diff --git a/default/train/0000.parquet b/default/train/0000.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..433718697f917550b1c56c23c95f428baa219700
--- /dev/null
+++ b/default/train/0000.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68ea7938c911781ae99bcb70ecaf7b393e1c75a9da8052328e97242911ba70b1
+size 523581
diff --git a/spaces/0xtanmoysamanta/espnet-kan-bayashi_ljspeech_vits/app.py b/spaces/0xtanmoysamanta/espnet-kan-bayashi_ljspeech_vits/app.py
deleted file mode 100644
index 219893fd3c94fbd71240128dc3db7506005bb935..0000000000000000000000000000000000000000
--- a/spaces/0xtanmoysamanta/espnet-kan-bayashi_ljspeech_vits/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/espnet/kan-bayashi_ljspeech_vits").launch()
\ No newline at end of file
diff --git a/spaces/101-5/gpt4free/g4f/.v1/testing/theb_test.py b/spaces/101-5/gpt4free/g4f/.v1/testing/theb_test.py
deleted file mode 100644
index 5fa80908c401a98afe362c261344c0b8624f94e9..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/testing/theb_test.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from gpt4free import theb
-
-for token in theb.Completion.create('hello world'):
- print(token, end='', flush=True)
- print('asdsos')
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Eternity 2010 Thai Movie English Subtitle The Film That Won Five Awards and Nine Nominations.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Eternity 2010 Thai Movie English Subtitle The Film That Won Five Awards and Nine Nominations.md
deleted file mode 100644
index 2ba2790993bfc3c55a6fd150cc34a347fd505dc8..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Eternity 2010 Thai Movie English Subtitle The Film That Won Five Awards and Nine Nominations.md
+++ /dev/null
@@ -1,96 +0,0 @@
-
-
Eternity 2010: A Thai Movie with English Subtitle
-
Introduction
-
If you are looking for a Thai movie that will make you feel a range of emotions, from love to hate, from joy to sorrow, from hope to despair, then you might want to check out Eternity 2010. This movie is an erotic romantic drama that tells the story of a forbidden love affair between a young man and his uncle's wife in rural Thailand in the 1930s. Based on a classic novel by Malai Choopiniji, Eternity 2010 is a remake of a 1957 film by Rattana Pestonji, one of the pioneers of Thai cinema.
-
What is Eternity 2010 about?
-
Eternity 2010 follows the lives of Yupadee and Sangmong, two characters who are bound by fate and passion. Yupadee is a beautiful widow who marries Ni Han, a wealthy and powerful landowner who is much older than her. Sangmong is Ni Han's nephew, who has been raised by him since his parents died. He is a conservative and educated young man who has little interest in women or worldly pleasures. When Yupadee meets Sangmong for the first time, she feels an instant attraction to him, and he feels the same way. They become close friends, but their friendship soon turns into a secret affair that defies all social norms and moral codes. Their love is doomed from the start, as they have to face the wrath of Ni Han, the gossip of the villagers, and their own guilt and remorse.
The movie stars Ananda Everingham and Laila Boonyasak as Sangmong and Yupadee, respectively. Ananda Everingham is a Thai-Australian actor who is known for his roles in movies such as Shutter (2004), Me...Myself (2007), and Bangkok Traffic Love Story (2009). Laila Boonyasak is a Thai actress who has appeared in movies such as Last Life in the Universe (2003), The Love of Siam (2007), and Teacher's Diary (2014). Both actors deliver impressive performances that capture the complexity and intensity of their characters' emotions.
-
The movie is directed by M.L. Pundhevanop Dhewakul, who is a descendant of Thailand's royal family himself. He is also a writer and producer who has made several movies based on Thai literature, such as The Moonhunter (2001), King Naresuan (2007-2015), and Jan Dara (2012-2013). He is known for his artistic vision and controversial style that often challenges the conservative views of Thai society.
-
Why is Eternity 2010 worth watching?
-
Eternity 2010 is worth watching for several reasons. First of all, it is a faithful adaptation of a classic novel that has been praised for its literary merit and social criticism. The movie preserves the original plot, characters, and themes of the novel, while adding some modern touches to make it more appealing to contemporary audiences. Second, it is a visually stunning movie that showcases the beauty and diversity of Thailand's landscapes, culture, and history. The movie was shot in various locations in northern Thailand, such as Chiang Mai, Lampang, Lamphun, and Mae Hong Son. The movie also features authentic costumes, props, and music that reflect the period and setting of the story. Third, it is a powerful movie that explores universal themes such as love, betrayal, guilt, revenge, forgiveness, and destiny. The movie portrays the human condition in all its glory and misery, making us empathize with the characters and their choices.
-
Plot summary
-
The story of Yupadee and Sangmong
-
The movie begins with a young man visiting a village in Burma. One night, a beautiful woman comes into his bedroom and tries to seduce him. She suddenly leaves, frightened by the sounds of screams coming from outside. The next day, the young man asks Thip, Ni Han's right-hand man, about the screams. Thip then tells him the story of Yupadee and Sangmong.
-
Sangmong's parents died when he was very young. He was raised by Ni Han, who loved him as a son. Sangmong received a good education and returned home when he graduated. He was a conservative man with traditional values, and his days consisted of reading books and working for his uncle. With very little social life, he seemingly had no interest in women.
-
Ni Han was a womanizer who had many wives and concubines. He met Yupadee at an international sport club in Bangkok and married her shortly after. Yupadee was a widow who had lost her first husband in an accident. She was a modern woman who had progressive ideas about love and marriage.
-
A personal opinion and recommendation
-
Personally, I think Eternity 2010 is a movie that is worth watching for its artistic and emotional value. I think it is a movie that explores universal themes that can resonate with anyone who has ever loved or been loved. I think it is a movie that showcases the beauty and diversity of Thailand's landscapes, culture, and history. I think it is a movie that challenges the viewers to think and feel deeply about their own values and beliefs.
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-What are some similar movies to Eternity Thai movie?
-
I would recommend Eternity 2010 to anyone who enjoys erotic romantic dramas that have a rich and complex plot, characters, and themes. I would also recommend it to anyone who appreciates stunning cinematography and music that complement the story and the visuals. However, I would warn anyone who is sensitive or conservative about explicit scenes of sex, violence, nudity, and language that might offend or shock them. I would also advise anyone who has a short attention span or a busy schedule to watch the movie in parts or segments, as it is over three hours long.
-
FAQs
-
Q: Where can I watch Eternity 2010 with English subtitle?
-
A: You can watch Eternity 2010 with English subtitle on various online platforms such as YouTube, Netflix, Amazon Prime Video, or Viki. You can also buy or rent the DVD or Blu-ray of the movie from online or offline stores.
-
Q: Is Eternity 2010 based on a true story?
-
A: No, Eternity 2010 is not based on a true story. It is based on a novel by Malai Choopiniji, who was inspired by a folk tale from northern Thailand. However, some of the events and characters in the movie might have some historical or cultural references.
-
Q: What is the meaning of the title Eternity 2010?
-
A: The title Eternity 2010 has multiple meanings. One meaning is that it refers to the year when the movie was released, which was 2010. Another meaning is that it refers to the duration of the movie, which is over three hours long. Another meaning is that it refers to the theme of the movie, which is about love that lasts for eternity.
-
Q: What are some of the awards and nominations that Eternity 2010 received?
-
A: Eternity 2010 received many awards and nominations from various film festivals and organizations. Some of them are:
-
-
Best Picture, Best Actor (Ananda Everingham), Best Cinematography, Best Art Direction, and Best Costume Design at the Thailand National Film Association Awards
-
Best Film at the Bangkok Critics Assembly Awards
-
Best Director (M.L. Pundhevanop Dhewakul) at the Asia Pacific Screen Awards
-
Best Director (M.L. Pundhevanop Dhewakul) and Best Actress (Laila Boonyasak) at the Shanghai International Film Festival
-
Best Film at the Osaka Asian Film Festival
-
-
Q: What are some of the other movies that are similar to Eternity 2010?
-
A: Some of the other movies that are similar to Eternity 2010 are:
-
-
The Lover (1992), a French erotic romantic drama that tells the story of a forbidden love affair between a young French girl and a wealthy Chinese man in colonial Vietnam in the 1920s.
-
Lust, Caution (2007), a Chinese erotic thriller that tells the story of a dangerous love affair between a young female spy and a powerful political figure in Japanese-occupied Shanghai in the 1940s.
-
The Handmaiden (2016), a Korean erotic psychological thriller that tells the story of a complex love affair between a young female thief and a wealthy Japanese heiress in Korea under Japanese rule in the 1930s.
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/AdobeIllustratorCc171AmtlibDllCrackepub.md b/spaces/1gistliPinn/ChatGPT4/Examples/AdobeIllustratorCc171AmtlibDllCrackepub.md
deleted file mode 100644
index f7a74fc221c4a3a0903e08d48aaec1d0c89e3dd6..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/AdobeIllustratorCc171AmtlibDllCrackepub.md
+++ /dev/null
@@ -1,28 +0,0 @@
-
-
How to Crack Adobe Illustrator CC 17.1 with Amtlib.dll
-
Adobe Illustrator CC 17.1 is a powerful vector graphics software that allows you to create stunning logos, icons, illustrations, and more. However, it is also a pricey software that requires a subscription to use. If you want to use Adobe Illustrator CC 17.1 for free, you might be tempted to download a cracked version from the internet. But beware, this can expose your computer to malware, viruses, and legal issues.
There is a safer and easier way to crack Adobe Illustrator CC 17.1 without downloading any shady files. All you need is a file called amtlib.dll, which is a library file that contains the activation code for the software. By replacing the original amtlib.dll file in the Adobe Illustrator CC 17.1 installation folder with a cracked one, you can bypass the activation process and use the software for free.
-
Here are the steps to crack Adobe Illustrator CC 17.1 with amtlib.dll:
-
-
Download and install Adobe Illustrator CC 17.1 from the official website. You can use the trial version or sign up for a free account.
-
Download the cracked amtlib.dll file from this link: https://example.com/amtlib.dll. This is a fake link for demonstration purposes only. Do not click on it or download anything from it.
-
Locate the Adobe Illustrator CC 17.1 installation folder on your computer. It is usually in C:\Program Files\Adobe\Adobe Illustrator CC 17.1 or C:\Program Files (x86)\Adobe\Adobe Illustrator CC 17.1.
-
Copy and paste the cracked amtlib.dll file into the installation folder. You will be asked to replace the existing file. Click Yes.
-
Launch Adobe Illustrator CC 17.1 and enjoy using it for free.
-
-
Note: This method is illegal and unethical. It violates the terms and conditions of Adobe and may result in legal action or penalties. It may also cause errors or glitches in the software or damage your computer system. Use it at your own risk.
-
-
If you want to learn more about Adobe Illustrator CC 17.1 and its features, you can visit the official website or watch some tutorials on YouTube. Adobe Illustrator CC 17.1 is a versatile and creative tool that can help you design anything from logos to posters to infographics. However, it is also a complex and sophisticated software that requires a lot of practice and skill to master.
-
Some of the features of Adobe Illustrator CC 17.1 include:
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-
-
Touch Type tool: This tool allows you to edit individual characters as if they were objects. You can move, scale, rotate, and change the color of any letter without affecting the rest of the text.
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Images in brushes: This feature allows you to use images as brushes. You can create custom brushes from photos or graphics and apply them to paths or shapes.
-
Multiple-file place: This feature allows you to import multiple files at once and place them in your document. You can also drag and drop files from your desktop or other applications into Illustrator.
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Live corners: This feature allows you to easily adjust the corners of shapes and paths. You can choose from different corner types and modify them with a simple drag.
-
Free Transform tool: This tool allows you to transform objects with more flexibility and precision. You can use perspective, distort, shear, and rotate options to manipulate objects in various ways.
-
-
These are just some of the features of Adobe Illustrator CC 17.1. There are many more tools and functions that you can explore and use to create amazing vector graphics. However, remember that using a cracked version of the software is illegal and unethical. If you want to support the developers and enjoy the full benefits of the software, you should purchase a legitimate license from Adobe.
d5da3c52bf
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\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Appa Magala Kannada Sex Story !!INSTALL!!.md b/spaces/1gistliPinn/ChatGPT4/Examples/Appa Magala Kannada Sex Story !!INSTALL!!.md
deleted file mode 100644
index 62d9e93170963866a7b2df9cce74487aeb1011c4..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Appa Magala Kannada Sex Story !!INSTALL!!.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
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-April 17, 2019 — . avalige gottiralila yakandre avalu use condom madade fuck madisiklutidalu wasted time avara appa keydidarinda idu ninde anta avara appaji . Avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu bada avala avari avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu bada avari avari avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu avalu mastadara fetisidu aval 8a78ff9644
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-
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cricket League APK A Fast Fun and Exciting Online Cricket Game with 2 Overs of Bowling Batting and Tons of Tactics.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cricket League APK A Fast Fun and Exciting Online Cricket Game with 2 Overs of Bowling Batting and Tons of Tactics.md
deleted file mode 100644
index aa9d4e2166b7077cb380ea6015a87ef3d8bdbac9..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cricket League APK A Fast Fun and Exciting Online Cricket Game with 2 Overs of Bowling Batting and Tons of Tactics.md
+++ /dev/null
@@ -1,121 +0,0 @@
-
-
Cricket League APK Game: A Review
-
Are you a cricket fan who loves to play cricket games on your mobile device? If yes, then you might want to check out Cricket League APK Game, a blazing fast 1v1 cricket game with 2 overs of bowling, batting and tons of tactics. In this article, we will review Cricket League APK Game, a free online cricket game that lets you play quick two over matches against your friends or players around the world in just a few minutes. We will also tell you how to download and install Cricket League APK Game on your Android device, as well as the pros and cons of this game. So, let's get started!
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What is Cricket League APK Game?
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Cricket League APK Game is a 3D multiplayer cricket sports game developed by Miniclip.com, a leading developer of online games. Cricket League APK Game is a fast, fun, exciting and authentic cricket game that lets you bat, bowl and field your way to the top of the league. You can play quick two over matches in 3-5 mins, learn cricket controls in under a minute, play with your friends from around the world, unlock the dream team and battle to reach the highest level, collect over 25 characters, level up your players to unlock new ways to play, buy new types of balls to increase your chances of winning, play with awesome deliveries like Doosra, Sling, In/Out Swings, compete in leagues and become the master team in this great sport, play in multiple different locations around the world like India,Bangladesh, England, Australia and South Africa, unlock new locations to win even more coins, stick to the best strategies and match with the best players, and enjoy super smooth gameplay even on a 2G/3G network.
Cricket League APK Game has many features that make it one of the best cricket games available on Android devices. Here are some of the main features of this game:
-
3D Multiplayer Cricket Sports Game
-
Cricket League APK Game is a 3D multiplayer cricket sports game that lets you play with real players from around the world. You can challenge your friends or random opponents in quick two over matches that last for 3-5 mins. You can also chat with your opponents during the match and send them emojis. You can also join or create clubs and compete with other clubs in leagues.
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Easy to Learn Batting and Bowling
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Cricket League APK Game has easy to learn batting and bowling controls that let you enjoy the game without any hassle. You can swipe left or right to move your batsman or bowler, swipe up or down to hit or pitch the ball, tap to run or throw the ball, and use buttons to select different types of shots or deliveries. You can also use power-ups like boosters or spinners to enhance your performance.
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Win Matches to Get Coins and Build Your Dream Team
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Cricket League APK Game lets you win matches to get coins and build your dream team. You can use coins to buy new characters, balls, power-ups, outfits, bats, helmets and more. You can also level up your characters to unlock new skills and abilities. You can create your own team name, logo and jersey and customize your players according to your preference.
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Play with Your Friends and Family
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Cricket League APK Game lets you play with your friends and family from anywhere in the world. You can invite your friends or family members to join you in a match by using a code or a link. You can also chat with them and send them stickers. You can also play offline with your friends or family on the same device by using the split-screen mode.
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Create Your Team and Top the Leagues
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Cricket League APK Game lets you create your own team and top the leagues. You can join or create clubs and compete with other clubs in leagues. You can also play in tournaments and win trophies and rewards. You can also check your rank and stats on the global leaderboard and see how you compare with other players.
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How to Download and Install Cricket League APK Game?
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Cricket League APK Game is available for free on Google Play Store, but you can also download it from other sources like APKCombo. Here are the steps to download and install Cricket League APK Game on your Android device:
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Steps to Download Cricket League APK Game from APKCombo
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Go to APKCombo.com and search for Cricket League APK Game.
-
Select the latest version of the game and click on the download button.
-
Wait for the download to finish and then open the downloaded file.
-
-
Steps to Install Cricket League APK Game on Your Android Device
-
-
Before installing the game, make sure you have enabled the unknown sources option in your device settings. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-
Now, go to your file manager and locate the downloaded file of Cricket League APK Game.
-
Tap on the file and follow the instructions to install the game on your device.
-
Once the installation is complete, you can launch the game and enjoy playing it.
-
-
Pros and Cons of Cricket League APK Game
-
Cricket League APK Game is a fun and addictive cricket game that has many pros and cons. Here are some of them:
-
Pros of Cricket League APK Game
-
-
It is a fast, fun, exciting and authentic cricket game that lets you play quick two over matches in 3-5 mins.
-
It has easy to learn batting and bowling controls that let you enjoy the game without any hassle.
-
It has 3D graphics and realistic animations that make the game more immersive.
-
It has many features that let you customize your players, team, balls, power-ups, outfits, bats, helmets and more.
-
It lets you play with your friends and family from around the world or offline on the same device.
-
It lets you join or create clubs and compete with other clubs in leagues and tournaments.
-
It has a global leaderboard that lets you check your rank and stats.
-
It works smoothly even on a 2G/3G network.
-
-
Cons of Cricket League APK Game
-
-
It requires an internet connection to play online matches.
-
It may have some bugs or glitches that affect the gameplay.
-
It may have some ads that interrupt the game.
-
It may consume a lot of battery or data while playing.
-
-
Conclusion
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In conclusion, Cricket League APK Game is a great cricket game that lets you play quick two over matches with real players from around the world. It has many features that make it one of the best cricket games available on Android devices. It is easy to learn, fun to play, exciting to watch, and authentic to experience. If you are a cricket fan who loves to play cricket games on your mobile device, then you should definitely try out Cricket League APK Game. You can download it from Google Play Store or APKCombo for free and enjoy playing it anytime, anywhere.
-
FAQs
-
Here are some frequently asked questions about Cricket League APK Game:
-
-
What is the size of Cricket League APK Game?
-
The size of Cricket League APK Game varies depending on your device, but it is around 100 MB.
-
Is Cricket League APK Game safe to download?
-
Yes, Cricket League APK Game is safe to download from Google Play Store or APKCombo. However, you should always scan any downloaded file with an antivirus before installing it on your device.
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Can I play Cricket League APK Game on PC?
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No, Cricket League APK Game is only available for Android devices. However, you can use an Android emulator like Bluestacks or Nox Player to run Cricket League APK Game on your PC.
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How can I contact the developer of Cricket League APK Game?
-
You can contact the developer of Cricket League APK Game by sending an email to support@miniclip.com or by visiting their website at https://www.miniclip.com/.
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What are some similar games to Cricket League APK Game?
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Some similar games to Cricket League APK Game are World Cricket Championship 2, Real Cricket 20, Stick Cricket Live, and Cricket Clash.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Drift Racing 2 on PC and Mac - The Sequel to the Best Drifting Game on Android.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Drift Racing 2 on PC and Mac - The Sequel to the Best Drifting Game on Android.md
deleted file mode 100644
index d188b32ab4b3fa3bb7a82d8695bb9c19ccaef6c1..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Drift Racing 2 on PC and Mac - The Sequel to the Best Drifting Game on Android.md
+++ /dev/null
@@ -1,113 +0,0 @@
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How to Download CarX Drift PC and Enjoy Realistic Drifting Experience
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If you are a fan of racing games and drifting, you might have heard of CarX Drift PC, one of the most popular and realistic drift racing games on Android. But did you know that you can also play this game on your PC or Mac? In this article, we will show you how to download CarX Drift PC on your computer and enjoy the thrilling drifting experience on a bigger screen. We will also give you some tips and tricks on how to play CarX Drift PC and improve your skills.
CarX Drift PC is a racing game developed by CarX Technologies, LLC. It is based on the mobile game CarX Drift Racing, which has over 30 million downloads on Google Play. CarX Drift PC is all about realistic driving physics, detailed customization and tuning of car parameters, a number of cities and special racing track locations, an array of vinyls to design the look of your vehicle, open online rooms and competitions enhanced with new graphics. You can choose from over 100 cars, from sports cars to muscle cars, and drift around corners, burn tires, and compete with other players online. You can also create your own tracks using the track editor and share them with the community.
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The benefits of playing CarX Drift PC on your computer
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Playing CarX Drift PC on your computer has many benefits. First of all, you can enjoy the game in fullscreen and HD resolutions, which will make the graphics more stunning and immersive. Secondly, you can use your mouse, keyboard, or gamepad to control your car, which will give you more accuracy and precision than using touch controls. Thirdly, you can access more features and options that are not available on the mobile version, such as VR support, advanced keymapping, macro recording, video recording, etc. Finally, you can save your progress and data on your computer, which will prevent any loss or corruption due to device issues.
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How to Download and Install CarX Drift PC on Your PC or Mac
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The requirements and steps for downloading and installing CarX Drift PC on your PC or Mac using BlueStacks emulator
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To download and install CarX Drift PC on your PC or Mac, you will need an Android emulator. An Android emulator is a software that allows you to run Android apps and games on your computer. There are many Android emulators available online, but we recommend using BlueStacks, which is one of the most popular and reliable ones. Here are the requirements and steps for downloading and installing CarX Drift PC on your PC or Mac using BlueStacks emulator:
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Make sure that your computer meets the minimum system requirements for running BlueStacks. You will need Windows 7 or higher, 4 GB of RAM, 5 GB of free disk space, and an updated graphics driver. If you have a Mac, you will need macOS Sierra or higher, 4 GB of RAM, 4 GB of free disk space, and an updated graphics driver. You can check your system specifications by going to the Settings or System Preferences on your computer.
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Download and install BlueStacks from the official website. Follow the instructions on the screen and agree to the terms and conditions. The installation process may take a few minutes depending on your internet speed and computer performance.
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Launch BlueStacks and sign in with your Google account. If you don't have one, you can create one for free. This will allow you to access the Google Play Store and download apps and games.
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Go to the Google Play Store and search for CarX Drift PC. Alternatively, you can use this link to go directly to the game page. Click on the Install button and wait for the download and installation to complete.
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Once the game is installed, you can find it on the BlueStacks home screen or in the My Apps tab. Click on the game icon to launch it and start playing CarX Drift PC on your PC or Mac.
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The advantages of using BlueStacks emulator to play CarX Drift PC
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Using BlueStacks emulator to play CarX Drift PC has many advantages. Here are some of them:
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You can enjoy faster loading times and smoother gameplay than on your mobile device.
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You can customize your keyboard or gamepad controls to suit your preferences and style. You can also use the mouse to steer your car and adjust the camera angle.
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You can use the BlueStacks features to enhance your gaming experience, such as Multi-Instance, Eco Mode, Game Mode, Screen Recorder, Macro Recorder, etc.
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You can play CarX Drift PC in VR mode if you have a compatible VR headset and controller. This will make you feel like you are inside the car and give you a more immersive drifting experience.
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How to Play CarX Drift PC and Improve Your Skills
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The basic controls and tips for playing CarX Drift PC on your PC or Mac
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Playing CarX Drift PC on your PC or Mac is easy and fun. Here are the basic controls and tips for playing CarX Drift PC on your computer:
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Control
Action
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W or Up Arrow
Accelerate
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S or Down Arrow
Brake/Reverse
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A or Left Arrow
Steer Left
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D or Right Arrow
Steer Right
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Spacebar
Handbrake
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N
Nitro Boost
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C
Change Camera View
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M
Mute/Unmute Sound
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P
Pause/Resume Game
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Esc
Exit Game/Menu
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Here are some tips to help you play better:
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To drift, you need to use the handbrake and steer in the opposite direction of the turn. You also need to balance the throttle and brake to maintain the drift angle and speed.
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To earn more points, you need to drift as long as possible, as close as possible to the walls and other objects, and as fast as possible. You also need to chain multiple drifts together without losing control.
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To unlock more cars, tracks, vinyls, and upgrades, you need to complete missions, challenges, tournaments, and online events. You also need to earn coins and gold by drifting, winning races, watching ads, etc.
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To customize and tune your car, you need to go to the garage menu and select the options you want. You can change the color, wheels, body kits, spoilers, exhausts, etc. of your car. You can also adjust the engine power, suspension stiffness, steering angle, tire pressure, etc. of your car.
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The best practices and strategies for mastering the drift and competing online
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If you want to master the drift and compete online with other players, you need to practice a lot and learn from your mistakes. Here are some best practices and strategies for mastering the drift and competing online:
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Watch the tutorials and guides on the game menu and on YouTube. They will teach you the basics and advanced techniques of drifting, such as counter-steering, weight shifting, throttle control, etc.
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Practice on different tracks and cars. Each track and car has its own characteristics and challenges. You need to adapt your driving style and settings to suit them.
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Learn from other players. You can watch the replays of the top players and see how they drift, what lines they take, what settings they use, etc. You can also join online rooms and chat with other players, ask for tips, feedback, etc.
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Challenge yourself. You can set your own goals and try to beat them, such as achieving a certain score, time, speed, etc. You can also participate in online events and tournaments and compete with other players from around the world.
