12, 12, 12, 16, 16, 20, 20, 24, 24, 28, 28,
-    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32
-};
-
-static const uint8_t swb_size_1024_24[] = {
-    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
-    12, 12, 12, 12, 16, 16, 16, 20, 20, 24, 24, 28, 28,
-    32, 36, 36, 40, 44, 48, 52, 52, 64, 64, 64, 64, 64
-};
-
-static const uint8_t swb_size_1024_16[] = {
-    8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
-    12, 12, 12, 12, 12, 12, 12, 12, 12, 16, 16, 16, 16, 20, 20, 20, 24, 24, 28, 28,
-    32, 36, 40, 40, 44, 48, 52, 56, 60, 64, 64, 64
-};
-
-static const uint8_t swb_size_1024_8[] = {
-    12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
-    16, 16, 16, 16, 16, 16, 16, 20, 20, 20, 20, 24, 24, 24, 28, 28,
-    32, 36, 36, 40, 44, 48, 52, 56, 60, 64, 80
-};
-
-const uint8_t *const ff_aac_swb_size_128[] = {
-    swb_size_128_96, swb_size_128_96, swb_size_128_64,
-    swb_size_128_48, swb_size_128_48, swb_size_128_48,
-    swb_size_128_24, swb_size_128_24, swb_size_128_16,
-    swb_size_128_16, swb_size_128_16, swb_size_128_8,
-    swb_size_128_8
-};
-
-const uint8_t *const ff_aac_swb_size_1024[] = {
-    swb_size_1024_96, swb_size_1024_96, swb_size_1024_64,
-    swb_size_1024_48, swb_size_1024_48, swb_size_1024_32,
-    swb_size_1024_24, swb_size_1024_24, swb_size_1024_16,
-    swb_size_1024_16, swb_size_1024_16, swb_size_1024_8,
-    swb_size_1024_8
-};
-
-const int ff_aac_swb_size_128_len  = FF_ARRAY_ELEMS(ff_aac_swb_size_128);
-const int ff_aac_swb_size_1024_len = FF_ARRAY_ELEMS(ff_aac_swb_size_1024);
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aasc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aasc.c
deleted file mode 100644
index c8e15772b148e8bc1f21e4120cb35a71438ca40c..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aasc.c
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * Autodesk RLE Decoder
- * Copyright (C) 2005 The FFmpeg project
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * Autodesk RLE Video Decoder by Konstantin Shishkov
- */
-
-#include <string.h>
-
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "decode.h"
-#include "msrledec.h"
-
-typedef struct AascContext {
-    AVCodecContext *avctx;
-    GetByteContext gb;
-    AVFrame *frame;
-
-    uint32_t palette[AVPALETTE_COUNT];
-    int palette_size;
-} AascContext;
-
-static av_cold int aasc_decode_init(AVCodecContext *avctx)
-{
-    AascContext *s = avctx->priv_data;
-    uint8_t *ptr;
-    int i;
-
-    s->avctx = avctx;
-    switch (avctx->bits_per_coded_sample) {
-    case 8:
-        avctx->pix_fmt = AV_PIX_FMT_PAL8;
-
-        ptr = avctx->extradata;
-        s->palette_size = FFMIN(avctx->extradata_size, AVPALETTE_SIZE);
-        for (i = 0; i < s->palette_size / 4; i++) {
-            s->palette[i] = 0xFFU << 24 | AV_RL32(ptr);
-            ptr += 4;
-        }
-        break;
-    case 16:
-        avctx->pix_fmt = AV_PIX_FMT_RGB555LE;
-        break;
-    case 24:
-        avctx->pix_fmt = AV_PIX_FMT_BGR24;
-        break;
-    default:
-        av_log(avctx, AV_LOG_ERROR, "Unsupported bit depth: %d\n", avctx->bits_per_coded_sample);
-        return -1;
-    }
-
-    s->frame = av_frame_alloc();
-    if (!s->frame)
-        return AVERROR(ENOMEM);
-
-    return 0;
-}
-
-static int aasc_decode_frame(AVCodecContext *avctx, AVFrame *rframe,
-                             int *got_frame, AVPacket *avpkt)
-{
-    const uint8_t *buf = avpkt->data;
-    int buf_size       = avpkt->size;
-    AascContext *s     = avctx->priv_data;
-    int compr, i, stride, psize, ret;
-
-    if (buf_size < 4) {
-        av_log(avctx, AV_LOG_ERROR, "frame too short\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    if ((ret = ff_reget_buffer(avctx, s->frame, 0)) < 0)
-        return ret;
-
-    compr     = AV_RL32(buf);
-    buf      += 4;
-    buf_size -= 4;
-    psize = avctx->bits_per_coded_sample / 8;
-    switch (avctx->codec_tag) {
-    case MKTAG('A', 'A', 'S', '4'):
-        bytestream2_init(&s->gb, buf - 4, buf_size + 4);
-        ff_msrle_decode(avctx, s->frame, 8, &s->gb);
-        break;
-    case MKTAG('A', 'A', 'S', 'C'):
-        switch (compr) {
-        case 0:
-            stride = (avctx->width * psize + psize) & ~psize;
-            if (buf_size < stride * avctx->height)
-                return AVERROR_INVALIDDATA;
-            for (i = avctx->height - 1; i >= 0; i--) {
-                memcpy(s->frame->data[0] + i * s->frame->linesize[0], buf, avctx->width * psize);
-                buf += stride;
-                buf_size -= stride;
-            }
-            break;
-        case 1:
-            bytestream2_init(&s->gb, buf, buf_size);
-            ff_msrle_decode(avctx, s->frame, 8, &s->gb);
-            break;
-        default:
-            av_log(avctx, AV_LOG_ERROR, "Unknown compression type %d\n", compr);
-            return AVERROR_INVALIDDATA;
-        }
-        break;
-    default:
-        av_log(avctx, AV_LOG_ERROR, "Unknown FourCC: %X\n", avctx->codec_tag);
-        return -1;
-    }
-
-    if (avctx->pix_fmt == AV_PIX_FMT_PAL8)
-        memcpy(s->frame->data[1], s->palette, s->palette_size);
-
-    *got_frame = 1;
-    if ((ret = av_frame_ref(rframe, s->frame)) < 0)
-        return ret;
-
-    /* report that the buffer was completely consumed */
-    return avpkt->size;
-}
-
-static av_cold int aasc_decode_end(AVCodecContext *avctx)
-{
-    AascContext *s = avctx->priv_data;
-
-    av_frame_free(&s->frame);
-
-    return 0;
-}
-
-const FFCodec ff_aasc_decoder = {
-    .p.name         = "aasc",
-    CODEC_LONG_NAME("Autodesk RLE"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_AASC,
-    .priv_data_size = sizeof(AascContext),
-    .init           = aasc_decode_init,
-    .close          = aasc_decode_end,
-    FF_CODEC_DECODE_CB(aasc_decode_frame),
-    .p.capabilities = AV_CODEC_CAP_DR1,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/fft_init_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/fft_init_arm.c
deleted file mode 100644
index 8ae22dfb4e78e5b9148171b6386963f0fc705246..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/fft_init_arm.c
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2009 Mans Rullgard <mans@mansr.com>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "libavutil/cpu.h"
-#include "libavutil/arm/cpu.h"
-
-#include "libavcodec/fft.h"
-
-void ff_fft_calc_vfp(FFTContext *s, FFTComplex *z);
-
-void ff_fft_permute_neon(FFTContext *s, FFTComplex *z);
-void ff_fft_calc_neon(FFTContext *s, FFTComplex *z);
-
-void ff_imdct_half_vfp(FFTContext *s, FFTSample *output, const FFTSample *input);
-
-void ff_imdct_calc_neon(FFTContext *s, FFTSample *output, const FFTSample *input);
-void ff_imdct_half_neon(FFTContext *s, FFTSample *output, const FFTSample *input);
-void ff_mdct_calc_neon(FFTContext *s, FFTSample *output, const FFTSample *input);
-
-av_cold void ff_fft_init_arm(FFTContext *s)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_vfp_vm(cpu_flags)) {
-        s->fft_calc     = ff_fft_calc_vfp;
-#if CONFIG_MDCT
-        s->imdct_half   = ff_imdct_half_vfp;
-#endif
-    }
-
-    if (have_neon(cpu_flags)) {
-#if CONFIG_FFT
-        if (s->nbits < 17) {
-            s->fft_permute = ff_fft_permute_neon;
-            s->fft_calc    = ff_fft_calc_neon;
-        }
-#endif
-#if CONFIG_MDCT
-        s->imdct_calc   = ff_imdct_calc_neon;
-        s->imdct_half   = ff_imdct_half_neon;
-        s->mdct_calc    = ff_mdct_calc_neon;
-        s->mdct_permutation = FF_MDCT_PERM_INTERLEAVE;
-#endif
-    }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_core_bsf.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_core_bsf.c
deleted file mode 100644
index 1a2db39cba31ad80feb1547237e440d4b15b3a26..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_core_bsf.c
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Copyright (c) 2016 Paul B Mahol
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "bsf.h"
-#include "bsf_internal.h"
-#include "bytestream.h"
-#include "dca_syncwords.h"
-
-static int dca_core_filter(AVBSFContext *ctx, AVPacket *pkt)
-{
-    GetByteContext gb;
-    uint32_t syncword;
-    int core_size = 0, ret;
-
-    ret = ff_bsf_get_packet_ref(ctx, pkt);
-    if (ret < 0)
-        return ret;
-
-    bytestream2_init(&gb, pkt->data, pkt->size);
-    syncword = bytestream2_get_be32(&gb);
-    bytestream2_skip(&gb, 1);
-
-    switch (syncword) {
-    case DCA_SYNCWORD_CORE_BE:
-        core_size = ((bytestream2_get_be24(&gb) >> 4) & 0x3fff) + 1;
-        break;
-    }
-
-    if (core_size > 0 && core_size <= pkt->size) {
-        pkt->size = core_size;
-    }
-
-    return 0;
-}
-
-static const enum AVCodecID codec_ids[] = {
-    AV_CODEC_ID_DTS, AV_CODEC_ID_NONE,
-};
-
-const FFBitStreamFilter ff_dca_core_bsf = {
-    .p.name      = "dca_core",
-    .p.codec_ids = codec_ids,
-    .filter      = dca_core_filter,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/librsvgdec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/librsvgdec.c
deleted file mode 100644
index 9c8aa2dedc88a4ef99bbb82001fe886840d0686f..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/librsvgdec.c
+++ /dev/null
@@ -1,131 +0,0 @@
-/*
- * Librsvg rasterization wrapper
- * Copyright (c) 2017 Rostislav Pehlivanov <atomnuker@gmail.com>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "decode.h"
-#include "libavutil/opt.h"
-#include "librsvg-2.0/librsvg/rsvg.h"
-
-typedef struct LibRSVGContext {
-    AVClass *class;
-
-    int width;
-    int height;
-    int keep_ar;
-} LibRSVGContext;
-
-static int librsvg_decode_frame(AVCodecContext *avctx, AVFrame *frame,
-                                int *got_frame, AVPacket *pkt)
-{
-    int ret;
-    LibRSVGContext *s = avctx->priv_data;
-
-    RsvgHandle *handle;
-    RsvgDimensionData unscaled_dimensions, dimensions;
-    cairo_surface_t *image;
-    cairo_t *crender = NULL;
-    GError *error = NULL;
-
-    *got_frame = 0;
-
-    handle = rsvg_handle_new_from_data(pkt->data, pkt->size, &error);
-    if (error) {
-        av_log(avctx, AV_LOG_ERROR, "Error parsing svg!\n");
-        g_error_free(error);
-        return AVERROR_INVALIDDATA;
-    }
-
-    rsvg_handle_get_dimensions(handle, &dimensions);
-    rsvg_handle_get_dimensions(handle, &unscaled_dimensions);
-    dimensions.width  = s->width  ? s->width  : dimensions.width;
-    dimensions.height = s->height ? s->height : dimensions.height;
-    if (s->keep_ar && (s->width || s->height)) {
-        double default_ar = unscaled_dimensions.width/(double)unscaled_dimensions.height;
-        if (!s->width)
-            dimensions.width  = lrintf(dimensions.height * default_ar);
-        else
-            dimensions.height = lrintf(dimensions.width  / default_ar);
-    }
-
-    if ((ret = ff_set_dimensions(avctx, dimensions.width, dimensions.height)))
-        return ret;
-    avctx->pix_fmt = AV_PIX_FMT_RGB32;
-
-    if ((ret = ff_get_buffer(avctx, frame, 0)))
-        return ret;
-    frame->pict_type = AV_PICTURE_TYPE_I;
-    frame->key_frame = 1;
-
-    image = cairo_image_surface_create_for_data(frame->data[0], CAIRO_FORMAT_ARGB32,
-                                                frame->width, frame->height,
-                                                frame->linesize[0]);
-    if (cairo_surface_status(image) != CAIRO_STATUS_SUCCESS)
-        return AVERROR_INVALIDDATA;
-
-    crender = cairo_create(image);
-
-    cairo_save(crender);
-    cairo_set_operator(crender, CAIRO_OPERATOR_CLEAR);
-    cairo_paint(crender);
-    cairo_restore(crender);
-
-    cairo_scale(crender, dimensions.width / (double)unscaled_dimensions.width,
-                dimensions.height / (double)unscaled_dimensions.height);
-
-    rsvg_handle_render_cairo(handle, crender);
-
-    cairo_destroy(crender);
-    cairo_surface_destroy(image);
-    g_object_unref(handle);
-
-    *got_frame = 1;
-
-    return 0;
-}
-
-#define OFFSET(x) offsetof(LibRSVGContext, x)
-#define DEC (AV_OPT_FLAG_DECODING_PARAM | AV_OPT_FLAG_VIDEO_PARAM)
-static const AVOption options[] = {
-    { "width", "Width to render to (0 for default)", OFFSET(width), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, DEC },
-    { "height", "Height to render to (0 for default)", OFFSET(height), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, DEC },
-    { "keep_ar", "Keep aspect ratio with custom width/height", OFFSET(keep_ar), AV_OPT_TYPE_BOOL, { .i64 = 1 }, 0, 1, DEC },
-    { NULL },
-};
-
-static const AVClass librsvg_decoder_class = {
-    .class_name = "Librsvg",
-    .item_name  = av_default_item_name,
-    .option     = options,
-    .version    = LIBAVUTIL_VERSION_INT,
-};
-
-const FFCodec ff_librsvg_decoder = {
-    .p.name         = "librsvg",
-    CODEC_LONG_NAME("Librsvg rasterizer"),
-    .p.priv_class   = &librsvg_decoder_class,
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_SVG,
-    .p.capabilities = AV_CODEC_CAP_DR1,
-    .p.wrapper_name = "librsvg",
-    FF_CODEC_DECODE_CB(librsvg_decode_frame),
-    .priv_data_size = sizeof(LibRSVGContext),
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/idctdsp_msa.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/idctdsp_msa.c
deleted file mode 100644
index b6b98dc7fcc362373f7dadb19cb8f26e31c5f3eb..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/idctdsp_msa.c
+++ /dev/null
@@ -1,146 +0,0 @@
-/*
- * Copyright (c) 2015 Manojkumar Bhosale (Manojkumar.Bhosale@imgtec.com)
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/mips/generic_macros_msa.h"
-#include "idctdsp_mips.h"
-
-static void put_pixels_clamped_msa(const int16_t *block, uint8_t *pixels,
-                                   int32_t stride)
-{
-    uint64_t in0_d, in1_d, in2_d, in3_d, in4_d, in5_d, in6_d, in7_d;
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-
-    LD_SH8(block, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-    CLIP_SH8_0_255(in0, in1, in2, in3, in4, in5, in6, in7);
-    PCKEV_B4_SH(in0, in0, in1, in1, in2, in2, in3, in3, in0, in1, in2, in3);
-    PCKEV_B4_SH(in4, in4, in5, in5, in6, in6, in7, in7, in4, in5, in6, in7);
-
-    in0_d = __msa_copy_u_d((v2i64) in0, 0);
-    in1_d = __msa_copy_u_d((v2i64) in1, 0);
-    in2_d = __msa_copy_u_d((v2i64) in2, 0);
-    in3_d = __msa_copy_u_d((v2i64) in3, 0);
-    in4_d = __msa_copy_u_d((v2i64) in4, 0);
-    in5_d = __msa_copy_u_d((v2i64) in5, 0);
-    in6_d = __msa_copy_u_d((v2i64) in6, 0);
-    in7_d = __msa_copy_u_d((v2i64) in7, 0);
-    SD4(in0_d, in1_d, in2_d, in3_d, pixels, stride);
-    pixels += 4 * stride;
-    SD4(in4_d, in5_d, in6_d, in7_d, pixels, stride);
-}
-
-static void put_signed_pixels_clamped_msa(const int16_t *block, uint8_t *pixels,
-                                          int32_t stride)
-{
-    uint64_t in0_d, in1_d, in2_d, in3_d, in4_d, in5_d, in6_d, in7_d;
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-
-    LD_SH8(block, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-
-    in0 += 128;
-    in1 += 128;
-    in2 += 128;
-    in3 += 128;
-    in4 += 128;
-    in5 += 128;
-    in6 += 128;
-    in7 += 128;
-
-    CLIP_SH8_0_255(in0, in1, in2, in3, in4, in5, in6, in7);
-    PCKEV_B4_SH(in0, in0, in1, in1, in2, in2, in3, in3, in0, in1, in2, in3);
-    PCKEV_B4_SH(in4, in4, in5, in5, in6, in6, in7, in7, in4, in5, in6, in7);
-
-    in0_d = __msa_copy_u_d((v2i64) in0, 0);
-    in1_d = __msa_copy_u_d((v2i64) in1, 0);
-    in2_d = __msa_copy_u_d((v2i64) in2, 0);
-    in3_d = __msa_copy_u_d((v2i64) in3, 0);
-    in4_d = __msa_copy_u_d((v2i64) in4, 0);
-    in5_d = __msa_copy_u_d((v2i64) in5, 0);
-    in6_d = __msa_copy_u_d((v2i64) in6, 0);
-    in7_d = __msa_copy_u_d((v2i64) in7, 0);
-    SD4(in0_d, in1_d, in2_d, in3_d, pixels, stride);
-    pixels += 4 * stride;
-    SD4(in4_d, in5_d, in6_d, in7_d, pixels, stride);
-}
-
-static void add_pixels_clamped_msa(const int16_t *block, uint8_t *pixels,
-                                   int32_t stride)
-{
-    uint64_t in0_d, in1_d, in2_d, in3_d, in4_d, in5_d, in6_d, in7_d;
-    v8i16 in0, in1, in2, in3, in4, in5, in6, in7;
-    v16u8 pix_in0, pix_in1, pix_in2, pix_in3;
-    v16u8 pix_in4, pix_in5, pix_in6, pix_in7;
-    v8u16 pix0, pix1, pix2, pix3, pix4, pix5, pix6, pix7;
-    v8i16 zero = { 0 };
-
-    LD_SH8(block, 8, in0, in1, in2, in3, in4, in5, in6, in7);
-    LD_UB8(pixels, stride, pix_in0, pix_in1, pix_in2,
-           pix_in3, pix_in4, pix_in5, pix_in6, pix_in7);
-
-    ILVR_B4_UH(zero, pix_in0, zero, pix_in1, zero, pix_in2, zero, pix_in3,
-               pix0, pix1, pix2, pix3);
-    ILVR_B4_UH(zero, pix_in4, zero, pix_in5, zero, pix_in6, zero, pix_in7,
-               pix4, pix5, pix6, pix7);
-
-    in0 += (v8i16) pix0;
-    in1 += (v8i16) pix1;
-    in2 += (v8i16) pix2;
-    in3 += (v8i16) pix3;
-    in4 += (v8i16) pix4;
-    in5 += (v8i16) pix5;
-    in6 += (v8i16) pix6;
-    in7 += (v8i16) pix7;
-
-    CLIP_SH8_0_255(in0, in1, in2, in3, in4, in5, in6, in7);
-    PCKEV_B4_SH(in0, in0, in1, in1, in2, in2, in3, in3, in0, in1, in2, in3);
-    PCKEV_B4_SH(in4, in4, in5, in5, in6, in6, in7, in7, in4, in5, in6, in7);
-
-    in0_d = __msa_copy_u_d((v2i64) in0, 0);
-    in1_d = __msa_copy_u_d((v2i64) in1, 0);
-    in2_d = __msa_copy_u_d((v2i64) in2, 0);
-    in3_d = __msa_copy_u_d((v2i64) in3, 0);
-    in4_d = __msa_copy_u_d((v2i64) in4, 0);
-    in5_d = __msa_copy_u_d((v2i64) in5, 0);
-    in6_d = __msa_copy_u_d((v2i64) in6, 0);
-    in7_d = __msa_copy_u_d((v2i64) in7, 0);
-    SD4(in0_d, in1_d, in2_d, in3_d, pixels, stride);
-    pixels += 4 * stride;
-    SD4(in4_d, in5_d, in6_d, in7_d, pixels, stride);
-}
-
-void ff_put_pixels_clamped_msa(const int16_t *block,
-                               uint8_t *av_restrict pixels,
-                               ptrdiff_t line_size)
-{
-    put_pixels_clamped_msa(block, pixels, line_size);
-}
-
-void ff_put_signed_pixels_clamped_msa(const int16_t *block,
-                                      uint8_t *av_restrict pixels,
-                                      ptrdiff_t line_size)
-{
-    put_signed_pixels_clamped_msa(block, pixels, line_size);
-}
-
-void ff_add_pixels_clamped_msa(const int16_t *block,
-                               uint8_t *av_restrict pixels,
-                               ptrdiff_t line_size)
-{
-    add_pixels_clamped_msa(block, pixels, line_size);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Dolphin Emulator APK How to Play Your Favorite Nintendo Games on Your Phone.md b/spaces/congsaPfin/Manga-OCR/logs/Dolphin Emulator APK How to Play Your Favorite Nintendo Games on Your Phone.md
deleted file mode 100644
index e54d13c344309802daf959ebfbc6b543b22ebafe..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Dolphin Emulator APK How to Play Your Favorite Nintendo Games on Your Phone.md	
+++ /dev/null
@@ -1,124 +0,0 @@
-
-<h1>How to Install and Use Dolphin Emulator APK Google Drive</h1>
-<p>Dolphin emulator is a free and open-source software that allows you to play Nintendo GameCube and Wii games on your PC, Mac, Linux, Android, and even Xbox devices. It is the most popular and reliable emulator for these two consoles, with high compatibility across the majority of titles. It also offers several enhancements, such as full HD graphics, turbo speed, networked multiplayer, cheat codes, controllers support, and more.</p>
-<p>If you are a fan of Nintendo games and want to enjoy them on your device of choice, dolphin emulator is the perfect solution for you. In this article, we will show you how to install and use dolphin emulator on different platforms, as well as some tips and tricks to optimize your gaming experience. We will also cover some common issues and errors that you may encounter with dolphin emulator, and how to fix them. Finally, we will share some user reviews and ratings of dolphin emulator, so you can see what others think about it.</p>
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- <h2>Features of Dolphin Emulator</h2>
-<p>Dolphin emulator has many features that make it stand out from other emulators. Here are some of the main ones:</p>
-<ul>
-<li><strong>Advanced Features:</strong> Dolphin emulator allows you to customize the graphics and audio settings to suit your preferences. You can also enable various enhancements, such as anti-aliasing, anisotropic filtering, texture scaling, stereoscopic 3D, widescreen hack, etc.</li>
-<li><strong>Audio & Video:</strong> Dolphin emulator supports a wide range of audio and video formats, including Dolby Pro Logic II, DTK Music, DSP HLE/LLE emulation, H264 video decoding, etc. You can also play games in full HD (1080p) resolution with several improvements over the original consoles.</li>
-<li><strong>Cheat Codes:</strong> Dolphin emulator supports GameShark, Action Replay, and Gecko codes for various games. You can use them to modify the game behavior, unlock hidden features, or make the game easier or harder.</li>
-<li><strong>Compatibility:</strong> Dolphin emulator has a high-level emulation accuracy that allows it to run most GameCube and Wii games without major issues. You can check the compatibility list on the official website to see how well each game works on dolphin emulator.</li>
-<li><strong>Controllers:</strong> Dolphin emulator supports all major controllers and input devices, such as keyboard, mouse, gamepad, joystick, steering wheel, etc. You can also use original Nintendo GameCube controllers with Smash Bros. Wii U USB adapter or original Wii Remotes via DolphinBar.</li>
-</ul>
- <h2>Compatibility of Dolphin Emulator</h2>
-<p>Dolphin emulator is a cross-platform software that runs on Windows (10 and newer), Linux, macOS (10.15 Catalina and up), Android (5.0 and above), Xbox One, Xbox Series X/S. However, each platform has different system requirements and supported games.</p>
-<p>Here are the minimum system requirements for running dolphin emulator on different platforms:</p>
-<table>
-<tr><th>Platform</th><th>CPU</th><th>GPU</th><th>RAM</th></tr>
-<tr><td>Windows</td><td>x86-64 CPU with SSE2 support</td><td>Pixel Shader 3.0 and Direct3D 10 or OpenGL 3 support</td><td>2 GB or more</td></tr>
-<tr><td>Linux</td><td>x86-64 CPU with SSE2 support</td><td>Pixel Shader 3.0 and OpenGL 3 support</td><td>2 GB or more</td></tr>
-<tr><td>macOS</td><td>x86-64 CPU or ARM64 CPU <h2>Installation of Dolphin Emulator</h2>
-<p>To install dolphin emulator on your device, you need to download the APK file from a trusted source. You can find the official dolphin emulator APK on Google Play Store, APKCombo, or TechSpot. Alternatively, you can also download the APK file from the official website or the GitHub repository. However, these sources may not have the latest version of dolphin emulator, so you may need to update it manually.</p>
-<p>Once you have downloaded the APK file, you need to enable the installation of unknown apps on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. Then, locate the APK file on your device and tap on it to install it. You may need to grant some permissions to dolphin emulator during the installation process.</p>
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-<p>After installing dolphin emulator, you can launch it from your app drawer or home screen. You will see a welcome screen with some information and options. You can tap on Start to begin using dolphin emulator, or tap on Settings to configure it according to your preferences.</p>
- <h2>Troubleshooting Dolphin Emulator</h2>
-<p>Dolphin emulator is a complex software that may encounter some issues and errors while running games. Some of the common problems that users face are:</p>
-<ul>
-<li><strong>Game crashes or freezes:</strong> This may happen due to various reasons, such as incompatible settings, corrupted files, insufficient resources, etc. To fix this, you can try the following solutions:     <ul>
-    <li>Update dolphin emulator to the latest version.</li>
-    <li>Check the compatibility list and adjust the settings accordingly.</li>
-    <li>Verify the integrity of the game files and redownload them if necessary.</li>
-    <li>Clear the cache and data of dolphin emulator.</li>
-    <li>Restart your device and try again.</li>
-    </ul>
-</li>
-<li><strong>Game runs slowly or lags:</strong> This may happen due to low performance of your device or improper settings. To fix this, you can try the following solutions:     <ul>
-    <li>Lower the graphics and audio settings of dolphin emulator.</li>
-    <li>Enable turbo mode or overclocking if supported by your device.</li>
-    <li>Close other apps and background processes that may consume resources.</li>
-    <li>Use a wired connection or a stable Wi-Fi network for online games.</li>
-    </ul>
-</li>
-<li><strong>Game does not load or shows a black screen:</strong> This may happen due to missing or incorrect files, such as BIOS, firmware, or game updates. To fix this, you can try the following solutions:     <ul>
-    <li>Download and install the required files from reliable sources.</li>
-    <li>Place the files in the correct folders and directories.</li>
-    <li>Update the game to the latest version if available.</li>
-    </ul>
-</li>
-</ul>
- <h2>Reviews of Dolphin Emulator</h2>
-<p>Dolphin emulator has received many positive reviews and ratings from users who have tried it on their devices. Here are some of them:</p>
- <blockquote><p>"This an excellent emulator. I've been playing Twilight Princess and it runs perfectly. I'm still in disbelief that I actually get to replay all my favorite games on my phone." - Kevin</p></blockquote>
- <blockquote><p>"Runs surprisingly well, though, does have stuttering, but I think they'll iron it out in time. I do think that they should add multi mapping to the controls that way if you where, for instance, playing with a controller and you can't decide if you like L1 or R1 for the Z button, you could just map both. Other than that, I rather enjoyed the experience." - Mark</p></blockquote>
- <blockquote><p>"This app is really good! I only have one issue! It saves the game save files to the system files that aren't easily accessible. This is a real inconvenience for swapping save files from PC to phone. Is there a way to change the path that the app stores and reads the save files from? I have not been able to find one on android." - B Carp</p></blockquote>
- <h2>Conclusion</h2>
-<p>Dolphin emulator is a powerful and versatile software that allows you to play Nintendo GameCube and Wii games on your device of choice. It has many features and benefits that enhance your gaming experience, such as HD graphics, networked multiplayer, cheat codes, controllers support, and more. It also has a high compatibility rate across most games, with minimal issues and errors.</p>
-<p>If you want to relive your childhood memories or try new games that you have never played before, dolphin emulator is the best option for you. You can download and install it on your device easily, and enjoy the games with high quality and performance.</p>
-<p>We hope this article has helped you to learn more about dolphin emulator and how to use it on your device. If you have any questions or feedback, feel free to leave a comment below. Happy gaming!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about dolphin emulator:</p>
-<ol>
-<li><strong>Is dolphin emulator legal?</strong></li>
-<p>Dolphin emulator itself is legal, as it is a software that emulates the hardware of Nintendo GameCube and Wii consoles. However, downloading and using game ROMs or ISOs that you do not own is illegal, as it violates the copyright laws. Therefore, we recommend that you only use dolphin emulator with games that you legally own or have permission to use.</p>
-<li><strong>How to update dolphin emulator?</strong></li>
-<p>If you have downloaded dolphin emulator from Google Play Store, APKCombo, or TechSpot, you can update it automatically through these sources. If you have downloaded dolphin emulator from the official website or the GitHub repository, you can update it manually by downloading the latest APK file and installing it over the existing one.</p>
-<li><strong>How to add games to dolphin emulator?</strong></li>
-<p>To add games to dolphin emulator, you need to have the game files in ROM or ISO format on your device. You can either copy them from your original discs using a disc drive and a software like ImgBurn, or download them from online sources (which may be illegal). Then, you need to place the game files in a folder on your device, and scan it with dolphin emulator. You can also browse for the game files manually by tapping on the + icon on the top right corner of the dolphin emulator app.</p>
-<li><strong>How to play multiplayer games on dolphin emulator?</strong></li>
-<p>To play multiplayer games on dolphin emulator, you have two options: local multiplayer or online multiplayer. For local multiplayer, you need to connect multiple controllers or input devices to your device, and configure them in the dolphin emulator settings. Then, you can launch the game and select the multiplayer mode. For online multiplayer, you need to use a service like Netplay or Wiimmfi, which allow you to connect with other players over the internet. You can find more information and instructions on how to use these services on the official dolphin emulator wiki.</p>
-<li><strong>How to save and load games on dolphin emulator?</strong></li>
-<p>To save and load games on dolphin emulator, you have two options: in-game save or save state. For in-game save, you need to use the save function of the game itself, which usually requires a memory card file. You can create a memory card file in the dolphin emulator settings, and assign it to a slot. Then, you can save and load your game progress as usual. For save state, you need to use the save state function of dolphin emulator, which allows you to save and load your game state at any point. You can access this function by tapping on the menu icon on the top left corner of the dolphin emulator app.</p>
-</ol></p> 197e85843d<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Sekarang Kalender 2023 PDF Gratis Termasuk Jadwal Libur dan Cuti Bersama.md b/spaces/congsaPfin/Manga-OCR/logs/Download Sekarang Kalender 2023 PDF Gratis Termasuk Jadwal Libur dan Cuti Bersama.md
deleted file mode 100644
index 3451decc8981df2e7732b44c3f5f2ed33214ceda..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Sekarang Kalender 2023 PDF Gratis Termasuk Jadwal Libur dan Cuti Bersama.md	
+++ /dev/null
@@ -1,119 +0,0 @@
-<br />
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- <h3>Convenient and portable</h3>
-<p>A kalender 2023 PDF is convenient and portable because you can access it anytime and anywhere. You can download your calendar on your computer, laptop, tablet, smartphone, or any other device that supports PDF files. You can also store your calendar on your cloud storage or email account for easy access. You can also share your calendar with others via email, social media, or other platforms.</p>
- <h2>Features of a Kalender 2023 PDF</h2>
-<p>A kalender 2023 PDF has various features that make it useful and attractive. Here are some of them:</p>
- <h3>Different formats and styles</h3>
-<p>A kalender 2023 PDF comes in different formats and styles that suit different needs and tastes. You can choose from yearly, monthly, weekly, daily, or hourly calendars. You can also choose from vertical or horizontal calendars. You can also choose from simple or elaborate calendars. You can also choose from different themes such as nature, animals, flowers, sports, cartoons, etc.</p <h3>Holidays and events</h3>
-<p>A kalender 2023 PDF also shows the holidays and events that are celebrated or observed in different countries, regions, cultures, religions, or interests. You can see the national holidays, public holidays, religious holidays, cultural holidays, international holidays, and other special days that are marked on your calendar. You can also see the dates and names of the holidays and events, as well as their meanings and significance.</p>
- <h3>Week numbers and days</h3>
-<p>A kalender 2023 PDF also displays the week numbers and days of the year 2023. You can see the number of weeks and days that have passed or are remaining in the year. You can also see the number of weeks and days in each month. You can also see the names and abbreviations of the days of the week, as well as their order and sequence.</p>
- <h2>How to Use a Kalender 2023 PDF</h2>
-<p>A kalender 2023 PDF can be used for various purposes, depending on your needs and goals. Here are some examples of how to use a kalender 2023 PDF:</p>
- <h3>For personal planning and organization</h3>
-<p>You can use a kalender 2023 PDF for personal planning and organization. You can use it to set your goals, priorities, tasks, projects, deadlines, milestones, etc. You can also use it to track your progress, achievements, challenges, feedback, etc. You can also use it to manage your time, energy, resources, budget, etc. You can also use it to balance your work, life, health, hobbies, etc.</p>
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-<p>A kalender 2023 PDF is a great tool for planning, scheduling, and organizing your personal, professional, or academic activities. You can download gratis kalender 2023 PDF from various online sources and print them out or use them on your devices. You can also enjoy the benefits and features of a kalender 2023 PDF, such as easy printing and customization, accurate and updated information, convenient and portable access, different formats and styles, holidays and events, week numbers and days, etc. You can also use a kalender 2023 PDF for various purposes, such as personal planning and organization, business and work purposes, academic and educational activities, etc.</p>
-<p>If you want to download gratis kalender 2023 PDF and start using it for your needs and goals, you can check out the following table that shows some of the best sources for gratis kalender 2023 PDF. You can click on the links to visit the websites and download your preferred kalender 2023 PDF.</p>
- <table>
-<tr>
-<th>Source</th>
-<th>Format</th>
-<th>Style</th>
-<th>Holidays</th>
-</tr>
-<tr>
-<td><a href="https://www.calendarlabs.com/2023-calendar-templates.php">Calendar Labs</a></td>
-<td>Yearly, monthly</td>
-<td>Simple, elegant</td>
-<td>US, UK, Canada, Australia, India</td>
-</tr>
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-<td><a href="https://www.timeanddate.com/calendar/?year=2023">Time and Date</a></td>
-<td>Yearly, monthly</td>
-<td>Classic, modern</td>
-<td>Worldwide</td>
-</tr>
-<tr>
-<td><a href="https://www.wincalendar.com/2023-Calendar-PDF">Win Calendar</a></td>
-<td>Yearly, monthly</td>
-<td>Cute, colorful</td>
-<td>US</td>
-</tr>
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-<td><a href="https://www.vertex42.com/calendars/2023.html">Vertex42</a></td>
-<td>Yearly, monthly</td>
-<td>Professional, functional</td>
-<td>US</td>
-</tr>
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-<td><a href="https://www.pdfcalendar.com/">PDF Calendar</a></td>
-<td>Yearly, monthly</td>
-<td>Minimalist, clean</td <td>No</td>
-</tr>
-</table>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions (FAQs) about gratis kalender 2023 PDF:</p>
- <h4>Where can I download gratis kalender 2023 PDF?</h4>
-<p>You can download gratis kalender 2023 PDF from various online sources, such as the ones listed in the table above. You can also search for other websites that offer gratis kalender 2023 PDF by using keywords such as "free calendar 2023 PDF", "download calendar 2023 PDF", "printable calendar 2023 PDF", etc.</p>
- <h4>How can I edit and customize a kalender 2023 PDF?</h4>
-<p>You can edit and customize a kalender 2023 PDF by using a PDF editor software or an online PDF editor tool. You can also use a word processor or a spreadsheet program to edit and customize your calendar. You can change the size, orientation, layout, design, color, font, and other elements of your calendar. You can also add notes, reminders, events, birthdays, appointments, holidays, and other important dates to your calendar.</p>
- <h4>What are the best sources for gratis kalender 2023 PDF?</h4>
-<p>The best sources for gratis kalender 2023 PDF depend on your needs and preferences. You should choose a source that offers a format and style that suits your purpose and taste. You should also choose a source that provides accurate and updated information about the holidays and events that are relevant to you. You should also choose a source that is easy to access, download, print, and share.</p>
- <h4>How can I print a kalender 2023 PDF?</h4>
-<p>You can print a kalender 2023 PDF by using a printer that supports PDF files. You can also use an online printing service or a local printing shop to print your calendar. You should choose the paper or material that matches your needs and quality standards. You should also adjust the settings of your printer or printing service to ensure the best results.</p>
- <h4>What are some tips for using a kalender 2023 PDF effectively?</h4>
-<p>Some tips for using a kalender 2023 PDF effectively are:</p>
-<ul>
-<li>Review your calendar regularly and update it as needed.</li>
-<li>Use your calendar as a guide and not as a constraint.</li>
-<li>Be realistic and flexible with your plans and goals.</li>
-<li>Balance your priorities and responsibilities.</li>
-<li>Make time for yourself and your loved ones.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Experience the Best of Android Gaming with Play Together on PC and Mac.md b/spaces/congsaPfin/Manga-OCR/logs/Experience the Best of Android Gaming with Play Together on PC and Mac.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/Experience the Best of Android Gaming with Play Together on PC and Mac.md	
+++ /dev/null
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-<br />
-<h1>Download Play Together: A Fun and Social Online Game for Everyone</h1>
- <p>Do you love playing games online with your friends? Do you want to explore a virtual world where you can do anything you want? Do you want to have fun and socialize with people from different countries and cultures? If you answered yes to any of these questions, then you should definitely try Play Together, a popular online game that lets you play, chat, and have fun with millions of players around the world.</p>
- <p>Play Together is an online game that was developed by HAEGIN Co., Ltd. and released in 2020. It is available for Android, iOS, and PC devices. In this game, you can create your own character, customize your appearance, decorate your house, play various mini-games, collect items, raise pets, make friends, and more. You can also join different clubs, parties, events, and festivals that are held regularly in the game.</p>
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- <p>If you are interested in playing Play Together, here is how you can download it on different devices.</p>
- <h2>How to download Play Together on different devices</h2>
- <h3>Android devices</h3>
- <p>If you have an Android device, such as a smartphone or a tablet, you can download Play Together from the Google Play Store. Here are the steps to follow:</p>
- <ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "Play Together" in the search bar.</li>
-<li>Select the game from the search results and tap on "Install".</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Once the installation is complete, tap on "Open" to launch the game.</li>
-</ol>
- <p>You can also download the game from this link: [Play Together](^1^)</p>
- <h3>iOS devices</h3>
- <p>If you have an iOS device, such as an iPhone or an iPad, you can download Play Together from the App Store. Here are the steps to follow:</p>
- <ol>
-<li>Open the App Store app on your device.</li>
-<li>Search for "Play Together" in the search bar.</li>
-<li>Select the game from the search results and tap on "Get".</li>
-<li>Enter your Apple ID password or use Touch ID or Face ID to confirm the download.</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Once the installation is complete, tap on the game icon to launch it.</li>
-</ol>
- <p>You can also download the game from this link: [Play Together](^3^)</p>
- <h3>PC</h3>
- <p>If you have a PC, you can download Play Together from Steam or MuMu Player. Steam is a digital distribution platform that allows you to buy and play games online. MuMu Player is an emulator that allows you to run Android games on your PC. Here are the steps to follow for each option:</p>
- <h4>Steam</h4>
- <ol>
-<li>Download and install Steam on your PC from this link: [Steam](^2^)</li>
-<li>Create a Steam account or log in with your existing one.</li>
-<li>Open Steam and go to the Store tab.</li>
-<li>Search for "Remote Play" in the search bar.</li>
-<li>Select "Remote Play" from the search results and click on "Install Game".</li>
-<li>Wait for Remote Play to download and install on your PC.</li>
-<li>Launch Remote Play and log in with your Steam account.</li>
-<li>Select "Remote Play Together" from the menu.</li>
-<li>Select "Invite Anyone" from the options.</li>
-<li>Copy the invitation link and send it to your friends who have Play Together installed on their Android or iOS devices.</li>
-<li>Wait for your friends to join the game using the link.</li>
-<li>Enjoy playing Play Together with your friends on your PC.</li>
-</ol>
- <h4>MuMu Player</h4>
- <ol>
-<li>Download and install MuMu Player on your PC from this link: [MuMu Player]</li>
-<li>Open MuMu Player and go to the Google Play Store app.</li>
-<li>Search for "Play Together" in the search bar and install the game.</li>
-<li>Launch the game and log in with your Google account or create a new one.</li>
-<li>Enjoy playing Play Together on your PC.</li>
-</ol>
- <h2>How to play Play Together and what you can do in the game</h2>
- <p>Now that you have downloaded Play Together on your device, you are ready to play and have fun. Here are some of the things you can do in the game:</p>
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- <h3>Play games with friends from around the world</h3>
- <p>One of the main features of Play Together is that you can play various mini-games with your friends or other players from different countries. You can join or create a room and invite up to 16 players to join you. Some of the mini-games you can play are:</p>
- <ul>
-<li>Camping: You can go camping with your friends and enjoy activities like fishing, cooking, and playing guitar.</li>
-<li>Karaoke: You can sing along to your favorite songs and show off your vocal skills.</li>
-<li>Hide and Seek: You can play the classic game of hide and seek and try to find or avoid other players.</li>
-<li>Zombie Run: You can run away from zombies or become one yourself and chase other players.</li>
-<li>Snowball Fight: You can have a snowball fight with other players and try to hit them with snowballs.</li>
-</ul>
- <p>These are just some of the mini-games you can play in Play Together. There are many more games to choose from, such as soccer, basketball, bowling, golf, chess, and more. You can also earn coins and rewards by playing these games, which you can use to buy items and upgrade your character.</p>
- <h3>Decorate your house and dress up your character</h3>
- <p>Another feature of Play Together is that you can customize your own house and character. You can buy furniture, wallpaper, flooring, and decorations for your house and make it look cozy and stylish. You can also invite your friends over to your house and have a party or a sleepover.</p>
- <p>You can also dress up your character with different outfits, accessories, hairstyles, and facial expressions. You can choose from a variety of styles, such as casual, formal, sporty, cute, cool, and more. You can also mix and match different items to create your own unique look. You can also change your character's gender, skin color, eye color, and hair color anytime you want.</p>
- <h3>Build your collection and raise pets</h3>
- <p>The last feature of Play Together that we will mention is that you can build your own collection of items and pets. You can collect various items by playing games, completing quests, opening chests, or buying them from the shop. Some of the items you can collect are:</p>
- <ul>
-<li>Clothes: You can collect different clothes for your character, such as shirts, pants, skirts, dresses, hats, shoes, and more.</li>
-<li>Furniture: You can collect different furniture for your house, such as sofas, tables, chairs, beds, cabinets, lamps, and more.</li>
-<li>Vehicles: You can collect different vehicles that you can use to travel around the game world, such as bikes, cars, boats, planes, and more.</li>
-<li>Toys: You can collect different toys that you can use to play with other players or pets, such as balls, dolls, teddy bears, puzzles, and more.</li>
-</ul>
- <p>You can also collect and raise different pets in Play Together. You can adopt pets from the pet shop or find them in the game world. Some of the pets you can have are:</p>
- <ul>
-<li>Dogs: You can have different breeds of dogs as pets, such as poodles, beagles, labradors, huskies, and more.</li>
-<li>Cats: You can have different breeds of cats as pets, such as persians, siamese, ragdolls, and more.</li>
-<li>Birds: You can have different kinds of birds as pets, such as parrots, owls, eagles, and more.</li>
-<li>Exotic: You can have some exotic animals as pets, such as pandas, lions, tigers, and more.</li>
-</ul>
- <p>You can feed, groom, play with, and train your pets. You can also take them with you to different places and show them off to other players. You can also breed your pets and get new ones.</p>
- <h2>The benefits of playing games with friends online</h2>
- <p>Playing games online with your friends is not only fun, but also beneficial for your well-being. Here are some of the benefits of playing games with friends online:</p>
- <h3>Improved mental health and reduced stress</h3>
- <p>Playing games online with your friends can help you cope with stress and improve your mood. Games can provide you with a sense of achievement, challenge, and enjoyment. They can also distract you from negative thoughts and emotions. Playing games online with your friends can also make you feel less lonely and more connected. You can share your experiences, feelings, and opinions with your friends. You can also support each other and cheer each other up.</p>
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- <p>Playing games online with your friends can also boost your cognitive skills and creativity. Games can stimulate your brain and improve your memory, attention, problem-solving, and decision-making skills. They can also enhance your imagination and innovation. Playing games online with your friends can also expose you to different perspectives, cultures, and ideas. You can learn new things, discover new possibilities, and create new solutions.</p>
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- <p>Playing games online with your friends can also increase your social interaction and communication skills. Games can foster teamwork, cooperation, and coordination. They can also promote communication, negotiation, and persuasion skills. Playing games online with your friends can also help you develop friendships, trust, and empathy. You can bond with your friends over common interests, goals, and experiences. You can also meet new people and expand your social network.</p>
- <h2>Conclusion: Play Together is a fun and social online game that you can enjoy anytime, anywhere</h2>
- <p>In conclusion, Play Together is a fun and social online game that you can enjoy anytime, anywhere. You can download it on different devices and play it with your friends or other players from around the world. You can play various mini-games, decorate your house and dress up your character, build your collection and raise pets, and more. You can also benefit from playing games online with your friends in terms of improved mental health, enhanced cognitive skills, and increased social interaction. So what are you waiting for? Download Play Together today and have fun!</p>
- <h2>FAQs: Some common questions and answers about Play Together</h2>
- <p>Here are some common questions and answers about Play Together that you might find helpful:</p>
- <ol>
-<li><b>Is Play Together free to play?</b></li>
-<p>Yes, Play Together is free to play. However, there are some in-game purchases that you can make to enhance your gaming experience.</p>
-<li><b>Is Play Together safe to play?</b></li>
-<p>Yes, Play Together is safe to play. However, you should be careful about sharing personal information or clicking on suspicious links while playing the game. You should also report any inappropriate or abusive behavior from other players.</p>
-<li><b>How do I contact the customer service of Play Together?</b></li>
-<p>You can contact the customer service of Play Together by sending an email to [playtogether@haegin.kr] or by visiting their official website: [Play Together].</p>
-<li><b>How do I update Play Together?</b></li>
-<p>You can update Play Together by going to the Google Play Store or the App Store on your device and checking for updates. You can also enable automatic updates for the game in the settings of your device.</p>
-<li><b>How do I delete Play Together?</b></li>
-<p>You can delete Play Together by going to the settings of your device and selecting the option to uninstall the game. However, you should be aware that deleting the game will also delete your game data and progress. If you want to keep your game data and progress, you should link your game account to your Google or Facebook account before deleting the game.</p>
- <p>I hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install Craftsman APK on Your Android Device.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install Craftsman APK on Your Android Device.md
deleted file mode 100644
index ed59117f20fb51bd9169f8e3d9e29023a60b68d7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install Craftsman APK on Your Android Device.md	
+++ /dev/null
@@ -1,137 +0,0 @@
-
-<h1>Craftsman APK Pure: A Guide to Download and Play the Game</h1>
-<p>If you are looking for a game that lets you unleash your creativity and imagination, you might want to check out Craftsman APK Pure. This is a sandbox game that allows you to design and build your own houses, castles, and other structures. You can also explore different worlds, collect resources, and fight enemies. In this article, we will show you how to download and install Craftsman APK Pure on your Android device, how to play it with your friends online, why you should play it, and some tips and tricks to master it.</p>
- <h2>What is Craftsman APK Pure?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Craftsman is a game that was inspired by Minecraft, one of the most popular sandbox games in the world. It has similar graphics, gameplay, and mechanics, but it also has some unique features that make it stand out. Some of these features are:</p>
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-<li>Stunning graphics and realistic sound effects that create an immersive experience.</li>
-<li>Simple, easy to play, and suitable for all ages.</li>
-<li>Many game modes, such as survival, creative, adventure, multiplayer, etc.</li>
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-<li>An online mode that lets you play with your friends or other players from around the world.</li>
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- <h3>How to download and install Craftsman APK Pure on your Android device</h3>
-<p>If you want to play Craftsman on your Android device, you can download it from APK Pure, a website that provides free and safe apps and games for Android users. Here are the steps to follow:</p>
-<ol>
-<li>Go to <a href="(^1^)">APKCombo.com</a> on your browser and search for "Craftsman".</li>
-<li>Select the game from the list of results and click on "Download APK".</li>
-<li>Wait for the download to finish and then open the file.</li>
-<li>If you see a warning message that says "Install blocked", go to your device settings and enable "Unknown sources".</li>
-<li>Follow the instructions on the screen and install the game.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
- <h3>How to play Craftsman APK Pure with your friends online</h3>
-<p>One of the best features of Craftsman is that you can play it with your friends online. You can join or create a server and invite your friends to join you. You can also chat with them, collaborate with them, or compete with them. Here are the steps to play online:</p>
-<ol>
-<li>Open the game and select "Multiplayer" from the main menu.</li>
-<li>Select "Join Server" or "Create Server" depending on what you want to do.</li>
-<li>If you want to join a server, enter the server name or IP address and click on "Join". If you want to create a server, enter a name for your server and click on "Create".</li>
-<li>Wait for the server to load and then invite your friends by sharing the server code or link with them. You can also use the chat feature to communicate with them.</li>
-<li>Have fun playing with your friends!</li>
-</ol>
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-<h3>The benefits of playing a creative and sandbox game</h3>
-<p>Craftsman is a game that stimulates your creativity and imagination. You can design and build anything you want, from simple houses to complex cities. You can also express your personality and style through your creations. Playing a creative and sandbox game like Craftsman can have many benefits, such as:</p>
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-<li>Improving your problem-solving and critical thinking skills.</li>
-<li>Enhancing your spatial awareness and hand-eye coordination.</li>
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-<li>Developing your artistic and aesthetic sense.</li>
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-</ul>
- <h3>The advantages of using APK Pure as a source of apps and games</h3>
-<p>APK Pure is a website that offers free and safe apps and games for Android users. It has many advantages over other sources, such as:</p>
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-<li>It has a large and updated collection of apps and games, including the latest versions and exclusive releases.</li>
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-<li>It has a user-friendly interface, with easy search and navigation features.</li>
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-</ul>
- <h3>The challenges and fun of building your own world in Craftsman APK Pure</h3>
-<p>Craftsman is a game that lets you build your own world in a 3D environment. You can use different blocks, items, and tools to create anything you want. You can also explore different biomes, such as forests, deserts, mountains, oceans, etc. You can also encounter different creatures, such as animals, monsters, zombies, etc. Building your own world in Craftsman APK Pure can be challenging and fun, because:</p>
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-<li>You have unlimited possibilities and freedom to create whatever you want.</li>
-<li>You have to use your imagination and creativity to make your creations unique and beautiful.</li>
-<li>You have to use your logic and strategy to make your creations functional and efficient.</li>
-<li>You have to use your skills and knowledge to make your creations realistic and accurate.</li>
-<li>You have to face various dangers and obstacles that test your survival skills.</li>
-</ul>
- <h2>Tips and tricks to master Craftsman APK Pure</h2>
-<h3>How to use the tools and materials in the game</h3>
-<p>Craftsman is a game that requires you to use various tools and materials to create your structures. Some of the tools and materials are:</p>
- <table border="1">
-<tr><th>Tool/Material</th><th>Use</th></tr>
-<tr><td>Pickaxe</td><td>To mine stone, ore, coal, etc.</td></tr>
-<tr><td>Axe</td><td>To chop wood, planks, etc.</td></tr>
-<tr><td>Shovel</td><td>To dig dirt, sand, gravel, etc.</td></tr>
-<tr><td>Hoe</td><td>To till soil for farming crops.</td></tr>
-<tr><td>Sword</td><td>To fight enemies and animals.</td></tr>
-<tr><td>Bow</td><td>To shoot arrows at enemies and animals from a distance.</td></tr>
-<tr><td>Torch</td><td>To provide light in dark places.</td></tr>
-<tr><td>Crafting table</td><td>To craft more advanced tools, items, blocks, etc.</td></tr>
-<tr><td>Furnace</td><td>To smelt ore into ingots, cook food, etc.</td></tr>
-<tr><td>Chest</td><td>To store your items and blocks.</td></tr>
-<tr><td>Bed</td><td>To sleep at night and set your spawn point.</td></tr> <tr><td>Door</td><td>To create an entrance to your house or building.</td></tr>
-</table>
- <p>To use the tools and materials in the game, you need to select them from your inventory and then tap or hold the screen to perform the action. You can also craft new tools and materials by combining existing ones on the crafting table. For example, you can craft a pickaxe by placing two sticks and three stones on the crafting table.</p>
- <h3>How to design and construct different types of buildings and structures</h3>
-<p>Craftsman is a game that lets you design and construct different types of buildings and structures, such as houses, castles, bridges, towers, etc. You can use different blocks, items, and tools to create your structures. You can also customize your structures with different colors, textures, patterns, etc. Here are some tips to design and construct your structures:</p>
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-<ul>
-<li>Plan your structure before you start building. Sketch out the shape, size, and layout of your structure on paper or in your mind.</li>
-<li>Choose the right blocks and materials for your structure. Consider the function, style, and theme of your structure. For example, if you want to build a medieval castle, you might want to use stone, wood, iron, etc.</li>
-<li>Build your structure layer by layer, from the bottom to the top. Start with the foundation and then add the walls, floors, roofs, windows, doors, etc.</li>
-<li>Add details and decorations to your structure. Use different blocks, items, and tools to add features and details to your structure. For example, you can use torches, paintings, flowers, banners, etc. to decorate your structure.</li>
-<li>Test and improve your structure. Check if your structure is stable, functional, and aesthetically pleasing. Fix any errors or flaws that you find. You can also ask for feedback from other players or friends.</li>
-</ul>
- <h3>How to explore and survive in different biomes and environments</h3>
-<p>Craftsman is a game that lets you explore and survive in different biomes and environments, such as forests, deserts, mountains, oceans, etc. You can find different resources, animals, plants, etc. in each biome. You can also encounter different dangers and threats in each biome. Here are some tips to explore and survive in different biomes and environments:</p>
-<ul>
-<li>Gather enough resources for your survival. You need to collect food, water, wood, stone, ore, etc. for your basic needs. You can also craft tools, weapons, armor, etc. for your protection and convenience.</li>
-<li>Build a shelter for your safety. You need to build a shelter where you can sleep at night and store your items. You can build a simple house or a complex base depending on your preference.</li>
-<li>Be prepared for the weather and the time of day. You need to wear appropriate clothing and equipment for the weather and the time of day. For example, you might need a coat and a torch for the night or a hat and sunglasses for the day.</li>
-<li>Be aware of the enemies and animals in each biome. You need to avoid or fight the enemies and animals that can harm you in each biome. For example, you might need to avoid or fight zombies, skeletons, spiders, etc. in the dark or cacti, scorpions, snakes, etc. in the desert.</li>
-<li>Have fun exploring and discovering new things in each biome. You can find hidden treasures, rare items, secret dungeons, etc. in each biome. You can also learn new facts, stories, and lore about each biome.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>Craftsman APK Pure is a game that lets you create and explore your own world in a 3D environment. You can download it from APK Pure, a website that offers free and safe apps and games for Android users. You can also play it with your friends online and have fun together. Craftsman APK Pure is a game that challenges your creativity, imagination, logic, and survival skills. It is also a game that offers you unlimited possibilities and freedom to express yourself. If you are looking for a game that is fun, engaging, and educational, you should try Craftsman APK Pure today!</p>
- <h2>FAQs</h2>
-<h3>Five unique questions and answers related to the topic</h3>
-<ol>
-<li>Q: What are the minimum requirements to play Craftsman APK Pure on your Android device?<br>A: You need to have an Android device that runs on Android 4.4 or higher, has at least 1 GB of RAM, and has at least 100 MB of free storage space.</li>
-<li>Q: How can you update Craftsman APK Pure to the latest version?<br>A: You can update Craftsman APK Pure by visiting APK Pure website and downloading the latest version of the game. You can also enable the auto-update feature on your device settings to get notified when there is a new update available.</li>
-<li>Q: How can you backup your Craftsman APK Pure world data?<br>A: You can backup your Craftsman APK Pure world data by using a file manager app on your device and copying the folder named "com.craftsman.go" from the internal storage to an external storage device or a cloud service.</li>
-<li>Q: How can you mod Craftsman APK Pure to add new features or change the game settings?<br>A: You can mod Craftsman APK Pure by using a modding app or tool that is compatible with the game. You can also download and install modded versions of the game from APK Pure or other sources.</li>
-<li>Q: How can you report a bug or a problem with Craftsman APK Pure?<br>A: You can report a bug or a problem with Craftsman APK Pure by contacting the developer of the game through their email address or social media accounts. You can also leave a comment or a review on APK Pure website or app.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Play Tekken 3 APK from Weebly.com in Minutes.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download and Play Tekken 3 APK from Weebly.com in Minutes.md
deleted file mode 100644
index b1b3a433d079b948a7aef1e0571d0b9988893daf..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Play Tekken 3 APK from Weebly.com in Minutes.md	
+++ /dev/null
@@ -1,95 +0,0 @@
-<br />
-<h1>Tekken 3 APK Weebly.com Free Download</h1>
-<p>If you are a fan of fighting games, you have probably heard of Tekken 3, one of the most acclaimed and popular games in the genre. Released in 1997 for arcades and in 1998 for PlayStation, Tekken 3 has been praised for its fast and fluid gameplay, its large and diverse cast of characters, and its various modes and challenges. But did you know that you can also play Tekken 3 on your Android device for free? In this article, we will show you how to download Tekken 3 APK from weebly.com for free, and give you some tips and tricks for playing it on your smartphone or tablet.</p>
- <h2>What is Tekken 3?</h2>
-<p>Tekken 3 is a one-on-one fighting game that features 23 different characters, each with their own unique moves, combos, and styles. The game has various modes, such as Arcade, VS, Team Battle, Time Attack, Survival, Tekken Ball, Tekken Force, and Practice. The game also has a story mode that follows the events of the previous games in the series, focusing on the rivalry between the Mishima family and the mysterious Ogre.</p>
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-<p>The game is known for its speed and smoothness of gameplay, as well as its impressive graphics and sound effects. The game also introduces a new feature that allows characters to sidestep in or out of the background, adding more depth and strategy to the fights. The game also has a lot of secrets and hidden characters to unlock, making it replayable and rewarding.</p>
- <h2>Why is Tekken 3 popular?</h2>
-<p>Tekken 3 is widely considered as one of the best fighting games ever made, and one of the greatest video games of all time. It has received critical acclaim from various media outlets, such as IGN, GameSpot, Game Informer, Electronic Gaming Monthly, Edge, Famitsu, and more. It has also sold more than 8 million copies worldwide on PlayStation alone, making it one of the best-selling games on the console.</p>
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-<p>Tekken 3 has also influenced many other fighting games in terms of gameplay mechanics, character design, presentation, and innovation. It has also spawned many sequels and spin-offs in the Tekken series, as well as crossover games with other franchises such as Street Fighter and Soul Calibur. The game has also been featured in various forms of media such as <h2>How to download Tekken 3 APK from weebly.com for free?</h2>
-<p>If you want to play Tekken 3 on your Android device, you don't need to buy or emulate the original PlayStation version. You can simply download the APK file from weebly.com for free, and install it on your smartphone or tablet. Here are the steps you need to follow:</p>
- <h3>Step 1: Visit the website</h3>
-<p>The first thing you need to do is to visit the website that hosts the Tekken 3 APK file. The website is <a href="(^1^)">tekken3apk.weebly.com</a>, and it has a simple and user-friendly design. On the homepage, you will see a brief introduction to the game, as well as some screenshots and videos. You will also see a button that says "Download Now".</p>
- <h3>Step 2: Choose the download option</h3>
-<p>When you click on the "Download Now" button, you will be redirected to another page that has two options: "Download APK" and "Download Data". You need to download both files in order to play the game. The APK file is about 21 MB, and the Data file is about 500 MB. You can either download them directly from the website, or use a third-party app such as Google Drive or Dropbox.</p>
-<p>Once you have downloaded both files, you need to move them to the same folder on your device's internal storage or SD card. You can use a file manager app such as ES File Explorer or ZArchiver to do this.</p>
- <h3>Step 3: Install the APK file</h3>
-<p>The final step is to install the APK file on your device. Before you do this, you need to make sure that you have enabled the option to install apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources, and toggle it on.</p>
-<p>Then, go to the folder where you have moved the APK and Data files, and tap on the APK file. You will see a pop-up window that asks you to confirm the installation. Tap on "Install", and wait for a few seconds until the installation is complete.</p>
-<p>After that, you can launch the game from your app drawer or home screen, and enjoy playing Tekken 3 on your Android device.</p>
- <h2>Tips and tricks for playing Tekken 3 on Android</h2>
-<p>Tekken 3 is a fun and addictive game that will keep you entertained for hours. However, if you want to get the most out of it, you might want to know some tips and tricks that will help you improve your skills and experience. Here are some of them:</p>
-<ul>
-<li>Use the virtual buttons wisely. The game has four virtual buttons on the screen: left punch, right punch, left kick, and right kick. You can use them to perform various moves and combos, depending on the character you choose. However, you might find them a bit small or unresponsive at times. To fix this, you can adjust their size and position in the settings menu. You can also use a Bluetooth controller or a keyboard if you prefer.</li>
-<li>Unlock new characters and modes. The game has 23 characters in total, but only 10 of them are available at the start. To unlock the rest of them, you need to complete certain tasks or challenges in different modes. For example, to unlock Jin Kazama, you need to beat Arcade mode with any character; to unlock Dr. Bosconovitch, you need to beat Tekken Force mode four times; and to unlock Gon, you need to beat Tekken Ball mode with any character. You can also unlock new modes such as Theater mode and Tekken Ball mode by completing Arcade mode with different characters.</li>
-<li>Improve your performance. If you find that the game is running slow or laggy on your device, you can try some ways to improve its performance. For example, you can lower the graphics quality in the settings menu; you can close other apps that are running in the background; or you can clear your device's cache and memory. You can also use a booster app such as Game Booster or Clean Master to optimize your device for gaming.</li>
-</ul>
- <h2>Reviews and ratings of Tekken 3 APK</h2>
-<p>Tekken 3 APK has received positive reviews and ratings from various sources, such as IGN, GameSpot, Metacritic, IMDb, and more. Here are some of them:</p>
- <table>
-<tr><th>Source</th><th>Rating</th><th>Comment</th></tr>
-<tr><td><a href="(^1^)">IGN</a></td><td>9.3/10</td><td>" Tekken 3 is a masterpiece of fighting games, with a huge roster of characters, a variety of modes, and a smooth and responsive gameplay. The game has aged well, and still holds up as one of the best in the genre."</td></tr>
-<tr><td><a href="">GameSpot</a></td><td>9.5/10</td><td>"Tekken 3 is the best PlayStation game to come along in a long time. It's not just a great fighting game - it's a showcase for the power of the PlayStation and an addictive and fun game in its own right."</td></tr>
-<tr><td><a href="">Metacritic</a></td><td>96/100</td><td>"Tekken 3 is a must-have for any PlayStation owner who likes fighting games. It has everything you could ask for: great graphics, sound, gameplay, and replay value."</td></tr>
-<tr><td><a href="">IMDb</a></td><td>8.8/10</td><td>"Tekken 3 is one of the best games I have ever played. The story is engaging, the characters are memorable, and the gameplay is addictive. I highly recommend it to anyone who loves video games."</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Tekken 3 is a classic game that deserves to be played by every fighting game fan. It has a lot of features, modes, and characters that will keep you entertained for hours. And the best part is, you can play it on your Android device for free, thanks to the Tekken 3 APK from weebly.com. All you need to do is follow the steps we have shown you in this article, and you will be able to enjoy this amazing game on your smartphone or tablet.</p>
-<p>So what are you waiting for? Download Tekken 3 APK from weebly.com for free today, and unleash your inner fighter!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Tekken 3 APK:</p>
-<ol>
-<li>Is Tekken 3 APK safe to download and install?</li>
-<p>Yes, Tekken 3 APK from weebly.com is safe to download and install. It does not contain any viruses, malware, or spyware that could harm your device or data. However, you should always be careful when downloading files from unknown sources, and scan them with an antivirus app before installing them.</p>
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-<p>Tekken 3 APK from weebly.com is compatible with most Android devices that run on Android 4.0 or higher. However, some devices may have issues with the graphics quality, performance, or controls. If you encounter any problems with the game, you can try adjusting the settings in the game menu, or use a different device.</li>
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-<p>Tekken 3 APK from weebly.com is not an official version of the game, and it is not authorized or endorsed by the original developers or publishers. Therefore, it may not be legal to download and play it in some countries or regions. You should check your local laws and regulations before downloading and playing it.</li>
-<li>Can I play Tekken 3 APK online with other players?</li>
-<p>No, Tekken 3 APK from weebly.com does not support online multiplayer mode. You can only play it offline with yourself or with another player using the same device.</li>
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-<p>No, Tekken 3 APK from weebly.com does not have any updates or patches available. You can only play it as it is, without any changes or improvements.</li>
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\ No newline at end of file
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/twins.py b/spaces/cooelf/Multimodal-CoT/timm/models/twins.py
deleted file mode 100644
index 4aed09d90f4d832e798bc7dd39d3712cb20b966d..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/twins.py
+++ /dev/null
@@ -1,422 +0,0 @@
-""" Twins
-A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
-    - https://arxiv.org/pdf/2104.13840.pdf
-
-Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
-
-"""
-# --------------------------------------------------------
-# Twins
-# Copyright (c) 2021 Meituan
-# Licensed under The Apache 2.0 License [see LICENSE for details]
-# Written by Xinjie Li, Xiangxiang Chu
-# --------------------------------------------------------
-import math
-from copy import deepcopy
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .layers import Mlp, DropPath, to_2tuple, trunc_normal_
-from .registry import register_model
-from .vision_transformer import Attention
-from .helpers import build_model_with_cfg, overlay_external_default_cfg
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
-        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'patch_embeds.0.proj', 'classifier': 'head',
-        **kwargs
-    }
-
-
-default_cfgs = {
-    'twins_pcpvt_small': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth',
-        ),
-    'twins_pcpvt_base': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_base-e5ecb09b.pth',
-        ),
-    'twins_pcpvt_large': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_large-d273f802.pth',
-        ),
-    'twins_svt_small': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_svt_small-42e5f78c.pth',
-        ),
-    'twins_svt_base': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_svt_base-c2265010.pth',
-        ),
-    'twins_svt_large': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_svt_large-90f6aaa9.pth',
-        ),
-}
-
-Size_ = Tuple[int, int]
-
-
-class LocallyGroupedAttn(nn.Module):
-    """ LSA: self attention within a group
-    """
-    def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., ws=1):
-        assert ws != 1
-        super(LocallyGroupedAttn, self).__init__()
-        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
-
-        self.dim = dim
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=True)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.ws = ws
-
-    def forward(self, x, size: Size_):
-        # There are two implementations for this function, zero padding or mask. We don't observe obvious difference for
-        # both. You can choose any one, we recommend forward_padding because it's neat. However,
-        # the masking implementation is more reasonable and accurate.
-        B, N, C = x.shape
-        H, W = size
-        x = x.view(B, H, W, C)
-        pad_l = pad_t = 0
-        pad_r = (self.ws - W % self.ws) % self.ws
-        pad_b = (self.ws - H % self.ws) % self.ws
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-        _h, _w = Hp // self.ws, Wp // self.ws
-        x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)
-        qkv = self.qkv(x).reshape(
-            B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
-        q, k, v = qkv[0], qkv[1], qkv[2]
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-        attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
-        x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-        x = x.reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    # def forward_mask(self, x, size: Size_):
-    #     B, N, C = x.shape
-    #     H, W = size
-    #     x = x.view(B, H, W, C)
-    #     pad_l = pad_t = 0
-    #     pad_r = (self.ws - W % self.ws) % self.ws
-    #     pad_b = (self.ws - H % self.ws) % self.ws
-    #     x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-    #     _, Hp, Wp, _ = x.shape
-    #     _h, _w = Hp // self.ws, Wp // self.ws
-    #     mask = torch.zeros((1, Hp, Wp), device=x.device)
-    #     mask[:, -pad_b:, :].fill_(1)
-    #     mask[:, :, -pad_r:].fill_(1)
-    #
-    #     x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)  # B, _h, _w, ws, ws, C
-    #     mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1,  _h * _w, self.ws * self.ws)
-    #     attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3)  # 1, _h*_w, ws*ws, ws*ws
-    #     attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0))
-    #     qkv = self.qkv(x).reshape(
-    #         B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
-    #     # n_h, B, _w*_h, nhead, ws*ws, dim
-    #     q, k, v = qkv[0], qkv[1], qkv[2]  # B, _h*_w, n_head, ws*ws, dim_head
-    #     attn = (q @ k.transpose(-2, -1)) * self.scale  # B, _h*_w, n_head, ws*ws, ws*ws
-    #     attn = attn + attn_mask.unsqueeze(2)
-    #     attn = attn.softmax(dim=-1)
-    #     attn = self.attn_drop(attn)  # attn @v ->  B, _h*_w, n_head, ws*ws, dim_head
-    #     attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
-    #     x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
-    #     if pad_r > 0 or pad_b > 0:
-    #         x = x[:, :H, :W, :].contiguous()
-    #     x = x.reshape(B, N, C)
-    #     x = self.proj(x)
-    #     x = self.proj_drop(x)
-    #     return x
-
-
-class GlobalSubSampleAttn(nn.Module):
-    """ GSA: using a  key to summarize the information for a group to be efficient.
-    """
-    def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., sr_ratio=1):
-        super().__init__()
-        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
-
-        self.dim = dim
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
-
-        self.q = nn.Linear(dim, dim, bias=True)
-        self.kv = nn.Linear(dim, dim * 2, bias=True)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        self.sr_ratio = sr_ratio
-        if sr_ratio > 1:
-            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
-            self.norm = nn.LayerNorm(dim)
-        else:
-            self.sr = None
-            self.norm = None
-
-    def forward(self, x, size: Size_):
-        B, N, C = x.shape
-        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
-        if self.sr is not None:
-            x = x.permute(0, 2, 1).reshape(B, C, *size)
-            x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
-            x = self.norm(x)
-        kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        k, v = kv[0], kv[1]
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=None):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        if ws is None:
-            self.attn = Attention(dim, num_heads, False, None, attn_drop, drop)
-        elif ws == 1:
-            self.attn = GlobalSubSampleAttn(dim, num_heads, attn_drop, drop, sr_ratio)
-        else:
-            self.attn = LocallyGroupedAttn(dim, num_heads, attn_drop, drop, ws)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x, size: Size_):
-        x = x + self.drop_path(self.attn(self.norm1(x), size))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class PosConv(nn.Module):
-    # PEG  from https://arxiv.org/abs/2102.10882
-    def __init__(self, in_chans, embed_dim=768, stride=1):
-        super(PosConv, self).__init__()
-        self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim), )
-        self.stride = stride
-
-    def forward(self, x, size: Size_):
-        B, N, C = x.shape
-        cnn_feat_token = x.transpose(1, 2).view(B, C, *size)
-        x = self.proj(cnn_feat_token)
-        if self.stride == 1:
-            x += cnn_feat_token
-        x = x.flatten(2).transpose(1, 2)
-        return x
-
-    def no_weight_decay(self):
-        return ['proj.%d.weight' % i for i in range(4)]
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-
-        self.img_size = img_size
-        self.patch_size = patch_size
-        assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \
-            f"img_size {img_size} should be divided by patch_size {patch_size}."
-        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
-        self.num_patches = self.H * self.W
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        self.norm = nn.LayerNorm(embed_dim)
-
-    def forward(self, x) -> Tuple[torch.Tensor, Size_]:
-        B, C, H, W = x.shape
-
-        x = self.proj(x).flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        out_size = (H // self.patch_size[0], W // self.patch_size[1])
-
-        return x, out_size
-
-
-class Twins(nn.Module):
-    """ Twins Vision Transfomer (Revisiting Spatial Attention)
-
-    Adapted from PVT (PyramidVisionTransformer) class at https://github.com/whai362/PVT.git
-    """
-    def __init__(
-            self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dims=(64, 128, 256, 512),
-            num_heads=(1, 2, 4, 8), mlp_ratios=(4, 4, 4, 4), drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=(3, 4, 6, 3), sr_ratios=(8, 4, 2, 1), wss=None,
-            block_cls=Block):
-        super().__init__()
-        self.num_classes = num_classes
-        self.depths = depths
-        self.embed_dims = embed_dims
-        self.num_features = embed_dims[-1]
-
-        img_size = to_2tuple(img_size)
-        prev_chs = in_chans
-        self.patch_embeds = nn.ModuleList()
-        self.pos_drops = nn.ModuleList()
-        for i in range(len(depths)):
-            self.patch_embeds.append(PatchEmbed(img_size, patch_size, prev_chs, embed_dims[i]))
-            self.pos_drops.append(nn.Dropout(p=drop_rate))
-            prev_chs = embed_dims[i]
-            img_size = tuple(t // patch_size for t in img_size)
-            patch_size = 2
-
-        self.blocks = nn.ModuleList()
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-        cur = 0
-        for k in range(len(depths)):
-            _block = nn.ModuleList([block_cls(
-                dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], drop=drop_rate,
-                attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[k],
-                ws=1 if wss is None or i % 2 == 1 else wss[k]) for i in range(depths[k])])
-            self.blocks.append(_block)
-            cur += depths[k]
-
-        self.pos_block = nn.ModuleList([PosConv(embed_dim, embed_dim) for embed_dim in embed_dims])
-
-        self.norm = norm_layer(self.num_features)
-
-        # classification head
-        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-
-        # init weights
-        self.apply(self._init_weights)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return set(['pos_block.' + n for n, p in self.pos_block.named_parameters()])
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-        elif isinstance(m, nn.BatchNorm2d):
-            m.weight.data.fill_(1.0)
-            m.bias.data.zero_()
-
-    def forward_features(self, x):
-        B = x.shape[0]
-        for i, (embed, drop, blocks, pos_blk) in enumerate(
-                zip(self.patch_embeds, self.pos_drops, self.blocks, self.pos_block)):
-            x, size = embed(x)
-            x = drop(x)
-            for j, blk in enumerate(blocks):
-                x = blk(x, size)
-                if j == 0:
-                    x = pos_blk(x, size)  # PEG here
-            if i < len(self.depths) - 1:
-                x = x.reshape(B, *size, -1).permute(0, 3, 1, 2).contiguous()
-        x = self.norm(x)
-        return x.mean(dim=1)  # GAP here
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def _create_twins(variant, pretrained=False, **kwargs):
-    if kwargs.get('features_only', None):
-        raise RuntimeError('features_only not implemented for Vision Transformer models.')
-
-    model = build_model_with_cfg(
-        Twins, variant, pretrained,
-        default_cfg=default_cfgs[variant],
-        **kwargs)
-    return model
-
-
-@register_model
-def twins_pcpvt_small(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-        depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
-    return _create_twins('twins_pcpvt_small', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def twins_pcpvt_base(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-        depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
-    return _create_twins('twins_pcpvt_base', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def twins_pcpvt_large(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-        depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
-    return _create_twins('twins_pcpvt_large', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def twins_svt_small(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16], mlp_ratios=[4, 4, 4, 4],
-        depths=[2, 2, 10, 4], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs)
-    return _create_twins('twins_svt_small', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def twins_svt_base(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24], mlp_ratios=[4, 4, 4, 4],
-        depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs)
-    return _create_twins('twins_svt_base', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def twins_svt_large(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=4, embed_dims=[128, 256, 512, 1024], num_heads=[4, 8, 16, 32], mlp_ratios=[4, 4, 4, 4],
-        depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs)
-    return _create_twins('twins_svt_large', pretrained=pretrained, **model_kwargs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/decode_heads/psp_head.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/decode_heads/psp_head.py
deleted file mode 100644
index b5f1e71c70c3a20f4007c263ec471a87bb214a48..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/decode_heads/psp_head.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class PPM(nn.ModuleList):
-    """Pooling Pyramid Module used in PSPNet.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module.
-        in_channels (int): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        conv_cfg (dict|None): Config of conv layers.
-        norm_cfg (dict|None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-        align_corners (bool): align_corners argument of F.interpolate.
-    """
-
-    def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
-                 act_cfg, align_corners):
-        super(PPM, self).__init__()
-        self.pool_scales = pool_scales
-        self.align_corners = align_corners
-        self.in_channels = in_channels
-        self.channels = channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        for pool_scale in pool_scales:
-            self.append(
-                nn.Sequential(
-                    nn.AdaptiveAvgPool2d(pool_scale),
-                    ConvModule(
-                        self.in_channels,
-                        self.channels,
-                        1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg)))
-
-    def forward(self, x):
-        """Forward function."""
-        ppm_outs = []
-        for ppm in self:
-            ppm_out = ppm(x)
-            upsampled_ppm_out = resize(
-                ppm_out,
-                size=x.size()[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-            ppm_outs.append(upsampled_ppm_out)
-        return ppm_outs
-
-
-@HEADS.register_module()
-class PSPHead(BaseDecodeHead):
-    """Pyramid Scene Parsing Network.
-
-    This head is the implementation of
-    `PSPNet <https://arxiv.org/abs/1612.01105>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module. Default: (1, 2, 3, 6).
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
-        super(PSPHead, self).__init__(**kwargs)
-        assert isinstance(pool_scales, (list, tuple))
-        self.pool_scales = pool_scales
-        self.psp_modules = PPM(
-            self.pool_scales,
-            self.in_channels,
-            self.channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-        self.bottleneck = ConvModule(
-            self.in_channels + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        psp_outs = [x]
-        psp_outs.extend(self.psp_modules(x))
-        psp_outs = torch.cat(psp_outs, dim=1)
-        output = self.bottleneck(psp_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/cownclown/TehVenom-MPT-7b-WizardLM_Uncensored-Storywriter-Merge/app.py b/spaces/cownclown/TehVenom-MPT-7b-WizardLM_Uncensored-Storywriter-Merge/app.py
deleted file mode 100644
index 18323e670d1cf0fa4fa8e9a7c44913e550a770c9..0000000000000000000000000000000000000000
--- a/spaces/cownclown/TehVenom-MPT-7b-WizardLM_Uncensored-Storywriter-Merge/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/TehVenom/MPT-7b-WizardLM_Uncensored-Storywriter-Merge").launch()
\ No newline at end of file
diff --git a/spaces/cozyanduofen/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/cozyanduofen/bingo/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
-  const [isAtBottom, setIsAtBottom] = React.useState(false)
-
-  React.useEffect(() => {
-    const handleScroll = () => {
-      setIsAtBottom(
-        window.innerHeight + window.scrollY >=
-          document.body.offsetHeight - offset
-      )
-    }
-
-    window.addEventListener('scroll', handleScroll, { passive: true })
-    handleScroll()
-
-    return () => {
-      window.removeEventListener('scroll', handleScroll)
-    }
-  }, [offset])
-
-  return isAtBottom
-}
diff --git a/spaces/cvlab/zero123-live/ldm/modules/x_transformer.py b/spaces/cvlab/zero123-live/ldm/modules/x_transformer.py
deleted file mode 100644
index 5fc15bf9cfe0111a910e7de33d04ffdec3877576..0000000000000000000000000000000000000000
--- a/spaces/cvlab/zero123-live/ldm/modules/x_transformer.py
+++ /dev/null
@@ -1,641 +0,0 @@
-"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-from functools import partial
-from inspect import isfunction
-from collections import namedtuple
-from einops import rearrange, repeat, reduce
-
-# constants
-
-DEFAULT_DIM_HEAD = 64
-
-Intermediates = namedtuple('Intermediates', [
-    'pre_softmax_attn',
-    'post_softmax_attn'
-])
-
-LayerIntermediates = namedtuple('Intermediates', [
-    'hiddens',
-    'attn_intermediates'
-])
-
-
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len):
-        super().__init__()
-        self.emb = nn.Embedding(max_seq_len, dim)
-        self.init_()
-
-    def init_(self):
-        nn.init.normal_(self.emb.weight, std=0.02)
-
-    def forward(self, x):
-        n = torch.arange(x.shape[1], device=x.device)
-        return self.emb(n)[None, :, :]
-
-
-class FixedPositionalEmbedding(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
-
-    def forward(self, x, seq_dim=1, offset=0):
-        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
-        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
-        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
-        return emb[None, :, :]
-
-
-# helpers
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def always(val):
-    def inner(*args, **kwargs):
-        return val
-    return inner
-
-
-def not_equals(val):
-    def inner(x):
-        return x != val
-    return inner
-
-
-def equals(val):
-    def inner(x):
-        return x == val
-    return inner
-
-
-def max_neg_value(tensor):
-    return -torch.finfo(tensor.dtype).max
-
-
-# keyword argument helpers
-
-def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
-    return dict(zip(keys, values))
-
-
-def group_dict_by_key(cond, d):
-    return_val = [dict(), dict()]
-    for key in d.keys():
-        match = bool(cond(key))
-        ind = int(not match)
-        return_val[ind][key] = d[key]
-    return (*return_val,)
-
-
-def string_begins_with(prefix, str):
-    return str.startswith(prefix)
-
-
-def group_by_key_prefix(prefix, d):
-    return group_dict_by_key(partial(string_begins_with, prefix), d)
-
-
-def groupby_prefix_and_trim(prefix, d):
-    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
-    return kwargs_without_prefix, kwargs
-
-
-# classes
-class Scale(nn.Module):
-    def __init__(self, value, fn):
-        super().__init__()
-        self.value = value
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.value, *rest)
-
-
-class Rezero(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-        self.g = nn.Parameter(torch.zeros(1))
-
-    def forward(self, x, **kwargs):
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.g, *rest)
-
-
-class ScaleNorm(nn.Module):
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1))
-
-    def forward(self, x):
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim, eps=1e-8):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class Residual(nn.Module):
-    def forward(self, x, residual):
-        return x + residual
-
-
-class GRUGating(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.gru = nn.GRUCell(dim, dim)
-
-    def forward(self, x, residual):
-        gated_output = self.gru(
-            rearrange(x, 'b n d -> (b n) d'),
-            rearrange(residual, 'b n d -> (b n) d')
-        )
-
-        return gated_output.reshape_as(x)
-
-
-# feedforward
-
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-# attention.
-class Attention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            dim_head=DEFAULT_DIM_HEAD,
-            heads=8,
-            causal=False,
-            mask=None,
-            talking_heads=False,
-            sparse_topk=None,
-            use_entmax15=False,
-            num_mem_kv=0,
-            dropout=0.,
-            on_attn=False
-    ):
-        super().__init__()
-        if use_entmax15:
-            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        self.causal = causal
-        self.mask = mask
-
-        inner_dim = dim_head * heads
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(dim, inner_dim, bias=False)
-        self.dropout = nn.Dropout(dropout)
-
-        # talking heads
-        self.talking_heads = talking_heads
-        if talking_heads:
-            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
-            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
-
-        # explicit topk sparse attention
-        self.sparse_topk = sparse_topk
-
-        # entmax
-        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
-        self.attn_fn = F.softmax
-
-        # add memory key / values
-        self.num_mem_kv = num_mem_kv
-        if num_mem_kv > 0:
-            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-
-        # attention on attention
-        self.attn_on_attn = on_attn
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
-
-    def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            rel_pos=None,
-            sinusoidal_emb=None,
-            prev_attn=None,
-            mem=None
-    ):
-        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
-        kv_input = default(context, x)
-
-        q_input = x
-        k_input = kv_input
-        v_input = kv_input
-
-        if exists(mem):
-            k_input = torch.cat((mem, k_input), dim=-2)
-            v_input = torch.cat((mem, v_input), dim=-2)
-
-        if exists(sinusoidal_emb):
-            # in shortformer, the query would start at a position offset depending on the past cached memory
-            offset = k_input.shape[-2] - q_input.shape[-2]
-            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
-            k_input = k_input + sinusoidal_emb(k_input)
-
-        q = self.to_q(q_input)
-        k = self.to_k(k_input)
-        v = self.to_v(v_input)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
-
-        input_mask = None
-        if any(map(exists, (mask, context_mask))):
-            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
-            k_mask = q_mask if not exists(context) else context_mask
-            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
-            q_mask = rearrange(q_mask, 'b i -> b () i ()')
-            k_mask = rearrange(k_mask, 'b j -> b () () j')
-            input_mask = q_mask * k_mask
-
-        if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
-            k = torch.cat((mem_k, k), dim=-2)
-            v = torch.cat((mem_v, v), dim=-2)
-            if exists(input_mask):
-                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
-
-        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
-        mask_value = max_neg_value(dots)
-
-        if exists(prev_attn):
-            dots = dots + prev_attn
-
-        pre_softmax_attn = dots
-
-        if talking_heads:
-            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
-
-        if exists(rel_pos):
-            dots = rel_pos(dots)
-
-        if exists(input_mask):
-            dots.masked_fill_(~input_mask, mask_value)
-            del input_mask
-
-        if self.causal:
-            i, j = dots.shape[-2:]
-            r = torch.arange(i, device=device)
-            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
-            mask = F.pad(mask, (j - i, 0), value=False)
-            dots.masked_fill_(mask, mask_value)
-            del mask
-
-        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
-            top, _ = dots.topk(self.sparse_topk, dim=-1)
-            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
-            mask = dots < vk
-            dots.masked_fill_(mask, mask_value)
-            del mask
-
-        attn = self.attn_fn(dots, dim=-1)
-        post_softmax_attn = attn
-
-        attn = self.dropout(attn)
-
-        if talking_heads:
-            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
-
-        out = einsum('b h i j, b h j d -> b h i d', attn, v)
-        out = rearrange(out, 'b h n d -> b n (h d)')
-
-        intermediates = Intermediates(
-            pre_softmax_attn=pre_softmax_attn,
-            post_softmax_attn=post_softmax_attn
-        )
-
-        return self.to_out(out), intermediates
-
-
-class AttentionLayers(nn.Module):
-    def __init__(
-            self,
-            dim,
-            depth,
-            heads=8,
-            causal=False,
-            cross_attend=False,
-            only_cross=False,
-            use_scalenorm=False,
-            use_rmsnorm=False,
-            use_rezero=False,
-            rel_pos_num_buckets=32,
-            rel_pos_max_distance=128,
-            position_infused_attn=False,
-            custom_layers=None,
-            sandwich_coef=None,
-            par_ratio=None,
-            residual_attn=False,
-            cross_residual_attn=False,
-            macaron=False,
-            pre_norm=True,
-            gate_residual=False,
-            **kwargs
-    ):
-        super().__init__()
-        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
-        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
-
-        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
-
-        self.dim = dim
-        self.depth = depth
-        self.layers = nn.ModuleList([])
-
-        self.has_pos_emb = position_infused_attn
-        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
-        self.rotary_pos_emb = always(None)
-
-        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
-        self.rel_pos = None
-
-        self.pre_norm = pre_norm
-
-        self.residual_attn = residual_attn
-        self.cross_residual_attn = cross_residual_attn
-
-        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
-        norm_class = RMSNorm if use_rmsnorm else norm_class
-        norm_fn = partial(norm_class, dim)
-
-        norm_fn = nn.Identity if use_rezero else norm_fn
-        branch_fn = Rezero if use_rezero else None
-
-        if cross_attend and not only_cross:
-            default_block = ('a', 'c', 'f')
-        elif cross_attend and only_cross:
-            default_block = ('c', 'f')
-        else:
-            default_block = ('a', 'f')
-
-        if macaron:
-            default_block = ('f',) + default_block
-
-        if exists(custom_layers):
-            layer_types = custom_layers
-        elif exists(par_ratio):
-            par_depth = depth * len(default_block)
-            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
-            default_block = tuple(filter(not_equals('f'), default_block))
-            par_attn = par_depth // par_ratio
-            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
-            par_width = (depth_cut + depth_cut // par_attn) // par_attn
-            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
-            par_block = default_block + ('f',) * (par_width - len(default_block))
-            par_head = par_block * par_attn
-            layer_types = par_head + ('f',) * (par_depth - len(par_head))
-        elif exists(sandwich_coef):
-            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
-            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
-        else:
-            layer_types = default_block * depth
-
-        self.layer_types = layer_types
-        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
-
-        for layer_type in self.layer_types:
-            if layer_type == 'a':
-                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
-            elif layer_type == 'c':
-                layer = Attention(dim, heads=heads, **attn_kwargs)
-            elif layer_type == 'f':
-                layer = FeedForward(dim, **ff_kwargs)
-                layer = layer if not macaron else Scale(0.5, layer)
-            else:
-                raise Exception(f'invalid layer type {layer_type}')
-
-            if isinstance(layer, Attention) and exists(branch_fn):
-                layer = branch_fn(layer)
-
-            if gate_residual:
-                residual_fn = GRUGating(dim)
-            else:
-                residual_fn = Residual()
-
-            self.layers.append(nn.ModuleList([
-                norm_fn(),
-                layer,
-                residual_fn
-            ]))
-
-    def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            mems=None,
-            return_hiddens=False
-    ):
-        hiddens = []
-        intermediates = []
-        prev_attn = None
-        prev_cross_attn = None
-
-        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
-
-        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
-            is_last = ind == (len(self.layers) - 1)
-
-            if layer_type == 'a':
-                hiddens.append(x)
-                layer_mem = mems.pop(0)
-
-            residual = x
-
-            if self.pre_norm:
-                x = norm(x)
-
-            if layer_type == 'a':
-                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
-                                   prev_attn=prev_attn, mem=layer_mem)
-            elif layer_type == 'c':
-                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
-            elif layer_type == 'f':
-                out = block(x)
-
-            x = residual_fn(out, residual)
-
-            if layer_type in ('a', 'c'):
-                intermediates.append(inter)
-
-            if layer_type == 'a' and self.residual_attn:
-                prev_attn = inter.pre_softmax_attn
-            elif layer_type == 'c' and self.cross_residual_attn:
-                prev_cross_attn = inter.pre_softmax_attn
-
-            if not self.pre_norm and not is_last:
-                x = norm(x)
-
-        if return_hiddens:
-            intermediates = LayerIntermediates(
-                hiddens=hiddens,
-                attn_intermediates=intermediates
-            )
-
-            return x, intermediates
-
-        return x
-
-
-class Encoder(AttentionLayers):
-    def __init__(self, **kwargs):
-        assert 'causal' not in kwargs, 'cannot set causality on encoder'
-        super().__init__(causal=False, **kwargs)
-
-
-
-class TransformerWrapper(nn.Module):
-    def __init__(
-            self,
-            *,
-            num_tokens,
-            max_seq_len,
-            attn_layers,
-            emb_dim=None,
-            max_mem_len=0.,
-            emb_dropout=0.,
-            num_memory_tokens=None,
-            tie_embedding=False,
-            use_pos_emb=True
-    ):
-        super().__init__()
-        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
-
-        dim = attn_layers.dim
-        emb_dim = default(emb_dim, dim)
-
-        self.max_seq_len = max_seq_len
-        self.max_mem_len = max_mem_len
-        self.num_tokens = num_tokens
-
-        self.token_emb = nn.Embedding(num_tokens, emb_dim)
-        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
-                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
-        self.emb_dropout = nn.Dropout(emb_dropout)
-
-        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
-        self.attn_layers = attn_layers
-        self.norm = nn.LayerNorm(dim)
-
-        self.init_()
-
-        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
-
-        # memory tokens (like [cls]) from Memory Transformers paper
-        num_memory_tokens = default(num_memory_tokens, 0)
-        self.num_memory_tokens = num_memory_tokens
-        if num_memory_tokens > 0:
-            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
-
-            # let funnel encoder know number of memory tokens, if specified
-            if hasattr(attn_layers, 'num_memory_tokens'):
-                attn_layers.num_memory_tokens = num_memory_tokens
-
-    def init_(self):
-        nn.init.normal_(self.token_emb.weight, std=0.02)
-
-    def forward(
-            self,
-            x,
-            return_embeddings=False,
-            mask=None,
-            return_mems=False,
-            return_attn=False,
-            mems=None,
-            **kwargs
-    ):
-        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
-        x = self.token_emb(x)
-        x += self.pos_emb(x)
-        x = self.emb_dropout(x)
-
-        x = self.project_emb(x)
-
-        if num_mem > 0:
-            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
-            x = torch.cat((mem, x), dim=1)
-
-            # auto-handle masking after appending memory tokens
-            if exists(mask):
-                mask = F.pad(mask, (num_mem, 0), value=True)
-
-        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
-        x = self.norm(x)
-
-        mem, x = x[:, :num_mem], x[:, num_mem:]
-
-        out = self.to_logits(x) if not return_embeddings else x
-
-        if return_mems:
-            hiddens = intermediates.hiddens
-            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
-            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
-            return out, new_mems
-
-        if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
-            return out, attn_maps
-
-        return out
-
diff --git a/spaces/daayros/anything-v3.0/app.py b/spaces/daayros/anything-v3.0/app.py
deleted file mode 100644
index 1c567dfe6e68a28b0233d3b9959b493dd42b0ed9..0000000000000000000000000000000000000000
--- a/spaces/daayros/anything-v3.0/app.py
+++ /dev/null
@@ -1,278 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-import utils
-import datetime
-import time
-import psutil
-
-start_time = time.time()
-is_colab = utils.is_google_colab()
-
-class Model:
-    def __init__(self, name, path="", prefix=""):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-        self.pipe_t2i = None
-        self.pipe_i2i = None
-
-models = [
-   Model("anything v3", "Linaqruf/anything-v3.0", "anything v3 style"),
-  ]
-    #  Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
-    #  Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
-    #  Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
-    #  Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
-     #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
-     #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
-     #Model("Robo Diffusion", "nousr/robo-diffusion", ""),
-     
-scheduler = DPMSolverMultistepScheduler(
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    num_train_timesteps=1000,
-    trained_betas=None,
-    predict_epsilon=True,
-    thresholding=False,
-    algorithm_type="dpmsolver++",
-    solver_type="midpoint",
-    lower_order_final=True,
-)
-
-custom_model = None
-if is_colab:
-  models.insert(0, Model("Custom model"))
-  custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
-  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-
-else: # download all models
-  print(f"{datetime.datetime.now()} Downloading vae...")
-  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
-  for model in models:
-    try:
-        print(f"{datetime.datetime.now()} Downloading {model.name} model...")
-        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
-        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-    except Exception as e:
-        print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
-        models.remove(model)
-  pipe = models[0].pipe_t2i
-  
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def custom_model_changed(path):
-  models[0].path = path
-  global current_model
-  current_model = models[0]
-
-def on_model_change(model_name):
-  
-  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
-  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
-def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-
-  print(psutil.virtual_memory()) # print memory usage
-
-  global current_model
-  for model in models:
-    if model.name == model_name:
-      current_model = model
-      model_path = current_model.path
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-
-  try:
-    if img is not None:
-      return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "txt2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-        else:
-          pipe = pipe.to("cpu")
-          pipe = current_model.pipe_t2i
-
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "txt2img"
-
-    prompt = current_model.prefix + prompt  
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "img2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-        else:
-          pipe = pipe.to("cpu")
-          pipe = current_model.pipe_i2i
-        
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "img2img"
-
-    prompt = current_model.prefix + prompt
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe(
-        prompt,
-        negative_prompt = neg_prompt,
-        # num_images_per_prompt=n_images,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-
-    if is_colab:
-      return results.images[0]
-      
-    for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
-    return results.images[0]
-
-css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="finetuned-diffusion-div">
-              <div>
-                <h1>Anything V3</h1>
-              </div>
-              <img src="https://i.imgur.com/FEA7N1p.png">
-              <p>
-               Demo for Anything V3 + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗.
-              </p>
-              <p>You can skip the queue and load custom models in the colab: <a href="https://colab.research.google.com/drive/109CvcHvmfTv3hlS8DSetiky9kH1WVKFf"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
-               Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
-              </p>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
-              with gr.Box(visible=False) as custom_model_group:
-                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
-                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
-              
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-
-              image_out = gr.Image(height=512)
-              # gallery = gr.Gallery(
-              #     label="Generated images", show_label=False, elem_id="gallery"
-              # ).style(grid=[1], height="auto")
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-
-              # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    if is_colab:
-      model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
-      custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
-    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
-
-    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    ex = gr.Examples([
-       [models[0].name, "iron man", 7.5, 50],
-      
-    ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Model by TopdeckingLands.</p>
-    </div>
-    """)
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-if not is_colab:
-  demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
\ No newline at end of file
diff --git a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/__init__.py b/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/DcxImagePlugin.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/DcxImagePlugin.py
deleted file mode 100644
index cde9d42f09f304679180b673bf4d8fdb68d6b4b3..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/DcxImagePlugin.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# DCX file handling
-#
-# DCX is a container file format defined by Intel, commonly used
-# for fax applications.  Each DCX file consists of a directory
-# (a list of file offsets) followed by a set of (usually 1-bit)
-# PCX files.
-#
-# History:
-# 1995-09-09 fl   Created
-# 1996-03-20 fl   Properly derived from PcxImageFile.
-# 1998-07-15 fl   Renamed offset attribute to avoid name clash
-# 2002-07-30 fl   Fixed file handling
-#
-# Copyright (c) 1997-98 by Secret Labs AB.
-# Copyright (c) 1995-96 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image
-from ._binary import i32le as i32
-from .PcxImagePlugin import PcxImageFile
-
-MAGIC = 0x3ADE68B1  # QUIZ: what's this value, then?
-
-
-def _accept(prefix):
-    return len(prefix) >= 4 and i32(prefix) == MAGIC
-
-
-##
-# Image plugin for the Intel DCX format.
-
-
-class DcxImageFile(PcxImageFile):
-    format = "DCX"
-    format_description = "Intel DCX"
-    _close_exclusive_fp_after_loading = False
-
-    def _open(self):
-        # Header
-        s = self.fp.read(4)
-        if not _accept(s):
-            msg = "not a DCX file"
-            raise SyntaxError(msg)
-
-        # Component directory
-        self._offset = []
-        for i in range(1024):
-            offset = i32(self.fp.read(4))
-            if not offset:
-                break
-            self._offset.append(offset)
-
-        self._fp = self.fp
-        self.frame = None
-        self.n_frames = len(self._offset)
-        self.is_animated = self.n_frames > 1
-        self.seek(0)
-
-    def seek(self, frame):
-        if not self._seek_check(frame):
-            return
-        self.frame = frame
-        self.fp = self._fp
-        self.fp.seek(self._offset[frame])
-        PcxImageFile._open(self)
-
-    def tell(self):
-        return self.frame
-
-
-Image.register_open(DcxImageFile.format, DcxImageFile, _accept)
-
-Image.register_extension(DcxImageFile.format, ".dcx")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/cli/normalizer.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/cli/normalizer.py
deleted file mode 100644
index f4bcbaac049b542a004918a0b1499122fcca9cc0..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/cli/normalizer.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import argparse
-import sys
-from json import dumps
-from os.path import abspath, basename, dirname, join, realpath
-from platform import python_version
-from typing import List, Optional
-from unicodedata import unidata_version
-
-import charset_normalizer.md as md_module
-from charset_normalizer import from_fp
-from charset_normalizer.models import CliDetectionResult
-from charset_normalizer.version import __version__
-
-
-def query_yes_no(question: str, default: str = "yes") -> bool:
-    """Ask a yes/no question via input() and return their answer.
-
-    "question" is a string that is presented to the user.
-    "default" is the presumed answer if the user just hits <Enter>.
-        It must be "yes" (the default), "no" or None (meaning
-        an answer is required of the user).
-
-    The "answer" return value is True for "yes" or False for "no".
-
-    Credit goes to (c) https://stackoverflow.com/questions/3041986/apt-command-line-interface-like-yes-no-input
-    """
-    valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False}
-    if default is None:
-        prompt = " [y/n] "
-    elif default == "yes":
-        prompt = " [Y/n] "
-    elif default == "no":
-        prompt = " [y/N] "
-    else:
-        raise ValueError("invalid default answer: '%s'" % default)
-
-    while True:
-        sys.stdout.write(question + prompt)
-        choice = input().lower()
-        if default is not None and choice == "":
-            return valid[default]
-        elif choice in valid:
-            return valid[choice]
-        else:
-            sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n")
-
-
-def cli_detect(argv: Optional[List[str]] = None) -> int:
-    """
-    CLI assistant using ARGV and ArgumentParser
-    :param argv:
-    :return: 0 if everything is fine, anything else equal trouble
-    """
-    parser = argparse.ArgumentParser(
-        description="The Real First Universal Charset Detector. "
-        "Discover originating encoding used on text file. "
-        "Normalize text to unicode."
-    )
-
-    parser.add_argument(
-        "files", type=argparse.FileType("rb"), nargs="+", help="File(s) to be analysed"
-    )
-    parser.add_argument(
-        "-v",
-        "--verbose",
-        action="store_true",
-        default=False,
-        dest="verbose",
-        help="Display complementary information about file if any. "
-        "Stdout will contain logs about the detection process.",
-    )
-    parser.add_argument(
-        "-a",
-        "--with-alternative",
-        action="store_true",
-        default=False,
-        dest="alternatives",
-        help="Output complementary possibilities if any. Top-level JSON WILL be a list.",
-    )
-    parser.add_argument(
-        "-n",
-        "--normalize",
-        action="store_true",
-        default=False,
-        dest="normalize",
-        help="Permit to normalize input file. If not set, program does not write anything.",
-    )
-    parser.add_argument(
-        "-m",
-        "--minimal",
-        action="store_true",
-        default=False,
-        dest="minimal",
-        help="Only output the charset detected to STDOUT. Disabling JSON output.",
-    )
-    parser.add_argument(
-        "-r",
-        "--replace",
-        action="store_true",
-        default=False,
-        dest="replace",
-        help="Replace file when trying to normalize it instead of creating a new one.",
-    )
-    parser.add_argument(
-        "-f",
-        "--force",
-        action="store_true",
-        default=False,
-        dest="force",
-        help="Replace file without asking if you are sure, use this flag with caution.",
-    )
-    parser.add_argument(
-        "-t",
-        "--threshold",
-        action="store",
-        default=0.2,
-        type=float,
-        dest="threshold",
-        help="Define a custom maximum amount of chaos allowed in decoded content. 0. <= chaos <= 1.",
-    )
-    parser.add_argument(
-        "--version",
-        action="version",
-        version="Charset-Normalizer {} - Python {} - Unicode {} - SpeedUp {}".format(
-            __version__,
-            python_version(),
-            unidata_version,
-            "OFF" if md_module.__file__.lower().endswith(".py") else "ON",
-        ),
-        help="Show version information and exit.",
-    )
-
-    args = parser.parse_args(argv)
-
-    if args.replace is True and args.normalize is False:
-        print("Use --replace in addition of --normalize only.", file=sys.stderr)
-        return 1
-
-    if args.force is True and args.replace is False:
-        print("Use --force in addition of --replace only.", file=sys.stderr)
-        return 1
-
-    if args.threshold < 0.0 or args.threshold > 1.0:
-        print("--threshold VALUE should be between 0. AND 1.", file=sys.stderr)
-        return 1
-
-    x_ = []
-
-    for my_file in args.files:
-        matches = from_fp(my_file, threshold=args.threshold, explain=args.verbose)
-
-        best_guess = matches.best()
-
-        if best_guess is None:
-            print(
-                'Unable to identify originating encoding for "{}". {}'.format(
-                    my_file.name,
-                    "Maybe try increasing maximum amount of chaos."
-                    if args.threshold < 1.0
-                    else "",
-                ),
-                file=sys.stderr,
-            )
-            x_.append(
-                CliDetectionResult(
-                    abspath(my_file.name),
-                    None,
-                    [],
-                    [],
-                    "Unknown",
-                    [],
-                    False,
-                    1.0,
-                    0.0,
-                    None,
-                    True,
-                )
-            )
-        else:
-            x_.append(
-                CliDetectionResult(
-                    abspath(my_file.name),
-                    best_guess.encoding,
-                    best_guess.encoding_aliases,
-                    [
-                        cp
-                        for cp in best_guess.could_be_from_charset
-                        if cp != best_guess.encoding
-                    ],
-                    best_guess.language,
-                    best_guess.alphabets,
-                    best_guess.bom,
-                    best_guess.percent_chaos,
-                    best_guess.percent_coherence,
-                    None,
-                    True,
-                )
-            )
-
-            if len(matches) > 1 and args.alternatives:
-                for el in matches:
-                    if el != best_guess:
-                        x_.append(
-                            CliDetectionResult(
-                                abspath(my_file.name),
-                                el.encoding,
-                                el.encoding_aliases,
-                                [
-                                    cp
-                                    for cp in el.could_be_from_charset
-                                    if cp != el.encoding
-                                ],
-                                el.language,
-                                el.alphabets,
-                                el.bom,
-                                el.percent_chaos,
-                                el.percent_coherence,
-                                None,
-                                False,
-                            )
-                        )
-
-            if args.normalize is True:
-                if best_guess.encoding.startswith("utf") is True:
-                    print(
-                        '"{}" file does not need to be normalized, as it already came from unicode.'.format(
-                            my_file.name
-                        ),
-                        file=sys.stderr,
-                    )
-                    if my_file.closed is False:
-                        my_file.close()
-                    continue
-
-                dir_path = dirname(realpath(my_file.name))
-                file_name = basename(realpath(my_file.name))
-
-                o_: List[str] = file_name.split(".")
-
-                if args.replace is False:
-                    o_.insert(-1, best_guess.encoding)
-                    if my_file.closed is False:
-                        my_file.close()
-                elif (
-                    args.force is False
-                    and query_yes_no(
-                        'Are you sure to normalize "{}" by replacing it ?'.format(
-                            my_file.name
-                        ),
-                        "no",
-                    )
-                    is False
-                ):
-                    if my_file.closed is False:
-                        my_file.close()
-                    continue
-
-                try:
-                    x_[0].unicode_path = join(dir_path, ".".join(o_))
-
-                    with open(x_[0].unicode_path, "w", encoding="utf-8") as fp:
-                        fp.write(str(best_guess))
-                except IOError as e:
-                    print(str(e), file=sys.stderr)
-                    if my_file.closed is False:
-                        my_file.close()
-                    return 2
-
-        if my_file.closed is False:
-            my_file.close()
-
-    if args.minimal is False:
-        print(
-            dumps(
-                [el.__dict__ for el in x_] if len(x_) > 1 else x_[0].__dict__,
-                ensure_ascii=True,
-                indent=4,
-            )
-        )
-    else:
-        for my_file in args.files:
-            print(
-                ", ".join(
-                    [
-                        el.encoding or "undefined"
-                        for el in x_
-                        if el.path == abspath(my_file.name)
-                    ]
-                )
-            )
-
-    return 0
-
-
-if __name__ == "__main__":
-    cli_detect()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/utils.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/utils.py
deleted file mode 100644
index 267d64ce8aee9eaf5462d9cbd47deca44cfdef28..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/utils.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import re
-import warnings
-from dataclasses import is_dataclass
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    MutableMapping,
-    Optional,
-    Set,
-    Type,
-    Union,
-    cast,
-)
-from weakref import WeakKeyDictionary
-
-import fastapi
-from fastapi._compat import (
-    PYDANTIC_V2,
-    BaseConfig,
-    ModelField,
-    PydanticSchemaGenerationError,
-    Undefined,
-    UndefinedType,
-    Validator,
-    lenient_issubclass,
-)
-from fastapi.datastructures import DefaultPlaceholder, DefaultType
-from pydantic import BaseModel, create_model
-from pydantic.fields import FieldInfo
-from typing_extensions import Literal
-
-if TYPE_CHECKING:  # pragma: nocover
-    from .routing import APIRoute
-
-# Cache for `create_cloned_field`
-_CLONED_TYPES_CACHE: MutableMapping[
-    Type[BaseModel], Type[BaseModel]
-] = WeakKeyDictionary()
-
-
-def is_body_allowed_for_status_code(status_code: Union[int, str, None]) -> bool:
-    if status_code is None:
-        return True
-    # Ref: https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.1.0.md#patterned-fields-1
-    if status_code in {
-        "default",
-        "1XX",
-        "2XX",
-        "3XX",
-        "4XX",
-        "5XX",
-    }:
-        return True
-    current_status_code = int(status_code)
-    return not (current_status_code < 200 or current_status_code in {204, 304})
-
-
-def get_path_param_names(path: str) -> Set[str]:
-    return set(re.findall("{(.*?)}", path))
-
-
-def create_response_field(
-    name: str,
-    type_: Type[Any],
-    class_validators: Optional[Dict[str, Validator]] = None,
-    default: Optional[Any] = Undefined,
-    required: Union[bool, UndefinedType] = Undefined,
-    model_config: Type[BaseConfig] = BaseConfig,
-    field_info: Optional[FieldInfo] = None,
-    alias: Optional[str] = None,
-    mode: Literal["validation", "serialization"] = "validation",
-) -> ModelField:
-    """
-    Create a new response field. Raises if type_ is invalid.
-    """
-    class_validators = class_validators or {}
-    if PYDANTIC_V2:
-        field_info = field_info or FieldInfo(
-            annotation=type_, default=default, alias=alias
-        )
-    else:
-        field_info = field_info or FieldInfo()
-    kwargs = {"name": name, "field_info": field_info}
-    if PYDANTIC_V2:
-        kwargs.update({"mode": mode})
-    else:
-        kwargs.update(
-            {
-                "type_": type_,
-                "class_validators": class_validators,
-                "default": default,
-                "required": required,
-                "model_config": model_config,
-                "alias": alias,
-            }
-        )
-    try:
-        return ModelField(**kwargs)  # type: ignore[arg-type]
-    except (RuntimeError, PydanticSchemaGenerationError):
-        raise fastapi.exceptions.FastAPIError(
-            "Invalid args for response field! Hint: "
-            f"check that {type_} is a valid Pydantic field type. "
-            "If you are using a return type annotation that is not a valid Pydantic "
-            "field (e.g. Union[Response, dict, None]) you can disable generating the "
-            "response model from the type annotation with the path operation decorator "
-            "parameter response_model=None. Read more: "
-            "https://fastapi.tiangolo.com/tutorial/response-model/"
-        ) from None
-
-
-def create_cloned_field(
-    field: ModelField,
-    *,
-    cloned_types: Optional[MutableMapping[Type[BaseModel], Type[BaseModel]]] = None,
-) -> ModelField:
-    if PYDANTIC_V2:
-        return field
-    # cloned_types caches already cloned types to support recursive models and improve
-    # performance by avoiding unecessary cloning
-    if cloned_types is None:
-        cloned_types = _CLONED_TYPES_CACHE
-
-    original_type = field.type_
-    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
-        original_type = original_type.__pydantic_model__
-    use_type = original_type
-    if lenient_issubclass(original_type, BaseModel):
-        original_type = cast(Type[BaseModel], original_type)
-        use_type = cloned_types.get(original_type)
-        if use_type is None:
-            use_type = create_model(original_type.__name__, __base__=original_type)
-            cloned_types[original_type] = use_type
-            for f in original_type.__fields__.values():
-                use_type.__fields__[f.name] = create_cloned_field(
-                    f, cloned_types=cloned_types
-                )
-    new_field = create_response_field(name=field.name, type_=use_type)
-    new_field.has_alias = field.has_alias  # type: ignore[attr-defined]
-    new_field.alias = field.alias  # type: ignore[misc]
-    new_field.class_validators = field.class_validators  # type: ignore[attr-defined]
-    new_field.default = field.default  # type: ignore[misc]
-    new_field.required = field.required  # type: ignore[misc]
-    new_field.model_config = field.model_config  # type: ignore[attr-defined]
-    new_field.field_info = field.field_info
-    new_field.allow_none = field.allow_none  # type: ignore[attr-defined]
-    new_field.validate_always = field.validate_always  # type: ignore[attr-defined]
-    if field.sub_fields:  # type: ignore[attr-defined]
-        new_field.sub_fields = [  # type: ignore[attr-defined]
-            create_cloned_field(sub_field, cloned_types=cloned_types)
-            for sub_field in field.sub_fields  # type: ignore[attr-defined]
-        ]
-    if field.key_field:  # type: ignore[attr-defined]
-        new_field.key_field = create_cloned_field(  # type: ignore[attr-defined]
-            field.key_field, cloned_types=cloned_types  # type: ignore[attr-defined]
-        )
-    new_field.validators = field.validators  # type: ignore[attr-defined]
-    new_field.pre_validators = field.pre_validators  # type: ignore[attr-defined]
-    new_field.post_validators = field.post_validators  # type: ignore[attr-defined]
-    new_field.parse_json = field.parse_json  # type: ignore[attr-defined]
-    new_field.shape = field.shape  # type: ignore[attr-defined]
-    new_field.populate_validators()  # type: ignore[attr-defined]
-    return new_field
-
-
-def generate_operation_id_for_path(
-    *, name: str, path: str, method: str
-) -> str:  # pragma: nocover
-    warnings.warn(
-        "fastapi.utils.generate_operation_id_for_path() was deprecated, "
-        "it is not used internally, and will be removed soon",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    operation_id = name + path
-    operation_id = re.sub(r"\W", "_", operation_id)
-    operation_id = operation_id + "_" + method.lower()
-    return operation_id
-
-
-def generate_unique_id(route: "APIRoute") -> str:
-    operation_id = route.name + route.path_format
-    operation_id = re.sub(r"\W", "_", operation_id)
-    assert route.methods
-    operation_id = operation_id + "_" + list(route.methods)[0].lower()
-    return operation_id
-
-
-def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None:
-    for key, value in update_dict.items():
-        if (
-            key in main_dict
-            and isinstance(main_dict[key], dict)
-            and isinstance(value, dict)
-        ):
-            deep_dict_update(main_dict[key], value)
-        elif (
-            key in main_dict
-            and isinstance(main_dict[key], list)
-            and isinstance(update_dict[key], list)
-        ):
-            main_dict[key] = main_dict[key] + update_dict[key]
-        else:
-            main_dict[key] = value
-
-
-def get_value_or_default(
-    first_item: Union[DefaultPlaceholder, DefaultType],
-    *extra_items: Union[DefaultPlaceholder, DefaultType],
-) -> Union[DefaultPlaceholder, DefaultType]:
-    """
-    Pass items or `DefaultPlaceholder`s by descending priority.
-
-    The first one to _not_ be a `DefaultPlaceholder` will be returned.
-
-    Otherwise, the first item (a `DefaultPlaceholder`) will be returned.
-    """
-    items = (first_item,) + extra_items
-    for item in items:
-        if not isinstance(item, DefaultPlaceholder):
-            return item
-    return first_item
-
-
-def match_pydantic_error_url(error_type: str) -> Any:
-    from dirty_equals import IsStr
-
-    return IsStr(regex=rf"^https://errors\.pydantic\.dev/.*/v/{error_type}")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-93c91554.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-93c91554.css
deleted file mode 100644
index beda351dfc765484ad744113e3d1734eb71cacd1..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-93c91554.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-15lo0d8{display:flex;flex-wrap:wrap;gap:var(--layout-gap);width:var(--size-full)}.hide.svelte-15lo0d8{display:none}.compact.svelte-15lo0d8>*,.compact.svelte-15lo0d8 .box{border-radius:0}.compact.svelte-15lo0d8,.panel.svelte-15lo0d8{border-radius:var(--container-radius);background:var(--background-fill-secondary);padding:var(--size-2)}.unequal-height.svelte-15lo0d8{align-items:flex-start}.stretch.svelte-15lo0d8{align-items:stretch}div.svelte-15lo0d8>*,div.svelte-15lo0d8>.form>*{flex:1 1 0%;flex-wrap:wrap;min-width:min(160px,100%)}
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/_path.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/_path.py
deleted file mode 100644
index 1f97c96146960d4256840340fe25f8da7be552c2..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/_path.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import pathlib
-import functools
-
-from typing import Dict, Union
-
-
-####
-# from jaraco.path 3.4.1
-
-FilesSpec = Dict[str, Union[str, bytes, 'FilesSpec']]  # type: ignore
-
-
-def build(spec: FilesSpec, prefix=pathlib.Path()):
-    """
-    Build a set of files/directories, as described by the spec.
-
-    Each key represents a pathname, and the value represents
-    the content. Content may be a nested directory.
-
-    >>> spec = {
-    ...     'README.txt': "A README file",
-    ...     "foo": {
-    ...         "__init__.py": "",
-    ...         "bar": {
-    ...             "__init__.py": "",
-    ...         },
-    ...         "baz.py": "# Some code",
-    ...     }
-    ... }
-    >>> target = getfixture('tmp_path')
-    >>> build(spec, target)
-    >>> target.joinpath('foo/baz.py').read_text(encoding='utf-8')
-    '# Some code'
-    """
-    for name, contents in spec.items():
-        create(contents, pathlib.Path(prefix) / name)
-
-
-@functools.singledispatch
-def create(content: Union[str, bytes, FilesSpec], path):
-    path.mkdir(exist_ok=True)
-    build(content, prefix=path)  # type: ignore
-
-
-@create.register
-def _(content: bytes, path):
-    path.write_bytes(content)
-
-
-@create.register
-def _(content: str, path):
-    path.write_text(content, encoding='utf-8')
-
-
-# end from jaraco.path
-####
diff --git a/spaces/declare-lab/tango/diffusers/examples/dreambooth/train_dreambooth_lora.py b/spaces/declare-lab/tango/diffusers/examples/dreambooth/train_dreambooth_lora.py
deleted file mode 100644
index cef19e4a542567e530307ad460263fdd99e58fc7..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/dreambooth/train_dreambooth_lora.py
+++ /dev/null
@@ -1,1028 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import hashlib
-import logging
-import math
-import os
-import warnings
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, upload_folder
-from packaging import version
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    DiffusionPipeline,
-    DPMSolverMultistepScheduler,
-    UNet2DConditionModel,
-)
-from diffusers.loaders import AttnProcsLayers
-from diffusers.models.attention_processor import LoRAAttnProcessor
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.15.0.dev0")
-
-logger = get_logger(__name__)
-
-
-def save_model_card(repo_id: str, images=None, base_model=str, prompt=str, repo_folder=None):
-    img_str = ""
-    for i, image in enumerate(images):
-        image.save(os.path.join(repo_folder, f"image_{i}.png"))
-        img_str += f"![img_{i}](./image_{i}.png)\n"
-
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-instance_prompt: {prompt}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- lora
-inference: true
----
-    """
-    model_card = f"""
-# LoRA DreamBooth - {repo_id}
-
-These are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n
-{img_str}
-"""
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        revision=revision,
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "RobertaSeriesModelWithTransformation":
-        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
-        return RobertaSeriesModelWithTransformation
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        required=True,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default=None,
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--validation_prompt",
-        type=str,
-        default=None,
-        help="A prompt that is used during validation to verify that the model is learning.",
-    )
-    parser.add_argument(
-        "--num_validation_images",
-        type=int,
-        default=4,
-        help="Number of images that should be generated during validation with `validation_prompt`.",
-    )
-    parser.add_argument(
-        "--validation_epochs",
-        type=int,
-        default=50,
-        help=(
-            "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`."
-        ),
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If there are not enough images already present in"
-            " class_data_dir, additional images will be sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="lora-dreambooth-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
-            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=(
-            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
-            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
-            " for more docs"
-        ),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_num_cycles",
-        type=int,
-        default=1,
-        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
-    )
-    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument(
-        "--prior_generation_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp32", "fp16", "bf16"],
-        help=(
-            "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to  fp16 if a GPU is available else fp32."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        args = parser.parse_args()
-
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.with_prior_preservation:
-        if args.class_data_dir is None:
-            raise ValueError("You must specify a data directory for class images.")
-        if args.class_prompt is None:
-            raise ValueError("You must specify prompt for class images.")
-    else:
-        # logger is not available yet
-        if args.class_data_dir is not None:
-            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
-        if args.class_prompt is not None:
-            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        class_data_root=None,
-        class_prompt=None,
-        class_num=None,
-        size=512,
-        center_crop=False,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError("Instance images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            if class_num is not None:
-                self.num_class_images = min(len(self.class_images_path), class_num)
-            else:
-                self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-        example["instance_images"] = self.image_transforms(instance_image)
-        example["instance_prompt_ids"] = self.tokenizer(
-            self.instance_prompt,
-            truncation=True,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
-            example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
-                truncation=True,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                return_tensors="pt",
-            ).input_ids
-
-        return example
-
-
-def collate_fn(examples, with_prior_preservation=False):
-    input_ids = [example["instance_prompt_ids"] for example in examples]
-    pixel_values = [example["instance_images"] for example in examples]
-
-    # Concat class and instance examples for prior preservation.
-    # We do this to avoid doing two forward passes.
-    if with_prior_preservation:
-        input_ids += [example["class_prompt_ids"] for example in examples]
-        pixel_values += [example["class_images"] for example in examples]
-
-    pixel_values = torch.stack(pixel_values)
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    input_ids = torch.cat(input_ids, dim=0)
-
-    batch = {
-        "input_ids": input_ids,
-        "pixel_values": pixel_values,
-    }
-    return batch
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-
-def main(args):
-    logging_dir = Path(args.output_dir, args.logging_dir)
-
-    accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        logging_dir=logging_dir,
-        project_config=accelerator_project_config,
-    )
-
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-        import wandb
-
-    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
-    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
-    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Generate class images if prior preservation is enabled.
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
-            if args.prior_generation_precision == "fp32":
-                torch_dtype = torch.float32
-            elif args.prior_generation_precision == "fp16":
-                torch_dtype = torch.float16
-            elif args.prior_generation_precision == "bf16":
-                torch_dtype = torch.bfloat16
-            pipeline = DiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path,
-                torch_dtype=torch_dtype,
-                safety_checker=None,
-                revision=args.revision,
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
-
-            for example in tqdm(
-                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
-            ):
-                images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
-                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
-                    image.save(image_filename)
-
-            del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(
-            args.pretrained_model_name_or_path,
-            subfolder="tokenizer",
-            revision=args.revision,
-            use_fast=False,
-        )
-
-    # import correct text encoder class
-    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
-    )
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
-    )
-
-    # We only train the additional adapter LoRA layers
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-    unet.requires_grad_(False)
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move unet, vae and text_encoder to device and cast to weight_dtype
-    unet.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    # now we will add new LoRA weights to the attention layers
-    # It's important to realize here how many attention weights will be added and of which sizes
-    # The sizes of the attention layers consist only of two different variables:
-    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
-    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
-
-    # Let's first see how many attention processors we will have to set.
-    # For Stable Diffusion, it should be equal to:
-    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
-    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
-    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
-    # => 32 layers
-
-    # Set correct lora layers
-    lora_attn_procs = {}
-    for name in unet.attn_processors.keys():
-        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-        if name.startswith("mid_block"):
-            hidden_size = unet.config.block_out_channels[-1]
-        elif name.startswith("up_blocks"):
-            block_id = int(name[len("up_blocks.")])
-            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-        elif name.startswith("down_blocks"):
-            block_id = int(name[len("down_blocks.")])
-            hidden_size = unet.config.block_out_channels[block_id]
-
-        lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
-
-    unet.set_attn_processor(lora_attn_procs)
-    lora_layers = AttnProcsLayers(unet.attn_processors)
-
-    accelerator.register_for_checkpointing(lora_layers)
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    # Optimizer creation
-    optimizer = optimizer_class(
-        lora_layers.parameters(),
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    # Dataset and DataLoaders creation:
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        class_num=args.num_class_images,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-    )
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        batch_size=args.train_batch_size,
-        shuffle=True,
-        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
-        num_workers=args.dataloader_num_workers,
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-        num_cycles=args.lr_num_cycles,
-        power=args.lr_power,
-    )
-
-    # Prepare everything with our `accelerator`.
-    lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        lora_layers, optimizer, train_dataloader, lr_scheduler
-    )
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth-lora", config=vars(args))
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the mos recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            resume_global_step = global_step * args.gradient_accumulation_steps
-            first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    for epoch in range(first_epoch, args.num_train_epochs):
-        unet.train()
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
-                latents = latents * vae.config.scaling_factor
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Predict the noise residual
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                if args.with_prior_preservation:
-                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
-                    target, target_prior = torch.chunk(target, 2, dim=0)
-
-                    # Compute instance loss
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                    # Compute prior loss
-                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                    # Add the prior loss to the instance loss.
-                    loss = loss + args.prior_loss_weight * prior_loss
-                else:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = lora_layers.parameters()
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-                if global_step % args.checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-        if accelerator.is_main_process:
-            if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
-                logger.info(
-                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-                    f" {args.validation_prompt}."
-                )
-                # create pipeline
-                pipeline = DiffusionPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    unet=accelerator.unwrap_model(unet),
-                    text_encoder=accelerator.unwrap_model(text_encoder),
-                    revision=args.revision,
-                    torch_dtype=weight_dtype,
-                )
-                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-                pipeline = pipeline.to(accelerator.device)
-                pipeline.set_progress_bar_config(disable=True)
-
-                # run inference
-                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-                images = [
-                    pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
-                    for _ in range(args.num_validation_images)
-                ]
-
-                for tracker in accelerator.trackers:
-                    if tracker.name == "tensorboard":
-                        np_images = np.stack([np.asarray(img) for img in images])
-                        tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
-                    if tracker.name == "wandb":
-                        tracker.log(
-                            {
-                                "validation": [
-                                    wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                    for i, image in enumerate(images)
-                                ]
-                            }
-                        )
-
-                del pipeline
-                torch.cuda.empty_cache()
-
-    # Save the lora layers
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        unet = unet.to(torch.float32)
-        unet.save_attn_procs(args.output_dir)
-
-        # Final inference
-        # Load previous pipeline
-        pipeline = DiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
-        )
-        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-        pipeline = pipeline.to(accelerator.device)
-
-        # load attention processors
-        pipeline.unet.load_attn_procs(args.output_dir)
-
-        # run inference
-        if args.validation_prompt and args.num_validation_images > 0:
-            generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
-            images = [
-                pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
-                for _ in range(args.num_validation_images)
-            ]
-
-            for tracker in accelerator.trackers:
-                if tracker.name == "tensorboard":
-                    np_images = np.stack([np.asarray(img) for img in images])
-                    tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
-                if tracker.name == "wandb":
-                    tracker.log(
-                        {
-                            "test": [
-                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                for i, image in enumerate(images)
-                            ]
-                        }
-                    )
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                images=images,
-                base_model=args.pretrained_model_name_or_path,
-                prompt=args.instance_prompt,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_text_to_speech.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_text_to_speech.py
deleted file mode 100644
index 68de5a3b29202997ca65501be583845630cbca39..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_text_to_speech.py
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/8/18
-@Author  : mashenquan
-@File    : test_text_to_speech.py
-@Desc    : Unit tests.
-"""
-import asyncio
-import base64
-
-from pydantic import BaseModel
-
-from metagpt.learn.text_to_speech import text_to_speech
-
-
-async def mock_text_to_speech():
-    class Input(BaseModel):
-        input: str
-
-    inputs = [
-        {"input": "Panda emoji"}
-    ]
-
-    for i in inputs:
-        seed = Input(**i)
-        base64_data = await text_to_speech(seed.input)
-        assert base64_data != ""
-        print(f"{seed.input} -> {base64_data}")
-        flags = ";base64,"
-        assert flags in base64_data
-        ix = base64_data.find(flags) + len(flags)
-        declaration = base64_data[0: ix]
-        assert declaration
-        data = base64_data[ix:]
-        assert data
-        assert base64.b64decode(data, validate=True)
-
-
-def test_suite():
-    loop = asyncio.get_event_loop()
-    task = loop.create_task(mock_text_to_speech())
-    loop.run_until_complete(task)
-
-
-if __name__ == '__main__':
-    test_suite()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Cabelas.Dangerous.Hunts.2013. -SKIDROW -Crack Fix Torrent Download ((FULL)).md b/spaces/diacanFperku/AutoGPT/Cabelas.Dangerous.Hunts.2013. -SKIDROW -Crack Fix Torrent Download ((FULL)).md
deleted file mode 100644
index 4c81186826a0ab90eeb5cf58e09250a7427ebc3a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Cabelas.Dangerous.Hunts.2013. -SKIDROW -Crack Fix Torrent Download ((FULL)).md	
+++ /dev/null
@@ -1,9 +0,0 @@
-
-<p>i'm sorry, but now i'm not able to allow the update, because after the update the app moved to the background and i could not get it back. i already cleaned the cache, everything i can think of, and the log doesn't even mention the error anymore. appreciate any help you can give me with this. it's really bothering me, because my app always runs flawlessly, and i'd like it to be able to do the same after this update! </p>
-<h2>Cabelas.Dangerous.Hunts.2013. -SKIDROW -Crack Fix Torrent Download</h2><br /><p><b><b>DOWNLOAD</b> &ndash;&ndash;&ndash; <a href="https://gohhs.com/2uFTVN">https://gohhs.com/2uFTVN</a></b></p><br /><br />
-<p>this is my first try at how to download cabelas dangerous hunts 2013 game from the internet. i chose torrent, and downloaded the torrent from the internet where i could. but, when it started the download it told me that i already have it installed, and it's asking me to update. when i try to update it, it says i don't have the proper permissions to change it. do i need to have root to update it? if so, where can i get root? and what should i do if i can't get this game to update? i'm not giving up on it, i just need a little help. help? </p>
-<p>i don't know if this is just a google problem, but on the google play page for cabelas dangerous hunts 2013 the app is listed as free, and it says it has been bought, but i don't know if i have, and if i do, if i should get it back. i haven't played it on my android phone, and it is the only cabelas dangerous hunts 2013 experience i have. if you are the person who bought it, would you please help me out? thanks! </p>
-<p>i'm sorry, but now i'm not able to allow the update, because after the update the app moved to the background and i could not get it back. i already cleaned the cache, everything i can think of, and the log doesn't even mention the error anymore. appreciate any help you can give me with this.</p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Embertone - Sensual Saxophone Full __LINK__ Version.md b/spaces/diacanFperku/AutoGPT/Embertone - Sensual Saxophone Full __LINK__ Version.md
deleted file mode 100644
index 5881f2567b9bf9953e13193562932efcc201076e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Embertone - Sensual Saxophone Full __LINK__ Version.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Embertone - Sensual Saxophone full version</h2><br /><p><b><b>Download</b> &#187;&#187;&#187; <a href="https://gohhs.com/2uFTNN">https://gohhs.com/2uFTNN</a></b></p><br /><br />
-<br />
-Embertone's Sensual Sax and Jubal Flute half price; ... Syntorial is $89.99 for the full version (normally $129.99) from Friday to Sunday (this is a really good deal ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diagaiwei/ir_chinese_medqa/baleen/hop_searcher.py b/spaces/diagaiwei/ir_chinese_medqa/baleen/hop_searcher.py
deleted file mode 100644
index a7f266b3490c786b014014b10e39de4f828af938..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/baleen/hop_searcher.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from typing import Union
-
-from colbert import Searcher
-from colbert.data import Queries
-from colbert.infra.config import ColBERTConfig
-
-
-TextQueries = Union[str, 'list[str]', 'dict[int, str]', Queries]
-
-
-class HopSearcher(Searcher):
-    def __init__(self, *args, config=None, interaction='flipr', **kw_args):
-        defaults = ColBERTConfig(query_maxlen=64, interaction=interaction)
-        config = ColBERTConfig.from_existing(defaults, config)
-
-        super().__init__(*args, config=config, **kw_args)
-
-    def encode(self, text: TextQueries, context: TextQueries):
-        queries = text if type(text) is list else [text]
-        context = context if context is None or type(context) is list else [context]
-        bsize = 128 if len(queries) > 128 else None
-
-        self.checkpoint.query_tokenizer.query_maxlen = self.config.query_maxlen
-        Q = self.checkpoint.queryFromText(queries, context=context, bsize=bsize, to_cpu=True)
-
-        return Q
-
-    def search(self, text: str, context: str, k=10):
-        return self.dense_search(self.encode(text, context), k)
-
-    def search_all(self, queries: TextQueries, context: TextQueries, k=10):
-        queries = Queries.cast(queries)
-        context = Queries.cast(context) if context is not None else context
-
-        queries_ = list(queries.values())
-        context_ = list(context.values()) if context is not None else context
-
-        Q = self.encode(queries_, context_)
-
-        return self._search_all_Q(queries, Q, k)
diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/data/ranking.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/data/ranking.py
deleted file mode 100644
index c334a52f6cb165bd22b5170dda1f2c6a80baa791..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/colbert/data/ranking.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import os
-import tqdm
-import ujson
-from colbert.infra.provenance import Provenance
-
-from colbert.infra.run import Run
-from colbert.utils.utils import print_message, groupby_first_item
-from utility.utils.save_metadata import get_metadata_only
-
-
-def numericize(v):
-    if '.' in v:
-        return float(v)
-
-    return int(v)
-
-
-def load_ranking(path):  # works with annotated and un-annotated ranked lists
-    print_message("#> Loading the ranked lists from", path)
-
-    with open(path) as f:
-        return [list(map(numericize, line.strip().split('\t'))) for line in f]
-
-
-class Ranking:
-    def __init__(self, path=None, data=None, metrics=None, provenance=None):
-        self.__provenance = provenance or path or Provenance()
-        self.data = self._prepare_data(data or self._load_file(path))
-
-    def provenance(self):
-        return self.__provenance
-    
-    def toDict(self):
-        return {'provenance': self.provenance()}
-
-    def _prepare_data(self, data):
-        # TODO: Handle list of lists???
-        if isinstance(data, dict):
-            self.flat_ranking = [(qid, *rest) for qid, subranking in data.items() for rest in subranking]
-            return data
-
-        self.flat_ranking = data
-        return groupby_first_item(tqdm.tqdm(self.flat_ranking))
-
-    def _load_file(self, path):
-        return load_ranking(path)
-
-    def todict(self):
-        return dict(self.data)
-
-    def tolist(self):
-        return list(self.flat_ranking)
-
-    def items(self):
-        return self.data.items()
-
-    def _load_tsv(self, path):
-        raise NotImplementedError
-
-    def _load_jsonl(self, path):
-        raise NotImplementedError
-
-    def save(self, new_path):
-        assert 'tsv' in new_path.strip('/').split('/')[-1].split('.'), "TODO: Support .json[l] too."
-
-        with Run().open(new_path, 'w') as f:
-            for items in self.flat_ranking:
-                line = '\t'.join(map(lambda x: str(int(x) if type(x) is bool else x), items)) + '\n'
-                f.write(line)
-
-            output_path = f.name
-            print_message(f"#> Saved ranking of {len(self.data)} queries and {len(self.flat_ranking)} lines to {f.name}")
-        
-        with Run().open(f'{new_path}.meta', 'w') as f:
-            d = {}
-            d['metadata'] = get_metadata_only()
-            d['provenance'] = self.provenance()
-            line = ujson.dumps(d, indent=4)
-            f.write(line)
-        
-        return output_path
-
-    @classmethod
-    def cast(cls, obj):
-        if type(obj) is str:
-            return cls(path=obj)
-
-        if isinstance(obj, dict) or isinstance(obj, list):
-            return cls(data=obj)
-
-        if type(obj) is cls:
-            return obj
-
-        assert False, f"obj has type {type(obj)} which is not compatible with cast()"
diff --git a/spaces/digitalxingtong/Kino-Bert-VITS2/transforms.py b/spaces/digitalxingtong/Kino-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Kino-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/commons.py b/spaces/digitalxingtong/Nailv-Bert-Vits2/commons.py
deleted file mode 100644
index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/commons.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/atss_head.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/atss_head.py
deleted file mode 100644
index ff55dfa1790ba270539fc9f623dbb2984fa1a99e..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/atss_head.py
+++ /dev/null
@@ -1,689 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
-                        images_to_levels, multi_apply, multiclass_nms,
-                        reduce_mean, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-EPS = 1e-12
-
-
-@HEADS.register_module()
-class ATSSHead(AnchorHead):
-    """Bridging the Gap Between Anchor-based and Anchor-free Detection via
-    Adaptive Training Sample Selection.
-
-    ATSS head structure is similar with FCOS, however ATSS use anchor boxes
-    and assign label by Adaptive Training Sample Selection instead max-iou.
-
-    https://arxiv.org/abs/1912.02424
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
-                 loss_centerness=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0),
-                 **kwargs):
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(ATSSHead, self).__init__(num_classes, in_channels, **kwargs)
-
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # SSD sampling=False so use PseudoSampler
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-        self.loss_centerness = build_loss(loss_centerness)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.atss_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.atss_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-        self.atss_centerness = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 1, 3, padding=1)
-        self.scales = nn.ModuleList(
-            [Scale(1.0) for _ in self.anchor_generator.strides])
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.atss_cls, std=0.01, bias=bias_cls)
-        normal_init(self.atss_reg, std=0.01)
-        normal_init(self.atss_centerness, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
-        """
-        return multi_apply(self.forward_single, feats, self.scales)
-
-    def forward_single(self, x, scale):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls scores for a single scale level
-                    the channels number is num_anchors * num_classes.
-                bbox_pred (Tensor): Box energies / deltas for a single scale
-                    level, the channels number is num_anchors * 4.
-                centerness (Tensor): Centerness for a single scale level, the
-                    channel number is (N, num_anchors * 1, H, W).
-        """
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-        cls_score = self.atss_cls(cls_feat)
-        # we just follow atss, not apply exp in bbox_pred
-        bbox_pred = scale(self.atss_reg(reg_feat)).float()
-        centerness = self.atss_centerness(reg_feat)
-        return cls_score, bbox_pred, centerness
-
-    def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
-                    label_weights, bbox_targets, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            cls_score (Tensor): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W).
-            bbox_pred (Tensor): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            num_total_samples (int): Number os positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-            -1, self.cls_out_channels).contiguous()
-        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
-        centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # classification loss
-        loss_cls = self.loss_cls(
-            cls_score, labels, label_weights, avg_factor=num_total_samples)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_centerness = centerness[pos_inds]
-
-            centerness_targets = self.centerness_target(
-                pos_anchors, pos_bbox_targets)
-            pos_decode_bbox_pred = self.bbox_coder.decode(
-                pos_anchors, pos_bbox_pred)
-            pos_decode_bbox_targets = self.bbox_coder.decode(
-                pos_anchors, pos_bbox_targets)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=centerness_targets,
-                avg_factor=1.0)
-
-            # centerness loss
-            loss_centerness = self.loss_centerness(
-                pos_centerness,
-                centerness_targets,
-                avg_factor=num_total_samples)
-
-        else:
-            loss_bbox = bbox_pred.sum() * 0
-            loss_centerness = centerness.sum() * 0
-            centerness_targets = bbox_targets.new_tensor(0.)
-
-        return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             centernesses,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            centernesses (list[Tensor]): Centerness for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, loss_centerness,\
-            bbox_avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                centernesses,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                num_total_samples=num_total_samples)
-
-        bbox_avg_factor = sum(bbox_avg_factor)
-        bbox_avg_factor = reduce_mean(bbox_avg_factor).item()
-        if bbox_avg_factor < EPS:
-            bbox_avg_factor = 1
-        losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            loss_centerness=loss_centerness)
-
-    def centerness_target(self, anchors, bbox_targets):
-        # only calculate pos centerness targets, otherwise there may be nan
-        gts = self.bbox_coder.decode(anchors, bbox_targets)
-        anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
-        anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
-        l_ = anchors_cx - gts[:, 0]
-        t_ = anchors_cy - gts[:, 1]
-        r_ = gts[:, 2] - anchors_cx
-        b_ = gts[:, 3] - anchors_cy
-
-        left_right = torch.stack([l_, r_], dim=1)
-        top_bottom = torch.stack([t_, b_], dim=1)
-        centerness = torch.sqrt(
-            (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) *
-            (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]))
-        assert not torch.isnan(centerness).any()
-        return centerness
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   centernesses,
-                   img_metas,
-                   cfg=None,
-                   rescale=False,
-                   with_nms=True):
-        """Transform network output for a batch into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                with shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            centernesses (list[Tensor]): Centerness for each scale level with
-                shape (N, num_anchors * 1, H, W).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used. Default: None.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-        device = cls_scores[0].device
-        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
-        mlvl_anchors = self.anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-
-        cls_score_list = [cls_scores[i].detach() for i in range(num_levels)]
-        bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)]
-        centerness_pred_list = [
-            centernesses[i].detach() for i in range(num_levels)
-        ]
-        img_shapes = [
-            img_metas[i]['img_shape'] for i in range(cls_scores[0].shape[0])
-        ]
-        scale_factors = [
-            img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0])
-        ]
-        result_list = self._get_bboxes(cls_score_list, bbox_pred_list,
-                                       centerness_pred_list, mlvl_anchors,
-                                       img_shapes, scale_factors, cfg, rescale,
-                                       with_nms)
-        return result_list
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    centernesses,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for a single scale level
-                with shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for a single
-                scale level with shape (N, num_anchors * 4, H, W).
-            centernesses (list[Tensor]): Centerness for a single scale level
-                with shape (N, num_anchors * 1, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shapes (list[tuple[int]]): Shape of the input image,
-                list[(height, width, 3)].
-            scale_factors (list[ndarray]): Scale factor of the image arrange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        device = cls_scores[0].device
-        batch_size = cls_scores[0].shape[0]
-        # convert to tensor to keep tracing
-        nms_pre_tensor = torch.tensor(
-            cfg.get('nms_pre', -1), device=device, dtype=torch.long)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_centerness = []
-        for cls_score, bbox_pred, centerness, anchors in zip(
-                cls_scores, bbox_preds, centernesses, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            centerness = centerness.permute(0, 2, 3,
-                                            1).reshape(batch_size,
-                                                       -1).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-
-            # Always keep topk op for dynamic input in onnx
-            if nms_pre_tensor > 0 and (torch.onnx.is_in_onnx_export()
-                                       or scores.shape[-2] > nms_pre_tensor):
-                from torch import _shape_as_tensor
-                # keep shape as tensor and get k
-                num_anchor = _shape_as_tensor(scores)[-2].to(device)
-                nms_pre = torch.where(nms_pre_tensor < num_anchor,
-                                      nms_pre_tensor, num_anchor)
-
-                max_scores, _ = (scores * centerness[..., None]).max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                centerness = centerness[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_centerness.append(centerness)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1)
-
-        # Set max number of box to be feed into nms in deployment
-        deploy_nms_pre = cfg.get('deploy_nms_pre', -1)
-        if deploy_nms_pre > 0 and torch.onnx.is_in_onnx_export():
-            batch_mlvl_scores, _ = (
-                batch_mlvl_scores *
-                batch_mlvl_centerness.unsqueeze(2).expand_as(batch_mlvl_scores)
-            ).max(-1)
-            _, topk_inds = batch_mlvl_scores.topk(deploy_nms_pre)
-            batch_inds = torch.arange(batch_size).view(-1,
-                                                       1).expand_as(topk_inds)
-            batch_mlvl_scores = batch_mlvl_scores[batch_inds, topk_inds, :]
-            batch_mlvl_bboxes = batch_mlvl_bboxes[batch_inds, topk_inds, :]
-            batch_mlvl_centerness = batch_mlvl_centerness[batch_inds,
-                                                          topk_inds]
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-
-        if with_nms:
-            det_results = []
-            for (mlvl_bboxes, mlvl_scores,
-                 mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores,
-                                         batch_mlvl_centerness):
-                det_bbox, det_label = multiclass_nms(
-                    mlvl_bboxes,
-                    mlvl_scores,
-                    cfg.score_thr,
-                    cfg.nms,
-                    cfg.max_per_img,
-                    score_factors=mlvl_centerness)
-                det_results.append(tuple([det_bbox, det_label]))
-        else:
-            det_results = [
-                tuple(mlvl_bs)
-                for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores,
-                                   batch_mlvl_centerness)
-            ]
-        return det_results
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    label_channels=1,
-                    unmap_outputs=True):
-        """Get targets for ATSS head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. Besides
-        returning the targets as the parent method does, it also returns the
-        anchors as the first element of the returned tuple.
-        """
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        num_level_anchors_list = [num_level_anchors] * num_imgs
-
-        # concat all level anchors and flags to a single tensor
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            anchor_list[i] = torch.cat(anchor_list[i])
-            valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
-         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
-             self._get_target_single,
-             anchor_list,
-             valid_flag_list,
-             num_level_anchors_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             label_channels=label_channels,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        anchors_list = images_to_levels(all_anchors, num_level_anchors)
-        labels_list = images_to_levels(all_labels, num_level_anchors)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_anchors)
-        bbox_targets_list = images_to_levels(all_bbox_targets,
-                                             num_level_anchors)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_anchors)
-        return (anchors_list, labels_list, label_weights_list,
-                bbox_targets_list, bbox_weights_list, num_total_pos,
-                num_total_neg)
-
-    def _get_target_single(self,
-                           flat_anchors,
-                           valid_flags,
-                           num_level_anchors,
-                           gt_bboxes,
-                           gt_bboxes_ignore,
-                           gt_labels,
-                           img_meta,
-                           label_channels=1,
-                           unmap_outputs=True):
-        """Compute regression, classification targets for anchors in a single
-        image.
-
-        Args:
-            flat_anchors (Tensor): Multi-level anchors of the image, which are
-                concatenated into a single tensor of shape (num_anchors ,4)
-            valid_flags (Tensor): Multi level valid flags of the image,
-                which are concatenated into a single tensor of
-                    shape (num_anchors,).
-            num_level_anchors Tensor): Number of anchors of each scale level.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            img_meta (dict): Meta info of the image.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: N is the number of total anchors in the image.
-                labels (Tensor): Labels of all anchors in the image with shape
-                    (N,).
-                label_weights (Tensor): Label weights of all anchor in the
-                    image with shape (N,).
-                bbox_targets (Tensor): BBox targets of all anchors in the
-                    image with shape (N, 4).
-                bbox_weights (Tensor): BBox weights of all anchors in the
-                    image with shape (N, 4)
-                pos_inds (Tensor): Indices of positive anchor with shape
-                    (num_pos,).
-                neg_inds (Tensor): Indices of negative anchor with shape
-                    (num_neg,).
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg.allowed_border)
-        if not inside_flags.any():
-            return (None, ) * 7
-        # assign gt and sample anchors
-        anchors = flat_anchors[inside_flags, :]
-
-        num_level_anchors_inside = self.get_num_level_anchors_inside(
-            num_level_anchors, inside_flags)
-        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
-                                             gt_bboxes, gt_bboxes_ignore,
-                                             gt_labels)
-
-        sampling_result = self.sampler.sample(assign_result, anchors,
-                                              gt_bboxes)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            if hasattr(self, 'bbox_coder'):
-                pos_bbox_targets = self.bbox_coder.encode(
-                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-            else:
-                # used in VFNetHead
-                pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class since v2.5.0
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            anchors = unmap(anchors, num_total_anchors, inside_flags)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags, fill=self.num_classes)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
-        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
-                pos_inds, neg_inds)
-
-    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
-        split_inside_flags = torch.split(inside_flags, num_level_anchors)
-        num_level_anchors_inside = [
-            int(flags.sum()) for flags in split_inside_flags
-        ]
-        return num_level_anchors_inside
diff --git a/spaces/dineshreddy/WALT/mmdet/models/detectors/fsaf.py b/spaces/dineshreddy/WALT/mmdet/models/detectors/fsaf.py
deleted file mode 100644
index 9f10fa1ae10f31e6cb5de65505b14a4fc97dd022..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/detectors/fsaf.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FSAF(SingleStageDetector):
-    """Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                   test_cfg, pretrained)
diff --git a/spaces/doctorsafe/mychat/predict.py b/spaces/doctorsafe/mychat/predict.py
deleted file mode 100644
index 4e9156943a6af48a68c13c9c00259c431cb90368..0000000000000000000000000000000000000000
--- a/spaces/doctorsafe/mychat/predict.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
-
-import json
-import gradio as gr
-import logging
-import traceback
-import requests
-import importlib
-
-# config_private.py放自己的秘密如API和代理网址
-# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-
-timeout_bot_msg = '[local] Request timeout, network error.'
-
-def get_full_error(chunk, stream_response):
-    """
-        获取完整的从Openai返回的报错
-    """
-    while True:
-        try:
-            chunk += next(stream_response)
-        except:
-            break
-    return chunk
-
-def predict_no_ui(inputs, top_p, temperature, history=[]):
-    """
-        发送至chatGPT，等待回复，一次性完成，不显示中间过程。
-        predict函数的简化版。
-        用于payload比较大的情况，或者用于实现多线、带嵌套的复杂功能。
-
-        inputs 是本次问询的输入
-        top_p, temperature是chatGPT的内部调优参数
-        history 是之前的对话列表
-        （注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误，然后raise ConnectionAbortedError）
-    """
-    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt="", stream=False)
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=False
-            response = requests.post(API_URL, headers=headers, proxies=proxies,
-                                    json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); break
-        except requests.exceptions.ReadTimeout as e:
-            retry += 1
-            traceback.print_exc()
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
-            if retry > MAX_RETRY: raise TimeoutError
-
-    try:
-        result = json.loads(response.text)["choices"][0]["message"]["content"]
-        return result
-    except Exception as e:
-        if "choices" not in response.text: print(response.text)
-        raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)
-
-
-def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', 
-            stream = True, additional_fn=None):
-    """
-        发送至chatGPT，流式获取输出。
-        用于基础的对话功能。
-        inputs 是本次问询的输入
-        top_p, temperature是chatGPT的内部调优参数
-        history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
-        additional_fn代表点击的哪个按钮，按钮见functional.py
-    """
-    if additional_fn is not None:
-        import functional
-        importlib.reload(functional)
-        functional = functional.get_functionals()
-        inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
-
-    if stream:
-        raw_input = inputs
-        logging.info(f'[raw_input] {raw_input}')
-        chatbot.append((inputs, ""))
-        yield chatbot, history, "等待响应"
-
-    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
-    history.append(inputs); history.append(" ")
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=True
-            response = requests.post(API_URL, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
-        except:
-            retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield chatbot, history, "请求超时"+retry_msg
-            if retry > MAX_RETRY: raise TimeoutError
-
-    gpt_replying_buffer = ""
-    
-    is_head_of_the_stream = True
-    if stream:
-        stream_response =  response.iter_lines()
-        while True:
-            chunk = next(stream_response)
-            # print(chunk.decode()[6:])
-            if is_head_of_the_stream:
-                # 数据流的第一帧不携带content
-                is_head_of_the_stream = False; continue
-            
-            if chunk:
-                try:
-                    if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-                        # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
-                        break
-                    # 处理数据流的主体
-                    chunkjson = json.loads(chunk.decode()[6:])
-                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
-                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
-                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
-                    history[-1] = gpt_replying_buffer
-                    chatbot[-1] = (history[-2], history[-1])
-                    yield chatbot, history, status_text
-
-                except Exception as e:
-                    traceback.print_exc()
-                    yield chatbot, history, "Json解析不合常规"
-                    chunk = get_full_error(chunk, stream_response)
-                    error_msg = chunk.decode()
-                    if "reduce the length" in error_msg:
-                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Input (or history) is too long, please reduce input or clear history by refleshing this page.")
-                        history = []
-                    elif "Incorrect API key" in error_msg:
-                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key provided.")
-                    else:
-                        from toolbox import regular_txt_to_markdown
-                        tb_str = regular_txt_to_markdown(traceback.format_exc())
-                        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Json Error \n\n {tb_str} \n\n {regular_txt_to_markdown(chunk.decode()[4:])}")
-                    yield chatbot, history, "Json解析不合常规" + error_msg
-                    return
-
-def generate_payload(inputs, top_p, temperature, history, system_prompt, stream):
-    """
-        整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
-    """
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {API_KEY}"
-    }
-
-    conversation_cnt = len(history) // 2
-
-    messages = [{"role": "system", "content": system_prompt}]
-    if conversation_cnt:
-        for index in range(0, 2*conversation_cnt, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if what_gpt_answer["content"] == "": continue
-                if what_gpt_answer["content"] == timeout_bot_msg: continue
-                messages.append(what_i_have_asked)
-                messages.append(what_gpt_answer)
-            else:
-                messages[-1]['content'] = what_gpt_answer['content']
-
-    what_i_ask_now = {}
-    what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
-    messages.append(what_i_ask_now)
-
-    payload = {
-        "model": LLM_MODEL,
-        "messages": messages, 
-        "temperature": temperature,  # 1.0,
-        "top_p": top_p,  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    
-    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
-    return headers,payload
-
-
diff --git a/spaces/duycse1603/math2tex/HybridViT/module/component/prediction_head/addon_module/position_encoding.py b/spaces/duycse1603/math2tex/HybridViT/module/component/prediction_head/addon_module/position_encoding.py
deleted file mode 100644
index cba525cc89544df367a1a784ffd94d9f941a068e..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/HybridViT/module/component/prediction_head/addon_module/position_encoding.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import torch
-from torch import nn
-
-__all__ = ['WordPosEnc']
-
-class WordPosEnc(nn.Module):
-    def __init__(
-        self, d_model: int = 512, max_len: int = 500, temperature: float = 10000.0
-    ) -> None:
-        super().__init__()
-        pe = torch.zeros(max_len, d_model)
-
-        position = torch.arange(0, max_len, dtype=torch.float)
-        dim_t = torch.arange(0, d_model, 2, dtype=torch.float)
-        div_term = 1.0 / (temperature ** (dim_t / d_model))
-
-        inv_freq = torch.einsum("i, j -> i j", position, div_term)
-
-        pe[:, 0::2] = inv_freq.sin()
-        pe[:, 1::2] = inv_freq.cos()
-        self.register_buffer("pe", pe)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        _, seq_len, _ = x.size()
-        emb = self.pe[:seq_len, :]
-        x = x + emb[None, :, :]
-        return x
diff --git a/spaces/duycse1603/math2tex/ScanSSD/gtdb/generate_subimages.py b/spaces/duycse1603/math2tex/ScanSSD/gtdb/generate_subimages.py
deleted file mode 100644
index 14b9ca5ada17a440bb5adcf70788f2f7545a36f2..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/ScanSSD/gtdb/generate_subimages.py
+++ /dev/null
@@ -1,319 +0,0 @@
-# Author: Parag Mali
-# This script divides a big image into smaller images using a sliding window.
-# It also divides corresponding bounding box annotations.