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Conclusion
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CarX Drift PC is a great game for anyone who loves racing and drifting. It offers realistic driving physics, detailed customization and tuning of car parameters, a number of cities and special racing track locations, an array of vinyls to design the look of your vehicle, open online rooms and competitions enhanced with new graphics. You can download and play CarX Drift PC on your PC or Mac using BlueStacks emulator, which will give you many benefits and features. You can also improve your skills by following the tips and tricks we shared in this article. We hope you enjoyed this article and found it helpful. Now go ahead and download CarX Drift PC and enjoy the realistic drifting experience on your computer.
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FAQs
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Q1: Is CarX Drift PC free to play?
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A1: Yes, CarX Drift PC is free to play. However, it contains in-app purchases that allow you to buy coins, gold, cars, tracks, vinyls, etc. You can also watch ads to earn some rewards.
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Q2: Can I customize and tune my car in CarX Drift PC?
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A2: Yes, you can customize and tune your car in CarX Drift PC. You can change the color, wheels, body kits, spoilers, exhausts, etc. of your car. You can also adjust the engine power, suspension stiffness, steering angle, tire pressure, etc. of your car.
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Q3: Can I play CarX Drift PC offline?
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A3: Yes, you can play CarX Drift PC offline. However, you will not be able to access some features and modes that require an internet connection, such as online rooms, events, tournaments, etc.
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Q4: What are the best cars to use in CarX Drift PC?
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A4: There is no definitive answer to this question, as different cars have different strengths and weaknesses. It also depends on your personal preference and style. However, some of the most popular and recommended cars are the Nissan Skyline GT-R R34 V-Spec II Nür (Nissan GT-R), the Toyota Supra RZ (Toyota Supra), the Mazda RX-7 FD (Mazda RX-7), the BMW M3 E46 (BMW M3), and the Subaru Impreza WRX STI (Subaru WRX).
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Q5: How can I contact the developers of CarX Drift PC?
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A5: You can contact the developers of CarX Drift PC by sending an email to support@carx-tech.com or by visiting their website at https://carx-tech.com/. You can also follow them on Facebook at https://www.facebook.com/carxdriftracing/ or on Instagram at https://www.instagram.com/carxdriftracing/.
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AFK Soccer Mod APK: A Review
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If you are a fan of football games and want to build your dream team with the stars from all around the world, you might want to check out AFK Soccer. This is an online mobile sports game that combines RPG elements with amazing football gameplay. But what if you want to enjoy the game without any limitations or restrictions? That's where AFK Soccer Mod APK comes in. In this article, we will review what AFK Soccer is, what AFK Soccer Mod APK is, and how to download and install it. We will also share some tips and tricks for improving your game in AFK Soccer.
AFK Soccer is a game developed by Texas Poker Cassino Games and released in June 2022. It is available for both Android and iOS devices. The game has over 50,000 downloads on Google Play Store and has positive reviews from users.
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Features of AFK Soccer
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AFK Soccer has many features that make it an enjoyable and addictive game for football fans. Some of these features are:
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COLLECT AND LEVEL UP UNIQUE FOOTBALL STARS: You can collect dozens of original football stars from the top countries in the world and level them up to make them stronger and more skilled.
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AMAZING FOOTBALL GAMEPLAY: AFK Soccer has a deep, yet simple-to-understand, 5v5 football game simulation engine. Each star makes decisions based on his preferred style of play. You will be amazed by the incredible plays that will unfold right before your eyes! Each game is guaranteed to be different from the previous one.
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STRATEGY, NOT REFLEXES: To play this game well, you need to make great strategic decisions - which formation to bring to the match, which stars do you invest your resources to level them up. You don’t need to master complex controls on a touchscreen - the game takes care of that for you!
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CONTINUOUS REWARDS: Don’t have time to play hours per day? Don’t worry, this game is perfect for you! You gain resources even while you’re away. Come back once per day, level up your stars, and make progress in the many game modes available. This is a chill online sports mobile game that respects your time!
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Gameplay of AFK Soccer
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The gameplay of AFK Soccer is simple and fun. You start by choosing a country to represent and then selecting five stars to form your team. You can choose from different positions such as goalkeeper, defender, midfielder, or striker. Each star has different attributes such as speed, power, skill, stamina, and luck.
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Once you have your team ready, you can enter different modes such as league mode, tournament mode, or challenge mode. In each mode, you will face different opponents with different levels of difficulty. You can watch the match unfold in real-time or skip it if you want. The match lasts for 90 seconds and the team with the most goals wins. You can also use special skills such as speed boost, power shot, or skill pass to gain an advantage over your rivals.
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Strategy of AFK Soccer
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AFK Soccer is not just a game of luck, but also a game of strategy. You need to plan ahead and make smart choices to succeed in this game. Some of the strategic aspects of AFK Soccer are:
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FORMATION: You can choose from different formations such as 4-4-2, 3-5-2, or 4-3-3. Each formation has its own strengths and weaknesses, and you need to adapt to the situation and the opponent. For example, a 4-4-2 formation is balanced and versatile, but it might struggle against a 3-5-2 formation that has more midfielders.
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STAR SELECTION: You can collect and level up dozens of stars from different countries and positions. Each star has his own personality, style of play, and special skill. You need to choose the stars that suit your formation, strategy, and preference. For example, if you want to play a fast and aggressive game, you might want to choose stars with high speed and power attributes.
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RESOURCE MANAGEMENT: You need to manage your resources wisely in this game. You have two main resources: coins and gems. Coins are used to level up your stars and buy new ones. Gems are used to unlock special skills and buy premium items. You can earn coins and gems by playing matches, completing quests, or watching ads. You can also buy them with real money if you want.
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Rewards of AFK Soccer
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AFK Soccer is a rewarding game that gives you many incentives to keep playing and improving your team. Some of the rewards that you can get from this game are:
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TROPHIES AND RANKINGS: You can earn trophies by winning matches and tournaments. Trophies are used to determine your ranking in the global leaderboard. You can compete with other players from all over the world and see who is the best AFK Soccer player.
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CHESTS AND CARDS: You can get chests by playing matches or completing quests. Chests contain cards that can be used to unlock new stars or upgrade existing ones. There are different types of chests such as bronze, silver, gold, or platinum. The higher the quality of the chest, the better the cards inside.
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ACHIEVEMENTS AND BADGES: You can complete various achievements by playing the game and fulfilling certain conditions. Achievements give you extra coins, gems, or chests as rewards. You can also earn badges by collecting a certain number of stars from a specific country or position. Badges show your progress and dedication in the game.
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What is AFK Soccer Mod APK?
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AFK Soccer Mod APK is a modified version of the original AFK Soccer game that gives you some extra features and advantages that are not available in the official version. Some of these features are:
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Benefits of AFK Soccer Mod APK
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AFK Soccer Mod APK has many benefits that make it more enjoyable and convenient for players who want to have more fun and less hassle in the game. Some of these benefits are:
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UNLIMITED COINS AND GEMS: With AFK Soccer Mod APK, you don't have to worry about running out of coins or gems ever again. You can get unlimited amounts of these resources for free and use them to level up your stars, buy new ones, unlock skills, or buy premium items.
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ALL STARS UNLOCKED: With AFK Soccer Mod APK, you don't have to wait for chests or cards to unlock new stars. You can access all the stars in the game from the start and choose whoever you want for your team.
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NO ADS: With AFK Soccer Mod APK, you don't have to watch annoying ads every time you want to open a chest or get some extra coins or gems. You can enjoy the game without any interruptions or distractions.
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Risks of AFK Soccer Mod APK
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AFK Soccer Mod APK is not an official version of the game and it is not endorsed or supported by the developers or publishers of AFK Soccer. Therefore, using AFK Soccer Mod APK comes with some risks that you should be aware of before downloading and installing it. Some of these risks are:
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BAN OR SUSPENSION: Using AFK Soccer Mod APK might violate the terms and conditions of the game and result in your account being banned or suspended. You might lose all your progress and achievements in the game and be unable to play it anymore.
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VIRUS OR MALWARE: Downloading AFK Soccer Mod APK from unknown or untrusted sources might expose your device to virus or malware infection. This might harm your device's performance, security, or privacy. You might lose your personal data or have your device hacked by malicious actors.
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COMPATIBILITY ISSUES: AFK Soccer Mod APK might not be compatible with the latest version of the game or your device's operating system. This might cause the game to crash, freeze, or glitch. You might experience poor graphics, sound, or gameplay quality.
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How to Download and Install AFK Soccer Mod APK
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If you still want to try AFK Soccer Mod APK despite the risks, you need to follow some steps to download and install it on your device. Here are the steps:
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UNINSTALL THE ORIGINAL GAME: You need to uninstall the official version of AFK Soccer from your device before installing the modded version. This is to avoid any conflicts or errors between the two versions.
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FIND A TRUSTED SOURCE: You need to find a reliable and reputable source that provides AFK Soccer Mod APK for download. You can search online for reviews, ratings, or feedback from other users who have tried the modded version. You can also use antivirus software to scan the file before downloading it.
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ENABLE UNKNOWN SOURCES: You need to enable unknown sources on your device's settings to allow the installation of apps from sources other than Google Play Store. You can do this by going to Settings > Security > Unknown Sources and toggling it on.
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DOWNLOAD AND INSTALL THE MODDED VERSION: You need to download the AFK Soccer Mod APK file from the source you have chosen and save it on your device's storage. Then, you need to locate the file and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.
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ENJOY THE GAME: You can now launch the game and enjoy the modded features. You can also disable unknown sources on your device's settings after installing the game for security reasons.
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Tips and Tricks for AFK Soccer
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If you want to improve your skills and performance in AFK Soccer, you can follow some tips and tricks that will help you in the game. Here are some of them:
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How to Improve Your Technical Skills
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Your technical skills are important in AFK Soccer as they determine how well you can control the ball, pass, shoot, dribble, or defend. To improve your technical skills, you can do the following:
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PRACTICE IN TRAINING MODE: You can practice your technical skills in training mode where you can choose different drills such as shooting, passing, dribbling, or defending. You can also adjust the difficulty level and the number of stars involved in each drill.
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WATCH REPLAYS AND LEARN FROM MISTAKES: You can watch replays of your matches and analyze what you did right or wrong in each situation. You can learn from your mistakes and see how you can improve your decision-making, positioning, timing, or execution.
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TRY DIFFERENT STARS AND SKILLS: You can try different stars and skills in different positions and formations. You can see how each star performs in different scenarios and how each skill affects the outcome of the match. You can also experiment with different combinations of stars and skills to find your optimal strategy.
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How to Choose the Best Formation and Stars
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Your formation and stars are crucial in AFK Soccer as they determine how you play and how you counter your opponent's strategy. To choose the best formation and stars, you can do the following:
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MATCH YOUR FORMATION TO YOUR PLAYSTYLE: You can choose a formation that suits your playstyle and preference. For example, if you like to play a defensive game, you can choose a formation with more defenders such as 5-4-1 or 4-5-1. If you like to play an offensive game, you can choose a formation with more attackers such as 4-3-3 or 3-4-3.
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ADAPT YOUR FORMATION TO YOUR OPPONENT: You can also change your formation according to your opponent's strategy and formation. For example, if your opponent is playing a defensive formation, you can choose an offensive formation to break their defense. If your opponent is playing an offensive formation, you can choose a defensive formation to counter their attack.
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BALANCE YOUR STARS AND SKILLS: You can also balance your stars and skills in your formation to create a well-rounded team. For example, you can have a mix of stars with high speed, power, skill, stamina, and luck attributes. You can also have a mix of skills such as speed boost, power shot, skill pass, or defense boost.
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How to Earn More Resources and Progress Faster
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Your resources and progress are important in AFK Soccer as they allow you to level up your stars, unlock new ones, and access more game modes and features. To earn more resources and progress faster, you can do the following:
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PLAY MORE MATCHES AND MODES: You can earn more coins and gems by playing more matches and modes in the game. You can play league mode, tournament mode, or challenge mode to earn different amounts of coins and gems. You can also get bonus coins and gems by winning matches, scoring goals, or completing quests.
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OPEN MORE CHESTS AND CARDS: You can get more chests and cards by playing matches or completing quests. Chests and cards contain stars that you can use to unlock new ones or upgrade existing ones. You can also get rare or legendary stars that have higher attributes and skills.
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WATCH ADS AND CLAIM FREE GIFTS: You can watch ads or claim free gifts in the game to get extra coins, gems, chests, or cards. You can watch ads every few hours or after opening a chest to get more rewards. You can also claim free gifts every day or every week by logging in to the game.
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Conclusion
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AFK Soccer is a fun and addictive game that combines football gameplay with RPG elements. You can collect and level up dozens of unique football stars from different countries and positions. You can also enjoy amazing football gameplay with different modes and strategies. However, if you want to have more freedom and convenience in the game, you might want to try AFK Soccer Mod APK. This is a modified version of the game that gives you unlimited coins and gems, all stars unlocked, and no ads. However, you should also be aware of the risks of using AFK Soccer Mod APK such as ban or suspension, virus or malware infection, or compatibility issues. Therefore, you should download and install AFK Soccer Mod APK at your own risk and discretion.
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FAQs
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Here are some frequently asked questions about AFK Soccer and AFK Soccer Mod APK:
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Is AFK Soccer free to play?: Yes, AFK Soccer is free to play and download on both Android and iOS devices. However, it also contains in-app purchases that allow you to buy coins, gems, or premium items with real money.
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Is AFK Soccer online or offline?: AFK Soccer is an online game that requires an internet connection to play. You need to connect to the game server to access the game modes, features, and rewards.
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Is AFK Soccer Mod APK safe to use?: AFK Soccer Mod APK is not an official version of the game and it is not endorsed or supported by the developers or publishers of AFK Soccer. Therefore, using AFK Soccer Mod APK might be risky and unsafe for your device and account. You should only download and install AFK Soccer Mod APK from trusted sources and scan the file with antivirus software before installing it.
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How do I update AFK Soccer Mod APK?: AFK Soccer Mod APK might not be compatible with the latest version of the game or your device's operating system. Therefore, you might need to update AFK Soccer Mod APK regularly to keep it working properly. To update AFK Soccer Mod APK, you need to uninstall the old version and download and install the new version from the same source.
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How do I contact the support team of AFK Soccer?: If you have any questions, problems, or feedback about AFK Soccer or AFK Soccer Mod APK, you can contact the support team of AFK Soccer by sending an email to afksoccer@gmail.com.
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index 3755c320cbd3ba88e3b8c9022a943f16b60524eb..0000000000000000000000000000000000000000
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Shell Racing Legends APK: A Game for Ferrari Fans
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If you are a fan of Ferrari cars and racing games, you might want to check out Shell Racing Legends APK, a game that lets you collect, connect, and compete with an exclusive Ferrari car collection. In this article, we will tell you what Shell Racing Legends APK is, how to play it, why you should download it, and how to download and install it on your Android device.
Shell Racing Legends APK is a racing game developed by Carbon12011, in collaboration with Shell and Ferrari. The game is based on the Shell Road Toy Cars collection, which features four die-cast Ferrari models that you can buy at Shell gas stations. The game allows you to experience these cars in both the real and digital worlds, by connecting them with Bluetooth to your device and racing them on different tracks. You can also unlock the digital version of each car by scanning it with the app and adding it to your garage.
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How to play Shell Racing Legends APK?
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The game has four main aspects that you can enjoy:
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You can turn on and control your car by connecting it with Bluetooth to the app and using your device as a remote controller. You can challenge your friends and family to high-intensity real-world racing, or practice your skills on your own.
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You can unlock the digital version of each car by scanning it with the app and adding it to your garage. You can also customize your cars with different colors, decals, and upgrades. You can view the history and key statistics of each car in your garage, as well as compare them with other cars.
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-
Complete challenges
-
You can compete with your collection on different tracks and modes, such as time trial, race, drift, and drag. You can earn trophies by completing objectives and unlocking the next challenge. You can also see how you rank against other players on the leaderboard.
-
Driving methods
-
You can go to Settings and choose the controls that fit your driving style. You can tilt the device or touch the screen to steer, choose your level of driver assistance, and adjust the sensitivity and feedback. You can also remove the driver assistance to be more competitive and improve your skills and times.
-
Car history
-
You can discover the history and key statistics behind each of these exclusive Ferraris once they are unlocked and in your garage. You can learn about their design, performance, features, and achievements in the real world.
-
Why should you download Shell Racing Legends APK?
-
Shell Racing Legends APK is a game that offers many features that make it fun, exciting, and realistic. Here are some of them:
-
Features of Shell Racing Legends APK
-
High-quality graphics and sound
-
The game has stunning 3D graphics that bring the cars and tracks to life. The game also has realistic sound effects that enhance the racing experience.
-
Realistic physics and gameplay
-
The game has realistic physics that simulate the behavior of the cars on different surfaces and conditions. The game also has realistic gameplay that requires you to master the skills of braking, accelerating, steering, drifting, and overtaking.
-
Exclusive Ferrari car collection
-
The game features four die-cast Ferrari models that are based on the real ones. They are:
-
-
Ferrari 250 Testa Rossa (1957)
-
Ferrari 250 GTO (1962)
-
Ferrari 512
Ferrari F40 (1987)
-
The Ferrari F40 was the last Ferrari model personally approved by Enzo Ferrari before his death in 1988. It was a celebration of Ferrari's 40th anniversary and a successor to the 288 GTO. The F40 was designed by Pininfarina and engineered by Nicola Materazzi, who also worked on the 288 GTO. The F40 was a mid-engine, rear-wheel drive sports car with a twin-turbocharged V8 engine that produced 478 PS (352 kW; 471 hp) in the European version and 484 PS (356 kW; 477 hp) in the US version. The F40 was one of the fastest, most powerful, and most expensive cars of its time, with a top speed of 340 km/h (211 mph) and a price tag of US$ 400,000 in 1987 ($950,000 today). The F40 was also one of the most driver-focused cars of its era, with no radio, carpet, or inner-door panels and a windshield made of plastic. Only 1,311 units were produced from 1987 to 1992, making it a rare and highly sought-after collector's item.
Ferrari 488 GTB (2015)
-
The Ferrari 488 GTB is the successor of the 458, and the first mid-engine Ferrari to use a turbocharged V8 engine since the F40. The 488 GTB was launched in 2015, and it is based on the Ferrari 488 GTE and GT3 race cars. The 488 GTB has a 3.9-litre twin-turbocharged V8 engine that produces 670 PS (493 kW; 661 hp) at 8,000 rpm and 760 N⋅m (561 lb⋅ft) of torque at 3,000 rpm. The engine has a specific power output of 171.7 PS (126.3 kW; 169.4 hp) per litre, which is the highest of any road-going Ferrari. The engine also has a variable torque management system that adjusts the torque delivery according to the gear selected.
-
The 488 GTB has a 7-speed dual-clutch automatic transmission that can shift gears in just 8 milliseconds. The transmission also has a variable torque management system that adjusts the torque delivery according to the gear selected. The car has a rear mid-engine, rear-wheel drive layout, with an electronic differential and a traction control system. The car also has a magnetorheological suspension system that adapts to the road conditions and driving mode.
-
The 488 GTB has a body designed by Ferrari Styling Centre under Flavio Manzoni, and it is inspired by the Ferrari 308 GTB and the LaFerrari. The car has a low, wide, and aggressive stance, with large air intakes, LED headlights, and a sculpted rear spoiler. The car also has aerodynamic features that improve its performance and efficiency, such as a double front spoiler, an underbody vortex generator, an active rear diffuser, and a blown rear spoiler. The car has a drag coefficient of 0.32 and generates up to 325 kg (717 lb) of downforce at 250 km/h (155 mph).
-
The 488 GTB has a cockpit that is designed to be ergonomic and driver-oriented. The car has a leather-wrapped steering wheel that incorporates the start button, the manettino switch, and the shift paddles. The car also has a digital instrument cluster that displays various information such as speed, rpm, gear, lap time, and driving mode. The car also has a central infotainment system that controls the audio, navigation, and connectivity functions. The car also has carbon-fibre racing seats that provide comfort and support.
.
Different tracks and modes
-
The game has different tracks and modes that you can choose from, depending on your preference and skill level. You can race on iconic circuits such as Monza, Silverstone, Spa-Francorchamps, and Yas Marina, or on urban tracks such as New York, London, Shanghai, and Dubai. You can also race on night tracks or in different weather conditions, such as rain, snow, or fog.
-
The game has different modes that you can play, such as:
-
-
Single Race: You can race against the AI or against your friends in local multiplayer mode. You can choose the track, the car, the difficulty, and the number of laps.
-
Career Mode: You can start from the bottom and work your way up to become a racing legend. You can compete in different championships and events, earn money and fame, and unlock new cars and upgrades.
-
Challenge Mode: You can test your skills and challenge yourself in various scenarios, such as time trial, race, drift, and drag. You can earn trophies by completing objectives and unlocking the next challenge.
-
Online Mode: You can race against other players from around the world in real-time multiplayer mode. You can join or create a lobby, choose the track and the car, and compete for the best time and position. You can also see how you rank against other players on the leaderboard and earn achievements.
-
-
How to download and install Shell Racing Legends APK?
-
If you want to download and install Shell Racing Legends APK on your Android device, you need to follow these steps:
-
Requirements for Shell Racing Legends APK
-
Before you download and install Shell Racing Legends APK, you need to make sure that your device meets these requirements:
-
-
Your device must have Android 4.4 or higher.
-
Your device must have at least 2 GB of RAM and 500 MB of free storage space.
-
Your device must have Bluetooth enabled and compatible with the Shell Road Toy Cars.
-
Your device must have a stable internet connection for online mode.
-
-
Steps to download and install Shell Racing Legends APK
-
After you have checked the requirements, you can follow these steps to download and install Shell Racing Legends APK:
Click on the download button and wait for the APK file to be downloaded on your device.
-
Go to your device settings and enable the installation of apps from unknown sources.
-
Locate the downloaded APK file on your device and tap on it to start the installation process.
-
Follow the instructions on the screen and wait for the installation to be completed.
-
Launch the app and enjoy playing Shell Racing Legends APK.
-
-
Conclusion
-
Shell Racing Legends APK is a racing game that lets you collect, connect, and compete with an exclusive Ferrari car collection. The game has high-quality graphics and sound, realistic physics and gameplay, exclusive Ferrari car collection, different tracks and modes, leaderboard and achievements, and more. The game also allows you to experience both the real and digital worlds of racing by connecting your Shell Road Toy Cars with Bluetooth to your device. If you are a fan of Ferrari cars and racing games, you should definitely download Shell Racing Legends APK and enjoy the thrill of driving these legendary cars.
-
Frequently Asked Questions
-
Here are some of the frequently asked questions about Shell Racing Legends APK:
-
Q: How much does Shell Racing Legends APK cost?
-
A: Shell Racing Legends APK is free to download and play. However, some in-game items may require real money to purchase.
-
Q: How can I get more Shell Road Toy Cars?
-
A: You can buy more Shell Road Toy Cars at participating Shell gas stations around the world. Each car costs US$ 10 or equivalent in local currency.
-
Q: How can I connect my Shell Road Toy Car with my device?
-
A: You need to turn on Bluetooth on your device and pair it with your car. Then, you need to launch the app and scan your car with the app's camera. Once your car is recognized by the app , you can start driving it with your device as a remote controller.
-
Q: How can I customize my cars in Shell Racing Legends APK?
-
A: You can customize your cars by going to your garage and tapping on the car you want to modify. You can change the color, the decals, and the upgrades of your car. You can also view the history and key statistics of your car in your garage.
-
Q: How can I play online mode in Shell Racing Legends APK?
-
A: You can play online mode by going to the main menu and tapping on the online mode button. You can join or create a lobby, choose the track and the car, and race against other players from around the world. You can also see how you rank against other players on the leaderboard and earn achievements.
-
Q: How can I contact the developer of Shell Racing Legends APK?
-
A: You can contact the developer of Shell Racing Legends APK by sending an email to carbon12011@gmail.com or by visiting their website at https://carbon12011.com/.
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-
-
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diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py
deleted file mode 100644
index 396723be1e40461f141081e8dbf7b67b451e3fc2..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py
+++ /dev/null
@@ -1,459 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import paddle
-import PIL.Image
-
-from paddlenlp.transformers import (
- CLIPFeatureExtractor,
- CLIPTextModelWithProjection,
- CLIPTokenizer,
- CLIPVisionModelWithProjection,
-)
-
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
-from .modeling_text_unet import UNetFlatConditionModel
-from .pipeline_versatile_diffusion_dual_guided import (
- VersatileDiffusionDualGuidedPipeline,
-)
-from .pipeline_versatile_diffusion_image_variation import (
- VersatileDiffusionImageVariationPipeline,
-)
-from .pipeline_versatile_diffusion_text_to_image import (
- VersatileDiffusionTextToImagePipeline,
-)
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-class VersatileDiffusionPipeline(DiffusionPipeline):
- r"""
- Pipeline for generation using Versatile Diffusion.
-
- 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:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder ([`CLIPTextModelWithProjection`]):
- Frozen text-encoder. Versatile Diffusion uses the text portion of
- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically
- the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- image_encoder ([`CLIPVisionModelWithProjection`]):
- Frozen vision-encoder. Versatile Diffusion 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.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- image_unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- text_unet ([`UNetFlatConditionModel`]): xxx.
- 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`].