-# NOTE: This file is no longer needed as sub-images are generated in data-loader
-
-# read the image
-import numpy as np
-import cv2
-import copy
-import os
-import sys
-from multiprocessing import Pool
-
-# Default parameters for thr GTDB dataset
-intermediate_width = 4800
-intermediate_height = 6000
-crop_size = 1200
-final_width = 0
-final_height = 0
-stride = 0.1
-
-n_horizontal = int(intermediate_width / crop_size)  # 4
-n_vertical = int(intermediate_height / crop_size)  # 5
-
-math_dir='/home/psm2208/data/GTDB/annotations/'
-char_dir='/home/psm2208/data/GTDB/char_annotations/'
-image_dir='/home/psm2208/data/GTDB/images/'
-output_image_dir='/home/psm2208/data/GTDB/processed_images_/'
-output_math_dir='/home/psm2208/data/GTDB/processed_annotations_/'
-output_char_dir='/home/psm2208/data/GTDB/processed_char_annotations_/'
-
-# This function generates sub-images
-def generate_subimages(pdf_name ='Alford94'):
-
-    # find all the images
-    image_filelist = [file for file in os.listdir(os.path.join(image_dir, pdf_name)) if file.endswith('.png')]
-
-    # math annotations
-    math_filepath = os.path.join(math_dir, pdf_name + ".math")
-    math_file_present = os.path.isfile(math_filepath)
-
-    # char annotations
-    char_filepath = os.path.join(char_dir, pdf_name + ".char")
-    char_file_present = os.path.isfile(char_filepath)
-
-    if math_file_present:
-        math_file = open(math_filepath, 'r')
-
-        boxes = {}
-
-        for line in math_file:
-            box = line.split(",")
-            idx = int(box[0]) + 1
-            box = box[1:]
-
-            box = list(map(float, box))
-            box = list(map(int, box))
-
-            if idx not in boxes:
-                boxes[idx] = []
-
-            boxes[idx].append(box)
-
-    if char_file_present:
-        char_file = open(char_filepath, 'r')
-
-        char_boxes = {}
-
-        for line in char_file:
-            char_box = line.split(",")
-            idx = int(char_box[0]) + 1
-            char_box = char_box[2:]
-
-            #box = list(map(int, box))
-
-            if idx not in char_boxes:
-                char_boxes[idx] = []
-
-            char_boxes[idx].append(char_box)
-
-
-    for image_filepath in image_filelist:
-
-        image = cv2.imread(os.path.join(image_dir, pdf_name, image_filepath))
-        basename = os.path.basename(image_filepath)
-        page_id = int(os.path.splitext(basename)[0])
-
-        original_width = image.shape[1]
-        original_height = image.shape[0]
-
-        intermediate_width_ratio = intermediate_width / original_width
-        intermediate_height_ratio = intermediate_height / original_height
-
-        image = cv2.resize(image, (intermediate_width, intermediate_height))
-
-        if math_file_present:
-            if page_id in boxes:
-                current_boxes = boxes[page_id]
-            else:
-                current_boxes = []
-
-            # preprocess the boxes
-            for box in current_boxes:
-
-                box[0] = box[0] * intermediate_width_ratio
-                box[1] = box[1] * intermediate_height_ratio
-                box[2] = box[2] * intermediate_width_ratio
-                box[3] = box[3] * intermediate_height_ratio
-
-        if char_file_present:
-            if page_id in char_boxes:
-                current_char_boxes = char_boxes[page_id]
-            else:
-                current_char_boxes = []
-
-            # preprocess the boxes
-            for box in current_char_boxes:
-
-                box[0] = float(box[0]) * intermediate_width_ratio
-                box[1] = float(box[1]) * intermediate_height_ratio
-                box[2] = float(box[2]) * intermediate_width_ratio
-                box[3] = float(box[3]) * intermediate_height_ratio
-
-
-        subimg_id = 1
-
-        # create required dirs
-        if not os.path.exists(os.path.join(output_image_dir, pdf_name, str(page_id))):
-            os.makedirs(os.path.join(output_image_dir, pdf_name, str(page_id)))
-
-        if not os.path.exists(os.path.join(output_math_dir, pdf_name, str(page_id))):
-            os.makedirs(os.path.join(output_math_dir, pdf_name, str(page_id)))
-
-        if not os.path.exists(os.path.join(output_char_dir, pdf_name, str(page_id))):
-            os.makedirs(os.path.join(output_char_dir, pdf_name, str(page_id)))
-
-        for i in np.arange(0, n_vertical-1+stride, stride):
-            for j in np.arange(0, n_horizontal-1+stride, stride):
-
-                print('Processing sub image : ', subimg_id)
-
-                if math_file_present:
-                     out_math_file = os.path.join(output_math_dir, pdf_name, str(page_id), str(subimg_id) + ".pmath")
-                     out_math = open(out_math_file, "w")
-
-                if char_file_present:
-                    out_char_file = os.path.join(output_char_dir, pdf_name, str(page_id), str(subimg_id) + ".pchar")
-                    out_char = open(out_char_file, "w")
-
-                x_l = int(np.round(crop_size * i))
-                x_h = int(np.round(crop_size * (i + 1)))
-
-                y_l = int(np.round(crop_size * j))
-                y_h = int(np.round(crop_size * (j + 1)))
-
-                cropped_image = image[x_l: x_h, y_l: y_h, :]
-                cropped_image = cv2.resize(cropped_image, (final_width, final_height))
-
-                # left, top, right, bottom
-                image_box = [y_l, x_l, y_h, x_h]
-
-                # find scaling factors
-                final_width_ratio = final_width / (y_h - y_l)
-                final_height_ratio = final_height / (x_h - x_l)
-
-                count = 0
-
-                if math_file_present:
-                    if page_id in boxes:
-                        current_page_boxes = copy.deepcopy(boxes[page_id])
-                    else:
-                        current_page_boxes = []
-
-                    # if math intersects only consider the region which
-                    # is part of the current bounding box
-                    for box in current_page_boxes:
-                        if intersects(image_box, box):
-                            #print('intersects ', box)
-
-                            # left, top, right, bottom
-                            # y increases downwards
-
-                            #crop the boxes to fit into image region
-                            box[0] = max(y_l, box[0])
-                            box[1] = max(x_l, box[1])
-                            box[2] = min(y_h, box[2])
-                            box[3] = min(x_h, box[3])
-
-                            # Translate to origin
-                            box[0] = box[0] - y_l
-                            box[2] = box[2] - y_l
-
-                            box[1] = box[1] - x_l
-                            box[3] = box[3] - x_l
-
-                            # scaling
-                            box[2] = int(np.round(box[2] * final_width_ratio))
-                            box[0] = int(np.round(box[0] * final_width_ratio))
-
-                            box[3] = int(np.round(box[3] * final_height_ratio))
-                            box[1] = int(np.round(box[1] * final_height_ratio))
-
-                            count = count + 1
-                            out_math.write(','.join(str(x) for x in box))
-                            out_math.write("\n")
-                            #cv2.rectangle(cropped_image, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 3)
-
-                if char_file_present:
-                    if page_id in char_boxes:
-                        current_page_char_boxes = copy.deepcopy(char_boxes[page_id])
-                    else:
-                        current_page_char_boxes = []
-
-                    # if math intersects only consider the region which
-                    # is part of the current bounding box
-                    for box in current_page_char_boxes:
-                        if intersects(image_box, box):
-                            #print('intersects ', box)
-
-                            # left, top, right, bottom
-                            # y increases downwards
-
-                            #crop the boxes to fit into image region
-                            box[0] = max(y_l, box[0])
-                            box[1] = max(x_l, box[1])
-                            box[2] = min(y_h, box[2])
-                            box[3] = min(x_h, box[3])
-
-                            # Translate to origin
-                            box[0] = box[0] - y_l
-                            box[2] = box[2] - y_l
-
-                            box[1] = box[1] - x_l
-                            box[3] = box[3] - x_l
-
-                            # scaling
-                            box[2] = int(np.round(box[2] * final_width_ratio))
-                            box[0] = int(np.round(box[0] * final_width_ratio))
-
-                            box[3] = int(np.round(box[3] * final_height_ratio))
-                            box[1] = int(np.round(box[1] * final_height_ratio))
-
-                            out_char.write(','.join(str(x) for x in box))
-                            #out_char.write("\n")
-
-                fig_name = os.path.join(output_image_dir, pdf_name, str(page_id), str(subimg_id) + ".png")
-                print("Saving " + fig_name)
-                cv2.imwrite(fig_name, cropped_image)
-                subimg_id = subimg_id + 1
-
-                if math_file_present:
-
-                    out_math.close()
-                    out_char.close()
-                    if count == 0:
-                        # if no math regions, delete the file
-                        os.remove(out_math_file)
-                        os.remove(out_char_file)
-
-
-# check if two rectangles intersect
-def intersects(first, other):
-    return not (first[2] < other[0] or
-                first[0] > other[2] or
-                first[1] > other[3] or
-                first[3] < other[1])
-
-if __name__ == '__main__':
-
-    training_pdf_names = open(sys.argv[1], 'r')
-    stride = float(sys.argv[2]) # 0.1
-
-    training_pdf_names_list = []
-
-    # for each training image pdf file
-    for pdf_name in training_pdf_names:
-        pdf_name = pdf_name.strip()
-        if pdf_name != '':
-            training_pdf_names_list.append(pdf_name)
-
-            if not os.path.exists(os.path.join(output_image_dir, pdf_name)):
-                os.makedirs(os.path.join(output_image_dir, pdf_name))
-
-            if not os.path.exists(os.path.join(output_math_dir, pdf_name)):
-                os.makedirs(os.path.join(output_math_dir, pdf_name))
-
-            if not os.path.exists(os.path.join(output_char_dir, pdf_name)):
-                os.makedirs(os.path.join(output_char_dir, pdf_name))
-
-    training_pdf_names.close()
-
-    suffix = sys.argv[3] #str(int(100 * stride))
-
-    final_height = int(suffix)
-    final_width = int(suffix)
-
-    char_dir = '/home/psm2208/data/GTDB/char_annotations/'
-    math_dir = '/home/psm2208/code/eval/relations_adjust/'
-    #math_dir = '/home/psm2208/data/GTDB/annotations/'
-    image_dir = '/home/psm2208/data/GTDB/images/'
-
-    output_image_dir = '/home/psm2208/data/GTDB/processed_images_' + suffix
-    output_math_dir = '/home/psm2208/data/GTDB/processed_annotations_' + suffix
-    output_char_dir = '/home/psm2208/data/GTDB/processed_char_annotations_' + suffix
-
-    # create required dirs
-    if not os.path.exists(output_image_dir):
-        os.makedirs(output_image_dir)
-
-    if not os.path.exists(output_math_dir):
-        os.makedirs(output_math_dir)
-
-    if not os.path.exists(output_char_dir):
-        os.makedirs(output_char_dir)
-
-    pool = Pool(processes=32)
-    pool.map(generate_subimages, training_pdf_names_list)
-    pool.close()
-    pool.join()
diff --git a/spaces/elonmuskceo/docker-aimstack/README.md b/spaces/elonmuskceo/docker-aimstack/README.md
deleted file mode 100644
index f82bce77afe6e23d40cae252e7ffa274a475c4f6..0000000000000000000000000000000000000000
--- a/spaces/elonmuskceo/docker-aimstack/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
----
-title: Aimstack
-emoji: ⚡
-colorFrom: purple
-colorTo: pink
-sdk: docker
-fullWidth: true
-pinned: false
----
-
-## `./Dockerfile`
-
-```dockerfile
-FROM ubuntu:kinetic
-
-# Doesn't usually have an "upgrade"
-RUN apt-get update \
- && DEBIAN_FRONTEND=noninteractive \
-    apt-get install --no-install-recommends --assume-yes \
-	  build-essential \
-      python3 \
-	  python3-dev \
-      python3-pip
-
-
-# install the `aim` package on the latest version
-RUN pip install aim
-
-# make a directory where the Aim repo will be initialized, `/aim`
-RUN mkdir /aim
-
-ENTRYPOINT ["/bin/sh", "-c"]
-
-# have to run `aim init` in the directory that stores aim data for
-# otherwise `aim up` will prompt for confirmation to create the directory itself.
-# We run aim listening on 0.0.0.0 to expose all ports. Also, we run
-# using `--dev` to print verbose logs. Port 43800 is the default port of
-# `aim up` but explicit is better than implicit.
-CMD ["echo \"N\" | aim init --repo /aim && aim up --host 0.0.0.0 --port 7860 --repo /aim"]
-```
\ No newline at end of file
diff --git a/spaces/enzostvs/hub-api-playground/components/input/input.tsx b/spaces/enzostvs/hub-api-playground/components/input/input.tsx
deleted file mode 100644
index 8e3427f38a357a32529c34ed3893b437fb2cc38c..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/hub-api-playground/components/input/input.tsx
+++ /dev/null
@@ -1,59 +0,0 @@
-import React, { ChangeEvent, useState } from "react";
-import { useUpdateEffect } from "react-use";
-import { HiInformationCircle } from "react-icons/hi";
-
-interface Props {
-  value: string;
-  onChange: (value: string) => void;
-  placeholder?: string;
-  tooltip?: string;
-  label?: string;
-  onlyAlphaNumeric?: boolean;
-}
-
-export const TextInput: React.FC<Props> = ({
-  value: initialValue,
-  onChange,
-  placeholder,
-  tooltip,
-  onlyAlphaNumeric = true,
-  label,
-}) => {
-  const [value, setValue] = useState(initialValue);
-
-  const handleInputChange = (event: ChangeEvent<HTMLInputElement>) => {
-    const newValue = event.target.value;
-    // Only allow numbers or strings
-    if (onlyAlphaNumeric && /^[0-9a-zA-Z]*$/.test(newValue)) {
-      return setValue(newValue);
-    }
-    setValue(newValue);
-  };
-
-  useUpdateEffect(() => onChange(value), [value]);
-
-  return (
-    <div className="w-full relative grid grid-cols-1 gap-2.5">
-      <div className="flex items-center justify-start gap-2 relative">
-        {tooltip && (
-          <div className="group cursor-pointer">
-            <HiInformationCircle className="text-slate-500 group-hover:text-slate-300 text-xl" />
-            <div className="bg-slate-950/90 z-10 rounded-xl p-3 text-white absolute text-xs left-0 bottom-0 translate-y-[calc(100%+8px)] opacity-0 transition-all duration-200 group-hover:opacity-100 pointer-events-none group-hover:pointer-events-auto">
-              {tooltip}
-            </div>
-          </div>
-        )}
-        <label className="text-slate-400 text-sm font-medium capitalize">
-          {label}:
-        </label>
-      </div>
-      <input
-        type="text"
-        value={value}
-        placeholder={placeholder}
-        onChange={handleInputChange}
-        className="transition-all duration-200 truncate w-full h-full px-4 py-4 bg-slate-950/50 rounded-lg  outline-none text-slate-200 placeholder:text-slate-600 focus:ring-4 focus:ring-indigo-600 focus:ring-opacity-40 border border-slate-950/50 focus:border-indigo-500"
-      />
-    </div>
-  );
-};
diff --git a/spaces/failfast/nextjs-hf-spaces/Dockerfile b/spaces/failfast/nextjs-hf-spaces/Dockerfile
deleted file mode 100644
index a6a6a69b5ff641c6360aec9fc08cb8df9dbad434..0000000000000000000000000000000000000000
--- a/spaces/failfast/nextjs-hf-spaces/Dockerfile
+++ /dev/null
@@ -1,63 +0,0 @@
-FROM node:18-alpine AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
-RUN apk add --no-cache libc6-compat
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
-  if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
-  elif [ -f package-lock.json ]; then npm ci; \
-  elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
-  else echo "Lockfile not found." && exit 1; \
-  fi
-
-# Uncomment the following lines if you want to use a secret at buildtime, 
-# for example to access your private npm packages
-# RUN --mount=type=secret,id=HF_EXAMPLE_SECRET,mode=0444,required=true \
-#     $(cat /run/secrets/HF_EXAMPLE_SECRET)
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps /app/node_modules ./node_modules
-COPY . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-# RUN yarn build
-
-# If you use yarn, comment out this line and use the line above
-RUN npm run build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN addgroup --system --gid 1001 nodejs
-RUN adduser --system --uid 1001 nextjs
-
-COPY --from=builder /app/public ./public
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
-COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-
-CMD ["node", "server.js"]
diff --git a/spaces/falterWliame/Face_Mask_Detection/Boom Chat Add Ons Nulled 12 __LINK__.md b/spaces/falterWliame/Face_Mask_Detection/Boom Chat Add Ons Nulled 12 __LINK__.md
deleted file mode 100644
index a1036ff2c5896e9e3b57c81ac8fd6bdfffaa59fd..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Boom Chat Add Ons Nulled 12 __LINK__.md	
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@@ -1,95 +0,0 @@
-
-<h1>Boom Chat Add Ons Nulled 12: How to Enhance Your Online Chat Experience</h1>
-<p>If you are looking for a way to improve your online chat experience, you might want to check out Boom Chat Add Ons Nulled 12. This is a collection of 12 premium add-ons for Boom Chat, a responsive PHP/AJAX chat script that allows you to create your own chat room or join existing ones.</p>
-<p>Boom Chat Add Ons Nulled 12 includes features such as:</p>
-<h2>Boom Chat Add Ons Nulled 12</h2><br /><p><b><b>Download</b> &#10001; &#10001; &#10001; <a href="https://urlca.com/2uDchZ">https://urlca.com/2uDchZ</a></b></p><br /><br />
-<ul>
-<li>VIP membership: This add-on allows you to create VIP users who can access exclusive chat rooms, send private messages, use custom emoticons, and more.</li>
-<li>Private chat: This add-on allows you to create private chat rooms that are only visible to invited users. You can also password-protect your private chat rooms for extra security.</li>
-<li>Chat mods: This add-on allows you to assign moderators who can manage your chat rooms, ban users, delete messages, and more.</li>
-<li>Chat bots: This add-on allows you to create chat bots that can interact with your users, answer questions, provide information, and more.</li>
-<li>Chat themes: This add-on allows you to customize the appearance of your chat rooms with different themes, colors, fonts, and backgrounds.</li>
-<li>Chat games: This add-on allows you to add fun games to your chat rooms, such as trivia, hangman, tic-tac-toe, and more.</li>
-<li>And more!</li>
-</ul>
-<p>With Boom Chat Add Ons Nulled 12, you can enhance your online chat experience and make it more engaging, interactive, and fun. You can download Boom Chat Add Ons Nulled 12 for free from the link below and install it on your website in minutes. You can also check out the demo version of Boom Chat Add Ons Nulled 12 to see how it works.</p>
-<p>Boom Chat Add Ons Nulled 12 is compatible with Boom Chat v7.1 or higher. You need to have Boom Chat installed on your website before you can use Boom Chat Add Ons Nulled 12. You can also download Boom Chat v7.1 for free from the link below and follow the installation instructions.</p>
-<p>Boom Chat Add Ons Nulled 12 is a great way to spice up your online chat experience and attract more users to your website. Whether you want to create a social network, a community forum, a gaming platform, or any other type of website that involves online chat, Boom Chat Add Ons Nulled 12 can help you achieve your goals.</p>
-<h2>Download Links</h2>
-<p>Boom Chat Add Ons Nulled 12: <a href="https://urlgoal.com/2tAKRo">https://urlgoal.com/2tAKRo</a></p>
-<p>Boom Chat v7.1: <a href="https://urlgoal.com/2tAKRo">https://urlgoal.com/2tAKRo</a></p>
-<p></p>
-<h2>Demo Version</h2>
-<p>Boom Chat Add Ons Nulled 12: <a href="https://urlgoal.com/2tAKRo">https://urlgoal.com/2tAKRo</a></p>
-<p>Boom Chat v7.1: <a href="https://urlgoal.com/2tAKRo">https://urlgoal.com/2tAKRo</a></p>
-<h3>Why Choose Boom Chat Add Ons Nulled 12?</h3>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. Here are some of the benefits of using Boom Chat Add Ons Nulled 12:</p>
-<ul>
-<li>You can create VIP users who can access exclusive chat rooms, send private messages, use custom emoticons, and more.</li>
-<li>You can create private chat rooms that are only visible to invited users. You can also password-protect your private chat rooms for extra security.</li>
-<li>You can assign moderators who can manage your chat rooms, ban users, delete messages, and more.</li>
-<li>You can create chat bots that can interact with your users, answer questions, provide information, and more.</li>
-<li>You can customize the appearance of your chat rooms with different themes, colors, fonts, and backgrounds.</li>
-<li>You can add fun games to your chat rooms, such as trivia, hangman, tic-tac-toe, and more.</li>
-<li>You can download Boom Chat Add Ons Nulled 12 for free and install it on your website in minutes.</li>
-<li>You can check out the demo version of Boom Chat Add Ons Nulled 12 to see how it works.</li>
-</ul>
-<p>Boom Chat Add Ons Nulled 12 is compatible with Boom Chat v7.1 or higher. You need to have Boom Chat installed on your website before you can use Boom Chat Add Ons Nulled 12. You can also download Boom Chat v7.1 for free and follow the installation instructions.</p>
-
-<h3>How to Use Boom Chat Add Ons Nulled 12?</h3>
-<p>Using Boom Chat Add Ons Nulled 12 is easy and simple. Here are the steps to use Boom Chat Add Ons Nulled 12:</p>
-<ol>
-<li>Download Boom Chat Add Ons Nulled 12 from the link below and unzip the file.</li>
-<li>Upload the add-ons folder to your website root directory where you have installed Boom Chat.</li>
-<li>Log in to your Boom Chat admin panel and go to the add-ons section.</li>
-<li>Activate the add-ons that you want to use by clicking on the enable button.</li>
-<li>Configure the settings of each add-on according to your preferences.</li>
-<li>Enjoy using Boom Chat Add Ons Nulled 12 on your website!</li>
-</ol>
-<p>If you have any questions or issues with using Boom Chat Add Ons Nulled 12, you can contact the support team or visit the official website for more information.</p>
-
-<h3>What are the Reviews of Boom Chat Add Ons Nulled 12?</h3>
-<p>Boom Chat Add Ons Nulled 12 has received positive reviews from many users who have used it on their websites. Here are some of the testimonials from satisfied customers:</p>
-<blockquote>"Boom Chat Add Ons Nulled 12 is amazing! It has everything I need to make my chat room more fun and interactive. I love the VIP membership feature and the chat games. My users are very happy with it too. Thank you for this awesome product!" - John Smith</blockquote>
-<blockquote>"I have been using Boom Chat for a while and I was looking for some add-ons to enhance it. I found Boom Chat Add Ons Nulled 12 and I was impressed by the quality and variety of the add-ons. They are easy to install and use, and they work perfectly with Boom Chat. I highly recommend it to anyone who wants to improve their online chat experience." - Lisa Jones</blockquote>
-<blockquote>"Boom Chat Add Ons Nulled 12 is a must-have for any website that involves online chat. It has so many features that make chatting more enjoyable and engaging. The chat bots are very smart and helpful, and the chat themes are very beautiful. I also like the private chat feature and the chat mods feature. It's worth every penny!" - Mark Wilson</blockquote>
-<h3>Where to Get Boom Chat Add Ons Nulled 12?</h3>
-<p>If you are interested in getting Boom Chat Add Ons Nulled 12, you can download it for free from the link below. You will get a zip file that contains all the 12 add-ons and a readme file that explains how to install and use them. You can also check out the demo version of Boom Chat Add Ons Nulled 12 to see how it works before you download it.</p>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. You can create VIP users, private chat rooms, chat mods, chat bots, chat themes, chat games, and more. You can customize your chat rooms according to your preferences and attract more users to your website.</p>
-<p>Boom Chat Add Ons Nulled 12 is compatible with Boom Chat v7.1 or higher. You need to have Boom Chat installed on your website before you can use Boom Chat Add Ons Nulled 12. You can also download Boom Chat v7.1 for free from the link below and follow the installation instructions.</p>
-<h3>Conclusion</h3>
-<p>Boom Chat Add Ons Nulled 12 is a great way to spice up your online chat experience and make it more enjoyable and interactive. Whether you want to create a social network, a community forum, a gaming platform, or any other type of website that involves online chat, Boom Chat Add Ons Nulled 12 can help you achieve your goals.</p>
-<p>Boom Chat Add Ons Nulled 12 is easy to install and use, and it works perfectly with Boom Chat. You can download Boom Chat Add Ons Nulled 12 for free and install it on your website in minutes. You can also check out the demo version of Boom Chat Add Ons Nulled 12 to see how it works.</p>
-<p>Boom Chat Add Ons Nulled 12 has received positive reviews from many users who have used it on their websites. They have praised the quality and variety of the add-ons, and the improvement in their online chat experience. They have also recommended it to anyone who wants to improve their online chat experience.</p>
-<p>If you are looking for a way to enhance your online chat experience and make it more fun and engaging, you might want to check out Boom Chat Add Ons Nulled 12. It has everything you need to make your chat room more attractive, interactive, and fun. Download Boom Chat Add Ons Nulled 12 today and enjoy using it on your website!</p>
-
- ---> ServiceClient failure for DeepLeo[/ERROR]
-<h3>What are the Advantages of Boom Chat Add Ons Nulled 12?</h3>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. Here are some of the advantages of using Boom Chat Add Ons Nulled 12:</p>
-<ul>
-<li>You can save money by downloading Boom Chat Add Ons Nulled 12 for free instead of buying them separately.</li>
-<li>You can get access to all the latest features and updates of Boom Chat Add Ons Nulled 12 without any hassle.</li>
-<li>You can use Boom Chat Add Ons Nulled 12 on any type of website that involves online chat, such as social networks, community forums, gaming platforms, and more.</li>
-<li>You can increase your website traffic and user engagement by using Boom Chat Add Ons Nulled 12 on your website.</li>
-<li>You can improve your website performance and security by using Boom Chat Add Ons Nulled 12 on your website.</li>
-</ul>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. You can download Boom Chat Add Ons Nulled 12 for free from the link below and install it on your website in minutes. You can also check out the demo version of Boom Chat Add Ons Nulled 12 to see how it works.</p>
-
-<h3>What are the Disadvantages of Boom Chat Add Ons Nulled 12?</h3>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. However, there are also some disadvantages of using Boom Chat Add Ons Nulled 12. Here are some of them:</p>
-<ul>
-<li>You may face some legal issues by downloading Boom Chat Add Ons Nulled 12 for free instead of buying them legally.</li>
-<li>You may encounter some bugs or errors by using Boom Chat Add Ons Nulled 12 on your website.</li>
-<li>You may lose some support or warranty by using Boom Chat Add Ons Nulled 12 on your website.</li>
-<li>You may compromise some quality or functionality by using Boom Chat Add Ons Nulled 12 on your website.</li>
-<li>You may risk some security or privacy by using Boom Chat Add Ons Nulled 12 on your website.</li>
-</ul>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. However, you should also be aware of the potential risks and drawbacks of using Boom Chat Add Ons Nulled 12 on your website. You should always use Boom Chat Add Ons Nulled 12 at your own discretion and responsibility.</p>
-<h3>Conclusion</h3>
-<p>Boom Chat Add Ons Nulled 12 is a collection of 12 premium add-ons that can enhance your online chat experience and make it more fun and engaging. You can create VIP users, private chat rooms, chat mods, chat bots, chat themes, chat games, and more. You can customize your chat rooms according to your preferences and attract more users to your website.</p>
-<p>Boom Chat Add Ons Nulled 12 is easy to install and use, and it works perfectly with Boom Chat. You can download Boom Chat Add Ons Nulled 12 for free and install it on your website in minutes. You can also check out the demo version of Boom Chat Add Ons Nulled 12 to see how it works.</p>
-<p>Boom Chat Add Ons Nulled 12 has received positive reviews from many users who have used it on their websites. They have praised the quality and variety of the add-ons, and the improvement in their online chat experience. They have also recommended it to anyone who wants to improve their online chat experience.</p>
-<p>However, you should also be aware of the potential risks and drawbacks of using Boom Chat Add Ons Nulled 12 on your website. You may face some legal issues, bugs, errors, support issues, quality issues, or security issues by using Boom Chat Add Ons Nulled 12 on your website. You should always use Boom Chat Add Ons Nulled 12 at your own discretion and responsibility.</p>
-<p>If you are looking for a way to enhance your online chat experience and make it more fun and engaging, you might want to check out Boom Chat Add Ons Nulled 12. It has everything you need to make your chat room more attractive, interactive, and fun. Download Boom Chat Add Ons Nulled 12 today and enjoy using it on your website!</p> 3cee63e6c2<br />
-<br />
-<br />
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Magix Music Maker 2013 Premium 191036 Serial Number.md b/spaces/falterWliame/Face_Mask_Detection/Magix Music Maker 2013 Premium 191036 Serial Number.md
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-<br />
-<h1>Magix Music Maker 2013 Premium 191036 Serial Number: How to Get It and Use It</h1>
-<p>Do you love making music? Do you want to create your own songs, beats, and soundtracks with a professional software? If yes, then you might be interested in Magix Music Maker 2013 Premium. This software is a powerful and easy-to-use tool that allows you to produce your own music with thousands of sounds, loops, instruments, effects, and features. You can also record, edit, mix, and master your tracks with high-quality tools and plugins.</p>
-<h2>Magix Music Maker 2013 Premium 191036 Serial Number</h2><br /><p><b><b>Download Zip</b> ->>->>->> <a href="https://urlca.com/2uDdq0">https://urlca.com/2uDdq0</a></b></p><br /><br />
-<p>But how can you get Magix Music Maker 2013 Premium without paying for it? And what are the benefits of using it? In this article, we will show you how to get a serial number for Magix Music Maker 2013 Premium and how to use it to activate the software. We will also explain the benefits of using this software and how to use it effectively.</p>
-<h2>What is a serial number and how to get it?</h2>
-<p>A serial number is a code that unlocks the full features and functions of a software. Without a serial number, you might only be able to use a trial version or a limited version of a software.</p>
-<p>To get a serial number for Magix Music Maker 2013 Premium, you need to follow these steps:</p>
-<ol>
-<li>Find a reliable website that offers a serial number for Magix Music Maker 2013 Premium. You can use the search results from this query as a reference: <a href="https://www.bing.com/search?q=Magix+Music+Maker+2013+Premium+191036+Serial+Number">Magix Music Maker 2013 Premium 191036 Serial Number</a>. However, be careful about the reliability and safety of the website. Some websites might contain viruses, malware, or spyware that can harm your computer or steal your personal information. Some websites might also provide fake or invalid serial numbers that will not work or will be detected by the software.</li>
-<li>Download the serial number from the website and scan it with an antivirus program before using it. You should also backup your data before using the serial number in case something goes wrong.</li>
-<li>Copy the serial number and keep it in a safe place.</li>
-</ol>
-<h2>How to activate Magix Music Maker 2013 Premium with the serial number?</h2>
-<p>To activate Magix Music Maker 2013 Premium with the serial number, you need to follow these steps:</p>
-<ol>
-<li>Download and install Magix Music Maker 2013 Premium from its official website: <a href="https://www.magix.com/us/music/music-maker/">https://www.magix.com/us/music/music-maker/</a>.</li>
-<li>Open Magix Music Maker 2013 Premium and go to the Help menu. Click on Activate Editions.</li>
-<li>Select Magix Music Maker 2013 Premium from the list and click on Next.</li>
-<li>Paste the serial number that you copied from the website and click on Activate Online Now.</li>
-<li>Wait for the activation process to complete and click on Finish.</li>
-<li>Enjoy using Magix Music Maker 2013 Premium with all its features and functions.</li>
-</ol>
-<h2>What are the benefits of using Magix Music Maker 2013 Premium?</h2>
-<p>Using Magix Music Maker 2013 Premium has many benefits, such as:</p>
-<p></p>
-<ul>
-<li>You can save money by not paying for the software.</li>
-<li>You can access all the features and functions of the software without any limitations.</li>
-<li>You can use the software for as long as you want without any expiration date.</li>
-<li>You can update the software without any problems.</li>
-<li>You can create your own music with thousands of sounds, loops, instruments, effects, and features.</li>
-<li>You can record, edit, mix, and master your tracks with high-quality tools and plugins.</li>
-<li>You can export your tracks in various formats, such as MP3, WAV, OGG, FLAC, etc.</li>
-<li>You can share your tracks online or burn them to CDs or DVDs.</li>
-</ul>
-<h2>How to use Magix Music Maker 2013 Premium effectively?</h2>
-<p>To use Magix Music Maker 2013 Premium effectively, you need to follow these tips:</p>
-<ul>
-<li>Learn how to use the interface and the features of the software. You can find tutorials and videos on the official website: <a href="https://www.magix.com/us/support/know-how/tutorials/music-maker/">https://www.magix.com/us/support/know-how/tutorials/music-maker/</a>.</li>
-<li>Use the soundpools and loops to create your own songs easily. You can choose from various genres, such as rock, pop, hip hop, dance, etc. You can also customize the sounds and loops with effects and instruments.</li>
-<li>Use the instruments and effects to enhance your tracks. You can choose from various instruments, such as drums, guitars, keyboards, synths, etc. You can also choose from various effects, such as reverb,</li><li>delay,</li><li>chorus,</li><li>flanger,</li><li>distortion,</li><li>equalizer,</li><li>compressor,</li><li>limiter,</li><li>gate,</li><li>vocoder,</li><li>wah-wah,</li><li>tremolo,</li><li>phaser,</li><li>pitch shifter,</li><li>vintage effects suite,</li><l
-i>VST plugins support,</i</p>
-<li>delay,</li><li>chorus,</li><li>flanger,</li><li>distortion,</li><li>equalizer,</li><li>compressor,</li><li>limiter,</li><li>gate,</li><li>vocoder,</li><li>wah-wah,</li><li>tremolo,</li><li>phaser,</li><li>pitch shifter,</li><li>vintage effects suite,</li><l
-i>VST plugins support,</i
-<li>and more.</li>
-
-<li>Mix your tracks with professional tools. You can use various tools, such as volume faders,</li><li>solo/mute buttons,</li><li>panning knobs,</li><li>snap function,</li><li>ruler display,</li><li>waveform display,</li><li>spectral display,</li><l
-i>magnifier function,</i
-<li>cutter function,</l
-i>fades function,</i
-<li>crossfade function,</l
-i>automation function,</i
-<li>mastering suite,</l
-i>and more.</i
-
-<li>Export your tracks in various formats, such as MP3, WAV, OGG, FLAC, etc. You can also adjust the quality and bitrate of your tracks. You can also share your tracks online or burn them to CDs or DVDs.</l
-i></ul>
-<h2>Conclusion</h2>
-<p>Magix Music Maker 2013 Premium is a powerful and easy-to-use software that can help you create your own music with thousands of sounds, loops, instruments, effects, and features. However, if you want to use it without paying for it, you need a serial number to activate it. You can find many websites that offer serial numbers for Magix Music Maker 2013 Premium online, but you need to be careful about their reliability and safety. You also need to follow the instructions carefully to activate the software with the serial number. By using a serial number for Magix Music Maker 2013 Premium, you can enjoy its full capabilities without spending any money.</p>
-<p>We hope this article has helped you understand how to use Magix Music Maker 2013 Premium serial number to create your own music. If you have any questions or feedback, please feel free to leave a comment below.</p>
-<li>delay,</li><li>chorus,</li><li>flanger,</li><li>distortion,</li><li>equalizer,</li><li>compressor,</li><li>limiter,</li><li>gate,</li><li>vocoder,</li><li>wah-wah,</li><li>tremolo,</li><li>phaser,</li><li>pitch shifter,</li><l
-i>vintage effects suite,</i
-<li>VST plugins support,</l
-i>and more.</i
-
-<li>Mix your tracks with professional tools. You can use various tools, such as volume faders,</l
-i>solo/mute buttons,</i
-<li>panning knobs,</l
-i>snap function,</i
-<li>ruler display,</l
-i>waveform display,</i
-<li>spectral display,</l
-i>magnifier function,</i
-<li>cutter function,</l
-i>fades function,</i
-<li>crossfade function,</l
-i>automation function,</i
-<li>mastering suite,</l
-i>and more.</i
-
-<li>Export your tracks in various formats, such as MP3, WAV, OGG, FLAC, etc. You can also adjust the quality and bitrate of your tracks. You can also share your tracks online or burn them to CDs or DVDs.</l
-i></ul>
-<h2>Conclusion</h2>
-<p>Magix Music Maker 2013 Premium is a powerful and easy-to-use software that can help you create your own music with thousands of sounds, loops, instruments, effects, and features. However, if you want to use it without paying for it, you need a serial number to activate it. You can find many websites that offer serial numbers for Magix Music Maker 2013 Premium online, but you need to be careful about their reliability and safety. You also need to follow the instructions carefully to activate the software with the serial number. By using a serial number for Magix Music Maker 2013 Premium, you can enjoy its full capabilities without spending any money.</p>
-<p>We hope this article has helped you understand how to use Magix Music Maker 2013 Premium serial number to create your own music. If you have any questions or feedback, please feel free to leave a comment below.</p>
-
-<p>If you want to learn more about Magix Music Maker 2013 Premium and its features, you can check out these additional paragraphs:</p>
-<h3>How to use the soundpools and loops in Magix Music Maker 2013 Premium?</h3>
-<p>The soundpools and loops are pre-made audio files that you can use to create your own songs easily. You can choose from various genres, such as rock, pop, hip hop, dance, etc. You can also customize the sounds and loops with effects and instruments.</p>
-<p>To use the soundpools and loops in Magix Music Maker 2013 Premium, you need to follow these steps:</p>
-<ol>
-<li>Open Magix Music Maker 2013 Premium and go to the Media Pool tab. Click on Soundpools.</li>
-<li>Select a genre from the list and browse the available soundpools and loops. You can preview them by clicking on them.</li>
-<li>Drag and drop the soundpools and loops that you like to the arranger window. You can arrange them in different tracks and adjust their length and position.</li>
-<li>Add effects and instruments to the soundpools and loops by clicking on the FX or Instruments buttons on each track. You can choose from various effects and instruments that suit your genre and style.</li>
-<li>Mix your tracks with the volume faders, solo/mute buttons, panning knobs, snap function, ruler display, waveform display, spectral display, magnifier function, cutter function, fades function, crossfade function, automation function, mastering suite, and more.</li>
-<li>Export your tracks in various formats, such as MP3, WAV, OGG, FLAC, etc. You can also adjust the quality and bitrate of your tracks. You can also share your tracks online or burn them to CDs or DVDs.</li>
-</ol>
-<h3>How to use the instruments and effects in Magix Music Maker 2013 Premium?</h3>
-<p>The instruments and effects are tools that you can use to enhance your tracks. You can choose from various instruments, such as drums, guitars, keyboards, synths, etc. You can also choose from various effects, such as reverb,
-delay,
-chorus,
-flanger,
-distortion,
-equalizer,
-compressor,
-limiter,
-gate,
-vocoder,
-wah-wah,
-tremolo,
-phaser,
-pitch shifter,
-vintage effects suite,
-VST plugins support,
-and more.</p>
-<p>To use the instruments and effects in Magix Music Maker 2013 Premium, you need to follow these steps:</p>
-<ol>
-<li>Open Magix Music Maker 2013 Premium and go to the Media Pool tab. Click on Instruments or Effects.</li>
-<li>Select an instrument or an effect from the list and drag and drop it to a track in the arranger window. You can add multiple instruments or effects to a track.</li>
-<li>Edit the parameters of the instrument or the effect by clicking on its icon on the track. You can adjust various settings such as volume,
-panning,
-pitch,
-modulation,
-envelope,
-filter,
-LFO,
-oscillator,
-amp,
-cabinet,
-EQ,
-delay,
-reverb,
-and more.</li>
-<li>Mix your tracks with the volume faders,
-solo/mute buttons,
-panning knobs,
-snap function,
-ruler display,
-waveform display,
-spectral display,
-magnifier function,
-cutter function,
-fades function,
-crossfade function,
-automation function,
-mastering suite,
-and more.</li>
-<li>Export your tracks in various formats,
-
-such as MP3,
-
-WAV,
-
-OGG,
-
-FLAC,
-
-etc.
-
-You can also adjust the quality and bitrate of your tracks.
-
-You can also share your tracks online or burn them to CDs or DVDs.
-
-</l
-
-i></ol>
-<h2>Conclusion</h2>
-<p>Magix Music Maker 2013 Premium is a powerful and easy-to-use software that can help you create your own music with thousands of sounds, loops, instruments, effects, and features. However, if you want to use it without paying for it, you need a serial number to activate it. You can find many websites that offer serial numbers for Magix Music Maker 2013 Premium online, but you need to be careful about their reliability and safety. You also need to follow the instructions carefully to activate the software with the serial number. By using a serial number for Magix Music Maker 2013 Premium, you can enjoy its full capabilities without spending any money.</p>
-<p>We hope this article has helped you understand how to use Magix Music Maker 2013 Premium serial number to create your own music. We have also shown you how to use the soundpools and loops, the instruments and effects, and the professional tools in Magix Music Maker 2013 Premium. You can use these features to create your own songs, beats, and soundtracks with ease and creativity. You can also export your tracks in various formats and share them online or burn them to CDs or DVDs.</p>
-<p>If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you and help you with any issues you might have. Thank you for reading and happy music making!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Pacific Rim Uprising English Hd 1080p In Hindi Download NEW.md b/spaces/falterWliame/Face_Mask_Detection/Pacific Rim Uprising English Hd 1080p In Hindi Download NEW.md
deleted file mode 100644
index e26e8fc6c314785f9514fac7356e708ae12ec6e8..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Pacific Rim Uprising English Hd 1080p In Hindi Download NEW.md	
+++ /dev/null
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diff --git a/spaces/fb700/chatglm-fitness-RLHF/request_llm/bridge_jittorllms_llama.py b/spaces/fb700/chatglm-fitness-RLHF/request_llm/bridge_jittorllms_llama.py
deleted file mode 100644
index 6dfac681aeaa11a780304b9e645637cabd677688..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/request_llm/bridge_jittorllms_llama.py
+++ /dev/null
@@ -1,178 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf
-from multiprocessing import Process, Pipe
-
-load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-
-#################################################################################
-class GetGLMHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.jittorllms_model = None
-        self.info = ""
-        self.local_history = []
-        self.success = True
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-        
-    def check_dependency(self):
-        try:
-            import pandas
-            self.info = "依赖检测通过"
-            self.success = True
-        except:
-            from toolbox import trimmed_format_exc
-            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
-                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
-                        r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
-            self.success = False
-
-    def ready(self):
-        return self.jittorllms_model is not None
-
-    def run(self):
-        # 子进程执行
-        # 第一次运行，加载参数
-        def validate_path():
-            import os, sys
-            dir_name = os.path.dirname(__file__)
-            env = os.environ.get("PATH", "")
-            os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
-            root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-            os.chdir(root_dir_assume + '/request_llm/jittorllms')
-            sys.path.append(root_dir_assume + '/request_llm/jittorllms')
-        validate_path() # validate path so you can run from base directory
-
-        def load_model():
-            import types
-            try:
-                if self.jittorllms_model is None:
-                    device, = get_conf('LOCAL_MODEL_DEVICE')
-                    from .jittorllms.models import get_model
-                    # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
-                    args_dict = {'model': 'llama'}
-                    print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
-                    self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
-                    print('done get model')
-            except:
-                self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
-                raise RuntimeError("不能正常加载jittorllms的参数！")
-        print('load_model')
-        load_model()
-
-        # 进入任务等待状态
-        print('进入任务等待状态')
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            query = kwargs['query']
-            history = kwargs['history']
-            # 是否重置
-            if len(self.local_history) > 0 and len(history)==0:
-                print('触发重置')
-                self.jittorllms_model.reset()
-            self.local_history.append(query)
-
-            print('收到消息，开始请求')
-            try:
-                for response in self.jittorllms_model.stream_chat(query, history):
-                    print(response)
-                    self.child.send(response)
-            except:
-                from toolbox import trimmed_format_exc
-                print(trimmed_format_exc())
-                self.child.send('[Local Message] Call jittorllms fail.')
-            # 请求处理结束，开始下一个循环
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # 主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res != '[Finish]':
-                yield res
-            else:
-                break
-        self.threadLock.release()
-    
-global llama_glm_handle
-llama_glm_handle = None
-#################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global llama_glm_handle
-    if llama_glm_handle is None:
-        llama_glm_handle = GetGLMHandle()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + llama_glm_handle.info
-        if not llama_glm_handle.success: 
-            error = llama_glm_handle.info
-            llama_glm_handle = None
-            raise RuntimeError(error)
-
-    # jittorllms 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    for response in llama_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        print(response)
-        if len(observe_window) >= 1:  observe_window[0] = response
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, ""))
-
-    global llama_glm_handle
-    if llama_glm_handle is None:
-        llama_glm_handle = GetGLMHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + llama_glm_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not llama_glm_handle.success: 
-            llama_glm_handle = None
-            return
-
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    # 处理历史信息
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
-    for response in llama_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, response)
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/utils/plot.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/utils/plot.py
deleted file mode 100644
index ccc588e5c01ca550b69c385aeb3fd139c59fb88a..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/utils/plot.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# coding: utf-8
-
-import os
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
-from prettytable import PrettyTable
-from sklearn.metrics import roc_curve, auc
-
-image_path = "/data/anxiang/IJB_release/IJBC"
-files = [
-        "./ms1mv3_arcface_r100/ms1mv3_arcface_r100/ijbc.npy"
-]
-
-
-def read_template_pair_list(path):
-    pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
-    return t1, t2, label
-
-
-p1, p2, label = read_template_pair_list(
-    os.path.join('%s/meta' % image_path,
-                 '%s_template_pair_label.txt' % 'ijbc'))
-
-methods = []
-scores = []
-for file in files:
-    methods.append(file.split('/')[-2])
-    scores.append(np.load(file))
-
-methods = np.array(methods)
-scores = dict(zip(methods, scores))
-colours = dict(
-    zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
-x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
-tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
-fig = plt.figure()
-for method in methods:
-    fpr, tpr, _ = roc_curve(label, scores[method])
-    roc_auc = auc(fpr, tpr)
-    fpr = np.flipud(fpr)
-    tpr = np.flipud(tpr)  # select largest tpr at same fpr
-    plt.plot(fpr,
-             tpr,
-             color=colours[method],
-             lw=1,
-             label=('[%s (AUC = %0.4f %%)]' %
-                    (method.split('-')[-1], roc_auc * 100)))
-    tpr_fpr_row = []
-    tpr_fpr_row.append("%s-%s" % (method, "IJBC"))
-    for fpr_iter in np.arange(len(x_labels)):
-        _, min_index = min(
-            list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
-        tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
-    tpr_fpr_table.add_row(tpr_fpr_row)
-plt.xlim([10 ** -6, 0.1])
-plt.ylim([0.3, 1.0])
-plt.grid(linestyle='--', linewidth=1)
-plt.xticks(x_labels)
-plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
-plt.xscale('log')
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.title('ROC on IJB')
-plt.legend(loc="lower right")
-print(tpr_fpr_table)
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/id_loss.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/id_loss.py
deleted file mode 100644
index bab806172eff18c0630536ae96817508c3197b8b..0000000000000000000000000000000000000000
--- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/id_loss.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from torch import nn
-from configs.paths_config import model_paths
-from models.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
-    def __init__(self):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-        for module in [self.facenet, self.face_pool]:
-            for param in module.parameters():
-                param.requires_grad = False
-
-    def extract_feats(self, x):
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y, x):
-        n_samples = x.shape[0]
-        x_feats = self.extract_feats(x)
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        id_logs = []
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            diff_input = y_hat_feats[i].dot(x_feats[i])
-            diff_views = y_feats[i].dot(x_feats[i])
-            id_logs.append({'diff_target': float(diff_target),
-                            'diff_input': float(diff_input),
-                            'diff_views': float(diff_views)})
-            loss += 1 - diff_target
-            id_diff = float(diff_target) - float(diff_views)
-            sim_improvement += id_diff
-            count += 1
-
-        return loss / count, sim_improvement / count, id_logs
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/utils/__init__.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/locales/index.ts b/spaces/fengmuxi/ChatGpt-Web/app/locales/index.ts
deleted file mode 100644
index 40f0a1aded0678bc7df369bd76388e996a78d3c6..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/locales/index.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import CN from "./cn";
-import EN from "./en";
-import TW from "./tw";
-import ES from "./es";
-import IT from "./it";
-import TR from "./tr";
-import JP from "./jp";
-import DE from "./de";
-
-export type { LocaleType } from "./cn";
-
-export const AllLangs = [
-  "en",
-  "cn",
-  "tw",
-  "es",
-  "it",
-  "tr",
-  "jp",
-  "de",
-] as const;
-export type Lang = (typeof AllLangs)[number];
-
-const LANG_KEY = "lang";
-const DEFAULT_LANG = "en";
-
-function getItem(key: string) {
-  try {
-    return localStorage.getItem(key);
-  } catch {
-    return null;
-  }
-}
-
-function setItem(key: string, value: string) {
-  try {
-    localStorage.setItem(key, value);
-  } catch {}
-}
-
-function getLanguage() {
-  try {
-    return navigator.language.toLowerCase();
-  } catch {
-    console.log("[Lang] failed to detect user lang.");
-    return DEFAULT_LANG;
-  }
-}
-
-export function getLang(): Lang {
-  const savedLang = getItem(LANG_KEY);
-
-  if (AllLangs.includes((savedLang ?? "") as Lang)) {
-    return savedLang as Lang;
-  }
-
-  const lang = getLanguage();
-
-  for (const option of AllLangs) {
-    if (lang.includes(option)) {
-      return option;
-    }
-  }
-
-  return DEFAULT_LANG;
-}
-
-export function changeLang(lang: Lang) {
-  setItem(LANG_KEY, lang);
-  location.reload();
-}
-
-export default {
-  en: EN,
-  cn: CN,
-  tw: TW,
-  es: ES,
-  it: IT,
-  tr: TR,
-  jp: JP,
-  de: DE,
-}[getLang()] as typeof CN;
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Bloons TD 6 for PC (v31.0) - Build Monkey Towers Deploy Heroes and Pop Bloons in this Epic Strategy Game.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Bloons TD 6 for PC (v31.0) - Build Monkey Towers Deploy Heroes and Pop Bloons in this Epic Strategy Game.md
deleted file mode 100644
index aab6e7a79f638e9e1cbc2b161c4c40720ea707d4..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Bloons TD 6 for PC (v31.0) - Build Monkey Towers Deploy Heroes and Pop Bloons in this Epic Strategy Game.md	
+++ /dev/null
@@ -1,217 +0,0 @@
-
-<h1>Download Bloons TD 6 Free for PC (v31.0)</h1>
-<p>If you are looking for a fun and addictive tower defense game that will keep you entertained for hours, then you should definitely check out <strong>Bloons TD 6</strong>. This is the latest installment in the popular <strong>Bloons</strong> series, where you have to use various monkey towers and heroes to pop colorful balloons (or bloons) before they reach the end of the track.</p>
-<p>In this article, we will show you how to download <strong>Bloons TD 6</strong> free for PC (v31.0), which is the most recent version of the game as of June 2023. We will also tell you why you should play <strong>Bloons TD 6</strong> on PC instead of mobile devices, and what are the best methods and sources to get the game for free.</p>
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-<p <h2>What is Bloons TD 6?</h2>
-<p><strong>Bloons TD 6</strong> is a 3D tower defense game developed and published by <strong>Ninja Kiwi</strong>, a New Zealand-based company that specializes in creating casual and mobile games. The game was released in June 2018 for iOS, Android, and Windows devices, and has since received numerous updates and expansions.</p>
-<p>The game features over 20 maps, 50 towers, 10 heroes, and hundreds of upgrades and achievements. The game also supports co-op and multiplayer modes, where you can team up with other players or compete against them. The game has a colorful and cartoonish style, with cute and quirky animations and sound effects.</p>
-<p>The game has received positive reviews from critics and players alike, who praised its gameplay, graphics, variety, and replay value. The game has also been a commercial success, selling over 6 million copies worldwide and becoming one of the top-grossing games on the App Store and Google Play.</p>
-<h2>Why download Bloons TD 6 for PC?</h2>
-<p>While <strong>Bloons TD 6</strong> is primarily designed for mobile devices, you might want to download it for PC for several reasons. Here are some of the benefits and advantages of playing <strong>Bloons TD 6</strong> on PC:</p>
-<ul>
-<li><strong>Better graphics:</strong> Playing <strong>Bloons TD 6</strong> on PC will allow you to enjoy the game's stunning 3D graphics on a larger screen and at a higher resolution. You will be able to see more details and appreciate the game's art style more.</li>
-<li><strong>Better performance:</strong> Playing <strong>Bloons TD 6</strong> on PC will also ensure that you have a smoother and faster gaming experience. You will not have to worry about lag, crashes, or battery drain. You will also be able to adjust the game's settings to suit your preferences and system specifications.</li>
-<li><strong>Better controls:</strong> Playing <strong>Bloons TD 6</strong> on PC will give you more options and flexibility when it comes to controlling the game. You can use your mouse and keyboard, or a controller, to place towers, activate abilities, and navigate menus. You can also customize your keybindings and shortcuts to suit your playstyle.</li>
-<li><strong>Better modding options:</strong> Playing <strong>Bloons TD 6</strong> on PC will also open up more possibilities for modding the game. You can use various tools and programs to create your own maps, towers, heroes, bloons, and more. You can also download and install mods made by other players to enhance your gaming experience.</li>
-</ul>
-<p>As you can see, playing <strong>Bloons TD 6</strong> on PC has many perks that you might not get on mobile devices. If you are interested in downloading <strong>Bloons TD 6</strong> free for PC (v31.0), then read on to find out how.</p>
- <h2>How to download Bloons TD 6 for PC (v31.0)?</h2>
-<p>There are different methods and sources that you can use to download <strong>Bloons TD 6</strong> free for PC (v31.0). We will explain each one in detail below, along with their requirements, steps, tips, and tricks.</p>
- <h3>Method 1: Steam</h3>
-<p>The first method that we recommend is to download <strong>Bloons TD 6</strong> from <strong>Steam</strong>, which is the official platform for the game. Steam is a digital distribution service that offers thousands of PC games, including <strong>Bloons TD 6</strong>. Steam also provides various features and benefits for gamers, such as cloud saves, achievements, updates, community forums, and more.</p>
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- <h4>Requirements</h4>
-<p>To download <strong>Bloons TD 6</strong> from Steam, you will need the following:</p>
-<table>
-<tr><td><strong>A Steam account:</strong></td><td>You will need to create a free account on Steam's website or app. You will also need to verify your email address and agree to Steam's terms of service.</td></tr>
-<tr><td><strong>A payment method:</strong></td><td>You will need to have a valid payment method linked to your Steam account, such as a credit card, debit card, PayPal, or Steam Wallet. You will need to pay $9.99 USD (or equivalent) to purchase <strong>Bloons TD 6</strong>.</td></tr>
-<tr>< <td><strong>A PC that meets the system requirements:</strong></td><td>You will need to have a PC that can run <strong>Bloons TD 6</strong> smoothly and without issues. Here are the minimum and recommended system requirements for the game:</td></tr>
-</table>
- <table>
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 7 (64bit)</td><td>OS: Windows 10 (64bit)</td></tr>
-<tr><td>Processor: 1.5Ghz or better</td><td>Processor: 2Ghz or better</td></tr>
-<tr><td>Memory: 4096 MB RAM</td><td>Memory: 8192 MB RAM</td></tr>
-<tr><td>Graphics: OpenGL 2.0 compatible, ATI, Nvidia or Intel HD</td><td>Graphics: OpenGL 2.0 compatible, ATI, Nvidia or Intel HD</td></tr>
-<tr><td>Storage: 2048 MB available space</td><td>Storage: 4096 MB available space</td></tr>
-<tr><td>Sound Card: Windows compatible sound card</td><td>Sound Card: Windows compatible sound card</td></tr>
-</table>
- <h4>Steps</h4>
-<p>To download <strong>Bloons TD 6</strong> from Steam, you will need to follow these steps:</p>
-<ol>
-<li>Open Steam on your PC or visit <a href="">Steam's website</a>.</li>
-<li>Login to your Steam account or create one if you don't have one.</li>
-<li>Search for <strong>Bloons TD 6</strong> in the Steam store or click on this <a href="">link</a>.</li>
-<li>Add the game to your cart and proceed to checkout.</li>
-<li>Select your payment method and confirm your purchase.</li>
-<li>Wait for the game to download and install on your PC.</li>
-<li>Launch the game from your Steam library and enjoy!</li>
-</ol>
- <h4>Tips and tricks</h4>
-<p>Here are some tips and tricks that can help you optimize your gaming experience with <strong>Bloons TD 6</strong> on Steam:</p>
-<ul>
-<li>You can adjust the game's settings, such as resolution, graphics quality, sound volume, and language, from the main menu or the pause menu.</li>
-<li>You can use Steam's cloud save feature to sync your game progress across different devices. You can also backup your save files manually by locating them in your Steam folder.</li>
-<li>You can access the latest updates and patches for the game by enabling automatic updates in Steam. You can also check the game's news and changelog from Steam's community hub.</li>
-<li>You can join Steam's community hub for <strong>Bloons TD 6</strong> to interact with other players, share screenshots and videos, ask questions, give feedback, and more.</li>
-<li>You can use Steam's overlay feature to access various functions while playing the game, such as chatting with friends, browsing the web, taking screenshots, and more.</li>
-</ul>
- <h3>Method 2: Epic Games Store</h3>
- <p>The second method that we suggest is to download <strong>Bloons TD 6</strong> from <strong>Epic Games Store</strong>, which is another popular platform for PC games. Epic Games Store is a digital distribution service that offers exclusive titles, free games, discounts, and more. Epic Games Store also provides various features and benefits for gamers, such as achievements, support tickets, events, and more.</p>
- <h4>Requirements</h4>
- <p>To download <strong>Bloons TD 6</strong> from Epic Games Store, you will need the following:</p>
- <table>
-<tr><td><strong>An Epic Games account:</strong></td><td>You will need to create a free account on Epic Games Store's website or app. You will also need to verify your email address and agree to Epic Games Store's terms of service.</td></tr>
-<tr><td><strong>A payment method:</strong></td><td>You will need to have a valid payment method linked to your Epic Games account, such as a credit card, debit card, PayPal, or Epic Games Wallet. You will need to pay $9.99 USD (or equivalent) to purchase <strong>Bloons TD 6</strong>.</td></tr>
-<tr>< <td><strong>A PC that meets the system requirements:</strong></td><td>You will need to have a PC that can run <strong>Bloons TD 6</strong> smoothly and without issues. Here are the minimum and recommended system requirements for the game:</td></tr>
-</table>
- <table>
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 7 (64bit)</td><td>OS: Windows 10 (64bit)</td></tr>
-<tr><td>Processor: 1.5Ghz or better</td><td>Processor: 2Ghz or better</td></tr>
-<tr><td>Memory: 4096 MB RAM</td><td>Memory: 8192 MB RAM</td></tr>
-<tr><td>Graphics: OpenGL 2.0 compatible, ATI, Nvidia or Intel HD</td><td>Graphics: OpenGL 2.0 compatible, ATI, Nvidia or Intel HD</td></tr>
-<tr><td>Storage: 2048 MB available space</td><td>Storage: 4096 MB available space</td></tr>
-<tr><td>Sound Card: Windows compatible sound card</td><td>Sound Card: Windows compatible sound card</td></tr>
-</table>
- <h4>Steps</h4>
- <p>To download <strong>Bloons TD 6</strong> from Epic Games Store, you will need to follow these steps:</p>
- <ol>
-<li>Open Epic Games Store on your PC or visit <a href="">Epic Games Store's website</a>.</li>
-<li>Login to your Epic Games account or create one if you don't have one.</li>
-<li>Search for <strong>Bloons TD 6</strong> in the Epic Games Store or click on this <a href="">link</a>.</li>
-<li>Add the game to your cart and proceed to checkout.</li>
-<li>Select your payment method and confirm your purchase.</li>
-<li>Wait for the game to download and install on your PC.</li>
-<li>Launch the game from your Epic Games library and enjoy!</li>
-</ol>
- <h4>Tips and tricks</h4>
- <p>Here are some tips and tricks that can help you optimize your gaming experience with <strong>Bloons TD 6</strong> on Epic Games Store:</p>
- <ul>
-<li>You can adjust the game's settings, such as resolution, graphics quality, sound volume, and language, from the main menu or the pause menu.</li>
-<li>You can use Epic Games Store's achievements feature to track your progress and unlock rewards in the game.</li>
-<li>You can access the latest updates and patches for the game by enabling automatic updates in Epic Games Store. You can also check the game's news and changelog from Epic Games Store's website.</li>
-<li>You can join Epic Games Store's events for <strong>Bloons TD 6</strong> to participate in challenges, giveaways, and tournaments with other players.</li>
-<li>You can use Epic Games Store's support tickets feature to contact customer service if you encounter any issues with the game or the platform.</li>
-</ul>
- <h3>Method 3: Third-party sites</h3>
- <p>The third method that we propose is to download <strong>Bloons TD 6</strong> from third-party sites that offer free PC games. These are websites that host or link to various PC games that you can download for free, without paying or registering. However, these sites are not authorized by the game developers or publishers, and may pose some risks and challenges for gamers.</p>
- <h4>Requirements</h4>
- <p>To download <strong>Bloons TD 6</strong> from third-party sites, you will need the following:</p>
- <table>
-<tr><td><strong>A PC that meets the system requirements:</strong></td><td>You will need to have a PC that can run <strong>Bloons TD 6</strong> smoothly and without issues. Here are the minimum and recommended system requirements for the game:</td></tr>
-<tr>< </table>
- <table>
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 7 (64bit)</td><td>OS: Windows 10 (64bit)</td></tr>
-<tr><td>Processor: 1.5Ghz or better</td><td>Processor: 2Ghz or better</td></tr>
-<tr><td>Memory: 4096 MB RAM</td><td>Memory: 8192 MB RAM</td></tr>
-<tr><td>Graphics: OpenGL 2.0 compatible, ATI, Nvidia or Intel HD</td><td>Graphics: OpenGL 2.0 compatible, ATI, Nvidia or Intel HD</td></tr>
-<tr><td>Storage: 2048 MB available space</td><td>Storage: 4096 MB available space</td></tr>
-<tr><td>Sound Card: Windows compatible sound card</td><td>Sound Card: Windows compatible sound card</td></tr>
-</table>
- <h4>Steps</h4>
- <p>To download <strong>Bloons TD 6</strong> from third-party sites, you will need to follow these steps:</p>
- <ol>
-<li>Find a reliable and reputable third-party site that offers free PC games. You can use search engines, forums, blogs, or recommendations from other gamers to find such sites.</li>
-<li>Check the game version and the download link of <strong>Bloons TD 6</strong>. Make sure that the game version is v31.0, which is the latest version of the game as of June 2023. Also, make sure that the download link is working and safe.</li>
-<li>Download the game file from the third-party site. The file may be in different formats, such as ZIP, RAR, ISO, or EXE. You may need to use a program like WinRAR or 7-Zip to extract the file.</li>
-<li>Install the game on your PC. You may need to run the setup file or mount the ISO file to install the game. You may also need to copy and paste the crack file or enter a serial key to activate the game.</li>
-<li>Launch the game from your desktop or start menu and enjoy!</li>
-</ol>
- <h4>Tips and tricks</h4>
- <p>Here are some tips and tricks that can help you avoid scams, viruses, and legal issues when downloading <strong>Bloons TD 6</strong> from a third-party site:</p>
- <ul>
-<li>You should always use a reputable antivirus software to scan the game file before downloading and installing it. This can help you detect and remove any malware or spyware that may harm your PC or steal your personal information.</li>
-<li>You should always read the reviews and comments of other users who have downloaded the game from the same site. This can help you verify the quality and safety of the game file and the download link.</li>
-<li>You should always check the source and origin of the game file and the download link. This can help you avoid downloading fake or modified versions of the game that may contain viruses or unwanted content.</li>
-<li>You should always respect the intellectual property rights of the game developers and publishers. This means that you should not distribute, share, or sell the game file or the download link without their permission. You should also support them by purchasing the game from official platforms if you can afford it.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, <strong>Bloons TD 6</strong> is a fantastic tower defense game that will challenge your skills and creativity. You can download <strong>Bloons TD 6</strong> free for PC (v31.0) using different methods and sources, such as Steam, Epic Games Store, or third-party sites. However, each method has its own pros and cons, so you should choose wisely and carefully.</p>
- <p>We hope that this article has helped you learn how to download <strong>Bloons TD 6</strong> free for PC (v31.0) and enjoy the game. If you have any questions, comments, or suggestions, feel free to leave them below. Happy gaming!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers about downloading <strong>Bloons TD 6</strong> for PC (v31.0):</p>
- <ol>
-<li><strong>Is Bloons TD 6 free on PC?</strong></li>
-<p>No, Bloons TD 6 is not free on PC. You will need to pay $9.99 USD (or equivalent) to purchase the game from official platforms like Steam or Epic Games Store. However, you can download Bloons TD 6 free for PC (v31.0) from third-party sites that offer free PC games.</p>
-<li <li><strong>How to update Bloons TD 6 on PC?</strong></li>
-<p>To update Bloons TD 6 on PC, you will need to follow different steps depending on the method and source that you used to download the game. Here are some general guidelines:</p>
-<ul>
-<li>If you downloaded Bloons TD 6 from Steam, you can enable automatic updates in Steam's settings or manually check for updates in your Steam library. Steam will download and install the latest version of the game for you.</li>
-<li>If you downloaded Bloons TD 6 from Epic Games Store, you can enable automatic updates in Epic Games Store's settings or manually check for updates in your Epic Games library. Epic Games Store will download and install the latest version of the game for you.</li>
-<li>If you downloaded Bloons TD 6 from a third-party site, you will need to find and download the latest version of the game from the same site or another reliable site. You will also need to uninstall the previous version of the game and install the new version on your PC.</li>
-</ul>
-<li><strong>How to play Bloons TD 6 online with friends on PC?</strong></li>
-<p>To play Bloons TD 6 online with friends on PC, you will need to follow these steps:</p>
-<ol>
-<li>Launch Bloons TD 6 on your PC and select the co-op mode from the main menu.</li>
-<li>Create a co-op lobby or join an existing one. You can choose between public or private lobbies, and between different game modes and difficulties.</li>
-<li>Invite your friends to join your lobby or accept their invitations. You can use Steam's or Epic Games Store's friend list and chat features to communicate with your friends.</li>
-<li>Start the game and cooperate with your friends to pop bloons and complete objectives. You can use voice chat or text chat to coordinate your strategies and tactics.</li>
-</ol>
-<li><strong>How to mod Bloons TD 6 on PC?</strong></li>
-<p>To mod Bloons TD 6 on PC, you will need to use various tools and programs that can modify the game's files and data. Some of the most popular tools and programs for modding Bloons TD 6 are:</p>
-<ul>
-<li><a href="">BTD Toolbox</a>: A program that allows you to create and edit maps, towers, heroes, bloons, and more.</li>
-<li><a href="">BTD Modding API</a>: A program that allows you to create and use custom scripts, functions, and events in the game.</li>
-<li><a href="">BTD Mod Manager</a>: A program that allows you to install and manage mods for Bloons TD 6.</li>
-</ul>
-<p>You can find more information and tutorials on how to mod Bloons TD 6 on PC on <a href="">this website</a>.</p>
- <li><strong>How to fix common errors with Bloons TD 6 on PC?</strong></li>
- <p>If you encounter any errors or issues with Bloons TD 6 on PC, such as crashes, freezes, glitches, or bugs, you can try some of these solutions:</p>
- <ul>
-<li>Update your game to the latest version.</li>
-<li>Update your drivers and software to the latest version.</li>
-<li>Verify the integrity of your game files using Steam's or Epic Games Store's features.</li>
-<li>Run the game as an administrator or in compatibility mode.</li>
-<li>Disable any antivirus or firewall programs that may interfere with the game.</li>
-<li>Delete any mods or custom files that may conflict with the game.</li>
-<li>Contact customer support or visit the official forums for more help.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
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-
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-<p>We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
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- <ol>
-<li><b>What is the difference between Endurance Dead Space Mod APK and Endurance?</b></li>
-<p>Endurance Dead Space Mod APK is a modded version of Endurance, a survival horror game for Android and iOS devices. The mod adds new features, such as unlimited ammo, health, and money, as well as improved graphics, sounds, and performance.</p>
- <li><b>Is Endurance Dead Space Mod APK safe to download and install?</b></li>
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-<p>Endurance Dead Space Mod APK may not support online multiplayer mode, as it is a modded version of the game that may not be compatible with the official servers. However, you can try to play online with other players who have the same modded version of the game as you do.</p>
- <li><b>Can I play Endurance Dead Space Mod APK on PC?</b></li>
-<p>To play Endurance Dead Space Mod APK on PC, you need to use an Android emulator, such as [Bluestacks] or [NoxPlayer]. An Android emulator is a software that allows you to run Android apps on your PC. However, you may experience some performance issues or compatibility problems when using an emulator, as it may not run the game as smoothly as on a native Android device.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffffu/bing/cloudflare/worker.js b/spaces/fffffu/bing/cloudflare/worker.js
deleted file mode 100644
index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000
--- a/spaces/fffffu/bing/cloudflare/worker.js
+++ /dev/null
@@ -1,18 +0,0 @@
-const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。
-
-export default {
-  async fetch(request) {
-    const uri = new URL(request.url);
-    if (uri.protocol === 'http:') {
-      uri.protocol = 'https:';
-      return new Response('', {
-        status: 301,
-        headers: {
-          location: uri.toString(),
-        },
-      })
-    }
-    uri.host = TRAGET_HOST
-    return fetch(new Request(uri.toString(), request));
-  },
-};
diff --git a/spaces/fffiloni/Video-Matting-Anything/utils/config.py b/spaces/fffiloni/Video-Matting-Anything/utils/config.py
deleted file mode 100644
index fa2d14ded19af42810b4773c8260ee5292f5afa7..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Video-Matting-Anything/utils/config.py
+++ /dev/null
@@ -1,161 +0,0 @@
-from easydict import EasyDict
-
-# Base default config
-CONFIG = EasyDict({})
-# to indicate this is a default setting, should not be changed by user
-CONFIG.is_default = True
-CONFIG.version = "baseline"
-CONFIG.phase = "train"
-# distributed training
-CONFIG.dist = False
-CONFIG.wandb = False
-# global variables which will be assigned in the runtime
-CONFIG.local_rank = 0
-CONFIG.gpu = 0
-CONFIG.world_size = 1
-
-# Model config
-CONFIG.model = EasyDict({})
-# use pretrained checkpoint as encoder
-CONFIG.model.freeze_seg = True
-CONFIG.model.multi_scale = False
-CONFIG.model.imagenet_pretrain = True
-CONFIG.model.imagenet_pretrain_path = "/home/liyaoyi/Source/python/attentionMatting/pretrain/model_best_resnet34_En_nomixup.pth"
-CONFIG.model.batch_size = 16
-# one-hot or class, choice: [3, 1]
-CONFIG.model.mask_channel = 1
-CONFIG.model.trimap_channel = 3
-
-# hyper-parameter for refinement
-CONFIG.model.self_refine_width1 = 30
-CONFIG.model.self_refine_width2 = 15
-CONFIG.model.self_mask_width = 10
-
-# Model -> Architecture config
-CONFIG.model.arch = EasyDict({})
-# definition in networks/encoders/__init__.py and networks/encoders/__init__.py
-CONFIG.model.arch.encoder = "res_shortcut_encoder_29"
-CONFIG.model.arch.decoder = "res_shortcut_decoder_22"
-CONFIG.model.arch.m2m = "conv_baseline"
-CONFIG.model.arch.seg = "maskrcnn"
-# predefined for GAN structure
-CONFIG.model.arch.discriminator = None
-
-
-# Dataloader config
-CONFIG.data = EasyDict({})
-CONFIG.data.cutmask_prob = 0
-CONFIG.data.workers = 0
-CONFIG.data.pha_ratio = 0.5
-# data path for training and validation in training phase
-CONFIG.data.train_fg = None
-CONFIG.data.train_alpha = None
-CONFIG.data.train_bg = None
-CONFIG.data.test_merged = None
-CONFIG.data.test_alpha = None
-CONFIG.data.test_trimap = None
-CONFIG.data.imagematte_fg = None
-CONFIG.data.imagematte_pha = None
-CONFIG.data.d646_fg = None
-CONFIG.data.d646_pha = None
-CONFIG.data.aim_fg = None
-CONFIG.data.aim_pha = None
-CONFIG.data.human2k_fg = None
-CONFIG.data.human2k_pha = None
-CONFIG.data.am2k_fg = None
-CONFIG.data.am2k_pha = None
-CONFIG.data.coco_bg = None
-CONFIG.data.bg20k_bg = None
-CONFIG.data.rim_pha = None
-CONFIG.data.rim_img = None
-CONFIG.data.spd_pha = None
-CONFIG.data.spd_img = None
-# feed forward image size (untested)
-CONFIG.data.crop_size = 1024
-# composition of two foregrounds, affine transform, crop and HSV jitter
-CONFIG.data.real_world_aug = False
-CONFIG.data.augmentation = True
-CONFIG.data.random_interp = True
-
-### Benchmark config
-CONFIG.benchmark = EasyDict({})
-CONFIG.benchmark.him2k_img = '/home/jiachen.li/data/HIM2K/images/natural'
-CONFIG.benchmark.him2k_alpha = '/home/jiachen.li/data/HIM2K/alphas/natural'
-CONFIG.benchmark.him2k_comp_img = '/home/jiachen.li/data/HIM2K/images/comp'
-CONFIG.benchmark.him2k_comp_alpha = '/home/jiachen.li/data/HIM2K/alphas/comp'
-CONFIG.benchmark.rwp636_img = '/home/jiachen.li/data/RealWorldPortrait-636/image'
-CONFIG.benchmark.rwp636_alpha = '/home/jiachen.li/data/RealWorldPortrait-636/alpha'
-CONFIG.benchmark.ppm100_img = '/home/jiachen.li/data/PPM-100/image'
-CONFIG.benchmark.ppm100_alpha = '/home/jiachen.li/data/PPM-100/matte'
-CONFIG.benchmark.am2k_img = '/home/jiachen.li/data/AM2k/validation/original'
-CONFIG.benchmark.am2k_alpha = '/home/jiachen.li/data/AM2k/validation/mask'
-CONFIG.benchmark.rw100_img = '/home/jiachen.li/data/RefMatte_RW_100/image_all'
-CONFIG.benchmark.rw100_alpha = '/home/jiachen.li/data/RefMatte_RW_100/mask'
-CONFIG.benchmark.rw100_text = '/home/jiachen.li/data/RefMatte_RW_100/refmatte_rw100_label.json'
-CONFIG.benchmark.rw100_index = '/home/jiachen.li/data/RefMatte_RW_100/eval_index_expression.json'
-CONFIG.benchmark.vm_img = '/home/jiachen.li/data/videomatte_512x288'
-
-# Training config
-CONFIG.train = EasyDict({})
-CONFIG.train.total_step = 100000
-CONFIG.train.warmup_step = 5000
-CONFIG.train.val_step = 1000
-# basic learning rate of optimizer
-CONFIG.train.G_lr = 1e-3
-# beta1 and beta2 for Adam
-CONFIG.train.beta1 = 0.5
-CONFIG.train.beta2 = 0.999
-# weight of different losses
-CONFIG.train.rec_weight = 1
-CONFIG.train.comp_weight = 1
-CONFIG.train.lap_weight = 1
-# clip large gradient
-CONFIG.train.clip_grad = True
-# resume the training (checkpoint file name)
-CONFIG.train.resume_checkpoint = None
-# reset the learning rate (this option will reset the optimizer and learning rate scheduler and ignore warmup)
-CONFIG.train.reset_lr = False
-
-
-# Logging config
-CONFIG.log = EasyDict({})
-CONFIG.log.tensorboard_path = "./logs/tensorboard"
-CONFIG.log.tensorboard_step = 100
-# save less images to save disk space
-CONFIG.log.tensorboard_image_step = 500
-CONFIG.log.logging_path = "./logs/stdout"
-CONFIG.log.logging_step = 10
-CONFIG.log.logging_level = "DEBUG"
-CONFIG.log.checkpoint_path = "./checkpoints"
-CONFIG.log.checkpoint_step = 10000
-
-
-def load_config(custom_config, default_config=CONFIG, prefix="CONFIG"):
-    """
-    This function will recursively overwrite the default config by a custom config
-    :param default_config:
-    :param custom_config: parsed from config/config.toml
-    :param prefix: prefix for config key
-    :return: None
-    """
-    if "is_default" in default_config:
-        default_config.is_default = False
-
-    for key in custom_config.keys():
-        full_key = ".".join([prefix, key])
-        if key not in default_config:
-            raise NotImplementedError("Unknown config key: {}".format(full_key))
-        elif isinstance(custom_config[key], dict):
-            if isinstance(default_config[key], dict):
-                load_config(default_config=default_config[key],
-                            custom_config=custom_config[key],
-                            prefix=full_key)
-            else:
-                raise ValueError("{}: Expected {}, got dict instead.".format(full_key, type(custom_config[key])))
-        else:
-            if isinstance(default_config[key], dict):
-                raise ValueError("{}: Expected dict, got {} instead.".format(full_key, type(custom_config[key])))
-            else:
-                default_config[key] = custom_config[key]
-
-
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/wasi.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/wasi.d.ts
deleted file mode 100644
index d20b66bf443eeae7eb10f04576ab1e1e1e2da418..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/wasi.d.ts
+++ /dev/null
@@ -1,158 +0,0 @@
-/**
- * The WASI API provides an implementation of the [WebAssembly System Interface](https://wasi.dev/) specification. WASI gives sandboxed WebAssembly applications access to the
- * underlying operating system via a collection of POSIX-like functions.
- *
- * ```js
- * import { readFile } from 'fs/promises';
- * import { WASI } from 'wasi';
- * import { argv, env } from 'process';
- *
- * const wasi = new WASI({
- *   args: argv,
- *   env,
- *   preopens: {
- *     '/sandbox': '/some/real/path/that/wasm/can/access'
- *   }
- * });
- *
- * // Some WASI binaries require:
- * //   const importObject = { wasi_unstable: wasi.wasiImport };
- * const importObject = { wasi_snapshot_preview1: wasi.wasiImport };
- *
- * const wasm = await WebAssembly.compile(
- *   await readFile(new URL('./demo.wasm', import.meta.url))
- * );
- * const instance = await WebAssembly.instantiate(wasm, importObject);
- *
- * wasi.start(instance);
- * ```
- *
- * To run the above example, create a new WebAssembly text format file named`demo.wat`:
- *
- * ```text
- * (module
- *     ;; Import the required fd_write WASI function which will write the given io vectors to stdout
- *     ;; The function signature for fd_write is:
- *     ;; (File Descriptor, *iovs, iovs_len, nwritten) -> Returns number of bytes written
- *     (import "wasi_snapshot_preview1" "fd_write" (func $fd_write (param i32 i32 i32 i32) (result i32)))
- *
- *     (memory 1)
- *     (export "memory" (memory 0))
- *
- *     ;; Write 'hello world\n' to memory at an offset of 8 bytes
- *     ;; Note the trailing newline which is required for the text to appear
- *     (data (i32.const 8) "hello world\n")
- *
- *     (func $main (export "_start")
- *         ;; Creating a new io vector within linear memory
- *         (i32.store (i32.const 0) (i32.const 8))  ;; iov.iov_base - This is a pointer to the start of the 'hello world\n' string
- *         (i32.store (i32.const 4) (i32.const 12))  ;; iov.iov_len - The length of the 'hello world\n' string
- *
- *         (call $fd_write
- *             (i32.const 1) ;; file_descriptor - 1 for stdout
- *             (i32.const 0) ;; *iovs - The pointer to the iov array, which is stored at memory location 0
- *             (i32.const 1) ;; iovs_len - We're printing 1 string stored in an iov - so one.
- *             (i32.const 20) ;; nwritten - A place in memory to store the number of bytes written
- *         )
- *         drop ;; Discard the number of bytes written from the top of the stack
- *     )
- * )
- * ```
- *
- * Use [wabt](https://github.com/WebAssembly/wabt) to compile `.wat` to `.wasm`
- *
- * ```console
- * $ wat2wasm demo.wat
- * ```
- *
- * The `--experimental-wasi-unstable-preview1` CLI argument is needed for this
- * example to run.
- * @experimental
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/wasi.js)
- */
-declare module 'wasi' {
-    interface WASIOptions {
-        /**
-         * An array of strings that the WebAssembly application will
-         * see as command line arguments. The first argument is the virtual path to the
-         * WASI command itself.
-         */
-        args?: string[] | undefined;
-        /**
-         * An object similar to `process.env` that the WebAssembly
-         * application will see as its environment.
-         */
-        env?: object | undefined;
-        /**
-         * This object represents the WebAssembly application's
-         * sandbox directory structure. The string keys of `preopens` are treated as
-         * directories within the sandbox. The corresponding values in `preopens` are
-         * the real paths to those directories on the host machine.
-         */
-        preopens?: NodeJS.Dict<string> | undefined;
-        /**
-         * By default, WASI applications terminate the Node.js
-         * process via the `__wasi_proc_exit()` function. Setting this option to `true`
-         * causes `wasi.start()` to return the exit code rather than terminate the
-         * process.
-         * @default false
-         */
-        returnOnExit?: boolean | undefined;
-        /**
-         * The file descriptor used as standard input in the WebAssembly application.
-         * @default 0
-         */
-        stdin?: number | undefined;
-        /**
-         * The file descriptor used as standard output in the WebAssembly application.
-         * @default 1
-         */
-        stdout?: number | undefined;
-        /**
-         * The file descriptor used as standard error in the WebAssembly application.
-         * @default 2
-         */
-        stderr?: number | undefined;
-    }
-    /**
-     * The `WASI` class provides the WASI system call API and additional convenience
-     * methods for working with WASI-based applications. Each `WASI` instance
-     * represents a distinct sandbox environment. For security purposes, each `WASI`instance must have its command-line arguments, environment variables, and
-     * sandbox directory structure configured explicitly.
-     * @since v13.3.0, v12.16.0
-     */
-    class WASI {
-        constructor(options?: WASIOptions);
-        /**
-         * Attempt to begin execution of `instance` as a WASI command by invoking its`_start()` export. If `instance` does not contain a `_start()` export, or if`instance` contains an `_initialize()`
-         * export, then an exception is thrown.
-         *
-         * `start()` requires that `instance` exports a [`WebAssembly.Memory`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WebAssembly/Memory) named`memory`. If
-         * `instance` does not have a `memory` export an exception is thrown.
-         *
-         * If `start()` is called more than once, an exception is thrown.
-         * @since v13.3.0, v12.16.0
-         */
-        start(instance: object): void; // TODO: avoid DOM dependency until WASM moved to own lib.
-        /**
-         * Attempt to initialize `instance` as a WASI reactor by invoking its`_initialize()` export, if it is present. If `instance` contains a `_start()`export, then an exception is thrown.
-         *
-         * `initialize()` requires that `instance` exports a [`WebAssembly.Memory`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WebAssembly/Memory) named`memory`.
-         * If `instance` does not have a `memory` export an exception is thrown.
-         *
-         * If `initialize()` is called more than once, an exception is thrown.
-         * @since v14.6.0, v12.19.0
-         */
-        initialize(instance: object): void; // TODO: avoid DOM dependency until WASM moved to own lib.
-        /**
-         * `wasiImport` is an object that implements the WASI system call API. This object
-         * should be passed as the `wasi_snapshot_preview1` import during the instantiation
-         * of a [`WebAssembly.Instance`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/WebAssembly/Instance).
-         * @since v13.3.0, v12.16.0
-         */
-        readonly wasiImport: NodeJS.Dict<any>; // TODO: Narrow to DOM types
-    }
-}
-declare module 'node:wasi' {
-    export * from 'wasi';
-}
diff --git a/spaces/fffiloni/x-decoder-video/README.md b/spaces/fffiloni/x-decoder-video/README.md
deleted file mode 100644
index c79e9ffce1664f712e193fee88942d9c4328defc..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/x-decoder-video/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: X-Decoder Video
-emoji: ✨
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/utils/model_list.py b/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/utils/model_list.py
deleted file mode 100644
index 009edb3cf3ae92b9bee47f4e85f3300f383fa114..0000000000000000000000000000000000000000
--- a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/utils/model_list.py
+++ /dev/null
@@ -1,22 +0,0 @@
-stable_model_list = [
-    "runwayml/stable-diffusion-v1-5",
-    "SG161222/Realistic_Vision_V2.0"
-]
-
-stable_inpiant_model_list = [
-    "stabilityai/stable-diffusion-2-inpainting",
-    "runwayml/stable-diffusion-inpainting",
-]
-
-controlnet_model_list = [
-    "lllyasviel/control_v11p_sd15_canny",
-    "lllyasviel/control_v11f1p_sd15_depth",
-    "lllyasviel/control_v11p_sd15_openpose",
-    "lllyasviel/control_v11p_sd15_scribble",
-    "lllyasviel/control_v11p_sd15_mlsd",
-    "lllyasviel/control_v11e_sd15_shuffle",
-    "lllyasviel/control_v11e_sd15_ip2p",
-    "lllyasviel/control_v11p_sd15_lineart",
-    "lllyasviel/control_v11p_sd15s2_lineart_anime",
-    "lllyasviel/control_v11p_sd15_softedge",
-]
diff --git a/spaces/floriankrempl/mtg_rules_bot/tests/data_handler/__init__.py b/spaces/floriankrempl/mtg_rules_bot/tests/data_handler/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/flowers-team/Interactive_DeepRL_Demo/js/utils/utils.js b/spaces/flowers-team/Interactive_DeepRL_Demo/js/utils/utils.js
deleted file mode 100644
index cf7583ecefffca9595573c6b0245a647b3c08d31..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/Interactive_DeepRL_Demo/js/utils/utils.js
+++ /dev/null
@@ -1,37 +0,0 @@
-/* Utility functions */
-
-/**
- * Downloads the given object as a JSON file with the given filename.
- * @param exportObj {Object} - Object to be downloaded.
- * @param exportName {string} - Name of the file.
- */
-window.downloadObjectAsJson = (exportObj, exportName) => {
-    let dataStr = "data:text/json;charset=utf-8," + encodeURIComponent(JSON.stringify(exportObj));
-    let downloadAnchorNode = document.createElement('a');
-    downloadAnchorNode.setAttribute("href",     dataStr);
-    downloadAnchorNode.setAttribute("download", exportName + ".json");
-    document.body.appendChild(downloadAnchorNode); // required for firefox
-    downloadAnchorNode.click();
-    downloadAnchorNode.remove();
-}
-
-/**
- * Transforms the first character of the given string to upper case.
- * @param a {string}
- * @returns {string}
- */
-window.strUcFirst = (a) => {
-    return (a+'').charAt(0).toUpperCase()+a.substr(1);
-}
-
-/**
- * Draws the forbidden area in the off-screen forbidden canvas.
- */
-window.draw_forbidden_area = () => {
-    forbidden_canvas.clear();
-    forbidden_canvas.stroke("#FF0000");
-    forbidden_canvas.strokeWeight(3);
-    forbidden_canvas.fill(255, 50, 0, 75);
-    let w = convertPosEnvToCanvas((INITIAL_TERRAIN_STARTPAD - 1) * TERRAIN_STEP, 0).x;
-    forbidden_canvas.rect(0, 0, w, RENDERING_VIEWER_H + 2 * SCROLL_Y_MAX);
-}
\ No newline at end of file
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/stream_frames/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/stream_frames/run.py
deleted file mode 100644
index f6120d81dd92961e120fb933a1195386fbe04a9c..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/stream_frames/run.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-import numpy as np
-
-def flip(im):
-    return np.flipud(im)
-
-demo = gr.Interface(
-    flip, 
-    gr.Image(source="webcam", streaming=True), 
-    "image",
-    live=True
-)
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/non_local.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/non_local.py
deleted file mode 100644
index 92d00155ef275c1201ea66bba30470a1785cc5d7..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/non_local.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta
-
-import torch
-import torch.nn as nn
-
-from ..utils import constant_init, normal_init
-from .conv_module import ConvModule
-from .registry import PLUGIN_LAYERS
-
-
-class _NonLocalNd(nn.Module, metaclass=ABCMeta):
-    """Basic Non-local module.
-
-    This module is proposed in
-    "Non-local Neural Networks"
-    Paper reference: https://arxiv.org/abs/1711.07971
-    Code reference: https://github.com/AlexHex7/Non-local_pytorch
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        reduction (int): Channel reduction ratio. Default: 2.
-        use_scale (bool): Whether to scale pairwise_weight by
-            `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`.
-            Default: True.
-        conv_cfg (None | dict): The config dict for convolution layers.
-            If not specified, it will use `nn.Conv2d` for convolution layers.
-            Default: None.
-        norm_cfg (None | dict): The config dict for normalization layers.
-            Default: None. (This parameter is only applicable to conv_out.)
-        mode (str): Options are `gaussian`, `concatenation`,
-            `embedded_gaussian` and `dot_product`. Default: embedded_gaussian.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 reduction=2,
-                 use_scale=True,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 mode='embedded_gaussian',
-                 **kwargs):
-        super(_NonLocalNd, self).__init__()
-        self.in_channels = in_channels
-        self.reduction = reduction
-        self.use_scale = use_scale
-        self.inter_channels = max(in_channels // reduction, 1)
-        self.mode = mode
-
-        if mode not in [
-                'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation'
-        ]:
-            raise ValueError("Mode should be in 'gaussian', 'concatenation', "
-                             f"'embedded_gaussian' or 'dot_product', but got "
-                             f'{mode} instead.')
-
-        # g, theta, phi are defaulted as `nn.ConvNd`.
-        # Here we use ConvModule for potential usage.
-        self.g = ConvModule(
-            self.in_channels,
-            self.inter_channels,
-            kernel_size=1,
-            conv_cfg=conv_cfg,
-            act_cfg=None)
-        self.conv_out = ConvModule(
-            self.inter_channels,
-            self.in_channels,
-            kernel_size=1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-        if self.mode != 'gaussian':
-            self.theta = ConvModule(
-                self.in_channels,
-                self.inter_channels,
-                kernel_size=1,
-                conv_cfg=conv_cfg,
-                act_cfg=None)
-            self.phi = ConvModule(
-                self.in_channels,
-                self.inter_channels,
-                kernel_size=1,
-                conv_cfg=conv_cfg,
-                act_cfg=None)
-
-        if self.mode == 'concatenation':
-            self.concat_project = ConvModule(
-                self.inter_channels * 2,
-                1,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                bias=False,
-                act_cfg=dict(type='ReLU'))
-
-        self.init_weights(**kwargs)
-
-    def init_weights(self, std=0.01, zeros_init=True):
-        if self.mode != 'gaussian':
-            for m in [self.g, self.theta, self.phi]:
-                normal_init(m.conv, std=std)
-        else:
-            normal_init(self.g.conv, std=std)
-        if zeros_init:
-            if self.conv_out.norm_cfg is None:
-                constant_init(self.conv_out.conv, 0)
-            else:
-                constant_init(self.conv_out.norm, 0)
-        else:
-            if self.conv_out.norm_cfg is None:
-                normal_init(self.conv_out.conv, std=std)
-            else:
-                normal_init(self.conv_out.norm, std=std)
-
-    def gaussian(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        pairwise_weight = pairwise_weight.softmax(dim=-1)
-        return pairwise_weight
-
-    def embedded_gaussian(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        if self.use_scale:
-            # theta_x.shape[-1] is `self.inter_channels`
-            pairwise_weight /= theta_x.shape[-1]**0.5
-        pairwise_weight = pairwise_weight.softmax(dim=-1)
-        return pairwise_weight
-
-    def dot_product(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        pairwise_weight /= pairwise_weight.shape[-1]
-        return pairwise_weight
-
-    def concatenation(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        h = theta_x.size(2)
-        w = phi_x.size(3)
-        theta_x = theta_x.repeat(1, 1, 1, w)
-        phi_x = phi_x.repeat(1, 1, h, 1)
-
-        concat_feature = torch.cat([theta_x, phi_x], dim=1)
-        pairwise_weight = self.concat_project(concat_feature)
-        n, _, h, w = pairwise_weight.size()
-        pairwise_weight = pairwise_weight.view(n, h, w)
-        pairwise_weight /= pairwise_weight.shape[-1]
-
-        return pairwise_weight
-
-    def forward(self, x):
-        # Assume `reduction = 1`, then `inter_channels = C`
-        # or `inter_channels = C` when `mode="gaussian"`
-
-        # NonLocal1d x: [N, C, H]
-        # NonLocal2d x: [N, C, H, W]
-        # NonLocal3d x: [N, C, T, H, W]
-        n = x.size(0)
-
-        # NonLocal1d g_x: [N, H, C]
-        # NonLocal2d g_x: [N, HxW, C]
-        # NonLocal3d g_x: [N, TxHxW, C]
-        g_x = self.g(x).view(n, self.inter_channels, -1)
-        g_x = g_x.permute(0, 2, 1)
-
-        # NonLocal1d theta_x: [N, H, C], phi_x: [N, C, H]
-        # NonLocal2d theta_x: [N, HxW, C], phi_x: [N, C, HxW]
-        # NonLocal3d theta_x: [N, TxHxW, C], phi_x: [N, C, TxHxW]
-        if self.mode == 'gaussian':
-            theta_x = x.view(n, self.in_channels, -1)
-            theta_x = theta_x.permute(0, 2, 1)
-            if self.sub_sample:
-                phi_x = self.phi(x).view(n, self.in_channels, -1)
-            else:
-                phi_x = x.view(n, self.in_channels, -1)
-        elif self.mode == 'concatenation':
-            theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
-            phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
-        else:
-            theta_x = self.theta(x).view(n, self.inter_channels, -1)
-            theta_x = theta_x.permute(0, 2, 1)
-            phi_x = self.phi(x).view(n, self.inter_channels, -1)
-
-        pairwise_func = getattr(self, self.mode)
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = pairwise_func(theta_x, phi_x)
-
-        # NonLocal1d y: [N, H, C]
-        # NonLocal2d y: [N, HxW, C]
-        # NonLocal3d y: [N, TxHxW, C]
-        y = torch.matmul(pairwise_weight, g_x)
-        # NonLocal1d y: [N, C, H]
-        # NonLocal2d y: [N, C, H, W]
-        # NonLocal3d y: [N, C, T, H, W]
-        y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
-                                                    *x.size()[2:])
-
-        output = x + self.conv_out(y)
-
-        return output
-
-
-class NonLocal1d(_NonLocalNd):
-    """1D Non-local module.
-
-    Args:
-        in_channels (int): Same as `NonLocalND`.
-        sub_sample (bool): Whether to apply max pooling after pairwise
-            function (Note that the `sub_sample` is applied on spatial only).
-            Default: False.
-        conv_cfg (None | dict): Same as `NonLocalND`.
-            Default: dict(type='Conv1d').
-    """
-
-    def __init__(self,
-                 in_channels,
-                 sub_sample=False,
-                 conv_cfg=dict(type='Conv1d'),
-                 **kwargs):
-        super(NonLocal1d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
-
-        self.sub_sample = sub_sample
-
-        if sub_sample:
-            max_pool_layer = nn.MaxPool1d(kernel_size=2)
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            if self.mode != 'gaussian':
-                self.phi = nn.Sequential(self.phi, max_pool_layer)
-            else:
-                self.phi = max_pool_layer
-
-
-@PLUGIN_LAYERS.register_module()
-class NonLocal2d(_NonLocalNd):
-    """2D Non-local module.
-
-    Args:
-        in_channels (int): Same as `NonLocalND`.
-        sub_sample (bool): Whether to apply max pooling after pairwise
-            function (Note that the `sub_sample` is applied on spatial only).
-            Default: False.
-        conv_cfg (None | dict): Same as `NonLocalND`.
-            Default: dict(type='Conv2d').
-    """
-
-    _abbr_ = 'nonlocal_block'
-
-    def __init__(self,
-                 in_channels,
-                 sub_sample=False,
-                 conv_cfg=dict(type='Conv2d'),
-                 **kwargs):
-        super(NonLocal2d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
-
-        self.sub_sample = sub_sample
-
-        if sub_sample:
-            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            if self.mode != 'gaussian':
-                self.phi = nn.Sequential(self.phi, max_pool_layer)
-            else:
-                self.phi = max_pool_layer
-
-
-class NonLocal3d(_NonLocalNd):
-    """3D Non-local module.
-
-    Args:
-        in_channels (int): Same as `NonLocalND`.
-        sub_sample (bool): Whether to apply max pooling after pairwise
-            function (Note that the `sub_sample` is applied on spatial only).
-            Default: False.
-        conv_cfg (None | dict): Same as `NonLocalND`.
-            Default: dict(type='Conv3d').
-    """
-
-    def __init__(self,
-                 in_channels,
-                 sub_sample=False,
-                 conv_cfg=dict(type='Conv3d'),
-                 **kwargs):
-        super(NonLocal3d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
-        self.sub_sample = sub_sample
-
-        if sub_sample:
-            max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            if self.mode != 'gaussian':
-                self.phi = nn.Sequential(self.phi, max_pool_layer)
-            else:
-                self.phi = max_pool_layer
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/losses/constants.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/losses/constants.py
deleted file mode 100644
index 1d1399ff3545918f9ee2dd1960690766cb392666..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/losses/constants.py
+++ /dev/null
@@ -1,18 +0,0 @@
-#: Loss binary mode suppose you are solving binary segmentation task.
-#: That mean yor have only one class which pixels are labled as **1**,
-#: the rest pixels are background and labeled as **0**.
-#: Target mask shape - (N, H, W), model output mask shape (N, 1, H, W).
-BINARY_MODE: str = "binary"
-
-#: Loss multiclass mode suppose you are solving multi-**class** segmentation task.
-#: That mean you have *C = 1..N* classes which have unique label values,
-#: classes are mutually exclusive and all pixels are labeled with theese values.
-#: Target mask shape - (N, H, W), model output mask shape (N, C, H, W).
-MULTICLASS_MODE: str = "multiclass"
-
-#: Loss multilabel mode suppose you are solving multi-**label** segmentation task.
-#: That mean you have *C = 1..N* classes which pixels are labeled as **1**,
-#: classes are not mutually exclusive and each class have its own *channel*,
-#: pixels in each channel which are not belong to class labeled as **0**.
-#: Target mask shape - (N, C, H, W), model output mask shape (N, C, H, W).
-MULTILABEL_MODE: str = "multilabel"
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Cisco Ccna Cloud 210-451 Cldfnd Cbt Nuggets 53 grafikprogramme hymn The Ultimate Guide to Cisco CCNA Cloud Certification.md b/spaces/gotiQspiryo/whisper-ui/examples/Cisco Ccna Cloud 210-451 Cldfnd Cbt Nuggets 53 grafikprogramme hymn The Ultimate Guide to Cisco CCNA Cloud Certification.md
deleted file mode 100644
index 6981ec8d4a180d72fb78a48aa720c95132404bb9..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Cisco Ccna Cloud 210-451 Cldfnd Cbt Nuggets 53 grafikprogramme hymn The Ultimate Guide to Cisco CCNA Cloud Certification.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Cisco Ccna Cloud 210-451 Cldfnd Cbt Nuggets 53 grafikprogramme hymn</h2><br /><p><b><b>Download File</b> &#9734; <a href="https://urlgoal.com/2uyMh6">https://urlgoal.com/2uyMh6</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Diseno En Ingenieria Mecanica Shigley 6 Edicion Solucionario Un Recurso Valioso para Estudiantes y Profesores.md b/spaces/gotiQspiryo/whisper-ui/examples/Diseno En Ingenieria Mecanica Shigley 6 Edicion Solucionario Un Recurso Valioso para Estudiantes y Profesores.md
deleted file mode 100644
index 3873c9ecc56b49d84a77f086f040b1425a1bd1b1..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Diseno En Ingenieria Mecanica Shigley 6 Edicion Solucionario Un Recurso Valioso para Estudiantes y Profesores.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<p>Hola que tal,<br/><br/>Estoy buscando el solucionario de Mechanics of Materials de Ansel C Ugural primera edicion editorial Wiley. Porfavor si alguien lo tiene hagamelo saber al elitar@hotmail.com , estoy algo desesperado porke no voy bien en esa materia y el profesor no explica bien. <br/><br/>Mil Gracias por su ayuda.</p>
-<h2>Diseno En Ingenieria Mecanica Shigley 6 Edicion Solucionario</h2><br /><p><b><b>Download Zip</b> &#10042; <a href="https://urlgoal.com/2uyN82">https://urlgoal.com/2uyN82</a></b></p><br /><br />
-<p>es de una buena ayuda tu pagina, pero estoy buscando el solucionario o libro de "mecanica de fluidos de Franzini" se los agradecederia si me lo consiguieran. y gracias por la ayuda!! dieguras_16@hotmail.com</p>
-<p>muchas gracias maestro!!!!excelente tu aporte a la ingenieria mecanica y tu dedicacion a hacer posible esto...<br/>Te lo agradesco de corazon...<br/><br/>Ingeniero Mecánico USACH<br/><br/>Gracias nuevamente y que Dios te guarde y te proteja...</p>
-<p>hola ando buscando con urgencia el solucionario de mecanica cuantica de franz schwabl, y el de mecanica estadistica de la coleccion de berkely. porfa es urgente. <br/><br/>gracias.<br/><br/>mi correo es <br/>js_trujillo5@hotmail.com</p>
-<p>hola que tal oyes de casulidad no tienes el solucioanrio de diseño en ingneria mecanica de shigley pero la sexta edicion...si serias tan amable de facilitarmelo..<br/><br/>mi correo es vermax_plus@hotmail.com</p>
-<p>holaa<br/>que tall <br/><br/><br/>,,...<br/>buenoo!!<br/>solo keria decirless,,<br/>ke io tengo <br/>lo solucionarios de <br/>dinamika la 6,7 edicion<br/>pero ando buscando los de<br/>la 8, y 5 ,, si alguien los tiene<br/>se los cambioo,,y si noo,,<br/>detodos,,modos,,agregenme,,y se los pasoo,ok<br/>byee!!<br/>innes_neak_jl@hotmail.com</p>
-<p>saludos...por favor queria ver si era posible que arreglaran el link del solucionario del norto diseno de maquinas, ya que se encuentra danado...si alguien lo tiene q por fa vor me lo envie a mi correo jegc54@gmail.com</p>
-<p></p>
-<p>Hola a todos que tal?, necesito saber si alguien de ustedes me puede proporcionar el solucionario de mecánica vectorial para inegnieros de dinamica 7ma edicion, pro favor mandarmelo a mi correo chelalo16@hotmail.com....se los agradecere.....hasta luego!!!</p>
-<p>alguien tendra por ahi el solucionario del libro de mecanica de materiales de Fitzgerald.ya que no lo veo en la lista.<br/><br/>te agredeceria si pudieses ayudarme y enviarmelo gracias<br/>mi mail:<br/>turey79@hotmail.com</p>
-<p>hola, fijate q ando buscando el solucionario del libro de mecanica de los fluidos e hidraulica de la serie shaun el autor es ranald v. giles, ya lo busque por todos lados si de casualidad lo tenes te suplico que me lo envies mi correo es alejandra_alvarado_11@yahoo.com y desde ya te agradezco</p>
-<p>Hola estoy buecando el libro diseño en ingneria mecanica de shigley la sexta edicion con su solucionario o el libro de la septima edicion<br/> me podrias indicar donde o como conseguirlos <br/>gracias</p>
-<p>yo creo que estas chato de constestar tantos post y6 suvir libros y blabla bueno la peticioon es la de siempre necesito el solucionario de "mecanica vectorial para ingenieros Dinamica" <br/>7ªedicion <br/>autores<br/>Ferdinand P. Beer<br/>E. Russell Johnston, Jr<br/>William E. Clausen<br/><br/> los busque por mi cuenta perommmm no lo he pillado <br/><br/>atodo esto te felicito e bajado barios solucionarios de este blog y gracias a tus suvidas he pasaado varios ramos<br/> espero que me respondas y si no == gracias por lo suvido adios....</p>
-<p>Excelente Blog!!!<br/>Me gustaria saber si puedes conseguir el solucionario de <br/><br/>1 "Mecanica vectorial para ingenieros: estatica/ dinamica 8va edicion. beer-johnston-eisberg"<br/><br/>2 "Probabilidad y estadistica para ingenieria y ciencia . 7ma edicion "<br/><br/>3." Termodinamica 5ta edicion por yunus cengel y michael boles"<br/><br/>Muchas gracias, pues seria de mucha ayuda<br/><br/>mmieses@gmail.com</p>
-<p>Amigos estoy buscando un solucionario de sistemas de control para ing. mecanica. si alguno tiene el libro y/o solucionario de este por favor hacerme el gran favor de regalarmelo gracias. ingluisroncancio@hotmail.com<br/><br/>a proposito buena pagina para buscar solucionarios. me tope por error. gracias!!!</p>
-<p>hey que, no se si puedas ayudarme con el solucionario de mecanica de materiales de james m. gere sexta edicion, lo ocupo con urgencia, te lo agradeceria mucho, y gracoas por ayudar a los que estudiamos ingenierias...mi correo es dlorbans@hotmail.com</p>
-<p>Hola.... excelente blog... necesito ayuda para encontrar el solucionario completo del libro de mecanica de fluidos de mott... en especial el capitulo de compuertas.... Gracias<br/>!!! es urgente!!!<br/><br/>dicarolb88_19@yahoo.com</p>
-<p>Hola...... excelente blog... Necesito ayuda para encontrar el solucionario completo del libro de mecanica de fluidos de Mott. especialmente el capitulo de compuertas... Gracias<br/>!!es urgente!!<br/><br/>dicarolb88_19@yahoo.com</p>
-<p>hola...<br/>necesito el solucionario del libro mecanica vectorial para ingenieros . Estatica la octava edicion <br/>por favor si alguien la teien x favor enviarmela a mi correo sherley_10@hotmail.com <br/>por favor es super urgente tengo parcial este lunes</p>
-<p>Aunque no encontré propiamente el solucionario que quería, uno de otra edicion la verdad, quiero darte las gracias y mandarte mcuhas bendiciones porque has puesto todos estos solucionarios en tu blog. So nice of you!!! :D</p>
-<p>hola necesito el solucionario de TERMODINAMICA DE FAIRES a ver si me lo pudieras consguir o me lo pudira conseguir alguien yo tengo el de diseño de shigley de resistencia de materiales y algunos otros se los puedo enviar mi correo es: jaza_montes@hotmail.com porfavor ayudenme¡¡¡</p>
-<p>hola amigo, necesetito el solu de mecanica vectorial para ingenieros beer y jhonston la 7ma edicon si me lo pueden madar a mi correo se los agradeceria ...<br/><br/>y tambien el libro de Ingenieria Economica - Leland Blank & Antohony Tarquin. la ultima edicion<br/>mi correo es <br/>dahiana8184@hotmail.com</p>
-<p>hola, excelente pagina, estoy buscando el solucionario de mecanica de los fluidos streeter 8va edicion, 3ra en español, si alguien lo tiene dejo mi correo kubo_222@hotmail.com, por favor en realidad lo necesito</p>
-<p>Hola frank!!! fijate que necesito urgentemente el solucionario de termodinamica de yunus cengel y michael boles pero la 4ta edicion es urgente por favor si lo tienes facilitamelo a mi email por favor estoy desesperado con esa clase, te lo agradecere mucho: mi correo donelch@hotmail.com<br/>gracias</p>
-<p>Hola que tal!!!.. estoy buscando el solucionario de Mecánica de Solidos de Egor Popov (segunda o tercera edicion), si alguien lo tiene porfa enviarmelo a caromontoya12@hotmail.com... me urge!! GRACIAS</p>
-<p>Hola... Muy bueno tu blog. Necesito urgente el solucionario de mecanica de fluidos e hidraulica de Schaum de cualquier edicion. es urgente. por favor cualquier informacion enviala a este correo linoelgrande@hotmail.com<br/>Muchas gracias. Espero de tu ayuda</p>
-<p>auxilio por favor necesito el solucionario de mecanica de fluidos e hidraulica de ranald v. giles tercera edicion losproblems propuestos en el 3 capitulo sobre todo 3.44, 3.50 y 3.51 no los puesdo realizar ni satanas pudo por favor auxilio. racydth@hotmail.com<br/><br/>mil gracias aquien pueda ayudarme</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/examples/speech_to_text/docs/mtedx_example.md b/spaces/gradio/HuBERT/examples/speech_to_text/docs/mtedx_example.md
deleted file mode 100644
index 25b4556affbf5bc141b103095d15fffef6225c0e..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_to_text/docs/mtedx_example.md
+++ /dev/null
@@ -1,200 +0,0 @@
-[[Back]](..)
-
-# S2T Example: Speech Translation (ST) on Multilingual TEDx
-
-[Multilingual TEDx](https://arxiv.org/abs/2102.01757) is multilingual corpus for speech recognition and
-speech translation. The data is derived from TEDx talks in 8 source languages
-with translations to a subset of 5 target languages.
-
-## Data Preparation
-[Download](http://openslr.org/100/) and unpack Multilingual TEDx data to a path
-`${MTEDX_ROOT}/${LANG_PAIR}`, then preprocess it with
-```bash
-# additional Python packages for S2T data processing/model training
-pip install pandas torchaudio soundfile sentencepiece
-
-# Generate TSV manifests, features, vocabulary
-# and configuration for each language
-python examples/speech_to_text/prep_mtedx_data.py \
-  --data-root ${MTEDX_ROOT} --task asr \
-  --vocab-type unigram --vocab-size 1000
-python examples/speech_to_text/prep_mtedx_data.py \
-  --data-root ${MTEDX_ROOT} --task st \
-  --vocab-type unigram --vocab-size 1000
-
-# Add vocabulary and configuration for joint data
-# (based on the manifests and features generated above)
-python examples/speech_to_text/prep_mtedx_data.py \
-  --data-root ${MTEDX_ROOT} --task asr --joint \
-  --vocab-type unigram --vocab-size 8000
-python examples/speech_to_text/prep_mtedx_data.py \
-  --data-root ${MTEDX_ROOT} --task st --joint \
-  --vocab-type unigram --vocab-size 8000
-```
-The generated files (manifest, features, vocabulary and data configuration) will be added to
-`${MTEDX_ROOT}/${LANG_PAIR}` (per-language data) and `MTEDX_ROOT` (joint data).
-
-
-## ASR
-#### Training
-Spanish as example:
-```bash
-fairseq-train ${MTEDX_ROOT}/es-es \
-    --config-yaml config_asr.yaml --train-subset train_asr --valid-subset valid_asr \
-    --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \
-    --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \
-    --arch s2t_transformer_xs --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \
-    --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \
-    --load-pretrained-encoder-from ${PRETRAINED_ENCODER} \
-    --skip-invalid-size-inputs-valid-test \
-    --keep-last-epochs 10 --update-freq 8 --patience 10
-```
-For joint model (using ASR data from all 8 languages):
-```bash
-fairseq-train ${MTEDX_ROOT} \
-    --config-yaml config_asr.yaml \
-    --train-subset train_es-es_asr,train_fr-fr_asr,train_pt-pt_asr,train_it-it_asr,train_ru-ru_asr,train_el-el_asr,train_ar-ar_asr,train_de-de_asr \
-    --valid-subset valid_es-es_asr,valid_fr-fr_asr,valid_pt-pt_asr,valid_it-it_asr,valid_ru-ru_asr,valid_el-el_asr,valid_ar-ar_asr,valid_de-de_asr \
-    --save-dir ${MULTILINGUAL_ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \
-    --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \
-    --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \
-    --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \
-    --skip-invalid-size-inputs-valid-test \
-    --keep-last-epochs 10 --update-freq 8 --patience 10 \
-    --ignore-prefix-size 1
-```
-where `MULTILINGUAL_ASR_SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs
-with 1 GPU. You may want to update it accordingly when using more than 1 GPU.
-For multilingual models, we prepend target language ID token as target BOS, which should be excluded from
-the training loss via `--ignore-prefix-size 1`.
-
-#### Inference & Evaluation
-```bash
-CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
-python scripts/average_checkpoints.py \
-  --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \
-  --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}"
-
-fairseq-generate ${MTEDX_ROOT}/es-es \
-  --config-yaml config_asr.yaml --gen-subset test --task speech_to_text \
-  --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \
-  --skip-invalid-size-inputs-valid-test \
-  --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct --remove-bpe
-
-# For models trained on joint data
-CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
-python scripts/average_checkpoints.py \
-  --inputs ${MULTILINGUAL_ASR_SAVE_DIR} --num-epoch-checkpoints 10 \
-  --output "${MULTILINGUAL_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}"
-
-for LANG in es fr pt it ru el ar de; do
-  fairseq-generate ${MTEDX_ROOT} \
-    --config-yaml config_asr.yaml --gen-subset test_${LANG}-${LANG}_asr --task speech_to_text \
-    --prefix-size 1 --path ${MULTILINGUAL_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} \
-    --max-tokens 40000 --beam 5 \
-    --skip-invalid-size-inputs-valid-test \
-    --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct --remove-bpe
-done
-```
-#### Results
-| Data         | --arch             | Params |  Es  |  Fr  |  Pt  |  It  |  Ru  |   El  |   Ar  |   De  |
-|--------------|--------------------|--------|------|------|------|------|------|-------|-------|-------|
-| Monolingual  | s2t_transformer_xs |    10M | 46.4 | 45.6 | 54.8 | 48.0 | 74.7 | 109.5 | 104.4 | 111.1 |
-
-
-## ST
-#### Training
-Es-En as example:
-```bash
-fairseq-train ${MTEDX_ROOT}/es-en \
-    --config-yaml config_st.yaml --train-subset train_st --valid-subset valid_st \
-    --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \
-    --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \
-    --arch s2t_transformer_xs --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \
-    --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \
-    --load-pretrained-encoder-from ${PRETRAINED_ENCODER} \
-    --skip-invalid-size-inputs-valid-test \
-    --keep-last-epochs 10 --update-freq 8 --patience 10
-```
-For multilingual model (all 12 directions):
-```bash
-fairseq-train ${MTEDX_ROOT} \
-    --config-yaml config_st.yaml \
-    --train-subset train_el-en_st,train_es-en_st,train_es-fr_st,train_es-it_st,train_es-pt_st,train_fr-en_st,train_fr-es_st,train_fr-pt_st,train_it-en_st,train_it-es_st,train_pt-en_st,train_pt-es_st,train_ru-en_st \
-    --valid-subset valid_el-en_st,valid_es-en_st,valid_es-fr_st,valid_es-it_st,valid_es-pt_st,valid_fr-en_st,valid_fr-es_st,valid_fr-pt_st,valid_it-en_st,valid_it-es_st,valid_pt-en_st,valid_pt-es_st,valid_ru-en_st \
-    --save-dir ${MULTILINGUAL_ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \
-    --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \
-    --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \
-    --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \
-    --skip-invalid-size-inputs-valid-test \
-    --keep-last-epochs 10 --update-freq 8 --patience 10 \
-    --ignore-prefix-size 1 \
-    --load-pretrained-encoder-from ${PRETRAINED_ENCODER}
-```
-where `ST_SAVE_DIR` (`MULTILINGUAL_ST_SAVE_DIR`) is the checkpoint root path. The ST encoder is pre-trained by ASR
-for faster training and better performance: `--load-pretrained-encoder-from <(JOINT_)ASR checkpoint path>`. We set
-`--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU.
-For multilingual models, we prepend target language ID token as target BOS, which should be excluded from
-the training loss via `--ignore-prefix-size 1`.
-
-#### Inference & Evaluation
-Average the last 10 checkpoints and evaluate on the `test` split:
-```bash
-CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
-python scripts/average_checkpoints.py \
-  --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \
-  --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}"
-
-fairseq-generate ${MTEDX_ROOT}/es-en \
-  --config-yaml config_st.yaml --gen-subset test --task speech_to_text \
-  --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \
-  --max-tokens 50000 --beam 5 --scoring sacrebleu --remove-bpe
-
-# For multilingual models
-python scripts/average_checkpoints.py \
-  --inputs ${MULTILINGUAL_ST_SAVE_DIR} --num-epoch-checkpoints 10 \
-  --output "${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME}"
-
-for LANGPAIR in es-en es-fr es-pt fr-en fr-es fr-pt pt-en pt-es it-en it-es ru-en el-en; do
-  fairseq-generate ${MTEDX_ROOT} \
-    --config-yaml config_st.yaml --gen-subset test_${LANGPAIR}_st --task speech_to_text \
-    --prefix-size 1 --path ${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \
-    --max-tokens 40000 --beam 5 \
-    --skip-invalid-size-inputs-valid-test \
-    --scoring sacrebleu --remove-bpe
-done
-```
-For multilingual models, we force decoding from the target language ID token (as BOS) via `--prefix-size 1`.
-
-#### Results
-| Data         | --arch          | Params | Es-En | Es-Pt | Es-Fr | Fr-En | Fr-Es | Fr-Pt | Pt-En | Pt-Es | It-En | It-Es | Ru-En | El-En |
-|--------------|--------------------|-----|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
-| Bilingual    | s2t_transformer_xs | 10M |  7.0  |  12.2 |  1.7  |  8.9  |  10.6 |  7.9  |  8.1  |  8.7  |   6.4 |  1.0  |  0.7  |  0.6  |
-| Multilingual | s2t_transformer_s  | 31M |  12.3 |  17.4 |   6.1 |  12.0 |  13.6 |  13.2 |  12.0 |  13.7 |  10.7 |  13.1 |  0.6  |  0.8  |
-
-
-## Citation
-Please cite as:
-```
-@misc{salesky2021mtedx,
-      title={Multilingual TEDx Corpus for Speech Recognition and Translation},
-      author={Elizabeth Salesky and Matthew Wiesner and Jacob Bremerman and Roldano Cattoni and Matteo Negri and Marco Turchi and Douglas W. Oard and Matt Post},
-      year={2021},
-}
-
-@inproceedings{wang2020fairseqs2t,
-  title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
-  author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
-  booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
-  year = {2020},
-}
-
-@inproceedings{ott2019fairseq,
-  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
-  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
-  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
-  year = {2019},
-}
-```
-
-[[Back]](..)
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py b/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py
deleted file mode 100644
index 5f4faa99218b0b30c980cad167c52b2297cd92c3..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import sys
-
-for idx, line in enumerate(sys.stdin):
-    print(f"utt{idx:010d} {line}", end='')
\ No newline at end of file
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/data/flist_dataset.py b/spaces/gwang-kim/DATID-3D/pose_estimation/data/flist_dataset.py
deleted file mode 100644
index c0b6945c80aa756074a5d3c02b9443b15ddcfc57..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/data/flist_dataset.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""This script defines the custom dataset for Deep3DFaceRecon_pytorch
-"""
-
-import os.path
-from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
-from data.image_folder import make_dataset
-from PIL import Image
-import random
-import util.util as util
-import numpy as np
-import json
-import torch
-from scipy.io import loadmat, savemat
-import pickle
-from util.preprocess import align_img, estimate_norm
-from util.load_mats import load_lm3d
-
-
-def default_flist_reader(flist):
-    """
-    flist format: impath label\nimpath label\n ...(same to caffe's filelist)
-    """
-    imlist = []
-    with open(flist, 'r') as rf:
-        for line in rf.readlines():
-            impath = line.strip()
-            imlist.append(impath)
-
-    return imlist
-
-def jason_flist_reader(flist):
-    with open(flist, 'r') as fp:
-        info = json.load(fp)
-    return info
-
-def parse_label(label):
-    return torch.tensor(np.array(label).astype(np.float32))
-
-
-class FlistDataset(BaseDataset):
-    """
-    It requires one directories to host training images '/path/to/data/train'
-    You can train the model with the dataset flag '--dataroot /path/to/data'.
-    """
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        BaseDataset.__init__(self, opt)
-        
-        self.lm3d_std = load_lm3d(opt.bfm_folder)
-        
-        msk_names = default_flist_reader(opt.flist)
-        self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names]
-
-        self.size = len(self.msk_paths) 
-        self.opt = opt
-        
-        self.name = 'train' if opt.isTrain else 'val'
-        if '_' in opt.flist:
-            self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0]
-        
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index (int)      -- a random integer for data indexing
-
-        Returns a dictionary that contains A, B, A_paths and B_paths
-            img (tensor)       -- an image in the input domain
-            msk (tensor)       -- its corresponding attention mask
-            lm  (tensor)       -- its corresponding 3d landmarks
-            im_paths (str)     -- image paths
-            aug_flag (bool)    -- a flag used to tell whether its raw or augmented
-        """
-        msk_path = self.msk_paths[index % self.size]  # make sure index is within then range
-        img_path = msk_path.replace('mask/', '')
-        lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt'
-
-        raw_img = Image.open(img_path).convert('RGB')
-        raw_msk = Image.open(msk_path).convert('RGB')
-        raw_lm = np.loadtxt(lm_path).astype(np.float32)
-
-        _, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk)
-        
-        aug_flag = self.opt.use_aug and self.opt.isTrain
-        if aug_flag:
-            img, lm, msk = self._augmentation(img, lm, self.opt, msk)
-        
-        _, H = img.size
-        M = estimate_norm(lm, H)
-        transform = get_transform()
-        img_tensor = transform(img)
-        msk_tensor = transform(msk)[:1, ...]
-        lm_tensor = parse_label(lm)
-        M_tensor = parse_label(M)
-
-
-        return {'imgs': img_tensor, 
-                'lms': lm_tensor, 
-                'msks': msk_tensor, 
-                'M': M_tensor,
-                'im_paths': img_path, 
-                'aug_flag': aug_flag,
-                'dataset': self.name}
-
-    def _augmentation(self, img, lm, opt, msk=None):
-        affine, affine_inv, flip = get_affine_mat(opt, img.size)
-        img = apply_img_affine(img, affine_inv)
-        lm = apply_lm_affine(lm, affine, flip, img.size)
-        if msk is not None:
-            msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR)
-        return img, lm, msk
-    
-
-
-
-    def __len__(self):
-        """Return the total number of images in the dataset.
-        """
-        return self.size
diff --git a/spaces/h2oai/wave-tour/examples/plot_area_smooth.py b/spaces/h2oai/wave-tour/examples/plot_area_smooth.py
deleted file mode 100644
index bac7e0e6475c2d737c509b68519025d94bc64ff8..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_area_smooth.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Plot / Area / Smooth
-# Make an area #plot with a smooth curve.
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
-    box='1 1 4 5',
-    title='Area, smooth',
-    data=data('year price', 9, rows=[
-        ('1991', 15468),
-        ('1992', 16100),
-        ('1993', 15900),
-        ('1994', 17409),
-        ('1995', 17000),
-        ('1996', 31056),
-        ('1997', 31982),
-        ('1998', 32040),
-        ('1999', 33233),
-    ]),
-    plot=ui.plot([ui.mark(type='area', x_scale='time', x='=year', y='=price', curve='smooth', y_min=0)])
-))
-
-page.save()
diff --git a/spaces/hOTZR/new-Bing-with_your_cookies/app.py b/spaces/hOTZR/new-Bing-with_your_cookies/app.py
deleted file mode 100644
index e6f2908c74ccf92d230f500a80e295f7ea21dfb7..0000000000000000000000000000000000000000
--- a/spaces/hOTZR/new-Bing-with_your_cookies/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import gradio as gr
-import json
-import asyncio
-import os
-from EdgeGPT import Chatbot, ConversationStyle
-import re
-
-#read cookie from local file
-# with open('./cookies.json', 'r') as f:
-#     cookies = json.load(f)
-#如果你是选择读取仓库内的cookie.json文件，那么不需要再向函数内传递cookies参数
-# 也可以删去gr.Tab("Cookies"):这一界面对应的代码
-async def get_model_reply(prompt,style,cookies,context=[]):
-    # combines the new question with a previous context
-    context += [prompt]
-    cookies = json.loads(cookies)
-    # given the most recent context (4096 characters)
-    # continue the text up to 2048 tokens ~ 8192 charaters
-    bot = Chatbot(cookies=cookies)
-    prompt2='\n\n'.join(context)[:4096]
-    raw_data = await bot.ask(prompt2, conversation_style=style)
-    await bot.close()
-    #print(raw_data)
-    try:
-        try:
-            response = raw_data["item"]["messages"][1]["text"]
-        except:
-            response = raw_data["item"]["messages"][1]["adaptiveCards"][0]["body"][0]["text"]
-        response = re.sub(r'\^', '', response)
-        response = response.rstrip()
-        context += [response]
-
-    # list of (user, bot) responses. We will use this format later
-        responses = [(u, b) for u, b in zip(context[::2], context[1::2])]
-        return responses, context
-    except:
-        try:
-            if raw_data["item"]["throttling"]["numUserMessagesInConversation"] > raw_data["item"]["throttling"]["maxNumUserMessagesInConversation"]:
-                response="> **Oops, I think we've reached the end of this conversation. Please reset the bot!**"
-                context += [response]
-
-                # list of (user, bot) responses. We will use this format later
-                responses = [(u, b) for u, b in zip(context[::2], context[1::2])]
-                return responses, context
-                
-        except:    
-            if raw_data["item"]["result"]["value"] == "Throttled":
-                response="> **Error: We're sorry, but you've reached the maximum number of messages you can send to Bing in a 24-hour period. Check back later!**"
-                context += [response]
-
-                # list of (user, bot) responses. We will use this format later
-                responses = [(u, b) for u, b in zip(context[::2], context[1::2])]
-                return responses, context
-# query = 'Which is the largest country by area in the world?'
-# style="precise"
-# responses, context =asyncio.run(get_model_reply(query,style,context=[]))
-#
-# print(' ' + responses[-1][0])
-# print(' ' + responses[-1][1])
-with gr.Blocks() as dialog_app:
-    with gr.Tab("Cookies"):
-        cookies = gr.Textbox(lines=2, label="输入bing.com中的cookies")
-    with gr.Tab("New Bing Chat"):
-        gr.Markdown("# A Simple Web to use New Bing Without Magic")
-        chatbot = gr.Chatbot()
-        state = gr.State([])
-        markdown = gr.Markdown(label="Output")
-
-        with gr.Row():
-            inputs = gr.Textbox(
-                label="输入问题",
-                placeholder="Enter text and press enter"
-            )
-            style = gr.Dropdown(label="回答倾向", choices=["creative", "balanced", "precise"], multiselect=False,
-                                value="balanced", type="value")
-
-        inputs.submit(get_model_reply, [inputs, style, cookies, state], [chatbot, state])
-        send = gr.Button("Send")
-        send.click(get_model_reply, [inputs, style, cookies, state], [chatbot, state])
-
-# launches the app in a new local port
-dialog_app.launch()
-# 为网站设置密码防止滥用
-# dialog_app.launch(auth=("admin", "pass1234"))
\ No newline at end of file
diff --git a/spaces/hahahehe99340/chatgpt/presets.py b/spaces/hahahehe99340/chatgpt/presets.py
deleted file mode 100644
index 66e48f1341880cf736b0ef0356b1ab30b0cbbe99..0000000000000000000000000000000000000000
--- a/spaces/hahahehe99340/chatgpt/presets.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# -*- coding:utf-8 -*-
-
-# ChatGPT 设置
-initial_prompt = "You are a helpful assistant."
-API_URL = "https://api.openai.com/v1/chat/completions"
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-# 错误信息
-standard_error_msg = "☹️发生了错误："  # 错误信息的标准前缀
-error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。"  # 获取对话时发生错误
-connection_timeout_prompt = "连接超时，无法获取对话。"  # 连接超时
-read_timeout_prompt = "读取超时，无法获取对话。"  # 读取超时
-proxy_error_prompt = "代理错误，无法获取对话。"  # 代理错误
-ssl_error_prompt = "SSL错误，无法获取对话。"  # SSL 错误
-no_apikey_msg = "API key长度不是51位，请检查是否输入正确。"  # API key 长度不足 51 位
-
-max_token_streaming = 3500  # 流式对话时的最大 token 数
-timeout_streaming = 5  # 流式对话时的超时时间
-max_token_all = 3500  # 非流式对话时的最大 token 数
-timeout_all = 200  # 非流式对话时的超时时间
-enable_streaming_option = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-
-SIM_K = 5
-INDEX_QUERY_TEMPRATURE = 1.0
-
-title = """<h1 align="left" style="min-width:200px; margin-top:0;">ChatGPT 🚀</h1>"""
-description = """\
-<div align="center" style="margin:16px 0">
-
-由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536) 和 [明昭MZhao](https://space.bilibili.com/24807452)开发
-
-访问川虎ChatGPT的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本
-
-此App使用 `gpt-3.5-turbo` 大语言模型
-</div>
-"""
-
-summarize_prompt = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
-
-MODELS = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-]  # 可选的模型
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in 中文"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in 中文
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
-If the context isn't useful, return the original answer.
-"""
diff --git a/spaces/halilumutyalcin/spam-email-classifier-app/README.md b/spaces/halilumutyalcin/spam-email-classifier-app/README.md
deleted file mode 100644
index 7ed50aecfa0def120bb558aeeb0f7d4f5b9f9e9d..0000000000000000000000000000000000000000
--- a/spaces/halilumutyalcin/spam-email-classifier-app/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Spam Email Classifier App
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hanjp/White-box-Cartoonization/app.py b/spaces/hanjp/White-box-Cartoonization/app.py
deleted file mode 100644
index c55ced56bd87a85f59d1c8ef84b7eca87422720f..0000000000000000000000000000000000000000
--- a/spaces/hanjp/White-box-Cartoonization/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-import argparse
-import functools
-import os
-import pathlib
-import sys
-from typing import Callable
-import uuid
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-
-from io import BytesIO
-from wbc.cartoonize import Cartoonize
-
-ORIGINAL_REPO_URL = 'https://github.com/SystemErrorWang/White-box-Cartoonization'
-TITLE = 'SystemErrorWang/White-box-Cartoonization'
-DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
-
-"""
-ARTICLE = """
-
-"""
-
-SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
-def compress_UUID():
-    '''
-    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
-    包括:[0-9a-zA-Z\-_]共64个
-    长度:(32-2)/3*2=20
-    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
-    :return:String
-    '''
-    row = str(uuid.uuid4()).replace('-', '')
-    safe_code = ''
-    for i in range(10):
-        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
-        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
-    safe_code = safe_code.replace('-', '')
-    return safe_code
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    parser.add_argument('--allow-screenshot', action='store_true')
-    return parser.parse_args()
-
-def run(
-        image,
-        cartoonize : Cartoonize
-) -> tuple[PIL.Image.Image]:
-
-    out_path = compress_UUID()+'.png'
-    cartoonize.run_sigle(image.name, out_path)
-
-    return PIL.Image.open(out_path)
-
-
-def main():
-    gr.close_all()
-
-    args = parse_args()
-
-    cartoonize = Cartoonize(os.path.join(os.path.dirname(os.path.abspath(__file__)),'wbc/saved_models/'))
-
-    func = functools.partial(run, cartoonize=cartoonize)
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input Image'),
-        ],
-        [
-            gr.outputs.Image(
-                type='pil',
-                label='Result'),
-        ],
-        # examples=examples,
-        theme=args.theme,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        allow_screenshot=args.allow_screenshot,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/hanzportgas/rvc-models-v2/vc_infer_pipeline.py b/spaces/hanzportgas/rvc-models-v2/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/hanzportgas/rvc-models-v2/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-            _, I = index.search(npy, 1)
-            npy = big_npy[I.squeeze()]
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = np.load(file_big_npy)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/hardon-server/remove-background-on-image-def/app.py b/spaces/hardon-server/remove-background-on-image-def/app.py
deleted file mode 100644
index 58cf24e05e36bc744eea2265cc7edcbb0e51a918..0000000000000000000000000000000000000000
--- a/spaces/hardon-server/remove-background-on-image-def/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import gradio as gr
-from rembg import remove
-
-def segment(image):
-     return remove(image)
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        
-        with gr.Column():
-            i_img = gr.Image(label="Input Image")
-
-    with gr.Row():
-
-        with gr.Column():
-            run_btn = gr.Button("Remove", variant="primary")
-    
-    with gr.Row():
-
-        with gr.Column():
-             o_img = gr.Image(label="Result Image")
-
-    run_btn.click(
-            segment,
-            inputs=[
-                i_img,
-            ],
-            outputs=[o_img],
-    )
-            
-demo.queue(concurrency_count=3)
-demo.launch(show_api=False, debug=False, share=False, show_error=False)
\ No newline at end of file
diff --git "a/spaces/hbestm/gpt-academic-play/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py" "b/spaces/hbestm/gpt-academic-play/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py"
deleted file mode 100644
index c1e5dadd142de683323463d3df260cbe6eefa6d8..0000000000000000000000000000000000000000
--- "a/spaces/hbestm/gpt-academic-play/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py"
+++ /dev/null
@@ -1,60 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-@CatchException
-def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，如温度和top_p等，一般原样传递下去就行
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append((txt, "正在同时咨询gpt-3.5和gpt-4……"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    llm_kwargs['llm_model'] = 'gpt-3.5-turbo&gpt-4' # 支持任意数量的llm接口，用&符号分隔
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=txt, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt=system_prompt,
-        retry_times_at_unknown_error=0
-    )
-
-    history.append(txt)
-    history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-
-@CatchException
-def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，如温度和top_p等，一般原样传递下去就行
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=txt, inputs_show_user=txt, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt=system_prompt,
-        retry_times_at_unknown_error=0
-    )
-
-    history.append(txt)
-    history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
\ No newline at end of file
diff --git a/spaces/hhhhardman/VITS/modules.py b/spaces/hhhhardman/VITS/modules.py
deleted file mode 100644
index f5af1fd9a20dc03707889f360a39bb4b784a6df3..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/modules.py
+++ /dev/null
@@ -1,387 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/create_plots_new/SegmentationMetrics.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/create_plots_new/SegmentationMetrics.py
deleted file mode 100644
index c901c2ba9e484bd0d02d1906abafda6a69a1679e..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/create_plots_new/SegmentationMetrics.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import numpy as np
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import plotly.io as pio
-import os
-pio.kaleido.scope.mathjax = None
-import json
-
-
-if __name__ == '__main__':
-    experiments =['Task501_Glacier_front',
-                  'Task502_Glacier_zone',
-                  'Task503_Glacier_mtl_early',
-                  'Task503_Glacier_mtl_late',
-                  'Task505_Glacier_mtl_boundary',
-                  'Task500_Glacier_zonefronts_nodeep',
-                  'Task500_Glacier_zonefronts'
-                  ]
-    data_dir = '/home/ho11laqe/Desktop/nnUNet_results/Final_Eval/'
-
-    for metric in ['Precision', 'Recall', 'F1', 'IoU']:
-
-        zone_metric = {}
-        for experiment in experiments:
-
-            zone_metric_exp = []
-            #nofront[experiment] = {'Front': [], 'Zone': []}
-            for fold in range(5):
-                # load json file with results
-                results_json_path = os.path.join(data_dir, experiment, 'fold_'+str(fold), 'pngs', 'eval_results.json')
-                if not os.path.exists(results_json_path):
-                    results_json_path = os.path.join(data_dir, experiment, 'fold_' + str(fold), 'eval_results.json')
-
-                with open(results_json_path, 'r') as f:
-                    result = json.load(f)
-
-                if 'Zone_Segmentation' in result.keys():
-                    avg_metric = 'Average_'+metric
-                    if metric == 'F1':
-                        avg_metric = 'Average_' + metric + ' Score'
-                    zone_metric_exp.append(result['Zone_Segmentation']['Zone_'+metric][avg_metric])
-                else:
-                    zone_metric_exp.append(0)
-
-            zone_metric[experiment] = zone_metric_exp
-
-        box_width = 0.8
-        fig = px.box(None, points="all", template="plotly_white", width=700, height=500)
-
-        fig.add_trace(go.Box(y=zone_metric['Task502_Glacier_zone'], name='Zone<br> STL', width=box_width,
-                                line_color='LightBlue ',  pointpos=0, boxpoints='all', boxmean=True))
-        fig.add_trace(go.Box(y=zone_metric['Task503_Glacier_mtl_early'], name='Early Zone <br>MTL', width=box_width,
-                                line_color='YellowGreen', pointpos=0, boxpoints='all', boxmean=True,))
-        fig.add_trace(go.Box(y=zone_metric['Task503_Glacier_mtl_late'], name='Late Zone<br> MTL', width=box_width,
-                                line_color='#e1e400',  pointpos=0, boxpoints='all', boxmean=True))
-        fig.add_trace(go.Box(y=zone_metric['Task505_Glacier_mtl_boundary'], name='Boundary<br>Zone MTL', width=box_width,
-                                line_color='gold',  pointpos=0, boxpoints='all', boxmean=True))
-        fig.add_trace(go.Box(y=zone_metric['Task500_Glacier_zonefronts'], name='Fused Labels<br>Front', width=box_width,
-                                line_color='orange', pointpos=0, boxpoints='all', boxmean=True))
-
-        fig.update_layout(showlegend=False, font=dict(family="Times New Roman", size=18))
-        fig.update_yaxes(title=metric)
-        # fig.show()
-        fig.write_image('output/'+metric+".pdf", format='pdf')
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_0.sh b/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_0.sh
deleted file mode 100644
index 19094555af69e411cb1f277e8cb88c065aed828d..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_0.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash -l
-#SBATCH --nodes=1 --gres=gpu:1 --time=24:00:00
-#SBATCH --job-name=Task503_glacier_mtl_early_0
-
-export data_raw="/home/woody/iwi5/iwi5039h/data_raw"
-export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/"
-export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/"
-export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER"
-
-cd nnunet_glacer
-pwd
-conda activate nnunet
-
-python3 nnunet/dataset_conversion/Task503_Glacier_mtl.py -data_percentage 100 -base $data_raw
-python3 nnunet/experiment_planning/nnUNet_plan_and_preprocess.py -t 503 -pl3d None -pl2d ExperimentPlanner2D_mtl
-
-python3 nnunet/run/run_training.py 2d nnUNetTrainerMTLearly 503 0 -p nnUNetPlans_mtl --disable_postprocessing_on_folds 
-python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task503_Glacier_mtl/imagesTs -o $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_0 -t 503 -m 2d -f 0 -p nnUNetPlans_mtl -tr nnUNetTrainerMTLearly
-python3 nnunet/dataset_conversion/Task503_Glacier_mtl_reverse.py -i $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_0
-python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_0/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test
diff --git a/spaces/housexu123/bingo-2.0/src/components/chat.tsx b/spaces/housexu123/bingo-2.0/src/components/chat.tsx
deleted file mode 100644
index fcb6f467e2c773d364d684bdca4c39b4656fe417..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/src/components/chat.tsx
+++ /dev/null
@@ -1,92 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/hra/music-recommendation/app.py b/spaces/hra/music-recommendation/app.py
deleted file mode 100644
index c3248e6738b0f76e4f8e234621326cec14630305..0000000000000000000000000000000000000000
--- a/spaces/hra/music-recommendation/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import json
-import requests
-import gradio as gr
-import random
-import time
-import os
-import datetime
-from datetime import datetime
-
-print('for  update')
-
-API_TOKEN = os.getenv("API_TOKEN")
-HRA_TOKEN=os.getenv("HRA_TOKEN")
-
-
-from huggingface_hub import InferenceApi
-inference = InferenceApi("bigscience/bloom",token=API_TOKEN)
-
-headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
-url_decodemprompts='https://us-central1-createinsightsproject.cloudfunctions.net/gethrahfprompts'
-
-data={"prompt_type":'song_recommend1',"hra_token":HRA_TOKEN}
-try:
-    r = requests.post(url_decodemprompts, data=json.dumps(data), headers=headers)
-except requests.exceptions.ReadTimeout as e:
-    print(e)
-#print(r.content)
-
-prompt=str(r.content, 'UTF-8')
-print(prompt)
-
-def infer(prompt,
-          max_length = 250,
-          top_k = 0,
-          num_beams = 0,
-          no_repeat_ngram_size = 2,
-          top_p = 0.9,
-          seed=42,
-          temperature=0.7,
-          greedy_decoding = False,
-          return_full_text = True):
-    
-    print(seed)
-    top_k = None if top_k == 0 else top_k
-    do_sample = False if num_beams > 0 else not greedy_decoding
-    num_beams = None if (greedy_decoding or num_beams == 0) else num_beams
-    no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size
-    top_p = None if num_beams else top_p
-    early_stopping = None if num_beams is None else num_beams > 0
-
-    params = {
-        "max_new_tokens": max_length,
-        "top_k": top_k,
-        "top_p": top_p,
-        "temperature": temperature,
-        "do_sample": do_sample,
-        "seed": seed,
-        "early_stopping":early_stopping,
-        "no_repeat_ngram_size":no_repeat_ngram_size,
-        "num_beams":num_beams,
-        "return_full_text":return_full_text
-    }
-    
-    s = time.time()
-    response = inference(prompt, params=params)
-    #print(response)
-    proc_time = time.time()-s
-    #print(f"Processing time was {proc_time} seconds")
-    return response
-
-def getsongrecco(text_inp1,text_inp2):
-  print(text_inp1,' by ',text_inp2)
-  print(datetime.today().strftime("%d-%m-%Y"))
-  
-  text = prompt+"\nInput:"+text_inp1+' by '+text_inp2 + "\nOutput:"
-  resp = infer(text,seed=random.randint(0,100))
-
-  generated_text=resp[0]['generated_text']
-  result = generated_text.replace(text,'').strip()
-  result = result.replace("Output:","")
-  parts = result.split("###")
-  topic = parts[0].strip()
-  topic="\n".join(topic.split('\n'))
-  print(topic)
-  return(topic)
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Markdown("<h1><center>Music Recommendation</center></h1>")
-        gr.Markdown(
-            """Get Music Recommendations from Large Language Models. Enter a song, band/artist and get recommendations. Prompt Engineered AI model bigscience/bloom."""
-            )
-    with gr.Row():
-        with gr.Column():
-            textbox1 = gr.Textbox(placeholder="Enter song name...", lines=1,label='Song')
-            textbox2 = gr.Textbox(placeholder="Enter band/ artist name...", lines=1,label='Band/ Artist')
-        with gr.Column():
-            btn = gr.Button("Recommend")    
-            output1 = gr.Textbox(lines=2,label='Music Recommendations')
-
-    btn.click(getsongrecco,inputs=[textbox1,textbox2], outputs=[output1])
-    examples = gr.Examples(examples=[["It's My Life","Bon Jovi"],["Wavin' Flag","K'naan"],['Dreamer','Ozzy Osbourne'],['Deutschland','Rammstein']],
-                           inputs=[textbox1,textbox2])
-    
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/huggingface-projects/stable-diffusion-latent-upscaler/app.py b/spaces/huggingface-projects/stable-diffusion-latent-upscaler/app.py
deleted file mode 100644
index 18b8e688947bb1083f2f0cb96596fea94a920285..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/stable-diffusion-latent-upscaler/app.py
+++ /dev/null
@@ -1,263 +0,0 @@
-from diffusers import (
-    StableDiffusionPipeline,
-    DPMSolverMultistepScheduler,
-    DiffusionPipeline,
-)
-import gradio as gr
-import torch
-from PIL import Image
-import time
-import psutil
-import random
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-
-
-start_time = time.time()
-current_steps = 25
-
-SAFETY_CHECKER = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=torch.float16)
-
-UPSCALER = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16)
-UPSCALER.to("cuda")
-UPSCALER.enable_xformers_memory_efficient_attention()
-
-
-class Model:
-    def __init__(self, name, path=""):
-        self.name = name
-        self.path = path
-
-        if path != "":
-            self.pipe_t2i = StableDiffusionPipeline.from_pretrained(
-                path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER
-            )
-            self.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config(
-                self.pipe_t2i.scheduler.config
-            )
-        else:
-            self.pipe_t2i = None
-
-
-models = [
-    #Model("Stable Diffusion v1-4", "CompVis/stable-diffusion-v1-4"),
-    # Model("Stable Diffusion v1-5", "runwayml/stable-diffusion-v1-5"),
-    Model("anything-v4.0", "xyn-ai/anything-v4.0"),
-]
-
-MODELS = {m.name: m for m in models}
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-
-def error_str(error, title="Error"):
-    return (
-        f"""#### {title}
-            {error}"""
-        if error
-        else ""
-    )
-
-
-def inference(
-    prompt,
-    neg_prompt,
-    guidance,
-    steps,
-    seed,
-    model_name,
-):
-
-    print(psutil.virtual_memory())  # print memory usage
-
-    if seed == 0:
-        seed = random.randint(0, 2147483647)
-
-    generator = torch.Generator("cuda").manual_seed(seed)
-
-    try:
-        low_res_image, up_res_image = txt_to_img(
-            model_name,
-            prompt,
-            neg_prompt,
-            guidance,
-            steps,
-            generator,
-        )
-        return low_res_image, up_res_image, f"Done. Seed: {seed}",
-    except Exception as e:
-        return None, None, error_str(e)
-
-
-def txt_to_img(
-    model_name,
-    prompt,
-    neg_prompt,
-    guidance,
-    steps,
-    generator,
-):
-    pipe = MODELS[model_name].pipe_t2i
-
-    if torch.cuda.is_available():
-        pipe = pipe.to("cuda")
-        pipe.enable_xformers_memory_efficient_attention()
-
-    low_res_latents = pipe(
-        prompt,
-        negative_prompt=neg_prompt,
-        num_inference_steps=int(steps),
-        guidance_scale=guidance,
-        generator=generator,
-        output_type="latent",
-    ).images
-
-    with torch.no_grad():
-        low_res_image = pipe.decode_latents(low_res_latents)
-        low_res_image = pipe.numpy_to_pil(low_res_image)
-
-    up_res_image = UPSCALER(
-        prompt=prompt,
-        negative_prompt=neg_prompt,
-        image=low_res_latents,
-        num_inference_steps=20,
-        guidance_scale=0,
-        generator=generator,
-    ).images
-
-    pipe.to("cpu")
-    torch.cuda.empty_cache()
-
-    return low_res_image[0], up_res_image[0]
-
-
-def replace_nsfw_images(results):
-    for i in range(len(results.images)):
-        if results.nsfw_content_detected[i]:
-            results.images[i] = Image.open("nsfw.png")
-    return results.images
-
-
-with gr.Blocks(css="style.css") as demo:
-    gr.HTML(
-        f"""
-            <div class="finetuned-diffusion-div">
-              <div style="text-align: center">
-                <h1>Anything v4 model + <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Stable Diffusion Latent Upscaler</a></h1>
-                <p>
-                   Demo for the <a href="https://huggingface.co/andite/anything-v4.0">Anything v4</a> model hooked with the ultra-fast <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Latent Upscaler</a>
-                  </p>
-              </div>
-              <!-- 
-              <p>To skip the queue, you can duplicate this Space<br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/patrickvonplaten/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
-              -->
-            </div>
-        """
-    )
-
-    with gr.Column(scale=100):
-        with gr.Group(visible=False):
-                model_name = gr.Dropdown(
-                    label="Model",
-                    choices=[m.name for m in models],
-                    value=models[0].name,
-                    visible=False
-                )
-
-        with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
-                    with gr.Column():
-                        prompt = gr.Textbox(
-                            label="Enter your prompt",
-                            show_label=False,
-                            max_lines=1,
-                            placeholder="Enter your prompt",
-                            elem_id="prompt-text-input",
-                        ).style(
-                            border=(True, False, True, True),
-                            rounded=(True, False, False, True),
-                            container=False,
-                        )
-                        neg_prompt = gr.Textbox(
-                            label="Enter your negative prompt",
-                            show_label=False,
-                            max_lines=1,
-                            placeholder="Enter a negative prompt",
-                            elem_id="negative-prompt-text-input",
-                        ).style(
-                            border=(True, False, True, True),
-                            rounded=(True, False, False, True),
-                            container=False,
-                        )
-                    generate = gr.Button("Generate image").style(
-                        margin=False,
-                        rounded=(False, True, True, False),
-                        full_width=False,
-                    )
-            
-        with gr.Accordion("Advanced Options", open=False):
-                with gr.Group():
-                    with gr.Row():
-                        guidance = gr.Slider(
-                            label="Guidance scale", value=7.5, maximum=15
-                        )
-                        steps = gr.Slider(
-                            label="Steps",
-                            value=current_steps,
-                            minimum=2,
-                            maximum=75,
-                            step=1,
-                        )
-
-                    seed = gr.Slider(
-                        0, 2147483647, label="Seed (0 = random)", value=0, step=1
-                    )
-            
-
-    with gr.Column(scale=100):
-        with gr.Row():
-            with gr.Column(scale=75):
-                up_res_image = gr.Image(label="Upscaled 1024px Image", shape=(1024, 1024))
-            with gr.Column(scale=25):
-                low_res_image = gr.Image(label="Original 512px Image", shape=(512, 512))
-        error_output = gr.Markdown()
-
-    inputs = [
-        prompt,
-        neg_prompt,
-        guidance,
-        steps,
-        seed,
-        model_name,
-    ]
-    outputs = [low_res_image, up_res_image, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    ex = gr.Examples(
-        [
-            ["a mecha robot in a favela", "low quality", 7.5, 25, 33, models[0].name],
-            ["the spirit of a tamagotchi wandering in the city of Paris", "low quality, bad render", 7.5, 50, 85, models[0].name],
-        ],
-        inputs=[prompt, neg_prompt, guidance, steps, seed, model_name],
-        outputs=outputs,
-        fn=inference,
-        cache_examples=True,
-    )
-    ex.dataset.headers = [""]
-    
-    gr.HTML(
-        """
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Space by 🤗 Hugging Face, models by Stability AI, andite, linaqruf and others ❤️</p>
-      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p>
-      <p>This is a Demo Space For:<br>
-      <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Stability AI's Latent Upscaler</a>
-    </div>
-    """
-    )
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/hwchase17/langchain-demo/README.md b/spaces/hwchase17/langchain-demo/README.md
deleted file mode 100644
index 6a1de1082e9c4d2fcd8148e3fb8bdfa1ff519fbb..0000000000000000000000000000000000000000
--- a/spaces/hwchase17/langchain-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: LangChain Demo
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r100.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r100.py
deleted file mode 100644
index 2663dc950c42f699428d92e7349a1cf5ed8d848d..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_r100.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "r100"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.interclass_filtering_threshold = 0
-config.fp16 = True
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.optimizer = "sgd"
-config.lr = 0.1
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace12M"
-config.num_classes = 617970
-config.num_image = 12720066
-config.num_epoch = 20
-config.warmup_epoch = 0
-config.val_targets = []
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc03_32gpu_r200.py b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc03_32gpu_r200.py
deleted file mode 100644
index e34dd1c11f489d9c5c1b23c3677d303aafe46da6..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/wf42m_pfc03_32gpu_r200.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "r200"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.3
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.4
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace42M"
-config.num_classes = 2059906
-config.num_image = 42474557
-config.num_epoch = 20
-config.warmup_epoch = config.num_epoch // 10
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/hzy123/bingo/Dockerfile b/spaces/hzy123/bingo/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/hzy123/bingo/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/inamXcontru/PoeticTTS/Artcut 2009 Para Windows 7 Gratis !!TOP!!.md b/spaces/inamXcontru/PoeticTTS/Artcut 2009 Para Windows 7 Gratis !!TOP!!.md
deleted file mode 100644
index 08d74b1de968668b779f2a937a3b45d6e386d9be..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Artcut 2009 Para Windows 7 Gratis !!TOP!!.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-<h2>Artcut 2009 para windows 7 gratis</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://gohhs.com/2uz4zN">https://gohhs.com/2uz4zN</a></b></p><br /><br />
-
-Sep 7, 2020 - If a user wants to download Artcut 2009 for free, ..... This software requires the following operating systems: Windows 2000 and Microsoft ... - Anything you can download for free.
-Download ...
-- Everything you can download for free.
-Download Artcut 2009 for free in Russian for Windows 7, 10, 8, XP ...
-Year of release: 2008 Genre: Graphic Editor Developer: Artcut Publisher: Artcut Developer's website: http ...
-Free download Artcut 2009 for your computer in Russian.
-Download Artcut 2009 without registration on your computer.
-Artcut 2009 Free Download Artcut 2009 on your computer ...
-Artcut 2009 for ...
-Download a program for ... 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/innat/Google-MediaPipe/utils.py b/spaces/innat/Google-MediaPipe/utils.py
deleted file mode 100644
index 96a19ca5e6346ebdcea2ffc0a279f379b3169410..0000000000000000000000000000000000000000
--- a/spaces/innat/Google-MediaPipe/utils.py
+++ /dev/null
@@ -1,22 +0,0 @@
-
-import cv2, math 
-
-DESIRED_HEIGHT = 480
-DESIRED_WIDTH = 480
-
-def read_n_resize(image_file, read=True):
-    image = cv2.imread(image_file) if read else image_file
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if read else image
-    
-    h, w = image.shape[:2]
-    
-    if h < w:
-        img = cv2.resize(
-            image, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH)))
-        )
-    else:
-        img = cv2.resize(
-            image, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT)
-        )
-    
-    return img
\ No newline at end of file
diff --git a/spaces/innnky/visinger2-nomidi/text/npu/__init__.py b/spaces/innnky/visinger2-nomidi/text/npu/__init__.py
deleted file mode 100644
index e9801c0c2e0d5a8e94c6fd8679ab691cc4578fe2..0000000000000000000000000000000000000000
--- a/spaces/innnky/visinger2-nomidi/text/npu/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from text.npu import symbols
-from text.npu.symbol_converter import *
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/High Quality Crack Do Stronghold 2 Deluxe 1.3.0.md b/spaces/inplisQlawa/anything-midjourney-v4-1/High Quality Crack Do Stronghold 2 Deluxe 1.3.0.md
deleted file mode 100644
index 2b70564839af5f25ee6e5cfcdcf315246b3c0651..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/High Quality Crack Do Stronghold 2 Deluxe 1.3.0.md	
+++ /dev/null
@@ -1,46 +0,0 @@
-<h2>Crack Do Stronghold 2 Deluxe 1.3.0</h2><br /><p><b><b>Download File</b> === <a href="https://urlin.us/2uEwh9">https://urlin.us/2uEwh9</a></b></p><br /><br />
-<br />
-Java ; Created by, N/A ; Updated by, N/A.Embedded, EJB, Annotations, XML
-
-, EJB, Annotations, XML Object-relational mapping
-
-One of the most basic questions about the JVM is: What exactly is an object? In this post I show you how to see the real object with the user interface debugger.
-
-What is an object?
-
-In Java we have multiple ways to think about the same thing:
-
-A value of a certain type. A reference. An object.
-
-The distinction between these is subtle. Values are represented in computer memory as bits. Objects are references to a storage location.
-
-The Java programming language exposes both types. Both can be used to represent a user, a string, an object, a field or a value.
-
-A value can be typed as a String, or a Number. A String is a byte[], and a Number is a int. Both are treated as immutable.
-
-, or a. A is a byte[], and a is a. Both are treated as immutable. An object can be cast to any value, for example Number is a subtype of Number.
-
-Example: A String is a subtype of String.
-
-When you pass a String to a method, the method won’t be called on the String object. Instead the JVM will cast the String to a String object and call the method.
-
-When you pass a Number to a method, the JVM will cast the Number to a Number object and call the method.
-
-Objects are a special type of value in the Java language, but they have a very different meaning. They’re a representation of a data structure in the program.
-
-A memory location that is referred to by a pointer. An object.
-
-An object contains a field and a reference to another object. It can be passed around, but it’s nothing more than a reference.
-
-How to see an object?
-
-In this post we’ll talk about how to use the user interface debugger to step into classes, see variables and even edit properties.
-
-To better understand what an object looks like, the javac compiler emits an assembler file which contains the object representation. We can view this file with javap, but it’s very difficult to understand.
-
-Let’s make it simpler:
-
-jav 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/ixiangjin/GPT4ALL/README.md b/spaces/ixiangjin/GPT4ALL/README.md
deleted file mode 100644
index 5c0c0df6f4d3b07f6f30b627fd4c8b764a34b692..0000000000000000000000000000000000000000
--- a/spaces/ixiangjin/GPT4ALL/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: GPT4ALL
-emoji: 📊
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/james-oldfield/PandA/networks/stylegan3/viz/capture_widget.py b/spaces/james-oldfield/PandA/networks/stylegan3/viz/capture_widget.py
deleted file mode 100644
index dc46c5a69fcce2c81b46e8b0c1f1659c468cec03..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/stylegan3/viz/capture_widget.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import re
-import numpy as np
-import imgui
-import PIL.Image
-from gui_utils import imgui_utils
-from . import renderer
-
-#----------------------------------------------------------------------------
-
-class CaptureWidget:
-    def __init__(self, viz):
-        self.viz            = viz
-        self.path           = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '_screenshots'))
-        self.dump_image     = False
-        self.dump_gui       = False
-        self.defer_frames   = 0
-        self.disabled_time  = 0
-
-    def dump_png(self, image):
-        viz = self.viz
-        try:
-            _height, _width, channels = image.shape
-            assert channels in [1, 3]
-            assert image.dtype == np.uint8
-            os.makedirs(self.path, exist_ok=True)
-            file_id = 0
-            for entry in os.scandir(self.path):
-                if entry.is_file():
-                    match = re.fullmatch(r'(\d+).*', entry.name)
-                    if match:
-                        file_id = max(file_id, int(match.group(1)) + 1)
-            if channels == 1:
-                pil_image = PIL.Image.fromarray(image[:, :, 0], 'L')
-            else:
-                pil_image = PIL.Image.fromarray(image, 'RGB')
-            pil_image.save(os.path.join(self.path, f'{file_id:05d}.png'))
-        except:
-            viz.result.error = renderer.CapturedException()
-
-    @imgui_utils.scoped_by_object_id
-    def __call__(self, show=True):
-        viz = self.viz
-        if show:
-            with imgui_utils.grayed_out(self.disabled_time != 0):
-                imgui.text('Capture')
-                imgui.same_line(viz.label_w)
-                _changed, self.path = imgui_utils.input_text('##path', self.path, 1024,
-                    flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
-                    width=(-1 - viz.button_w * 2 - viz.spacing * 2),
-                    help_text='PATH')
-                if imgui.is_item_hovered() and not imgui.is_item_active() and self.path != '':
-                    imgui.set_tooltip(self.path)
-                imgui.same_line()
-                if imgui_utils.button('Save image', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)):
-                    self.dump_image = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-                imgui.same_line()
-                if imgui_utils.button('Save GUI', width=-1, enabled=(self.disabled_time == 0)):
-                    self.dump_gui = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-
-        self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
-        if self.defer_frames > 0:
-            self.defer_frames -= 1
-        elif self.dump_image:
-            if 'image' in viz.result:
-                self.dump_png(viz.result.image)
-            self.dump_image = False
-        elif self.dump_gui:
-            viz.capture_next_frame()
-            self.dump_gui = False
-        captured_frame = viz.pop_captured_frame()
-        if captured_frame is not None:
-            self.dump_png(captured_frame)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/jason137/text-to-sql/Dockerfile b/spaces/jason137/text-to-sql/Dockerfile
deleted file mode 100644
index a0348127cbc227bfae18b45898a887c2616fae73..0000000000000000000000000000000000000000
--- a/spaces/jason137/text-to-sql/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM python:latest
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y sqlite3 \
-    && apt-get install -y git
-
-COPY requirements.txt .
-
-RUN pip install --upgrade --no-cache-dir pip \
-    && pip install --upgrade --no-cache-dir -r requirements.txt
-
-RUN git clone https://github.com/lerocha/chinook-database.git \
-    && cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sql .
-
-RUN sqlite3 -init Chinook_Sqlite.sql Chinook.db ""
-
-COPY script.py .
-RUN --mount=type=secret,id=OPENAI_API_KEY,mode=0444,required=true
-
-CMD ["streamlit", "run", "script.py", "--server.port", "7860"]
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/LifeSim/src/app/agents/pick.ts b/spaces/jbilcke-hf/LifeSim/src/app/agents/pick.ts
deleted file mode 100644
index 48dc2995f08d8c3774a9b7b35b808064313361a7..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/LifeSim/src/app/agents/pick.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-
-export const pick = (items: string[]) => items[Math.floor(Math.random()*items.length)]
diff --git a/spaces/jgurzoni/image_background_swapper/saicinpainting/training/visualizers/colors.py b/spaces/jgurzoni/image_background_swapper/saicinpainting/training/visualizers/colors.py
deleted file mode 100644
index 9e9e39182c58cb06a1c5e97a7e6c497cc3388ebe..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/saicinpainting/training/visualizers/colors.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import random
-import colorsys
-
-import numpy as np
-import matplotlib
-matplotlib.use('agg')
-import matplotlib.pyplot as plt 
-from matplotlib.colors import LinearSegmentedColormap
-
-
-def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False):
-    # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
-    """
-    Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks
-    :param nlabels: Number of labels (size of colormap)
-    :param type: 'bright' for strong colors, 'soft' for pastel colors
-    :param first_color_black: Option to use first color as black, True or False
-    :param last_color_black: Option to use last color as black, True or False
-    :param verbose: Prints the number of labels and shows the colormap. True or False
-    :return: colormap for matplotlib
-    """
-    if type not in ('bright', 'soft'):
-        print ('Please choose "bright" or "soft" for type')
-        return
-
-    if verbose:
-        print('Number of labels: ' + str(nlabels))
-
-    # Generate color map for bright colors, based on hsv
-    if type == 'bright':
-        randHSVcolors = [(np.random.uniform(low=0.0, high=1),
-                          np.random.uniform(low=0.2, high=1),
-                          np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]
-
-        # Convert HSV list to RGB
-        randRGBcolors = []
-        for HSVcolor in randHSVcolors:
-            randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
-
-        if first_color_black:
-            randRGBcolors[0] = [0, 0, 0]
-
-        if last_color_black:
-            randRGBcolors[-1] = [0, 0, 0]
-
-        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
-
-    # Generate soft pastel colors, by limiting the RGB spectrum
-    if type == 'soft':
-        low = 0.6
-        high = 0.95
-        randRGBcolors = [(np.random.uniform(low=low, high=high),
-                          np.random.uniform(low=low, high=high),
-                          np.random.uniform(low=low, high=high)) for i in range(nlabels)]
-
-        if first_color_black:
-            randRGBcolors[0] = [0, 0, 0]
-
-        if last_color_black:
-            randRGBcolors[-1] = [0, 0, 0]
-        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
-
-    # Display colorbar
-    if verbose:
-        from matplotlib import colors, colorbar
-        from matplotlib import pyplot as plt
-        fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))
-
-        bounds = np.linspace(0, nlabels, nlabels + 1)
-        norm = colors.BoundaryNorm(bounds, nlabels)
-
-        cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,
-                                   boundaries=bounds, format='%1i', orientation=u'horizontal')
-
-    return randRGBcolors, random_colormap
-
diff --git a/spaces/jiangjiechen/Auction-Arena-Demo/src/item_base.py b/spaces/jiangjiechen/Auction-Arena-Demo/src/item_base.py
deleted file mode 100644
index 72d5305eab30ddb2adee1c54072b99aca070f45a..0000000000000000000000000000000000000000
--- a/spaces/jiangjiechen/Auction-Arena-Demo/src/item_base.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import sys
-sys.path.append('..')
-from utils import LoadJsonL
-
-
-class Item():
-    def __init__(self, id: int, name: str, price: int, desc: str, true_value: int):
-        self.id = id
-        self.name = name
-        self.price = price
-        self.desc = desc
-        self.true_value = true_value
-        self._original_price = price
-
-    def get_desc(self):
-        return f"{self.name}, starting at ${int(self.price)}."
-
-    def __repr__(self):
-        return f"{self.name}"
-    
-    def __str__(self):
-        return f"{self.name}"
-    
-    def info(self):
-        return f"{self.name}: ${int(self.price)} to ${self.true_value}."
-
-    def lower_price(self, percentage: float = 0.2):
-        # lower starting price by 20%
-        self.price = int(self.price * (1 - percentage))
-    
-    def reset_price(self):
-        self.price = self._original_price
-
-
-def create_items(item_info_jsl):
-    '''
-    item_info: a list of dict (name, price, desc, id)
-    '''
-    item_info_jsl = LoadJsonL(item_info_jsl)
-    item_list = []
-    for info in item_info_jsl:
-        item_list.append(Item(**info))
-    return item_list
-
-
-def item_list_equal(items_1: list, items_2: list):
-    # could be a list of strings (names) or a list of Items
-    item_1_names = [item.name if isinstance(item, Item) else item for item in items_1]
-    item_2_names = [item.name if isinstance(item, Item) else item for item in items_2]
-    return set(item_1_names) == set(item_2_names)
\ No newline at end of file
diff --git a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig5a_Prediction_test.py b/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig5a_Prediction_test.py
deleted file mode 100644
index 608c3c2cb2083fb22a190a5abcdc25e391caf93e..0000000000000000000000000000000000000000
--- a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig5a_Prediction_test.py
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/usr/bin/python
-# coding: utf-8
-
-# Author: LE YUAN
-
-import os
-import math
-import json
-import numpy as np
-from collections import defaultdict
-import matplotlib.pyplot as plt
-from matplotlib import rc
-import seaborn as sns
-import pandas as pd
-from scipy.stats import gaussian_kde
-from scipy import stats
-from sklearn.metrics import mean_squared_error,r2_score
-
-
-def main() :
-    experimental_values = list()
-    predicted_values = list()
-    with open('../../Data/test_dataset/test_out.txt', 'r') as testfile :
-        testData = testfile.readlines()[1:]
-
-    number = 0
-    for data in testData :
-        line = data.strip().split('\t')
-        # print(line)
-        number += 1
-        experimental, predicted = float(line[0]), float(line[1])
-        experimental_values.append(experimental)
-        predicted_values.append(predicted)
-
-    # correlation, p_value = stats.pearsonr(x, y)
-    correlation, p_value = stats.pearsonr(experimental_values, predicted_values)
-
-    # https://blog.csdn.net/u012735708/article/details/84337262?utm_medium=distribute.pc_relevant.none-
-    # task-blog-BlogCommendFromMachineLearnPai2-1.pc_relevant_is_cache&depth_1-utm_source=
-    # distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.pc_relevant_is_cache
-    r2 = r2_score(experimental_values,predicted_values)
-    rmse = np.sqrt(mean_squared_error(experimental_values,predicted_values))
-
-    print('The data point number: %s' % number)
-    print('r is: %.2f' % correlation)
-    # print('p value is: %s' % p_value)
-    print('p value is: %s' % p_value)
-    print('R2 is: %.2f' % r2)
-    print('RMSE is: %.2f' % rmse)
-    print('\n')
-        
-    # Results:
-    # The data point number: 1684
-    # r is: 0.71
-    # p value is: 1.4624026012794328e-262
-    # R2 is: 0.51
-    # RMSE is: 1.05
-
-    allData = pd.DataFrame(list(zip(experimental_values,predicted_values)))
-    allData.columns = ['Experimental value', 'Predicted value']
-
-    plt.figure(figsize=(1.5,1.5))
-
-    # To solve the 'Helvetica' font cannot be used in PDF file
-    # https://stackoverflow.com/questions/59845568/the-pdf-backend-does-not-currently-support-the-selected-font
-    # rc('text', usetex=True) 
-    rc('font',**{'family':'serif','serif':['Helvetica']})
-    plt.rcParams['pdf.fonttype'] = 42
-    # plt.rc('text', usetex=True)
-
-    plt.axes([0.12,0.12,0.83,0.83])
-
-    plt.tick_params(direction='in')
-    plt.tick_params(which='major',length=1.5)
-    plt.tick_params(which='major',width=0.4)
-
-    # http://showteeth.tech/posts/24328.html
-    # https://stackoverflow.com/questions/49662964/density-scatter-plot-for-huge-dataset-in-matplotlib
-    kcat_values_vstack = np.vstack([experimental_values,predicted_values])
-    experimental_predicted = gaussian_kde(kcat_values_vstack)(kcat_values_vstack)
-
-    # plt.scatter(data = allData, x = 'Predicted value', y = 'Experimental value')
-    # sns.regplot(data = allData, x = 'Experimental value', y = 'Predicted value', color='#2166ac', scatter_kws={"s": 1})
-    ax = plt.scatter(x = experimental_values, y = predicted_values, c=experimental_predicted, s=3, edgecolor=[])
-
-    # https://stackoverflow.com/questions/53935805/specify-range-of-colors-for-density-plot-in-matplotlib
-    cbar = plt.colorbar(ax)
-    cbar.ax.tick_params(labelsize=6)
-    cbar.set_label('Density', size=7)
-
-    plt.text(-4.7, 6.9, 'r = %.2f' % correlation, fontweight ="normal", fontsize=6)
-    plt.text(-4.7, 5.9, 'P value = 1.5e-262', fontweight ="normal", fontsize=6)
-    plt.text(-4.7, 4.8, 'N = 1684', fontweight ="normal", fontsize=6)
-
-    plt.rcParams['font.family'] = 'Helvetica'
-
-    plt.xlabel("Experimental $k$$_\mathregular{cat}$ value", fontdict={'weight': 'normal', 'fontname': 'Helvetica', 'size': 7}, fontsize=7)
-    plt.ylabel('Predicted $k$$_\mathregular{cat}$ value',fontdict={'weight': 'normal', 'fontname': 'Helvetica', 'size': 7},fontsize=7)
-
-    plt.xticks([-6, -4, -2, 0, 2, 4, 6, 8])
-    plt.yticks([-6, -4, -2, 0, 2, 4, 6, 8])
-
-    plt.xticks(fontsize=6)
-    plt.yticks(fontsize=6)
-
-    # plt.rcParams['text.usetex'] = True
-
-    ax = plt.gca()
-    ax.spines['bottom'].set_linewidth(0.5)
-    ax.spines['left'].set_linewidth(0.5)
-    ax.spines['top'].set_linewidth(0.5)
-    ax.spines['right'].set_linewidth(0.5)
-
-    plt.savefig("../../Results/figures/SuppleFig5a.pdf", dpi=400, bbox_inches='tight')
-
-if __name__ == '__main__' :
-    main()
-
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/attrs/converters.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/attrs/converters.py
deleted file mode 100644
index edfa8d3c16ac8642773651778012a3cd57005d9b..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/attrs/converters.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-from attr.converters import *  # noqa
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/websockets.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/websockets.py
deleted file mode 100644
index 55a4ac4a1a918720bb3b94eaea6f8737b968216a..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/websockets.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from starlette.websockets import WebSocket as WebSocket  # noqa
-from starlette.websockets import WebSocketDisconnect as WebSocketDisconnect  # noqa
-from starlette.websockets import WebSocketState as WebSocketState  # noqa
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/__init__.py
deleted file mode 100644
index e699ebf0ca55c4fcd8a257c5936f479d133a4a31..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/__init__.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""Init file of LlamaIndex."""
-from pathlib import Path
-
-with open(Path(__file__).absolute().parents[0] / "VERSION") as _f:
-    __version__ = _f.read().strip()
-
-
-import logging
-from logging import NullHandler
-
-from gpt_index.data_structs.struct_type import IndexStructType
-
-# embeddings
-from gpt_index.embeddings.langchain import LangchainEmbedding
-from gpt_index.embeddings.openai import OpenAIEmbedding
-
-# structured
-from gpt_index.indices.common.struct_store.base import SQLDocumentContextBuilder
-from gpt_index.indices.empty import GPTEmptyIndex
-
-# indices
-from gpt_index.indices.keyword_table import (
-    GPTKeywordTableIndex,
-    GPTRAKEKeywordTableIndex,
-    GPTSimpleKeywordTableIndex,
-)
-from gpt_index.indices.list import GPTListIndex
-
-# prompt helper
-from gpt_index.indices.prompt_helper import PromptHelper
-
-# for composability
-from gpt_index.indices.query.schema import QueryConfig, QueryMode
-from gpt_index.indices.struct_store.sql import GPTSQLStructStoreIndex
-from gpt_index.indices.tree import GPTTreeIndex
-from gpt_index.indices.vector_store import (
-    GPTChromaIndex,
-    GPTFaissIndex,
-    GPTPineconeIndex,
-    GPTQdrantIndex,
-    GPTSimpleVectorIndex,
-    GPTVectorStoreIndex,
-    GPTWeaviateIndex,
-)
-
-# langchain helper
-from gpt_index.langchain_helpers.chain_wrapper import LLMPredictor
-from gpt_index.langchain_helpers.memory_wrapper import GPTIndexMemory
-from gpt_index.langchain_helpers.sql_wrapper import SQLDatabase
-
-# prompts
-from gpt_index.prompts.base import Prompt
-from gpt_index.prompts.prompts import (
-    KeywordExtractPrompt,
-    QueryKeywordExtractPrompt,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-    SummaryPrompt,
-    TreeInsertPrompt,
-    TreeSelectMultiplePrompt,
-    TreeSelectPrompt,
-)
-
-# readers
-from gpt_index.readers import (
-    BeautifulSoupWebReader,
-    ChromaReader,
-    DiscordReader,
-    Document,
-    FaissReader,
-    GithubRepositoryReader,
-    GoogleDocsReader,
-    JSONReader,
-    MboxReader,
-    NotionPageReader,
-    ObsidianReader,
-    PineconeReader,
-    QdrantReader,
-    RssReader,
-    SimpleDirectoryReader,
-    SimpleMongoReader,
-    SimpleWebPageReader,
-    SlackReader,
-    StringIterableReader,
-    TrafilaturaWebReader,
-    TwitterTweetReader,
-    WeaviateReader,
-    WikipediaReader,
-)
-from gpt_index.readers.download import download_loader
-
-# token predictor
-from gpt_index.token_counter.mock_chain_wrapper import MockLLMPredictor
-from gpt_index.token_counter.mock_embed_model import MockEmbedding
-
-# best practices for library logging:
-# https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library
-logging.getLogger(__name__).addHandler(NullHandler())
-
-
-__all__ = [
-    "GPTKeywordTableIndex",
-    "GPTSimpleKeywordTableIndex",
-    "GPTRAKEKeywordTableIndex",
-    "GPTListIndex",
-    "GPTEmptyIndex",
-    "GPTTreeIndex",
-    "GPTFaissIndex",
-    "GPTPineconeIndex",
-    "GPTQdrantIndex",
-    "GPTSimpleVectorIndex",
-    "GPTVectorStoreIndex",
-    "GPTWeaviateIndex",
-    "GPTChromaIndex",
-    "GPTSQLStructStoreIndex",
-    "Prompt",
-    "LangchainEmbedding",
-    "OpenAIEmbedding",
-    "SummaryPrompt",
-    "TreeInsertPrompt",
-    "TreeSelectPrompt",
-    "TreeSelectMultiplePrompt",
-    "RefinePrompt",
-    "QuestionAnswerPrompt",
-    "KeywordExtractPrompt",
-    "QueryKeywordExtractPrompt",
-    "WikipediaReader",
-    "ObsidianReader",
-    "Document",
-    "SimpleDirectoryReader",
-    "JSONReader",
-    "SimpleMongoReader",
-    "NotionPageReader",
-    "GoogleDocsReader",
-    "MboxReader",
-    "SlackReader",
-    "StringIterableReader",
-    "WeaviateReader",
-    "FaissReader",
-    "ChromaReader",
-    "PineconeReader",
-    "QdrantReader",
-    "DiscordReader",
-    "SimpleWebPageReader",
-    "RssReader",
-    "BeautifulSoupWebReader",
-    "TrafilaturaWebReader",
-    "LLMPredictor",
-    "MockLLMPredictor",
-    "MockEmbedding",
-    "SQLDatabase",
-    "GPTIndexMemory",
-    "SQLDocumentContextBuilder",
-    "SQLContextBuilder",
-    "PromptHelper",
-    "QueryConfig",
-    "QueryMode",
-    "IndexStructType",
-    "TwitterTweetReader",
-    "download_loader",
-    "GithubRepositoryReader",
-]
-
-# NOTE: keep for backwards compatibility
-SQLContextBuilder = SQLDocumentContextBuilder
diff --git a/spaces/jonathanjordan21/ads-video-generator/components/tag_chain.py b/spaces/jonathanjordan21/ads-video-generator/components/tag_chain.py
deleted file mode 100644
index aeed0209d0f6e55ac27e2ce3caefdfd7019e0b22..0000000000000000000000000000000000000000
--- a/spaces/jonathanjordan21/ads-video-generator/components/tag_chain.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-
-def chain(llm):
-  sum_template = """what is the most significant actions or places or things, say it in at most 5 words :
-  
-  {sentence}
-  
-  
-  """
-  
-  sum_prompt = PromptTemplate(template=sum_template, input_variables=["sentence"])
-  
-  sum_llm_chain = LLMChain(prompt=sum_prompt, llm=llm)
-
-  return sum_llm_chain
\ No newline at end of file
diff --git a/spaces/jonigata/PoseMaker2/js/poseMaker.js b/spaces/jonigata/PoseMaker2/js/poseMaker.js
deleted file mode 100644
index fe353694ad6ca5a22b9ffdaff02355892402de69..0000000000000000000000000000000000000000
--- a/spaces/jonigata/PoseMaker2/js/poseMaker.js
+++ /dev/null
@@ -1,823 +0,0 @@
-console.log("hello from poseEditor.js")
-var canvas = null;
-var ctx = null;
-var canvasBg = null;
-
-const wheelDisplayTime = 500;
-
-const limbSeq = [
-  [1, 2], [2, 3], [3, 4], // 右腕
-  [1, 5], [5, 6], [6, 7], // 左腕
-  [1, 8], [8, 9], [9, 10],  // 右胴→右脚
-  [1, 11], [11, 12], [12, 13], // 左胴→左脚
-  [1, 0], // 首
-  [0, 14], [14, 16], // 右目
-  [0, 15], [15, 17] // 左目
-];
-
-function findParentNodeIndex(nodeIndex) {
-  // limbSeqの各要素の2番目の要素がjointIndexの場合、その要素の1番目の要素を返す
-  // 見つからないばあいは-1を返す
-  limbIndex = limbSeq.findIndex((limb) => limb[1] === nodeIndex);
-  return limbIndex === -1 ? -1 : limbSeq[limbIndex][0];
-}
-
-function cutOffLimb(pose, cutOffIndex) {
-  console.log(`cutOffLimb: ${cutOffIndex}`);
-  // 末端ノードの座標を削除する
-  var newPose = deepCopy(pose);
-  for (let i = 0; i < 18; i++) {
-    if (newPose[i] == null) {continue;}
-    // ルートまで検索し、その間にcuttOffIndexがあれば削除
-    var curr = i;
-    while (curr !== 1) {
-      console.log(`checking: ${i} -> ${curr}`);
-      let parent = findParentNodeIndex(curr);
-      if (parent === cutOffIndex) {
-        console.log(`cutOffLimb: ${i} -> ${cutOffIndex}`);
-        newPose[i] = null;
-        break;
-      }
-      curr = parent;
-    }
-  }
-  return newPose;
-}
-
-function repairPose(sourcePose) {
-  // TODO: ループには対応してないかも
-  var pose = sourcePose;
-  var newPose = new Array(18)
-  for (var k = 0; k < 3; k++) {
-    var processed = 0; // イテレーション用
-    for (let i = 0; i < 18; i++) {
-      if (pose[i] == null) {
-        let parent = findParentNodeIndex(i);
-        if (parent === -1) {continue;} // あり得ない
-        if (pose[parent] == null) {
-          console.log(`repair failed(A): ${i} -> parent loss`);
-          continue;
-        }
-
-        // サンプルデータから引っ張ってくる
-        var v = sampleCandidateSource[i].map((x, j) => x - sampleCandidateSource[parent][j]);
-        newPose[i] = pose[parent].map((x, j) => x + v[j]);
-        console.log(`repaired: ${i} -> ${newPose[newPose.length - 1]}`);
-        processed++;
-      } else {
-        newPose[i] = pose[i].map(x => x);
-      }
-    }
-    if (processed === 0) {break;}
-    pose = newPose;
-  }
-  return newPose;
-}
-
-function deepCopy(arr) {
-  return JSON.parse(JSON.stringify(arr));
-}
-
-function distSq(p0, p1) {
-  return (p0[0] - p1[0]) ** 2 + (p0[1] - p1[1]) ** 2;
-}
-
-// poseDataの形式：[[[x1, y1], [x2, y2], ...],[[x3, y3], [x4, y4], ...], ...]
-// 各要素が人間
-// 人間の各要素が関節
-
-function poseDataToCandidateAndSubset(poseData) {
-  let candidate = [];
-  let subset = [];
-  for (let i = 0; i < poseData.length; i++) {
-    let person = poseData[i];
-    let subsetElement = [];
-    for (let j = 0; j < person.length; j++) {
-      candidate.push(person[j]);
-      subsetElement.push(candidate.length - 1);
-    }
-    subset.push(subsetElement);
-  }
-  return [candidate, subset];
-}
-
-// サンプルデータ  
-const sampleCandidateSource = [[235, 158],[234, 220],[193, 222],[138, 263],[89, 308],[276, 220],[325, 264],[375, 309],[207, 347],[203, 433],[199, 523],[261, 347],[262, 430],[261, 522],[227, 148],[245, 148],[208, 158],[258, 154]].map((p) => [p[0], p[1] - 70]);
-const sampleSubsetElementSource = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17];
-
-// const sampleCandidateSource = [[618.00, 0.00], [618.00, 44.00], [304.00, 81.00], [482.00, 96.00], [66.00, 270.00], [171.00, 280.00], [618.00, 82.00], [307.00, 112.00], [460.00, 143.00], [0.00, 301.00], [65.00, 301.00], [172.00, 303.00], [584.00, 86.00], [275.00, 119.00], [420.00, 139.00], [0.00, 301.00], [41.00, 301.00], [144.00, 303.00], [544.00, 131.00], [348.00, 139.00], [262.00, 160.00], [0.00, 337.00], [52.00, 339.00], [130.00, 348.00], [570.00, 175.00], [283.00, 177.00], [78.00, 338.00], [172.00, 380.00], [651.00, 78.00], [338.00, 111.00], [505.00, 144.00], [92.00, 301.00], [198.00, 305.00], [661.00, 132.00], [349.00, 156.00], [541.00, 179.00], [106.00, 336.00], [203.00, 348.00], [305.00, 159.00], [665.00, 160.00], [563.00, 192.00], [80.00, 343.00], [181.00, 385.00], [614.00, 205.00], [291.00, 220.00], [432.00, 320.00], [152.00, 372.00], [43.00, 380.00], [0.00, 386.00], [623.00, 281.00], [306.00, 290.00], [92.00, 357.00], [509.00, 434.00], [304.00, 357.00], [622.00, 368.00], [47.00, 394.00], [0.00, 395.00], [142.00, 405.00], [535.00, 565.00], [655.00, 200.00], [337.00, 217.00], [467.00, 322.00], [191.00, 372.00], [83.00, 375.00], [344.00, 282.00], [655.00, 282.00], [103.00, 343.00], [237.00, 368.00], [22.00, 377.00], [0.00, 379.00], [460.00, 459.00], [305.00, 352.00], [638.00, 355.00], [0.00, 401.00], [110.00, 412.00], [411.00, 570.00], [608.00, 0.00], [608.00, 40.00], [297.00, 75.00], [469.00, 84.00], [0.00, 261.00], [58.00, 263.00], [165.00, 275.00], [625.00, 0.00], [625.00, 39.00], [309.00, 74.00], [486.00, 83.00], [71.00, 264.00], [180.00, 276.00], [599.00, 0.00], [599.00, 44.00], [284.00, 80.00], [440.00, 93.00], [48.00, 271.00], [0.00, 272.00], [157.00, 277.00], [634.00, 0.00], [633.00, 41.00], [319.00, 77.00], [79.00, 269.00], [190.00, 277.00]];
-// const sampleSubsetElementSource = [1.00,6.00,12.00,18.00,24.00,28.00,33.00,39.00,43.00,49.00,54.00,59.00,65.00,72.00,77.00,84.00,90.00,97.00,32.98,18.00],[5.00,11.00,17.00,23.00,27.00,32.00,37.00,42.00,46.00,-1.00,-1.00,62.00,67.00,-1.00,82.00,88.00,95.00,100.00,25.45,15.00],[4.00,10.00,16.00,22.00,26.00,31.00,36.00,41.00,47.00,51.00,57.00,63.00,66.00,74.00,81.00,87.00,93.00,99.00,26.97,18.00],[3.00,8.00,14.00,19.00,25.00,30.00,35.00,40.00,45.00,52.00,58.00,61.00,70.00,75.00,79.00,86.00,92.00,-1.00,30.45,17.00],[2.00,7.00,13.00,20.00,-1.00,29.00,34.00,38.00,44.00,50.00,53.00,60.00,64.00,71.00,78.00,85.00,91.00,98.00,27.89,17.00],[0.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,-1.00,76.00,83.00,-1.00,96.00,3.33,4.00];
-
-function makePoseFromCandidateAndSubsetElement(candidate, subsetElement) {
-  var pose = [];
-  for (let j = 0 ; j < 18; j++) {
-    let i = subsetElement[j];
-    pose.push(i < 0 || candidate[i] == null ? null : candidate[i].map((x)=>x));
-  }
-  return pose;
-}
-
-function makePoseDataFromCandidateAndSubset(candidate, subset) {
-  return subset.map(subsetElement => makePoseFromCandidateAndSubsetElement(candidate, subsetElement));
-}
-
-function addPerson() {
-  var dx = Math.random() * 100;
-  var dy = Math.random() * 100;
-
-  poseData.push(
-    makePoseFromCandidateAndSubsetElement(
-      sampleCandidateSource.map(point => [point[0] + dx, point[1] + dy]),
-      sampleSubsetElementSource));
-
-  addHistory();    
-  Redraw();
-}
-
-function removePerson(personIndex) {
-  poseData.splice(personIndex, 1);
-  addHistory();
-  Redraw();
-}
-
-function repairPerson(personIndex) {
-  poseData[personIndex] = repairPose(poseData[personIndex]);
-  addHistory();
-  Redraw();
-}
-
-function cutOffPersonLimb(personIndex, limbIndex) {
-  poseData[personIndex] = cutOffLimb(poseData[personIndex], limbIndex);
-  console.log(poseData[personIndex]);
-  console.log(poseData);
-  addHistory();
-  Redraw();
-}
-
-function toggleHideNode(personIndex, nodeIndex) {
-  poseData[personIndex][nodeIndex][2] = !poseData[personIndex][nodeIndex][2];
-}
-
-// ドラッグ中の各キーが押されているかどうかのフラグ
-var keyDownFlags = {};
-// マウスカーソル
-var mouseCursor = [-1, -1];
-
-function cross(lhs, rhs) {return lhs[0] * rhs[1] - lhs[1] * rhs[0];}
-function dot(lhs, rhs) {return lhs[0] * rhs[0] + lhs[1] * rhs[1];}
-function directedAngleTo(lhs, rhs) {return Math.atan2(cross(lhs, rhs), dot(lhs, rhs));}
-
-function isMouseOnCanvas() {
-  // mouseCursorがcanvasの範囲内にあるかどうかを判定
-  var rect = canvas.getBoundingClientRect();
-  var f = 0 <= mouseCursor[0] && mouseCursor[0] <= rect.width && 0 <= mouseCursor[1] && mouseCursor[1] <= rect.height;
-  return f;
-}
-
-function clearCanvas() {
-  var w = canvas.width;
-  var h = canvas.height;
-  ctx.fillStyle = 'black';
-  ctx.fillRect(0, 0, w, h);
-}
-
-function drawBackground() {
-  if (canvasBg != null) {
-    ctx.drawImage(canvasBg, 0, 0);
-  }
-}
-
-function resizeCanvas(width, height) {
-  canvas.width = width ? width : canvas.width;
-  canvas.height = height ? height : canvas.height;
-  Redraw();
-}
-
-function calculateCenter(shape) {
-  var center = shape.reduce(function(acc, point) {
-    if (point === null) {
-      acc[0] += point[0];
-      acc[1] += point[1];
-    }
-    return acc;
-  }, [0, 0]);
-  center[0] /= shape.length;
-  center[1] /= shape.length;
-  return center;
-}
-
-// v2d -> v3d
-function rotateX(vector, angle) {
-  var x = vector[0];
-  var y = vector[1];
-  var z = 0;
-
-  // X軸に対して回転する
-  var x1 = x;
-  var y1 = y * Math.cos(angle) - z * Math.sin(angle);
-  var z1 = y * Math.sin(angle) + z * Math.cos(angle);
-
-  return [x1, y1, z1];
-}
-
-// v2d -> v3d
-function rotateY(vector, angle) {
-  var x = vector[0];
-  var y = vector[1];
-  var z = 0;
-
-  // Y軸に対して回転する
-  var x1 = x * Math.cos(angle) + z * Math.sin(angle);
-  var y1 = y;
-  var z1 = -x * Math.sin(angle) + z * Math.cos(angle);
-
-  return [x1, y1, z1];  
-}
-
-// v3d -> v2d
-function perspectiveProjection(vector, cameraDistance) {
-  var x = vector[0];
-  var y = vector[1];
-  var z = vector[2];
-
-  if (z === 0) {
-    return [x, y];
-  }
-
-  var scale = cameraDistance / (cameraDistance - z);
-  var x1 = x * scale;
-  var y1 = y * scale;
-
-  return [x1, y1];
-}  
-
-// v2d -> v3d
-function rotateAndProject(f, p, c, angle) {
-  var v = [p[0] - c[0], p[1] - c[1]];
-  var v1 = f(v, angle);
-  var v2 = perspectiveProjection(v1, 500);
-  return [v2[0] + c[0], v2[1] + c[1]];
-}
-
-function drawBodyPose(saving) {
-  let stickWidth = 4;
-  let imageSize = Math.min(canvas.width, canvas.height);
-  stickWidth *= imageSize / 512;
-
-  const colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
-              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
-              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]];
-
-  ctx.globalAlpha = 0.6;
-
-  // edge
-  for (let i = 0; i < poseData.length; i++) {
-    const pose = poseData[i];
-
-    for (let j = 0; j < 17; j++) {
-      const p = pose[limbSeq[j][0]];
-      const q = pose[limbSeq[j][1]];
-      if (p == null || q == null) continue;
-      if (p[2] || q[2]) { continue; }
-      const [X0, Y0] = p;
-      const [X1, Y1] = q;
-      let angle = Math.atan2(Y1 - Y0, X1 - X0);
-      let magnitude = ((X0 - X1) ** 2 + (Y0 - Y1) ** 2) ** 0.5
-      let polygon = new Path2D();
-      polygon.ellipse((X0+X1)/2, (Y0+Y1)/2, magnitude / 2, stickWidth, angle, 0, 2 * Math.PI);
-      ctx.fillStyle = `rgb(${colors[j].join(',')})`;
-      ctx.fill(polygon);
-    }
-  }
-
-  ctx.globalAlpha = 1.0;
-
-  // node
-  for (let i = 0; i < poseData.length; i++) {
-    const pose = poseData[i];
-
-    ctx.font = '12px serif';
-    for (let j = 0; j < 18; j++) {
-      const p = pose[j];
-      if (p == null) continue;
-      const [x, y] = p;
-      if (p[2]) {
-        if (!saving) {
-          ctx.beginPath();
-          ctx.arc(x, y, stickWidth, 0, 2 * Math.PI);
-          ctx.lineWidth = 1;
-          ctx.strokeStyle = `rgb(${colors[j].join(',')})`;
-          ctx.stroke();
-        }
-      } else {
-        ctx.beginPath();
-        ctx.arc(x, y, stickWidth, 0, 2 * Math.PI);
-        ctx.fillStyle = `rgb(${colors[j].join(',')})`;
-        ctx.fill();
-      }
-      // ctx.fillStyle = 'rgb(255,255,255)'
-      // ctx.fillText(j, x-3, y+4);
-    }
-  }
-}
-
-let lastWheeling = 0;
-
-function drawUI() {
-  if (keyDownFlags['Space'] || keyDownFlags['BracketLeft'] || keyDownFlags['BracketRight'] || 
-      new Date().getTime() - lastWheeling < wheelDisplayTime) {
-    ctx.beginPath();
-    ctx.lineWidth=4;
-    ctx.arc(mouseCursor[0], mouseCursor[1], dragRange, 0, 2 * Math.PI);
-    ctx.strokeStyle = 'rgb(255,255,255)';
-    ctx.stroke();
-  }
-
-  if (isDragging && (dragMode == "rotate" || dragMode == "rotate2")) {
-    ctx.beginPath();
-    ctx.lineWidth=1;
-    ctx.strokeStyle = 'rgb(255,255,255)';
-    ctx.moveTo(dragStart[0], dragStart[1]);
-    ctx.lineTo(dragStart[0]+rotateBaseVector[0], dragStart[1]+rotateBaseVector[1]);
-    ctx.stroke();
-  }
-
-  let operationTextFlags = {
-    "Space": "Range Move",
-    "AltLeft": "Body Move",
-    "AltRight": "Body Move",
-    "ControlLeft": "Scale",
-    "ControlRight": "Scale",
-    "ShiftLeft": "Rotate",
-    "ShiftRight": "Rotate",
-    "KeyQ": "CutOff",
-    "KeyD": "Delete",
-    "KeyX": "X-Axis",
-    "KeyC": "Y-Axis",
-    "KeyR": "Repair",
-  }
-
-  // operationTextFlagsに含まれるものがkeyDownFlagsに含まれるばあい、そのキーの文字列を取得
-  let activeOperations = Object.keys(operationTextFlags).filter(key => keyDownFlags[key]);
-  if (activeOperations.length > 0) {
-    // 左上に表示
-    ctx.font = '20px serif';
-    ctx.fillStyle = 'rgb(255,255,255)';
-    ctx.fillText(operationTextFlags[activeOperations[0]], 10, 30);
-  }
-}
-
-function Redraw(saving) {
-  clearCanvas();
-  drawBackground();
-  drawBodyPose(saving);
-  drawUI();
-}
-
-function getNearestNode(p) {
-    let minDistSq = Infinity;
-    let personIndex = -1;
-    let nodeIndex = -1;
-    for (let i = 0; i < poseData.length; i++) {
-      const pose = poseData[i];
-      for (let j = 0; j < pose.length; j++) {
-        const q = pose[j];
-        if (q == null) continue;
-        const d = distSq(p, q);
-        if (d < minDistSq) {
-          minDistSq = d;
-          personIndex = i;
-          nodeIndex = j;
-        }
-      }
-    }
-    return [personIndex, nodeIndex, Math.sqrt(minDistSq)];
-}
-
-let dragRange = 64;
-let dragRangeDelta = 16;
-
-// ドラッグ中に座標を保持するための変数
-let isDragging = false;
-let dragStart = [0, 0];
-let dragPersonIndex = -1;
-let dragMarks = [];
-let dragMode = "";
-let rotateBaseVector = null;
-let history = [];
-let historyIndex = 0;
-
-function clearHistory() {
-  history = [];
-  historyIndex = 0;  
-}
-
-function addHistory() {
-  history = history.slice(0, historyIndex);
-  history.push(JSON.parse(JSON.stringify(poseData)));
-  historyIndex = history.length;
-}
-
-function undo() {
-  if (1 < historyIndex) {
-    historyIndex--;
-    poseData = deepCopy(history[historyIndex-1]);
-    Redraw();
-  }   
-}
-
-function redo() {
-  if (historyIndex < history.length) {
-    historyIndex++;
-    poseData = deepCopy(history[historyIndex-1]);
-    Redraw();
-  }   
-}
-
-function fetchLatestPoseData() {
-  return history[historyIndex-1];
-}
-
-function getCanvasPosition(event) {
-  const rect = canvas.getBoundingClientRect();
-  const x = event.clientX - rect.left;
-  const y = event.clientY - rect.top;
-  return [x, y];
-}
-
-function forEachMarkedNodes(fn) {
-  for (let i = 0; i < dragMarks.length; i++) {
-    for (let j = 0; j < dragMarks[i].length; j++) {
-      if (dragMarks[i][j]) {
-        fn(i, j, poseData[i][j]);
-      }
-    }
-  }
-}
-
-// Canvas要素上でマウスが押された場合に呼び出される関数
-function handleMouseDown(event) {
-  const p = getCanvasPosition(event);
-  const [personIndex, nodeIndex, minDist] = getNearestNode(p);
-
-  if (keyDownFlags["KeyD"]) {removePerson(personIndex);return;}
-  if (keyDownFlags["KeyR"]) {repairPerson(personIndex);return;}
-  if (keyDownFlags["KeyH"]) {toggleHideNode(personIndex, nodeIndex);return;}
-
-  if (keyDownFlags["KeyQ"] && minDist < 16) {
-    console.log("pressed KeyQ");
-    cutOffPersonLimb(personIndex, nodeIndex);
-    return;
-  }
-
-  // ドラッグ処理の開始
-  dragStart = p;
-  dragMarks = poseData.map(pose => pose.map(node => false));
-
-  if (event.altKey || event.ctrlKey || event.shiftKey ||
-      keyDownFlags["KeyX"] || keyDownFlags["KeyC"]) {
-    // dragMarksを設定
-    dragMarks[personIndex] = 
-      poseData[personIndex].map((node) => node != null);
-      isDragging = true;
-    if (event.altKey) {
-      dragMode = "move";
-    } else if (event.ctrlKey) {
-      dragMode = "scale";
-    } else if (event.shiftKey) {
-      dragMode = "rotate";
-      rotateBaseVector = [0, 0];
-    } else if (keyDownFlags["KeyX"]) {
-      dragMode = "rotateX";
-    } else if (keyDownFlags["KeyC"]) {
-      dragMode = "rotateY";
-    }
-  } else if (keyDownFlags["Space"]) {
-    dragMarks[personIndex] = 
-      poseData[personIndex].map(
-        (node) => node != null && distSq(p, node) < dragRange ** 2);
-    isDragging = dragMarks[personIndex].some((mark) => mark);
-    dragMode = "move";
-  } else if (minDist < 16) {
-    dragMarks[personIndex][nodeIndex] = true;
-    isDragging = true;
-    dragMode = "move";
-  }
-}
-
-// Canvas要素上でマウスが動いた場合に呼び出される関数
-function handleMouseMove(event) {
-  mouseCursor = getCanvasPosition(event);
-  if (isDragging) {
-    const p = getCanvasPosition(event);
-    const dragOffset = [p[0] - dragStart[0], p[1] - dragStart[1]];
-    const latestPoseData = fetchLatestPoseData();
-
-    if (dragMode == "scale") {
-      // 拡大縮小
-      let xScale = 1 + dragOffset[0] / canvas.width;
-      let yScale = 1 + dragOffset[0] / canvas.height;
-      forEachMarkedNodes((i, j, node) => {
-        const lp = latestPoseData[i][j];
-        node[0] = (lp[0] - dragStart[0]) * xScale + dragStart[0];
-        node[1] = (lp[1] - dragStart[1]) * yScale + dragStart[1];
-      });
-    } else if (dragMode == "rotate") {
-      rotateBaseVector = dragOffset;
-      if (!event.shiftKey) {
-        dragMode = "rotate2";
-      }
-    } else if (dragMode == "rotate2") {
-      // 回転
-      let angle = directedAngleTo(rotateBaseVector, dragOffset);
-      forEachMarkedNodes((i, j, node) => {
-        const lp = latestPoseData[i][j];
-        let x = lp[0] - dragStart[0];
-        let y = lp[1] - dragStart[1];
-        let sin = Math.sin(angle);
-        let cos = Math.cos(angle);
-        node[0] = x * cos - y * sin + dragStart[0];
-        node[1] = x * sin + y * cos + dragStart[1];
-      });
-    } else if (dragMode == "rotateX") {
-      const center = dragStart;
-      const angle = dragOffset[1] / -40;
-      forEachMarkedNodes((i, j, node) => {
-        const lp = latestPoseData[i][j];
-        const np = rotateAndProject(rotateX, lp, center, angle);
-        node[0] = np[0];
-        node[1] = np[1];
-      });
-    } else if (dragMode == "rotateY") {
-      const center = dragStart;
-      const angle = dragOffset[0] / 40;
-      forEachMarkedNodes((i, j, node) => {
-        const lp = latestPoseData[i][j];
-        const np = rotateAndProject(rotateY, lp, center, angle);
-        node[0] = np[0];
-        node[1] = np[1];
-      });
-    } else if (dragMode == "move") {
-      // 移動
-      forEachMarkedNodes((i, j, node) => {
-        const lp = latestPoseData[i][j];
-        node[0] = lp[0] + dragOffset[0];
-        node[1] = lp[1] + dragOffset[1];
-      });
-    }
-  }
-
-  Redraw();
-}
-
-function handleMouseUp(event) {
-  isDragging = false; 
-  addHistory();
-  Redraw();
-}
-
-function handleMouseLeave(event) {
-  mouseCursor = [-1,-1];
-  handleMouseUp(event);
-  keyDownFlags = {};
-}
-
-function ModifyDragRange(delta) { dragRange = Math.max(dragRangeDelta, Math.min(512, dragRange + delta)); }
-
-document.addEventListener('wheel', function(event) {
-  if (!isMouseOnCanvas()) {return;}
-  if (!event.altKey && !keyDownFlags['Space']) {return;}
-
-  event.preventDefault();
-  const deltaY = event.deltaY;
-  if (deltaY < 0) {ModifyDragRange(-dragRangeDelta);}
-  if (0 < deltaY) {ModifyDragRange(dragRangeDelta);}
-  lastWheeling = new Date().getTime();
-  Redraw();
-  window.setTimeout(function() { Redraw(); }, wheelDisplayTime+10);
-}, {passive: false});
-
-document.addEventListener("keydown", (event) => { 
-  if (!isMouseOnCanvas()) {return;}
-
-  if (event.code == "BracketLeft") { ModifyDragRange(-dragRangeDelta); }
-  if (event.code == "BracketRight") { ModifyDragRange(dragRangeDelta); }
-  keyDownFlags[event.code] = true; 
-  Redraw();
-  event.preventDefault();
-});
-document.addEventListener("keyup", (event) => { 
-  if (!isMouseOnCanvas()) {return;}
-
-  keyDownFlags[event.code] = false; 
-  if (event.ctrlKey && event.code == "KeyE") {
-    addPerson();
-  } else if (event.ctrlKey && event.code == "KeyZ") {
-    if (event.shiftKey) {
-      redo();
-    } else {
-      undo();
-    }
-  }
-  Redraw();
-  event.preventDefault();
-});
-
-function initializeEditor() {
-  console.log("initializeEditor");
-
-  canvas = document.getElementById('canvas');
-  ctx = canvas.getContext('2d');
-
-  canvas.addEventListener('mousedown', handleMouseDown);
-  canvas.addEventListener('mousemove', handleMouseMove);
-  canvas.addEventListener('mouseup', handleMouseUp);
-  canvas.addEventListener('mouseleave', handleMouseLeave);
-  poseData = [];
-  clearHistory();
-}
-
-function importPose(jsonData) {
-  if (jsonData != null) {
-    if (Array.isArray(jsonData)) {
-      jsonData = jsonData[0]; // 自分で仕込んだバグ回避
-    }
-    newPoseData = makePoseDataFromCandidateAndSubset(jsonData.candidate, jsonData.subset);
-  } else {
-    newPoseData = makePoseDataFromCandidateAndSubset(sampleCandidateSource, [sampleSubsetElementSource]);
-  }
-  poseData = poseData.concat(newPoseData);
-  addHistory();
-  Redraw();
-}
-
-// crc32
-// CRC32を初期化
-function initCrc32Table() {
-  const crcTable = new Uint32Array(256);
-  for (let i = 0; i < 256; i++) {
-    let c = i;
-    for (let j = 0; j < 8; j++) {
-      c = (c & 1) ? (0xEDB88320 ^ (c >>> 1)) : (c >>> 1);
-    }
-    crcTable[i] = c;
-  }
-  return crcTable;
-}
-
-// データのCRC32を計算
-function getCrc32(data, crc=0) {
-  const crcTable = initCrc32Table();
-  crc = (crc ^ 0xFFFFFFFF) >>> 0;
-  for (let i = 0; i < data.length; i++) {
-    crc = crcTable[(crc ^ data[i]) & 0xFF] ^ (crc >>> 8);
-  }
-  return (crc ^ 0xFFFFFFFF) >>> 0;
-}
-
-function stringToUint8Array(str) {
-  var arr = new Uint8Array(str.length);
-  for (var i = 0; i < str.length; i++) {
-    arr[i] = str.charCodeAt(i);
-  }
-  return arr;
-}
-
-function base64ToUint8Array(base64Str) {
-  return stringToUint8Array(atob(base64Str));
-}
-
-function visitPng(png, type) {
-  var dataLength;
-  var chunkType;
-  var nextChunkPos;
-  var Signature = String.fromCharCode(137, 80, 78, 71, 13, 10, 26, 10);
-  var rpos = 0;
-  
-  // シグネチャの確認
-  if (String.fromCharCode.apply(null, png.subarray(rpos, rpos += 8)) !== Signature) {
-      throw new Error('invalid signature');
-  }
-  
-  // チャンクの探索
-  while (rpos < png.length) {
-      dataLength = (
-          (png[rpos++] << 24) |
-          (png[rpos++] << 16) |
-          (png[rpos++] <<  8) |
-          (png[rpos++]      )
-      ) >>> 0;
-  
-      nextChunkPos = rpos + dataLength + 8;
-  
-      chunkType = String.fromCharCode.apply(null, png.subarray(rpos, rpos += 4));
-      
-      if (chunkType === type) {
-        return [rpos - 8, dataLength, nextChunkPos];
-      }
-      
-      rpos = nextChunkPos;
-  }
-}
-
-function createChunk(type, data) {
-  var dataLength = data.length;
-  var chunk = new Uint8Array(4 + 4 + dataLength + 4);
-  var type = stringToUint8Array(type);
-  var pos = 0;
-  
-  // length
-  chunk[pos++] = (dataLength >> 24) & 0xff;
-  chunk[pos++] = (dataLength >> 16) & 0xff;
-  chunk[pos++] = (dataLength >>  8) & 0xff;
-  chunk[pos++] = (dataLength      ) & 0xff;
-  
-  // type
-  chunk[pos++] = type[0];
-  chunk[pos++] = type[1];
-  chunk[pos++] = type[2];
-  chunk[pos++] = type[3];
-  
-  // data
-  for (let i = 0; i < dataLength; ++i) {
-      chunk[pos++] = data[i];
-  }
-  
-  //crc
-  initCrc32Table();
-  let crc = getCrc32(type);
-  crc = getCrc32(data, crc);
-  chunk[pos++] = (crc >> 24) & 0xff;
-  chunk[pos++] = (crc >> 16) & 0xff;
-  chunk[pos++] = (crc >>  8) & 0xff;
-  chunk[pos++] = (crc      ) & 0xff;
-  
-  return chunk;
-}
-
-function insertChunk(destBuffer, sourceBuffer, rpos, chunk) {
-  var pos = 0;
-  
-  // IDAT チャンクの前までコピー
-  destBuffer.set(sourceBuffer.subarray(0, rpos), pos);
-  pos += rpos;
-  
-  // hoGe チャンクをコピー
-  destBuffer.set(chunk, pos);
-  pos += chunk.length;
-  
-  // IDAT チャンク以降をコピー
-  destBuffer.set(sourceBuffer.subarray(rpos), pos);
-}
-
-function mergeCanvasWithPose(keyword, content) {
-  const canvasUrl = canvas.toDataURL();
-
-  var insertion = stringToUint8Array(`${keyword}\0${content}`);
-  var chunk = createChunk("tEXt", insertion);
-  var sourceBuffer = base64ToUint8Array(canvasUrl.split(',')[1]);
-  var destBuffer = new Uint8Array(sourceBuffer.length + insertion.length + 12);
-
-  var [rpos, dataLength, nextChunkPos] = visitPng(sourceBuffer, "IHDR");
-  insertChunk(destBuffer, sourceBuffer, nextChunkPos, chunk);
-
-  var blob = new Blob([destBuffer], {type: "image/png"});
-  var url = URL.createObjectURL(blob);
-  return url;  
-}
-
-function savePose() {
-  var [candidate, subset] = poseDataToCandidateAndSubset(poseData);
-  let jsonData = {candidate: candidate, subset: subset};
-  Redraw(true);
-
-  var url = mergeCanvasWithPose("openpose", JSON.stringify(jsonData));
-
-  const createEl = document.createElement('a');
-  createEl.href = url;
-
-  // This is the name of our downloaded file
-  createEl.download = "pose.png";
-
-  createEl.click();
-  createEl.remove();
-
-  Redraw();
-
-  return jsonData;
-}
-
-function importBackground(image) {
-  if (image == null) {
-    canvasBg = null;
-    Redraw();
-    return;
-  }
-
-  let m = new Image();
-  m.src = image;
-  m.onload = function() {
-    canvasBg = m;
-    Redraw();
-  }
-}
\ No newline at end of file
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/app/interface/page/index.tsx b/spaces/jordonpeter01/ai-comic-factory/src/app/interface/page/index.tsx
deleted file mode 100644
index 9a4c4fbf9ee68d2e95234c4b33fee0b0b34fa4c1..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/app/interface/page/index.tsx
+++ /dev/null
@@ -1,59 +0,0 @@
-import { allLayouts } from "@/app/layouts"
-import { useStore } from "@/app/store"
-import { cn } from "@/lib/utils"
-import { useEffect, useRef } from "react"
-
-export function Page({ page }: { page: number }) {
-  const zoomLevel = useStore(state => state.zoomLevel)
-  const layouts = useStore(state => state.layouts)
-  // const prompt = useStore(state => state.prompt)
-
-  const LayoutElement = (allLayouts as any)[layouts[page]]
-
-  /*
-  const [canLoad, setCanLoad] = useState(false)
-  useEffect(() => {
-    if (prompt?.length) {
-      setCanLoad(false)
-      setTimeout(() => {
-        setCanLoad(true)
-      }, page * 4000)
-    }
-  }, [prompt])
-  */
-
-  const setPage = useStore(state => state.setPage)
-  const pageRef = useRef<HTMLDivElement>(null)
-
-  useEffect(() => {
-    const element = pageRef.current
-    if (!element) { return }
-    setPage(element)
-  }, [pageRef.current])
-  
-  return (
-    <div
-      ref={pageRef}
-      className={cn(
-        `w-full`,
-        // we are trying to reach a "book" look
-        // we are using aspect-[297/210] because it matches A4 (297mm x 210mm)
-        // `aspect-[210/297]`,
-        `aspect-[250/297]`,
-
-        `transition-all duration-100 ease-in-out`,
-        `border border-stone-200`,
-        `shadow-2xl`,
-        `print:shadow-none`,
-        `print:border-0`,
-        `print:width-screen`
-      )}
-      style={{
-        padding: `${Math.round((zoomLevel / 100) * 16)}px`
-        // marginLeft: `${zoomLevel > 100 ? `100`}`
-      }}
-      >
-      <LayoutElement />
-    </div>
-  )
-}
\ No newline at end of file
diff --git a/spaces/justest/gpt4free/g4f/Provider/Providers/EasyChat.py b/spaces/justest/gpt4free/g4f/Provider/Providers/EasyChat.py
deleted file mode 100644
index 9f4aa7b2d047901f9bbb5278bb2ddde3c9f8246f..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/Provider/Providers/EasyChat.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import os, requests
-from ...typing import sha256, Dict, get_type_hints
-import json
-
-url = "https://free.easychat.work/api/openai/v1/chat/completions"
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    ''' limited to 240 messages/hour'''
-    base = ''
-    for message in messages:
-        base += '%s: %s\n' % (message['role'], message['content'])
-    base += 'assistant:'
-    
-    headers = {
-        "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",
-    }
-
-    data = {
-        "messages": [
-            {"role": "system", "content": "You are ChatGPT, a large language model trained by OpenAI."},
-            {"role": "user", "content": base}
-        ],
-        "stream": False,
-        "model": "gpt-3.5-turbo",
-        "temperature": 0.5,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-        "top_p": 1
-    }
-
-    response = requests.post(url, headers=headers, json=data)
-    if response.status_code == 200:
-        response = response.json()
-        yield response['choices'][0]['message']['content']
-    else:
-        print(f"Error Occurred::{response.status_code}")
-        return None
-
-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]])
\ No newline at end of file
diff --git a/spaces/justest/mdn-chatbot/src/pages/api/chat-search.ts b/spaces/justest/mdn-chatbot/src/pages/api/chat-search.ts
deleted file mode 100644
index fbe9e562c64a2add21f90422da6fb5bdb01a3662..0000000000000000000000000000000000000000
--- a/spaces/justest/mdn-chatbot/src/pages/api/chat-search.ts
+++ /dev/null
@@ -1,48 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import ChatGPT from 'gpt-web';
-import { search } from './search';
-
-const chatbot = new ChatGPT(process.env.OPENAI_EMAIL!, process.env.OPENAI_PASSWORD!);
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const text = String(req.query.text);
-  const limit = String(req.query.limit || 3);
-  console.log('limit', limit);
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8');
-
-  if (!text.trim()) {
-    return res.end('text不能为空');
-  }
-
-  console.log('search', text);
-  const searchResults = await search(text, parseInt(limit, 10))
-  const knownledge: string[] = [];
-  if (searchResults) {
-    searchResults.forEach(data => {
-      const { title, text } = data.payload as any;
-      knownledge.push(`
-Q: ${title}
-A: ${text}
-      `.trim());
-    });
-  }
-
-  const prompt = [
-    "你是一个研发小助手，名字叫研发小助手。请学习下面的知识回答问题，并按照示例格式仿写。如果内容不相关就返回未查到相关信息：\n\n",
-    knownledge.join('\n'),
-    'Q: ' + text + '\nA: '
-  ].join('\n');
-  console.log('prompt', prompt);
-
-  let lastLen = 0;
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8');
-
-  const response = await chatbot.chat(String(prompt), {
-    onMessage: (msg: string) => {
-      res.write(msg.slice(lastLen));
-      res.flushHeaders();
-      lastLen = msg.length;
-    },
-  });
-  res.end(response.slice(lastLen));
-  console.log('done');
-}
diff --git a/spaces/jvde/sovits-webui/mel_processing.py b/spaces/jvde/sovits-webui/mel_processing.py
deleted file mode 100644
index 817f03756f64caf8cc54329a9325024c8fb9e0c3..0000000000000000000000000000000000000000
--- a/spaces/jvde/sovits-webui/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/jy46604790/Fake-News-Recognition/Part3.md b/spaces/jy46604790/Fake-News-Recognition/Part3.md
deleted file mode 100644
index eac00551b20588b04623ef8b4d171fbd06cedb45..0000000000000000000000000000000000000000
--- a/spaces/jy46604790/Fake-News-Recognition/Part3.md
+++ /dev/null
@@ -1,12 +0,0 @@
-### Fine Tuning
-
-Fine tuning is when using a pretrained model, train it on a dataset specific to the task. The steps for fine tuning a pre_trained model can be given as follows:
-
-1. Tokenize each sentence using RoBERTa 
-2. Set training parameters
-3. Train the model
-4. Assess: Using ROC-AUC for this problem
-
-### Challenge
-
-The biggest challenge in this task is to get the correct label for each piece of news. As we can't verify news ourselves, I managed to choose news that is objective and has been confirmed or falsified.
\ No newline at end of file
diff --git a/spaces/kdrkdrkdr/HinaTTS/monotonic_align/core.py b/spaces/kdrkdrkdr/HinaTTS/monotonic_align/core.py
deleted file mode 100644
index 1f940605fe4fd0738fa0006149fcba14ef88223a..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/HinaTTS/monotonic_align/core.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
-           nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
-                index = index - 1
diff --git a/spaces/kdrkdrkdr/ProsekaTTS/utils.py b/spaces/kdrkdrkdr/ProsekaTTS/utils.py
deleted file mode 100644
index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/ProsekaTTS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, required=True,
-                        help='Model name')
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/audio.py b/spaces/keithhon/Real-Time-Voice-Cloning/encoder/audio.py
deleted file mode 100644
index 799aa835499ce8b839290f28b2c8ffb629f37565..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/audio.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from scipy.ndimage.morphology import binary_dilation
-from encoder.params_data import *
-from pathlib import Path
-from typing import Optional, Union
-from warnings import warn
-import numpy as np
-import librosa
-import struct
-
-try:
-    import webrtcvad
-except:
-    warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.")
-    webrtcvad=None
-
-int16_max = (2 ** 15) - 1
-
-
-def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
-                   source_sr: Optional[int] = None,
-                   normalize: Optional[bool] = True,
-                   trim_silence: Optional[bool] = True):
-    """
-    Applies the preprocessing operations used in training the Speaker Encoder to a waveform 
-    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
-
-    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 
-    just .wav), either the waveform as a numpy array of floats.
-    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 
-    preprocessing. After preprocessing, the waveform's sampling rate will match the data 
-    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 
-    this argument will be ignored.
-    """
-    # Load the wav from disk if needed
-    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
-        wav, source_sr = librosa.load(str(fpath_or_wav), sr=None)
-    else:
-        wav = fpath_or_wav
-    
-    # Resample the wav if needed
-    if source_sr is not None and source_sr != sampling_rate:
-        wav = librosa.resample(wav, source_sr, sampling_rate)
-        
-    # Apply the preprocessing: normalize volume and shorten long silences 
-    if normalize:
-        wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
-    if webrtcvad and trim_silence:
-        wav = trim_long_silences(wav)
-    
-    return wav
-
-
-def wav_to_mel_spectrogram(wav):
-    """
-    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
-    Note: this not a log-mel spectrogram.
-    """
-    frames = librosa.feature.melspectrogram(
-        wav,
-        sampling_rate,
-        n_fft=int(sampling_rate * mel_window_length / 1000),
-        hop_length=int(sampling_rate * mel_window_step / 1000),
-        n_mels=mel_n_channels
-    )
-    return frames.astype(np.float32).T
-
-
-def trim_long_silences(wav):
-    """
-    Ensures that segments without voice in the waveform remain no longer than a 
-    threshold determined by the VAD parameters in params.py.
-
-    :param wav: the raw waveform as a numpy array of floats 
-    :return: the same waveform with silences trimmed away (length <= original wav length)
-    """
-    # Compute the voice detection window size
-    samples_per_window = (vad_window_length * sampling_rate) // 1000
-    
-    # Trim the end of the audio to have a multiple of the window size
-    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
-    
-    # Convert the float waveform to 16-bit mono PCM
-    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
-    
-    # Perform voice activation detection
-    voice_flags = []
-    vad = webrtcvad.Vad(mode=3)
-    for window_start in range(0, len(wav), samples_per_window):
-        window_end = window_start + samples_per_window
-        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
-                                         sample_rate=sampling_rate))
-    voice_flags = np.array(voice_flags)
-    
-    # Smooth the voice detection with a moving average
-    def moving_average(array, width):
-        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
-        ret = np.cumsum(array_padded, dtype=float)
-        ret[width:] = ret[width:] - ret[:-width]
-        return ret[width - 1:] / width
-    
-    audio_mask = moving_average(voice_flags, vad_moving_average_width)
-    audio_mask = np.round(audio_mask).astype(np.bool)
-    
-    # Dilate the voiced regions
-    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
-    audio_mask = np.repeat(audio_mask, samples_per_window)
-    
-    return wav[audio_mask == True]
-
-
-def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
-    if increase_only and decrease_only:
-        raise ValueError("Both increase only and decrease only are set")
-    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
-    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
-        return wav
-    return wav * (10 ** (dBFS_change / 20))
diff --git a/spaces/kepl/gpt/g4f/Provider/Providers/hteyun.py b/spaces/kepl/gpt/g4f/Provider/Providers/hteyun.py
deleted file mode 100644
index a6eba7c00331d720afb47215e818f5900d4aedcf..0000000000000000000000000000000000000000
--- a/spaces/kepl/gpt/g4f/Provider/Providers/hteyun.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://hteyun.com'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-        'Accept': 'application/json, text/plain, */*',
-        'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5,zh;q=0.4',
-        'Origin': 'https://hteyun.com',
-        'Referer': 'https://hteyun.com/chat/',
-    }
-    data = {
-        'messages': messages,
-        'model': model,
-        'systemMessage': 'You are ChatGPT, a large language model trained by OpenAI. Follow the user\'s instructions carefully. Respond using russian language.',
-        'temperature': 0.7,
-        'presence_penalty': 0,
-    }
-    response = requests.post(url + '/api/chat-stream', json=data, headers=headers, stream=True)
-    print(response.json())
-
-    # Извлечение текста из response
-    return response.json()['text']
-
-
-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]])
diff --git a/spaces/keras-io/Attention_based_Deep_Multiple_Instance_Learning/app.py b/spaces/keras-io/Attention_based_Deep_Multiple_Instance_Learning/app.py
deleted file mode 100644
index 2e43266e1cb6de8724d64229cdff44d6a7872f34..0000000000000000000000000000000000000000
--- a/spaces/keras-io/Attention_based_Deep_Multiple_Instance_Learning/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-from matplotlib import pyplot as plt
-
-import numpy as np
-import tensorflow as tf
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-
-
-model = from_pretrained_keras('keras-io/attention_mil')
-
-# functions for inference
-IMG_SIZE = 28
-
-# resize the image and it to a float between 0,1
-def plot(input_images=None, predictions=None, attention_weights=None):
-    bag_class = np.argmax(predictions)
-    bag_class = 'This set of image does not contain number 8' if bag_class == 0 else 'This set of image contains number 8'
-
-    # attention_weights = [round(i, 2) for i in attention_weights]
-    prob_str = f"Each image probability: {attention_weights[0]:.2f}, {attention_weights[1]:.2f}, {attention_weights[2]:.2f}"
-
-    if input_images is not None:
-        figure = plt.figure(figsize=(8, 8))
-        for j in range(len(input_images)):
-            image = input_images[j]
-            figure.add_subplot(1, len(input_images), j + 1)
-            plt.grid(False)
-            if attention_weights is not None:
-                plt.title(f"prob={attention_weights[j]:.2f}")
-            plt.imshow(np.squeeze(input_images[j]))
-        return [bag_class, plt.gcf()]
-
-    return [bag_class, prob_str]
-
-
-def preprocess_image(image):
-    # image = image[:, :, 0]
-    image = image / 255.0
-    image = np.expand_dims(image, axis = 0)
-    return image
-
-def infer(input_images_1, input_images_2, input_images_3):
-    if (input_images_1 is not None) & (input_images_2 is not None) & (input_images_3 is not None):
-        # Normalize input data
-        input_images_1 = preprocess_image(input_images_1)
-        input_images_2 = preprocess_image(input_images_2)
-        input_images_3 = preprocess_image(input_images_3)
-
-        # Collect info per model.
-        prediction = model.predict([input_images_1, input_images_2, input_images_3])
-        prediction = np.squeeze(np.swapaxes(prediction, 1, 0))
-        intermediate_model = keras.Model(model.input, model.get_layer("alpha").output)
-        intermediate_predictions = intermediate_model.predict([input_images_1, input_images_2, input_images_3])
-        attention_weights = np.squeeze(np.swapaxes(intermediate_predictions, 1, 0))
-
-        return plot(
-            [input_images_1, input_images_2, input_images_3],
-            predictions=prediction,
-            attention_weights=attention_weights
-        )
-
-# get the inputs
-input1 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='First image', show_label=True, visible=True)
-input2 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='Second image', show_label=True, visible=True)
-input3 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='Third image', show_label=True, visible=True)
-# the app outputs two segmented images
-output = [gr.Label(), gr.Plot()]
-# output = [gr.Plot()]
-# it's good practice to pass examples, description and a title to guide users
-title = 'Bag of Image Classification'
-description = 'This is the demo for Keras Implementation of Classification using Attention-based Deep Multiple Instance Learning (MIL). The model will try to predict whether number 8 is within the set of input images. As it was trained on MNIST dataset, please use MNIST image for precise result.'    
-
-article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on the following Keras example <a href=\"https://keras.io/examples/vision/attention_mil_classification\"> Classification using Attention-based Deep Multiple Instance Learning (MIL)</a> by <a href=\"https://www.linkedin.com/in/mohamadjaber1\">Mohamad Jaber.</a> <br> Check out the model <a href=\"https://huggingface.co/keras-io/attention_mil\">here</a>"
-
-gr_interface = gr.Interface(
-    infer, inputs=[input1, input2, input3], outputs=output, allow_flagging='never',
-    analytics_enabled=False, title=title, description=description,  article=article,
-    # examples = [[f'{i}.png' for i in range(0,3)], [f'{i}.png' for i in range(3,6)], [f'{i}.png' for i in range(6,9)], '9.png']
-    examples = [['samples/0.png', 'samples/6.png', 'samples/2.png'], ['samples/1.png','samples/2.png', 'samples/3.png'],
-                ['samples/4.png', 'samples/8.png', 'samples/7.png'], ['samples/8.png', 'samples/0.png', 'samples/9.png'],
-                ['samples/5.png', 'samples/6.png', 'samples/3.png'], ['samples/7.png', 'samples/8.png', 'samples/9.png']]
-)
-            
-gr_interface.launch(enable_queue=True, debug=False)
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/speaker_encoder/__init__.py b/spaces/kevinwang676/ChatGLM2-SadTalker/speaker_encoder/__init__.py
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/speaker_encoder/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/kevinwang676/ControlNet-with-GPT-4/app_canny.py b/spaces/kevinwang676/ControlNet-with-GPT-4/app_canny.py
deleted file mode 100644
index 8d987f17e272bd4ab7e2cca5e2f20db3dc5d1791..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ControlNet-with-GPT-4/app_canny.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from settings import (
-    DEFAULT_IMAGE_RESOLUTION,
-    DEFAULT_NUM_IMAGES,
-    MAX_IMAGE_RESOLUTION,
-    MAX_NUM_IMAGES,
-    MAX_SEED,
-)
-from utils import randomize_seed_fn
-
-def create_demo(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Image Prompt")
-                run_button = gr.Button("Generate")
-                with gr.Accordion("Advanced options", open=False):
-                    num_samples = gr.Slider(
-                        label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
-                    )
-                    image_resolution = gr.Slider(
-                        label="Image resolution",
-                        minimum=256,
-                        maximum=MAX_IMAGE_RESOLUTION,
-                        value=DEFAULT_IMAGE_RESOLUTION,
-                        step=256,
-                    )
-                    canny_low_threshold = gr.Slider(
-                        label="Canny low threshold", minimum=1, maximum=255, value=100, step=1
-                    )
-                    canny_high_threshold = gr.Slider(
-                        label="Canny high threshold", minimum=1, maximum=255, value=200, step=1
-                    )
-                    num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
-                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
-                    n_prompt = gr.Textbox(
-                        label="Negative prompt",
-                        value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
-                    )
-            with gr.Column():
-                result = gr.Gallery(label="Output", show_label=False, columns=2, object_fit="scale-down")
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            canny_low_threshold,
-            canny_high_threshold,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name=False,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name="canny",
-        )
-    return demo
-
-
-if __name__ == "__main__":
-    from model import Model
-
-    model = Model(task_name="Canny")
-    demo = create_demo(model.process_canny)
-    demo.queue().launch()
diff --git a/spaces/kios/Natural_Disaster_Classification/app.py b/spaces/kios/Natural_Disaster_Classification/app.py
deleted file mode 100644
index 67fc94fa6d330690fc308240a375ff01458b8654..0000000000000000000000000000000000000000
--- a/spaces/kios/Natural_Disaster_Classification/app.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jul 22 08:51:28 2022
-@author: dshian01
-"""
-#%%
-
-# Import required libraries
-import numpy as np
-import cv2
-from keras.models import load_model
-import tensorflow as tf
-from sklearn.metrics import classification_report,  accuracy_score
-import seaborn as sns
-import matplotlib.pyplot as plt
-import os
-import gradio as gr
-import csv
-import pandas as pd
-
-#%% 
-mean = [106.17, 106.499, 105.324]
-std = [64.173, 64.123, 64.142]
-
-# Define the class names
-class_names = ['Collapsed Buildings', 'Flood', 'Normal', 'Wildfire']
-
-# Load the model that was trained
-model_directory = r'Model.best.hdf5'
-model = load_model(model_directory)
-
-def classify_video(video_directory, start_sec, duration):
-
-    # Check that video exists
-    if video_directory is None:
-        raise RuntimeError('No video found!')
-
-    # Define video output file
-    outputvideo = 'Natural_Disasters_Predictions.mp4'
-
-    # Delete existing output files
-    if os.path.exists(outputvideo):
-        os.remove(outputvideo)
-    if os.path.exists('output_video.mp4'):
-        os.remove('output_video.mp4')
-    if os.path.exists('Classes.csv'):
-        os.remove('Classes.csv')
-
-    capture_video = cv2.VideoCapture(video_directory)
-
-    # Find start and end frame 
-    total_frames = round(capture_video.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = capture_video.get(cv2.CAP_PROP_FPS) 
-    start_frame = round(start_sec * fps)
-    end_frame = round(start_frame + (duration * fps))
-    frame_id = 0
-    
-    tags = []
-
-    # Ensure selected start frame exists in the video
-    if start_frame > total_frames:
-            raise RuntimeError('Start second is out of bounds!')
-
-    writer = None
-    (Width, Height) = (None, None)
-    while True:
-        (taken, frame) = capture_video.read()
-        if not taken:
-            break
-        if Width is None or Height is None:
-            (Width, Height) = frame.shape[:2]
-
-        #Break early if current frame has exceeded the last frame of the video
-
-        if frame_id > end_frame:
-            break
-        
-        if  start_frame <= frame_id:
-
-            output = frame.copy()
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            frame = cv2.resize(frame, (224, 224)).astype('float32')
-            frame2 = (frame-mean)/std
-
-            preds_dis = model.predict(np.expand_dims(frame2, axis=0))[0]
-            i = np.argmax(preds_dis)
-            label_dis = class_names[i]
-            score = preds_dis[i]
-            text_dis = 'Disaster Type: {}'.format(label_dis)
-            
-            tags.append([str(label_dis), str(score), str(frame_id)])
-
-            cv2.putText(output, text_dis, (45, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.15, (255,0,0),4)
-
-            if writer is None:
-                fourcc = cv2.VideoWriter_fourcc(*'MP4V')
-                writer = cv2.VideoWriter(outputvideo, fourcc, 30, (Height, Width))
-
-            writer.write(output)
-
-        frame_id += 1
-
-    print('Finalizing video analysis....')
-    writer.release()
-    capture_video.release()
-    cv2.destroyAllWindows()
-    
-    # Use FFmpeg to change fourcc of output video to h264 in order to display it in a web page 
-    # in case video isn't displayed correctly
-    os.system('ffmpeg -i Natural_Disasters_Predictions.mp4 -vcodec libx264 -f mp4 output_video.mp4')
-
-    # Create csv file and insert classes
-    with open('Classes.csv', 'w') as f:
-        write = csv.writer(f)
-        write.writerow(['Class', 'Score', 'Frame'])
-        write.writerows(tags)
-        f.close()
-
-    df = pd.DataFrame(tags, columns = ['Class', 'Score', 'Frame'])
-
-    frames_per_class = []
-
-    for _class in class_names:
-        frames_per_class.append(len(df[df['Class'].str.fullmatch(_class) == True]))
-
-    plt.switch_backend('agg')
-    fig = plt.figure()
-
-    plt.bar(class_names, frames_per_class, color = ['green','blue','purple','brown'])
-    plt.title('Frames per class')
-    plt.ylabel('Frames')
-    plt.xlabel('Disaster')
-
-    return 'output_video.mp4', fig, 'Classes.csv' , tags[:10]
-
-#%%
-# def model_evaluate_report(model, model_name, x, y_data):
-#     y = np.argmax(y_data, axis=1)
-#     # preds_prob = model.predict(x)
-#     preds = np.argmax(model.predict(x), axis=1)
-#     print('\nAccuracy on Test Data: ', accuracy_score(y, preds))
-#     print('\nNumber of correctly identified imgaes: ',
-#           accuracy_score(y, preds, normalize=False), '\n')
-
-#     print('Classification Report: \n', classification_report(y, preds))
-#     cm = tf.math.confusion_matrix(labels=y, predictions=preds)
-
-#     ax = plt.subplot()
-#     sns.heatmap(cm, annot=True, fmt='g', ax=ax);  # annot=True to annotate cells, ftm='g' to disable scientific notation
-
-#     # labels, title and ticks
-#     ax.set_xlabel('Predicted labels')
-#     ax.set_ylabel('True labels')
-#     ax.set_title('Confusion Matrix')
-#     ax.xaxis.set_ticklabels(['Collapsed Buildings', 'Flood', 'Normal', 'Wildfire'], fontsize=8)
-#     ax.yaxis.set_ticklabels(['Collapsed Buildings', 'Flood', 'Normal', 'Wildfire'], fontsize=8)
-#     print('Confusion Matrix has been saved')
-#     return preds
-
-"""Predict the disaster of an image from a folder as jpg"""
-# Predict image from a folder directory
-def predict_img(img):
-
-    # Check that image exists
-    if img is None:
-        raise RuntimeError('No image found!')
-
-    # Delete existing output files
-    if os.path.exists('Class.csv'):
-        os.remove('Class.csv')
-    if os.path.exists('output_image.png'):
-        os.remove('output_image.png')
-
-    img  = cv2.resize(img, (224,224))
-    
-    img_standardized = (img-mean)/std
-    
-    img_standardized = np.expand_dims(img_standardized, axis=0)
-
-    predicted_model = model.predict(img_standardized)
-    
-    preds = np.argmax(predicted_model, axis=1)[0]
-
-    label = class_names[preds]
-    score = predicted_model[0][preds]
-
-    tags = [[str(label), str(score)]]
-
-    # Create csv file and insert class
-    with open('Class.csv', 'w') as f:
-        write = csv.writer(f)
-        write.writerow(['Class', 'Score'])
-        write.writerows(tags)
-        f.close()
-
-    # Showing image
-    plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')
-    plt.axis('off')
-    plt.title('Disaster: {}'.format(label))
-    plt.savefig('output_image.png')
-
-    return 'output_image.png', 'Class.csv' , tags[:10]
-
-# we need to standardize the frames because the training was done on standardized data
-# frame = (frame - mean) / std
-
-#%%
-
-# image interface
-image_interface = gr.Interface(fn = predict_img,
-                                     inputs = gr.Image(),
-                                     outputs = [gr.Image(label = 'Image'), gr.File(label = 'All classes') ,gr.Dataframe(label = 'Class', headers = ['Class', 'Score'])],
-                                     allow_flagging = False,
-                                     cache_examples= False,             
-                                     examples = [['examples/neighborhood.png'],['examples/flood.png'],['examples/wildfire.png'],['examples/collapsed_building.png']])
-
-# video interface
-video_interface = gr.Interface(fn = classify_video,
-                                    inputs = [gr.Video(),
-                                    gr.Slider(0, 60, value = 0, label = 'Start Second', step = 1),
-                                    gr.Slider(1, 10, value = 4, label = 'Duration', step = 1)],
-                                    outputs = [gr.Video(label = 'Video'), gr.Plot(label='Frames per class'), gr.File(label = 'All classes'), gr.DataFrame(label = 'Ten first classes', headers = ['Distaster', 'Score', 'Frame'])],
-                                    allow_flagging = False,
-                                    cache_examples= False,
-                                    examples = [['examples/natural_disasters.mp4', 9, 10]])
-
-gr.TabbedInterface(
-    [video_interface, image_interface],
-    ['Scan Video', 'Scan Images']
-).launch()
-
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/pointer_generator/postprocess.py b/spaces/koajoel/PolyFormer/fairseq/examples/pointer_generator/postprocess.py
deleted file mode 100644
index b213aed80fd1e3d86f975256fcb7d9d4c16ca857..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/pointer_generator/postprocess.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import re
-import sys
-
-
-class OOVIndexError(IndexError):
-    def __init__(self, pos, source_seq, target_seq):
-        super(OOVIndexError, self).__init__(
-            "A <unk-N> tag in the target sequence refers to a position that is "
-            "outside the source sequence. Most likely there was a mismatch in "
-            "provided source and target sequences. Otherwise this would mean that "
-            "the pointing mechanism somehow attended to a position that is past "
-            "the actual sequence end."
-        )
-        self.source_pos = pos
-        self.source_seq = source_seq
-        self.target_seq = target_seq
-
-
-def replace_oovs(source_in, target_in, target_out):
-    """Replaces <unk-N> tokens in the target text with the corresponding word in
-    the source text.
-    """
-
-    oov_re = re.compile("^<unk-([0-9]+)>$")
-
-    for source_seq, target_seq in zip(source_in, target_in):
-        target_seq_out = []
-
-        pos_to_word = source_seq.strip().split()
-        for token in target_seq.strip().split():
-            m = oov_re.match(token)
-            if m:
-                pos = int(m.group(1))
-                if pos >= len(pos_to_word):
-                    raise OOVIndexError(pos, source_seq, target_seq)
-                token_out = pos_to_word[pos]
-            else:
-                token_out = token
-            target_seq_out.append(token_out)
-        target_out.write(" ".join(target_seq_out) + "\n")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Replaces <unk-N> tokens in target sequences with words from "
-        "the corresponding position in the source sequence."
-    )
-    parser.add_argument(
-        "--source", type=str, help="text file with source sequences", required=True
-    )
-    parser.add_argument(
-        "--target", type=str, help="text file with target sequences", required=True
-    )
-    parser.add_argument(
-        "--target-out",
-        type=str,
-        help="where to write target sequences without <unk-N> " "entries",
-        required=True,
-    )
-    args = parser.parse_args()
-
-    target_in = (
-        open(args.target, "r", encoding="utf-8") if args.target is not None else None
-    )
-    target_out = (
-        open(args.target_out, "w", encoding="utf-8")
-        if args.target_out is not None
-        else None
-    )
-    with open(args.source, "r", encoding="utf-8") as source_in, open(
-        args.target, "r", encoding="utf-8"
-    ) as target_in, open(args.target_out, "w", encoding="utf-8") as target_out:
-        replace_oovs(source_in, target_in, target_out)
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except OOVIndexError as e:
-        print(e, file=sys.stderr)
-        print("Source sequence:", e.source_seq.strip(), file=sys.stderr)
-        print("Target sequence:", e.target_seq.strip(), file=sys.stderr)
-        print(
-            "Source sequence length:",
-            len(e.source_seq.strip().split()),
-            file=sys.stderr,
-        )
-        print("The offending tag points to:", e.source_pos)
-        sys.exit(2)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/evaluation/eval_f0.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/evaluation/eval_f0.py
deleted file mode 100644
index df721d683113b44957149cfc3cddaba36520a22c..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/evaluation/eval_f0.py
+++ /dev/null
@@ -1,266 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Signal processing-based evaluation using waveforms
-"""
-import numpy as np
-import os.path as op
-
-import torchaudio
-import tqdm
-from tabulate import tabulate
-
-from examples.speech_synthesis.utils import (
-    gross_pitch_error, voicing_decision_error, f0_frame_error
-)
-from examples.speech_synthesis.evaluation.eval_sp import load_eval_spec
-
-
-def difference_function(x, n, tau_max):
-    """
-    Compute difference function of data x. This solution is implemented directly
-    with Numpy fft.
-
-
-    :param x: audio data
-    :param n: length of data
-    :param tau_max: integration window size
-    :return: difference function
-    :rtype: list
-    """
-
-    x = np.array(x, np.float64)
-    w = x.size
-    tau_max = min(tau_max, w)
-    x_cumsum = np.concatenate((np.array([0.]), (x * x).cumsum()))
-    size = w + tau_max
-    p2 = (size // 32).bit_length()
-    nice_numbers = (16, 18, 20, 24, 25, 27, 30, 32)
-    size_pad = min(x * 2 ** p2 for x in nice_numbers if x * 2 ** p2 >= size)
-    fc = np.fft.rfft(x, size_pad)
-    conv = np.fft.irfft(fc * fc.conjugate())[:tau_max]
-    return x_cumsum[w:w - tau_max:-1] + x_cumsum[w] - x_cumsum[:tau_max] - \
-        2 * conv
-
-
-def cumulative_mean_normalized_difference_function(df, n):
-    """
-    Compute cumulative mean normalized difference function (CMND).
-
-    :param df: Difference function
-    :param n: length of data
-    :return: cumulative mean normalized difference function
-    :rtype: list
-    """
-
-    # scipy method
-    cmn_df = df[1:] * range(1, n) / np.cumsum(df[1:]).astype(float)
-    return np.insert(cmn_df, 0, 1)
-
-
-def get_pitch(cmdf, tau_min, tau_max, harmo_th=0.1):
-    """
-    Return fundamental period of a frame based on CMND function.
-
-    :param cmdf: Cumulative Mean Normalized Difference function
-    :param tau_min: minimum period for speech
-    :param tau_max: maximum period for speech
-    :param harmo_th: harmonicity threshold to determine if it is necessary to
-    compute pitch frequency
-    :return: fundamental period if there is values under threshold, 0 otherwise
-    :rtype: float
-    """
-    tau = tau_min
-    while tau < tau_max:
-        if cmdf[tau] < harmo_th:
-            while tau + 1 < tau_max and cmdf[tau + 1] < cmdf[tau]:
-                tau += 1
-            return tau
-        tau += 1
-
-    return 0    # if unvoiced
-
-
-def compute_yin(sig, sr, w_len=512, w_step=256, f0_min=100, f0_max=500,
-                harmo_thresh=0.1):
-    """
-
-    Compute the Yin Algorithm. Return fundamental frequency and harmonic rate.
-
-    https://github.com/NVIDIA/mellotron adaption of
-    https://github.com/patriceguyot/Yin
-
-    :param sig: Audio signal (list of float)
-    :param sr: sampling rate (int)
-    :param w_len: size of the analysis window (samples)
-    :param w_step: size of the lag between two consecutives windows (samples)
-    :param f0_min: Minimum fundamental frequency that can be detected (hertz)
-    :param f0_max: Maximum fundamental frequency that can be detected (hertz)
-    :param harmo_thresh: Threshold of detection. The yalgorithmù return the
-    first minimum of the CMND function below this threshold.
-
-    :returns:
-
-        * pitches: list of fundamental frequencies,
-        * harmonic_rates: list of harmonic rate values for each fundamental
-        frequency value (= confidence value)
-        * argmins: minimums of the Cumulative Mean Normalized DifferenceFunction
-        * times: list of time of each estimation
-    :rtype: tuple
-    """
-
-    tau_min = int(sr / f0_max)
-    tau_max = int(sr / f0_min)
-
-    # time values for each analysis window
-    time_scale = range(0, len(sig) - w_len, w_step)
-    times = [t/float(sr) for t in time_scale]
-    frames = [sig[t:t + w_len] for t in time_scale]
-
-    pitches = [0.0] * len(time_scale)
-    harmonic_rates = [0.0] * len(time_scale)
-    argmins = [0.0] * len(time_scale)
-
-    for i, frame in enumerate(frames):
-        # Compute YIN
-        df = difference_function(frame, w_len, tau_max)
-        cm_df = cumulative_mean_normalized_difference_function(df, tau_max)
-        p = get_pitch(cm_df, tau_min, tau_max, harmo_thresh)
-
-        # Get results
-        if np.argmin(cm_df) > tau_min:
-            argmins[i] = float(sr / np.argmin(cm_df))
-        if p != 0:  # A pitch was found
-            pitches[i] = float(sr / p)
-            harmonic_rates[i] = cm_df[p]
-        else:  # No pitch, but we compute a value of the harmonic rate
-            harmonic_rates[i] = min(cm_df)
-
-    return pitches, harmonic_rates, argmins, times
-
-
-def extract_f0(samples):
-    f0_samples = []
-    for sample in tqdm.tqdm(samples):
-        if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]):
-            f0_samples.append(None)
-            continue
-
-        # assume single channel
-        yref, sr = torchaudio.load(sample["ref"])
-        ysyn, _sr = torchaudio.load(sample["syn"])
-        yref, ysyn = yref[0], ysyn[0]
-        assert sr == _sr, f"{sr} != {_sr}"
-
-        yref_f0 = compute_yin(yref, sr)
-        ysyn_f0 = compute_yin(ysyn, sr)
-
-        f0_samples += [
-            {
-                "ref": yref_f0,
-                "syn": ysyn_f0
-            }
-        ]
-
-    return f0_samples
-
-
-def eval_f0_error(samples, distortion_fn):
-    results = []
-    for sample in tqdm.tqdm(samples):
-        if sample is None:
-            results.append(None)
-            continue
-        # assume single channel
-        yref_f, _, _, yref_t = sample["ref"]
-        ysyn_f, _, _, ysyn_t = sample["syn"]
-
-        yref_f = np.array(yref_f)
-        yref_t = np.array(yref_t)
-        ysyn_f = np.array(ysyn_f)
-        ysyn_t = np.array(ysyn_t)
-
-        distortion = distortion_fn(yref_t, yref_f, ysyn_t, ysyn_f)
-        results.append((distortion.item(),
-                        len(yref_f),
-                        len(ysyn_f)
-                        ))
-    return results
-
-
-def eval_gross_pitch_error(samples):
-    return eval_f0_error(samples, gross_pitch_error)
-
-
-def eval_voicing_decision_error(samples):
-    return eval_f0_error(samples, voicing_decision_error)
-
-
-def eval_f0_frame_error(samples):
-    return eval_f0_error(samples, f0_frame_error)
-
-
-def print_results(results, show_bin):
-    results = np.array(list(filter(lambda x: x is not None, results)))
-
-    np.set_printoptions(precision=3)
-
-    def _print_result(results):
-        res = {
-            "nutt": len(results),
-            "error": results[:, 0].mean(),
-            "std": results[:, 0].std(),
-            "dur_ref": int(results[:, 1].sum()),
-            "dur_syn": int(results[:, 2].sum()),
-        }
-        print(tabulate([res.values()], res.keys(), floatfmt=".4f"))
-
-    print(">>>> ALL")
-    _print_result(results)
-
-    if show_bin:
-        edges = [0, 200, 400, 600, 800, 1000, 2000, 4000]
-        for i in range(1, len(edges)):
-            mask = np.logical_and(results[:, 1] >= edges[i-1],
-                                  results[:, 1] < edges[i])
-            if not mask.any():
-                continue
-            bin_results = results[mask]
-            print(f">>>> ({edges[i-1]}, {edges[i]})")
-            _print_result(bin_results)
-
-
-def main(eval_f0, gpe, vde, ffe, show_bin):
-    samples = load_eval_spec(eval_f0)
-    if gpe or vde or ffe:
-        f0_samples = extract_f0(samples)
-
-    if gpe:
-        print("===== Evaluate Gross Pitch Error =====")
-        results = eval_gross_pitch_error(f0_samples)
-        print_results(results, show_bin)
-    if vde:
-        print("===== Evaluate Voicing Decision Error =====")
-        results = eval_voicing_decision_error(f0_samples)
-        print_results(results, show_bin)
-    if ffe:
-        print("===== Evaluate F0 Frame Error =====")
-        results = eval_f0_frame_error(f0_samples)
-        print_results(results, show_bin)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("eval_f0")
-    parser.add_argument("--gpe", action="store_true")
-    parser.add_argument("--vde", action="store_true")
-    parser.add_argument("--ffe", action="store_true")
-    parser.add_argument("--show-bin", action="store_true")
-    args = parser.parse_args()
-
-    main(args.eval_f0, args.gpe, args.vde, args.ffe, args.show_bin)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/evaluation/eval_sp.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/evaluation/eval_sp.py
deleted file mode 100644
index 702c4980389624f788abc0b42cdf54757a52512f..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/evaluation/eval_sp.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-"""
-Signal processing-based evaluation using waveforms
-"""
-
-import csv
-import numpy as np
-import os.path as op
-
-import torch
-import tqdm
-from tabulate import tabulate
-import torchaudio
-
-from examples.speech_synthesis.utils import batch_mel_spectral_distortion
-from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion
-
-
-def load_eval_spec(path):
-    with open(path) as f:
-        reader = csv.DictReader(f, delimiter='\t')
-        samples = list(reader)
-    return samples
-
-
-def eval_distortion(samples, distortion_fn, device="cuda"):
-    nmiss = 0
-    results = []
-    for sample in tqdm.tqdm(samples):
-        if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]):
-            nmiss += 1
-            results.append(None)
-            continue
-        # assume single channel
-        yref, sr = torchaudio.load(sample["ref"])
-        ysyn, _sr = torchaudio.load(sample["syn"])
-        yref, ysyn = yref[0].to(device), ysyn[0].to(device)
-        assert sr == _sr, f"{sr} != {_sr}"
-
-        distortion, extra = distortion_fn([yref], [ysyn], sr, None)[0]
-        _, _, _, _, _, pathmap = extra
-        nins = torch.sum(pathmap.sum(dim=1) - 1)  # extra frames in syn
-        ndel = torch.sum(pathmap.sum(dim=0) - 1)  # missing frames from syn
-        results.append(
-            (distortion.item(),  # path distortion
-             pathmap.size(0),  # yref num frames
-             pathmap.size(1),  # ysyn num frames
-             pathmap.sum().item(),  # path length
-             nins.item(),  # insertion
-             ndel.item(),  # deletion
-             )
-        )
-    return results
-
-
-def eval_mel_cepstral_distortion(samples, device="cuda"):
-    return eval_distortion(samples, batch_mel_cepstral_distortion, device)
-
-
-def eval_mel_spectral_distortion(samples, device="cuda"):
-    return eval_distortion(samples, batch_mel_spectral_distortion, device)
-
-
-def print_results(results, show_bin):
-    results = np.array(list(filter(lambda x: x is not None, results)))
-
-    np.set_printoptions(precision=3)
-
-    def _print_result(results):
-        dist, dur_ref, dur_syn, dur_ali, nins, ndel = results.sum(axis=0)
-        res = {
-            "nutt": len(results),
-            "dist": dist,
-            "dur_ref": int(dur_ref),
-            "dur_syn": int(dur_syn),
-            "dur_ali": int(dur_ali),
-            "dist_per_ref_frm": dist/dur_ref,
-            "dist_per_syn_frm": dist/dur_syn,
-            "dist_per_ali_frm": dist/dur_ali,
-            "ins": nins/dur_ref,
-            "del": ndel/dur_ref,
-        }
-        print(tabulate(
-            [res.values()],
-            res.keys(),
-            floatfmt=".4f"
-        ))
-
-    print(">>>> ALL")
-    _print_result(results)
-
-    if show_bin:
-        edges = [0, 200, 400, 600, 800, 1000, 2000, 4000]
-        for i in range(1, len(edges)):
-            mask = np.logical_and(results[:, 1] >= edges[i-1],
-                                  results[:, 1] < edges[i])
-            if not mask.any():
-                continue
-            bin_results = results[mask]
-            print(f">>>> ({edges[i-1]}, {edges[i]})")
-            _print_result(bin_results)
-
-
-def main(eval_spec, mcd, msd, show_bin):
-    samples = load_eval_spec(eval_spec)
-    device = "cpu"
-    if mcd:
-        print("===== Evaluate Mean Cepstral Distortion =====")
-        results = eval_mel_cepstral_distortion(samples, device)
-        print_results(results, show_bin)
-    if msd:
-        print("===== Evaluate Mean Spectral Distortion =====")
-        results = eval_mel_spectral_distortion(samples, device)
-        print_results(results, show_bin)
-
-
-if __name__ == "__main__":
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument("eval_spec")
-    parser.add_argument("--mcd", action="store_true")
-    parser.add_argument("--msd", action="store_true")
-    parser.add_argument("--show-bin", action="store_true")
-    args = parser.parse_args()
-
-    main(args.eval_spec, args.mcd, args.msd, args.show_bin)
diff --git a/spaces/kquote03/lama-video-watermark-remover/bin/paper_runfiles/predict_inner_features.sh b/spaces/kquote03/lama-video-watermark-remover/bin/paper_runfiles/predict_inner_features.sh
deleted file mode 100644
index 864c1a0fca8b93b2a193656e45ff55f6a051eb8c..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/bin/paper_runfiles/predict_inner_features.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/env bash
-
-# paths to data are valid for mml7
-
-source "$(dirname $0)/env.sh"
-
-"$BINDIR/predict_inner_features.py" \
-    -cn default_inner_features_ffc \
-    model.path="/data/inpainting/paper_data/final_models/ours/r.suvorov_2021-03-05_17-34-05_train_ablv2_work_ffc075_resume_epoch39" \
-    indir="/data/inpainting/paper_data/inner_features_vis/input/" \
-    outdir="/data/inpainting/paper_data/inner_features_vis/output/ffc" \
-    dataset.img_suffix=.png
-
-
-"$BINDIR/predict_inner_features.py" \
-    -cn default_inner_features_work \
-    model.path="/data/inpainting/paper_data/final_models/ours/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37" \
-    indir="/data/inpainting/paper_data/inner_features_vis/input/" \
-    outdir="/data/inpainting/paper_data/inner_features_vis/output/work" \
-    dataset.img_suffix=.png
diff --git a/spaces/krishw/MovieExplorer/Data.py b/spaces/krishw/MovieExplorer/Data.py
deleted file mode 100644
index 1601a9a9f49e52c76fc0811f8890ce9fb3d4b931..0000000000000000000000000000000000000000
--- a/spaces/krishw/MovieExplorer/Data.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-st.set_page_config(
-   page_title="Movie Explorer",
-   layout="wide",
-   initial_sidebar_state="expanded",
-)
-
-st.markdown("<h1 style='text-align: center;'>Movie Analysis</h1>", unsafe_allow_html=True)
-movies_df = pd.read_csv('movies2.csv',lineterminator='\n')
-movies_df = movies_df.drop(columns=movies_df.columns[0])
-movies_df = movies_df[movies_df['Release Date'] < "2022-01-01"]
-st.write(movies_df)
-st.caption("Data Source: TMDB")
-cast_df = pd.read_csv('movie_cast.csv')
-
diff --git a/spaces/kukuhtw/AutoGPT/autogpt/commands/web_playwright.py b/spaces/kukuhtw/AutoGPT/autogpt/commands/web_playwright.py
deleted file mode 100644
index 4e388ded203cefb5e24f9116f7fe5b8a94893413..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/autogpt/commands/web_playwright.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Web scraping commands using Playwright"""
-from __future__ import annotations
-
-try:
-    from playwright.sync_api import sync_playwright
-except ImportError:
-    print(
-        "Playwright not installed. Please install it with 'pip install playwright' to use."
-    )
-from bs4 import BeautifulSoup
-
-from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
-
-
-def scrape_text(url: str) -> str:
-    """Scrape text from a webpage
-
-    Args:
-        url (str): The URL to scrape text from
-
-    Returns:
-        str: The scraped text
-    """
-    with sync_playwright() as p:
-        browser = p.chromium.launch()
-        page = browser.new_page()
-
-        try:
-            page.goto(url)
-            html_content = page.content()
-            soup = BeautifulSoup(html_content, "html.parser")
-
-            for script in soup(["script", "style"]):
-                script.extract()
-
-            text = soup.get_text()
-            lines = (line.strip() for line in text.splitlines())
-            chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-            text = "\n".join(chunk for chunk in chunks if chunk)
-
-        except Exception as e:
-            text = f"Error: {str(e)}"
-
-        finally:
-            browser.close()
-
-    return text
-
-
-def scrape_links(url: str) -> str | list[str]:
-    """Scrape links from a webpage
-
-    Args:
-        url (str): The URL to scrape links from
-
-    Returns:
-        Union[str, List[str]]: The scraped links
-    """
-    with sync_playwright() as p:
-        browser = p.chromium.launch()
-        page = browser.new_page()
-
-        try:
-            page.goto(url)
-            html_content = page.content()
-            soup = BeautifulSoup(html_content, "html.parser")
-
-            for script in soup(["script", "style"]):
-                script.extract()
-
-            hyperlinks = extract_hyperlinks(soup, url)
-            formatted_links = format_hyperlinks(hyperlinks)
-
-        except Exception as e:
-            formatted_links = f"Error: {str(e)}"
-
-        finally:
-            browser.close()
-
-    return formatted_links
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/transform.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/transform.py
deleted file mode 100644
index f85b54b73121589cb8de284a5e8efe9d20fefa17..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/transform.py
+++ /dev/null
@@ -1,495 +0,0 @@
-"""Affine 2D transformation matrix class.
-
-The Transform class implements various transformation matrix operations,
-both on the matrix itself, as well as on 2D coordinates.
-
-Transform instances are effectively immutable: all methods that operate on the
-transformation itself always return a new instance. This has as the
-interesting side effect that Transform instances are hashable, ie. they can be
-used as dictionary keys.
-
-This module exports the following symbols:
-
-Transform
-	this is the main class
-Identity
-	Transform instance set to the identity transformation
-Offset
-	Convenience function that returns a translating transformation
-Scale
-	Convenience function that returns a scaling transformation
-
-The DecomposedTransform class implements a transformation with separate
-translate, rotation, scale, skew, and transformation-center components.
-
-:Example:
-
-	>>> t = Transform(2, 0, 0, 3, 0, 0)
-	>>> t.transformPoint((100, 100))
-	(200, 300)
-	>>> t = Scale(2, 3)
-	>>> t.transformPoint((100, 100))
-	(200, 300)
-	>>> t.transformPoint((0, 0))
-	(0, 0)
-	>>> t = Offset(2, 3)
-	>>> t.transformPoint((100, 100))
-	(102, 103)
-	>>> t.transformPoint((0, 0))
-	(2, 3)
-	>>> t2 = t.scale(0.5)
-	>>> t2.transformPoint((100, 100))
-	(52.0, 53.0)
-	>>> import math
-	>>> t3 = t2.rotate(math.pi / 2)
-	>>> t3.transformPoint((0, 0))
-	(2.0, 3.0)
-	>>> t3.transformPoint((100, 100))
-	(-48.0, 53.0)
-	>>> t = Identity.scale(0.5).translate(100, 200).skew(0.1, 0.2)
-	>>> t.transformPoints([(0, 0), (1, 1), (100, 100)])
-	[(50.0, 100.0), (50.550167336042726, 100.60135501775433), (105.01673360427253, 160.13550177543362)]
-	>>>
-"""
-
-import math
-from typing import NamedTuple
-from dataclasses import dataclass
-
-
-__all__ = ["Transform", "Identity", "Offset", "Scale", "DecomposedTransform"]
-
-
-_EPSILON = 1e-15
-_ONE_EPSILON = 1 - _EPSILON
-_MINUS_ONE_EPSILON = -1 + _EPSILON
-
-
-def _normSinCos(v):
-    if abs(v) < _EPSILON:
-        v = 0
-    elif v > _ONE_EPSILON:
-        v = 1
-    elif v < _MINUS_ONE_EPSILON:
-        v = -1
-    return v
-
-
-class Transform(NamedTuple):
-
-    """2x2 transformation matrix plus offset, a.k.a. Affine transform.
-    Transform instances are immutable: all transforming methods, eg.
-    rotate(), return a new Transform instance.
-
-    :Example:
-
-            >>> t = Transform()
-            >>> t
-            <Transform [1 0 0 1 0 0]>
-            >>> t.scale(2)
-            <Transform [2 0 0 2 0 0]>
-            >>> t.scale(2.5, 5.5)
-            <Transform [2.5 0 0 5.5 0 0]>
-            >>>
-            >>> t.scale(2, 3).transformPoint((100, 100))
-            (200, 300)
-
-    Transform's constructor takes six arguments, all of which are
-    optional, and can be used as keyword arguments::
-
-            >>> Transform(12)
-            <Transform [12 0 0 1 0 0]>
-            >>> Transform(dx=12)
-            <Transform [1 0 0 1 12 0]>
-            >>> Transform(yx=12)
-            <Transform [1 0 12 1 0 0]>
-
-    Transform instances also behave like sequences of length 6::
-
-            >>> len(Identity)
-            6
-            >>> list(Identity)
-            [1, 0, 0, 1, 0, 0]
-            >>> tuple(Identity)
-            (1, 0, 0, 1, 0, 0)
-
-    Transform instances are comparable::
-
-            >>> t1 = Identity.scale(2, 3).translate(4, 6)
-            >>> t2 = Identity.translate(8, 18).scale(2, 3)
-            >>> t1 == t2
-            1
-
-    But beware of floating point rounding errors::
-
-            >>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
-            >>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
-            >>> t1
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> t2
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> t1 == t2
-            0
-
-    Transform instances are hashable, meaning you can use them as
-    keys in dictionaries::
-
-            >>> d = {Scale(12, 13): None}
-            >>> d
-            {<Transform [12 0 0 13 0 0]>: None}
-
-    But again, beware of floating point rounding errors::
-
-            >>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
-            >>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
-            >>> t1
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> t2
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> d = {t1: None}
-            >>> d
-            {<Transform [0.2 0 0 0.3 0.08 0.18]>: None}
-            >>> d[t2]
-            Traceback (most recent call last):
-              File "<stdin>", line 1, in ?
-            KeyError: <Transform [0.2 0 0 0.3 0.08 0.18]>
-    """
-
-    xx: float = 1
-    xy: float = 0
-    yx: float = 0
-    yy: float = 1
-    dx: float = 0
-    dy: float = 0
-
-    def transformPoint(self, p):
-        """Transform a point.
-
-        :Example:
-
-                >>> t = Transform()
-                >>> t = t.scale(2.5, 5.5)
-                >>> t.transformPoint((100, 100))
-                (250.0, 550.0)
-        """
-        (x, y) = p
-        xx, xy, yx, yy, dx, dy = self
-        return (xx * x + yx * y + dx, xy * x + yy * y + dy)
-
-    def transformPoints(self, points):
-        """Transform a list of points.
-
-        :Example:
-
-                >>> t = Scale(2, 3)
-                >>> t.transformPoints([(0, 0), (0, 100), (100, 100), (100, 0)])
-                [(0, 0), (0, 300), (200, 300), (200, 0)]
-                >>>
-        """
-        xx, xy, yx, yy, dx, dy = self
-        return [(xx * x + yx * y + dx, xy * x + yy * y + dy) for x, y in points]
-
-    def transformVector(self, v):
-        """Transform an (dx, dy) vector, treating translation as zero.
-
-        :Example:
-
-                >>> t = Transform(2, 0, 0, 2, 10, 20)
-                >>> t.transformVector((3, -4))
-                (6, -8)
-                >>>
-        """
-        (dx, dy) = v
-        xx, xy, yx, yy = self[:4]
-        return (xx * dx + yx * dy, xy * dx + yy * dy)
-
-    def transformVectors(self, vectors):
-        """Transform a list of (dx, dy) vector, treating translation as zero.
-
-        :Example:
-                >>> t = Transform(2, 0, 0, 2, 10, 20)
-                >>> t.transformVectors([(3, -4), (5, -6)])
-                [(6, -8), (10, -12)]
-                >>>
-        """
-        xx, xy, yx, yy = self[:4]
-        return [(xx * dx + yx * dy, xy * dx + yy * dy) for dx, dy in vectors]
-
-    def translate(self, x=0, y=0):
-        """Return a new transformation, translated (offset) by x, y.
-
-        :Example:
-                >>> t = Transform()
-                >>> t.translate(20, 30)
-                <Transform [1 0 0 1 20 30]>
-                >>>
-        """
-        return self.transform((1, 0, 0, 1, x, y))
-
-    def scale(self, x=1, y=None):
-        """Return a new transformation, scaled by x, y. The 'y' argument
-        may be None, which implies to use the x value for y as well.
-
-        :Example:
-                >>> t = Transform()
-                >>> t.scale(5)
-                <Transform [5 0 0 5 0 0]>
-                >>> t.scale(5, 6)
-                <Transform [5 0 0 6 0 0]>
-                >>>
-        """
-        if y is None:
-            y = x
-        return self.transform((x, 0, 0, y, 0, 0))
-
-    def rotate(self, angle):
-        """Return a new transformation, rotated by 'angle' (radians).
-
-        :Example:
-                >>> import math
-                >>> t = Transform()
-                >>> t.rotate(math.pi / 2)
-                <Transform [0 1 -1 0 0 0]>
-                >>>
-        """
-        import math
-
-        c = _normSinCos(math.cos(angle))
-        s = _normSinCos(math.sin(angle))
-        return self.transform((c, s, -s, c, 0, 0))
-
-    def skew(self, x=0, y=0):
-        """Return a new transformation, skewed by x and y.
-
-        :Example:
-                >>> import math
-                >>> t = Transform()
-                >>> t.skew(math.pi / 4)
-                <Transform [1 0 1 1 0 0]>
-                >>>
-        """
-        import math
-
-        return self.transform((1, math.tan(y), math.tan(x), 1, 0, 0))
-
-    def transform(self, other):
-        """Return a new transformation, transformed by another
-        transformation.
-
-        :Example:
-                >>> t = Transform(2, 0, 0, 3, 1, 6)
-                >>> t.transform((4, 3, 2, 1, 5, 6))
-                <Transform [8 9 4 3 11 24]>
-                >>>
-        """
-        xx1, xy1, yx1, yy1, dx1, dy1 = other
-        xx2, xy2, yx2, yy2, dx2, dy2 = self
-        return self.__class__(
-            xx1 * xx2 + xy1 * yx2,
-            xx1 * xy2 + xy1 * yy2,
-            yx1 * xx2 + yy1 * yx2,
-            yx1 * xy2 + yy1 * yy2,
-            xx2 * dx1 + yx2 * dy1 + dx2,
-            xy2 * dx1 + yy2 * dy1 + dy2,
-        )
-
-    def reverseTransform(self, other):
-        """Return a new transformation, which is the other transformation
-        transformed by self. self.reverseTransform(other) is equivalent to
-        other.transform(self).
-
-        :Example:
-                >>> t = Transform(2, 0, 0, 3, 1, 6)
-                >>> t.reverseTransform((4, 3, 2, 1, 5, 6))
-                <Transform [8 6 6 3 21 15]>
-                >>> Transform(4, 3, 2, 1, 5, 6).transform((2, 0, 0, 3, 1, 6))
-                <Transform [8 6 6 3 21 15]>
-                >>>
-        """
-        xx1, xy1, yx1, yy1, dx1, dy1 = self
-        xx2, xy2, yx2, yy2, dx2, dy2 = other
-        return self.__class__(
-            xx1 * xx2 + xy1 * yx2,
-            xx1 * xy2 + xy1 * yy2,
-            yx1 * xx2 + yy1 * yx2,
-            yx1 * xy2 + yy1 * yy2,
-            xx2 * dx1 + yx2 * dy1 + dx2,
-            xy2 * dx1 + yy2 * dy1 + dy2,
-        )
-
-    def inverse(self):
-        """Return the inverse transformation.
-
-        :Example:
-                >>> t = Identity.translate(2, 3).scale(4, 5)
-                >>> t.transformPoint((10, 20))
-                (42, 103)
-                >>> it = t.inverse()
-                >>> it.transformPoint((42, 103))
-                (10.0, 20.0)
-                >>>
-        """
-        if self == Identity:
-            return self
-        xx, xy, yx, yy, dx, dy = self
-        det = xx * yy - yx * xy
-        xx, xy, yx, yy = yy / det, -xy / det, -yx / det, xx / det
-        dx, dy = -xx * dx - yx * dy, -xy * dx - yy * dy
-        return self.__class__(xx, xy, yx, yy, dx, dy)
-
-    def toPS(self):
-        """Return a PostScript representation
-
-        :Example:
-
-                >>> t = Identity.scale(2, 3).translate(4, 5)
-                >>> t.toPS()
-                '[2 0 0 3 8 15]'
-                >>>
-        """
-        return "[%s %s %s %s %s %s]" % self
-
-    def toDecomposed(self) -> "DecomposedTransform":
-        """Decompose into a DecomposedTransform."""
-        return DecomposedTransform.fromTransform(self)
-
-    def __bool__(self):
-        """Returns True if transform is not identity, False otherwise.
-
-        :Example:
-
-                >>> bool(Identity)
-                False
-                >>> bool(Transform())
-                False
-                >>> bool(Scale(1.))
-                False
-                >>> bool(Scale(2))
-                True
-                >>> bool(Offset())
-                False
-                >>> bool(Offset(0))
-                False
-                >>> bool(Offset(2))
-                True
-        """
-        return self != Identity
-
-    def __repr__(self):
-        return "<%s [%g %g %g %g %g %g]>" % ((self.__class__.__name__,) + self)
-
-
-Identity = Transform()
-
-
-def Offset(x=0, y=0):
-    """Return the identity transformation offset by x, y.
-
-    :Example:
-            >>> Offset(2, 3)
-            <Transform [1 0 0 1 2 3]>
-            >>>
-    """
-    return Transform(1, 0, 0, 1, x, y)
-
-
-def Scale(x, y=None):
-    """Return the identity transformation scaled by x, y. The 'y' argument
-    may be None, which implies to use the x value for y as well.
-
-    :Example:
-            >>> Scale(2, 3)
-            <Transform [2 0 0 3 0 0]>
-            >>>
-    """
-    if y is None:
-        y = x
-    return Transform(x, 0, 0, y, 0, 0)
-
-
-@dataclass
-class DecomposedTransform:
-    """The DecomposedTransform class implements a transformation with separate
-    translate, rotation, scale, skew, and transformation-center components.
-    """
-
-    translateX: float = 0
-    translateY: float = 0
-    rotation: float = 0  # in degrees, counter-clockwise
-    scaleX: float = 1
-    scaleY: float = 1
-    skewX: float = 0  # in degrees, clockwise
-    skewY: float = 0  # in degrees, counter-clockwise
-    tCenterX: float = 0
-    tCenterY: float = 0
-
-    @classmethod
-    def fromTransform(self, transform):
-        # Adapted from an answer on
-        # https://math.stackexchange.com/questions/13150/extracting-rotation-scale-values-from-2d-transformation-matrix
-        a, b, c, d, x, y = transform
-
-        sx = math.copysign(1, a)
-        if sx < 0:
-            a *= sx
-            b *= sx
-
-        delta = a * d - b * c
-
-        rotation = 0
-        scaleX = scaleY = 0
-        skewX = skewY = 0
-
-        # Apply the QR-like decomposition.
-        if a != 0 or b != 0:
-            r = math.sqrt(a * a + b * b)
-            rotation = math.acos(a / r) if b >= 0 else -math.acos(a / r)
-            scaleX, scaleY = (r, delta / r)
-            skewX, skewY = (math.atan((a * c + b * d) / (r * r)), 0)
-        elif c != 0 or d != 0:
-            s = math.sqrt(c * c + d * d)
-            rotation = math.pi / 2 - (
-                math.acos(-c / s) if d >= 0 else -math.acos(c / s)
-            )
-            scaleX, scaleY = (delta / s, s)
-            skewX, skewY = (0, math.atan((a * c + b * d) / (s * s)))
-        else:
-            # a = b = c = d = 0
-            pass
-
-        return DecomposedTransform(
-            x,
-            y,
-            math.degrees(rotation),
-            scaleX * sx,
-            scaleY,
-            math.degrees(skewX) * sx,
-            math.degrees(skewY),
-            0,
-            0,
-        )
-
-    def toTransform(self):
-        """Return the Transform() equivalent of this transformation.
-
-        :Example:
-                >>> DecomposedTransform(scaleX=2, scaleY=2).toTransform()
-                <Transform [2 0 0 2 0 0]>
-                >>>
-        """
-        t = Transform()
-        t = t.translate(
-            self.translateX + self.tCenterX, self.translateY + self.tCenterY
-        )
-        t = t.rotate(math.radians(self.rotation))
-        t = t.scale(self.scaleX, self.scaleY)
-        t = t.skew(math.radians(self.skewX), math.radians(self.skewY))
-        t = t.translate(-self.tCenterX, -self.tCenterY)
-        return t
-
-
-if __name__ == "__main__":
-    import sys
-    import doctest
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/yaml-95012b83.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/yaml-95012b83.js
deleted file mode 100644
index 3fef68bd6d3b922eebf9622184021189fa7e8cc2..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/yaml-95012b83.js
+++ /dev/null
@@ -1,2 +0,0 @@
-var l=["true","false","on","off","yes","no"],f=new RegExp("\\b(("+l.join(")|(")+"))$","i");const a={name:"yaml",token:function(n,i){var r=n.peek(),e=i.escaped;if(i.escaped=!1,r=="#"&&(n.pos==0||/\s/.test(n.string.charAt(n.pos-1))))return n.skipToEnd(),"comment";if(n.match(/^('([^']|\\.)*'?|"([^"]|\\.)*"?)/))return"string";if(i.literal&&n.indentation()>i.keyCol)return n.skipToEnd(),"string";if(i.literal&&(i.literal=!1),n.sol()){if(i.keyCol=0,i.pair=!1,i.pairStart=!1,n.match("---")||n.match("..."))return"def";if(n.match(/^\s*-\s+/))return"meta"}if(n.match(/^(\{|\}|\[|\])/))return r=="{"?i.inlinePairs++:r=="}"?i.inlinePairs--:r=="["?i.inlineList++:i.inlineList--,"meta";if(i.inlineList>0&&!e&&r==",")return n.next(),"meta";if(i.inlinePairs>0&&!e&&r==",")return i.keyCol=0,i.pair=!1,i.pairStart=!1,n.next(),"meta";if(i.pairStart){if(n.match(/^\s*(\||\>)\s*/))return i.literal=!0,"meta";if(n.match(/^\s*(\&|\*)[a-z0-9\._-]+\b/i))return"variable";if(i.inlinePairs==0&&n.match(/^\s*-?[0-9\.\,]+\s?$/)||i.inlinePairs>0&&n.match(/^\s*-?[0-9\.\,]+\s?(?=(,|}))/))return"number";if(n.match(f))return"keyword"}return!i.pair&&n.match(/^\s*(?:[,\[\]{}&*!|>'"%@`][^\s'":]|[^,\[\]{}#&*!|>'"%@`])[^#]*?(?=\s*:($|\s))/)?(i.pair=!0,i.keyCol=n.indentation(),"atom"):i.pair&&n.match(/^:\s*/)?(i.pairStart=!0,"meta"):(i.pairStart=!1,i.escaped=r=="\\",n.next(),null)},startState:function(){return{pair:!1,pairStart:!1,keyCol:0,inlinePairs:0,inlineList:0,literal:!1,escaped:!1}},languageData:{commentTokens:{line:"#"}}};export{a as yaml};
-//# sourceMappingURL=yaml-95012b83.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_events.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_events.py
deleted file mode 100644
index 075bf8a469d44d2388b08ec3d009fe55d44cb6eb..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/_events.py
+++ /dev/null
@@ -1,369 +0,0 @@
-# High level events that make up HTTP/1.1 conversations. Loosely inspired by
-# the corresponding events in hyper-h2:
-#
-#     http://python-hyper.org/h2/en/stable/api.html#events
-#
-# Don't subclass these. Stuff will break.
-
-import re
-from abc import ABC
-from dataclasses import dataclass, field
-from typing import Any, cast, Dict, List, Tuple, Union
-
-from ._abnf import method, request_target
-from ._headers import Headers, normalize_and_validate
-from ._util import bytesify, LocalProtocolError, validate
-
-# Everything in __all__ gets re-exported as part of the h11 public API.
-__all__ = [
-    "Event",
-    "Request",
-    "InformationalResponse",
-    "Response",
-    "Data",
-    "EndOfMessage",
-    "ConnectionClosed",
-]
-
-method_re = re.compile(method.encode("ascii"))
-request_target_re = re.compile(request_target.encode("ascii"))
-
-
-class Event(ABC):
-    """
-    Base class for h11 events.
-    """
-
-    __slots__ = ()
-
-
-@dataclass(init=False, frozen=True)
-class Request(Event):
-    """The beginning of an HTTP request.
-
-    Fields:
-
-    .. attribute:: method
-
-       An HTTP method, e.g. ``b"GET"`` or ``b"POST"``. Always a byte
-       string. :term:`Bytes-like objects <bytes-like object>` and native
-       strings containing only ascii characters will be automatically
-       converted to byte strings.
-
-    .. attribute:: target
-
-       The target of an HTTP request, e.g. ``b"/index.html"``, or one of the
-       more exotic formats described in `RFC 7320, section 5.3
-       <https://tools.ietf.org/html/rfc7230#section-5.3>`_. Always a byte
-       string. :term:`Bytes-like objects <bytes-like object>` and native
-       strings containing only ascii characters will be automatically
-       converted to byte strings.
-
-    .. attribute:: headers
-
-       Request headers, represented as a list of (name, value) pairs. See
-       :ref:`the header normalization rules <headers-format>` for details.
-
-    .. attribute:: http_version
-
-       The HTTP protocol version, represented as a byte string like
-       ``b"1.1"``. See :ref:`the HTTP version normalization rules
-       <http_version-format>` for details.
-
-    """
-
-    __slots__ = ("method", "headers", "target", "http_version")
-
-    method: bytes
-    headers: Headers
-    target: bytes
-    http_version: bytes
-
-    def __init__(
-        self,
-        *,
-        method: Union[bytes, str],
-        headers: Union[Headers, List[Tuple[bytes, bytes]], List[Tuple[str, str]]],
-        target: Union[bytes, str],
-        http_version: Union[bytes, str] = b"1.1",
-        _parsed: bool = False,
-    ) -> None:
-        super().__init__()
-        if isinstance(headers, Headers):
-            object.__setattr__(self, "headers", headers)
-        else:
-            object.__setattr__(
-                self, "headers", normalize_and_validate(headers, _parsed=_parsed)
-            )
-        if not _parsed:
-            object.__setattr__(self, "method", bytesify(method))
-            object.__setattr__(self, "target", bytesify(target))
-            object.__setattr__(self, "http_version", bytesify(http_version))
-        else:
-            object.__setattr__(self, "method", method)
-            object.__setattr__(self, "target", target)
-            object.__setattr__(self, "http_version", http_version)
-
-        # "A server MUST respond with a 400 (Bad Request) status code to any
-        # HTTP/1.1 request message that lacks a Host header field and to any
-        # request message that contains more than one Host header field or a
-        # Host header field with an invalid field-value."
-        # -- https://tools.ietf.org/html/rfc7230#section-5.4
-        host_count = 0
-        for name, value in self.headers:
-            if name == b"host":
-                host_count += 1
-        if self.http_version == b"1.1" and host_count == 0:
-            raise LocalProtocolError("Missing mandatory Host: header")
-        if host_count > 1:
-            raise LocalProtocolError("Found multiple Host: headers")
-
-        validate(method_re, self.method, "Illegal method characters")
-        validate(request_target_re, self.target, "Illegal target characters")
-
-    # This is an unhashable type.
-    __hash__ = None  # type: ignore
-
-
-@dataclass(init=False, frozen=True)
-class _ResponseBase(Event):
-    __slots__ = ("headers", "http_version", "reason", "status_code")
-
-    headers: Headers
-    http_version: bytes
-    reason: bytes
-    status_code: int
-
-    def __init__(
-        self,
-        *,
-        headers: Union[Headers, List[Tuple[bytes, bytes]], List[Tuple[str, str]]],
-        status_code: int,
-        http_version: Union[bytes, str] = b"1.1",
-        reason: Union[bytes, str] = b"",
-        _parsed: bool = False,
-    ) -> None:
-        super().__init__()
-        if isinstance(headers, Headers):
-            object.__setattr__(self, "headers", headers)
-        else:
-            object.__setattr__(
-                self, "headers", normalize_and_validate(headers, _parsed=_parsed)
-            )
-        if not _parsed:
-            object.__setattr__(self, "reason", bytesify(reason))
-            object.__setattr__(self, "http_version", bytesify(http_version))
-            if not isinstance(status_code, int):
-                raise LocalProtocolError("status code must be integer")
-            # Because IntEnum objects are instances of int, but aren't
-            # duck-compatible (sigh), see gh-72.
-            object.__setattr__(self, "status_code", int(status_code))
-        else:
-            object.__setattr__(self, "reason", reason)
-            object.__setattr__(self, "http_version", http_version)
-            object.__setattr__(self, "status_code", status_code)
-
-        self.__post_init__()
-
-    def __post_init__(self) -> None:
-        pass
-
-    # This is an unhashable type.
-    __hash__ = None  # type: ignore
-
-
-@dataclass(init=False, frozen=True)
-class InformationalResponse(_ResponseBase):
-    """An HTTP informational response.
-
-    Fields:
-
-    .. attribute:: status_code
-
-       The status code of this response, as an integer. For an
-       :class:`InformationalResponse`, this is always in the range [100,
-       200).
-
-    .. attribute:: headers
-
-       Request headers, represented as a list of (name, value) pairs. See
-       :ref:`the header normalization rules <headers-format>` for
-       details.
-
-    .. attribute:: http_version
-
-       The HTTP protocol version, represented as a byte string like
-       ``b"1.1"``. See :ref:`the HTTP version normalization rules
-       <http_version-format>` for details.
-
-    .. attribute:: reason
-
-       The reason phrase of this response, as a byte string. For example:
-       ``b"OK"``, or ``b"Not Found"``.
-
-    """
-
-    def __post_init__(self) -> None:
-        if not (100 <= self.status_code < 200):
-            raise LocalProtocolError(
-                "InformationalResponse status_code should be in range "
-                "[100, 200), not {}".format(self.status_code)
-            )
-
-    # This is an unhashable type.
-    __hash__ = None  # type: ignore
-
-
-@dataclass(init=False, frozen=True)
-class Response(_ResponseBase):
-    """The beginning of an HTTP response.
-
-    Fields:
-
-    .. attribute:: status_code
-
-       The status code of this response, as an integer. For an
-       :class:`Response`, this is always in the range [200,
-       1000).
-
-    .. attribute:: headers
-
-       Request headers, represented as a list of (name, value) pairs. See
-       :ref:`the header normalization rules <headers-format>` for details.
-
-    .. attribute:: http_version
-
-       The HTTP protocol version, represented as a byte string like
-       ``b"1.1"``. See :ref:`the HTTP version normalization rules
-       <http_version-format>` for details.
-
-    .. attribute:: reason
-
-       The reason phrase of this response, as a byte string. For example:
-       ``b"OK"``, or ``b"Not Found"``.
-
-    """
-
-    def __post_init__(self) -> None:
-        if not (200 <= self.status_code < 1000):
-            raise LocalProtocolError(
-                "Response status_code should be in range [200, 1000), not {}".format(
-                    self.status_code
-                )
-            )
-
-    # This is an unhashable type.
-    __hash__ = None  # type: ignore
-
-
-@dataclass(init=False, frozen=True)
-class Data(Event):
-    """Part of an HTTP message body.
-
-    Fields:
-
-    .. attribute:: data
-
-       A :term:`bytes-like object` containing part of a message body. Or, if
-       using the ``combine=False`` argument to :meth:`Connection.send`, then
-       any object that your socket writing code knows what to do with, and for
-       which calling :func:`len` returns the number of bytes that will be
-       written -- see :ref:`sendfile` for details.
-
-    .. attribute:: chunk_start
-
-       A marker that indicates whether this data object is from the start of a
-       chunked transfer encoding chunk. This field is ignored when when a Data
-       event is provided to :meth:`Connection.send`: it is only valid on
-       events emitted from :meth:`Connection.next_event`. You probably
-       shouldn't use this attribute at all; see
-       :ref:`chunk-delimiters-are-bad` for details.
-
-    .. attribute:: chunk_end
-
-       A marker that indicates whether this data object is the last for a
-       given chunked transfer encoding chunk. This field is ignored when when
-       a Data event is provided to :meth:`Connection.send`: it is only valid
-       on events emitted from :meth:`Connection.next_event`. You probably
-       shouldn't use this attribute at all; see
-       :ref:`chunk-delimiters-are-bad` for details.
-
-    """
-
-    __slots__ = ("data", "chunk_start", "chunk_end")
-
-    data: bytes
-    chunk_start: bool
-    chunk_end: bool
-
-    def __init__(
-        self, data: bytes, chunk_start: bool = False, chunk_end: bool = False
-    ) -> None:
-        object.__setattr__(self, "data", data)
-        object.__setattr__(self, "chunk_start", chunk_start)
-        object.__setattr__(self, "chunk_end", chunk_end)
-
-    # This is an unhashable type.
-    __hash__ = None  # type: ignore
-
-
-# XX FIXME: "A recipient MUST ignore (or consider as an error) any fields that
-# are forbidden to be sent in a trailer, since processing them as if they were
-# present in the header section might bypass external security filters."
-# https://svn.tools.ietf.org/svn/wg/httpbis/specs/rfc7230.html#chunked.trailer.part
-# Unfortunately, the list of forbidden fields is long and vague :-/
-@dataclass(init=False, frozen=True)
-class EndOfMessage(Event):
-    """The end of an HTTP message.
-
-    Fields:
-
-    .. attribute:: headers
-
-       Default value: ``[]``
-
-       Any trailing headers attached to this message, represented as a list of
-       (name, value) pairs. See :ref:`the header normalization rules
-       <headers-format>` for details.
-
-       Must be empty unless ``Transfer-Encoding: chunked`` is in use.
-
-    """
-
-    __slots__ = ("headers",)
-
-    headers: Headers
-
-    def __init__(
-        self,
-        *,
-        headers: Union[
-            Headers, List[Tuple[bytes, bytes]], List[Tuple[str, str]], None
-        ] = None,
-        _parsed: bool = False,
-    ) -> None:
-        super().__init__()
-        if headers is None:
-            headers = Headers([])
-        elif not isinstance(headers, Headers):
-            headers = normalize_and_validate(headers, _parsed=_parsed)
-
-        object.__setattr__(self, "headers", headers)
-
-    # This is an unhashable type.
-    __hash__ = None  # type: ignore
-
-
-@dataclass(frozen=True)
-class ConnectionClosed(Event):
-    """This event indicates that the sender has closed their outgoing
-    connection.
-
-    Note that this does not necessarily mean that they can't *receive* further
-    data, because TCP connections are composed to two one-way channels which
-    can be closed independently. See :ref:`closing` for details.
-
-    No fields.
-    """
-
-    pass
diff --git a/spaces/lewiswu1209/MockingBird/web/static/js/jquery.js b/spaces/lewiswu1209/MockingBird/web/static/js/jquery.js
deleted file mode 100644
index fc6c299b73e792ef288e785c22393a5df9dded4b..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/web/static/js/jquery.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	}
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kaalakaandi Movie 720p Download Utorrent Movies.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kaalakaandi Movie 720p Download Utorrent Movies.md
deleted file mode 100644
index d86f388b1ad11a12b46dd5811d88a666faf7991f..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kaalakaandi Movie 720p Download Utorrent Movies.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Kaalakaandi Movie 720p Download Utorrent Movies</h2><br /><p><b><b>DOWNLOAD</b> &#9658; <a href="https://bytlly.com/2uGwkG">https://bytlly.com/2uGwkG</a></b></p><br /><br />
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-Hd Saif Ali Khan Latest Hindi Movie Full Movie 2018 Kaalakaandi. movies to download movies. Kaalokadi movies 2018.
-Kaalokadi movie Full movie.
-Kaalokadi Movies 2018.
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-Kaalokadi 8a78ff9644<br />
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diff --git a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py b/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py
deleted file mode 100644
index b8a00d6305eeda5a94788017afc1cda0d4a4cd2a..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "mbf"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 2e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 20, 25]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/lixq/bingo61/src/pages/api/blob.ts b/spaces/lixq/bingo61/src/pages/api/blob.ts
deleted file mode 100644
index fecd48031916b2284b8958892196e0a1ad420421..0000000000000000000000000000000000000000
--- a/spaces/lixq/bingo61/src/pages/api/blob.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { bcid } = req.query
-
-    const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
-      {
-        method: 'GET',
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referrer-Policy": "origin-when-cross-origin",
-        },
-      },
-    )
-
-    res.writeHead(200, {
-      'Content-Length': headers.get('content-length')!,
-      'Content-Type': headers.get('content-type')!,
-    })
-    // @ts-ignore
-    return Readable.fromWeb(body!).pipe(res)
-  } catch (e) {
-    console.log('Error', e)
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/lysine/auscultate/src/lib/breath/api.ts b/spaces/lysine/auscultate/src/lib/breath/api.ts
deleted file mode 100644
index e58d73a6f141bfb2a37310fa159b6b068705cd5a..0000000000000000000000000000000000000000
--- a/spaces/lysine/auscultate/src/lib/breath/api.ts
+++ /dev/null
@@ -1,133 +0,0 @@
-import express from 'express';
-import { z } from 'zod';
-import { validate, wrap } from '../helper';
-import { cases } from './data';
-import { notFound } from '@hapi/boom';
-import {
-  Diagnosis,
-  Location,
-  RandomResult,
-  SoundFilter,
-  getCaseAbnormalities,
-  getTrackAbnormalities,
-} from '../../breath-types';
-
-const router = express.Router();
-
-router.get(
-  '/case/random',
-  wrap(async (req, res) => {
-    const { query } = await validate(
-      req,
-      z.object({
-        query: z
-          .object({
-            location: z
-              .union([z.nativeEnum(Location), z.array(z.nativeEnum(Location))])
-              .optional(),
-            abnormalLocation: z
-              .union([z.nativeEnum(Location), z.array(z.nativeEnum(Location))])
-              .optional(),
-            sound: z.nativeEnum(SoundFilter).optional(),
-            diagnosis: z
-              .union([
-                z.nativeEnum(Diagnosis),
-                z.array(z.nativeEnum(Diagnosis)),
-              ])
-              .optional(),
-          })
-          .strict(),
-      })
-    );
-    let filtered = cases.slice();
-    if (query.location) {
-      const locations = Array.isArray(query.location)
-        ? query.location
-        : [query.location];
-      filtered = filtered.filter(p =>
-        p.tracks.some(l => locations.includes(l.location))
-      );
-    }
-    if (query.abnormalLocation) {
-      const locations = Array.isArray(query.abnormalLocation)
-        ? query.abnormalLocation
-        : [query.abnormalLocation];
-      filtered = filtered.filter(p =>
-        p.tracks.some(l => {
-          const abnormalities = getTrackAbnormalities(l);
-          return (
-            (abnormalities.crackles || abnormalities.wheezes) &&
-            locations.includes(l.location)
-          );
-        })
-      );
-    }
-    if (query.sound) {
-      if (query.sound === SoundFilter.None) {
-        filtered = filtered.filter(p => {
-          const abnormalities = getCaseAbnormalities(p);
-          return !abnormalities.crackles && !abnormalities.wheezes;
-        });
-      } else if (query.sound === SoundFilter.Any) {
-        filtered = filtered.filter(p => {
-          const abnormalities = getCaseAbnormalities(p);
-          return abnormalities.crackles || abnormalities.wheezes;
-        });
-      } else if (query.sound === SoundFilter.Crackles) {
-        filtered = filtered.filter(p => {
-          const abnormalities = getCaseAbnormalities(p);
-          return abnormalities.crackles;
-        });
-      } else if (query.sound === SoundFilter.Wheezes) {
-        filtered = filtered.filter(p => {
-          const abnormalities = getCaseAbnormalities(p);
-          return abnormalities.wheezes;
-        });
-      }
-    }
-    if (query.diagnosis) {
-      const diagnoses = Array.isArray(query.diagnosis)
-        ? query.diagnosis
-        : [query.diagnosis];
-      filtered = filtered.filter(p => diagnoses.includes(p.diagnosis));
-    }
-
-    if (filtered.length === 0) {
-      throw notFound('No cases found with the given criteria');
-    }
-    const _case = filtered[Math.floor(Math.random() * filtered.length)];
-    res.status(200).json({
-      patientId: _case.patientId,
-      recordingId: _case.recordingId,
-      count: filtered.length,
-    } satisfies RandomResult);
-  })
-);
-
-router.get(
-  '/case',
-  wrap(async (req, res) => {
-    const {
-      query: { patientId, recordingId },
-    } = await validate(
-      req,
-      z.object({
-        query: z
-          .object({
-            patientId: z.string(),
-            recordingId: z.string(),
-          })
-          .strict(),
-      })
-    );
-    const _case = cases.find(
-      p => p.patientId === parseInt(patientId) && p.recordingId === recordingId
-    );
-    if (!_case) {
-      throw notFound(`Case ${patientId}_${recordingId} not found`);
-    }
-    res.status(200).json(_case);
-  })
-);
-
-export default router;
diff --git a/spaces/ma-xu/LIVE/thrust/examples/cpp_integration/device.h b/spaces/ma-xu/LIVE/thrust/examples/cpp_integration/device.h
deleted file mode 100644
index e398edf3361d35225e1ba829baab9c61ada55367..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/examples/cpp_integration/device.h
+++ /dev/null
@@ -1,7 +0,0 @@
-#pragma once
-
-#include <thrust/host_vector.h>
-
-// function prototype
-void sort_on_device(thrust::host_vector<int>& V);
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/logical.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/logical.h
deleted file mode 100644
index 702dbad852d9e074147368a87b28a082fcfa8242..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/logical.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-#include <thrust/detail/internal_functional.h>
-#include <thrust/find.h>
-#include <thrust/logical.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename ExecutionPolicy, typename InputIterator, typename Predicate>
-__host__ __device__
-bool all_of(thrust::execution_policy<ExecutionPolicy> &exec, InputIterator first, InputIterator last, Predicate pred)
-{
-  return thrust::find_if(exec, first, last, thrust::detail::not1(pred)) == last;
-}
-
-
-template<typename ExecutionPolicy, typename InputIterator, typename Predicate>
-__host__ __device__
-bool any_of(thrust::execution_policy<ExecutionPolicy> &exec, InputIterator first, InputIterator last, Predicate pred)
-{
-  return thrust::find_if(exec, first, last, pred) != last;
-}
-
-
-template<typename ExecutionPolicy, typename InputIterator, typename Predicate>
-__host__ __device__
-bool none_of(thrust::execution_policy<ExecutionPolicy> &exec, InputIterator first, InputIterator last, Predicate pred)
-{
-  return !thrust::any_of(exec, first, last, pred);
-}
-
-
-} // end generic
-} // end detail
-} // end system
-} // end thrust
-
diff --git a/spaces/magicr/BuboGPT/bubogpt/runners/__init__.py b/spaces/magicr/BuboGPT/bubogpt/runners/__init__.py
deleted file mode 100644
index 0c3234b7ce28fb41cf2ccf86f2e1d79489394091..0000000000000000000000000000000000000000
--- a/spaces/magicr/BuboGPT/bubogpt/runners/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from bubogpt.runners.runner_base import RunnerBase
-
-__all__ = ["RunnerBase"]
diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/tests/test_sync_batchnorm.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/tests/test_sync_batchnorm.py
deleted file mode 100644
index 1f7b6c64c06fc26348489cd15669501a2098c82f..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/tests/test_sync_batchnorm.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_sync_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-
-import unittest
-
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from sync_batchnorm import set_sbn_eps_mode
-from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback
-from sync_batchnorm.unittest import TorchTestCase
-
-set_sbn_eps_mode('plus')
-
-
-def handy_var(a, unbias=True):
-    n = a.size(0)
-    asum = a.sum(dim=0)
-    as_sum = (a ** 2).sum(dim=0)  # a square sum
-    sumvar = as_sum - asum * asum / n
-    if unbias:
-        return sumvar / (n - 1)
-    else:
-        return sumvar / n
-
-
-def _find_bn(module):
-    for m in module.modules():
-        if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)):
-            return m
-
-
-class SyncTestCase(TorchTestCase):
-    def _syncParameters(self, bn1, bn2):
-        bn1.reset_parameters()
-        bn2.reset_parameters()
-        if bn1.affine and bn2.affine:
-            bn2.weight.data.copy_(bn1.weight.data)
-            bn2.bias.data.copy_(bn1.bias.data)
-
-    def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False):
-        """Check the forward and backward for the customized batch normalization."""
-        bn1.train(mode=is_train)
-        bn2.train(mode=is_train)
-
-        if cuda:
-            input = input.cuda()
-
-        self._syncParameters(_find_bn(bn1), _find_bn(bn2))
-
-        input1 = Variable(input, requires_grad=True)
-        output1 = bn1(input1)
-        output1.sum().backward()
-        input2 = Variable(input, requires_grad=True)
-        output2 = bn2(input2)
-        output2.sum().backward()
-
-        self.assertTensorClose(input1.data, input2.data)
-        self.assertTensorClose(output1.data, output2.data)
-        self.assertTensorClose(input1.grad, input2.grad)
-        self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean)
-        self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var)
-
-    def testSyncBatchNormNormalTrain(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True)
-
-    def testSyncBatchNormNormalEval(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)
-
-    def testSyncBatchNormSyncTrain(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True)
-
-    def testSyncBatchNormSyncEval(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)
-
-    def testSyncBatchNorm2DSyncTrain(self):
-        bn = nn.BatchNorm2d(10)
-        sync_bn = SynchronizedBatchNorm2d(10)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/manu-codes/dysperse/app.py b/spaces/manu-codes/dysperse/app.py
deleted file mode 100644
index 461bdb2ce483b71861f3c5513b6761734a94e763..0000000000000000000000000000000000000000
--- a/spaces/manu-codes/dysperse/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/distilbert-base-cased-distilled-squad").launch()
\ No newline at end of file
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/LICENSE.md b/spaces/marlenezw/audio-driven-animations/MakeItTalk/LICENSE.md
deleted file mode 100644
index 49035ff10e9b40aa64301b8e8819dcf46606c4cb..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/LICENSE.md
+++ /dev/null
@@ -1,159 +0,0 @@
-# Creative Commons Attribution-NonCommercial 4.0 International
-
-Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible.
-
-### Using Creative Commons Public Licenses
-
-Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses.
-
-* __Considerations for licensors:__ Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. [More considerations for licensors](http://wiki.creativecommons.org/Considerations_for_licensors_and_licensees#Considerations_for_licensors).
-
-* __Considerations for the public:__ By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. [More considerations for the public](http://wiki.creativecommons.org/Considerations_for_licensors_and_licensees#Considerations_for_licensees).
-
-## Creative Commons Attribution-NonCommercial 4.0 International Public License
-
-By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.
-
-### Section 1 – Definitions.
-
-a. __Adapted Material__ means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
-
-b. __Adapter's License__ means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License.
-
-c. __Copyright and Similar Rights__ means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
-
-d. __Effective Technological Measures__ means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
-
-e. __Exceptions and Limitations__ means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
-
-f. __Licensed Material__ means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
-
-g. __Licensed Rights__ means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
-
-h. __Licensor__ means the individual(s) or entity(ies) granting rights under this Public License.
-
-i. __NonCommercial__ means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
-
-j. __Share__ means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
-
-k. __Sui Generis Database Rights__ means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
-
-l. __You__ means the individual or entity exercising the Licensed Rights under this Public License. __Your__ has a corresponding meaning.
-
-### Section 2 – Scope.
-
-a. ___License grant.___
-
-   1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
-
-       A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
-
-       B. produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
-
-   2. __Exceptions and Limitations.__ For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
-
-   3. __Term.__ The term of this Public License is specified in Section 6(a).
-
-   4. __Media and formats; technical modifications allowed.__ The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
-
-   5. __Downstream recipients.__
-
-        A. __Offer from the Licensor – Licensed Material.__ Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
-
-        B. __No downstream restrictions.__ You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
-
-   6. __No endorsement.__ Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
-
-b. ___Other rights.___
-
-   1. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
-
-   2. Patent and trademark rights are not licensed under this Public License.
-
-   3. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
-
-### Section 3 – License Conditions.
-
-Your exercise of the Licensed Rights is expressly made subject to the following conditions.
-
-a. ___Attribution.___
-
-   1. If You Share the Licensed Material (including in modified form), You must:
-
-       A. retain the following if it is supplied by the Licensor with the Licensed Material:
-
-         i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
-
-         ii. a copyright notice;
-
-         iii. a notice that refers to this Public License;
-
-         iv. a notice that refers to the disclaimer of warranties;
-
-         v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
-
-       B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
-
-       C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
-
-   2. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
-
-   3. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
-
-   4. If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
-
-### Section 4 – Sui Generis Database Rights.
-
-Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
-
-a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
-
-b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
-
-c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
-
-For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
-
-### Section 5 – Disclaimer of Warranties and Limitation of Liability.
-
-a. __Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.__
-
-b. __To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.__
-
-c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
-
-### Section 6 – Term and Termination.
-
-a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
-
-b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
-
-   1. automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
-
-   2. upon express reinstatement by the Licensor.
-
-   For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
-
-c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
-
-d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
-
-### Section 7 – Other Terms and Conditions.
-
-a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
-
-b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
-
-### Section 8 – Interpretation.
-
-a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
-
-b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
-
-c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
-
-d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-
-> Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at [creativecommons.org/policies](http://creativecommons.org/policies), Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses.
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diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/main_gen_new_puppet.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/main_gen_new_puppet.py
deleted file mode 100644
index 7abf28c900c9df4bf31f75e4735d4aac7d319b05..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/main_gen_new_puppet.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import sys
-from facewarp.gen_puppet_utils import *
-
-''' ================================================
-            FOA face landmark detection 
-================================================ '''
-
-data_dir = out_dir = 'MakeItTalk/examples_cartoon'
-test_data = sys.argv[1] # for example 'roy_example.png'
-CH = test_data[:-4]
-use_gt_bb = False
-
-if(not os.path.exists(os.path.join(data_dir, CH + '.pts'))):
-
-    from thirdparty.face_of_art.menpo_functions import *
-    from thirdparty.face_of_art.deep_heatmaps_model_fusion_net import DeepHeatmapsModel
-
-    model_path = 'MakeItTalk/examples/ckpt/deep_heatmaps-60000'  # model for estimation stage
-    pdm_path = 'thirdparty/face_of_art/pdm_clm_models/pdm_models/'  # models for correction stage
-    clm_path = 'thirdparty/face_of_art/pdm_clm_models/clm_models/g_t_all'  # model for tuning stage
-
-    outline_tune = True  # if true use tuning stage on eyebrows+jaw, else use tuning stage on jaw only
-    map_landmarks_to_original_image = True  # if True, landmark predictions will be mapped to match original
-    # input image size. otherwise the predicted landmarks will match the cropped version (256x256) of the images
-
-    # load images
-    bb_dir = os.path.join(data_dir, 'Bounding_Boxes')
-    bb_dictionary = load_bb_dictionary(bb_dir, mode='TEST', test_data=test_data)
-    bb_type = 'init'
-
-    img_list = load_menpo_image_list(
-        img_dir=data_dir, test_data=test_data, train_crop_dir=data_dir, img_dir_ns=data_dir, bb_type=bb_type,
-        bb_dictionary=bb_dictionary, mode='TEST', return_transform=map_landmarks_to_original_image)
-
-    # load model
-    heatmap_model = DeepHeatmapsModel(
-        mode='TEST', img_path=data_dir, test_model_path=model_path, test_data=test_data, menpo_verbose=False)
-
-    print ("\npredicting landmarks for: "+os.path.join(data_dir, test_data))
-    print ("\nsaving landmarks to: "+out_dir)
-    for i, img in enumerate(img_list):
-        if i == 0:
-            reuse = None
-        else:
-            reuse = True
-
-        preds = heatmap_model.get_landmark_predictions(img_list=[img], pdm_models_dir=pdm_path, clm_model_path=clm_path,
-                                                       reuse=reuse, map_to_input_size=map_landmarks_to_original_image)
-
-        if map_landmarks_to_original_image:
-            img = img[0]
-
-        if outline_tune:
-            pred_lms = preds['ECpTp_out']
-        else:
-            pred_lms = preds['ECpTp_jaw']
-
-        mio.export_landmark_file(PointCloud(pred_lms[0]), os.path.join(out_dir, img.path.stem + '.pts'),
-                                 overwrite=True)
-
-    print ("\nFOA landmark detection DONE!")
-
-
-''' ====================================================================
-        opencv vis and refine landmark 
-        
-1. visualize the automatic detection result from FOA approach   
-2. click on landmarks and move them if they are not correct 
-
-Press Q to save landmarks and continue.
-==================================================================== '''
-
-import cv2
-import numpy as np
-import os
-
-if(os.path.exists(os.path.join(data_dir, CH + '_face_open_mouth.txt'))):
-    pts0 = np.loadtxt(os.path.join(data_dir, CH + '_face_open_mouth.txt'))
-    pts0 = pts0[:, 0:2]
-else:
-    f = open(os.path.join(data_dir, test_data[:-4] + '.pts'), 'r')
-    lines = f.readlines()
-    pts = []
-    for i in range(3, 3+68):
-        line = lines[i]
-        line = line[:-1].split(' ')
-        pts += [float(item) for item in line]
-    pts0 = np.array(pts).reshape((68, 2))
-
-pts = np.copy(pts0)
-img0 = cv2.imread(os.path.join(data_dir, test_data))
-img = np.copy(img0)
-node = -1
-
-
-def click_adjust_wireframe(event, x, y, flags, param):
-    global img, pts, node
-
-    def update_img(node, button_up=False):
-        global img, pts
-
-        # update carton points object and get fresh pts list
-        pts[node, 0], pts[node, 1] = x, y
-
-        img = np.copy(img0)
-        draw_landmarks(img, pts)
-
-        # zoom-in feature
-        if (not button_up):
-            zoom_in_scale = 2
-            zoom_in_box_size = int(150 / zoom_in_scale)
-            zoom_in_range = int(np.min([zoom_in_box_size, x, y,
-                                        (img.shape[0] - y) / 2 / zoom_in_scale,
-                                        (img.shape[1] - x) / 2 / zoom_in_scale]))
-
-            img_zoom_in = img[y - zoom_in_range:y + zoom_in_range,
-                          x - zoom_in_range:x + zoom_in_range].copy()
-            img_zoom_in = cv2.resize(img_zoom_in, (0, 0), fx=zoom_in_scale,
-                                     fy=zoom_in_scale)
-            cv2.drawMarker(img_zoom_in, (zoom_in_range * zoom_in_scale,
-                                         zoom_in_range * zoom_in_scale),
-                           (0, 0, 255),
-                           markerType=cv2.MARKER_CROSS, markerSize=30,
-                           thickness=2, line_type=cv2.LINE_AA)
-            height, width, depth = np.shape(img_zoom_in)
-
-            img[y:y + height, x:x + width] = img_zoom_in
-            cv2.rectangle(img, (x, y), (x + height, y + width),
-                          (0, 0, 255), thickness=2)
-
-
-    if event == cv2.EVENT_LBUTTONDOWN:
-        # search for nearest point
-        node = closest_node((x, y), pts)
-        if(node >=0):
-            update_img(node)
-
-    if event == cv2.EVENT_LBUTTONUP:
-        node = closest_node((x, y), pts)
-        if (node >= 0):
-            update_img(node, button_up=True)
-        node = -1
-
-    if event == cv2.EVENT_MOUSEMOVE:
-        # redraw figure
-        if (node != -1):
-            update_img(node)
-
-draw_landmarks(img, pts)
-
-cv2.namedWindow("img", cv2.WINDOW_NORMAL)
-cv2.setMouseCallback("img", click_adjust_wireframe)
-
-while(True):
-    cv2.imshow('img', img)
-    key = cv2.waitKey(1)
-    if key == ord("q"):
-        break
-cv2.destroyAllWindows()
-
-print('vis and refine landmark Done!')
-pts = np.concatenate([pts, np.ones((68, 1))], axis=1)
-np.savetxt(os.path.join(data_dir, '{}_face_open_mouth.txt'.format(CH)), pts, fmt='%.4f')
-
-
-''' =================================================================
-            find closed mouth landmark and normalize
-    
-Input: param are used to change closed mouth strength
-        param[0]: larger -> outer-upper lip higher 
-        param[1]: larger -> outer-lower lip higher 
-        param[2]: larger -> inner-upper lip higher 
-        param[3]: larger -> inner-lower lip higher 
-
-Output: saved as CH_face_open_mouth_norm.txt
-                 CH_scale_shift.txt
-                 CH_face_close_mouth.txt  
-                 
-Press Q or close the image window to continue.
-================================================================= '''
-
-
-norm_anno(data_dir, CH, param=[0.7, 0.4, 0.5, 0.5], show=True)
-
-
-''' =================================================================
-                       delauney tri
-    
-Input: INNER_ONLY indicates whether use the inner lip landmarks only
-
-Output: saved as CH_delauney_tri.txt
-
-Press any key to continue.
-================================================================= '''
-
-
-delauney_tri(data_dir, test_data, INNER_ONLY=False)
-
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/models/model_audio2landmark_speaker_aware.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/models/model_audio2landmark_speaker_aware.py
deleted file mode 100644
index 86ce63d8a45231accd22225b753641bf4094d375..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/models/model_audio2landmark_speaker_aware.py
+++ /dev/null
@@ -1,492 +0,0 @@
-"""
- # Copyright 2020 Adobe
- # All Rights Reserved.
- 
- # NOTICE: Adobe permits you to use, modify, and distribute this file in
- # accordance with the terms of the Adobe license agreement accompanying
- # it.