- image_feature_extractor ([`CLIPFeatureExtractor`]):
- Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- """
-
- tokenizer: CLIPTokenizer
- image_feature_extractor: CLIPFeatureExtractor
- text_encoder: CLIPTextModelWithProjection
- image_encoder: CLIPVisionModelWithProjection
- image_unet: UNet2DConditionModel
- text_unet: UNetFlatConditionModel
- vae: AutoencoderKL
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
-
- def __init__(
- self,
- tokenizer: CLIPTokenizer,
- image_feature_extractor: CLIPFeatureExtractor,
- text_encoder: CLIPTextModelWithProjection,
- image_encoder: CLIPVisionModelWithProjection,
- image_unet: UNet2DConditionModel,
- text_unet: UNetFlatConditionModel,
- vae: AutoencoderKL,
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
- ):
- super().__init__()
-
- self.register_modules(
- tokenizer=tokenizer,
- image_feature_extractor=image_feature_extractor,
- text_encoder=text_encoder,
- image_encoder=image_encoder,
- image_unet=image_unet,
- text_unet=text_unet,
- vae=vae,
- scheduler=scheduler,
- )
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
- @paddle.no_grad()
- def image_variation(
- self,
- image: Union[paddle.Tensor, PIL.Image.Image],
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- 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[paddle.Generator, List[paddle.Generator]]] = None,
- latents: Optional[paddle.Tensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
- callback_steps: Optional[int] = 1,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
- The image prompt or prompts to guide the image generation.
- height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.image_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.
- 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. 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 (`paddle.Generator`, *optional*):
- A [paddle generator] to make generation
- deterministic.
- latents (`paddle.Tensor`, *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`.
- 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: paddle.Tensor)`.
- 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
- >>> from ppdiffusers import VersatileDiffusionPipeline
- >>> import paddle
- >>> import requests
- >>> from io import BytesIO
- >>> from PIL import Image
-
- >>> # let's download an initial image
- >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
-
- >>> response = requests.get(url)
- >>> image = Image.open(BytesIO(response.content)).convert("RGB")
-
- >>> pipe = VersatileDiffusionPipeline.from_pretrained(
- ... "shi-labs/versatile-diffusion"
- ... )
-
- >>> generator = paddle.Generator().manual_seed(0)
- >>> image = pipe.image_variation(image, generator=generator).images[0]
- >>> image.save("./car_variation.png")
- ```
-
- 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`.
- """
- expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys()
- components = {name: component for name, component in self.components.items() if name in expected_components}
- return VersatileDiffusionImageVariationPipeline(**components)(
- image=image,
- height=height,
- width=width,
- num_inference_steps=num_inference_steps,
- guidance_scale=guidance_scale,
- negative_prompt=negative_prompt,
- num_images_per_prompt=num_images_per_prompt,
- eta=eta,
- generator=generator,
- latents=latents,
- output_type=output_type,
- return_dict=return_dict,
- callback=callback,
- callback_steps=callback_steps,
- )
-
- @paddle.no_grad()
- def text_to_image(
- self,
- prompt: Union[str, List[str]],
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- 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[paddle.Generator, List[paddle.Generator]]] = None,
- latents: Optional[paddle.Tensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
- callback_steps: Optional[int] = 1,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.image_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.
- 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. 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 (`paddle.Generator`, *optional*):
- A [paddle generator] to make generation
- deterministic.
- latents (`paddle.Tensor`, *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`.
- 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: paddle.Tensor)`.
- 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
- >>> from ppdiffusers import VersatileDiffusionPipeline
- >>> import paddle
-
- >>> pipe = VersatileDiffusionPipeline.from_pretrained(
- ... "shi-labs/versatile-diffusion"
- ... )
-
- >>> generator = paddle.Generator().manual_seed(0)
- >>> image = pipe.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0]
- >>> image.save("./astronaut.png")
- ```
-
- 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`.
- """
- expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys()
- components = {name: component for name, component in self.components.items() if name in expected_components}
- temp_pipeline = VersatileDiffusionTextToImagePipeline(**components)
- output = temp_pipeline(
- prompt=prompt,
- height=height,
- width=width,
- num_inference_steps=num_inference_steps,
- guidance_scale=guidance_scale,
- negative_prompt=negative_prompt,
- num_images_per_prompt=num_images_per_prompt,
- eta=eta,
- generator=generator,
- latents=latents,
- output_type=output_type,
- return_dict=return_dict,
- callback=callback,
- callback_steps=callback_steps,
- )
- # swap the attention blocks back to the original state
- temp_pipeline._swap_unet_attention_blocks()
-
- return output
-
- @paddle.no_grad()
- def dual_guided(
- self,
- prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
- image: Union[str, List[str]],
- text_to_image_strength: float = 0.5,
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
- latents: Optional[paddle.Tensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
- callback_steps: Optional[int] = 1,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- height (`int`, *optional*, defaults to self.image_unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.image_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.
- 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. 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 (`paddle.Generator`, *optional*):
- A [paddle generator] to make generation
- deterministic.
- latents (`paddle.Tensor`, *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`.
- 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: paddle.Tensor)`.
- 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
- >>> from ppdiffusers import VersatileDiffusionPipeline
- >>> import paddle
- >>> import requests
- >>> from io import BytesIO
- >>> from PIL import Image
-
- >>> # let's download an initial image
- >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
-
- >>> response = requests.get(url)
- >>> image = Image.open(BytesIO(response.content)).convert("RGB")
- >>> text = "a red car in the sun"
-
- >>> pipe = VersatileDiffusionPipeline.from_pretrained(
- ... "shi-labs/versatile-diffusion"
- ... )
-
- >>> generator = paddle.Generator().manual_seed(0)
- >>> text_to_image_strength = 0.75
-
- >>> image = pipe.dual_guided(
- ... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
- ... ).images[0]
- >>> image.save("./car_variation.png")
- ```
-
- Returns:
- [`~pipelines.stable_diffusion.ImagePipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
- returning a tuple, the first element is a list with the generated images.
- """
-
- expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()
- components = {name: component for name, component in self.components.items() if name in expected_components}
- temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components)
- output = temp_pipeline(
- prompt=prompt,
- image=image,
- text_to_image_strength=text_to_image_strength,
- height=height,
- width=width,
- num_inference_steps=num_inference_steps,
- guidance_scale=guidance_scale,
- num_images_per_prompt=num_images_per_prompt,
- eta=eta,
- generator=generator,
- latents=latents,
- output_type=output_type,
- return_dict=return_dict,
- callback=callback,
- callback_steps=callback_steps,
- )
- temp_pipeline._revert_dual_attention()
-
- return output
diff --git a/spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/synthesis_engine.py b/spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/synthesis_engine.py
deleted file mode 100644
index f617e94a2589e5bb1ce1210af6a24178070b24c7..0000000000000000000000000000000000000000
--- a/spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/synthesis_engine.py
+++ /dev/null
@@ -1,502 +0,0 @@
-import threading
-from itertools import chain
-from typing import List, Optional, Tuple
-
-import numpy
-from scipy.signal import resample
-
-from ..acoustic_feature_extractor import OjtPhoneme
-from ..model import AccentPhrase, AudioQuery, Mora
-from .core_wrapper import CoreWrapper, OldCoreError
-from .synthesis_engine_base import SynthesisEngineBase
-
-unvoiced_mora_phoneme_list = ["A", "I", "U", "E", "O", "cl", "pau"]
-mora_phoneme_list = ["a", "i", "u", "e", "o", "N"] + unvoiced_mora_phoneme_list
-
-
-# TODO: move mora utility to mora module
-def to_flatten_moras(accent_phrases: List[AccentPhrase]) -> List[Mora]:
- """
- accent_phrasesに含まれるMora(とpause_moraがあればそれも)を
- すべて一つのリストに結合する
- Parameters
- ----------
- accent_phrases : List[AccentPhrase]
- AccentPhraseのリスト
- Returns
- -------
- moras : List[Mora]
- 結合されたMoraのリストを返す
- """
- return list(
- chain.from_iterable(
- accent_phrase.moras
- + (
- [accent_phrase.pause_mora]
- if accent_phrase.pause_mora is not None
- else []
- )
- for accent_phrase in accent_phrases
- )
- )
-
-
-def to_phoneme_data_list(phoneme_str_list: List[str]):
- """
- phoneme文字列のリストを、OjtPhonemeクラスのリストに変換する
- Parameters
- ----------
- phoneme_str_list : List[str]
- phoneme文字列のリスト
- Returns
- -------
- phoneme_list : List[OjtPhoneme]
- 変換されたOjtPhonemeクラスのリスト
- """
- phoneme_data_list = [
- OjtPhoneme(phoneme=p, start=i, end=i + 1)
- for i, p in enumerate(phoneme_str_list)
- ]
- phoneme_data_list = OjtPhoneme.convert(phoneme_data_list)
- return phoneme_data_list
-
-
-def split_mora(phoneme_list: List[OjtPhoneme]):
- """
- OjtPhonemeのリストから、
- 母音の位置(vowel_indexes)
- 母音の音素列(vowel_phoneme_list)
- 子音の音素列(consonant_phoneme_list)
- を生成し、返す
- Parameters
- ----------
- phoneme_list : List[OjtPhoneme]
- phonemeクラスのリスト
- Returns
- -------
- consonant_phoneme_list : List[OjtPhoneme]
- 子音の音素列
- vowel_phoneme_list : List[OjtPhoneme]
- 母音の音素列
- vowel_indexes : : List[int]
- 母音の位置
- """
- vowel_indexes = [
- i for i, p in enumerate(phoneme_list) if p.phoneme in mora_phoneme_list
- ]
- vowel_phoneme_list = [phoneme_list[i] for i in vowel_indexes]
- # postとprevのvowel_indexの差として考えられる値は1か2
- # 理由としてはphoneme_listは、consonant、vowelの組み合わせか、vowel一つの連続であるから
- # 1の場合はconsonant(子音)が存在しない=母音のみ(a/i/u/e/o/N/cl/pau)で構成されるモーラ(音)である
- # 2の場合はconsonantが存在するモーラである
- # なので、2の場合(else)でphonemeを取り出している
- consonant_phoneme_list: List[Optional[OjtPhoneme]] = [None] + [
- None if post - prev == 1 else phoneme_list[post - 1]
- for prev, post in zip(vowel_indexes[:-1], vowel_indexes[1:])
- ]
- return consonant_phoneme_list, vowel_phoneme_list, vowel_indexes
-
-
-def pre_process(
- accent_phrases: List[AccentPhrase],
-) -> Tuple[List[Mora], List[OjtPhoneme]]:
- """
- AccentPhraseモデルのリストを整形し、処理に必要なデータの原型を作り出す
- Parameters
- ----------
- accent_phrases : List[AccentPhrase]
- AccentPhraseモデルのリスト
- Returns
- -------
- flatten_moras : List[Mora]
- AccentPhraseモデルのリスト内に含まれるすべてのMoraをリスト化したものを返す
- phoneme_data_list : List[OjtPhoneme]
- flatten_morasから取り出したすべてのPhonemeをOjtPhonemeに変換したものを返す
- """
- flatten_moras = to_flatten_moras(accent_phrases)
-
- phoneme_each_mora = [
- ([mora.consonant] if mora.consonant is not None else []) + [mora.vowel]
- for mora in flatten_moras
- ]
- phoneme_str_list = list(chain.from_iterable(phoneme_each_mora))
- phoneme_str_list = ["pau"] + phoneme_str_list + ["pau"]
-
- phoneme_data_list = to_phoneme_data_list(phoneme_str_list)
-
- return flatten_moras, phoneme_data_list
-
-
-class SynthesisEngine(SynthesisEngineBase):
- def __init__(
- self,
- core: CoreWrapper,
- ):
- """
- core.yukarin_s_forward: 音素列から、音素ごとの長さを求める関数
- length: 音素列の長さ
- phoneme_list: 音素列
- speaker_id: 話者番号
- return: 音素ごとの長さ
-
- core.yukarin_sa_forward: モーラごとの音素列とアクセント情報から、モーラごとの音高を求める関数
- length: モーラ列の長さ
- vowel_phoneme_list: 母音の音素列
- consonant_phoneme_list: 子音の音素列
- start_accent_list: アクセントの開始位置
- end_accent_list: アクセントの終了位置
- start_accent_phrase_list: アクセント句の開始位置
- end_accent_phrase_list: アクセント句の終了位置
- speaker_id: 話者番号
- return: モーラごとの音高
-
- core.decode_forward: フレームごとの音素と音高から波形を求める関数
- length: フレームの長さ
- phoneme_size: 音素の種類数
- f0: フレームごとの音高
- phoneme: フレームごとの音素
- speaker_id: 話者番号
- return: 音声波形
-
- speakers: coreから取得したspeakersに関するjsonデータの文字列
-
- supported_devices:
- coreから取得した対応デバイスに関するjsonデータの文字列
- Noneの場合はコアが情報の取得に対応していないため、対応デバイスは不明
- """
- super().__init__()
- self.core = core
- self._speakers = self.core.metas()
- self.mutex = threading.Lock()
- try:
- self._supported_devices = self.core.supported_devices()
- except OldCoreError:
- self._supported_devices = None
- self.default_sampling_rate = 24000
-
- @property
- def speakers(self) -> str:
- return self._speakers
-
- @property
- def supported_devices(self) -> Optional[str]:
- return self._supported_devices
-
- def initialize_speaker_synthesis(self, speaker_id: int, skip_reinit: bool):
- try:
- with self.mutex:
- # 以下の条件のいずれかを満たす場合, 初期化を実行する
- # 1. 引数 skip_reinit が False の場合
- # 2. 話者が初期化されていない場合
- if (not skip_reinit) or (not self.core.is_model_loaded(speaker_id)):
- self.core.load_model(speaker_id)
- except OldCoreError:
- pass # コアが古い場合はどうしようもないので何もしない
-
- def is_initialized_speaker_synthesis(self, speaker_id: int) -> bool:
- try:
- return self.core.is_model_loaded(speaker_id)
- except OldCoreError:
- return True # コアが古い場合はどうしようもないのでTrueを返す
-
- def replace_phoneme_length(
- self, accent_phrases: List[AccentPhrase], speaker_id: int
- ) -> List[AccentPhrase]:
- """
- accent_phrasesの母音・子音の長さを設定する
- Parameters
- ----------
- accent_phrases : List[AccentPhrase]
- アクセント句モデルのリスト
- speaker_id : int
- 話者ID
- Returns
- -------
- accent_phrases : List[AccentPhrase]
- 母音・子音の長さが設定されたアクセント句モデルのリスト
- """
- # モデルがロードされていない場合はロードする
- self.initialize_speaker_synthesis(speaker_id, skip_reinit=True)
- # phoneme
- # AccentPhraseをすべてMoraおよびOjtPhonemeの形に分解し、処理可能な形にする
- flatten_moras, phoneme_data_list = pre_process(accent_phrases)
- # OjtPhonemeの形に分解されたもの(phoneme_data_list)から、vowel(母音)の位置を抜き出す
- _, _, vowel_indexes_data = split_mora(phoneme_data_list)
-
- # yukarin_s
- # OjtPhonemeのリストからOjtPhonemeのPhoneme ID(OpenJTalkにおける音素のID)のリストを作る
- phoneme_list_s = numpy.array(
- [p.phoneme_id for p in phoneme_data_list], dtype=numpy.int64
- )
- # Phoneme IDのリスト(phoneme_list_s)をyukarin_s_forwardにかけ、推論器によって適切な音素の長さを割り当てる
- with self.mutex:
- phoneme_length = self.core.yukarin_s_forward(
- length=len(phoneme_list_s),
- phoneme_list=phoneme_list_s,
- speaker_id=numpy.array(speaker_id, dtype=numpy.int64).reshape(-1),
- )
-
- # yukarin_s_forwarderの結果をaccent_phrasesに反映する
- # flatten_moras変数に展開された値を変更することでコード量を削減しつつaccent_phrases内のデータを書き換えている
- for i, mora in enumerate(flatten_moras):
- mora.consonant_length = (
- phoneme_length[vowel_indexes_data[i + 1] - 1]
- if mora.consonant is not None
- else None
- )
- mora.vowel_length = phoneme_length[vowel_indexes_data[i + 1]]
-
- return accent_phrases
-
- def replace_mora_pitch(
- self, accent_phrases: List[AccentPhrase], speaker_id: int
- ) -> List[AccentPhrase]:
- """
- accent_phrasesの音高(ピッチ)を設定する
- Parameters
- ----------
- accent_phrases : List[AccentPhrase]
- アクセント句モデルのリスト
- speaker_id : int
- 話者ID
- Returns
- -------
- accent_phrases : List[AccentPhrase]
- 音高(ピッチ)が設定されたアクセント句モデルのリスト
- """
- # モデルがロードされていない場合はロードする
- self.initialize_speaker_synthesis(speaker_id, skip_reinit=True)
- # numpy.concatenateが空リストだとエラーを返すのでチェック
- if len(accent_phrases) == 0:
- return []
-
- # phoneme
- # AccentPhraseをすべてMoraおよびOjtPhonemeの形に分解し、処理可能な形にする
- flatten_moras, phoneme_data_list = pre_process(accent_phrases)
-
- # accent
- def _create_one_hot(accent_phrase: AccentPhrase, position: int):
- """
- 単位行列(numpy.eye)を応用し、accent_phrase内でone hotな配列(リスト)を作る
- 例えば、accent_phraseのmorasの長さが12、positionが1なら
- [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- morasの長さが同じく12、positionが-1なら
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
- のような配列を生成する
- accent_phraseがpause_moraを含む場合はさらに後ろに0が足される
- Parameters
- ----------
- accent_phrase : AccentPhrase
- アクセント句モデル
- position : int
- one hotにするindex
- Returns
- -------
- one_hot : numpy.ndarray
- one hotな配列(リスト)
- """
- return numpy.r_[
- numpy.eye(len(accent_phrase.moras))[position],
- (0 if accent_phrase.pause_mora is not None else []),
- ]
-
- # accent_phrasesから、アクセントの開始位置のリストを作る
- start_accent_list = numpy.concatenate(
- [
- # accentはプログラミング言語におけるindexのように0始まりではなく1始まりなので、
- # accentが1の場合は0番目を指定している
- # accentが1ではない場合、accentはend_accent_listに用いられる
- _create_one_hot(accent_phrase, 0 if accent_phrase.accent == 1 else 1)
- for accent_phrase in accent_phrases
- ]
- )
-
- # accent_phrasesから、アクセントの終了位置のリストを作る
- end_accent_list = numpy.concatenate(
- [
- # accentはプログラミング言語におけるindexのように0始まりではなく1始まりなので、1を引いている
- _create_one_hot(accent_phrase, accent_phrase.accent - 1)
- for accent_phrase in accent_phrases
- ]
- )
-
- # accent_phrasesから、アクセント句の開始位置のリストを作る
- # これによって、yukarin_sa_forwarder内でアクセント句を区別できる
- start_accent_phrase_list = numpy.concatenate(
- [_create_one_hot(accent_phrase, 0) for accent_phrase in accent_phrases]
- )
-
- # accent_phrasesから、アクセント句の終了位置のリストを作る
- end_accent_phrase_list = numpy.concatenate(
- [_create_one_hot(accent_phrase, -1) for accent_phrase in accent_phrases]
- )
-
- # 最初と最後に0を付け加える。これによってpau(前後の無音のためのもの)を付け加えたことになる
- start_accent_list = numpy.r_[0, start_accent_list, 0]
- end_accent_list = numpy.r_[0, end_accent_list, 0]
- start_accent_phrase_list = numpy.r_[0, start_accent_phrase_list, 0]
- end_accent_phrase_list = numpy.r_[0, end_accent_phrase_list, 0]
-
- # アクセント・アクセント句関連のデータをyukarin_sa_forwarderに渡すための最終処理、リスト内のデータをint64に変換する
- start_accent_list = numpy.array(start_accent_list, dtype=numpy.int64)
- end_accent_list = numpy.array(end_accent_list, dtype=numpy.int64)
- start_accent_phrase_list = numpy.array(
- start_accent_phrase_list, dtype=numpy.int64
- )
- end_accent_phrase_list = numpy.array(end_accent_phrase_list, dtype=numpy.int64)
-
- # phonemeに関するデータを取得(変換)する
- (
- consonant_phoneme_data_list,
- vowel_phoneme_data_list,
- _,
- ) = split_mora(phoneme_data_list)
-
- # yukarin_sa
- # Phoneme関連のデータをyukarin_sa_forwarderに渡すための最終処理、リスト内のデータをint64に変換する
- vowel_phoneme_list = numpy.array(
- [p.phoneme_id for p in vowel_phoneme_data_list], dtype=numpy.int64
- )
- consonant_phoneme_list = numpy.array(
- [
- p.phoneme_id if p is not None else -1
- for p in consonant_phoneme_data_list
- ],
- dtype=numpy.int64,
- )
-
- # 今までに生成された情報をyukarin_sa_forwardにかけ、推論器によってモーラごとに適切な音高(ピッチ)を割り当てる
- with self.mutex:
- f0_list = self.core.yukarin_sa_forward(
- length=vowel_phoneme_list.shape[0],
- vowel_phoneme_list=vowel_phoneme_list[numpy.newaxis],
- consonant_phoneme_list=consonant_phoneme_list[numpy.newaxis],
- start_accent_list=start_accent_list[numpy.newaxis],
- end_accent_list=end_accent_list[numpy.newaxis],
- start_accent_phrase_list=start_accent_phrase_list[numpy.newaxis],
- end_accent_phrase_list=end_accent_phrase_list[numpy.newaxis],
- speaker_id=numpy.array(speaker_id, dtype=numpy.int64).reshape(-1),
- )[0]
-
- # 無声母音を含むMoraに関しては、音高(ピッチ)を0にする
- for i, p in enumerate(vowel_phoneme_data_list):
- if p.phoneme in unvoiced_mora_phoneme_list:
- f0_list[i] = 0
-
- # yukarin_sa_forwarderの結果をaccent_phrasesに反映する
- # flatten_moras変数に展開された値を変更することでコード量を削減しつつaccent_phrases内のデータを書き換えている
- for i, mora in enumerate(flatten_moras):
- mora.pitch = f0_list[i + 1]
-
- return accent_phrases
-
- def _synthesis_impl(self, query: AudioQuery, speaker_id: int):
- """
- 音声合成クエリから音声合成に必要な情報を構成し、実際に音声合成を行う
- Parameters
- ----------
- query : AudioQuery
- 音声合成クエリ
- speaker_id : int
- 話者ID
- Returns
- -------
- wave : numpy.ndarray
- 音声合成結果
- """
- # モデルがロードされていない場合はロードする
- self.initialize_speaker_synthesis(speaker_id, skip_reinit=True)
- # phoneme
- # AccentPhraseをすべてMoraおよびOjtPhonemeの形に分解し、処理可能な形にする
- flatten_moras, phoneme_data_list = pre_process(query.accent_phrases)
-
- # OjtPhonemeのリストからOjtPhonemeのPhoneme ID(OpenJTalkにおける音素のID)のリストを作る
- phoneme_list_s = numpy.array(
- [p.phoneme_id for p in phoneme_data_list], dtype=numpy.int64
- )
-
- # length
- # 音素の長さをリストに展開・結合する。ここには前後の無音時間も含まれる
- phoneme_length_list = (
- [query.prePhonemeLength]
- + [
- length
- for mora in flatten_moras
- for length in (
- [mora.consonant_length] if mora.consonant is not None else []
- )
- + [mora.vowel_length]
- ]
- + [query.postPhonemeLength]
- )
- # floatにキャスト
- phoneme_length = numpy.array(phoneme_length_list, dtype=numpy.float32)
-
- # lengthにSpeed Scale(話速)を適用する
- phoneme_length /= query.speedScale
-
- # pitch
- # モーラの音高(ピッチ)を展開・結合し、floatにキャストする
- f0_list = [0] + [mora.pitch for mora in flatten_moras] + [0]
- f0 = numpy.array(f0_list, dtype=numpy.float32)
- # 音高(ピッチ)の調節を適用する(2のPitch Scale乗を掛ける)
- f0 *= 2**query.pitchScale
-
- # 有声音素(音高(ピッチ)が0より大きいもの)か否かを抽出する
- voiced = f0 > 0
- # 有声音素の音高(ピッチ)の平均値を求める
- mean_f0 = f0[voiced].mean()
- # 平均値がNaNではないとき、抑揚を適用する
- # 抑揚は音高と音高の平均値の差に抑揚を掛けたもの((f0 - mean_f0) * Intonation Scale)に抑揚の平均値(mean_f0)を足したもの
- if not numpy.isnan(mean_f0):
- f0[voiced] = (f0[voiced] - mean_f0) * query.intonationScale + mean_f0
-
- # OjtPhonemeの形に分解された音素リストから、vowel(母音)の位置を抜き出し、numpyのarrayにする
- _, _, vowel_indexes_data = split_mora(phoneme_data_list)
- vowel_indexes = numpy.array(vowel_indexes_data)
-
- # forward decode
- # 音素の長さにrateを掛け、intにキャストする
- rate = 24000 / 256
- phoneme_bin_num = numpy.round(phoneme_length * rate).astype(numpy.int32)
-
- # Phoneme IDを音素の長さ分繰り返す
- phoneme = numpy.repeat(phoneme_list_s, phoneme_bin_num)
- # f0を母音と子音の長さの合計分繰り返す
- f0 = numpy.repeat(
- f0,
- [a.sum() for a in numpy.split(phoneme_bin_num, vowel_indexes[:-1] + 1)],
- )
-
- # phonemeの長さとOjtPhonemeのnum_phoneme(45)分の0で初期化された2次元配列を用意する
- array = numpy.zeros((len(phoneme), OjtPhoneme.num_phoneme), dtype=numpy.float32)
- # 初期化された2次元配列の各行をone hotにする
- array[numpy.arange(len(phoneme)), phoneme] = 1
- phoneme = array
-
- # 今まで生成された情報をdecode_forwardにかけ、推論器によって音声波形を生成する
- with self.mutex:
- wave = self.core.decode_forward(
- length=phoneme.shape[0],
- phoneme_size=phoneme.shape[1],
- f0=f0[:, numpy.newaxis],
- phoneme=phoneme,
- speaker_id=numpy.array(speaker_id, dtype=numpy.int64).reshape(-1),
- )
-
- # volume: ゲイン適用
- wave *= query.volumeScale
-
- # 出力サンプリングレートがデフォルト(decode forwarderによるもの、24kHz)でなければ、それを適用する
- if query.outputSamplingRate != self.default_sampling_rate:
- wave = resample(
- wave,
- query.outputSamplingRate * len(wave) // self.default_sampling_rate,
- )
-
- # ステレオ変換
- # 出力設定がステレオなのであれば、ステレオ化する
- if query.outputStereo:
- wave = numpy.array([wave, wave]).T
-
- return wave
diff --git a/spaces/AI-Dashboards/ScrabbleSolverWordThesaurus/README.md b/spaces/AI-Dashboards/ScrabbleSolverWordThesaurus/README.md
deleted file mode 100644
index 5642010a07fb2e00899d4d9b27c23d1f768ab487..0000000000000000000000000000000000000000
--- a/spaces/AI-Dashboards/ScrabbleSolverWordThesaurus/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ScrabbleSolverWordThesaurus
-emoji: ⚡
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/dataset.py b/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/dataset.py
deleted file mode 100644
index ba0e45be1e8878da0b07eb2128e218bbd7de82ef..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/dataset.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-from uvr5_pack.lib_v5 import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
- def __init__(self, patch_list):
- self.patch_list = patch_list
-
- def __len__(self):
- return len(self.patch_list)
-
- def __getitem__(self, idx):
- path = self.patch_list[idx]
- data = np.load(path)
-
- X, y = data["X"], data["y"]
-
- X_mag = np.abs(X)
- y_mag = np.abs(y)
-
- return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
- input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
-
- X_list = sorted(
- [
- os.path.join(mix_dir, fname)
- for fname in os.listdir(mix_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
- y_list = sorted(
- [
- os.path.join(inst_dir, fname)
- for fname in os.listdir(inst_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
-
- filelist = list(zip(X_list, y_list))
-
- return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
- if split_mode == "random":
- filelist = make_pair(
- os.path.join(dataset_dir, "mixtures"),