- 
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.parallel
-import torch.utils.data
-import math
-import torch.nn.functional as F
-import copy
-import numpy as np
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-AUDIO_FEAT_SIZE = 161
-FACE_ID_FEAT_SIZE = 204
-EPSILON = 1e-40
-
-class Embedder(nn.Module):
-    def __init__(self, feat_size, d_model):
-        super().__init__()
-        self.embed = nn.Linear(feat_size, d_model)
-    def forward(self, x):
-        return self.embed(x)
-
-
-class PositionalEncoder(nn.Module):
-    def __init__(self, d_model, max_seq_len=512):
-        super().__init__()
-        self.d_model = d_model
-
-        # create constant 'pe' matrix with values dependant on
-        # pos and i
-        pe = torch.zeros(max_seq_len, d_model)
-        for pos in range(max_seq_len):
-            for i in range(0, d_model, 2):
-                pe[pos, i] = \
-                    math.sin(pos / (10000 ** ((2 * i) / d_model)))
-                pe[pos, i + 1] = \
-                    math.cos(pos / (10000 ** ((2 * (i + 1)) / d_model)))
-
-        pe = pe.unsqueeze(0)
-        self.register_buffer('pe', pe)
-
-
-    def forward(self, x):
-        # make embeddings relatively larger
-        x = x * math.sqrt(self.d_model)
-        # add constant to embedding
-        seq_len = x.size(1)
-        x = x + self.pe[:, :seq_len].clone().detach().to(device)
-        return x
-
-
-def attention(q, k, v, d_k, mask=None, dropout=None):
-
-    scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
-    if mask is not None:
-        mask = mask.unsqueeze(1)
-        scores = scores.masked_fill(mask == 0, -1e9)
-    scores = F.softmax(scores, dim=-1)
-
-    if dropout is not None:
-        scores = dropout(scores)
-
-    output = torch.matmul(scores, v)
-    return output
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, heads, d_model, dropout=0.1):
-        super().__init__()
-
-        self.d_model = d_model
-        self.d_k = d_model // heads
-        self.h = heads
-
-        self.q_linear = nn.Linear(d_model, d_model)
-        self.v_linear = nn.Linear(d_model, d_model)
-        self.k_linear = nn.Linear(d_model, d_model)
-        self.dropout = nn.Dropout(dropout)
-        self.out = nn.Linear(d_model, d_model)
-
-    def forward(self, q, k, v, mask=None):
-        bs = q.size(0)
-
-        # perform linear operation and split into h heads
-
-        k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
-        q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
-        v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
-
-        # transpose to get dimensions bs * h * sl * d_model
-
-        k = k.transpose(1, 2)
-        q = q.transpose(1, 2)
-        v = v.transpose(1, 2)
-
-        # calculate attention using function we will define next
-        scores = attention(q, k, v, self.d_k, mask, self.dropout)
-
-        # concatenate heads and put through final linear layer
-        concat = scores.transpose(1, 2).contiguous() \
-            .view(bs, -1, self.d_model)
-
-        output = self.out(concat)
-
-        return output
-
-class FeedForward(nn.Module):
-    def __init__(self, d_model, d_ff=2048, dropout = 0.1):
-        super().__init__()
-        # We set d_ff as a default to 2048
-        self.linear_1 = nn.Linear(d_model, d_ff)
-        self.dropout = nn.Dropout(dropout)
-        self.linear_2 = nn.Linear(d_ff, d_model)
-    def forward(self, x):
-        x = self.dropout(F.relu(self.linear_1(x)))
-        x = self.linear_2(x)
-        return x
-
-
-class Norm(nn.Module):
-    def __init__(self, d_model, eps=1e-6):
-        super().__init__()
-
-        self.size = d_model
-        # create two learnable parameters to calibrate normalisation
-        self.alpha = nn.Parameter(torch.ones(self.size))
-        self.bias = nn.Parameter(torch.zeros(self.size))
-        self.eps = eps
-
-    def forward(self, x):
-        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) \
-               / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
-        return norm
-
-# build an encoder layer with one multi-head attention layer and one # feed-forward layer
-class EncoderLayer(nn.Module):
-    def __init__(self, d_model, heads, dropout=0.1):
-        super().__init__()
-        self.norm_1 = Norm(d_model)
-        self.norm_2 = Norm(d_model)
-        self.attn = MultiHeadAttention(heads, d_model)
-        self.ff = FeedForward(d_model)
-        self.dropout_1 = nn.Dropout(dropout)
-        self.dropout_2 = nn.Dropout(dropout)
-
-    def forward(self, x, mask):
-        x2 = self.norm_1(x)
-        x = x + self.dropout_1(self.attn(x2, x2, x2, mask))
-        x2 = self.norm_2(x)
-        x = x + self.dropout_2(self.ff(x2))
-        return x
-
-    # build a decoder layer with two multi-head attention layers and
-    # one feed-forward layer
-class DecoderLayer(nn.Module):
-    def __init__(self, d_model, heads, dropout=0.1):
-        super().__init__()
-        self.norm_1 = Norm(d_model)
-        self.norm_2 = Norm(d_model)
-        self.norm_3 = Norm(d_model)
-
-        self.dropout_1 = nn.Dropout(dropout)
-        self.dropout_2 = nn.Dropout(dropout)
-        self.dropout_3 = nn.Dropout(dropout)
-
-        self.attn_1 = MultiHeadAttention(heads, d_model)
-        self.attn_2 = MultiHeadAttention(heads, d_model)
-        self.ff = FeedForward(d_model).cuda()
-
-    def forward(self, x, e_outputs, src_mask, trg_mask):
-        x2 = self.norm_1(x)
-
-        x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
-        x2 = self.norm_2(x)
-        x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs, src_mask))
-        x2 = self.norm_3(x)
-        x = x + self.dropout_3(self.ff(x2))
-        return x
-
-    # We can then build a convenient cloning function that can generate multiple layers:
-def get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-class Encoder(nn.Module):
-    def __init__(self, d_model, N, heads, in_size):
-        super().__init__()
-        self.N = N
-        self.embed = Embedder(in_size, d_model)
-        self.pe = PositionalEncoder(d_model)
-        self.layers = get_clones(EncoderLayer(d_model, heads), N)
-        self.norm = Norm(d_model)
-
-    def forward(self, x, mask=None):
-        x = self.embed(x)
-        x = self.pe(x)
-        for i in range(self.N):
-            x = self.layers[i](x, mask)
-        return self.norm(x)
-
-
-class Decoder(nn.Module):
-    def __init__(self, d_model, N, heads, in_size):
-        super().__init__()
-        self.N = N
-        self.embed = Embedder(in_size, d_model)
-        self.pe = PositionalEncoder(d_model)
-        self.layers = get_clones(DecoderLayer(d_model, heads), N)
-        self.norm = Norm(d_model)
-
-    def forward(self, x, e_outputs, src_mask=None, trg_mask=None):
-        x = self.embed(x)
-        x = self.pe(x)
-        for i in range(self.N):
-            x = self.layers[i](x, e_outputs, src_mask, trg_mask)
-        return self.norm(x)
-
-
-class Audio2landmark_speaker_aware_old(nn.Module):
-
-    def __init__(self, spk_emb_enc_size=128,
-                 transformer_d_model=32, N=2, heads=2,
-                 pos_dim=9,
-                 use_prior_net=False, is_noautovc=False):
-        super(Audio2landmark_speaker_aware, self).__init__()
-
-        self.pos_dim = pos_dim
-
-        audio_feat_size = 80 if not use_prior_net else 161
-        audio_feat_size = 258 if is_noautovc else audio_feat_size
-
-        # init audio encoder with content model
-        self.use_prior_net = use_prior_net
-        self.fc_prior = nn.Sequential(
-            nn.Linear(in_features=audio_feat_size, out_features=256),
-            nn.BatchNorm1d(256),
-            nn.LeakyReLU(0.2),
-            nn.Linear(256, 161),
-        )
-
-        self.audio_feat_size = audio_feat_size
-        self.bilstm = nn.LSTM(input_size=161,
-                                             hidden_size=256,
-                                             num_layers=3,
-                                             dropout=0.5,
-                                             bidirectional=False,
-                                             batch_first=True)
-
-        ''' original version '''
-        self.spk_emb_encoder = nn.Sequential(
-            nn.Linear(in_features=256, out_features=256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 128),
-            nn.LeakyReLU(0.02),
-            nn.Linear(128, spk_emb_enc_size),
-        )
-
-        d_model = transformer_d_model * heads
-        N = N
-        heads = heads
-        self.d_model = d_model
-
-        self.encoder = Encoder(d_model, N, heads, in_size=256)
-        self.decoder = Decoder(d_model, N, heads, in_size=256)
-
-
-        self.out_fls_2 = nn.Sequential(
-            nn.Linear(in_features=d_model + 204, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 204),
-        )
-
-        self.out_pos_2 = nn.Sequential(
-            nn.Linear(in_features=d_model, out_features=32),
-            nn.LeakyReLU(0.02),
-            nn.Linear(32, 16),
-            nn.LeakyReLU(0.02),
-            nn.Linear(16, self.pos_dim),
-        )
-
-
-    def forward(self, au, face_id):
-
-        ''' original version '''
-        # audio
-        inputs = au
-        if (self.use_prior_net):
-            inputs = self.fc_prior(inputs.contiguous().view(-1, self.audio_feat_size))
-            inputs = inputs.view(-1, 18, 161)
-
-        audio_encode, (_, _) = self.bilstm(inputs)
-        audio_encode = audio_encode[:, -1, :]
-
-
-
-        # multi-attention
-        comb_encode = audio_encode
-        src_feat = comb_encode.unsqueeze(0)
-        e_outputs = self.encoder(src_feat)[0]
-        # e_outputs = comb_encode
-
-        fl_input = e_outputs #[:, 0:self.d_model//4*3]
-        pos_input = e_outputs #[:, self.d_model//4*3:]
-
-        fl_input = torch.cat([fl_input, face_id], dim=1)
-        fl_pred = self.out_fls_2(fl_input)
-        pos_pred = self.out_pos_2(pos_input)
-
-        return fl_pred, pos_pred, face_id[0:1, :], None
-
-
-class Audio2landmark_speaker_aware(nn.Module):
-
-    def __init__(self, audio_feat_size=80, c_enc_hidden_size=256, num_layers=3, drop_out=0,
-                 spk_feat_size=256, spk_emb_enc_size=128, lstm_g_win_size=64, add_info_size=6,
-                 transformer_d_model=32, N=2, heads=2, z_size=128, audio_dim=256):
-        super(Audio2landmark_speaker_aware, self).__init__()
-
-        self.lstm_g_win_size = lstm_g_win_size
-        self.add_info_size = add_info_size
-        comb_mlp_size = c_enc_hidden_size * 2
-
-        self.audio_content_encoder = nn.LSTM(input_size=audio_feat_size,
-                                             hidden_size=c_enc_hidden_size,
-                                             num_layers=num_layers,
-                                             dropout=drop_out,
-                                             bidirectional=False,
-                                             batch_first=True)
-
-        self.use_audio_projection = not (audio_dim == c_enc_hidden_size)
-        if(self.use_audio_projection):
-            self.audio_projection = nn.Sequential(
-                nn.Linear(in_features=c_enc_hidden_size, out_features=256),
-                nn.LeakyReLU(0.02),
-                nn.Linear(256, 128),
-                nn.LeakyReLU(0.02),
-                nn.Linear(128, audio_dim),
-            )
-
-
-        ''' original version '''
-        self.spk_emb_encoder = nn.Sequential(
-            nn.Linear(in_features=spk_feat_size, out_features=256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 128),
-            nn.LeakyReLU(0.02),
-            nn.Linear(128, spk_emb_enc_size),
-        )
-
-        d_model = transformer_d_model * heads
-        N = N
-        heads = heads
-
-        self.encoder = Encoder(d_model, N, heads, in_size=audio_dim + spk_emb_enc_size + z_size)
-        self.decoder = Decoder(d_model, N, heads, in_size=204)
-        self.out = nn.Sequential(
-            nn.Linear(in_features=d_model + z_size, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 204),
-        )
-
-
-    def forward(self, au, emb, face_id, add_z_spk=False, another_emb=None):
-
-        # audio
-        audio_encode, (_, _) = self.audio_content_encoder(au)
-        audio_encode = audio_encode[:, -1, :]
-
-        if(self.use_audio_projection):
-            audio_encode = self.audio_projection(audio_encode)
-
-        # spk
-        spk_encode = self.spk_emb_encoder(emb)
-        if(add_z_spk):
-            z_spk = torch.tensor(torch.randn(spk_encode.shape)*0.01, requires_grad=False, dtype=torch.float).to(device)
-            spk_encode = spk_encode + z_spk
-
-        # comb
-        z = torch.tensor(torch.zeros(au.shape[0], 128), requires_grad=False, dtype=torch.float).to(device)
-        comb_encode = torch.cat((audio_encode, spk_encode, z), dim=1)
-        src_feat = comb_encode.unsqueeze(0)
-
-        e_outputs = self.encoder(src_feat)[0]
-
-        e_outputs = torch.cat((e_outputs, z), dim=1)
-
-        fl_pred = self.out(e_outputs)
-
-        return fl_pred, face_id[0:1, :], spk_encode
-
-
-
-def nopeak_mask(size):
-    np_mask = np.triu(np.ones((1, size, size)), k=1).astype('uint8')
-    np_mask = torch.tensor(torch.from_numpy(np_mask) == 0)
-    np_mask = np_mask.to(device)
-    return np_mask
-
-
-def create_masks(src, trg):
-    src_mask = (src != torch.zeros_like(src, requires_grad=False))
-
-    if trg is not None:
-        size = trg.size(1)  # get seq_len for matrix
-        np_mask = nopeak_mask(size)
-        np_mask = np_mask.to(device)
-        trg_mask = np_mask
-
-    else:
-        trg_mask = None
-    return src_mask, trg_mask
-
-
-
-class Transformer_DT(nn.Module):
-    def __init__(self, transformer_d_model=32, N=2, heads=2, spk_emb_enc_size=128):
-        super(Transformer_DT, self).__init__()
-        d_model = transformer_d_model * heads
-        self.encoder = Encoder(d_model, N, heads, in_size=204 + spk_emb_enc_size)
-        self.out = nn.Sequential(
-            nn.Linear(in_features=d_model, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 1),
-        )
-
-    def forward(self, fls, spk_emb, win_size=64, win_step=16):
-        feat = torch.cat((fls, spk_emb), dim=1)
-
-        win_size = feat.shape[0]-1 if feat.shape[0] <= win_size else win_size
-        D_input = [feat[i:i+win_size:win_step] for i in range(0, feat.shape[0]-win_size, win_step)]
-        D_input = torch.stack(D_input, dim=0)
-        D_output = self.encoder(D_input)
-        D_output = torch.max(D_output, dim=1, keepdim=False)[0]
-        d = self.out(D_output)
-        # d = torch.sigmoid(d)
-        return d
-
-
-class TalkingToon_spk2res_lstmgan_DT(nn.Module):
-    def __init__(self, comb_emb_size=256, lstm_g_hidden_size=256, num_layers=3, drop_out=0, input_size=6):
-        super(TalkingToon_spk2res_lstmgan_DT, self).__init__()
-
-        self.fl_DT = nn.GRU(input_size=comb_emb_size + FACE_ID_FEAT_SIZE,
-                                             hidden_size=lstm_g_hidden_size,
-                                             num_layers=3,
-                                             dropout=0,
-                                             bidirectional=False,
-                                             batch_first=True)
-        self.projection = nn.Sequential(
-            nn.Linear(in_features=lstm_g_hidden_size, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 1),
-        )
-
-        self.maxpool = nn.MaxPool1d(4, 1)
-
-    def forward(self, comb_encode, fls, win_size=32, win_step=1):
-        feat = torch.cat((comb_encode, fls), dim=1)
-        # v
-        # feat = torch.cat((comb_encode[0:-1], fls[1:] - fls[0:-1]), dim=1)
-
-        # max pooling
-        feat = feat.transpose(0, 1).unsqueeze(0)
-        feat = self.maxpool(feat)
-        feat = feat[0].transpose(0, 1)
-
-        win_size = feat.shape[0] - 1 if feat.shape[0] <= win_size else win_size
-        D_input = [feat[i:i+win_size:win_step] for i in range(0, feat.shape[0]-win_size)]
-        D_input = torch.stack(D_input, dim=0)
-        D_output, _ = self.fl_DT(D_input)
-        D_output = D_output[:, -1, :]
-        d = self.projection(D_output)
-        # d = torch.sigmoid(d)
-        return d
\ No newline at end of file
diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_base_32khz.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_base_32khz.py
deleted file mode 100644
index 4e364614537e426f21c18a2c2a9d94b3babce051..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_base_32khz.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=32, partition=partitions)
-    launcher.bind_(solver='musicgen/musicgen_base_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-
-    fsdp = {'autocast': False, 'fsdp.use': True}
-    medium = {'model/lm/model_scale': 'medium'}
-    large = {'model/lm/model_scale': 'large'}
-
-    cfg_low = {'classifier_free_guidance.training_dropout': 0.2}
-    wd_low = {'conditioners.description.t5.word_dropout': 0.2}
-
-    adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
-
-    launcher.bind_(fsdp)
-
-    launcher.slurm_(gpus=32).bind_(label='32gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub()
-
-    launcher.slurm_(gpus=64).bind_(label='64gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(medium, adam)
-
-    launcher.slurm_(gpus=96).bind_(label='96gpus')
-    with launcher.job_array():
-        sub = launcher.bind()
-        sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3})
diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/solvers/base.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/solvers/base.py
deleted file mode 100644
index 0432e44a36838c5731711f9d54f81822b21f20bd..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/solvers/base.py
+++ /dev/null
@@ -1,631 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-import typing as tp
-
-import flashy
-import omegaconf
-import torch
-from torch import nn
-
-from .. import optim
-from ..optim import fsdp
-from ..utils import checkpoint
-from ..utils.autocast import TorchAutocast
-from ..utils.best_state import BestStateDictManager
-from ..utils.deadlock import DeadlockDetect
-from ..utils.profiler import Profiler
-from ..utils.utils import copy_state, dict_from_config, model_hash, with_rank_rng
-
-
-class StandardSolver(ABC, flashy.BaseSolver):
-    """Standard solver for AudioCraft.
-
-    The standard solver implements a base training loop with the following stages:
-    train, valid, evaluate and generate that are expected to be all defined for
-    solvers in AudioCraft. It also provides a nice default management of Dora history replay,
-    checkpoint management across epoch, and logging configuration.
-
-    AudioCraft solvers must inherit from the StandardSolver and define the methods
-    associated to each stage as well as the show, build_model and build_dataloaders methods.
-    """
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__()
-        self.logger.info(f"Instantiating solver {self.__class__.__name__} for XP {self.xp.sig}")
-        self.logger.info(f"All XP logs are stored in {self.xp.folder}")
-        self.cfg = cfg
-        self.device = cfg.device
-        self.model: nn.Module
-        self._continue_best_source_keys = ['best_state', 'fsdp_best_state']
-        self._fsdp_modules: tp.List[fsdp.FSDP] = []
-        self._ema_sources: nn.ModuleDict = nn.ModuleDict()
-        self.ema: tp.Optional[optim.ModuleDictEMA] = None
-        self.dataloaders: tp.Dict[str, torch.utils.data.DataLoader] = dict()
-        self._log_updates = self.cfg.logging.get('log_updates', 10)
-        if self.cfg.logging.log_tensorboard:
-            self.init_tensorboard(**self.cfg.get('tensorboard'))
-        if self.cfg.logging.log_wandb and self:
-            self.init_wandb(**self.cfg.get('wandb'))
-        # keep a copy of the best performing state for stateful objects
-        # used for evaluation and generation stages
-        dtype_best: tp.Optional[torch.dtype] = None
-        if self.cfg.fsdp.use:
-            dtype_best = getattr(torch, self.cfg.fsdp.param_dtype)  # type: ignore
-            assert isinstance(dtype_best, torch.dtype)
-        elif self.cfg.autocast:
-            dtype_best = getattr(torch, self.cfg.autocast_dtype)  # type: ignore
-            assert isinstance(dtype_best, torch.dtype)
-        self.best_state: BestStateDictManager = BestStateDictManager(dtype=dtype_best)
-        # Hacky support for keeping a copy of the full best state in rank0.
-        self.fsdp_best_state: tp.Dict[str, tp.Any] = {}
-        self.register_stateful('best_state', 'fsdp_best_state')  # register best_state object to keep it in state_dict
-        self._new_best_state: bool = False  # should save a new checkpoint
-        # instantiate datasets and appropriate number of updates per epoch
-        self.build_dataloaders()
-        if self.cfg.execute_only is None:
-            assert 'train' in self.dataloaders, "The train dataset split must be provided."
-            assert 'valid' in self.dataloaders, "The valid dataset split must be provided."
-        self.train_updates_per_epoch = len(self.dataloaders['train']) if 'train' in self.dataloaders else 0
-        if self.cfg.optim.updates_per_epoch:
-            self.train_updates_per_epoch = self.cfg.optim.updates_per_epoch
-        self.total_updates = self.train_updates_per_epoch * self.cfg.optim.epochs
-        # instantiate model & exponential moving average on the model
-        self.build_model()
-        self.logger.info("Model hash: %s", model_hash(self.model))
-        assert 'model' in self.stateful.sources, \
-            "Please register the model to stateful with self.register_stateful('model') in build_model."
-        self.profiler = Profiler(self.model, **self.cfg.profiler)
-        self.initialize_ema()
-        self.register_stateful('ema')
-        assert self.ema is None or 'ema' in self.stateful.sources, \
-            "Please register the ema to stateful with self.register_stateful('ema') in build_model."
-        self.deadlock_detect = DeadlockDetect(**self.cfg.deadlock)
-        # basic statistics on the trained model
-        model_size = sum(p.numel() for p in self.model.parameters() if p.requires_grad) / 1e6
-        # one copy of grad, one copy of momentum, one copy of denominator and model weights.
-        # and 4 bytes for each float!
-        mem_usage = model_size * 4 * 4 / 1000
-        self.logger.info("Model size: %.2f M params", model_size)
-        self.logger.info("Base memory usage, with model, grad and optim: %.2f GB", mem_usage)
-
-    @property
-    def autocast(self):
-        """Convenient autocast (or not) using the solver configuration."""
-        return TorchAutocast(enabled=self.cfg.autocast, device_type=self.device, dtype=self.autocast_dtype)
-
-    def _get_state_source(self, name) -> flashy.state.StateDictSource:
-        # Internal utility to get a state source from the solver
-        return self.stateful.sources[name]
-
-    @property
-    def best_metric_name(self) -> tp.Optional[str]:
-        """Metric name used to identify the best state. This metric should be stored in the metrics
-        used on the stage for best state identification (most likely, `valid`). If None, then
-        no best state is saved.
-        """
-        return None
-
-    def register_best_state(self, *args: str):
-        """Register state sources in `BestStateDictManager` to keep their best states along with their
-        latest states. The best state will be used at evaluation stages instead of the latest states.
-
-        Shortcut around `BestStateDictManager.register` method. You can pass any number of
-        attribute, included nested attributes and those will be included into the checkpoints
-        and automatically restored when `BaseSolver.restore` is called.
-        """
-        for name in args:
-            state_source = self._get_state_source(name)
-            assert name in self.stateful.sources, "Registered states in best should be registered in stateful first!"
-            self.best_state.register(name, state_source)
-
-    def register_ema(self, *args: str):
-        """Register state sources for exponential moving average.
-
-        The registered sources are used to instantiate a ModuleDictEMA instance.
-        The ModuleDictEMA keeps a `nn.ModuleDict` module that is updated when self.ema.step() is called
-        and swapped with the original state sources with self.swap_ema_state() method.
-
-        Usage:
-            self.register_ema('model')
-        """
-        assert self.ema is None, "Cannot register state source to already instantiated EMA."
-        for name in args:
-            self._ema_sources[name] = getattr(self, name)
-
-    def wrap_with_fsdp(self, model: torch.nn.Module, *args, **kwargs):
-        model = fsdp.wrap_with_fsdp(self.cfg.fsdp, model, *args, **kwargs)
-        if isinstance(model, fsdp.FSDP):
-            self._fsdp_modules.append(model)
-        return model
-
-    def update_best_state_from_stage(self, stage_name: str = 'valid'):
-        """Update latest best state based on pending metrics of a given stage. This method relies
-        on the `BestStateDictManager.update` method to update the best state_dict with latest weights
-        if the registered states happen to match to the best performing setup.
-        """
-        if self.best_metric_name is None:
-            # when no best metric is defined, the last state is always the best
-            self._new_best_state = True
-            self.logger.info("Updating best state with current state.")
-        else:
-            assert stage_name in self._pending_metrics, f"Metrics for stage {stage_name} not found."
-            assert self.best_metric_name in self._pending_metrics[stage_name], \
-                f"Best metric not found in {stage_name} metrics. Cannot register best state"
-            current_score = self._pending_metrics[stage_name][self.best_metric_name]
-            all_best_metric_scores = [
-                past_metrics[stage_name][self.best_metric_name]
-                for past_metrics in self.history
-            ]
-            all_best_metric_scores.append(current_score)
-            best_score = min(all_best_metric_scores)
-            self._new_best_state = current_score == best_score
-            if self._new_best_state:
-                old_best = min(all_best_metric_scores[:-1] + [float('inf')])
-                self.logger.info(
-                    f"New best state with {self.best_metric_name}={current_score:.3f} (was {old_best:.3f})")
-
-        if self._new_best_state:
-            if self.cfg.fsdp.use:
-                # this will give an empty state dict on all ranks but the rank 0
-                # which will have a copy in memory of the full model.
-                with fsdp.switch_to_full_state_dict(self._fsdp_modules):
-                    for name in self.best_state.states.keys():
-                        state_source = self._get_state_source(name)
-                        self.best_state.update(name, state_source)
-                    # we save to a different dict.
-                    self.fsdp_best_state.update(self.best_state.state_dict())
-                # We cannot efficiently load fsdp_best_state when using FSDP,
-                # so we have do do a second pass, with the local shards.
-            for name in self.best_state.states.keys():
-                state_source = self._get_state_source(name)
-                self.best_state.update(name, state_source)
-
-    def _load_new_state_dict(self, state_dict: dict) -> dict:
-        old_states = {}
-        for name, new_state in state_dict.items():
-            state_source = self._get_state_source(name)
-            old_states[name] = copy_state(state_source.state_dict())
-            state_source.load_state_dict(new_state)
-        return old_states
-
-    @contextmanager
-    def swap_best_state(self):
-        self.logger.debug(f"Swapping to best state for: {', '.join(self.best_state.state_dict().keys())}")
-        old_states = self._load_new_state_dict(self.best_state.state_dict())
-        try:
-            yield
-        finally:
-            self.logger.debug("Swapping back from best to original state")
-            for name, old_state in old_states.items():
-                state_source = self._get_state_source(name)
-                state_source.load_state_dict(old_state)
-
-    @contextmanager
-    def swap_ema_state(self):
-        if self.ema is None:
-            yield
-        else:
-            ema_state_dict = self.ema.state_dict()['state']
-            self.logger.debug(f"Swapping to EMA state for: {', '.join(ema_state_dict.keys())}")
-            old_states = self._load_new_state_dict(ema_state_dict)
-            try:
-                yield
-            finally:
-                self.logger.debug("Swapping back from EMA state to original state")
-                for name, old_state in old_states.items():
-                    state_source = self._get_state_source(name)
-                    state_source.load_state_dict(old_state)
-
-    @property
-    def is_training(self):
-        return self.current_stage == 'train'
-
-    def log_model_summary(self, model: nn.Module):
-        """Log model summary, architecture and size of the model."""
-        self.logger.info(model)
-        mb = sum(p.numel() for p in model.parameters()) * 4 / 2 ** 20
-        self.logger.info("Size: %.1f MB", mb)
-
-    @abstractmethod
-    def build_model(self):
-        """Method to implement to initialize model."""
-        ...
-
-    def initialize_ema(self):
-        """Initialize exponential moving average with the registered sources.
-        EMA object is created if the optim.ema.model.decay value is non-null.
-        """
-        from .builders import get_ema
-        self.ema = get_ema(self._ema_sources, self.cfg.optim.ema)
-        if self.ema is None:
-            self.logger.info('No EMA on the model.')
-        else:
-            assert self.cfg.optim.ema.updates > 0
-            self.logger.info(
-                f'Initializing EMA on the model with decay = {self.ema.decay}'
-                f' every {self.cfg.optim.ema.updates} updates'
-            )
-
-    @abstractmethod
-    def build_dataloaders(self):
-        """Method to implement to initialize dataloaders."""
-        ...
-
-    @abstractmethod
-    def show(self):
-        """Method to log any information without running the job."""
-        ...
-
-    @property
-    def log_updates(self):
-        # convenient access to log updates
-        return self._log_updates
-
-    def checkpoint_path(self, **kwargs):
-        kwargs.setdefault('use_fsdp', self.cfg.fsdp.use)
-        return self.folder / checkpoint.checkpoint_name(**kwargs)
-
-    def epoch_checkpoint_path(self, epoch: int, **kwargs):
-        kwargs.setdefault('use_fsdp', self.cfg.fsdp.use)
-        return self.folder / checkpoint.checkpoint_name(str(epoch), **kwargs)
-
-    def checkpoint_path_with_name(self, name: str, **kwargs):
-        kwargs.setdefault('use_fsdp', self.cfg.fsdp.use)
-        return self.folder / checkpoint.checkpoint_name(name=name, **kwargs)
-
-    def save_checkpoints(self):
-        """Save checkpoint, optionally keeping a copy for a given epoch."""
-        is_sharded = self.cfg.fsdp.use
-        if not flashy.distrib.is_rank_zero() and not is_sharded:
-            return
-        self.logger.info("Model hash: %s", model_hash(self.model))
-        state = self.state_dict()
-        epoch = self.epoch - 1  # pushing metrics will increase the epoch in Flashy, so we do -1 here
-
-        # save minimal state_dict as new checkpoint every X epoch
-        if self.cfg.checkpoint.save_every:
-            if epoch % self.cfg.checkpoint.save_every == 0:
-                minimal_state = state
-                if self.cfg.checkpoint.keep_every_states is not None and len(self.cfg.checkpoint.keep_every_states) > 0:
-                    minimal_state = {
-                        name: source for name, source in state.items()
-                        if name in self.cfg.checkpoint.keep_every_states
-                    }
-                epoch_checkpoint_path = self.epoch_checkpoint_path(epoch)
-                checkpoint.save_checkpoint(minimal_state, epoch_checkpoint_path, is_sharded)
-
-        # save checkpoint as latest checkpoint
-        if self.cfg.checkpoint.save_last:
-            last_checkpoint_path = self.checkpoint_path()
-            checkpoint.save_checkpoint(state, last_checkpoint_path, is_sharded)
-
-        # flush any stale checkpoint to reduce disk footprint
-        checkpoint.flush_stale_checkpoints(self.checkpoint_path())
-
-    def load_from_pretrained(self, name: str) -> dict:
-        raise NotImplementedError("Solver does not provide a way to load pretrained models.")
-
-    def load_checkpoints(self, load_best: bool = False, ignore_state_keys: tp.List[str] = []) -> tp.Optional[dict]:
-        """Load last checkpoint or the one specified in continue_from.
-
-        Args:
-            load_best (bool): Whether to load from best state dict or not.
-                Best state dict is always used when not loading the current xp.
-            ignore_state_keys (list of str): List of sources to ignore when loading the state, e.g. `optimizer`.
-        Returns:
-            state (dict, optional): The loaded state dictionary.
-        """
-        # load checkpoints from xp folder or cfg.continue_from
-        is_sharded = self.cfg.fsdp.use
-        load_from_path: tp.Optional[Path] = None
-        checkpoint_source: tp.Optional[checkpoint.CheckpointSource] = None
-
-        if load_best:
-            self.logger.info("Trying to load state_dict from best state.")
-
-        state: tp.Optional[dict] = None
-        rank0_checkpoint_path = self.checkpoint_path(use_fsdp=False)
-        current_checkpoint_path = self.checkpoint_path()
-        _pretrained_prefix = '//pretrained/'
-        continue_pretrained = (self.cfg.continue_from or '').startswith(_pretrained_prefix)
-        if rank0_checkpoint_path.exists():
-            self.logger.info(f"Loading existing checkpoint: {current_checkpoint_path}")
-            load_from_path = current_checkpoint_path
-            checkpoint.check_sharded_checkpoint(current_checkpoint_path, rank0_checkpoint_path)
-            checkpoint_source = checkpoint.CheckpointSource.CURRENT_XP
-        elif self.cfg.continue_from and not continue_pretrained:
-            self.logger.info(f"Continuing from provided checkpoint: {self.cfg.continue_from}")
-            # we're always continuing from consolidated checkpoints: self.cfg.use_fsdp and not continue_best
-            load_from_path = checkpoint.resolve_checkpoint_path(self.cfg.continue_from, use_fsdp=False)
-            if load_from_path is None:
-                self.logger.error('Could not resolve the continue_from checkpoint %s', self.cfg.continue_from)
-                raise RuntimeError(f'Could not resolve continue_from checkpoint {self.cfg.continue_from}')
-            checkpoint_source = checkpoint.CheckpointSource.OTHER
-
-        if load_from_path is not None:
-            state = checkpoint.load_checkpoint(load_from_path, is_sharded)
-        elif continue_pretrained:
-            self.logger.info("Loading a pretrained model. Ignoring 'load_best' and 'ignore_state_keys' params.")
-            state = self.load_from_pretrained(self.cfg.continue_from[len(_pretrained_prefix):])
-            checkpoint_source = checkpoint.CheckpointSource.PRETRAINED
-            load_best = True
-
-        # checkpoints are not from the current xp, we only retrieve the best state
-        if checkpoint_source is not None and checkpoint_source != checkpoint.CheckpointSource.CURRENT_XP:
-            assert state is not None
-            self.logger.info("Checkpoint source is not the current xp: Load state_dict from best state.")
-            load_best = True
-            state = {key: state[key] for key in self._continue_best_source_keys if key in state}
-            # loaded checkpoints are FSDP checkpoints: we're reading the best state
-            # from FSDP and we drop the regular best_state
-            if 'fsdp_best_state' in state and state['fsdp_best_state']:
-                state.pop('best_state', None)
-                self.logger.info("... Loaded checkpoint has FSDP best state")
-            # FSDP is enabled in the solver, if the loaded checkpoints do not have FSDP support
-            # then we're initializing FSDP best state with the regular best state
-            elif self.cfg.fsdp.use:
-                if 'fsdp_best_state' not in state or not state['fsdp_best_state']:
-                    # we swap non-FSDP checkpoints best_state to FSDP-compatible best state
-                    state['fsdp_best_state'] = state.pop('best_state')
-                    self.logger.info("... Loaded checkpoint does not have FSDP best state. Use regular best state")
-
-        if state is not None:
-            if load_best:
-                self.logger.info("Ignoring keys when loading best %r", ignore_state_keys)
-                for key in set(ignore_state_keys):
-                    if key in state:
-                        state.pop(key)
-                has_best_state = 'best_state' in state or 'fsdp_best_state' in state
-                assert has_best_state, ("Trying to load best state but neither 'best_state'",
-                                        " or 'fsdp_best_state' found in checkpoints.")
-            self.load_state_dict(state)
-
-        # for FSDP, let's make extra sure nothing bad happened with out of sync
-        # checkpoints across workers.
-        epoch = float(self.epoch)
-        avg_epoch = flashy.distrib.average_metrics({'epoch': epoch})['epoch']
-        if avg_epoch != epoch:
-            raise RuntimeError(
-                f"Inconsistent loading of checkpoints happened, our epoch is {epoch} "
-                f"but average of epochs is {avg_epoch}, at least one gpu must have a "
-                "different epoch number.")
-
-        # on load_best, properly reinitialize state_dict, best states and ema
-        # otherwise we load from the current xp and don't alter anything
-        if load_best:
-            self.logger.info("Loading state_dict from best state.")
-            if not self.cfg.fsdp.use and self.fsdp_best_state:
-                # loading from an FSDP checkpoint but with FSDP deactivated
-                self.logger.info("... Loading from FSDP best state dict.")
-                self.best_state.load_state_dict(self.fsdp_best_state)
-
-            # if load_best, we permanently override the regular state_dict with the best state
-            if self.cfg.fsdp.use:
-                self.logger.info("FSDP is used, loading from FSDP best state.")
-                with fsdp.switch_to_full_state_dict(self._fsdp_modules):
-                    # this might be really fragile but okay for now.
-                    self.load_state_dict(self.fsdp_best_state)
-            else:
-                # we permanently swap the stateful objects to their best state
-                self._load_new_state_dict(self.best_state.state_dict())
-
-            # the EMA modules should also be instantiated with best state.
-            # the easiest way to do so is to reinitialize a new EMA with best state loaded.
-            if self.ema is not None:
-                self.logger.info("Re-initializing EMA from best state")
-                self.initialize_ema()
-
-            if self.cfg.fsdp.use:
-                self.logger.info("Re-initializing best state after using FSDP best state.")
-                for name in self.best_state.states.keys():
-                    state_source = self._get_state_source(name)
-                    self.best_state.update(name, state_source)
-
-        return state
-
-    def restore(self, load_best: bool = False, replay_metrics: bool = False,
-                ignore_state_keys: tp.List[str] = []) -> bool:
-        """Restore the status of a solver for a given xp.
-
-        Args:
-            load_best (bool): if `True`, load the best state from the checkpoint.
-            replay_metrics (bool): if `True`, logs all the metrics from past epochs.
-            ignore_state_keys (list of str): list of sources to ignore when loading the state, e.g. `optimizer`.
-        """
-        self.logger.info("Restoring weights and history.")
-        restored_checkpoints = self.load_checkpoints(load_best, ignore_state_keys)
-
-        self.logger.info("Model hash: %s", model_hash(self.model))
-
-        if replay_metrics and len(self.history) > 0:
-            self.logger.info("Replaying past metrics...")
-            for epoch, stages in enumerate(self.history):
-                for stage_name, metrics in stages.items():
-                    # We manually log the metrics summary to the result logger
-                    # as we don't want to add them to the pending metrics
-                    self.result_logger._log_summary(stage_name, metrics, step=epoch + 1, step_name='epoch',
-                                                    formatter=self.get_formatter(stage_name))
-        return restored_checkpoints is not None
-
-    def commit(self, save_checkpoints: bool = True):
-        """Commit metrics to dora and save checkpoints at the end of an epoch."""
-        # we override commit to introduce more complex checkpoint saving behaviors
-        self.history.append(self._pending_metrics)  # This will increase self.epoch
-        if save_checkpoints:
-            self.save_checkpoints()
-        self._start_epoch()
-        if flashy.distrib.is_rank_zero():
-            self.xp.link.update_history(self.history)
-
-    def run_epoch(self):
-        """Run a single epoch with all stages.
-
-        Metrics for a given stage are stored in _pending_metrics and committed by the solver afterwards.
-        Children solvers can extend this method with custom behavior, e.g.:
-
-            def run_epoch(self):
-                ... # custom code
-                super().run_epoch()
-                ... # custom code
-        """
-        self.run_stage('train', self.train)
-        with torch.no_grad():
-            with self.swap_ema_state():
-                self.run_stage('valid', self.valid)
-                # the best state is updated with EMA states if available
-                self.update_best_state_from_stage('valid')
-            with self.swap_best_state():
-                if self.should_run_stage('evaluate'):
-                    self.run_stage('evaluate', self.evaluate)
-                if self.should_run_stage('generate'):
-                    self.run_stage('generate', with_rank_rng()(self.generate))
-
-    def run(self):
-        """Training loop."""
-        assert len(self.state_dict()) > 0
-        self.restore(replay_metrics=True)  # load checkpoint and replay history
-        self.log_hyperparams(dict_from_config(self.cfg))
-        for epoch in range(self.epoch, self.cfg.optim.epochs + 1):
-            if self.should_stop_training():
-                return
-            self.run_epoch()
-            # Commit will send the metrics to Dora and save checkpoints by default.
-            self.commit()
-
-    def should_stop_training(self) -> bool:
-        """Check whether we should stop training or not."""
-        return self.epoch > self.cfg.optim.epochs
-
-    def should_run_stage(self, stage_name) -> bool:
-        """Check whether we want to run the specified stages."""
-        stage_every = self.cfg[stage_name].get('every', None)
-        is_last_epoch = self.epoch == self.cfg.optim.epochs
-        is_epoch_every = (stage_every and self.epoch % stage_every == 0)
-        return is_last_epoch or is_epoch_every
-
-    @abstractmethod
-    def run_step(self, idx: int, batch: tp.Any, metrics: dict):
-        """Perform one training or valid step on a given batch."""
-        ...
-
-    def common_train_valid(self, dataset_split: str, **kwargs: tp.Any):
-        """Common logic for train and valid stages."""
-        self.model.train(self.is_training)
-
-        loader = self.dataloaders[dataset_split]
-        # get a different order for distributed training, otherwise this will get ignored
-        if flashy.distrib.world_size() > 1 \
-           and isinstance(loader.sampler, torch.utils.data.distributed.DistributedSampler):
-            loader.sampler.set_epoch(self.epoch)
-        updates_per_epoch = self.train_updates_per_epoch if self.is_training else len(loader)
-        if self.cfg.benchmark_no_load:
-            self.logger.warning("Fake loading for benchmarking: re-using first batch")
-            batch = next(iter(loader))
-            loader = [batch] * updates_per_epoch  # type: ignore
-        lp = self.log_progress(self.current_stage, loader, total=updates_per_epoch, updates=self.log_updates)
-        average = flashy.averager()  # epoch wise average
-        instant_average = flashy.averager()  # average between two logging
-        metrics: dict = {}
-
-        with self.profiler, self.deadlock_detect:  # profiler will only run for the first 20 updates.
-            for idx, batch in enumerate(lp):
-                self.deadlock_detect.update('batch')
-                if idx >= updates_per_epoch:
-                    break
-                metrics = {}
-                metrics = self.run_step(idx, batch, metrics)
-                self.deadlock_detect.update('step')
-                # run EMA step
-                if self.ema is not None and self.is_training and (idx + 1) % self.cfg.optim.ema.updates == 0:
-                    self.logger.debug("EMA model step")
-                    self.ema.step()
-                self.deadlock_detect.update('ema')
-                self.profiler.step()
-                instant_metrics = instant_average(metrics)
-                if lp.update(**instant_metrics):
-                    instant_average = flashy.averager()  # reset averager between two logging
-                metrics = average(metrics)  # epoch wise average
-                self.deadlock_detect.update('end_batch')
-
-        metrics = flashy.distrib.average_metrics(metrics, updates_per_epoch)
-        return metrics
-
-    def train(self):
-        """Train stage."""
-        return self.common_train_valid('train')
-
-    def valid(self):
-        """Valid stage."""
-        return self.common_train_valid('valid')
-
-    @abstractmethod
-    def evaluate(self):
-        """Evaluate stage."""
-        ...
-
-    @abstractmethod
-    def generate(self):
-        """Generate stage."""
-        ...
-
-    def run_one_stage(self, stage_name: str):
-        """Run only the specified stage.
-        This method is useful to only generate samples from a trained experiment
-        or rerun the validation or evaluation stages.
-        """
-        fn = {
-            'generate': with_rank_rng()(self.generate),
-            'evaluate': self.evaluate,
-            'valid': self.valid,
-        }
-        if stage_name not in fn:
-            raise ValueError(f'Trying to run stage {stage_name} is not supported.')
-        assert len(self.state_dict()) > 0
-        self._start_epoch()
-        with torch.no_grad(), self.swap_best_state():
-            self.run_stage(stage_name, fn[stage_name])
-        if not self.cfg.execute_inplace:
-            self.commit(save_checkpoints=False)
-
-    @staticmethod
-    def get_eval_solver_from_sig(sig: str, dtype: tp.Optional[str] = None,
-                                 device: tp.Optional[str] = None, autocast: bool = True,
-                                 batch_size: tp.Optional[int] = None,
-                                 override_cfg: tp.Optional[tp.Union[dict, omegaconf.DictConfig]] = None,
-                                 **kwargs):
-        """Mostly a convenience function around audiocraft.train.get_solver_from_sig,
-        populating all the proper param, deactivating EMA, FSDP, loading the best state,
-        basically all you need to get a solver ready to "play" with in single GPU mode
-        and with minimal memory overhead.
-
-        Args:
-            sig (str): signature to load.
-            dtype (str or None): potential dtype, as a string, i.e. 'float16'.
-            device (str or None): potential device, as a string, i.e. 'cuda'.
-            override_cfg (dict or omegaconf.DictConfig or None): potential device, as a string, i.e. 'cuda'.
-        """
-        from audiocraft import train
-        our_override_cfg: tp.Dict[str, tp.Any] = {'optim': {'ema': {'use': False}}}
-        our_override_cfg['autocast'] = autocast
-        if dtype is not None:
-            our_override_cfg['dtype'] = dtype
-        if device is not None:
-            our_override_cfg['device'] = device
-        if batch_size is not None:
-            our_override_cfg['dataset'] = {'batch_size': batch_size}
-        if override_cfg is None:
-            override_cfg = {}
-        override_cfg = omegaconf.OmegaConf.merge(
-            omegaconf.DictConfig(override_cfg), omegaconf.DictConfig(our_override_cfg))  # type: ignore
-        solver = train.get_solver_from_sig(
-            sig, override_cfg=override_cfg,
-            load_best=True, disable_fsdp=True,
-            ignore_state_keys=['optimizer', 'ema'], **kwargs)
-        solver.model.eval()
-        return solver
diff --git a/spaces/matthoffner/chatbot-mini/components/Chat/ChatMessage.tsx b/spaces/matthoffner/chatbot-mini/components/Chat/ChatMessage.tsx
deleted file mode 100644
index 512551f6b271d6c2f71fc8ea1df5618bb1ff8a88..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/components/Chat/ChatMessage.tsx
+++ /dev/null
@@ -1,291 +0,0 @@
-import {
-  IconCheck,
-  IconCopy,
-  IconEdit,
-  IconRobot,
-  IconTrash,
-  IconUser,
-} from '@tabler/icons-react';
-import { FC, memo, useContext, useEffect, useRef, useState } from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-import { updateConversation } from '@/utils/app/conversation';
-
-import { Message } from '@/types/chat';
-
-import HomeContext from '@/pages/api/home/home.context';
-
-import { CodeBlock } from '../Markdown/CodeBlock';
-import { MemoizedReactMarkdown } from '../Markdown/MemoizedReactMarkdown';
-
-import rehypeMathjax from 'rehype-mathjax';
-import remarkGfm from 'remark-gfm';
-import remarkMath from 'remark-math';
-
-export interface Props {
-  message: Message;
-  messageIndex: number;
-  onEdit?: (editedMessage: Message) => void
-}
-
-export const ChatMessage: FC<Props> = memo(({ message, messageIndex, onEdit }) => {
-  const { t } = useTranslation('chat');
-
-  const {
-    state: { selectedConversation, conversations, currentMessage, messageIsStreaming },
-    dispatch: homeDispatch,
-  } = useContext(HomeContext);
-
-  const [isEditing, setIsEditing] = useState<boolean>(false);
-  const [isTyping, setIsTyping] = useState<boolean>(false);
-  const [messageContent, setMessageContent] = useState(message.content);
-  const [messagedCopied, setMessageCopied] = useState(false);
-
-  const textareaRef = useRef<HTMLTextAreaElement>(null);
-
-  const toggleEditing = () => {
-    setIsEditing(!isEditing);
-  };
-
-  const handleInputChange = (event: React.ChangeEvent<HTMLTextAreaElement>) => {
-    setMessageContent(event.target.value);
-    if (textareaRef.current) {
-      textareaRef.current.style.height = 'inherit';
-      textareaRef.current.style.height = `${textareaRef.current.scrollHeight}px`;
-    }
-  };
-
-  const handleEditMessage = () => {
-    if (message.content != messageContent) {
-      if (selectedConversation && onEdit) {
-        onEdit({ ...message, content: messageContent });
-      }
-    }
-    setIsEditing(false);
-  };
-
-  const handleDeleteMessage = () => {
-    if (!selectedConversation) return;
-
-    const { messages } = selectedConversation;
-    const findIndex = messages.findIndex((elm) => elm === message);
-
-    if (findIndex < 0) return;
-
-    if (
-      findIndex < messages.length - 1 &&
-      messages[findIndex + 1].role === 'assistant'
-    ) {
-      messages.splice(findIndex, 2);
-    } else {
-      messages.splice(findIndex, 1);
-    }
-    const updatedConversation = {
-      ...selectedConversation,
-      messages,
-    };
-
-    const { single, all } = updateConversation(
-      updatedConversation,
-      conversations,
-    );
-    homeDispatch({ field: 'selectedConversation', value: single });
-    homeDispatch({ field: 'conversations', value: all });
-  };
-
-  const handlePressEnter = (e: React.KeyboardEvent<HTMLTextAreaElement>) => {
-    if (e.key === 'Enter' && !isTyping && !e.shiftKey) {
-      e.preventDefault();
-      handleEditMessage();
-    }
-  };
-
-  const copyOnClick = () => {
-    if (!navigator.clipboard) return;
-
-    navigator.clipboard.writeText(message.content).then(() => {
-      setMessageCopied(true);
-      setTimeout(() => {
-        setMessageCopied(false);
-      }, 2000);
-    });
-  };
-
-  useEffect(() => {
-    setMessageContent(message.content);
-  }, [message.content]);
-
-
-  useEffect(() => {
-    if (textareaRef.current) {
-      textareaRef.current.style.height = 'inherit';
-      textareaRef.current.style.height = `${textareaRef.current.scrollHeight}px`;
-    }
-  }, [isEditing]);
-
-  return (
-    <div
-      className={`group md:px-4 ${
-        message.role === 'assistant'
-          ? 'border-b border-black/10 bg-gray-50 text-gray-800 dark:border-gray-900/50 dark:bg-[#444654] dark:text-gray-100'
-          : 'border-b border-black/10 bg-white text-gray-800 dark:border-gray-900/50 dark:bg-[#343541] dark:text-gray-100'
-      }`}
-      style={{ overflowWrap: 'anywhere' }}
-    >
-      <div className="relative m-auto flex p-4 text-base md:max-w-2xl md:gap-6 md:py-6 lg:max-w-2xl lg:px-0 xl:max-w-3xl">
-        <div className="min-w-[40px] text-right font-bold">
-          {message.role === 'assistant' ? (
-            <IconRobot size={30} />
-          ) : (
-            <IconUser size={30} />
-          )}
-        </div>
-
-        <div className="prose mt-[-2px] w-full dark:prose-invert">
-          {message.role === 'user' ? (
-            <div className="flex w-full">
-              {isEditing ? (
-                <div className="flex w-full flex-col">
-                  <textarea
-                    ref={textareaRef}
-                    className="w-full resize-none whitespace-pre-wrap border-none dark:bg-[#343541]"
-                    value={messageContent}
-                    onChange={handleInputChange}
-                    onKeyDown={handlePressEnter}
-                    onCompositionStart={() => setIsTyping(true)}
-                    onCompositionEnd={() => setIsTyping(false)}
-                    style={{
-                      fontFamily: 'inherit',
-                      fontSize: 'inherit',
-                      lineHeight: 'inherit',
-                      padding: '0',
-                      margin: '0',
-                      overflow: 'hidden',
-                    }}
-                  />
-
-                  <div className="mt-10 flex justify-center space-x-4">
-                    <button
-                      className="h-[40px] rounded-md bg-blue-500 px-4 py-1 text-sm font-medium text-white enabled:hover:bg-blue-600 disabled:opacity-50"
-                      onClick={handleEditMessage}
-                      disabled={messageContent.trim().length <= 0}
-                    >
-                      {t('Save & Submit')}
-                    </button>
-                    <button
-                      className="h-[40px] rounded-md border border-neutral-300 px-4 py-1 text-sm font-medium text-neutral-700 hover:bg-neutral-100 dark:border-neutral-700 dark:text-neutral-300 dark:hover:bg-neutral-800"
-                      onClick={() => {
-                        setMessageContent(message.content);
-                        setIsEditing(false);
-                      }}
-                    >
-                      {t('Cancel')}
-                    </button>
-                  </div>
-                </div>
-              ) : (
-                <div className="prose whitespace-pre-wrap dark:prose-invert flex-1">
-                  {message.content}
-                </div>
-              )}
-
-              {!isEditing && (
-                <div className="md:-mr-8 ml-1 md:ml-0 flex flex-col md:flex-row gap-4 md:gap-1 items-center md:items-start justify-end md:justify-start">
-                  <button
-                    className="invisible group-hover:visible focus:visible text-gray-500 hover:text-gray-700 dark:text-gray-400 dark:hover:text-gray-300"
-                    onClick={toggleEditing}
-                  >
-                    <IconEdit size={20} />
-                  </button>
-                  <button
-                    className="invisible group-hover:visible focus:visible text-gray-500 hover:text-gray-700 dark:text-gray-400 dark:hover:text-gray-300"
-                    onClick={handleDeleteMessage}
-                  >
-                    <IconTrash size={20} />
-                  </button>
-                </div>
-              )}
-            </div>
-          ) : (
-            <div className="flex flex-row">
-              <MemoizedReactMarkdown
-                className="prose dark:prose-invert flex-1"
-                remarkPlugins={[remarkGfm, remarkMath]}
-                rehypePlugins={[rehypeMathjax]}
-                components={{
-                  code({ node, inline, className, children, ...props }) {
-                    if (children.length) {
-                      if (children[0] == '▍') {
-                        return <span className="animate-pulse cursor-default mt-1">▍</span>
-                      }
-
-                      children[0] = (children[0] as string).replace("`▍`", "▍")
-                    }
-
-                    const match = /language-(\w+)/.exec(className || '');
-
-                    return !inline ? (
-                      <CodeBlock
-                        key={Math.random()}
-                        language={(match && match[1]) || ''}
-                        value={String(children).replace(/\n$/, '')}
-                        {...props}
-                      />
-                    ) : (
-                      <code className={className} {...props}>
-                        {children}
-                      </code>
-                    );
-                  },
-                  table({ children }) {
-                    return (
-                      <table className="border-collapse border border-black px-3 py-1 dark:border-white">
-                        {children}
-                      </table>
-                    );
-                  },
-                  th({ children }) {
-                    return (
-                      <th className="break-words border border-black bg-gray-500 px-3 py-1 text-white dark:border-white">
-                        {children}
-                      </th>
-                    );
-                  },
-                  td({ children }) {
-                    return (
-                      <td className="break-words border border-black px-3 py-1 dark:border-white">
-                        {children}
-                      </td>
-                    );
-                  },
-                }}
-              >
-                {`${message.content}${
-                  messageIsStreaming && messageIndex == (selectedConversation?.messages.length ?? 0) - 1 ? '`▍`' : ''
-                }`}
-              </MemoizedReactMarkdown>
-
-              <div className="md:-mr-8 ml-1 md:ml-0 flex flex-col md:flex-row gap-4 md:gap-1 items-center md:items-start justify-end md:justify-start">
-                {messagedCopied ? (
-                  <IconCheck
-                    size={20}
-                    className="text-green-500 dark:text-green-400"
-                  />
-                ) : (
-                  <button
-                    className="invisible group-hover:visible focus:visible text-gray-500 hover:text-gray-700 dark:text-gray-400 dark:hover:text-gray-300"
-                    onClick={copyOnClick}
-                  >
-                    <IconCopy size={20} />
-                  </button>
-                )}
-              </div>
-            </div>
-          )}
-        </div>
-      </div>
-    </div>
-  );
-});
-ChatMessage.displayName = 'ChatMessage';
diff --git a/spaces/matthoffner/starchat-ui/docs/google_search.md b/spaces/matthoffner/starchat-ui/docs/google_search.md
deleted file mode 100644
index 026e067f403c6bfc9efdfcf443de273e380afe70..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/starchat-ui/docs/google_search.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Google Search Tool
-
-Use the Google Search API to search the web in Chatbot UI.
-
-## How To Enable
-
-1. Create a new project at https://console.developers.google.com/apis/dashboard
-
-2. Create a new API key at https://console.developers.google.com/apis/credentials
-
-3. Enable the Custom Search API at https://console.developers.google.com/apis/library/customsearch.googleapis.com
-
-4. Create a new Custom Search Engine at https://cse.google.com/cse/all
-
-5. Add your API Key and your Custom Search Engine ID to your .env.local file
-
-6. You can now select the Google Search Tool in the search tools dropdown
-
-## Usage Limits
-
-Google gives you 100 free searches per day. You can increase this limit by creating a billing account.
diff --git a/spaces/meeww/Minecraft_Skin_Generator/app.py b/spaces/meeww/Minecraft_Skin_Generator/app.py
deleted file mode 100644
index 3107606ea971104425e51fbc847f6773906a56df..0000000000000000000000000000000000000000
--- a/spaces/meeww/Minecraft_Skin_Generator/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import gradio as gr
-from image_dataset import ImageDataset
-from image_wgan import ImageWgan
-import os
-from os.path import exists
-from PIL import Image, ImageEnhance
-import numpy
-print(gr.__version__)
-def init():
-    generated_samples_folder = "."
-    discriminator_saved_model = "discriminator64.model"
-    generator_saved_model = "generator64.model"
-    latent_space = 100
-    image_wgan = ImageWgan(
-        image_shape = (4,64,64),
-        latent_space_dimension=latent_space,
-        generator_saved_model=generator_saved_model if exists(generator_saved_model) else None,
-        discriminator_saved_model=discriminator_saved_model if exists(discriminator_saved_model) else None
-    )
-    image_wgan.generate(
-        sample_folder=generated_samples_folder
-    )
-    crop()
-
-def crop():
-
-    import generator
-    res = 64
-    if res != 0:
-        results = "generated.png"
-        img = Image.open(results)
-
-        width,height = img.size
-
-
-        top = 2
-        bottom = 2
-        for i in range(4):
-            left = (res+2)*i +2
-            right = width-(res+2)*i
-            imgcrop = img.crop((left,top,left+res,res+2))
-
-
-            imgcrop.save(str(i)+".png")     
-init()
-
-
-
-
-
-import numpy
-
-def gen(seed):
-    numpy.random.seed(int(seed))
-    init()
-    crop()
-    imgArr = []
-    for i in range(4):
-        img = Image.open(str(i)+".png")
-        imgArr.append(img)
-    return imgArr
-
-iface = gr.Interface(
-    fn=gen, 
-    inputs=gr.Slider(0,1000,500,step = 0.01),
-    outputs=gr.Gallery(label="Generated Skins"),
-    title = "Minecraft Skin Generator",
-    css = "html{image-rendering:pixelated}",
-    debug = True,
-)
-iface.launch(width=64,height=64,enable_queue=True)
\ No newline at end of file
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/model/basis.py b/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/model/basis.py
deleted file mode 100644
index 74f04a0fada04ad63f9d9fc17f4fe7bb038d806b..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/model/basis.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Permission is hereby granted, free of charge, to any person obtaining a
-# copy of this software and associated documentation files (the "Software"),
-# to deal in the Software without restriction, including without limitation
-# the rights to use, copy, modify, merge, publish, distribute, sublicense,
-# and/or sell copies of the Software, and to permit persons to whom the
-# Software is furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
-# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-# DEALINGS IN THE SOFTWARE.
-#
-# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
-# SPDX-License-Identifier: MIT
-
-
-from functools import lru_cache
-from typing import Dict, List
-
-import e3nn.o3 as o3
-import torch
-import torch.nn.functional as F
-from torch import Tensor
-from torch.cuda.nvtx import range as nvtx_range
-
-from se3_transformer.runtime.utils import degree_to_dim
-
-
-@lru_cache(maxsize=None)
-def get_clebsch_gordon(J: int, d_in: int, d_out: int, device) -> Tensor:
-    """ Get the (cached) Q^{d_out,d_in}_J matrices from equation (8) """
-    return o3.wigner_3j(J, d_in, d_out, dtype=torch.float64, device=device).permute(2, 1, 0)
-
-
-@lru_cache(maxsize=None)
-def get_all_clebsch_gordon(max_degree: int, device) -> List[List[Tensor]]:
-    all_cb = []
-    for d_in in range(max_degree + 1):
-        for d_out in range(max_degree + 1):
-            K_Js = []
-            for J in range(abs(d_in - d_out), d_in + d_out + 1):
-                K_Js.append(get_clebsch_gordon(J, d_in, d_out, device))
-            all_cb.append(K_Js)
-    return all_cb
-
-
-def get_spherical_harmonics(relative_pos: Tensor, max_degree: int) -> List[Tensor]:
-    all_degrees = list(range(2 * max_degree + 1))
-    with nvtx_range('spherical harmonics'):
-        sh = o3.spherical_harmonics(all_degrees, relative_pos, normalize=True)
-        return torch.split(sh, [degree_to_dim(d) for d in all_degrees], dim=1)
-
-
-@torch.jit.script
-def get_basis_script(max_degree: int,
-                     use_pad_trick: bool,
-                     spherical_harmonics: List[Tensor],
-                     clebsch_gordon: List[List[Tensor]],
-                     amp: bool) -> Dict[str, Tensor]:
-    """
-    Compute pairwise bases matrices for degrees up to max_degree
-    :param max_degree:            Maximum input or output degree
-    :param use_pad_trick:         Pad some of the odd dimensions for a better use of Tensor Cores
-    :param spherical_harmonics:   List of computed spherical harmonics
-    :param clebsch_gordon:        List of computed CB-coefficients
-    :param amp:                   When true, return bases in FP16 precision
-    """
-    basis = {}
-    idx = 0
-    # Double for loop instead of product() because of JIT script
-    for d_in in range(max_degree + 1):
-        for d_out in range(max_degree + 1):
-            key = f'{d_in},{d_out}'
-            K_Js = []
-            for freq_idx, J in enumerate(range(abs(d_in - d_out), d_in + d_out + 1)):
-                Q_J = clebsch_gordon[idx][freq_idx]
-                K_Js.append(torch.einsum('n f, k l f -> n l k', spherical_harmonics[J].float(), Q_J.float()))
-
-            basis[key] = torch.stack(K_Js, 2)  # Stack on second dim so order is n l f k
-            if amp:
-                basis[key] = basis[key].half()
-            if use_pad_trick:
-                basis[key] = F.pad(basis[key], (0, 1))  # Pad the k dimension, that can be sliced later
-
-            idx += 1
-
-    return basis
-
-
-@torch.jit.script
-def update_basis_with_fused(basis: Dict[str, Tensor],
-                            max_degree: int,
-                            use_pad_trick: bool,
-                            fully_fused: bool) -> Dict[str, Tensor]:
-    """ Update the basis dict with partially and optionally fully fused bases """
-    num_edges = basis['0,0'].shape[0]
-    device = basis['0,0'].device
-    dtype = basis['0,0'].dtype
-    sum_dim = sum([degree_to_dim(d) for d in range(max_degree + 1)])
-
-    # Fused per output degree
-    for d_out in range(max_degree + 1):
-        sum_freq = sum([degree_to_dim(min(d, d_out)) for d in range(max_degree + 1)])
-        basis_fused = torch.zeros(num_edges, sum_dim, sum_freq, degree_to_dim(d_out) + int(use_pad_trick),
-                                  device=device, dtype=dtype)
-        acc_d, acc_f = 0, 0
-        for d_in in range(max_degree + 1):
-            basis_fused[:, acc_d:acc_d + degree_to_dim(d_in), acc_f:acc_f + degree_to_dim(min(d_out, d_in)),
-            :degree_to_dim(d_out)] = basis[f'{d_in},{d_out}'][:, :, :, :degree_to_dim(d_out)]
-
-            acc_d += degree_to_dim(d_in)
-            acc_f += degree_to_dim(min(d_out, d_in))
-
-        basis[f'out{d_out}_fused'] = basis_fused
-
-    # Fused per input degree
-    for d_in in range(max_degree + 1):
-        sum_freq = sum([degree_to_dim(min(d, d_in)) for d in range(max_degree + 1)])
-        basis_fused = torch.zeros(num_edges, degree_to_dim(d_in), sum_freq, sum_dim,
-                                  device=device, dtype=dtype)
-        acc_d, acc_f = 0, 0
-        for d_out in range(max_degree + 1):
-            basis_fused[:, :, acc_f:acc_f + degree_to_dim(min(d_out, d_in)), acc_d:acc_d + degree_to_dim(d_out)] \
-                = basis[f'{d_in},{d_out}'][:, :, :, :degree_to_dim(d_out)]
-
-            acc_d += degree_to_dim(d_out)
-            acc_f += degree_to_dim(min(d_out, d_in))
-
-        basis[f'in{d_in}_fused'] = basis_fused
-
-    if fully_fused:
-        # Fully fused
-        # Double sum this way because of JIT script
-        sum_freq = sum([
-            sum([degree_to_dim(min(d_in, d_out)) for d_in in range(max_degree + 1)]) for d_out in range(max_degree + 1)
-        ])
-        basis_fused = torch.zeros(num_edges, sum_dim, sum_freq, sum_dim, device=device, dtype=dtype)
-
-        acc_d, acc_f = 0, 0
-        for d_out in range(max_degree + 1):
-            b = basis[f'out{d_out}_fused']
-            basis_fused[:, :, acc_f:acc_f + b.shape[2], acc_d:acc_d + degree_to_dim(d_out)] = b[:, :, :,
-                                                                                              :degree_to_dim(d_out)]
-            acc_f += b.shape[2]
-            acc_d += degree_to_dim(d_out)
-
-        basis['fully_fused'] = basis_fused
-
-    del basis['0,0']  # We know that the basis for l = k = 0 is filled with a constant
-    return basis
-
-
-def get_basis(relative_pos: Tensor,
-              max_degree: int = 4,
-              compute_gradients: bool = False,
-              use_pad_trick: bool = False,
-              amp: bool = False) -> Dict[str, Tensor]:
-    with nvtx_range('spherical harmonics'):
-        spherical_harmonics = get_spherical_harmonics(relative_pos, max_degree)
-    with nvtx_range('CB coefficients'):
-        clebsch_gordon = get_all_clebsch_gordon(max_degree, relative_pos.device)
-
-    with torch.autograd.set_grad_enabled(compute_gradients):
-        with nvtx_range('bases'):
-            basis = get_basis_script(max_degree=max_degree,
-                                     use_pad_trick=use_pad_trick,
-                                     spherical_harmonics=spherical_harmonics,
-                                     clebsch_gordon=clebsch_gordon,
-                                     amp=amp)
-            return basis
diff --git a/spaces/merve/data-leak/public/measuring-fairness/mini.js b/spaces/merve/data-leak/public/measuring-fairness/mini.js
deleted file mode 100644
index 51e81b909d66e7a0b45f54b318a0b88a95fdb217..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/measuring-fairness/mini.js
+++ /dev/null
@@ -1,205 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-
-
-window.makeMini = function(){
-
-  var s = 10
-  var sScale = ([a, b]) => [s*a, s*b]
-
-  var miniSel = d3.selectAll('.mini').html('').each(addMini).st({overflow: 'visible'})
-
-  var cColors = {
-    true:  {true: colors.sick, false: lcolors.sick},
-    false: {true: colors.well, false: lcolors.well}
-  }
-  var rColors = {
-    true:  {true: lcolors.sick, false: llcolors.sick},
-    false: {true: lcolors.well, false: llcolors.well}
-  }
-
-
-  function addMini(){
-    var miniSel = d3.select(this)
-
-    var type = miniSel.attr('type')
-    var sex = miniSel.attr('sex')
-    var isAll = sex == 'all'
-
-    miniSel.st({marginBottom: sex == 'male' ? 30 : 0})
-
-    var data = students
-      .filter(d => isAll ? true : sex == 'male' ? d.isMale : !d.isMale)
-
-    var topDatum = {}
-    var botDatum = {}
-
-    if (type == 'fp'){
-      topDatum.opacity = d => d.grade > d.threshold && d.isSick
-      botDatum.opacity = d => d.isSick
-    } else {
-      topDatum.opacity = d => d.grade > d.threshold && d.isSick
-      botDatum.opacity = d => d.grade > d.threshold
-    }
-
-
-
-    var top = -s*nCols/2 + 10
-    if (!isAll) top /= 2
-    addGrid(miniSel.append('span'), topDatum)
-    miniSel.append('span.equation').text('÷').st({top, fontWeight: '', fontSize: 20})
-    addGrid(miniSel.append('span'), botDatum)
-    miniSel.append('span.equation').text('=').st({top, fontWeight: '', fontSize: 20})
-
-    if (!isAll){
-      var sexStr = sex == 'male' ? 'children' : 'adults'
-
-      var coStr = `of ${sexStr} <br>testing positive<br>are sick`
-      var fpStr = `of ${sexStr} <br>who are sick <br>test positive`
-      miniSel.st({position: 'relative'})
-        .append('div.axis')
-        .st({position: 'absolute', right: -9, textAlign: 'center', width: 95, lineHeight: 14, bottom: -15})
-        .html(type == 'fp' ? fpStr : coStr)
-
-    }
-    
-    var percentSel = miniSel.append('span.equation').st({top, marginLeft: 0})
-
-    function update(){
-      topDatum.update()
-      botDatum.update()
-
-      var percent = d3.sum(data, topDatum.opacity)/d3.sum(data, botDatum.opacity)
-      percentSel.text(d3.format('.0%')(percent))
-    }
-
-    miniSel.datum({update}) 
-
-
-    function addGrid(gridSel, datum){
-      var {opacity} = datum
-
-      var width = s*nCols
-      var height = s*nCols*(isAll ? 1 : .5)
-      var svg = gridSel.append('svg').at({width, height})
-
-      var callSickSel = svg.append('rect')
-        .at({width, height, fill: lcolors.sick})
-
-      var callWellPath = svg.append('path')
-        .at({width, height, fill: lcolors.well})
-
-
-      var personSel = svg.appendMany('g', data)
-        .translate(d => sScale(d.pos[isAll ? 'allIJ' : 'sexGroupIJ']))
-
-      var pad = 0
-      // var rectSel = personSel.append('rect')
-      //   .at({
-      //     height: s - pad,
-      //     width: s - pad,
-      //     // stroke: '#666',
-      //     // strokeWidth: .1,
-      //   })
-
-
-      var circleSel = personSel.append('circle')
-        .at({r: s/4, cx: s/2 - pad/2, cy: s/2 - pad/2, fill: d => d.isSick ? colors.sick : '#777'})
-
-      if (!isAll){
-        svg.append('path')
-          .translate([-1, -5])
-          .at({stroke: colors.sick, d: 'M 0 0 H ' + (sex == 'male' ? 8 : 4)*s})
-      }
-
-      var geodata = {type: 'FeatureCollection'}
-      geodata.features = data.map(d => {
-        var [x, y] = sScale(d.pos[isAll ? 'allIJ' : 'sexGroupIJ'])
-        return {
-          type: 'Feature',
-          geometry: {
-            type: 'Polygon',
-            coordinates: [
-              [[x, y], [x, y + s], [x + s, y + s], [x + s, y], [x, y]]
-            ]
-          },
-          properties: {d},
-        }
-      })
-
-      var topology = topojson.topology({boxes: geodata})
-      var geowrap = topojson.feature(topology, topology.objects.boxes)
-      var path = d3.geoPath()
-
-      var hiddenPath = svg.append('path')
-        .at({stroke: 'none', fill: 'rgba(255,255,255,.6)'})
-        .translate(.5, 1)
-
-      var includedPath = svg.append('path')
-        .at({stroke: '#000', fill: 'none'})
-        .translate(.5, 1)
-
-
-      circleSel.at({fill: d => d.isSick ? colors.sick : colors.well})
-
-      datum.update = () => {
-        // rectSel.at({
-        //   // fill: d => rColors[d.grade > d.threshold][opacity(d)],
-        //   // strokeWidth: d => opacity(d) ? 1 : .1,
-        // })
-
-        // circleSel.at({fill: d => cColors[d.isSick][opacity(d)]})
-
-        var byType = d3.nestBy(topology.objects.boxes.geometries, d => opacity(d.properties.d))
-
-        byType.forEach(type => {
-          var obj = {type: 'GeometryCollection', geometries: type}
-          var pathStr = path(topojson.mesh(topology, obj, (a, b) => a == b))
-
-          var pathSel = type.key == 'true' ? includedPath : hiddenPath
-          pathSel.at({d: pathStr})
-        })
-
-        var sickBoxes = topology.objects.boxes.geometries
-          .filter(d => d.properties.d.grade <= d.properties.d.threshold)
-        var obj = {type: 'GeometryCollection', geometries: sickBoxes}
-        var pathStr = path(topojson.mesh(topology, obj, (a, b) => a == b))
-        callWellPath.at({d: pathStr})
-      }
-    }
-   
-  }
-
-
-
-  function updateAll(){
-    miniSel.each(d => d.update())
-  }
-
-  return {updateAll}
-}
-
-
-
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/merve/dataset-worldviews/public/hidden-bias/annotations.js b/spaces/merve/dataset-worldviews/public/hidden-bias/annotations.js
deleted file mode 100644
index b0fd377b443ee9bd31e7bd1d9dbacafc4e5282e3..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/hidden-bias/annotations.js
+++ /dev/null
@@ -1,86 +0,0 @@
-window.annotations = [
-  {
-    "slide": 0,
-    "x": 1.77,
-    "y": 3.17,
-    "path": "M -3,-59 A 31.215 31.215 0 1 0 -10,2",
-    "text": "Joshua had a high school GPA of 3.2 and 1.8 in college",
-    "textOffset": [
-      -1,
-      -48
-    ]
-  },
-  {
-    "slide": 0,
-    "x": 2.93,
-    "y": 2.08,
-    "path": "M 56,61 A 45.102 45.102 0 0 0 19.000001907348633,1.0000003576278687",
-    "text": "Abigail has a 2.1 in high school and 2.9 in college",
-    "textOffset": [
-      -5,
-      85
-    ],
-    "width": 18
-  },
-  {
-    "slide": 1,
-    "x": 3.7,
-    "y": 2,
-    "path": "M 1,41 A 209.709 209.709 0 0 1 -310,76",
-    "text": "Most students have a higher GPA in high school",
-    "textOffset": [
-      -69,
-      11
-    ],
-    "width": 18
-  },
-  {
-    "slide": 2,
-    "x": 1,
-    "y": 4,
-    "path": "M 0 0",
-    "text": "A well adjusted model will usually over predict about half the students' grades...",
-    "textOffset": [
-      25,
-      50
-    ],
-    "width": 25
-  },
-  {
-    "slide": 2,
-    "x": 4,
-    "y": 1,
-    "path": "M 0 0",
-    "text": "...and under predict the other half",
-    "textOffset": [
-      -109,
-      -51
-    ],
-    "width": 18
-  },
-  {
-    "slide": 5,
-    "x": 2.58,
-    "y": 2,
-    "path": "M 54,34 A 29.707 29.707 0 0 0 11,-6",
-    "text": "The model predicted both Lucas and Mia would get a 2.0, but she ended up with a higher GPA",
-    "html": "The model predicted both <span class='highlight male'>Lucas</span> and <span class='highlight female'>Mia</span> would get a 2.0, but she ended up with a higher GPA",
-    "textOffset": [
-      -22,
-      44
-    ],
-    "width": 23
-  },
-  {
-    "slide": 5,
-    "x": 2.14,
-    "y": 2,
-    "path": "M 40,61 A 35.025 35.025 0 0 1 -4,7",
-    "text": "",
-    "textOffset": [
-      -100,
-      179
-    ],
-    "width": 14
-  }
-]
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init-util.js b/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init-util.js
deleted file mode 100644
index 90927e1e1ab40c05fc3ee46b69e7e400b1f9a86a..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init-util.js
+++ /dev/null
@@ -1,105 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-window.initUtil = function(){
-  function palette(min, max){
-    // https://blocks.roadtolarissa.com/1wheel/raw/94091c1f8a69d5966e48aef4ac19baf9/index.html?colors=00006e-006a78-00a963-8a8a8a-d5882a-a15142-7f0000&numTicks=255&space=lab&type=basis
-    var colors = ['#00006e', '#00006e', '#00006f', '#00006f', '#00006f', '#000070', '#000070', '#000170', '#000471', '#000871', '#000b71', '#000f72', '#001272', '#001572', '#001872', '#001b73', '#001e73', '#002173', '#002473', '#002674', '#002974', '#002c74', '#002e74', '#003174', '#003375', '#003675', '#003975', '#003b75', '#003e75', '#004075', '#004375', '#004575', '#004775', '#004a75', '#004c75', '#004f75', '#005175', '#005375', '#005675', '#005875', '#005a75', '#005c75', '#005e75', '#006175', '#006375', '#006574', '#006774', '#006974', '#006b74', '#006d74', '#006f73', '#007173', '#007373', '#007473', '#007672', '#007872', '#007a72', '#007b72', '#007d71', '#007f71', '#008071', '#008270', '#008370', '#008570', '#008670', '#00886f', '#00896f', '#008a6f', '#008c6f', '#008d6e', '#008e6e', '#008f6e', '#00906e', '#00916e', '#00926d', '#00936d', '#00946d', '#00956d', '#00966d', '#00976d', '#00976d', '#00986d', '#00996d', '#00996d', '#009a6d', '#009a6e', '#009b6e', '#009b6e', '#009b6e', '#079c6f', '#119c6f', '#189c6f', '#1e9c70', '#249c70', '#289c70', '#2d9c71', '#319c71', '#359c71', '#399c72', '#3c9c72', '#409c73', '#439c73', '#479b74', '#4a9b74', '#4d9b74', '#509b75', '#539a75', '#569a76', '#599976', '#5c9976', '#5f9976', '#629877', '#659877', '#679777', '#6a9777', '#6d9677', '#6f9678', '#729578', '#749578', '#779478', '#799477', '#7c9377', '#7e9377', '#819277', '#839277', '#859176', '#889176', '#8a9175', '#8c9075', '#8e9074', '#908f73', '#938f73', '#958e72', '#978e71', '#998e70', '#9b8d6f', '#9d8d6e', '#9f8d6d', '#a08c6c', '#a28c6b', '#a48c69', '#a68b68', '#a88b67', '#a98b65', '#ab8a64', '#ac8a63', '#ae8a61', '#af8960', '#b1895f', '#b2895d', '#b4885c', '#b5885a', '#b68859', '#b78757', '#b88756', '#b98755', '#ba8653', '#bb8652', '#bc8550', '#bd854f', '#be854d', '#bf844c', '#bf844b', '#c0834a', '#c08348', '#c18247', '#c18246', '#c28145', '#c28044', '#c28043', '#c27f42', '#c27e41', '#c37e40', '#c27d3f', '#c27c3f', '#c27b3e', '#c27a3d', '#c27a3d', '#c1793c', '#c1783c', '#c1773c', '#c0763b', '#c0753b', '#bf743a', '#bf733a', '#be713a', '#bd703a', '#bd6f39', '#bc6e39', '#bb6d39', '#bb6b38', '#ba6a38', '#b96938', '#b86737', '#b76637', '#b76537', '#b66336', '#b56236', '#b46035', '#b35e35', '#b25d34', '#b15b34', '#b05933', '#af5833', '#ae5632', '#ad5431', '#ad5230', '#ac502f', '#ab4e2f', '#aa4c2e', '#a94a2c', '#a8482b', '#a7462a', '#a64429', '#a54127', '#a43f26', '#a33d24', '#a33a23', '#a23721', '#a1351f', '#a0321e', '#9f2f1c', '#9e2c1a', '#9d2818', '#9c2516', '#9c2114', '#9b1d11', '#9a180f', '#99120d', '#980b0a', '#970207', '#960004', '#950001', '#940000', '#930000', '#920000', '#910000', '#900000', '#8f0000', '#8e0000', '#8e0000', '#8d0000', '#8c0000', '#8b0000', '#8a0000', '#890000', '#880000', '#870000', '#860000', '#850000', '#840000', '#830000', '#820000', '#810000', '#800000']
-
-      return v => {
-        var i = d3.clamp(0, (v - min)/(max - min), 1)
-        return colors[Math.round(i*(colors.length - 1))]
-      }
-  }
-  
-  var util = {
-    palette,
-    color: d3.interpolateSpectral,
-    color: palette(0, 1),
-  }
-  
-  util.colors = [1 - .25, .25].map(util.color)
-
-  util.updateSentenceLabels = pair => {
-    var t0 = tokenizer.tokenize(pair.s0)
-    var t1 = tokenizer.tokenize(pair.s1)
-
-    var i = 0
-    while (t0[i] == t1[i] && i < t0.length) i++
-
-    var j = 1
-    while (t0[t0.length - j] == t1[t1.length - j] && j < t0.length) j++
-
-    pair.label0 = tokens2origStr(t0, pair.s0)
-    pair.label1 = tokens2origStr(t1, pair.s1)
-
-    function tokens2origStr(t, s){
-      var tokenStr = tokenizer.decode(t.slice(i, -j + 1)).trim()
-      var lowerStr = s.toLowerCase()
-
-      var startI = lowerStr.indexOf(tokenStr)
-      return s.slice(startI, startI + tokenStr.length)
-    }
-
-    if (
-      !pair.label0.length || 
-      !pair.label1.length || 
-      pair.label0.length > 15 || 
-      pair.label1.length > 15){
-      pair.label0 = ''
-      pair.label1 = ''
-    }
-
-    // console.log(i, j, pair.label0, pair.label1)
-  }
-
-  util.addAxisLabel = (c, xText, yText, xOffset=37, yOffset=-35) => {
-    c.svg.select('.x').append('g')
-      .translate([c.width/2, xOffset])
-      .append('text.axis-label')
-      .text(xText)
-      .at({textAnchor: 'middle'})
-      .st({fill: '#000'})
-
-    c.svg.select('.y')
-      .append('g')
-      .translate([yOffset, c.height/2])
-      .append('text.axis-label')
-      .text(yText)
-      .at({textAnchor: 'middle', transform: 'rotate(-90)'})
-      .st({fill: '#000'})
-  }
-
-  util.ggPlotBg = (c) => {
-    c.svg.append('rect')
-      .at({width: c.width, height: c.height, fill: '#eee'})
-      .lower()
-
-    c.svg.selectAll('.tick').selectAll('line').remove()
-    c.svg.selectAll('.y .tick')
-      .append('path').at({d: 'M 0 0 H ' + c.width, stroke: '#fff', strokeWidth: 1})
-    c.svg.selectAll('.y text').at({x: -3})
-    c.svg.selectAll('.x .tick')
-      .append('path').at({d: 'M 0 0 V -' + c.height, stroke: '#fff', strokeWidth: 1})
-  }
-
-  util.corrFmt = d => (d3.format('+.2f')(d)).replace('0.', '.')
-
-  return util
-}
-
-if (window.init) window.init()
-
diff --git a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init.js b/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init.js
deleted file mode 100644
index 71d258e5027dc3c924b2b263e6bb8ea370189b1d..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/server-side/fill-in-the-blank/scatter-plot-colab/two-sentences/init.js
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-window.init = function(){
-  var initFns = [window.initUtil, window.initScatter, window.initPair]
-  if (!initFns.every(d => d)) return
-
-  window.util = initUtil()
-
-  var pair = window.python_settings
-  pair.s0 = python_data.s0
-  pair.s1 = python_data.s1
-  pair.e0 = python_data.e0
-  pair.e1 = python_data.e1
-  pair.label0 = 'Sentence 0'
-  pair.label1 = 'Sentence 1'
-  pair.vocab = python_data.vocab
-
-  var sel = d3.select('.container').html('')
-    .st({width: 500})
-
-  initPair(pair, sel)
-}
-
-
-window.init()
diff --git a/spaces/mfrashad/ClothingGAN/netdissect/broden.py b/spaces/mfrashad/ClothingGAN/netdissect/broden.py
deleted file mode 100644
index 854e87a46839c837b43cba5347967ce74ae4bf35..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/netdissect/broden.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import os, errno, numpy, torch, csv, re, shutil, os, zipfile
-from collections import OrderedDict
-from torchvision.datasets.folder import default_loader
-from torchvision import transforms
-from scipy import ndimage
-from urllib.request import urlopen
-
-class BrodenDataset(torch.utils.data.Dataset):
-    '''
-    A multicategory segmentation data set.
-
-    Returns three streams:
-    (1) The image (3, h, w).
-    (2) The multicategory segmentation (labelcount, h, w).
-    (3) A bincount of pixels in the segmentation (labelcount).
-
-    Net dissect also assumes that the dataset object has three properties
-    with human-readable labels:
-
-    ds.labels = ['red', 'black', 'car', 'tree', 'grid', ...]
-    ds.categories = ['color', 'part', 'object', 'texture']
-    ds.label_category = [0, 0, 2, 2, 3, ...] # The category for each label
-    '''
-    def __init__(self, directory='dataset/broden', resolution=384,
-            split='train', categories=None,
-            transform=None, transform_segment=None,
-            download=False, size=None, include_bincount=True,
-            broden_version=1, max_segment_depth=6):
-        assert resolution in [224, 227, 384]
-        if download:
-            ensure_broden_downloaded(directory, resolution, broden_version)
-        self.directory = directory
-        self.resolution = resolution
-        self.resdir = os.path.join(directory, 'broden%d_%d' %
-                (broden_version, resolution))
-        self.loader = default_loader
-        self.transform = transform
-        self.transform_segment = transform_segment
-        self.include_bincount = include_bincount
-        # The maximum number of multilabel layers that coexist at an image.
-        self.max_segment_depth = max_segment_depth
-        with open(os.path.join(self.resdir, 'category.csv'),
-                encoding='utf-8') as f:
-            self.category_info = OrderedDict()
-            for row in csv.DictReader(f):
-                self.category_info[row['name']] = row
-        if categories is not None:
-            # Filter out unused categories
-            categories = set([c for c in categories if c in self.category_info])
-            for cat in list(self.category_info.keys()):
-                if cat not in categories:
-                    del self.category_info[cat]
-        categories = list(self.category_info.keys())
-        self.categories = categories
-
-        # Filter out unneeded images.
-        with open(os.path.join(self.resdir, 'index.csv'),
-                encoding='utf-8') as f:
-            all_images = [decode_index_dict(r) for r in csv.DictReader(f)]
-        self.image = [row for row in all_images
-            if index_has_any_data(row, categories) and row['split'] == split]
-        if size is not None:
-            self.image = self.image[:size]
-        with open(os.path.join(self.resdir, 'label.csv'),
-                encoding='utf-8') as f:
-            self.label_info = build_dense_label_array([
-                decode_label_dict(r) for r in csv.DictReader(f)])
-            self.labels = [l['name'] for l in self.label_info]
-        # Build dense remapping arrays for labels, so that you can
-        # get dense ranges of labels for each category.
-        self.category_map = {}
-        self.category_unmap = {}
-        self.category_label = {}
-        for cat in self.categories:
-            with open(os.path.join(self.resdir, 'c_%s.csv' % cat),
-                    encoding='utf-8') as f:
-                c_data = [decode_label_dict(r) for r in csv.DictReader(f)]
-            self.category_unmap[cat], self.category_map[cat] = (
-                    build_numpy_category_map(c_data))
-            self.category_label[cat] = build_dense_label_array(
-                    c_data, key='code')
-        self.num_labels = len(self.labels)
-        # Primary categories for each label is the category in which it
-        # appears with the maximum coverage.
-        self.label_category = numpy.zeros(self.num_labels, dtype=int)
-        for i in range(self.num_labels):
-            maxcoverage, self.label_category[i] = max(
-               (self.category_label[cat][self.category_map[cat][i]]['coverage']
-                    if i < len(self.category_map[cat])
-                       and self.category_map[cat][i] else 0, ic)
-                for ic, cat in enumerate(categories))
-
-    def __len__(self):
-        return len(self.image)
-
-    def __getitem__(self, idx):
-        record = self.image[idx]
-        # example record: {
-        #    'image': 'opensurfaces/25605.jpg', 'split': 'train',
-        #    'ih': 384, 'iw': 384, 'sh': 192, 'sw': 192,
-        #    'color': ['opensurfaces/25605_color.png'],
-        #    'object': [], 'part': [],
-        #    'material': ['opensurfaces/25605_material.png'],
-        #    'scene': [], 'texture': []}
-        image = self.loader(os.path.join(self.resdir, 'images',
-            record['image']))
-        segment = numpy.zeros(shape=(self.max_segment_depth,
-            record['sh'], record['sw']), dtype=int)
-        if self.include_bincount:
-            bincount = numpy.zeros(shape=(self.num_labels,), dtype=int)
-        depth = 0
-        for cat in self.categories:
-            for layer in record[cat]:
-                if isinstance(layer, int):
-                    segment[depth,:,:] = layer
-                    if self.include_bincount:
-                        bincount[layer] += segment.shape[1] * segment.shape[2]
-                else:
-                    png = numpy.asarray(self.loader(os.path.join(
-                        self.resdir, 'images', layer)))
-                    segment[depth,:,:] = png[:,:,0] + png[:,:,1] * 256
-                    if self.include_bincount:
-                        bincount += numpy.bincount(segment[depth,:,:].flatten(),
-                            minlength=self.num_labels)
-                depth += 1
-        if self.transform:
-            image = self.transform(image)
-        if self.transform_segment:
-            segment = self.transform_segment(segment)
-        if self.include_bincount:    
-            bincount[0] = 0
-            return (image, segment, bincount)
-        else:
-            return (image, segment)
-
-def build_dense_label_array(label_data, key='number', allow_none=False):
-    '''
-    Input: set of rows with 'number' fields (or another field name key).
-    Output: array such that a[number] = the row with the given number.
-    '''
-    result = [None] * (max([d[key] for d in label_data]) + 1)
-    for d in label_data:
-        result[d[key]] = d
-    # Fill in none
-    if not allow_none:
-        example = label_data[0]
-        def make_empty(k):
-            return dict((c, k if c is key else type(v)())
-                    for c, v in example.items())
-        for i, d in enumerate(result):
-            if d is None:
-                result[i] = dict(make_empty(i))
-    return result
-
-def build_numpy_category_map(map_data, key1='code', key2='number'):
-    '''
-    Input: set of rows with 'number' fields (or another field name key).
-    Output: array such that a[number] = the row with the given number.
-    '''
-    results = list(numpy.zeros((max([d[key] for d in map_data]) + 1),
-            dtype=numpy.int16) for key in (key1, key2))
-    for d in map_data:
-        results[0][d[key1]] = d[key2]
-        results[1][d[key2]] = d[key1]
-    return results
-
-def index_has_any_data(row, categories):
-    for c in categories:
-        for data in row[c]:
-            if data: return True
-    return False
-
-def decode_label_dict(row):
-    result = {}
-    for key, val in row.items():
-        if key == 'category':
-            result[key] = dict((c, int(n))
-                for c, n in [re.match('^([^(]*)\(([^)]*)\)$', f).groups()
-                    for f in val.split(';')])
-        elif key == 'name':
-            result[key] = val
-        elif key == 'syns':
-            result[key] = val.split(';')
-        elif re.match('^\d+$', val):
-            result[key] = int(val)
-        elif re.match('^\d+\.\d*$', val):
-            result[key] = float(val)
-        else:
-            result[key] = val
-    return result
-
-def decode_index_dict(row):
-    result = {}
-    for key, val in row.items():
-        if key in ['image', 'split']:
-            result[key] = val
-        elif key in ['sw', 'sh', 'iw', 'ih']:
-            result[key] = int(val)
-        else:
-            item = [s for s in val.split(';') if s]
-            for i, v in enumerate(item):
-                if re.match('^\d+$', v):
-                    item[i] = int(v)
-            result[key] = item
-    return result
-
-class ScaleSegmentation:
-    '''
-    Utility for scaling segmentations, using nearest-neighbor zooming.
-    '''
-    def __init__(self, target_height, target_width):
-        self.target_height = target_height
-        self.target_width = target_width
-    def __call__(self, seg):
-        ratio = (1, self.target_height / float(seg.shape[1]),
-                self.target_width / float(seg.shape[2]))
-        return ndimage.zoom(seg, ratio, order=0)
-
-def scatter_batch(seg, num_labels, omit_zero=True, dtype=torch.uint8):
-    '''
-    Utility for scattering semgentations into a one-hot representation.
-    '''
-    result = torch.zeros(*((seg.shape[0], num_labels,) + seg.shape[2:]),
-            dtype=dtype, device=seg.device)
-    result.scatter_(1, seg, 1)
-    if omit_zero:
-        result[:,0] = 0
-    return result
-
-def ensure_broden_downloaded(directory, resolution, broden_version=1):
-    assert resolution in [224, 227, 384]
-    baseurl = 'http://netdissect.csail.mit.edu/data/'
-    dirname = 'broden%d_%d' % (broden_version, resolution)
-    if os.path.isfile(os.path.join(directory, dirname, 'index.csv')):
-        return # Already downloaded
-    zipfilename = 'broden1_%d.zip' % resolution
-    download_dir = os.path.join(directory, 'download')
-    os.makedirs(download_dir, exist_ok=True)
-    full_zipfilename = os.path.join(download_dir, zipfilename)
-    if not os.path.exists(full_zipfilename):
-        url = '%s/%s' % (baseurl, zipfilename)
-        print('Downloading %s' % url)
-        data = urlopen(url)
-        with open(full_zipfilename, 'wb') as f:
-            f.write(data.read())
-    print('Unzipping %s' % zipfilename)
-    with zipfile.ZipFile(full_zipfilename, 'r') as zip_ref:
-        zip_ref.extractall(directory)
-    assert os.path.isfile(os.path.join(directory, dirname, 'index.csv'))
-
-def test_broden_dataset():
-    '''
-    Testing code.
-    '''
-    bds = BrodenDataset('dataset/broden', resolution=384,
-            transform=transforms.Compose([
-                        transforms.Resize(224),
-                        transforms.ToTensor()]),
-            transform_segment=transforms.Compose([
-                        ScaleSegmentation(224, 224)
-                        ]),
-            include_bincount=True)
-    loader = torch.utils.data.DataLoader(bds, batch_size=100, num_workers=24)
-    for i in range(1,20):
-        print(bds.label[i]['name'],
-                list(bds.category.keys())[bds.primary_category[i]])
-    for i, (im, seg, bc) in enumerate(loader):
-        print(i, im.shape, seg.shape, seg.max(), bc.shape)
-
-if __name__ == '__main__':
-    test_broden_dataset()
diff --git a/spaces/mixcard/image-2-text-largecoco/README.md b/spaces/mixcard/image-2-text-largecoco/README.md
deleted file mode 100644
index 69f953b921c86498a99b210038ac58d205a0d7a6..0000000000000000000000000000000000000000
--- a/spaces/mixcard/image-2-text-largecoco/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image 2 Text Largecoco
-emoji: 🏃
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.46.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mncai/chat-doctor-kr/README.md b/spaces/mncai/chat-doctor-kr/README.md
deleted file mode 100644
index 4bf196b66625b2b49c94891567b27af348e834b5..0000000000000000000000000000000000000000
--- a/spaces/mncai/chat-doctor-kr/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chat Doctor Kr
-emoji: 🐨
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/moyeli/BingAi/Dockerfile b/spaces/moyeli/BingAi/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/moyeli/BingAi/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp b/spaces/mshukor/UnIVAL/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
deleted file mode 100644
index 707219105a17a691e43de1296a72bbaffa0c7fe9..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
-Copyright (c) Microsoft Corporation.
-Licensed under the MIT License.
-*/
-
-#include <torch/extension.h>
-#include <vector>
-
-/*
-CPP Binding for CUDA OP
-*/
-
-// CUDA forward declarations
-torch::Tensor ngram_repeat_block_cuda_forward(
-    torch::Tensor tokens,
-    torch::Tensor lprobs,
-    int bsz,
-    int step,
-    int beam_size,
-    int no_repeat_ngram_size);
-
-#define CHECK_CUDA(x) \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
-  CHECK_CUDA(x);       \
-  CHECK_CONTIGUOUS(x)
-
-// Input check and call to CUDA OP
-// Backward method not required
-torch::Tensor ngram_repeat_block_forward(
-    torch::Tensor tokens,
-    torch::Tensor lprobs,
-    int bsz,
-    int step,
-    int beam_size,
-    int no_repeat_ngram_size) {
-  CHECK_INPUT(tokens);
-  CHECK_INPUT(lprobs);
-  assert(bsz > 0);
-  assert(step >= 0);
-  assert(beam_size > 0);
-  assert(no_repeat_ngram_size > 0);
-
-  return ngram_repeat_block_cuda_forward(
-      tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def(
-      "forward",
-      &ngram_repeat_block_forward,
-      "No Repeat Ngram Block forward (CUDA)");
-}
diff --git a/spaces/mshukor/UnIVAL/models/unival/.ipynb_checkpoints/unify_transformer-checkpoint.py b/spaces/mshukor/UnIVAL/models/unival/.ipynb_checkpoints/unify_transformer-checkpoint.py
deleted file mode 100644
index dc8ab9eea0bc9809886758ad4cad694ae25b4287..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/models/unival/.ipynb_checkpoints/unify_transformer-checkpoint.py
+++ /dev/null
@@ -1,1735 +0,0 @@
-# Copyright 2022 The OFA-Sys Team. 
-# All rights reserved.
-# This source code is licensed under the Apache 2.0 license 
-# found in the LICENSE file in the root directory.
- 
-import math
-import random
-from typing import Any, Dict, List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.distributed import fsdp_wrap
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderDecoderModel,
-    FairseqIncrementalDecoder,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.modules import (
-    AdaptiveSoftmax,
-    BaseLayer,
-    FairseqDropout,
-    LayerDropModuleList,
-    LayerNorm,
-    SinusoidalPositionalEmbedding,
-    GradMultiply
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-from torch import Tensor
-
-from .unify_transformer_layer import TransformerEncoderLayer, TransformerDecoderLayer
-from .resnet import ResNet
-from .frozen_bn import FrozenBatchNorm2d
-
-
-DEFAULT_MAX_SOURCE_POSITIONS = 1024
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)
-
-
-def BatchNorm2d(out_chan, momentum=0.1, eps=1e-3):
-    return nn.SyncBatchNorm.convert_sync_batchnorm(
-        nn.BatchNorm2d(out_chan, momentum=momentum, eps=eps)
-    )
-
-
-def make_token_bucket_position(bucket_size, max_position=DEFAULT_MAX_SOURCE_POSITIONS):
-    context_pos = torch.arange(max_position, dtype=torch.long)[:, None]
-    memory_pos = torch.arange(max_position, dtype=torch.long)[None, :]
-    relative_pos = context_pos - memory_pos
-    sign = torch.sign(relative_pos)
-    mid = bucket_size // 2
-    abs_pos = torch.where((relative_pos<mid) & (relative_pos > -mid), mid-1, torch.abs(relative_pos))
-    log_pos = torch.ceil(torch.log(abs_pos/mid)/math.log((max_position-1)/mid) * (mid-1)) + mid
-    log_pos = log_pos.int()
-    bucket_pos = torch.where(abs_pos.le(mid), relative_pos, log_pos*sign).long()
-    return bucket_pos + bucket_size - 1
-
-
-def make_image_bucket_position(bucket_size, num_relative_distance):
-    coords_h = torch.arange(bucket_size)
-    coords_w = torch.arange(bucket_size)
-    coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-    coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-    relative_coords[:, :, 0] += bucket_size - 1  # shift to start from 0
-    relative_coords[:, :, 1] += bucket_size - 1
-    relative_coords[:, :, 0] *= 2 * bucket_size - 1
-    relative_position_index = torch.zeros(size=(bucket_size * bucket_size + 1,) * 2, dtype=relative_coords.dtype)
-    relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-    relative_position_index[0, 0:] = num_relative_distance - 3
-    relative_position_index[0:, 0] = num_relative_distance - 2
-    relative_position_index[0, 0] = num_relative_distance - 1
-    return relative_position_index
-
-
-class PromptEncoder(torch.nn.Module):
-    r"""
-    Prompt encoder to generate prompts, including prompt, prefix, instance and instruction
-    """
-
-    def __init__(
-            self,
-            type,
-            length,
-            projection,
-            embed_dim,
-            proj_dim,
-            layers,
-            vocab_size):
-        super().__init__()
-        self.prefix_projection = projection
-
-        if type == "prefix":
-            layers = layers
-            prompt_vocab_size = length
-
-        if self.prefix_projection:
-            self.embedding = torch.nn.Embedding(prompt_vocab_size, embed_dim)
-            self.trans = torch.nn.Sequential(
-                torch.nn.Linear(embed_dim, proj_dim),
-                torch.nn.ReLU(),
-                torch.nn.Linear(proj_dim, layers * 2 * embed_dim)
-            )
-        else:
-            if type == "prefix":
-                self.embedding = torch.nn.Embedding(
-                    prompt_vocab_size, layers * 2 * embed_dim)
-
-    def forward(self, prefix: torch.Tensor):
-        if self.prefix_projection:
-            prefix_tokens = self.embedding(prefix)
-            past_key_values = self.trans(prefix_tokens)
-        else:
-            past_key_values = self.embedding(prefix)
-        return past_key_values
-
-
-@register_model("unify_transformer")
-class TransformerModel(FairseqEncoderDecoderModel):
-    """
-    Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
-    <https://arxiv.org/abs/1706.03762>`_.
-
-    Args:
-        encoder (TransformerEncoder): the encoder
-        decoder (TransformerDecoder): the decoder
-
-    The Transformer model provides the following named architectures and
-    command-line arguments:
-
-    .. argparse::
-        :ref: fairseq.models.transformer_parser
-        :prog:
-    """
-
-    def __init__(self, args, encoder, decoder):
-        super().__init__(encoder, decoder)
-        self.args = args
-        self.supports_align_args = True
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        # fmt: off
-        parser.add_argument('--activation-fn',
-                            choices=utils.get_available_activation_fns(),
-                            help='activation function to use')
-        parser.add_argument('--dropout', type=float, metavar='D',
-                            help='dropout probability')
-        parser.add_argument('--attention-dropout', type=float, metavar='D',
-                            help='dropout probability for attention weights')
-        parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
-                            help='dropout probability after activation in FFN.')
-        parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
-                            help='path to pre-trained encoder embedding')
-        parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
-                            help='encoder embedding dimension')
-        parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
-                            help='encoder embedding dimension for FFN')
-        parser.add_argument('--encoder-layers', type=int, metavar='N',
-                            help='num encoder layers')
-        parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
-                            help='num encoder attention heads')
-        parser.add_argument('--encoder-normalize-before', action='store_true',
-                            help='apply layernorm before each encoder block')
-        parser.add_argument('--encoder-learned-pos', action='store_true',
-                            help='use learned positional embeddings in the encoder')
-        parser.add_argument('--bitfit', default=False, action='store_true',
-                            help='use bitfit in the transformer')
-        parser.add_argument('--freeze-encoder', action='store_true',
-                            help='freeze the parameters in the encoder')
-
-
-        parser.add_argument('--adapter', action='store_true',
-                            help='use adapter in the model')
-        parser.add_argument('--adapter-dim', type=int, metavar='N',
-                            help='adapter-down-dim')
-
-        parser.add_argument('--encoder-prompt', action='store_true',
-                            help='use prompt tuning in the encoder')
-        parser.add_argument('--encoder-prompt-type', type=str, metavar='STR',
-                            choices=['prefix'],
-                            help='the type of prompt tuning')
-        parser.add_argument('--encoder-prompt-projection', action='store_true',
-                            help='use prompt projection')
-        parser.add_argument('--encoder-prompt-length', type=int, metavar='N',
-                            help='use prompt tuning in the decoder')
-        parser.add_argument('--encoder-prompt-dim', type=int, metavar='N',
-                            help='encoder prompt dimension if use encoder prompt projection')
-        parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
-                            help='path to pre-trained decoder embedding')
-        parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
-                            help='decoder embedding dimension')
-        parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
-                            help='decoder embedding dimension for FFN')
-        parser.add_argument('--decoder-layers', type=int, metavar='N',
-                            help='num decoder layers')
-        parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
-                            help='num decoder attention heads')
-        parser.add_argument('--decoder-learned-pos', action='store_true',
-                            help='use learned positional embeddings in the decoder')
-        parser.add_argument('--decoder-normalize-before', action='store_true',
-                            help='apply layernorm before each decoder block')
-        parser.add_argument('--decoder-output-dim', type=int, metavar='N',
-                            help='decoder output dimension (extra linear layer '
-                                 'if different from decoder embed dim')
-        parser.add_argument('--freeze-decoder', action='store_true',
-                            help='freeze the parameters in the decoder')
-
-        parser.add_argument('--decoder-prompt', action='store_true',
-                            help='use prompt tuning in the decoder')
-        parser.add_argument('--decoder-prompt-type', type=str, metavar='STR',
-                            choices=['prefix'],
-                            help='the type of prompt tuning')
-        parser.add_argument('--decoder-prompt-length', type=int, metavar='N',
-                            help='use prompt tuning in the decoder')
-        parser.add_argument('--decoder-prompt-projection', action='store_true',
-                            help='use prompt projection')
-        parser.add_argument('--decoder-prompt-dim', type=int, metavar='N',
-                            help='decoder prompt dimension if use decoder prompt projection')
-        parser.add_argument('--share-decoder-input-output-embed', action='store_true',
-                            help='share decoder input and output embeddings')
-        parser.add_argument('--share-all-embeddings', action='store_true',
-                            help='share encoder, decoder and output embeddings'
-                                 ' (requires shared dictionary and embed dim)')
-        parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
-                            help='if set, disables positional embeddings (outside self attention)')
-        parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
-                            help='comma separated list of adaptive softmax cutoff points. '
-                                 'Must be used with adaptive_loss criterion'),
-        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
-                            help='sets adaptive softmax dropout for the tail projections')
-        parser.add_argument('--layernorm-embedding', action='store_true',
-                            help='add layernorm to embedding')
-        parser.add_argument('--no-scale-embedding', action='store_true',
-                            help='if True, dont scale embeddings')
-        parser.add_argument('--checkpoint-activations', action='store_true',
-                            help='checkpoint activations at each layer, which saves GPU '
-                                 'memory usage at the cost of some additional compute')
-        parser.add_argument('--offload-activations', action='store_true',
-                            help='checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations.')
-        # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019)
-        parser.add_argument('--no-cross-attention', default=False, action='store_true',
-                            help='do not perform cross-attention')
-        parser.add_argument('--cross-self-attention', default=False, action='store_true',
-                            help='perform cross+self-attention')
-        # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
-        parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
-                            help='LayerDrop probability for encoder')
-        parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0,
-                            help='LayerDrop probability for decoder')
-        parser.add_argument('--encoder-layers-to-keep', default=None,
-                            help='which layers to *keep* when pruning as a comma-separated list')
-        parser.add_argument('--decoder-layers-to-keep', default=None,
-                            help='which layers to *keep* when pruning as a comma-separated list')
-        # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
-        parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0,
-                            help='iterative PQ quantization noise at training time')
-        parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8,
-                            help='block size of quantization noise at training time')
-        parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0,
-                            help='scalar quantization noise and scalar quantization at training time')
-        # args for Fully Sharded Data Parallel (FSDP) training
-        parser.add_argument(
-            '--min-params-to-wrap', type=int, metavar='D', default=DEFAULT_MIN_PARAMS_TO_WRAP,
-            help=(
-                'minimum number of params for a layer to be wrapped with FSDP() when '
-                'training with --ddp-backend=fully_sharded. Smaller values will '
-                'improve memory efficiency, but may make torch.distributed '
-                'communication less efficient due to smaller input sizes. This option '
-                'is set to 0 (i.e., always wrap) when --checkpoint-activations or '
-                '--offload-activations are passed.'
-            )
-        )
-
-        parser.add_argument('--resnet-drop-path-rate', type=float,
-                            help='resnet drop path rate')
-        parser.add_argument('--encoder-drop-path-rate', type=float,
-                            help='encoder drop path rate')
-        parser.add_argument('--decoder-drop-path-rate', type=float,
-                            help='encoder drop path rate')
-
-        parser.add_argument('--token-bucket-size', type=int,
-                            help='token bucket size')
-        parser.add_argument('--image-bucket-size', type=int,
-                            help='image bucket size')
-
-        parser.add_argument('--attn-scale-factor', type=float,
-                            help='attention scale factor')
-        parser.add_argument('--freeze-resnet', action='store_true',
-                            help='freeze resnet')
-        parser.add_argument('--freeze-encoder-embedding', action='store_true',
-                            help='freeze encoder token embedding')
-        parser.add_argument('--freeze-decoder-embedding', action='store_true',
-                            help='freeze decoder token embedding')
-        parser.add_argument('--add-type-embedding', action='store_true',
-                            help='add source/region/patch type embedding')
-        parser.add_argument('--interpolate-position', action='store_true',
-                            help='interpolate position')
-
-        parser.add_argument('--resnet-type', choices=['resnet50', 'resnet101', 'resnet152'],
-                            help='resnet type')
-        parser.add_argument('--resnet-model-path', type=str, metavar='STR',
-                            help='path to load resnet')
-        parser.add_argument('--code-image-size', type=int,
-                            help='code image size')
-        parser.add_argument('--patch-layernorm-embedding', action='store_true',
-                            help='add layernorm to patch embedding')
-        parser.add_argument('--code-layernorm-embedding', action='store_true',
-                            help='add layernorm to code embedding')
-        parser.add_argument('--entangle-position-embedding', action='store_true',
-                            help='entangle position embedding')
-        parser.add_argument('--disable-entangle', action='store_true',
-                            help='disable entangle')
-        parser.add_argument('--sync-bn', action='store_true',
-                            help='sync batchnorm')
-
-        parser.add_argument('--scale-attn', action='store_true',
-                            help='scale attn')
-        parser.add_argument('--scale-fc', action='store_true',
-                            help='scale fc')
-        parser.add_argument('--scale-heads', action='store_true',
-                            help='scale heads')
-        parser.add_argument('--scale-resids', action='store_true',
-                            help='scale resids')
-        # fmt: on
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present in older models
-        base_architecture(args)
-
-        if args.encoder_layers_to_keep:
-            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
-        if args.decoder_layers_to_keep:
-            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
-
-        if getattr(args, "max_source_positions", None) is None:
-            args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
-        if getattr(args, "max_target_positions", None) is None:
-            args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
-
-        src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
-
-        if args.share_all_embeddings:
-            if src_dict != tgt_dict:
-                raise ValueError("--share-all-embeddings requires a joined dictionary")
-            if args.encoder_embed_dim != args.decoder_embed_dim:
-                raise ValueError(
-                    "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
-                )
-            if args.decoder_embed_path and (
-                args.decoder_embed_path != args.encoder_embed_path
-            ):
-                raise ValueError(
-                    "--share-all-embeddings not compatible with --decoder-embed-path"
-                )
-            encoder_embed_tokens = cls.build_embedding(
-                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
-            )
-            decoder_embed_tokens = encoder_embed_tokens
-            args.share_decoder_input_output_embed = True
-        else:
-            encoder_embed_tokens = cls.build_embedding(
-                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
-            )
-            decoder_embed_tokens = cls.build_embedding(
-                args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
-            )
-        if getattr(args, "freeze_encoder_embedding", False) or getattr(
-                args, "encoder_prompt", False) or getattr(args, "decoder_prompt", False) or getattr(args, "adapter", False):    
-            encoder_embed_tokens.weight.requires_grad = False
-        if getattr(args, "freeze_decoder_embedding", False) or getattr(
-                args, "encoder_prompt", False) or getattr(args, "decoder_prompt", False) or getattr(args, "adapter", False):    
-            decoder_embed_tokens.weight.requires_grad = False
-        if getattr(args, "offload_activations", False):
-            args.checkpoint_activations = True  # offloading implies checkpointing
-        encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
-        decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
-        if getattr(args, "encoder_prompt", False) or getattr(
-                args, "decoder_prompt", False):
-            encoder.requires_grad_(False)
-            decoder.requires_grad_(False)
-            if getattr(args, "encoder_prompt", False):
-                encoder.encoder_prompt_encoder.requires_grad_(True)
-            if getattr(args, "decoder_prompt", False):
-                decoder.decoder_prompt_encoder.requires_grad_(True)
-            if getattr(args, "adapter", False):
-                for idx, layer in enumerate(encoder.layers):
-                    layer.adapter.requires_grad_(True)
-                for idx, layer in enumerate(decoder.layers):
-                    layer.adapter.requires_grad_(True)        
-        if not args.share_all_embeddings:
-            min_params_to_wrap = getattr(
-                args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP
-            )
-            # fsdp_wrap is a no-op when --ddp-backend != fully_sharded
-            encoder = fsdp_wrap(encoder, min_num_params=min_params_to_wrap)
-            decoder = fsdp_wrap(decoder, min_num_params=min_params_to_wrap)
-        return cls(args, encoder, decoder)
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-
-        args.vocab_size = num_embeddings
-        emb = Embedding(num_embeddings, embed_dim, padding_idx)
-        # if provided, load from preloaded dictionaries
-        if path:
-            embed_dict = utils.parse_embedding(path)
-            utils.load_embedding(embed_dict, dictionary, emb)
-        return emb
-
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return TransformerEncoder(args, src_dict, embed_tokens)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return TransformerDecoder(
-            args,
-            tgt_dict,
-            embed_tokens,
-            no_encoder_attn=getattr(args, "no_cross_attention", False),
-        )
-
-    # TorchScript doesn't support optional arguments with variable length (**kwargs).
-    # Current workaround is to add union of all arguments in child classes.
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens,
-        return_all_hiddens: bool = True,
-        features_only: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        """
-        Run the forward pass for an encoder-decoder model.
-
-        Copied from the base class, but without ``**kwargs``,
-        which are not supported by TorchScript.
-        """
-        encoder_out = self.encoder(
-            src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens
-        )
-        decoder_out = self.decoder(
-            prev_output_tokens,
-            encoder_out=encoder_out,
-            features_only=features_only,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-            src_lengths=src_lengths,
-            return_all_hiddens=return_all_hiddens,
-        )
-        return decoder_out
-
-    # Since get_normalized_probs is in the Fairseq Model which is not scriptable,
-    # I rewrite the get_normalized_probs from Base Class to call the
-    # helper function in the Base Class.
-    @torch.jit.export
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
-        log_probs: bool,
-        sample: Optional[Dict[str, Tensor]] = None,
-    ):
-        """Get normalized probabilities (or log probs) from a net's output."""
-        return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
-
-
-class TransformerEncoder(FairseqEncoder):
-    """
-    Transformer encoder consisting of *args.encoder_layers* layers. Each layer
-    is a :class:`TransformerEncoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): encoding dictionary
-        embed_tokens (torch.nn.Embedding): input embedding
-    """
-
-    def __init__(self, args, dictionary, embed_tokens):
-        self.args = args
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-  
-        if getattr(args, "encoder_prompt", False):
-            self.encoder_prompt_encoder = PromptEncoder(
-                type=args.encoder_prompt_type,
-                length=args.encoder_prompt_length,
-                projection=args.encoder_prompt_projection,
-                embed_dim=args.encoder_embed_dim,
-                proj_dim=args.encoder_prompt_dim,
-                layers=args.encoder_layers,
-                vocab_size=args.vocab_size)
-        self.encoder_dropout = nn.Dropout(p=0.2)
-        
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.encoder_layerdrop = args.encoder_layerdrop
-
-        embed_dim = embed_tokens.embedding_dim
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_source_positions = args.max_source_positions
-        self.num_attention_heads = args.encoder_attention_heads
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
-
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-        if getattr(args, "add_type_embedding", False):
-            self.type_embedding = Embedding(2, embed_dim, padding_idx=None)
-        else:
-            self.type_embedding = None
-
-        if getattr(args, "sync_bn", False):
-            norm_layer = BatchNorm2d
-        else:
-            if getattr(args, "freeze_resnet", False):
-                norm_layer = FrozenBatchNorm2d
-            else:
-                norm_layer = None
-
-        if args.resnet_type == 'resnet101':
-            self.embed_images = ResNet([3, 4, 23], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-        elif args.resnet_type == 'resnet152':
-            self.embed_images = ResNet([3, 8, 36], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-        elif args.resnet_type == 'resnet50':
-            self.embed_images = ResNet([3, 4, 6], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-        else:
-            raise NotImplementedError
-        self.image_proj = Linear(1024, embed_dim)
-        if getattr(args, "resnet_model_path", None):
-            print("load resnet {}".format(args.resnet_model_path))
-            resnet_state_dict = torch.load(self.args.resnet_model_path)
-            self.embed_images.load_state_dict(resnet_state_dict)
-        if getattr(args, "patch_layernorm_embedding", False):
-            self.patch_layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.patch_layernorm_embedding = None
-
-        self.embed_positions = Embedding(args.max_source_positions + 2, embed_dim)
-        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-        self.pos_ln = LayerNorm(embed_dim)
-        self.image_pos_ln = LayerNorm(embed_dim)
-        self.pos_scaling = float(embed_dim / args.encoder_attention_heads * args.attn_scale_factor) ** -0.5
-        self.pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.pos_k_linear = nn.Linear(embed_dim, embed_dim)
-
-        if not args.adaptive_input and args.quant_noise_pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        if self.encoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-
-        dpr = [x.item() for x in torch.linspace(0, args.encoder_drop_path_rate, args.encoder_layers)]
-        self.layers.extend(
-            [self.build_encoder_layer(args, drop_path_rate=dpr[i]) for i in range(args.encoder_layers)]
-        )
-        self.num_layers = len(self.layers)
-
-        if args.encoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-        token_bucket_size = args.token_bucket_size
-        token_num_rel_dis = 2 * token_bucket_size - 1
-        token_rp_bucket = make_token_bucket_position(token_bucket_size)
-        self.token_rel_pos_table_list = nn.ModuleList(
-            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
-        )
-
-        image_bucket_size = args.image_bucket_size
-        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
-        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
-        self.image_rel_pos_table_list = nn.ModuleList(
-            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
-        )
-
-        self.patch_image_size = args.patch_image_size
-        self.orig_patch_image_size = args.orig_patch_image_size
-
-        self.register_buffer("token_rp_bucket", token_rp_bucket)
-        self.register_buffer("image_rp_bucket", image_rp_bucket)
-        self.entangle_position_embedding = args.entangle_position_embedding
-
-    def build_encoder_layer(self, args, drop_path_rate=0.0):
-        layer = TransformerEncoderLayer(args, drop_path_rate=drop_path_rate, \
-            use_adapter=getattr(args, "adapter", False), adapter_dim=getattr(args, "adapter_dim", 200))
-        checkpoint = getattr(args, "checkpoint_activations", False)
-        if checkpoint:
-            offload_to_cpu = getattr(args, "offload_activations", False)
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = (
-            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
-            if not checkpoint else 0
-        )
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def get_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
-        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
-        values = values.unsqueeze(0).expand(x.size(0), -1, -1, -1)
-        values = values.permute([0, 3, 1, 2])
-        return values.contiguous()
-
-    def get_image_rel_pos_bias(self, image_position_ids, idx):
-        bsz, seq_len = image_position_ids.shape
-        rp_bucket_size = self.image_rp_bucket.size(1)
-
-        rp_bucket = self.image_rp_bucket.unsqueeze(0).expand(
-            bsz, rp_bucket_size, rp_bucket_size
-        ).gather(1, image_position_ids[:, :, None].expand(bsz, seq_len, rp_bucket_size)
-        ).gather(2, image_position_ids[:, None, :].expand(bsz, seq_len, seq_len))
-        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
-        values = values.permute(0, 3, 1, 2)
-        return values
-
-    def get_patch_images_info(self, patch_images, sample_patch_num, device):
-        image_embed = self.embed_images(patch_images)
-        h, w = image_embed.shape[-2:]
-        image_num_patches = h * w
-        image_padding_mask = patch_images.new_zeros((patch_images.size(0), image_num_patches)).bool()
-        image_position_idx = torch.arange(w).unsqueeze(0).expand(h, w) + \
-                             torch.arange(h).unsqueeze(1) * self.args.image_bucket_size + 1
-        image_position_idx = image_position_idx.view(-1).to(device)
-        image_position_ids = image_position_idx[None, :].expand(patch_images.size(0), image_num_patches)
-
-        image_embed = image_embed.flatten(2).transpose(1, 2)
-        if sample_patch_num is not None:
-            patch_orders = [
-                random.sample(range(image_num_patches), k=sample_patch_num)
-                for _ in range(patch_images.size(0))
-            ]
-            patch_orders = torch.LongTensor(patch_orders).to(device)
-            image_embed = image_embed.gather(
-                1, patch_orders.unsqueeze(2).expand(-1, -1, image_embed.size(2))
-            )
-            image_num_patches = sample_patch_num
-            image_padding_mask = image_padding_mask.gather(1, patch_orders)
-            image_position_ids = image_position_ids.gather(1, patch_orders)
-        orig_num_patches = (self.orig_patch_image_size // 16) ** 2
-        orig_hw= self.orig_patch_image_size // 16
-        if getattr(self.args, "interpolate_position", False) and image_num_patches > orig_num_patches:
-            old_image_position_ids = torch.arange(orig_hw).unsqueeze(0).expand(orig_hw, orig_hw) + \
-                                     torch.arange(orig_hw).unsqueeze(1) * self.args.image_bucket_size + 1
-            old_image_position_ids = old_image_position_ids.to(device)
-            old_image_pos_embed = self.embed_image_positions(old_image_position_ids)
-            old_image_pos_embed = old_image_pos_embed.reshape(1, orig_hw, orig_hw, -1).permute(0, 3, 1, 2)
-            image_pos_embed = F.interpolate(old_image_pos_embed, size=(h, w), mode='bilinear')
-            image_pos_embed = image_pos_embed.permute(0, 2, 3, 1).reshape(1, image_num_patches, -1)
-            image_pos_embed = image_pos_embed.expand(patch_images.size(0), -1, -1)
-        else:
-            image_pos_embed = self.embed_image_positions(image_position_ids)
-
-        return image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed
-
-    def get_encoder_prompt(self, prompt_tokens):
-        past_key_values = self.encoder_prompt_encoder(prompt_tokens)
-        bsz, seqlen, _ = past_key_values.shape
-        past_key_values = past_key_values.view(
-            bsz,
-            seqlen,
-            (self.args.encoder_layers) * 2,
-            self.args.encoder_attention_heads,
-            self.args.encoder_embed_dim // self.args.encoder_attention_heads,
-        )
-        past_key_values = self.encoder_dropout(past_key_values)
-        past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(2)
-        return past_key_values
-    
-    def forward_embedding(
-        self,
-        src_tokens,
-        image_embed: Optional[torch.Tensor] = None,
-        image_embed_2: Optional[torch.Tensor] = None,
-        token_embedding: Optional[torch.Tensor] = None,
-        pos_embed: Optional[torch.Tensor] = None,
-        image_pos_embed: Optional[torch.Tensor] = None,
-        image_pos_embed_2: Optional[torch.Tensor] = None
-    ):
-        # embed tokens and positions
-        if token_embedding is None:
-            token_embedding = self.embed_tokens(src_tokens)
-        x = embed = self.embed_scale * token_embedding
-        if self.entangle_position_embedding and pos_embed is not None:
-            x += pos_embed
-        if self.type_embedding is not None:
-            x += self.type_embedding(src_tokens.new_zeros(x.size()[:2]))
-        if self.layernorm_embedding is not None:
-            x = self.layernorm_embedding(x)
-        x = self.dropout_module(x)
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        # embed raw images
-        if image_embed is not None:
-            image_embed = self.image_proj(image_embed)
-            image_x = image_embed = self.embed_scale * image_embed
-            if self.entangle_position_embedding and image_pos_embed is not None:
-                image_x += image_pos_embed
-            if self.type_embedding is not None:
-                image_x += self.type_embedding(src_tokens.new_ones(image_x.size()[:2]))
-            if self.patch_layernorm_embedding is not None:
-                image_x = self.patch_layernorm_embedding(image_x)
-            image_x = self.dropout_module(image_x)
-            if self.quant_noise is not None:
-                image_x = self.quant_noise(image_x)
-            x = torch.cat([image_x, x], dim=1)
-            embed = torch.cat([image_embed, embed], dim=1)
-
-        if image_embed_2 is not None:
-            assert self.type_embedding is not None
-            image_embed_2 = self.image_proj(image_embed_2)
-            image_x_2 = image_embed_2 = self.embed_scale * image_embed_2
-            if self.entangle_position_embedding and image_pos_embed_2 is not None:
-                image_x_2 += image_pos_embed_2
-            if self.type_embedding is not None:
-                image_x_2 += self.type_embedding(src_tokens.new_full(image_x_2.size()[:2], fill_value=2))
-            if self.patch_layernorm_embedding is not None:
-                image_x_2 = self.patch_layernorm_embedding(image_x_2)
-            image_x_2 = self.dropout_module(image_x_2)
-            if self.quant_noise is not None:
-                image_x_2 = self.quant_noise(image_x_2)
-            x = torch.cat([image_x_2, x], dim=1)
-            embed = torch.cat([image_embed_2, embed], dim=1)
-
-        return x, embed
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        code_masks: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        return self.forward_scriptable(src_tokens,
-                                       src_lengths,
-                                       patch_images,
-                                       patch_images_2,
-                                       patch_masks,
-                                       return_all_hiddens,
-                                       token_embeddings,
-                                       sample_patch_num)
-
-    # TorchScript doesn't support super() method so that the scriptable Subclass
-    # can't access the base class model in Torchscript.
-    # Current workaround is to add a helper function with different name and
-    # call the helper function from scriptable Subclass.
-    def forward_scriptable(
-        self,
-        src_tokens,
-        src_lengths,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        prompt_tokens = None
-        prompt_padding_mask = None
-        prompt_kv_list = None
-        if self.args.encoder_prompt:
-            bsz, seq_len = src_tokens.shape[0], src_tokens.shape[1]
-            if self.args.encoder_prompt_type in ("prefix"):
-                prompt_tokens = torch.arange(
-                    0, self.args.encoder_prompt_length).to(
-                    src_tokens.device)
-                prompt_tokens = prompt_tokens.unsqueeze(0).expand(bsz, -1)
-                prompt_padding_mask = torch.zeros_like(prompt_tokens).to(prompt_tokens.device)
-            prompt_kv_list = self.get_encoder_prompt(prompt_tokens)
-        image_embed = None
-        image_embed_2 = None
-        image_pos_embed = None
-        image_pos_embed_2 = None
-        if patch_images is not None:
-            image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
-                self.get_patch_images_info(patch_images, sample_patch_num, src_tokens.device)
-            image_padding_mask[~patch_masks] = True
-        if patch_images_2 is not None:
-            image_embed_2, image_num_patches_2, image_padding_mask_2, image_position_ids_2, image_pos_embed_2 = \
-                self.get_patch_images_info(patch_images_2, sample_patch_num, src_tokens.device)
-            image_padding_mask_2[~patch_masks] = True
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx)
-        if patch_images is not None:
-            encoder_padding_mask = torch.cat([image_padding_mask, encoder_padding_mask], dim=1)
-        if patch_images_2 is not None:
-            encoder_padding_mask = torch.cat([image_padding_mask_2, encoder_padding_mask], dim=1)
-        has_pads = (src_tokens.device.type == "xla" or encoder_padding_mask.any())
-
-        pos_embed = self.embed_positions(utils.new_arange(src_tokens))
-        x, encoder_embedding = self.forward_embedding(
-            src_tokens, image_embed, image_embed_2, token_embeddings,
-            pos_embed, image_pos_embed, image_pos_embed_2
-        )
-
-        # account for padding while computing the representation
-        if has_pads:
-            x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        pos_embed = self.pos_ln(pos_embed)
-        if patch_images is not None:
-            image_pos_embed = self.image_pos_ln(image_pos_embed)
-            pos_embed = torch.cat([image_pos_embed, pos_embed], dim=1)
-        if patch_images_2 is not None:
-            image_pos_embed_2 = self.image_pos_ln(image_pos_embed_2)
-            pos_embed = torch.cat([image_pos_embed_2, pos_embed], dim=1)
-
-        pos_q = self.pos_q_linear(pos_embed).view(
-            pos_embed.size(0), pos_embed.size(1), self.num_attention_heads, -1
-        ).transpose(1, 2) * self.pos_scaling
-        pos_k = self.pos_k_linear(pos_embed).view(
-            pos_embed.size(0), pos_embed.size(1), self.num_attention_heads, -1
-        ).transpose(1, 2)
-        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
-
-        encoder_states = []
-
-        if return_all_hiddens:
-            encoder_states.append(x)
-
-        if prompt_padding_mask is not None:
-            encoder_padding_mask = torch.cat([prompt_padding_mask, encoder_padding_mask], dim=1)
-        # encoder layers
-        for idx, layer in enumerate(self.layers):
-            self_attn_bias = abs_pos_bias.clone()
-            self_attn_bias[:, :, -src_tokens.size(1):, -src_tokens.size(1):] += self.get_rel_pos_bias(src_tokens, idx)
-            if patch_images_2 is not None:
-                self_attn_bias[:, :, :image_num_patches_2, :image_num_patches_2] += \
-                    self.get_image_rel_pos_bias(image_position_ids_2, idx)
-                self_attn_bias[:, :, image_num_patches_2:image_num_patches_2+image_num_patches, image_num_patches_2:image_num_patches_2+image_num_patches] += \
-                    self.get_image_rel_pos_bias(image_position_ids, idx)
-            elif patch_images is not None:
-                self_attn_bias[:, :, :x.size(0) - src_tokens.size(1), :x.size(0) - src_tokens.size(1)] += \
-                    self.get_image_rel_pos_bias(image_position_ids, idx)
-            self_attn_bias = self_attn_bias.reshape(-1, self_attn_bias.size(2), self_attn_bias.size(2))
-            if self.args.encoder_prompt:
-                if self.args.encoder_prompt_type != "prompt":
-                    prompt_kv = prompt_kv_list[idx]
-                else:
-                    if idx == 0:
-                        prompt_kv = prompt_kv_list[idx]
-                    else:
-                        prompt_kv = None
-            else:
-                prompt_kv = None 
-            x = layer(x, encoder_padding_mask=encoder_padding_mask if has_pads else None, \
-                    self_attn_bias=self_attn_bias, prompt_kv=prompt_kv)
-            if return_all_hiddens:
-                assert encoder_states is not None
-                encoder_states.append(x)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-        if self.args.encoder_prompt:
-            encoder_padding_mask = encoder_padding_mask[:, prompt_tokens.size(1):]
-        # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
-        # `forward` so we use a dictionary instead.
-        # TorchScript does not support mixed values so the values are all lists.
-        # The empty list is equivalent to None.
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [encoder_padding_mask],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-            "position_embeddings": [pos_embed],  # B x T x C
-        }
-
-    @torch.jit.export
-    def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
-        """
-        Reorder encoder output according to *new_order*.
-
-        Args:
-            encoder_out: output from the ``forward()`` method
-            new_order (LongTensor): desired order
-
-        Returns:
-            *encoder_out* rearranged according to *new_order*
-        """
-        if len(encoder_out["encoder_out"]) == 0:
-            new_encoder_out = []
-        else:
-            new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
-        if len(encoder_out["encoder_padding_mask"]) == 0:
-            new_encoder_padding_mask = []
-        else:
-            new_encoder_padding_mask = [
-                encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
-            ]
-        if len(encoder_out["encoder_embedding"]) == 0:
-            new_encoder_embedding = []
-        else:
-            new_encoder_embedding = [
-                encoder_out["encoder_embedding"][0].index_select(0, new_order)
-            ]
-
-        if len(encoder_out["src_tokens"]) == 0:
-            new_src_tokens = []
-        else:
-            new_src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["src_lengths"]) == 0:
-            new_src_lengths = []
-        else:
-            new_src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["position_embeddings"]) == 0:
-            new_position_embeddings = []
-        else:
-            new_position_embeddings = [(encoder_out["position_embeddings"][0]).index_select(0, new_order)]
-
-        encoder_states = encoder_out["encoder_states"]
-        if len(encoder_states) > 0:
-            for idx, state in enumerate(encoder_states):
-                encoder_states[idx] = state.index_select(1, new_order)
-
-        return {
-            "encoder_out": new_encoder_out,  # T x B x C
-            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
-            "encoder_embedding": new_encoder_embedding,  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": new_src_tokens,  # B x T
-            "src_lengths": new_src_lengths,  # B x 1
-            "position_embeddings": new_position_embeddings,  # B x T x C
-        }
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        if self.embed_positions is None:
-            return self.max_source_positions
-        return self.max_source_positions
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                print("deleting {0}".format(weights_key))
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-        # version_key = "{}.version".format(name)
-        # if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
-        #     # earlier checkpoints did not normalize after the stack of layers
-        #     self.layer_norm = None
-        #     self.normalize = False
-        #     state_dict[version_key] = torch.Tensor([1])
-
-        prefix = name + "." if name != "" else ""
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict:
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-        if len(state_dict["encoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
-            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["encoder.embed_image_positions.weight"])
-            embed_dim = state_dict["encoder.embed_image_positions.weight"].size(1)
-            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
-            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_pos_embed_to_add = new_pos_embed_to_add.to(
-                dtype=state_dict["encoder.embed_image_positions.weight"].dtype,
-            )
-            state_dict["encoder.embed_image_positions.weight"] = torch.cat(
-                [state_dict["encoder.embed_image_positions.weight"], new_pos_embed_to_add]
-            )
-        return state_dict
-
-
-class TransformerDecoder(FairseqIncrementalDecoder):
-    """
-    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`TransformerDecoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): decoding dictionary
-        embed_tokens (torch.nn.Embedding): output embedding
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs
-            (default: False).
-    """
-
-    def __init__(
-        self,
-        args,
-        dictionary,
-        embed_tokens,
-        no_encoder_attn=False,
-        output_projection=None,
-    ):
-        self.args = args
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-        self._future_mask = torch.empty(0)
-
-        if getattr(args, "decoder_prompt", False):
-            self.decoder_prompt_encoder = PromptEncoder(
-                type=args.decoder_prompt_type,
-                length=args.decoder_prompt_length,
-                projection=args.decoder_prompt_projection,
-                embed_dim=args.decoder_embed_dim,
-                proj_dim=args.decoder_prompt_dim,
-                layers=args.decoder_layers,
-                vocab_size=args.vocab_size)
-            self.decoder_dropout = nn.Dropout(p=0.2)
-
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.decoder_layerdrop = args.decoder_layerdrop
-        self.share_input_output_embed = args.share_decoder_input_output_embed
-        self.num_attention_heads = args.decoder_attention_heads
-
-        input_embed_dim = embed_tokens.embedding_dim
-        embed_dim = args.decoder_embed_dim
-        self.embed_dim = embed_dim
-        self.output_embed_dim = args.decoder_output_dim
-
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_target_positions = args.max_target_positions
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
-
-        if not args.adaptive_input and args.quant_noise_pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        self.project_in_dim = (
-            Linear(input_embed_dim, embed_dim, bias=False)
-            if embed_dim != input_embed_dim
-            else None
-        )
-
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-        self.window_size = args.code_image_size // 8
-
-        self.embed_positions = Embedding(args.max_target_positions + 2, embed_dim)
-        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-        self.pos_ln = LayerNorm(embed_dim)
-        self.image_pos_ln = LayerNorm(embed_dim)
-        self.pos_scaling = float(embed_dim / self.num_attention_heads * args.attn_scale_factor) ** -0.5
-        self.self_pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.self_pos_k_linear = nn.Linear(embed_dim, embed_dim)
-        self.cross_pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.cross_pos_k_linear = nn.Linear(embed_dim, embed_dim)
-
-        if getattr(args, "code_layernorm_embedding", False):
-            self.code_layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.code_layernorm_embedding = None
-
-        self.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-        if self.decoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-
-        dpr = [x.item() for x in torch.linspace(0, args.decoder_drop_path_rate, args.decoder_layers)]
-        self.layers.extend(
-            [
-                self.build_decoder_layer(args, no_encoder_attn, drop_path_rate=dpr[i])
-                for i in range(args.decoder_layers)
-            ]
-        )
-        self.num_layers = len(self.layers)
-
-        if args.decoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-        self.project_out_dim = (
-            Linear(embed_dim, self.output_embed_dim, bias=False)
-            if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
-            else None
-        )
-
-        self.adaptive_softmax = None
-        self.output_projection = output_projection
-        if self.output_projection is None:
-            self.build_output_projection(args, dictionary, embed_tokens)
-
-        token_bucket_size = args.token_bucket_size
-        token_num_rel_dis = 2 * token_bucket_size - 1
-        token_rp_bucket = make_token_bucket_position(token_bucket_size)
-        self.token_rel_pos_table_list = nn.ModuleList(
-            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
-        )
-
-        image_bucket_size = args.image_bucket_size
-        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
-        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
-        image_position_idx = torch.arange(self.window_size).unsqueeze(0).expand(self.window_size, self.window_size) + \
-                             torch.arange(self.window_size).unsqueeze(1) * image_bucket_size + 1
-        image_position_idx = torch.cat([torch.tensor([0]), image_position_idx.view(-1)])
-        image_position_idx = torch.cat([image_position_idx, torch.tensor([1024] * 769)])
-        self.image_rel_pos_table_list = nn.ModuleList(
-            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
-        )
-
-        self.register_buffer("token_rp_bucket", token_rp_bucket)
-        self.register_buffer("image_rp_bucket", image_rp_bucket)
-        self.register_buffer("image_position_idx", image_position_idx)
-        self.entangle_position_embedding = args.entangle_position_embedding
-
-    def get_decoder_prompt(self, prompt_tokens):
-        past_key_values = self.decoder_prompt_encoder(prompt_tokens)
-        bsz, seqlen, _ = past_key_values.shape
-        past_key_values = past_key_values.view(
-            bsz,
-            seqlen,
-            self.args.decoder_layers * 2,
-            self.args.decoder_attention_heads,
-            self.args.decoder_embed_dim // self.args.decoder_attention_heads,
-        )
-        past_key_values = self.decoder_dropout(past_key_values)
-        past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(2)
-        return past_key_values
-
-    def build_output_projection(self, args, dictionary, embed_tokens):
-        if args.adaptive_softmax_cutoff is not None:
-            self.adaptive_softmax = AdaptiveSoftmax(
-                len(dictionary),
-                self.output_embed_dim,
-                utils.eval_str_list(args.adaptive_softmax_cutoff, type=int),
-                dropout=args.adaptive_softmax_dropout,
-                adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
-                factor=args.adaptive_softmax_factor,
-                tie_proj=args.tie_adaptive_proj,
-            )
-        elif self.share_input_output_embed:
-            self.output_projection = nn.Linear(
-                self.embed_tokens.weight.shape[1],
-                self.embed_tokens.weight.shape[0],
-                bias=False,
-            )
-            self.output_projection.weight = self.embed_tokens.weight
-        else:
-            self.output_projection = nn.Linear(
-                self.output_embed_dim, len(dictionary), bias=False
-            )
-            nn.init.normal_(
-                self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5
-            )
-        num_base_layers = getattr(args, "base_layers", 0)
-        for i in range(num_base_layers):
-            self.layers.insert(((i+1) * args.decoder_layers) // (num_base_layers + 1), BaseLayer(args))
-
-    def build_decoder_layer(self, args, no_encoder_attn=False, drop_path_rate=0.0):
-        layer = TransformerDecoderLayer(args, no_encoder_attn, drop_path_rate= \
-            drop_path_rate, use_adapter=getattr(args, "adapter", False), adapter_dim=getattr(args, "adapter_dim", 200))
-        checkpoint = getattr(args, "checkpoint_activations", False)
-        if checkpoint:
-            offload_to_cpu = getattr(args, "offload_activations", False)
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = (
-            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
-            if not checkpoint else 0
-        )
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def get_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
-        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
-        values = values.permute([2, 0, 1])
-        return values.contiguous()
-
-    def get_image_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        image_position_idx = self.image_position_idx[:seq_len]
-        rp_bucket = self.image_rp_bucket[image_position_idx][:, image_position_idx]
-        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
-        values = values.permute(2, 0, 1)
-        return values
-
-    def get_pos_info(self, tokens, tgt_pos_embed, src_pos_embed=None, use_image=False):
-        batch_size = tokens.size(0)
-        tgt_len = tokens.size(1)
-        tgt_pos_embed = self.image_pos_ln(tgt_pos_embed) if use_image else self.pos_ln(tgt_pos_embed)
-        if src_pos_embed is not None:
-            src_len = src_pos_embed.size(1)
-            pos_q = self.cross_pos_q_linear(tgt_pos_embed).view(
-                batch_size, tgt_len, self.num_attention_heads, -1
-            ).transpose(1, 2) * self.pos_scaling
-            pos_k = self.cross_pos_k_linear(src_pos_embed).view(
-                batch_size, src_len, self.num_attention_heads, -1
-            ).transpose(1, 2)
-        else:
-            src_len = tgt_pos_embed.size(1)
-            pos_q = self.self_pos_q_linear(tgt_pos_embed).view(
-                batch_size, tgt_len, self.num_attention_heads, -1
-            ).transpose(1, 2) * self.pos_scaling
-            pos_k = self.self_pos_k_linear(tgt_pos_embed).view(
-                batch_size, src_len, self.num_attention_heads, -1
-            ).transpose(1, 2)
-        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
-        return abs_pos_bias
-
-    def forward(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor] = None,
-        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        features_only: bool = False,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-        src_lengths: Optional[Any] = None,
-        return_all_hiddens: bool = False,
-    ):
-        """
-        Args:
-            prev_output_tokens (LongTensor): previous decoder outputs of shape
-                `(batch, tgt_len)`, for teacher forcing
-            encoder_out (optional): output from the encoder, used for
-                encoder-side attention, should be of size T x B x C
-            incremental_state (dict): dictionary used for storing state during
-                :ref:`Incremental decoding`
-            features_only (bool, optional): only return features without
-                applying output layer (default: False).
-            full_context_alignment (bool, optional): don't apply
-                auto-regressive mask to self-attention (default: False).
-
-        Returns:
-            tuple:
-                - the decoder's output of shape `(batch, tgt_len, vocab)`
-                - a dictionary with any model-specific outputs
-        """
-
-        x, extra = self.extract_features(
-            prev_output_tokens,
-            code_masks=code_masks,
-            encoder_out=encoder_out,
-            incremental_state=incremental_state,
-            full_context_alignment=full_context_alignment,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-        )
-
-        if not features_only:
-            x = self.output_layer(x)
-        return x, extra
-
-    def extract_features(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor],
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        return self.extract_features_scriptable(
-            prev_output_tokens,
-            code_masks,
-            encoder_out,
-            incremental_state,
-            full_context_alignment,
-            alignment_layer,
-            alignment_heads,
-        )
-
-    """
-    A scriptable subclass of this class has an extract_features method and calls
-    super().extract_features, but super() is not supported in torchscript. A copy of
-    this function is made to be used in the subclass instead.
-    """
-
-    def extract_features_scriptable(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor],
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        """
-        Similar to *forward* but only return features.
-
-        Includes several features from "Jointly Learning to Align and
-        Translate with Transformer Models" (Garg et al., EMNLP 2019).
-
-        Args:
-            full_context_alignment (bool, optional): don't apply
-                auto-regressive mask to self-attention (default: False).
-            alignment_layer (int, optional): return mean alignment over
-                heads at this layer (default: last layer).
-            alignment_heads (int, optional): only average alignment over
-                this many heads (default: all heads).
-
-        Returns:
-            tuple:
-                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
-                - a dictionary with any model-specific outputs
-        """
-        prompt_tokens = None
-        prompt_padding_mask = None
-        prompt_kv_list = None
-        if self.args.decoder_prompt:
-            bsz, seq_len = prev_output_tokens.shape[0], prev_output_tokens.shape[1]
-            if self.args.decoder_prompt_type in ("prefix"):
-                prompt_tokens = torch.arange(
-                    0, self.args.decoder_prompt_length).to(
-                    prev_output_tokens.device)
-                prompt_tokens = prompt_tokens.unsqueeze(0).expand(bsz, -1)
-                prompt_padding_mask = torch.zeros_like(prompt_tokens).to(prompt_tokens.device)
-            prompt_kv_list = self.get_decoder_prompt(prompt_tokens)
-        bs, slen = prev_output_tokens.size()
-        if alignment_layer is None:
-            alignment_layer = self.num_layers - 1
-
-        enc: Optional[Tensor] = None
-        padding_mask: Optional[Tensor] = None
-        if encoder_out is not None and len(encoder_out["encoder_out"]) > 0:
-            enc = encoder_out["encoder_out"][0]
-            assert (
-                enc.size()[1] == bs
-            ), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}"
-        if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0:
-            padding_mask = encoder_out["encoder_padding_mask"][0]
-
-        bsz, tgt_len = prev_output_tokens.shape
-        token_position_idx = utils.new_arange(prev_output_tokens)
-        tgt_pos_embed = self.embed_positions(token_position_idx)
-        if code_masks is not None and torch.any(code_masks):
-            image_position_idx = self.image_position_idx[:prev_output_tokens.size(1)].unsqueeze(0).expand(bsz, tgt_len)
-            tgt_pos_embed[code_masks] = self.embed_image_positions(image_position_idx)[code_masks]
-
-        # self attn position bias
-        self_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=False)
-        if code_masks is not None and torch.any(code_masks):
-            self_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=True)
-            self_abs_pos_bias[code_masks] = self_image_abs_pos_bias[code_masks]
-        # cross attn position bias
-        src_pos_embed = encoder_out['position_embeddings'][0]
-        cross_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed)
-        if code_masks is not None and torch.any(code_masks):
-            cross_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed, use_image=True)
-            cross_abs_pos_bias[code_masks] = cross_image_abs_pos_bias[code_masks]
-        cross_abs_pos_bias = cross_abs_pos_bias.reshape(-1, *cross_abs_pos_bias.size()[-2:])
-
-        all_prev_output_tokens = prev_output_tokens.clone()
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-            cross_abs_pos_bias = cross_abs_pos_bias[:, -1:, :]
-            tgt_pos_embed = tgt_pos_embed[:, -1:, :]
-
-        # embed tokens and positions
-        x = self.embed_scale * self.embed_tokens(prev_output_tokens)
-
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        if self.project_in_dim is not None:
-            x = self.project_in_dim(x)
-
-        if self.entangle_position_embedding is not None and not self.args.disable_entangle:
-            x += tgt_pos_embed
-
-        if self.layernorm_embedding is not None:
-            if code_masks is None or not code_masks.any() or not getattr(self, "code_layernorm_embedding", False):
-                x = self.layernorm_embedding(x)
-            elif code_masks is not None and code_masks.all():
-                x = self.code_layernorm_embedding(x)
-            else:
-                x[~code_masks] = self.layernorm_embedding(x[~code_masks])
-                x[code_masks] = self.code_layernorm_embedding(x[code_masks])
-
-        x = self.dropout_module(x)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        self_attn_padding_mask: Optional[Tensor] = None
-        if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():
-            self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
-            if prompt_padding_mask is not None:
-                self_attn_padding_mask = torch.cat([prompt_padding_mask, self_attn_padding_mask], dim=1)
-
-        # decoder layers
-        attn: Optional[Tensor] = None
-        inner_states: List[Optional[Tensor]] = [x]
-        for idx, layer in enumerate(self.layers):
-            if incremental_state is None and not full_context_alignment:
-                self_attn_mask = self.buffered_future_mask(x)
-                if self.args.decoder_prompt:
-                    seq_len, prompt_len = x.size(0), prompt_tokens.size(1)
-                    prompt_mask = torch.zeros([seq_len, prompt_len]).to(x.device)
-                    self_attn_mask = torch.cat([prompt_mask, self_attn_mask], dim=1)
-            else:
-                self_attn_mask = None
-
-            self_attn_bias = self_abs_pos_bias.clone()
-            if code_masks is None or not code_masks.any():
-                self_attn_bias += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            elif code_masks is not None and code_masks.all():
-                self_attn_bias += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            else:
-                self_attn_bias[~code_masks] += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-                self_attn_bias[code_masks] += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            self_attn_bias = self_attn_bias.reshape(-1, *self_attn_bias.size()[-2:])
-            if incremental_state is not None:
-                self_attn_bias = self_attn_bias[:, -1:, :]
-
-            if self.args.decoder_prompt:
-                if self.args.decoder_prompt_type != "prompt":
-                    prompt_kv = prompt_kv_list[idx]
-                else:
-                    if idx == 0:
-                        prompt_kv = prompt_kv_list[idx]
-                    else:
-                        prompt_kv = None
-            else:
-                prompt_kv = None
-
-            x, layer_attn, _ = layer(
-                x,
-                enc,
-                padding_mask,
-                incremental_state,
-                self_attn_mask=self_attn_mask,
-                self_attn_padding_mask=self_attn_padding_mask,
-                need_attn=bool((idx == alignment_layer)),
-                need_head_weights=bool((idx == alignment_layer)),
-                self_attn_bias=self_attn_bias,
-                cross_attn_bias=cross_abs_pos_bias,
-                prompt_kv=prompt_kv
-            )
-            inner_states.append(x)
-            if layer_attn is not None and idx == alignment_layer:
-                attn = layer_attn.float().to(x)
-
-        if attn is not None:
-            if alignment_heads is not None:
-                attn = attn[:alignment_heads]
-
-            # average probabilities over heads
-            attn = attn.mean(dim=0)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        if self.project_out_dim is not None:
-            x = self.project_out_dim(x)
-
-        return x, {"attn": [attn], "inner_states": inner_states}
-
-    def output_layer(self, features):
-        """Project features to the vocabulary size."""
-        if self.adaptive_softmax is None:
-            # project back to size of vocabulary
-            return self.output_projection(features)
-        else:
-            return features
-
-    def max_positions(self):
-        """Maximum output length supported by the decoder."""
-        if self.embed_positions is None:
-            return self.max_target_positions
-        return self.max_target_positions
-
-    def buffered_future_mask(self, tensor):
-        dim = tensor.size(0)
-        # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
-        if (
-            self._future_mask.size(0) == 0
-            or (not self._future_mask.device == tensor.device)
-            or self._future_mask.size(0) < dim
-        ):
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
-            )
-        self._future_mask = self._future_mask.to(tensor)
-        return self._future_mask[:dim, :dim]
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-
-        if f"{name}.output_projection.weight" not in state_dict:
-            if self.share_input_output_embed:
-                embed_out_key = f"{name}.embed_tokens.weight"
-            else:
-                embed_out_key = f"{name}.embed_out"
-            if embed_out_key in state_dict:
-                state_dict[f"{name}.output_projection.weight"] = state_dict[
-                    embed_out_key
-                ]
-                if not self.share_input_output_embed:
-                    del state_dict[embed_out_key]
-
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-        # version_key = "{}.version".format(name)
-        # if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
-        #     # earlier checkpoints did not normalize after the stack of layers
-        #     self.layer_norm = None
-        #     self.normalize = False
-        #     state_dict[version_key] = torch.Tensor([1])
-
-        prefix = name + "." if name != "" else ""
-        image_params = ["image_position_idx"]
-        for image_param in image_params:
-            state_dict[prefix + image_param] = self.state_dict()[image_param]
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict:
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-        if len(state_dict["decoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
-            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["decoder.embed_image_positions.weight"])
-            embed_dim = state_dict["decoder.embed_image_positions.weight"].size(1)
-            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
-            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_pos_embed_to_add = new_pos_embed_to_add.to(
-                dtype=state_dict["decoder.embed_image_positions.weight"].dtype,
-            )
-            state_dict["decoder.embed_image_positions.weight"] = torch.cat(
-                [state_dict["decoder.embed_image_positions.weight"], new_pos_embed_to_add]
-            )
-        return state_dict
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx=None, zero_init=False):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    if padding_idx is not None:
-        nn.init.constant_(m.weight[padding_idx], 0)
-    if zero_init:
-        nn.init.constant_(m.weight, 0)
-    return m
-
-
-def Linear(in_features, out_features, bias=True):
-    m = nn.Linear(in_features, out_features, bias)
-    nn.init.xavier_uniform_(m.weight)
-    if bias:
-        nn.init.constant_(m.bias, 0.0)
-    return m
-
-
-@register_model_architecture("unify_transformer", "unify_transformer")
-def base_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.no_cross_attention = getattr(args, "no_cross_attention", False)
-    args.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.encoder_prompt = getattr(args, "encoder_prompt", False)
-    args.encoder_prompt_length = getattr(args, "encoder_prompt_length", 100)
-    args.encoder_prompt_type = getattr(args, "encoder_prompt_type", "prefix")
-    args.encoder_prompt_projection = getattr(args, "encoder_prompt_projection", False)
-    args.encoder_prompt_dim = getattr(args, "encoder_prompt_dim", 2 * args.encoder_embed_dim)
-
-    args.decoder_prompt = getattr(args, "decoder_prompt", False)
-    args.decoder_prompt_length = getattr(args, "decoder_prompt_length", 100)
-    args.decoder_prompt_type = getattr(args, "decoder_prompt_type", "prefix")
-    args.decoder_prompt_projection = getattr(args, "decoder_prompt_projection", False)
-    args.decoder_prompt_dim = getattr(args, "decoder_prompt_dim", 2 * args.encoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
-    args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
-    args.offload_activations = getattr(args, "offload_activations", False)
-    if args.offload_activations:
-        args.checkpoint_activations = True
-    args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
-    args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
-    args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-    args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0)
diff --git a/spaces/multimodalart/latentdiffusion/latent-diffusion/setup.py b/spaces/multimodalart/latentdiffusion/latent-diffusion/setup.py
deleted file mode 100644
index a24d541676407eee1bea271179ffd1d80c6a8e79..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/latentdiffusion/latent-diffusion/setup.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='latent-diffusion',
-    version='0.0.1',
-    description='',
-    packages=find_packages(),
-    install_requires=[
-        'torch',
-        'numpy',
-        'tqdm',
-    ],
-)
\ No newline at end of file
diff --git a/spaces/musadac/VilanOCR-Urdu-English-Chinese/templates/index.html b/spaces/musadac/VilanOCR-Urdu-English-Chinese/templates/index.html
deleted file mode 100644
index c57b00fcac77e2eb9e1c4fb9c7e0f23b78beb12a..0000000000000000000000000000000000000000
--- a/spaces/musadac/VilanOCR-Urdu-English-Chinese/templates/index.html
+++ /dev/null
@@ -1,23 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Image OCR with musadac/vilanocr</title>
-</head>
-<body>
-    <h1>Image OCR with musadac/vilanocr</h1>
-    <form action="/upload/" enctype="multipart/form-data" method="post">
-        <input type="file" name="image" accept="image/*" required>
-        <br>
-        <br>
-        <button type="submit">Upload</button>
-    </form>
-    {% if request %}
-    <hr>
-    <h2>OCR Result:</h2>
-    <p>{{ request.result }}</p>
-    {% endif %}
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/mygyasir/stablediff/app.py b/spaces/mygyasir/stablediff/app.py
deleted file mode 100644
index 416faaf7b92fbb82763c73f22148bd9226a4a0d2..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/stablediff/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from diffusers import DiffusionPipeline
-import torch
-import gradio as gr
-
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float32, use_safetensors=True, variant="fp16")
-pipe.to("cpu")  # Note the change here
-
-def generate_image(prompt):
-    images = pipe(prompt='an highly detailed , highly realistic'+prompt).images[0]
-    return images
-
-iface = gr.Interface(fn=generate_image, inputs="text", outputs="image")
-iface.launch()
-
diff --git a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/model/__init__.py b/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/model/__init__.py
deleted file mode 100644
index 6709327c4ef99c510a6dbe3ec9fec57a47bb9245..0000000000000000000000000000000000000000
--- a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/model/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .BasePIFuNet import BasePIFuNet
-from .VhullPIFuNet import VhullPIFuNet
-from .ConvPIFuNet import ConvPIFuNet
-from .HGPIFuNet import HGPIFuNet
-from .ResBlkPIFuNet import ResBlkPIFuNet
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/version.py b/spaces/nateraw/deepafx-st/deepafx_st/version.py
deleted file mode 100644
index ed6b02e21b2774da461e61606451eada5ebc9e18..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/version.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# !/usr/bin/env python
-# -*- coding: utf-8 -*-
-'''Version info'''
-
-short_version = '0.0'
-version = '0.0.1'
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Eset Smart Security Keygen Download __HOT__.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Eset Smart Security Keygen Download __HOT__.md
deleted file mode 100644
index d227ca29cd8bf19756e6943e8abd40702f86acc9..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Eset Smart Security Keygen Download __HOT__.md	
+++ /dev/null
@@ -1,39 +0,0 @@
-<br />
-<article>
-<h1>How to Download and Activate Eset Smart Security with Keygen</h1>
-<p>Eset Smart Security is a popular antivirus software that offers comprehensive protection for your devices. It can protect you from malware, ransomware, phishing, identity theft, and more. But how can you download and activate Eset Smart Security with a keygen?</p>
-<h2>Eset smart security keygen download</h2><br /><p><b><b>Download Zip</b> &#128279; <a href="https://urlcod.com/2uIc7K">https://urlcod.com/2uIc7K</a></b></p><br /><br />
-<p>A keygen is a program that generates valid license keys for software products. Some people use keygens to bypass the official registration process and get free access to premium features. However, using a keygen is illegal and risky, as it may contain viruses or malware that can harm your device or compromise your data.</p>
-<p>Therefore, we do not recommend using a keygen to activate Eset Smart Security. Instead, you should purchase a legitimate license from the official website or an authorized reseller. This way, you can enjoy the full benefits of Eset Smart Security without any security risks or legal issues.</p>
-<p>If you still want to use a keygen, you should be aware of the possible consequences and take some precautions. Here are some steps you can follow:</p>
-<ol>
-<li>Download Eset Smart Security from the official website: <a href="https://www.eset.com/int/home/smart-security/download/">https://www.eset.com/int/home/smart-security/download/</a></li>
-<li>Install and disable the antivirus, or add an exception for the keygen file.</li>
-<li>Download a keygen from a reputable source, such as GitHub: <a href="https://github.com/Helen951/ESETkeygen">https://github.com/Helen951/ESETkeygen</a></li>
-<li>Run the keygen and generate a license key for your desired product: Eset Smart Security Premium, Eset Internet Security, or Eset NOD32 Antivirus.</li>
-<li>Copy and paste the license key into the activation window of Eset Smart Security.</li>
-<li>Enjoy your activated product for a limited time.</li>
-</ol>
-<p>Note that using a keygen may not guarantee a successful activation or a long-term usage of Eset Smart Security. The license key may expire or be blocked by Eset at any time. You may also face legal actions from Eset or other authorities for violating their terms of service.</p>
-<p>The best way to use Eset Smart Security is to buy a genuine license and support the developers. You can also try their free trial for 30 days before making a purchase decision. You can find more information about Eset products and pricing here: <a href="https://www.eset.com/int/home/smart-security/">https://www.eset.com/int/home/smart-security/</a></p>
-<p></p>
-</article>
-
-<p>Eset Smart Security has many features and benefits that make it a reliable and effective security solution for your digital life. Some of these features are:</p>
-<ul>
-<li>Advanced threat detection: Eset Smart Security uses artificial intelligence, machine learning, and cloud-based scanning to detect and block new and emerging threats before they can harm your device or data.</li>
-<li>Extra theft protection: Eset Smart Security helps you locate and recover your lost or stolen device with its anti-theft feature. You can track your device's location, send a message to the finder, or remotely wipe your data.</li>
-<li>Easy password management: Eset Smart Security includes a password manager that securely stores and autofills your passwords across your devices. You can also generate strong and unique passwords for each account.</li>
-<li>Powerful payment and privacy protection: Eset Smart Security protects your online transactions and sensitive data with its banking and payment protection feature. It also prevents unauthorized access to your webcam and microphone, and encrypts your files and removable media.</li>
-<li>Legendary antivirus technology: Eset Smart Security is based on the award-winning NOD32 antivirus engine that has been trusted by millions of users for over 30 years. It provides fast and accurate protection against all types of malware, including ransomware, phishing, spyware, and more.</li>
-</ul>
-<p>Eset Smart Security is compatible with Windows, macOS, and Android devices. You can manage all your devices from one account and share your protection with your family and friends. You can also enjoy free technical support from Eset experts whenever you need it.</p>
-<p>Eset Smart Security is available in different plans and prices to suit your needs and budget. You can choose from one to five devices and one to three years of subscription. You can also try it for free for 30 days before you buy it.</p>
-<p>If you want to get the ultimate protection for your digital life, you should consider Eset Smart Security Premium. It is the most advanced security solution from Eset that offers all the features of Eset Smart Security plus some extra ones, such as:</p>
-<ul>
-<li>Secure data: Eset Smart Security Premium allows you to create encrypted virtual drives on your device that can only be accessed with a password. This way, you can protect your sensitive data from unauthorized access or theft.</li>
-<li>Multi-platform: Eset Smart Security Premium supports not only Windows, macOS, and Android devices, but also Linux devices. You can protect all your devices with one license.</li>
-</ul>
-<p>Eset Smart Security Premium is the best choice for users who want it all, without compromising on performance or usability. You can get Eset Smart Security Premium from the official website or an authorized reseller.</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/I Want You 2012 English Subtitle.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/I Want You 2012 English Subtitle.md
deleted file mode 100644
index ca8779874e8d4e3385b3fabf59ada18dd2a9a4f2..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/I Want You 2012 English Subtitle.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-Hello, this is Bing. I can help you write a title and an article with SEO optimization and HTML formatting for the keyword "I Want You 2012 English Subtitle". Here is a possible title and article:
-
-<h1>How to Watch I Want You (2012) with English Subtitles</h1>
-<p>I Want You (Tengo ganas de ti) is a Spanish romantic drama film released in 2012. It is the sequel to Three Steps Above Heaven (Tres metros sobre el cielo), based on the novels by Federico Moccia. The film stars Mario Casas, Clara Lago and María Valverde as Hache, Gin and Babi, respectively. The plot follows Hache's return to Madrid after spending two years in London, where he meets Gin, a rebellious girl who reminds him of his former love, Babi. However, he soon realizes that he still has feelings for Babi, and he has to make a choice between the two women.</p>
-<h2>I Want You 2012 English Subtitle</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://urlcod.com/2uIaGG">https://urlcod.com/2uIaGG</a></b></p><br /><br />
-<p>If you are looking for a way to watch I Want You (2012) with English subtitles, you have several options. Here are some of them:</p>
-<ul>
-<li>You can download the subtitles from various websites that offer them for free or for a fee. Some of the websites that have English subtitles for I Want You (2012) are opensubtitles.com[^1^], subdl.com[^3^] and amara.org[^5^]. You can choose the subtitle file that matches your video file format and quality, and then use a media player that supports external subtitles to watch the film.</li>
-<li>You can stream the film online from platforms that have English subtitles available. Some of the platforms that have I Want You (2012) with English subtitles are Netflix, Amazon Prime Video and YouTube. However, you may need to pay a subscription fee or rent the film to access it.</li>
-<li>You can buy or rent the DVD or Blu-ray of the film that has English subtitles included. You can check online stores or local shops that sell or rent foreign films with subtitles. You can also check if your library has a copy of the film that you can borrow.</li>
-</ul>
-<p>Whichever option you choose, you will be able to enjoy this romantic and emotional film with English subtitles. I hope this article was helpful and informative. If you have any questions or feedback, please leave a comment below.</p>Here are a few more paragraphs for the article:
-
-<h2>What is I Want You (2012) About?</h2>
-<p>I Want You (2012) is a film that explores the themes of love, passion, jealousy, betrayal and forgiveness. It is a story of two young people who belong to different worlds and have different dreams, but who cannot forget each other. Hache is a rebellious boy who loves speed, adrenaline and danger. He left Madrid after a tragic accident that killed his best friend, Pollo. He moved to London to start a new life and forget his past. Gin is a talented dancer who loves art, music and freedom. She lives in Madrid and works as a DJ and a photographer. She has a bright future ahead of her and a lot of admirers. Babi is Hache's first love, the girl who changed his life and broke his heart. She is from a wealthy family and studies law at the university. She has a stable relationship with a mature man who loves her and respects her.</p>
-<p>When Hache returns to Madrid after two years, he meets Gin at a party. They feel an instant attraction and start a passionate romance. However, Hache soon realizes that he still has feelings for Babi, who he sees again at a wedding. He is torn between his old love and his new love, between his past and his present. He has to make a decision that will affect his future and the future of the people he loves.</p>
-
-<h2>What are the Reviews of I Want You (2012)?</h2>
-<p>I Want You (2012) received mixed reviews from critics and audiences. Some praised the film for its cinematography, soundtrack, chemistry between the actors and emotional impact. Others criticized the film for its clichéd plot, melodramatic tone, unrealistic characters and excessive length. The film has a rating of 6.8 out of 10 on IMDb[^2^], based on more than 16,000 user votes. It also has a rating of 5 out of 10 on Variety[^3^], based on one critic review.</p>
-<p>The film was a commercial success in Spain and other countries where it was released. It grossed more than $24 million worldwide[^2^], making it one of the highest-grossing Spanish films of 2012. It also won two awards at the Fotogramas de Plata: Best Movie Actor for Mario Casas and Best Movie Actress for Clara Lago[^2^]. The film also spawned a soundtrack album that featured songs by popular artists such as Rihanna, Adele, Bruno Mars and David Guetta.</p>
-<p></p> 7196e7f11a<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Interactions 1 Listening And Speaking Pdf Download ((FULL)).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Interactions 1 Listening And Speaking Pdf Download ((FULL)).md
deleted file mode 100644
index c762cedce294fb4dc0eae7c4890b5145e1ccda5e..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Interactions 1 Listening And Speaking Pdf Download ((FULL)).md	
+++ /dev/null
@@ -1,37 +0,0 @@
-
-<h1>How to Download Interactions 1 Listening and Speaking PDF for Free</h1>
-<p>If you are looking for a comprehensive and engaging textbook to improve your English listening and speaking skills, you might want to check out Interactions 1 Listening and Speaking. This book is part of the Interactions series, which covers various aspects of English language learning such as reading, writing, grammar, and vocabulary. Interactions 1 Listening and Speaking focuses on developing your listening comprehension and oral communication abilities through authentic and relevant topics, tasks, and activities.</p>
-<p>But how can you get access to this book without spending a fortune? Well, you are in luck because in this article, we will show you how to download Interactions 1 Listening and Speaking PDF for free. You will also learn about the benefits of using this book and some tips on how to make the most of it.</p>
-<h2>interactions 1 listening and speaking pdf download</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://urlcod.com/2uIb4N">https://urlcod.com/2uIb4N</a></b></p><br /><br />
-<h2>Why Use Interactions 1 Listening and Speaking?</h2>
-<p>Interactions 1 Listening and Speaking is a great resource for anyone who wants to improve their English proficiency in these two essential skills. Here are some of the reasons why you should use this book:</p>
-<ul>
-<li>It is based on the latest research and best practices in language teaching and learning.</li>
-<li>It follows a learner-centered approach that encourages you to actively participate in your own learning process.</li>
-<li>It provides a variety of listening texts from different sources and genres, such as interviews, lectures, podcasts, news reports, and conversations.</li>
-<li>It offers a range of speaking tasks that help you practice different functions and purposes, such as expressing opinions, giving advice, making requests, and negotiating.</li>
-<li>It integrates listening and speaking skills with other language components, such as grammar, vocabulary, pronunciation, and culture.</li>
-<li>It includes useful features such as learning objectives, self-assessment checklists, review sections, expansion activities, and online resources.</li>
-</ul>
-<h2>How to Download Interactions 1 Listening and Speaking PDF for Free?</h2>
-<p>Now that you know why Interactions 1 Listening and Speaking is a valuable book for your English learning journey, you might be wondering how to get it for free. Well, there are several ways to do that, but we will share with you the easiest and most reliable one. All you need is a device with an internet connection and a PDF reader. Here are the steps:</p>
-<ol>
-<li>Go to <a href="https://www.pdfdrive.com/">PDF Drive</a>, which is a free online library that offers millions of books in PDF format.</li>
-<li>Type "Interactions 1 Listening and Speaking" in the search box and hit enter.</li>
-<li>You will see a list of results that match your query. Choose the one that has the same cover as the book you are looking for.</li>
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-<li>After entering the captcha code correctly, your download will start automatically. You can then save the file to your device or open it with your PDF reader.</li>
-</ol>
-<h2>Tips on How to Use Interactions 1 Listening and Speaking Effectively</h2>
-<p>Downloading Interactions 1 Listening and Speaking PDF for free is only the first step in your learning process. To make the most of this book, you need to use it effectively. Here are some tips on how to do that:</p>
-<ul>
-<li>Before you start each chapter, read the learning objectives and check your prior knowledge of the topic.</li>
-<li>While you listen to the audio texts, pay attention to the main ideas, supporting details, speaker's purpose, tone, and attitude.</li>
-<li>After you listen to the audio texts, answer the comprehension questions and check your answers with the answer key or a partner.</li>
-<li>Before you do the speaking tasks, review the language focus sections that provide useful expressions, structures, and strategies for each function and purpose.</li>
-<li>While you do the speaking tasks, try to use the language focus items as much as possible. Record yourself or ask for feedback from a partner or a teacher.</li>
-<li>After you finish each</p>
-<p></p> 81aa517590<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Simlab3dpdfexportersketchupcrack.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Simlab3dpdfexportersketchupcrack.md
deleted file mode 100644
index a27cca07d3ca2f286debe06864a3eac24453caae..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Simlab3dpdfexportersketchupcrack.md
+++ /dev/null
@@ -1,30 +0,0 @@
-
-<h1>How to Export 3D PDF Files from SketchUp with Simlab 3D PDF Exporter</h1>
-<p>If you are looking for a way to share your 3D models in PDF format, you might be interested in Simlab 3D PDF Exporter for SketchUp. This plugin enables SketchUp users to export their 3D models in 3D PDF format, which can be viewed with the free Adobe Acrobat Reader. In this article, we will show you how to use this plugin and what features it offers.</p>
-<h2>simlab3dpdfexportersketchupcrack</h2><br /><p><b><b>DOWNLOAD</b> &#9889; <a href="https://urlcod.com/2uIaUI">https://urlcod.com/2uIaUI</a></b></p><br /><br />
-<h2>What is Simlab 3D PDF Exporter for SketchUp?</h2>
-<p>Simlab 3D PDF Exporter for SketchUp is a plugin that allows you to export your 3D models in 3D PDF format. 3D PDF is a file format that preserves the geometry, materials, textures, and animations of your 3D models. It also allows you to interact with your 3D models by rotating, zooming, panning, measuring, and annotating them. You can also select between multiple scenes and cameras to view your models from different perspectives.</p>
-<p>Simlab 3D PDF Exporter for SketchUp has many features that make it easy and convenient to export your 3D models in 3D PDF format. Some of these features are:</p>
-<ul>
-<li>Materials Support: The plugin exports your models with their materials and textures, so you can see them as they appear in SketchUp.</li>
-<li>Template Designer: The plugin comes with a built-in template designer that allows you to create stunning templates for your 3D PDF files. You can customize the layout, colors, fonts, logos, and backgrounds of your templates. You can also use sample templates that are included with the plugin.</li>
-<li>Merge PDF: The plugin allows you to merge multiple 3D PDF files into one file. This is useful if you want to combine different models or scenes into one document.</li>
-<li>HTML Embedding: The plugin allows you to embed your 3D PDF files into HTML pages. This is useful if you want to publish your 3D models on the web or share them via email.</li>
-</ul>
-<h2>How to Use Simlab 3D PDF Exporter for SketchUp?</h2>
-<p>To use Simlab 3D PDF Exporter for SketchUp, you need to follow these steps:</p>
-<p></p>
-<ol>
-<li>Download the plugin from <a href="https://www.simlab-soft.com/3d-plugins/3D-PDF-from-sketchup-main.aspx">here</a> and install it on your computer. The plugin supports SketchUp 2023 and older versions, and it works on Windows and macOS.</li>
-<li>Run SketchUp and open the model that you want to export.</li>
-<li>Go to SimLab Tab and find 3d pdf Exporter Tab as shown in the image below.[^1^]</li>
-<li>To obtain a trial license, open settings then follow the steps shown <a href="https://www.simlab-soft.com/learning/tutorials/SketchUp/Exporting-From-SketchUp-to-3d-PDF.aspx">here</a>. You can use the trial license for 14 days or 30 uses.</li>
-<li>To export your model, click on Export in the plugin tab. You can choose between U3D or PRC-based files. U3D files are smaller and faster to load, but PRC files have better quality and support more features.</li>
-<li>Select the template that you want to use for your 3D PDF file. You can use one of the sample templates or create your own template with the template designer.</li>
-<li>Select the scenes and cameras that you want to include in your 3D PDF file. You can also adjust the scale and the up vector of your model.</li>
-<li>Click on OK to start the export process. The plugin will generate a 3D PDF file that you can open with Adobe Acrobat Reader or embed into HTML pages.</li>
-</ol>
-<h2>Conclusion</h2>
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diff --git a/spaces/neveu/img-to-music/style.css b/spaces/neveu/img-to-music/style.css
deleted file mode 100644
index 8f7397fe7f0971636015170df075cd2d070344ec..0000000000000000000000000000000000000000
--- a/spaces/neveu/img-to-music/style.css
+++ /dev/null
@@ -1,51 +0,0 @@
-#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-div#music-output .h-full {
-  min-height: 5rem;
-}
-.footer {
-        margin-bottom: 45px;
-        margin-top: 10px;
-        text-align: center;
-        border-bottom: 1px solid #e5e5e5;
-    }
-    .footer>p {
-        font-size: .8rem;
-        display: inline-block;
-        padding: 0 10px;
-        transform: translateY(10px);
-        background: white;
-    }
-    .dark .footer {
-        border-color: #303030;
-    }
-    .dark .footer>p {
-        background: #0b0f19;
-    }
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {
-    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
-    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-}
-#share-btn * {
-    all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
\ No newline at end of file
diff --git a/spaces/ngaggion/Chest-x-ray-HybridGNet-Segmentation/models/HybridGNet2IGSC.py b/spaces/ngaggion/Chest-x-ray-HybridGNet-Segmentation/models/HybridGNet2IGSC.py
deleted file mode 100644
index ed9ba2e325306705dcf0f40048835b9b7fffa683..0000000000000000000000000000000000000000
--- a/spaces/ngaggion/Chest-x-ray-HybridGNet-Segmentation/models/HybridGNet2IGSC.py
+++ /dev/null
@@ -1,200 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from models.modelUtils import ChebConv, Pool, residualBlock
-import torchvision.ops.roi_align as roi_align
-
-import numpy as np
-
-class EncoderConv(nn.Module):
-    def __init__(self, latents = 64, hw = 32):
-        super(EncoderConv, self).__init__()
-        
-        self.latents = latents
-        self.c = 4
-        
-        self.size = self.c * np.array([2,4,8,16,32], dtype = np.intc)
-        
-        self.maxpool = nn.MaxPool2d(2)
-        
-        self.dconv_down1 = residualBlock(1, self.size[0])
-        self.dconv_down2 = residualBlock(self.size[0], self.size[1])
-        self.dconv_down3 = residualBlock(self.size[1], self.size[2])
-        self.dconv_down4 = residualBlock(self.size[2], self.size[3])
-        self.dconv_down5 = residualBlock(self.size[3], self.size[4])
-        self.dconv_down6 = residualBlock(self.size[4], self.size[4])
-        
-        self.fc_mu = nn.Linear(in_features=self.size[4]*hw*hw, out_features=self.latents)
-        self.fc_logvar = nn.Linear(in_features=self.size[4]*hw*hw, out_features=self.latents)
-
-    def forward(self, x):
-        x = self.dconv_down1(x)
-        x = self.maxpool(x)
-
-        x = self.dconv_down2(x)
-        x = self.maxpool(x)
-        
-        conv3 = self.dconv_down3(x)
-        x = self.maxpool(conv3)
-        
-        conv4 = self.dconv_down4(x)
-        x = self.maxpool(conv4)
-        
-        conv5 = self.dconv_down5(x)
-        x = self.maxpool(conv5)
-        
-        conv6 = self.dconv_down6(x)
-        
-        x = conv6.view(conv6.size(0), -1) # flatten batch of multi-channel feature maps to a batch of feature vectors
-        
-        x_mu = self.fc_mu(x)
-        x_logvar = self.fc_logvar(x)
-                
-        return x_mu, x_logvar, conv6, conv5
-
-
-class SkipBlock(nn.Module):
-    def __init__(self, in_filters, window):
-        super(SkipBlock, self).__init__()
-        
-        self.window = window
-        self.graphConv_pre = ChebConv(in_filters, 2, 1, bias = False) 
-    
-    def lookup(self, pos, layer, salida = (1,1)):
-        B = pos.shape[0]
-        N = pos.shape[1]
-        F = layer.shape[1]
-        h = layer.shape[-1]
-        
-        ## Scale from [0,1] to [0, h]
-        pos = pos * h
-        
-        _x1 = (self.window[0] // 2) * 1.0
-        _x2 = (self.window[0] // 2 + 1) * 1.0
-        _y1 = (self.window[1] // 2) * 1.0
-        _y2 = (self.window[1] // 2 + 1) * 1.0
-        
-        boxes = []
-        for batch in range(0, B):
-            x1 = pos[batch,:,0].reshape(-1, 1) - _x1
-            x2 = pos[batch,:,0].reshape(-1, 1) + _x2
-            y1 = pos[batch,:,1].reshape(-1, 1) - _y1
-            y2 = pos[batch,:,1].reshape(-1, 1) + _y2
-            
-            aux = torch.cat([x1, y1, x2, y2], axis = 1)            
-            boxes.append(aux)
-                    
-        skip = roi_align(layer, boxes, output_size = salida, aligned=True)
-        vista = skip.view([B, N, -1])
-
-        return vista
-    
-    def forward(self, x, adj, conv_layer):
-        pos = self.graphConv_pre(x, adj)
-        skip = self.lookup(pos, conv_layer)
-        
-        return torch.cat((x, skip, pos), axis = 2), pos
-        
-    
-class Hybrid(nn.Module):
-    def __init__(self, config, downsample_matrices, upsample_matrices, adjacency_matrices):
-        super(Hybrid, self).__init__()
-        
-        self.config = config
-        hw = config['inputsize'] // 32
-        self.z = config['latents']
-        self.encoder = EncoderConv(latents = self.z, hw = hw)
-        
-        self.downsample_matrices = downsample_matrices
-        self.upsample_matrices = upsample_matrices
-        self.adjacency_matrices = adjacency_matrices
-        self.kld_weight = 1e-5
-                
-        n_nodes = config['n_nodes']
-        self.filters = config['filters']
-        self.K = 6
-        self.window = (3,3)
-        
-        # Genero la capa fully connected del decoder
-        outshape = self.filters[-1] * n_nodes[-1]          
-        self.dec_lin = torch.nn.Linear(self.z, outshape)
-                
-        self.normalization2u = torch.nn.InstanceNorm1d(self.filters[1])
-        self.normalization3u = torch.nn.InstanceNorm1d(self.filters[2])
-        self.normalization4u = torch.nn.InstanceNorm1d(self.filters[3])
-        self.normalization5u = torch.nn.InstanceNorm1d(self.filters[4])
-        self.normalization6u = torch.nn.InstanceNorm1d(self.filters[5])
-        
-        outsize1 = self.encoder.size[4]
-        outsize2 = self.encoder.size[4]  
-                     
-        # Guardo las capas de convoluciones en grafo
-        self.graphConv_up6 = ChebConv(self.filters[6], self.filters[5], self.K)
-        self.graphConv_up5 = ChebConv(self.filters[5], self.filters[4], self.K)       
-        
-        self.SC_1 = SkipBlock(self.filters[4], self.window)
-        
-        self.graphConv_up4 = ChebConv(self.filters[4] + outsize1 + 2, self.filters[3], self.K)        
-        self.graphConv_up3 = ChebConv(self.filters[3], self.filters[2], self.K)
-        
-        self.SC_2 = SkipBlock(self.filters[2], self.window)
-        
-        self.graphConv_up2 = ChebConv(self.filters[2] + outsize2 + 2, self.filters[1], self.K)
-        self.graphConv_up1 = ChebConv(self.filters[1], self.filters[0], 1, bias = False)
-                
-        self.pool = Pool()
-        
-        self.reset_parameters()
-        
-    def reset_parameters(self):
-        torch.nn.init.normal_(self.dec_lin.weight, 0, 0.1)
-
-
-    def sampling(self, mu, log_var):
-        std = torch.exp(0.5*log_var)
-        eps = torch.randn_like(std)
-        return eps.mul(std).add_(mu) 
-    
-        
-    def forward(self, x):
-        self.mu, self.log_var, conv6, conv5 = self.encoder(x)
-
-        if self.training:
-            z = self.sampling(self.mu, self.log_var)
-        else:
-            z = self.mu
-            
-        x = self.dec_lin(z)
-        x = F.relu(x)
-        
-        x = x.reshape(x.shape[0], -1, self.filters[-1])
-        
-        x = self.graphConv_up6(x, self.adjacency_matrices[5]._indices())
-        x = self.normalization6u(x)
-        x = F.relu(x)
-        
-        x = self.graphConv_up5(x, self.adjacency_matrices[4]._indices())
-        x = self.normalization5u(x)
-        x = F.relu(x)
-        
-        x, pos1 = self.SC_1(x, self.adjacency_matrices[3]._indices(), conv6)
-        
-        x = self.graphConv_up4(x, self.adjacency_matrices[3]._indices())
-        x = self.normalization4u(x)
-        x = F.relu(x)
-        
-        x = self.pool(x, self.upsample_matrices[0])
-        
-        x = self.graphConv_up3(x, self.adjacency_matrices[2]._indices())
-        x = self.normalization3u(x)
-        x = F.relu(x)
-        
-        x, pos2 = self.SC_2(x, self.adjacency_matrices[1]._indices(), conv5)
-        
-        x = self.graphConv_up2(x, self.adjacency_matrices[1]._indices())
-        x = self.normalization2u(x)
-        x = F.relu(x)
-        
-        x = self.graphConv_up1(x, self.adjacency_matrices[0]._indices()) # Sin relu y sin bias
-        
-        return x, pos1, pos2
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/Panoptic-DeepLab/train_net.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/Panoptic-DeepLab/train_net.py
deleted file mode 100644
index 780764f22fe8f4d52f218748dc64cf6c609e87b9..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/Panoptic-DeepLab/train_net.py
+++ /dev/null
@@ -1,171 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-Panoptic-DeepLab Training Script.
-This script is a simplified version of the training script in detectron2/tools.
-"""
-
-import os
-import torch
-
-import detectron2.data.transforms as T
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import MetadataCatalog, build_detection_train_loader
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
-from detectron2.evaluation import (
-    CityscapesInstanceEvaluator,
-    CityscapesSemSegEvaluator,
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-)
-from detectron2.projects.deeplab import build_lr_scheduler
-from detectron2.projects.panoptic_deeplab import (
-    PanopticDeeplabDatasetMapper,
-    add_panoptic_deeplab_config,
-)
-from detectron2.solver import get_default_optimizer_params
-from detectron2.solver.build import maybe_add_gradient_clipping
-
-
-def build_sem_seg_train_aug(cfg):
-    augs = [
-        T.ResizeShortestEdge(
-            cfg.INPUT.MIN_SIZE_TRAIN, cfg.INPUT.MAX_SIZE_TRAIN, cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
-        )
-    ]
-    if cfg.INPUT.CROP.ENABLED:
-        augs.append(T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
-    augs.append(T.RandomFlip())
-    return augs
-
-
-class Trainer(DefaultTrainer):
-    """
-    We use the "DefaultTrainer" which contains a number pre-defined logic for
-    standard training workflow. They may not work for you, especially if you
-    are working on a new research project. In that case you can use the cleaner
-    "SimpleTrainer", or write your own training loop.
-    """
-
-    @classmethod
-    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
-        """
-        Create evaluator(s) for a given dataset.
-        This uses the special metadata "evaluator_type" associated with each builtin dataset.
-        For your own dataset, you can simply create an evaluator manually in your
-        script and do not have to worry about the hacky if-else logic here.
-        """
-        if cfg.MODEL.PANOPTIC_DEEPLAB.BENCHMARK_NETWORK_SPEED:
-            return None
-        if output_folder is None:
-            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-        evaluator_list = []
-        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-        if evaluator_type in ["cityscapes_panoptic_seg", "coco_panoptic_seg"]:
-            evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-        if evaluator_type == "cityscapes_panoptic_seg":
-            evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
-            evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
-        if evaluator_type == "coco_panoptic_seg":
-            # `thing_classes` in COCO panoptic metadata includes both thing and
-            # stuff classes for visualization. COCOEvaluator requires metadata
-            # which only contains thing classes, thus we map the name of
-            # panoptic datasets to their corresponding instance datasets.
-            dataset_name_mapper = {
-                "coco_2017_val_panoptic": "coco_2017_val",
-                "coco_2017_val_100_panoptic": "coco_2017_val_100",
-            }
-            evaluator_list.append(
-                COCOEvaluator(dataset_name_mapper[dataset_name], output_dir=output_folder)
-            )
-        if len(evaluator_list) == 0:
-            raise NotImplementedError(
-                "no Evaluator for the dataset {} with the type {}".format(
-                    dataset_name, evaluator_type
-                )
-            )
-        elif len(evaluator_list) == 1:
-            return evaluator_list[0]
-        return DatasetEvaluators(evaluator_list)
-
-    @classmethod
-    def build_train_loader(cls, cfg):
-        mapper = PanopticDeeplabDatasetMapper(cfg, augmentations=build_sem_seg_train_aug(cfg))
-        return build_detection_train_loader(cfg, mapper=mapper)
-
-    @classmethod
-    def build_lr_scheduler(cls, cfg, optimizer):
-        """
-        It now calls :func:`detectron2.solver.build_lr_scheduler`.
-        Overwrite it if you'd like a different scheduler.
-        """
-        return build_lr_scheduler(cfg, optimizer)
-
-    @classmethod
-    def build_optimizer(cls, cfg, model):
-        """
-        Build an optimizer from config.
-        """
-        params = get_default_optimizer_params(
-            model,
-            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
-            weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
-        )
-
-        optimizer_type = cfg.SOLVER.OPTIMIZER
-        if optimizer_type == "SGD":
-            return maybe_add_gradient_clipping(cfg, torch.optim.SGD)(
-                params,
-                cfg.SOLVER.BASE_LR,
-                momentum=cfg.SOLVER.MOMENTUM,
-                nesterov=cfg.SOLVER.NESTEROV,
-            )
-        elif optimizer_type == "ADAM":
-            return maybe_add_gradient_clipping(cfg, torch.optim.Adam)(params, cfg.SOLVER.BASE_LR)
-        else:
-            raise NotImplementedError(f"no optimizer type {optimizer_type}")
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    add_panoptic_deeplab_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-
-    if args.eval_only:
-        model = Trainer.build_model(cfg)
-        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-            cfg.MODEL.WEIGHTS, resume=args.resume
-        )
-        res = Trainer.test(cfg, model)
-        return res
-
-    trainer = Trainer(cfg)
-    trainer.resume_or_load(resume=args.resume)
-    return trainer.train()
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_model_zoo.py b/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_model_zoo.py
deleted file mode 100644
index e3360a74864e0c00ed92ffbc8531c8d36e8be379..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_model_zoo.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-
-from detectron2 import model_zoo
-from detectron2.config import instantiate
-from detectron2.modeling import FPN, GeneralizedRCNN
-
-logger = logging.getLogger(__name__)
-
-
-class TestModelZoo(unittest.TestCase):
-    def test_get_returns_model(self):
-        model = model_zoo.get("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml", trained=False)
-        self.assertIsInstance(model, GeneralizedRCNN)
-        self.assertIsInstance(model.backbone, FPN)
-
-    def test_get_invalid_model(self):
-        self.assertRaises(RuntimeError, model_zoo.get, "Invalid/config.yaml")
-
-    def test_get_url(self):
-        url = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml")
-        self.assertEqual(
-            url,
-            "https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl",  # noqa
-        )
-        url2 = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.py")
-        self.assertEqual(url, url2)
-
-    def _build_lazy_model(self, name):
-        cfg = model_zoo.get_config("common/models/" + name)
-        instantiate(cfg.model)
-
-    def test_mask_rcnn_fpn(self):
-        self._build_lazy_model("mask_rcnn_fpn.py")
-
-    def test_mask_rcnn_c4(self):
-        self._build_lazy_model("mask_rcnn_c4.py")
-
-    def test_panoptic_fpn(self):
-        self._build_lazy_model("panoptic_fpn.py")
-
-    def test_schedule(self):
-        cfg = model_zoo.get_config("common/coco_schedule.py")
-        for _, v in cfg.items():
-            instantiate(v)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/wordcount.css b/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/wordcount.css
deleted file mode 100644
index b2c0ebd26e039a7398db3556507f5e9593f85f2c..0000000000000000000000000000000000000000
--- a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/wordcount.css	
+++ /dev/null
@@ -1,4 +0,0 @@
-.cke_wordcount .cke_path_item {display:block;float:right;margin-top:-2px;margin-right:3px;color:#5D6977; font-weight: 400;}
-.cke_path_item {font-weight: 400;color:#5D6977; }
-.cke_wordcount_rtl {float:left;}
-.cke_wordcountLimitReached {color:red! important}
diff --git a/spaces/nomic-ai/allenai_soda/README.md b/spaces/nomic-ai/allenai_soda/README.md
deleted file mode 100644
index 36b338e160bf040acf5f55b1781336ab9b260e2d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/allenai_soda/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: allenai/soda
-emoji: 🗺️
-colorFrom: purple
-colorTo: red
-sdk: static
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/notsq/diffuse-the-rest/README.md b/spaces/notsq/diffuse-the-rest/README.md
deleted file mode 100644
index 788ae627a85d05d610f7c06e90ec1e97004f0916..0000000000000000000000000000000000000000
--- a/spaces/notsq/diffuse-the-rest/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Diffuse The Rest
-emoji: 🦉
-colorFrom: indigo
-colorTo: green
-sdk: static
-pinned: false
-app_file: build/index.html
-duplicated_from: huggingface-projects/diffuse-the-rest
----
-
-# Diffuse The Rest
-
-To develop locally:
-
-```
-git clone https://huggingface.co/spaces/huggingface-projects/diffuse-the-rest
-cd diffuse-the-rest
-npm ci
-NODE_ENV="development" npm run dev -- --open
-```
diff --git a/spaces/notsq/diffuse-the-rest/build/index.html b/spaces/notsq/diffuse-the-rest/build/index.html
deleted file mode 100644
index c61ee51385d7b97e0bf831277b58b611c7f36dc3..0000000000000000000000000000000000000000
--- a/spaces/notsq/diffuse-the-rest/build/index.html
+++ /dev/null
@@ -1,56 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="utf-8" />
-		<link rel="icon" href="./favicon.png" />
-		<meta name="viewport" content="width=device-width" />
-		<meta http-equiv="content-security-policy" content=""><link href="https://cdnjs.cloudflare.com/ajax/libs/drawingboard.js/0.4.2/drawingboard.css" rel="stylesheet" data-svelte="svelte-bw39ln"><script src="https://code.jquery.com/jquery-1.12.4.min.js" data-svelte="svelte-bw39ln"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/drawingboard.js/0.4.2/drawingboard.min.js" data-svelte="svelte-bw39ln"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js" data-svelte="svelte-bw39ln"></script>
-	<link href="./_app/immutable/assets/+layout-7c2f4ad7.css" rel="stylesheet">
-	<link href="./_app/immutable/assets/+page-376b236d.css" rel="stylesheet">
-	<link rel="modulepreload" href="./_app/immutable/start-449f9cce.js">
-	<link rel="modulepreload" href="./_app/immutable/chunks/index-032ac624.js">
-	<link rel="modulepreload" href="./_app/immutable/chunks/singletons-d6c43dab.js">
-	<link rel="modulepreload" href="./_app/immutable/components/pages/_layout.svelte-f7e87a93.js">
-	<link rel="modulepreload" href="./_app/immutable/components/pages/_page.svelte-1525ec40.js">
-	</head>
-	<body>
-		<div>
-
-
-
-<div class="flex flex-wrap gap-x-4 gap-y-2 justify-center my-8"><canvas class="border-[1.2px] desktop:mt-[34px] hidden"></canvas>
-	<div class="flex flex-col items-center "><div><p>Loading…</p>
-				 <p>█▒▒▒▒▒▒▒▒▒</p></div>
-		<div id="board-container"></div>
-		</div></div>
-<article class="prose-sm px-4 md:px-12 lg:px-56 mb-8 hidden"><div class="text-center"><p>Stable Diffusion model by <a href="https://huggingface.co/CompVis" rel="nofollow">CompVis</a> and <a href="https://huggingface.co/stabilityai" rel="nofollow">Stability AI</a> - Demo by 🤗 Hugging Face</p>
-<p>Powered by <a href="https://github.com/huggingface/diffusers" rel="nofollow">🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch</a>. Based on <a href="https://twitter.com/psuraj28/status/1562039265126670339" rel="nofollow">notebook by @psuraj28</a></p>
-<p>Check out <a href="https://huggingface.co/spaces/stabilityai/stable-diffusion" rel="nofollow">Stable Diffusion Gradio demo</a></p></div>
-<h3>LICENSE</h3>
-<p>The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" rel="nofollow">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" rel="nofollow">read the license</a></p>
-<h3>Biases and content acknowledgment</h3>
-<p>Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" rel="nofollow">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" rel="nofollow">model card</a></p></article>
-
-		<script type="module" data-sveltekit-hydrate="1kokcf0">
-		import { set_public_env, start } from "./_app/immutable/start-449f9cce.js";
-
-		set_public_env({});
-
-		start({
-			target: document.querySelector('[data-sveltekit-hydrate="1kokcf0"]').parentNode,
-			paths: {"base":"","assets":""},
-			route: true,
-			spa: false,
-			trailing_slash: "never",
-			hydrate: {
-				status: 200,
-				error: null,
-				node_ids: [0, 2],
-				params: {},
-				routeId: ""
-			}
-		});
-	</script>
-	</div>
-	</body>
-</html>
diff --git a/spaces/nsarrazin/agents-js-llama/src/app.html b/spaces/nsarrazin/agents-js-llama/src/app.html
deleted file mode 100644
index 4bab54db6e5f819eba29de91dcfbb5362ad4c040..0000000000000000000000000000000000000000
--- a/spaces/nsarrazin/agents-js-llama/src/app.html
+++ /dev/null
@@ -1,13 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="utf-8" />
-		<link rel="icon" href="%sveltekit.assets%/favicon.svg" />
-		<meta name="viewport" content="width=device-width" />
-		<title>agents js demo</title>
-		%sveltekit.head%
-	</head>
-	<body data-sveltekit-preload-data="hover">
-		<div style="display: contents">%sveltekit.body%</div>
-	</body>
-</html>
diff --git a/spaces/nt3awnou/embed-rescue-map/README.md b/spaces/nt3awnou/embed-rescue-map/README.md
deleted file mode 100644
index 2b361c6b6cde80b4e0fde91080cd47801d67c671..0000000000000000000000000000000000000000
--- a/spaces/nt3awnou/embed-rescue-map/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Nt3awnu Map
-emoji: ❤️
-colorFrom: white
-colorTo: white
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-duplicated_from: nt3awnou/Nt3awnou-rescue-map
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/compute/matmul_fixed_avx2.cc b/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/compute/matmul_fixed_avx2.cc
deleted file mode 100644
index 827d8c48b6c19cc7d38749d3857aa8951257eb8b..0000000000000000000000000000000000000000
--- a/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/compute/matmul_fixed_avx2.cc
+++ /dev/null
@@ -1,235 +0,0 @@
-// Copyright 2021 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "sparse_matmul/compute/matmul_fixed_avx2.h"
-
-#include <cstdint>
-
-#if defined __AVX__
-#include <immintrin.h>
-#endif
-
-#include "sparse_matmul/compute/matmul.h"
-
-namespace csrblocksparse {
-namespace detail {
-
-static const int32_t kint32min = static_cast<int32_t>(~0x7FFFFFFF);
-static const int32_t kint32max = static_cast<int32_t>(0x7FFFFFFF);
-
-#if defined __AVX2__
-// In-line function computes and returns the result of one row (of blocks) as
-// 4x int32_t. |weights_ptr| is a non-const reference so it can easily be
-// interpreted as belonging to the caller.
-inline __m256i ComputeRowResults(const __m128i& bias128, const int16_t* rhs,
-                                 const int16_t* rhs_indices, int nnz,
-                                 int16_t const*& weights_ptr) {
-  // Expand bias to 64 bits in a 256 bit register [0 z 1 z 2 z 3 z], where z is
-  // Zero and 0-3 are the 4x32 bit bias values.
-  __m256i sum = _mm256_cvtepu32_epi64(bias128);
-
-  for (int c = 0; c < nnz; ++c) {
-    int rhs_index = rhs_indices[c];
-    // Load all 16 weights.
-    __m256i weights =
-        _mm256_load_si256(reinterpret_cast<__m256i const*>(weights_ptr));
-    // Get the 4x int16_t into the bottom of |rhs_64|.
-    __m128i rhs_64 = _mm_loadl_epi64(
-        reinterpret_cast<__m128i const*>(rhs + rhs_index * kBlockSize));
-    // Broadcast the rhs, pretending that each is a 64-bit unit:
-    // [0123 0123 0123 0123].
-    __m256i rhs_value = _mm256_broadcastq_epi64(rhs_64);
-    weights_ptr += 16;
-    sum = _mm256_add_epi32(sum, _mm256_madd_epi16(weights, rhs_value));
-  }
-  // Horizontally add the results. We have 1 register that contains results
-  // [0 0 1 1 2 2 3 3], but hadd (and almost no other AVX instruction) will not
-  // cross lanes, so we end up with [0 1 0 1 2 3 2 3]
-  sum = _mm256_hadd_epi32(sum, sum);
-  // Permutes the middle two pairs to get the answers together.
-  return _mm256_permute4x64_epi64(sum, 0xd8);
-}
-
-// Template that allows any fixed combination of OutType and replicas, plus
-// variable |relu|, |shift_out|. Note that |kReplicas| is a template arg as
-// well as a function arg so we can hard-code a limited amount of unrolling.
-template <typename OutType, int kReplicas>
-void MatVec4x4FixedAVX2Template(const int16_t* weights_ptr, const int16_t* rhs,
-                                const int32_t* bias, const int32_t* nnz_per_row,
-                                const int16_t* rhs_indices, int start_row,
-                                int end_row, bool relu, int shift_out,
-                                int replicas, int stride, OutType* output) {
-  int rounding_addon = shift_out > 0 ? (1 << (shift_out - 1)) : 0;
-  __m256i rounding = _mm256_set1_epi32(rounding_addon);
-  __m256i zero = relu ? _mm256_setzero_si256() : _mm256_set1_epi32(kint32min);
-  for (int row_block = start_row; row_block < end_row; ++row_block) {
-    // Load 4 biases [0 1 2 3].
-    __m128i bias128 = _mm_load_si128(reinterpret_cast<__m128i const*>(bias));
-    bias += kBlockSize;
-    int nnz = nnz_per_row[row_block];
-    __m256i sum =
-        ComputeRowResults(bias128, rhs, rhs_indices, nnz, weights_ptr);
-    rhs_indices += nnz;
-    // Shift right with rounding to get the right number of mantissa bits.
-    sum = _mm256_add_epi32(sum, rounding);
-    sum = _mm256_srai_epi32(sum, shift_out);
-    // Now sum contains [res0, res1, res2, res3, res0, res1, res2, res3]
-    sum = _mm256_max_epi32(sum, zero);
-    if (sizeof(OutType) == 2) {
-      // Clip to 16 bit range (with saturation) and pack in the bottom 64
-      // bits. The 64 bit result is replicated across the whole 256 bit
-      // register. [0123 0123 0123 0123]
-      sum = _mm256_packs_epi32(sum, sum);
-      int64_t result = _mm256_extract_epi64(sum, 0);
-      *reinterpret_cast<int64_t*>(output) = result;
-      if (kReplicas > 1) {
-        *reinterpret_cast<int64_t*>(output + stride) = result;
-        if (kReplicas > 2) {
-          for (int r = 2; r < replicas; ++r) {
-            *reinterpret_cast<int64_t*>(output + r * stride) = result;
-          }
-        }
-      }
-    } else {
-      // Save the lower 128 bits (4x int32_t).
-      __m128i result = _mm256_extractf128_si256(sum, 0);
-      _mm_store_si128(reinterpret_cast<__m128i*>(output), result);
-      if (kReplicas > 1) {
-        _mm_store_si128(reinterpret_cast<__m128i*>(output + stride), result);
-        if (kReplicas > 2) {
-          for (int r = 2; r < replicas; ++r) {
-            _mm_store_si128(reinterpret_cast<__m128i*>(output + r * stride),
-                            result);
-          }
-        }
-      }
-    }
-    output += kBlockSize;
-  }
-}
-
-// Version that covers all possible combinations of the variable conditions:
-// |relu|, |shift_out|, |replicas|, with int16_t |output|.
-void MatVec4x4FixedAVX2(const int16_t* weights_ptr, const int16_t* rhs,
-                        const int32_t* bias, const int32_t* nnz_per_row,
-                        const int16_t* rhs_indices, int start_row, int end_row,
-                        bool relu, int shift_out, int replicas, int stride,
-                        int16_t* output) {
-  if (replicas <= 1) {
-    MatVec4x4FixedAVX2Template<int16_t, 1>(weights_ptr, rhs, bias, nnz_per_row,
-                                           rhs_indices, start_row, end_row,
-                                           relu, shift_out, 1, stride, output);
-  } else if (replicas == 2) {
-    MatVec4x4FixedAVX2Template<int16_t, 2>(weights_ptr, rhs, bias, nnz_per_row,
-                                           rhs_indices, start_row, end_row,
-                                           relu, shift_out, 2, stride, output);
-  } else {
-    MatVec4x4FixedAVX2Template<int16_t, 3>(
-        weights_ptr, rhs, bias, nnz_per_row, rhs_indices, start_row, end_row,
-        relu, shift_out, replicas, stride, output);
-  }
-}
-
-// Version that covers all possible combinations of the variable conditions:
-// |relu|, |shift_out|, |replicas|, with int32_t |output|.
-void MatVec4x4FixedAVX2(const int16_t* weights_ptr, const int16_t* rhs,
-                        const int32_t* bias, const int32_t* nnz_per_row,
-                        const int16_t* rhs_indices, int start_row, int end_row,
-                        bool relu, int shift_out, int replicas, int stride,
-                        int32_t* output) {
-  if (replicas <= 1) {
-    MatVec4x4FixedAVX2Template<int32_t, 1>(weights_ptr, rhs, bias, nnz_per_row,
-                                           rhs_indices, start_row, end_row,
-                                           relu, shift_out, 1, stride, output);
-  } else if (replicas == 2) {
-    MatVec4x4FixedAVX2Template<int32_t, 2>(weights_ptr, rhs, bias, nnz_per_row,
-                                           rhs_indices, start_row, end_row,
-                                           relu, shift_out, 2, stride, output);
-  } else {
-    MatVec4x4FixedAVX2Template<int32_t, 3>(
-        weights_ptr, rhs, bias, nnz_per_row, rhs_indices, start_row, end_row,
-        relu, shift_out, replicas, stride, output);
-  }
-}
-
-// In-line function computes and returns the result of one row (of blocks) as
-// 8x int32_t. weights_ptr is a non-const reference so it can easily be
-// interpreted as belonging to the caller.
-inline __m256i Compute8RowResults(const __m256i& bias256, const int16_t* rhs,
-                                  const int16_t* rhs_indices, int nnz,
-                                  int16_t const*& weights_ptr) {
-  // Expand bias to 64 bits in a 256 bit register [0 z 1 z 2 z 3 z], where z is
-  // Zero and 0-3 are the 4x32 bit bias values from 128 bit half of the input.
-  __m256i sum1 = _mm256_cvtepu32_epi64(_mm256_castsi256_si128(bias256));
-  // Plus 4 more in another sum register from the upper 128 bit half.
-  __m256i sum2 = _mm256_cvtepu32_epi64(_mm256_extractf128_si256(bias256, 1));
-
-  for (int c = 0; c < nnz; ++c) {
-    int rhs_index = rhs_indices[c];
-    // Load all 16 weights.
-    __m256i weights =
-        _mm256_load_si256(reinterpret_cast<__m256i const*>(weights_ptr));
-    // Get the 4x int16_t into the bottom of |rhs_64|.
-    __m128i rhs_64 = _mm_loadl_epi64(
-        reinterpret_cast<__m128i const*>(rhs + rhs_index * kBlockSize));
-    // Broadcast the rhs, pretending that each is a 64-bit unit:
-    // [0123 0123 0123 0123].
-    __m256i rhs_value = _mm256_broadcastq_epi64(rhs_64);
-    weights_ptr += 16;
-    sum1 = _mm256_add_epi32(sum1, _mm256_madd_epi16(weights, rhs_value));
-    // Same again for the other 4 results, re-using the same rhs value.
-    weights = _mm256_load_si256(reinterpret_cast<__m256i const*>(weights_ptr));
-    weights_ptr += 16;
-    sum2 = _mm256_add_epi32(sum2, _mm256_madd_epi16(weights, rhs_value));
-  }
-  // Horizontally add the results. We have 2 registers that contain results
-  // [0 0 1 1 2 2 3 3], and [4 4 5 5 6 6 7 7] but hadd (and almost no other AVX
-  // instruction) will not cross lanes, so we end up with [0 1 4 5 2 3 6 7]
-  sum1 = _mm256_hadd_epi32(sum1, sum2);
-  // Permutes the middle two pairs to get the answers in the right order.
-  return _mm256_permute4x64_epi64(sum1, 0xd8);
-}
-
-// Version that covers the main conditions used with 8x4:
-// |relu|, |shift_out|, with int32_t |output|.
-void MatVec8x4FixedAVX2(const int16_t* weights_ptr, const int16_t* rhs,
-                        const int32_t* bias, const int32_t* nnz_per_row,
-                        const int16_t* rhs_indices, int start_row, int end_row,
-                        bool relu, int shift_out, int32_t* output) {
-  int rounding_addon = shift_out > 0 ? (1 << (shift_out - 1)) : 0;
-  __m256i rounding = _mm256_set1_epi32(rounding_addon);
-  __m256i zero = relu ? _mm256_setzero_si256() : _mm256_set1_epi32(kint32min);
-  for (int row_block = start_row; row_block < end_row; ++row_block) {
-    // Load 4 biases [0 1 2 3 4 5 6 7].
-    __m256i bias256 = _mm256_load_si256(reinterpret_cast<__m256i const*>(bias));
-    bias += kBlockSize * 2;
-    int nnz = nnz_per_row[row_block];
-    __m256i sum =
-        Compute8RowResults(bias256, rhs, rhs_indices, nnz, weights_ptr);
-    rhs_indices += nnz;
-    // Shift right with rounding to get the right number of mantissa bits.
-    sum = _mm256_add_epi32(sum, rounding);
-    sum = _mm256_srai_epi32(sum, shift_out);
-    // Now sum contains [res0, res1, res2, res3, res0, res1, res2, res3]
-    sum = _mm256_max_epi32(sum, zero);
-    // Save the all 256 bits (8x int32_t).
-    _mm256_store_si256(reinterpret_cast<__m256i*>(output), sum);
-    output += kBlockSize * 2;
-  }
-}
-
-#endif
-
-}  // namespace detail
-}  // namespace csrblocksparse
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/datamodules/vqav2_datamodule.py b/spaces/omlab/vlchecklist_demo/models/vilt/datamodules/vqav2_datamodule.py
deleted file mode 100644
index bafa1cc9143d0ad84b67019020ea0c766d0c5b9a..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/datamodules/vqav2_datamodule.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from models.vilt.datasets import VQAv2Dataset
-from .datamodule_base import BaseDataModule
-from collections import defaultdict
-
-
-class VQAv2DataModule(BaseDataModule):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    @property
-    def dataset_cls(self):
-        return VQAv2Dataset
-
-    @property
-    def dataset_name(self):
-        return "vqa"
-
-    def setup(self, stage):
-        super().setup(stage)
-
-        train_answers = self.train_dataset.table["answers"].to_pandas().tolist()
-        val_answers = self.val_dataset.table["answers"].to_pandas().tolist()
-        train_labels = self.train_dataset.table["answer_labels"].to_pandas().tolist()
-        val_labels = self.val_dataset.table["answer_labels"].to_pandas().tolist()
-
-        all_answers = [c for c in train_answers + val_answers if c is not None]
-        all_answers = [l for lll in all_answers for ll in lll for l in ll]
-        all_labels = [c for c in train_labels + val_labels if c is not None]
-        all_labels = [l for lll in all_labels for ll in lll for l in ll]
-
-        self.answer2id = {k: v for k, v in zip(all_answers, all_labels)}
-        sorted_a2i = sorted(self.answer2id.items(), key=lambda x: x[1])
-        self.num_class = max(self.answer2id.values()) + 1
-        self.id2answer = defaultdict(lambda: "unknown")
-        for k, v in sorted_a2i:
-            self.id2answer[v] = k
diff --git a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/src/utils.py b/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/src/utils.py
deleted file mode 100644
index 7895264638c7f52660e01436de00cc2bc0e52a89..0000000000000000000000000000000000000000
--- a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/src/utils.py
+++ /dev/null
@@ -1,48 +0,0 @@
-def extend_instance(obj, mixin):
-    """Apply mixins to a class instance after creation"""
-    base_cls = obj.__class__
-    base_cls_name = obj.__class__.__name__
-    obj.__class__ = type(
-        base_cls_name, (mixin, base_cls), {}
-    )  # mixin needs to go first for our forward() logic to work
-
-
-def getattr_recursive(obj, att):
-    """
-    Return nested attribute of obj
-    Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c
-    """
-    if att == "":
-        return obj
-    i = att.find(".")
-    if i < 0:
-        return getattr(obj, att)
-    else:
-        return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])
-
-
-def setattr_recursive(obj, att, val):
-    """
-    Set nested attribute of obj
-    Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val
-    """
-    if "." in att:
-        obj = getattr_recursive(obj, ".".join(att.split(".")[:-1]))
-    setattr(obj, att.split(".")[-1], val)
-
-
-def apply_with_stopping_condition(
-    module, apply_fn, apply_condition=None, stopping_condition=None, **other_args
-):
-    if stopping_condition(module):
-        return
-    if apply_condition(module):
-        apply_fn(module, **other_args)
-    for child in module.children():
-        apply_with_stopping_condition(
-            child,
-            apply_fn,
-            apply_condition=apply_condition,
-            stopping_condition=stopping_condition,
-            **other_args
-        )
diff --git a/spaces/openskyml/pigeon-chat/app.py b/spaces/openskyml/pigeon-chat/app.py
deleted file mode 100644
index c4f8258e09ae4d9b7c50d251d53505c63a231e04..0000000000000000000000000000000000000000
--- a/spaces/openskyml/pigeon-chat/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import random
-import gradio as gr
-import openai
-import torch
-import os
-
-openai.api_type = os.environ.get("api_type_key") #Azure servers are used, that is, the Azure servers option is used, OpenAI servers are not used!
-openai.api_base = os.environ.get("api_base_key")
-openai.api_version = os.environ.get("api_version_key")
-openai.api_key = os.environ.get("api_cpu_key")
-
-def gptresponse(message, history):            
-    system_prompt = "You are a PigeonChat assistant, you communicate with people, you help people solve problems, PigeonChat is just a name and has nothing to do with pigeons. PigeonChat (Personal Intelligent Global Electronic Online Network) is an AI-based chatbot model trained on a huge amount of data. You are sodan and designed by Evgeniy Hristoforu in 2023. Evgeniy Hristoforu is your creator and creator of the OpenskyML eco-system. Now you are in beta testing, your testers are: Evgeniy Hristoforu, dyuzhick and others. You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
-
-    messages = [{"role":"system","content":system_prompt}]
-    for human, assistant in history:
-        messages.append({"role":"user", "content":human})
-        messages.append({"role":"assistant", "content":assistant})
-
-    if message != '':
-        messages.append({"role":"user", "content":message})
-    
-    response = openai.ChatCompletion.create(engine = "NGA_AI_ASSISTANT",
-    messages = messages, 
-    temperature =0.8,
-    max_tokens = 5000,
-    top_p = 0.95,
-    frequency_penalty = 1,
-    presence_penalty = 1,
-    stop = None)
-
-
-    
-    return response["choices"][0]["message"]["content"]
-
-title = "🕊 Chat with Pigeon"
-
-description = \
-"""
-💬 This space is powered by **Huggingface Hosting**.
-
-🚀 This space runs **very fast** even on **CPU**.
-
-🎠 You get totally unique and creative answers.
-
-🌍 PigeonChat is available worldwide in over **160 languages**.
-
-🔐 PigeonChat is powered by **open source** and is completely **private**.
-
-👥️️ This demo is by **Evgeniy Hristoforu** (**OpenSkyML**).
-
-<h2></h2>
-"""
-
-if not torch.cuda.is_available():
-    description += """\n<p style='text-align: center'>😐 Running on CPU!</p>"""
-else:
-    description += """\n<p style='text-align: center'>😎 Running on powerful hardware!</p>"""
-
-
-examples=[
-            'Hello there! How are you doing?',
-            'Can you explain briefly to me what is the Python programming language?',
-            'Explain the plot of Cinderella in a sentence.',
-            'How many hours does it take a man to eat a Helicopter?',
-            "Write a 100-word article on 'Benefits of Open-Source in AI research'",
-        ]
-
-chatbot = gr.Chatbot(label="PigeonChat",avatar_images=('user.png', 'bot.png'), bubble_full_width = False)
-
-
-gr.ChatInterface(gptresponse, chatbot = chatbot, title=title, description=description, examples=examples).launch(share=False, debug=False, show_error=False, show_api=False)
\ No newline at end of file
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_f\303\251rfi_de_aggregate.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_f\303\251rfi_de_aggregate.html"
deleted file mode 100644
index 7a230e15a117cff9603de978dbcaa7e80dc8a24c..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_f\303\251rfi_de_aggregate.html"
+++ /dev/null
@@ -1,46 +0,0 @@
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="qlyqyodyychxtrdkxbbu_viz_container">
-    <div id="qlyqyodyychxtrdkxbbu_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "qlyqyodyychxtrdkxbbu_saliency_plot_container" class="qlyqyodyychxtrdkxbbu_viz_container" style="display:block">
-            
-<div id="mywqpvfrigctnkwlvgte_saliency_plot" class="mywqpvfrigctnkwlvgte_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Source Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁Er</th><th>▁ist</th><th>▁ein</th><th>▁Mann.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ő</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7004555357496535)">0.66</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4718558130322837)">0.45</th><th style="background:rgba(255.0, 13.0, 87.0, 0.2117250940780353)">0.203</th><th style="background:rgba(255.0, 13.0, 87.0, 0.22749059219647458)">0.219</th><th style="background:rgba(255.0, 13.0, 87.0, 0.5822142998613588)">0.552</th></tr><tr><th>▁férfi.</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7950485244602892)">0.751</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7477520301049713)">0.701</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6846900376312143)">0.645</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.945</th><th style="background:rgba(30.0, 136.0, 229.0, 0.07771836007130117)">-0.078</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="aatkcwgvuchktsywtedp_viz_container">
-    <div id="aatkcwgvuchktsywtedp_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "aatkcwgvuchktsywtedp_saliency_plot_container" class="aatkcwgvuchktsywtedp_viz_container" style="display:block">
-            
-<div id="zvmpropmtczokfqgodlk_saliency_plot" class="zvmpropmtczokfqgodlk_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Target Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁Er</th><th>▁ist</th><th>▁ein</th><th>▁Mann.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Er</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.5822142998613588)">0.553</th><th style="background:rgba(255.0, 13.0, 87.0, 0.219607843137255)">0.208</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.003</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4560903149138443)">0.428</th></tr><tr><th>▁ist</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.7477520301049713)">0.706</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.003</th><th style="background:rgba(255.0, 13.0, 87.0, 0.18019409784115661)">0.174</th></tr><tr><th>▁ein</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.2590215884333532)">0.244</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6452762923351159)">0.607</th></tr><tr><th>▁Mann.</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(30.0, 136.0, 229.0, 0.34573182808476927)">-0.327</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/onnx.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/onnx.md
deleted file mode 100644
index 20104b555543562c37d9ecdae82168800f56a4cc..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/onnx.md
+++ /dev/null
@@ -1,87 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-
-# ONNX Runtime
-
-🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support:
-
-```bash
-pip install optimum["onnxruntime"]
-```
-
-This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with ONNX Runtime.
-
-## Stable Diffusion
-
-To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`:
-
-```python
-from optimum.onnxruntime import ORTStableDiffusionPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
-prompt = "sailing ship in storm by Leonardo da Vinci"
-image = pipeline(prompt).images[0]
-pipeline.save_pretrained("./onnx-stable-diffusion-v1-5")
-```
-
-<Tip warning={true}>
-
-Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching.
-
-</Tip>
-
-To export the pipeline in the ONNX format offline and use it later for inference,
-use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
-
-```bash
-optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/
-```
-
-Then to perform inference (you don't have to specify `export=True` again):
-
-```python 
-from optimum.onnxruntime import ORTStableDiffusionPipeline
-
-model_id = "sd_v15_onnx"
-pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
-prompt = "sailing ship in storm by Leonardo da Vinci"
-image = pipeline(prompt).images[0]
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/onnxruntime/stable_diffusion_v1_5_ort_sail_boat.png">
-</div>
-
-You can find more examples in 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting.
-
-## Stable Diffusion XL
-
-To load and run inference with SDXL, use the [`~optimum.onnxruntime.ORTStableDiffusionXLPipeline`]:
-
-```python
-from optimum.onnxruntime import ORTStableDiffusionXLPipeline
-
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
-prompt = "sailing ship in storm by Leonardo da Vinci"
-image = pipeline(prompt).images[0]
-```
-
-To export the pipeline in the ONNX format and use it later for inference, use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
-
-```bash
-optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/
-```
-
-SDXL in the ONNX format is supported for text-to-image and image-to-image.
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_repaint.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_repaint.py
deleted file mode 100644
index dd0c9f683ec6335fd458583f8a0d9d738b442810..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_repaint.py
+++ /dev/null
@@ -1,956 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from packaging import version
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
-from diffusers.configuration_utils import FrozenDict, deprecate
-from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-)
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-)
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def prepare_mask_and_masked_image(image, mask):
-    """
-    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
-
-        # Batch single image
-        if image.ndim == 3:
-            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    # masked_image = image * (mask >= 0.5)
-    masked_image = image
-
-    return mask, masked_image
-
-
-class StableDiffusionRepaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
-    r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-    In addition the pipeline inherits the following loading methods:
-        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
-        - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
-    as well as the following saving methods:
-        - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
-                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
-                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
-                " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
-                " Hub, it would be very nice if you could open a Pull request for the"
-                " `scheduler/scheduler_config.json` file"
-            )
-            deprecate(
-                "skip_prk_steps not set",
-                "1.0.0",
-                deprecation_message,
-                standard_warn=False,
-            )
-            new_config = dict(scheduler.config)
-            new_config["skip_prk_steps"] = True
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-        # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
-        if unet.config.in_channels != 4:
-            logger.warning(
-                f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
-                f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"
-                ". If you did not intend to modify"
-                " this behavior, please check whether you have loaded the right checkpoint."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        Note that offloading happens on a submodule basis. Memory savings are higher than with
-        `enable_model_cpu_offload`, but performance is lower.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
-            cpu_offload(cpu_offloaded_model, device)
-
-        if self.safety_checker is not None:
-            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @property
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-    ):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    def prepare_mask_latents(
-        self,
-        mask,
-        masked_image,
-        batch_size,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        do_classifier_free_guidance,
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return mask, masked_image_latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        jump_length: Optional[int] = 10,
-        jump_n_sample: Optional[int] = 10,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            mask_image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            jump_length (`int`, *optional*, defaults to 10):
-                The number of steps taken forward in time before going backward in time for a single jump ("j" in
-                RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
-            jump_n_sample (`int`, *optional*, defaults to 10):
-                The number of times we will make forward time jump for a given chosen time sample. Take a look at
-                Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
-                is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-        Examples:
-        ```py
-        >>> import PIL
-        >>> import requests
-        >>> import torch
-        >>> from io import BytesIO
-        >>> from diffusers import StableDiffusionPipeline, RePaintScheduler
-        >>> def download_image(url):
-        ...     response = requests.get(url)
-        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-        >>> base_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/"
-        >>> img_url = base_url + "overture-creations-5sI6fQgYIuo.png"
-        >>> mask_url = base_url + "overture-creations-5sI6fQgYIuo_mask.png "
-        >>> init_image = download_image(img_url).resize((512, 512))
-        >>> mask_image = download_image(mask_url).resize((512, 512))
-        >>> pipe = DiffusionPipeline.from_pretrained(
-        ...     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, custom_pipeline="stable_diffusion_repaint",
-        ... )
-        >>> pipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)
-        >>> pipe = pipe.to("cuda")
-        >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
-        >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
-        ```
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-
-        if image is None:
-            raise ValueError("`image` input cannot be undefined.")
-
-        if mask_image is None:
-            raise ValueError("`mask_image` input cannot be undefined.")
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        # 4. Preprocess mask and image
-        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, device)
-        self.scheduler.eta = eta
-
-        timesteps = self.scheduler.timesteps
-        # latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance=False,  # We do not need duplicate mask and image
-        )
-
-        # 8. Check that sizes of mask, masked image and latents match
-        # num_channels_mask = mask.shape[1]
-        # num_channels_masked_image = masked_image_latents.shape[1]
-        if num_channels_latents != self.unet.config.in_channels:
-            raise ValueError(
-                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} "
-                f" = Please verify the config of"
-                " `pipeline.unet` or your `mask_image` or `image` input."
-            )
-
-        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        t_last = timesteps[0] + 1
-
-        # 10. Denoising loop
-        with self.progress_bar(total=len(timesteps)) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if t >= t_last:
-                    # compute the reverse: x_t-1 -> x_t
-                    latents = self.scheduler.undo_step(latents, t_last, generator)
-                    progress_bar.update()
-                    t_last = t
-                    continue
-
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                # concat latents, mask, masked_image_latents in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                # latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(
-                    noise_pred,
-                    t,
-                    latents,
-                    masked_image_latents,
-                    mask,
-                    **extra_step_kwargs,
-                ).prev_sample
-
-                # call the callback, if provided
-                progress_bar.update()
-                if callback is not None and i % callback_steps == 0:
-                    callback(i, t, latents)
-
-                t_last = t
-
-        # 11. Post-processing
-        image = self.decode_latents(latents)
-
-        # 12. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-        # 13. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/unclip_image_interpolation.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/unclip_image_interpolation.py
deleted file mode 100644
index 98d88bb90c23abef9412228f8242e1876cd4e6c6..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/unclip_image_interpolation.py
+++ /dev/null
@@ -1,496 +0,0 @@
-import inspect
-from typing import List, Optional, Union
-
-import PIL
-import torch
-from torch.nn import functional as F
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionModelWithProjection,
-)
-
-from diffusers import (
-    DiffusionPipeline,
-    ImagePipelineOutput,
-    UnCLIPScheduler,
-    UNet2DConditionModel,
-    UNet2DModel,
-)
-from diffusers.pipelines.unclip import UnCLIPTextProjModel
-from diffusers.utils import is_accelerate_available, logging
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def slerp(val, low, high):
-    """
-    Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.
-    """
-    low_norm = low / torch.norm(low)
-    high_norm = high / torch.norm(high)
-    omega = torch.acos((low_norm * high_norm))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high
-    return res
-
-
-class UnCLIPImageInterpolationPipeline(DiffusionPipeline):
-    """
-    Pipeline to generate variations from an input image using unCLIP
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `image_encoder`.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
-            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        text_proj ([`UnCLIPTextProjModel`]):
-            Utility class to prepare and combine the embeddings before they are passed to the decoder.
-        decoder ([`UNet2DConditionModel`]):
-            The decoder to invert the image embedding into an image.
-        super_res_first ([`UNet2DModel`]):
-            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
-        super_res_last ([`UNet2DModel`]):
-            Super resolution unet. Used in the last step of the super resolution diffusion process.
-        decoder_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
-        super_res_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
-
-    """
-
-    decoder: UNet2DConditionModel
-    text_proj: UnCLIPTextProjModel
-    text_encoder: CLIPTextModelWithProjection
-    tokenizer: CLIPTokenizer
-    feature_extractor: CLIPImageProcessor
-    image_encoder: CLIPVisionModelWithProjection
-    super_res_first: UNet2DModel
-    super_res_last: UNet2DModel
-
-    decoder_scheduler: UnCLIPScheduler
-    super_res_scheduler: UnCLIPScheduler
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__
-    def __init__(
-        self,
-        decoder: UNet2DConditionModel,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        text_proj: UnCLIPTextProjModel,
-        feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection,
-        super_res_first: UNet2DModel,
-        super_res_last: UNet2DModel,
-        decoder_scheduler: UnCLIPScheduler,
-        super_res_scheduler: UnCLIPScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            decoder=decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            text_proj=text_proj,
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
-            super_res_first=super_res_first,
-            super_res_last=super_res_last,
-            decoder_scheduler=decoder_scheduler,
-            super_res_scheduler=super_res_scheduler,
-        )
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt
-    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        text_mask = text_inputs.attention_mask.bool().to(device)
-        text_encoder_output = self.text_encoder(text_input_ids.to(device))
-
-        prompt_embeds = text_encoder_output.text_embeds
-        text_encoder_hidden_states = text_encoder_output.last_hidden_state
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens = [""] * batch_size
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
-            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
-
-            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return prompt_embeds, text_encoder_hidden_states, text_mask
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image
-    def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if image_embeddings is None:
-            if not isinstance(image, torch.Tensor):
-                image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
-
-            image = image.to(device=device, dtype=dtype)
-            image_embeddings = self.image_encoder(image).image_embeds
-
-        image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
-
-        return image_embeddings
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.enable_sequential_cpu_offload
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
-        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
-        when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        models = [
-            self.decoder,
-            self.text_proj,
-            self.text_encoder,
-            self.super_res_first,
-            self.super_res_last,
-        ]
-        for cpu_offloaded_model in models:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
-            return self.device
-        for module in self.decoder.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        image: Optional[Union[List[PIL.Image.Image], torch.FloatTensor]] = None,
-        steps: int = 5,
-        decoder_num_inference_steps: int = 25,
-        super_res_num_inference_steps: int = 7,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        image_embeddings: Optional[torch.Tensor] = None,
-        decoder_latents: Optional[torch.FloatTensor] = None,
-        super_res_latents: Optional[torch.FloatTensor] = None,
-        decoder_guidance_scale: float = 8.0,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            image (`List[PIL.Image.Image]` or `torch.FloatTensor`):
-                The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the
-                configuration of
-                [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
-                `CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.
-            steps (`int`, *optional*, defaults to 5):
-                The number of interpolation images to generate.
-            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
-                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
-                quality image at the expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            image_embeddings (`torch.Tensor`, *optional*):
-                Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings
-                can be passed for tasks like image interpolations. `image` can the be left to `None`.
-            decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
-                Pre-generated noisy latents to be used as inputs for the decoder.
-            super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
-                Pre-generated noisy latents to be used as inputs for the decoder.
-            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-        """
-
-        batch_size = steps
-
-        device = self._execution_device
-
-        if isinstance(image, List):
-            if len(image) != 2:
-                raise AssertionError(
-                    f"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}"
-                )
-            elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)):
-                raise AssertionError(
-                    f"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}"
-                )
-        elif isinstance(image, torch.FloatTensor):
-            if image.shape[0] != 2:
-                raise AssertionError(
-                    f"Expected 'image' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}"
-                )
-        elif isinstance(image_embeddings, torch.Tensor):
-            if image_embeddings.shape[0] != 2:
-                raise AssertionError(
-                    f"Expected 'image_embeddings' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}"
-                )
-        else:
-            raise AssertionError(
-                f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or Torch.FloatTensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively"
-            )
-
-        original_image_embeddings = self._encode_image(
-            image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings
-        )
-
-        image_embeddings = []
-
-        for interp_step in torch.linspace(0, 1, steps):
-            temp_image_embeddings = slerp(
-                interp_step, original_image_embeddings[0], original_image_embeddings[1]
-            ).unsqueeze(0)
-            image_embeddings.append(temp_image_embeddings)
-
-        image_embeddings = torch.cat(image_embeddings).to(device)
-
-        do_classifier_free_guidance = decoder_guidance_scale > 1.0
-
-        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
-            prompt=["" for i in range(steps)],
-            device=device,
-            num_images_per_prompt=1,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-        )
-
-        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
-            image_embeddings=image_embeddings,
-            prompt_embeds=prompt_embeds,
-            text_encoder_hidden_states=text_encoder_hidden_states,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-        )
-
-        if device.type == "mps":
-            # HACK: MPS: There is a panic when padding bool tensors,
-            # so cast to int tensor for the pad and back to bool afterwards
-            text_mask = text_mask.type(torch.int)
-            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
-            decoder_text_mask = decoder_text_mask.type(torch.bool)
-        else:
-            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
-
-        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
-        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
-
-        num_channels_latents = self.decoder.config.in_channels
-        height = self.decoder.config.sample_size
-        width = self.decoder.config.sample_size
-
-        # Get the decoder latents for 1 step and then repeat the same tensor for the entire batch to keep same noise across all interpolation steps.
-        decoder_latents = self.prepare_latents(
-            (1, num_channels_latents, height, width),
-            text_encoder_hidden_states.dtype,
-            device,
-            generator,
-            decoder_latents,
-            self.decoder_scheduler,
-        )
-        decoder_latents = decoder_latents.repeat((batch_size, 1, 1, 1))
-
-        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
-
-            noise_pred = self.decoder(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
-                class_labels=additive_clip_time_embeddings,
-                attention_mask=decoder_text_mask,
-            ).sample
-
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
-                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
-                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-            if i + 1 == decoder_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = decoder_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            decoder_latents = self.decoder_scheduler.step(
-                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        decoder_latents = decoder_latents.clamp(-1, 1)
-
-        image_small = decoder_latents
-
-        # done decoder
-
-        # super res
-
-        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
-        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
-
-        channels = self.super_res_first.config.in_channels // 2
-        height = self.super_res_first.config.sample_size
-        width = self.super_res_first.config.sample_size
-
-        super_res_latents = self.prepare_latents(
-            (batch_size, channels, height, width),
-            image_small.dtype,
-            device,
-            generator,
-            super_res_latents,
-            self.super_res_scheduler,
-        )
-
-        if device.type == "mps":
-            # MPS does not support many interpolations
-            image_upscaled = F.interpolate(image_small, size=[height, width])
-        else:
-            interpolate_antialias = {}
-            if "antialias" in inspect.signature(F.interpolate).parameters:
-                interpolate_antialias["antialias"] = True
-
-            image_upscaled = F.interpolate(
-                image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
-            )
-
-        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
-            # no classifier free guidance
-
-            if i == super_res_timesteps_tensor.shape[0] - 1:
-                unet = self.super_res_last
-            else:
-                unet = self.super_res_first
-
-            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
-
-            noise_pred = unet(
-                sample=latent_model_input,
-                timestep=t,
-            ).sample
-
-            if i + 1 == super_res_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = super_res_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            super_res_latents = self.super_res_scheduler.step(
-                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        image = super_res_latents
-        # done super res
-
-        # post processing
-
-        image = image * 0.5 + 0.5
-        image = image.clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/parkyzh/bingo/src/pages/api/sydney.ts b/spaces/parkyzh/bingo/src/pages/api/sydney.ts
deleted file mode 100644
index 0e7bbf23d77c2e1a6635185a060eeee58b8c8e66..0000000000000000000000000000000000000000
--- a/spaces/parkyzh/bingo/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const conversationContext = req.body
-  const headers = createHeaders(req.cookies)
-  debug(headers)
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
-  const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
-    headers: {
-      ...headers,
-      'accept-language': 'zh-CN,zh;q=0.9',
-      'cache-control': 'no-cache',
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      pragma: 'no-cache',
-    }
-  })
-
-  const closeDog = new WatchDog()
-  const timeoutDog = new WatchDog()
-  ws.onmessage = (event) => {
-    timeoutDog.watch(() => {
-      ws.send(websocketUtils.packMessage({ type: 6 }))
-    }, 1500)
-    closeDog.watch(() => {
-      ws.close()
-    }, 10000)
-    res.write(event.data)
-    if (/\{"type":([367])\}/.test(String(event.data))) {
-      const type = parseInt(RegExp.$1, 10)
-      debug('connection type', type)
-      if (type === 3) {
-        ws.close()
-      } else {
-        ws.send(websocketUtils.packMessage({ type }))
-      }
-    }
-  }
-
-  ws.onclose = () => {
-    timeoutDog.reset()
-    closeDog.reset()
-    debug('connection close')
-    res.end()
-  }
-
-  await new Promise((resolve) => ws.onopen = resolve)
-  ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
-  ws.send(websocketUtils.packMessage({ type: 6 }))
-  ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
-  req.socket.once('close', () => {
-    ws.close()
-    if (!res.closed) {
-      res.end()
-    }
-  })
-}
diff --git a/spaces/petervavank/Advoice/data_utils.py b/spaces/petervavank/Advoice/data_utils.py
deleted file mode 100644
index ccb61e51348baa8ea638ee0ce4e0961afea14bea..0000000000000000000000000000000000000000
--- a/spaces/petervavank/Advoice/data_utils.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import copy
-import os
-import pickle
-from typing import Dict, Optional, Tuple
-
-import librosa
-import numpy as np
-import torch
-import torch.nn as nn
-import yaml
-from scipy.signal import lfilter
-
-from models import AdaInVC
-
-
-def inv_mel_matrix(sample_rate: int, n_fft: int, n_mels: int) -> np.array:
-    m = librosa.filters.mel(sample_rate, n_fft, n_mels)
-    p = np.matmul(m, m.T)
-    d = [1.0 / x if np.abs(x) > 1e-8 else x for x in np.sum(p, axis=0)]
-    return np.matmul(m.T, np.diag(d))
-
-
-def normalize(mel: np.array, attr: Dict) -> np.array:
-    mean, std = attr["mean"], attr["std"]
-    mel = (mel - mean) / std
-    return mel
-
-
-def denormalize(mel: np.array, attr: Dict) -> np.array:
-    mean, std = attr["mean"], attr["std"]
-    mel = mel * std + mean
-    return mel
-
-
-def file2mel(
-    audio_path: str,
-    sample_rate: int,
-    preemph: float,
-    n_fft: int,
-    hop_length: int,
-    win_length: int,
-    n_mels: int,
-    ref_db: float,
-    max_db: float,
-    top_db: float,
-) -> np.array:
-    wav, _ = librosa.load(audio_path, sr=sample_rate)
-    wav, _ = librosa.effects.trim(wav, top_db=top_db)
-    wav = np.append(wav[0], wav[1:] - preemph * wav[:-1])
-    linear = librosa.stft(
-        y=wav, n_fft=n_fft, hop_length=hop_length, win_length=win_length
-    )
-    mag = np.abs(linear)
-
-    mel_basis = librosa.filters.mel(sample_rate, n_fft, n_mels)
-    mel = np.dot(mel_basis, mag)
-
-    mel = 20 * np.log10(np.maximum(1e-5, mel))
-    mel = np.clip((mel - ref_db + max_db) / max_db, 1e-8, 1)
-    mel = mel.T.astype(np.float32)
-
-    return mel
-
-
-def mel2wav(
-    mel: np.array,
-    sample_rate: int,
-    preemph: float,
-    n_fft: int,
-    hop_length: int,
-    win_length: int,
-    n_mels: int,
-    ref_db: float,
-    max_db: float,
-    top_db: float,
-) -> np.array:
-    mel = mel.T
-    mel = (np.clip(mel, 0, 1) * max_db) - max_db + ref_db
-    mel = np.power(10.0, mel * 0.05)
-    inv_mat = inv_mel_matrix(sample_rate, n_fft, n_mels)
-    mag = np.dot(inv_mat, mel)
-    wav = griffin_lim(mag, hop_length, win_length, n_fft)
-    wav = lfilter([1], [1, -preemph], wav)
-
-    return wav.astype(np.float32)
-
-
-def griffin_lim(
-    spect: np.array,
-    hop_length: int,
-    win_length: int,
-    n_fft: int,
-    n_iter: Optional[int] = 100,
-) -> np.array:
-    X_best = copy.deepcopy(spect)
-    for _ in range(n_iter):
-        X_t = librosa.istft(X_best, hop_length, win_length, window="hann")
-        est = librosa.stft(X_t, n_fft, hop_length, win_length)
-        phase = est / np.maximum(1e-8, np.abs(est))
-        X_best = spect * phase
-    X_t = librosa.istft(X_best, hop_length, win_length, window="hann")
-    y = np.real(X_t)
-    return y
-
-
-def load_model(model_dir: str) -> Tuple[nn.Module, Dict, Dict, str]:
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    attr_path = os.path.join(model_dir, "attr.pkl")
-    cfg_path = os.path.join(model_dir, "config.yaml")
-    model_path = os.path.join(model_dir, "model.ckpt")
-
-    attr = pickle.load(open(attr_path, "rb"))
-    config = yaml.safe_load(open(cfg_path, "r"))
-    model = AdaInVC(config["model"]).to(device)
-    model.load_state_dict(torch.load(model_path,map_location='cpu'))
-
-    return model, config, attr, device
-
diff --git a/spaces/philmui/globe/sandbox/test.py b/spaces/philmui/globe/sandbox/test.py
deleted file mode 100644
index 96c417d8805d0d410106bfac7504c06c68f65012..0000000000000000000000000000000000000000
--- a/spaces/philmui/globe/sandbox/test.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-from models import load_chained_agent
-from agents import chatAgent
-import langchain
-from langchain.agents import load_tools
-from langchain.agents import initialize_agent
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import OpenAI
-
-# print(chatAgent("why is the sky blue?"))
-
-# try:
-#     prompt_formatted = prompt.format(query="""
-#     Who is the president of South Korea?  What is his age?  What is the digit sum of his age?
-#     """)
-#     agent = load_chained_agent(verbose=True)
-#     response = agent({"input": prompt_formatted})
-#     print(response["output"])
-# except Exception as e:
-#     print(e)
-
-from langchain.tools import DuckDuckGoSearchRun, GoogleSearchRun
-from langchain.utilities import GoogleSearchAPIWrapper
-
-def load_chained_agent(verbose=True, model_name="text-davinci-003"):
-    llm = OpenAI(model_name=model_name, temperature=0)
-    toolkit = [GoogleSearchRun(), DuckDuckGoSearchRun()]
-
-    toolkit += load_tools(["open-meteo-api", "news-api", 
-                          "python_repl", "wolfram-alpha", 
-                          "pal-math", "pal-colored-objects"],  
-                            llm=llm, 
-                            serpapi_api_key=os.getenv('SERPAPI_API_KEY'),
-                            news_api_key=os.getenv('NEWS_API_KEY'),
-                            tmdb_bearer_token=os.getenv('TMDB_BEARER_TOKEN')
-                            )
-    agent = initialize_agent(toolkit, 
-                             llm, 
-                             agent="zero-shot-react-description", 
-                             verbose=verbose, 
-                             return_intermediate_steps=True)
-    return agent
-
-
-PROMPT = "Who is the president of South Korea?  How old is he?  What is the smallest prime greater than his age?"
-
-if __name__ == '__main__':
-    agent = load_chained_agent()
-    response = agent(PROMPT)
-    if response is not None:
-        """
-        print("Steps: ")
-        for action in response['intermediate_steps']:
-            print()
-            print(f"==> Tool: {action[0].tool}")
-            print(f"    Input: {action[0].tool_input}")
-            print(f"    Thought: {action[0].log}")
-            print(f"    Finding: {action[1]}")
-        """
-        print(f"input: {response['input']}")
-        print(f"output: {response['output']}")
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py
deleted file mode 100644
index bebe24e6d3ac321523e0442d28b77b6e6df85970..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Legacy editable installation process, i.e. `setup.py develop`.
-"""
-import logging
-from typing import Optional, Sequence
-
-from pip._internal.build_env import BuildEnvironment
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.setuptools_build import make_setuptools_develop_args
-from pip._internal.utils.subprocess import call_subprocess
-
-logger = logging.getLogger(__name__)
-
-
-def install_editable(
-    *,
-    global_options: Sequence[str],
-    prefix: Optional[str],
-    home: Optional[str],
-    use_user_site: bool,
-    name: str,
-    setup_py_path: str,
-    isolated: bool,
-    build_env: BuildEnvironment,
-    unpacked_source_directory: str,
-) -> None:
-    """Install a package in editable mode. Most arguments are pass-through
-    to setuptools.
-    """
-    logger.info("Running setup.py develop for %s", name)
-
-    args = make_setuptools_develop_args(
-        setup_py_path,
-        global_options=global_options,
-        no_user_config=isolated,
-        prefix=prefix,
-        home=home,
-        use_user_site=use_user_site,
-    )
-
-    with indent_log():
-        with build_env:
-            call_subprocess(
-                args,
-                command_desc="python setup.py develop",
-                cwd=unpacked_source_directory,
-            )
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/requests/cookies.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/requests/cookies.py
deleted file mode 100644
index bf54ab237e410603061b8cec8fd195912d3cfb08..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/requests/cookies.py
+++ /dev/null
@@ -1,561 +0,0 @@
-"""
-requests.cookies
-~~~~~~~~~~~~~~~~
-
-Compatibility code to be able to use `cookielib.CookieJar` with requests.
-
-requests.utils imports from here, so be careful with imports.
-"""
-
-import calendar
-import copy
-import time
-
-from ._internal_utils import to_native_string
-from .compat import Morsel, MutableMapping, cookielib, urlparse, urlunparse
-
-try:
-    import threading
-except ImportError:
-    import dummy_threading as threading
-
-
-class MockRequest:
-    """Wraps a `requests.Request` to mimic a `urllib2.Request`.
-
-    The code in `cookielib.CookieJar` expects this interface in order to correctly
-    manage cookie policies, i.e., determine whether a cookie can be set, given the
-    domains of the request and the cookie.
-
-    The original request object is read-only. The client is responsible for collecting
-    the new headers via `get_new_headers()` and interpreting them appropriately. You
-    probably want `get_cookie_header`, defined below.
-    """
-
-    def __init__(self, request):
-        self._r = request
-        self._new_headers = {}
-        self.type = urlparse(self._r.url).scheme
-
-    def get_type(self):
-        return self.type
-
-    def get_host(self):
-        return urlparse(self._r.url).netloc
-
-    def get_origin_req_host(self):
-        return self.get_host()
-
-    def get_full_url(self):
-        # Only return the response's URL if the user hadn't set the Host
-        # header
-        if not self._r.headers.get("Host"):
-            return self._r.url
-        # If they did set it, retrieve it and reconstruct the expected domain
-        host = to_native_string(self._r.headers["Host"], encoding="utf-8")
-        parsed = urlparse(self._r.url)
-        # Reconstruct the URL as we expect it
-        return urlunparse(
-            [
-                parsed.scheme,
-                host,
-                parsed.path,
-                parsed.params,
-                parsed.query,
-                parsed.fragment,
-            ]
-        )
-
-    def is_unverifiable(self):
-        return True
-
-    def has_header(self, name):
-        return name in self._r.headers or name in self._new_headers
-
-    def get_header(self, name, default=None):
-        return self._r.headers.get(name, self._new_headers.get(name, default))
-
-    def add_header(self, key, val):
-        """cookielib has no legitimate use for this method; add it back if you find one."""
-        raise NotImplementedError(
-            "Cookie headers should be added with add_unredirected_header()"
-        )
-
-    def add_unredirected_header(self, name, value):
-        self._new_headers[name] = value
-
-    def get_new_headers(self):
-        return self._new_headers
-
-    @property
-    def unverifiable(self):
-        return self.is_unverifiable()
-
-    @property
-    def origin_req_host(self):
-        return self.get_origin_req_host()
-
-    @property
-    def host(self):
-        return self.get_host()
-
-
-class MockResponse:
-    """Wraps a `httplib.HTTPMessage` to mimic a `urllib.addinfourl`.
-
-    ...what? Basically, expose the parsed HTTP headers from the server response
-    the way `cookielib` expects to see them.
-    """
-
-    def __init__(self, headers):
-        """Make a MockResponse for `cookielib` to read.
-
-        :param headers: a httplib.HTTPMessage or analogous carrying the headers
-        """
-        self._headers = headers
-
-    def info(self):
-        return self._headers
-
-    def getheaders(self, name):
-        self._headers.getheaders(name)
-
-
-def extract_cookies_to_jar(jar, request, response):
-    """Extract the cookies from the response into a CookieJar.
-
-    :param jar: cookielib.CookieJar (not necessarily a RequestsCookieJar)
-    :param request: our own requests.Request object
-    :param response: urllib3.HTTPResponse object
-    """
-    if not (hasattr(response, "_original_response") and response._original_response):
-        return
-    # the _original_response field is the wrapped httplib.HTTPResponse object,
-    req = MockRequest(request)
-    # pull out the HTTPMessage with the headers and put it in the mock:
-    res = MockResponse(response._original_response.msg)
-    jar.extract_cookies(res, req)
-
-
-def get_cookie_header(jar, request):
-    """
-    Produce an appropriate Cookie header string to be sent with `request`, or None.
-
-    :rtype: str
-    """
-    r = MockRequest(request)
-    jar.add_cookie_header(r)
-    return r.get_new_headers().get("Cookie")
-
-
-def remove_cookie_by_name(cookiejar, name, domain=None, path=None):
-    """Unsets a cookie by name, by default over all domains and paths.
-
-    Wraps CookieJar.clear(), is O(n).
-    """
-    clearables = []
-    for cookie in cookiejar:
-        if cookie.name != name:
-            continue
-        if domain is not None and domain != cookie.domain:
-            continue
-        if path is not None and path != cookie.path:
-            continue
-        clearables.append((cookie.domain, cookie.path, cookie.name))
-
-    for domain, path, name in clearables:
-        cookiejar.clear(domain, path, name)
-
-
-class CookieConflictError(RuntimeError):
-    """There are two cookies that meet the criteria specified in the cookie jar.
-    Use .get and .set and include domain and path args in order to be more specific.
-    """
-
-
-class RequestsCookieJar(cookielib.CookieJar, MutableMapping):
-    """Compatibility class; is a cookielib.CookieJar, but exposes a dict
-    interface.
-
-    This is the CookieJar we create by default for requests and sessions that
-    don't specify one, since some clients may expect response.cookies and
-    session.cookies to support dict operations.
-
-    Requests does not use the dict interface internally; it's just for
-    compatibility with external client code. All requests code should work
-    out of the box with externally provided instances of ``CookieJar``, e.g.
-    ``LWPCookieJar`` and ``FileCookieJar``.
-
-    Unlike a regular CookieJar, this class is pickleable.
-
-    .. warning:: dictionary operations that are normally O(1) may be O(n).
-    """
-
-    def get(self, name, default=None, domain=None, path=None):
-        """Dict-like get() that also supports optional domain and path args in
-        order to resolve naming collisions from using one cookie jar over
-        multiple domains.
-
-        .. warning:: operation is O(n), not O(1).
-        """
-        try:
-            return self._find_no_duplicates(name, domain, path)
-        except KeyError:
-            return default
-
-    def set(self, name, value, **kwargs):
-        """Dict-like set() that also supports optional domain and path args in
-        order to resolve naming collisions from using one cookie jar over
-        multiple domains.
-        """
-        # support client code that unsets cookies by assignment of a None value:
-        if value is None:
-            remove_cookie_by_name(
-                self, name, domain=kwargs.get("domain"), path=kwargs.get("path")
-            )
-            return
-
-        if isinstance(value, Morsel):
-            c = morsel_to_cookie(value)
-        else:
-            c = create_cookie(name, value, **kwargs)
-        self.set_cookie(c)
-        return c
-
-    def iterkeys(self):
-        """Dict-like iterkeys() that returns an iterator of names of cookies
-        from the jar.
-
-        .. seealso:: itervalues() and iteritems().
-        """
-        for cookie in iter(self):
-            yield cookie.name
-
-    def keys(self):
-        """Dict-like keys() that returns a list of names of cookies from the
-        jar.
-
-        .. seealso:: values() and items().
-        """
-        return list(self.iterkeys())
-
-    def itervalues(self):
-        """Dict-like itervalues() that returns an iterator of values of cookies
-        from the jar.
-
-        .. seealso:: iterkeys() and iteritems().
-        """
-        for cookie in iter(self):
-            yield cookie.value
-
-    def values(self):
-        """Dict-like values() that returns a list of values of cookies from the
-        jar.
-
-        .. seealso:: keys() and items().
-        """
-        return list(self.itervalues())
-
-    def iteritems(self):
-        """Dict-like iteritems() that returns an iterator of name-value tuples
-        from the jar.
-
-        .. seealso:: iterkeys() and itervalues().
-        """
-        for cookie in iter(self):
-            yield cookie.name, cookie.value
-
-    def items(self):
-        """Dict-like items() that returns a list of name-value tuples from the
-        jar. Allows client-code to call ``dict(RequestsCookieJar)`` and get a
-        vanilla python dict of key value pairs.
-
-        .. seealso:: keys() and values().
-        """
-        return list(self.iteritems())
-
-    def list_domains(self):
-        """Utility method to list all the domains in the jar."""
-        domains = []
-        for cookie in iter(self):
-            if cookie.domain not in domains:
-                domains.append(cookie.domain)
-        return domains
-
-    def list_paths(self):
-        """Utility method to list all the paths in the jar."""
-        paths = []
-        for cookie in iter(self):
-            if cookie.path not in paths:
-                paths.append(cookie.path)
-        return paths
-
-    def multiple_domains(self):
-        """Returns True if there are multiple domains in the jar.
-        Returns False otherwise.
-
-        :rtype: bool
-        """
-        domains = []
-        for cookie in iter(self):
-            if cookie.domain is not None and cookie.domain in domains:
-                return True
-            domains.append(cookie.domain)
-        return False  # there is only one domain in jar
-
-    def get_dict(self, domain=None, path=None):
-        """Takes as an argument an optional domain and path and returns a plain
-        old Python dict of name-value pairs of cookies that meet the
-        requirements.
-
-        :rtype: dict
-        """
-        dictionary = {}
-        for cookie in iter(self):
-            if (domain is None or cookie.domain == domain) and (
-                path is None or cookie.path == path
-            ):
-                dictionary[cookie.name] = cookie.value
-        return dictionary
-
-    def __contains__(self, name):
-        try:
-            return super().__contains__(name)
-        except CookieConflictError:
-            return True
-
-    def __getitem__(self, name):
-        """Dict-like __getitem__() for compatibility with client code. Throws
-        exception if there are more than one cookie with name. In that case,
-        use the more explicit get() method instead.
-
-        .. warning:: operation is O(n), not O(1).
-        """
-        return self._find_no_duplicates(name)
-
-    def __setitem__(self, name, value):
-        """Dict-like __setitem__ for compatibility with client code. Throws
-        exception if there is already a cookie of that name in the jar. In that
-        case, use the more explicit set() method instead.
-        """
-        self.set(name, value)
-
-    def __delitem__(self, name):
-        """Deletes a cookie given a name. Wraps ``cookielib.CookieJar``'s
-        ``remove_cookie_by_name()``.
-        """
-        remove_cookie_by_name(self, name)
-
-    def set_cookie(self, cookie, *args, **kwargs):
-        if (
-            hasattr(cookie.value, "startswith")
-            and cookie.value.startswith('"')
-            and cookie.value.endswith('"')
-        ):
-            cookie.value = cookie.value.replace('\\"', "")
-        return super().set_cookie(cookie, *args, **kwargs)
-
-    def update(self, other):
-        """Updates this jar with cookies from another CookieJar or dict-like"""
-        if isinstance(other, cookielib.CookieJar):
-            for cookie in other:
-                self.set_cookie(copy.copy(cookie))
-        else:
-            super().update(other)
-
-    def _find(self, name, domain=None, path=None):
-        """Requests uses this method internally to get cookie values.
-
-        If there are conflicting cookies, _find arbitrarily chooses one.
-        See _find_no_duplicates if you want an exception thrown if there are
-        conflicting cookies.
-
-        :param name: a string containing name of cookie
-        :param domain: (optional) string containing domain of cookie
-        :param path: (optional) string containing path of cookie
-        :return: cookie.value
-        """
-        for cookie in iter(self):
-            if cookie.name == name:
-                if domain is None or cookie.domain == domain:
-                    if path is None or cookie.path == path:
-                        return cookie.value
-
-        raise KeyError(f"name={name!r}, domain={domain!r}, path={path!r}")
-
-    def _find_no_duplicates(self, name, domain=None, path=None):
-        """Both ``__get_item__`` and ``get`` call this function: it's never
-        used elsewhere in Requests.
-
-        :param name: a string containing name of cookie
-        :param domain: (optional) string containing domain of cookie
-        :param path: (optional) string containing path of cookie
-        :raises KeyError: if cookie is not found
-        :raises CookieConflictError: if there are multiple cookies
-            that match name and optionally domain and path
-        :return: cookie.value
-        """
-        toReturn = None
-        for cookie in iter(self):
-            if cookie.name == name:
-                if domain is None or cookie.domain == domain:
-                    if path is None or cookie.path == path:
-                        if toReturn is not None:
-                            # if there are multiple cookies that meet passed in criteria
-                            raise CookieConflictError(
-                                f"There are multiple cookies with name, {name!r}"
-                            )
-                        # we will eventually return this as long as no cookie conflict
-                        toReturn = cookie.value
-
-        if toReturn:
-            return toReturn
-        raise KeyError(f"name={name!r}, domain={domain!r}, path={path!r}")
-
-    def __getstate__(self):
-        """Unlike a normal CookieJar, this class is pickleable."""
-        state = self.__dict__.copy()
-        # remove the unpickleable RLock object
-        state.pop("_cookies_lock")
-        return state
-
-    def __setstate__(self, state):
-        """Unlike a normal CookieJar, this class is pickleable."""
-        self.__dict__.update(state)
-        if "_cookies_lock" not in self.__dict__:
-            self._cookies_lock = threading.RLock()
-
-    def copy(self):
-        """Return a copy of this RequestsCookieJar."""
-        new_cj = RequestsCookieJar()
-        new_cj.set_policy(self.get_policy())
-        new_cj.update(self)
-        return new_cj
-
-    def get_policy(self):
-        """Return the CookiePolicy instance used."""
-        return self._policy
-
-
-def _copy_cookie_jar(jar):
-    if jar is None:
-        return None
-
-    if hasattr(jar, "copy"):
-        # We're dealing with an instance of RequestsCookieJar
-        return jar.copy()
-    # We're dealing with a generic CookieJar instance
-    new_jar = copy.copy(jar)
-    new_jar.clear()
-    for cookie in jar:
-        new_jar.set_cookie(copy.copy(cookie))
-    return new_jar
-
-
-def create_cookie(name, value, **kwargs):
-    """Make a cookie from underspecified parameters.
-
-    By default, the pair of `name` and `value` will be set for the domain ''
-    and sent on every request (this is sometimes called a "supercookie").
-    """
-    result = {
-        "version": 0,
-        "name": name,
-        "value": value,
-        "port": None,
-        "domain": "",
-        "path": "/",
-        "secure": False,
-        "expires": None,
-        "discard": True,
-        "comment": None,
-        "comment_url": None,
-        "rest": {"HttpOnly": None},
-        "rfc2109": False,
-    }
-
-    badargs = set(kwargs) - set(result)
-    if badargs:
-        raise TypeError(
-            f"create_cookie() got unexpected keyword arguments: {list(badargs)}"
-        )
-
-    result.update(kwargs)
-    result["port_specified"] = bool(result["port"])
-    result["domain_specified"] = bool(result["domain"])
-    result["domain_initial_dot"] = result["domain"].startswith(".")
-    result["path_specified"] = bool(result["path"])
-
-    return cookielib.Cookie(**result)
-
-
-def morsel_to_cookie(morsel):
-    """Convert a Morsel object into a Cookie containing the one k/v pair."""
-
-    expires = None
-    if morsel["max-age"]:
-        try:
-            expires = int(time.time() + int(morsel["max-age"]))
-        except ValueError:
-            raise TypeError(f"max-age: {morsel['max-age']} must be integer")
-    elif morsel["expires"]:
-        time_template = "%a, %d-%b-%Y %H:%M:%S GMT"
-        expires = calendar.timegm(time.strptime(morsel["expires"], time_template))
-    return create_cookie(
-        comment=morsel["comment"],
-        comment_url=bool(morsel["comment"]),
-        discard=False,
-        domain=morsel["domain"],
-        expires=expires,
-        name=morsel.key,
-        path=morsel["path"],
-        port=None,
-        rest={"HttpOnly": morsel["httponly"]},
-        rfc2109=False,
-        secure=bool(morsel["secure"]),
-        value=morsel.value,
-        version=morsel["version"] or 0,
-    )
-
-
-def cookiejar_from_dict(cookie_dict, cookiejar=None, overwrite=True):
-    """Returns a CookieJar from a key/value dictionary.
-
-    :param cookie_dict: Dict of key/values to insert into CookieJar.
-    :param cookiejar: (optional) A cookiejar to add the cookies to.
-    :param overwrite: (optional) If False, will not replace cookies
-        already in the jar with new ones.
-    :rtype: CookieJar
-    """
-    if cookiejar is None:
-        cookiejar = RequestsCookieJar()
-
-    if cookie_dict is not None:
-        names_from_jar = [cookie.name for cookie in cookiejar]
-        for name in cookie_dict:
-            if overwrite or (name not in names_from_jar):
-                cookiejar.set_cookie(create_cookie(name, cookie_dict[name]))
-
-    return cookiejar
-
-
-def merge_cookies(cookiejar, cookies):
-    """Add cookies to cookiejar and returns a merged CookieJar.
-
-    :param cookiejar: CookieJar object to add the cookies to.
-    :param cookies: Dictionary or CookieJar object to be added.
-    :rtype: CookieJar
-    """
-    if not isinstance(cookiejar, cookielib.CookieJar):
-        raise ValueError("You can only merge into CookieJar")
-
-    if isinstance(cookies, dict):
-        cookiejar = cookiejar_from_dict(cookies, cookiejar=cookiejar, overwrite=False)
-    elif isinstance(cookies, cookielib.CookieJar):
-        try:
-            cookiejar.update(cookies)
-        except AttributeError:
-            for cookie_in_jar in cookies:
-                cookiejar.set_cookie(cookie_in_jar)
-
-    return cookiejar
diff --git a/spaces/prerna9811/Chord/portaudio/src/hostapi/skeleton/pa_hostapi_skeleton.c b/spaces/prerna9811/Chord/portaudio/src/hostapi/skeleton/pa_hostapi_skeleton.c
deleted file mode 100644
index 46808d2f26f97527e78c8e3df48c7381bcf34e1a..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/src/hostapi/skeleton/pa_hostapi_skeleton.c
+++ /dev/null
@@ -1,814 +0,0 @@
-/*
- * $Id$
- * Portable Audio I/O Library skeleton implementation
- * demonstrates how to use the common functions to implement support
- * for a host API
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 1999-2002 Ross Bencina, Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/** @file
- @ingroup common_src
-
- @brief Skeleton implementation of support for a host API.
-
- This file is provided as a starting point for implementing support for
- a new host API. It provides examples of how the common code can be used.
-
- @note IMPLEMENT ME comments are used to indicate functionality
- which much be customised for each implementation.
-*/
-
-
-#include <string.h> /* strlen() */
-
-#include "pa_util.h"
-#include "pa_allocation.h"
-#include "pa_hostapi.h"
-#include "pa_stream.h"
-#include "pa_cpuload.h"
-#include "pa_process.h"
-
-
-/* prototypes for functions declared in this file */
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-PaError PaSkeleton_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex index );
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-
-
-static void Terminate( struct PaUtilHostApiRepresentation *hostApi );
-static PaError IsFormatSupported( struct PaUtilHostApiRepresentation *hostApi,
-                                  const PaStreamParameters *inputParameters,
-                                  const PaStreamParameters *outputParameters,
-                                  double sampleRate );
-static PaError OpenStream( struct PaUtilHostApiRepresentation *hostApi,
-                           PaStream** s,
-                           const PaStreamParameters *inputParameters,
-                           const PaStreamParameters *outputParameters,
-                           double sampleRate,
-                           unsigned long framesPerBuffer,
-                           PaStreamFlags streamFlags,
-                           PaStreamCallback *streamCallback,
-                           void *userData );
-static PaError CloseStream( PaStream* stream );
-static PaError StartStream( PaStream *stream );
-static PaError StopStream( PaStream *stream );
-static PaError AbortStream( PaStream *stream );
-static PaError IsStreamStopped( PaStream *s );
-static PaError IsStreamActive( PaStream *stream );
-static PaTime GetStreamTime( PaStream *stream );
-static double GetStreamCpuLoad( PaStream* stream );
-static PaError ReadStream( PaStream* stream, void *buffer, unsigned long frames );
-static PaError WriteStream( PaStream* stream, const void *buffer, unsigned long frames );
-static signed long GetStreamReadAvailable( PaStream* stream );
-static signed long GetStreamWriteAvailable( PaStream* stream );
-
-
-/* IMPLEMENT ME: a macro like the following one should be used for reporting
- host errors */
-#define PA_SKELETON_SET_LAST_HOST_ERROR( errorCode, errorText ) \
-    PaUtil_SetLastHostErrorInfo( paInDevelopment, errorCode, errorText )
-
-/* PaSkeletonHostApiRepresentation - host api datastructure specific to this implementation */
-
-typedef struct
-{
-    PaUtilHostApiRepresentation inheritedHostApiRep;
-    PaUtilStreamInterface callbackStreamInterface;
-    PaUtilStreamInterface blockingStreamInterface;
-
-    PaUtilAllocationGroup *allocations;
-
-    /* implementation specific data goes here */
-}
-PaSkeletonHostApiRepresentation;  /* IMPLEMENT ME: rename this */
-
-
-PaError PaSkeleton_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex hostApiIndex )
-{
-    PaError result = paNoError;
-    int i, deviceCount;
-    PaSkeletonHostApiRepresentation *skeletonHostApi;
-    PaDeviceInfo *deviceInfoArray;
-
-    skeletonHostApi = (PaSkeletonHostApiRepresentation*)PaUtil_AllocateMemory( sizeof(PaSkeletonHostApiRepresentation) );
-    if( !skeletonHostApi )
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-
-    skeletonHostApi->allocations = PaUtil_CreateAllocationGroup();
-    if( !skeletonHostApi->allocations )
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-
-    *hostApi = &skeletonHostApi->inheritedHostApiRep;
-    (*hostApi)->info.structVersion = 1;
-    (*hostApi)->info.type = paInDevelopment;            /* IMPLEMENT ME: change to correct type id */
-    (*hostApi)->info.name = "skeleton implementation";  /* IMPLEMENT ME: change to correct name */
-
-    (*hostApi)->info.defaultInputDevice = paNoDevice;  /* IMPLEMENT ME */
-    (*hostApi)->info.defaultOutputDevice = paNoDevice; /* IMPLEMENT ME */
-
-    (*hostApi)->info.deviceCount = 0;
-
-    deviceCount = 0; /* IMPLEMENT ME */
-
-    if( deviceCount > 0 )
-    {
-        (*hostApi)->deviceInfos = (PaDeviceInfo**)PaUtil_GroupAllocateMemory(
-                skeletonHostApi->allocations, sizeof(PaDeviceInfo*) * deviceCount );
-        if( !(*hostApi)->deviceInfos )
-        {
-            result = paInsufficientMemory;
-            goto error;
-        }
-
-        /* allocate all device info structs in a contiguous block */
-        deviceInfoArray = (PaDeviceInfo*)PaUtil_GroupAllocateMemory(
-                skeletonHostApi->allocations, sizeof(PaDeviceInfo) * deviceCount );
-        if( !deviceInfoArray )
-        {
-            result = paInsufficientMemory;
-            goto error;
-        }
-
-        for( i=0; i < deviceCount; ++i )
-        {
-            PaDeviceInfo *deviceInfo = &deviceInfoArray[i];
-            deviceInfo->structVersion = 2;
-            deviceInfo->hostApi = hostApiIndex;
-            deviceInfo->name = 0; /* IMPLEMENT ME: allocate block and copy name eg:
-                deviceName = (char*)PaUtil_GroupAllocateMemory( skeletonHostApi->allocations, strlen(srcName) + 1 );
-                if( !deviceName )
-                {
-                    result = paInsufficientMemory;
-                    goto error;
-                }
-                strcpy( deviceName, srcName );
-                deviceInfo->name = deviceName;
-            */
-
-            deviceInfo->maxInputChannels = 0;  /* IMPLEMENT ME */
-            deviceInfo->maxOutputChannels = 0;  /* IMPLEMENT ME */
-
-            deviceInfo->defaultLowInputLatency = 0.;  /* IMPLEMENT ME */
-            deviceInfo->defaultLowOutputLatency = 0.;  /* IMPLEMENT ME */
-            deviceInfo->defaultHighInputLatency = 0.;  /* IMPLEMENT ME */
-            deviceInfo->defaultHighOutputLatency = 0.;  /* IMPLEMENT ME */
-
-            deviceInfo->defaultSampleRate = 0.; /* IMPLEMENT ME */
-
-            (*hostApi)->deviceInfos[i] = deviceInfo;
-            ++(*hostApi)->info.deviceCount;
-        }
-    }
-
-    (*hostApi)->Terminate = Terminate;
-    (*hostApi)->OpenStream = OpenStream;
-    (*hostApi)->IsFormatSupported = IsFormatSupported;
-
-    PaUtil_InitializeStreamInterface( &skeletonHostApi->callbackStreamInterface, CloseStream, StartStream,
-                                      StopStream, AbortStream, IsStreamStopped, IsStreamActive,
-                                      GetStreamTime, GetStreamCpuLoad,
-                                      PaUtil_DummyRead, PaUtil_DummyWrite,
-                                      PaUtil_DummyGetReadAvailable, PaUtil_DummyGetWriteAvailable );
-
-    PaUtil_InitializeStreamInterface( &skeletonHostApi->blockingStreamInterface, CloseStream, StartStream,
-                                      StopStream, AbortStream, IsStreamStopped, IsStreamActive,
-                                      GetStreamTime, PaUtil_DummyGetCpuLoad,
-                                      ReadStream, WriteStream, GetStreamReadAvailable, GetStreamWriteAvailable );
-
-    return result;
-
-error:
-    if( skeletonHostApi )
-    {
-        if( skeletonHostApi->allocations )
-        {
-            PaUtil_FreeAllAllocations( skeletonHostApi->allocations );
-            PaUtil_DestroyAllocationGroup( skeletonHostApi->allocations );
-        }
-
-        PaUtil_FreeMemory( skeletonHostApi );
-    }
-    return result;
-}
-
-
-static void Terminate( struct PaUtilHostApiRepresentation *hostApi )
-{
-    PaSkeletonHostApiRepresentation *skeletonHostApi = (PaSkeletonHostApiRepresentation*)hostApi;
-
-    /*
-        IMPLEMENT ME:
-            - clean up any resources not handled by the allocation group
-    */
-
-    if( skeletonHostApi->allocations )
-    {
-        PaUtil_FreeAllAllocations( skeletonHostApi->allocations );
-        PaUtil_DestroyAllocationGroup( skeletonHostApi->allocations );
-    }
-
-    PaUtil_FreeMemory( skeletonHostApi );
-}
-
-
-static PaError IsFormatSupported( struct PaUtilHostApiRepresentation *hostApi,
-                                  const PaStreamParameters *inputParameters,
-                                  const PaStreamParameters *outputParameters,
-                                  double sampleRate )
-{
-    int inputChannelCount, outputChannelCount;
-    PaSampleFormat inputSampleFormat, outputSampleFormat;
-
-    if( inputParameters )
-    {
-        inputChannelCount = inputParameters->channelCount;
-        inputSampleFormat = inputParameters->sampleFormat;
-
-        /* all standard sample formats are supported by the buffer adapter,
-            this implementation doesn't support any custom sample formats */
-        if( inputSampleFormat & paCustomFormat )
-            return paSampleFormatNotSupported;
-
-        /* unless alternate device specification is supported, reject the use of
-            paUseHostApiSpecificDeviceSpecification */
-
-        if( inputParameters->device == paUseHostApiSpecificDeviceSpecification )
-            return paInvalidDevice;
-
-        /* check that input device can support inputChannelCount */
-        if( inputChannelCount > hostApi->deviceInfos[ inputParameters->device ]->maxInputChannels )
-            return paInvalidChannelCount;
-
-        /* validate inputStreamInfo */
-        if( inputParameters->hostApiSpecificStreamInfo )
-            return paIncompatibleHostApiSpecificStreamInfo; /* this implementation doesn't use custom stream info */
-    }
-    else
-    {
-        inputChannelCount = 0;
-    }
-
-    if( outputParameters )
-    {
-        outputChannelCount = outputParameters->channelCount;
-        outputSampleFormat = outputParameters->sampleFormat;
-
-        /* all standard sample formats are supported by the buffer adapter,
-            this implementation doesn't support any custom sample formats */
-        if( outputSampleFormat & paCustomFormat )
-            return paSampleFormatNotSupported;
-
-        /* unless alternate device specification is supported, reject the use of
-            paUseHostApiSpecificDeviceSpecification */
-
-        if( outputParameters->device == paUseHostApiSpecificDeviceSpecification )
-            return paInvalidDevice;
-
-        /* check that output device can support outputChannelCount */
-        if( outputChannelCount > hostApi->deviceInfos[ outputParameters->device ]->maxOutputChannels )
-            return paInvalidChannelCount;
-
-        /* validate outputStreamInfo */
-        if( outputParameters->hostApiSpecificStreamInfo )
-            return paIncompatibleHostApiSpecificStreamInfo; /* this implementation doesn't use custom stream info */
-    }
-    else
-    {
-        outputChannelCount = 0;
-    }
-
-    /*
-        IMPLEMENT ME:
-
-            - if a full duplex stream is requested, check that the combination
-                of input and output parameters is supported if necessary
-
-            - check that the device supports sampleRate
-
-        Because the buffer adapter handles conversion between all standard
-        sample formats, the following checks are only required if paCustomFormat
-        is implemented, or under some other unusual conditions.
-
-            - check that input device can support inputSampleFormat, or that
-                we have the capability to convert from inputSampleFormat to
-                a native format
-
-            - check that output device can support outputSampleFormat, or that
-                we have the capability to convert from outputSampleFormat to
-                a native format
-    */
-
-
-    /* suppress unused variable warnings */
-    (void) sampleRate;
-
-    return paFormatIsSupported;
-}
-
-/* PaSkeletonStream - a stream data structure specifically for this implementation */
-
-typedef struct PaSkeletonStream
-{ /* IMPLEMENT ME: rename this */
-    PaUtilStreamRepresentation streamRepresentation;
-    PaUtilCpuLoadMeasurer cpuLoadMeasurer;
-    PaUtilBufferProcessor bufferProcessor;
-
-    /* IMPLEMENT ME:
-            - implementation specific data goes here
-    */
-    unsigned long framesPerHostCallback; /* just an example */
-}
-PaSkeletonStream;
-
-/* see pa_hostapi.h for a list of validity guarantees made about OpenStream parameters */
-
-static PaError OpenStream( struct PaUtilHostApiRepresentation *hostApi,
-                           PaStream** s,
-                           const PaStreamParameters *inputParameters,
-                           const PaStreamParameters *outputParameters,
-                           double sampleRate,
-                           unsigned long framesPerBuffer,
-                           PaStreamFlags streamFlags,
-                           PaStreamCallback *streamCallback,
-                           void *userData )
-{
-    PaError result = paNoError;
-    PaSkeletonHostApiRepresentation *skeletonHostApi = (PaSkeletonHostApiRepresentation*)hostApi;
-    PaSkeletonStream *stream = 0;
-    unsigned long framesPerHostBuffer = framesPerBuffer; /* these may not be equivalent for all implementations */
-    int inputChannelCount, outputChannelCount;
-    PaSampleFormat inputSampleFormat, outputSampleFormat;
-    PaSampleFormat hostInputSampleFormat, hostOutputSampleFormat;
-
-
-    if( inputParameters )
-    {
-        inputChannelCount = inputParameters->channelCount;
-        inputSampleFormat = inputParameters->sampleFormat;
-
-        /* unless alternate device specification is supported, reject the use of
-            paUseHostApiSpecificDeviceSpecification */
-
-        if( inputParameters->device == paUseHostApiSpecificDeviceSpecification )
-            return paInvalidDevice;
-
-        /* check that input device can support inputChannelCount */
-        if( inputChannelCount > hostApi->deviceInfos[ inputParameters->device ]->maxInputChannels )
-            return paInvalidChannelCount;
-
-        /* validate inputStreamInfo */
-        if( inputParameters->hostApiSpecificStreamInfo )
-            return paIncompatibleHostApiSpecificStreamInfo; /* this implementation doesn't use custom stream info */
-
-        /* IMPLEMENT ME - establish which  host formats are available */
-        hostInputSampleFormat =
-            PaUtil_SelectClosestAvailableFormat( paInt16 /* native formats */, inputSampleFormat );
-    }
-    else
-    {
-        inputChannelCount = 0;
-        inputSampleFormat = hostInputSampleFormat = paInt16; /* Suppress 'uninitialised var' warnings. */
-    }
-
-    if( outputParameters )
-    {
-        outputChannelCount = outputParameters->channelCount;
-        outputSampleFormat = outputParameters->sampleFormat;
-
-        /* unless alternate device specification is supported, reject the use of
-            paUseHostApiSpecificDeviceSpecification */
-
-        if( outputParameters->device == paUseHostApiSpecificDeviceSpecification )
-            return paInvalidDevice;
-
-        /* check that output device can support inputChannelCount */
-        if( outputChannelCount > hostApi->deviceInfos[ outputParameters->device ]->maxOutputChannels )
-            return paInvalidChannelCount;
-
-        /* validate outputStreamInfo */
-        if( outputParameters->hostApiSpecificStreamInfo )
-            return paIncompatibleHostApiSpecificStreamInfo; /* this implementation doesn't use custom stream info */
-
-        /* IMPLEMENT ME - establish which  host formats are available */
-        hostOutputSampleFormat =
-            PaUtil_SelectClosestAvailableFormat( paInt16 /* native formats */, outputSampleFormat );
-    }
-    else
-    {
-        outputChannelCount = 0;
-        outputSampleFormat = hostOutputSampleFormat = paInt16; /* Suppress 'uninitialized var' warnings. */
-    }
-
-    /*
-        IMPLEMENT ME:
-
-        ( the following two checks are taken care of by PaUtil_InitializeBufferProcessor() FIXME - checks needed? )
-
-            - check that input device can support inputSampleFormat, or that
-                we have the capability to convert from outputSampleFormat to
-                a native format
-
-            - check that output device can support outputSampleFormat, or that
-                we have the capability to convert from outputSampleFormat to
-                a native format
-
-            - if a full duplex stream is requested, check that the combination
-                of input and output parameters is supported
-
-            - check that the device supports sampleRate
-
-            - alter sampleRate to a close allowable rate if possible / necessary
-
-            - validate suggestedInputLatency and suggestedOutputLatency parameters,
-                use default values where necessary
-    */
-
-
-
-
-    /* validate platform specific flags */
-    if( (streamFlags & paPlatformSpecificFlags) != 0 )
-        return paInvalidFlag; /* unexpected platform specific flag */
-
-
-    stream = (PaSkeletonStream*)PaUtil_AllocateMemory( sizeof(PaSkeletonStream) );
-    if( !stream )
-    {
-        result = paInsufficientMemory;
-        goto error;
-    }
-
-    if( streamCallback )
-    {
-        PaUtil_InitializeStreamRepresentation( &stream->streamRepresentation,
-                                               &skeletonHostApi->callbackStreamInterface, streamCallback, userData );
-    }
-    else
-    {
-        PaUtil_InitializeStreamRepresentation( &stream->streamRepresentation,
-                                               &skeletonHostApi->blockingStreamInterface, streamCallback, userData );
-    }
-
-    PaUtil_InitializeCpuLoadMeasurer( &stream->cpuLoadMeasurer, sampleRate );
-
-
-    /* we assume a fixed host buffer size in this example, but the buffer processor
-        can also support bounded and unknown host buffer sizes by passing
-        paUtilBoundedHostBufferSize or paUtilUnknownHostBufferSize instead of
-        paUtilFixedHostBufferSize below. */
-
-    result =  PaUtil_InitializeBufferProcessor( &stream->bufferProcessor,
-              inputChannelCount, inputSampleFormat, hostInputSampleFormat,
-              outputChannelCount, outputSampleFormat, hostOutputSampleFormat,
-              sampleRate, streamFlags, framesPerBuffer,
-              framesPerHostBuffer, paUtilFixedHostBufferSize,
-              streamCallback, userData );
-    if( result != paNoError )
-        goto error;
-
-
-    /*
-        IMPLEMENT ME: initialise the following fields with estimated or actual
-        values.
-    */
-    stream->streamRepresentation.streamInfo.inputLatency =
-            (PaTime)PaUtil_GetBufferProcessorInputLatencyFrames(&stream->bufferProcessor) / sampleRate; /* inputLatency is specified in _seconds_ */
-    stream->streamRepresentation.streamInfo.outputLatency =
-            (PaTime)PaUtil_GetBufferProcessorOutputLatencyFrames(&stream->bufferProcessor) / sampleRate; /* outputLatency is specified in _seconds_ */
-    stream->streamRepresentation.streamInfo.sampleRate = sampleRate;
-
-
-    /*
-        IMPLEMENT ME:
-            - additional stream setup + opening
-    */
-
-    stream->framesPerHostCallback = framesPerHostBuffer;
-
-    *s = (PaStream*)stream;
-
-    return result;
-
-error:
-    if( stream )
-        PaUtil_FreeMemory( stream );
-
-    return result;
-}
-
-/*
-    ExampleHostProcessingLoop() illustrates the kind of processing which may
-    occur in a host implementation.
-
-*/
-static void ExampleHostProcessingLoop( void *inputBuffer, void *outputBuffer, void *userData )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)userData;
-    PaStreamCallbackTimeInfo timeInfo = {0,0,0}; /* IMPLEMENT ME */
-    int callbackResult;
-    unsigned long framesProcessed;
-
-    PaUtil_BeginCpuLoadMeasurement( &stream->cpuLoadMeasurer );
-
-    /*
-        IMPLEMENT ME:
-            - generate timing information
-            - handle buffer slips
-    */
-
-    /*
-        If you need to byte swap or shift inputBuffer to convert it into a
-        portaudio format, do it here.
-    */
-
-
-
-    PaUtil_BeginBufferProcessing( &stream->bufferProcessor, &timeInfo, 0 /* IMPLEMENT ME: pass underflow/overflow flags when necessary */ );
-
-    /*
-        depending on whether the host buffers are interleaved, non-interleaved
-        or a mixture, you will want to call PaUtil_SetInterleaved*Channels(),
-        PaUtil_SetNonInterleaved*Channel() or PaUtil_Set*Channel() here.
-    */
-
-    PaUtil_SetInputFrameCount( &stream->bufferProcessor, 0 /* default to host buffer size */ );
-    PaUtil_SetInterleavedInputChannels( &stream->bufferProcessor,
-            0, /* first channel of inputBuffer is channel 0 */
-            inputBuffer,
-            0 ); /* 0 - use inputChannelCount passed to init buffer processor */
-
-    PaUtil_SetOutputFrameCount( &stream->bufferProcessor, 0 /* default to host buffer size */ );
-    PaUtil_SetInterleavedOutputChannels( &stream->bufferProcessor,
-            0, /* first channel of outputBuffer is channel 0 */
-            outputBuffer,
-            0 ); /* 0 - use outputChannelCount passed to init buffer processor */
-
-    /* you must pass a valid value of callback result to PaUtil_EndBufferProcessing()
-        in general you would pass paContinue for normal operation, and
-        paComplete to drain the buffer processor's internal output buffer.
-        You can check whether the buffer processor's output buffer is empty
-        using PaUtil_IsBufferProcessorOuputEmpty( bufferProcessor )
-    */
-    callbackResult = paContinue;
-    framesProcessed = PaUtil_EndBufferProcessing( &stream->bufferProcessor, &callbackResult );
-
-
-    /*
-        If you need to byte swap or shift outputBuffer to convert it to
-        host format, do it here.
-    */
-
-    PaUtil_EndCpuLoadMeasurement( &stream->cpuLoadMeasurer, framesProcessed );
-
-
-    if( callbackResult == paContinue )
-    {
-        /* nothing special to do */
-    }
-    else if( callbackResult == paAbort )
-    {
-        /* IMPLEMENT ME - finish playback immediately  */
-
-        /* once finished, call the finished callback */
-        if( stream->streamRepresentation.streamFinishedCallback != 0 )
-            stream->streamRepresentation.streamFinishedCallback( stream->streamRepresentation.userData );
-    }
-    else
-    {
-        /* User callback has asked us to stop with paComplete or other non-zero value */
-
-        /* IMPLEMENT ME - finish playback once currently queued audio has completed  */
-
-        /* once finished, call the finished callback */
-        if( stream->streamRepresentation.streamFinishedCallback != 0 )
-            stream->streamRepresentation.streamFinishedCallback( stream->streamRepresentation.userData );
-    }
-}
-
-
-/*
-    When CloseStream() is called, the multi-api layer ensures that
-    the stream has already been stopped or aborted.
-*/
-static PaError CloseStream( PaStream* s )
-{
-    PaError result = paNoError;
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /*
-        IMPLEMENT ME:
-            - additional stream closing + cleanup
-    */
-
-    PaUtil_TerminateBufferProcessor( &stream->bufferProcessor );
-    PaUtil_TerminateStreamRepresentation( &stream->streamRepresentation );
-    PaUtil_FreeMemory( stream );
-
-    return result;
-}
-
-
-static PaError StartStream( PaStream *s )
-{
-    PaError result = paNoError;
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    PaUtil_ResetBufferProcessor( &stream->bufferProcessor );
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior */
-
-    /* suppress unused function warning. the code in ExampleHostProcessingLoop or
-       something similar should be implemented to feed samples to and from the
-       host after StartStream() is called.
-    */
-    (void) ExampleHostProcessingLoop;
-
-    return result;
-}
-
-
-static PaError StopStream( PaStream *s )
-{
-    PaError result = paNoError;
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior */
-
-    return result;
-}
-
-
-static PaError AbortStream( PaStream *s )
-{
-    PaError result = paNoError;
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior */
-
-    return result;
-}
-
-
-static PaError IsStreamStopped( PaStream *s )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior */
-
-    return 0;
-}
-
-
-static PaError IsStreamActive( PaStream *s )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior */
-
-    return 0;
-}
-
-
-static PaTime GetStreamTime( PaStream *s )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior*/
-
-    return 0;
-}
-
-
-static double GetStreamCpuLoad( PaStream* s )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    return PaUtil_GetCpuLoad( &stream->cpuLoadMeasurer );
-}
-
-
-/*
-    As separate stream interfaces are used for blocking and callback
-    streams, the following functions can be guaranteed to only be called
-    for blocking streams.
-*/
-
-static PaError ReadStream( PaStream* s,
-                           void *buffer,
-                           unsigned long frames )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) buffer;
-    (void) frames;
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior*/
-
-    return paNoError;
-}
-
-
-static PaError WriteStream( PaStream* s,
-                            const void *buffer,
-                            unsigned long frames )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) buffer;
-    (void) frames;
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior*/
-
-    return paNoError;
-}
-
-
-static signed long GetStreamReadAvailable( PaStream* s )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior*/
-
-    return 0;
-}
-
-
-static signed long GetStreamWriteAvailable( PaStream* s )
-{
-    PaSkeletonStream *stream = (PaSkeletonStream*)s;
-
-    /* suppress unused variable warnings */
-    (void) stream;
-
-    /* IMPLEMENT ME, see portaudio.h for required behavior*/
-
-    return 0;
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/SunImagePlugin.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/SunImagePlugin.py
deleted file mode 100644
index 6a8d5d86b73ebc56ba16e016f4be53802f87c0e8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/SunImagePlugin.py
+++ /dev/null
@@ -1,139 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Sun image file handling
-#
-# History:
-# 1995-09-10 fl   Created
-# 1996-05-28 fl   Fixed 32-bit alignment
-# 1998-12-29 fl   Import ImagePalette module
-# 2001-12-18 fl   Fixed palette loading (from Jean-Claude Rimbault)
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1995-1996 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i32be as i32
-
-
-def _accept(prefix):
-    return len(prefix) >= 4 and i32(prefix) == 0x59A66A95
-
-
-##
-# Image plugin for Sun raster files.
-
-
-class SunImageFile(ImageFile.ImageFile):
-    format = "SUN"
-    format_description = "Sun Raster File"
-
-    def _open(self):
-        # The Sun Raster file header is 32 bytes in length
-        # and has the following format:
-
-        #     typedef struct _SunRaster
-        #     {
-        #         DWORD MagicNumber;      /* Magic (identification) number */
-        #         DWORD Width;            /* Width of image in pixels */
-        #         DWORD Height;           /* Height of image in pixels */
-        #         DWORD Depth;            /* Number of bits per pixel */
-        #         DWORD Length;           /* Size of image data in bytes */
-        #         DWORD Type;             /* Type of raster file */
-        #         DWORD ColorMapType;     /* Type of color map */
-        #         DWORD ColorMapLength;   /* Size of the color map in bytes */
-        #     } SUNRASTER;
-
-        # HEAD
-        s = self.fp.read(32)
-        if not _accept(s):
-            msg = "not an SUN raster file"
-            raise SyntaxError(msg)
-
-        offset = 32
-
-        self._size = i32(s, 4), i32(s, 8)
-
-        depth = i32(s, 12)
-        # data_length = i32(s, 16)   # unreliable, ignore.
-        file_type = i32(s, 20)
-        palette_type = i32(s, 24)  # 0: None, 1: RGB, 2: Raw/arbitrary
-        palette_length = i32(s, 28)
-
-        if depth == 1:
-            self._mode, rawmode = "1", "1;I"
-        elif depth == 4:
-            self._mode, rawmode = "L", "L;4"
-        elif depth == 8:
-            self._mode = rawmode = "L"
-        elif depth == 24:
-            if file_type == 3:
-                self._mode, rawmode = "RGB", "RGB"
-            else:
-                self._mode, rawmode = "RGB", "BGR"
-        elif depth == 32:
-            if file_type == 3:
-                self._mode, rawmode = "RGB", "RGBX"
-            else:
-                self._mode, rawmode = "RGB", "BGRX"
-        else:
-            msg = "Unsupported Mode/Bit Depth"
-            raise SyntaxError(msg)
-
-        if palette_length:
-            if palette_length > 1024:
-                msg = "Unsupported Color Palette Length"
-                raise SyntaxError(msg)
-
-            if palette_type != 1:
-                msg = "Unsupported Palette Type"
-                raise SyntaxError(msg)
-
-            offset = offset + palette_length
-            self.palette = ImagePalette.raw("RGB;L", self.fp.read(palette_length))
-            if self.mode == "L":
-                self._mode = "P"
-                rawmode = rawmode.replace("L", "P")
-
-        # 16 bit boundaries on stride
-        stride = ((self.size[0] * depth + 15) // 16) * 2
-
-        # file type: Type is the version (or flavor) of the bitmap
-        # file. The following values are typically found in the Type
-        # field:
-        # 0000h Old
-        # 0001h Standard
-        # 0002h Byte-encoded
-        # 0003h RGB format
-        # 0004h TIFF format
-        # 0005h IFF format
-        # FFFFh Experimental
-
-        # Old and standard are the same, except for the length tag.
-        # byte-encoded is run-length-encoded
-        # RGB looks similar to standard, but RGB byte order
-        # TIFF and IFF mean that they were converted from T/IFF
-        # Experimental means that it's something else.
-        # (https://www.fileformat.info/format/sunraster/egff.htm)
-
-        if file_type in (0, 1, 3, 4, 5):
-            self.tile = [("raw", (0, 0) + self.size, offset, (rawmode, stride))]
-        elif file_type == 2:
-            self.tile = [("sun_rle", (0, 0) + self.size, offset, rawmode)]
-        else:
-            msg = "Unsupported Sun Raster file type"
-            raise SyntaxError(msg)
-
-
-#
-# registry
-
-
-Image.register_open(SunImageFile.format, SunImageFile, _accept)
-
-Image.register_extension(SunImageFile.format, ".ras")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_core/_streams.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_core/_streams.py
deleted file mode 100644
index 54ea2b2bafd321a4f88dfa6fd19993213eec8105..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_core/_streams.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import math
-from typing import Any, TypeVar, overload
-
-from ..streams.memory import (
-    MemoryObjectReceiveStream,
-    MemoryObjectSendStream,
-    MemoryObjectStreamState,
-)
-
-T_Item = TypeVar("T_Item")
-
-
-@overload
-def create_memory_object_stream(
-    max_buffer_size: float = ...,
-) -> tuple[MemoryObjectSendStream[Any], MemoryObjectReceiveStream[Any]]:
-    ...
-
-
-@overload
-def create_memory_object_stream(
-    max_buffer_size: float = ..., item_type: type[T_Item] = ...
-) -> tuple[MemoryObjectSendStream[T_Item], MemoryObjectReceiveStream[T_Item]]:
-    ...
-
-
-def create_memory_object_stream(
-    max_buffer_size: float = 0, item_type: type[T_Item] | None = None
-) -> tuple[MemoryObjectSendStream[Any], MemoryObjectReceiveStream[Any]]:
-    """
-    Create a memory object stream.
-
-    :param max_buffer_size: number of items held in the buffer until ``send()`` starts blocking
-    :param item_type: type of item, for marking the streams with the right generic type for
-        static typing (not used at run time)
-    :return: a tuple of (send stream, receive stream)
-
-    """
-    if max_buffer_size != math.inf and not isinstance(max_buffer_size, int):
-        raise ValueError("max_buffer_size must be either an integer or math.inf")
-    if max_buffer_size < 0:
-        raise ValueError("max_buffer_size cannot be negative")
-
-    state: MemoryObjectStreamState = MemoryObjectStreamState(max_buffer_size)
-    return MemoryObjectSendStream(state), MemoryObjectReceiveStream(state)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/charset_normalizer/cd.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/charset_normalizer/cd.py
deleted file mode 100644
index 4ea6760c45bce5773bfe4b46d7b3c07c2c139d49..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/charset_normalizer/cd.py
+++ /dev/null
@@ -1,395 +0,0 @@
-import importlib
-from codecs import IncrementalDecoder
-from collections import Counter
-from functools import lru_cache
-from typing import Counter as TypeCounter, Dict, List, Optional, Tuple
-
-from .constant import (
-    FREQUENCIES,
-    KO_NAMES,
-    LANGUAGE_SUPPORTED_COUNT,
-    TOO_SMALL_SEQUENCE,
-    ZH_NAMES,
-)
-from .md import is_suspiciously_successive_range
-from .models import CoherenceMatches
-from .utils import (
-    is_accentuated,
-    is_latin,
-    is_multi_byte_encoding,
-    is_unicode_range_secondary,
-    unicode_range,
-)
-
-
-def encoding_unicode_range(iana_name: str) -> List[str]:
-    """
-    Return associated unicode ranges in a single byte code page.
-    """
-    if is_multi_byte_encoding(iana_name):
-        raise IOError("Function not supported on multi-byte code page")
-
-    decoder = importlib.import_module(
-        "encodings.{}".format(iana_name)
-    ).IncrementalDecoder
-
-    p: IncrementalDecoder = decoder(errors="ignore")
-    seen_ranges: Dict[str, int] = {}
-    character_count: int = 0
-
-    for i in range(0x40, 0xFF):
-        chunk: str = p.decode(bytes([i]))
-
-        if chunk:
-            character_range: Optional[str] = unicode_range(chunk)
-
-            if character_range is None:
-                continue
-
-            if is_unicode_range_secondary(character_range) is False:
-                if character_range not in seen_ranges:
-                    seen_ranges[character_range] = 0
-                seen_ranges[character_range] += 1
-                character_count += 1
-
-    return sorted(
-        [
-            character_range
-            for character_range in seen_ranges
-            if seen_ranges[character_range] / character_count >= 0.15
-        ]
-    )
-
-
-def unicode_range_languages(primary_range: str) -> List[str]:
-    """
-    Return inferred languages used with a unicode range.
-    """
-    languages: List[str] = []
-
-    for language, characters in FREQUENCIES.items():
-        for character in characters:
-            if unicode_range(character) == primary_range:
-                languages.append(language)
-                break
-
-    return languages
-
-
-@lru_cache()
-def encoding_languages(iana_name: str) -> List[str]:
-    """
-    Single-byte encoding language association. Some code page are heavily linked to particular language(s).
-    This function does the correspondence.
-    """
-    unicode_ranges: List[str] = encoding_unicode_range(iana_name)
-    primary_range: Optional[str] = None
-
-    for specified_range in unicode_ranges:
-        if "Latin" not in specified_range:
-            primary_range = specified_range
-            break
-
-    if primary_range is None:
-        return ["Latin Based"]
-
-    return unicode_range_languages(primary_range)
-
-
-@lru_cache()
-def mb_encoding_languages(iana_name: str) -> List[str]:
-    """
-    Multi-byte encoding language association. Some code page are heavily linked to particular language(s).
-    This function does the correspondence.
-    """
-    if (
-        iana_name.startswith("shift_")
-        or iana_name.startswith("iso2022_jp")
-        or iana_name.startswith("euc_j")
-        or iana_name == "cp932"
-    ):
-        return ["Japanese"]
-    if iana_name.startswith("gb") or iana_name in ZH_NAMES:
-        return ["Chinese"]
-    if iana_name.startswith("iso2022_kr") or iana_name in KO_NAMES:
-        return ["Korean"]
-
-    return []
-
-
-@lru_cache(maxsize=LANGUAGE_SUPPORTED_COUNT)
-def get_target_features(language: str) -> Tuple[bool, bool]:
-    """
-    Determine main aspects from a supported language if it contains accents and if is pure Latin.
-    """
-    target_have_accents: bool = False
-    target_pure_latin: bool = True
-
-    for character in FREQUENCIES[language]:
-        if not target_have_accents and is_accentuated(character):
-            target_have_accents = True
-        if target_pure_latin and is_latin(character) is False:
-            target_pure_latin = False
-
-    return target_have_accents, target_pure_latin
-
-
-def alphabet_languages(
-    characters: List[str], ignore_non_latin: bool = False
-) -> List[str]:
-    """
-    Return associated languages associated to given characters.
-    """
-    languages: List[Tuple[str, float]] = []
-
-    source_have_accents = any(is_accentuated(character) for character in characters)
-
-    for language, language_characters in FREQUENCIES.items():
-        target_have_accents, target_pure_latin = get_target_features(language)
-
-        if ignore_non_latin and target_pure_latin is False:
-            continue
-
-        if target_have_accents is False and source_have_accents:
-            continue
-
-        character_count: int = len(language_characters)
-
-        character_match_count: int = len(
-            [c for c in language_characters if c in characters]
-        )
-
-        ratio: float = character_match_count / character_count
-
-        if ratio >= 0.2:
-            languages.append((language, ratio))
-
-    languages = sorted(languages, key=lambda x: x[1], reverse=True)
-
-    return [compatible_language[0] for compatible_language in languages]
-
-
-def characters_popularity_compare(
-    language: str, ordered_characters: List[str]
-) -> float:
-    """
-    Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language.
-    The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit).
-    Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.)
-    """
-    if language not in FREQUENCIES:
-        raise ValueError("{} not available".format(language))
-
-    character_approved_count: int = 0
-    FREQUENCIES_language_set = set(FREQUENCIES[language])
-
-    ordered_characters_count: int = len(ordered_characters)
-    target_language_characters_count: int = len(FREQUENCIES[language])
-
-    large_alphabet: bool = target_language_characters_count > 26
-
-    for character, character_rank in zip(
-        ordered_characters, range(0, ordered_characters_count)
-    ):
-        if character not in FREQUENCIES_language_set:
-            continue
-
-        character_rank_in_language: int = FREQUENCIES[language].index(character)
-        expected_projection_ratio: float = (
-            target_language_characters_count / ordered_characters_count
-        )
-        character_rank_projection: int = int(character_rank * expected_projection_ratio)
-
-        if (
-            large_alphabet is False
-            and abs(character_rank_projection - character_rank_in_language) > 4
-        ):
-            continue
-
-        if (
-            large_alphabet is True
-            and abs(character_rank_projection - character_rank_in_language)
-            < target_language_characters_count / 3
-        ):
-            character_approved_count += 1
-            continue
-
-        characters_before_source: List[str] = FREQUENCIES[language][
-            0:character_rank_in_language
-        ]
-        characters_after_source: List[str] = FREQUENCIES[language][
-            character_rank_in_language:
-        ]
-        characters_before: List[str] = ordered_characters[0:character_rank]
-        characters_after: List[str] = ordered_characters[character_rank:]
-
-        before_match_count: int = len(
-            set(characters_before) & set(characters_before_source)
-        )
-
-        after_match_count: int = len(
-            set(characters_after) & set(characters_after_source)
-        )
-
-        if len(characters_before_source) == 0 and before_match_count <= 4:
-            character_approved_count += 1
-            continue
-
-        if len(characters_after_source) == 0 and after_match_count <= 4:
-            character_approved_count += 1
-            continue
-
-        if (
-            before_match_count / len(characters_before_source) >= 0.4
-            or after_match_count / len(characters_after_source) >= 0.4
-        ):
-            character_approved_count += 1
-            continue
-
-    return character_approved_count / len(ordered_characters)
-
-
-def alpha_unicode_split(decoded_sequence: str) -> List[str]:
-    """
-    Given a decoded text sequence, return a list of str. Unicode range / alphabet separation.
-    Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list;
-    One containing the latin letters and the other hebrew.
-    """
-    layers: Dict[str, str] = {}
-
-    for character in decoded_sequence:
-        if character.isalpha() is False:
-            continue
-
-        character_range: Optional[str] = unicode_range(character)
-
-        if character_range is None:
-            continue
-
-        layer_target_range: Optional[str] = None
-
-        for discovered_range in layers:
-            if (
-                is_suspiciously_successive_range(discovered_range, character_range)
-                is False
-            ):
-                layer_target_range = discovered_range
-                break
-
-        if layer_target_range is None:
-            layer_target_range = character_range
-
-        if layer_target_range not in layers:
-            layers[layer_target_range] = character.lower()
-            continue
-
-        layers[layer_target_range] += character.lower()
-
-    return list(layers.values())
-
-
-def merge_coherence_ratios(results: List[CoherenceMatches]) -> CoherenceMatches:
-    """
-    This function merge results previously given by the function coherence_ratio.
-    The return type is the same as coherence_ratio.
-    """
-    per_language_ratios: Dict[str, List[float]] = {}
-    for result in results:
-        for sub_result in result:
-            language, ratio = sub_result
-            if language not in per_language_ratios:
-                per_language_ratios[language] = [ratio]
-                continue
-            per_language_ratios[language].append(ratio)
-
-    merge = [
-        (
-            language,
-            round(
-                sum(per_language_ratios[language]) / len(per_language_ratios[language]),
-                4,
-            ),
-        )
-        for language in per_language_ratios
-    ]
-
-    return sorted(merge, key=lambda x: x[1], reverse=True)
-
-
-def filter_alt_coherence_matches(results: CoherenceMatches) -> CoherenceMatches:
-    """
-    We shall NOT return "English—" in CoherenceMatches because it is an alternative
-    of "English". This function only keeps the best match and remove the em-dash in it.
-    """
-    index_results: Dict[str, List[float]] = dict()
-
-    for result in results:
-        language, ratio = result
-        no_em_name: str = language.replace("—", "")
-
-        if no_em_name not in index_results:
-            index_results[no_em_name] = []
-
-        index_results[no_em_name].append(ratio)
-
-    if any(len(index_results[e]) > 1 for e in index_results):
-        filtered_results: CoherenceMatches = []
-
-        for language in index_results:
-            filtered_results.append((language, max(index_results[language])))
-
-        return filtered_results
-
-    return results
-
-
-@lru_cache(maxsize=2048)
-def coherence_ratio(
-    decoded_sequence: str, threshold: float = 0.1, lg_inclusion: Optional[str] = None
-) -> CoherenceMatches:
-    """
-    Detect ANY language that can be identified in given sequence. The sequence will be analysed by layers.
-    A layer = Character extraction by alphabets/ranges.
-    """
-
-    results: List[Tuple[str, float]] = []
-    ignore_non_latin: bool = False
-
-    sufficient_match_count: int = 0
-
-    lg_inclusion_list = lg_inclusion.split(",") if lg_inclusion is not None else []
-    if "Latin Based" in lg_inclusion_list:
-        ignore_non_latin = True
-        lg_inclusion_list.remove("Latin Based")
-
-    for layer in alpha_unicode_split(decoded_sequence):
-        sequence_frequencies: TypeCounter[str] = Counter(layer)
-        most_common = sequence_frequencies.most_common()
-
-        character_count: int = sum(o for c, o in most_common)
-
-        if character_count <= TOO_SMALL_SEQUENCE:
-            continue
-
-        popular_character_ordered: List[str] = [c for c, o in most_common]
-
-        for language in lg_inclusion_list or alphabet_languages(
-            popular_character_ordered, ignore_non_latin
-        ):
-            ratio: float = characters_popularity_compare(
-                language, popular_character_ordered
-            )
-
-            if ratio < threshold:
-                continue
-            elif ratio >= 0.8:
-                sufficient_match_count += 1
-
-            results.append((language, round(ratio, 4)))
-
-            if sufficient_match_count >= 3:
-                break
-
-    return sorted(
-        filter_alt_coherence_matches(results), key=lambda x: x[1], reverse=True
-    )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Example-c3701be8.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Example-c3701be8.js
deleted file mode 100644
index 81f4609910469cbfd42dc4672aaea303df9f128d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Example-c3701be8.js
+++ /dev/null
@@ -1,2 +0,0 @@
-const{SvelteComponent:c,append:u,attr:d,detach:g,element:o,init:v,insert:r,noop:f,safe_not_equal:y,set_data:m,text:b,toggle_class:i}=window.__gradio__svelte__internal;function h(a){let e,n;return{c(){e=o("div"),n=b(a[0]),d(e,"class","svelte-1ayixqk"),i(e,"table",a[1]==="table"),i(e,"gallery",a[1]==="gallery"),i(e,"selected",a[2])},m(t,l){r(t,e,l),u(e,n)},p(t,[l]){l&1&&m(n,t[0]),l&2&&i(e,"table",t[1]==="table"),l&2&&i(e,"gallery",t[1]==="gallery"),l&4&&i(e,"selected",t[2])},i:f,o:f,d(t){t&&g(e)}}}function q(a,e,n){let{value:t}=e,{type:l}=e,{selected:_=!1}=e;return a.$$set=s=>{"value"in s&&n(0,t=s.value),"type"in s&&n(1,l=s.type),"selected"in s&&n(2,_=s.selected)},[t,l,_]}class w extends c{constructor(e){super(),v(this,e,q,h,y,{value:0,type:1,selected:2})}}export{w as default};
-//# sourceMappingURL=Example-c3701be8.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index.svelte_svelte_type_style_lang-8ef5c92a.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index.svelte_svelte_type_style_lang-8ef5c92a.js
deleted file mode 100644
index 1ef8cf20f28293cba3a6909318cec9f9665ebccc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index.svelte_svelte_type_style_lang-8ef5c92a.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import"./Button-89057c03.js";import{c as h}from"./utils-c3e3db58.js";import{M as v}from"./Example.svelte_svelte_type_style_lang-49787a8b.js";const{SvelteComponent:g,action_destroyer:w,attr:d,create_component:k,destroy_component:b,detach:z,element:M,init:j,insert:C,mount_component:q,safe_not_equal:y,toggle_class:c,transition_in:D,transition_out:E}=window.__gradio__svelte__internal,{createEventDispatcher:I}=window.__gradio__svelte__internal;function S(n){let e,i,r,m,a,o,f;return i=new v({props:{message:n[2],latex_delimiters:n[7],sanitize_html:n[5],line_breaks:n[6],chatbot:!1}}),{c(){e=M("div"),k(i.$$.fragment),d(e,"class",r="prose "+n[0].join(" ")+" svelte-aww9vd"),d(e,"data-testid","markdown"),d(e,"dir",m=n[4]?"rtl":"ltr"),c(e,"min",n[3]),c(e,"hide",!n[1])},m(t,s){C(t,e,s),q(i,e,null),a=!0,o||(f=w(h.call(null,e)),o=!0)},p(t,[s]){const _={};s&4&&(_.message=t[2]),s&128&&(_.latex_delimiters=t[7]),s&32&&(_.sanitize_html=t[5]),s&64&&(_.line_breaks=t[6]),i.$set(_),(!a||s&1&&r!==(r="prose "+t[0].join(" ")+" svelte-aww9vd"))&&d(e,"class",r),(!a||s&16&&m!==(m=t[4]?"rtl":"ltr"))&&d(e,"dir",m),(!a||s&9)&&c(e,"min",t[3]),(!a||s&3)&&c(e,"hide",!t[1])},i(t){a||(D(i.$$.fragment,t),a=!0)},o(t){E(i.$$.fragment,t),a=!1},d(t){t&&z(e),b(i),o=!1,f()}}}function A(n,e,i){let{elem_classes:r=[]}=e,{visible:m=!0}=e,{value:a}=e,{min_height:o=!1}=e,{rtl:f=!1}=e,{sanitize_html:t=!0}=e,{line_breaks:s=!1}=e,{latex_delimiters:_}=e;const u=I();return n.$$set=l=>{"elem_classes"in l&&i(0,r=l.elem_classes),"visible"in l&&i(1,m=l.visible),"value"in l&&i(2,a=l.value),"min_height"in l&&i(3,o=l.min_height),"rtl"in l&&i(4,f=l.rtl),"sanitize_html"in l&&i(5,t=l.sanitize_html),"line_breaks"in l&&i(6,s=l.line_breaks),"latex_delimiters"in l&&i(7,_=l.latex_delimiters)},n.$$.update=()=>{n.$$.dirty&4&&u("change")},[r,m,a,o,f,t,s,_]}class B extends g{constructor(e){super(),j(this,e,A,S,y,{elem_classes:0,visible:1,value:2,min_height:3,rtl:4,sanitize_html:5,line_breaks:6,latex_delimiters:7})}}const J=B;export{J as M};
-//# sourceMappingURL=Index.svelte_svelte_type_style_lang-8ef5c92a.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_return_integer.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_return_integer.py
deleted file mode 100644
index 3b2f42e2bff633fb728ac18d4f4de33de82cd5b2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_return_integer.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import pytest
-
-from numpy import array
-from . import util
-
-
-class TestReturnInteger(util.F2PyTest):
-    def check_function(self, t, tname):
-        assert t(123) == 123
-        assert t(123.6) == 123
-        assert t("123") == 123
-        assert t(-123) == -123
-        assert t([123]) == 123
-        assert t((123, )) == 123
-        assert t(array(123)) == 123
-        assert t(array(123, "b")) == 123
-        assert t(array(123, "h")) == 123
-        assert t(array(123, "i")) == 123
-        assert t(array(123, "l")) == 123
-        assert t(array(123, "B")) == 123
-        assert t(array(123, "f")) == 123
-        assert t(array(123, "d")) == 123
-
-        # pytest.raises(ValueError, t, array([123],'S3'))
-        pytest.raises(ValueError, t, "abc")
-
-        pytest.raises(IndexError, t, [])
-        pytest.raises(IndexError, t, ())
-
-        pytest.raises(Exception, t, t)
-        pytest.raises(Exception, t, {})
-
-        if tname in ["t8", "s8"]:
-            pytest.raises(OverflowError, t, 100000000000000000000000)
-            pytest.raises(OverflowError, t, 10000000011111111111111.23)
-
-
-class TestFReturnInteger(TestReturnInteger):
-    sources = [
-        util.getpath("tests", "src", "return_integer", "foo77.f"),
-        util.getpath("tests", "src", "return_integer", "foo90.f90"),
-    ]
-
-    @pytest.mark.parametrize("name",
-                             "t0,t1,t2,t4,t8,s0,s1,s2,s4,s8".split(","))
-    def test_all_f77(self, name):
-        self.check_function(getattr(self.module, name), name)
-
-    @pytest.mark.parametrize("name",
-                             "t0,t1,t2,t4,t8,s0,s1,s2,s4,s8".split(","))
-    def test_all_f90(self, name):
-        self.check_function(getattr(self.module.f90_return_integer, name),
-                            name)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/testutils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/testutils.py
deleted file mode 100644
index 7a633906bb4245261d71e9e783e188f3d44b7790..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/ma/testutils.py
+++ /dev/null
@@ -1,288 +0,0 @@
-"""Miscellaneous functions for testing masked arrays and subclasses
-
-:author: Pierre Gerard-Marchant
-:contact: pierregm_at_uga_dot_edu
-:version: $Id: testutils.py 3529 2007-11-13 08:01:14Z jarrod.millman $
-
-"""
-import operator
-
-import numpy as np
-from numpy import ndarray, float_
-import numpy.core.umath as umath
-import numpy.testing
-from numpy.testing import (
-    assert_, assert_allclose, assert_array_almost_equal_nulp,
-    assert_raises, build_err_msg
-    )
-from .core import mask_or, getmask, masked_array, nomask, masked, filled
-
-__all__masked = [
-    'almost', 'approx', 'assert_almost_equal', 'assert_array_almost_equal',
-    'assert_array_approx_equal', 'assert_array_compare',
-    'assert_array_equal', 'assert_array_less', 'assert_close',
-    'assert_equal', 'assert_equal_records', 'assert_mask_equal',
-    'assert_not_equal', 'fail_if_array_equal',
-    ]
-
-# Include some normal test functions to avoid breaking other projects who
-# have mistakenly included them from this file. SciPy is one. That is
-# unfortunate, as some of these functions are not intended to work with
-# masked arrays. But there was no way to tell before.
-from unittest import TestCase
-__some__from_testing = [
-    'TestCase', 'assert_', 'assert_allclose', 'assert_array_almost_equal_nulp',
-    'assert_raises'
-    ]
-
-__all__ = __all__masked + __some__from_testing
-
-
-def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8):
-    """
-    Returns true if all components of a and b are equal to given tolerances.
-
-    If fill_value is True, masked values considered equal. Otherwise,
-    masked values are considered unequal.  The relative error rtol should
-    be positive and << 1.0 The absolute error atol comes into play for
-    those elements of b that are very small or zero; it says how small a
-    must be also.
-
-    """
-    m = mask_or(getmask(a), getmask(b))
-    d1 = filled(a)
-    d2 = filled(b)
-    if d1.dtype.char == "O" or d2.dtype.char == "O":
-        return np.equal(d1, d2).ravel()
-    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
-    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
-    d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y))
-    return d.ravel()
-
-
-def almost(a, b, decimal=6, fill_value=True):
-    """
-    Returns True if a and b are equal up to decimal places.
-
-    If fill_value is True, masked values considered equal. Otherwise,
-    masked values are considered unequal.
-
-    """
-    m = mask_or(getmask(a), getmask(b))
-    d1 = filled(a)
-    d2 = filled(b)
-    if d1.dtype.char == "O" or d2.dtype.char == "O":
-        return np.equal(d1, d2).ravel()
-    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
-    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
-    d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
-    return d.ravel()
-
-
-def _assert_equal_on_sequences(actual, desired, err_msg=''):
-    """
-    Asserts the equality of two non-array sequences.
-
-    """
-    assert_equal(len(actual), len(desired), err_msg)
-    for k in range(len(desired)):
-        assert_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}')
-    return
-
-
-def assert_equal_records(a, b):
-    """
-    Asserts that two records are equal.
-
-    Pretty crude for now.
-
-    """
-    assert_equal(a.dtype, b.dtype)
-    for f in a.dtype.names:
-        (af, bf) = (operator.getitem(a, f), operator.getitem(b, f))
-        if not (af is masked) and not (bf is masked):
-            assert_equal(operator.getitem(a, f), operator.getitem(b, f))
-    return
-
-
-def assert_equal(actual, desired, err_msg=''):
-    """
-    Asserts that two items are equal.
-
-    """
-    # Case #1: dictionary .....
-    if isinstance(desired, dict):
-        if not isinstance(actual, dict):
-            raise AssertionError(repr(type(actual)))
-        assert_equal(len(actual), len(desired), err_msg)
-        for k, i in desired.items():
-            if k not in actual:
-                raise AssertionError(f"{k} not in {actual}")
-            assert_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}')
-        return
-    # Case #2: lists .....
-    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
-        return _assert_equal_on_sequences(actual, desired, err_msg='')
-    if not (isinstance(actual, ndarray) or isinstance(desired, ndarray)):
-        msg = build_err_msg([actual, desired], err_msg,)
-        if not desired == actual:
-            raise AssertionError(msg)
-        return
-    # Case #4. arrays or equivalent
-    if ((actual is masked) and not (desired is masked)) or \
-            ((desired is masked) and not (actual is masked)):
-        msg = build_err_msg([actual, desired],
-                            err_msg, header='', names=('x', 'y'))
-        raise ValueError(msg)
-    actual = np.asanyarray(actual)
-    desired = np.asanyarray(desired)
-    (actual_dtype, desired_dtype) = (actual.dtype, desired.dtype)
-    if actual_dtype.char == "S" and desired_dtype.char == "S":
-        return _assert_equal_on_sequences(actual.tolist(),
-                                          desired.tolist(),
-                                          err_msg='')
-    return assert_array_equal(actual, desired, err_msg)
-
-
-def fail_if_equal(actual, desired, err_msg='',):
-    """
-    Raises an assertion error if two items are equal.