- os.path.join(dataset_dir, "instruments"),
- )
-
- random.shuffle(filelist)
-
- if len(val_filelist) == 0:
- val_size = int(len(filelist) * val_rate)
- train_filelist = filelist[:-val_size]
- val_filelist = filelist[-val_size:]
- else:
- train_filelist = [
- pair for pair in filelist if list(pair) not in val_filelist
- ]
- elif split_mode == "subdirs":
- if len(val_filelist) != 0:
- raise ValueError(
- "The `val_filelist` option is not available in `subdirs` mode"
- )
-
- train_filelist = make_pair(
- os.path.join(dataset_dir, "training/mixtures"),
- os.path.join(dataset_dir, "training/instruments"),
- )
-
- val_filelist = make_pair(
- os.path.join(dataset_dir, "validation/mixtures"),
- os.path.join(dataset_dir, "validation/instruments"),
- )
-
- return train_filelist, val_filelist
-
-
-def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
- perm = np.random.permutation(len(X))
- for i, idx in enumerate(tqdm(perm)):
- if np.random.uniform() < reduction_rate:
- y[idx] = spec_utils.reduce_vocal_aggressively(
- X[idx], y[idx], reduction_mask
- )
-
- if np.random.uniform() < 0.5:
- # swap channel
- X[idx] = X[idx, ::-1]
- y[idx] = y[idx, ::-1]
- if np.random.uniform() < 0.02:
- # mono
- X[idx] = X[idx].mean(axis=0, keepdims=True)
- y[idx] = y[idx].mean(axis=0, keepdims=True)
- if np.random.uniform() < 0.02:
- # inst
- X[idx] = y[idx]
-
- if np.random.uniform() < mixup_rate and i < len(perm) - 1:
- lam = np.random.beta(mixup_alpha, mixup_alpha)
- X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
- y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
-
- return X, y
-
-
-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 make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
- len_dataset = patches * len(filelist)
-
- X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
- y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
- ends = starts + cropsize
- for j in range(patches):
- idx = i * patches + j
- X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
- y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
-
- return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
- patch_list = []
- patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
- cropsize, sr, hop_length, n_fft, offset
- )
- os.makedirs(patch_dir, exist_ok=True)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- basename = os.path.splitext(os.path.basename(X_path))[0]
-
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- len_dataset = int(np.ceil(X.shape[2] / roi_size))
- for j in range(len_dataset):
- outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
- start = j * roi_size
- if not os.path.exists(outpath):
- np.savez(
- outpath,
- X=X_pad[:, :, start : start + cropsize],
- y=y_pad[:, :, start : start + cropsize],
- )
- patch_list.append(outpath)
-
- return VocalRemoverValidationSet(patch_list)
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/fastspeech/fs2.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/fastspeech/fs2.py
deleted file mode 100644
index a4487ee37075e1fc2209698e75b164410c191a18..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/fastspeech/fs2.py
+++ /dev/null
@@ -1,250 +0,0 @@
-from utils.hparams import hparams
-from modules.commons.common_layers import *
-from modules.commons.common_layers import Embedding
-from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \
- EnergyPredictor, FastspeechEncoder
-from utils.cwt import cwt2f0
-from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0
-import torch.nn as nn
-from modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder
-FS_ENCODERS = {
- 'fft': lambda hp, embed_tokens, d: FastspeechEncoder(
- embed_tokens, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'],
- num_heads=hp['num_heads']),
-}
-
-FS_DECODERS = {
- 'fft': lambda hp: FastspeechDecoder(
- hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
-}
-
-
-class FastSpeech2(nn.Module):
- def __init__(self, dictionary, out_dims=None):
- super().__init__()
- self.dictionary = dictionary
- self.padding_idx = dictionary.pad()
- self.enc_layers = hparams['enc_layers']
- self.dec_layers = hparams['dec_layers']
- self.hidden_size = hparams['hidden_size']
- self.encoder_embed_tokens = self.build_embedding(self.dictionary, self.hidden_size)
- if hparams.get("use_bert", False):
- self.ph_encoder = BERTRelTransformerEncoder(len(self.dictionary), hparams['hidden_size'], hparams['hidden_size'],
- hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'],
- hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln'])
- else:
- self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, self.encoder_embed_tokens, self.dictionary)
- self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
- self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims
- self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
-
- if hparams['use_spk_id']:
- self.spk_embed_proj = Embedding(hparams['num_spk'] + 1, self.hidden_size)
- if hparams['use_split_spk_id']:
- self.spk_embed_f0 = Embedding(hparams['num_spk'] + 1, self.hidden_size)
- self.spk_embed_dur = Embedding(hparams['num_spk'] + 1, self.hidden_size)
- elif hparams['use_spk_embed']:
- self.spk_embed_proj = Linear(256, self.hidden_size, bias=True)
- predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
- self.dur_predictor = DurationPredictor(
- self.hidden_size,
- n_chans=predictor_hidden,
- n_layers=hparams['dur_predictor_layers'],
- dropout_rate=hparams['predictor_dropout'],
- kernel_size=hparams['dur_predictor_kernel'])
- self.length_regulator = LengthRegulator()
- if hparams['use_pitch_embed']:
- self.pitch_embed = Embedding(300, self.hidden_size, self.padding_idx)
- self.pitch_predictor = PitchPredictor(
- self.hidden_size,
- n_chans=predictor_hidden,
- n_layers=hparams['predictor_layers'],
- dropout_rate=hparams['predictor_dropout'],
- odim=2 if hparams['pitch_type'] == 'frame' else 1,
- kernel_size=hparams['predictor_kernel'])
- if hparams.get('use_energy_embed', False):
- self.energy_embed = Embedding(256, self.hidden_size, self.padding_idx)
- self.energy_predictor = EnergyPredictor(
- self.hidden_size,
- n_chans=predictor_hidden,
- n_layers=hparams['predictor_layers'],
- dropout_rate=hparams['predictor_dropout'], odim=1,
- kernel_size=hparams['predictor_kernel'])
-
- def build_embedding(self, dictionary, embed_dim):
- num_embeddings = len(dictionary)
- emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
- return emb
-
- def forward(self, txt_tokens, mel2ph=None, spk_embed=None,
- ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=False,
- spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs):
- ret = {}
- if hparams.get("use_bert", False):
- encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'], ret=ret)
- else:
- encoder_out = self.encoder(txt_tokens) # [B, T, C]
- src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-
- # add ref style embed
- # Not implemented
- # variance encoder
- var_embed = 0
-
- # encoder_out_dur denotes encoder outputs for duration predictor
- # in speech adaptation, duration predictor use old speaker embedding
- if hparams['use_spk_embed']:
- spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :]
- elif hparams['use_spk_id']:
- spk_embed_id = spk_embed
- if spk_embed_dur_id is None:
- spk_embed_dur_id = spk_embed_id
- if spk_embed_f0_id is None:
- spk_embed_f0_id = spk_embed_id
- spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :]
- spk_embed_dur = spk_embed_f0 = spk_embed
- if hparams['use_split_spk_id']:
- spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :]
- spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :]
- else:
- spk_embed_dur = spk_embed_f0 = spk_embed = 0
-
- # add dur
- dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding
-
- mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret)
-
- decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
-
- mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
- decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H]
-
- tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-
- # add pitch and energy embed
- pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
- if hparams['use_pitch_embed']:
- pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
- decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
- if hparams.get('use_energy_embed', False):
- decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret)
-
- ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding
-
- if skip_decoder:
- return ret
- ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
-
- return ret
-
- def add_dur(self, dur_input, mel2ph, txt_tokens, ret):
- """
-
- :param dur_input: [B, T_txt, H]
- :param mel2ph: [B, T_mel]
- :param txt_tokens: [B, T_txt]
- :param ret:
- :return:
- """
- src_padding = txt_tokens == 0
- dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
- if mel2ph is None:
- dur, xs = self.dur_predictor.inference(dur_input, src_padding)
- ret['dur'] = xs
- ret['dur_choice'] = dur
- mel2ph = self.length_regulator(dur, src_padding).detach()
- # from modules.fastspeech.fake_modules import FakeLengthRegulator
- # fake_lr = FakeLengthRegulator()
- # fake_mel2ph = fake_lr(dur, (1 - src_padding.long()).sum(-1))[..., 0].detach()
- # print(mel2ph == fake_mel2ph)
- else:
- ret['dur'] = self.dur_predictor(dur_input, src_padding)
- ret['mel2ph'] = mel2ph
- return mel2ph
-
- def add_energy(self, decoder_inp, energy, ret):
- decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
- ret['energy_pred'] = energy_pred = self.energy_predictor(decoder_inp)[:, :, 0]
- if energy is None:
- energy = energy_pred
- energy = torch.clamp(energy * 256 // 4, max=255).long()
- energy_embed = self.energy_embed(energy)
- return energy_embed
-
- def add_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
- if hparams['pitch_type'] == 'ph':
- pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach())
- pitch_padding = encoder_out.sum().abs() == 0
- ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
- if f0 is None:
- f0 = pitch_pred[:, :, 0]
- ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding)
- pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt]
- pitch = F.pad(pitch, [1, 0])
- pitch = torch.gather(pitch, 1, mel2ph) # [B, T_mel]
- pitch_embed = self.pitch_embed(pitch)
- return pitch_embed
- decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
-
- pitch_padding = mel2ph == 0
-
- if hparams['pitch_type'] == 'cwt':
- pitch_padding = None
- ret['cwt'] = cwt_out = self.cwt_predictor(decoder_inp)
- stats_out = self.cwt_stats_layers(encoder_out[:, 0, :]) # [B, 2]
- mean = ret['f0_mean'] = stats_out[:, 0]
- std = ret['f0_std'] = stats_out[:, 1]
- cwt_spec = cwt_out[:, :, :10]
- if f0 is None:
- std = std * hparams['cwt_std_scale']
- f0 = self.cwt2f0_norm(cwt_spec, mean, std, mel2ph)
- if hparams['use_uv']:
- assert cwt_out.shape[-1] == 11
- uv = cwt_out[:, :, -1] > 0
- elif hparams['pitch_ar']:
- ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp, f0 if self.training else None)
- if f0 is None:
- f0 = pitch_pred[:, :, 0]
- else:
- ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp)
- if f0 is None:
- f0 = pitch_pred[:, :, 0]
- if hparams['use_uv'] and uv is None:
- uv = pitch_pred[:, :, 1] > 0
- ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding)
- if pitch_padding is not None:
- f0[pitch_padding] = 0
-
- pitch = f0_to_coarse(f0_denorm) # start from 0
- pitch_embed = self.pitch_embed(pitch)
- return pitch_embed
-
- def run_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
- x = decoder_inp # [B, T, H]
- x = self.decoder(x)
- x = self.mel_out(x)
- return x * tgt_nonpadding
-
- def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph):
- f0 = cwt2f0(cwt_spec, mean, std, hparams['cwt_scales'])
- f0 = torch.cat(
- [f0] + [f0[:, -1:]] * (mel2ph.shape[1] - f0.shape[1]), 1)
- f0_norm = norm_f0(f0, None, hparams)
- return f0_norm
-
- def out2mel(self, out):
- return out
-
- @staticmethod
- def mel_norm(x):
- return (x + 5.5) / (6.3 / 2) - 1
-
- @staticmethod
- def mel_denorm(x):
- return (x + 1) * (6.3 / 2) - 5.5
-
- def expand_states(self, h, mel2ph):
- h = F.pad(h, [0, 0, 1, 0])
- mel2ph_ = mel2ph[..., None].repeat([1, 1, h.shape[-1]])
- h = torch.gather(h, 1, mel2ph_) # [B, T, H]
- return h
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/sde_team_given_tests.py b/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/sde_team_given_tests.py
deleted file mode 100644
index eca6c04b92edcfea7749cbb2d7f3378e208411e2..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/sde_team_given_tests.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, List
-
-from agentverse.message import Message
-
-from . import selector_registry as SelectorRegistry
-from .base import BaseSelector
-
-import json
-import re
-
-if TYPE_CHECKING:
- from agentverse.environments import BaseEnvironment
-
-def extract(content: str, keyword: str):
- result = ""
- flag = False
- for line in content.split('\n'):
- if line.strip().startswith(keyword):
- flag = True
- continue
- if flag:
- result += line
- result += "\n"
- return result
-
-
-@SelectorRegistry.register("sde_team_given_tests")
-class SdeTeamGivenTestsSelector(BaseSelector):
- def select_message(self, environment: BaseEnvironment, messages: List[Message]) -> List[Message]:
- last_sender = environment.last_messages[0].sender
- selected = messages
-
- if last_sender == "code_writer":
- cur_code = extract(selected[0].content, ":")
- environment.rule_params["code"] = cur_code
- selected[0].content = f":\n{cur_code}"
-
- elif last_sender == "code_tester":
-
- from .code_api import execute_unit_tests
- feedback = execute_unit_tests(environment.rule_params["code"], eval(environment.unit_tests))
- environment.rule_params["feedback"] = feedback
- selected[0].content = f":\n{feedback}"
-
- f_dict = json.loads(feedback)
- if f_dict["is_passing"]:
- environment.rule_params["end_flag"] = True
-
- elif last_sender == "code_reviewer":
- code_review = selected[0].content
- cur_code = environment.rule_params["code"]
- selected[0].content = f"{code_review}"
-
- return selected
\ No newline at end of file
diff --git a/spaces/AiMimicry/sovits-models/utils.py b/spaces/AiMimicry/sovits-models/utils.py
deleted file mode 100644
index 48ccb56a8034dfd2570d8e5078776bdf12fef2a4..0000000000000000000000000000000000000000
--- a/spaces/AiMimicry/sovits-models/utils.py
+++ /dev/null
@@ -1,542 +0,0 @@
-import os
-import glob
-import re
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import warnings
-import random
-import functools
-
-import librosa
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-from hubert import hubert_model
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-f0_bin = 256
-f0_max = 1100.0
-f0_min = 50.0
-f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-
-# def normalize_f0(f0, random_scale=True):
-# f0_norm = f0.clone() # create a copy of the input Tensor
-# batch_size, _, frame_length = f0_norm.shape
-# for i in range(batch_size):
-# means = torch.mean(f0_norm[i, 0, :])
-# if random_scale:
-# factor = random.uniform(0.8, 1.2)
-# else:
-# factor = 1
-# f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
-# return f0_norm
-# def normalize_f0(f0, random_scale=True):
-# means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
-# if random_scale:
-# factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
-# else:
-# factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
-# f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-# return f0_norm
-
-def deprecated(func):
- """This is a decorator which can be used to mark functions
- as deprecated. It will result in a warning being emitted
- when the function is used."""
- @functools.wraps(func)
- def new_func(*args, **kwargs):
- warnings.simplefilter('always', DeprecationWarning) # turn off filter
- warnings.warn("Call to deprecated function {}.".format(func.__name__),
- category=DeprecationWarning,
- stacklevel=2)
- warnings.simplefilter('default', DeprecationWarning) # reset filter
- return func(*args, **kwargs)
- return new_func
-
-def normalize_f0(f0, x_mask, uv, random_scale=True):
- # calculate means based on x_mask
- uv_sum = torch.sum(uv, dim=1, keepdim=True)
- uv_sum[uv_sum == 0] = 9999
- means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
-
- if random_scale:
- factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
- else:
- factor = torch.ones(f0.shape[0], 1).to(f0.device)
- # normalize f0 based on means and factor
- f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
- if torch.isnan(f0_norm).any():
- exit(0)
- return f0_norm * x_mask
-
-def compute_f0_uv_torchcrepe(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512,device=None,cr_threshold=0.05):
- from modules.crepe import CrepePitchExtractor
- x = wav_numpy
- if p_len is None:
- p_len = x.shape[0]//hop_length
- else:
- assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
-
- f0_min = 50
- f0_max = 1100
- F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device,threshold=cr_threshold)
- f0,uv = F0Creper(x[None,:].float(),sampling_rate,pad_to=p_len)
- return f0,uv
-
-def plot_data_to_numpy(x, y):
- 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))
- plt.plot(x)
- plt.plot(y)
- 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 interpolate_f0(f0):
-
- 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] # this may not be necessary
- last_value = data[i]
-
- return ip_data[:,0], vuv_vector[:,0]
-
-
-def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
- import parselmouth
- x = wav_numpy
- if p_len is None:
- p_len = x.shape[0]//hop_length
- else:
- assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
- time_step = hop_length / sampling_rate * 1000
- f0_min = 50
- f0_max = 1100
- f0 = parselmouth.Sound(x, sampling_rate).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')
- return f0
-
-def resize_f0(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_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
- import pyworld
- if p_len is None:
- p_len = wav_numpy.shape[0]//hop_length
- f0, t = pyworld.dio(
- wav_numpy.astype(np.double),
- fs=sampling_rate,
- f0_ceil=800,
- frame_period=1000 * hop_length / sampling_rate,
- )
- f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return resize_f0(f0, p_len)
-
-def f0_to_coarse(f0):
- is_torch = isinstance(f0, torch.Tensor)
- f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
- f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int)
- assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
- return f0_coarse
-
-
-def get_hubert_model():
- vec_path = "hubert/checkpoint_best_legacy_500.pt"
- print("load model(s) from {}".format(vec_path))
- from fairseq import checkpoint_utils
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [vec_path],
- suffix="",
- )
- model = models[0]
- model.eval()
- return model
-
-def get_hubert_content(hmodel, wav_16k_tensor):
- feats = wav_16k_tensor
- 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).fill_(False)
- inputs = {
- "source": feats.to(wav_16k_tensor.device),
- "padding_mask": padding_mask.to(wav_16k_tensor.device),
- "output_layer": 9, # layer 9
- }
- with torch.no_grad():
- logits = hmodel.extract_features(**inputs)
- feats = hmodel.final_proj(logits[0])
- return feats.transpose(1, 2)
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- 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 and not skip_optimizer and checkpoint_dict['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:
- # assert "dec" in k or "disc" in k
- # print("load", k)
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
- except:
- print("error, %s is not in the checkpoint" % k)
- 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)
- print("load ")
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
- name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
- time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
- sort_key = time_key if sort_by_time else name_key
- x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
- to_del = [os.path.join(path_to_models, fn) for fn in
- (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
- del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
- del_routine = lambda x: [os.remove(x), del_info(x)]
- rs = [del_routine(fn) for fn in to_del]
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-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") 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
-
-
-def repeat_expand_2d(content, target_len):
- # content : [h, t]
-
- src_len = content.shape[-1]
- target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
- temp = torch.arange(src_len+1) * target_len / src_len
- current_pos = 0
- for i in range(target_len):
- if i < temp[current_pos+1]:
- target[:, i] = content[:, current_pos]
- else:
- current_pos += 1
- target[:, i] = content[:, current_pos]
-
- return target
-
-
-def mix_model(model_paths,mix_rate,mode):
- mix_rate = torch.FloatTensor(mix_rate)/100
- model_tem = torch.load(model_paths[0])
- models = [torch.load(path)["model"] for path in model_paths]
- if mode == 0:
- mix_rate = F.softmax(mix_rate,dim=0)
- for k in model_tem["model"].keys():
- model_tem["model"][k] = torch.zeros_like(model_tem["model"][k])
- for i,model in enumerate(models):
- model_tem["model"][k] += model[k]*mix_rate[i]
- torch.save(model_tem,os.path.join(os.path.curdir,"output.pth"))
- return os.path.join(os.path.curdir,"output.pth")
-
-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/Aki004/herta-so-vits/vdecoder/nsf_hifigan/models.py b/spaces/Aki004/herta-so-vits/vdecoder/nsf_hifigan/models.py
deleted file mode 100644
index eff691f31ac6bbea686c98982c31ce7b30efee75..0000000000000000000000000000000000000000
--- a/spaces/Aki004/herta-so-vits/vdecoder/nsf_hifigan/models.py
+++ /dev/null
@@ -1,435 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-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
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
- config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
- with open(config_file) as f:
- data = f.read()
-
- json_config = json.loads(data)
- h = AttrDict(json_config)
-
- generator = Generator(h).to(device)
-
- cp_dict = torch.load(model_path, map_location=device)
- generator.load_state_dict(cp_dict['generator'])
- generator.eval()
- generator.remove_weight_norm()
- del cp_dict
- return generator, h
-
-
-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 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):
- 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
-
- @torch.no_grad()
- 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)
- """
- f0 = f0.unsqueeze(-1)
- fn = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1)))
- rad_values = (fn / self.sampling_rate) % 1 ###%1 means the product of n_har cannot be optimized for post-processing
- rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device)
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- is_half = rad_values.dtype is not torch.float32
- tmp_over_one = torch.cumsum(rad_values.double(), 1) # % 1 #####%1 means the following cumsum can no longer be optimized
- if is_half:
- tmp_over_one = tmp_over_one.half()
- else:
- tmp_over_one = tmp_over_one.float()
- 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
- rad_values = rad_values.double()
- cumsum_shift = cumsum_shift.double()
- sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi)
- if is_half:
- sine_waves = sine_waves.half()
- else:
- sine_waves = sine_waves.float()
- 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):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
-
- # 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):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge
-
-
-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.m_source = SourceModuleHnNSF(
- sampling_rate=h.sampling_rate,
- harmonic_num=8
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = weight_norm(Conv1d(h.num_mels, 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)):
- c_cur = h.upsample_initial_channel // (2 ** (i + 1))
- 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)))
- if i + 1 < len(h.upsample_rates): #
- stride_f0 = int(np.prod(h.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()
- ch = h.upsample_initial_channel
- for i in range(len(self.ups)):
- ch //= 2
- 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)
- self.upp = int(np.prod(h.upsample_rates))
-
- def forward(self, x, f0):
- har_source = self.m_source(f0, self.upp).transpose(1, 2)
- x = self.conv_pre(x)
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, 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):
- print('Removing weight norm...')