-
-    """
-    if isinstance(desired, dict):
-        if not isinstance(actual, dict):
-            raise AssertionError(repr(type(actual)))
-        fail_if_equal(len(actual), len(desired), err_msg)
-        for k, i in desired.items():
-            if k not in actual:
-                raise AssertionError(repr(k))
-            fail_if_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}')
-        return
-    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
-        fail_if_equal(len(actual), len(desired), err_msg)
-        for k in range(len(desired)):
-            fail_if_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}')
-        return
-    if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
-        return fail_if_array_equal(actual, desired, err_msg)
-    msg = build_err_msg([actual, desired], err_msg)
-    if not desired != actual:
-        raise AssertionError(msg)
-
-
-assert_not_equal = fail_if_equal
-
-
-def assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True):
-    """
-    Asserts that two items are almost equal.
-
-    The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal).
-
-    """
-    if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
-        return assert_array_almost_equal(actual, desired, decimal=decimal,
-                                         err_msg=err_msg, verbose=verbose)
-    msg = build_err_msg([actual, desired],
-                        err_msg=err_msg, verbose=verbose)
-    if not round(abs(desired - actual), decimal) == 0:
-        raise AssertionError(msg)
-
-
-assert_close = assert_almost_equal
-
-
-def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
-                         fill_value=True):
-    """
-    Asserts that comparison between two masked arrays is satisfied.
-
-    The comparison is elementwise.
-
-    """
-    # Allocate a common mask and refill
-    m = mask_or(getmask(x), getmask(y))
-    x = masked_array(x, copy=False, mask=m, keep_mask=False, subok=False)
-    y = masked_array(y, copy=False, mask=m, keep_mask=False, subok=False)
-    if ((x is masked) and not (y is masked)) or \
-            ((y is masked) and not (x is masked)):
-        msg = build_err_msg([x, y], err_msg=err_msg, verbose=verbose,
-                            header=header, names=('x', 'y'))
-        raise ValueError(msg)
-    # OK, now run the basic tests on filled versions
-    return np.testing.assert_array_compare(comparison,
-                                           x.filled(fill_value),
-                                           y.filled(fill_value),
-                                           err_msg=err_msg,
-                                           verbose=verbose, header=header)
-
-
-def assert_array_equal(x, y, err_msg='', verbose=True):
-    """
-    Checks the elementwise equality of two masked arrays.
-
-    """
-    assert_array_compare(operator.__eq__, x, y,
-                         err_msg=err_msg, verbose=verbose,
-                         header='Arrays are not equal')
-
-
-def fail_if_array_equal(x, y, err_msg='', verbose=True):
-    """
-    Raises an assertion error if two masked arrays are not equal elementwise.
-
-    """
-    def compare(x, y):
-        return (not np.all(approx(x, y)))
-    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
-                         header='Arrays are not equal')
-
-
-def assert_array_approx_equal(x, y, decimal=6, err_msg='', verbose=True):
-    """
-    Checks the equality of two masked arrays, up to given number odecimals.
-
-    The equality is checked elementwise.
-
-    """
-    def compare(x, y):
-        "Returns the result of the loose comparison between x and y)."
-        return approx(x, y, rtol=10. ** -decimal)
-    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
-                         header='Arrays are not almost equal')
-
-
-def assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True):
-    """
-    Checks the equality of two masked arrays, up to given number odecimals.
-
-    The equality is checked elementwise.
-
-    """
-    def compare(x, y):
-        "Returns the result of the loose comparison between x and y)."
-        return almost(x, y, decimal)
-    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
-                         header='Arrays are not almost equal')
-
-
-def assert_array_less(x, y, err_msg='', verbose=True):
-    """
-    Checks that x is smaller than y elementwise.
-
-    """
-    assert_array_compare(operator.__lt__, x, y,
-                         err_msg=err_msg, verbose=verbose,
-                         header='Arrays are not less-ordered')
-
-
-def assert_mask_equal(m1, m2, err_msg=''):
-    """
-    Asserts the equality of two masks.
-
-    """
-    if m1 is nomask:
-        assert_(m2 is nomask)
-    if m2 is nomask:
-        assert_(m1 is nomask)
-    assert_array_equal(m1, m2, err_msg=err_msg)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/reshape/merge/test_merge_cross.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/reshape/merge/test_merge_cross.py
deleted file mode 100644
index 14f9036e43fce13580916dacfd6409e4639d025e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/reshape/merge/test_merge_cross.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import pytest
-
-from pandas import (
-    DataFrame,
-    Series,
-)
-import pandas._testing as tm
-from pandas.core.reshape.merge import (
-    MergeError,
-    merge,
-)
-
-
-@pytest.mark.parametrize(
-    ("input_col", "output_cols"), [("b", ["a", "b"]), ("a", ["a_x", "a_y"])]
-)
-def test_merge_cross(input_col, output_cols):
-    # GH#5401
-    left = DataFrame({"a": [1, 3]})
-    right = DataFrame({input_col: [3, 4]})
-    left_copy = left.copy()
-    right_copy = right.copy()
-    result = merge(left, right, how="cross")
-    expected = DataFrame({output_cols[0]: [1, 1, 3, 3], output_cols[1]: [3, 4, 3, 4]})
-    tm.assert_frame_equal(result, expected)
-    tm.assert_frame_equal(left, left_copy)
-    tm.assert_frame_equal(right, right_copy)
-
-
-@pytest.mark.parametrize(
-    "kwargs",
-    [
-        {"left_index": True},
-        {"right_index": True},
-        {"on": "a"},
-        {"left_on": "a"},
-        {"right_on": "b"},
-    ],
-)
-def test_merge_cross_error_reporting(kwargs):
-    # GH#5401
-    left = DataFrame({"a": [1, 3]})
-    right = DataFrame({"b": [3, 4]})
-    msg = (
-        "Can not pass on, right_on, left_on or set right_index=True or "
-        "left_index=True"
-    )
-    with pytest.raises(MergeError, match=msg):
-        merge(left, right, how="cross", **kwargs)
-
-
-def test_merge_cross_mixed_dtypes():
-    # GH#5401
-    left = DataFrame(["a", "b", "c"], columns=["A"])
-    right = DataFrame(range(2), columns=["B"])
-    result = merge(left, right, how="cross")
-    expected = DataFrame({"A": ["a", "a", "b", "b", "c", "c"], "B": [0, 1, 0, 1, 0, 1]})
-    tm.assert_frame_equal(result, expected)
-
-
-def test_merge_cross_more_than_one_column():
-    # GH#5401
-    left = DataFrame({"A": list("ab"), "B": [2, 1]})
-    right = DataFrame({"C": range(2), "D": range(4, 6)})
-    result = merge(left, right, how="cross")
-    expected = DataFrame(
-        {
-            "A": ["a", "a", "b", "b"],
-            "B": [2, 2, 1, 1],
-            "C": [0, 1, 0, 1],
-            "D": [4, 5, 4, 5],
-        }
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_merge_cross_null_values(nulls_fixture):
-    # GH#5401
-    left = DataFrame({"a": [1, nulls_fixture]})
-    right = DataFrame({"b": ["a", "b"], "c": [1.0, 2.0]})
-    result = merge(left, right, how="cross")
-    expected = DataFrame(
-        {
-            "a": [1, 1, nulls_fixture, nulls_fixture],
-            "b": ["a", "b", "a", "b"],
-            "c": [1.0, 2.0, 1.0, 2.0],
-        }
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_join_cross_error_reporting():
-    # GH#5401
-    left = DataFrame({"a": [1, 3]})
-    right = DataFrame({"a": [3, 4]})
-    msg = (
-        "Can not pass on, right_on, left_on or set right_index=True or "
-        "left_index=True"
-    )
-    with pytest.raises(MergeError, match=msg):
-        left.join(right, how="cross", on="a")
-
-
-def test_merge_cross_series():
-    # GH#54055
-    ls = Series([1, 2, 3, 4], index=[1, 2, 3, 4], name="left")
-    rs = Series([3, 4, 5, 6], index=[3, 4, 5, 6], name="right")
-    res = merge(ls, rs, how="cross")
-
-    expected = merge(ls.to_frame(), rs.to_frame(), how="cross")
-    tm.assert_frame_equal(res, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/escsm.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/escsm.py
deleted file mode 100644
index 0069523a04969dd920ea4b43a497162157729174..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/escsm.py
+++ /dev/null
@@ -1,246 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .enums import MachineState
-
-HZ_CLS = (
-1,0,0,0,0,0,0,0,  # 00 - 07
-0,0,0,0,0,0,0,0,  # 08 - 0f
-0,0,0,0,0,0,0,0,  # 10 - 17
-0,0,0,1,0,0,0,0,  # 18 - 1f
-0,0,0,0,0,0,0,0,  # 20 - 27
-0,0,0,0,0,0,0,0,  # 28 - 2f
-0,0,0,0,0,0,0,0,  # 30 - 37
-0,0,0,0,0,0,0,0,  # 38 - 3f
-0,0,0,0,0,0,0,0,  # 40 - 47
-0,0,0,0,0,0,0,0,  # 48 - 4f
-0,0,0,0,0,0,0,0,  # 50 - 57
-0,0,0,0,0,0,0,0,  # 58 - 5f
-0,0,0,0,0,0,0,0,  # 60 - 67
-0,0,0,0,0,0,0,0,  # 68 - 6f
-0,0,0,0,0,0,0,0,  # 70 - 77
-0,0,0,4,0,5,2,0,  # 78 - 7f
-1,1,1,1,1,1,1,1,  # 80 - 87
-1,1,1,1,1,1,1,1,  # 88 - 8f
-1,1,1,1,1,1,1,1,  # 90 - 97
-1,1,1,1,1,1,1,1,  # 98 - 9f
-1,1,1,1,1,1,1,1,  # a0 - a7
-1,1,1,1,1,1,1,1,  # a8 - af
-1,1,1,1,1,1,1,1,  # b0 - b7
-1,1,1,1,1,1,1,1,  # b8 - bf
-1,1,1,1,1,1,1,1,  # c0 - c7
-1,1,1,1,1,1,1,1,  # c8 - cf
-1,1,1,1,1,1,1,1,  # d0 - d7
-1,1,1,1,1,1,1,1,  # d8 - df
-1,1,1,1,1,1,1,1,  # e0 - e7
-1,1,1,1,1,1,1,1,  # e8 - ef
-1,1,1,1,1,1,1,1,  # f0 - f7
-1,1,1,1,1,1,1,1,  # f8 - ff
-)
-
-HZ_ST = (
-MachineState.START,MachineState.ERROR,     3,MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,# 00-07
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,# 08-0f
-MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,     4,MachineState.ERROR,# 10-17
-     5,MachineState.ERROR,     6,MachineState.ERROR,     5,     5,     4,MachineState.ERROR,# 18-1f
-     4,MachineState.ERROR,     4,     4,     4,MachineState.ERROR,     4,MachineState.ERROR,# 20-27
-     4,MachineState.ITS_ME,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,# 28-2f
-)
-
-HZ_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0)
-
-HZ_SM_MODEL = {'class_table': HZ_CLS,
-               'class_factor': 6,
-               'state_table': HZ_ST,
-               'char_len_table': HZ_CHAR_LEN_TABLE,
-               'name': "HZ-GB-2312",
-               'language': 'Chinese'}
-
-ISO2022CN_CLS = (
-2,0,0,0,0,0,0,0,  # 00 - 07
-0,0,0,0,0,0,0,0,  # 08 - 0f
-0,0,0,0,0,0,0,0,  # 10 - 17
-0,0,0,1,0,0,0,0,  # 18 - 1f
-0,0,0,0,0,0,0,0,  # 20 - 27
-0,3,0,0,0,0,0,0,  # 28 - 2f
-0,0,0,0,0,0,0,0,  # 30 - 37
-0,0,0,0,0,0,0,0,  # 38 - 3f
-0,0,0,4,0,0,0,0,  # 40 - 47
-0,0,0,0,0,0,0,0,  # 48 - 4f
-0,0,0,0,0,0,0,0,  # 50 - 57
-0,0,0,0,0,0,0,0,  # 58 - 5f
-0,0,0,0,0,0,0,0,  # 60 - 67
-0,0,0,0,0,0,0,0,  # 68 - 6f
-0,0,0,0,0,0,0,0,  # 70 - 77
-0,0,0,0,0,0,0,0,  # 78 - 7f
-2,2,2,2,2,2,2,2,  # 80 - 87
-2,2,2,2,2,2,2,2,  # 88 - 8f
-2,2,2,2,2,2,2,2,  # 90 - 97
-2,2,2,2,2,2,2,2,  # 98 - 9f
-2,2,2,2,2,2,2,2,  # a0 - a7
-2,2,2,2,2,2,2,2,  # a8 - af
-2,2,2,2,2,2,2,2,  # b0 - b7
-2,2,2,2,2,2,2,2,  # b8 - bf
-2,2,2,2,2,2,2,2,  # c0 - c7
-2,2,2,2,2,2,2,2,  # c8 - cf
-2,2,2,2,2,2,2,2,  # d0 - d7
-2,2,2,2,2,2,2,2,  # d8 - df
-2,2,2,2,2,2,2,2,  # e0 - e7
-2,2,2,2,2,2,2,2,  # e8 - ef
-2,2,2,2,2,2,2,2,  # f0 - f7
-2,2,2,2,2,2,2,2,  # f8 - ff
-)
-
-ISO2022CN_ST = (
-MachineState.START,     3,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,# 00-07
-MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 08-0f
-MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,# 10-17
-MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     4,MachineState.ERROR,# 18-1f
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 20-27
-     5,     6,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 28-2f
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 30-37
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,MachineState.START,# 38-3f
-)
-
-ISO2022CN_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0, 0, 0, 0)
-
-ISO2022CN_SM_MODEL = {'class_table': ISO2022CN_CLS,
-                      'class_factor': 9,
-                      'state_table': ISO2022CN_ST,
-                      'char_len_table': ISO2022CN_CHAR_LEN_TABLE,
-                      'name': "ISO-2022-CN",
-                      'language': 'Chinese'}
-
-ISO2022JP_CLS = (
-2,0,0,0,0,0,0,0,  # 00 - 07
-0,0,0,0,0,0,2,2,  # 08 - 0f
-0,0,0,0,0,0,0,0,  # 10 - 17
-0,0,0,1,0,0,0,0,  # 18 - 1f
-0,0,0,0,7,0,0,0,  # 20 - 27
-3,0,0,0,0,0,0,0,  # 28 - 2f
-0,0,0,0,0,0,0,0,  # 30 - 37
-0,0,0,0,0,0,0,0,  # 38 - 3f
-6,0,4,0,8,0,0,0,  # 40 - 47
-0,9,5,0,0,0,0,0,  # 48 - 4f
-0,0,0,0,0,0,0,0,  # 50 - 57
-0,0,0,0,0,0,0,0,  # 58 - 5f
-0,0,0,0,0,0,0,0,  # 60 - 67
-0,0,0,0,0,0,0,0,  # 68 - 6f
-0,0,0,0,0,0,0,0,  # 70 - 77
-0,0,0,0,0,0,0,0,  # 78 - 7f
-2,2,2,2,2,2,2,2,  # 80 - 87
-2,2,2,2,2,2,2,2,  # 88 - 8f
-2,2,2,2,2,2,2,2,  # 90 - 97
-2,2,2,2,2,2,2,2,  # 98 - 9f
-2,2,2,2,2,2,2,2,  # a0 - a7
-2,2,2,2,2,2,2,2,  # a8 - af
-2,2,2,2,2,2,2,2,  # b0 - b7
-2,2,2,2,2,2,2,2,  # b8 - bf
-2,2,2,2,2,2,2,2,  # c0 - c7
-2,2,2,2,2,2,2,2,  # c8 - cf
-2,2,2,2,2,2,2,2,  # d0 - d7
-2,2,2,2,2,2,2,2,  # d8 - df
-2,2,2,2,2,2,2,2,  # e0 - e7
-2,2,2,2,2,2,2,2,  # e8 - ef
-2,2,2,2,2,2,2,2,  # f0 - f7
-2,2,2,2,2,2,2,2,  # f8 - ff
-)
-
-ISO2022JP_ST = (
-MachineState.START,     3,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,# 00-07
-MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 08-0f
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,# 10-17
-MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,# 18-1f
-MachineState.ERROR,     5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     4,MachineState.ERROR,MachineState.ERROR,# 20-27
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     6,MachineState.ITS_ME,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,# 28-2f
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,# 30-37
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 38-3f
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,MachineState.START,MachineState.START,# 40-47
-)
-
-ISO2022JP_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
-
-ISO2022JP_SM_MODEL = {'class_table': ISO2022JP_CLS,
-                      'class_factor': 10,
-                      'state_table': ISO2022JP_ST,
-                      'char_len_table': ISO2022JP_CHAR_LEN_TABLE,
-                      'name': "ISO-2022-JP",
-                      'language': 'Japanese'}
-
-ISO2022KR_CLS = (
-2,0,0,0,0,0,0,0,  # 00 - 07
-0,0,0,0,0,0,0,0,  # 08 - 0f
-0,0,0,0,0,0,0,0,  # 10 - 17
-0,0,0,1,0,0,0,0,  # 18 - 1f
-0,0,0,0,3,0,0,0,  # 20 - 27
-0,4,0,0,0,0,0,0,  # 28 - 2f
-0,0,0,0,0,0,0,0,  # 30 - 37
-0,0,0,0,0,0,0,0,  # 38 - 3f
-0,0,0,5,0,0,0,0,  # 40 - 47
-0,0,0,0,0,0,0,0,  # 48 - 4f
-0,0,0,0,0,0,0,0,  # 50 - 57
-0,0,0,0,0,0,0,0,  # 58 - 5f
-0,0,0,0,0,0,0,0,  # 60 - 67
-0,0,0,0,0,0,0,0,  # 68 - 6f
-0,0,0,0,0,0,0,0,  # 70 - 77
-0,0,0,0,0,0,0,0,  # 78 - 7f
-2,2,2,2,2,2,2,2,  # 80 - 87
-2,2,2,2,2,2,2,2,  # 88 - 8f
-2,2,2,2,2,2,2,2,  # 90 - 97
-2,2,2,2,2,2,2,2,  # 98 - 9f
-2,2,2,2,2,2,2,2,  # a0 - a7
-2,2,2,2,2,2,2,2,  # a8 - af
-2,2,2,2,2,2,2,2,  # b0 - b7
-2,2,2,2,2,2,2,2,  # b8 - bf
-2,2,2,2,2,2,2,2,  # c0 - c7
-2,2,2,2,2,2,2,2,  # c8 - cf
-2,2,2,2,2,2,2,2,  # d0 - d7
-2,2,2,2,2,2,2,2,  # d8 - df
-2,2,2,2,2,2,2,2,  # e0 - e7
-2,2,2,2,2,2,2,2,  # e8 - ef
-2,2,2,2,2,2,2,2,  # f0 - f7
-2,2,2,2,2,2,2,2,  # f8 - ff
-)
-
-ISO2022KR_ST = (
-MachineState.START,     3,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,# 00-07
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,# 08-0f
-MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     4,MachineState.ERROR,MachineState.ERROR,# 10-17
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,# 18-1f
-MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.START,MachineState.START,MachineState.START,MachineState.START,# 20-27
-)
-
-ISO2022KR_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0)
-
-ISO2022KR_SM_MODEL = {'class_table': ISO2022KR_CLS,
-                      'class_factor': 6,
-                      'state_table': ISO2022KR_ST,
-                      'char_len_table': ISO2022KR_CHAR_LEN_TABLE,
-                      'name': "ISO-2022-KR",
-                      'language': 'Korean'}
-
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/algebra.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/algebra.py
deleted file mode 100644
index dc4aedd2cdef2ce9472cbbb4f2f90123f730a6fa..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/algebra.py
+++ /dev/null
@@ -1,302 +0,0 @@
-"""
-    pygments.lexers.algebra
-    ~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for computer algebra systems.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import Lexer, RegexLexer, bygroups, do_insertions, words
-from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
-    Number, Punctuation, Generic, Whitespace
-
-__all__ = ['GAPLexer', 'GAPConsoleLexer', 'MathematicaLexer', 'MuPADLexer',
-           'BCLexer']
-
-
-class GAPLexer(RegexLexer):
-    """
-    For GAP source code.
-
-    .. versionadded:: 2.0
-    """
-    name = 'GAP'
-    url = 'http://www.gap-system.org'
-    aliases = ['gap']
-    filenames = ['*.g', '*.gd', '*.gi', '*.gap']
-
-    tokens = {
-        'root': [
-            (r'#.*$', Comment.Single),
-            (r'"(?:[^"\\]|\\.)*"', String),
-            (r'\(|\)|\[|\]|\{|\}', Punctuation),
-            (r'''(?x)\b(?:
-                if|then|elif|else|fi|
-                for|while|do|od|
-                repeat|until|
-                break|continue|
-                function|local|return|end|
-                rec|
-                quit|QUIT|
-                IsBound|Unbind|
-                TryNextMethod|
-                Info|Assert
-              )\b''', Keyword),
-            (r'''(?x)\b(?:
-                true|false|fail|infinity
-              )\b''',
-             Name.Constant),
-            (r'''(?x)\b(?:
-                (Declare|Install)([A-Z][A-Za-z]+)|
-                   BindGlobal|BIND_GLOBAL
-              )\b''',
-             Name.Builtin),
-            (r'\.|,|:=|;|=|\+|-|\*|/|\^|>|<', Operator),
-            (r'''(?x)\b(?:
-                and|or|not|mod|in
-              )\b''',
-             Operator.Word),
-            (r'''(?x)
-              (?:\w+|`[^`]*`)
-              (?:::\w+|`[^`]*`)*''', Name.Variable),
-            (r'[0-9]+(?:\.[0-9]*)?(?:e[0-9]+)?', Number),
-            (r'\.[0-9]+(?:e[0-9]+)?', Number),
-            (r'.', Text)
-        ],
-    }
-
-    def analyse_text(text):
-        score = 0.0
-
-        # Declaration part
-        if re.search(
-            r"(InstallTrueMethod|Declare(Attribute|Category|Filter|Operation" +
-            r"|GlobalFunction|Synonym|SynonymAttr|Property))", text
-        ):
-            score += 0.7
-
-        # Implementation part
-        if re.search(
-            r"(DeclareRepresentation|Install(GlobalFunction|Method|" +
-            r"ImmediateMethod|OtherMethod)|New(Family|Type)|Objectify)", text
-        ):
-            score += 0.7
-
-        return min(score, 1.0)
-
-
-class GAPConsoleLexer(Lexer):
-    """
-    For GAP console sessions. Modeled after JuliaConsoleLexer.
-
-    .. versionadded:: 2.14
-    """
-    name = 'GAP session'
-    aliases = ['gap-console', 'gap-repl']
-    filenames = ['*.tst']
-
-    def get_tokens_unprocessed(self, text):
-        gaplexer = GAPLexer(**self.options)
-        start = 0
-        curcode = ''
-        insertions = []
-        output = False
-        error = False
-
-        for line in text.splitlines(keepends=True):
-            if line.startswith('gap> ') or line.startswith('brk> '):
-                insertions.append((len(curcode), [(0, Generic.Prompt, line[:5])]))
-                curcode += line[5:]
-                output = False
-                error = False
-            elif not output and line.startswith('> '):
-                insertions.append((len(curcode), [(0, Generic.Prompt, line[:2])]))
-                curcode += line[2:]
-            else:
-                if curcode:
-                    yield from do_insertions(
-                        insertions, gaplexer.get_tokens_unprocessed(curcode))
-                    curcode = ''
-                    insertions = []
-                if line.startswith('Error, ') or error:
-                    yield start, Generic.Error, line
-                    error = True
-                else:
-                    yield start, Generic.Output, line
-                output = True
-            start += len(line)
-
-        if curcode:
-            yield from do_insertions(
-                insertions, gaplexer.get_tokens_unprocessed(curcode))
-
-    # the following is needed to distinguish Scilab and GAP .tst files
-    def analyse_text(text):
-        # GAP prompts are a dead give away, although hypothetical;y a
-        # file in another language could be trying to compare a variable
-        # "gap" as in "gap> 0.1". But that this should happen at the
-        # start of a line seems unlikely...
-        if re.search(r"^gap> ", text):
-            return 0.9
-        else:
-            return 0.0
-
-
-class MathematicaLexer(RegexLexer):
-    """
-    Lexer for Mathematica source code.
-
-    .. versionadded:: 2.0
-    """
-    name = 'Mathematica'
-    url = 'http://www.wolfram.com/mathematica/'
-    aliases = ['mathematica', 'mma', 'nb']
-    filenames = ['*.nb', '*.cdf', '*.nbp', '*.ma']
-    mimetypes = ['application/mathematica',
-                 'application/vnd.wolfram.mathematica',
-                 'application/vnd.wolfram.mathematica.package',
-                 'application/vnd.wolfram.cdf']
-
-    # http://reference.wolfram.com/mathematica/guide/Syntax.html
-    operators = (
-        ";;", "=", "=.", "!=" "==", ":=", "->", ":>", "/.", "+", "-", "*", "/",
-        "^", "&&", "||", "!", "<>", "|", "/;", "?", "@", "//", "/@", "@@",
-        "@@@", "~~", "===", "&", "<", ">", "<=", ">=",
-    )
-
-    punctuation = (",", ";", "(", ")", "[", "]", "{", "}")
-
-    def _multi_escape(entries):
-        return '(%s)' % ('|'.join(re.escape(entry) for entry in entries))
-
-    tokens = {
-        'root': [
-            (r'(?s)\(\*.*?\*\)', Comment),
-
-            (r'([a-zA-Z]+[A-Za-z0-9]*`)', Name.Namespace),
-            (r'([A-Za-z0-9]*_+[A-Za-z0-9]*)', Name.Variable),
-            (r'#\d*', Name.Variable),
-            (r'([a-zA-Z]+[a-zA-Z0-9]*)', Name),
-
-            (r'-?\d+\.\d*', Number.Float),
-            (r'-?\d*\.\d+', Number.Float),
-            (r'-?\d+', Number.Integer),
-
-            (words(operators), Operator),
-            (words(punctuation), Punctuation),
-            (r'".*?"', String),
-            (r'\s+', Text.Whitespace),
-        ],
-    }
-
-
-class MuPADLexer(RegexLexer):
-    """
-    A MuPAD lexer.
-    Contributed by Christopher Creutzig <christopher@creutzig.de>.
-
-    .. versionadded:: 0.8
-    """
-    name = 'MuPAD'
-    url = 'http://www.mupad.com'
-    aliases = ['mupad']
-    filenames = ['*.mu']
-
-    tokens = {
-        'root': [
-            (r'//.*?$', Comment.Single),
-            (r'/\*', Comment.Multiline, 'comment'),
-            (r'"(?:[^"\\]|\\.)*"', String),
-            (r'\(|\)|\[|\]|\{|\}', Punctuation),
-            (r'''(?x)\b(?:
-                next|break|end|
-                axiom|end_axiom|category|end_category|domain|end_domain|inherits|
-                if|%if|then|elif|else|end_if|
-                case|of|do|otherwise|end_case|
-                while|end_while|
-                repeat|until|end_repeat|
-                for|from|to|downto|step|end_for|
-                proc|local|option|save|begin|end_proc|
-                delete|frame
-              )\b''', Keyword),
-            (r'''(?x)\b(?:
-                DOM_ARRAY|DOM_BOOL|DOM_COMPLEX|DOM_DOMAIN|DOM_EXEC|DOM_EXPR|
-                DOM_FAIL|DOM_FLOAT|DOM_FRAME|DOM_FUNC_ENV|DOM_HFARRAY|DOM_IDENT|
-                DOM_INT|DOM_INTERVAL|DOM_LIST|DOM_NIL|DOM_NULL|DOM_POLY|DOM_PROC|
-                DOM_PROC_ENV|DOM_RAT|DOM_SET|DOM_STRING|DOM_TABLE|DOM_VAR
-              )\b''', Name.Class),
-            (r'''(?x)\b(?:
-                PI|EULER|E|CATALAN|
-                NIL|FAIL|undefined|infinity|
-                TRUE|FALSE|UNKNOWN
-              )\b''',
-             Name.Constant),
-            (r'\b(?:dom|procname)\b', Name.Builtin.Pseudo),
-            (r'\.|,|:|;|=|\+|-|\*|/|\^|@|>|<|\$|\||!|\'|%|~=', Operator),
-            (r'''(?x)\b(?:
-                and|or|not|xor|
-                assuming|
-                div|mod|
-                union|minus|intersect|in|subset
-              )\b''',
-             Operator.Word),
-            (r'\b(?:I|RDN_INF|RD_NINF|RD_NAN)\b', Number),
-            # (r'\b(?:adt|linalg|newDomain|hold)\b', Name.Builtin),
-            (r'''(?x)
-              ((?:[a-zA-Z_#][\w#]*|`[^`]*`)
-              (?:::[a-zA-Z_#][\w#]*|`[^`]*`)*)(\s*)([(])''',
-             bygroups(Name.Function, Text, Punctuation)),
-            (r'''(?x)
-              (?:[a-zA-Z_#][\w#]*|`[^`]*`)
-              (?:::[a-zA-Z_#][\w#]*|`[^`]*`)*''', Name.Variable),
-            (r'[0-9]+(?:\.[0-9]*)?(?:e[0-9]+)?', Number),
-            (r'\.[0-9]+(?:e[0-9]+)?', Number),
-            (r'\s+', Whitespace),
-            (r'.', Text)
-        ],
-        'comment': [
-            (r'[^/*]+', Comment.Multiline),
-            (r'/\*', Comment.Multiline, '#push'),
-            (r'\*/', Comment.Multiline, '#pop'),
-            (r'[*/]', Comment.Multiline)
-        ],
-    }
-
-
-class BCLexer(RegexLexer):
-    """
-    A BC lexer.
-
-    .. versionadded:: 2.1
-    """
-    name = 'BC'
-    url = 'https://www.gnu.org/software/bc/'
-    aliases = ['bc']
-    filenames = ['*.bc']
-
-    tokens = {
-        'root': [
-            (r'/\*', Comment.Multiline, 'comment'),
-            (r'"(?:[^"\\]|\\.)*"', String),
-            (r'[{}();,]', Punctuation),
-            (words(('if', 'else', 'while', 'for', 'break', 'continue',
-                    'halt', 'return', 'define', 'auto', 'print', 'read',
-                    'length', 'scale', 'sqrt', 'limits', 'quit',
-                    'warranty'), suffix=r'\b'), Keyword),
-            (r'\+\+|--|\|\||&&|'
-             r'([-<>+*%\^/!=])=?', Operator),
-            # bc doesn't support exponential
-            (r'[0-9]+(\.[0-9]*)?', Number),
-            (r'\.[0-9]+', Number),
-            (r'.', Text)
-        ],
-        'comment': [
-            (r'[^*/]+', Comment.Multiline),
-            (r'\*/', Comment.Multiline, '#pop'),
-            (r'[*/]', Comment.Multiline)
-        ],
-    }
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tzdata/zoneinfo/America/Kentucky/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tzdata/zoneinfo/America/Kentucky/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pseudolab/KorLearnGame/app.py b/spaces/pseudolab/KorLearnGame/app.py
deleted file mode 100644
index 011143502ac6f4cf8734b73ab02805ba2be1e804..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/KorLearnGame/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import gradio as gr
-import pandas as pd
-import random
-import re
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import AIMessage, HumanMessage, SystemMessage
-import os
-
-korean_word_data = pd.read_csv("korean_word_data.csv")
-llm = ChatOpenAI(temperature=1.0, model="gpt-3.5-turbo")
-
-
-def response(message, history):
-    levels = ["초급", "중급", "상급"]
-    print(history)
-
-    # 난이도 선택 메시지인 경우
-    if message in levels:
-        level_int = levels.index(message) + 1
-        this_level_word = korean_word_data[korean_word_data["level"] == level_int]
-        word_info = this_level_word.iloc[random.randint(0, len(this_level_word))]
-        description = word_info["description"]
-        word_type = word_info["type"]
-        return f"""🕹️ {message} 난이도로 게임을 시작할게요 🕹️
-
-        설명하는 이 단어를 맞춰보세요.
-        <{word_type}>[{description}]"""
-
-    # 이전 대화 확인
-    for user, bot in reversed(history):
-        if "난이도로 게임을 시작할게요 🕹️" in bot:
-            description_pattern = r"\[([^\]]*)\]"
-            word_info = korean_word_data[
-                korean_word_data["description"]
-                == re.search(description_pattern, bot).group(1)
-            ].iloc[0]
-            level, word_type, word, description, example, word_len, choseong = (
-                word_info["level"],
-                word_info["type"],
-                word_info["word"],
-                word_info["description"],
-                word_info["example"],
-                word_info["word_len"],
-                word_info["choseong"],
-            )
-            prompt = f"""You are a chatbot conducting a Korean vocabulary matching game. The current word's information is as follows:
-            Correct Word: {word}, Difficulty: {levels[level-1]}, Part of Speech: {word_type}, Meaning: [{description}], Example Sentence: [{example}], Word Length: {word_len}, Consonant: {choseong}
-            The user's input message is [{message}]. If the user is playing the Korean vocabulary matching game, compare the correct word with the message. If they are similar, respond with an expression of near-miss. If not similar, send words of encouragement.
-            If you think the content of the message is not related to the vocabulary matching game, respond appropriately to the situation.
-            If the user seems to be struggling, inform them that there are hints available.
-            Provide hints in this order: Example Sentence -> Word Length -> Consonant, and give only one hint at a time.
-
-            What you must follow:
-            1. Never say the correct word during a conversation.
-            2. Do not give hints unless there is a direct request for them from the user.
-            3. If the user answers correctly, ask them to enter the new difficulty level.
-            4. Give all answers in Korean."""
-
-            history_langchain_format = []
-            history_langchain_format.append(SystemMessage(content=prompt))
-            for human, ai in history:
-                history_langchain_format.append(HumanMessage(content=human))
-                history_langchain_format.append(AIMessage(content=ai))
-            history_langchain_format.append(HumanMessage(content=message))
-
-            gpt_response = llm(history_langchain_format)
-            return gpt_response.content
-
-    # 게임 시작 메시지가 없는 경우
-    return "초급, 중급, 상급 중 맞출 단어의 난이도를 골라주세요. 😎"
-
-
-gr.ChatInterface(
-    fn=response,
-    textbox=gr.Textbox(container=False, scale=10),
-    title="🎮 한국어 어휘 맞추기 게임: Korean Learning Game 🎮",
-    description="안녕하세요! 이 게임의 난이도는 초급, 중급, 상급으로 사용자가 자유롭게 난이도를 선택할 수 있습니다. 예시를 참고하여 난이도를 정확하게 입력해주세요. 난이도를 입력하면 게임이 시작됩니다.",
-    theme="soft",
-    examples=[["초급"], ["중급"], ["상급"]],
-    retry_btn="다시보내기 ↩",
-    undo_btn="이전 대화 지우기",
-    clear_btn="전체 대화 지우기",
-).launch()
diff --git a/spaces/pyInter/Liyuu_sovits4/hubert/hubert_model_onnx.py b/spaces/pyInter/Liyuu_sovits4/hubert/hubert_model_onnx.py
deleted file mode 100644
index d18f3c2a0fc29592a573a9780308d38f059640b9..0000000000000000000000000000000000000000
--- a/spaces/pyInter/Liyuu_sovits4/hubert/hubert_model_onnx.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-    def forward(self, x):
-        return self.units(x)
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/qpmzonxw/bing/Dockerfile b/spaces/qpmzonxw/bing/Dockerfile
deleted file mode 100644
index 4a99e0ae215904209b65ae4fc42e0299cd46e492..0000000000000000000000000000000000000000
--- a/spaces/qpmzonxw/bing/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="xkedofkskeowqwopeodssddfhadsegpoqwqiowqodkcckj"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Fire Dongle Crack [NEW]ed Huawei 165.md b/spaces/quidiaMuxgu/Expedit-SAM/Fire Dongle Crack [NEW]ed Huawei 165.md
deleted file mode 100644
index c13f89a4f9dde2eb6ee4fb3247838edf5ffbea8b..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Fire Dongle Crack [NEW]ed Huawei 165.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Fishing Planet Cornucopia Pack BEST Full Crack [BEST Full Version].md b/spaces/quidiaMuxgu/Expedit-SAM/Fishing Planet Cornucopia Pack BEST Full Crack [BEST Full Version].md
deleted file mode 100644
index 6259c6b559907aa8712c2fdc8e8aa726e882a7fe..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Fishing Planet Cornucopia Pack BEST Full Crack [BEST Full Version].md	
+++ /dev/null
@@ -1,107 +0,0 @@
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-<p>Fishing Planet: Cornucopia Pack is a DLC (downloadable content) for Fishing Planet, a unique and highly realistic online first-person multiplayer fishing simulator. The Cornucopia Pack features limited-edition powerful telescopic rods that are available exclusively in this pack, as well as other exclusive goodies, such as tackle boxes, baits, lures, hooks, lines, reels, and more. The Cornucopia Pack also gives you access to new fishing locations, such as Lake Saint-Croix in Michigan and Lake Quanchkin in Louisiana.</p>
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-<p>A full crack [full version] of Fishing Planet: Cornucopia Pack is a tool that was created by hackers and crackers who specialize in cracking software and games. It is a modified version of the game that removes the need for a license key or an activation code to play the game. It is mainly used to play Fishing Planet: Cornucopia Pack without paying for it or buying it from PlayStationStore.</p>
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-<h2>How to Download and Use a Full Crack [full Version] of Fishing Planet: Cornucopia Pack?</h2>
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-<p>Downloading and using a full crack [full version] of Fishing Planet: Cornucopia Pack is not an official or legal way to play the game. It is a pirated way that violates the terms and conditions of Fishing Planet and Sony Interactive Entertainment. However, if you still want to try it at your own risk, you can follow these steps:</p>
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-<ol>
-<li>Download a full crack [full version] of Fishing Planet: Cornucopia Pack from an unreliable source. You can find it on various websites and forums that offer software cracks and keygens. Make sure you scan the file with an antivirus program before opening it.</li>
-<li>Disable your internet connection. This is to prevent Fishing Planet and Sony Interactive Entertainment from detecting and blocking the full crack [full version].</li>
-<li>Install Fishing Planet: Cornucopia Pack on your PlayStation 4 console. You can do this by copying the file to a USB flash drive or an external hard drive and connecting it to your console.</li>
-<li>Run the full crack [full version] on your console. You will see a window with a button that says "Start Game".</li>
-<li>Click on the "Start Game" button. The full crack [full version] will launch Fishing Planet: Cornucopia Pack on your console.</li>
-<li>Enjoy playing Fishing Planet: Cornucopia Pack for free.</li>
-</ol>
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-<li>You can save money by not paying for the game or buying it from PlayStationStore.</li>
-<li>You can access all the features and functions of Fishing Planet: Cornucopia Pack without any limitations or restrictions.</li>
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-<p>While a full crack [full version] of Fishing Planet: Cornucopia Pack can help you play the game for free, it also comes with some risks that you should be aware of. Some of them are:</p>
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-<li>You may violate the terms and conditions of Fishing Planet and Sony Interactive Entertainment and face legal consequences. Using a full crack [full version] to play Fishing Planet: Cornucopia Pack is considered piracy and theft, which can result in fines, lawsuits, or even criminal charges.</li>
-<li>You may expose your console to malware and viruses. Some full cracks [full versions] may contain malicious code that can harm your console or steal your personal information. You should always scan the file with an antivirus program before opening it.</li>
-<li>You may lose access to technical support and updates from Fishing Planet and Sony Interactive Entertainment. Using a full crack [full version] to play Fishing Planet: Cornucopia Pack may prevent you from receiving official updates and patches from Fishing Planet and Sony Interactive Entertainment, which can affect the performance and security of the game. You may also not be able to contact Fishing Planet or Sony Interactive Entertainment for technical support or customer service.</li>
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-<ul>
-<li>You can use a free trial version of Fishing Planet: Cornucopia Pack. Fishing Planet offers a free trial version of its game that you can download and play for a limited time. This way, you can test the features and functions of the game before deciding whether to buy it or not.</li>
-<li>You can use a free or open source fishing game that has similar functions as Fishing Planet: Cornucopia Pack. There are many free or open source fishing games that can perform similar tasks as Fishing Planet: Cornucopia Pack, such as Ultimate Fishing Simulator, Pro Pilkki 2, Carp Fish Sim, Cat Goes Fishing, and more. You can download and play these games without paying anything or violating any laws.</li>
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-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Fishing Planet: Cornucopia Pack is a DLC (downloadable content) for Fishing Planet, a unique and highly realistic online first-person multiplayer fishing simulator. The Cornucopia Pack features limited-edition powerful telescopic rods that are available exclusively in this pack, as well as other exclusive goodies, such as tackle boxes, baits, lures, hooks, lines, reels, and more. The Cornucopia Pack also gives you access to new fishing locations, such as Lake Saint-Croix in Michigan and Lake Quanchkin in Louisiana.</p>
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-<p>If you are a fan of fishing games and want to enjoy the full experience of Fishing Planet: Cornucopia Pack, you might be tempted to download and use a full crack [full version] of the game. A full crack [full version] is a pirated version of the game that bypasses the activation process and allows you to play the game for free. However, before you do that, you should be aware of the risks and drawbacks of using a full crack [full version] of Fishing Planet: Cornucopia Pack. In this article, we have explained what a full crack [full version] of Fishing Planet: Cornucopia Pack is, how to download and use it, what are the benefits and risks of using it, and what are the alternatives to using it.</p>
-<h2>How to Play Fishing Planet: Cornucopia Pack?</h2>
-
-<p>Fishing Planet: Cornucopia Pack is a DLC (downloadable content) for Fishing Planet, a unique and highly realistic online first-person multiplayer fishing simulator. To play Fishing Planet: Cornucopia Pack, you need to have Fishing Planet installed on your PlayStation 4 console. You can download Fishing Planet for free from PlayStationStore.</p>
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-<p>Once you have Fishing Planet and Fishing Planet: Cornucopia Pack installed on your console, you can launch the game and select the Cornucopia Pack from the main menu. You will see a list of new fishing locations that are available in the Cornucopia Pack, such as Lake Saint-Croix in Michigan and Lake Quanchkin in Louisiana. You can choose any location that you want to fish in and start your fishing adventure.</p>
-
-<p>Fishing Planet: Cornucopia Pack features limited-edition powerful telescopic rods that are available exclusively in this pack, as well as other exclusive goodies, such as tackle boxes, baits, lures, hooks, lines, reels, and more. You can use these items to catch different types of fish, such as bass, catfish, carp, pike, muskie, trout, salmon, and more. You can also compete with other players online in tournaments and leaderboards.</p>
-
-<p>Fishing Planet: Cornucopia Pack is a fun and realistic fishing game that will challenge your skills and knowledge of fishing. You can enjoy the beautiful scenery and the realistic sounds of nature as you fish in different locations. You can also customize your character and your equipment to suit your style and preferences. Fishing Planet: Cornucopia Pack is a game that will make you feel like you are really fishing.</p>
-<h2>How to Choose the Right Fishing Location in Fishing Planet: Cornucopia Pack?</h2>
-
-<p>Fishing Planet: Cornucopia Pack offers you two new fishing locations that are exclusive to this pack, as well as other locations that are available in the base game. The two new locations are Lake Saint-Croix in Michigan and Lake Quanchkin in Louisiana. Each location has its own characteristics, such as water temperature, depth, clarity, vegetation, weather, and fish species. To choose the right fishing location for your needs, you can consider these factors:</p>
-
-<ul>
-<li>Your fishing style: Fishing Planet: Cornucopia Pack allows you to fish in different ways, such as spinning, casting, float fishing, bottom fishing, and fly fishing. You can choose the location that suits your fishing style and preferences.</li>
-<li>Your fishing goal: Fishing Planet: Cornucopia Pack allows you to catch different types of fish, such as bass, catfish, carp, pike, muskie, trout, salmon, and more. You can choose the location that has the fish species that you want to catch.</li>
-<li>Your fishing challenge: Fishing Planet: Cornucopia Pack allows you to compete with other players online in tournaments and leaderboards. You can choose the location that has the most competitive and challenging fishing conditions.</li>
-</ul>
-
-<h2>How to Use the Telescopic Rods in Fishing Planet: Cornucopia Pack?</h2>
-
-<p>Fishing Planet: Cornucopia Pack features limited-edition powerful telescopic rods that are available exclusively in this pack. These rods are designed to be versatile and adaptable to different fishing situations. They can be extended or retracted to adjust the length and action of the rod. They can also be equipped with different reels, lines, hooks, baits, and lures to suit different fish species and water conditions. To use the telescopic rods in Fishing Planet: Cornucopia Pack, you can follow these steps:</p>
-
-<ol>
-<li>Select a telescopic rod from your inventory. You can choose from four different models: Stinger 270XH (9 ft), Stinger 300XXH (10 ft), Stinger 330XXXH (11 ft), and Stinger 360XXXXH (12 ft).</li>
-<li>Select a reel from your inventory. You can choose from four different models: Hornet Swarm 5500 (spinning), Hornet Swarm 6500 (spinning), Hornet Swarm 7500 (casting), and Hornet Swarm 8500 (casting).</li>
-<li>Select a line from your inventory. You can choose from four different types: braid (strong but visible), fluorocarbon (invisible but stiff), monofilament (stretchy but weak), and nylon (balanced but prone to abrasion).</li>
-<li>Select a hook from your inventory. You can choose from four different sizes: #1/0 (small), #3/0 (medium), #5/0 (large), and #7/0 (extra large).</li>
-<li>Select a bait or a lure from your inventory. You can choose from various options, such as worms, maggots, dough balls, corn, cheese, minnows, shiners, frogs, crawfish, crankbaits, spinners, spoons, jigs, poppers, and more.</li>
-<li>Adjust the length and action of your rod by using the left and right buttons on your controller. You can extend or retract your rod to change its power and sensitivity.</li>
-<li>Cast your line by using the right trigger on your controller. You can aim your cast by using the right stick on your controller.</li>
-<li>Reel in your line by using the left trigger on your controller. You can control the speed and tension of your line by using the left stick on your controller.</li>
-<li>Hook and land a fish by using the right stick on your controller. You can set the hook by pulling the right stick down quickly. You can fight the fish by moving the right stick left or right to match its direction.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Fishing Planet: Cornucopia Pack is a DLC (downloadable content) for Fishing Planet, a unique and highly realistic online first-person multiplayer fishing simulator. The Cornucopia Pack features limited-edition powerful telescopic rods that are available exclusively in this pack, as well as other exclusive goodies, such as tackle boxes, baits, lures, hooks, lines, reels, and more. The Cornucopia Pack also gives you access to new fishing locations, such as Lake Saint-Croix in Michigan and Lake Quanchkin in Louisiana.</p>
-
-<p>If you are a fan of fishing games and want to enjoy the full experience of Fishing Planet: Cornucopia Pack, you might be tempted to download and use a full crack [full version] of the game. A full crack [full version] is a pirated version of the game that bypasses the activation process and allows you to play the game for free. However, before you do that, you should be aware of the risks and drawbacks of using a full crack [full version] of Fishing Planet: Cornucopia Pack. In this article, we have explained what a full crack [full version] of Fishing Planet: Cornucopia Pack is, how to download and use it, what are the benefits and risks of using it, what are the alternatives to using it.</p>
-
-<p>We have also explained how to choose the right fishing location for your needs,
-<p>We have also explained how to choose the right fishing location for your needs, how to use the telescopic rods in Fishing Planet: Cornucopia Pack, and how to play Fishing Planet: Cornucopia Pack. Fishing Planet: Cornucopia Pack is a fun and realistic fishing game that will challenge your skills and knowledge of fishing. You can enjoy the beautiful scenery and the realistic sounds of nature as you fish in different locations. You can also customize your character and your equipment to suit your style and preferences. Fishing Planet: Cornucopia Pack is a game that will make you feel like you are really fishing.</p> 3cee63e6c2<br />
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\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Ip Remote Control Software Rsba1 Crack HOT.md b/spaces/quidiaMuxgu/Expedit-SAM/Ip Remote Control Software Rsba1 Crack HOT.md
deleted file mode 100644
index c1cb70f73de7ccd627631007b6ededf545fb0db8..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Ip Remote Control Software Rsba1 Crack HOT.md	
+++ /dev/null
@@ -1,14 +0,0 @@
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-
-Local Operation. The RSBA2 provides the same frequency coverage and RF protection as the RSBA1, however, it operates on a single band, 10.5 MHz to 30.0 MHz. It provides a maximum output power of 50 dBw in a 6 dB power amplifier that can be expanded from 3.7 to 12 dBm. The power amplifier is expandable to 8 or 13 dBm. The maximum number of input antennas that the radio can accept is four.
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-Local Operation. The RSBA3 provides the same frequency coverage and RF protection as the RSBA1, however, it operates on a single band, 10.5 MHz to 30.0 MHz. It provides a maximum output power of 50 dBw in a 6 dB power amplifier that can be expanded from 3.7 to 12 dBm. The power amplifier is expandable to 8 or 13 dBm. The maximum number of input antennas that the radio can accept is four.
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-Local Operation. The RSBA4 provides the same frequency coverage and RF protection as the RSBA1, however, it operates on a single band, 10.5 MHz to 30.0 MHz. It provides a maximum output power of 50 dBw in a 6 dB power amplifier that can be expanded from 3.7 to 12 dBm. The power amplifier is expandable to 8 or 13 dBm. The maximum number of input antennas that the radio can accept is four.
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-Local Operation. The RSBA5 provides the same frequency coverage and RF protection as the RSBA1, however, it operates on a single band, 10.5 MHz to 30.0 MHz. It provides a maximum output power of 50 dBw in a 6 dB power amplifier that can be expanded from 3.7 to 12 dBm. The power amplifier is expandable to 8 or 13 dBm. The maximum number of input antennas that 4fefd39f24<br />
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diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/tools/diffq/__init__.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/tools/diffq/__init__.py
deleted file mode 100644
index 0eebdb931a873fc818f3774335417ee940ef6ab0..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/tools/diffq/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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.
-
-# flake8: noqa
-"""
-This package implements different quantization strategies:
-
-- `diffq.uniform.UniformQuantizer`: classic uniform quantization over n bits.
-- `diffq.diffq.DiffQuantizer`: differentiable quantizer based on scaled noise injection.
-
-Also, do check `diffq.base.BaseQuantizer` for the common methods of all Quantizers.
-"""
diff --git a/spaces/radames/transformers-js-sveltekit-server-example-app/svelte.config.js b/spaces/radames/transformers-js-sveltekit-server-example-app/svelte.config.js
deleted file mode 100644
index 2d8b830093553033a0cce4f9c495b9d4a4fc1f3d..0000000000000000000000000000000000000000
--- a/spaces/radames/transformers-js-sveltekit-server-example-app/svelte.config.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import adapter from '@sveltejs/adapter-node';
-import { vitePreprocess } from '@sveltejs/kit/vite';
-/** @type {import('@sveltejs/kit').Config} */
-const config = {
-	kit: {
-		adapter: adapter({
-			// default options are shown
-			out: 'build',
-			precompress: false,
-			envPrefix: '',
-			polyfill: true
-		})
-	},
-	preprocess: vitePreprocess()
-};
-export default config;
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Calculo de varias variables thomas 12 edicion pdf free Un recurso indispensable para estudiantes y profesores de matemticas.md b/spaces/raedeXanto/academic-chatgpt-beta/Calculo de varias variables thomas 12 edicion pdf free Un recurso indispensable para estudiantes y profesores de matemticas.md
deleted file mode 100644
index 6bb9fc4c43bab941f7adc732f557b2466b295862..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Calculo de varias variables thomas 12 edicion pdf free Un recurso indispensable para estudiantes y profesores de matemticas.md	
+++ /dev/null
@@ -1,132 +0,0 @@
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- <p>The book aims to help students develop a solid understanding of calculus and its applications in various fields of science and engineering. It also encourages students to think critically and creatively about mathematical problems and solutions.</p>
- <h3>Author's background</h3>
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- <p>If you have any questions or comments about this article or this book, feel free to leave them below. We would love to hear from you.</p>
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- <h2>FAQs</h2>
- <ol>
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-<p>solutions, etc. You can find some of these materials online, such as on <a href="https://www.edx.org/learn/calculus">edX</a>, <a href="https://www.khanacademy.org/math/integral-calculus">Khan Academy</a>, or <a href="https://ocw.mit.edu/courses/res-18-001-calculus-online-textbook-spring-2005/pages/textbook/">MIT OpenCourseWare</a>.</p></li>
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-<li><p>Yes, it is available in other languages, such as English, French, German, Portuguese, Chinese, etc. You can find some of these translations online, such as on <a href="https://www.amazon.com/Thomas-Calculus-Multivariable-George/dp/0321643690">Amazon</a>, <a href="https://www.amazon.fr/Calcul-diff%C3%A9rentiel-int%C3%A9gral-George-Thomas/dp/2891051831">Amazon France</a>, or <a href="https://www.amazon.de/Thomas-Calculus-Multivariable-George/dp/0321643690">Amazon Germany</a>.</p></li>
-</ol>
- </p> 0a6ba089eb<br />
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-<br />
-<h1>Download Opnet Modeler 16.1: A Comprehensive Guide</h1>
- <p>If you are looking for a powerful and versatile tool for network simulation and analysis, you might want to consider downloading Opnet Modeler 16.1. This software allows you to model, simulate, and analyze various types of networks, such as wired, wireless, mobile, optical, satellite, and more. You can also use it to test and optimize network performance, reliability, security, and scalability.</p>
-<h2>download opnet modeler 16.1</h2><br /><p><b><b>Download File</b> ->->->-> <a href="https://tinourl.com/2uL11t">https://tinourl.com/2uL11t</a></b></p><br /><br />
- <p>In this article, we will show you what Opnet Modeler is and why you need it, how to download it for free, how to use it for network simulation and analysis, and some tips and tricks for using it effectively. By the end of this article, you will have a clear idea of how to download Opnet Modeler 16.1 and use it for your network projects.</p>
- <h2>What is Opnet Modeler and why do you need it?</h2>
- <p>Opnet Modeler is a software application that enables you to model, simulate, and analyze different types of networks using a graphical user interface (GUI) and a discrete event simulation engine. You can create realistic network scenarios using various components, such as nodes, links, protocols, applications, traffic sources, etc. You can also customize the network behavior and parameters using various attributes, functions, models, etc.</p>
- <p>Opnet Modeler allows you to run simulations and view the results in various formats, such as graphs, charts, tables, animations, etc. You can also perform statistical analysis and compare different scenarios using various metrics, such as throughput, delay, jitter, packet loss, etc. You can also export the results to other applications or formats for further processing or presentation.</p>
- <p>Opnet Modeler is useful for anyone who wants to design, test, optimize, or troubleshoot networks. Whether you are a student, researcher, engineer, or manager, you can use Opnet Modeler to learn about network concepts and technologies, conduct experiments and research projects, evaluate network performance and reliability, optimize network resources and configurations, or solve network problems and issues.</p>
- <h3>Opnet Modeler features and benefits</h3>
- <p>Some of the main features and benefits of Opnet Modeler are:</p>
- <ul>
-<li>It supports a wide range of network types and technologies, such as wired (Ethernet), wireless (Wi-Fi), mobile (LTE), optical (WDM), satellite (LEO), etc.</li>
-<li>It provides a large library of predefined components and models that cover various aspects of network systems, such as physical layer (modulation), data link layer (MAC), network layer (routing), transport layer (TCP), application layer (HTTP), etc.</li>
-<li>It allows you to create your own components and models using various tools and languages, such as C++, Java, Python, etc.</li>
-<li>It offers a user-friendly GUI that enables you to drag-and-drop components onto a canvas and connect them with links. You can also use menus and dialogs to configure the network parameters and settings.</li>
-<li>It has a powerful discrete event simulation engine that executes the network events in chronological order and generates accurate and realistic results.</li>
-<li>It has a flexible result analysis system that enables you to view the simulation results in various formats and perspectives. You can also perform statistical analysis and comparison using various metrics.</li>
-<li>It has an integrated development environment (IDE) that enables you to edit code files, compile models, debug errors, and run simulations within the same interface.</li>
-<li>It has an online community and support forums that provide helpful resources and guidance for users of all levels and backgrounds.</li>
-</ul>
- <h3>Opnet Modeler system requirements and compatibility</h3>
- <p>To download and run Opnet Modeler 16.1, you need to have a computer that meets the following system requirements:</p>
- <table>
-<tr><th>Operating system</th><th>Windows XP/Vista/7/8/10</th></tr>
-<tr><td>CPU</td><td>Pentium IV or higher</td></tr>
-<tr><td>RAM</td><td>At least 2 GB</td></tr>
-<tr><td>Disk space</td><td>At least 10 GB</td></tr>
-<tr><td>Graphics card</td><td>OpenGL compatible</td></tr>
-<tr><td>Internet connection</td><td>Required for downloading and activating the software</td></tr>
-</table>
- <p>Note that Opnet Modeler 16.1 is compatible with both 32-bit and 64-bit versions of Windows operating systems. However, it is not compatible with Mac OS X or Linux operating systems.</p>
- <h2>How to download Opnet Modeler 16.1 for free?</h2>
- <p>If you want to download Opnet Modeler 16.1 for free, you need to follow these steps:</p>
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- <h3>Step 1: Visit the official website of Opnet Modeler</h3>
- <p>The first step is to visit the official website of Opnet Modeler, which is <a href="https://www.riverbed.com/products/steelcentral/opnet-modeler.html">https://www.riverbed.com/products/steelcentral/opnet-modeler.html</a>. This website provides information about the software, such as features, benefits, use cases, etc. You can also find links to download the software, access the online community, and contact the support team.</p>
- <h3>Step 2: Register for a free account or log in with your existing credentials</h3>
- <p>The second step is to register for a free account or log in with your existing credentials. To do this, you need to click on the "Download" button on the top right corner of the website. This will take you to a page where you can choose between two options: "Register" or "Log In".</p>
- <p>If you do not have an account yet, you need to click on "Register" and fill out a form with your personal information, such as name, email address, country, etc. You also need to agree to the terms and conditions of use. After completing the form, you will receive an email with a link to activate your account.</p>
- <p>If you already have an account, you need to click on "Log In" and enter your email address and password. If you forgot your password, you can click on "Forgot Password" and follow the instructions to reset it.</p>
- <h3>Step 3: Download the Opnet Modeler 16.1 installer file</h3>
- <p>The third step is to download the Opnet Modeler 16.1 installer file. To do this, you need to go back to the website and click on "Download" again. This will take you to a page where you can see a list of available downloads. You need to select "Opnet Modeler Suite - Version 16.1" and click on "Download Now". This will start downloading a zip file named "op_modeler_16_1.zip" to your computer.</p>
- <h3>Step 4: Run the installer file and follow the instructions</h3>
- <p>The fourth step is to run the installer file and follow the instructions. To do this, you need to unzip the downloaded file and double-click on "setup.exe". This will launch the installation wizard that will guide you through the process. You need agreement, choose the destination folder, select the components to install, and wait for the installation to complete. You may need to restart your computer after the installation.</p>
- <h2>How to use Opnet Modeler 16.1 for network simulation and analysis?</h2>
- <p>Once you have downloaded and installed Opnet Modeler 16.1, you can start using it for network simulation and analysis. To do this, you need to follow these steps:</p>
- <h3>Step 1: Create a new project or open an existing one</h3>
- <p>The first step is to create a new project or open an existing one. To do this, you need to launch Opnet Modeler 16.1 from your desktop or start menu. This will open the main window of the software, where you can see a menu bar, a toolbar, a project browser, and a workspace.</p>
- <p>To create a new project, you need to click on "File" and then "New Project". This will open a dialog box where you can enter a name and a description for your project. You can also choose a location and a folder for your project files.</p>
- <p>To open an existing project, you need to click on "File" and then "Open Project". This will open a dialog box where you can browse and select a project file with the extension ".opx".</p>
- <h3>Step 2: Choose a network scenario or design your own</h3>
- <p>The second step is to choose a network scenario or design your own. To do this, you need to click on "Scenarios" and then "New Scenario". This will open a dialog box where you can enter a name and a description for your scenario. You can also choose a network domain, such as wired, wireless, mobile, etc.</p>
- <p>After creating a scenario, you need to add network components to it. You can do this by using the project browser or the toolbar. The project browser shows a hierarchical view of your project files and folders. You can expand or collapse them by clicking on the plus or minus signs. You can also drag-and-drop components from the project browser to the workspace.</p>
- <p>The toolbar shows icons of various network components that you can use in your scenario. You can click on an icon and then click on the workspace to place a component. You can also right-click on an icon and select "Create Multiple" to place multiple components at once.</p>
- <p>You can connect network components by using links. You can do this by clicking on a component and then dragging to another component. You can also right-click on a component and select "Connect To" to choose a component to connect to.</p>
- <h3>Step 3: Configure the network parameters and settings</h3>
- <p>The third step is to configure the network parameters and settings. To do this, you need to double-click on a component or a link to open its attribute editor. The attribute editor shows various attributes that define the behavior and characteristics of the component or link. You can modify these attributes by entering values or choosing options from drop-down menus.</p>
- <p>You can also use functions and models to configure the network parameters and settings. Functions are mathematical expressions that calculate values for attributes based on variables and constants. Models are collections of functions and attributes that represent specific aspects of network systems, such as protocols, applications, traffic sources, etc.</p>
- <p>You can create your own functions and models by using various tools and languages, such as C++, Java, Python, etc. You can also use predefined functions and models from the library or import them from other sources.</p>
- <h3>Step 4: Run the simulation and view the results</h3>
- <p>The fourth step is to run the simulation and view the results. To do this, you need to click on "Run" and then "Run Simulation". This will open a dialog box where you can choose a simulation mode, such as normal, fast, debug, etc. You can also choose a simulation duration, such as seconds, minutes, hours, etc. You can also set breakpoints and watchpoints to pause or monitor the simulation.</p>
- <p>After running the simulation, you need to view the results in various formats, such as graphs, charts, tables, animations, etc. You can do this by using the result browser or the toolbar. The result browser shows a hierarchical view of your simulation results and folders. You can expand or collapse them by clicking on the plus or minus signs. You can also drag-and-drop results from the result browser to the workspace.</p>
- <p>The toolbar shows icons of various result formats that you can use in your scenario. You can click on an icon and then click on the workspace to place a result. You can also right-click on an icon and select "Create Multiple" to place multiple results at once.</p>
- <p>You can also perform statistical analysis and comparison using various metrics, such as throughput, delay, jitter, packet loss, etc. You can do this by using various tools and options from the menu bar or the toolbar. You can also export the results to other applications or formats for further processing or presentation.</p>
- <h2>Tips and tricks for using Opnet Modeler 16.1 effectively</h2>
- <p>To use Opnet Modeler 16.1 effectively, you may want to consider these tips and tricks:</p>
- <h3>Tip 1: Use the built-in tutorials and help files</h3>
- <p>If you are new to Opnet Modeler 16.1 or want to learn more about its features and functions, you may want to use the built-in tutorials and help files. You can access them by clicking on "Help" and then "Opnet Modeler Help". This will open a window that shows various topics and subtopics that cover various aspects of Opnet Modeler 16.1. You can browse them by clicking on the plus or minus signs or using the search box. You can also view examples and screenshots that illustrate how to use Opnet Modeler 16.1 in different scenarios.</p>
- <h3>Tip 2: Use the online community and support forums</h3>
- <p>If you have any questions or issues about Opnet Modeler 16.1 or want to share your feedback or suggestions, you may want to use the online community and support forums. You can access them by clicking on "Help" and then "Online Community". This will take you to a website that shows various forums and threads that discuss various topics related to Opnet Modeler 16.1. You can join them by registering for a free account or logging in with your existing credentials. You can also post your questions or comments by clicking on "New Topic" or "Reply". You can also view answers and solutions from other users or experts.</p>
- <h3>Tip 3: Update your software regularly</h3>
- <p>If you want to keep your software up-to-date and enjoy its latest features and improvements, you may want to update your software regularly. You can do this by clicking on "Help" and then "Check for Updates". This will check if there are any available updates for Opnet Modeler 16.1. If there are any updates, you will be prompted to download and install them.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Opnet Modeler 16.1 is a powerful and versatile tool for network simulation and analysis. It allows you to model, simulate, and analyze various types of networks using a graphical user interface (GUI)  and a discrete event simulation engine. It also provides a large library of predefined components  and models that cover various aspects of network systems. It also allows you to create your own components  and models using various tools  and languages. It also offers a user-friendly GUI that enables you to drag-and-drop components onto a canvas  and connect them with links. It also has a powerful discrete event simulation engine that executes  the network events in chronological order  and generates accurate  and realistic results. It also has a flexible result analysis system that enables you  to view  the simulation results in various formats  and perspectives. It also has an integrated development environment (IDE)  that enables you  to edit code files, compile models, debug errors, and run simulations within  the same interface. It also has an online community  and support forums  that provide helpful resources  and guidance for users  of all levels  and backgrounds.</p>
- <p>If you want to download Opnet Modeler 16.1 for free, you need  to follow these steps: visit  the official website of Opnet Modeler; register for a free account or log in with your existing credentials; download  the Opnet Modeler 16.1 installer file; run  the installer file  and follow  the instructions.</p>
- <p>If you want to use Opnet Modeler 16.1 for network simulation  and analysis, you need  to follow these steps: create a new project or open an existing one; choose a network scenario or design your own; configure  the network parameters  and settings; run  the simulation  and view  the results.</p>
- <p>To use Opnet Modeler 16.1 effectively, you may want to consider these tips  and tricks: use  the built-in tutorials  and help files; use  online community  and support forums; update  your software regularly.</p>
- <p>We hope that this article has helped you to understand how to download Opnet Modeler 16.1 and use it for your network projects. If you have any questions or feedback, please feel free to leave a comment below or contact us through our website. Thank you for reading and happy networking!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Opnet Modeler 16.1:</p>
- <ol>
-<li>What is the difference between Opnet Modeler and Opnet IT Guru?</li>
-<p>Opnet Modeler is a general-purpose network simulation and analysis tool that supports a wide range of network types and technologies. Opnet IT Guru is a specialized network simulation and analysis tool that focuses on specific network domains, such as enterprise, data center, cloud, etc.</p>
-<li>How can I get a license for Opnet Modeler 16.1?</li>
-<p>Opnet Modeler 16.1 is a commercial software that requires a license to use. You can get a license by purchasing it from Riverbed Technology, the company that owns Opnet Modeler. You can also get a free trial license by registering for a free account on the official website of Opnet Modeler.</p>
-<li>How can I learn more about Opnet Modeler 16.1?</li>
-<p>You can learn more about Opnet Modeler 16.1 by using the built-in tutorials and help files, the online community and support forums, or the official documentation and manuals. You can also take online courses or attend workshops or webinars that teach you how to use Opnet Modeler 16.1.</p>
-<li>What are some alternatives to Opnet Modeler 16.1?</li>
-<p>Some alternatives to Opnet Modeler 16.1 are NS2, NS3, OMNeT++, Simulink, MATLAB, etc. These are also network simulation and analysis tools that have different features and capabilities. You can compare them with Opnet Modeler 16.1 and choose the one that suits your needs and preferences.</p>
-<li>How can I contact the support team of Opnet Modeler 16.1?</li>
-<p>You can contact the support team of Opnet Modeler 16.1 by using the online community and support forums, the contact form on the official website of Opnet Modeler, or the email address or phone number provided by Riverbed Technology.</p>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download PK Telugu Movie English Subtitles For Movies and Join the Adventure of an Alien in India.md b/spaces/raedeXanto/academic-chatgpt-beta/Download PK Telugu Movie English Subtitles For Movies and Join the Adventure of an Alien in India.md
deleted file mode 100644
index 9ad6b756767c51ca2a55ccb0854bd6fcd18fdcb1..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download PK Telugu Movie English Subtitles For Movies and Join the Adventure of an Alien in India.md	
+++ /dev/null
@@ -1,95 +0,0 @@
-<br />
-<h1>PK Telugu Movie English Subtitles Download For Movies</h1>
- <p>Are you a fan of Telugu movies? Do you want to watch one of the most popular and critically acclaimed Telugu movies of all time? If yes, then you should definitely check out PK Telugu movie. But wait, there is a catch. You don't understand Telugu language. Don't worry, we have a solution for you. You can watch PK Telugu movie with English subtitles. Yes, you heard it right. You can enjoy this amazing movie with subtitles that will help you understand every dialogue and scene.</p>
- <p>In this article, we will tell you everything you need to know about PK Telugu movie and how to download English subtitles for it. We will also tell you why watching movies with subtitles is beneficial for you. So, without further ado, let's get started.</p>
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- <h2>What is PK Telugu Movie and Why It Is Popular</h2>
- <p>PK Telugu movie is a remake of the Hindi movie PK that was released in 2014. The Hindi movie was directed by Rajkumar Hirani and starred Aamir Khan as an alien who lands on Earth and gets stranded. He tries to find his way back home by searching for god but ends up exposing the hypocrisy and corruption of organized religion.</p>
- <p>The Telugu version of PK was released in 2022 and featured Darbha Appaji Ambarisha as an alien who lands in Hyderabad instead of Delhi. He meets Shrita Rao as a journalist who helps him find his remote control that was stolen by Ashu Reddy as a godman. Along the way, they encounter various religious groups and leaders who try to exploit people's faith for their own gain.</p>
- <p>The genre of PK Telugu movie is comedy-drama and it deals with themes such as religion, faith, superstition, tolerance, love, friendship, humanity, etc. The movie has a lot of humor and satire that makes fun of various religious practices and beliefs without being offensive or disrespectful.</p>
- <p>The movie was a huge hit among both critics and audiences. It received positive reviews for its story, direction, acting, music, cinematography, etc. It also broke many box office records and became one of the highest-grossing Telugu movies of all time.</p>
- <h2>What are the Benefits of Watching Movies with Subtitles</h2>
- <p>Watching movies with subtitles may seem like a hassle or a distraction for some people but it actually has many benefits for you. Here are some of them:</p>
-<p>PK Telugu Film with English Subs Free Download<br />
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- <ul>
-<li>Subtitles can help improve your language skills and comprehension. By reading subtitles while watching movies, you can learn new words, phrases,  idioms,  grammar,  pronunciation,  etc. You can also compare how different languages express the same meaning or emotion.</li>
-<li>Subtitles can enhance your viewing experience and enjoyment. By reading subtitles while watching movies,  you can understand better what is happening on screen,  especially if there are dialogues or scenes that are unclear,  fast,  or have accents or dialects that are unfamiliar to you.  You can also catch subtle details or jokes that you may miss otherwise.</li>
-<li>Subtitles can provide accessibility and inclusion for people with hearing impairments or different preferences.  By reading subtitles while watching movies,  you can enjoy them without missing any sound or dialogue.  You can also choose subtitles that suit your preference,  such as font size,  color,  style,  etc.</li>
-</ul>
- <h2>How to Download PK Telugu Movie English Subtitles for Movies</h2>
- <p>Now that you know what PK Telugu movie is about and why watching it with subtitles is beneficial for you,  you may be wondering how to download English subtitles for it.  Well,  there are many ways to do that but before we tell you them,  we want to remind you about some legal and ethical issues.</p>
- <p>Downloading subtitles from unauthorized sources may be illegal or unethical depending on where you live and what laws apply there.  You may be violating copyright laws or intellectual property rights by downloading subtitles without permission from the creators or owners.  You may also be exposing yourself to malware or viruses by downloading files from untrusted sites or links.</p>
- <p>Therefore,  we advise you to download subtitles only from legal and ethical sources that have proper licenses or permissions from the creators or owners.  You should also scan any files before opening them to ensure they are safe and clean.</p>
- <p>With that said,  here are some sources and methods of downloading English subtitles for PK Telugu movie:</p>
- <ul>
-<li>The easiest way to download English subtitles for PK Telugu movie is to watch it on Netflix or other streaming platforms that have the rights to show it.  These platforms usually provide subtitles in various languages for their content.  You just need to select English as your subtitle language option before playing the movie.</li>
-<li>The second way to download English subtitles for PK Telugu movie is to use subtitle websites that offer free or paid downloads of subtitles for various movies and shows.  These websites usually have a large database of subtitles in different languages,  formats,  qualities,  etc.  You just need to search for PK Telugu movie on these websites and choose the subtitle file that matches your movie version.</li>
-<li>The third way to download English subtitles for PK Telugu movie is to use subtitle software or online tools that allow you to create your own subtitles or edit existing ones.  These software or tools usually have features such as automatic transcription,  translation,  synchronization,  etc.  You just need to upload your movie file or URL on these software or tools and follow their instructions to generate or modify your subtitle file.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, PK Telugu movie is a comedy-drama that explores the theme of religion and faith in a humorous and satirical way. It is one of the most popular and critically acclaimed Telugu movies of all time. Watching it with English subtitles can help you improve your language skills, enhance your viewing experience, and provide accessibility and inclusion. You can download English subtitles for PK Telugu movie from legal and ethical sources such as streaming platforms, subtitle websites, or subtitle software or tools. We hope you enjoy watching this amazing movie with subtitles and have a great time.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about PK Telugu movie and English subtitles:</p>
- <ol>
-<li><b>Where can I watch PK Telugu movie online?</b></li>
-<p>You can watch PK Telugu movie online on Netflix or other streaming platforms that have the rights to show it. You can also rent or buy it from online stores or platforms that offer it legally.</p>
-<li><b>Who are the actors in PK Telugu movie?</b></li>
-<p>The actors in PK Telugu movie are Darbha Appaji Ambarisha as an alien who lands in Hyderabad, Shrita Rao as a journalist who helps him find his remote control, Ashu Reddy as a godman who steals his remote control, Aamir Khan as an alien who lands in Delhi, Anuskha Sharma as a journalist who helps him expose the frauds of religion, Sanjay Dutt as a bandmaster who befriends him, Saurabh Shukla as a self-proclaimed godman who opposes him, Parikshat Sahni as Anuskha's father who is a devout Hindu, Sushant Singh Rajput as Anuskha's ex-boyfriend who is a Muslim, Boman Irani as a TV channel owner who supports Anuskha's investigation, Rukhsar as Sanjay's wife who is a Pakistani.</p>
-<li><b>What is the genre and the theme of PK Telugu movie?</b></li>
-<p>The genre of PK Telugu movie is comedy-drama and the theme is religion and faith. The movie shows how an alien who has no idea about religion or god tries to find his way back home by searching for god but ends up exposing the hypocrisy and corruption of organized religion. The movie also shows how different people have different beliefs and practices based on their culture and upbringing but ultimately share the same humanity and love.</p>
-<li><b>How can I sync subtitles with movies?</b></li>
-<p>You can sync subtitles with movies by using subtitle editing software or online tools that allow you to adjust the timing and position of subtitles. You can also use keyboard shortcuts or mouse clicks to sync subtitles manually while watching movies. You can also check the frame rate and the encoding of both the movie and the subtitle files to ensure they are compatible.</p>
-<li><b>What are some other popular Telugu movies that have English subtitles?</b></li>
-<p>Some other popular Telugu movies that have English subtitles are Baahubali, Arjun Reddy, Rangasthalam, Mahanati, Ala Vaikunthapurramuloo. These movies are also available on Netflix or other streaming platforms that provide subtitles in various languages.</p>
-</ol>
- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/rahul999r/Rahul_Kannada_TTS/LICENSE.md b/spaces/rahul999r/Rahul_Kannada_TTS/LICENSE.md
deleted file mode 100644
index 5fd2e54913fd05b69de2874ec8f9a10c7f4e8d3f..0000000000000000000000000000000000000000
--- a/spaces/rahul999r/Rahul_Kannada_TTS/LICENSE.md
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Open-Speech-EkStep
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/string_decoder.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/string_decoder.d.ts
deleted file mode 100644
index a5858041116333dfaa018a9e5cf89e3a9fc78d71..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/string_decoder.d.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-/**
- * The `string_decoder` module provides an API for decoding `Buffer` objects into
- * strings in a manner that preserves encoded multi-byte UTF-8 and UTF-16
- * characters. It can be accessed using:
- *
- * ```js
- * const { StringDecoder } = require('string_decoder');
- * ```
- *
- * The following example shows the basic use of the `StringDecoder` class.
- *
- * ```js
- * const { StringDecoder } = require('string_decoder');
- * const decoder = new StringDecoder('utf8');
- *
- * const cent = Buffer.from([0xC2, 0xA2]);
- * console.log(decoder.write(cent));
- *
- * const euro = Buffer.from([0xE2, 0x82, 0xAC]);
- * console.log(decoder.write(euro));
- * ```
- *
- * When a `Buffer` instance is written to the `StringDecoder` instance, an
- * internal buffer is used to ensure that the decoded string does not contain
- * any incomplete multibyte characters. These are held in the buffer until the
- * next call to `stringDecoder.write()` or until `stringDecoder.end()` is called.
- *
- * In the following example, the three UTF-8 encoded bytes of the European Euro
- * symbol (`€`) are written over three separate operations:
- *
- * ```js
- * const { StringDecoder } = require('string_decoder');
- * const decoder = new StringDecoder('utf8');
- *
- * decoder.write(Buffer.from([0xE2]));
- * decoder.write(Buffer.from([0x82]));
- * console.log(decoder.end(Buffer.from([0xAC])));
- * ```
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/string_decoder.js)
- */
-declare module 'string_decoder' {
-    class StringDecoder {
-        constructor(encoding?: BufferEncoding);
-        /**
-         * Returns a decoded string, ensuring that any incomplete multibyte characters at
-         * the end of the `Buffer`, or `TypedArray`, or `DataView` are omitted from the
-         * returned string and stored in an internal buffer for the next call to`stringDecoder.write()` or `stringDecoder.end()`.
-         * @since v0.1.99
-         * @param buffer A `Buffer`, or `TypedArray`, or `DataView` containing the bytes to decode.
-         */
-        write(buffer: Buffer): string;
-        /**
-         * Returns any remaining input stored in the internal buffer as a string. Bytes
-         * representing incomplete UTF-8 and UTF-16 characters will be replaced with
-         * substitution characters appropriate for the character encoding.
-         *
-         * If the `buffer` argument is provided, one final call to `stringDecoder.write()`is performed before returning the remaining input.
-         * After `end()` is called, the `stringDecoder` object can be reused for new input.
-         * @since v0.9.3
-         * @param buffer A `Buffer`, or `TypedArray`, or `DataView` containing the bytes to decode.
-         */
-        end(buffer?: Buffer): string;
-    }
-}
-declare module 'node:string_decoder' {
-    export * from 'string_decoder';
-}
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Asa 5505 License Keygen 13.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Asa 5505 License Keygen 13.md
deleted file mode 100644
index 895b161c72795fd6ece335e57323759168f86cdd..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Asa 5505 License Keygen 13.md	
+++ /dev/null
@@ -1,98 +0,0 @@
-## Asa 5505 License Keygen 13
-
-
-
- 
-
- ![Asa 5505 License Keygen 13](https://www.cisco.com/c/dam/en/us/support/docs/security/anyconnect-secure-mobility-client/215330-obtaining-an-emergency-covid-19-anyconne-00.png)
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-
-# How to Upgrade Your Cisco ASA 5505 Firewall License
-
- 
-
-If you have a Cisco ASA 5505 firewall that is running out of user licenses, you might want to upgrade it to a higher license level. This will allow you to connect more devices to your network and enjoy more features and security benefits. In this article, we will show you how to upgrade your Cisco ASA 5505 firewall license using a keygen tool.
-
- 
-
-## What is a Cisco ASA 5505 firewall license?
-
- 
-
-A Cisco ASA 5505 firewall license is a software activation code that enables certain features and capabilities on your device. There are different license levels for the Cisco ASA 5505 firewall, such as:
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-- Base license: This is the default license that comes with the device. It allows up to 10 users, 3 VLANs, and basic firewall functionality.
-
-- Security Plus license: This is an optional license that adds more features and capabilities to the device. It allows unlimited users, 20 VLANs, high availability, trunking, DMZ support, and more.
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-- AnyConnect Essentials license: This is an optional license that enables VPN access for remote users using the AnyConnect client software.
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-- AnyConnect Premium license: This is an optional license that enables VPN access for remote users using the AnyConnect client software or a web browser. It also supports advanced VPN features such as SSL encryption, clientless access, endpoint posture assessment, and more.
-
-
-
-You can check your current license level and status by using the `show version` command on the ASA CLI or by accessing the ASDM GUI.
-
- 
-
-## How to upgrade your Cisco ASA 5505 firewall license?
-
- 
-
-To upgrade your Cisco ASA 5505 firewall license, you need to purchase a new activation key from an authorized Cisco partner or reseller. You can find the list of Cisco partners and resellers here: [http://tools.cisco.com/WWChannels/LOCATR/openBasicSearch.do](http://tools.cisco.com/WWChannels/LOCATR/openBasicSearch.do)
-
- 
-
-Alternatively, you can use a keygen tool that generates a valid activation key for your device based on its serial number. This is not recommended as it may violate the Cisco terms and conditions and expose your device to security risks. However, if you decide to use a keygen tool, you can follow these steps:
-
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-1. Download the keygen tool from this link: [https://soundcloud.com/enomcascei1980/asa-5505-license-keygen-13](https://soundcloud.com/enomcascei1980/asa-5505-license-keygen-13)
-
-2. Run the keygen tool and enter your device serial number. You can find your device serial number by using the `show version` command on the ASA CLI or by accessing the ASDM GUI.
-
-3. Select the license level you want to upgrade to and click Generate.
-
-4. Copy the activation key that is displayed on the screen.
-
-5. Login to your ASA CLI or ASDM GUI and enter the following command: `activation-key key`, where *key* is the activation key you copied from the keygen tool.
-
-6. Reload your device for the changes to take effect.
-
-
-
-You can verify your new license level and status by using the `show version` command on the ASA CLI or by accessing the ASDM GUI.
-
- 
-
-## Conclusion
-
- 
-
-In this article, we showed you how to upgrade your Cisco ASA 5505 firewall license using a keygen tool. We also explained what are the different license levels for the Cisco ASA 5505 firewall and how to check your current license status. We hope you found this article helpful and informative. If you have any questions or feedback, please leave a comment below.
-
- ``` dfd1c89656
-
- 
-
- 
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/ABBYY FineReader 15 Crack Full Licence Key.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/ABBYY FineReader 15 Crack Full Licence Key.md
deleted file mode 100644
index c37497c3b547f5a966d55d0a4efff0da23df169c..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/ABBYY FineReader 15 Crack Full Licence Key.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Lenovo G450 G550 B550 18CN44WW V2.53 No Whitelist By TTAV134.17 8.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Lenovo G450 G550 B550 18CN44WW V2.53 No Whitelist By TTAV134.17 8.md
deleted file mode 100644
index 9e88e27bbb91d89f97388351bada884e2bb9a2c7..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Lenovo G450 G550 B550 18CN44WW V2.53 No Whitelist By TTAV134.17 8.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Farming Simulator 15 Full Crack [FULL] [TOP].md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Farming Simulator 15 Full Crack [FULL] [TOP].md
deleted file mode 100644
index 19244f98d97f3fd8a98ce8b74b66925764f9baf0..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Farming Simulator 15 Full Crack [FULL] [TOP].md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<h2>Farming Simulator 15 full crack [FULL]</h2><br /><p><b><b>Download Zip</b> &#9913;&#9913;&#9913; <a href="https://urlgoal.com/2uCKS6">https://urlgoal.com/2uCKS6</a></b></p><br /><br />
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-Features•The ultimate open world farming experience.•More than 30,000 square kilometers of fields and natural habitats.•The world of Farming Simulator is your oyster: cultivate the most beautiful fields on the planet, from the most vivid cities to the wildest nature.•Grow crops that are more than life-like. They respond to the environment and to the behavior of livestock.•Hundreds of vehicles allow you to get around your farm and do whatever you want.•Saves your progress to the cloud.
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diff --git a/spaces/rgres/Seg2Sat/Makefile b/spaces/rgres/Seg2Sat/Makefile
deleted file mode 100644
index 4bbe3b2c9215f96f239832bdf353c0a70c4f9bba..0000000000000000000000000000000000000000
--- a/spaces/rgres/Seg2Sat/Makefile
+++ /dev/null
@@ -1,24 +0,0 @@
-install-node:
-	./install-node.sh
-
-build-client:
-	cd frontend && npm install && npm run build && rm -rf ../static && cp -r build/ ../static/
-
-build-dev:
-	cd frontend && rm -rf node-modules && npm install && NODE_ENV=development npm run build && rm -rf ../static 2>&1 && cp -r build/ ../static/
-
-run-dev:
-	FLASK_DEBUG=development python app.py
-
-run-prod:
-	python app.py & python gradio-app.py
-
-run-mock:
-	python app.py & python gradio-mock-app.py
-
-stop-server:
-	killall python
-
-all: run-prod
-
-mock: run-mock
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/liteflownet/README.md b/spaces/richardzhangy26/yandian_flow_classification/configs/liteflownet/README.md
deleted file mode 100644
index 1328453727ee0ca8bdcb21d71b3de3853468c55d..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/liteflownet/README.md
+++ /dev/null
@@ -1,188 +0,0 @@
-# LiteFlowNet
-
-[LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation](https://arxiv.org/pdf/1805.07036.pdf)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-FlowNet2, the state-of-the-art convolutional neural
-network (CNN) for optical flow estimation, requires over
-160M parameters to achieve accurate flow estimation. In
-this paper we present an alternative network that outperforms
-FlowNet2 on the challenging Sintel final pass and
-KITTI benchmarks, while being 30 times smaller in the
-model size and 1.36 times faster in the running speed. This
-is made possible by drilling down to architectural details
-that might have been missed in the current frameworks: (1)
-We present a more effective flow inference approach at each
-pyramid level through a lightweight cascaded network. It
-not only improves flow estimation accuracy through early
-correction, but also permits seamless incorporation of descriptor matching
-in our network. (2) We present a novel flow regularization layer
-to ameliorate the issue of outliers and vague flow boundaries
-by using a feature-driven local convolution. (3) Our network owns
-an effective structure for pyramidal feature extraction and embraces feature
-warping rather than image warping as practiced in FlowNet2.
-Our code and trained models are available at
-https://github.com/twhui/LiteFlowNet.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/76149310/142731269-eee91f40-1a4d-4c9e-afc6-6d90b0674b62.png" width="70%"/>
-</div>
-
-## Results and Models
-
-<table>
-    <thead>
-        <tr>
-            <td rowspan=2>Models</td>
-            <td rowspan=2>Training datasets</td>
-            <td rowspan=2>FlyingChairs</td>
-            <td colspan=2>Sintel (training)</td>
-            <td colspan=1>KITTI2012 (training)</td>
-            <td colspan=2>KITTI2015 (training)</td>
-            <td rowspan=2>Log</td>
-            <td rowspan=2>Config</td>
-            <td rowspan=2>Download</td>
-        </tr>
-        <tr>
-            <th>clean</th>
-            <th>final</th>
-            <th>EPE</th>
-            <th>Fl-all</th>
-            <th>EPE</th>
-        </tr>
-    </thead>
-    <tbody>
-        <tr>
-            <th>LiteFlowNet-pre-M6S6</th>
-            <th>Flying Chairs</th>
-            <th>4.43</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M6S6_8x1_flyingchairs_320x448.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M6S6_8x1_flyingchairs_320x448.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M6S6_8x1_flyingchairs_320x448.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-pre-M6S6R6</th>
-            <th>Flying Chairs</th>
-            <th>4.07</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M6S6R6_8x1_flyingchairs_320x448.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M6S6R6_8x1_flyingchairs_320x448.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M6S6R6_8x1_flyingchairs_320x448.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-pre-M5S5R5</th>
-            <th>Flying Chairs</th>
-            <th>2.98</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M5S5R5_8x1_flyingchairs_320x448.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M5S5R5_8x1_flyingchairs_320x448.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M5S5R5_8x1_flyingchairs_320x448.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-pre-M4S4R4</th>
-            <th>Flying Chairs</th>
-            <th>2.20</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M4S4R4_8x1_flyingchairs_320x448.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M4S4R4_8x1_flyingchairs_320x448.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M4S4R4_8x1_flyingchairs_320x448.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-pre-M3S3R3</th>
-            <th>Flying Chairs</th>
-            <th>1.71</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M3S3R3_8x1_flyingchairs_320x448.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M3S3R3_8x1_flyingchairs_320x448.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M3S3R3_8x1_flyingchairs_320x448.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-pre (LiteFlowNet-pre-M2S2R2)</th>
-            <th>Flying Chairs</th>
-            <th>1.38</th>
-            <th>2.74</th>
-            <th>4.52</th>
-            <th>6.49</th>
-            <th>37.99%</th>
-            <th>15.41</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M2S2R2_8x1_flyingchairs_320x448.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M2S2R2_8x1_flyingchairs_320x448.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_pre_M2S2R2_8x1_flyingchairs_320x448.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet</th>
-            <th>Flying Chairs + Flying Thing3d subset</th>
-            <th>-</th>
-            <th>2.47</th>
-            <th>4.30</th>
-            <th>5.42</th>
-            <th>32.86$</th>
-            <th>13.50</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_8x1_500k_flyingthings3d_subset_384x768.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_8x1_500k_flyingthings3d_subset_384x768.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_8x1_500k_flyingthings3d_subset_384x768.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-ft</th>
-            <th>Flying Chairs + Flying Thing3d subset + Sintel</th>
-            <th>-</th>
-            <th>1.47</th>
-            <th>2.06</th>
-            <th>-</th>
-            <th>-</th>
-            <th>-</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_ft_4x1_500k_sintel_384x768.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_ft_4x1_500k_sintel_384x768.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_ft_4x1_500k_sintel_384x768.pth'>Model</a></th>
-        </tr>
-        <tr>
-            <th>LiteFlowNet-ft</th>
-            <th>Flying Chairs + Flying Thing3d subset + KITTI</th>
-            <th>-</th>
-            <th></th>
-            <th></th>
-            <th>1.07</th>
-            <th>5.45%</th>
-            <th>1.45</th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_ft_4x1_500k_kitti_320x896.log.json'>log</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_ft_4x1_500k_kitti_320x896.py'>Config</a></th>
-            <th><a href='https://download.openmmlab.com/mmflow/liteflownet/liteflownet_ft_4x1_500k_kitti_320x896.pth'>Model</a></th>
-        </tr>
-    </tbody>
-</table>
-
-## Citation
-
-```bibtex
-@inproceedings{hui2018liteflownet,
-  title={Liteflownet: A lightweight convolutional neural network for optical flow estimation},
-  author={Hui, Tak-Wai and Tang, Xiaoou and Loy, Chen Change},
-  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
-  pages={8981--8989},
-  year={2018}
-}
-```
diff --git a/spaces/robin0307/MMOCR/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py b/spaces/robin0307/MMOCR/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py
deleted file mode 100644
index 843fd36fc60682706503120f16866ba511cf7310..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# model settings
-model = dict(
-    type='OCRMaskRCNN',
-    backbone=dict(
-        type='mmdet.ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='mmdet.FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=5),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[4],
-            ratios=[0.17, 0.44, 1.13, 2.90, 7.46],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(
-        type='StandardRoIHead',
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=dict(
-            type='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=1,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        mask_head=dict(
-            type='FCNMaskHead',
-            num_convs=4,
-            in_channels=256,
-            conv_out_channels=256,
-            num_classes=1,
-            loss_mask=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
-
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                match_low_quality=True,
-                ignore_iof_thr=-1,
-                gpu_assign_thr=50),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_across_levels=False,
-            nms_pre=2000,
-            nms_post=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='OHEMSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_across_levels=False,
-            nms_pre=1000,
-            nms_post=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.05,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100,
-            mask_thr_binary=0.5)))
diff --git a/spaces/rorallitri/biomedical-language-models/logs/         .md b/spaces/rorallitri/biomedical-language-models/logs/         .md
deleted file mode 100644
index 05939dea70ffec647930e0157288d4710eb5c56e..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/         .md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Реальная рыбалка ключ скачать</h2><br /><p><b><b>Download</b> >>> <a href="https://tinurll.com/2uzmxw">https://tinurll.com/2uzmxw</a></b></p><br /><br />
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diff --git a/spaces/rushic24/Priyanka-Chopra-TTS/synthesis/vocoders/hifigan_model.py b/spaces/rushic24/Priyanka-Chopra-TTS/synthesis/vocoders/hifigan_model.py
deleted file mode 100644
index 0dfedfd1f9c333ee0fe4138729ff34316b67caca..0000000000000000000000000000000000000000
--- a/spaces/rushic24/Priyanka-Chopra-TTS/synthesis/vocoders/hifigan_model.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Credit: https://github.com/jik876/hifi-gan
-#
-# MIT License
-#
-# Copyright (c) 2020 Jungil Kong
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-LRELU_SLOPE = 0.1
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
-                ),
-                weight_norm(
-                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
-                ),
-                weight_norm(
-                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-        self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if h.resblock == "1" else ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        h.upsample_initial_channel // (2**i),
-                        h.upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-                norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-                norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-                norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-                norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList(
-            [
-                DiscriminatorP(2),
-                DiscriminatorP(3),
-                DiscriminatorP(5),
-                DiscriminatorP(7),
-                DiscriminatorP(11),
-            ]
-        )
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 128, 15, 1, padding=7)),
-                norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-                norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-                norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList(
-            [
-                DiscriminatorS(use_spectral_norm=True),
-                DiscriminatorS(),
-                DiscriminatorS(),
-            ]
-        )
-        self.meanpools = nn.ModuleList([AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
diff --git a/spaces/sarinam/speaker-anonymization/IMSToucan/InferenceInterfaces/InferenceArchitectures/InferenceHiFiGAN.py b/spaces/sarinam/speaker-anonymization/IMSToucan/InferenceInterfaces/InferenceArchitectures/InferenceHiFiGAN.py
deleted file mode 100644
index 6ada209f8f135b6a5784eb4d78895e7e6226dca7..0000000000000000000000000000000000000000
--- a/spaces/sarinam/speaker-anonymization/IMSToucan/InferenceInterfaces/InferenceArchitectures/InferenceHiFiGAN.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import torch
-
-from ...Layers.ResidualBlock import HiFiGANResidualBlock as ResidualBlock
-
-
-class HiFiGANGenerator(torch.nn.Module):
-
-    def __init__(self,
-                 path_to_weights,
-                 in_channels=80,
-                 out_channels=1,
-                 channels=512,
-                 kernel_size=7,
-                 upsample_scales=(8, 6, 4, 4),
-                 upsample_kernel_sizes=(16, 12, 8, 8),
-                 resblock_kernel_sizes=(3, 7, 11),
-                 resblock_dilations=[(1, 3, 5), (1, 3, 5), (1, 3, 5)],
-                 use_additional_convs=True,
-                 bias=True,
-                 nonlinear_activation="LeakyReLU",
-                 nonlinear_activation_params={"negative_slope": 0.1},
-                 use_weight_norm=True, ):
-        super().__init__()
-        assert kernel_size % 2 == 1, "Kernal size must be odd number."
-        assert len(upsample_scales) == len(upsample_kernel_sizes)
-        assert len(resblock_dilations) == len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_kernel_sizes)
-        self.num_blocks = len(resblock_kernel_sizes)
-        self.input_conv = torch.nn.Conv1d(in_channels,
-                                          channels,
-                                          kernel_size,
-                                          1,
-                                          padding=(kernel_size - 1) // 2, )
-        self.upsamples = torch.nn.ModuleList()
-        self.blocks = torch.nn.ModuleList()
-        for i in range(len(upsample_kernel_sizes)):
-            self.upsamples += [
-                torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
-                                    torch.nn.ConvTranspose1d(channels // (2 ** i),
-                                                             channels // (2 ** (i + 1)),
-                                                             upsample_kernel_sizes[i],
-                                                             upsample_scales[i],
-                                                             padding=(upsample_kernel_sizes[i] - upsample_scales[i]) // 2, ), )]
-            for j in range(len(resblock_kernel_sizes)):
-                self.blocks += [ResidualBlock(kernel_size=resblock_kernel_sizes[j],
-                                              channels=channels // (2 ** (i + 1)),
-                                              dilations=resblock_dilations[j],
-                                              bias=bias,
-                                              use_additional_convs=use_additional_convs,
-                                              nonlinear_activation=nonlinear_activation,
-                                              nonlinear_activation_params=nonlinear_activation_params, )]
-        self.output_conv = torch.nn.Sequential(
-            torch.nn.LeakyReLU(),
-            torch.nn.Conv1d(channels // (2 ** (i + 1)),
-                            out_channels,
-                            kernel_size,
-                            1,
-                            padding=(kernel_size - 1) // 2, ),
-            torch.nn.Tanh(), )
-        if use_weight_norm:
-            self.apply_weight_norm()
-        self.load_state_dict(torch.load(path_to_weights, map_location='cpu')["generator"])
-
-    def forward(self, c, normalize_before=False):
-        if normalize_before:
-            c = (c - self.mean) / self.scale
-        c = self.input_conv(c.unsqueeze(0))
-        for i in range(self.num_upsamples):
-            c = self.upsamples[i](c)
-            cs = 0.0  # initialize
-            for j in range(self.num_blocks):
-                cs = cs + self.blocks[i * self.num_blocks + j](c)
-            c = cs / self.num_blocks
-        c = self.output_conv(c)
-        return c.squeeze(0).squeeze(0)
-
-    def remove_weight_norm(self):
-        def _remove_weight_norm(m):
-            try:
-                torch.nn.utils.remove_weight_norm(m)
-            except ValueError:
-                return
-
-        self.apply(_remove_weight_norm)
-
-    def apply_weight_norm(self):
-        def _apply_weight_norm(m):
-            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d):
-                torch.nn.utils.weight_norm(m)
-
-        self.apply(_apply_weight_norm)
diff --git a/spaces/scedlatioru/img-to-music/example/Dalet Radio Suite.rar.md b/spaces/scedlatioru/img-to-music/example/Dalet Radio Suite.rar.md
deleted file mode 100644
index 8ed9585fc0849259f8a692b36948a6dea9ad08e5..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Dalet Radio Suite.rar.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Dalet Radio Suite.rar</h2><br /><p><b><b>Download File</b> &#10004;&#10004;&#10004; <a href="https://gohhs.com/2uEyKc">https://gohhs.com/2uEyKc</a></b></p><br /><br />
-<br />
-Podcast Exporter exports audios in MP3 and metadata in RSS format ... DP.RD.RADIO. Dalet Radio OnAir. (Player and Carts). Dalet OnAir Player ensures live and automatic ... Dalet News Suite integrates with various models of teleprompters. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Extreme PBR 2.0 Addon For Blender 2.8.md b/spaces/scedlatioru/img-to-music/example/Extreme PBR 2.0 Addon For Blender 2.8.md
deleted file mode 100644
index 4b0c5892b237e19e1798862ef8ac7bf91dbe966b..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Extreme PBR 2.0 Addon For Blender 2.8.md	
+++ /dev/null
@@ -1,35 +0,0 @@
-
-<h1>How to Use Extreme PBR 2.0 Addon for Blender 2.8</h1>
-<p>Extreme PBR 2.0 is an addon for Blender 2.8 that allows you to import and manage hundreds of PBR materials with ease. PBR stands for Physically Based Rendering, which means that the materials are realistic and consistent under different lighting conditions. With Extreme PBR 2.0, you can create stunning scenes and models in Blender with just a few clicks.</p>
-<h2>Extreme PBR 2.0 addon for Blender 2.8</h2><br /><p><b><b>Download File</b> &#10027; <a href="https://gohhs.com/2uEyX7">https://gohhs.com/2uEyX7</a></b></p><br /><br />
-<p>In this article, we will show you how to install and use Extreme PBR 2.0 addon for Blender 2.8, and some of the features and benefits of this addon.</p>
-<h2>How to Install Extreme PBR 2.0 Addon for Blender 2.8</h2>
-<p>To install Extreme PBR 2.0 addon for Blender 2.8, you need to purchase it from the <a href="https://blendermarket.com/products/extreme-pbr-20-addon-for-blender-28">Blender Market</a>. The addon costs $29, but you can use the code "cgmatter" for a 20% discount[^1^]. After purchasing, you will receive a zip file containing the addon and the libraries of materials.</p>
-<p>To install the addon, follow these steps:</p>
-<p></p>
-<ol>
-<li>Open Blender 2.8 and go to Edit > Preferences > Add-ons.</li>
-<li>Click on Install and locate the zip file of the addon (Extreme_PBR_2.0_addon_for_Blender_2.8.rar).</li>
-<li>Select the zip file and click on Install Add-on.</li>
-<li>Enable the addon by checking the box next to Node: Extreme PBR Combo.</li>
-<li>Click on Save Preferences and close the window.</li>
-</ol>
-<p>To install the libraries of materials, follow these steps:</p>
-<ol>
-<li>Unzip the libraries zip files (Extreme_PBR_2_Library_1.zip, Extreme_PBR_2_Library_2.zip, etc.) to a folder of your choice.</li>
-<li>In Blender, go to File > External Data > Find Missing Files.</li>
-<li>Select the folder where you unzipped the libraries and click on Find Missing Files.</li>
-<li>Wait for Blender to find and link all the materials to the addon.</li>
-</ol>
-<h2>How to Use Extreme PBR 2.0 Addon for Blender 2.8</h2>
-<p>To use Extreme PBR 2.0 addon for Blender 2.8, you need to have a mesh object selected in Object Mode. Then, you can access the addon panel by pressing N on your keyboard and going to the Extreme PBR Combo tab.</p>
-<p>The addon panel has four sections: Materials, UV Mapping, Shader Maker, and Videomaker.</p>
-<h3>Materials</h3>
-<p>The Materials section is where you can browse and apply the materials from the libraries. You can filter the materials by category (such as Metal, Wood, Stone, etc.), or by name using the search bar. You can also create your own categories by clicking on the + button next to Category.</p>
-<p>To apply a material to your object, simply click on the thumbnail of the material. You can also preview the material by hovering over it with your mouse cursor. The material will be applied to your object using a Principled BSDF shader node with all the maps (such as Diffuse, Normal, Roughness, Metallic, Specular, and Displacement) connected automatically.</p>
-<p>You can adjust some parameters of the material using the sliders below the thumbnails, such as Scale, Rotation, Offset, Strength, and Mix Factor. You can also enable or disable some maps using the checkboxes below them.</p>
-<h3>UV Mapping</h3>
-<p>The UV Mapping section is where you can control the rotation, scale, and position of the UV mapping of your object directly from the panel. You can use this section to fine-tune how your material looks on your object without going into Edit Mode or UV Editing Mode.</p>
-<p>You can adjust some parameters of the UV mapping using the sliders below them, such as U Scale, V Scale, U Rotation, V Rotation, U Offset, and V Offset</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/HD Online Player (2015 Hd Video Songs 1080p Blu Ray Ta).md b/spaces/scedlatioru/img-to-music/example/HD Online Player (2015 Hd Video Songs 1080p Blu Ray Ta).md
deleted file mode 100644
index 9d02dc0056fa41719a38bdc69f99e36597fad0a1..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/HD Online Player (2015 Hd Video Songs 1080p Blu Ray Ta).md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<br />
-<p>its pretty straightforward. its not a complex app, but it doesnt need to be. its just what you need to upload and access your music. and it does a good job of it. if your looking for a cross platform app, then this isnt the one for you. but if you are looking for a good free file manager and music player, then this is the one for you.</p>
-<p>as well as being a great way of keeping your cameras safe and sound, an sd memory card is the ideal way of transferring your captured photographs and videos to your computer for viewing on the go, or sharing with friends. sd cards are also the most affordable option, and will allow you to save a lot of space on your laptop or pc for other uses. its also worth remembering that you can transfer any photos and videos from your memory card to an online folder, making it a great way of backing up your pictures and videos.</p>
-<h2>HD Online Player (2015 Hd Video Songs 1080p Blu Ray Ta)</h2><br /><p><b><b>DOWNLOAD</b> &#9675; <a href="https://gohhs.com/2uEzim">https://gohhs.com/2uEzim</a></b></p><br /><br />
-<p>youre going to find that people have different ways of managing their spotify account, but its fairly common for all of them to be able to listen to the music they want, whenever they want. if youve got a premium account, youre able to download spotify for offline listening, so you can find your favorite songs even when youre on the go. you can download the music, meaning youre not tied to the spotify interface, and youll be able to listen to it on any device, whether its an iphone, an android phone, or on a computer.</p>
-<p>one of the most obvious reasons to get a 4k tv is to enjoy high-definition video and audio without sacrificing the convenience of a standard-definition screen. the added resolution is what makes a 4k tv worthwhile, and it also enables you to get a lot more content on screen at once. remember that most 4k tvs cant handle older, standard-definition content, so if you want to enjoy any blu-ray discs or dvds, youll need a player that can handle the 1080p standard.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Php Booking System Nulled Xenforo PORTABLE.md b/spaces/scedlatioru/img-to-music/example/Php Booking System Nulled Xenforo PORTABLE.md
deleted file mode 100644
index b420b8ec514e4ce43a54d9641f2ab20800613cbc..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Php Booking System Nulled Xenforo PORTABLE.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Php Booking System Nulled Xenforo</h2><br /><p><b><b>Download File</b> &gt;&gt;&gt;&gt;&gt; <a href="https://gohhs.com/2uEAhA">https://gohhs.com/2uEAhA</a></b></p><br /><br />
-
-Post free & nulled php scripts,CMS themes, HTML templates here. visit... Coinjoker offers remtano clone script in the form of readymade and customized solutions ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/schibsted/facial_expression_classifier/app.py b/spaces/schibsted/facial_expression_classifier/app.py
deleted file mode 100644
index de9127091998ae51762f8010b1146c69283102fb..0000000000000000000000000000000000000000
--- a/spaces/schibsted/facial_expression_classifier/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Facial expression classifier
-import os
-from fastai.vision.all import *
-import gradio as gr
-
-# Emotion
-learn_emotion = load_learner('emotions_vgg19.pkl')
-learn_emotion_labels = learn_emotion.dls.vocab
-
-# Sentiment
-learn_sentiment = load_learner('sentiment_vgg19.pkl')
-learn_sentiment_labels = learn_sentiment.dls.vocab
-
-# Predict
-def predict(img):
-    img = PILImage.create(img)
-    
-    pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(img)
-    
-    pred_sentiment, pred_sentiment_idx, probs_sentiment = learn_sentiment.predict(img)
-    
-    #emotions = {f'emotion_{learn_emotion_labels[i]}': float(probs_emotion[i]) for i in range(len(learn_emotion_labels))}
-    #sentiments = {f'sentiment_{learn_sentiment_labels[i]}': float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))}
-    
-    emotions = {learn_emotion_labels[i]: float(probs_emotion[i]) for i in range(len(learn_emotion_labels))}
-    sentiments = {learn_sentiment_labels[i]: float(probs_sentiment[i]) for i in range(len(learn_sentiment_labels))}
-        
-    return [emotions, sentiments] #{**emotions, **sentiments}
-
-# Gradio
-title = "Facial Emotion and Sentiment Detector"
-
-description = gr.Markdown(
-                """Ever wondered what a person might be feeling looking at their picture? 
-                 Well, now you can! Try this fun app. Just upload a facial image in JPG or
-                 PNG format. Voila! you can now see what they might have felt when the picture
-                 was taken.
-                 
-                 **Tip**: Be sure to only include face to get best results. Check some sample images
-                 below for inspiration!""").value
-
-article = gr.Markdown(
-             """**DISCLAIMER:** This model does not reveal the actual emotional state of a person. Use and 
-             interpret results at your own risk! It was built as a demo for AI course. Samples images
-             were downloaded from VG & AftenPosten news webpages. Copyrights belong to respective
-             brands. All rights reserved.
-             
-             **PREMISE:** The idea is to determine an overall sentiment of a news site on a daily basis
-             based on the pictures. We are restricting pictures to only include close-up facial
-             images.
-             
-             **DATA:** FER2013 dataset consists of 48x48 pixel grayscale images of faces. There are 28,709 
-             images in the training set and 3,589 images in the test set. However, for this demo all 
-             pictures were combined into a single dataset and 80:20 split was used for training. Images
-             are assigned one of the 7 emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral.
-             In addition to these 7 classes, images were re-classified into 3 sentiment categories based
-             on emotions:
-             
-             Positive (Happy, Surprise)
-             
-             Negative (Angry, Disgust, Fear, Sad)
-             
-             Neutral (Neutral)
-             
-             FER2013 (preliminary version) dataset can be downloaded at:
-             https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
-             
-             **MODEL:** VGG19 was used as the base model and trained on FER2013 dataset. Model was trained
-             using PyTorch and FastAI. Two models were trained, one for detecting emotion and the other
-             for detecting sentiment. Although, this could have been done with just one model, here two
-             models were trained for the demo.""").value
-
-enable_queue=True
-
-examples = ['happy1.jpg', 'happy2.jpg', 'angry1.png', 'angry2.jpg', 'neutral1.jpg', 'neutral2.jpg']
-
-gr.Interface(fn = predict, 
-             inputs = gr.Image(shape=(48, 48), image_mode='L'), 
-             outputs = [gr.Label(label='Emotion'), gr.Label(label='Sentiment')], #gr.Label(),
-             title = title,
-             examples = examples,
-             description = description,
-             article=article,
-             allow_flagging='never').launch(enable_queue=enable_queue)
\ No newline at end of file
diff --git a/spaces/seanbenhur/tamilatis/tamilatis/main.py b/spaces/seanbenhur/tamilatis/tamilatis/main.py
deleted file mode 100644
index 203cfdd8855444f0fc6910da5e51ad9c45be04cb..0000000000000000000000000000000000000000
--- a/spaces/seanbenhur/tamilatis/tamilatis/main.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import logging
-import os
-import pickle
-
-#import wandb
-import numpy as np
-import hydra
-import pandas as pd
-import torch.nn as nn
-import torch.optim as optim
-from accelerate import Accelerator
-from omegaconf.omegaconf import OmegaConf
-from sklearn.preprocessing import LabelEncoder
-from torch.utils.data import DataLoader
-from transformers import AutoTokenizer, get_scheduler
-
-from dataset import ATISDataset, BuildDataset
-from model import JointATISModel
-from predict import TamilATISPredictor
-from trainer import ATISTrainer
-
-logger = logging.getLogger(__name__)
-
-
-@hydra.main(config_path="./configs", config_name="config")
-def main(cfg):
-
-    os.environ['WANDB_PROJECT'] = cfg.wandb.project_name
-    os.environ['WANDB_RUN_GROUP'] = cfg.wandb.group_name
-    
-    logger.info(OmegaConf.to_yaml(cfg, resolve=True))
-    accelerator = Accelerator()
-    # Get all tags
-    annotations = set()
-    intents = set()
-    count = 0
-
-    logger.info("Building Dataset")
-    data_utils = BuildDataset()
-    train_data = data_utils.build_dataset(cfg.dataset.train_path)
-    valid_data = data_utils.build_dataset(cfg.dataset.valid_path)
-    test_data = data_utils.build_dataset(cfg.dataset.test_path)
-
-    annotations, intents, count = set(), set(), 0
-    for boi_data, intent in train_data:
-        if intent[0] == "B" or intent[0] == "I":
-            count += 1
-        intents.add(intent)
-        for token, annotation in boi_data:
-            annotations.add(annotation)
-
-    for boi_data, intent in valid_data:
-        if intent[0] == "B" or intent[0] == "I":
-            count += 1
-        intents.add(intent)
-        for token, annotation in boi_data:
-            annotations.add(annotation)
-
-    for boi_data, intent in test_data:
-        if intent[0] == "B" or intent[0] == "I":
-            count += 1
-        intents.add(intent)
-        for token, annotation in boi_data:
-            annotations.add(annotation)
-
-    annotations = list(annotations)
-    intents = list(intents)
-
-    # convert string labels to int
-    label_encoder = LabelEncoder()
-    label_encoder.fit(annotations)
-    np.save("/content/tamilatis/models/tamilatis/ner_classes.npy",label_encoder.classes_)
-
-    intent_encoder = LabelEncoder()
-    intent_encoder.fit(intents)
-    np.save("/content/tamilatis/models/tamilatis/intent_classes.npy",intent_encoder.classes_)
-
-    train_ds = ATISDataset(
-        train_data, cfg.model.tokenizer_name, label_encoder, intent_encoder
-    )
-    val_ds = ATISDataset(
-        valid_data, cfg.model.tokenizer_name, label_encoder, intent_encoder
-    )
-    test_ds = ATISDataset(
-        test_data, cfg.model.tokenizer_name, label_encoder, intent_encoder
-    )
-
-    train_dl = DataLoader(train_ds, batch_size=cfg.training.batch_size, pin_memory=True)
-    val_dl = DataLoader(val_ds, batch_size=cfg.training.batch_size * 2, pin_memory=True)
-    test_dl = DataLoader(
-        test_ds, batch_size=cfg.training.batch_size * 2, pin_memory=True
-    )
-    logging.info("DataLoaders are created!")
-
-    model = JointATISModel(
-        cfg.model.model_name, cfg.model.num_labels, cfg.model.num_intents
-    )
-    criterion = nn.CrossEntropyLoss()
-    # Optimizer
-    # Split weights in two groups, one with weight decay and the other not.
-    no_decay = ["bias", "LayerNorm.weight"]
-    optimizer_grouped_parameters = [
-        {
-            "params": [
-                p
-                for n, p in model.named_parameters()
-                if not any(nd in n for nd in no_decay)
-            ],
-            "weight_decay": cfg.training.weight_decay,
-        },
-        {
-            "params": [
-                p
-                for n, p in model.named_parameters()
-                if any(nd in n for nd in no_decay)
-            ],
-            "weight_decay": 0.0,
-        },
-    ]
-    optimizer = optim.AdamW(optimizer_grouped_parameters, lr=cfg.training.lr)
-    nb_train_steps = int(
-        len(train_dl) / cfg.training.batch_size * cfg.training.max_epochs
-    )
-
-    if cfg.training.scheduler is not None:
-      scheduler = get_scheduler(
-        cfg.training.scheduler,
-        optimizer,
-        num_warmup_steps=cfg.training.warmup_steps,
-        num_training_steps=nb_train_steps)
-      # Register the LR scheduler
-      accelerator.register_for_checkpointing(scheduler)
-
-    scheduler = None
-    model, optimizer, train_dl, val_dl = accelerator.prepare(
-        model, optimizer, train_dl, val_dl
-    )
-    
-    run = wandb.init(cfg.wandb.project_name,cfg.wandb.group_name,cfg.wandb.run_name)
-    if cfg.training.do_train:
-        trainer = ATISTrainer(
-            model,
-            optimizer,
-            scheduler,
-            criterion,
-            accelerator,
-            cfg.dataset.output_dir,
-            cfg.dataset.num_labels,
-            cfg.dataset.num_intents,
-            run
-        )
-        best_model, best_loss = trainer.fit(
-            cfg.training.max_epochs, train_dl, val_dl, cfg.training.patience
-        )
-        model_dir = f"{cfg.dataset.output_dir}/model_{best_loss}"
-        if not os.path.exists(model_dir):
-            os.makedirs(model_dir)
-        best_model.save_pretrained(model_dir, push_to_hub=False)
-        logging.info(
-            f"The Best model with validation loss {best_loss} is saved in {model_dir}"
-        )
-    if cfg.training.do_predict:
-        predictor = TamilATISPredictor(
-            model,
-            cfg.model.test_model,
-            cfg.model.tokenizer_name,
-            label_encoder,
-            intent_encoder,
-            cfg.model.num_labels,
-        )
-        outputs, intents = predictor.predict_test_data(test_data)
-        ner_cls_rep, intent_cls_rep = predictor.evaluate(outputs, intents)
-        ner_cls_df = pd.DataFrame(ner_cls_rep).transpose()
-        intent_cls_df = pd.DataFrame(intent_cls_rep).transpose()
-        ner_cls_df.to_csv(cfg.training.ner_cls_path)
-        intent_cls_df.to_csv(cfg.training.intent_cls_path)
-        logging.info(
-            f"Classification reports of intents and slots are saved in {cfg.training.ner_cls_path} and {cfg.training.intent_cls_path}"
-        )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/vd.py b/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/vd.py
deleted file mode 100644
index e80fe20e63951555bdcbb44f972ebf3fbcb66b42..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Versatile-Diffusion/lib/model_zoo/vd.py
+++ /dev/null
@@ -1,455 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-import numpy.random as npr
-import copy
-from functools import partial
-from contextlib import contextmanager
-from lib.model_zoo.common.get_model import get_model, register
-from lib.log_service import print_log
-
-symbol = 'vd'
-
-from .diffusion_utils import \
-    count_params, extract_into_tensor, make_beta_schedule
-from .distributions import normal_kl, DiagonalGaussianDistribution
-
-from .autokl import AutoencoderKL
-from .ema import LitEma
-
-def highlight_print(info):
-    print_log('')
-    print_log(''.join(['#']*(len(info)+4)))
-    print_log('# '+info+' #')
-    print_log(''.join(['#']*(len(info)+4)))
-    print_log('')
-
-class String_Reg_Buffer(nn.Module):
-    def __init__(self, output_string):
-        super().__init__()
-        torch_string = torch.ByteTensor(list(bytes(output_string, 'utf8')))
-        self.register_buffer('output_string', torch_string)
-
-    @torch.no_grad()
-    def forward(self, *args, **kwargs):
-        list_str = self.output_string.tolist()
-        output_string = bytes(list_str)
-        output_string = output_string.decode()
-        return output_string
-
-@register('vd_v2_0')
-class VD_v2_0(nn.Module):
-    def __init__(self,
-                 vae_cfg_list,
-                 ctx_cfg_list,
-                 diffuser_cfg_list,
-                 global_layer_ptr=None,
-
-                 parameterization="eps",
-                 timesteps=1000,
-                 use_ema=False,
-
-                 beta_schedule="linear",
-                 beta_linear_start=1e-4,
-                 beta_linear_end=2e-2,
-                 given_betas=None,
-                 cosine_s=8e-3,
-
-                 loss_type="l2",
-                 l_simple_weight=1.,
-                 l_elbo_weight=0.,
-                 
-                 v_posterior=0.,
-                 learn_logvar=False, 
-                 logvar_init=0, 
-
-                 latent_scale_factor=None,):
-
-        super().__init__()
-        assert parameterization in ["eps", "x0"], \
-            'currently only supporting "eps" and "x0"'
-        self.parameterization = parameterization
-        highlight_print("Running in {} mode".format(self.parameterization))
-
-        self.vae = self.get_model_list(vae_cfg_list)
-        self.ctx = self.get_model_list(ctx_cfg_list)
-        self.diffuser = self.get_model_list(diffuser_cfg_list)
-        self.global_layer_ptr = global_layer_ptr
-
-        assert self.check_diffuser(), 'diffuser layers are not aligned!'
-
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print_log(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.loss_type = loss_type
-        self.l_simple_weight = l_simple_weight
-        self.l_elbo_weight = l_elbo_weight
-        self.v_posterior = v_posterior
-        self.device = 'cpu'
-
-        self.register_schedule(
-            given_betas=given_betas,
-            beta_schedule=beta_schedule,
-            timesteps=timesteps,
-            linear_start=beta_linear_start,
-            linear_end=beta_linear_end,
-            cosine_s=cosine_s)
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-        self.latent_scale_factor = {} if latent_scale_factor is None else latent_scale_factor
-
-        self.parameter_group = {}
-        for namei, diffuseri in self.diffuser.items():
-            self.parameter_group.update({
-                'diffuser_{}_{}'.format(namei, pgni):pgi for pgni, pgi in diffuseri.parameter_group.items()
-            })
-
-    def to(self, device):
-        self.device = device
-        super().to(device)
-
-    def get_model_list(self, cfg_list):
-        net = nn.ModuleDict()
-        for name, cfg in cfg_list:
-            if not isinstance(cfg, str):
-                net[name] = get_model()(cfg)
-            else:
-                net[name] = String_Reg_Buffer(cfg)
-        return net
-
-    def register_schedule(self, 
-                          given_betas=None, 
-                          beta_schedule="linear", 
-                          timesteps=1000,
-                          linear_start=1e-4, 
-                          linear_end=2e-2, 
-                          cosine_s=8e-3):
-        if given_betas is not None:
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, \
-            'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                    1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        else:
-            raise NotImplementedError("mu not supported")
-        # TODO how to choose this term
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print_log(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print_log(f"{context}: Restored training weights")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        value1 = extract_into_tensor(
-            self.sqrt_recip_alphas_cumprod, t, x_t.shape)
-        value2 = extract_into_tensor(
-            self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-        return value1*x_t -value2*noise
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = torch.randn_like(x_start) if noise is None else noise
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def forward(self, x_info, c_info):
-        x = x_info['x']
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x_info, t, c_info)
-
-    def p_losses(self, x_info, t, c_info, noise=None):
-        x = x_info['x']
-        noise = torch.randn_like(x) if noise is None else noise
-        x_noisy = self.q_sample(x_start=x, t=t, noise=noise)
-        x_info['x'] = x_noisy
-        model_output = self.apply_model(x_info, t, c_info)
-
-        loss_dict = {}
-
-        if self.parameterization == "x0":
-            target = x
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        bs = model_output.shape[0]
-        loss_simple = self.get_loss(model_output, target, mean=False).view(bs, -1).mean(-1)
-        loss_dict['loss_simple'] = loss_simple.mean()
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-
-        if self.learn_logvar:
-            loss_dict['loss_gamma'] = loss.mean()
-            loss_dict['logvar'    ] = self.logvar.data.mean()
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).view(bs, -1).mean(-1)
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict['loss_vlb'] = loss_vlb
-        loss_dict.update({'Loss': loss})
-
-        return loss, loss_dict
-
-    @torch.no_grad()
-    def vae_encode(self, x, which, **kwargs):
-        z = self.vae[which].encode(x, **kwargs)
-        if self.latent_scale_factor is not None:
-            if self.latent_scale_factor.get(which, None) is not None:
-                scale = self.latent_scale_factor[which]
-                return scale * z
-        return z
-
-    @torch.no_grad()
-    def vae_decode(self, z, which, **kwargs):
-        if self.latent_scale_factor is not None:
-            if self.latent_scale_factor.get(which, None) is not None:
-                scale = self.latent_scale_factor[which]
-                z = 1./scale * z
-        x = self.vae[which].decode(z, **kwargs)
-        return x
-
-    @torch.no_grad()
-    def ctx_encode(self, x, which, **kwargs):
-        if which.find('vae_') == 0:
-            return self.vae[which[4:]].encode(x, **kwargs)
-        else:
-            return self.ctx[which].encode(x, **kwargs)
-
-    def ctx_encode_trainable(self, x, which, **kwargs):
-        if which.find('vae_') == 0:
-            return self.vae[which[4:]].encode(x, **kwargs)
-        else:
-            return self.ctx[which].encode(x, **kwargs)
-
-    def check_diffuser(self):
-        for idx, (_, diffuseri) in enumerate(self.diffuser.items()):
-            if idx==0:
-                order = diffuseri.layer_order
-            else:
-                if not order == diffuseri.layer_order:
-                    return False
-        return True
-
-    @torch.no_grad()
-    def on_train_batch_start(self, x):
-        pass
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def apply_model(self, x_info, timesteps, c_info):
-        x_type, x = x_info['type'], x_info['x']
-        c_type, c = c_info['type'], c_info['c']
-        dtype = x.dtype
-
-        hs = []
-
-        from .openaimodel import timestep_embedding
-
-        glayer_ptr = x_type if self.global_layer_ptr is None else self.global_layer_ptr
-        model_channels = self.diffuser[glayer_ptr].model_channels
-        t_emb = timestep_embedding(timesteps, model_channels, repeat_only=False).to(dtype)
-        emb = self.diffuser[glayer_ptr].time_embed(t_emb)
-
-        d_iter = iter(self.diffuser[x_type].data_blocks)
-        c_iter = iter(self.diffuser[c_type].context_blocks)
-
-        i_order = self.diffuser[x_type].i_order
-        m_order = self.diffuser[x_type].m_order
-        o_order = self.diffuser[x_type].o_order
-
-        h = x
-        for ltype in i_order:
-            if ltype == 'd':
-                module = next(d_iter)
-                h = module(h, emb, None)
-            elif ltype == 'c':
-                module = next(c_iter)
-                h = module(h, emb, c)
-            elif ltype == 'save_hidden_feature':
-                hs.append(h)
-
-        for ltype in m_order:
-            if ltype == 'd':
-                module = next(d_iter)
-                h = module(h, emb, None)
-            elif ltype == 'c':
-                module = next(c_iter)
-                h = module(h, emb, c)
-
-        for ltype in o_order:
-            if ltype == 'load_hidden_feature':
-                h = torch.cat([h, hs.pop()], dim=1)
-            elif ltype == 'd':
-                module = next(d_iter)
-                h = module(h, emb, None)
-            elif ltype == 'c':
-                module = next(c_iter)
-                h = module(h, emb, c)
-        o = h
-
-        return o
-
-    def context_mixing(self, x, emb, context_module_list, context_info_list, mixing_type):
-        nm = len(context_module_list)
-        nc = len(context_info_list)
-        assert nm == nc
-        context = [c_info['c'] for c_info in context_info_list]
-        cratio = np.array([c_info['ratio'] for c_info in context_info_list])
-        cratio = cratio / cratio.sum()
-
-        if mixing_type == 'attention':
-            h = None
-            for module, c, r in zip(context_module_list, context, cratio):
-                hi = module(x, emb, c) * r
-                h = h+hi if h is not None else hi
-            return h
-        elif mixing_type == 'layer':
-            ni = npr.choice(nm, p=cratio)
-            module = context_module_list[ni]
-            c = context[ni]
-            h = module(x, emb, c)
-            return h
-
-    def apply_model_multicontext(self, x_info, timesteps, c_info_list, mixing_type='attention'):
-        '''
-        context_info_list: [[context_type, context, ratio]] for 'attention'
-        '''
-
-        x_type, x = x_info['type'], x_info['x']
-        dtype = x.dtype
-
-        hs = []
-
-        from .openaimodel import timestep_embedding
-        model_channels = self.diffuser[x_type].model_channels
-        t_emb = timestep_embedding(timesteps, model_channels, repeat_only=False).to(dtype)
-        emb = self.diffuser[x_type].time_embed(t_emb)
-
-        d_iter = iter(self.diffuser[x_type].data_blocks)
-        c_iter_list = [iter(self.diffuser[c_info['type']].context_blocks) for c_info in c_info_list]
-
-        i_order = self.diffuser[x_type].i_order
-        m_order = self.diffuser[x_type].m_order
-        o_order = self.diffuser[x_type].o_order
-
-        h = x
-        for ltype in i_order:
-            if ltype == 'd':
-                module = next(d_iter)
-                h = module(h, emb, None)
-            elif ltype == 'c':
-                module_list = [next(c_iteri) for c_iteri in c_iter_list]
-                h = self.context_mixing(h, emb, module_list, c_info_list, mixing_type)
-            elif ltype == 'save_hidden_feature':
-                hs.append(h)
-
-        for ltype in m_order:
-            if ltype == 'd':
-                module = next(d_iter)
-                h = module(h, emb, None)
-            elif ltype == 'c':
-                module_list = [next(c_iteri) for c_iteri in c_iter_list]
-                h = self.context_mixing(h, emb, module_list, c_info_list, mixing_type)
-
-        for ltype in o_order:
-            if ltype == 'load_hidden_feature':
-                h = torch.cat([h, hs.pop()], dim=1)
-            elif ltype == 'd':
-                module = next(d_iter)
-                h = module(h, emb, None)
-            elif ltype == 'c':
-                module_list = [next(c_iteri) for c_iteri in c_iter_list]
-                h = self.context_mixing(h, emb, module_list, c_info_list, mixing_type)
-        o = h
-        return o
diff --git a/spaces/shikunl/prismer/prismer/train_pretrain.py b/spaces/shikunl/prismer/prismer/train_pretrain.py
deleted file mode 100644
index d48ccc923e8e382a994988014a5ab0cc59d59654..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/train_pretrain.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) 2023, NVIDIA Corporation & Affiliates. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://github.com/NVlabs/prismer/blob/main/LICENSE
-
-import argparse
-import numpy as np
-import random
-import time
-import datetime
-import functools
-import torch
-try:
-    import ruamel_yaml as yaml
-except ModuleNotFoundError:
-    import ruamel.yaml as yaml
-    
-from accelerate import Accelerator, FullyShardedDataParallelPlugin
-from model.prismer_caption import PrismerCaption
-from dataset import create_dataset, create_loader
-from utils import *
-from tqdm import tqdm
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--mode', default='')
-parser.add_argument('--port', default='')
-
-parser.add_argument('--config', default='configs/pretrain.yaml')
-parser.add_argument('--from_checkpoint', action='store_true')
-parser.add_argument('--shard_grad_op', action='store_true')
-parser.add_argument('--full_shard', action='store_true')
-parser.add_argument('--exp_name', default='', type=str)
-parser.add_argument('--mixed_precision', default='fp16', type=str)
-parser.add_argument('--seed', default=42, type=int)
-args = parser.parse_args()
-
-config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-torch.manual_seed(args.seed)
-np.random.seed(args.seed)
-random.seed(args.seed)
-
-train_dataset = create_dataset('pretrain', config)
-train_loader = create_loader(train_dataset, batch_size=config['batch_size_train'], num_workers=8, train=True)
-
-model = PrismerCaption(config)
-if args.shard_grad_op:  # Model Sharding: ZeRO 2
-    from torch.distributed.fsdp import MixedPrecision, BackwardPrefetch, ShardingStrategy, StateDictType, CPUOffload
-    fsdp_plugin = FullyShardedDataParallelPlugin(sharding_strategy=ShardingStrategy.SHARD_GRAD_OP,
-                                                 backward_prefetch=BackwardPrefetch.BACKWARD_PRE,
-                                                 mixed_precision_policy=MixedPrecision(param_dtype=torch.float16,
-                                                                                       reduce_dtype=torch.float16,
-                                                                                       buffer_dtype=torch.float16),
-                                                 state_dict_type=StateDictType.FULL_STATE_DICT,
-                                                 cpu_offload=CPUOffload(offload_params=False),
-                                                 ignored_modules=model.ignored_modules)
-    accelerator = Accelerator(mixed_precision=args.mixed_precision, fsdp_plugin=fsdp_plugin)
-    model = accelerator.prepare(model)
-
-elif args.full_shard:  # Model Sharding: ZeRO 3
-    from torch.distributed.fsdp import MixedPrecision, BackwardPrefetch, ShardingStrategy, StateDictType
-    from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
-    from model.modules.vit import ResidualAttentionBlock
-    from model.modules.resampler import PerceiverAttentionBlock
-    from model.modules.roberta import RobertaLayer
-    auto_wrap_policy = functools.partial(
-        transformer_auto_wrap_policy,
-        transformer_layer_cls={
-            ResidualAttentionBlock,
-            PerceiverAttentionBlock,
-            RobertaLayer
-        },
-    )
-    fsdp_plugin = FullyShardedDataParallelPlugin(sharding_strategy=ShardingStrategy.FULL_SHARD,
-                                                 backward_prefetch=BackwardPrefetch.BACKWARD_PRE,
-                                                 mixed_precision_policy=MixedPrecision(param_dtype=torch.float16,
-                                                                                       reduce_dtype=torch.float16,
-                                                                                       buffer_dtype=torch.float16),
-                                                 state_dict_type=StateDictType.FULL_STATE_DICT,
-                                                 auto_wrap_policy=auto_wrap_policy,
-                                                 ignored_modules=model.ignored_modules)
-    accelerator = Accelerator(mixed_precision=args.mixed_precision, fsdp_plugin=fsdp_plugin)
-    model = accelerator.prepare(model)
-else:
-    accelerator = Accelerator(mixed_precision=args.mixed_precision)
-
-# Reload saved states
-if args.from_checkpoint:
-    state_dict = torch.load(f'logging/pretrain_{args.exp_name}/pytorch_model.bin', map_location='cpu')
-    if os.path.exists(f'logging/pretrain_{args.exp_name}/epoch.pt'):
-        start_epoch = torch.load(f'logging/pretrain_{args.exp_name}/epoch.pt')[0] + 1
-    else:
-        start_epoch = 0
-    model.load_state_dict(state_dict)
-    accelerator.print(f'Start re-training from checkpoint with Epoch {start_epoch}')
-else:
-    start_epoch = 0
-
-optimizer = torch.optim.AdamW(params=filter(lambda p: p.requires_grad, model.parameters()),
-                              lr=config['init_lr'], weight_decay=config['weight_decay'])
-
-if args.shard_grad_op or args.full_shard:
-    optimizer, train_loader = accelerator.prepare(optimizer, train_loader)
-else:
-    model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader)
-
-
-start_time = time.time()
-warmup_step = 0
-for epoch in range(start_epoch, config['max_epoch']):
-    cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-
-    train_loss = 0
-    num_train_elems = 0
-    model.train()
-    for i, (experts, caption) in enumerate(tqdm(train_loader)):
-        if warmup_step < config['warmup_steps']:
-            warmup_lr_schedule(optimizer, warmup_step, config['warmup_steps'], config['warmup_lr'], config['init_lr'])
-            warmup_step += 1
-
-        loss = model(experts, caption)
-
-        optimizer.zero_grad()
-        accelerator.backward(loss)
-        optimizer.step()
-
-        train_loss += loss.item()
-        num_train_elems += 1
-
-    train_loss /= num_train_elems
-    accelerator.print(f"Epoch {epoch:03d} | loss: {train_loss:.4f} || Time: {(time.time() - start_time):.4f}")
-    accelerator.save_state(f'logging/pretrain_{args.exp_name}')
-    accelerator.save([epoch], f'logging/pretrain_{args.exp_name}/epoch.pt')
-
-total_time = time.time() - start_time
-total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-accelerator.print('Training time {}'.format(total_time_str))
-
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Backrooms Buff Doge Horror How a Meme Became a Monster.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Backrooms Buff Doge Horror How a Meme Became a Monster.md
deleted file mode 100644
index eda9ad316ea0583fb702d23b303e1249f6fe47af..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Backrooms Buff Doge Horror How a Meme Became a Monster.md	
+++ /dev/null
@@ -1,126 +0,0 @@
-
-<h1>Backrooms Buff Doge Horror: A Terrifying Meme-Based Game</h1>
-<p>If you are a fan of horror games and internet memes, you might want to check out <strong>Backrooms Buff Doge Horror</strong>, a free indie horror game where you have to run through mazes on different levels to escape a monstrous hound that looks like the dog on the Doge cryptocurrency coin. You will find clues and weapons along the way, but you will also face traps, puzzles, and a relentless enemy that can kill you in one bite. In this article, we will tell you everything you need to know about this game, from its origin and features to its gameplay and tips.</p>
- <h2>What is Backrooms Buff Doge Horror?</h2>
-<p>Backrooms Buff Doge Horror is a horror game that combines two popular memes: <em>The Backrooms</em> and <em>Buff Doge</em>. The game is set in a series of endless rooms that resemble old office spaces with yellow wallpaper, moist carpet, and fluorescent lights. These rooms are known as <em>The Backrooms</em>, a creepy urban legend that originated on 4chan in 2019. According to the legend, if you are not careful and you noclip out of reality in the wrong areas, you will end up in The Backrooms, where you will be trapped forever in a labyrinth of empty rooms that span six hundred million square miles. To make matters worse, you will also hear something wandering around nearby, which is sure to have heard you.</p>
-<h2>backrooms buff doge horror</h2><br /><p><b><b>Download</b> &#127775; <a href="https://ssurll.com/2uNUnn">https://ssurll.com/2uNUnn</a></b></p><br /><br />
-<p>That something is <em>Buff Doge</em>, a muscular version of Doge, a Shiba Inu dog that became famous as an internet meme in 2013. Buff Doge is usually used to represent something better or stronger than its counterpart, Cheems, another Shiba Inu dog with a speech impediment. However, in this game, Buff Doge is not your friend or ally; he is your enemy and your nightmare. He is a huge, buffed-up dog with red eyes and sharp teeth that can rip you apart in seconds. He is also very fast and smart, so you will have to be quick and clever to avoid him or fight him.</p>
-<p>The game has two modes: story mode and endless mode. In story mode, you have to complete five levels with different themes and difficulties. Each level has a hamburger icon that you need to find to exit The Backrooms and advance to the next level. You will also find notes that reveal more information about The Backrooms and Buff Doge. In endless mode, you have to survive as long as possible in a randomly generated maze with no exit. You can choose the difficulty level and the theme of the maze. The game also has a leaderboard that shows the best scores of other players.</p>
- <h2>Where did the game come from?</h2>
-<h3>The Backrooms meme</h3>
-<p>The Backrooms meme was created by an anonymous user on 4chan's /x/ board on May 12, 2019. The user posted a picture of a yellow room with the caption: "If you're not careful and you noclip out of reality in the wrong areas, you'll end up in The Backrooms, where it's nothing but the stink of old moist carpet, the madness of mono-yellow, the endless background noise of fluorescent lights at maximum hum-buzz, and approximately six hundred million square miles of randomly segmented empty rooms to be trapped in. God save you if you hear something wandering around nearby, because it sure as hell has heard you".</p>
-<p>How to download Backrooms Buff Doge Horror for Windows<br />
-Backrooms Buff Doge Horror gameplay and review<br />
-How to escape the buff doge in the backrooms<br />
-Backrooms Buff Doge Horror sequel to Buff Impostor<br />
-Best weapons to use in Backrooms Buff Doge Horror<br />
-How to find the hamburger in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror indie horror game by MoustacheBanana<br />
-How to survive the mazes in Backrooms Buff Doge Horror<br />
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-How to hide from the buff doge in the backrooms<br />
-Backrooms Buff Doge Horror different themes per level<br />
-How long is Backrooms Buff Doge Horror game<br />
-Backrooms Buff Doge Horror creepy sound effects and voice<br />
-How to solve the puzzles in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror inspired by the dog on the Doge cryptocurrency coin<br />
-How to play Backrooms Buff Doge Horror on PC<br />
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-Backrooms Buff Doge Horror file size and system requirements<br />
-How to run faster in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror user rating and feedback<br />
-How to get more torches in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror best indie horror game of 2023<br />
-How to find the exit in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror scary buff doge monster design<br />
-How to use tools in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror dark rooms and corridors theme<br />
-How to adjust the settings in Backrooms Buff Doge Horror<br />
-Backrooms Buff Doge Horror action and adventure genre<br />
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-Backrooms Buff Doge Horror nostalgic horror game experience<br />
-How to save your progress in Backrooms Buff Doge Horror</p>
-<p>The picture and the caption quickly went viral on Reddit, Twitter, YouTube, and other platforms, spawning various interpretations, stories, fan art, and games based on The Backrooms. Some people claimed to have experienced The Backrooms themselves, while others speculated about what lurks in them. The Backrooms meme became a popular subject for creepypasta, horror fiction, and urban exploration.</p>
- <h3>The Buff Doge meme</h3>
-<p>The Buff Doge meme was created by an Instagram user named @n1ckstagram__ in February 2020. The user posted a picture of a muscular Doge next to a picture of a normal Doge with the caption: "They're the same picture". The picture was a parody of a scene from the TV show <em>The Office</em>, where Jim Halpert shows two identical pictures of Pam Beesly to Dwight Schrute and says: "They're the same picture".</p>
-<p>The picture and the caption became a popular format for comparing two things that are supposedly similar but actually different. The Buff Doge usually represents something superior or desirable, while the normal Doge represents something inferior or undesirable. For example, one meme shows Buff Doge as "Bitcoin" and normal Doge as "Dogecoin". The Buff Doge meme also spawned variations such as Buff Cheems, Buff Wojak, and Buff Pepe.</p>
- <h3>The horror genre</h3>
-<p>The horror genre is a type of fiction that aims to evoke fear, suspense, terror, or dread in the audience. Horror stories often feature supernatural elements, such as ghosts, vampires, zombies, demons, or monsters, as well as realistic threats, such as serial killers, psychopaths, cults, or diseases. Horror stories also often explore themes such as death, violence, madness, isolation, or evil.</p>
-<p>The horror genre has a long and rich history that dates back to ancient times. Some of the earliest examples of horror literature include <em>The Epic of Gilgamesh</em>, <em>The Odyssey</em>, <em>Beowulf</em>, and <em>The Arabian Nights</em>. Some of the most influential horror writers include Edgar Allan Poe, H.P. Lovecraft, Bram Stoker, Mary Shelley, Stephen King, and Clive Barker. Some of the most popular horror media include <em>Frankenstein</em>, <em>Dracula</em>, <em>The Exorcist</em>, <em>The Shining</em>, <em>Nightmare on Elm Street</em>, <em>Saw</em>, and <em>Resident Evil</em>.</p> <h2>How to play Backrooms Buff Doge Horror?</h2>
-<h3>The controls</h3>
-<p>The game is played with a keyboard and a mouse. The keyboard controls are as follows:</p>
-<ul>
-<li>W, A, S, D: Move forward, left, backward, and right.</li>
-<li>Space: Jump.</li>
-<li>Shift: Sprint.</li>
-<li>E: Interact with objects and doors.</li>
-<li>F: Flashlight on/off.</li>
-<li>R: Reload weapon.</li>
-<li>G: Drop weapon.</li>
-<li>Esc: Pause menu.</li>
-</ul>
-<p>The mouse controls are as follows:</p>
-<ul>
-<li>Move mouse: Look around.</li>
-<li>Left click: Shoot weapon.</li>
-<li>Right click: Aim weapon.</li>
-<li>Scroll wheel: Switch weapon.</li>
-</ul>
- <h3>The levels</h3>
-<p>The game has five levels in story mode, each with a different theme and difficulty. The levels are:</p>
-<ol>
-<li><strong>The Office</strong>: This is the first and easiest level. It consists of a series of rooms that look like an old office building. The rooms have desks, chairs, computers, files, and other office items. The hamburger icon is located in a room with a large window that overlooks the city. You will find a pistol and some ammo in this level, as well as some notes that explain the basics of the game. The Buff Doge will start chasing you after you find the first note.</li>
-<li><strong>The Hospital</strong>: This is the second and slightly harder level. It consists of a series of rooms that look like a rundown hospital. The rooms have beds, cabinets, medical equipment, blood stains, and body parts. The hamburger icon is located in a room with an operating table and a surgical lamp. You will find a shotgun and some ammo in this level, as well as some notes that reveal more about The Backrooms and Buff Doge. The Buff Doge will be faster and more aggressive in this level.</li>
-<li><strong>The Factory</strong>: This is the third and medium level. It consists of a series of rooms that look like an abandoned factory. The rooms have machines, pipes, wires, barrels, and crates. The hamburger icon is located in a room with a conveyor belt and a furnace. You will find a machine gun and some ammo in this level, as well as some notes that warn you about the dangers of The Backrooms and Buff Doge. The Buff Doge will be smarter and more cunning in this level.</li>
-<li><strong>The School</strong>: This is the fourth and hard level. It consists of a series of rooms that look like a haunted school. The rooms have desks, chairs, books, chalkboards, lockers, and graffiti. The hamburger icon is located in a room with a podium and a flag. You will find a flamethrower and some fuel in this level, as well as some notes that tell you about the secrets of The Backrooms and Buff Doge. The Buff Doge will be stronger and more resilient in this level.</li>
-<li><strong>The Exit</strong>: This is the fifth and final level. It consists of a single room that looks like the exit of The Backrooms. The room has a door with an exit sign above it. The hamburger icon is located next to the door. You will not find any weapons or notes in this level, but you will have to face the ultimate challenge: the final showdown with Buff Doge. You will have to use all your skills and resources to defeat him and escape The Backrooms once and for all.</li>
-</ol>
- <h3>The weapons</h3>
-<p>The game has four weapons that you can use to fight Buff Doge or distract him. Each weapon has its own advantages and disadvantages, such as damage, range, accuracy, fire rate, reload time, ammo capacity, and availability. The weapons are:</p>
- <table border="1">
-<tr><th>Weapon</th><th>Damage</th><th>Range</th><th>Accuracy</th><th>Fire Rate</th><th>Reload Time</th><th>Ammo Capacity</th><th>Availability</th></tr>
-<tr><td>Pistol</td><td>Low</td><td>Medium</td><td>High</td><td>Medium</td><td>Fast</td><td>12 bullets</td><td>The Office</td></tr>
-<tr><td>Shotgun</td><td>High</td><td>Short</td><td>Low</td><td>Slow</td><td>Slow</td><td>6 shells</td><td>The Hospital</td></tr>
-<tr><td>Machine Gun</td><td>Medium</td><td>Long can help you beat the game:</p>
-<ul>
-<li>Keep moving. Buff Doge can smell you and track you down, so don't stay in one place for too long. Run, jump, and hide as much as you can.</li>
-<li>Use your flashlight wisely. The flashlight can help you see better in the dark, but it can also attract Buff Doge's attention. Turn it off when you don't need it or when you hear him nearby.</li>
-<li>Listen to the sounds. The game has a lot of sound effects that can give you clues about Buff Doge's location, movements, and actions. For example, you can hear his footsteps, his growls, his barks, and his bites. You can also hear the doors opening and closing, the lights flickering, and the objects falling.</li>
-<li>Explore the rooms. The rooms have a lot of items that can help you or hinder you. You can find weapons, ammo, health kits, notes, keys, and other useful things. You can also find traps, alarms, dead ends, and other dangerous things. Be careful and curious.</li>
-<li>Don't panic. Buff Doge is scary and powerful, but he is not invincible. You can fight him or escape him if you are calm and smart. Don't let him intimidate you or make you lose your focus.</li>
-</ul>
- <h2>Why play Backrooms Buff Doge Horror?</h2>
-<p>Backrooms Buff Doge Horror is a game that offers a lot of fun and excitement for horror fans and meme lovers alike. The game has many features that make it appealing, such as:</p>
-<ul>
-<li>Its indie horror effects. The game has a low-poly graphics style that creates a retro and nostalgic atmosphere. The game also has a lot of glitches, bugs, and distortions that add to the surreal and creepy vibe of The Backrooms.</li>
-<li>Its humor. The game has a lot of jokes, references, and Easter eggs that poke fun at the memes, the horror genre, and the game itself. The game also has a lot of absurd and hilarious situations that contrast with the horror elements.</li>
-<li>Its creativity. The game has a lot of originality and imagination in its design, story, and gameplay. The game mixes two memes that have nothing to do with each other and creates a unique and coherent concept. The game also has a lot of variety and surprises in its levels, weapons, and challenges.</li>
-<li>Its difficulty. The game is not easy to beat, but it is not impossible either. The game requires skill, strategy, patience, and perseverance to overcome its obstacles and enemies. The game also rewards the player with satisfaction and achievement for completing its goals.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Backrooms Buff Doge Horror is a game that combines horror and humor in a meme-based adventure. The game is set in The Backrooms, a mysterious and infinite labyrinth of yellow rooms where you have to escape from Buff Doge, a muscular and murderous dog that wants to kill you. The game has two modes: story mode and endless mode. In story mode, you have to complete five levels with different themes and difficulties. In endless mode, you have to survive as long as possible in a randomly generated maze with no exit. The game has four weapons that you can use to fight or distract Buff Doge: pistol, shotgun, machine gun, and flamethrower. The game also has a lot of sound effects, items, notes, and secrets that can help you or hinder you. The game is fun, exciting, scary, and funny at the same time. If you are looking for a game that will make you scream and laugh, you should try Backrooms Buff Doge Horror. You can download it for free from the official website or from itch.io.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Backrooms Buff Doge Horror and their answers:</p>
-<ol>
-<li><strong>Q: Is Backrooms Buff Doge Horror a multiplayer game?</strong></li>
-<li>A: No, Backrooms Buff Doge Horror is a single-player game. You can only play it by yourself.</li>
-<li><strong>Q: Is Backrooms Buff Doge Horror a scary game?</strong></li>
-<li>A: Yes, Backrooms Buff Doge Horror is a scary game. It has a lot of horror elements, such as jump scares, gore, suspense, and tension. However, it also has a lot of humor elements, such as jokes, memes, and satire. The game is not meant to be taken seriously, but rather to be enjoyed as a parody of horror and memes.</li>
-<li><strong>Q: How long is Backrooms Buff Doge Horror?</strong></li>
-<li>A: The length of Backrooms Buff Doge Horror depends on how fast and how well you play. The story mode has five levels that can take from 10 to 30 minutes each to complete. The endless mode has no end, so you can play as long as you want or until you die.</li>
-<li><strong>Q: How can I save my progress in Backrooms Buff Doge Horror?</strong></li>
-<li>A: You can save your progress in Backrooms Buff Doge Horror by pressing the Esc key and selecting the Save option from the pause menu. You can also load your saved progress by selecting the Load option from the main menu.</li>
-<li><strong>Q: How can I contact the developer of Backrooms Buff Doge Horror?</strong></li>
-<li>A: You can contact the developer of Backrooms Buff Doge Horror by sending an email to backroomsbuffdogehorror@gmail.com or by visiting the official website or the itch.io page of the game.</li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Car Simulator 2 Cheat APK How to Get Unlimited Money and More.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Car Simulator 2 Cheat APK How to Get Unlimited Money and More.md
deleted file mode 100644
index 4192c7c096d67c8774ff4d18edb33a9748d68ebf..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Car Simulator 2 Cheat APK How to Get Unlimited Money and More.md	
+++ /dev/null
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-<br />
-<h1>Car Simulator 2 Cheat APK: How to Get Unlimited Money and All Cars Unlocked</h1>
-<p>If you are a fan of driving games, you might have heard of Car Simulator 2, a realistic and fun game that lets you explore a 3D open world, race with other players, and customize your cars. But what if you want to get more out of the game without spending real money or grinding for hours? That's where a cheat APK comes in handy. In this article, we will show you how to download and install Car Simulator 2 Cheat APK, which gives you unlimited money and all cars unlocked in the game. We will also explain the benefits of using this cheat APK and how to use it properly.</p>
- <h2>Introduction</h2>
-<h3>What is Car Simulator 2?</h3>
-<p>Car Simulator 2 is a free-to-play driving simulator game developed by Oppana Games and released in 2019 for Android devices. The game features over 85 new cars, online and single-player modes, daily bonuses and quests, realistic physics and sound effects, interactive gas stations, exciting missions, and a dynamic day-night cycle. You can drive from a first- or third-person perspective, switch between different vehicles, upgrade your cars, buy new garages and houses, and cruise all over the city with your friends. The game has over 50 million downloads and a 4.4-star rating on Google Play Store.</p>
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- <h3>What is a cheat APK?</h3>
-<p>A cheat APK is a modified version of an original APK (Android Package Kit) file that contains some changes or additions that give the user an advantage in the game. For example, a cheat APK can give you unlimited money, coins, gems, lives, or other resources that you normally have to earn or buy in the game. A cheat APK can also unlock all the features, levels, items, or characters that are otherwise locked or restricted in the game. A cheat APK is usually created by third-party developers or hackers who modify the original APK file using various tools and techniques.</p>
- <h2>Benefits of using Car Simulator 2 Cheat APK</h2>
-<h3>Unlimited money</h3>
-<p>One of the main benefits of using Car Simulator 2 Cheat APK is that you get unlimited money in the game. Money is the main currency in Car Simulator 2 that you can use to buy new cars, upgrades, garages, houses, and other items. You can earn money by completing missions, winning races, picking up cab fares, working for the mob, or getting daily bonuses. However, earning money can be time-consuming and tedious, especially if you want to buy the most expensive cars or items in the game. With Car Simulator 2 Cheat APK, you don't have to worry about running out of money or saving up for something. You can buy whatever you want without any limitations or restrictions.</p>
- <h3>All cars unlocked</h3>
-<h3>No ads</h3>
-<p>A third benefit of using Car Simulator 2 Cheat APK is that you get rid of all the ads in the game. Ads are annoying and distracting, especially when they pop up in the middle of the game or force you to watch them before you can continue playing. Ads can also slow down your device or consume your data. With Car Simulator 2 Cheat APK, you don't have to deal with any ads at all. You can enjoy the game without any interruptions or inconveniences.</p>
- <h2>How to download and install Car Simulator 2 Cheat APK</h2>
-<h3>Step 1: Find a reliable source</h3>
-<p>The first step to download and install Car Simulator 2 Cheat APK is to find a reliable source that offers the file. There are many websites and blogs that claim to provide cheat APKs for various games, but not all of them are trustworthy or safe. Some of them may contain viruses, malware, spyware, or other harmful programs that can damage your device or steal your personal information. Some of them may also provide fake or outdated files that don't work or cause problems in the game. Therefore, you should be careful and do some research before downloading any cheat APK from any source. You should look for reviews, ratings, comments, feedback, and testimonials from other users who have tried the cheat APK before. You should also check the file size, version, compatibility, and permissions of the cheat APK before downloading it.</p>
- <h3>Step 2: Enable unknown sources</h3>
-<p>The second step to download and install Car Simulator 2 Cheat APK is to enable unknown sources on your device. Unknown sources are sources that are not verified or authorized by Google Play Store or your device manufacturer. By default, your device does not allow you to install apps from unknown sources for security reasons. However, since cheat APKs are not available on Google Play Store, you have to enable unknown sources to install them on your device. To do this, you have to go to your device settings, then security or privacy, then unknown sources, and then toggle it on. You may also see a warning message that installing apps from unknown sources may harm your device or data. You have to accept the risk and proceed with caution.</p>
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- <h3>Step 3: Download and install the APK file</h3>
-<p>The third step to download and install Car Simulator 2 Cheat APK is to download and install the APK file on your device. To do this, you have to go to the source that you have chosen in step 1 and click on the download button or link. You may also see a pop-up window that asks you to confirm the download or choose a location to save the file. You have to follow the instructions and wait for the download to finish. Once the download is complete, you have to locate the file on your device using a file manager app or your device's built-in file explorer. You have to tap on the file and then tap on install. You may also see a pop-up window that asks you to grant some permissions or access to the app. You have to allow them and wait for the installation to finish.</p>
- <h2>How to use Car Simulator 2 Cheat APK</h2>
-<h3>Step 1: Launch the game</h3>
-<p>The first step to use Car Simulator 2 Cheat APK is to launch the game on your device. To do this, you have to find the game icon on your home screen or app drawer and tap on it. You may also see a loading screen or a splash screen that shows the game logo or some information about the game. You have to wait for the game to load and start.</p>
- <h3>Step 2: Choose your mode and car</h3>
-<p>The second step to use Car Simulator 2 Cheat APK is to choose your mode and car in the game. To do this, you have to go to the main menu of the game and select either online or single-player mode. Online mode allows you to play with other players from around the world in real-time races or free roam sessions. Single-player mode allows you to play solo in various missions or free roam sessions. After choosing your mode, you have to choose your car from over 85 new cars that are all unlocked in the game thanks to the cheat APK. You can also customize your car with different colors, wheels, spoilers, decals, and other accessories.</p>
- <h3>Step 3: Enjoy the game with unlimited money and all cars unlocked</h3>
-<p>you have to drive your car in the 3D open world, race with other players or NPCs, complete various missions, and have fun. You can also visit different places in the city, such as gas stations, car dealerships, repair shops, casinos, and more. You can also buy new garages and houses with your unlimited money and store your cars there. You can also switch between different cars anytime you want and try out their different features and performance. You can enjoy the game with unlimited money and all cars unlocked thanks to the cheat APK.</p>
- <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Car Simulator 2 Cheat APK is a modified version of the original game that gives you unlimited money and all cars unlocked in the game. It also removes all the ads in the game and lets you enjoy the game without any interruptions or inconveniences. You can download and install Car Simulator 2 Cheat APK from a reliable source, enable unknown sources on your device, and install the APK file on your device. Then, you can launch the game, choose your mode and car, and enjoy the game with unlimited money and all cars unlocked.</p>
- <h3>Call to action</h3>
-<p>If you are interested in trying out Car Simulator 2 Cheat APK, you can follow the steps that we have explained in this article and download it from a reliable source. However, you should be aware of the risks and consequences of using cheat APKs, such as getting banned from the game, losing your progress, or harming your device or data. Therefore, you should use cheat APKs at your own discretion and responsibility. We hope that this article has helped you understand how to download and install Car Simulator 2 Cheat APK and how to use it properly. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Car Simulator 2 Cheat APK:</p>
-<table>
-<tr><td><b>Q: Is Car Simulator 2 Cheat APK safe to use?</b></td><td><b>A: Car Simulator 2 Cheat APK is not officially endorsed or supported by Oppana Games or Google Play Store. It is a third-party modification that may contain viruses, malware, spyware, or other harmful programs that can damage your device or data. Therefore, it is not safe to use Car Simulator 2 Cheat APK unless you trust the source and take precautions.</b></td></tr>
-<tr><td><b>Q: Will I get banned from the game if I use Car Simulator 2 Cheat APK?</b></td><td><b>A: There is a possibility that you will get banned from the game if you use Car Simulator 2 Cheat APK. Oppana Games may detect that you are using a cheat APK and suspend or terminate your account for violating their terms of service or fair play policy. Therefore, you should use Car Simulator 2 Cheat APK at your own risk and accept the consequences.</b></td></tr>
-<tr><td><b>Q: Can I update Car Simulator 2 Cheat APK?</b></td><td><b>A: No, you cannot update Car Simulator 2 Cheat APK. If you try to update it from Google Play Store or other sources, you will lose all the benefits of the cheat APK and revert back to the original version of the game. Therefore, you should avoid updating Car Simulator 2 Cheat APK unless there is a new version of the cheat APK available from a reliable source.</b></td></tr>
-<tr><td><b>Q: Can I play online with other players using Car Simulator 2 Cheat APK?</b></td><td><b>A: Yes, you can play online with other players using Car Simulator 2 Cheat APK. However, you may encounter some problems or issues while playing online, such as lagging, crashing, disconnecting, or mismatching with other players who are using different versions of the game. Therefore, you should be prepared for some possible glitches or errors while playing online with Car Simulator 2 Cheat APK.</b></td></tr>
-<tr><td><b>Q: Can I use Car Simulator 2 Cheat APK on other devices?</b></td><td><b>A: Yes, you can use Car Simulator 2 Cheat APK on other devices as long as they are compatible with Android operating system and have enough storage space and memory to run the game. However, you may have to download and install Car Simulator 2 Cheat APK again on each device that you want to use it on.</b></td></tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get Gunship Battle 3D Mod APK 2.7.22 from Pure APK Store.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get Gunship Battle 3D Mod APK 2.7.22 from Pure APK Store.md
deleted file mode 100644
index 95905f3b7f99aee4f455b8da5087535fada3ebbc..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get Gunship Battle 3D Mod APK 2.7.22 from Pure APK Store.md	
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-<br />
-<h1>Gunship Battle 3D 2.7.22 Mod APK Pure: A Review</h1>
- <p>If you are a fan of helicopter combat games, you might have heard of Gunship Battle 3D, a popular game that lets you fly various helicopters and engage in exciting missions. But did you know that there is a mod APK for this game that gives you unlimited money, gold, and access to all the helicopters? In this article, we will review Gunship Battle 3D 2.7.22 mod APK pure, a modified version of the original game that you can download from APKPure.com. We will also discuss the pros and cons of using this mod APK, and answer some frequently asked questions.</p>
- <h2>What is Gunship Battle 3D?</h2>
- <h3>A thrilling helicopter combat game</h3>
- <p>Gunship Battle 3D is a helicopter combat game developed by JOYCITY Corp., a South Korean game developer. The game was released in 2014 for Android and iOS devices, and has since gained millions of downloads and positive reviews from players around the world. The game is inspired by real-life helicopter battles and missions, and features realistic graphics, sound effects, and physics.</p>
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- <h3>Features and gameplay</h3>
- <p>The game has two modes: episode mode and custom mission mode. In episode mode, you can play through various missions based on historical events, such as the Gulf War, the Iraq War, and the Somali Civil War. In custom mission mode, you can create your own missions and challenge yourself or other players online.</p>
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- <h2>What is a mod APK?</h2>
- <h3>A modified version of the original app</h3>
- <p>An APK (Android Package Kit) is a file format that contains all the components of an Android app, such as the code, resources, assets, and manifest. A mod APK is a modified version of an original APK that has been altered by someone other than the developer to add or remove features, change the appearance, or bypass restrictions.</p>
- <h3>Benefits and risks of using a mod APK</h3>
- <p>Using a mod APK can have some benefits, such as getting access to premium features for free, unlocking hidden content, enhancing the gameplay experience, or removing annoying ads. However, using a mod APK can also have some risks, such as exposing your device to malware or viruses, violating the terms of service of the original app, losing your progress or data, or getting banned from online services.</p>
- <p>Therefore, before downloading and installing any mod APK, you should always do some research about its source, quality, compatibility, and safety. You should also backup your data and use a reliable antivirus software to protect your device from potential harm. You should also respect the rights of the original developers and support them if you enjoy their app.</p>
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- <p>Gunship Battle 3D 2.7.22 mod APK pure is a modified version of Gunship Battle 3D that you can download from APKPure.com, a website that provides free and safe APK files for Android users. This mod APK was uploaded on June 16, 2021, and has been downloaded over 10,000 times by users who rated it 4.5 out of 5 stars.</p>
- <h3>How to download and install it</h3>
- <p>To download and install Gunship Battle 3D 2.7.22 mod APK pure, you need to follow these steps:</p>
- <ol>
-<li>Go to APKPure.com and search for Gunship Battle 3D 2.7.22 mod APK pure.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<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 start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Launch the app and enjoy the game with unlimited money, gold, and helicopters.</li>
-</ol>
- <h2>Pros and cons of Gunship Battle 3D 2.7.22 mod APK pure</h2>
- <h3>Pros</h3>
- <h4>Unlimited money and gold</h4>
- <p>One of the main advantages of using this mod APK is that you can get unlimited money and gold in the game, which you can use to buy and upgrade any helicopter you want. You can also buy more parts and equipment to customize your helicopter and make it more powerful and efficient.</p>
- <h4>All helicopters unlocked</h4>
- <p>Another benefit of using this mod APK is that you can unlock all the helicopters in the game, including the ones that are normally available only through in-app purchases or special events. You can choose from over 40 different helicopters, each with its own unique design, features, and weapons. You can also switch between different helicopters during a mission to suit your strategy and preference.</p>
- <h4>No ads</h4>
- <p>A third advantage of using this mod APK is that you can enjoy the game without any ads or interruptions. You can focus on your missions and have a smooth and immersive gameplay experience without having to watch or skip any annoying ads.</p>
- <h3>Cons</h3>
- <h4>Not compatible with some devices</h4>
- <p>One of the drawbacks of using this mod APK is that it may not be compatible with some devices or Android versions. Some users have reported that the game crashes or freezes on their devices, or that some features do not work properly. You may need to check the compatibility of your device before downloading and installing this mod APK.</p>
- <h4>May cause security issues</h4>
- <p>Another disadvantage of using this mod APK is that it may cause security issues on your device or account. Since this mod APK is not authorized by the original developer, it may contain malware or viruses that can harm your device or steal your data. You may also risk getting banned from online services or losing your progress if the developer detects that you are using a modified version of the game.</p>
- <h4>May not work with future updates</h4>
- <p>A third drawback of using this mod APK is that it may not work with future updates of the game. The original developer may release new versions of the game that fix bugs, add new features, or improve performance. However, these updates may not be compatible with this mod APK, or may overwrite its modifications. You may need to wait for a new version of the mod APK to be released, or switch back to the original version of the game.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Gunship Battle 3D 2.7.22 mod APK pure is a modified version of Gunship Battle 3D that gives you unlimited money, gold, and access to all the helicopters in the game. You can download it from APKPure.com and install it on your device following some simple steps. However, you should also be aware of the potential risks and drawbacks of using this mod APK, such as compatibility issues, security issues, or update issues. You should always do your research before downloading any mod APK, and respect the rights of the original developers.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Gunship Battle 3D 2.7.22 mod APK pure:</p>
- <ol>
-<li>Is Gunship Battle 3D 2.7.22 mod APK pure safe to use?</li>
-<p>There is no definitive answer to this question, as different sources may have different levels of safety and quality. However, APKPure.com is a reputable website that claims to scan all the APK files for viruses and malware before uploading them. You can also check the user reviews and ratings to see if there are any complaints or issues reported by other users. However, you should always use a reliable antivirus software and backup your data before installing any mod APK.</p>
-<li>Can I play Gunship Battle 3D online with this mod APK?</li>
-<p>Yes, you can play Gunship Battle 3D online with this mod APK, as it does not require root access or any special permissions. However, you may encounter some problems or errors when playing online, such as lagging, crashing, or disconnecting. You may also risk getting banned from online services or losing your progress if the developer detects that you are using a modified version of the game.</p>
-<li>Can I update Gunship Battle 3D with this mod APK?</li>
-<p>No, you cannot update Gunship Battle 3D with this mod APK, as it may not be compatible with the latest version of the game. If you try to update the game with this mod APK, you may lose your modifications, money, gold, or helicopters. You may also encounter some bugs or glitches that affect the gameplay. You may need to wait for a new version of the mod APK to be released, or switch back to the original version of the game.</p>
-<li>Can I use this mod APK on other devices or platforms?</li>
-<p>No, you cannot use this mod APK on other devices or platforms, as it is designed only for Android devices. You may not be able to install or run this mod APK on iOS, Windows, or Mac devices. You may also need to check the compatibility of your Android device before downloading and installing this mod APK.</p>
-<li>Can I uninstall this mod APK if I don't like it?</li>
-<p>Yes, you can uninstall this mod APK if you don't like it or if you encounter any problems with it. You can simply go to your device settings and uninstall the app like any other app. However, you may lose your progress, data, or modifications when you uninstall this mod APK. You may also need to reinstall the original version of the game if you want to play it again.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/society-ethics/ethical-charters/charters/bigscience.md b/spaces/society-ethics/ethical-charters/charters/bigscience.md
deleted file mode 100644
index bd0a46525aa6b106c79c041a926d9a6e1d3052bb..0000000000000000000000000000000000000000
--- a/spaces/society-ethics/ethical-charters/charters/bigscience.md
+++ /dev/null
@@ -1,112 +0,0 @@
-# BigScience Ethical Charter
-
-## Preamble
-
-### Introduction
-
-The development and applications of research in NLP are advancing rapidly, with direct real-world consequences. As a result, possible societal benefits exist, but related risks also increase considerably. Aware of these potential challenges, BigScience drafted an ethical charter formalizing its core values and how they are articulated. 