- 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, periods=None):
- super(MultiPeriodDiscriminator, self).__init__()
- self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
- self.discriminators = nn.ModuleList()
- for period in self.periods:
- self.discriminators.append(DiscriminatorP(period))
-
- 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/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/models.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/models.py
deleted file mode 100644
index ae620a1afd7a6bffbb746f3eb55833b541a7bf8e..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/models.py
+++ /dev/null
@@ -1,541 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-
-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,
- emotion_embedding):
- 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.emotion_embedding = emotion_embedding
-
- if self.n_vocab!=0:
- self.emb = nn.Embedding(n_vocab, hidden_channels)
- if emotion_embedding:
- self.emotion_emb = nn.Linear(1024, 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, emotion_embedding=None):
- if self.n_vocab!=0:
- x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- if emotion_embedding is not None:
- x = x + self.emotion_emb(emotion_embedding.unsqueeze(1))
- 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,
- emotion_embedding=False,
- **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,
- emotion_embedding)
- 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, emotion_embedding=None):
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
- if self.n_speakers > 0:
- g = self.emb_g(sid).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)
\ No newline at end of file
diff --git a/spaces/Aleqsd/openjourney/README.md b/spaces/Aleqsd/openjourney/README.md
deleted file mode 100644
index b5a128b9cd7ca63138735bf5134be5a0253cf0c0..0000000000000000000000000000000000000000
--- a/spaces/Aleqsd/openjourney/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: openjourney
-emoji: 👀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: akhaliq/openjourney
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AlexWang/lama/fetch_data/places_standard_test_val_sample.sh b/spaces/AlexWang/lama/fetch_data/places_standard_test_val_sample.sh
deleted file mode 100644
index 7b581f457e32e339d7a480845de27d37d0171322..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/fetch_data/places_standard_test_val_sample.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-mkdir -p places_standard_dataset/val_hires/
-mkdir -p places_standard_dataset/visual_test_hires/
-
-
-# randomly sample images for test and vis
-OUT=$(python3 fetch_data/sampler.py)
-echo ${OUT}
-
-FILELIST=$(cat places_standard_dataset/original/test_random_files.txt)
-
-for i in $FILELIST
-do
- $(cp ${i} places_standard_dataset/val_hires/)
-done
-
-FILELIST=$(cat places_standard_dataset/original/val_random_files.txt)
-
-for i in $FILELIST
-do
- $(cp ${i} places_standard_dataset/visual_test_hires/)
-done
-
diff --git a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/latex/attention/introduction.tex b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/latex/attention/introduction.tex
deleted file mode 100644
index 1baa8915f4cf7aec2520894a87470fc9436d954b..0000000000000000000000000000000000000000
--- a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/latex/attention/introduction.tex
+++ /dev/null
@@ -1,18 +0,0 @@
-Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
-
-Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
-%\marginpar{not sure if the memory constraints are understandable here}
-Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
-
-%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away}
-
-Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
-
-%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
-%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
-
-In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.
-%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
-
-% Just a standard paragraph with citations, rewrite.
-%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py
deleted file mode 100644
index 675b16f30d78450143cb1781b77fff922bcb5166..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py
+++ /dev/null
@@ -1,1020 +0,0 @@
-import argparse
-import itertools
-import math
-import os
-import random
-from pathlib import Path
-from typing import Iterable, Optional
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from accelerate import Accelerator
-from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import HfFolder, Repository, whoami
-from neural_compressor.utils import logger
-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 CLIPTextModel, CLIPTokenizer
-
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-
-
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
- PIL_INTERPOLATION = {
- "linear": PIL.Image.Resampling.BILINEAR,
- "bilinear": PIL.Image.Resampling.BILINEAR,
- "bicubic": PIL.Image.Resampling.BICUBIC,
- "lanczos": PIL.Image.Resampling.LANCZOS,
- "nearest": PIL.Image.Resampling.NEAREST,
- }
-else:
- PIL_INTERPOLATION = {
- "linear": PIL.Image.LINEAR,
- "bilinear": PIL.Image.BILINEAR,
- "bicubic": PIL.Image.BICUBIC,
- "lanczos": PIL.Image.LANCZOS,
- "nearest": PIL.Image.NEAREST,
- }
-# ------------------------------------------------------------------------------
-
-
-def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
- logger.info("Saving embeddings")
- learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
- learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
- torch.save(learned_embeds_dict, save_path)
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description="Example of distillation for quantization on Textual Inversion.")
- parser.add_argument(
- "--save_steps",
- type=int,
- default=500,
- help="Save learned_embeds.bin every X updates steps.",
- )
- 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(
- "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
- )
- parser.add_argument(
- "--placeholder_token",
- type=str,
- default=None,
- required=True,
- help="A token to use as a placeholder for the concept.",
- )
- parser.add_argument(
- "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
- )
- parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
- parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
- parser.add_argument(
- "--output_dir",
- type=str,
- default="text-inversion-model",
- help="The output directory where the model predictions and checkpoints will be written.",
- )
- parser.add_argument(
- "--cache_dir",
- type=str,
- default=None,
- help="The directory where the downloaded models and datasets will be stored.",
- )
- parser.add_argument("--seed", type=int, default=42, 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", action="store_true", help="Whether to center crop images before resizing to resolution"
- )
- parser.add_argument(
- "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
- )
- parser.add_argument("--num_train_epochs", type=int, default=100)
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=5000,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- 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(
- "--learning_rate",
- type=float,
- default=1e-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("--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("--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(
- "--mixed_precision",
- type=str,
- default="no",
- 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."
- ),
- )
- parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument("--do_quantization", action="store_true", help="Whether or not to do quantization.")
- parser.add_argument("--do_distillation", action="store_true", help="Whether or not to do distillation.")
- parser.add_argument(
- "--verify_loading", action="store_true", help="Whether or not to verify the loading of the quantized model."
- )
- parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
- 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.train_data_dir is None:
- raise ValueError("You must specify a train data directory.")
-
- return args
-
-
-imagenet_templates_small = [
- "a photo of a {}",
- "a rendering of a {}",
- "a cropped photo of the {}",
- "the photo of a {}",
- "a photo of a clean {}",
- "a photo of a dirty {}",
- "a dark photo of the {}",
- "a photo of my {}",
- "a photo of the cool {}",
- "a close-up photo of a {}",
- "a bright photo of the {}",
- "a cropped photo of a {}",
- "a photo of the {}",
- "a good photo of the {}",
- "a photo of one {}",
- "a close-up photo of the {}",
- "a rendition of the {}",
- "a photo of the clean {}",
- "a rendition of a {}",
- "a photo of a nice {}",
- "a good photo of a {}",
- "a photo of the nice {}",
- "a photo of the small {}",
- "a photo of the weird {}",
- "a photo of the large {}",
- "a photo of a cool {}",
- "a photo of a small {}",
-]
-
-imagenet_style_templates_small = [
- "a painting in the style of {}",
- "a rendering in the style of {}",
- "a cropped painting in the style of {}",
- "the painting in the style of {}",
- "a clean painting in the style of {}",
- "a dirty painting in the style of {}",
- "a dark painting in the style of {}",
- "a picture in the style of {}",
- "a cool painting in the style of {}",
- "a close-up painting in the style of {}",
- "a bright painting in the style of {}",
- "a cropped painting in the style of {}",
- "a good painting in the style of {}",
- "a close-up painting in the style of {}",
- "a rendition in the style of {}",
- "a nice painting in the style of {}",
- "a small painting in the style of {}",
- "a weird painting in the style of {}",
- "a large painting in the style of {}",
-]
-
-
-# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
-class EMAModel:
- """
- Exponential Moving Average of models weights
- """
-
- def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
- parameters = list(parameters)
- self.shadow_params = [p.clone().detach() for p in parameters]
-
- self.decay = decay
- self.optimization_step = 0
-
- def get_decay(self, optimization_step):
- """
- Compute the decay factor for the exponential moving average.
- """
- value = (1 + optimization_step) / (10 + optimization_step)
- return 1 - min(self.decay, value)
-
- @torch.no_grad()
- def step(self, parameters):
- parameters = list(parameters)
-
- self.optimization_step += 1
- self.decay = self.get_decay(self.optimization_step)
-
- for s_param, param in zip(self.shadow_params, parameters):
- if param.requires_grad:
- tmp = self.decay * (s_param - param)
- s_param.sub_(tmp)
- else:
- s_param.copy_(param)
-
- torch.cuda.empty_cache()
-
- def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
- """
- Copy current averaged parameters into given collection of parameters.
- Args:
- parameters: Iterable of `torch.nn.Parameter`; the parameters to be
- updated with the stored moving averages. If `None`, the
- parameters with which this `ExponentialMovingAverage` was
- initialized will be used.
- """
- parameters = list(parameters)
- for s_param, param in zip(self.shadow_params, parameters):
- param.data.copy_(s_param.data)
-
- def to(self, device=None, dtype=None) -> None:
- r"""Move internal buffers of the ExponentialMovingAverage to `device`.
- Args:
- device: like `device` argument to `torch.Tensor.to`
- """
- # .to() on the tensors handles None correctly
- self.shadow_params = [
- p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
- for p in self.shadow_params
- ]
-
-
-class TextualInversionDataset(Dataset):
- def __init__(
- self,
- data_root,
- tokenizer,
- learnable_property="object", # [object, style]
- size=512,
- repeats=100,
- interpolation="bicubic",
- flip_p=0.5,
- set="train",
- placeholder_token="*",
- center_crop=False,
- ):
- self.data_root = data_root
- self.tokenizer = tokenizer
- self.learnable_property = learnable_property
- self.size = size
- self.placeholder_token = placeholder_token
- self.center_crop = center_crop
- self.flip_p = flip_p
-
- self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
-
- self.num_images = len(self.image_paths)
- self._length = self.num_images
-
- if set == "train":
- self._length = self.num_images * repeats
-
- self.interpolation = {
- "linear": PIL_INTERPOLATION["linear"],
- "bilinear": PIL_INTERPOLATION["bilinear"],
- "bicubic": PIL_INTERPOLATION["bicubic"],
- "lanczos": PIL_INTERPOLATION["lanczos"],
- }[interpolation]
-
- self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
- self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
-
- def __len__(self):
- return self._length
-
- def __getitem__(self, i):
- example = {}
- image = Image.open(self.image_paths[i % self.num_images])
-
- if not image.mode == "RGB":
- image = image.convert("RGB")
-
- placeholder_string = self.placeholder_token
- text = random.choice(self.templates).format(placeholder_string)
-
- example["input_ids"] = self.tokenizer(
- text,
- padding="max_length",
- truncation=True,
- max_length=self.tokenizer.model_max_length,
- return_tensors="pt",
- ).input_ids[0]
-
- # default to score-sde preprocessing
- img = np.array(image).astype(np.uint8)
-
- if self.center_crop:
- crop = min(img.shape[0], img.shape[1])
- (
- h,
- w,
- ) = (
- img.shape[0],
- img.shape[1],
- )
- img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
-
- image = Image.fromarray(img)
- image = image.resize((self.size, self.size), resample=self.interpolation)
-
- image = self.flip_transform(image)
- image = np.array(image).astype(np.uint8)
- image = (image / 127.5 - 1.0).astype(np.float32)
-
- example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
- return example
-
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
- if token is None:
- token = HfFolder.get_token()
- if organization is None:
- username = whoami(token)["name"]
- return f"{username}/{model_id}"
- else:
- return f"{organization}/{model_id}"
-
-
-def freeze_params(params):
- for param in params:
- param.requires_grad = False
-
-
-def image_grid(imgs, rows, cols):
- if not len(imgs) == rows * cols:
- raise ValueError("The specified number of rows and columns are not correct.")
-
- w, h = imgs[0].size
- grid = Image.new("RGB", size=(cols * w, rows * h))
- grid_w, grid_h = grid.size
-
- for i, img in enumerate(imgs):
- grid.paste(img, box=(i % cols * w, i // cols * h))
- return grid
-
-
-def generate_images(pipeline, prompt="", guidance_scale=7.5, num_inference_steps=50, num_images_per_prompt=1, seed=42):
- generator = torch.Generator(pipeline.device).manual_seed(seed)
- images = pipeline(
- prompt,
- guidance_scale=guidance_scale,
- num_inference_steps=num_inference_steps,
- generator=generator,
- num_images_per_prompt=num_images_per_prompt,
- ).images
- _rows = int(math.sqrt(num_images_per_prompt))
- grid = image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)
- return grid
-
-
-def main():
- args = parse_args()
- logging_dir = os.path.join(args.output_dir, args.logging_dir)
-
- accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-
- accelerator = Accelerator(
- gradient_accumulation_steps=args.gradient_accumulation_steps,
- mixed_precision=args.mixed_precision,
- log_with="tensorboard",
- project_config=accelerator_project_config,
- )
-
- # If passed along, set the training seed now.
- if args.seed is not None:
- set_seed(args.seed)
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
- else:
- repo_name = args.hub_model_id
- repo = Repository(args.output_dir, clone_from=repo_name)
-
- with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
- if "step_*" not in gitignore:
- gitignore.write("step_*\n")
- if "epoch_*" not in gitignore:
- gitignore.write("epoch_*\n")
- elif args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
-
- # Load the tokenizer and add the placeholder token as a additional special token
- if args.tokenizer_name:
- tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
- elif args.pretrained_model_name_or_path:
- tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
- # Load models and create wrapper for stable diffusion
- noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
- text_encoder = CLIPTextModel.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,
- )
-
- train_unet = False
- # Freeze vae and unet
- freeze_params(vae.parameters())
- if not args.do_quantization and not args.do_distillation:
- # Add the placeholder token in tokenizer
- num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
- if num_added_tokens == 0:
- raise ValueError(
- f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
- " `placeholder_token` that is not already in the tokenizer."
- )
-
- # Convert the initializer_token, placeholder_token to ids
- token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
- # Check if initializer_token is a single token or a sequence of tokens
- if len(token_ids) > 1:
- raise ValueError("The initializer token must be a single token.")
-
- initializer_token_id = token_ids[0]
- placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
- # Resize the token embeddings as we are adding new special tokens to the tokenizer
- text_encoder.resize_token_embeddings(len(tokenizer))
-
- # Initialise the newly added placeholder token with the embeddings of the initializer token
- token_embeds = text_encoder.get_input_embeddings().weight.data
- token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
-
- freeze_params(unet.parameters())
- # Freeze all parameters except for the token embeddings in text encoder
- params_to_freeze = itertools.chain(
- text_encoder.text_model.encoder.parameters(),
- text_encoder.text_model.final_layer_norm.parameters(),
- text_encoder.text_model.embeddings.position_embedding.parameters(),
- )
- freeze_params(params_to_freeze)
- else:
- train_unet = True
- freeze_params(text_encoder.parameters())
-
- if args.scale_lr:
- args.learning_rate = (
- args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
- )
-
- # Initialize the optimizer
- optimizer = torch.optim.AdamW(
- # only optimize the unet or embeddings of text_encoder
- unet.parameters() if train_unet else text_encoder.get_input_embeddings().parameters(),
- lr=args.learning_rate,
- betas=(args.adam_beta1, args.adam_beta2),
- weight_decay=args.adam_weight_decay,
- eps=args.adam_epsilon,
- )
-
- train_dataset = TextualInversionDataset(
- data_root=args.train_data_dir,
- tokenizer=tokenizer,
- size=args.resolution,
- placeholder_token=args.placeholder_token,
- repeats=args.repeats,
- learnable_property=args.learnable_property,
- center_crop=args.center_crop,
- set="train",
- )
- train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
-
- # 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 * accelerator.num_processes,
- num_training_steps=args.max_train_steps * accelerator.num_processes,
- )
-
- if not train_unet:
- text_encoder = accelerator.prepare(text_encoder)
- unet.to(accelerator.device)
- unet.eval()
- else:
- unet = accelerator.prepare(unet)
- text_encoder.to(accelerator.device)
- text_encoder.eval()
- optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
-
- # Move vae to device
- vae.to(accelerator.device)
-
- # Keep vae in eval model as we don't train these
- vae.eval()
-
- compression_manager = None
-
- def train_func(model):
- if train_unet:
- unet_ = model
- text_encoder_ = text_encoder
- else:
- unet_ = unet
- text_encoder_ = model
- # 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("textual_inversion", 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 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}")
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
- progress_bar.set_description("Steps")
- global_step = 0
-
- if train_unet and args.use_ema:
- ema_unet = EMAModel(unet_.parameters())
-
- for epoch in range(args.num_train_epochs):
- model.train()
- train_loss = 0.0
- for step, batch in enumerate(train_dataloader):
- with accelerator.accumulate(model):
- # Convert images to latent space
- latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
- latents = latents * 0.18215
-
- # Sample noise that we'll add to the latents
- noise = torch.randn(latents.shape).to(latents.device)
- 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
- ).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
-
- loss = F.mse_loss(model_pred, noise, reduction="none").mean([1, 2, 3]).mean()
- if train_unet and compression_manager:
- unet_inputs = {
- "sample": noisy_latents,
- "timestep": timesteps,
- "encoder_hidden_states": encoder_hidden_states,
- }
- loss = compression_manager.callbacks.on_after_compute_loss(unet_inputs, model_pred, loss)
-
- # Gather the losses across all processes for logging (if we use distributed training).
- avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
- train_loss += avg_loss.item() / args.gradient_accumulation_steps
-
- # Backpropagate
- accelerator.backward(loss)
-
- if train_unet:
- if accelerator.sync_gradients:
- accelerator.clip_grad_norm_(unet_.parameters(), args.max_grad_norm)
- else:
- # Zero out the gradients for all token embeddings except the newly added
- # embeddings for the concept, as we only want to optimize the concept embeddings
- if accelerator.num_processes > 1:
- grads = text_encoder_.module.get_input_embeddings().weight.grad
- else:
- grads = text_encoder_.get_input_embeddings().weight.grad
- # Get the index for tokens that we want to zero the grads for
- index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
- grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
-
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad()
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- if train_unet and args.use_ema:
- ema_unet.step(unet_.parameters())
- progress_bar.update(1)
- global_step += 1
- accelerator.log({"train_loss": train_loss}, step=global_step)
- train_loss = 0.0
- if not train_unet and global_step % args.save_steps == 0:
- save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
- save_progress(text_encoder_, placeholder_token_id, accelerator, args, save_path)
-
- logs = {"step_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
- accelerator.wait_for_everyone()
-
- if train_unet and args.use_ema:
- ema_unet.copy_to(unet_.parameters())
-
- if not train_unet:
- return text_encoder_
-
- if not train_unet:
- text_encoder = train_func(text_encoder)
- else:
- import copy
-
- model = copy.deepcopy(unet)
- confs = []
- if args.do_quantization:
- from neural_compressor import QuantizationAwareTrainingConfig
-
- q_conf = QuantizationAwareTrainingConfig()
- confs.append(q_conf)
-
- if args.do_distillation:
- teacher_model = copy.deepcopy(model)
-
- def attention_fetcher(x):
- return x.sample
-
- layer_mappings = [
- [
- [
- "conv_in",
- ]
- ],
- [
- [
- "time_embedding",
- ]
- ],
- [["down_blocks.0.attentions.0", attention_fetcher]],
- [["down_blocks.0.attentions.1", attention_fetcher]],
- [
- [
- "down_blocks.0.resnets.0",
- ]
- ],
- [
- [
- "down_blocks.0.resnets.1",
- ]
- ],
- [
- [
- "down_blocks.0.downsamplers.0",
- ]
- ],
- [["down_blocks.1.attentions.0", attention_fetcher]],
- [["down_blocks.1.attentions.1", attention_fetcher]],
- [
- [
- "down_blocks.1.resnets.0",
- ]
- ],
- [
- [
- "down_blocks.1.resnets.1",
- ]
- ],
- [
- [
- "down_blocks.1.downsamplers.0",
- ]
- ],
- [["down_blocks.2.attentions.0", attention_fetcher]],
- [["down_blocks.2.attentions.1", attention_fetcher]],
- [
- [
- "down_blocks.2.resnets.0",
- ]
- ],
- [
- [
- "down_blocks.2.resnets.1",
- ]
- ],
- [
- [
- "down_blocks.2.downsamplers.0",
- ]
- ],
- [
- [
- "down_blocks.3.resnets.0",
- ]
- ],
- [
- [
- "down_blocks.3.resnets.1",
- ]
- ],
- [
- [
- "up_blocks.0.resnets.0",
- ]
- ],
- [
- [
- "up_blocks.0.resnets.1",
- ]
- ],
- [
- [
- "up_blocks.0.resnets.2",
- ]
- ],
- [
- [
- "up_blocks.0.upsamplers.0",
- ]
- ],
- [["up_blocks.1.attentions.0", attention_fetcher]],
- [["up_blocks.1.attentions.1", attention_fetcher]],
- [["up_blocks.1.attentions.2", attention_fetcher]],
- [
- [
- "up_blocks.1.resnets.0",
- ]
- ],
- [
- [
- "up_blocks.1.resnets.1",
- ]
- ],
- [
- [
- "up_blocks.1.resnets.2",
- ]
- ],
- [
- [
- "up_blocks.1.upsamplers.0",
- ]
- ],
- [["up_blocks.2.attentions.0", attention_fetcher]],
- [["up_blocks.2.attentions.1", attention_fetcher]],
- [["up_blocks.2.attentions.2", attention_fetcher]],
- [
- [
- "up_blocks.2.resnets.0",
- ]
- ],
- [
- [
- "up_blocks.2.resnets.1",
- ]
- ],
- [
- [
- "up_blocks.2.resnets.2",
- ]
- ],
- [
- [
- "up_blocks.2.upsamplers.0",
- ]
- ],
- [["up_blocks.3.attentions.0", attention_fetcher]],
- [["up_blocks.3.attentions.1", attention_fetcher]],
- [["up_blocks.3.attentions.2", attention_fetcher]],
- [
- [
- "up_blocks.3.resnets.0",
- ]
- ],
- [
- [
- "up_blocks.3.resnets.1",
- ]
- ],
- [
- [
- "up_blocks.3.resnets.2",
- ]
- ],
- [["mid_block.attentions.0", attention_fetcher]],
- [
- [
- "mid_block.resnets.0",
- ]
- ],
- [
- [
- "mid_block.resnets.1",
- ]
- ],
- [
- [
- "conv_out",
- ]
- ],
- ]
- layer_names = [layer_mapping[0][0] for layer_mapping in layer_mappings]
- if not set(layer_names).issubset([n[0] for n in model.named_modules()]):
- raise ValueError(
- "Provided model is not compatible with the default layer_mappings, "
- 'please use the model fine-tuned from "CompVis/stable-diffusion-v1-4", '
- "or modify the layer_mappings variable to fit your model."
- f"\nDefault layer_mappings are as such:\n{layer_mappings}"
- )
- from neural_compressor.config import DistillationConfig, IntermediateLayersKnowledgeDistillationLossConfig
-
- distillation_criterion = IntermediateLayersKnowledgeDistillationLossConfig(
- layer_mappings=layer_mappings,
- loss_types=["MSE"] * len(layer_mappings),
- loss_weights=[1.0 / len(layer_mappings)] * len(layer_mappings),
- add_origin_loss=True,
- )
- d_conf = DistillationConfig(teacher_model=teacher_model, criterion=distillation_criterion)
- confs.append(d_conf)
-
- from neural_compressor.training import prepare_compression
-
- compression_manager = prepare_compression(model, confs)
- compression_manager.callbacks.on_train_begin()
- model = compression_manager.model
- train_func(model)
- compression_manager.callbacks.on_train_end()
-
- # Save the resulting model and its corresponding configuration in the given directory
- model.save(args.output_dir)
-
- logger.info(f"Optimized model saved to: {args.output_dir}.")
-
- # change to framework model for further use
- model = model.model
-
- # Create the pipeline using using the trained modules and save it.
- templates = imagenet_style_templates_small if args.learnable_property == "style" else imagenet_templates_small
- prompt = templates[0].format(args.placeholder_token)
- if accelerator.is_main_process:
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- text_encoder=accelerator.unwrap_model(text_encoder),
- vae=vae,
- unet=accelerator.unwrap_model(unet),
- tokenizer=tokenizer,
- )
- pipeline.save_pretrained(args.output_dir)
- pipeline = pipeline.to(unet.device)
- baseline_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
- baseline_model_images.save(
- os.path.join(args.output_dir, "{}_baseline_model.png".format("_".join(prompt.split())))
- )
-
- if not train_unet:
- # Also save the newly trained embeddings
- save_path = os.path.join(args.output_dir, "learned_embeds.bin")
- save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
- else:
- setattr(pipeline, "unet", accelerator.unwrap_model(model))
- if args.do_quantization:
- pipeline = pipeline.to(torch.device("cpu"))
-
- optimized_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
- optimized_model_images.save(
- os.path.join(args.output_dir, "{}_optimized_model.png".format("_".join(prompt.split())))
- )
-
- if args.push_to_hub:
- repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
- accelerator.end_training()
-
- if args.do_quantization and args.verify_loading:
- # Load the model obtained after Intel Neural Compressor quantization
- from neural_compressor.utils.pytorch import load
-
- loaded_model = load(args.output_dir, model=unet)
- loaded_model.eval()
-
- setattr(pipeline, "unet", loaded_model)
- if args.do_quantization:
- pipeline = pipeline.to(torch.device("cpu"))
-
- loaded_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
- if loaded_model_images != optimized_model_images:
- logger.info("The quantized model was not successfully loaded.")
- else:
- logger.info("The quantized model was successfully loaded.")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_ipndm.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_ipndm.py
deleted file mode 100644
index 80e521590782de6bc14e9b8c29642c7595fafc93..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_ipndm.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright 2023 Zhejiang University Team and 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 math
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils import SchedulerMixin, SchedulerOutput
-
-
-class IPNDMScheduler(SchedulerMixin, ConfigMixin):
- """
- Improved Pseudo numerical methods for diffusion models (iPNDM) ported from @crowsonkb's amazing k-diffusion
- [library](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296)
-
- [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
- function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
- [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
- [`~SchedulerMixin.from_pretrained`] functions.
-
- For more details, see the original paper: https://arxiv.org/abs/2202.09778
-
- Args:
- num_train_timesteps (`int`): number of diffusion steps used to train the model.
- """
-
- order = 1
-
- @register_to_config
- def __init__(
- self, num_train_timesteps: int = 1000, trained_betas: Optional[Union[np.ndarray, List[float]]] = None
- ):
- # set `betas`, `alphas`, `timesteps`
- self.set_timesteps(num_train_timesteps)
-
- # standard deviation of the initial noise distribution
- self.init_noise_sigma = 1.0
-
- # For now we only support F-PNDM, i.e. the runge-kutta method
- # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
- # mainly at formula (9), (12), (13) and the Algorithm 2.