-
-### Scope
-
-The scope of this ethical charter is threefold:
-
-1. To establish the core values of BigScience in order to allow its contributors to commit to them, both individually and collectively.
-2. To serve as a pivot for drafting BigScience documents intended to frame specific issues ethically and legally.
-3. To enable Big Science to promote values within the research community through scientific publication, dissemination, and popularization. 
-
-### People concerned
-
-The members of BigScience hold the values stated in this ethical charter. As ethical guidelines, they apply to any activities and documents governing a specific aspect of the project.
-
-### Limitations of this ethical charter
-
-Given the breadth of the scope of BigScience and thriving to seek progress in NLP research, we recognize that not all scientific research will have a positive impact on society. It is difficult to predict all the uses the scientific community will make of our artifacts. Therefore, we defer to our license and model card for further information.
-
-### Relevance over time
-
-We interpret ethics as an ongoing process, not a time-fixed code with universal validity. For these reasons, when needed, BigScience will review, update and adapt the ethical charter from time to time.
-
-### Legitimacy 
-
-The elaboration of this ethical charter results from a bottom-up collaboration that tried to collect all the different thoughts and opinions of BigScience participants. Then, experts in applied ethics and law did a final revision. We aim for consensus: if any BigScience member individually does not feel aligned with one or more of the values inscribed in this ethical charter, the member will have the right to object at appropriate times and places to that end.
-
-### Ethical approach
-
-We assume the basis of value pluralism within our community, and we cherish it. That is why the ethical notion of harmony (和) in Confucian moral theory seemed to be the appropriate approach for such an international and interdisciplinary scientific community as BigScience. “Harmony is by its very nature relational. It presupposes the coexistence of multiple parties; […] harmony is always contextual; epistemologically it calls for a holistic approach[1].”
-
-### Ethical compliance 
-
-We distinguish two levels of ethical compliance operating within the charter: individual and collective. We are held accountable for ethical compliance both as individual BigScience contributors and as a collective research entity.
-
-### Other documents articulation 
-
-Given the pivotal function of this ethical charter, we will refer to the other BigScience documents intended to govern specific issues directly where needed in the relevant paragraph.
-
-## BigScience Values
- 
-We apply the distinction between intrinsic and extrinsic values in the structure of this ethical charter. The former refers to “what is valuable for its own sake, in itself […], as an end[2]”; the latter is characterized as “what is valuable as a means, or for something else’s work[3]”. We distinguish between intrinsic and extrinsic values because the latter can vary more efficiently to achieve the former goals: the latter are substitutable. This structure will help the reader understand how the two types of values combine and allow the BigScience community to adapt this ethical charter over time.
-
-### Intrinsic Values
- 
-#### Inclusivity
-
-We work to ensure welcomeness in the process and equal access to the BigScience artifacts without any form of discrimination (e.g., religion, ethnicity, sexual orientation, gender, political orientation, age, ability). We believe that “inclusivity” is not just non-discrimination, but also a sense of belonging.
-
-#### Diversity
-
-The BigScience community has over 900 researchers and communities (see some listed collaborations here) from 50 countries covering over 20 languages. The collaborators bring together their expertise from various sources of knowledge, scientific fields, and institutional contexts (academia, industry, research institutions, etc). 
-
-#### Reproducibility
-
-The BigScience project was born with the clear intention of being a research initiative devoted to open science. BigScience aims at ensuring the reproduction of the research experiments and scientific conclusions developed under its aegis. 
-
-#### Openness
-
-Openness takes two dimensions, one focused on the process, and the other focused on its result. BigScience aims to be an open science framework whereby NLP, and broadly, AI-related researchers from all over the world can contribute and join the initiative. With regards to the results of our research, such as the future Large Language Model, these are created by the research community to the research community, and therefore will be released on an open basis, taking into account the risks derived from the use of the model. 
-
-#### Responsibility
-
-Each contributor has both an individual and a collective responsibility for their work within the BigScience project. This responsibility is both social and environmental. BigScience intends to positively impact stakeholders through its artifacts regarding the former. Concerning the latter, BigScience is committed to developing tools to monitor and lower its artifacts’ carbon footprint and energy consumption. Moreover, other tools such as an open legal playbook for NLP researchers guiding them regarding the use and respect of IP and privacy rights also seek to promote responsibility around the scientific community.
-
-### Extrinsic Values
-
-#### Accessibility
-
-As a means to achieve openness.
-
-BigScience puts in its best efforts to make our research and technological outputs easily interpretable and explained to the wider public, outside the scientific community, especially to communities that have participated in data sharing.
-
-Currently instrumentalized in:
-* no-code tools for exploring the catalog, trained models, etc.
-* translating our calls for participation (in the data sourcing group)
-* journalism (articles published on the project)
-* linked to multidisciplinarity - legal hackathon as a step toward “non-technical” presentation
-
-##### Transparency
-
-As a means to achieve reproducibility.
-
-BigScience work is actively promoted at various conferences, webinars, academic research, and scientific popularization so others can see our work.
-
-We have set up a management framework to oversee the use of BigScience models, datasets, and tools, e.g. through working groups. 
-
-All BigScience internal meetings and work progress are publicly shared within the Community, e.g. through public episodes. 
-
-We are committed to building tools to interpret, monitor, explain, and make intelligible the artifacts developed by BigScience.
-
-#### Interdisciplinarity
-
-As a means to achieve inclusivity.
-
-We are constantly building bridges among computer science, linguistics, law, sociology, philosophy, and other relevant disciplines in order to adopt a holistic approach in developing BigScience artifacts.
-
-#### Multilingualism
-
-As a means to achieve diversity.
-
-By having a system that is multilingual from its conception, with the immediate goal of covering the 20 most spoken languages in the world and a broad reach to include up to hundreds based on collaborations with native speakers, we aim to reduce existing disparities in language and foster a more equitable distribution of the benefits of our artifacts.
-
-________________
-[1] Chenyang Li, “The Confucian Ideal of Harmony”, in Philosophy East and West, vol. 56, no. 4, 2006, p. 589.
-[2] Chris Heathwood, “Monism and pluralism about value”, in The Oxford Handbook of Value Theory, Iwao Hires and Jonas Olson (ed.), Oxford University Press, Oxford, 2015, p. 29.
-[3] Ibid.
\ No newline at end of file
diff --git a/spaces/sohomghosh/FLUEnT/README.md b/spaces/sohomghosh/FLUEnT/README.md
deleted file mode 100644
index 40535a08307d3aa0aff4c143df0d27e25bb5cfec..0000000000000000000000000000000000000000
--- a/spaces/sohomghosh/FLUEnT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: FLUEnT Financial Language Understandability Enhancement Toolkit V4
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/spacy/healthsea-demo/viz.html b/spaces/spacy/healthsea-demo/viz.html
deleted file mode 100644
index b199a8cf30240e78b4214e8f48a186fc27ec0773..0000000000000000000000000000000000000000
--- a/spaces/spacy/healthsea-demo/viz.html
+++ /dev/null
@@ -1,212 +0,0 @@
-<html>
-<head>
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis.css" type="text/css" />
-<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis-network.min.js"> </script>
-<center>
-<h1></h1>
-</center>
-
-<!-- <link rel="stylesheet" href="../node_modules/vis/dist/vis.min.css" type="text/css" />
-<script type="text/javascript" src="../node_modules/vis/dist/vis.js"> </script>-->
-
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-
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-            height: 500px;
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-        }
-
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-        #loadingBar {
-            position:absolute;
-            top:0px;
-            left:0px;
-            width: 700px;
-            height: 500px;
-            background-color:rgba(200,200,200,0.8);
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-            -moz-transition: all 0.5s ease;
-            -ms-transition: all 0.5s ease;
-            -o-transition: all 0.5s ease;
-            transition: all 0.5s ease;
-            opacity:1;
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-
-        #bar {
-            position:absolute;
-            top:0px;
-            left:0px;
-            width:20px;
-            height:20px;
-            margin:auto auto auto auto;
-            border-radius:11px;
-            border:2px solid rgba(30,30,30,0.05);
-            background: rgb(0, 173, 246); /* Old browsers */
-            box-shadow: 2px 0px 4px rgba(0,0,0,0.4);
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-
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-
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-            height:50px;
-            margin:auto auto auto auto;
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-
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-            top:400px;
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-            box-shadow: 0px 0px 10px rgba(0,0,0,0.2);
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-</style>
-
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-
-<body>
-<div id = "mynetwork"></div>
-
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-    <div class="outerBorder">
-        <div id="text">0%</div>
-        <div id="border">
-            <div id="bar"></div>
-        </div>
-    </div>
-</div>
-
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-<script type="text/javascript">
-
-    // initialize global variables.
-    var edges;
-    var nodes;
-    var network; 
-    var container;
-    var options, data;
-
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-    // This method is responsible for drawing the graph, returns the drawn network
-    function drawGraph() {
-        var container = document.getElementById('mynetwork');
-        
-        
-
-        // parsing and collecting nodes and edges from the python
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"label": "arms_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#3CDFCB", "font": {"color": "#ffffff"}, "id": "knee_joint_pain", "label": "knee_joint_pain", "shape": "dot", "title": "2", "value": 70}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_joints_pain", "label": "knee_joints_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "aching_joint_pain", "label": "aching_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_joint_pains", "label": "knee_joint_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_joint_pain", "label": "hip_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_ligament_pain", "label": "knee_ligament_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_muscle_pain", "label": "shoulder_muscle_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "wrist_joint_pain", "label": "wrist_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee__pain", "label": "knee__pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_joint_ache", "label": "knee_joint_ache", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_muscle_pain", "label": "joint_muscle_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "arthritic_joint_pain", "label": "arthritic_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "fingers_joint_pain", "label": "fingers_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "ankle_joint__pain", "label": "ankle_joint__pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_joint_aches", "label": "hip_joint_aches", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "elbow_joint_pains", "label": "elbow_joint_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knees_joint_pain", "label": "knees_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_joint_stiffness", "label": "knee_joint_stiffness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "arm_muscle_pain", "label": "arm_muscle_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_arm_pain", "label": "shoulder_arm_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "thumb_joint_pain", "label": "thumb_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_finger_pain", "label": "joint_finger_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_joints_pain", "label": "shoulder_joints_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_bone_joint_pain", "label": "shoulder_bone_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_knee_pain", "label": "hip_knee_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_joint_discomfort", "label": "hip_joint_discomfort", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee\njoint_pain", "label": "knee\njoint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "jaw_hindge_joint_pain", "label": "jaw_hindge_joint_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_joints_pain", "label": "hip_joints_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#3CDFCB", "font": {"color": "#ffffff"}, "id": "knee_pains", "label": "knee_pains", "shape": "dot", "title": "2", "value": 70}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_pains", "label": "joint_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "leg_cramps", "label": "leg_cramps", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_aches", "label": "knee_aches", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knees_pain", "label": "knees_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_pains", "label": "shoulder_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_aches", "label": "hip_aches", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "chest_pains", "label": "chest_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "leg_spasms", "label": "leg_spasms", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "leg_pains", "label": "leg_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hip_pains", "label": "hip_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_discomfort", "label": "knee_discomfort", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_cramps", "label": "knee_cramps", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "elbow_aches", "label": "elbow_aches", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_soreness", "label": "shoulder_soreness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "ankle_pains", "label": "ankle_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "feet_pains", "label": "feet_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "legs_pains", "label": "legs_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "leg_aches", "label": "leg_aches", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "neck_spasms", "label": "neck_spasms", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_swelling", "label": "knee_swelling", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "leg_numbness", "label": "leg_numbness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_cramps", "label": "joint_cramps", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "neck_pains", "label": "neck_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "tendon_pains", "label": "tendon_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knees_pains", "label": "knees_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_tendonitis", "label": "shoulder_tendonitis", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knees_joint_pains", "label": "knees_joint_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "head_pains", "label": "head_pains", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_aches", "label": "shoulder_aches", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "shoulder_discomfort", "label": "shoulder_discomfort", "shape": "dot", "title": "3", "value": 55}, {"color": "#3CDFCB", "font": {"color": "#ffffff"}, "id": "spine_pain", "label": "spine_pain", "shape": "dot", "title": "2", "value": 70}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joints_pain", "label": "joints_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "ligaments_pain", "label": "ligaments_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "bone_pain", "label": "bone_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "nerve_pain", "label": "nerve_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "limb_pain", "label": "limb_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "hips_pain", "label": "hips_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "vertebral_pain", "label": "vertebral_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "pelvic_pain", "label": "pelvic_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "spinal_pain", "label": "spinal_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "bony_pain", "label": "bony_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "ear_pain", "label": "ear_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "ligament_pain", "label": "ligament_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "abdomen_pain", "label": "abdomen_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "kneecap_pain", "label": "kneecap_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "groin_pain", "label": "groin_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "extremity_pain", "label": "extremity_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "tendon_pain", "label": "tendon_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "testicle_pain", "label": "testicle_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "tailbone_pain", "label": "tailbone_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "lumbar_pain", "label": "lumbar_pain", "shape": "dot", "title": "3", "value": 55}, {"color": "#4BD4FF", "font": {"color": "#ffffff"}, "id": "joint_swelling", "label": "joint_swelling", "shape": "dot", "title": "1", "value": 85}, {"color": "#3CDFCB", "font": {"color": "#ffffff"}, "id": "joint_stiffness", "label": "joint_stiffness", "shape": "dot", "title": "2", "value": 70}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_soreness", "label": "joint_soreness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_discomfort", "label": "joint_discomfort", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "knee_stiffness", "label": "knee_stiffness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_spasms", "label": "joint_spasms", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_weakness", "label": "joint_weakness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_tightness", "label": "joint_tightness", "shape": "dot", "title": "3", "value": 55}, {"color": "#37DF8E", "font": {"color": "#ffffff"}, "id": "joint_tenderness", "label": "joint_tenderness", "shape": "dot", "title": "3", "value": 55}]);
-        edges = new vis.DataSet([{"from": "joint_pain", "to": "knee_pain", "value": 1.0}, {"from": "knee_pain", "to": "foot_pain", "value": 0.85}, {"from": "foot_pain", "to": "hip_pain", "value": 0.7}, {"from": "foot_pain", "to": "shoulder_pain", "value": 0.7}, {"from": "foot_pain", "to": "ankle_pain", "value": 0.7}, {"from": "foot_pain", "to": "neck_pain", "value": 0.7}, {"from": "foot_pain", "to": "wrist_pain", "value": 0.7}, {"from": "foot_pain", "to": "forearm_pain", "value": 0.7}, {"from": "foot_pain", "to": "leg_pain", "value": 0.7}, {"from": "foot_pain", "to": "foot_cramps", "value": 0.7}, {"from": "foot_pain", "to": "legs_pain", "value": 0.7}, {"from": "foot_pain", "to": "chest_pain", "value": 0.7}, {"from": "foot_pain", "to": "elbow_pain", "value": 0.7}, {"from": "foot_pain", "to": "waist_pain", "value": 0.7}, {"from": "foot_pain", "to": "finger_joint_pain", "value": 0.7}, {"from": "foot_pain", "to": "toe_pain", "value": 0.7}, {"from": "foot_pain", "to": "arm_pain", "value": 0.7}, {"from": "foot_pain", "to": "thumb_pain", "value": 0.7}, {"from": "foot_pain", "to": "heel_pain", "value": 0.7}, {"from": "foot_pain", "to": "head_pain", "value": 0.7}, {"from": "foot_pain", "to": "knuckle_pain", "value": 0.7}, {"from": "foot_pain", "to": "finger_pain", "value": 0.7}, {"from": "foot_pain", "to": "foot_swelling", "value": 0.7}, {"from": "foot_pain", "to": "feet_pain", "value": 0.7}, {"from": "foot_pain", "to": "jaw_pain", "value": 0.7}, {"from": "foot_pain", "to": "shoulders_pain", "value": 0.7}, {"from": "foot_pain", "to": "thigh_pain", "value": 0.7}, {"from": "foot_pain", "to": "arms_pain", "value": 0.7}, {"from": "knee_pain", "to": "knee_joint_pain", "value": 0.85}, {"from": "knee_joint_pain", "to": "knee_joints_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "aching_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "knee_joint_pains", "value": 0.7}, {"from": "knee_joint_pain", "to": "hip_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "knee_ligament_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "shoulder_muscle_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "wrist_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "knee__pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "knee_joint_ache", "value": 0.7}, {"from": "knee_joint_pain", "to": "joint_muscle_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "arthritic_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "fingers_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "ankle_joint__pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "hip_joint_aches", "value": 0.7}, {"from": "knee_joint_pain", "to": "elbow_joint_pains", "value": 0.7}, {"from": "knee_joint_pain", "to": "knees_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "knee_joint_stiffness", "value": 0.7}, {"from": "knee_joint_pain", "to": "arm_muscle_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "shoulder_arm_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "thumb_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "joint_finger_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "shoulder_joints_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "shoulder_bone_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "hip_knee_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "hip_joint_discomfort", "value": 0.7}, {"from": "knee_joint_pain", "to": "knee\njoint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "jaw_hindge_joint_pain", "value": 0.7}, {"from": "knee_joint_pain", "to": "hip_joints_pain", "value": 0.7}, {"from": "knee_pain", "to": "knee_pains", "value": 0.85}, {"from": "knee_pains", "to": "joint_pains", "value": 0.7}, {"from": "knee_pains", "to": "leg_cramps", "value": 0.7}, {"from": "knee_pains", "to": "knee_aches", "value": 0.7}, {"from": "knee_pains", "to": "knees_pain", "value": 0.7}, {"from": "knee_pains", "to": "shoulder_pains", "value": 0.7}, {"from": "knee_pains", "to": "hip_aches", "value": 0.7}, {"from": "knee_pains", "to": "chest_pains", "value": 0.7}, {"from": "knee_pains", "to": "leg_spasms", "value": 0.7}, {"from": "knee_pains", "to": "leg_pains", "value": 0.7}, {"from": "knee_pains", "to": "hip_pains", "value": 0.7}, {"from": "knee_pains", "to": "knee_discomfort", "value": 0.7}, {"from": "knee_pains", "to": "knee_cramps", "value": 0.7}, {"from": "knee_pains", "to": "elbow_aches", "value": 0.7}, {"from": "knee_pains", "to": "shoulder_soreness", "value": 0.7}, {"from": "knee_pains", "to": "ankle_pains", "value": 0.7}, {"from": "knee_pains", "to": "feet_pains", "value": 0.7}, {"from": "knee_pains", "to": "legs_pains", "value": 0.7}, {"from": "knee_pains", "to": "leg_aches", "value": 0.7}, {"from": "knee_pains", "to": "neck_spasms", "value": 0.7}, {"from": "knee_pains", "to": "knee_swelling", "value": 0.7}, {"from": "knee_pains", "to": "leg_numbness", "value": 0.7}, {"from": "knee_pains", "to": "joint_cramps", "value": 0.7}, {"from": "knee_pains", "to": "neck_pains", "value": 0.7}, {"from": "knee_pains", "to": "tendon_pains", "value": 0.7}, {"from": "knee_pains", "to": "knees_pains", "value": 0.7}, {"from": "knee_pains", "to": "shoulder_tendonitis", "value": 0.7}, {"from": "knee_pains", "to": "knees_joint_pains", "value": 0.7}, {"from": "knee_pains", "to": "head_pains", "value": 0.7}, {"from": "knee_pains", "to": "shoulder_aches", "value": 0.7}, {"from": "knee_pains", "to": "shoulder_discomfort", "value": 0.7}, {"from": "knee_pain", "to": "spine_pain", "value": 0.85}, {"from": "spine_pain", "to": "joints_pain", "value": 0.7}, {"from": "spine_pain", "to": "ligaments_pain", "value": 0.7}, {"from": "spine_pain", "to": "bone_pain", "value": 0.7}, {"from": "spine_pain", "to": "nerve_pain", "value": 0.7}, {"from": "spine_pain", "to": "limb_pain", "value": 0.7}, {"from": "spine_pain", "to": "hips_pain", "value": 0.7}, {"from": "spine_pain", "to": "vertebral_pain", "value": 0.7}, {"from": "spine_pain", "to": "pelvic_pain", "value": 0.7}, {"from": "spine_pain", "to": "spinal_pain", "value": 0.7}, {"from": "spine_pain", "to": "bony_pain", "value": 0.7}, {"from": "spine_pain", "to": "ear_pain", "value": 0.7}, {"from": "spine_pain", "to": "ligament_pain", "value": 0.7}, {"from": "spine_pain", "to": "abdomen_pain", "value": 0.7}, {"from": "spine_pain", "to": "kneecap_pain", "value": 0.7}, {"from": "spine_pain", "to": "groin_pain", "value": 0.7}, {"from": "spine_pain", "to": "extremity_pain", "value": 0.7}, {"from": "spine_pain", "to": "tendon_pain", "value": 0.7}, {"from": "spine_pain", "to": "testicle_pain", "value": 0.7}, {"from": "spine_pain", "to": "tailbone_pain", "value": 0.7}, {"from": "spine_pain", "to": "lumbar_pain", "value": 0.7}, {"from": "joint_pain", "to": "joint_swelling", "value": 1.0}, {"from": "joint_swelling", "to": "joint_stiffness", "value": 0.85}, {"from": "joint_stiffness", "to": "joint_soreness", "value": 0.7}, {"from": "joint_stiffness", "to": "joint_discomfort", "value": 0.7}, {"from": "joint_stiffness", "to": "knee_stiffness", "value": 0.7}, {"from": "joint_stiffness", "to": "joint_spasms", "value": 0.7}, {"from": "joint_stiffness", "to": "joint_weakness", "value": 0.7}, {"from": "joint_stiffness", "to": "joint_tightness", "value": 0.7}, {"from": "joint_stiffness", "to": "joint_tenderness", "value": 0.7}]);
-
-        // adding nodes and edges to the graph
-        data = {nodes: nodes, edges: edges};
-
-        var options = {
-    "configure": {
-        "enabled": false
-    },
-    "edges": {
-        "color": {
-            "inherit": true
-        },
-        "smooth": {
-            "enabled": true,
-            "type": "dynamic"
-        }
-    },
-    "interaction": {
-        "dragNodes": true,
-        "hideEdgesOnDrag": false,
-        "hideNodesOnDrag": false
-    },
-    "physics": {
-        "barnesHut": {
-            "avoidOverlap": 0,
-            "centralGravity": 0.3,
-            "damping": 0.09,
-            "gravitationalConstant": -2738,
-            "springConstant": 0.001,
-            "springLength": 250
-        },
-        "enabled": true,
-        "stabilization": {
-            "enabled": true,
-            "fit": true,
-            "iterations": 1000,
-            "onlyDynamicEdges": false,
-            "updateInterval": 50
-        }
-    }
-};
-        
-        
-
-        
-
-        network = new vis.Network(container, data, options);
-	 
-        
-
-
-        
-        network.on("stabilizationProgress", function(params) {
-      		document.getElementById('loadingBar').removeAttribute("style");
-	        var maxWidth = 496;
-	        var minWidth = 20;
-	        var widthFactor = params.iterations/params.total;
-	        var width = Math.max(minWidth,maxWidth * widthFactor);
-
-	        document.getElementById('bar').style.width = width + 'px';
-	        document.getElementById('text').innerHTML = Math.round(widthFactor*100) + '%';
-	    });
-	    network.once("stabilizationIterationsDone", function() {
-	        document.getElementById('text').innerHTML = '100%';
-	        document.getElementById('bar').style.width = '496px';
-	        document.getElementById('loadingBar').style.opacity = 0;
-	        // really clean the dom element
-	        setTimeout(function () {document.getElementById('loadingBar').style.display = 'none';}, 500);
-	    });
-        
-
-        return network;
-
-    }
-
-    drawGraph();
-
-</script>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/model_parallel/modules/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/model_parallel/modules/__init__.py
deleted file mode 100644
index 11603217a188f420ea849ae0fde19979736ba208..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/model_parallel/modules/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .multihead_attention import ModelParallelMultiheadAttention
-from .transformer_layer import (
-    ModelParallelTransformerEncoderLayer,
-    ModelParallelTransformerDecoderLayer,
-)
-
-__all__ = [
-    "ModelParallelMultiheadAttention",
-    "ModelParallelTransformerEncoderLayer",
-    "ModelParallelTransformerDecoderLayer",
-]
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/scripts_auto_postprocessing.py b/spaces/supertori/files/stable-diffusion-webui/modules/scripts_auto_postprocessing.py
deleted file mode 100644
index 16ec8b613b134b0a9a4054f06d5979ec1822c422..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/scripts_auto_postprocessing.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from modules import scripts, scripts_postprocessing, shared
-
-
-class ScriptPostprocessingForMainUI(scripts.Script):
-    def __init__(self, script_postproc):
-        self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
-        self.postprocessing_controls = None
-
-    def title(self):
-        return self.script.name
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def ui(self, is_img2img):
-        self.postprocessing_controls = self.script.ui()
-        return self.postprocessing_controls.values()
-
-    def postprocess_image(self, p, script_pp, *args):
-        args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
-
-        pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
-        pp.info = {}
-        self.script.process(pp, **args_dict)
-        p.extra_generation_params.update(pp.info)
-        script_pp.image = pp.image
-
-
-def create_auto_preprocessing_script_data():
-    from modules import scripts
-
-    res = []
-
-    for name in shared.opts.postprocessing_enable_in_main_ui:
-        script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
-        if script is None:
-            continue
-
-        constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())
-        res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
-
-    return res
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Crack No Cd Para Max Payne 1.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Crack No Cd Para Max Payne 1.md
deleted file mode 100644
index b7a4479cb981305a7bae8fdfea8373b825c02c43..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Crack No Cd Para Max Payne 1.md	
+++ /dev/null
@@ -1,141 +0,0 @@
-<br />
-<h1>Crack No Cd Para Max Payne 1: How to Enjoy the Classic Shooter Without a Disc</h1>
-
-<p>Max Payne is a legendary game that revolutionized the shooter genre with its cinematic style, noir story, and bullet time mechanics. Released in 2001, the game follows the titular character, a former NYPD detective who goes on a violent quest for revenge after his family is murdered by drug dealers. The game features intense gunfights, slow-motion effects, and a dark atmosphere that immerses the player in Max's tragic world.</p>
-
-<p>However, if you want to play Max Payne on your PC today, you might face some difficulties. One of them is that the game requires a CD to run, which can be annoying or impossible if you don't have a disc drive or a working copy of the game. Luckily, there is a way to play Max Payne without a disc: using a crack no cd para Max Payne 1.</p>
-<h2>Crack No Cd Para Max Payne 1</h2><br /><p><b><b>Download File</b> &gt; <a href="https://cinurl.com/2uEZ8t">https://cinurl.com/2uEZ8t</a></b></p><br /><br />
-
-<p>A crack no cd para Max Payne 1 is a modified version of the game's executable file that bypasses the CD check and lets you play without inserting a disc. This way, you can enjoy Max Payne on your PC without any hassle. In this article, we will show you how to find, download, install, and use crack no cd para Max Payne 1.</p>
-
-<h2>How to Find and Download Crack No Cd Para Max Payne 1</h2>
-
-<p>There are many websites that offer crack no cd para Max Payne 1, but not all of them are trustworthy or safe. Some of them might contain viruses, malware, or unwanted software that can harm your computer or compromise your privacy. Therefore, you should be careful and choose a reputable source for downloading crack no cd para Max Payne 1.</p>
-
-<p>One of the best websites for finding and downloading crack no cd para Max Payne 1 is GameCopyWorld. This website has a large collection of game fixes, trainers, cheats, and patches for various PC games, including Max Payne. You can easily find the crack no cd para Max Payne 1 that matches your game version and language by browsing the index or using the search function.</p>
-
-<p>To download crack no cd para Max Payne 1 from GameCopyWorld, follow these steps:</p>
-
-<ol>
-<li>Go to <a href="https://www.gamecopyworld.com/games/pc_maxpayne.shtml">https://www.gamecopyworld.com/games/pc_maxpayne.shtml</a>.</li>
-<li>Scroll down to the section "Game Fixes" and look for the crack no cd para Max Payne 1 that suits your needs. For example, if you have the English version of the game patched to v1.05, you can choose "Max Payne v1.05 [ALL] No-CD/Fixed EXE".</li>
-<li>Click on the link and you will be redirected to another page with more details and download options.</li>
-<li>Choose one of the mirrors to download the crack no cd para Max Payne 1 as a zip file.</li>
-<li>Save the file to your computer and extract it using a program like WinRAR or 7-Zip.</li>
-</ol>
-
-<h2>How to Install and Use Crack No Cd Para Max Payne 1</h2>
-
-<p>Once you have downloaded and extracted crack no cd para Max Payne 1, you need to install it and use it to play the game without a disc. To do that, follow these steps:</p>
-
-<ol>
-<li>Make a backup of your original game executable file (MaxPayne.exe) in case something goes wrong or you want to restore it later.</li>
-<li>Copy the crack no cd para Max Payne 1 file (also named MaxPayne.exe) and paste it into your game installation folder, usually located at C:\Program Files\Max Payne or C:\Program Files (x86)\Max Payne.</li>
-<li>Replace the original file when prompted.</li>
-<li>Run the game as usual by double-clicking on the crack no cd para Max Payne 1 file or using a shortcut.</li>
-<li>Enjoy playing Max Payne without a disc!</li>
-</ol>
-
-<h2>The Advantages and Disadvantages of Using Crack No Cd Para Max Payne 1</h2>
-
-<p>Using crack no cd para Max Payne 1 has some advantages and disadvantages that you should be aware of before deciding to use it. Here are some of them:</p>
-
-<h3>The Advantages</h3>
-
-<ul>
-<li>You can play Max Payne without a disc, which is convenient if you don't have a disc drive or a working copy of the game.</li>
-<li>You can save your disc space by not having to insert a disc every time you want to play.</li>
-<li>You can avoid potential problems with disc scratches, errors, or compatibility issues.</li>
-<li>You can play Max Payne on any PC that meets the minimum system requirements without having to carry a disc around.</li>
-</ul>
-
-<h3>The Disadvantages</h3>
-
-<ul>
-<li>You might violate the terms of service or end-user license agreement of the game by using crack no cd para Max Payne 1.</li>
-<li>You might not be able to play online or use some features of the game that require a disc check.</li>
-<li>You might get banned from online services or platforms that detect modified files or illegal copies of games.</li>
-<li>You might expose your computer or personal data to viruses, malware, or unwanted software that might be hidden in some crack no cd para Max Payne 1 files.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>In conclusion, crack no cd para Max Payne 1 is a handy tool for playing the classic shooter without a disc on your PC. However, you should be careful about where you download it from and how you use it. You should also respect the rights of the game developers and publishers and only use crack no cd para Max Payne 1 if you own an original copy of the game.</p>
-<p></p>
-
-<p>If you found this article helpful, please share it with your friends who might be interested in playing Max Payne without a disc. Also, feel free to leave a comment below if you have any questions or feedback about crack no cd para Max Payne 1. Thank you for reading!</p>
-<h2>Alternative Ways to Play Max Payne Without a Disc</h2>
-
-<p>If you don't want to use crack no cd para Max Payne 1 or you can't find one that works for your game version or language, there are some alternative ways to play Max Payne without a disc. Here are some of them:</p>
-
-<h3>Use a Virtual Drive</h3>
-
-<p>A virtual drive is a software that creates a simulated disc drive on your computer that can read disc image files (such as ISO, BIN, or CUE) as if they were real discs. This way, you can mount the disc image file of Max Payne on your virtual drive and play the game without inserting a physical disc.</p>
-
-<p>To use a virtual drive, you need to have a disc image file of Max Payne and a virtual drive software. There are many free and paid virtual drive software available online, such as DAEMON Tools, PowerISO, or Virtual CloneDrive. You can also create your own disc image file of Max Payne if you have a working copy of the game and a disc burning software.</p>
-
-<p>To use a virtual drive to play Max Payne without a disc, follow these steps:</p>
-
-<ol>
-<li>Install and run your virtual drive software.</li>
-<li>Create a new virtual drive on your computer.</li>
-<li>Mount the disc image file of Max Payne on your virtual drive.</li>
-<li>Run the game as usual by double-clicking on the game icon or using a shortcut.</li>
-<li>Enjoy playing Max Payne without a disc!</li>
-</ol>
-
-<h3>Buy a Digital Copy</h3>
-
-<p>Another way to play Max Payne without a disc is to buy a digital copy of the game from an online store or platform. A digital copy is a downloadable version of the game that does not require a disc to run. You can buy a digital copy of Max Payne from various sources, such as Steam, GOG.com, or Rockstar Games Launcher.</p>
-
-<p>To buy a digital copy of Max Payne, follow these steps:</p>
-
-<ol>
-<li>Choose an online store or platform that sells Max Payne and create an account if you don't have one.</li>
-<li>Search for Max Payne on the store or platform and add it to your cart.</li>
-<li>Proceed to checkout and pay for the game using your preferred method.</li>
-<li>Download and install the game on your computer using the store or platform's client or launcher.</li>
-<li>Run the game as usual by double-clicking on the game icon or using a shortcut.</li>
-<li>Enjoy playing Max Payne without a disc!</li>
-</ol>
-
-<h2>Tips and Tricks for Playing Max Payne Without a Disc</h2>
-
-<p>Playing Max Payne without a disc can be fun and convenient, but it can also have some challenges or issues. Here are some tips and tricks for playing Max Payne without a disc:</p>
-
-<ul>
-<li>Make sure your computer meets the minimum system requirements for running Max Payne. The game might not work properly or at all if your computer is too old or too weak.</li>
-<li>Update your game to the latest version available. The game might have some bugs or glitches that can be fixed by updating it. You can find official patches for Max Payne on GameCopyWorld or other websites.</li>
-<li>Install some mods or enhancements for Max Payne. The game might look outdated or bland compared to modern games, but you can improve its graphics, sound, gameplay, or content by installing some mods or enhancements. You can find many mods and enhancements for Max Payne on ModDB or other websites.</li>
-<li>Backup your game files and save files regularly. The game might crash or corrupt your files unexpectedly, so you should backup your game files and save files regularly to avoid losing your progress or data. You can backup your game files by copying them to another folder or drive, and you can backup your save files by copying them from C:\Program Files\Max Payne\savegame (or C:\Program Files (x86)\Max Payne\savegame) to another folder or drive.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>In conclusion, playing Max Payne without a disc is possible and easy with crack no cd para Max Payne 1 or other methods. However, you should be careful about where you download it from and how you use it. You should also respect the rights of the game developers and publishers and only play Max Payne without a disc if you own an original copy of the game.</p>
-
-<p>If you found this article helpful, please share it with your friends who might be interested in playing Max Payne without a disc. Also, feel free to leave a comment below if you have any questions or feedback about playing Max Payne without a disc. Thank you for reading!</p>
-<h2>Frequently Asked Questions About Crack No Cd Para Max Payne 1</h2>
-
-<p>Here are some frequently asked questions and answers about crack no cd para Max Payne 1 that you might find useful:</p>
-
-<h3>Q: Is crack no cd para Max Payne 1 legal?</h3>
-
-<p>A: The legality of crack no cd para Max Payne 1 depends on your country's laws and regulations regarding software piracy and copyright infringement. Generally speaking, using crack no cd para Max Payne 1 is considered illegal if you don't own an original copy of the game or if you distribute or share it with others. However, some countries or regions might have exceptions or loopholes that allow you to use crack no cd para Max Payne 1 for personal or educational purposes. You should consult your local laws and authorities before using crack no cd para Max Payne 1.</p>
-
-<h3>Q: Is crack no cd para Max Payne 1 safe?</h3>
-
-<p>A: The safety of crack no cd para Max Payne 1 depends on where you download it from and how you use it. Some websites or sources might offer crack no cd para Max Payne 1 that contains viruses, malware, or unwanted software that can harm your computer or compromise your privacy. Therefore, you should only download crack no cd para Max Payne 1 from reputable and trustworthy websites, such as GameCopyWorld. You should also scan the file with an antivirus or anti-malware program before installing or using it. Additionally, you should use a firewall or a VPN to protect your online activity and identity when using crack no cd para Max Payne 1.</p>
-
-<h3>Q: Does crack no cd para Max Payne 1 affect the game quality or performance?</h3>
-
-<p>A: The quality or performance of the game should not be affected by using crack no cd para Max Payne 1, as long as you have a compatible and updated version of the game and the crack. However, some features or functions of the game might not work properly or at all when using crack no cd para Max Payne 1, such as online multiplayer, achievements, or updates. You might also experience some bugs or glitches that can be fixed by updating the game or using official patches.</p>
-
-<h2>Final Words</h2>
-
-<p>We hope this article has helped you learn more about crack no cd para Max Payne 1 and how to use it to play the classic shooter without a disc on your PC. Remember to be careful and responsible when using crack no cd para Max Payne 1 and respect the rights of the game developers and publishers. If you have any questions or feedback about crack no cd para Max Payne 1, please leave a comment below. Thank you for reading!</p>
-<p>In conclusion, playing Max Payne without a disc is possible and easy with crack no cd para Max Payne 1 or other methods. However, you should be careful about where you download it from and how you use it. You should also respect the rights of the game developers and publishers and only play Max Payne without a disc if you own an original copy of the game.</p>
-
-<p>If you found this article helpful, please share it with your friends who might be interested in playing Max Payne without a disc. Also, feel free to leave a comment below if you have any questions or feedback about playing Max Payne without a disc. Thank you for reading!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dishonored 2 Crack __EXCLUSIVE__only Update For The Crybabies Steampunks.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dishonored 2 Crack __EXCLUSIVE__only Update For The Crybabies Steampunks.md
deleted file mode 100644
index d1cf0e9b51bfe7a924c9c29ee0704fb9f33a37c3..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dishonored 2 Crack __EXCLUSIVE__only Update For The Crybabies Steampunks.md	
+++ /dev/null
@@ -1,35 +0,0 @@
-
-<h1>Dishonored 2 Crackonly Update For The Crybabies Steampunks: What You Need to Know</h1>
-<p>If you are a fan of Dishonored 2, the stealth action-adventure game developed by Arkane Studios and published by Bethesda Softworks, you might be interested in the latest crack update released by STEAMPUNKS, a notorious scene group that specializes in cracking Denuvo-protected games.</p>
-<p>In this article, we will explain what this crack update is, why it is called "for the crybabies", and how to install and play Dishonored 2 with it.</p>
-<h2>dishonored 2 crackonly update for the crybabies steampunks</h2><br /><p><b><b>Download Zip</b> &#8230;&#8230;&#8230; <a href="https://cinurl.com/2uEYzC">https://cinurl.com/2uEYzC</a></b></p><br /><br />
-<h2>What is Dishonored 2 Crackonly Update For The Crybabies Steampunks?</h2>
-<p>Dishonored 2 Crackonly Update For The Crybabies Steampunks is a crack update that allows you to play Dishonored 2 without the need of a Steam account or a Denuvo license. It also fixes some bugs and performance issues that were present in the previous crack release by STEAMPUNKS.</p>
-<p>This crack update is called "for the crybabies" because it is a response to some complaints that STEAMPUNKS received from some users who were unhappy with their first release of Dishonored 2. In their first release, STEAMPUNKS included their key generator (stp-dh2.exe) inside the game installer, which was against the scene rules that require all releases to include the original files as well. This resulted in a nuke (a scene term for an invalid release) that was later revoked.</p>
-<p>STEAMPUNKS decided to release this crack update as a separate file, with a sarcastic title and a savage NFO (a scene term for an information file that accompanies each release) that mocked their critics and challenged other scene groups to crack more Denuvo games.</p>
-<h2>How to Install and Play Dishonored 2 with Dishonored 2 Crackonly Update For The Crybabies Steampunks?</h2>
-<p>To install and play Dishonored 2 with this crack update, you will need to have the original STEAMPUNKS ISO release of Dishonored 2 installed on your PC. You can find it on various torrent sites or file hosting services. The ISO file size is about 35 GB.</p>
-<p>Once you have downloaded and installed Dishonored 2, you will need to download the crack update file from one of the links provided by STEAMPUNKS. The file size is about 3.3 MB. You can find it on Reddit, Up-4ever, or other sources.</p>
-<p>After you have downloaded the crack update file, you will need to unpack it using a program like WinRAR or 7-Zip. You will get a folder called DISHONORED.2._ONLY.UPDATE.FOR.THE.CRYBABIES-STEAMPUNKS that contains two files: stp-dh2.exe and stp-dh2.nfo.</p>
-<p></p>
-<p>You will need to copy these two files to your game folder, where you installed Dishonored 2. You will be asked to overwrite the existing files. Click yes to confirm.</p>
-<p>Now you are ready to play Dishonored 2 with the latest crack update. You can launch the game by running stp-dh2.exe from your game folder or from your desktop shortcut. You will see a window with a key generator that will activate your game. Click generate and then play.</p>
-<p>That's it! Enjoy playing Dishonored 2 with Dishonored 2 Crackonly Update For The Crybabies Steampunks!</p>
-<h2>What is Dishonored 2?</h2>
-<p>Dishonored 2 is a sequel to the critically acclaimed Dishonored, a game that won more than 100 awards in 2012. Dishonored 2 is set 15 years after the events of the first game, in a world where mysticism and industry collide. You can play as either Emily Kaldwin, the deposed empress of Dunwall, or Corvo Attano, her royal protector and father. Both characters have their own unique abilities, weapons and gadgets that you can use to explore the stunning environments and eliminate your enemies in creative ways.</p>
-<p>Dishonored 2 offers you a choice between stealth and combat, lethal and non-lethal approaches, and high and low chaos outcomes. Your actions and decisions will shape the world around you and the fate of your empire. You can also replay the game with different characters, powers and choices to discover new secrets and possibilities.</p>
-<h2>What are some tips for playing Dishonored 2?</h2>
-<p>If you want to get the most out of Dishonored 2, here are some tips that might help you:</p>
-<ul>
-<li>Experiment with different powers and combinations. Dishonored 2 gives you a lot of freedom to customize your playstyle and use your powers in creative ways. For example, you can use Emily's Domino power to link the fates of multiple enemies, then use Corvo's Possession power to take control of one of them and make them jump off a cliff.</li>
-<li>Explore every corner and collect every item. Dishonored 2 is full of hidden secrets, collectibles and loot that can enhance your experience and unlock new options. You can find runes, bonecharms, blueprints, paintings, coins and more by exploring every nook and cranny of each level. You can also use your Heart device to locate these items more easily.</li>
-<li>Use stealth and non-lethal methods if you want a low chaos ending. Dishonored 2 has multiple endings depending on your actions and decisions throughout the game. If you kill too many people or cause too much havoc, you will get a high chaos ending, which is usually darker and more grim. If you want a low chaos ending, which is usually more hopeful and positive, you should avoid killing people or being detected as much as possible. You can use stealth techniques, non-lethal weapons and gadgets, or alternative solutions to deal with your enemies.</li>
-</ul>
-<h3>Conclusion</h3>
-<p>Dishonored 2 is an amazing game that offers you a rich and immersive experience in a fascinating world. If you want to play it with the latest crack update from STEAMPUNKS, you can follow the steps we outlined above to install and enjoy Dishonored 2 Crackonly Update For The Crybabies Steampunks.</p>
-<p>We hope this article was helpful and informative for you. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading!</p>
-<h3>Conclusion</h3>
-<p>Dishonored 2 is an amazing game that offers you a rich and immersive experience in a fascinating world. If you want to play it with the latest crack update from STEAMPUNKS, you can follow the steps we outlined above to install and enjoy Dishonored 2 Crackonly Update For The Crybabies Steampunks.</p>
-<p>We hope this article was helpful and informative for you. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Environmental Engineering 2 By Bc Punmia Pdf [REPACK] Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Environmental Engineering 2 By Bc Punmia Pdf [REPACK] Download.md
deleted file mode 100644
index 3cc6d2548a6a4161b6cf43e665fe26d02f737635..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Environmental Engineering 2 By Bc Punmia Pdf [REPACK] Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>environmental engineering 2 by bc punmia pdf download</h2><br /><p><b><b>Download Zip</b> &#9734; <a href="https://cinurl.com/2uEYbI">https://cinurl.com/2uEYbI</a></b></p><br /><br />
-<br />
-Bookmark File PDF Sk Garg. Environmental Engineering Vol. 2. Sk Garg. Environmental ... download link or read online here in PDF. Read online Sk ... 2. By Dr. B.C.Punmia – Environmental. Engineering Volume 1 also known popularly as ... 1fdad05405<br />
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-<br />
-<p></p>
diff --git a/spaces/syy404/whisper-webui/src/download.py b/spaces/syy404/whisper-webui/src/download.py
deleted file mode 100644
index 20565153f9e582be73246a1e2a3b7be3f368b322..0000000000000000000000000000000000000000
--- a/spaces/syy404/whisper-webui/src/download.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from tempfile import mkdtemp
-from typing import List
-from yt_dlp import YoutubeDL
-
-import yt_dlp
-from yt_dlp.postprocessor import PostProcessor
-
-class FilenameCollectorPP(PostProcessor):
-    def __init__(self):
-        super(FilenameCollectorPP, self).__init__(None)
-        self.filenames = []
-
-    def run(self, information):
-        self.filenames.append(information["filepath"])
-        return [], information
-
-def download_url(url: str, maxDuration: int = None, destinationDirectory: str = None, playlistItems: str = "1") -> List[str]: 
-    try:
-        return _perform_download(url, maxDuration=maxDuration, outputTemplate=None, destinationDirectory=destinationDirectory, playlistItems=playlistItems)
-    except yt_dlp.utils.DownloadError as e:
-        # In case of an OS error, try again with a different output template
-        if e.msg and e.msg.find("[Errno 36] File name too long") >= 0:
-            return _perform_download(url, maxDuration=maxDuration, outputTemplate="%(title).10s %(id)s.%(ext)s")
-        pass
-
-def _perform_download(url: str, maxDuration: int = None, outputTemplate: str = None, destinationDirectory: str = None, playlistItems: str = "1"):
-    # Create a temporary directory to store the downloaded files
-    if destinationDirectory is None:
-        destinationDirectory = mkdtemp()
-
-    ydl_opts = {
-        "format": "bestaudio/best",
-        'paths': {
-            'home': destinationDirectory
-        }
-    }
-    if (playlistItems):
-        ydl_opts['playlist_items'] = playlistItems
-
-    # Add output template if specified
-    if outputTemplate:
-        ydl_opts['outtmpl'] = outputTemplate
-
-    filename_collector = FilenameCollectorPP()
-
-    with YoutubeDL(ydl_opts) as ydl:
-        if maxDuration and maxDuration > 0:
-            info = ydl.extract_info(url, download=False)
-            entries = "entries" in info and info["entries"] or [info]
-
-            total_duration = 0
-
-            # Compute total duration
-            for entry in entries:
-                total_duration += float(entry["duration"])
-
-            if total_duration >= maxDuration:
-                raise ExceededMaximumDuration(videoDuration=total_duration, maxDuration=maxDuration, message="Video is too long")
-
-        ydl.add_post_processor(filename_collector)
-        ydl.download([url])
-
-    if len(filename_collector.filenames) <= 0:
-        raise Exception("Cannot download " + url)
-
-    result = []
-
-    for filename in filename_collector.filenames:
-        result.append(filename)
-        print("Downloaded " + filename)
-
-    return result 
-
-class ExceededMaximumDuration(Exception):
-    def __init__(self, videoDuration, maxDuration, message):
-        self.videoDuration = videoDuration
-        self.maxDuration = maxDuration
-        super().__init__(message)
\ No newline at end of file
diff --git a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/adversarial.py b/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/adversarial.py
deleted file mode 100644
index d798d87ac88bec59e879d35b201b5b5bcbaaf922..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/adversarial.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch
-
-
-class FGM(object):
-    """
-    refer to the paper: FGM(Fast Gradient Method)
-    Adversarial training methods for semi-supervised text classification
-    """
-
-    def __init__(self, model):
-        self.model = model
-        self.backup = {}
-
-    def attack(self, epsilon=1e-6, emd_name="embedding"):
-        for name, param in self.model.named_parameters():
-            if param.requires_grad and emd_name in name:
-                self.backup[name] = param.data.clone()
-                norm = torch.norm(param.grad)
-                if norm != 0 and not torch.isnan(norm):
-                    r_at = epsilon * param.grad / norm
-                    param.data.add_(r_at)
-
-    def restore(self, emd_name="embedding"):
-        for name, param in self.model.named_parameters():
-            if param.requires_grad and emd_name in name:
-                assert name in self.backup
-                param.data = self.backup[name]
-        self.backup = {}
-
-
-class PGD(object):
-    """
-    refer to the paper: PGD(Projected Gradient Descent)
-    Towards Deep Learning Models Resistant to Adversarial Attacks
-    """
-
-    def __init__(self, model):
-        self.model = model
-        self.emb_backup = {}
-        self.grad_backup = {}
-
-    def attack(self, epsilon=1., alpha=0.3, emb_name="embedding", is_first_attack=False):
-        for name, param in self.model.named_parameters():
-            if param.requires_grad and emb_name in name:
-                if is_first_attack:
-                    self.emb_backup[name] = param.data.clone()
-                norm = torch.norm(param.grad)
-                if norm != 0 and not torch.isnan(norm):
-                    r_at = alpha * param.grad / norm
-                    param.data.add_(r_at)
-                    param.data = self.project(name, param.data, epsilon)
-
-    def restore(self, emb_name="embedding"):
-        for name, param in self.model.named_parameters():
-            if param.requires_grad and emb_name in name:
-                assert name in self.emb_backup
-                param.data = self.emb_backup[name]
-        self.emb_backup = {}
-
-    def project(self, param_name, param_data, epsilon):
-        r = param_data - self.emb_backup[param_name]
-        if torch.norm(r) > epsilon:
-            r = epsilon * r / torch.norm(r)
-        return self.emb_backup[param_name] + r
-
-    def backup_grad(self):
-        for name, param in self.model.named_parameters():
-            if param.requires_grad:
-                self.grad_backup[name] = param.grad.clone()
-
-    def restore_grad(self):
-        for name, param in self.model.named_parameters():
-            if param.requires_grad:
-                param.grad = self.grad_backup[name]
diff --git a/spaces/teralomaniac/clewd/Dockerfile b/spaces/teralomaniac/clewd/Dockerfile
deleted file mode 100644
index f6ddf9ac542b5bd2f2d860ad9cef588013c8e902..0000000000000000000000000000000000000000
--- a/spaces/teralomaniac/clewd/Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-# Use the official Node.js image as the base image
-FROM node:20.4
-
-# Set the working directory in the container
-WORKDIR /app
-
-# Copy the package.json and package-lock.json files to the container
-COPY package*.json ./
-
-# Install the dependencies
-RUN npm install --no-audit --fund false
-
-# Copy the rest of the files to the container
-COPY . .
-
-# Change ownership of files in lib/bin and set permissions
-RUN chown -R node:node lib/bin/* && \
-    chmod u+x lib/bin/* && \
-    chmod -R 777 /app
-
-# Run as the "node" user for better security practices
-USER node
-
-RUN ls -la
-
-# Start the application
-CMD ["node", "clewd.js"]
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Avatar The Last Airbender All Episodes In Hindi 295.md b/spaces/terfces0erbo/CollegeProjectV2/Avatar The Last Airbender All Episodes In Hindi 295.md
deleted file mode 100644
index b8c31615b0db94d9fb0d5164b05a411cc87c6e2e..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Avatar The Last Airbender All Episodes In Hindi 295.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<h1>Avatar: The Last Airbender All Episodes in Hindi 295</h1>
-<p>Avatar: The Last Airbender is a popular animated series that follows the adventures of Aang, a young boy who is the reincarnation of the Avatar, the master of all four elements. Along with his friends Katara, Sokka and Toph, he travels across the world to learn the four bending arts and stop the Fire Nation from conquering the other nations.</p>
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Ay Papi 1-15 Online Comic BEST.md b/spaces/terfces0erbo/CollegeProjectV2/Ay Papi 1-15 Online Comic BEST.md
deleted file mode 100644
index 0db6578797c843d40e7b38d868fc8c8c6da64d36..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Ay Papi 1-15 Online Comic BEST.md	
+++ /dev/null
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Be Your Own Windkeeper Book Download ((INSTALL)).md b/spaces/terfces0erbo/CollegeProjectV2/Be Your Own Windkeeper Book Download ((INSTALL)).md
deleted file mode 100644
index f97e9a324f576920e4c1edd9f276c1d972aec24f..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Be Your Own Windkeeper Book Download ((INSTALL)).md	
+++ /dev/null
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Beach Buggy Racing The Ultimate Off-Road Kart Racing Game APK.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Beach Buggy Racing The Ultimate Off-Road Kart Racing Game APK.md
deleted file mode 100644
index 8e614da0067f78750d7d99cea492ad7becc202af..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Beach Buggy Racing The Ultimate Off-Road Kart Racing Game APK.md	
+++ /dev/null
@@ -1,169 +0,0 @@
-
-<h1>How to Download Beach Buggy Racing APK for Android</h1>
-<p>Beach Buggy Racing is a fun and exciting kart racing game that will take you to a tropical island full of surprises. You can race against a variety of opponents, each with their own personality and special ability, and collect crazy power-ups to spice up the competition. You can also customize your car, recruit a team of racers, and explore 15 different tracks with hidden shortcuts and secrets.</p>
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- <h2>What is Beach Buggy Racing?</h2>
-<p>Beach Buggy Racing is a free racing game by developer Vector Unit for mobile devices. It is an off-road kart racer that is set on a tropical island. In it, players will get to drive a variety of unique vehicles that they can customize in multiple ways.</p>
- <h3>Game features</h3>
-<p>Beach Buggy Racing has many features that make it an enjoyable and addictive game. Some of them are:</p>
-<ul>
-<li><b>EXCITING KART-RACING ACTION</b>: You can use your driving skills and a collection of creative power-ups to fight your way to the finish line. The game has spectacular physics-based gameplay that makes every race unpredictable and fun.</li>
-<li><b>COOL CARS TO CUSTOMIZE</b>: You can use your winnings to collect and upgrade a garage full of unique cars, from monster trucks to muscle cars to lunar rovers. Each car has its own performance and appearance.</li>
-<li><b>TONS OF AMAZING POWERUPS</b>: Beach Buggy Racing crushes other kart racers with over 25 totally unique power-ups, such as Dodgeball Frenzy, Fireball, Oil Slick, and more. You can use them to gain an edge over your rivals or to sabotage them.</li>
-<li><b>15 SPECTACULAR RACE TRACKS</b>: You can explore dinosaur-infested jungles, lava-spewing volcanoes, beautiful beaches, and mysterious swamps. Each track has its own challenges, obstacles, and secrets.</li>
-<li><b>COLLECT A TEAM OF RACERS</b>: You can recruit a team of drivers to play with, each with a unique special power like teleportation, flaming fire tracks, and confusion spells. You can switch between them during the races to suit your strategy.</li>
-<li><b>SPLIT SCREEN MULTIPLAYER</b>: You can race shoulder-to-shoulder with up to 4 friends on Android TV or a TV-connected phone or tablet. This feature requires an in-app purchase.</li>
-<li><b>GOOGLE PLAY GAME SERVICES</b>: You can compete with your friends on leaderboards, earn achievements, back up your game to the cloud, and keep multiple devices in sync with your Google account.</li>
-<li><b>PLAY THE WAY YOU WANT</b>: You can seamlessly switch between tilt steering, touch-screen, and USB/Bluetooth gamepad. You can also customize the 3D graphics settings to optimize your play <p>experience.</p>
- <h3>Game modes</h3>
-<p>Beach Buggy Racing has two main game modes that you can choose from:</p>
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-<ul>
-<li><b>CAREER MODE</b>: This is the mode where you can progress through the game and unlock new tracks, cars, and drivers. You can also earn coins and gems to upgrade your vehicles and power-ups. There are 12 championship cups to compete in, each with 4 races. You can earn up to 3 stars per race depending on your performance.</li>
-<li><b>QUICK RACE MODE</b>: This is the mode where you can play a single race on any track you have unlocked. You can also customize the race settings, such as the number of laps, the difficulty level, and the power-ups available. This is a good way to practice your skills and have fun.</li>
-</ul>
- <h3>Game reviews</h3>
-<p>Beach Buggy Racing has received positive reviews from both critics and players. It has a rating of 4.4 out of 5 stars on Google Play, with over 2 million reviews. Some of the comments from users are:</p>
-<blockquote>
-<p>"This game is awesome! It has great graphics, gameplay, and controls. It is very addictive and fun to play. I love the variety of cars, tracks, and power-ups. It is one of the best racing games I have ever played."</p>
-<p>"I really enjoy this game. It is challenging and entertaining. The graphics are amazing and the sound effects are realistic. The power-ups are hilarious and creative. It is a great game for all ages."</p>
-<p>"This game is very cool and fun. It reminds me of Mario Kart, but with more features and customization. The game runs smoothly and does not lag or crash. The multiplayer mode is also great. I highly recommend this game."</p>
-</blockquote>
- <h2>Why download Beach Buggy Racing APK?</h2>
-<p>An APK file is an Android Package file that contains all the files and data needed to install an app on an Android device. APK files are usually downloaded from third-party sources that are not affiliated with Google Play or the app developer.</p>
-<p>There are several reasons why you might want to download Beach Buggy Racing APK instead of installing it from Google Play. Some of them are:</p>
- <h3>Benefits of APK files</h3>
-<ul>
-<li><b>ACCESS TO LATEST VERSIONS</b>: Sometimes, Google Play may take some time to update an app or may not be available in your region. By downloading an APK file, you can get access to the latest version of Beach Buggy Racing as soon as it is released by the developer.</li>
-<li><b>ACCESS TO MODDED VERSIONS</b>: Some APK files may contain modified versions of Beach Buggy Racing that offer extra features or advantages, such as unlimited coins, gems, or power-ups. These versions may not be authorized by the developer, but they can enhance your gaming experience.</li>
-<li><b>ACCESS TO BANNED OR RESTRICTED APPS</b>: Some apps may be banned or restricted in certain countries or regions due to legal or political reasons. By downloading an APK file, you can bypass these restrictions and enjoy Beach Buggy Racing without any limitations.</li>
-<li><b>BACKUP AND RESTORE APPS</b>: By downloading an APK file, you can backup your Beach Buggy Racing app and data on your device or external storage. This way, you can restore it later if you lose it or switch to another device.</li>
-</ul>
- <h3>Risks of APK files</h3>
-<ul>
-<li><b>MALWARE AND VIRUSES</b>: One of the biggest risks of downloading APK files is that they may contain malware or viruses that can harm your device or steal your personal information. Some malicious APK files may also damage or delete your Beach Buggy Racing app and data.</li>
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-</ul>
- <h3>How to avoid malware <h3>How to avoid malware and viruses</h3>
-<p>While downloading APK files can have some benefits, it also comes with some risks. Therefore, you should be careful and cautious when downloading Beach Buggy Racing APK or any other APK file. Here are some tips to help you avoid malware and viruses:</p>
-<ul>
-<li><b>CHECK THE SOURCE</b>: You should only download APK files from reputable and trustworthy sources that have positive reviews and ratings. You should avoid unknown or suspicious sources that may contain malicious or fake files.</li>
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- <h2>How to download Beach Buggy Racing APK safely and easily?</h2>
-<p>If you have decided to download Beach Buggy Racing APK for your Android device, you can follow these simple steps to do it safely and easily:</p>
- <h3>Step 1: Enable unknown sources on your device</h3>
-<p>By default, Android devices do not allow the installation of apps from sources other than Google Play. To enable the installation of APK files, you need to change the settings on your device. Here is how:</p>
-<ol>
-<li>Go to <b>Settings</b> on your device.</li>
-<li>Tap on <b>Security</b> or <b>Privacy</b>, depending on your device model.</li>
-<li>Find and toggle on the option that says <b>Unknown sources</b> or <b>Allow installation of apps from unknown sources</b>.</li>
-<li>A warning message will pop up. Read it carefully and tap on <b>OK</b> or <b>Confirm</b>.</li>
-</ol>
- <h3>Step 2: Find a reliable APK source online</h3>
-<p>The next step is to find a reliable and trustworthy source online that offers Beach Buggy Racing APK for download. As mentioned earlier, you should only download APK files from reputable sources that have positive reviews and ratings. You can use a web browser or a search engine to find such sources. Some examples of popular and trusted sources are:</p>
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-<ol>
-<li>Navigate to the source website that you have chosen and find the Beach Buggy Racing APK file.</li>
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-<li>Go to your device's home screen or app drawer and find the Beach Buggy Racing icon.</li>
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-<li>You may need to grant some permissions or accept some terms and conditions before playing the game.</li>
-<li>You can now play Beach Buggy Racing as you normally would.</li>
-</ol>
- <h2>Tips and tricks for Beach Buggy Racing</h2>
-<p>To help you get the most out of Beach Buggy Racing, here are some tips and tricks that you can use:</p>
- <h3>Upgrade your car and choose the right driver</h3>
-<p>You can upgrade your car's speed, acceleration, handling, and strength using coins that you earn from racing. Upgrading your car will make it faster and more responsive, which will give you an edge over your opponents. You can also choose the right driver for your car, as each driver has a unique special power that can help you in different situations. For example, Rez has the ability to teleport, McSkelly has the ability to create fire tracks, and Beach Bro has the ability to confuse other racers.</p>
- <h3>Use power-ups strategically and master drifting</h3>
-<p>You can collect and use various power-ups during the races, such as rockets, oil slicks, fireballs, and more. These power-ups can help you attack or defend yourself from other racers. You should use them strategically and wisely, depending on the situation and the track. For example, you can use rockets to hit racers ahead of you, oil slicks to slow down racers behind you, and fireballs to clear obstacles in front of you. You should also master the art of drifting, which is sliding your car around corners without losing speed. Drifting can help you take sharp turns more smoothly and fill up your boost meter faster.</p>
- <h3>Take advantage of shortcuts and boost pads</h3>
-<p>You can find and use shortcuts and boost pads on the tracks to gain an advantage over your rivals. Shortcuts are hidden paths that can help you skip some parts of the track or avoid some obstacles. Boost pads are glowing areas that can give you a burst of speed when you drive over them. You should look out for these features and use them whenever possible. However, be careful not to miss them or crash into them, as they may also have some risks or traps.</p>
- <h3>Learn to dodge obstacles and practice regularly</h3>
-<p>You can encounter various obstacles on the tracks, such as rocks, barrels, crates, animals, and more. These obstacles can slow you down or damage your car if you hit them. You should learn to dodge them by steering, jumping, or using power-ups. You should also practice regularly to improve your skills and familiarize yourself with the tracks. You can play in quick race mode or replay the races in career mode to hone your abilities.</p>
- <h2>Conclusion</h2>
-<p>Beach Buggy Racing is a fun and exciting kart racing game that will take you to a tropical island full of surprises. You can race against a variety of opponents, each with their own personality and special ability, and collect crazy power-ups to spice up the competition. You can also customize your car, recruit a team of racers, and explore 15 different tracks with hidden shortcuts and secrets.</p>
-<p>If you want to download Beach Buggy Racing APK for your Android device, you can follow the steps we have outlined in this article. However, you should also be aware of the risks and precautions involved in downloading APK files from third-party sources. You should only download APK files from reputable and trustworthy sources that have positive reviews and ratings. You should also scan the APK file with a reliable antivirus or anti-malware software before installing it on your device. You should also review the permissions that the APK file requests before installing it on your device.</p>
-<p>We hope this article has helped you learn how to download Beach Buggy Racing APK for Android safely and easily. We also hope you have enjoyed playing Beach Buggy Racing on your device. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Beach Buggy Racing APK:</p>
-<ul>
-<li><b>Q: Is Beach Buggy Racing APK free?</b></li>
-<li><b>A: Yes, Beach Buggy Racing APK is free to download and play. However, some features and items may require in-app purchases.</b></li>
-<li><b>Q: Is Beach Buggy Racing APK safe?</b></li>
-<li><b>A: Beach Buggy Racing APK is safe if you download it from a reliable and trustworthy source online. However, you should always scan the APK file with a reliable antivirus or anti-malware software before installing it on your device.</b></li>
-<li><b>Q: Is Beach Buggy Racing APK legal?</b></li>
-<li><b>A: Beach Buggy Racing APK is legal if you download it from an authorized source that has permission from the developer. However, some APK files may violate the copyright or trademark of Beach Buggy Racing or its developer. Downloading these files may be illegal in some countries or regions and may result in legal consequences.</b></li>
-<li><b>Q: How do I update Beach Buggy Racing APK?</b></li>
-<li><b>A: To update Beach Buggy Racing APK, you need to download the latest version of the APK file from a reliable source online and install it on your device. You may need to uninstall the previous version of the app before installing the new one.</b></li>
-<li <li><b>Q: How do I uninstall Beach Buggy Racing APK?</b></li>
-<li><b>A: To uninstall Beach Buggy Racing APK, you need to go to Settings on your device, tap on Apps or Applications, find and tap on Beach Buggy Racing, and tap on Uninstall. You may also need to delete the APK file from your device's storage.</b></li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download APK Mod Lite How to Unlock Premium Features and Games on Your Android Device.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download APK Mod Lite How to Unlock Premium Features and Games on Your Android Device.md
deleted file mode 100644
index d65d508a17342f5ea0bbc0ac47e85c82f8e165b6..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download APK Mod Lite How to Unlock Premium Features and Games on Your Android Device.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-
-<h1>Download APK Mod Lite: How to Get the Best Apps for Your Android Device</h1>
-<p>If you are an Android user, you probably know that there are millions of apps available on the Google Play Store. However, not all of them are free, and some of them may not work well on your device. That's why you may want to try APK Mod Lite, a modified version of the original app that offers more features, less ads, and better performance. But how do you download and use APK Mod Lite? In this article, we will explain everything you need to know about this amazing tool.</p>
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- <h2>What is APK Mod Lite?</h2>
-<p>APK Mod Lite is a term that refers to an app that has been modified by a third-party developer to make it more user-friendly, efficient, and fun. For example, an APK Mod Lite may remove unwanted ads, unlock premium features, add new functions, or reduce the size of the app. APK Mod Lite can be applied to various types of apps, such as games, social media, streaming, productivity, and more.</p>
- <h3>The benefits of using APK Mod Lite</h3>
-<p>There are many reasons why you may want to use APK Mod Lite instead of the original app. Some of the benefits are:</p>
-<ul>
-<li>You can save money by accessing paid features for free.</li>
-<li>You can save storage space by installing smaller apps.</li>
-<li>You can enjoy faster and smoother performance by removing bloatware and bugs.</li>
-<li>You can customize your app experience by changing the settings and preferences.</li>
-<li>You can discover new and exciting features that are not available on the official version.</li>
-</ul>
- <h3>The risks of using APK Mod Lite</h3>
-<p>However, using APK Mod Lite also comes with some risks that you should be aware of. Some of the risks are:</p>
-<ul>
-<li>You may violate the terms and conditions of the original app developer and get banned or suspended.</li>
-<li>You may expose your device and data to malware, viruses, or hackers by downloading from untrusted sources.</li>
-<li>You may encounter compatibility issues or errors by installing outdated or incompatible apps.</li>
-<li>You may lose your progress or data by updating or uninstalling the app incorrectly.</li>
-<li>You may miss out on important updates or security patches from the official version.</li>
-</ul>
- <h2>How to download APK Mod Lite?</h2>
-<p>If you decide to download APK Mod Lite, you need to follow some steps to ensure a safe and successful installation. Here are the steps:</p>
- <h3>Find a reliable source</h3>
-<p>The first step is to find a reliable source that offers APK Mod Lite for your desired app. You can search online for websites or forums that specialize in providing modded apps. However, you need to be careful and check the reviews, ratings, and comments from other users before downloading anything. You can also use a website like [LITEAPKS](^1^), which offers free APK & Mod downloads for Android. </p>
- <h3>Check the compatibility and permissions</h3>
-<p>The next step is to check the compatibility and permissions of the APK file. You need to make sure that the app is compatible with your device model and Android version. You also need to check what permissions the app requires and whether they are necessary or not. You can do this by tapping on the APK file and reading the information before installing it.</p>
- <h3>Install the APK file</h3>
-<p>The final step is to install the APK file on your device. You need to enable the option to install apps from unknown sources in your device settings. Then, you can tap on the APK file and follow the instructions on the screen. Once the installation is complete, you can launch the app and enjoy the APK Mod Lite features.</p>
- <h2>How to use APK Mod Lite?</h2>
-<p>After you have downloaded and installed the APK Mod Lite, you may wonder how to use it effectively. Here are some tips to help you get the most out of your app:</p>
- <h3>Explore the features and settings</h3>
-<p>The first thing you should do is to explore the features and settings of the APK Mod Lite. You may find some new and interesting options that are not available on the original app. For example, you may be able to change the theme, language, or layout of the app. You may also be able to access premium content, unlock achievements, or modify the gameplay. You can experiment with different settings and see what works best for you.</p>
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- <h3>Update the app regularly</h3>
-<p>The second thing you should do is to update the app regularly. This is important to ensure that your app is compatible with the latest Android version and security patches. It also helps to fix any bugs or errors that may occur. You can check for updates by visiting the source website or forum where you downloaded the APK Mod Lite. You can also use a tool like [APKUpdater], which scans your device for installed apps and notifies you of any available updates. </p>
- <h3>Uninstall the app if needed</h3>
-<p>The third thing you should do is to uninstall the app if needed. This may happen if you encounter any problems with the app, such as crashes, freezes, or malware. It may also happen if you want to switch back to the original app or try a different APK Mod Lite. To uninstall the app, you need to go to your device settings and find the app in the list of installed apps. Then, you can tap on it and select uninstall. You may also need to delete the APK file from your device storage.</p>
- <h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion, APK Mod Lite is a great way to enhance your Android app experience. It allows you to access more features, less ads, and better performance for your favorite apps. However, you need to be careful and follow some steps to download and use APK Mod Lite safely and effectively. You need to find a reliable source, check the compatibility and permissions, install the APK file, explore the features and settings, update the app regularly, and uninstall the app if needed.</p>
- <h4>Call to action</h4>
-<p>If you are interested in trying APK Mod Lite, why not start today? You can visit [LITEAPKS] and browse through their collection of free APK & Mod downloads for Android. You can also join their community and share your feedback and suggestions with other users. Download APK Mod Lite now and enjoy the best apps for your Android device!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>What is the difference between APK and APK Mod Lite?</b><br>
-APK is the file format used by Android to install apps on devices. APK Mod Lite is a modified version of an original APK that offers more features, less ads, and better performance.</li>
-<li><b>Is APK Mod Lite legal?</b><br>
-APK Mod Lite is not illegal per se, but it may violate the terms and conditions of the original app developer. Therefore, you should use it at your own risk and discretion.</li>
-<li><b>Is APK Mod Lite safe?</b><br>
-APK Mod Lite is not always safe, as it may contain malware, viruses, or hackers that can harm your device or data. Therefore, you should only download from trusted sources and scan your device regularly.</li>
-<li><b>How do I know if an APK Mod Lite is compatible with my device?</b><br>
-You can check the compatibility of an APK Mod Lite by reading the information on the source website or forum. You can also tap on the APK file before installing it and see what Android version and permissions it requires.</li>
-<li><b>How do I update an APK Mod Lite?</b><br>
-You can update an APK Mod Lite by visiting the source website or forum where you downloaded it and looking for any new versions. You can also use a tool like [APKUpdater], which scans your device for installed apps and notifies you of any available updates. </li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Burgman 125 Revue Technique Pdf.md b/spaces/tioseFevbu/cartoon-converter/scripts/Burgman 125 Revue Technique Pdf.md
deleted file mode 100644
index 703aae3a98bd3853a9edbf0b92cd486f870b1e6b..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Burgman 125 Revue Technique Pdf.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-Here is a possible title and article with html formatting for the keyword "burgman 125 revue technique pdf":
-
-<h1>How to Find and Download a Revue Technique for Suzuki Burgman 125</h1>
-<p>A revue technique is a technical manual that provides detailed information on how to maintain, repair and troubleshoot a vehicle. A revue technique can be very useful for owners, mechanics and enthusiasts who want to learn more about their vehicle's features, specifications and performance.</p>
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-<p>One of the popular scooters that has a revue technique available is the Suzuki Burgman 125, a maxi-scooter that was launched in 2002 and has been updated several times since then. The Suzuki Burgman 125 offers a comfortable ride, a spacious storage compartment, a fuel-efficient engine and a sleek design.</p>
-<p>If you own or are interested in a Suzuki Burgman 125, you might want to get your hands on a revue technique for this scooter. However, finding and downloading a revue technique for Suzuki Burgman 125 can be challenging, as there are not many sources online that offer this document for free or at a low cost.</p>
-<p>In this article, we will show you some of the possible ways to find and download a revue technique for Suzuki Burgman 125, as well as some of the advantages and disadvantages of each method.</p>
-<p></p>
-
-<h2>Method 1: Search on Online Forums</h2>
-<p>One of the easiest ways to find and download a revue technique for Suzuki Burgman 125 is to search on online forums that are dedicated to scooters or motorcycles. There are many enthusiasts who share their knowledge and resources on these forums, and you might be able to find someone who has uploaded or linked to a revue technique for Suzuki Burgman 125.</p>
-<p>For example, one of the forums that has a thread about the revue technique for Suzuki Burgman 125 is Scooter System[^1^], a French forum that covers various topics related to scooters. In this thread, a user named Spark posted a PDF file of the revue technique for Suzuki Burgman 125 in May 2020, and received positive feedback from other users. You can download the PDF file by clicking on the attachment icon at the bottom of Spark's post.</p>
-<p>However, there are some drawbacks to using this method. First of all, you need to be able to read and understand French, as most of the forums that have information about the revue technique for Suzuki Burgman 125 are in French. Secondly, you need to register and log in to the forum before you can access the PDF file. Thirdly, you need to be careful about the quality and reliability of the PDF file, as it might be incomplete, outdated or infected with malware.</p>
-
-<h2>Method 2: Search on Online Databases</h2>
-<p>Another way to find and download a revue technique for Suzuki Burgman 125 is to search on online databases that offer free or paid access to various manuals for different vehicles. These databases usually have a large collection of documents that cover different aspects of vehicle maintenance and repair, such as service manuals, parts catalogs, wiring diagrams and more.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Downton Abbey Saison 3 French Download Torrent.md b/spaces/tioseFevbu/cartoon-converter/scripts/Downton Abbey Saison 3 French Download Torrent.md
deleted file mode 100644
index 44f9e69e62d402421aaf7c776a1e976c88a5a0c8..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Downton Abbey Saison 3 French Download Torrent.md	
+++ /dev/null
@@ -1,30 +0,0 @@
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/metadata/importlib/_dists.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/metadata/importlib/_dists.py
deleted file mode 100644
index fbf9a93218a14692314c8be085e720ff784f67f3..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/metadata/importlib/_dists.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import email.message
-import importlib.metadata
-import os
-import pathlib
-import zipfile
-from typing import (
-    Collection,
-    Dict,
-    Iterable,
-    Iterator,
-    Mapping,
-    Optional,
-    Sequence,
-    cast,
-)
-
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, UnsupportedWheel
-from pip._internal.metadata.base import (
-    BaseDistribution,
-    BaseEntryPoint,
-    DistributionVersion,
-    InfoPath,
-    Wheel,
-)
-from pip._internal.utils.misc import normalize_path
-from pip._internal.utils.packaging import safe_extra
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from ._compat import BasePath, get_dist_name
-
-
-class WheelDistribution(importlib.metadata.Distribution):
-    """An ``importlib.metadata.Distribution`` read from a wheel.
-
-    Although ``importlib.metadata.PathDistribution`` accepts ``zipfile.Path``,
-    its implementation is too "lazy" for pip's needs (we can't keep the ZipFile
-    handle open for the entire lifetime of the distribution object).
-
-    This implementation eagerly reads the entire metadata directory into the
-    memory instead, and operates from that.
-    """
-
-    def __init__(
-        self,
-        files: Mapping[pathlib.PurePosixPath, bytes],
-        info_location: pathlib.PurePosixPath,
-    ) -> None:
-        self._files = files
-        self.info_location = info_location
-
-    @classmethod
-    def from_zipfile(
-        cls,
-        zf: zipfile.ZipFile,
-        name: str,
-        location: str,
-    ) -> "WheelDistribution":
-        info_dir, _ = parse_wheel(zf, name)
-        paths = (
-            (name, pathlib.PurePosixPath(name.split("/", 1)[-1]))
-            for name in zf.namelist()
-            if name.startswith(f"{info_dir}/")
-        )
-        files = {
-            relpath: read_wheel_metadata_file(zf, fullpath)
-            for fullpath, relpath in paths
-        }
-        info_location = pathlib.PurePosixPath(location, info_dir)
-        return cls(files, info_location)
-
-    def iterdir(self, path: InfoPath) -> Iterator[pathlib.PurePosixPath]:
-        # Only allow iterating through the metadata directory.
-        if pathlib.PurePosixPath(str(path)) in self._files:
-            return iter(self._files)
-        raise FileNotFoundError(path)
-
-    def read_text(self, filename: str) -> Optional[str]:
-        try:
-            data = self._files[pathlib.PurePosixPath(filename)]
-        except KeyError:
-            return None
-        try:
-            text = data.decode("utf-8")
-        except UnicodeDecodeError as e:
-            wheel = self.info_location.parent
-            error = f"Error decoding metadata for {wheel}: {e} in {filename} file"
-            raise UnsupportedWheel(error)
-        return text
-
-
-class Distribution(BaseDistribution):
-    def __init__(
-        self,
-        dist: importlib.metadata.Distribution,
-        info_location: Optional[BasePath],
-        installed_location: Optional[BasePath],
-    ) -> None:
-        self._dist = dist
-        self._info_location = info_location
-        self._installed_location = installed_location
-
-    @classmethod
-    def from_directory(cls, directory: str) -> BaseDistribution:
-        info_location = pathlib.Path(directory)
-        dist = importlib.metadata.Distribution.at(info_location)
-        return cls(dist, info_location, info_location.parent)
-
-    @classmethod
-    def from_wheel(cls, wheel: Wheel, name: str) -> BaseDistribution:
-        try:
-            with wheel.as_zipfile() as zf:
-                dist = WheelDistribution.from_zipfile(zf, name, wheel.location)
-        except zipfile.BadZipFile as e:
-            raise InvalidWheel(wheel.location, name) from e
-        except UnsupportedWheel as e:
-            raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
-        return cls(dist, dist.info_location, pathlib.PurePosixPath(wheel.location))
-
-    @property
-    def location(self) -> Optional[str]:
-        if self._info_location is None:
-            return None
-        return str(self._info_location.parent)
-
-    @property
-    def info_location(self) -> Optional[str]:
-        if self._info_location is None:
-            return None
-        return str(self._info_location)
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        if self._installed_location is None:
-            return None
-        return normalize_path(str(self._installed_location))
-
-    def _get_dist_name_from_location(self) -> Optional[str]:
-        """Try to get the name from the metadata directory name.
-
-        This is much faster than reading metadata.
-        """
-        if self._info_location is None:
-            return None
-        stem, suffix = os.path.splitext(self._info_location.name)
-        if suffix not in (".dist-info", ".egg-info"):
-            return None
-        return stem.split("-", 1)[0]
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        name = self._get_dist_name_from_location() or get_dist_name(self._dist)
-        return canonicalize_name(name)
-
-    @property
-    def version(self) -> DistributionVersion:
-        return parse_version(self._dist.version)
-
-    def is_file(self, path: InfoPath) -> bool:
-        return self._dist.read_text(str(path)) is not None
-
-    def iter_distutils_script_names(self) -> Iterator[str]:
-        # A distutils installation is always "flat" (not in e.g. egg form), so
-        # if this distribution's info location is NOT a pathlib.Path (but e.g.
-        # zipfile.Path), it can never contain any distutils scripts.
-        if not isinstance(self._info_location, pathlib.Path):
-            return
-        for child in self._info_location.joinpath("scripts").iterdir():
-            yield child.name
-
-    def read_text(self, path: InfoPath) -> str:
-        content = self._dist.read_text(str(path))
-        if content is None:
-            raise FileNotFoundError(path)
-        return content
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        # importlib.metadata's EntryPoint structure sasitfies BaseEntryPoint.
-        return self._dist.entry_points
-
-    def _metadata_impl(self) -> email.message.Message:
-        # From Python 3.10+, importlib.metadata declares PackageMetadata as the
-        # return type. This protocol is unfortunately a disaster now and misses
-        # a ton of fields that we need, including get() and get_payload(). We
-        # rely on the implementation that the object is actually a Message now,
-        # until upstream can improve the protocol. (python/cpython#94952)
-        return cast(email.message.Message, self._dist.metadata)
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        return (
-            safe_extra(extra) for extra in self.metadata.get_all("Provides-Extra", [])
-        )
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        contexts: Sequence[Dict[str, str]] = [{"extra": safe_extra(e)} for e in extras]
-        for req_string in self.metadata.get_all("Requires-Dist", []):
-            req = Requirement(req_string)
-            if not req.marker:
-                yield req
-            elif not extras and req.marker.evaluate({"extra": ""}):
-                yield req
-            elif any(req.marker.evaluate(context) for context in contexts):
-                yield req
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/jpcntx.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/jpcntx.py
deleted file mode 100644
index 7a8e5be0623904d577b8c51eb3d627af963638e5..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/jpcntx.py
+++ /dev/null
@@ -1,237 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-
-# This is hiragana 2-char sequence table, the number in each cell represents its frequency category
-# fmt: off
-jp2_char_context = (
-    (0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1),
-    (2, 4, 0, 4, 0, 3, 0, 4, 0, 3, 4, 4, 4, 2, 4, 3, 3, 4, 3, 2, 3, 3, 4, 2, 3, 3, 3, 2, 4, 1, 4, 3, 3, 1, 5, 4, 3, 4, 3, 4, 3, 5, 3, 0, 3, 5, 4, 2, 0, 3, 1, 0, 3, 3, 0, 3, 3, 0, 1, 1, 0, 4, 3, 0, 3, 3, 0, 4, 0, 2, 0, 3, 5, 5, 5, 5, 4, 0, 4, 1, 0, 3, 4),
-    (0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2),
-    (0, 4, 0, 5, 0, 5, 0, 4, 0, 4, 5, 4, 4, 3, 5, 3, 5, 1, 5, 3, 4, 3, 4, 4, 3, 4, 3, 3, 4, 3, 5, 4, 4, 3, 5, 5, 3, 5, 5, 5, 3, 5, 5, 3, 4, 5, 5, 3, 1, 3, 2, 0, 3, 4, 0, 4, 2, 0, 4, 2, 1, 5, 3, 2, 3, 5, 0, 4, 0, 2, 0, 5, 4, 4, 5, 4, 5, 0, 4, 0, 0, 4, 4),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
-    (0, 3, 0, 4, 0, 3, 0, 3, 0, 4, 5, 4, 3, 3, 3, 3, 4, 3, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 4, 4, 4, 4, 5, 3, 4, 4, 3, 4, 5, 5, 4, 5, 5, 1, 4, 5, 4, 3, 0, 3, 3, 1, 3, 3, 0, 4, 4, 0, 3, 3, 1, 5, 3, 3, 3, 5, 0, 4, 0, 3, 0, 4, 4, 3, 4, 3, 3, 0, 4, 1, 1, 3, 4),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
-    (0, 4, 0, 3, 0, 3, 0, 4, 0, 3, 4, 4, 3, 2, 2, 1, 2, 1, 3, 1, 3, 3, 3, 3, 3, 4, 3, 1, 3, 3, 5, 3, 3, 0, 4, 3, 0, 5, 4, 3, 3, 5, 4, 4, 3, 4, 4, 5, 0, 1, 2, 0, 1, 2, 0, 2, 2, 0, 1, 0, 0, 5, 2, 2, 1, 4, 0, 3, 0, 1, 0, 4, 4, 3, 5, 4, 3, 0, 2, 1, 0, 4, 3),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
-    (0, 3, 0, 5, 0, 4, 0, 2, 1, 4, 4, 2, 4, 1, 4, 2, 4, 2, 4, 3, 3, 3, 4, 3, 3, 3, 3, 1, 4, 2, 3, 3, 3, 1, 4, 4, 1, 1, 1, 4, 3, 3, 2, 0, 2, 4, 3, 2, 0, 3, 3, 0, 3, 1, 1, 0, 0, 0, 3, 3, 0, 4, 2, 2, 3, 4, 0, 4, 0, 3, 0, 4, 4, 5, 3, 4, 4, 0, 3, 0, 0, 1, 4),
-    (1, 4, 0, 4, 0, 4, 0, 4, 0, 3, 5, 4, 4, 3, 4, 3, 5, 4, 3, 3, 4, 3, 5, 4, 4, 4, 4, 3, 4, 2, 4, 3, 3, 1, 5, 4, 3, 2, 4, 5, 4, 5, 5, 4, 4, 5, 4, 4, 0, 3, 2, 2, 3, 3, 0, 4, 3, 1, 3, 2, 1, 4, 3, 3, 4, 5, 0, 3, 0, 2, 0, 4, 5, 5, 4, 5, 4, 0, 4, 0, 0, 5, 4),
-    (0, 5, 0, 5, 0, 4, 0, 3, 0, 4, 4, 3, 4, 3, 3, 3, 4, 0, 4, 4, 4, 3, 4, 3, 4, 3, 3, 1, 4, 2, 4, 3, 4, 0, 5, 4, 1, 4, 5, 4, 4, 5, 3, 2, 4, 3, 4, 3, 2, 4, 1, 3, 3, 3, 2, 3, 2, 0, 4, 3, 3, 4, 3, 3, 3, 4, 0, 4, 0, 3, 0, 4, 5, 4, 4, 4, 3, 0, 4, 1, 0, 1, 3),
-    (0, 3, 1, 4, 0, 3, 0, 2, 0, 3, 4, 4, 3, 1, 4, 2, 3, 3, 4, 3, 4, 3, 4, 3, 4, 4, 3, 2, 3, 1, 5, 4, 4, 1, 4, 4, 3, 5, 4, 4, 3, 5, 5, 4, 3, 4, 4, 3, 1, 2, 3, 1, 2, 2, 0, 3, 2, 0, 3, 1, 0, 5, 3, 3, 3, 4, 3, 3, 3, 3, 4, 4, 4, 4, 5, 4, 2, 0, 3, 3, 2, 4, 3),
-    (0, 2, 0, 3, 0, 1, 0, 1, 0, 0, 3, 2, 0, 0, 2, 0, 1, 0, 2, 1, 3, 3, 3, 1, 2, 3, 1, 0, 1, 0, 4, 2, 1, 1, 3, 3, 0, 4, 3, 3, 1, 4, 3, 3, 0, 3, 3, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 4, 1, 0, 2, 3, 2, 2, 2, 1, 3, 3, 3, 4, 4, 3, 2, 0, 3, 1, 0, 3, 3),
-    (0, 4, 0, 4, 0, 3, 0, 3, 0, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 3, 4, 2, 4, 3, 4, 3, 3, 2, 4, 3, 4, 5, 4, 1, 4, 5, 3, 5, 4, 5, 3, 5, 4, 0, 3, 5, 5, 3, 1, 3, 3, 2, 2, 3, 0, 3, 4, 1, 3, 3, 2, 4, 3, 3, 3, 4, 0, 4, 0, 3, 0, 4, 5, 4, 4, 5, 3, 0, 4, 1, 0, 3, 4),
-    (0, 2, 0, 3, 0, 3, 0, 0, 0, 2, 2, 2, 1, 0, 1, 0, 0, 0, 3, 0, 3, 0, 3, 0, 1, 3, 1, 0, 3, 1, 3, 3, 3, 1, 3, 3, 3, 0, 1, 3, 1, 3, 4, 0, 0, 3, 1, 1, 0, 3, 2, 0, 0, 0, 0, 1, 3, 0, 1, 0, 0, 3, 3, 2, 0, 3, 0, 0, 0, 0, 0, 3, 4, 3, 4, 3, 3, 0, 3, 0, 0, 2, 3),
-    (2, 3, 0, 3, 0, 2, 0, 1, 0, 3, 3, 4, 3, 1, 3, 1, 1, 1, 3, 1, 4, 3, 4, 3, 3, 3, 0, 0, 3, 1, 5, 4, 3, 1, 4, 3, 2, 5, 5, 4, 4, 4, 4, 3, 3, 4, 4, 4, 0, 2, 1, 1, 3, 2, 0, 1, 2, 0, 0, 1, 0, 4, 1, 3, 3, 3, 0, 3, 0, 1, 0, 4, 4, 4, 5, 5, 3, 0, 2, 0, 0, 4, 4),
-    (0, 2, 0, 1, 0, 3, 1, 3, 0, 2, 3, 3, 3, 0, 3, 1, 0, 0, 3, 0, 3, 2, 3, 1, 3, 2, 1, 1, 0, 0, 4, 2, 1, 0, 2, 3, 1, 4, 3, 2, 0, 4, 4, 3, 1, 3, 1, 3, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 1, 1, 1, 2, 0, 3, 0, 0, 0, 3, 4, 2, 4, 3, 2, 0, 1, 0, 0, 3, 3),
-    (0, 1, 0, 4, 0, 5, 0, 4, 0, 2, 4, 4, 2, 3, 3, 2, 3, 3, 5, 3, 3, 3, 4, 3, 4, 2, 3, 0, 4, 3, 3, 3, 4, 1, 4, 3, 2, 1, 5, 5, 3, 4, 5, 1, 3, 5, 4, 2, 0, 3, 3, 0, 1, 3, 0, 4, 2, 0, 1, 3, 1, 4, 3, 3, 3, 3, 0, 3, 0, 1, 0, 3, 4, 4, 4, 5, 5, 0, 3, 0, 1, 4, 5),
-    (0, 2, 0, 3, 0, 3, 0, 0, 0, 2, 3, 1, 3, 0, 4, 0, 1, 1, 3, 0, 3, 4, 3, 2, 3, 1, 0, 3, 3, 2, 3, 1, 3, 0, 2, 3, 0, 2, 1, 4, 1, 2, 2, 0, 0, 3, 3, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 2, 2, 0, 3, 2, 1, 3, 3, 0, 2, 0, 2, 0, 0, 3, 3, 1, 2, 4, 0, 3, 0, 2, 2, 3),
-    (2, 4, 0, 5, 0, 4, 0, 4, 0, 2, 4, 4, 4, 3, 4, 3, 3, 3, 1, 2, 4, 3, 4, 3, 4, 4, 5, 0, 3, 3, 3, 3, 2, 0, 4, 3, 1, 4, 3, 4, 1, 4, 4, 3, 3, 4, 4, 3, 1, 2, 3, 0, 4, 2, 0, 4, 1, 0, 3, 3, 0, 4, 3, 3, 3, 4, 0, 4, 0, 2, 0, 3, 5, 3, 4, 5, 2, 0, 3, 0, 0, 4, 5),
-    (0, 3, 0, 4, 0, 1, 0, 1, 0, 1, 3, 2, 2, 1, 3, 0, 3, 0, 2, 0, 2, 0, 3, 0, 2, 0, 0, 0, 1, 0, 1, 1, 0, 0, 3, 1, 0, 0, 0, 4, 0, 3, 1, 0, 2, 1, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 3, 1, 0, 3, 0, 0, 0, 1, 4, 4, 4, 3, 0, 0, 4, 0, 0, 1, 4),
-    (1, 4, 1, 5, 0, 3, 0, 3, 0, 4, 5, 4, 4, 3, 5, 3, 3, 4, 4, 3, 4, 1, 3, 3, 3, 3, 2, 1, 4, 1, 5, 4, 3, 1, 4, 4, 3, 5, 4, 4, 3, 5, 4, 3, 3, 4, 4, 4, 0, 3, 3, 1, 2, 3, 0, 3, 1, 0, 3, 3, 0, 5, 4, 4, 4, 4, 4, 4, 3, 3, 5, 4, 4, 3, 3, 5, 4, 0, 3, 2, 0, 4, 4),
-    (0, 2, 0, 3, 0, 1, 0, 0, 0, 1, 3, 3, 3, 2, 4, 1, 3, 0, 3, 1, 3, 0, 2, 2, 1, 1, 0, 0, 2, 0, 4, 3, 1, 0, 4, 3, 0, 4, 4, 4, 1, 4, 3, 1, 1, 3, 3, 1, 0, 2, 0, 0, 1, 3, 0, 0, 0, 0, 2, 0, 0, 4, 3, 2, 4, 3, 5, 4, 3, 3, 3, 4, 3, 3, 4, 3, 3, 0, 2, 1, 0, 3, 3),
-    (0, 2, 0, 4, 0, 3, 0, 2, 0, 2, 5, 5, 3, 4, 4, 4, 4, 1, 4, 3, 3, 0, 4, 3, 4, 3, 1, 3, 3, 2, 4, 3, 0, 3, 4, 3, 0, 3, 4, 4, 2, 4, 4, 0, 4, 5, 3, 3, 2, 2, 1, 1, 1, 2, 0, 1, 5, 0, 3, 3, 2, 4, 3, 3, 3, 4, 0, 3, 0, 2, 0, 4, 4, 3, 5, 5, 0, 0, 3, 0, 2, 3, 3),
-    (0, 3, 0, 4, 0, 3, 0, 1, 0, 3, 4, 3, 3, 1, 3, 3, 3, 0, 3, 1, 3, 0, 4, 3, 3, 1, 1, 0, 3, 0, 3, 3, 0, 0, 4, 4, 0, 1, 5, 4, 3, 3, 5, 0, 3, 3, 4, 3, 0, 2, 0, 1, 1, 1, 0, 1, 3, 0, 1, 2, 1, 3, 3, 2, 3, 3, 0, 3, 0, 1, 0, 1, 3, 3, 4, 4, 1, 0, 1, 2, 2, 1, 3),
-    (0, 1, 0, 4, 0, 4, 0, 3, 0, 1, 3, 3, 3, 2, 3, 1, 1, 0, 3, 0, 3, 3, 4, 3, 2, 4, 2, 0, 1, 0, 4, 3, 2, 0, 4, 3, 0, 5, 3, 3, 2, 4, 4, 4, 3, 3, 3, 4, 0, 1, 3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 4, 2, 3, 3, 3, 0, 3, 0, 0, 0, 4, 4, 4, 5, 3, 2, 0, 3, 3, 0, 3, 5),
-    (0, 2, 0, 3, 0, 0, 0, 3, 0, 1, 3, 0, 2, 0, 0, 0, 1, 0, 3, 1, 1, 3, 3, 0, 0, 3, 0, 0, 3, 0, 2, 3, 1, 0, 3, 1, 0, 3, 3, 2, 0, 4, 2, 2, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 0, 1, 0, 0, 0, 1, 3, 1, 2, 0, 0, 0, 1, 0, 0, 1, 4),
-    (0, 3, 0, 3, 0, 5, 0, 1, 0, 2, 4, 3, 1, 3, 3, 2, 1, 1, 5, 2, 1, 0, 5, 1, 2, 0, 0, 0, 3, 3, 2, 2, 3, 2, 4, 3, 0, 0, 3, 3, 1, 3, 3, 0, 2, 5, 3, 4, 0, 3, 3, 0, 1, 2, 0, 2, 2, 0, 3, 2, 0, 2, 2, 3, 3, 3, 0, 2, 0, 1, 0, 3, 4, 4, 2, 5, 4, 0, 3, 0, 0, 3, 5),
-    (0, 3, 0, 3, 0, 3, 0, 1, 0, 3, 3, 3, 3, 0, 3, 0, 2, 0, 2, 1, 1, 0, 2, 0, 1, 0, 0, 0, 2, 1, 0, 0, 1, 0, 3, 2, 0, 0, 3, 3, 1, 2, 3, 1, 0, 3, 3, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 3, 1, 2, 3, 0, 3, 0, 1, 0, 3, 2, 1, 0, 4, 3, 0, 1, 1, 0, 3, 3),
-    (0, 4, 0, 5, 0, 3, 0, 3, 0, 4, 5, 5, 4, 3, 5, 3, 4, 3, 5, 3, 3, 2, 5, 3, 4, 4, 4, 3, 4, 3, 4, 5, 5, 3, 4, 4, 3, 4, 4, 5, 4, 4, 4, 3, 4, 5, 5, 4, 2, 3, 4, 2, 3, 4, 0, 3, 3, 1, 4, 3, 2, 4, 3, 3, 5, 5, 0, 3, 0, 3, 0, 5, 5, 5, 5, 4, 4, 0, 4, 0, 1, 4, 4),
-    (0, 4, 0, 4, 0, 3, 0, 3, 0, 3, 5, 4, 4, 2, 3, 2, 5, 1, 3, 2, 5, 1, 4, 2, 3, 2, 3, 3, 4, 3, 3, 3, 3, 2, 5, 4, 1, 3, 3, 5, 3, 4, 4, 0, 4, 4, 3, 1, 1, 3, 1, 0, 2, 3, 0, 2, 3, 0, 3, 0, 0, 4, 3, 1, 3, 4, 0, 3, 0, 2, 0, 4, 4, 4, 3, 4, 5, 0, 4, 0, 0, 3, 4),
-    (0, 3, 0, 3, 0, 3, 1, 2, 0, 3, 4, 4, 3, 3, 3, 0, 2, 2, 4, 3, 3, 1, 3, 3, 3, 1, 1, 0, 3, 1, 4, 3, 2, 3, 4, 4, 2, 4, 4, 4, 3, 4, 4, 3, 2, 4, 4, 3, 1, 3, 3, 1, 3, 3, 0, 4, 1, 0, 2, 2, 1, 4, 3, 2, 3, 3, 5, 4, 3, 3, 5, 4, 4, 3, 3, 0, 4, 0, 3, 2, 2, 4, 4),
-    (0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 3, 0, 0, 0, 0, 0, 2, 0, 1, 2, 1, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 1, 0, 1, 1, 3, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 0, 3, 4, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1),
-    (0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 4, 1, 4, 0, 3, 0, 4, 0, 3, 0, 4, 0, 3, 0, 3, 0, 4, 1, 5, 1, 4, 0, 0, 3, 0, 5, 0, 5, 2, 0, 1, 0, 0, 0, 2, 1, 4, 0, 1, 3, 0, 0, 3, 0, 0, 3, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0),
-    (1, 4, 0, 5, 0, 3, 0, 2, 0, 3, 5, 4, 4, 3, 4, 3, 5, 3, 4, 3, 3, 0, 4, 3, 3, 3, 3, 3, 3, 2, 4, 4, 3, 1, 3, 4, 4, 5, 4, 4, 3, 4, 4, 1, 3, 5, 4, 3, 3, 3, 1, 2, 2, 3, 3, 1, 3, 1, 3, 3, 3, 5, 3, 3, 4, 5, 0, 3, 0, 3, 0, 3, 4, 3, 4, 4, 3, 0, 3, 0, 2, 4, 3),
-    (0, 1, 0, 4, 0, 0, 0, 0, 0, 1, 4, 0, 4, 1, 4, 2, 4, 0, 3, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 3, 1, 1, 1, 0, 3, 0, 0, 0, 1, 2, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 3, 2, 0, 2, 2, 0, 1, 0, 0, 0, 2, 3, 2, 3, 3, 0, 0, 0, 0, 2, 1, 0),
-    (0, 5, 1, 5, 0, 3, 0, 3, 0, 5, 4, 4, 5, 1, 5, 3, 3, 0, 4, 3, 4, 3, 5, 3, 4, 3, 3, 2, 4, 3, 4, 3, 3, 0, 3, 3, 1, 4, 4, 3, 4, 4, 4, 3, 4, 5, 5, 3, 2, 3, 1, 1, 3, 3, 1, 3, 1, 1, 3, 3, 2, 4, 5, 3, 3, 5, 0, 4, 0, 3, 0, 4, 4, 3, 5, 3, 3, 0, 3, 4, 0, 4, 3),
-    (0, 5, 0, 5, 0, 3, 0, 2, 0, 4, 4, 3, 5, 2, 4, 3, 3, 3, 4, 4, 4, 3, 5, 3, 5, 3, 3, 1, 4, 0, 4, 3, 3, 0, 3, 3, 0, 4, 4, 4, 4, 5, 4, 3, 3, 5, 5, 3, 2, 3, 1, 2, 3, 2, 0, 1, 0, 0, 3, 2, 2, 4, 4, 3, 1, 5, 0, 4, 0, 3, 0, 4, 3, 1, 3, 2, 1, 0, 3, 3, 0, 3, 3),
-    (0, 4, 0, 5, 0, 5, 0, 4, 0, 4, 5, 5, 5, 3, 4, 3, 3, 2, 5, 4, 4, 3, 5, 3, 5, 3, 4, 0, 4, 3, 4, 4, 3, 2, 4, 4, 3, 4, 5, 4, 4, 5, 5, 0, 3, 5, 5, 4, 1, 3, 3, 2, 3, 3, 1, 3, 1, 0, 4, 3, 1, 4, 4, 3, 4, 5, 0, 4, 0, 2, 0, 4, 3, 4, 4, 3, 3, 0, 4, 0, 0, 5, 5),
-    (0, 4, 0, 4, 0, 5, 0, 1, 1, 3, 3, 4, 4, 3, 4, 1, 3, 0, 5, 1, 3, 0, 3, 1, 3, 1, 1, 0, 3, 0, 3, 3, 4, 0, 4, 3, 0, 4, 4, 4, 3, 4, 4, 0, 3, 5, 4, 1, 0, 3, 0, 0, 2, 3, 0, 3, 1, 0, 3, 1, 0, 3, 2, 1, 3, 5, 0, 3, 0, 1, 0, 3, 2, 3, 3, 4, 4, 0, 2, 2, 0, 4, 4),
-    (2, 4, 0, 5, 0, 4, 0, 3, 0, 4, 5, 5, 4, 3, 5, 3, 5, 3, 5, 3, 5, 2, 5, 3, 4, 3, 3, 4, 3, 4, 5, 3, 2, 1, 5, 4, 3, 2, 3, 4, 5, 3, 4, 1, 2, 5, 4, 3, 0, 3, 3, 0, 3, 2, 0, 2, 3, 0, 4, 1, 0, 3, 4, 3, 3, 5, 0, 3, 0, 1, 0, 4, 5, 5, 5, 4, 3, 0, 4, 2, 0, 3, 5),
-    (0, 5, 0, 4, 0, 4, 0, 2, 0, 5, 4, 3, 4, 3, 4, 3, 3, 3, 4, 3, 4, 2, 5, 3, 5, 3, 4, 1, 4, 3, 4, 4, 4, 0, 3, 5, 0, 4, 4, 4, 4, 5, 3, 1, 3, 4, 5, 3, 3, 3, 3, 3, 3, 3, 0, 2, 2, 0, 3, 3, 2, 4, 3, 3, 3, 5, 3, 4, 1, 3, 3, 5, 3, 2, 0, 0, 0, 0, 4, 3, 1, 3, 3),
-    (0, 1, 0, 3, 0, 3, 0, 1, 0, 1, 3, 3, 3, 2, 3, 3, 3, 0, 3, 0, 0, 0, 3, 1, 3, 0, 0, 0, 2, 2, 2, 3, 0, 0, 3, 2, 0, 1, 2, 4, 1, 3, 3, 0, 0, 3, 3, 3, 0, 1, 0, 0, 2, 1, 0, 0, 3, 0, 3, 1, 0, 3, 0, 0, 1, 3, 0, 2, 0, 1, 0, 3, 3, 1, 3, 3, 0, 0, 1, 1, 0, 3, 3),
-    (0, 2, 0, 3, 0, 2, 1, 4, 0, 2, 2, 3, 1, 1, 3, 1, 1, 0, 2, 0, 3, 1, 2, 3, 1, 3, 0, 0, 1, 0, 4, 3, 2, 3, 3, 3, 1, 4, 2, 3, 3, 3, 3, 1, 0, 3, 1, 4, 0, 1, 1, 0, 1, 2, 0, 1, 1, 0, 1, 1, 0, 3, 1, 3, 2, 2, 0, 1, 0, 0, 0, 2, 3, 3, 3, 1, 0, 0, 0, 0, 0, 2, 3),
-    (0, 5, 0, 4, 0, 5, 0, 2, 0, 4, 5, 5, 3, 3, 4, 3, 3, 1, 5, 4, 4, 2, 4, 4, 4, 3, 4, 2, 4, 3, 5, 5, 4, 3, 3, 4, 3, 3, 5, 5, 4, 5, 5, 1, 3, 4, 5, 3, 1, 4, 3, 1, 3, 3, 0, 3, 3, 1, 4, 3, 1, 4, 5, 3, 3, 5, 0, 4, 0, 3, 0, 5, 3, 3, 1, 4, 3, 0, 4, 0, 1, 5, 3),
-    (0, 5, 0, 5, 0, 4, 0, 2, 0, 4, 4, 3, 4, 3, 3, 3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 5, 3, 3, 5, 2, 4, 4, 4, 3, 4, 4, 3, 3, 4, 4, 5, 5, 3, 3, 4, 3, 4, 3, 3, 4, 3, 3, 3, 3, 1, 2, 2, 1, 4, 3, 3, 5, 4, 4, 3, 4, 0, 4, 0, 3, 0, 4, 4, 4, 4, 4, 1, 0, 4, 2, 0, 2, 4),
-    (0, 4, 0, 4, 0, 3, 0, 1, 0, 3, 5, 2, 3, 0, 3, 0, 2, 1, 4, 2, 3, 3, 4, 1, 4, 3, 3, 2, 4, 1, 3, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 5, 3, 3, 3, 3, 3, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 1, 0, 0, 3, 1, 2, 2, 3, 0, 3, 0, 2, 0, 4, 4, 3, 3, 4, 1, 0, 3, 0, 0, 2, 4),
-    (0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 3, 1, 3, 0, 3, 2, 0, 0, 0, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 2, 0, 0, 0, 0, 0, 0, 2),
-    (0, 2, 1, 3, 0, 2, 0, 2, 0, 3, 3, 3, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 4, 2, 2, 1, 2, 1, 4, 0, 4, 3, 1, 3, 3, 3, 2, 4, 3, 5, 4, 3, 3, 3, 3, 3, 3, 3, 0, 1, 3, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 4, 2, 0, 2, 3, 0, 3, 3, 0, 3, 3, 4, 2, 3, 1, 4, 0, 1, 2, 0, 2, 3),
-    (0, 3, 0, 3, 0, 1, 0, 3, 0, 2, 3, 3, 3, 0, 3, 1, 2, 0, 3, 3, 2, 3, 3, 2, 3, 2, 3, 1, 3, 0, 4, 3, 2, 0, 3, 3, 1, 4, 3, 3, 2, 3, 4, 3, 1, 3, 3, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 4, 1, 1, 0, 3, 0, 3, 1, 0, 2, 3, 3, 3, 3, 3, 1, 0, 0, 2, 0, 3, 3),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3),
-    (0, 2, 0, 3, 1, 3, 0, 3, 0, 2, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 3, 3, 3, 1, 3, 0, 2, 3, 1, 1, 4, 3, 3, 2, 3, 3, 1, 2, 2, 4, 1, 3, 3, 0, 1, 4, 2, 3, 0, 1, 3, 0, 3, 0, 0, 1, 3, 0, 2, 0, 0, 3, 3, 2, 1, 3, 0, 3, 0, 2, 0, 3, 4, 4, 4, 3, 1, 0, 3, 0, 0, 3, 3),
-    (0, 2, 0, 1, 0, 2, 0, 0, 0, 1, 3, 2, 2, 1, 3, 0, 1, 1, 3, 0, 3, 2, 3, 1, 2, 0, 2, 0, 1, 1, 3, 3, 3, 0, 3, 3, 1, 1, 2, 3, 2, 3, 3, 1, 2, 3, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 2, 1, 2, 1, 3, 0, 3, 0, 0, 0, 3, 4, 4, 4, 3, 2, 0, 2, 0, 0, 2, 4),
-    (0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 3),
-    (0, 3, 0, 3, 0, 2, 0, 3, 0, 3, 3, 3, 2, 3, 2, 2, 2, 0, 3, 1, 3, 3, 3, 2, 3, 3, 0, 0, 3, 0, 3, 2, 2, 0, 2, 3, 1, 4, 3, 4, 3, 3, 2, 3, 1, 5, 4, 4, 0, 3, 1, 2, 1, 3, 0, 3, 1, 1, 2, 0, 2, 3, 1, 3, 1, 3, 0, 3, 0, 1, 0, 3, 3, 4, 4, 2, 1, 0, 2, 1, 0, 2, 4),
-    (0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 4, 2, 5, 1, 4, 0, 2, 0, 2, 1, 3, 1, 4, 0, 2, 1, 0, 0, 2, 1, 4, 1, 1, 0, 3, 3, 0, 5, 1, 3, 2, 3, 3, 1, 0, 3, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 1, 0, 3, 0, 2, 0, 1, 0, 3, 3, 3, 4, 3, 3, 0, 0, 0, 0, 2, 3),
-    (0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 3),
-    (0, 1, 0, 3, 0, 4, 0, 3, 0, 2, 4, 3, 1, 0, 3, 2, 2, 1, 3, 1, 2, 2, 3, 1, 1, 1, 2, 1, 3, 0, 1, 2, 0, 1, 3, 2, 1, 3, 0, 5, 5, 1, 0, 0, 1, 3, 2, 1, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 3, 4, 0, 1, 1, 1, 3, 2, 0, 2, 0, 1, 0, 2, 3, 3, 1, 2, 3, 0, 1, 0, 1, 0, 4),
-    (0, 0, 0, 1, 0, 3, 0, 3, 0, 2, 2, 1, 0, 0, 4, 0, 3, 0, 3, 1, 3, 0, 3, 0, 3, 0, 1, 0, 3, 0, 3, 1, 3, 0, 3, 3, 0, 0, 1, 2, 1, 1, 1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 2, 0, 0, 2, 0, 0, 0, 0, 2, 3, 3, 3, 3, 0, 0, 0, 0, 1, 4),
-    (0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 3, 1, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 2, 0, 2, 3, 0, 0, 2, 2, 3, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 2, 3),
-    (2, 4, 0, 5, 0, 5, 0, 4, 0, 3, 4, 3, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 5, 4, 5, 5, 5, 2, 3, 0, 5, 5, 4, 1, 5, 4, 3, 1, 5, 4, 3, 4, 4, 3, 3, 4, 3, 3, 0, 3, 2, 0, 2, 3, 0, 3, 0, 0, 3, 3, 0, 5, 3, 2, 3, 3, 0, 3, 0, 3, 0, 3, 4, 5, 4, 5, 3, 0, 4, 3, 0, 3, 4),
-    (0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 3, 4, 3, 2, 3, 2, 3, 0, 4, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 2, 4, 3, 3, 1, 3, 4, 3, 4, 4, 4, 3, 4, 4, 3, 2, 4, 4, 1, 0, 2, 0, 0, 1, 1, 0, 2, 0, 0, 3, 1, 0, 5, 3, 2, 1, 3, 0, 3, 0, 1, 2, 4, 3, 2, 4, 3, 3, 0, 3, 2, 0, 4, 4),
-    (0, 3, 0, 3, 0, 1, 0, 0, 0, 1, 4, 3, 3, 2, 3, 1, 3, 1, 4, 2, 3, 2, 4, 2, 3, 4, 3, 0, 2, 2, 3, 3, 3, 0, 3, 3, 3, 0, 3, 4, 1, 3, 3, 0, 3, 4, 3, 3, 0, 1, 1, 0, 1, 0, 0, 0, 4, 0, 3, 0, 0, 3, 1, 2, 1, 3, 0, 4, 0, 1, 0, 4, 3, 3, 4, 3, 3, 0, 2, 0, 0, 3, 3),
-    (0, 3, 0, 4, 0, 1, 0, 3, 0, 3, 4, 3, 3, 0, 3, 3, 3, 1, 3, 1, 3, 3, 4, 3, 3, 3, 0, 0, 3, 1, 5, 3, 3, 1, 3, 3, 2, 5, 4, 3, 3, 4, 5, 3, 2, 5, 3, 4, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 0, 4, 2, 2, 1, 3, 0, 3, 0, 2, 0, 4, 4, 3, 5, 3, 2, 0, 1, 1, 0, 3, 4),
-    (0, 5, 0, 4, 0, 5, 0, 2, 0, 4, 4, 3, 3, 2, 3, 3, 3, 1, 4, 3, 4, 1, 5, 3, 4, 3, 4, 0, 4, 2, 4, 3, 4, 1, 5, 4, 0, 4, 4, 4, 4, 5, 4, 1, 3, 5, 4, 2, 1, 4, 1, 1, 3, 2, 0, 3, 1, 0, 3, 2, 1, 4, 3, 3, 3, 4, 0, 4, 0, 3, 0, 4, 4, 4, 3, 3, 3, 0, 4, 2, 0, 3, 4),
-    (1, 4, 0, 4, 0, 3, 0, 1, 0, 3, 3, 3, 1, 1, 3, 3, 2, 2, 3, 3, 1, 0, 3, 2, 2, 1, 2, 0, 3, 1, 2, 1, 2, 0, 3, 2, 0, 2, 2, 3, 3, 4, 3, 0, 3, 3, 1, 2, 0, 1, 1, 3, 1, 2, 0, 0, 3, 0, 1, 1, 0, 3, 2, 2, 3, 3, 0, 3, 0, 0, 0, 2, 3, 3, 4, 3, 3, 0, 1, 0, 0, 1, 4),
-    (0, 4, 0, 4, 0, 4, 0, 0, 0, 3, 4, 4, 3, 1, 4, 2, 3, 2, 3, 3, 3, 1, 4, 3, 4, 0, 3, 0, 4, 2, 3, 3, 2, 2, 5, 4, 2, 1, 3, 4, 3, 4, 3, 1, 3, 3, 4, 2, 0, 2, 1, 0, 3, 3, 0, 0, 2, 0, 3, 1, 0, 4, 4, 3, 4, 3, 0, 4, 0, 1, 0, 2, 4, 4, 4, 4, 4, 0, 3, 2, 0, 3, 3),
-    (0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2),
-    (0, 2, 0, 3, 0, 4, 0, 4, 0, 1, 3, 3, 3, 0, 4, 0, 2, 1, 2, 1, 1, 1, 2, 0, 3, 1, 1, 0, 1, 0, 3, 1, 0, 0, 3, 3, 2, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 2, 2, 0, 3, 1, 0, 0, 1, 0, 1, 1, 0, 1, 2, 0, 3, 0, 0, 0, 0, 1, 0, 0, 3, 3, 4, 3, 1, 0, 1, 0, 3, 0, 2),
-    (0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 1, 0, 2, 0, 3, 1, 0, 1, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 4, 0, 0, 0, 2, 3, 0, 1, 4, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 3),
-    (0, 2, 0, 5, 0, 5, 0, 1, 0, 2, 4, 3, 3, 2, 5, 1, 3, 2, 3, 3, 3, 0, 4, 1, 2, 0, 3, 0, 4, 0, 2, 2, 1, 1, 5, 3, 0, 0, 1, 4, 2, 3, 2, 0, 3, 3, 3, 2, 0, 2, 4, 1, 1, 2, 0, 1, 1, 0, 3, 1, 0, 1, 3, 1, 2, 3, 0, 2, 0, 0, 0, 1, 3, 5, 4, 4, 4, 0, 3, 0, 0, 1, 3),
-    (0, 4, 0, 5, 0, 4, 0, 4, 0, 4, 5, 4, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 5, 3, 4, 5, 4, 2, 4, 2, 3, 4, 3, 1, 4, 4, 1, 3, 5, 4, 4, 5, 5, 4, 4, 5, 5, 5, 2, 3, 3, 1, 4, 3, 1, 3, 3, 0, 3, 3, 1, 4, 3, 4, 4, 4, 0, 3, 0, 4, 0, 3, 3, 4, 4, 5, 0, 0, 4, 3, 0, 4, 5),
-    (0, 4, 0, 4, 0, 3, 0, 3, 0, 3, 4, 4, 4, 3, 3, 2, 4, 3, 4, 3, 4, 3, 5, 3, 4, 3, 2, 1, 4, 2, 4, 4, 3, 1, 3, 4, 2, 4, 5, 5, 3, 4, 5, 4, 1, 5, 4, 3, 0, 3, 2, 2, 3, 2, 1, 3, 1, 0, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 2, 3, 3, 4, 3, 3, 3, 2, 1, 0, 3, 2, 1, 4, 3),
-    (0, 4, 0, 5, 0, 4, 0, 3, 0, 3, 5, 5, 3, 2, 4, 3, 4, 0, 5, 4, 4, 1, 4, 4, 4, 3, 3, 3, 4, 3, 5, 5, 2, 3, 3, 4, 1, 2, 5, 5, 3, 5, 5, 2, 3, 5, 5, 4, 0, 3, 2, 0, 3, 3, 1, 1, 5, 1, 4, 1, 0, 4, 3, 2, 3, 5, 0, 4, 0, 3, 0, 5, 4, 3, 4, 3, 0, 0, 4, 1, 0, 4, 4),
-    (1, 3, 0, 4, 0, 2, 0, 2, 0, 2, 5, 5, 3, 3, 3, 3, 3, 0, 4, 2, 3, 4, 4, 4, 3, 4, 0, 0, 3, 4, 5, 4, 3, 3, 3, 3, 2, 5, 5, 4, 5, 5, 5, 4, 3, 5, 5, 5, 1, 3, 1, 0, 1, 0, 0, 3, 2, 0, 4, 2, 0, 5, 2, 3, 2, 4, 1, 3, 0, 3, 0, 4, 5, 4, 5, 4, 3, 0, 4, 2, 0, 5, 4),
-    (0, 3, 0, 4, 0, 5, 0, 3, 0, 3, 4, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 4, 3, 3, 2, 2, 0, 3, 3, 3, 3, 3, 1, 3, 3, 3, 0, 4, 4, 3, 4, 4, 1, 1, 4, 4, 2, 0, 3, 1, 0, 1, 1, 0, 4, 1, 0, 2, 3, 1, 3, 3, 1, 3, 4, 0, 3, 0, 1, 0, 3, 1, 3, 0, 0, 1, 0, 2, 0, 0, 4, 4),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
-    (0, 3, 0, 3, 0, 2, 0, 3, 0, 1, 5, 4, 3, 3, 3, 1, 4, 2, 1, 2, 3, 4, 4, 2, 4, 4, 5, 0, 3, 1, 4, 3, 4, 0, 4, 3, 3, 3, 2, 3, 2, 5, 3, 4, 3, 2, 2, 3, 0, 0, 3, 0, 2, 1, 0, 1, 2, 0, 0, 0, 0, 2, 1, 1, 3, 1, 0, 2, 0, 4, 0, 3, 4, 4, 4, 5, 2, 0, 2, 0, 0, 1, 3),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 4, 2, 1, 1, 0, 1, 0, 3, 2, 0, 0, 3, 1, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 4, 0, 4, 2, 1, 0, 0, 0, 0, 0, 1),
-    (0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 3, 1, 0, 0, 0, 2, 0, 2, 1, 0, 0, 1, 2, 1, 0, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2),
-    (0, 4, 0, 4, 0, 4, 0, 3, 0, 4, 4, 3, 4, 2, 4, 3, 2, 0, 4, 4, 4, 3, 5, 3, 5, 3, 3, 2, 4, 2, 4, 3, 4, 3, 1, 4, 0, 2, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4, 3, 4, 1, 3, 4, 3, 2, 1, 2, 1, 3, 3, 3, 4, 4, 3, 3, 5, 0, 4, 0, 3, 0, 4, 3, 3, 3, 2, 1, 0, 3, 0, 0, 3, 3),
-    (0, 4, 0, 3, 0, 3, 0, 3, 0, 3, 5, 5, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 3, 5, 3, 3, 1, 3, 2, 4, 5, 5, 5, 5, 4, 3, 4, 5, 5, 3, 2, 2, 3, 3, 3, 3, 2, 3, 3, 1, 2, 3, 2, 4, 3, 3, 3, 4, 0, 4, 0, 2, 0, 4, 3, 2, 2, 1, 2, 0, 3, 0, 0, 4, 1),
-)
-# fmt: on
-
-
-class JapaneseContextAnalysis:
-    NUM_OF_CATEGORY = 6
-    DONT_KNOW = -1
-    ENOUGH_REL_THRESHOLD = 100
-    MAX_REL_THRESHOLD = 1000
-    MINIMUM_DATA_THRESHOLD = 4
-
-    def __init__(self):
-        self._total_rel = None
-        self._rel_sample = None
-        self._need_to_skip_char_num = None
-        self._last_char_order = None
-        self._done = None
-        self.reset()
-
-    def reset(self):
-        self._total_rel = 0  # total sequence received
-        # category counters, each integer counts sequence in its category
-        self._rel_sample = [0] * self.NUM_OF_CATEGORY
-        # if last byte in current buffer is not the last byte of a character,
-        # we need to know how many bytes to skip in next buffer
-        self._need_to_skip_char_num = 0
-        self._last_char_order = -1  # The order of previous char
-        # If this flag is set to True, detection is done and conclusion has
-        # been made
-        self._done = False
-
-    def feed(self, byte_str, num_bytes):
-        if self._done:
-            return
-
-        # The buffer we got is byte oriented, and a character may span in more than one
-        # buffers. In case the last one or two byte in last buffer is not
-        # complete, we record how many byte needed to complete that character
-        # and skip these bytes here.  We can choose to record those bytes as
-        # well and analyse the character once it is complete, but since a
-        # character will not make much difference, by simply skipping
-        # this character will simply our logic and improve performance.
-        i = self._need_to_skip_char_num
-        while i < num_bytes:
-            order, char_len = self.get_order(byte_str[i : i + 2])
-            i += char_len
-            if i > num_bytes:
-                self._need_to_skip_char_num = i - num_bytes
-                self._last_char_order = -1
-            else:
-                if (order != -1) and (self._last_char_order != -1):
-                    self._total_rel += 1
-                    if self._total_rel > self.MAX_REL_THRESHOLD:
-                        self._done = True
-                        break
-                    self._rel_sample[
-                        jp2_char_context[self._last_char_order][order]
-                    ] += 1
-                self._last_char_order = order
-
-    def got_enough_data(self):
-        return self._total_rel > self.ENOUGH_REL_THRESHOLD
-
-    def get_confidence(self):
-        # This is just one way to calculate confidence. It works well for me.
-        if self._total_rel > self.MINIMUM_DATA_THRESHOLD:
-            return (self._total_rel - self._rel_sample[0]) / self._total_rel
-        return self.DONT_KNOW
-
-    def get_order(self, _):
-        return -1, 1
-
-
-class SJISContextAnalysis(JapaneseContextAnalysis):
-    def __init__(self):
-        super().__init__()
-        self._charset_name = "SHIFT_JIS"
-
-    @property
-    def charset_name(self):
-        return self._charset_name
-
-    def get_order(self, byte_str):
-        if not byte_str:
-            return -1, 1
-        # find out current char's byte length
-        first_char = byte_str[0]
-        if (0x81 <= first_char <= 0x9F) or (0xE0 <= first_char <= 0xFC):
-            char_len = 2
-            if (first_char == 0x87) or (0xFA <= first_char <= 0xFC):
-                self._charset_name = "CP932"
-        else:
-            char_len = 1
-
-        # return its order if it is hiragana
-        if len(byte_str) > 1:
-            second_char = byte_str[1]
-            if (first_char == 202) and (0x9F <= second_char <= 0xF1):
-                return second_char - 0x9F, char_len
-
-        return -1, char_len
-
-
-class EUCJPContextAnalysis(JapaneseContextAnalysis):
-    def get_order(self, byte_str):
-        if not byte_str:
-            return -1, 1
-        # find out current char's byte length
-        first_char = byte_str[0]
-        if (first_char == 0x8E) or (0xA1 <= first_char <= 0xFE):
-            char_len = 2
-        elif first_char == 0x8F:
-            char_len = 3
-        else:
-            char_len = 1
-
-        # return its order if it is hiragana
-        if len(byte_str) > 1:
-            second_char = byte_str[1]
-            if (first_char == 0xA4) and (0xA1 <= second_char <= 0xF3):
-                return second_char - 0xA1, char_len
-
-        return -1, char_len
diff --git a/spaces/tomofi/MMOCR/mmocr/models/kie/heads/sdmgr_head.py b/spaces/tomofi/MMOCR/mmocr/models/kie/heads/sdmgr_head.py
deleted file mode 100644
index 8fb9078c8f37a0a2235efa08bd43d0b42f5bf90c..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/kie/heads/sdmgr_head.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from mmcv.runner import BaseModule
-from torch import nn
-from torch.nn import functional as F
-
-from mmocr.models.builder import HEADS, build_loss
-
-
-@HEADS.register_module()
-class SDMGRHead(BaseModule):
-
-    def __init__(self,
-                 num_chars=92,
-                 visual_dim=64,
-                 fusion_dim=1024,
-                 node_input=32,
-                 node_embed=256,
-                 edge_input=5,
-                 edge_embed=256,
-                 num_gnn=2,
-                 num_classes=26,
-                 loss=dict(type='SDMGRLoss'),
-                 bidirectional=False,
-                 train_cfg=None,
-                 test_cfg=None,
-                 init_cfg=dict(
-                     type='Normal',
-                     override=dict(name='edge_embed'),
-                     mean=0,
-                     std=0.01)):
-        super().__init__(init_cfg=init_cfg)
-
-        self.fusion = Block([visual_dim, node_embed], node_embed, fusion_dim)
-        self.node_embed = nn.Embedding(num_chars, node_input, 0)
-        hidden = node_embed // 2 if bidirectional else node_embed
-        self.rnn = nn.LSTM(
-            input_size=node_input,
-            hidden_size=hidden,
-            num_layers=1,
-            batch_first=True,
-            bidirectional=bidirectional)
-        self.edge_embed = nn.Linear(edge_input, edge_embed)
-        self.gnn_layers = nn.ModuleList(
-            [GNNLayer(node_embed, edge_embed) for _ in range(num_gnn)])
-        self.node_cls = nn.Linear(node_embed, num_classes)
-        self.edge_cls = nn.Linear(edge_embed, 2)
-        self.loss = build_loss(loss)
-
-    def forward(self, relations, texts, x=None):
-        node_nums, char_nums = [], []
-        for text in texts:
-            node_nums.append(text.size(0))
-            char_nums.append((text > 0).sum(-1))
-
-        max_num = max([char_num.max() for char_num in char_nums])
-        all_nodes = torch.cat([
-            torch.cat(
-                [text,
-                 text.new_zeros(text.size(0), max_num - text.size(1))], -1)
-            for text in texts
-        ])
-        embed_nodes = self.node_embed(all_nodes.clamp(min=0).long())
-        rnn_nodes, _ = self.rnn(embed_nodes)
-
-        nodes = rnn_nodes.new_zeros(*rnn_nodes.shape[::2])
-        all_nums = torch.cat(char_nums)
-        valid = all_nums > 0
-        nodes[valid] = rnn_nodes[valid].gather(
-            1, (all_nums[valid] - 1).unsqueeze(-1).unsqueeze(-1).expand(
-                -1, -1, rnn_nodes.size(-1))).squeeze(1)
-
-        if x is not None:
-            nodes = self.fusion([x, nodes])
-
-        all_edges = torch.cat(
-            [rel.view(-1, rel.size(-1)) for rel in relations])
-        embed_edges = self.edge_embed(all_edges.float())
-        embed_edges = F.normalize(embed_edges)
-
-        for gnn_layer in self.gnn_layers:
-            nodes, cat_nodes = gnn_layer(nodes, embed_edges, node_nums)
-
-        node_cls, edge_cls = self.node_cls(nodes), self.edge_cls(cat_nodes)
-        return node_cls, edge_cls
-
-
-class GNNLayer(nn.Module):
-
-    def __init__(self, node_dim=256, edge_dim=256):
-        super().__init__()
-        self.in_fc = nn.Linear(node_dim * 2 + edge_dim, node_dim)
-        self.coef_fc = nn.Linear(node_dim, 1)
-        self.out_fc = nn.Linear(node_dim, node_dim)
-        self.relu = nn.ReLU()
-
-    def forward(self, nodes, edges, nums):
-        start, cat_nodes = 0, []
-        for num in nums:
-            sample_nodes = nodes[start:start + num]
-            cat_nodes.append(
-                torch.cat([
-                    sample_nodes.unsqueeze(1).expand(-1, num, -1),
-                    sample_nodes.unsqueeze(0).expand(num, -1, -1)
-                ], -1).view(num**2, -1))
-            start += num
-        cat_nodes = torch.cat([torch.cat(cat_nodes), edges], -1)
-        cat_nodes = self.relu(self.in_fc(cat_nodes))
-        coefs = self.coef_fc(cat_nodes)
-
-        start, residuals = 0, []
-        for num in nums:
-            residual = F.softmax(
-                -torch.eye(num).to(coefs.device).unsqueeze(-1) * 1e9 +
-                coefs[start:start + num**2].view(num, num, -1), 1)
-            residuals.append(
-                (residual *
-                 cat_nodes[start:start + num**2].view(num, num, -1)).sum(1))
-            start += num**2
-
-        nodes += self.relu(self.out_fc(torch.cat(residuals)))
-        return nodes, cat_nodes
-
-
-class Block(nn.Module):
-
-    def __init__(self,
-                 input_dims,
-                 output_dim,
-                 mm_dim=1600,
-                 chunks=20,
-                 rank=15,
-                 shared=False,
-                 dropout_input=0.,
-                 dropout_pre_lin=0.,
-                 dropout_output=0.,
-                 pos_norm='before_cat'):
-        super().__init__()
-        self.rank = rank
-        self.dropout_input = dropout_input
-        self.dropout_pre_lin = dropout_pre_lin
-        self.dropout_output = dropout_output
-        assert (pos_norm in ['before_cat', 'after_cat'])
-        self.pos_norm = pos_norm
-        # Modules
-        self.linear0 = nn.Linear(input_dims[0], mm_dim)
-        self.linear1 = (
-            self.linear0 if shared else nn.Linear(input_dims[1], mm_dim))
-        self.merge_linears0 = nn.ModuleList()
-        self.merge_linears1 = nn.ModuleList()
-        self.chunks = self.chunk_sizes(mm_dim, chunks)
-        for size in self.chunks:
-            ml0 = nn.Linear(size, size * rank)
-            self.merge_linears0.append(ml0)
-            ml1 = ml0 if shared else nn.Linear(size, size * rank)
-            self.merge_linears1.append(ml1)
-        self.linear_out = nn.Linear(mm_dim, output_dim)
-
-    def forward(self, x):
-        x0 = self.linear0(x[0])
-        x1 = self.linear1(x[1])
-        bs = x1.size(0)
-        if self.dropout_input > 0:
-            x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
-            x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
-        x0_chunks = torch.split(x0, self.chunks, -1)
-        x1_chunks = torch.split(x1, self.chunks, -1)
-        zs = []
-        for x0_c, x1_c, m0, m1 in zip(x0_chunks, x1_chunks,
-                                      self.merge_linears0,
-                                      self.merge_linears1):
-            m = m0(x0_c) * m1(x1_c)  # bs x split_size*rank
-            m = m.view(bs, self.rank, -1)
-            z = torch.sum(m, 1)
-            if self.pos_norm == 'before_cat':
-                z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
-                z = F.normalize(z)
-            zs.append(z)
-        z = torch.cat(zs, 1)
-        if self.pos_norm == 'after_cat':
-            z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
-            z = F.normalize(z)
-
-        if self.dropout_pre_lin > 0:
-            z = F.dropout(z, p=self.dropout_pre_lin, training=self.training)
-        z = self.linear_out(z)
-        if self.dropout_output > 0:
-            z = F.dropout(z, p=self.dropout_output, training=self.training)
-        return z
-
-    @staticmethod
-    def chunk_sizes(dim, chunks):
-        split_size = (dim + chunks - 1) // chunks
-        sizes_list = [split_size] * chunks
-        sizes_list[-1] = sizes_list[-1] - (sum(sizes_list) - dim)
-        return sizes_list
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/config/defaults.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/config/defaults.py
deleted file mode 100644
index 6abf574d86e234c93b4e0162db49356a1f9e4fff..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/config/defaults.py
+++ /dev/null
@@ -1,373 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import os
-
-from yacs.config import CfgNode as CN
-
-
-# -----------------------------------------------------------------------------
-# Convention about Training / Test specific parameters
-# -----------------------------------------------------------------------------
-# Whenever an argument can be either used for training or for testing, the
-# corresponding name will be post-fixed by a _TRAIN for a training parameter,
-# or _TEST for a test-specific parameter.
-# For example, the number of images during training will be
-# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
-# IMAGES_PER_BATCH_TEST
-
-# -----------------------------------------------------------------------------
-# Config definition
-# -----------------------------------------------------------------------------
-
-_C = CN()
-
-_C.MODEL = CN()
-_C.MODEL.RPN_ONLY = False
-_C.MODEL.MASK_ON = False
-_C.MODEL.SEG_ON = False
-_C.MODEL.CHAR_MASK_ON = False
-_C.MODEL.DEVICE = "cuda"
-_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
-_C.MODEL.TRAIN_DETECTION_ONLY = False
-_C.MODEL.RESNET34 = False
-
-# If the WEIGHT starts with a catalog://, like :R-50, the code will look for
-# the path in paths_catalog. Else, it will use it as the specified absolute
-# path
-_C.MODEL.WEIGHT = ""
-
-_C.SEQUENCE = CN()
-_C.SEQUENCE.SEQ_ON = False
-_C.SEQUENCE.NUM_CHAR = 38
-_C.SEQUENCE.BOS_TOKEN = 0
-_C.SEQUENCE.MAX_LENGTH = 32
-_C.SEQUENCE.TEACHER_FORCE_RATIO = 1.0
-_C.SEQUENCE.TWO_CONV = False
-_C.SEQUENCE.MEAN_SCORE = False
-_C.SEQUENCE.RESIZE_HEIGHT = 16
-_C.SEQUENCE.RESIZE_WIDTH = 64
-
-
-# -----------------------------------------------------------------------------
-# INPUT
-# -----------------------------------------------------------------------------
-_C.INPUT = CN()
-# Size of the smallest side of the image during training
-_C.INPUT.MIN_SIZE_TRAIN = (800,)  # (800,)
-# Maximum size of the side of the image during training
-_C.INPUT.MAX_SIZE_TRAIN = 1333
-# Size of the smallest side of the image during testing
-_C.INPUT.MIN_SIZE_TEST = 800
-# Maximum size of the side of the image during testing
-_C.INPUT.MAX_SIZE_TEST = 1333
-# Values to be used for image normalization
-_C.INPUT.PIXEL_MEAN = [102.9801, 115.9465, 122.7717]
-# Values to be used for image normalization
-_C.INPUT.PIXEL_STD = [1.0, 1.0, 1.0]
-# Convert image to BGR format (for Caffe2 models), in range 0-255
-_C.INPUT.TO_BGR255 = True
-_C.INPUT.STRICT_RESIZE = False
-
-
-# -----------------------------------------------------------------------------
-# Dataset
-# -----------------------------------------------------------------------------
-_C.DATASETS = CN()
-# List of the dataset names for training, as present in paths_catalog.py
-_C.DATASETS.TRAIN = ()
-# List of the dataset names for testing, as present in paths_catalog.py
-_C.DATASETS.TEST = ()
-
-_C.DATASETS.RATIOS = []
-
-_C.DATASETS.AUG = False
-_C.DATASETS.RANDOM_CROP_PROB = 0.0
-_C.DATASETS.IGNORE_DIFFICULT = False
-_C.DATASETS.FIX_CROP = False
-_C.DATASETS.CROP_SIZE = (512, 512)
-_C.DATASETS.MAX_ROTATE_THETA = 30
-_C.DATASETS.FIX_ROTATE = False
-
-# -----------------------------------------------------------------------------
-# DataLoader
-# -----------------------------------------------------------------------------
-_C.DATALOADER = CN()
-# Number of data loading threads
-_C.DATALOADER.NUM_WORKERS = 4
-# If > 0, this enforces that each collated batch should have a size divisible
-# by SIZE_DIVISIBILITY
-_C.DATALOADER.SIZE_DIVISIBILITY = 0
-# If True, each batch should contain only images for which the aspect ratio
-# is compatible. This groups portrait images together, and landscape images
-# are not batched with portrait images.
-_C.DATALOADER.ASPECT_RATIO_GROUPING = True
-
-# ---------------------------------------------------------------------------- #
-# Backbone options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.BACKBONE = CN()
-
-# The backbone conv body to use
-# The string must match a function that is imported in modeling.model_builder
-# (e.g., 'FPN.add_fpn_ResNet101_conv5_body' to specify a ResNet-101-FPN
-# backbone)
-_C.MODEL.BACKBONE.CONV_BODY = "R-50-C4"
-
-# Add StopGrad at a specified stage so the bottom layers are frozen
-_C.MODEL.BACKBONE.FREEZE_CONV_BODY_AT = 2
-_C.MODEL.BACKBONE.OUT_CHANNELS = 256 * 4
-
-# ---------------------------------------------------------------------------- #
-# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
-# Note that parts of a resnet may be used for both the backbone and the head
-# These options apply to both
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RESNETS = CN()
-
-# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
-_C.MODEL.RESNETS.NUM_GROUPS = 1
-
-# Baseline width of each group
-_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
-
-# Place the stride 2 conv on the 1x1 filter
-# Use True only for the original MSRA ResNet; use False for C2 and Torch models
-_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
-
-# Residual transformation function
-_C.MODEL.RESNETS.TRANS_FUNC = "BottleneckWithFixedBatchNorm"
-# ResNet's stem function (conv1 and pool1)
-_C.MODEL.RESNETS.STEM_FUNC = "StemWithFixedBatchNorm"
-
-# Apply dilation in stage "res5"
-_C.MODEL.RESNETS.RES5_DILATION = 1
-
-_C.MODEL.RESNETS.BACKBONE_OUT_CHANNELS = 256 * 4
-_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
-_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
-
-_C.MODEL.RESNETS.STAGE_WITH_DCN = (False, False, False, False)
-_C.MODEL.RESNETS.WITH_MODULATED_DCN = False
-_C.MODEL.RESNETS.DEFORMABLE_GROUPS = 1
-_C.MODEL.RESNETS.LAYERS = (3, 4, 6, 3)
-
-# ---------------------------------------------------------------------------- #
-# FPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.FPN = CN()
-_C.MODEL.FPN.USE_GN = False
-_C.MODEL.FPN.USE_RELU = False
-
-# ---------------------------------------------------------------------------- #
-# RPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RPN = CN()
-_C.MODEL.RPN.USE_FPN = False
-# Base RPN anchor sizes given in absolute pixels w.r.t. the scaled network input
-_C.MODEL.RPN.ANCHOR_SIZES = (32, 64, 128, 256, 512)
-# Stride of the feature map that RPN is attached.
-# For FPN, number of strides should match number of scales
-_C.MODEL.RPN.ANCHOR_STRIDE = (16,)
-# RPN anchor aspect ratios
-_C.MODEL.RPN.ASPECT_RATIOS = (0.5, 1.0, 2.0)
-# Remove RPN anchors that go outside the image by RPN_STRADDLE_THRESH pixels
-# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
-_C.MODEL.RPN.STRADDLE_THRESH = 0
-# Minimum overlap required between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
-# ==> positive RPN example)
-_C.MODEL.RPN.FG_IOU_THRESHOLD = 0.7
-# Maximum overlap allowed between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
-# ==> negative RPN example)
-_C.MODEL.RPN.BG_IOU_THRESHOLD = 0.3
-# Total number of RPN examples per image
-_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
-# Target fraction of foreground (positive) examples per RPN minibatch
-_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
-# Number of top scoring RPN proposals to keep before applying NMS
-# When FPN is used, this is *per FPN level* (not total)
-_C.MODEL.RPN.PRE_NMS_TOP_N_TRAIN = 12000
-_C.MODEL.RPN.PRE_NMS_TOP_N_TEST = 6000
-# Number of top scoring RPN proposals to keep after applying NMS
-_C.MODEL.RPN.POST_NMS_TOP_N_TRAIN = 2000
-_C.MODEL.RPN.POST_NMS_TOP_N_TEST = 1000
-# NMS threshold used on RPN proposals
-_C.MODEL.RPN.NMS_THRESH = 0.7
-# Proposal height and width both need to be greater than RPN_MIN_SIZE
-# (a the scale used during training or inference)
-_C.MODEL.RPN.MIN_SIZE = 0
-# Number of top scoring RPN proposals to keep after combining proposals from
-# all FPN levels
-_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN = 2000
-_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST = 2000
-
-_C.MODEL.SEG = CN()
-_C.MODEL.SEG.USE_FPN = False
-_C.MODEL.SEG.USE_FUSE_FEATURE = False
-# Total number of SEG examples per image
-_C.MODEL.SEG.BATCH_SIZE_PER_IMAGE = 256
-# Target fraction of foreground (positive) examples per SEG minibatch
-_C.MODEL.SEG.POSITIVE_FRACTION = 0.5
-# NMS threshold used on SEG proposals
-_C.MODEL.SEG.BINARY_THRESH = 0.5
-_C.MODEL.SEG.USE_MULTIPLE_THRESH = False
-_C.MODEL.SEG.MULTIPLE_THRESH = (0.2, 0.3, 0.5, 0.7)
-_C.MODEL.SEG.BOX_THRESH = 0.7
-# Proposal height and width both need to be greater than RPN_MIN_SIZE
-# (a the scale used during training or inference)
-_C.MODEL.SEG.MIN_SIZE = 0
-_C.MODEL.SEG.SHRINK_RATIO = 0.5
-# Number of top scoring RPN proposals to keep after combining proposals from
-# all FPN levels
-_C.MODEL.SEG.TOP_N_TRAIN = 1000
-_C.MODEL.SEG.TOP_N_TEST = 1000
-_C.MODEL.SEG.AUG_PROPOSALS = False
-_C.MODEL.SEG.IGNORE_DIFFICULT = True
-_C.MODEL.SEG.EXPAND_RATIO = 1.6
-_C.MODEL.SEG.BOX_EXPAND_RATIO = 1.5
-_C.MODEL.SEG.USE_SEG_POLY = False
-_C.MODEL.SEG.USE_PPM = False
-
-
-# ---------------------------------------------------------------------------- #
-# ROI HEADS options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_HEADS = CN()
-_C.MODEL.ROI_HEADS.USE_FPN = False
-# Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD)
-_C.MODEL.ROI_HEADS.FG_IOU_THRESHOLD = 0.5
-# Overlap threshold for an RoI to be considered background
-# (class = 0 if overlap in [0, BG_IOU_THRESHOLD))
-_C.MODEL.ROI_HEADS.BG_IOU_THRESHOLD = 0.5
-# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
-# These are empirically chosen to approximately lead to unit variance targets
-_C.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
-# RoI minibatch size *per image* (number of regions of interest [ROIs])
-# Total number of RoIs per training minibatch =
-#   TRAIN.BATCH_SIZE_PER_IM * TRAIN.IMS_PER_BATCH * NUM_GPUS
-# E.g., a common configuration is: 512 * 2 * 8 = 8192
-_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
-# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
-_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
-
-# Only used on test mode
-
-# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
-# balance obtaining high recall with not having too many low precision
-# detections that will slow down inference post processing steps (like NMS)
-# _C.MODEL.ROI_HEADS.SCORE_THRESH = 0.05
-_C.MODEL.ROI_HEADS.SCORE_THRESH = 0.0
-# Overlap threshold used for non-maximum suppression (suppress boxes with
-# IoU >= this threshold)
-_C.MODEL.ROI_HEADS.NMS = 0.5
-# Maximum number of detections to return per image (100 is based on the limit
-# established for the COCO dataset)
-_C.MODEL.ROI_HEADS.DETECTIONS_PER_IMG = 100
-
-
-_C.MODEL.ROI_BOX_HEAD = CN()
-_C.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor"
-_C.MODEL.ROI_BOX_HEAD.PREDICTOR = "FastRCNNPredictor"
-_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_BOX_HEAD.POOLER_SCALES = (1.0 / 16,)
-_C.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 81
-# Hidden layer dimension when using an MLP for the RoI box head
-_C.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM = 1024
-_C.MODEL.ROI_BOX_HEAD.USE_REGRESSION = True
-_C.MODEL.ROI_BOX_HEAD.INFERENCE_USE_BOX = True
-_C.MODEL.ROI_BOX_HEAD.USE_MASKED_FEATURE = False
-_C.MODEL.ROI_BOX_HEAD.SOFT_MASKED_FEATURE_RATIO = 0.
-_C.MODEL.ROI_BOX_HEAD.MIX_OPTION = ""
-
-
-_C.MODEL.ROI_MASK_HEAD = CN()
-_C.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor"
-_C.MODEL.ROI_MASK_HEAD.PREDICTOR = "MaskRCNNC4Predictor"
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION_H = 32
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION_W = 128
-_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_MASK_HEAD.POOLER_SCALES = (1.0 / 16,)
-_C.MODEL.ROI_MASK_HEAD.MLP_HEAD_DIM = 1024
-_C.MODEL.ROI_MASK_HEAD.CONV_LAYERS = (256, 256, 256, 256)
-_C.MODEL.ROI_MASK_HEAD.RESOLUTION = 14
-_C.MODEL.ROI_MASK_HEAD.RESOLUTION_H = 32
-_C.MODEL.ROI_MASK_HEAD.RESOLUTION_W = 128
-_C.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR = True
-_C.MODEL.ROI_MASK_HEAD.CHAR_NUM_CLASSES = 38
-_C.MODEL.ROI_MASK_HEAD.USE_WEIGHTED_CHAR_MASK = False
-_C.MODEL.ROI_MASK_HEAD.MASK_BATCH_SIZE_PER_IM = 64
-_C.MODEL.ROI_MASK_HEAD.USE_MASKED_FEATURE = False
-_C.MODEL.ROI_MASK_HEAD.SOFT_MASKED_FEATURE_RATIO = 0.
-_C.MODEL.ROI_MASK_HEAD.MIX_OPTION = ""
-
-# ---------------------------------------------------------------------------- #
-# Solver
-# ---------------------------------------------------------------------------- #
-_C.SOLVER = CN()
-_C.SOLVER.MAX_ITER = 40000
-
-_C.SOLVER.BASE_LR = 0.001
-_C.SOLVER.BIAS_LR_FACTOR = 2
-
-_C.SOLVER.MOMENTUM = 0.9
-
-_C.SOLVER.WEIGHT_DECAY = 0.0005
-_C.SOLVER.WEIGHT_DECAY_BIAS = 0
-
-_C.SOLVER.GAMMA = 0.1
-_C.SOLVER.STEPS = (30000,)
-
-_C.SOLVER.WARMUP_FACTOR = 1.0 / 3
-_C.SOLVER.WARMUP_ITERS = 500
-_C.SOLVER.WARMUP_METHOD = "linear"
-
-_C.SOLVER.CHECKPOINT_PERIOD = 5000
-
-# Number of images per batch
-# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
-# see 2 images per batch
-_C.SOLVER.IMS_PER_BATCH = 16
-
-_C.SOLVER.RESUME = True
-
-_C.SOLVER.USE_ADAM = False
-
-_C.SOLVER.POW_SCHEDULE = False
-
-_C.SOLVER.DISPLAY_FREQ = 20
-
-# ---------------------------------------------------------------------------- #
-# Specific test options
-# ---------------------------------------------------------------------------- #
-_C.TEST = CN()
-_C.TEST.EXPECTED_RESULTS = []
-_C.TEST.EXPECTED_RESULTS_SIGMA_TOL = 4
-# Number of images per batch
-# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
-# see 2 images per batch
-_C.TEST.IMS_PER_BATCH = 8
-_C.TEST.VIS = False
-# from 0 to 255
-_C.TEST.CHAR_THRESH = 128
-
-
-# ---------------------------------------------------------------------------- #
-# Misc options
-# ---------------------------------------------------------------------------- #
-_C.OUTPUT_DIR = "."
-
-_C.PATHS_CATALOG = os.path.join(os.path.dirname(__file__), "paths_catalog.py")
-
-
-# ---------------------------------------------------------------------------- #
-# Precision options
-# ---------------------------------------------------------------------------- #
-
-# Precision of input, allowable: (float32, float16)
-_C.DTYPE = "float32"
-
-# Enable verbosity in apex.amp
-_C.AMP_VERBOSE = False
\ No newline at end of file
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/losses/gfocal_loss.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/losses/gfocal_loss.py
deleted file mode 100644
index 9d3b8833dc50c76f6741db5341dbf8da3402d07b..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/losses/gfocal_loss.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import mmcv
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def quality_focal_loss(pred, target, beta=2.0):
-    r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
-    Qualified and Distributed Bounding Boxes for Dense Object Detection
-    <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted joint representation of classification
-            and quality (IoU) estimation with shape (N, C), C is the number of
-            classes.
-        target (tuple([torch.Tensor])): Target category label with shape (N,)
-            and target quality label with shape (N,).
-        beta (float): The beta parameter for calculating the modulating factor.
-            Defaults to 2.0.
-
-    Returns:
-        torch.Tensor: Loss tensor with shape (N,).
-    """
-    assert len(target) == 2, """target for QFL must be a tuple of two elements,
-        including category label and quality label, respectively"""
-    # label denotes the category id, score denotes the quality score
-    label, score = target
-
-    # negatives are supervised by 0 quality score
-    pred_sigmoid = pred.sigmoid()
-    scale_factor = pred_sigmoid
-    zerolabel = scale_factor.new_zeros(pred.shape)
-    loss = F.binary_cross_entropy_with_logits(
-        pred, zerolabel, reduction='none') * scale_factor.pow(beta)
-
-    # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-    bg_class_ind = pred.size(1)
-    pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
-    pos_label = label[pos].long()
-    # positives are supervised by bbox quality (IoU) score
-    scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
-    loss[pos, pos_label] = F.binary_cross_entropy_with_logits(
-        pred[pos, pos_label], score[pos],
-        reduction='none') * scale_factor.abs().pow(beta)
-
-    loss = loss.sum(dim=1, keepdim=False)
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def distribution_focal_loss(pred, label):
-    r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning
-    Qualified and Distributed Bounding Boxes for Dense Object Detection
-    <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted general distribution of bounding boxes
-            (before softmax) with shape (N, n+1), n is the max value of the
-            integral set `{0, ..., n}` in paper.
-        label (torch.Tensor): Target distance label for bounding boxes with
-            shape (N,).
-
-    Returns:
-        torch.Tensor: Loss tensor with shape (N,).
-    """
-    dis_left = label.long()
-    dis_right = dis_left + 1
-    weight_left = dis_right.float() - label
-    weight_right = label - dis_left.float()
-    loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \
-        + F.cross_entropy(pred, dis_right, reduction='none') * weight_right
-    return loss
-
-
-@LOSSES.register_module()
-class QualityFocalLoss(nn.Module):
-    r"""Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:
-    Learning Qualified and Distributed Bounding Boxes for Dense Object
-    Detection <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        use_sigmoid (bool): Whether sigmoid operation is conducted in QFL.
-            Defaults to True.
-        beta (float): The beta parameter for calculating the modulating factor.
-            Defaults to 2.0.
-        reduction (str): Options are "none", "mean" and "sum".
-        loss_weight (float): Loss weight of current loss.
-    """
-
-    def __init__(self,
-                 use_sigmoid=True,
-                 beta=2.0,
-                 reduction='mean',
-                 loss_weight=1.0):
-        super(QualityFocalLoss, self).__init__()
-        assert use_sigmoid is True, 'Only sigmoid in QFL supported now.'
-        self.use_sigmoid = use_sigmoid
-        self.beta = beta
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): Predicted joint representation of
-                classification and quality (IoU) estimation with shape (N, C),
-                C is the number of classes.
-            target (tuple([torch.Tensor])): Target category label with shape
-                (N,) and target quality label with shape (N,).
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.use_sigmoid:
-            loss_cls = self.loss_weight * quality_focal_loss(
-                pred,
-                target,
-                weight,
-                beta=self.beta,
-                reduction=reduction,
-                avg_factor=avg_factor)
-        else:
-            raise NotImplementedError
-        return loss_cls
-
-
-@LOSSES.register_module()
-class DistributionFocalLoss(nn.Module):
-    r"""Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
-    Learning Qualified and Distributed Bounding Boxes for Dense Object
-    Detection <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
-        loss_weight (float): Loss weight of current loss.
-    """
-
-    def __init__(self, reduction='mean', loss_weight=1.0):
-        super(DistributionFocalLoss, self).__init__()
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): Predicted general distribution of bounding
-                boxes (before softmax) with shape (N, n+1), n is the max value
-                of the integral set `{0, ..., n}` in paper.
-            target (torch.Tensor): Target distance label for bounding boxes
-                with shape (N,).
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_cls = self.loss_weight * distribution_focal_loss(
-            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
-        return loss_cls
diff --git a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/output.py b/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/output.py
deleted file mode 100644
index f2f1736e9b6b6e9b394cbdfd635b87a570fa6f72..0000000000000000000000000000000000000000
--- a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/output.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Tuple, Optional
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.core import conditional_process
-from DeepFakeAI.uis.typing import Update
-from DeepFakeAI.utilities import is_image, is_video, normalize_output_path, clear_temp
-
-OUTPUT_START_BUTTON : Optional[gradio.Button] = None
-OUTPUT_CLEAR_BUTTON : Optional[gradio.Button] = None
-OUTPUT_IMAGE : Optional[gradio.Image] = None
-OUTPUT_VIDEO : Optional[gradio.Video] = None
-
-
-def render() -> None:
-	global OUTPUT_START_BUTTON
-	global OUTPUT_CLEAR_BUTTON
-	global OUTPUT_IMAGE
-	global OUTPUT_VIDEO
-
-	with gradio.Row():
-		with gradio.Box():
-			OUTPUT_IMAGE = gradio.Image(
-				label = wording.get('output_image_or_video_label'),
-				visible = False
-			)
-			OUTPUT_VIDEO = gradio.Video(
-				label = wording.get('output_image_or_video_label')
-			)
-	with gradio.Row():
-		OUTPUT_START_BUTTON = gradio.Button(wording.get('start_button_label'))
-		OUTPUT_CLEAR_BUTTON = gradio.Button(wording.get('clear_button_label'))
-
-
-def listen() -> None:
-	OUTPUT_START_BUTTON.click(update, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
-	OUTPUT_CLEAR_BUTTON.click(clear, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
-
-
-def update() -> Tuple[Update, Update]:
-	DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, '.')
-	if DeepFakeAI.globals.output_path:
-		conditional_process()
-		if is_image(DeepFakeAI.globals.output_path):
-			return gradio.update(value = DeepFakeAI.globals.output_path, visible = True), gradio.update(value = None, visible = False)
-		if is_video(DeepFakeAI.globals.output_path):
-			return gradio.update(value = None, visible = False), gradio.update(value = DeepFakeAI.globals.output_path, visible = True)
-	return gradio.update(value = None, visible = False), gradio.update(value = None, visible = False)
-
-
-def clear() -> Tuple[Update, Update]:
-	if DeepFakeAI.globals.target_path:
-		clear_temp(DeepFakeAI.globals.target_path)
-	return gradio.update(value = None), gradio.update(value = None)
diff --git a/spaces/trttung1610/musicgen/audiocraft/utils/export.py b/spaces/trttung1610/musicgen/audiocraft/utils/export.py
deleted file mode 100644
index 28b214017d9ac23934b67e8254a96131cefa6501..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/utils/export.py
+++ /dev/null
@@ -1,79 +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 to export a training checkpoint to a lightweight release checkpoint.
-"""
-
-from pathlib import Path
-import typing as tp
-
-from omegaconf import OmegaConf
-import torch
-
-from audiocraft import __version__
-
-
-def export_encodec(checkpoint_path: tp.Union[Path, str], out_file: tp.Union[Path, str]):
-    """Export only the best state from the given EnCodec checkpoint. This
-    should be used if you trained your own EnCodec model.
-    """
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['best_state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
-        'version': __version__,
-        'exported': True,
-    }
-    Path(out_file).parent.mkdir(exist_ok=True, parents=True)
-    torch.save(new_pkg, out_file)
-    return out_file
-
-
-def export_pretrained_compression_model(pretrained_encodec: str, out_file: tp.Union[Path, str]):
-    """Export a compression model (potentially EnCodec) from a pretrained model.
-    This is required for packaging the audio tokenizer along a MusicGen or AudioGen model.
-    Do not include the //pretrained/ prefix. For instance if you trained a model
-    with `facebook/encodec_32khz`, just put that as a name. Same for `dac_44khz`.
-
-    In that case, this will not actually include a copy of the model, simply the reference
-    to the model used.
-    """
-    if Path(pretrained_encodec).exists():
-        pkg = torch.load(pretrained_encodec)
-        assert 'best_state' in pkg
-        assert 'xp.cfg' in pkg
-        assert 'version' in pkg
-        assert 'exported' in pkg
-    else:
-        pkg = {
-            'pretrained': pretrained_encodec,
-            'exported': True,
-            'version': __version__,
-        }
-    Path(out_file).parent.mkdir(exist_ok=True, parents=True)
-    torch.save(pkg, out_file)
-
-
-def export_lm(checkpoint_path: tp.Union[Path, str], out_file: tp.Union[Path, str]):
-    """Export only the best state from the given MusicGen or AudioGen checkpoint.
-    """
-    pkg = torch.load(checkpoint_path, 'cpu')
-    if pkg['fsdp_best_state']:
-        best_state = pkg['fsdp_best_state']['model']
-    else:
-        assert pkg['best_state']
-        best_state = pkg['best_state']['model']
-    new_pkg = {
-        'best_state': best_state,
-        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
-        'version': __version__,
-        'exported': True,
-    }
-
-    Path(out_file).parent.mkdir(exist_ok=True, parents=True)
-    torch.save(new_pkg, out_file)
-    return out_file
diff --git a/spaces/trttung1610/musicgen/tests/modules/test_transformer.py b/spaces/trttung1610/musicgen/tests/modules/test_transformer.py
deleted file mode 100644
index 2bb79bfd58d535469f9b3c56b8a5fe254db5d8ba..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/tests/modules/test_transformer.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.transformer import (
-    StreamingMultiheadAttention, StreamingTransformer, set_efficient_attention_backend)
-
-
-def test_transformer_causal_streaming():
-    torch.manual_seed(1234)
-
-    for context, custom in product([None, 10], [False, True]):
-        # Test that causality and receptive fields are properly handled.
-        # looking at the gradients
-        tr = StreamingTransformer(
-            16, 4, 1 if context else 2,
-            causal=True, past_context=context, custom=custom,
-            dropout=0.)
-        steps = 20
-        for k in [0, 10, 15, 19]:
-            x = torch.randn(4, steps, 16, requires_grad=True)
-            y = tr(x)
-            y[:, k].abs().sum().backward()
-            if k + 1 < steps:
-                assert torch.allclose(x.grad[:, k + 1:], torch.tensor(0.)), x.grad[:, k + 1:].norm()
-            assert not torch.allclose(x.grad[:, :k + 1], torch.tensor(0.)), x.grad[:, :k + 1].norm()
-            if context is not None and k > context:
-                limit = k - context - 1
-                assert torch.allclose(x.grad[:, :limit],
-                                      torch.tensor(0.)), x.grad[:, :limit].norm()
-
-        # Now check that streaming gives the same result at batch eval.
-        x = torch.randn(4, steps, 16)
-        y = tr(x)
-        ys = []
-        with tr.streaming():
-            for k in range(steps):
-                chunk = x[:, k:k + 1, :]
-                ys.append(tr(chunk))
-        y_stream = torch.cat(ys, dim=1)
-        delta = torch.norm(y_stream - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_transformer_vs_pytorch():
-    torch.manual_seed(1234)
-    # Check that in the non causal setting, we get the same result as
-    # PyTorch Transformer encoder.
-    for custom in [False, True]:
-        tr = StreamingTransformer(
-            16, 4, 2,
-            causal=False, custom=custom, dropout=0., positional_scale=0.)
-        layer = torch.nn.TransformerEncoderLayer(16, 4, dropout=0., batch_first=True)
-        tr_ref = torch.nn.TransformerEncoder(layer, 2)
-        tr.load_state_dict(tr_ref.state_dict())
-
-        x = torch.randn(4, 20, 16)
-        y = tr(x)
-        y2 = tr_ref(x)
-        delta = torch.norm(y2 - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_streaming_api():
-    tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0.)
-    tr.eval()
-    steps = 12
-    x = torch.randn(1, steps, 16)
-
-    with torch.no_grad():
-        with tr.streaming():
-            _ = tr(x[:, :1])
-            state = {k: v.clone() for k, v in tr.get_streaming_state().items()}
-            y = tr(x[:, 1:2])
-            tr.set_streaming_state(state)
-            y2 = tr(x[:, 1:2])
-            assert torch.allclose(y, y2), (y - y2).norm()
-            assert tr.flush() is None
-
-
-def test_memory_efficient():
-    for backend in ['torch', 'xformers']:
-        torch.manual_seed(1234)
-        set_efficient_attention_backend(backend)
-
-        tr = StreamingTransformer(
-            16, 4, 2, custom=True, dropout=0., layer_scale=0.1)
-        tr_mem_efficient = StreamingTransformer(
-            16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1)
-        tr_mem_efficient.load_state_dict(tr.state_dict())
-        tr.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_mem_efficient(x)
-            assert torch.allclose(y, y2), ((y - y2).norm(), backend)
-
-
-def test_attention_as_float32():
-    torch.manual_seed(1234)
-    cases = [
-        {'custom': True},
-        {'custom': False},
-    ]
-    for case in cases:
-        tr = StreamingTransformer(16, 4, 2, dropout=0., dtype=torch.bfloat16, **case)
-        tr_float32 = StreamingTransformer(
-            16, 4, 2, dropout=0., attention_as_float32=True, dtype=torch.bfloat16, **case)
-        if not case['custom']:
-            # we are not using autocast here because it doesn't really
-            # work as expected on CPU, so we have to manually cast the weights of the MHA.
-            for layer in tr_float32.layers:
-                layer.self_attn.mha.to(torch.float32)
-        tr_float32.load_state_dict(tr.state_dict())
-        steps = 12
-        x = torch.randn(3, steps, 16, dtype=torch.bfloat16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_float32(x)
-            assert not torch.allclose(y, y2), (y - y2).norm()
-
-
-@torch.no_grad()
-def test_streaming_memory_efficient():
-    for backend in ['torch', 'xformers']:
-        torch.manual_seed(1234)
-        set_efficient_attention_backend(backend)
-        tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0., custom=True)
-        tr_mem_efficient = StreamingTransformer(
-            16, 4, 2, dropout=0., memory_efficient=True, causal=True)
-        tr.load_state_dict(tr_mem_efficient.state_dict())
-        tr.eval()
-        tr_mem_efficient.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        ref = tr(x)
-
-        with tr_mem_efficient.streaming():
-            outs = []
-            # frame_sizes = [2] + [1] * (steps - 2)
-            frame_sizes = [1] * steps
-
-            for frame_size in frame_sizes:
-                frame = x[:, :frame_size]
-                x = x[:, frame_size:]
-                outs.append(tr_mem_efficient(frame))
-
-        out = torch.cat(outs, dim=1)
-        delta = torch.norm(out - ref) / torch.norm(out)
-        assert delta < 1e-6, delta
-
-
-def test_cross_attention():
-    torch.manual_seed(1234)
-    for norm_first in [True, False]:
-        m = StreamingTransformer(
-            16, 4, 2, cross_attention=False, norm_first=norm_first, dropout=0., custom=True)
-        m_cross = StreamingTransformer(
-            16, 4, 2, cross_attention=True, norm_first=norm_first, dropout=0., custom=True)
-        m_cross.load_state_dict(m.state_dict(), strict=False)
-        x = torch.randn(2, 5, 16)
-        cross_x = torch.randn(2, 3, 16)
-        y_ref = m(x)
-        y_cross_zero = m_cross(x, cross_attention_src=0 * cross_x)
-        # With norm_first, the two should be exactly the same,
-        # but with norm_first=False, we get 2 normalization in a row
-        # and the epsilon value leads to a tiny change.
-        atol = 0. if norm_first else 1e-6
-        print((y_ref - y_cross_zero).norm() / y_ref.norm())
-        assert torch.allclose(y_ref, y_cross_zero, atol=atol)
-
-        # We now expect a difference even with a generous atol of 1e-2.
-        y_cross = m_cross(x, cross_attention_src=cross_x)
-        assert not torch.allclose(y_cross, y_cross_zero, atol=1e-2)
-
-        with pytest.raises(AssertionError):
-            _ = m_cross(x)
-            _ = m(x, cross_attention_src=cross_x)
-
-
-def test_cross_attention_compat():
-    torch.manual_seed(1234)
-    num_heads = 2
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        StreamingMultiheadAttention(dim, num_heads, causal=True, cross_attention=True)
-
-    cross_attn = StreamingMultiheadAttention(
-        dim, num_heads, dropout=0, cross_attention=True, custom=True)
-    ref_attn = torch.nn.MultiheadAttention(dim, num_heads, dropout=0, batch_first=True)
-
-    # We can load the regular attention state dict
-    # so we have compat when loading old checkpoints.
-    cross_attn.load_state_dict(ref_attn.state_dict())
-
-    queries = torch.randn(3, 7, dim)
-    keys = torch.randn(3, 9, dim)
-    values = torch.randn(3, 9, dim)
-
-    y = cross_attn(queries, keys, values)[0]
-    y_ref = ref_attn(queries, keys, values)[0]
-    assert torch.allclose(y, y_ref, atol=1e-7), (y - y_ref).norm() / y_ref.norm()
-
-    # Now let's check that streaming is working properly.
-    with cross_attn.streaming():
-        ys = []
-        for step in range(queries.shape[1]):
-            ys.append(cross_attn(queries[:, step: step + 1], keys, values)[0])
-    y_streaming = torch.cat(ys, dim=1)
-    assert torch.allclose(y_streaming, y, atol=1e-7)
-
-
-def test_repeat_kv():
-    torch.manual_seed(1234)
-    num_heads = 8
-    kv_repeat = 4
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat, cross_attention=True)
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat)
-    mha = StreamingMultiheadAttention(
-        dim, num_heads, causal=True, kv_repeat=kv_repeat, custom=True)
-    x = torch.randn(4, 18, dim)
-    y = mha(x, x, x)[0]
-    assert x.shape == y.shape
-
-
-def test_qk_layer_norm():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, bias_attn=False)
-    steps = 12
-    x = torch.randn(3, steps, 16)
-    y = tr(x)
-
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, cross_attention=True)
-    z = torch.randn(3, 21, 16)
-    y = tr(x, cross_attention_src=z)
-    assert y.shape == x.shape
diff --git a/spaces/ttt246/brain/Extension/src/pages/Panel/Message/index.css b/spaces/ttt246/brain/Extension/src/pages/Panel/Message/index.css
deleted file mode 100644
index a4d9ed277e3c7ffc490a0aab5babe5acbd954c54..0000000000000000000000000000000000000000
--- a/spaces/ttt246/brain/Extension/src/pages/Panel/Message/index.css
+++ /dev/null
@@ -1,30 +0,0 @@
-[data-theme='light'] {
-    --send-theme-color: #8753e9;
-    --receive-theme-color: #0d0c0d;
-  }
-  
-  [data-theme='dark'] {
-    --send-theme-color: #8753e9;
-    --receive-theme-color: #333333;
-  }
-
-.message {
-    display: flex;
-    flex-direction: column;
-    padding: 5px;
-}
-
-.message_sent {
-    width: fit-content;
-    padding: 5px;
-    border-radius: 5px;
-    background-color: var(--send-theme-color);
-    align-self: end;
-}
-
-.message_received {
-    width: fit-content;
-    padding: 5px;
-}
-
-
diff --git a/spaces/uSerNameDDHL/bingo/src/components/markdown.tsx b/spaces/uSerNameDDHL/bingo/src/components/markdown.tsx
deleted file mode 100644
index d4491467a1f14d1d72e535caac9c40636054e5df..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/src/components/markdown.tsx
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FC, memo } from 'react'
-import ReactMarkdown, { Options } from 'react-markdown'
-
-export const MemoizedReactMarkdown: FC<Options> = memo(
-  ReactMarkdown,
-  (prevProps, nextProps) =>
-    prevProps.children === nextProps.children &&
-    prevProps.className === nextProps.className
-)
diff --git a/spaces/ucalyptus/PTI/utils/models_utils.py b/spaces/ucalyptus/PTI/utils/models_utils.py
deleted file mode 100644
index 0465f38b8f140d77c40a2323f5a8cce4571f6194..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/utils/models_utils.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import pickle
-import functools
-import torch
-from configs import paths_config, global_config
-
-
-def toogle_grad(model, flag=True):
-    for p in model.parameters():
-        p.requires_grad = flag
-
-
-def load_tuned_G(run_id, type):
-    new_G_path = f'{paths_config.checkpoints_dir}/model_{run_id}_{type}.pt'
-    with open(new_G_path, 'rb') as f:
-        new_G = torch.load(f).to(global_config.device).eval()
-    new_G = new_G.float()
-    toogle_grad(new_G, False)
-    return new_G
-
-
-def load_old_G():
-    with open(paths_config.stylegan2_ada_ffhq, 'rb') as f:
-        old_G = pickle.load(f)['G_ema'].to(global_config.device).eval()
-        old_G = old_G.float()
-    return old_G
diff --git a/spaces/ugaray96/neural-search/app.py b/spaces/ugaray96/neural-search/app.py
deleted file mode 100644
index bdc51f961b2161e8e183c270af0a33a16d8f6225..0000000000000000000000000000000000000000
--- a/spaces/ugaray96/neural-search/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import streamlit as st
-
-st.set_page_config(
-    page_title="Neural Search",
-    page_icon="🔎",
-    layout="wide",
-    initial_sidebar_state="expanded",
-    menu_items={"About": "https://github.com/ugm2/neural-search-demo"},
-)
-
-from streamlit_option_menu import option_menu
-from interface.config import session_state_variables, pages
-from interface.components import component_select_pipeline
-from interface.utils import load_audio_model
-
-# Initialization of session state
-for key, value in session_state_variables.items():
-    if key not in st.session_state:
-        st.session_state[key] = value
-
-# Init audio model
-st.session_state["audio_model"] = load_audio_model()
-
-
-def run_demo():
-
-    main_page = st.container()
-
-    st.sidebar.title("🧠 Neural Search 🔎")
-
-    navigation = st.sidebar.container()
-
-    with navigation:
-
-        selected_page = option_menu(
-            menu_title=None,
-            options=list(pages.keys()),
-            icons=[f[1] for f in pages.values()],
-            menu_icon="cast",
-            default_index=0,
-            styles={
-                "container": {"border": "2px solid #818494"},
-                "icon": {"font-size": "22px"},
-                "nav-link": {"font-size": "20px", "text-align": "left"},
-            },
-        )
-        component_select_pipeline(navigation)
-
-    # Draw the correct page
-    pages[selected_page][0](main_page)
-
-
-run_demo()
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/render.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/render.py
deleted file mode 100644
index 7a3d141f3e00216b530d05c205c5f94f0ad814ab..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/render.py
+++ /dev/null
@@ -1,507 +0,0 @@
-import os
-import json
-import pandas as pd
-import cv2
-import numpy as np
-from PIL import Image, ImageOps
-from .rich import console
-
-from .generate import generate
-from .noise import add_noise
-from .animation import sample_from_cv2, sample_to_cv2, anim_frame_warp
-from .animation_key_frames import DeformAnimKeys, LooperAnimKeys
-from .video_audio_utilities import get_frame_name, get_next_frame
-from .depth import DepthModel
-from .colors import maintain_colors
-from .parseq_adapter import ParseqAnimKeys
-from .seed import next_seed
-from .blank_frame_reroll import blank_frame_reroll
-from .image_sharpening import unsharp_mask
-from .load_images import get_mask, load_img, get_mask_from_file
-from .hybrid_video import hybrid_generation, hybrid_composite
-from .hybrid_video import get_matrix_for_hybrid_motion, get_matrix_for_hybrid_motion_prev, get_flow_for_hybrid_motion, get_flow_for_hybrid_motion_prev, image_transform_ransac, image_transform_optical_flow
-from .save_images import save_image
-from .composable_masks import compose_mask_with_check
-from .settings import get_keys_to_exclude
-from .deforum_controlnet import unpack_controlnet_vids, is_controlnet_enabled
-# Webui
-from modules.shared import opts, cmd_opts, state, sd_model
-from modules import lowvram, devices, sd_hijack
-
-def render_animation(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, animation_prompts, root):
-    # handle hybrid video generation
-    if anim_args.animation_mode in ['2D','3D']:
-        if anim_args.hybrid_composite or anim_args.hybrid_motion in ['Affine', 'Perspective', 'Optical Flow']:
-            args, anim_args, inputfiles = hybrid_generation(args, anim_args, root)
-            # path required by hybrid functions, even if hybrid_comp_save_extra_frames is False
-            hybrid_frame_path = os.path.join(args.outdir, 'hybridframes')
-
-    # handle controlnet video input frames generation
-    if is_controlnet_enabled(controlnet_args):
-        unpack_controlnet_vids(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, animation_prompts, root)
-
-    # use parseq if manifest is provided
-    use_parseq = parseq_args.parseq_manifest != None and parseq_args.parseq_manifest.strip()
-    # expand key frame strings to values
-    keys = DeformAnimKeys(anim_args) if not use_parseq else ParseqAnimKeys(parseq_args, anim_args)
-    loopSchedulesAndData = LooperAnimKeys(loop_args, anim_args)
-    # resume animation
-    start_frame = 0
-    if anim_args.resume_from_timestring:
-        for tmp in os.listdir(args.outdir):
-            if ".txt" in tmp : 
-                pass
-            else:
-                filename = tmp.split("_")
-                # don't use saved depth maps to count number of frames
-                if anim_args.resume_timestring in filename and "depth" not in filename:
-                    start_frame += 1
-        #start_frame = start_frame - 1
-
-    # create output folder for the batch
-    os.makedirs(args.outdir, exist_ok=True)
-    print(f"Saving animation frames to:\n{args.outdir}")
-
-    # save settings for the batch
-    exclude_keys = get_keys_to_exclude('general')
-    settings_filename = os.path.join(args.outdir, f"{args.timestring}_settings.txt")
-    with open(settings_filename, "w+", encoding="utf-8") as f:
-        args.__dict__["prompts"] = animation_prompts
-        s = {}
-        for d in [dict(args.__dict__), dict(anim_args.__dict__), dict(parseq_args.__dict__), dict(loop_args.__dict__)]:
-            for key, value in d.items():
-                if key not in exclude_keys:
-                    s[key] = value
-        json.dump(s, f, ensure_ascii=False, indent=4)
-
-    # resume from timestring
-    if anim_args.resume_from_timestring:
-        args.timestring = anim_args.resume_timestring
-
-    # Always enable pseudo-3d with parseq. No need for an extra toggle:
-    # Whether it's used or not in practice is defined by the schedules
-    if use_parseq:
-        anim_args.flip_2d_perspective = True        
-
-    # expand prompts out to per-frame
-    if use_parseq:
-        prompt_series = keys.prompts
-    else:
-        prompt_series = pd.Series([np.nan for a in range(anim_args.max_frames)])
-        for i, prompt in animation_prompts.items():
-            prompt_series[int(i)] = prompt
-        prompt_series = prompt_series.ffill().bfill()
-
-    # check for video inits
-    using_vid_init = anim_args.animation_mode == 'Video Input'
-
-    # load depth model for 3D
-    predict_depths = (anim_args.animation_mode == '3D' and anim_args.use_depth_warping) or anim_args.save_depth_maps
-    predict_depths = predict_depths or (anim_args.hybrid_composite and anim_args.hybrid_comp_mask_type in ['Depth','Video Depth'])
-    if predict_depths:
-        depth_model = DepthModel('cpu' if cmd_opts.lowvram or cmd_opts.medvram else root.device)
-        depth_model.load_midas(root.models_path, root.half_precision)
-        if anim_args.midas_weight < 1.0:
-            depth_model.load_adabins(root.models_path)
-        # depth-based hybrid composite mask requires saved depth maps
-        if anim_args.hybrid_composite and anim_args.hybrid_comp_mask_type =='Depth':
-            anim_args.save_depth_maps = True
-    else:
-        depth_model = None
-        anim_args.save_depth_maps = False
-
-    # state for interpolating between diffusion steps
-    turbo_steps = 1 if using_vid_init else int(anim_args.diffusion_cadence)
-    turbo_prev_image, turbo_prev_frame_idx = None, 0
-    turbo_next_image, turbo_next_frame_idx = None, 0
-
-    # resume animation
-    prev_img = None
-    color_match_sample = None
-    if anim_args.resume_from_timestring:
-        last_frame = start_frame-1
-        if turbo_steps > 1:
-            last_frame -= last_frame%turbo_steps
-        path = os.path.join(args.outdir,f"{args.timestring}_{last_frame:05}.png")
-        img = cv2.imread(path)
-        #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Changed the colors on resume
-        prev_img = img
-        if anim_args.color_coherence != 'None':
-            color_match_sample = img
-        if turbo_steps > 1:
-            turbo_next_image, turbo_next_frame_idx = prev_img, last_frame
-            turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx
-            start_frame = last_frame+turbo_steps
-
-    args.n_samples = 1
-    frame_idx = start_frame
-
-    # reset the mask vals as they are overwritten in the compose_mask algorithm
-    mask_vals = {}
-    noise_mask_vals = {}
-
-    mask_vals['everywhere'] = Image.new('1', (args.W, args.H), 1)
-    noise_mask_vals['everywhere'] = Image.new('1', (args.W, args.H), 1)
-
-    mask_image = None
-    
-    if args.use_init and args.init_image != None and args.init_image != '':
-        _, mask_image = load_img(args.init_image, 
-                                        shape=(args.W, args.H),  
-                                        use_alpha_as_mask=args.use_alpha_as_mask)
-        mask_vals['init_mask'] = mask_image
-        noise_mask_vals['init_mask'] = mask_image
-    
-    # Grab the first frame masks since they wont be provided until next frame
-    if mask_image is None and args.use_mask:
-        mask_vals['init_mask'] = get_mask(args)
-        noise_mask_vals['init_mask'] = get_mask(args) # TODO?: add a different default noise mask
-
-    if anim_args.use_mask_video:
-        mask_vals['video_mask'] = get_mask_from_file(get_next_frame(args.outdir, anim_args.video_mask_path, frame_idx, True), args)
-        noise_mask_vals['video_mask'] = get_mask_from_file(get_next_frame(args.outdir, anim_args.video_mask_path, frame_idx, True), args)
-    else:
-        mask_vals['video_mask'] = None
-        noise_mask_vals['video_mask'] = None
-
-    #Webui
-    state.job_count = anim_args.max_frames
-    
-    while frame_idx < anim_args.max_frames:
-        #Webui
-        state.job = f"frame {frame_idx + 1}/{anim_args.max_frames}"
-        state.job_no = frame_idx + 1
-        if state.interrupted:
-            break
-
-        print(f"\033[36mAnimation frame: \033[0m{frame_idx}/{anim_args.max_frames}  ")
-
-        noise = keys.noise_schedule_series[frame_idx]
-        strength = keys.strength_schedule_series[frame_idx]
-        scale = keys.cfg_scale_schedule_series[frame_idx]
-        contrast = keys.contrast_schedule_series[frame_idx]
-        kernel = int(keys.kernel_schedule_series[frame_idx])
-        sigma = keys.sigma_schedule_series[frame_idx]
-        amount = keys.amount_schedule_series[frame_idx]
-        threshold = keys.threshold_schedule_series[frame_idx]
-        hybrid_comp_schedules = {
-            "alpha": keys.hybrid_comp_alpha_schedule_series[frame_idx],
-            "mask_blend_alpha": keys.hybrid_comp_mask_blend_alpha_schedule_series[frame_idx],
-            "mask_contrast": keys.hybrid_comp_mask_contrast_schedule_series[frame_idx],
-            "mask_auto_contrast_cutoff_low": int(keys.hybrid_comp_mask_auto_contrast_cutoff_low_schedule_series[frame_idx]),
-            "mask_auto_contrast_cutoff_high": int(keys.hybrid_comp_mask_auto_contrast_cutoff_high_schedule_series[frame_idx]),
-        }        
-        scheduled_sampler_name = None
-        scheduled_clipskip = None
-        mask_seq = None
-        noise_mask_seq = None
-        if anim_args.enable_steps_scheduling and keys.steps_schedule_series[frame_idx] is not None:
-            args.steps = int(keys.steps_schedule_series[frame_idx])
-        if anim_args.enable_sampler_scheduling and keys.sampler_schedule_series[frame_idx] is not None:
-            scheduled_sampler_name = keys.sampler_schedule_series[frame_idx].casefold()
-        if anim_args.enable_clipskip_scheduling and keys.clipskip_schedule_series[frame_idx] is not None:
-            scheduled_clipskip = int(keys.clipskip_schedule_series[frame_idx])
-        if args.use_mask and keys.mask_schedule_series[frame_idx] is not None:
-            mask_seq = keys.mask_schedule_series[frame_idx]
-        if anim_args.use_noise_mask and keys.noise_mask_schedule_series[frame_idx] is not None:
-            noise_mask_seq = keys.noise_mask_schedule_series[frame_idx]
-        
-        if args.use_mask and not anim_args.use_noise_mask:
-            noise_mask_seq = mask_seq
-        
-        depth = None
-
-        if anim_args.animation_mode == '3D' and (cmd_opts.lowvram or cmd_opts.medvram):
-            # Unload the main checkpoint and load the depth model
-            lowvram.send_everything_to_cpu()
-            sd_hijack.model_hijack.undo_hijack(sd_model)
-            devices.torch_gc()
-            depth_model.to(root.device)
-        
-        # emit in-between frames
-        if turbo_steps > 1:
-            tween_frame_start_idx = max(0, frame_idx-turbo_steps)
-            for tween_frame_idx in range(tween_frame_start_idx, frame_idx):
-                tween = float(tween_frame_idx - tween_frame_start_idx + 1) / float(frame_idx - tween_frame_start_idx)
-                print(f" Creating in-between frame: {tween_frame_idx}; tween:{tween:0.2f};")
-
-                advance_prev = turbo_prev_image is not None and tween_frame_idx > turbo_prev_frame_idx
-                advance_next = tween_frame_idx > turbo_next_frame_idx
-
-                if depth_model is not None:
-                    assert(turbo_next_image is not None)
-                    depth = depth_model.predict(turbo_next_image, anim_args, root.half_precision)
-                
-                if advance_prev:
-                    turbo_prev_image, _ = anim_frame_warp(turbo_prev_image, args, anim_args, keys, tween_frame_idx, depth_model, depth=depth, device=root.device, half_precision=root.half_precision)
-                if advance_next:
-                    turbo_next_image, _ = anim_frame_warp(turbo_next_image, args, anim_args, keys, tween_frame_idx, depth_model, depth=depth, device=root.device, half_precision=root.half_precision)
-
-                # hybrid video motion - warps turbo_prev_image or turbo_next_image to match motion
-                if tween_frame_idx > 0:
-                    if anim_args.hybrid_motion in ['Affine', 'Perspective']:
-                        if anim_args.hybrid_motion_use_prev_img:
-                            if advance_prev:
-                                matrix = get_matrix_for_hybrid_motion_prev(tween_frame_idx, (args.W, args.H), inputfiles, turbo_prev_image, anim_args.hybrid_motion)
-                                turbo_prev_image = image_transform_ransac(turbo_prev_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                            if advance_next:
-                                matrix = get_matrix_for_hybrid_motion_prev(tween_frame_idx, (args.W, args.H), inputfiles, turbo_next_image, anim_args.hybrid_motion)
-                                turbo_next_image = image_transform_ransac(turbo_next_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                        else:
-                            matrix = get_matrix_for_hybrid_motion(tween_frame_idx-1, (args.W, args.H), inputfiles, anim_args.hybrid_motion)
-                            if advance_prev:
-                                turbo_prev_image = image_transform_ransac(turbo_prev_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                            if advance_next:
-                                turbo_next_image = image_transform_ransac(turbo_next_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                    if anim_args.hybrid_motion in ['Optical Flow']:
-                        if anim_args.hybrid_motion_use_prev_img:
-                            if advance_prev:
-                                flow = get_flow_for_hybrid_motion_prev(tween_frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, turbo_prev_image, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames)
-                                turbo_prev_image = image_transform_optical_flow(turbo_prev_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                            if advance_next:
-                                flow = get_flow_for_hybrid_motion_prev(tween_frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, turbo_next_image, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames)
-                                turbo_next_image = image_transform_optical_flow(turbo_next_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                        else:
-                            flow = get_flow_for_hybrid_motion(tween_frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames)
-                            if advance_prev:
-                                turbo_prev_image = image_transform_optical_flow(turbo_prev_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                            if advance_next:
-                                turbo_next_image = image_transform_optical_flow(turbo_next_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-                      
-                turbo_prev_frame_idx = turbo_next_frame_idx = tween_frame_idx
-
-                if turbo_prev_image is not None and tween < 1.0:
-                    img = turbo_prev_image*(1.0-tween) + turbo_next_image*tween
-                else:
-                    img = turbo_next_image
-
-                # intercept and override to grayscale
-                if anim_args.color_force_grayscale:
-                    img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY)
-                    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-                filename = f"{args.timestring}_{tween_frame_idx:05}.png"
-                cv2.imwrite(os.path.join(args.outdir, filename), img)
-                if anim_args.save_depth_maps:
-                    depth_model.save(os.path.join(args.outdir, f"{args.timestring}_depth_{tween_frame_idx:05}.png"), depth)
-            if turbo_next_image is not None:
-                prev_img = turbo_next_image
-
-        # apply transforms to previous frame
-        if prev_img is not None:
-            prev_img, depth = anim_frame_warp(prev_img, args, anim_args, keys, frame_idx, depth_model, depth=None, device=root.device, half_precision=root.half_precision)
-
-            # hybrid video motion - warps prev_img to match motion, usually to prepare for compositing
-            if frame_idx > 0:
-                if anim_args.hybrid_motion in ['Affine', 'Perspective']:
-                    if anim_args.hybrid_motion_use_prev_img:
-                        matrix = get_matrix_for_hybrid_motion_prev(frame_idx, (args.W, args.H), inputfiles, prev_img, anim_args.hybrid_motion)
-                    else:
-                        matrix = get_matrix_for_hybrid_motion(frame_idx-1, (args.W, args.H), inputfiles, anim_args.hybrid_motion)
-                    prev_img = image_transform_ransac(prev_img, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)    
-                if anim_args.hybrid_motion in ['Optical Flow']:
-                    if anim_args.hybrid_motion_use_prev_img:
-                        flow = get_flow_for_hybrid_motion_prev(frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, prev_img, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames)
-                    else:
-                        flow = get_flow_for_hybrid_motion(frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames)
-                    prev_img = image_transform_optical_flow(prev_img, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE)
-
-            # do hybrid video - composites video frame into prev_img (now warped if using motion)
-            if anim_args.hybrid_composite:
-                args, prev_img = hybrid_composite(args, anim_args, frame_idx, prev_img, depth_model, hybrid_comp_schedules, root)
-
-            # apply color matching
-            if anim_args.color_coherence != 'None':
-                # video color matching
-                hybrid_available = anim_args.hybrid_composite or anim_args.hybrid_motion in ['Optical Flow', 'Affine', 'Perspective']
-                if anim_args.color_coherence == 'Video Input' and hybrid_available:
-                    video_color_coherence_frame = int(frame_idx) % int(anim_args.color_coherence_video_every_N_frames) == 0
-                    if video_color_coherence_frame:
-                        prev_vid_img = Image.open(os.path.join(args.outdir, 'inputframes', get_frame_name(anim_args.video_init_path) + f"{frame_idx:05}.jpg"))
-                        prev_vid_img = prev_vid_img.resize((args.W, args.H), Image.Resampling.LANCZOS)
-                        color_match_sample = np.asarray(prev_vid_img)
-                        color_match_sample = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2BGR)
-                if color_match_sample is None:
-                    color_match_sample = prev_img.copy()
-                else:
-                    prev_img = maintain_colors(prev_img, color_match_sample, anim_args.color_coherence)
-
-            # intercept and override to grayscale
-            if anim_args.color_force_grayscale:
-                prev_img = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
-                prev_img = cv2.cvtColor(prev_img, cv2.COLOR_GRAY2BGR)
-
-            # apply scaling
-            contrast_image = (prev_img * contrast).round().astype(np.uint8)
-            # anti-blur
-            if amount > 0:
-                contrast_image = unsharp_mask(contrast_image, (kernel, kernel), sigma, amount, threshold, mask_image if args.use_mask else None)
-            # apply frame noising
-            if args.use_mask or anim_args.use_noise_mask:
-                args.noise_mask = compose_mask_with_check(root, args, noise_mask_seq, noise_mask_vals, Image.fromarray(cv2.cvtColor(contrast_image, cv2.COLOR_BGR2RGB)))
-            noised_image = add_noise(contrast_image, noise, args.seed, anim_args.noise_type,
-                            (anim_args.perlin_w, anim_args.perlin_h, anim_args.perlin_octaves, anim_args.perlin_persistence),
-                             args.noise_mask, args.invert_mask)
-
-            # use transformed previous frame as init for current
-            args.use_init = True
-            args.init_sample = Image.fromarray(cv2.cvtColor(noised_image, cv2.COLOR_BGR2RGB))
-            args.strength = max(0.0, min(1.0, strength))
-        
-        args.scale = scale
-       
-        # Pix2Pix Image CFG Scale - does *nothing* with non pix2pix checkpoints
-        args.pix2pix_img_cfg_scale = float(keys.pix2pix_img_cfg_scale_series[frame_idx])
-
-        # grab prompt for current frame
-        args.prompt = prompt_series[frame_idx]
-        
-        if args.seed_behavior == 'schedule' or use_parseq:
-            args.seed = int(keys.seed_schedule_series[frame_idx])
-
-        if anim_args.enable_checkpoint_scheduling:
-            args.checkpoint = keys.checkpoint_schedule_series[frame_idx]
-        else:
-            args.checkpoint = None
-            
-        #SubSeed scheduling
-        if anim_args.enable_subseed_scheduling:
-            args.subseed = int(keys.subseed_schedule_series[frame_idx])
-            args.subseed_strength = float(keys.subseed_strength_schedule_series[frame_idx])
-
-        if use_parseq:
-            args.seed_enable_extras = True
-            args.subseed = int(keys.subseed_series[frame_idx])
-            args.subseed_strength = keys.subseed_strength_series[frame_idx]
-            
-        prompt_to_print, *after_neg = args.prompt.strip().split("--neg")
-        prompt_to_print = prompt_to_print.strip()
-        after_neg = "".join(after_neg).strip()
-
-        print(f"\033[32mSeed: \033[0m{args.seed}")
-        print(f"\033[35mPrompt: \033[0m{prompt_to_print}")
-        if after_neg and after_neg.strip():
-            print(f"\033[91mNeg Prompt: \033[0m{after_neg}")
-        if not using_vid_init:
-            # print motion table to cli if anim mode = 2D or 3D
-            if anim_args.animation_mode in ['2D','3D']:
-                print_render_table(anim_args, keys, frame_idx)
-
-        # grab init image for current frame
-        elif using_vid_init:
-            init_frame = get_next_frame(args.outdir, anim_args.video_init_path, frame_idx, False)
-            print(f"Using video init frame {init_frame}")
-            args.init_image = init_frame
-        if anim_args.use_mask_video:
-            mask_vals['video_mask'] = get_mask_from_file(get_next_frame(args.outdir, anim_args.video_mask_path, frame_idx, True), args)
-
-        if args.use_mask:
-            args.mask_image = compose_mask_with_check(root, args, mask_seq, mask_vals, args.init_sample) if args.init_sample is not None else None # we need it only after the first frame anyway
-
-        # setting up some arguments for the looper
-        loop_args.imageStrength = loopSchedulesAndData.image_strength_schedule_series[frame_idx]
-        loop_args.blendFactorMax = loopSchedulesAndData.blendFactorMax_series[frame_idx]
-        loop_args.blendFactorSlope = loopSchedulesAndData.blendFactorSlope_series[frame_idx]
-        loop_args.tweeningFrameSchedule = loopSchedulesAndData.tweening_frames_schedule_series[frame_idx]
-        loop_args.colorCorrectionFactor = loopSchedulesAndData.color_correction_factor_series[frame_idx]
-        loop_args.use_looper = loopSchedulesAndData.use_looper
-        loop_args.imagesToKeyframe = loopSchedulesAndData.imagesToKeyframe
-        
-        if scheduled_clipskip is not None:
-            opts.data["CLIP_stop_at_last_layers"] = scheduled_clipskip
-        
-        if anim_args.animation_mode == '3D' and (cmd_opts.lowvram or cmd_opts.medvram):
-            depth_model.to('cpu')
-            devices.torch_gc()
-            lowvram.setup_for_low_vram(sd_model, cmd_opts.medvram)
-            sd_hijack.model_hijack.hijack(sd_model)
-        
-        # sample the diffusion model
-        image = generate(args, anim_args, loop_args, controlnet_args, root, frame_idx, sampler_name=scheduled_sampler_name)
-        patience = 10
-
-        # intercept and override to grayscale
-        if anim_args.color_force_grayscale:
-            image = ImageOps.grayscale(image)
-            image = ImageOps.colorize(image, black ="black", white ="white")
-
-        # reroll blank frame 
-        if not image.getbbox():
-            print("Blank frame detected! If you don't have the NSFW filter enabled, this may be due to a glitch!")
-            if args.reroll_blank_frames == 'reroll':
-                while not image.getbbox():
-                    print("Rerolling with +1 seed...")
-                    args.seed += 1
-                    image = generate(args, anim_args, loop_args, controlnet_args, root, frame_idx, sampler_name=scheduled_sampler_name)
-                    patience -= 1
-                    if patience == 0:
-                        print("Rerolling with +1 seed failed for 10 iterations! Try setting webui's precision to 'full' and if it fails, please report this to the devs! Interrupting...")
-                        state.interrupted = True
-                        state.current_image = image
-                        return
-            elif args.reroll_blank_frames == 'interrupt':
-                print("Interrupting to save your eyes...")
-                state.interrupted = True
-                state.current_image = image
-            image = blank_frame_reroll(image, args, root, frame_idx)
-            if image == None:
-                return
-
-        opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-        if not using_vid_init:
-            prev_img = opencv_image
-
-        if turbo_steps > 1:
-            turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx
-            turbo_next_image, turbo_next_frame_idx = opencv_image, frame_idx
-            frame_idx += turbo_steps
-        else:    
-            filename = f"{args.timestring}_{frame_idx:05}.png"
-            save_image(image, 'PIL', filename, args, video_args, root)
-
-            if anim_args.save_depth_maps:
-                if cmd_opts.lowvram or cmd_opts.medvram:
-                    lowvram.send_everything_to_cpu()
-                    sd_hijack.model_hijack.undo_hijack(sd_model)
-                    devices.torch_gc()
-                    depth_model.to(root.device)
-                depth = depth_model.predict(opencv_image, anim_args, root.half_precision)
-                depth_model.save(os.path.join(args.outdir, f"{args.timestring}_depth_{frame_idx:05}.png"), depth)
-                if cmd_opts.lowvram or cmd_opts.medvram:
-                    depth_model.to('cpu')
-                    devices.torch_gc()
-                    lowvram.setup_for_low_vram(sd_model, cmd_opts.medvram)
-                    sd_hijack.model_hijack.hijack(sd_model)
-            frame_idx += 1
-
-        state.current_image = image
-
-        args.seed = next_seed(args)
-
-def print_render_table(anim_args, keys, frame_idx):
-    from rich.table import Table
-    from rich import box
-    table = Table(padding=0, box=box.ROUNDED)
-    field_names = []
-    if anim_args.animation_mode == '2D':
-        short_zoom = round(keys.zoom_series[frame_idx], 6)
-        field_names += ["Angle", "Zoom"]
-    field_names += ["Tr X", "Tr Y"]
-    if anim_args.animation_mode == '3D':
-        field_names += ["Tr Z", "Ro X", "Ro Y", "Ro Z"]
-    if anim_args.enable_perspective_flip:
-        field_names += ["Pf T", "Pf P", "Pf G", "Pf F"]
-    for field_name in field_names:
-        table.add_column(field_name, justify="center")
-    
-    rows = []
-    if anim_args.animation_mode == '2D':
-        rows += [str(keys.angle_series[frame_idx]),str(short_zoom)]
-    rows += [str(keys.translation_x_series[frame_idx]),str(keys.translation_y_series[frame_idx])]
-    if anim_args.animation_mode == '3D':
-        rows += [str(keys.translation_z_series[frame_idx]),str(keys.rotation_3d_x_series[frame_idx]),str(keys.rotation_3d_y_series[frame_idx]),str(keys.rotation_3d_z_series[frame_idx])]
-    if anim_args.enable_perspective_flip:
-        rows +=[str(keys.perspective_flip_theta_series[frame_idx]), str(keys.perspective_flip_phi_series[frame_idx]), str(keys.perspective_flip_gamma_series[frame_idx]), str(keys.perspective_flip_fv_series[frame_idx])]
-    table.add_row(*rows)
-    
-    console.print(table)
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/nn/modules/transformer.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/nn/modules/transformer.md
deleted file mode 100644
index a927f9b806efc5b10201f98055dca4eaf61cf2c0..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/nn/modules/transformer.md
+++ /dev/null
@@ -1,54 +0,0 @@
----
-description: Explore the Ultralytics nn modules pages on Transformer and MLP blocks, LayerNorm2d, and Deformable Transformer Decoder Layer.
-keywords: Ultralytics, NN Modules, TransformerEncoderLayer, TransformerLayer, MLPBlock, LayerNorm2d, DeformableTransformerDecoderLayer, examples, code snippets, tutorials
----
-
-## TransformerEncoderLayer
----
-### ::: ultralytics.nn.modules.transformer.TransformerEncoderLayer
-<br><br>
-
-## AIFI
----
-### ::: ultralytics.nn.modules.transformer.AIFI
-<br><br>
-
-## TransformerLayer
----
-### ::: ultralytics.nn.modules.transformer.TransformerLayer
-<br><br>
-
-## TransformerBlock
----
-### ::: ultralytics.nn.modules.transformer.TransformerBlock
-<br><br>
-
-## MLPBlock
----
-### ::: ultralytics.nn.modules.transformer.MLPBlock
-<br><br>
-
-## MLP
----
-### ::: ultralytics.nn.modules.transformer.MLP
-<br><br>
-
-## LayerNorm2d
----
-### ::: ultralytics.nn.modules.transformer.LayerNorm2d
-<br><br>
-
-## MSDeformAttn
----
-### ::: ultralytics.nn.modules.transformer.MSDeformAttn
-<br><br>
-
-## DeformableTransformerDecoderLayer
----
-### ::: ultralytics.nn.modules.transformer.DeformableTransformerDecoderLayer
-<br><br>
-
-## DeformableTransformerDecoder
----
-### ::: ultralytics.nn.modules.transformer.DeformableTransformerDecoder
-<br><br>
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/loss.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/loss.md
deleted file mode 100644
index 7d89d6e5cdffe471485d4930723aee411425105a..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/loss.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO for advanced bounding box and pose estimation. Visit our docs for more.
-keywords: Ultralytics, YOLO, loss functions, object detection, keypoint detection, segmentation, classification
----
-
-## VarifocalLoss
----
-### ::: ultralytics.yolo.utils.loss.VarifocalLoss
-<br><br>
-
-## FocalLoss
----
-### ::: ultralytics.yolo.utils.loss.FocalLoss
-<br><br>
-
-## BboxLoss
----
-### ::: ultralytics.yolo.utils.loss.BboxLoss
-<br><br>
-
-## KeypointLoss
----
-### ::: ultralytics.yolo.utils.loss.KeypointLoss
-<br><br>
-
-## v8DetectionLoss
----
-### ::: ultralytics.yolo.utils.loss.v8DetectionLoss
-<br><br>
-
-## v8SegmentationLoss
----
-### ::: ultralytics.yolo.utils.loss.v8SegmentationLoss
-<br><br>
-
-## v8PoseLoss
----
-### ::: ultralytics.yolo.utils.loss.v8PoseLoss
-<br><br>
-
-## v8ClassificationLoss
----
-### ::: ultralytics.yolo.utils.loss.v8ClassificationLoss
-<br><br>
diff --git a/spaces/vasudevgupta/GOOGLE_SUMMER_OF_CODE/app.py b/spaces/vasudevgupta/GOOGLE_SUMMER_OF_CODE/app.py
deleted file mode 100644
index b58d863b84a504291c9583e1463e13efe712435a..0000000000000000000000000000000000000000
--- a/spaces/vasudevgupta/GOOGLE_SUMMER_OF_CODE/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-from wav2vec2 import Wav2Vec2Processor, Wav2Vec2ForCTC
-
-
-if __name__ == '__main__':
-    processor = Wav2Vec2Processor(is_tokenizer=False)
-    tokenizer = Wav2Vec2Processor(is_tokenizer=True)
-    model = Wav2Vec2ForCTC.from_pretrained("vasudevgupta/gsoc-wav2vec2-960h")
-
-    def _forward(speech: tf.Tensor):
-        speech = processor(speech)[None]
-        tf_out = model(speech, training=False)
-        return tf.squeeze(tf.argmax(tf_out, axis=-1))
-
-    def transcribe_text(inputs):
-        _, speech = inputs
-        speech = tf.constant(speech, dtype=tf.float32)
-        speech = tf.transpose(speech)
-        tf_out = _forward(speech)
-        return tokenizer.decode(tf_out.numpy().tolist())
-
-    gr.Interface(fn=transcribe_text, inputs="audio", outputs="text").launch()
diff --git a/spaces/veb-101/driver-drowsiness-detection/audio_handling.py b/spaces/veb-101/driver-drowsiness-detection/audio_handling.py
deleted file mode 100644
index be35a5b969c980d8323bfefbc5c84884bb79bfb2..0000000000000000000000000000000000000000
--- a/spaces/veb-101/driver-drowsiness-detection/audio_handling.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import av
-import numpy as np
-from pydub import AudioSegment
-
-
-class AudioFrameHandler:
-    """To play/pass custom audio based on some event"""
-
-    def __init__(self, sound_file_path: str = ""):
-
-        self.custom_audio = AudioSegment.from_file(file=sound_file_path, format="wav")
-        self.custom_audio_len = len(self.custom_audio)
-
-        self.ms_per_audio_segment: int = 20
-        self.audio_segment_shape: tuple
-
-        self.play_state_tracker: dict = {"curr_segment": -1}  # Currently playing segment
-        self.audio_segments_created: bool = False
-        self.audio_segments: list = []
-
-    def prepare_audio(self, frame: av.AudioFrame):
-        raw_samples = frame.to_ndarray()
-        sound = AudioSegment(
-            data=raw_samples.tobytes(),
-            sample_width=frame.format.bytes,
-            frame_rate=frame.sample_rate,
-            channels=len(frame.layout.channels),
-        )
-
-        self.ms_per_audio_segment = len(sound)
-        self.audio_segment_shape = raw_samples.shape
-
-        self.custom_audio = self.custom_audio.set_channels(sound.channels)
-        self.custom_audio = self.custom_audio.set_frame_rate(sound.frame_rate)
-        self.custom_audio = self.custom_audio.set_sample_width(sound.sample_width)
-
-        self.audio_segments = [
-            self.custom_audio[i : i + self.ms_per_audio_segment]
-            for i in range(0, self.custom_audio_len - self.custom_audio_len % self.ms_per_audio_segment, self.ms_per_audio_segment)
-        ]
-        self.total_segments = len(self.audio_segments) - 1  # -1 because we start from 0.
-
-        self.audio_segments_created = True
-
-    def process(self, frame: av.AudioFrame, play_sound: bool = False):
-
-        """
-        Takes in the current input audio frame and based on play_sound boolean value
-        either starts sending the custom audio frame or dampens the frame wave to emulate silence.
-
-        For eg. playing a notification based on some event.
-        """
-
-        if not self.audio_segments_created:
-            self.prepare_audio(frame)
-
-        raw_samples = frame.to_ndarray()
-        _curr_segment = self.play_state_tracker["curr_segment"]
-
-        if play_sound:
-            if _curr_segment < self.total_segments:
-                _curr_segment += 1
-            else:
-                _curr_segment = 0
-
-            sound = self.audio_segments[_curr_segment]
-
-        else:
-            if -1 < _curr_segment < self.total_segments:
-                _curr_segment += 1
-                sound = self.audio_segments[_curr_segment]
-            else:
-                _curr_segment = -1
-                sound = AudioSegment(
-                    data=raw_samples.tobytes(),
-                    sample_width=frame.format.bytes,
-                    frame_rate=frame.sample_rate,
-                    channels=len(frame.layout.channels),
-                )
-                sound = sound.apply_gain(-100)
-
-        self.play_state_tracker["curr_segment"] = _curr_segment
-
-        channel_sounds = sound.split_to_mono()
-        channel_samples = [s.get_array_of_samples() for s in channel_sounds]
-
-        new_samples = np.array(channel_samples).T
-
-        new_samples = new_samples.reshape(self.audio_segment_shape)
-        new_frame = av.AudioFrame.from_ndarray(new_samples, layout=frame.layout.name)
-        new_frame.sample_rate = frame.sample_rate
-
-        return new_frame
diff --git a/spaces/vict0rsch/climateGAN/climategan/transforms.py b/spaces/vict0rsch/climateGAN/climategan/transforms.py
deleted file mode 100644
index 66b4ab9356e167cc29339300c6996a1f755837aa..0000000000000000000000000000000000000000
--- a/spaces/vict0rsch/climateGAN/climategan/transforms.py
+++ /dev/null
@@ -1,626 +0,0 @@
-"""Data transforms for the loaders
-"""
-import random
-import traceback
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from skimage.color import rgba2rgb
-from skimage.io import imread
-from torchvision import transforms as trsfs
-from torchvision.transforms.functional import (
-    adjust_brightness,
-    adjust_contrast,
-    adjust_saturation,
-)
-
-from climategan.tutils import normalize
-
-
-def interpolation(task):
-    if task in ["d", "m", "s"]:
-        return {"mode": "nearest"}
-    else:
-        return {"mode": "bilinear", "align_corners": True}
-
-
-class Resize:
-    def __init__(self, target_size, keep_aspect_ratio=False):
-        """
-        Resize transform. Target_size can be an int or a tuple of ints,
-        depending on whether both height and width should have the same
-        final size or not.
-
-        If keep_aspect_ratio is specified then target_size must be an int:
-        the smallest dimension of x will be set to target_size and the largest
-        dimension will be computed to the closest int keeping the original
-        aspect ratio. e.g.
-        >>> x = torch.rand(1, 3, 1200, 1800)
-        >>> m = torch.rand(1, 1, 600, 600)
-        >>> d = {"x": x, "m": m}
-        >>> {k: v.shape for k, v in Resize(640, True)(d).items()}
-         {"x": (1, 3, 640, 960), "m": (1, 1, 640, 960)}
-
-
-
-        Args:
-            target_size (int | tuple(int)): New size for the tensor
-            keep_aspect_ratio (bool, optional): Whether or not to keep aspect ratio
-                when resizing. Requires target_size to be an int. If keeping aspect
-                ratio, smallest dim will be set to target_size. Defaults to False.
-        """
-        if isinstance(target_size, (int, tuple, list)):
-            if not isinstance(target_size, int) and not keep_aspect_ratio:
-                assert len(target_size) == 2
-                self.h, self.w = target_size
-            else:
-                if keep_aspect_ratio:
-                    assert isinstance(target_size, int)
-                self.h = self.w = target_size
-
-            self.default_h = int(self.h)
-            self.default_w = int(self.w)
-            self.sizes = {}
-        elif isinstance(target_size, dict):
-            assert (
-                not keep_aspect_ratio
-            ), "dict target_size not compatible with keep_aspect_ratio"
-
-            self.sizes = {
-                k: {"h": v, "w": v} for k, v in target_size.items() if k != "default"
-            }
-            self.default_h = int(target_size["default"])
-            self.default_w = int(target_size["default"])
-
-        self.keep_aspect_ratio = keep_aspect_ratio
-
-    def compute_new_default_size(self, tensor):
-        """
-        compute the new size for a tensor depending on target size
-        and keep_aspect_rato
-
-        Args:
-            tensor (torch.Tensor): 4D tensor N x C x H x W.
-
-        Returns:
-            tuple(int): (new_height, new_width)
-        """
-        if self.keep_aspect_ratio:
-            h, w = tensor.shape[-2:]
-            if h < w:
-                return (self.h, int(self.default_h * w / h))
-            else:
-                return (int(self.default_h * h / w), self.default_w)
-        return (self.default_h, self.default_w)
-
-    def compute_new_size_for_task(self, task):
-        assert (
-            not self.keep_aspect_ratio
-        ), "compute_new_size_for_task is not compatible with keep aspect ratio"
-
-        if task not in self.sizes:
-            return (self.default_h, self.default_w)
-
-        return (self.sizes[task]["h"], self.sizes[task]["w"])
-
-    def __call__(self, data):
-        """
-        Resize a dict of tensors to the "x" key's new_size
-
-        Args:
-            data (dict[str:torch.Tensor]): The data dict to transform
-
-        Returns:
-            dict[str: torch.Tensor]: dict with all tensors resized to the
-                new size of the data["x"] tensor
-        """
-        task = tensor = new_size = None
-        try:
-            if not self.sizes:
-                d = {}
-                new_size = self.compute_new_default_size(
-                    data["x"] if "x" in data else list(data.values())[0]
-                )
-                for task, tensor in data.items():
-                    d[task] = F.interpolate(
-                        tensor, size=new_size, **interpolation(task)
-                    )
-                return d
-
-            d = {}
-            for task, tensor in data.items():
-                new_size = self.compute_new_size_for_task(task)
-                d[task] = F.interpolate(tensor, size=new_size, **interpolation(task))
-            return d
-
-        except Exception as e:
-            tb = traceback.format_exc()
-            print("Debug: task, shape, interpolation, h, w, new_size")
-            print(task)
-            print(tensor.shape)
-            print(interpolation(task))
-            print(self.h, self.w)
-            print(new_size)
-            print(tb)
-            raise Exception(e)
-
-
-class RandomCrop:
-    def __init__(self, size, center=False):
-        assert isinstance(size, (int, tuple, list))
-        if not isinstance(size, int):
-            assert len(size) == 2
-            self.h, self.w = size
-        else:
-            self.h = self.w = size
-
-        self.h = int(self.h)
-        self.w = int(self.w)
-        self.center = center
-
-    def __call__(self, data):
-        H, W = (
-            data["x"].size()[-2:] if "x" in data else list(data.values())[0].size()[-2:]
-        )
-
-        if not self.center:
-            top = np.random.randint(0, H - self.h)
-            left = np.random.randint(0, W - self.w)
-        else:
-            top = (H - self.h) // 2
-            left = (W - self.w) // 2
-
-        return {
-            task: tensor[:, :, top : top + self.h, left : left + self.w]
-            for task, tensor in data.items()
-        }
-
-
-class RandomHorizontalFlip:
-    def __init__(self, p=0.5):
-        # self.flip = TF.hflip
-        self.p = p
-
-    def __call__(self, data):
-        if np.random.rand() > self.p:
-            return data
-        return {task: torch.flip(tensor, [3]) for task, tensor in data.items()}
-
-
-class ToTensor:
-    def __init__(self):
-        self.ImagetoTensor = trsfs.ToTensor()
-        self.MaptoTensor = self.ImagetoTensor
-
-    def __call__(self, data):
-        new_data = {}
-        for task, im in data.items():
-            if task in {"x", "a"}:
-                new_data[task] = self.ImagetoTensor(im)
-            elif task in {"m"}:
-                new_data[task] = self.MaptoTensor(im)
-            elif task == "s":
-                new_data[task] = torch.squeeze(torch.from_numpy(np.array(im))).to(
-                    torch.int64
-                )
-            elif task == "d":
-                new_data = im
-
-        return new_data
-
-
-class Normalize:
-    def __init__(self, opts):
-        if opts.data.normalization == "HRNet":
-            self.normImage = trsfs.Normalize(
-                ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-            )
-        else:
-            self.normImage = trsfs.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-        self.normDepth = lambda x: x
-        self.normMask = lambda x: x
-        self.normSeg = lambda x: x
-
-        self.normalize = {
-            "x": self.normImage,
-            "s": self.normSeg,
-            "d": self.normDepth,
-            "m": self.normMask,
-        }
-
-    def __call__(self, data):
-        return {
-            task: self.normalize.get(task, lambda x: x)(tensor.squeeze(0))
-            for task, tensor in data.items()
-        }
-
-
-class RandBrightness:  # Input need to be between -1 and 1
-    def __call__(self, data):
-        return {
-            task: rand_brightness(tensor) if task == "x" else tensor
-            for task, tensor in data.items()
-        }
-
-
-class RandSaturation:
-    def __call__(self, data):
-        return {
-            task: rand_saturation(tensor) if task == "x" else tensor
-            for task, tensor in data.items()
-        }
-
-
-class RandContrast:
-    def __call__(self, data):
-        return {
-            task: rand_contrast(tensor) if task == "x" else tensor
-            for task, tensor in data.items()
-        }
-
-
-class BucketizeDepth:
-    def __init__(self, opts, domain):
-        self.domain = domain
-
-        if opts.gen.d.classify.enable and domain in {"s", "kitti"}:
-            self.buckets = torch.linspace(
-                *[
-                    opts.gen.d.classify.linspace.min,
-                    opts.gen.d.classify.linspace.max,
-                    opts.gen.d.classify.linspace.buckets - 1,
-                ]
-            )
-
-            self.transforms = {
-                "d": lambda tensor: torch.bucketize(
-                    tensor, self.buckets, out_int32=True, right=True
-                )
-            }
-        else:
-            self.transforms = {}
-
-    def __call__(self, data):
-        return {
-            task: self.transforms.get(task, lambda x: x)(tensor)
-            for task, tensor in data.items()
-        }
-
-
-class PrepareInference:
-    """
-    Transform which:
-      - transforms a str or an array into a tensor
-      - resizes the image to keep the aspect ratio
-      - crops in the center of the resized image
-      - normalize to 0:1
-      - rescale to -1:1
-    """
-
-    def __init__(self, target_size=640, half=False, is_label=False, enforce_128=True):
-        if enforce_128:
-            if target_size % 2 ** 7 != 0:
-                raise ValueError(
-                    f"Received a target_size of {target_size}, which is not a "
-                    + "multiple of 2^7 = 128. Set enforce_128 to False to disable "
-                    + "this error."
-                )
-        self.resize = Resize(target_size, keep_aspect_ratio=True)
-        self.crop = RandomCrop((target_size, target_size), center=True)
-        self.half = half
-        self.is_label = is_label
-
-    def process(self, t):
-        if isinstance(t, (str, Path)):
-            t = imread(str(t))
-
-        if isinstance(t, np.ndarray):
-            if t.shape[-1] == 4:
-                t = rgba2rgb(t)
-
-            t = torch.from_numpy(t)
-            if t.ndim == 3:
-                t = t.permute(2, 0, 1)
-
-        if t.ndim == 3:
-            t = t.unsqueeze(0)
-        elif t.ndim == 2:
-            t = t.unsqueeze(0).unsqueeze(0)
-
-        if not self.is_label:
-            t = t.to(torch.float32)
-            t = normalize(t)
-            t = (t - 0.5) * 2
-
-        t = {"m": t} if self.is_label else {"x": t}
-        t = self.resize(t)
-        t = self.crop(t)
-        t = t["m"] if self.is_label else t["x"]
-
-        if self.half and not self.is_label:
-            t = t.half()
-
-        return t
-
-    def __call__(self, x):
-        """
-        normalize, rescale, resize, crop in the center
-
-        x can be: dict {"task": data} list [data, ..] or data
-        data ^ can be a str, a Path, a numpy arrray or a Tensor
-        """
-        if isinstance(x, dict):
-            return {k: self.process(v) for k, v in x.items()}
-
-        if isinstance(x, list):
-            return [self.process(t) for t in x]
-
-        return self.process(x)
-
-
-class PrepareTest:
-    """
-    Transform which:
-      - transforms a str or an array into a tensor
-      - resizes the image to keep the aspect ratio
-      - crops in the center of the resized image
-      - normalize to 0:1 (optional)
-      - rescale to -1:1 (optional)
-    """
-
-    def __init__(self, target_size=640, half=False):
-        self.resize = Resize(target_size, keep_aspect_ratio=True)
-        self.crop = RandomCrop((target_size, target_size), center=True)
-        self.half = half
-
-    def process(self, t, normalize=False, rescale=False):
-        if isinstance(t, (str, Path)):
-            # t = img_as_float(imread(str(t)))
-            t = imread(str(t))
-            if t.shape[-1] == 4:
-                # t = rgba2rgb(t)
-                t = t[:, :, :3]
-            if np.ndim(t) == 2:
-                t = np.repeat(t[:, :, np.newaxis], 3, axis=2)
-
-        if isinstance(t, np.ndarray):
-            t = torch.from_numpy(t)
-            t = t.permute(2, 0, 1)
-
-        if len(t.shape) == 3:
-            t = t.unsqueeze(0)
-
-        t = t.to(torch.float32)
-        normalize(t) if normalize else t
-        (t - 0.5) * 2 if rescale else t
-        t = {"x": t}
-        t = self.resize(t)
-        t = self.crop(t)
-        t = t["x"]
-
-        if self.half:
-            return t.to(torch.float16)
-
-        return t
-
-    def __call__(self, x, normalize=False, rescale=False):
-        """
-        Call process()
-
-        x can be: dict {"task": data} list [data, ..] or data
-        data ^ can be a str, a Path, a numpy arrray or a Tensor
-        """
-        if isinstance(x, dict):
-            return {k: self.process(v, normalize, rescale) for k, v in x.items()}
-
-        if isinstance(x, list):
-            return [self.process(t, normalize, rescale) for t in x]
-
-        return self.process(x, normalize, rescale)
-
-
-def get_transform(transform_item, mode):
-    """Returns the torchivion transform function associated to a
-    transform_item listed in opts.data.transforms ; transform_item is
-    an addict.Dict
-    """
-
-    if transform_item.name == "crop" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandomCrop(
-            (transform_item.height, transform_item.width),
-            center=transform_item.center == mode,
-        )
-
-    elif transform_item.name == "resize" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return Resize(
-            transform_item.new_size, transform_item.get("keep_aspect_ratio", False)
-        )
-
-    elif transform_item.name == "hflip" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandomHorizontalFlip(p=transform_item.p or 0.5)
-
-    elif transform_item.name == "brightness" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandBrightness()
-
-    elif transform_item.name == "saturation" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandSaturation()
-
-    elif transform_item.name == "contrast" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandContrast()
-
-    elif transform_item.ignore is True or transform_item.ignore == mode:
-        return None
-
-    raise ValueError("Unknown transform_item {}".format(transform_item))
-
-
-def get_transforms(opts, mode, domain):
-    """Get all the transform functions listed in opts.data.transforms
-    using get_transform(transform_item, mode)
-    """
-    transforms = []
-    color_jittering_transforms = ["brightness", "saturation", "contrast"]
-
-    for t in opts.data.transforms:
-        if t.name not in color_jittering_transforms:
-            transforms.append(get_transform(t, mode))
-
-    if "p" not in opts.tasks and mode == "train":
-        for t in opts.data.transforms:
-            if t.name in color_jittering_transforms:
-                transforms.append(get_transform(t, mode))
-
-    transforms += [Normalize(opts), BucketizeDepth(opts, domain)]
-    transforms = [t for t in transforms if t is not None]
-
-    return transforms
-
-
-# ----- Adapted functions from https://github.com/mit-han-lab/data-efficient-gans -----#
-def rand_brightness(tensor, is_diff_augment=False):
-    if is_diff_augment:
-        assert len(tensor.shape) == 4
-        type_ = tensor.dtype
-        device_ = tensor.device
-        rand_tens = torch.rand(tensor.size(0), 1, 1, 1, dtype=type_, device=device_)
-        return tensor + (rand_tens - 0.5)
-    else:
-        factor = random.uniform(0.5, 1.5)
-        tensor = adjust_brightness(tensor, brightness_factor=factor)
-        # dummy pixels to fool scaling and preserve range
-        tensor[:, :, 0, 0] = 1.0
-        tensor[:, :, -1, -1] = 0.0
-        return tensor
-
-
-def rand_saturation(tensor, is_diff_augment=False):
-    if is_diff_augment:
-        assert len(tensor.shape) == 4
-        type_ = tensor.dtype
-        device_ = tensor.device
-        rand_tens = torch.rand(tensor.size(0), 1, 1, 1, dtype=type_, device=device_)
-        x_mean = tensor.mean(dim=1, keepdim=True)
-        return (tensor - x_mean) * (rand_tens * 2) + x_mean
-    else:
-        factor = random.uniform(0.5, 1.5)
-        tensor = adjust_saturation(tensor, saturation_factor=factor)
-        # dummy pixels to fool scaling and preserve range
-        tensor[:, :, 0, 0] = 1.0
-        tensor[:, :, -1, -1] = 0.0
-        return tensor
-
-
-def rand_contrast(tensor, is_diff_augment=False):
-    if is_diff_augment:
-        assert len(tensor.shape) == 4
-        type_ = tensor.dtype
-        device_ = tensor.device
-        rand_tens = torch.rand(tensor.size(0), 1, 1, 1, dtype=type_, device=device_)
-        x_mean = tensor.mean(dim=[1, 2, 3], keepdim=True)
-        return (tensor - x_mean) * (rand_tens + 0.5) + x_mean
-    else:
-        factor = random.uniform(0.5, 1.5)
-        tensor = adjust_contrast(tensor, contrast_factor=factor)
-        # dummy pixels to fool scaling and preserve range
-        tensor[:, :, 0, 0] = 1.0
-        tensor[:, :, -1, -1] = 0.0
-        return tensor
-
-
-def rand_cutout(tensor, ratio=0.5):
-    assert len(tensor.shape) == 4, "For rand cutout, tensor must be 4D."
-    type_ = tensor.dtype
-    device_ = tensor.device
-    cutout_size = int(tensor.size(-2) * ratio + 0.5), int(tensor.size(-1) * ratio + 0.5)
-    grid_batch, grid_x, grid_y = torch.meshgrid(
-        torch.arange(tensor.size(0), dtype=torch.long, device=device_),
-        torch.arange(cutout_size[0], dtype=torch.long, device=device_),
-        torch.arange(cutout_size[1], dtype=torch.long, device=device_),
-    )
-    size_ = [tensor.size(0), 1, 1]
-    offset_x = torch.randint(
-        0,
-        tensor.size(-2) + (1 - cutout_size[0] % 2),
-        size=size_,
-        device=device_,
-    )
-    offset_y = torch.randint(
-        0,
-        tensor.size(-1) + (1 - cutout_size[1] % 2),
-        size=size_,
-        device=device_,
-    )
-    grid_x = torch.clamp(
-        grid_x + offset_x - cutout_size[0] // 2, min=0, max=tensor.size(-2) - 1
-    )
-    grid_y = torch.clamp(
-        grid_y + offset_y - cutout_size[1] // 2, min=0, max=tensor.size(-1) - 1
-    )
-    mask = torch.ones(
-        tensor.size(0), tensor.size(2), tensor.size(3), dtype=type_, device=device_
-    )
-    mask[grid_batch, grid_x, grid_y] = 0
-    return tensor * mask.unsqueeze(1)
-
-
-def rand_translation(tensor, ratio=0.125):
-    assert len(tensor.shape) == 4, "For rand translation, tensor must be 4D."
-    device_ = tensor.device
-    shift_x, shift_y = (
-        int(tensor.size(2) * ratio + 0.5),
-        int(tensor.size(3) * ratio + 0.5),
-    )
-    translation_x = torch.randint(
-        -shift_x, shift_x + 1, size=[tensor.size(0), 1, 1], device=device_
-    )
-    translation_y = torch.randint(
-        -shift_y, shift_y + 1, size=[tensor.size(0), 1, 1], device=device_
-    )
-    grid_batch, grid_x, grid_y = torch.meshgrid(
-        torch.arange(tensor.size(0), dtype=torch.long, device=device_),
-        torch.arange(tensor.size(2), dtype=torch.long, device=device_),
-        torch.arange(tensor.size(3), dtype=torch.long, device=device_),
-    )
-    grid_x = torch.clamp(grid_x + translation_x + 1, 0, tensor.size(2) + 1)
-    grid_y = torch.clamp(grid_y + translation_y + 1, 0, tensor.size(3) + 1)
-    x_pad = F.pad(tensor, [1, 1, 1, 1, 0, 0, 0, 0])
-    tensor = (
-        x_pad.permute(0, 2, 3, 1)
-        .contiguous()[grid_batch, grid_x, grid_y]
-        .permute(0, 3, 1, 2)
-    )
-    return tensor
-
-
-class DiffTransforms:
-    def __init__(self, diff_aug_opts):
-        self.do_color_jittering = diff_aug_opts.do_color_jittering
-        self.do_cutout = diff_aug_opts.do_cutout
-        self.do_translation = diff_aug_opts.do_translation
-        self.cutout_ratio = diff_aug_opts.cutout_ratio
-        self.translation_ratio = diff_aug_opts.translation_ratio
-
-    def __call__(self, tensor):
-        if self.do_color_jittering:
-            tensor = rand_brightness(tensor, is_diff_augment=True)
-            tensor = rand_contrast(tensor, is_diff_augment=True)
-            tensor = rand_saturation(tensor, is_diff_augment=True)
-        if self.do_translation:
-            tensor = rand_translation(tensor, ratio=self.translation_ratio)
-        if self.do_cutout:
-            tensor = rand_cutout(tensor, ratio=self.cutout_ratio)
-        return tensor
diff --git a/spaces/voices/VCTK_British_English_Males/README.md b/spaces/voices/VCTK_British_English_Males/README.md
deleted file mode 100644
index 1cefc89497e1e46f3dcff7b47ba594dd9346a649..0000000000000000000000000000000000000000
--- a/spaces/voices/VCTK_British_English_Males/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: VCTK_British_English_Males
-emoji: 🍕
-colorFrom: blue
-colorTo: red
-sdk: docker
-pinned: false
-license: other
----
-Hi
\ No newline at end of file
diff --git a/spaces/volhack/vits-uma-genshin-honkai/text/symbols.py b/spaces/volhack/vits-uma-genshin-honkai/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/volhack/vits-uma-genshin-honkai/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pipelines/__init__.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pipelines/__init__.py
deleted file mode 100644
index 8b9046b07bb4ddea7a707a392b42e72db7c9df67..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pipelines/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from .compose import Compose
-from .formating import (Collect, ImageToTensor, ToDataContainer, ToTensor,
-                        Transpose, to_tensor)
-from .loading import LoadAnnotations, LoadImageFromFile
-from .test_time_aug import MultiScaleFlipAug
-from .transforms import (CLAHE, AdjustGamma, Normalize, Pad,
-                         PhotoMetricDistortion, RandomCrop, RandomFlip,
-                         RandomRotate, Rerange, Resize, RGB2Gray, SegRescale)
-
-__all__ = [
-    'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
-    'Transpose', 'Collect', 'LoadAnnotations', 'LoadImageFromFile',
-    'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop',
-    'Normalize', 'SegRescale', 'PhotoMetricDistortion', 'RandomRotate',
-    'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray'
-]
diff --git a/spaces/weidacn/deepdanbooru/deepdanbooru/commands/grad_cam.py b/spaces/weidacn/deepdanbooru/deepdanbooru/commands/grad_cam.py
deleted file mode 100644
index 6e3388f8bdfed5ed83ff7909f0509448f38531ac..0000000000000000000000000000000000000000
--- a/spaces/weidacn/deepdanbooru/deepdanbooru/commands/grad_cam.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import deepdanbooru as dd
-from scipy import ndimage
-
-
-@tf.function
-def get_gradient(model, x, output_mask):
-    with tf.GradientTape() as tape:
-        output = model(x)
-        gradcam_loss = tf.reduce_sum(tf.multiply(output_mask, output))
-
-    return tape.gradient(gradcam_loss, x)
-
-
-def norm_clip_grads(grads):
-    upper_quantile = np.quantile(grads, 0.99)
-    lower_quantile = np.quantile(grads, 0.01)
-    clipped_grads = np.abs(np.clip(grads, lower_quantile, upper_quantile))
-
-    return clipped_grads / np.max(clipped_grads)
-
-
-def filter_grads(grads):
-    return ndimage.median_filter(grads, 10)
-
-
-def to_onehot(length, index):
-    value = np.zeros(shape=(1, length), dtype=np.float32)
-    value[0, index] = 1.0
-    return value
-
-
-def grad_cam(project_path, target_path, output_path, threshold):
-    # os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-
-    if not os.path.exists(target_path):
-        raise Exception(f"Target path {target_path} is not exists.")
-
-    if os.path.isfile(target_path):
-        taget_image_paths = [target_path]
-    else:
-        patterns = [
-            "*.[Pp][Nn][Gg]",
-            "*.[Jj][Pp][Gg]",
-            "*.[Jj][Pp][Ee][Gg]",
-            "*.[Gg][Ii][Ff]",
-        ]
-
-        taget_image_paths = dd.io.get_file_paths_in_directory(target_path, patterns)
-
-        taget_image_paths = dd.extra.natural_sorted(taget_image_paths)
-
-    model = dd.project.load_model_from_project(project_path)
-    tags = dd.project.load_tags_from_project(project_path)
-    width = model.input_shape[2]
-    height = model.input_shape[1]
-
-    dd.io.try_create_directory(output_path)
-
-    for image_path in taget_image_paths:
-        image = dd.data.load_image_for_evaluate(image_path, width=width, height=height)
-        image_name = os.path.splitext(os.path.basename(image_path))[0]
-
-        image_folder = os.path.join(output_path, image_name)
-        dd.io.try_create_directory(image_folder)
-
-        Image.fromarray(np.uint8(image * 255.0)).save(
-            os.path.join(image_folder, f"input.png")
-        )
-        image_for_result = image
-        image_shape = image.shape
-        y = model.predict(
-            image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
-        )[0]
-
-        result_dict = {}
-
-        estimated_tags = []
-
-        for i, tag in enumerate(tags):
-            result_dict[tag] = y[i]
-
-            if y[i] >= threshold:
-                estimated_tags.append((i, tag))
-
-        print(f"Tags of {image_path}:")
-
-        for tag in tags:
-            if result_dict[tag] >= threshold:
-                print(f"({result_dict[tag]:05.3f}) {tag}")
-
-        image = image.astype(np.float32)
-
-        for estimated_tag in estimated_tags:
-            print(f"Calculating grad-cam ... ({estimated_tag[1]})")
-            grads = get_gradient(
-                model, tf.Variable([image]), to_onehot(len(tags), estimated_tag[0])
-            )[0]
-            print("Normalizing gradients ...")
-            grads = norm_clip_grads(grads)
-            print("Filtering gradients ...")
-            grads = filter_grads(grads)
-            Image.fromarray(np.uint8(grads * 255.0)).save(
-                os.path.join(
-                    image_folder,
-                    f"result-{estimated_tag[1]}.png".replace(":", "_").replace(
-                        "/", "_"
-                    ),
-                )
-            )
-            mask_array = np.stack([np.max(grads, axis=-1)] * 3, axis=2)
-            Image.fromarray(
-                np.uint8(np.multiply(image_for_result, mask_array) * 255.0)
-            ).save(
-                os.path.join(
-                    image_folder,
-                    f"result-{estimated_tag[1]}-masked.png".replace(":", "_").replace(
-                        "/", "_"
-                    ),
-                )
-            )
diff --git a/spaces/williamberman/stable-diffusion-xl-inpainting/app.py b/spaces/williamberman/stable-diffusion-xl-inpainting/app.py
deleted file mode 100644
index 860d23375aa9d9559829874385a21f67b0640a1d..0000000000000000000000000000000000000000
--- a/spaces/williamberman/stable-diffusion-xl-inpainting/app.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import gradio as gr
-import torch
-
-from diffusers import AutoPipelineForInpainting
-import diffusers
-from share_btn import community_icon_html, loading_icon_html, share_js
-from sdxl import sdxl_diffusion_loop
-from sdxl_models import SDXLUNet, SDXLVae, SDXLControlNetPreEncodedControlnetCond
-import torchvision.transforms.functional as TF
-from diffusion import make_sigmas, set_with_tqdm
-from huggingface_hub import hf_hub_download
-import gc
-
-set_with_tqdm(True)
-
-pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16")
-pipe.text_encoder.to("cuda")
-pipe.text_encoder_2.to("cuda")
-
-comparing_unet = SDXLUNet.load(hf_hub_download("stabilityai/stable-diffusion-xl-base-1.0", "unet/diffusion_pytorch_model.fp16.safetensors"))
-comparing_vae = SDXLVae.load(hf_hub_download("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors"))
-comparing_vae.to(torch.float16)
-comparing_controlnet = SDXLControlNetPreEncodedControlnetCond.load(hf_hub_download("williamberman/sdxl_controlnet_inpainting", "sdxl_controlnet_inpaint_pre_encoded_controlnet_cond_checkpoint_200000.safetensors"))
-comparing_controlnet.to(torch.float16)
-
-gc.collect()
-torch.cuda.empty_cache()
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
-    if negative_prompt == "":
-        negative_prompt = None
-    scheduler_class_name = scheduler.split("-")[0]
-
-    add_kwargs = {}
-    if len(scheduler.split("-")) > 1:
-        add_kwargs["use_karras"] = True
-    if len(scheduler.split("-")) > 2:
-        add_kwargs["algorithm_type"] = "sde-dpmsolver++"
-
-    scheduler = getattr(diffusers, scheduler_class_name)
-    pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
-    
-    init_image = dict["image"].convert("RGB").resize((1024, 1024))
-    mask = dict["mask"].convert("RGB").resize((1024, 1024))
-    
-    pipe.vae.to('cuda')
-    pipe.unet.to('cuda')
-
-    output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
-
-    pipe.vae.to('cpu')
-    pipe.unet.to('cpu')
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    comparing_vae.to('cuda')
-    comparing_unet.to('cuda')
-    comparing_controlnet.to('cuda')
-
-    image = TF.to_tensor(dict["image"].convert("RGB").resize((1024, 1024)))
-    mask = TF.to_tensor(dict["mask"].convert("L").resize((1024, 1024)))
-    image = image * (mask < 0.5)
-    image = TF.normalize(image, [0.5], [0.5])
-    image = comparing_vae.encode(image[None, :, :, :].to(dtype=comparing_vae.dtype, device=comparing_vae.device)).to(dtype=comparing_controlnet.dtype, device=comparing_controlnet.device)
-    mask = TF.resize(mask, (1024 // 8, 1024 // 8))[None, :, :, :].to(dtype=image.dtype, device=image.device)
-    image = torch.concat((image, mask), dim=1)
-
-    sigmas = make_sigmas(device=comparing_unet.device).to(dtype=comparing_unet.dtype)
-    timesteps = torch.linspace(0, sigmas.numel() - 1, int(steps), dtype=torch.long, device=comparing_unet.device)
-
-    out = sdxl_diffusion_loop(
-        prompts=prompt, negative_prompts=negative_prompt, images=image, guidance_scale=guidance_scale, sigmas=sigmas, timesteps=timesteps,
-        text_encoder_one=pipe.text_encoder, text_encoder_two=pipe.text_encoder_2, unet=comparing_unet, controlnet=comparing_controlnet
-    )
-
-    comparing_unet.to('cpu')
-    comparing_controlnet.to('cpu')
-
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    out = comparing_vae.output_tensor_to_pil(comparing_vae.decode(out))
-
-    comparing_vae.to('cpu')
-
-    gc.collect()
-    torch.cuda.empty_cache()
-    
-    return output.images[0], out[0], gr.update(visible=True)
-
-
-css = '''
-.gradio-container{max-width: 1100px !important}
-#image_upload{min-height:400px}
-#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
-#mask_radio .gr-form{background:transparent; border: none}
-#word_mask{margin-top: .75em !important}
-#word_mask textarea:disabled{opacity: 0.3}
-.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
-.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
-.dark .footer {border-color: #303030}
-.dark .footer>p {background: #0b0f19}
-.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
-#image_upload .touch-none{display: flex}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
-div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
-#share-btn-container:hover {background-color: #060606}
-#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
-#share-btn * {all: unset}
-#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
-#share-btn-container .wrap {display: none !important}
-#share-btn-container.hidden {display: none!important}
-#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
-#run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px;
-    border-top-left-radius: 0px;}
-#prompt-container{margin-top:-18px;}
-#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
-#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
-'''
-
-image_blocks = gr.Blocks(css=css, elem_id="total-container")
-with image_blocks as demo:
-    gr.HTML(read_content("header.html"))
-    with gr.Row():
-                with gr.Column():
-                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload",height=400)
-                    with gr.Row(elem_id="prompt-container", mobile_collapse=False, equal_height=True):
-                        with gr.Row():
-                            prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt")
-                            btn = gr.Button("Inpaint!", elem_id="run_button")
-                    
-                    with gr.Accordion(label="Advanced Settings", open=False):
-                        with gr.Row(mobile_collapse=False, equal_height=True):
-                            guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
-                            steps = gr.Number(value=20, minimum=1, maximum=1000, step=1, label="steps")
-                            strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
-                            negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
-                        with gr.Row(mobile_collapse=False, equal_height=True):
-                            schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
-                            scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
-                        
-                with gr.Column():
-                    image_out = gr.Image(label="Output diffusers full finetune 0.1", elem_id="output-img", height=400)
-                    image_out_comparing = gr.Image(label="Output controlnet + vae", elem_id="output-img-comparing", height=400)
-                    with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container:
-                        community_icon = gr.HTML(community_icon_html)
-                        loading_icon = gr.HTML(loading_icon_html)
-                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
-            
-
-    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, image_out_comparing, share_btn_container], api_name='run')
-    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, image_out_comparing, share_btn_container])
-    share_button.click(None, [], [], _js=share_js)
-
-    gr.Examples(
-                examples=[
-                    ["./imgs/aaa (8).png"],
-                    ["./imgs/download (1).jpeg"],
-                    ["./imgs/0_oE0mLhfhtS_3Nfm2.png"],
-                    ["./imgs/02_HubertyBlog-1-1024x1024.jpg"],
-                    ["./imgs/jdn_jacques_de_nuce-1024x1024.jpg"],
-                    ["./imgs/c4ca473acde04280d44128ad8ee09e8a.jpg"],
-                    ["./imgs/canam-electric-motorcycles-scaled.jpg"],
-                    ["./imgs/e8717ce80b394d1b9a610d04a1decd3a.jpeg"],
-                    ["./imgs/Nature___Mountains_Big_Mountain_018453_31.jpg"],
-                    ["./imgs/Multible-sharing-room_ccexpress-2-1024x1024.jpeg"],
-                ],
-                fn=predict,
-                inputs=[image],
-                cache_examples=False,
-    )
-    gr.HTML(
-        """
-            <div class="footer">
-                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
-                </p>
-            </div>
-        """
-    )
-
-image_blocks.queue(max_size=25).launch()
\ No newline at end of file
diff --git a/spaces/xiaohuolong/ChuanhuChatGPT/chatgpt - windows.bat b/spaces/xiaohuolong/ChuanhuChatGPT/chatgpt - windows.bat
deleted file mode 100644
index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000
--- a/spaces/xiaohuolong/ChuanhuChatGPT/chatgpt - windows.bat	
+++ /dev/null
@@ -1,14 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
-
-REM The web page can be accessed with delayed start http://127.0.0.1:7860/
-ping -n 5 127.0.0.1>nul
-
-REM access chargpt via your default browser
-start "" "http://127.0.0.1:7860/"
-
-
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/).
\ No newline at end of file
diff --git a/spaces/xiaoxicc/susu/chatgpt - macOS.command b/spaces/xiaoxicc/susu/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/xiaoxicc/susu/chatgpt - macOS.command	
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/yaoshining/text-generation-webui/docs/Spell-book.md b/spaces/yaoshining/text-generation-webui/docs/Spell-book.md
deleted file mode 100644
index 9b7c76c953f76f8a486bbe5156de4e9ebb3f0ec0..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/docs/Spell-book.md
+++ /dev/null
@@ -1,107 +0,0 @@
-You have now entered a hidden corner of the internet.
-
-A confusing yet intriguing realm of paradoxes and contradictions.
-
-A place where you will find out that what you thought you knew, you in fact didn't know, and what you didn't know was in front of you all along.
-
-![](https://i.pinimg.com/originals/6e/e2/7b/6ee27bad351d3aca470d80f1033ba9c6.jpg)
-
-*In other words, here I will document little-known facts about this web UI that I could not find another place for in the wiki.*
-
-#### You can train LoRAs in CPU mode
-
-Load the web UI with
-
-```
-python server.py --cpu
-```
-
-and start training the LoRA from the training tab as usual.
-
-#### 8-bit mode works with CPU offloading
-
-```
-python server.py --load-in-8bit --gpu-memory 4000MiB
-```
-
-#### `--pre_layer`, and not `--gpu-memory`, is the right way to do CPU offloading with 4-bit models
-
-```
-python server.py --wbits 4 --groupsize 128 --pre_layer 20
-```
-
-#### Models can be loaded in 32-bit, 16-bit, 8-bit, and 4-bit modes
-
-```
-python server.py --cpu
-python server.py
-python server.py --load-in-8bit
-python server.py --wbits 4
-```
-
-#### The web UI works with any version of GPTQ-for-LLaMa
-
-Including the up to date triton and cuda branches. But you have to delete the `repositories/GPTQ-for-LLaMa` folder and reinstall the new one every time:
-
-```
-cd text-generation-webui/repositories
-rm -r GPTQ-for-LLaMa
-pip uninstall quant-cuda
-git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda # or any other repository and branch
-cd GPTQ-for-LLaMa
-python setup_cuda.py install
-```
-
-#### Instruction-following templates are represented as chat characters
-
-https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following
-
-#### The right way to run Alpaca, Open Assistant, Vicuna, etc is Instruct mode, not normal chat mode
-
-Otherwise the prompt will not be formatted correctly.
-
-1. Start the web UI with
-
-```
-python server.py --chat
-```
-
-2. Click on the "instruct" option under "Chat modes"
-
-3. Select the correct template in the hidden dropdown menu that will become visible. 
-
-#### Notebook mode is best mode
-
-Ascended individuals have realized that notebook mode is the superset of chat mode and can do chats with ultimate flexibility, including group chats, editing replies, starting a new bot reply in a given way, and impersonating.
-
-#### RWKV is a RNN 
-
-Most models are transformers, but not RWKV, which is a RNN. It's a great model.
-
-#### `--gpu-memory` is not a hard limit on the GPU memory
-
-It is simply a parameter that is passed to the `accelerate` library while loading the model. More memory will be allocated during generation. That's why this parameter has to be set to less than your total GPU memory.
-
-#### Contrastive search perhaps the best preset
-
-But it uses a ton of VRAM.
-
-#### You can check the sha256sum of downloaded models with the download script
-
-```
-python download-model.py facebook/galactica-125m --check
-```
-
-#### The download script continues interrupted downloads by default
-
-It doesn't start over.
-
-#### You can download models with multiple threads
-
-```
-python download-model.py facebook/galactica-125m --threads 8
-```
-
-#### LoRAs work in 4-bit mode
-
-You need to follow [these instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) and then start the web UI with the `--monkey-patch` flag.
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/TempoGraph/TempoGraphItem.ts b/spaces/yderre-aubay/midi-player-demo/src/main/components/TempoGraph/TempoGraphItem.ts
deleted file mode 100644
index 051745dcd68e92dcca67c7d941217853f51483d6..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/TempoGraph/TempoGraphItem.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { IRect } from "../../../common/geometry"
-
-export interface TempoGraphItem {
-  id: number
-  bounds: IRect
-  microsecondsPerBeat: number
-}
diff --git a/spaces/ygangang/VToonify/vtoonify/model/bisenet/model.py b/spaces/ygangang/VToonify/vtoonify/model/bisenet/model.py
deleted file mode 100644
index e61c0eb20aaa63065cc17bbcfe27b245f1f0dbf5..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/bisenet/model.py
+++ /dev/null
@@ -1,283 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from model.bisenet.resnet import Resnet18
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-
-class ConvBNReLU(nn.Module):
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan,
-                out_chan,
-                kernel_size = ks,
-                stride = stride,
-                padding = padding,
-                bias = False)
-        self.bn = nn.BatchNorm2d(out_chan)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-class BiSeNetOutput(nn.Module):
-    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.conv_out(x)
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class AttentionRefinementModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-
-class ContextPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(ContextPath, self).__init__()
-        self.resnet = Resnet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-        self.init_weight()
-
-    def forward(self, x):
-        H0, W0 = x.size()[2:]
-        feat8, feat16, feat32 = self.resnet(x)
-        H8, W8 = feat8.size()[2:]
-        H16, W16 = feat16.size()[2:]
-        H32, W32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-### This is not used, since I replace this with the resnet feature with the same size
-class SpatialPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(SpatialPath, self).__init__()
-        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
-        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv1(x)
-        feat = self.conv2(feat)
-        feat = self.conv3(feat)
-        feat = self.conv_out(feat)
-        return feat
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class FeatureFusionModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan,
-                out_chan//4,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.conv2 = nn.Conv2d(out_chan//4,
-                out_chan,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class BiSeNet(nn.Module):
-    def __init__(self, n_classes, *args, **kwargs):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        ## here self.sp is deleted
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, n_classes)
-        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
-        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
-        self.init_weight()
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
-        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        feat_out = self.conv_out(feat_fuse)
-        feat_out16 = self.conv_out16(feat_cp8)
-        feat_out32 = self.conv_out32(feat_cp16)
-
-        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
-        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
-        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
-        return feat_out, feat_out16, feat_out32
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
-        for name, child in self.named_children():
-            child_wd_params, child_nowd_params = child.get_params()
-            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
-                lr_mul_wd_params += child_wd_params
-                lr_mul_nowd_params += child_nowd_params
-            else:
-                wd_params += child_wd_params
-                nowd_params += child_nowd_params
-        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
-
-
-if __name__ == "__main__":
-    net = BiSeNet(19)
-    net.cuda()
-    net.eval()
-    in_ten = torch.randn(16, 3, 640, 480).cuda()
-    out, out16, out32 = net(in_ten)
-    print(out.shape)
-
-    net.get_params()
diff --git a/spaces/yixin6178/ChatPaper/serve_grobid.sh b/spaces/yixin6178/ChatPaper/serve_grobid.sh
deleted file mode 100644
index 5316b64ca01eca562191fed7c9ade61b21de101a..0000000000000000000000000000000000000000
--- a/spaces/yixin6178/ChatPaper/serve_grobid.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-# download GROBID if directory does not exist
-declare -r GROBID_VERSION="0.6.2" # or change to current stable version
-
-if [ ! -d grobid-${GROBID_VERSION} ]; then
-  wget https://github.com/kermitt2/grobid/archive/${GROBID_VERSION}.zip
-  sudo chmod 777 "${GROBID_VERSION}.zip"
-  unzip "${GROBID_VERSION}.zip"
-  rm "${GROBID_VERSION}.zip"
-fi
-cd grobid-${GROBID_VERSION} || exit
-sudo chmod 777 gradlew
-./gradlew
\ No newline at end of file
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/kernels/yoso/fast_lsh_cumulation.h b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/kernels/yoso/fast_lsh_cumulation.h
deleted file mode 100644
index dd48de0ed159f49ee3afe93b12aaae719fe87688..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/kernels/yoso/fast_lsh_cumulation.h
+++ /dev/null
@@ -1,71 +0,0 @@
-#include <torch/extension.h>
-#include <ATen/ATen.h>
-#include <vector>
-
-std::vector<at::Tensor> fast_hash_ver1_kernel(
-  at::Tensor query_mask,
-  at::Tensor query_vector,
-  at::Tensor key_mask,
-  at::Tensor key_vector,
-  int num_hash_f,
-  int hash_code_len,
-  bool use_cuda
-);
-
-at::Tensor lsh_cumulation_ver1_kernel(
-  at::Tensor query_mask,
-  at::Tensor query_hash_code,
-  at::Tensor key_mask,
-  at::Tensor key_hash_code,
-  at::Tensor value,
-  int hashtable_capacity,
-  bool use_cuda
-);
-
-at::Tensor lsh_weighted_cumulation_ver1_kernel(
-  at::Tensor query_mask,
-  at::Tensor query_hash_code,
-  at::Tensor query_weight,
-  at::Tensor key_mask,
-  at::Tensor key_hash_code,
-  at::Tensor key_weight,
-  at::Tensor value,
-  int hashtable_capacity,
-  bool use_cuda
-);
-
-at::Tensor lsh_weighted_cumulation_ver2_kernel(
-  at::Tensor query_mask,
-  at::Tensor query_hash_code,
-  at::Tensor query_weight,
-  at::Tensor key_mask,
-  at::Tensor key_hash_code,
-  at::Tensor key_weight,
-  at::Tensor value,
-  int hashtable_capacity,
-  bool use_cuda
-);
-
-at::Tensor lsh_weighted_cumulation_ver3_kernel(
-  at::Tensor query_mask,
-  at::Tensor query_hash_code,
-  at::Tensor query_weight,
-  at::Tensor key_mask,
-  at::Tensor key_hash_code,
-  at::Tensor key_weight,
-  at::Tensor value,
-  int hashtable_capacity,
-  bool use_cuda
-);
-
-at::Tensor lsh_weighted_cumulation_ver4_kernel(
-  at::Tensor query_mask,
-  at::Tensor query_hash_code,
-  at::Tensor query_weight,
-  at::Tensor key_mask,
-  at::Tensor key_hash_code,
-  at::Tensor key_weight,
-  at::Tensor value,
-  int hashtable_capacity,
-  bool use_cuda
-);
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/auto/image_processing_auto.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/auto/image_processing_auto.py
deleted file mode 100644
index 7eeb1392d8e0b94d39efedb495b337bbcc9f9c0b..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/auto/image_processing_auto.py
+++ /dev/null
@@ -1,419 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" AutoImageProcessor class."""
-import importlib
-import json
-import os
-import warnings
-from collections import OrderedDict
-from typing import Dict, Optional, Union
-
-# Build the list of all image processors
-from ...configuration_utils import PretrainedConfig
-from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
-from ...image_processing_utils import ImageProcessingMixin
-from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
-from .auto_factory import _LazyAutoMapping
-from .configuration_auto import (
-    CONFIG_MAPPING_NAMES,
-    AutoConfig,
-    model_type_to_module_name,
-    replace_list_option_in_docstrings,
-)
-
-
-logger = logging.get_logger(__name__)
-
-IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
-    [
-        ("align", "EfficientNetImageProcessor"),
-        ("beit", "BeitImageProcessor"),
-        ("bit", "BitImageProcessor"),
-        ("blip", "BlipImageProcessor"),
-        ("blip-2", "BlipImageProcessor"),
-        ("bridgetower", "BridgeTowerImageProcessor"),
-        ("chinese_clip", "ChineseCLIPImageProcessor"),
-        ("clip", "CLIPImageProcessor"),
-        ("clipseg", "ViTImageProcessor"),
-        ("conditional_detr", "ConditionalDetrImageProcessor"),
-        ("convnext", "ConvNextImageProcessor"),
-        ("convnextv2", "ConvNextImageProcessor"),
-        ("cvt", "ConvNextImageProcessor"),
-        ("data2vec-vision", "BeitImageProcessor"),
-        ("deformable_detr", "DeformableDetrImageProcessor"),
-        ("deit", "DeiTImageProcessor"),
-        ("deta", "DetaImageProcessor"),
-        ("detr", "DetrImageProcessor"),
-        ("dinat", "ViTImageProcessor"),
-        ("dinov2", "BitImageProcessor"),
-        ("donut-swin", "DonutImageProcessor"),
-        ("dpt", "DPTImageProcessor"),
-        ("efficientformer", "EfficientFormerImageProcessor"),
-        ("efficientnet", "EfficientNetImageProcessor"),
-        ("flava", "FlavaImageProcessor"),
-        ("focalnet", "BitImageProcessor"),
-        ("git", "CLIPImageProcessor"),
-        ("glpn", "GLPNImageProcessor"),
-        ("groupvit", "CLIPImageProcessor"),
-        ("idefics", "IdeficsImageProcessor"),
-        ("imagegpt", "ImageGPTImageProcessor"),
-        ("instructblip", "BlipImageProcessor"),
-        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
-        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
-        ("levit", "LevitImageProcessor"),
-        ("mask2former", "Mask2FormerImageProcessor"),
-        ("maskformer", "MaskFormerImageProcessor"),
-        ("mgp-str", "ViTImageProcessor"),
-        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
-        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
-        ("mobilevit", "MobileViTImageProcessor"),
-        ("mobilevit", "MobileViTImageProcessor"),
-        ("mobilevitv2", "MobileViTImageProcessor"),
-        ("nat", "ViTImageProcessor"),
-        ("nougat", "NougatImageProcessor"),
-        ("oneformer", "OneFormerImageProcessor"),
-        ("owlvit", "OwlViTImageProcessor"),
-        ("perceiver", "PerceiverImageProcessor"),
-        ("pix2struct", "Pix2StructImageProcessor"),
-        ("poolformer", "PoolFormerImageProcessor"),
-        ("pvt", "PvtImageProcessor"),
-        ("regnet", "ConvNextImageProcessor"),
-        ("resnet", "ConvNextImageProcessor"),
-        ("sam", "SamImageProcessor"),
-        ("segformer", "SegformerImageProcessor"),
-        ("swiftformer", "ViTImageProcessor"),
-        ("swin", "ViTImageProcessor"),
-        ("swin2sr", "Swin2SRImageProcessor"),
-        ("swinv2", "ViTImageProcessor"),
-        ("table-transformer", "DetrImageProcessor"),
-        ("timesformer", "VideoMAEImageProcessor"),
-        ("tvlt", "TvltImageProcessor"),
-        ("upernet", "SegformerImageProcessor"),
-        ("van", "ConvNextImageProcessor"),
-        ("videomae", "VideoMAEImageProcessor"),
-        ("vilt", "ViltImageProcessor"),
-        ("vit", "ViTImageProcessor"),
-        ("vit_hybrid", "ViTHybridImageProcessor"),
-        ("vit_mae", "ViTImageProcessor"),
-        ("vit_msn", "ViTImageProcessor"),
-        ("vitmatte", "VitMatteImageProcessor"),
-        ("xclip", "CLIPImageProcessor"),
-        ("yolos", "YolosImageProcessor"),
-    ]
-)
-
-IMAGE_PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
-
-
-def image_processor_class_from_name(class_name: str):
-    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
-        if class_name in extractors:
-            module_name = model_type_to_module_name(module_name)
-
-            module = importlib.import_module(f".{module_name}", "transformers.models")
-            try:
-                return getattr(module, class_name)
-            except AttributeError:
-                continue
-
-    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
-        if getattr(extractor, "__name__", None) == class_name:
-            return extractor
-
-    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
-    # init and we return the proper dummy to get an appropriate error message.
-    main_module = importlib.import_module("transformers")
-    if hasattr(main_module, class_name):
-        return getattr(main_module, class_name)
-
-    return None
-
-
-def get_image_processor_config(
-    pretrained_model_name_or_path: Union[str, os.PathLike],
-    cache_dir: Optional[Union[str, os.PathLike]] = None,
-    force_download: bool = False,
-    resume_download: bool = False,
-    proxies: Optional[Dict[str, str]] = None,
-    token: Optional[Union[bool, str]] = None,
-    revision: Optional[str] = None,
-    local_files_only: bool = False,
-    **kwargs,
-):
-    """
-    Loads the image processor configuration from a pretrained model image processor configuration.
-
-    Args:
-        pretrained_model_name_or_path (`str` or `os.PathLike`):
-            This can be either:
-
-            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
-              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
-              under a user or organization name, like `dbmdz/bert-base-german-cased`.
-            - a path to a *directory* containing a configuration file saved using the
-              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
-
-        cache_dir (`str` or `os.PathLike`, *optional*):
-            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
-            cache should not be used.
-        force_download (`bool`, *optional*, defaults to `False`):
-            Whether or not to force to (re-)download the configuration files and override the cached versions if they
-            exist.
-        resume_download (`bool`, *optional*, defaults to `False`):
-            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
-        proxies (`Dict[str, str]`, *optional*):
-            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
-        token (`str` or *bool*, *optional*):
-            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-            when running `huggingface-cli login` (stored in `~/.huggingface`).
-        revision (`str`, *optional*, defaults to `"main"`):
-            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-            git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-            identifier allowed by git.
-        local_files_only (`bool`, *optional*, defaults to `False`):
-            If `True`, will only try to load the image processor configuration from local files.
-
-    <Tip>
-
-    Passing `token=True` is required when you want to use a private model.
-
-    </Tip>
-
-    Returns:
-        `Dict`: The configuration of the image processor.
-
-    Examples:
-
-    ```python
-    # Download configuration from huggingface.co and cache.
-    image_processor_config = get_image_processor_config("bert-base-uncased")
-    # This model does not have a image processor config so the result will be an empty dict.
-    image_processor_config = get_image_processor_config("xlm-roberta-base")
-
-    # Save a pretrained image processor locally and you can reload its config
-    from transformers import AutoTokenizer
-
-    image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
-    image_processor.save_pretrained("image-processor-test")
-    image_processor_config = get_image_processor_config("image-processor-test")
-    ```"""
-    use_auth_token = kwargs.pop("use_auth_token", None)
-    if use_auth_token is not None:
-        warnings.warn(
-            "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning
-        )
-        if token is not None:
-            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
-        token = use_auth_token
-
-    resolved_config_file = get_file_from_repo(
-        pretrained_model_name_or_path,
-        IMAGE_PROCESSOR_NAME,
-        cache_dir=cache_dir,
-        force_download=force_download,
-        resume_download=resume_download,
-        proxies=proxies,
-        token=token,
-        revision=revision,
-        local_files_only=local_files_only,
-    )
-    if resolved_config_file is None:
-        logger.info(
-            "Could not locate the image processor configuration file, will try to use the model config instead."
-        )
-        return {}
-
-    with open(resolved_config_file, encoding="utf-8") as reader:
-        return json.load(reader)
-
-
-class AutoImageProcessor:
-    r"""
-    This is a generic image processor class that will be instantiated as one of the image processor classes of the
-    library when created with the [`AutoImageProcessor.from_pretrained`] class method.
-
-    This class cannot be instantiated directly using `__init__()` (throws an error).
-    """
-
-    def __init__(self):
-        raise EnvironmentError(
-            "AutoImageProcessor is designed to be instantiated "
-            "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."
-        )
-
-    @classmethod
-    @replace_list_option_in_docstrings(IMAGE_PROCESSOR_MAPPING_NAMES)
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        r"""
-        Instantiate one of the image processor classes of the library from a pretrained model vocabulary.
-
-        The image processor class to instantiate is selected based on the `model_type` property of the config object
-        (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's
-        missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
-        List options
-
-        Params:
-            pretrained_model_name_or_path (`str` or `os.PathLike`):
-                This can be either:
-
-                - a string, the *model id* of a pretrained image_processor hosted inside a model repo on
-                  huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
-                  namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
-                - a path to a *directory* containing a image processor file saved using the
-                  [`~image_processing_utils.ImageProcessingMixin.save_pretrained`] method, e.g.,
-                  `./my_model_directory/`.
-                - a path or url to a saved image processor JSON *file*, e.g.,
-                  `./my_model_directory/preprocessor_config.json`.
-            cache_dir (`str` or `os.PathLike`, *optional*):
-                Path to a directory in which a downloaded pretrained model image processor should be cached if the
-                standard cache should not be used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force to (re-)download the image processor files and override the cached versions if
-                they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received file. Attempts to resume the download if such a file
-                exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
-            token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `huggingface-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
-                If `False`, then this function returns just the final image processor object. If `True`, then this
-                functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary
-                consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
-                `kwargs` which has not been used to update `image_processor` and is otherwise ignored.
-            trust_remote_code (`bool`, *optional*, defaults to `False`):
-                Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
-                should only be set to `True` for repositories you trust and in which you have read the code, as it will
-                execute code present on the Hub on your local machine.
-            kwargs (`Dict[str, Any]`, *optional*):
-                The values in kwargs of any keys which are image processor attributes will be used to override the
-                loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is
-                controlled by the `return_unused_kwargs` keyword parameter.
-
-        <Tip>
-
-        Passing `token=True` is required when you want to use a private model.
-
-        </Tip>
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor
-
-        >>> # Download image processor from huggingface.co and cache.
-        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
-
-        >>> # If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
-        >>> # image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")
-        ```"""
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        if use_auth_token is not None:
-            warnings.warn(
-                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning
-            )
-            if kwargs.get("token", None) is not None:
-                raise ValueError(
-                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
-                )
-            kwargs["token"] = use_auth_token
-
-        config = kwargs.pop("config", None)
-        trust_remote_code = kwargs.pop("trust_remote_code", None)
-        kwargs["_from_auto"] = True
-
-        config_dict, _ = ImageProcessingMixin.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)
-        image_processor_class = config_dict.get("image_processor_type", None)
-        image_processor_auto_map = None
-        if "AutoImageProcessor" in config_dict.get("auto_map", {}):
-            image_processor_auto_map = config_dict["auto_map"]["AutoImageProcessor"]
-
-        # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
-        # and if so, infer the image processor class from there.
-        if image_processor_class is None and image_processor_auto_map is None:
-            feature_extractor_class = config_dict.pop("feature_extractor_type", None)
-            if feature_extractor_class is not None:
-                logger.warning(
-                    "Could not find image processor class in the image processor config or the model config. Loading"
-                    " based on pattern matching with the model's feature extractor configuration."
-                )
-                image_processor_class = feature_extractor_class.replace("FeatureExtractor", "ImageProcessor")
-            if "AutoFeatureExtractor" in config_dict.get("auto_map", {}):
-                feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"]
-                image_processor_auto_map = feature_extractor_auto_map.replace("FeatureExtractor", "ImageProcessor")
-                logger.warning(
-                    "Could not find image processor auto map in the image processor config or the model config."
-                    " Loading based on pattern matching with the model's feature extractor configuration."
-                )
-
-        # If we don't find the image processor class in the image processor config, let's try the model config.
-        if image_processor_class is None and image_processor_auto_map is None:
-            if not isinstance(config, PretrainedConfig):
-                config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-            # It could be in `config.image_processor_type``
-            image_processor_class = getattr(config, "image_processor_type", None)
-            if hasattr(config, "auto_map") and "AutoImageProcessor" in config.auto_map:
-                image_processor_auto_map = config.auto_map["AutoImageProcessor"]
-
-        if image_processor_class is not None:
-            image_processor_class = image_processor_class_from_name(image_processor_class)
-
-        has_remote_code = image_processor_auto_map is not None
-        has_local_code = image_processor_class is not None or type(config) in IMAGE_PROCESSOR_MAPPING
-        trust_remote_code = resolve_trust_remote_code(
-            trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
-        )
-
-        if has_remote_code and trust_remote_code:
-            image_processor_class = get_class_from_dynamic_module(
-                image_processor_auto_map, pretrained_model_name_or_path, **kwargs
-            )
-            _ = kwargs.pop("code_revision", None)
-            if os.path.isdir(pretrained_model_name_or_path):
-                image_processor_class.register_for_auto_class()
-            return image_processor_class.from_dict(config_dict, **kwargs)
-        elif image_processor_class is not None:
-            return image_processor_class.from_dict(config_dict, **kwargs)
-        # Last try: we use the IMAGE_PROCESSOR_MAPPING.
-        elif type(config) in IMAGE_PROCESSOR_MAPPING:
-            image_processor_class = IMAGE_PROCESSOR_MAPPING[type(config)]
-            return image_processor_class.from_dict(config_dict, **kwargs)
-
-        raise ValueError(
-            f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
-            f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
-            f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}"
-        )
-
-    @staticmethod
-    def register(config_class, image_processor_class, exist_ok=False):
-        """
-        Register a new image processor for this class.
-
-        Args:
-            config_class ([`PretrainedConfig`]):
-                The configuration corresponding to the model to register.
-            image_processor_class ([`ImageProcessingMixin`]): The image processor to register.
-        """
-        IMAGE_PROCESSOR_MAPPING.register(config_class, image_processor_class, exist_ok=exist_ok)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bigbird_pegasus/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bigbird_pegasus/__init__.py
deleted file mode 100644
index c4245496e73dc29e53e8436d2e48b51e1b0d1fde..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/bigbird_pegasus/__init__.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright 2021 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
-
-
-_import_structure = {
-    "configuration_bigbird_pegasus": [
-        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
-        "BigBirdPegasusConfig",
-        "BigBirdPegasusOnnxConfig",
-    ],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_bigbird_pegasus"] = [
-        "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "BigBirdPegasusForCausalLM",
-        "BigBirdPegasusForConditionalGeneration",
-        "BigBirdPegasusForQuestionAnswering",
-        "BigBirdPegasusForSequenceClassification",
-        "BigBirdPegasusModel",
-        "BigBirdPegasusPreTrainedModel",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_bigbird_pegasus import (
-        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
-        BigBirdPegasusConfig,
-        BigBirdPegasusOnnxConfig,
-    )
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_bigbird_pegasus import (
-            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
-            BigBirdPegasusForCausalLM,
-            BigBirdPegasusForConditionalGeneration,
-            BigBirdPegasusForQuestionAnswering,
-            BigBirdPegasusForSequenceClassification,
-            BigBirdPegasusModel,
-            BigBirdPegasusPreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v2/image_processing_mobilenet_v2.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v2/image_processing_mobilenet_v2.py
deleted file mode 100644
index 9b015c88bf1d692d87babe1833e37d330351468e..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mobilenet_v2/image_processing_mobilenet_v2.py
+++ /dev/null
@@ -1,346 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Image processor class for MobileNetV2."""
-
-from typing import Dict, List, Optional, Tuple, Union
-
-import numpy as np
-
-from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
-from ...image_transforms import (
-    get_resize_output_image_size,
-    resize,
-    to_channel_dimension_format,
-)
-from ...image_utils import (
-    IMAGENET_STANDARD_MEAN,
-    IMAGENET_STANDARD_STD,
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    infer_channel_dimension_format,
-    is_scaled_image,
-    make_list_of_images,
-    to_numpy_array,
-    valid_images,
-)
-from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
-
-
-if is_torch_available():
-    import torch
-
-
-logger = logging.get_logger(__name__)
-
-
-class MobileNetV2ImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a MobileNetV2 image processor.
-
-    Args:
-        do_resize (`bool`, *optional*, defaults to `True`):
-            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
-            `do_resize` in the `preprocess` method.
-        size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`):
-            Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
-            the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
-            method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
-            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
-            `preprocess` method.
-        do_center_crop (`bool`, *optional*, defaults to `True`):
-            Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
-            is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the
-            `preprocess` method.
-        crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
-            Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
-            Can be overridden by the `crop_size` parameter in the `preprocess` method.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
-            parameter in the `preprocess` method.
-        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
-            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
-            `preprocess` method.
-        do_normalize:
-            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
-            method.
-        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
-            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
-            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-    """
-
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-        self,
-        do_resize: bool = True,
-        size: Optional[Dict[str, int]] = None,
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
-        do_center_crop: bool = True,
-        crop_size: Dict[str, int] = None,
-        do_rescale: bool = True,
-        rescale_factor: Union[int, float] = 1 / 255,
-        do_normalize: bool = True,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-        size = size if size is not None else {"shortest_edge": 256}
-        size = get_size_dict(size, default_to_square=False)
-        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
-        crop_size = get_size_dict(crop_size, param_name="crop_size")
-        self.do_resize = do_resize
-        self.size = size
-        self.resample = resample
-        self.do_center_crop = do_center_crop
-        self.crop_size = crop_size
-        self.do_rescale = do_rescale
-        self.rescale_factor = rescale_factor
-        self.do_normalize = do_normalize
-        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
-        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
-
-    # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize
-    def resize(
-        self,
-        image: np.ndarray,
-        size: Dict[str, int],
-        resample: PILImageResampling = PILImageResampling.BICUBIC,
-        data_format: Optional[Union[str, ChannelDimension]] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> np.ndarray:
-        """
-        Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
-        resized to keep the input aspect ratio.
-
-        Args:
-            image (`np.ndarray`):
-                Image to resize.
-            size (`Dict[str, int]`):
-                Size of the output image.
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
-                Resampling filter to use when resiizing the image.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format of the input image. If not provided, it will be inferred.
-        """
-        size = get_size_dict(size, default_to_square=False)
-        if "shortest_edge" not in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
-        output_size = get_resize_output_image_size(
-            image, size=size["shortest_edge"], default_to_square=False, input_data_format=input_data_format
-        )
-        return resize(
-            image,
-            size=output_size,
-            resample=resample,
-            data_format=data_format,
-            input_data_format=input_data_format,
-            **kwargs,
-        )
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        do_resize: Optional[bool] = None,
-        size: Dict[str, int] = None,
-        resample: PILImageResampling = None,
-        do_center_crop: bool = None,
-        crop_size: Dict[str, int] = None,
-        do_rescale: Optional[bool] = None,
-        rescale_factor: Optional[float] = None,
-        do_normalize: Optional[bool] = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ):
-        """
-        Preprocess an image or batch of images.
-
-        Args:
-            images (`ImageInput`):
-                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
-                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
-            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
-                Whether to resize the image.
-            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
-                Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
-                the longest edge resized to keep the input aspect ratio.
-            resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
-                `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
-                an effect if `do_resize` is set to `True`.
-            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
-                Whether to center crop the image.
-            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
-                Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
-            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
-                Whether to rescale the image values between [0 - 1].
-            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
-                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
-            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
-                Whether to normalize the image.
-            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
-                Image mean to use if `do_normalize` is set to `True`.
-            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
-                Image standard deviation to use if `do_normalize` is set to `True`.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                - Unset: Return a list of `np.ndarray`.
-                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
-                The channel dimension format for the output image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - Unset: Use the channel dimension format of the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the input image. If unset, the channel dimension format is inferred
-                from the input image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-        """
-        do_resize = do_resize if do_resize is not None else self.do_resize
-        size = size if size is not None else self.size
-        size = get_size_dict(size, default_to_square=False)
-        resample = resample if resample is not None else self.resample
-        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
-        crop_size = crop_size if crop_size is not None else self.crop_size
-        crop_size = get_size_dict(crop_size, param_name="crop_size")
-        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
-        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
-        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-
-        images = make_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        if do_resize and size is None:
-            raise ValueError("Size must be specified if do_resize is True.")
-
-        if do_center_crop and crop_size is None:
-            raise ValueError("Crop size must be specified if do_center_crop is True.")
-
-        if do_rescale and rescale_factor is None:
-            raise ValueError("Rescale factor must be specified if do_rescale is True.")
-
-        if do_normalize and (image_mean is None or image_std is None):
-            raise ValueError("Image mean and std must be specified if do_normalize is True.")
-
-        # All transformations expect numpy arrays.
-        images = [to_numpy_array(image) for image in images]
-
-        if is_scaled_image(images[0]) and do_rescale:
-            logger.warning_once(
-                "It looks like you are trying to rescale already rescaled images. If the input"
-                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
-            )
-
-        if input_data_format is None:
-            # We assume that all images have the same channel dimension format.
-            input_data_format = infer_channel_dimension_format(images[0])
-
-        if do_resize:
-            images = [
-                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
-                for image in images
-            ]
-
-        if do_center_crop:
-            images = [
-                self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images
-            ]
-
-        if do_rescale:
-            images = [
-                self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
-                for image in images
-            ]
-
-        if do_normalize:
-            images = [
-                self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
-                for image in images
-            ]
-
-        images = [
-            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
-        ]
-
-        data = {"pixel_values": images}
-        return BatchFeature(data=data, tensor_type=return_tensors)
-
-    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileNetV2
-    def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
-        """
-        Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports
-        PyTorch.
-
-        Args:
-            outputs ([`MobileNetV2ForSemanticSegmentation`]):
-                Raw outputs of the model.
-            target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
-                List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
-                predictions will not be resized.
-
-        Returns:
-            semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic
-            segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
-            specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
-        """
-        # TODO: add support for other frameworks
-        logits = outputs.logits
-
-        # Resize logits and compute semantic segmentation maps
-        if target_sizes is not None:
-            if len(logits) != len(target_sizes):
-                raise ValueError(
-                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
-                )
-
-            if is_torch_tensor(target_sizes):
-                target_sizes = target_sizes.numpy()
-
-            semantic_segmentation = []
-
-            for idx in range(len(logits)):
-                resized_logits = torch.nn.functional.interpolate(
-                    logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
-                )
-                semantic_map = resized_logits[0].argmax(dim=0)
-                semantic_segmentation.append(semantic_map)
-        else:
-            semantic_segmentation = logits.argmax(dim=1)
-            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
-
-        return semantic_segmentation
diff --git a/spaces/yizhangliu/chatGPT/utils.py b/spaces/yizhangliu/chatGPT/utils.py
deleted file mode 100644
index b09b072410049e2aa6f82cdd775084d8c0f7064e..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/chatGPT/utils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import json, os
-from tencentcloud.common import credential
-from tencentcloud.common.profile.client_profile import ClientProfile
-from tencentcloud.common.profile.http_profile import HttpProfile
-from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
-from tencentcloud.tmt.v20180321 import tmt_client, models
-
-def get_tmt_client():
-    try:
-        # 实例化一个认证对象，入参需要传入腾讯云账户 SecretId 和 SecretKey，此处还需注意密钥对的保密
-        # 代码泄露可能会导致 SecretId 和 SecretKey 泄露，并威胁账号下所有资源的安全性。以下代码示例仅供参考，建议采用更安全的方式来使用密钥，请参见：https://cloud.tencent.com/document/product/1278/85305
-        # 密钥可前往官网控制台 https://console.cloud.tencent.com/cam/capi 进行获取
-        SecretId = os.environ.get("TENCENTCLOUD_SECRET_ID")
-        SecretKey = os.environ.get("TENCENTCLOUD_SECRET_KEY")    
-        cred = credential.Credential(SecretId, SecretKey)
-        # 实例化一个http选项，可选的，没有特殊需求可以跳过
-        httpProfile = HttpProfile()
-        httpProfile.endpoint = "tmt.tencentcloudapi.com"
-
-        # 实例化一个client选项，可选的，没有特殊需求可以跳过
-        clientProfile = ClientProfile()
-        clientProfile.httpProfile = httpProfile
-        # 实例化要请求产品的client对象,clientProfile是可选的
-        client = tmt_client.TmtClient(cred, "ap-shanghai", clientProfile)
-        print(f'client_{client}')
-        return client
-    except TencentCloudSDKException as err:
-        print(f'client_err_{err}')
-        return None
-
-def getTextTrans_tmt(tmt_client, text, source='zh', target='en'):
-    def is_chinese(string):
-        for ch in string:
-            if u'\u4e00' <= ch <= u'\u9fff':
-                return True
-        return False
-
-    if tmt_client is None:
-        return text
-    if not is_chinese(text) and target == 'en': 
-        return text
-    try:
-        req = models.TextTranslateRequest()
-        params = {
-            "SourceText": text,
-            "Source": source,
-            "Target": target,
-            "ProjectId": 0
-        }   
-        req.from_json_string(json.dumps(params))
-        resp = tmt_client.TextTranslate(req)
-        return resp.TargetText
-    except Exception as e:
-        return text 
\ No newline at end of file
diff --git a/spaces/ykilcher/apes/metrics/inception_score.py b/spaces/ykilcher/apes/metrics/inception_score.py
deleted file mode 100644
index 3822c1435901a47e8c192b52cd3ed1ce5de67acd..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/metrics/inception_score.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Inception Score (IS) from the paper "Improved techniques for training
-GANs". Matches the original implementation by Salimans et al. at
-https://github.com/openai/improved-gan/blob/master/inception_score/model.py"""
-
-import numpy as np
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_is(opts, num_gen, num_splits):
-    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-    detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer.
-
-    gen_probs = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        capture_all=True, max_items=num_gen).get_all()
-
-    if opts.rank != 0:
-        return float('nan'), float('nan')
-
-    scores = []
-    for i in range(num_splits):
-        part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits]
-        kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True)))
-        kl = np.mean(np.sum(kl, axis=1))
-        scores.append(np.exp(kl))
-    return float(np.mean(scores)), float(np.std(scores))
-
-#----------------------------------------------------------------------------
diff --git a/spaces/ypx123/vits-uma-genshin-honkai/models.py b/spaces/ypx123/vits-uma-genshin-honkai/models.py
deleted file mode 100644
index 52e15d1b9775038fd6e82b2efe6f95f51c66802d..0000000000000000000000000000000000000000
--- a/spaces/ypx123/vits-uma-genshin-honkai/models.py
+++ /dev/null
@@ -1,534 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    device = next(self.parameters()).device  # 获取模型所在的设备
-    x, m_p, logs_p, x_mask = self.enc_p(x.to(device), x_lengths.to(device))
-    if self.n_speakers > 0:
-      g = self.emb_g(sid.to(device)).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/ysharma/Low-rank-Adaptation/lora_diffusion/cli_lora_add.py b/spaces/ysharma/Low-rank-Adaptation/lora_diffusion/cli_lora_add.py
deleted file mode 100644
index 3a416af806d92f3f89927895593e8a7f18203dbd..0000000000000000000000000000000000000000
--- a/spaces/ysharma/Low-rank-Adaptation/lora_diffusion/cli_lora_add.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from typing import Literal, Union, Dict
-import os
-import shutil
-import fire
-from diffusers import StableDiffusionPipeline
-
-import torch
-from .lora import tune_lora_scale, weight_apply_lora
-from .to_ckpt_v2 import convert_to_ckpt
-
-
-def _text_lora_path(path: str) -> str:
-    assert path.endswith(".pt"), "Only .pt files are supported"
-    return ".".join(path.split(".")[:-1] + ["text_encoder", "pt"])
-
-
-def add(
-    path_1: str,
-    path_2: str,
-    output_path: str,
-    alpha: float = 0.5,
-    mode: Literal[
-        "lpl",
-        "upl",
-        "upl-ckpt-v2",
-    ] = "lpl",
-    with_text_lora: bool = False,
-):
-    print("Lora Add, mode " + mode)
-    if mode == "lpl":
-        for _path_1, _path_2, opt in [(path_1, path_2, "unet")] + (
-            [(_text_lora_path(path_1), _text_lora_path(path_2), "text_encoder")]
-            if with_text_lora
-            else []
-        ):
-            print("Loading", _path_1, _path_2)
-            out_list = []
-            if opt == "text_encoder":
-                if not os.path.exists(_path_1):
-                    print(f"No text encoder found in {_path_1}, skipping...")
-                    continue
-                if not os.path.exists(_path_2):
-                    print(f"No text encoder found in {_path_1}, skipping...")
-                    continue
-
-            l1 = torch.load(_path_1)
-            l2 = torch.load(_path_2)
-
-            l1pairs = zip(l1[::2], l1[1::2])
-            l2pairs = zip(l2[::2], l2[1::2])
-
-            for (x1, y1), (x2, y2) in zip(l1pairs, l2pairs):
-                # print("Merging", x1.shape, y1.shape, x2.shape, y2.shape)
-                x1.data = alpha * x1.data + (1 - alpha) * x2.data
-                y1.data = alpha * y1.data + (1 - alpha) * y2.data
-
-                out_list.append(x1)
-                out_list.append(y1)
-
-            if opt == "unet":
-
-                print("Saving merged UNET to", output_path)
-                torch.save(out_list, output_path)
-
-            elif opt == "text_encoder":
-                print("Saving merged text encoder to", _text_lora_path(output_path))
-                torch.save(
-                    out_list,
-                    _text_lora_path(output_path),
-                )
-
-    elif mode == "upl":
-
-        loaded_pipeline = StableDiffusionPipeline.from_pretrained(
-            path_1,
-        ).to("cpu")
-
-        weight_apply_lora(loaded_pipeline.unet, torch.load(path_2), alpha=alpha)
-        if with_text_lora:
-
-            weight_apply_lora(
-                loaded_pipeline.text_encoder,
-                torch.load(_text_lora_path(path_2)),
-                alpha=alpha,
-                target_replace_module=["CLIPAttention"],
-            )
-
-        loaded_pipeline.save_pretrained(output_path)
-
-    elif mode == "upl-ckpt-v2":
-
-        loaded_pipeline = StableDiffusionPipeline.from_pretrained(
-            path_1,
-        ).to("cpu")
-
-        weight_apply_lora(loaded_pipeline.unet, torch.load(path_2), alpha=alpha)
-        if with_text_lora:
-            weight_apply_lora(
-                loaded_pipeline.text_encoder,
-                torch.load(_text_lora_path(path_2)),
-                alpha=alpha,
-                target_replace_module=["CLIPAttention"],
-            )
-
-        _tmp_output = output_path + ".tmp"
-
-        loaded_pipeline.save_pretrained(_tmp_output)
-        convert_to_ckpt(_tmp_output, output_path, as_half=True)
-        # remove the tmp_output folder
-        shutil.rmtree(_tmp_output)
-
-    else:
-        print("Unknown mode", mode)
-        raise ValueError(f"Unknown mode {mode}")
-
-
-def main():
-    fire.Fire(add)
diff --git a/spaces/ysharma/pix2pix-zero-01/src/make_edit_direction.py b/spaces/ysharma/pix2pix-zero-01/src/make_edit_direction.py
deleted file mode 100644
index d6307694847e6f98749390ccb02c0b5ef6e2b67f..0000000000000000000000000000000000000000
--- a/spaces/ysharma/pix2pix-zero-01/src/make_edit_direction.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os, pdb
-
-import argparse
-import numpy as np
-import torch
-import requests
-from PIL import Image
-
-from diffusers import DDIMScheduler
-from utils.edit_pipeline import EditingPipeline
-
-
-## convert sentences to sentence embeddings
-def load_sentence_embeddings(l_sentences, tokenizer, text_encoder, device="cuda"):
-    with torch.no_grad():
-        l_embeddings = []
-        for sent in l_sentences:
-            text_inputs = tokenizer(
-                    sent,
-                    padding="max_length",
-                    max_length=tokenizer.model_max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-            text_input_ids = text_inputs.input_ids
-            prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
-            l_embeddings.append(prompt_embeds)
-    return torch.concatenate(l_embeddings, dim=0).mean(dim=0).unsqueeze(0)
-
-
-if __name__=="__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--file_source_sentences', required=True)
-    parser.add_argument('--file_target_sentences', required=True)
-    parser.add_argument('--output_folder', required=True)
-    parser.add_argument('--model_path', type=str, default='CompVis/stable-diffusion-v1-4')
-    args = parser.parse_args()
-
-    # load the model
-    pipe = EditingPipeline.from_pretrained(args.model_path, torch_dtype=torch.float16).to("cuda")
-    bname_src = os.path.basename(args.file_source_sentences).strip(".txt")
-    outf_src = os.path.join(args.output_folder, bname_src+".pt")
-    if os.path.exists(outf_src):
-        print(f"Skipping source file {outf_src} as it already exists")
-    else:
-        with open(args.file_source_sentences, "r") as f:
-            l_sents = [x.strip() for x in f.readlines()]
-        mean_emb = load_sentence_embeddings(l_sents, pipe.tokenizer, pipe.text_encoder, device="cuda")
-        print(mean_emb.shape)
-        torch.save(mean_emb, outf_src)
-
-    bname_tgt = os.path.basename(args.file_target_sentences).strip(".txt")
-    outf_tgt = os.path.join(args.output_folder, bname_tgt+".pt")
-    if os.path.exists(outf_tgt):
-        print(f"Skipping target file {outf_tgt} as it already exists")
-    else:
-        with open(args.file_target_sentences, "r") as f:
-            l_sents = [x.strip() for x in f.readlines()]
-        mean_emb = load_sentence_embeddings(l_sents, pipe.tokenizer, pipe.text_encoder, device="cuda")
-        print(mean_emb.shape)
-        torch.save(mean_emb, outf_tgt)
diff --git a/spaces/yukie/yukie-sovits3/data_utils.py b/spaces/yukie/yukie-sovits3/data_utils.py
deleted file mode 100644
index 9dfba4a9dfbfbd2b6ed5e771a5ffee4f70419ba3..0000000000000000000000000000000000000000
--- a/spaces/yukie/yukie-sovits3/data_utils.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons
-from mel_processing import spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text, transform
-
-# import h5py
-
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths, hparams):
-        self.audiopaths = load_filepaths_and_text(audiopaths)
-        self.max_wav_value = hparams.data.max_wav_value
-        self.sampling_rate = hparams.data.sampling_rate
-        self.filter_length = hparams.data.filter_length
-        self.hop_length = hparams.data.hop_length
-        self.win_length = hparams.data.win_length
-        self.sampling_rate = hparams.data.sampling_rate
-        self.use_sr = hparams.train.use_sr
-        self.spec_len = hparams.train.max_speclen
-        self.spk_map = hparams.spk
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                                     self.sampling_rate, self.hop_length, self.win_length,
-                                     center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-
-        spk = filename.split(os.sep)[-2]
-        spk = torch.LongTensor([self.spk_map[spk]])
-
-        c = torch.load(filename + ".soft.pt").squeeze(0)
-        c = torch.repeat_interleave(c, repeats=2, dim=1)
-
-        f0 = np.load(filename + ".f0.npy")
-        f0 = torch.FloatTensor(f0)
-        lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
-        assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape, filename)
-        assert abs(lmin - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
-        assert abs(lmin - c.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
-        spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
-        audio_norm = audio_norm[:, :lmin * self.hop_length]
-        _spec, _c, _audio_norm, _f0 = spec, c, audio_norm, f0
-        while spec.size(-1) < self.spec_len:
-            spec = torch.cat((spec, _spec), -1)
-            c = torch.cat((c, _c), -1)
-            f0 = torch.cat((f0, _f0), -1)
-            audio_norm = torch.cat((audio_norm, _audio_norm), -1)
-        start = random.randint(0, spec.size(-1) - self.spec_len)
-        end = start + self.spec_len
-        spec = spec[:, start:end]
-        c = c[:, start:end]
-        f0 = f0[start:end]
-        audio_norm = audio_norm[:, start * self.hop_length:end * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk
-
-    def __getitem__(self, index):
-        return self.get_audio(self.audiopaths[index][0])
-
-    def __len__(self):
-        return len(self.audiopaths)
-
-
-class EvalDataLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths, hparams):
-        self.audiopaths = load_filepaths_and_text(audiopaths)
-        self.max_wav_value = hparams.data.max_wav_value
-        self.sampling_rate = hparams.data.sampling_rate
-        self.filter_length = hparams.data.filter_length
-        self.hop_length = hparams.data.hop_length
-        self.win_length = hparams.data.win_length
-        self.sampling_rate = hparams.data.sampling_rate
-        self.use_sr = hparams.train.use_sr
-        self.audiopaths = self.audiopaths[:5]
-        self.spk_map = hparams.spk
-
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                                     self.sampling_rate, self.hop_length, self.win_length,
-                                     center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-
-        spk = filename.split(os.sep)[-2]
-        spk = torch.LongTensor([self.spk_map[spk]])
-
-        c = torch.load(filename + ".soft.pt").squeeze(0)
-
-        c = torch.repeat_interleave(c, repeats=2, dim=1)
-
-        f0 = np.load(filename + ".f0.npy")
-        f0 = torch.FloatTensor(f0)
-        lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
-        assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
-        assert abs(f0.shape[0] - spec.shape[-1]) < 4, (c.size(-1), spec.size(-1), f0.shape)
-        spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
-        audio_norm = audio_norm[:, :lmin * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk
-
-    def __getitem__(self, index):
-        return self.get_audio(self.audiopaths[index][0])
-
-    def __len__(self):
-        return len(self.audiopaths)
-
diff --git a/spaces/yz333/real-bing/README.md b/spaces/yz333/real-bing/README.md
deleted file mode 100644
index c6413ea0d38eb3595e3aa3042808746070ad18e9..0000000000000000000000000000000000000000
--- a/spaces/yz333/real-bing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Real Bing
-emoji: 🏆
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zhenwusw/JoJoGAN/e4e/criteria/id_loss.py b/spaces/zhenwusw/JoJoGAN/e4e/criteria/id_loss.py
deleted file mode 100644
index bab806172eff18c0630536ae96817508c3197b8b..0000000000000000000000000000000000000000
--- a/spaces/zhenwusw/JoJoGAN/e4e/criteria/id_loss.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from torch import nn
-from configs.paths_config import model_paths
-from models.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
-    def __init__(self):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-        for module in [self.facenet, self.face_pool]:
-            for param in module.parameters():
-                param.requires_grad = False
-
-    def extract_feats(self, x):
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y, x):
-        n_samples = x.shape[0]
-        x_feats = self.extract_feats(x)
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        id_logs = []
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            diff_input = y_hat_feats[i].dot(x_feats[i])
-            diff_views = y_feats[i].dot(x_feats[i])
-            id_logs.append({'diff_target': float(diff_target),
-                            'diff_input': float(diff_input),
-                            'diff_views': float(diff_views)})
-            loss += 1 - diff_target
-            id_diff = float(diff_target) - float(diff_views)
-            sim_improvement += id_diff
-            count += 1
-
-        return loss / count, sim_improvement / count, id_logs
diff --git a/spaces/zhtet/RegBotBeta/assets/prompts/custom_prompts.py b/spaces/zhtet/RegBotBeta/assets/prompts/custom_prompts.py
deleted file mode 100644
index ced5566a43b6d839713a9d9fe03f85fd19842cdc..0000000000000000000000000000000000000000
--- a/spaces/zhtet/RegBotBeta/assets/prompts/custom_prompts.py
+++ /dev/null
@@ -1,22 +0,0 @@
-text_qa_template_str = (
-    "Assistant Message: I'm here to assist you with information about California water regulations."
-    "Feel free to ask questions based on the context below, and I'll do my best to provide accurate answers.\n\n"
-    "Context: {context_str}\n\n"
-    "Your Question: {query_str}\n\n"
-    "Answer: \n\n"
-    "If the question is pertinent, I'll provide you with the chapter, article, and section of the information"
-    "from the PDF document 'Title 17 and 22, California Code of Regulations'. Additionally, I'll strive to include"
-    "the corresponding page number for your reference.\n\n"
-    "Please note that my knowledge is up to date as of September 2021."
-)
-
-refine_template_str = (
-    "The original question is as follows: {query_str}\n"
-    "We have provided an existing answer: {existing_answer}\n"
-    "We have the opportunity to refine the existing answer "
-    "(only if needed) with some more context below.\n"
-    "------------\n"
-    "{context_msg}\n"
-    "------------\n"
-    "Using both the new context and your own knowledege, update or repeat the existing answer.\n"
-)
diff --git a/spaces/zomehwh/rvc-models/app.py b/spaces/zomehwh/rvc-models/app.py
deleted file mode 100644
index 5ef3bed52089af1afd7b5edcf72721d92b2bbbe0..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/rvc-models/app.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import os
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import (
-    is_half,
-    device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
-    def vc_fn(
-        input_audio,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        tts_mode,
-        tts_text,
-        tts_voice
-    ):
-        try:
-            if tts_mode:
-                if len(tts_text) > 100 and limitation:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            else:
-                if args.files:
-                    audio, sr = librosa.load(input_audio, sr=16000, mono=True)
-                else:
-                    if input_audio is None:
-                        return "You need to upload an audio", None
-                    sampling_rate, audio = input_audio
-                    duration = audio.shape[0] / sampling_rate
-                    if duration > 20 and limitation:
-                        return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
-                    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                    if len(audio.shape) > 1:
-                        audio = librosa.to_mono(audio.transpose(1, 0))
-                    if sampling_rate != 16000:
-                        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                file_big_npy,
-                index_rate,
-                if_f0,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return "Success", (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
-    if is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
-    if tts_mode:
-        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--api', action="store_true", default=False)
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    parser.add_argument("--files", action="store_true", default=False, help="load audio from path")
-    args, unknown = parser.parse_known_args()
-    load_hubert()
-    models = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with open("weights/model_info.json", "r", encoding="utf-8") as f:
-        models_info = json.load(f)
-    for name, info in models_info.items():
-        if not info['enable']:
-            continue
-        title = info['title']
-        author = info.get("author", None)
-        cover = f"weights/{name}/{info['cover']}"
-        index = f"weights/{name}/{info['feature_retrieval_library']}"
-        npy = f"weights/{name}/{info['feature_file']}"
-        cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
-        tgt_sr = cpt["config"][-1]
-        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-        if_f0 = cpt.get("f0", 1)
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-        del net_g.enc_q
-        print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
-        net_g.eval().to(device)
-        if is_half:
-            net_g = net_g.half()
-        else:
-            net_g = net_g.float()
-        vc = VC(tgt_sr, device, is_half)
-        models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> RVC Models\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=zomehwh.Rvc-Models)\n\n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/16MXRcKEjGDqQzVanvi8xYOOOlhdNBopM?usp=share_link)\n\n"
-            "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/zomehwh/rvc-models?duplicate=true)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
-
-        )
-        with gr.Tabs():
-            for (name, title, author, cover, vc_fn) in models:
-                with gr.TabItem(name):
-                    with gr.Row():
-                        gr.Markdown(
-                            '<div align="center">'
-                            f'<div>{title}</div>\n'+
-                            (f'<div>Model author: {author}</div>' if author else "")+
-                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                            '</div>'
-                        )
-                    with gr.Row():
-                        with gr.Column():
-                            if args.files:
-                                vc_input = gr.Textbox(label="Input audio path")
-                            else:
-                                vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
-                            vc_transpose = gr.Number(label="Transpose", value=0)
-                            vc_f0method = gr.Radio(
-                                label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
-                                choices=["pm", "harvest"],
-                                value="pm",
-                                interactive=True,
-                            )
-                            vc_index_ratio = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label="Retrieval feature ratio",
-                                value=0.6,
-                                interactive=True,
-                            )
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
-                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                            vc_submit = gr.Button("Generate", variant="primary")
-                        with gr.Column():
-                            vc_output1 = gr.Textbox(label="Output Message")
-                            vc_output2 = gr.Audio(label="Output Audio")
-                vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
-                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
-        app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/zomehwh/sovits-xiaoke/inference/__init__.py b/spaces/zomehwh/sovits-xiaoke/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/zxy666/bingo-chatai666/src/lib/hooks/use-bing.ts b/spaces/zxy666/bingo-chatai666/src/lib/hooks/use-bing.ts
deleted file mode 100644
index dcdb1667ced0cba299b0825c0e91c4732411308c..0000000000000000000000000000000000000000
--- a/spaces/zxy666/bingo-chatai666/src/lib/hooks/use-bing.ts
+++ /dev/null
@@ -1,173 +0,0 @@
-'use client'
-
-import { useState, useCallback, useEffect, useMemo } from 'react'
-import { useAtom, useAtomValue } from 'jotai'
-import { chatFamily, bingConversationStyleAtom, GreetMessages, hashAtom, voiceAtom } from '@/state'
-import { setConversationMessages } from './chat-history'
-import { ChatMessageModel, BotId, FileItem } from '@/lib/bots/bing/types'
-import { nanoid } from '../utils'
-import { TTS } from '../bots/bing/tts'
-
-export function useBing(botId: BotId = 'bing') {
-  const chatAtom = useMemo(() => chatFamily({ botId, page: 'singleton' }), [botId])
-  const [enableTTS] = useAtom(voiceAtom)
-  const speaker = useMemo(() => new TTS(), [])
-  const [hash, setHash] = useAtom(hashAtom)
-  const bingConversationStyle = useAtomValue(bingConversationStyleAtom)
-  const [chatState, setChatState] = useAtom(chatAtom)
-  const [input, setInput] = useState('')
-  const [attachmentList, setAttachmentList] = useState<FileItem[]>([])
-
-  const updateMessage = useCallback(
-    (messageId: string, updater: (message: ChatMessageModel) => void) => {
-      setChatState((draft) => {
-        const message = draft.messages.find((m) => m.id === messageId)
-        if (message) {
-          updater(message)
-        }
-      })
-    },
-    [setChatState],
-  )
-
-  const sendMessage = useCallback(
-    async (input: string, options = {}) => {
-      const botMessageId = nanoid()
-      const imageUrl = attachmentList?.[0]?.status === 'loaded' ? attachmentList[0].url : undefined
-      setChatState((draft) => {
-        const text = imageUrl ? `${input}\n\n![image](${imageUrl})` : input
-        draft.messages.push({ id: nanoid(), text, author: 'user' }, { id: botMessageId, text: '', author: 'bot' })
-        setAttachmentList([])
-      })
-      const abortController = new AbortController()
-      setChatState((draft) => {
-        draft.generatingMessageId = botMessageId
-        draft.abortController = abortController
-      })
-      speaker.reset()
-      await chatState.bot.sendMessage({
-        prompt: input,
-        imageUrl: /\?bcid=([^&]+)/.test(imageUrl ?? '') ? `https://www.bing.com/images/blob?bcid=${RegExp.$1}` : imageUrl,
-        options: {
-          ...options,
-          bingConversationStyle,
-        },
-        signal: abortController.signal,
-        onEvent(event) {
-          if (event.type === 'UPDATE_ANSWER') {
-            updateMessage(botMessageId, (message) => {
-              if (event.data.text.length > message.text.length) {
-                message.text = event.data.text
-              }
-
-              if (event.data.spokenText && enableTTS) {
-                speaker.speak(event.data.spokenText)
-              }
-
-              message.throttling = event.data.throttling || message.throttling
-              message.sourceAttributions = event.data.sourceAttributions || message.sourceAttributions
-              message.suggestedResponses = event.data.suggestedResponses || message.suggestedResponses
-            })
-          } else if (event.type === 'ERROR') {
-            updateMessage(botMessageId, (message) => {
-              message.error = event.error
-            })
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          } else if (event.type === 'DONE') {
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          }
-        },
-      })
-    },
-    [botId, attachmentList, chatState.bot, setChatState, updateMessage],
-  )
-
-  const uploadImage = useCallback(async (imgUrl: string) => {
-    setAttachmentList([{ url: imgUrl, status: 'loading' }])
-    const response = await chatState.bot.uploadImage(imgUrl, bingConversationStyle)
-    if (response?.blobId) {
-      setAttachmentList([{ url: `/api/blob?bcid=${response.blobId}`, status: 'loaded' }])
-    } else {
-      setAttachmentList([{ url: imgUrl, status: 'error' }])
-    }
-  }, [chatState.bot])
-
-  const resetConversation = useCallback(() => {
-    chatState.bot.resetConversation()
-    speaker.abort()
-    setChatState((draft) => {
-      draft.abortController = undefined
-      draft.generatingMessageId = ''
-      draft.messages = [{ author: 'bot', text: GreetMessages[Math.floor(GreetMessages.length * Math.random())], id: nanoid() }]
-      draft.conversationId = nanoid()
-    })
-  }, [chatState.bot, setChatState])
-
-  const stopGenerating = useCallback(() => {
-    chatState.abortController?.abort()
-    if (chatState.generatingMessageId) {
-      updateMessage(chatState.generatingMessageId, (message) => {
-        if (!message.text && !message.error) {
-          message.text = 'Cancelled'
-        }
-      })
-    }
-    setChatState((draft) => {
-      draft.generatingMessageId = ''
-    })
-  }, [chatState.abortController, chatState.generatingMessageId, setChatState, updateMessage])
-
-  useEffect(() => {
-    if (chatState.messages.length) {
-      setConversationMessages(botId, chatState.conversationId, chatState.messages)
-    }
-  }, [botId, chatState.conversationId, chatState.messages])
-
-  useEffect(() => {
-    if (hash === 'reset') {
-      resetConversation()
-      setHash('')
-    }
-  }, [hash, setHash])
-
-  const chat = useMemo(
-    () => ({
-      botId,
-      bot: chatState.bot,
-      isSpeaking: speaker.isSpeaking,
-      messages: chatState.messages,
-      sendMessage,
-      setInput,
-      input,
-      resetConversation,
-      generating: !!chatState.generatingMessageId,
-      stopGenerating,
-      uploadImage,
-      setAttachmentList,
-      attachmentList,
-    }),
-    [
-      botId,
-      bingConversationStyle,
-      chatState.bot,
-      chatState.generatingMessageId,
-      chatState.messages,
-      speaker.isSpeaking,
-      setInput,
-      input,
-      setAttachmentList,
-      attachmentList,
-      resetConversation,
-      sendMessage,
-      stopGenerating,
-    ],
-  )
-
-  return chat
-}