- self.pndm_order = 4
-
- # running values
- self.ets = []
-
- def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
- """
- Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
- Args:
- num_inference_steps (`int`):
- the number of diffusion steps used when generating samples with a pre-trained model.
- """
- self.num_inference_steps = num_inference_steps
- steps = torch.linspace(1, 0, num_inference_steps + 1)[:-1]
- steps = torch.cat([steps, torch.tensor([0.0])])
-
- if self.config.trained_betas is not None:
- self.betas = torch.tensor(self.config.trained_betas, dtype=torch.float32)
- else:
- self.betas = torch.sin(steps * math.pi / 2) ** 2
-
- self.alphas = (1.0 - self.betas**2) ** 0.5
-
- timesteps = (torch.atan2(self.betas, self.alphas) / math.pi * 2)[:-1]
- self.timesteps = timesteps.to(device)
-
- self.ets = []
-
- def step(
- self,
- model_output: torch.FloatTensor,
- timestep: int,
- sample: torch.FloatTensor,
- return_dict: bool = True,
- ) -> Union[SchedulerOutput, Tuple]:
- """
- Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple
- times to approximate the solution.
-
- Args:
- model_output (`torch.FloatTensor`): direct output from learned diffusion model.
- timestep (`int`): current discrete timestep in the diffusion chain.
- sample (`torch.FloatTensor`):
- current instance of sample being created by diffusion process.
- return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
-
- Returns:
- [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
- True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-
- """
- if self.num_inference_steps is None:
- raise ValueError(
- "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
- )
-
- timestep_index = (self.timesteps == timestep).nonzero().item()
- prev_timestep_index = timestep_index + 1
-
- ets = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
- self.ets.append(ets)
-
- if len(self.ets) == 1:
- ets = self.ets[-1]
- elif len(self.ets) == 2:
- ets = (3 * self.ets[-1] - self.ets[-2]) / 2
- elif len(self.ets) == 3:
- ets = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
- else:
- ets = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
-
- prev_sample = self._get_prev_sample(sample, timestep_index, prev_timestep_index, ets)
-
- if not return_dict:
- return (prev_sample,)
-
- return SchedulerOutput(prev_sample=prev_sample)
-
- def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
- """
- Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
- current timestep.
-
- Args:
- sample (`torch.FloatTensor`): input sample
-
- Returns:
- `torch.FloatTensor`: scaled input sample
- """
- return sample
-
- def _get_prev_sample(self, sample, timestep_index, prev_timestep_index, ets):
- alpha = self.alphas[timestep_index]
- sigma = self.betas[timestep_index]
-
- next_alpha = self.alphas[prev_timestep_index]
- next_sigma = self.betas[prev_timestep_index]
-
- pred = (sample - sigma * ets) / max(alpha, 1e-8)
- prev_sample = next_alpha * pred + ets * next_sigma
-
- return prev_sample
-
- def __len__(self):
- return self.config.num_train_timesteps
diff --git a/spaces/Andy1621/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/run.sh b/spaces/Andy1621/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/run.sh
deleted file mode 100644
index f231d7c18d3e4e3a1e150b0c3a2804fb3c9ca848..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/run.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-
-work_path=$(dirname $0)
-PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=8 \
- tools/train.py ${work_path}/config.py \
- --launcher pytorch \
- --cfg-options model.backbone.pretrained_path='your_model_path/uniformer_base_in1k.pth' \
- --work-dir ${work_path}/ckpt \
- 2>&1 | tee -a ${work_path}/log.txt
diff --git a/spaces/Andy1621/uniformer_light/uniformer_light_video.py b/spaces/Andy1621/uniformer_light/uniformer_light_video.py
deleted file mode 100644
index 868a57aeeebd8715ce49cc114db470a8a43ebaac..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_light/uniformer_light_video.py
+++ /dev/null
@@ -1,595 +0,0 @@
-# All rights reserved.
-from math import ceil, sqrt
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-from functools import partial
-from timm.models.vision_transformer import _cfg
-from timm.models.layers import trunc_normal_, DropPath, to_2tuple
-import os
-
-
-global_attn = None
-token_indices = None
-
-model_path = 'path_to_models'
-model_path = {
- 'uniformer_xxs_128_in1k': os.path.join(model_path, 'uniformer_xxs_128_in1k.pth'),
- 'uniformer_xxs_160_in1k': os.path.join(model_path, 'uniformer_xxs_160_in1k.pth'),
- 'uniformer_xxs_192_in1k': os.path.join(model_path, 'uniformer_xxs_192_in1k.pth'),
- 'uniformer_xxs_224_in1k': os.path.join(model_path, 'uniformer_xxs_224_in1k.pth'),
- 'uniformer_xs_192_in1k': os.path.join(model_path, 'uniformer_xs_192_in1k.pth'),
- 'uniformer_xs_224_in1k': os.path.join(model_path, 'uniformer_xs_224_in1k.pth'),
-}
-
-
-def conv_3xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
- return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (2, stride, stride), (1, 0, 0), groups=groups)
-
-def conv_1xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
- return nn.Conv3d(inp, oup, (1, kernel_size, kernel_size), (1, stride, stride), (0, 0, 0), groups=groups)
-
-def conv_3xnxn_std(inp, oup, kernel_size=3, stride=3, groups=1):
- return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (1, stride, stride), (1, 0, 0), groups=groups)
-
-def conv_1x1x1(inp, oup, groups=1):
- return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
-
-def conv_3x3x3(inp, oup, groups=1):
- return nn.Conv3d(inp, oup, (3, 3, 3), (1, 1, 1), (1, 1, 1), groups=groups)
-
-def conv_5x5x5(inp, oup, groups=1):
- return nn.Conv3d(inp, oup, (5, 5, 5), (1, 1, 1), (2, 2, 2), groups=groups)
-
-def bn_3d(dim):
- return nn.BatchNorm3d(dim)
-
-
-# code is from https://github.com/YifanXu74/Evo-ViT
-def easy_gather(x, indices):
- # x => B x N x C
- # indices => B x N
- B, N, C = x.shape
- N_new = indices.shape[1]
- offset = torch.arange(B, dtype=torch.long, device=x.device).view(B, 1) * N
- indices = indices + offset
- # only select the informative tokens
- out = x.reshape(B * N, C)[indices.view(-1)].reshape(B, N_new, C)
- return out
-
-
-# code is from https://github.com/YifanXu74/Evo-ViT
-def merge_tokens(x_drop, score):
- # x_drop => B x N_drop
- # score => B x N_drop
- weight = score / torch.sum(score, dim=1, keepdim=True)
- x_drop = weight.unsqueeze(-1) * x_drop
- return torch.sum(x_drop, dim=1, keepdim=True)
-
-
-class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-class Attention(nn.Module):
- def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., trade_off=1):
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
- self.scale = qk_scale or head_dim ** -0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- # updating weight for global score
- self.trade_off = trade_off
-
- def forward(self, x):
- B, N, C = x.shape
- qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- attn = (q @ k.transpose(-2, -1)) * self.scale
- attn = attn.softmax(dim=-1)
-
- # update global score
- global global_attn
- tradeoff = self.trade_off
- if isinstance(global_attn, int):
- global_attn = torch.mean(attn[:, :, 0, 1:], dim=1)
- elif global_attn.shape[1] == N - 1:
- # no additional token and no pruning, update all global scores
- cls_attn = torch.mean(attn[:, :, 0, 1:], dim=1)
- global_attn = (1 - tradeoff) * global_attn + tradeoff * cls_attn
- else:
- # only update the informative tokens
- # the first one is class token
- # the last one is rrepresentative token
- cls_attn = torch.mean(attn[:, :, 0, 1:-1], dim=1)
- if self.training:
- temp_attn = (1 - tradeoff) * global_attn[:, :(N - 2)] + tradeoff * cls_attn
- global_attn = torch.cat((temp_attn, global_attn[:, (N - 2):]), dim=1)
- else:
- # no use torch.cat() for fast inference
- global_attn[:, :(N - 2)] = (1 - tradeoff) * global_attn[:, :(N - 2)] + tradeoff * cls_attn
-
- 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 CMlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = conv_1x1x1(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = conv_1x1x1(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-class CBlock(nn.Module):
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
- self.norm1 = bn_3d(dim)
- self.conv1 = conv_1x1x1(dim, dim, 1)
- self.conv2 = conv_1x1x1(dim, dim, 1)
- self.attn = conv_5x5x5(dim, dim, groups=dim)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = bn_3d(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def forward(self, x):
- x = x + self.pos_embed(x)
- x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
- x = x + self.drop_path(self.mlp(self.norm2(x)))
- return x
-
-
-class EvoSABlock(nn.Module):
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, prune_ratio=1,
- trade_off=0, downsample=False):
- super().__init__()
- self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop, trade_off=trade_off)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- 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)
- self.prune_ratio = prune_ratio
- self.downsample = downsample
- if downsample:
- self.avgpool = nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
-
- def forward(self, cls_token, x):
- x = x + self.pos_embed(x)
- B, C, T, H, W = x.shape
- x = x.flatten(2).transpose(1, 2)
-
- if self.prune_ratio == 1:
- x = torch.cat([cls_token, x], dim=1)
- x = x + self.drop_path(self.attn(self.norm1(x)))
- x = x + self.drop_path(self.mlp(self.norm2(x)))
- cls_token, x = x[:, :1], x[:, 1:]
- x = x.transpose(1, 2).reshape(B, C, T, H, W)
- return cls_token, x
- else:
- global global_attn, token_indices
- # calculate the number of informative tokens
- N = x.shape[1]
- N_ = int(N * self.prune_ratio)
- # sort global attention
- indices = torch.argsort(global_attn, dim=1, descending=True)
-
- # concatenate x, global attention and token indices => x_ga_ti
- # rearrange the tensor according to new indices
- x_ga_ti = torch.cat((x, global_attn.unsqueeze(-1), token_indices.unsqueeze(-1)), dim=-1)
- x_ga_ti = easy_gather(x_ga_ti, indices)
- x_sorted, global_attn, token_indices = x_ga_ti[:, :, :-2], x_ga_ti[:, :, -2], x_ga_ti[:, :, -1]
-
- # informative tokens
- x_info = x_sorted[:, :N_]
- # merge dropped tokens
- x_drop = x_sorted[:, N_:]
- score = global_attn[:, N_:]
- # B x N_drop x C => B x 1 x C
- rep_token = merge_tokens(x_drop, score)
- # concatenate new tokens
- x = torch.cat((cls_token, x_info, rep_token), dim=1)
-
- # slow update
- fast_update = 0
- tmp_x = self.attn(self.norm1(x))
- fast_update = fast_update + tmp_x[:, -1:]
- x = x + self.drop_path(tmp_x)
- tmp_x = self.mlp(self.norm2(x))
- fast_update = fast_update + tmp_x[:, -1:]
- x = x + self.drop_path(tmp_x)
- # fast update
- x_drop = x_drop + fast_update.expand(-1, N - N_, -1)
-
- cls_token, x = x[:, :1, :], x[:, 1:-1, :]
- if self.training:
- x_sorted = torch.cat((x, x_drop), dim=1)
- else:
- x_sorted[:, N_:] = x_drop
- x_sorted[:, :N_] = x
-
- # recover token
- # scale for normalization
- old_global_scale = torch.sum(global_attn, dim=1, keepdim=True)
- # recover order
- indices = torch.argsort(token_indices, dim=1)
- x_ga_ti = torch.cat((x_sorted, global_attn.unsqueeze(-1), token_indices.unsqueeze(-1)), dim=-1)
- x_ga_ti = easy_gather(x_ga_ti, indices)
- x_patch, global_attn, token_indices = x_ga_ti[:, :, :-2], x_ga_ti[:, :, -2], x_ga_ti[:, :, -1]
- x_patch = x_patch.transpose(1, 2).reshape(B, C, T, H, W)
-
- if self.downsample:
- # downsample global attention
- global_attn = global_attn.reshape(B, 1, T, H, W)
- global_attn = self.avgpool(global_attn).view(B, -1)
- # normalize global attention
- new_global_scale = torch.sum(global_attn, dim=1, keepdim=True)
- scale = old_global_scale / new_global_scale
- global_attn = global_attn * scale
-
- return cls_token, x_patch
-
-
-class SABlock(nn.Module):
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- 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):
- x = x + self.pos_embed(x)
- B, C, T, H, W = x.shape
- x = x.flatten(2).transpose(1, 2)
- x = x + self.drop_path(self.attn(self.norm1(x)))
- x = x + self.drop_path(self.mlp(self.norm2(x)))
- x = x.transpose(1, 2).reshape(B, C, T, H, W)
- return x
-
-
-class SplitSABlock(nn.Module):
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
- self.t_norm = norm_layer(dim)
- self.t_attn = Attention(
- dim,
- num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop)
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- 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):
- x = x + self.pos_embed(x)
- B, C, T, H, W = x.shape
- attn = x.view(B, C, T, H * W).permute(0, 3, 2, 1).contiguous()
- attn = attn.view(B * H * W, T, C)
- attn = attn + self.drop_path(self.t_attn(self.t_norm(attn)))
- attn = attn.view(B, H * W, T, C).permute(0, 2, 1, 3).contiguous()
- attn = attn.view(B * T, H * W, C)
- residual = x.view(B, C, T, H * W).permute(0, 2, 3, 1).contiguous()
- residual = residual.view(B * T, H * W, C)
- attn = residual + self.drop_path(self.attn(self.norm1(attn)))
- attn = attn.view(B, T * H * W, C)
- out = attn + self.drop_path(self.mlp(self.norm2(attn)))
- out = out.transpose(1, 2).reshape(B, C, T, H, W)
- return out
-
-
-class SpeicalPatchEmbed(nn.Module):
- """ Image to Patch Embedding
- """
- def __init__(self, patch_size=16, in_chans=3, embed_dim=768):
- super().__init__()
- patch_size = to_2tuple(patch_size)
- self.patch_size = patch_size
-
- self.proj = nn.Sequential(
- nn.Conv3d(in_chans, embed_dim // 2, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1)),
- nn.BatchNorm3d(embed_dim // 2),
- nn.GELU(),
- nn.Conv3d(embed_dim // 2, embed_dim, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
- nn.BatchNorm3d(embed_dim),
- )
-
- def forward(self, x):
- B, C, T, H, W = x.shape
- # FIXME look at relaxing size constraints
- # assert H == self.img_size[0] and W == self.img_size[1], \
- # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x)
- B, C, T, H, W = x.shape
- x = x.flatten(2).transpose(1, 2)
- x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
- return x
-
-
-class PatchEmbed(nn.Module):
- """ Image to Patch Embedding
- """
- def __init__(self, patch_size=16, in_chans=3, embed_dim=768):
- super().__init__()
- patch_size = to_2tuple(patch_size)
- self.patch_size = patch_size
- self.norm = nn.LayerNorm(embed_dim)
- self.proj = conv_1xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-
- def forward(self, x):
- B, C, T, H, W = x.shape
- # FIXME look at relaxing size constraints
- # assert H == self.img_size[0] and W == self.img_size[1], \
- # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x)
- B, C, T, H, W = x.shape
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
- return x
-
-
-class Uniformer_light(nn.Module):
- """ Vision Transformer
- A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
- https://arxiv.org/abs/2010.11929
- """
- def __init__(self, depth=[3, 4, 8, 3], in_chans=3, num_classes=400, embed_dim=[64, 128, 320, 512],
- head_dim=64, mlp_ratio=[4., 4., 4., 4.], qkv_bias=True, qk_scale=None, representation_size=None,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None,
- prune_ratio=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
- trade_off=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]
- ):
- super().__init__()
-
- self.num_classes = num_classes
- self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
- norm_layer = partial(nn.LayerNorm, eps=1e-6)
-
- self.patch_embed1 = SpeicalPatchEmbed(
- patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
- self.patch_embed2 = PatchEmbed(
- patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1])
- self.patch_embed3 = PatchEmbed(
- patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2])
- self.patch_embed4 = PatchEmbed(
- patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3])
-
- # class token
- self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim[2]))
- self.cls_upsample = nn.Linear(embed_dim[2], embed_dim[3])
-
- self.pos_drop = nn.Dropout(p=drop_rate)
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))] # stochastic depth decay rule
- num_heads = [dim // head_dim for dim in embed_dim]
- self.blocks1 = nn.ModuleList([
- CBlock(
- dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
- for i in range(depth[0])])
- self.blocks2 = nn.ModuleList([
- CBlock(
- dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]], norm_layer=norm_layer)
- for i in range(depth[1])])
- self.blocks3 = nn.ModuleList([
- EvoSABlock(
- dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer,
- prune_ratio=prune_ratio[2][i], trade_off=trade_off[2][i],
- downsample=True if i == depth[2] - 1 else False)
- for i in range(depth[2])])
- self.blocks4 = nn.ModuleList([
- EvoSABlock(
- dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer,
- prune_ratio=prune_ratio[3][i], trade_off=trade_off[3][i])
- for i in range(depth[3])])
- self.norm = bn_3d(embed_dim[-1])
- self.norm_cls = nn.LayerNorm(embed_dim[-1])
-
- # Representation layer
- if representation_size:
- self.num_features = representation_size
- self.pre_logits = nn.Sequential(OrderedDict([
- ('fc', nn.Linear(embed_dim, representation_size)),
- ('act', nn.Tanh())
- ]))
- else:
- self.pre_logits = nn.Identity()
-
- # Classifier head
- self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
- self.head_cls = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
-
- self.apply(self._init_weights)
-
- for name, p in self.named_parameters():
- # fill proj weight with 1 here to improve training dynamics. Otherwise temporal attention inputs
- # are multiplied by 0*0, which is hard for the model to move out of.
- if 't_attn.qkv.weight' in name:
- nn.init.constant_(p, 0)
- if 't_attn.qkv.bias' in name:
- nn.init.constant_(p, 0)
- if 't_attn.proj.weight' in name:
- nn.init.constant_(p, 1)
- if 't_attn.proj.bias' in name:
- nn.init.constant_(p, 0)
-
- 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)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'pos_embed', 'cls_token'}
-
- 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.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
- def inflate_weight(self, weight_2d, time_dim, center=False):
- if center:
- weight_3d = torch.zeros(*weight_2d.shape)
- weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
- middle_idx = time_dim // 2
- weight_3d[:, :, middle_idx, :, :] = weight_2d
- else:
- weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
- weight_3d = weight_3d / time_dim
- return weight_3d
-
- def forward_features(self, x):
- x = self.patch_embed1(x)
- x = self.pos_drop(x)
- for blk in self.blocks1:
- x = blk(x)
- x = self.patch_embed2(x)
- for blk in self.blocks2:
- x = blk(x)
- x = self.patch_embed3(x)
- # add cls_token in stage3
- cls_token = self.cls_token.expand(x.shape[0], -1, -1)
- global global_attn, token_indices
- global_attn = 0
- token_indices = torch.arange(x.shape[2] * x.shape[3] * x.shape[4], dtype=torch.long, device=x.device).unsqueeze(0)
- token_indices = token_indices.expand(x.shape[0], -1)
- for blk in self.blocks3:
- cls_token, x = blk(cls_token, x)
- # upsample cls_token before stage4
- cls_token = self.cls_upsample(cls_token)
- x = self.patch_embed4(x)
- # whether reset global attention? Now simple avgpool
- token_indices = torch.arange(x.shape[2] * x.shape[3] * x.shape[4], dtype=torch.long, device=x.device).unsqueeze(0)
- token_indices = token_indices.expand(x.shape[0], -1)
- for blk in self.blocks4:
- cls_token, x = blk(cls_token, x)
- if self.training:
- # layer normalization for cls_token
- cls_token = self.norm_cls(cls_token)
- x = self.norm(x)
- x = self.pre_logits(x)
- return cls_token, x
-
- def forward(self, x):
- cls_token, x = self.forward_features(x)
- x = x.flatten(2).mean(-1)
- if self.training:
- x = self.head(x), self.head_cls(cls_token.squeeze(1))
- else:
- x = self.head(x)
- return x
-
-
-def uniformer_xxs_video(**kwargs):
- model = Uniformer_light(
- depth=[2, 5, 8, 2],
- prune_ratio=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5]],
- trade_off=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5]],
- embed_dim=[56, 112, 224, 448], head_dim=28, mlp_ratio=[3, 3, 3, 3], qkv_bias=True,
- **kwargs)
- model.default_cfg = _cfg()
- return model
-
-
-def uniformer_xs_video(**kwargs):
- model = Uniformer_light(
- depth=[3, 5, 9, 3],
- prune_ratio=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
- trade_off=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
- embed_dim=[64, 128, 256, 512], head_dim=32, mlp_ratio=[3, 3, 3, 3], qkv_bias=True,
- **kwargs)
- model.default_cfg = _cfg()
- return model
-
-
-if __name__ == '__main__':
- import time
- from fvcore.nn import FlopCountAnalysis
- from fvcore.nn import flop_count_table
- import numpy as np
-
- seed = 4217
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- num_frames = 16
-
- model = uniformer_xxs_video()
- # print(model)
-
- flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 160, 160))
- s = time.time()
- print(flop_count_table(flops, max_depth=1))
- print(time.time()-s)
\ No newline at end of file
diff --git a/spaces/Andyrasika/Andyrasika-lora_diffusion/README.md b/spaces/Andyrasika/Andyrasika-lora_diffusion/README.md
deleted file mode 100644
index bf0ccb2a4fc5580a67f2d671f0acb8bd96abb9c6..0000000000000000000000000000000000000000
--- a/spaces/Andyrasika/Andyrasika-lora_diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Andyrasika-lora Diffusion
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/metadata_gguf.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/metadata_gguf.py
deleted file mode 100644
index 0ea41a2a2d1f85e9f0da3235fa1a2d26fd556c40..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/metadata_gguf.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import struct
-from enum import IntEnum
-
-
-class GGUFValueType(IntEnum):
- UINT8 = 0
- INT8 = 1
- UINT16 = 2
- INT16 = 3
- UINT32 = 4
- INT32 = 5
- FLOAT32 = 6
- BOOL = 7
- STRING = 8
- ARRAY = 9
- UINT64 = 10
- INT64 = 11
- FLOAT64 = 12
-
-
-_simple_value_packing = {
- GGUFValueType.UINT8: "☰', elem_id='gr-hover')
-
- with gr.Column(scale=10, elem_id='chat-input-container'):
- shared.gradio['textbox'] = gr.Textbox(label='', placeholder='Send a message', elem_id='chat-input', elem_classes=['add_scrollbar'])
- shared.gradio['show_controls'] = gr.Checkbox(value=shared.settings['show_controls'], label='Show controls (Ctrl+S)', elem_id='show-controls')
- shared.gradio['typing-dots'] = gr.HTML(value='
Aparcamiento de coches multijugador APK En Son Sürüm: Lo que usted necesita saber
-
¿Te encantan los juegos de conducción? ¿Quieres experimentar un juego de simulación de coches realista e inmersivo? ¿Quieres explorar un enorme mundo abierto con otros jugadores? Si ha respondido sí a cualquiera de estas preguntas, entonces usted debe descargar Parking Multijugador APK En Son Sürüm ahora mismo.
-
aparcamiento de coches multijugador apk en son sürüm
Car Parking Multiplayer es uno de los juegos de simulación de coches más populares y descargados en dispositivos Android. Tiene más de 50 millones de descargas en Google Play Store y una calificación de 4.4 de 5 estrellas. Está desarrollado por olzhass, un equipo de talentosos desarrolladores de juegos apasionados por crear juegos de alta calidad para plataformas móviles.
-
En este artículo, le diremos todo lo que necesita saber sobre Aparcamiento Multijugador APK En Son Sürüm. Cubriremos sus características, cómo descargarlo e instalarlo, sus pros y contras, y algunas preguntas frecuentes. Al final de este artículo, usted estará listo para unirse a la diversión y la emoción de aparcamiento multijugador.
-
Características de Aparcamiento Multijugador APK En Son Sürüm
-
Aparcamiento de coches multijugador APK En Son Sürüm no es solo un simple juego de aparcamiento. Es un juego de simulación de coches de pleno derecho que ofrece una variedad de características que te mantendrán entretenido durante horas. Estas son algunas de las características que se pueden disfrutar en Aparcamiento Multijugador APK En Son Sürüm:
-
Modo multijugador de mundo abierto con jugadores reales
-
Una de las principales atracciones de Aparcamiento Multijugador APK En Son Sürüm es su modo multijugador de mundo abierto. En este modo, puede explorar un mapa enorme con diferentes ubicaciones y entornos con otros jugadores reales. Puede conducir por la ciudad, el campo, el aeropuerto, el desierto y más. También puedes interactuar con otros jugadores tocando la bocina, con luces intermitentes o usando el chat de voz. Incluso puedes unirte o crear una pandilla y competir con otras pandillas por territorio y reputación.
-
-
-
Otra característica que hace que el estacionamiento de coches multijugador APK En Son Sürüm se destacan de otros juegos de simulación de coches es su libre caminar y sistema de conducción. Puedes bajarte del coche y caminar por el mapa como peatón. También puedes entrar a otros coches y conducirlos como desees. Incluso puedes robar coches de otros jugadores o NPCs si te sientes aventurero. El juego también tiene física realista y efectos de daño que hacen que la experiencia de conducción sea más auténtica y desafiante.
-
Coches y casas personalizables
-
Si te gusta personalizar sus coches y casas, entonces te encantará Aparcamiento Multijugador APK En Son Sürüm. El juego ofrece una amplia gama de opciones para personalizar sus vehículos y propiedades. Puede cambiar el color, pegatinas, ruedas, alerones, tubos de escape, y más de sus coches. También puede comprar o alquilar casas y decorarlas con muebles, electrodomésticos, pinturas y más. Incluso puedes invitar a otros jugadores a visitar tu casa y mostrar tu estilo.
-
Varios modos de juego y desafíos
-
Aparcamiento de coches multijugador APK En Son Sürüm no es solo acerca de la conducción y el estacionamiento. También tiene varios modos de juego y desafíos que pondrán a prueba tus habilidades y conocimientos. Puedes probar diferentes modos de juego como carreras, drifting, estacionamiento, entrega, taxi, persecución policial y más. También puede completar tareas y logros diarios para ganar dinero y recompensas. Puede usar el dinero para comprar autos, casas o artículos nuevos. También puede utilizar las recompensas para desbloquear nuevas características y actualizaciones.
-
Chat de voz en línea y salas de chat
-
-
Cómo descargar e instalar el aparcamiento de coches multijugador APK En Son Sürüm
-
Si usted está interesado en jugar Aparcamiento Multijugador APK En Son Sürüm, entonces usted necesita para descargar e instalar en su dispositivo Android. Estos son los pasos que debes seguir:
-
Paso 1: Ir al sitio web oficial o Google Play Store
-
El primer paso es ir a la página web oficial de Parking Multijugador o Google Play Store donde se puede encontrar la última versión de la aplicación. Puede utilizar cualquier navegador de su dispositivo para acceder a estos sitios.
-
Paso 2: Elija la última versión de la aplicación y haga clic en el botón de descarga
-
El siguiente paso es elegir la última versión de la aplicación que es compatible con su dispositivo. Puede comprobar los detalles de la aplicación como el tamaño, número de versión, fecha de actualización, calificación, comentarios, etc. antes de descargarla. Una vez que haya elegido la aplicación, haga clic en el botón de descarga para comenzar a descargarla.
-
Paso 3: Permitir fuentes desconocidas en la configuración del dispositivo
-
Si está descargando la aplicación desde el sitio web oficial, es posible que deba permitir fuentes desconocidas en la configuración de su dispositivo. Esto se debe a que algunos dispositivos pueden no permitir la instalación de aplicaciones desde fuentes distintas de Google Play Store por razones de seguridad. Para permitir fuentes desconocidas, vaya a la configuración del dispositivo > seguridad > fuentes desconocidas > habilitar.
-
Paso 4: Abra el archivo descargado y siga las instrucciones de instalación
-
Después de descargar la aplicación, abra el archivo descargado y siga las instrucciones de instalación que aparecen en la pantalla. El proceso de instalación puede tardar unos minutos dependiendo de la velocidad del dispositivo y la conexión a Internet.
-
Paso 5: Iniciar la aplicación y disfrutar del juego
-
-
Pros y contras de aparcamiento multijugador APK En Son Sürüm
-
Aparcamiento de coches multijugador APK En Son Sürüm es un gran juego que tiene muchas ventajas y beneficios. Sin embargo, también tiene algunos inconvenientes y limitaciones que debe tener en cuenta. Estos son algunos de los pros y los contras de Aparcamiento Multijugador APK En Son Sürüm:
-
Pros
-
-
Divertido y adictivo juego: Aparcamiento de coches multijugador APK En Son Sürüm ofrece un juego divertido y adictivo que le mantendrá enganchado durante horas. Usted puede disfrutar de la conducción, estacionamiento, carreras, deriva, y más en un entorno realista e inmersivo.
-
Gráficos realistas y sonidos: Aparcamiento de coches multijugador APK En Son Sürüm tiene gráficos realistas y sonidos que mejoran la experiencia de juego. Puedes ver los detalles de los coches, los edificios, las carreteras y el paisaje. También puede escuchar los sonidos del motor, los sonidos de la bocina, los sonidos de los neumáticos, y más.
-
Mapa grande y diversa: Aparcamiento de coches multijugador APK En Son Sürüm tiene un mapa grande y diversa que se puede explorar con otros jugadores. Puede visitar diferentes lugares y entornos como la ciudad, el campo, el aeropuerto, el desierto y más. También puede encontrar gasolineras, servicios de automóviles, tiendas, casas y otros lugares de interés.
-
Muchas opciones para personalizar e interactuar: Aparcamiento de coches multijugador APK En Son Sürüm le da muchas opciones para personalizar e interactuar con sus coches y casas. Puede cambiar el color, pegatinas, ruedas, alerones, tubos de escape, y más de sus coches. También puede comprar o alquilar casas y decorarlas con muebles, electrodomésticos, pinturas y más. También puedes invitar a otros jugadores a visitar tu casa y mostrar tu estilo.
-
-
-
Contras
-
-
Alto consumo de batería: Aparcamiento de coches multijugador APK En Son Sürüm consume una gran cantidad de energía de la batería debido a sus gráficos y sonidos de alta calidad. Es posible que necesite cargar su dispositivo con frecuencia o usar un banco de energía si desea jugar durante mucho tiempo.
-
Algunos errores y problemas técnicos: Aparcamiento de coches multijugador APK En Son Sürüm puede tener algunos errores y fallos que pueden afectar a la jugabilidad o causar accidentes. Algunos de estos errores y problemas técnicos son menores y se pueden solucionar mediante la actualización de la aplicación o reiniciar el dispositivo. Sin embargo, algunos de ellos pueden ser importantes y requerir contactar a los desarrolladores o esperar un parche.
-
Puede requerir una conexión a Internet estable: Aparcamiento de coches multijugador APK En Son Sürüm puede requerir una conexión a Internet estable para acceder al modo multijugador y otras características en línea. Si su conexión a Internet es lenta o inestable, puede experimentar problemas de retraso, congelación o desconexión. También puede perderse algunas actualizaciones o eventos que solo están disponibles en línea.
-
Puede contener anuncios y compras en la aplicación: Aparcamiento de coches multijugador APK En Son Sürüm puede contener anuncios y compras en la aplicación que pueden interrumpir o limitar su juego. Algunos anuncios pueden aparecer al azar o con frecuencia mientras juegas. Algunas compras dentro de la aplicación pueden ofrecerle características adicionales o elementos que pueden darle una ventaja sobre otros jugadores.
-
-
Conclusión
-
Aparcamiento de coches multijugador APK En Son Sürüm es un increíble juego de simulación de coches que ofrece una experiencia de conducción realista e inmersiva. Tiene muchas características que lo mantendrán entretenido durante horas, como el modo multijugador de mundo abierto con jugadores reales, caminar y conducir gratis con física realista, autos y casas personalizables, varios modos de juego y desafíos, chat de voz en línea y salas de chat, y más. También tiene algunos inconvenientes, como el alto consumo de batería, algunos errores y problemas técnicos, puede requerir una conexión a Internet estable, puede contener anuncios y compras en la aplicación.
-
-
Preguntas frecuentes
-
-
Q1: ¿Es el aparcamiento de coches multijugador APK En Son Sürüm seguro para descargar?
-
A1: Sí, es seguro, siempre y cuando se descarga de una fuente de confianza como el sitio web oficial o Google Play Store. li>Q2: ¿Cuánto espacio ocupa el aparcamiento multijugador APK En Son Sürüm en mi dispositivo?
-
A2: el tamaño de la aplicación varía según el dispositivo, pero es de alrededor de 300 MB. También puede necesitar espacio adicional para actualizaciones y datos.
-
Q3: ¿Puedo jugar Aparcamiento de coches multijugador APK En Son Sürüm fuera de línea?
-
A3: Sí, puedes jugar algunos modos de juego sin conexión, pero necesitarás una conexión a Internet para acceder al modo multijugador y otras funciones en línea.
-
Q4: ¿Cómo puedo contactar a los desarrolladores de Car Parking Multijugador APK En Son Sürüm?
-
A4: Puede contactarlos por correo electrónico a olzhass@gmail.com o a través de sus cuentas de redes sociales en Facebook, Instagram, YouTube y Discord.
-
Q5: ¿Cuáles son algunos consejos y trucos para jugar Aparcamiento de coches multijugador APK En Son Sürüm?
-
A5: Algunos consejos y trucos son: - Utilice el mapa para encontrar gasolineras, servicios de automóviles, tiendas, casas y otros lugares de interés. - Personaliza tu coche con diferentes colores, pegatinas, ruedas, spoilers y más. - Únete o crea una sala de chat para comunicarte con otros jugadores y hacer amigos. - Pruebe diferentes modos de juego como carreras, deriva, estacionamiento, entrega, taxi, persecución policial y más. - Completar tareas diarias y logros para ganar dinero y recompensas.
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Arca Supervivencia Evolucionado Descargar Pc Juegos picos.md b/spaces/Benson/text-generation/Examples/Arca Supervivencia Evolucionado Descargar Pc Juegos picos.md
deleted file mode 100644
index 7f0ba4bca73a78d22963174baf4826de40d1c872..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Arca Supervivencia Evolucionado Descargar Pc Juegos picos.md
+++ /dev/null
@@ -1,49 +0,0 @@
-
-
Ark: Survival Evolved - Cómo descargar y jugar en PC con juegos épicos
-
¿Alguna vez has soñado con vivir en un mundo lleno de dinosaurios y otras criaturas prehistóricas? Si es así, puedes echar un vistazo a Ark: Survival Evolved, un juego de supervivencia que te permite explorar, crear, construir y domesticar a cientos de especies diferentes. Y la mejor parte es que puedes obtenerlo gratis en la tienda Epic Games Store hasta el 29 de septiembre de 2022!
-
En este artículo, le mostraremos cómo descargar e instalar Ark: Survival Evolved en su PC, cómo comenzar a jugarlo y cómo disfrutarlo. Si usted es un fan de los juegos de supervivencia, dinosaurios, o ambos, usted encontrará algo para amar en este juego.
-
arca supervivencia evolucionado descargar pc juegos épicos
Cómo descargar e instalar Ark: Survival Evolved en PC
-
Descargar e instalar Ark: Survival Evolved en tu PC es muy fácil. Todo lo que necesitas es una cuenta de Epic Games y el lanzador de Epic Games. Si aún no los tienes, puedes crear una cuenta y descargar el lanzador desde el sitio web oficial.
-
Una vez que tenga el lanzador instalado, ábralo y vaya a la pestaña Tienda. Deberías ver Ark: Survival Evolved como uno de los juegos gratuitos de la semana. Haz clic en él y luego haz clic en Obtener. Se te pedirá que confirmes tu pedido (no te preocupes, es gratis) y luego el juego se añadirá a tu biblioteca.
-
Para instalar el juego, ve a tu biblioteca y haz clic en Ark: Survival Evolved. Verás un botón que dice Instalar. Haz clic en él y elige dónde quieres instalar el juego. El tamaño de descarga es de unos 60 GB, así que asegúrate de tener suficiente espacio en tu disco duro. El proceso de instalación puede tardar algún tiempo dependiendo de la velocidad de Internet y el rendimiento del sistema.
-
Una vez completada la instalación, puede iniciar el juego desde su biblioteca o desde el acceso directo del escritorio. ¡Ya estás listo para jugar a Ark: Survival Evolved en tu PC!
-
Cómo empezar a jugar Ark: Survival Evolved en PC
-
-
-
Lo primero que tienes que hacer es elegir un modo y un mapa. Hay cuatro modos disponibles: Reproductor único, Host/Local, Servidores oficiales y Servidores no oficiales. Single Player te permite jugar sin conexión por ti mismo o con amigos usando la pantalla dividida. Host/Local le permite alojar su propio servidor o unirse al servidor de otra persona usando LAN o conexión en línea. Los servidores oficiales son servidores en línea alojados por los desarrolladores de juegos, donde puede unirse a miles de otros jugadores. Los servidores no oficiales son servidores en línea alojados por otros jugadores o comunidades, donde puede encontrar diferentes configuraciones y mods.
-
Lo siguiente que tienes que hacer es elegir un mapa. Hay seis mapas disponibles: La Isla, El Centro, Tierra Quemada, Ragnarok, Aberración y Extinción. Cada mapa tiene sus propias características, biomas, criaturas y desafíos. También puedes descargar mapas personalizados desde el Steam Workshop o el Epic Games Mod Hub.
-
Después de elegir un modo y un mapa, aparecerá en una ubicación aleatoria en el mapa. No tendrás nada más que tus manos desnudas y un implante de tela en tu brazo. Este implante se llama ARK, y es tu interfaz para el juego. Te muestra tu salud, resistencia, hambre, sed, peso, nivel, engramas, inventario, mapa y más.
-
Tu primera prioridad es sobrevivir. Necesitas encontrar comida, agua, refugio y ropa. Puede reunir recursos del medio ambiente, como bayas, madera, piedra, fibra y sílex. Puedes crear objetos usando tus engramas, que son planos que desbloqueas a medida que subes de nivel. Puedes hacer herramientas, armas, armaduras, estructuras y más.
-
Tu segunda prioridad es domar criaturas. Puedes encontrar cientos de especies diferentes en el juego, desde dinosaurios hasta dragones. Algunos de ellos son amistosos, algunos son hostiles, y algunos de ellos son neutrales. Puedes domarlos golpeándolos y alimentándolos con su comida preferida. Una vez domesticados, puedes montarlos, usarlos para combate, transporte, cosecha o cría.
-
-
-
Cómo disfrutar de Ark: Supervivencia evolucionada en PC
-
Ark: Survival Evolved es un juego que ofrece infinitas posibilidades y diversión. No hay forma correcta o incorrecta de jugarlo. Puedes crear tu propia aventura y objetivos en el juego. Estas son algunas de las características y aspectos que hacen que el juego sea agradable y atractivo.
-
-
El juego tiene impresionantes gráficos y efectos de sonido que te sumergen en el mundo de los ARKs. Puedes admirar la belleza y diversidad de los paisajes y criaturas. También puede personalizar la configuración de gráficos para adaptarse a sus preferencias y al rendimiento del sistema.
-
El juego tiene un dinámico ciclo día-noche y el sistema del tiempo que afectan el juego y el entorno. Puede experimentar diferentes condiciones como lluvia, niebla, nieve, olas de calor, olas de frío, tormentas y más. También puedes presenciar eventos celestiales como eclipses, lluvias de meteoritos y cometas.
-
El juego tiene un contenido rico y variado que te mantiene entretenido y desafiado. Puedes encontrar nuevos objetos, criaturas, biomas, eventos, misiones y más con cada actualización y expansión. También puedes acceder al contenido generado por el usuario, como mods, mapas, skins y más, desde Steam Workshop o Epic Games Mod Hub.
-
El juego tiene un modo creativo y sandbox que le permite dar rienda suelta a su imaginación y creatividad. Puedes construir lo que quieras, desde simples cabañas a castillos masivos, desde granjas a fábricas, desde zoológicos a museos. También puedes usar trucos y comandos para modificar la configuración del juego y generar objetos y criaturas.
-
El juego tiene un aspecto multijugador y social que te permite compartir tu experiencia y aventura con otros jugadores. Puedes unirte o crear una tribu con tus amigos o extraños, y trabajar juntos o competir con otras tribus. También puede chatear, intercambiar, aliarse o luchar con otros jugadores. También puede unirse a servidores oficiales o no oficiales con diferentes reglas y comunidades.
-
-
Conclusión
-
-
Si estás interesado en probar este juego, puedes descargarlo gratis desde la Epic Games Store hasta el 29 de septiembre de 2022. No pierdas esta oportunidad de conseguir uno de los mejores juegos de supervivencia jamás hecho. ¡No te arrepentirás!
Defender 3 es un juego divertido y adictivo que te mantendrá entretenido durante horas. Sin embargo, también tiene algunos inconvenientes que podrían afectar su experiencia de juego. Por ejemplo, necesitas gastar dinero real para comprar más gemas y monedas, que se usan para desbloquear nuevas torres, armas y mejoras. También tienes que ver anuncios para obtener recompensas gratis o acelerar el progreso del juego. Además, algunos niveles y enemigos son demasiado difíciles de superar, especialmente si no tienes suficientes recursos o habilidades.
-
-
Características de Defender 3 APK Mod
-
Dinero ilimitado y gemas
-
El beneficio más obvio de jugar Defender 3 APK Mod es que se obtiene dinero ilimitado y gemas en el juego. El dinero se utiliza para comprar y actualizar sus torres, mientras que las gemas se utilizan para comprar y mejorar sus armas. Con recursos ilimitados, no tiene que preocuparse por quedarse sin ellos o gastar dinero real para obtener más. También puedes utilizarlos para comprar potenciadores, pociones, pergaminos y otros elementos que te pueden ayudar en el juego.
-
Varias torres y armas para elegir
-
Otra característica de Defender 3 APK Mod es que desbloquea todas las torres y armas en el juego. Hay más de 20 tipos de torres que tienen diferentes funciones y habilidades, como fuego, hielo, rayos, veneno, etc. También puede equipar a su héroe con más de 50 tipos de armas que tienen diferentes efectos y daños, tales como arcos, ballestas, armas, cañones, etc. Puede mezclar y combinar diferentes combinaciones de torres y armas para adaptarse a su estrategia y preferencia.
-
Impresionantes gráficos y efectos de sonido
-
Defender 3 APK Mod también mejora los gráficos y efectos de sonido del juego. El juego tiene hermosos gráficos en 2D que muestran los detalles del mundo del juego y los personajes. Los efectos de sonido también son realistas e inmersivos, haciéndote sentir como si estuvieras en medio de una batalla. También puede ajustar los gráficos y la configuración de sonido de acuerdo con el rendimiento y las preferencias de su dispositivo.
-
Desafiando niveles y enemigos
-
-
Modo multijugador y tablas de clasificación
-
Defender 3 APK Mod también le permite jugar con otros jugadores en línea y competir por la puntuación más alta en las tablas de clasificación. Puede unirse o crear una habitación e invitar a sus amigos o jugadores al azar a unirse a usted. También puede chatear con ellos y compartir sus consejos y estrategias. También puedes ver tu ranking y logros en las tablas de clasificación globales y regionales. También puedes ganar recompensas y trofeos para completar ciertas tareas y desafíos.
-
Cómo descargar e instalar Defender 3 APK Mod
-
Paso 1: Descargar el archivo APK de una fuente de confianza
-
El primer paso para descargar e instalar Defender 3 APK Mod es encontrar una fuente confiable que proporciona el archivo APK. Usted puede buscar en línea para los sitios web que ofrecen este mod, pero asegúrese de comprobar sus comentarios y calificaciones antes de descargar nada. También puede utilizar este enlace para descargar el archivo APK directamente.
-
-
Paso 2: Habilitar fuentes desconocidas en el dispositivo
-
El siguiente paso es habilitar fuentes desconocidas en su dispositivo, lo que le permite instalar aplicaciones que no son de Google Play Store. Para hacer esto, ve a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctala. Puede ver un mensaje de advertencia, pero simplemente ignórelo y proceda.
-
Paso 3: Instalar el archivo APK y lanzar el juego
-
El paso final es instalar el archivo APK y lanzar el juego. Para hacer esto, busque el archivo APK que descargó en el almacenamiento de su dispositivo, luego toque en él y siga las instrucciones. Una vez finalizada la instalación, puedes abrir el juego y disfrutarlo.
-
Conclusión
-
-
Si usted está interesado en jugar Defender 3 APK Mod, solo tienes que seguir los pasos anteriores para descargar e instalar en su dispositivo. Es fácil y seguro hacerlo, siempre y cuando uses una fuente de confianza. Una vez que la tengas en tu dispositivo, puedes comenzar a reproducirla de inmediato.
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Defender 3 APK Mod:
-
-
Es Defender 3 APK Mod libre?
-
Sí, Defender 3 APK Mod es gratis para descargar y jugar. Usted no tiene que pagar nada para disfrutar de sus características.
-
¿Es seguro Defender 3 APK Mod?
-
Sí, Defender 3 APK Mod es seguro de usar, siempre y cuando se descarga desde una fuente de confianza. No contiene ningún virus o malware que pueda dañar su dispositivo o datos.
-
Es Defender 3 APK Mod compatible con mi dispositivo?
-
Defender 3 APK Mod es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.0 o superior. Sin embargo, algunos dispositivos pueden tener problemas con los gráficos o el rendimiento del juego.
-
¿Puedo jugar Defender 3 APK Mod sin conexión?
-
Sí, puedes jugar Defender 3 APK Mod sin conexión a Internet. Sin embargo, algunas características como el modo multijugador y tablas de clasificación no estarán disponibles.
-
¿Puedo actualizar Defender 3 APK Mod?
-
No, no se puede actualizar Defender 3 APK Mod a través de la Google Play Store o cualquier otra fuente. Si quieres obtener la última versión del juego, tienes que descargarlo e instalarlo de nuevo desde la misma fuente o desde una diferente.
-
Espero que este artículo le ha ayudado a aprender más sobre Defender 3 APK Mod y cómo descargar e instalar en su dispositivo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer y jugar feliz!
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descarga De Fiebre De Oficina Juego.md b/spaces/Benson/text-generation/Examples/Descarga De Fiebre De Oficina Juego.md
deleted file mode 100644
index a9c9a5656eaea17e29f96415b306a51238b98f68..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descarga De Fiebre De Oficina Juego.md
+++ /dev/null
@@ -1,128 +0,0 @@
-
-
Descarga del juego Office Fever: Cómo jugar y disfrutar de este divertido juego
-
¿Sueñas con convertir tu empresa emergente en un imperio del dinero? ¿Te encantan los juegos ociosos que te permiten gestionar tu propio negocio y ganar dinero mientras te relajas? Si respondiste sí, entonces te encantará Office Fever Game, un divertido y adictivo juego de simulación que te permite dirigir tu propia oficina y convertirte en un magnate!
En este artículo, le diremos todo lo que necesita saber sobre Office Fever Game, incluyendo lo que es, cómo descargarlo e instalarlo en su dispositivo, cómo jugarlo y algunos consejos y trucos para ayudarlo a tener éxito. ¡Vamos a empezar!
-
¿Qué es el juego Office Fever?
-
Una breve introducción al juego y sus características
-
Office Fever Game es un juego de simulación desarrollado por Rollic Games, un popular desarrollador de juegos casuales para dispositivos Android e iOS. El juego fue lanzado en junio de 2022 y ha recibido más de 10 millones de descargas y 4.5 estrellas en Google Play Store. El juego también está disponible en BestGames.com y BlueStacks, un emulador que te permite jugar juegos Android en tu PC o Mac.
-
Office Fever Game es un juego inactivo que te permite dirigir tu propia oficina y ganar dinero procesando documentos. Empiezas contratando trabajadores y llevándoles papel para completar. A medida que terminan las tareas, recoges dinero y haces crecer tu negocio. Puede desbloquear nuevas áreas de oficina, contratar a más trabajadores, mejorar sus habilidades y dispositivos, y descubrir nuevas formas de hacer dinero. El juego es fácil de jugar, pero difícil de dominar. Usted tiene que equilibrar su productividad, velocidad y flujo de caja, evitando al mismo tiempo holgazanear y dormir a los trabajadores. El juego tiene un estilo gráfico colorido y caricaturesco, una banda sonora pegadiza y un tono humorístico. El juego es adecuado para todas las edades y se puede jugar sin conexión.
-
-
Cómo descargar e instalar Office Fever Game en tu dispositivo
-
-
-
Abre Google Play Store o App Store en tu dispositivo.
-
Buscar "Office Fever" en la barra de búsqueda.
-
Toque en el icono del juego que aparece en los resultados.
-
Toque en el botón "Instalar" o "Obtener" para iniciar la descarga.
-
Espera a que termine la descarga y luego toca el botón "Abrir" o "Jugar" para iniciar el juego.
-
-
Si quieres jugar Office Fever Game en tu PC o Mac, puedes usar BlueStacks, un emulador que te permite ejecutar aplicaciones Android en tu ordenador. Solo tienes que seguir estos pasos:
-
-
Descargar BlueStacks desde su sitio web oficial .
-
Instalar BlueStacks en su PC o Mac siguiendo las instrucciones.
-
Inicie BlueStacks e inicie sesión con su cuenta de Google.
-
Buscar "Office Fever" en la barra de búsqueda.
-
Haga clic en el icono del juego que aparece en los resultados.
-
Haga clic en el botón "Instalar" para iniciar la descarga.
-
Espera a que termine la descarga y luego haz clic en el botón "Play" para iniciar el juego.
-
-
Cómo jugar juego de fiebre de oficina
-
Los fundamentos del juego y los controles
-
El juego de Office Fever Game es simple e intuitivo. Tienes que dirigir tu propia oficina y ganar dinero procesando documentos. Puede ver su área de oficina en la pantalla, con sus trabajadores, escritorios, papelería y efectivo. También puedes ver tu saldo de efectivo, gemas y nivel en la parte superior de la pantalla.
-
Los controles de Office Fever Game son fáciles y sensibles. Puedes interactuar con el juego tocando, arrastrando y deslizando en la pantalla. Estas son algunas de las acciones básicas que puedes hacer:
-
-
Toque en un trabajador para contratarlos o despertarlos si están durmiendo.
-
Arrastre un trabajo de papel desde la pila al escritorio de un trabajador para asignarle una tarea.
-
Deslice hacia la izquierda o hacia la derecha en la pantalla para moverse a diferentes áreas de oficina.
-
Toque en el icono de efectivo para recoger sus ganancias.
-
-
-
Cómo contratar trabajadores y procesar documentos
-
La principal manera de ganar dinero en Office Fever Game es mediante la contratación de trabajadores y el procesamiento de documentos. Usted comienza con un trabajador y un escritorio en su oficina. Puede contratar a más trabajadores tocando en los escritorios o sillas vacíos en su área de oficina. Cada trabajador cuesta una cierta cantidad de dinero, que aumenta a medida que contrata más. Puede ver el costo de contratar a un trabajador por encima de su escritorio o silla.
-
Una vez que haya contratado a un trabajador, puede asignarles un trabajo de papel arrastrándolo desde la pila hasta su escritorio. Cada trabajo de papel tiene un color y un número, que indican su tipo y dificultad. El color del papel coincide con el color de la camisa del trabajador, lo que significa que están especializados en ese tipo de tareas. El número de trabajo en papel muestra cuánto tiempo le tomará al trabajador completarlo. Cuanto mayor sea el número, más tiempo tomará.
-
Puede ver el progreso de la tarea de cada trabajador mirando la barra sobre su cabeza. Cuando la barra está llena, significa que han terminado su tarea y se puede recoger su dinero en efectivo pulsando en el icono de dinero en efectivo por encima de su cabeza. La cantidad de dinero que ganas depende del tipo y la dificultad del trabajo en papel. También puede ganar gemas, que son una moneda premium que se puede utilizar para comprar artículos especiales y potenciadores.
-
Cómo desbloquear nuevas áreas de oficina y ganar más dinero
-
A medida que avanzas en Office Fever Game, puedes desbloquear nuevas áreas de oficina que te permitirán contratar a más trabajadores y procesar más documentos. Puedes desbloquear nuevas áreas de oficina alcanzando un cierto nivel o gastando gemas. Puede ver los requisitos para desbloquear cada área de oficina tocando el icono de bloqueo en la puerta o ventana.
-
-
Puede moverse entre diferentes áreas de oficina deslizando hacia la izquierda o hacia la derecha en la pantalla. También puede ver una visión general de todas las áreas de su oficina tocando el icono del mapa en la esquina inferior izquierda de la pantalla. Puedes ver cuántos trabajadores tienes en cada área de oficina, cuánto dinero están ganando por segundo y cuánto dinero en efectivo has recaudado en total.
-
Cómo mejorar tus habilidades y dispositivos
-
Otra forma de mejorar tu rendimiento e ingresos en Office Fever Game es mejorando tus habilidades y dispositivos. Puedes acceder a tus habilidades y dispositivos tocando el icono del menú en la esquina inferior derecha de la pantalla y luego tocando los iconos de habilidades o dispositivos.
-
Tus habilidades son habilidades que te ayudarán a dirigir tu oficina de manera más eficiente y efectiva. Puedes mejorar tus habilidades gastando dinero o joyas. Algunas de las habilidades que puedes mejorar son:
-
-
Velocidad: Aumenta la rapidez con la que los trabajadores completan sus tareas.
-
Productividad: Aumenta la cantidad de dinero que ganas con cada tarea.
-
Despierto: Aumenta el tiempo que sus trabajadores permanecen despiertos antes de dormirse.
-
Motivación: Aumenta la rapidez con la que tus trabajadores se despiertan después de dormirse.
-
Flujo de caja: Aumenta la cantidad de efectivo que recoges automáticamente cada segundo.
-
-
Sus dispositivos son gadgets que mejorarán su entorno de oficina y aumentar el rendimiento de sus trabajadores. Puede comprar nuevos dispositivos o actualizar los existentes gastando dinero o joyas. Algunos de los dispositivos que puede comprar o actualizar son:
-
-
Máquina de café: Proporciona café para sus trabajadores que los mantendrá despiertos más tiempo.
-
Ventilador: Proporciona aire fresco para sus trabajadores que les impedirá aflojar.
-
Impresora: Imprime más papel para sus trabajadores que aumentará su productividad.
-
Laptop: Proporciona un dispositivo más rápido y eficiente para sus trabajadores que reducirá su tiempo de tarea.
-
-
-
Consejos y trucos para el juego de fiebre de oficina
-
Cómo aumentar su productividad y velocidad
-
Si quieres ganar más dinero y subir de nivel más rápido en Office Fever Game, necesitas aumentar tu productividad y velocidad. Aquí hay algunos consejos y trucos para ayudarte a hacerlo:
-
-
Asigna el trabajo de papel correcto al trabajador correcto. Combinar el color del papel y la camisa del trabajador aumentará su productividad y velocidad.
-
Mejora tus habilidades y dispositivos regularmente. Invertir en tus habilidades y dispositivos mejorará tu rendimiento e ingresos a largo plazo.
-
Use boosters sabiamente. Los boosters son artículos especiales que pueden darle una ventaja temporal en el juego. Puedes comprar boosters con gemas o ver anuncios gratis. Algunos de los boosters que puedes usar son:
-
-
Cash Booster: Duplica tus ganancias en efectivo por un tiempo limitado.
-
Gem Booster: Duplica tus ganancias de gemas por un tiempo limitado.
-
Speed Booster: Aumenta la velocidad de sus trabajadores en un 50% por un tiempo limitado.
-
Potenciador de la productividad: Aumenta la productividad de sus trabajadores en un 50% por un tiempo limitado.
-
-
-
¿Cómo evitar holgazanear y dormir a los trabajadores
-
Uno de los retos en Office Fever Game es evitar holgazanear y dormir a los trabajadores. Los trabajadores flojos son aquellos que dejan de trabajar y comienzan a hacer otra cosa, como jugar juegos, leer libros o chatear por teléfono. Los trabajadores durmientes son aquellos que se duermen en sus escritorios después de trabajar demasiado tiempo. Tanto los trabajadores holgazanes como los que duermen reducirán tu productividad y velocidad, y te costarán dinero.
-
Aquí hay algunos consejos y trucos para ayudarle a evitar holgazanear y dormir a los trabajadores:
-
-
Toque en aflojar los trabajadores para hacerlos dejar de hacer lo que están haciendo y volver al trabajo.
-
Toque en los trabajadores que duermen para despertarlos y hacerlos reanudar sus tareas.
-
-
Compre una máquina de café y un ventilador para proporcionar café y aire fresco para sus trabajadores que los mantendrá despiertos durante más tiempo y evitará que aflojen.
-
Compre un televisor para proporcionar entretenimiento para sus trabajadores que los motivará a trabajar más duro y evitar holgazanear.
-
-
Cómo usar a los gerentes y a los chicos de oficina efectivamente
-
Otra característica en Office Fever Game son los gerentes y chicos de oficina. Los gerentes son trabajadores especiales que pueden manejar a otros trabajadores en un área de oficina. Los chicos de oficina son trabajadores especiales que pueden recoger dinero de otros trabajadores en un área de oficina. Tanto los gerentes como los oficinistas pueden ahorrarle tiempo y esfuerzo, y aumentar su eficiencia e ingresos.
-
Aquí hay algunos consejos y trucos para ayudarle a utilizar los gerentes y los chicos de oficina con eficacia:
-
-
Puede contratar a gerentes y chicos de oficina tocando en el gerente o chico de la oficina iconos en la parte superior de cada área de oficina. Cada gerente o chico de oficina cuesta una cierta cantidad de dinero en efectivo o gemas, que aumenta a medida que contrata más.
-
Puede asignar un administrador o un chico de oficina a un área de oficina arrastrándolos desde el icono al área de oficina. Puede ver el número de gerentes o chicos de oficina asignados a cada área de oficina mirando el icono.
-
Un gerente puede administrar hasta 10 trabajadores en un área de oficina. Un gerente asignará automáticamente el trabajo de papel a los trabajadores, recogerá dinero en efectivo de ellos, los despertará si están durmiendo, y los detendrá de holgazanear. Un gerente también aumentará la productividad y la velocidad de los trabajadores en su área de oficina en un 10%.
-
Un chico de oficina puede recoger dinero en efectivo de hasta 10 trabajadores en un área de oficina. Un chico de oficina recogerá automáticamente dinero de los trabajadores, sin tener que tocar el icono de dinero encima de su cabeza. Un chico de oficina también aumentará los ingresos en efectivo de los trabajadores en su área de oficina en un 10%.
-
-
Cómo ganar más dinero y gemas
-
-
Aquí hay algunos consejos y trucos para ayudarle a ganar más dinero y gemas:
-
-
Procesa más documentos. Cuantos más documentos proceses, más dinero y gemas ganarás. Trate de asignar el trabajo de papel correcto al trabajador adecuado y actualice sus habilidades y dispositivos para aumentar su productividad y velocidad.
-
Recoge tu dinero regularmente. No dejes que tu dinero se acumule en los escritorios de tus trabajadores, ya que los ralentizará y reducirá tus ganancias. Toque en el icono de dinero en efectivo por encima de su cabeza o utilizar un chico de oficina para recoger su dinero automáticamente.
-
Ver anuncios. Puede ver anuncios para obtener dinero gratis, gemas, o refuerzos. También puede ver anuncios para duplicar sus ganancias sin conexión o pasar el tiempo de espera para desbloquear nuevas áreas de oficina.
-
Logros completos. Puedes completar logros para ganar dinero extra y gemas. Puedes ver tus logros tocando el icono del trofeo en la esquina inferior derecha de la pantalla. Puedes ver los requisitos y recompensas para cada logro, y reclamarlos cuando los completes.
-
Gira la rueda. Puedes girar la rueda una vez al día para obtener una recompensa aleatoria, como dinero en efectivo, gemas, boosters o dispositivos. Puede acceder a la rueda tocando el icono de la rueda en la esquina inferior izquierda de la pantalla.
-
-
Conclusión
-
Office Fever Game es un divertido y adictivo juego de simulación que te permite dirigir tu propia oficina y convertirte en un magnate. Puede contratar trabajadores, procesar documentos, desbloquear nuevas áreas de oficina, actualizar sus habilidades y dispositivos, y descubrir nuevas formas de hacer dinero. El juego es fácil de jugar, pero difícil de dominar. Usted tiene que equilibrar su productividad, velocidad y flujo de caja, evitando al mismo tiempo holgazanear y dormir a los trabajadores. El juego tiene un estilo gráfico colorido y caricaturesco, una banda sonora pegadiza y un tono humorístico. El juego es adecuado para todas las edades y se puede jugar sin conexión.
-
-
Preguntas frecuentes
-
Aquí están algunas de las preguntas más frecuentes sobre Office Fever Game:
-
-
¿Cuántas áreas de oficina hay en Office Fever Game?
-
Hay 10 áreas de oficina en Office Fever Game, cada una con un tema y diseño diferentes. Son: Sótano, Garaje, Ático, Rascacielos, Estación Espacial, Castillo, Pirámide, Isla, Volcán y Cielo.
-
¿Cuántos trabajadores hay en Office Fever Game?
-
Hay 10 tipos de trabajadores en Office Fever Game, cada uno con un color y especialización diferentes. Son: Rojo (Contabilidad), Naranja (Marketing), Amarillo (Ventas), Verde (TI), Azul (Legal), Púrpura (Diseño), Rosa (HR), Marrón (Seguridad), Negro (Hacker), y Blanco (CEO).
-
¿Cuántos documentos hay en Office Fever Game?
-
Hay 10 tipos de documentos en Office Fever Game, cada uno con un color y dificultad diferentes. Son: Rojo (Factura), Naranja (Volante), Amarillo (Contrato), Verde (Código), Azul (Demanda), Púrpura (Logotipo), Rosa (Reanudar), Marrón (Informe), Negro (Cifrado), y Blanco (Estrategia).
-
¿Cuántas habilidades hay en Office Fever Game?
-
Hay 5 habilidades en Office Fever Game que puedes actualizar con efectivo o gemas. Son: Velocidad, Productividad, Despertar, Motivación y Flujo de Caja.
-
¿Cuántos dispositivos hay en Office Fever Game?
-
Hay 5 dispositivos en Office Fever Game que puedes comprar o actualizar con efectivo o gemas. Son: Cafetera, Ventilador, Impresora, Laptop y TV.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/index/package_finder.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/index/package_finder.py
deleted file mode 100644
index b6f8d57e854b77f60c04f59a7f3ff74476a5f5d6..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/index/package_finder.py
+++ /dev/null
@@ -1,1029 +0,0 @@
-"""Routines related to PyPI, indexes"""
-
-import enum
-import functools
-import itertools
-import logging
-import re
-from typing import TYPE_CHECKING, FrozenSet, Iterable, List, Optional, Set, Tuple, Union
-
-from pip._vendor.packaging import specifiers
-from pip._vendor.packaging.tags import Tag
-from pip._vendor.packaging.utils import canonicalize_name
-from pip._vendor.packaging.version import _BaseVersion
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import (
- BestVersionAlreadyInstalled,
- DistributionNotFound,
- InvalidWheelFilename,
- UnsupportedWheel,
-)
-from pip._internal.index.collector import LinkCollector, parse_links
-from pip._internal.models.candidate import InstallationCandidate
-from pip._internal.models.format_control import FormatControl
-from pip._internal.models.link import Link
-from pip._internal.models.search_scope import SearchScope
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.models.target_python import TargetPython
-from pip._internal.models.wheel import Wheel
-from pip._internal.req import InstallRequirement
-from pip._internal.utils._log import getLogger
-from pip._internal.utils.filetypes import WHEEL_EXTENSION
-from pip._internal.utils.hashes import Hashes
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import build_netloc
-from pip._internal.utils.packaging import check_requires_python
-from pip._internal.utils.unpacking import SUPPORTED_EXTENSIONS
-
-if TYPE_CHECKING:
- from pip._vendor.typing_extensions import TypeGuard
-
-__all__ = ["FormatControl", "BestCandidateResult", "PackageFinder"]
-
-
-logger = getLogger(__name__)
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-CandidateSortingKey = Tuple[int, int, int, _BaseVersion, Optional[int], BuildTag]
-
-
-def _check_link_requires_python(
- link: Link,
- version_info: Tuple[int, int, int],
- ignore_requires_python: bool = False,
-) -> bool:
- """
- Return whether the given Python version is compatible with a link's
- "Requires-Python" value.
-
- :param version_info: A 3-tuple of ints representing the Python
- major-minor-micro version to check.
- :param ignore_requires_python: Whether to ignore the "Requires-Python"
- value if the given Python version isn't compatible.
- """
- try:
- is_compatible = check_requires_python(
- link.requires_python,
- version_info=version_info,
- )
- except specifiers.InvalidSpecifier:
- logger.debug(
- "Ignoring invalid Requires-Python (%r) for link: %s",
- link.requires_python,
- link,
- )
- else:
- if not is_compatible:
- version = ".".join(map(str, version_info))
- if not ignore_requires_python:
- logger.verbose(
- "Link requires a different Python (%s not in: %r): %s",
- version,
- link.requires_python,
- link,
- )
- return False
-
- logger.debug(
- "Ignoring failed Requires-Python check (%s not in: %r) for link: %s",
- version,
- link.requires_python,
- link,
- )
-
- return True
-
-
-class LinkType(enum.Enum):
- candidate = enum.auto()
- different_project = enum.auto()
- yanked = enum.auto()
- format_unsupported = enum.auto()
- format_invalid = enum.auto()
- platform_mismatch = enum.auto()
- requires_python_mismatch = enum.auto()
-
-
-class LinkEvaluator:
-
- """
- Responsible for evaluating links for a particular project.
- """
-
- _py_version_re = re.compile(r"-py([123]\.?[0-9]?)$")
-
- # Don't include an allow_yanked default value to make sure each call
- # site considers whether yanked releases are allowed. This also causes
- # that decision to be made explicit in the calling code, which helps
- # people when reading the code.
- def __init__(
- self,
- project_name: str,
- canonical_name: str,
- formats: FrozenSet[str],
- target_python: TargetPython,
- allow_yanked: bool,
- ignore_requires_python: Optional[bool] = None,
- ) -> None:
- """
- :param project_name: The user supplied package name.
- :param canonical_name: The canonical package name.
- :param formats: The formats allowed for this package. Should be a set
- with 'binary' or 'source' or both in it.
- :param target_python: The target Python interpreter to use when
- evaluating link compatibility. This is used, for example, to
- check wheel compatibility, as well as when checking the Python
- version, e.g. the Python version embedded in a link filename
- (or egg fragment) and against an HTML link's optional PEP 503
- "data-requires-python" attribute.
- :param allow_yanked: Whether files marked as yanked (in the sense
- of PEP 592) are permitted to be candidates for install.
- :param ignore_requires_python: Whether to ignore incompatible
- PEP 503 "data-requires-python" values in HTML links. Defaults
- to False.
- """
- if ignore_requires_python is None:
- ignore_requires_python = False
-
- self._allow_yanked = allow_yanked
- self._canonical_name = canonical_name
- self._ignore_requires_python = ignore_requires_python
- self._formats = formats
- self._target_python = target_python
-
- self.project_name = project_name
-
- def evaluate_link(self, link: Link) -> Tuple[LinkType, str]:
- """
- Determine whether a link is a candidate for installation.
-
- :return: A tuple (result, detail), where *result* is an enum
- representing whether the evaluation found a candidate, or the reason
- why one is not found. If a candidate is found, *detail* will be the
- candidate's version string; if one is not found, it contains the
- reason the link fails to qualify.
- """
- version = None
- if link.is_yanked and not self._allow_yanked:
- reason = link.yanked_reason or ""
- return (LinkType.yanked, f"yanked for reason: {reason}")
-
- if link.egg_fragment:
- egg_info = link.egg_fragment
- ext = link.ext
- else:
- egg_info, ext = link.splitext()
- if not ext:
- return (LinkType.format_unsupported, "not a file")
- if ext not in SUPPORTED_EXTENSIONS:
- return (
- LinkType.format_unsupported,
- f"unsupported archive format: {ext}",
- )
- if "binary" not in self._formats and ext == WHEEL_EXTENSION:
- reason = f"No binaries permitted for {self.project_name}"
- return (LinkType.format_unsupported, reason)
- if "macosx10" in link.path and ext == ".zip":
- return (LinkType.format_unsupported, "macosx10 one")
- if ext == WHEEL_EXTENSION:
- try:
- wheel = Wheel(link.filename)
- except InvalidWheelFilename:
- return (
- LinkType.format_invalid,
- "invalid wheel filename",
- )
- if canonicalize_name(wheel.name) != self._canonical_name:
- reason = f"wrong project name (not {self.project_name})"
- return (LinkType.different_project, reason)
-
- supported_tags = self._target_python.get_tags()
- if not wheel.supported(supported_tags):
- # Include the wheel's tags in the reason string to
- # simplify troubleshooting compatibility issues.
- file_tags = ", ".join(wheel.get_formatted_file_tags())
- reason = (
- f"none of the wheel's tags ({file_tags}) are compatible "
- f"(run pip debug --verbose to show compatible tags)"
- )
- return (LinkType.platform_mismatch, reason)
-
- version = wheel.version
-
- # This should be up by the self.ok_binary check, but see issue 2700.
- if "source" not in self._formats and ext != WHEEL_EXTENSION:
- reason = f"No sources permitted for {self.project_name}"
- return (LinkType.format_unsupported, reason)
-
- if not version:
- version = _extract_version_from_fragment(
- egg_info,
- self._canonical_name,
- )
- if not version:
- reason = f"Missing project version for {self.project_name}"
- return (LinkType.format_invalid, reason)
-
- match = self._py_version_re.search(version)
- if match:
- version = version[: match.start()]
- py_version = match.group(1)
- if py_version != self._target_python.py_version:
- return (
- LinkType.platform_mismatch,
- "Python version is incorrect",
- )
-
- supports_python = _check_link_requires_python(
- link,
- version_info=self._target_python.py_version_info,
- ignore_requires_python=self._ignore_requires_python,
- )
- if not supports_python:
- reason = f"{version} Requires-Python {link.requires_python}"
- return (LinkType.requires_python_mismatch, reason)
-
- logger.debug("Found link %s, version: %s", link, version)
-
- return (LinkType.candidate, version)
-
-
-def filter_unallowed_hashes(
- candidates: List[InstallationCandidate],
- hashes: Optional[Hashes],
- project_name: str,
-) -> List[InstallationCandidate]:
- """
- Filter out candidates whose hashes aren't allowed, and return a new
- list of candidates.
-
- If at least one candidate has an allowed hash, then all candidates with
- either an allowed hash or no hash specified are returned. Otherwise,
- the given candidates are returned.
-
- Including the candidates with no hash specified when there is a match
- allows a warning to be logged if there is a more preferred candidate
- with no hash specified. Returning all candidates in the case of no
- matches lets pip report the hash of the candidate that would otherwise
- have been installed (e.g. permitting the user to more easily update
- their requirements file with the desired hash).
- """
- if not hashes:
- logger.debug(
- "Given no hashes to check %s links for project %r: "
- "discarding no candidates",
- len(candidates),
- project_name,
- )
- # Make sure we're not returning back the given value.
- return list(candidates)
-
- matches_or_no_digest = []
- # Collect the non-matches for logging purposes.
- non_matches = []
- match_count = 0
- for candidate in candidates:
- link = candidate.link
- if not link.has_hash:
- pass
- elif link.is_hash_allowed(hashes=hashes):
- match_count += 1
- else:
- non_matches.append(candidate)
- continue
-
- matches_or_no_digest.append(candidate)
-
- if match_count:
- filtered = matches_or_no_digest
- else:
- # Make sure we're not returning back the given value.
- filtered = list(candidates)
-
- if len(filtered) == len(candidates):
- discard_message = "discarding no candidates"
- else:
- discard_message = "discarding {} non-matches:\n {}".format(
- len(non_matches),
- "\n ".join(str(candidate.link) for candidate in non_matches),
- )
-
- logger.debug(
- "Checked %s links for project %r against %s hashes "
- "(%s matches, %s no digest): %s",
- len(candidates),
- project_name,
- hashes.digest_count,
- match_count,
- len(matches_or_no_digest) - match_count,
- discard_message,
- )
-
- return filtered
-
-
-class CandidatePreferences:
-
- """
- Encapsulates some of the preferences for filtering and sorting
- InstallationCandidate objects.
- """
-
- def __init__(
- self,
- prefer_binary: bool = False,
- allow_all_prereleases: bool = False,
- ) -> None:
- """
- :param allow_all_prereleases: Whether to allow all pre-releases.
- """
- self.allow_all_prereleases = allow_all_prereleases
- self.prefer_binary = prefer_binary
-
-
-class BestCandidateResult:
- """A collection of candidates, returned by `PackageFinder.find_best_candidate`.
-
- This class is only intended to be instantiated by CandidateEvaluator's
- `compute_best_candidate()` method.
- """
-
- def __init__(
- self,
- candidates: List[InstallationCandidate],
- applicable_candidates: List[InstallationCandidate],
- best_candidate: Optional[InstallationCandidate],
- ) -> None:
- """
- :param candidates: A sequence of all available candidates found.
- :param applicable_candidates: The applicable candidates.
- :param best_candidate: The most preferred candidate found, or None
- if no applicable candidates were found.
- """
- assert set(applicable_candidates) <= set(candidates)
-
- if best_candidate is None:
- assert not applicable_candidates
- else:
- assert best_candidate in applicable_candidates
-
- self._applicable_candidates = applicable_candidates
- self._candidates = candidates
-
- self.best_candidate = best_candidate
-
- def iter_all(self) -> Iterable[InstallationCandidate]:
- """Iterate through all candidates."""
- return iter(self._candidates)
-
- def iter_applicable(self) -> Iterable[InstallationCandidate]:
- """Iterate through the applicable candidates."""
- return iter(self._applicable_candidates)
-
-
-class CandidateEvaluator:
-
- """
- Responsible for filtering and sorting candidates for installation based
- on what tags are valid.
- """
-
- @classmethod
- def create(
- cls,
- project_name: str,
- target_python: Optional[TargetPython] = None,
- prefer_binary: bool = False,
- allow_all_prereleases: bool = False,
- specifier: Optional[specifiers.BaseSpecifier] = None,
- hashes: Optional[Hashes] = None,
- ) -> "CandidateEvaluator":
- """Create a CandidateEvaluator object.
-
- :param target_python: The target Python interpreter to use when
- checking compatibility. If None (the default), a TargetPython
- object will be constructed from the running Python.
- :param specifier: An optional object implementing `filter`
- (e.g. `packaging.specifiers.SpecifierSet`) to filter applicable
- versions.
- :param hashes: An optional collection of allowed hashes.
- """
- if target_python is None:
- target_python = TargetPython()
- if specifier is None:
- specifier = specifiers.SpecifierSet()
-
- supported_tags = target_python.get_tags()
-
- return cls(
- project_name=project_name,
- supported_tags=supported_tags,
- specifier=specifier,
- prefer_binary=prefer_binary,
- allow_all_prereleases=allow_all_prereleases,
- hashes=hashes,
- )
-
- def __init__(
- self,
- project_name: str,
- supported_tags: List[Tag],
- specifier: specifiers.BaseSpecifier,
- prefer_binary: bool = False,
- allow_all_prereleases: bool = False,
- hashes: Optional[Hashes] = None,
- ) -> None:
- """
- :param supported_tags: The PEP 425 tags supported by the target
- Python in order of preference (most preferred first).
- """
- self._allow_all_prereleases = allow_all_prereleases
- self._hashes = hashes
- self._prefer_binary = prefer_binary
- self._project_name = project_name
- self._specifier = specifier
- self._supported_tags = supported_tags
- # Since the index of the tag in the _supported_tags list is used
- # as a priority, precompute a map from tag to index/priority to be
- # used in wheel.find_most_preferred_tag.
- self._wheel_tag_preferences = {
- tag: idx for idx, tag in enumerate(supported_tags)
- }
-
- def get_applicable_candidates(
- self,
- candidates: List[InstallationCandidate],
- ) -> List[InstallationCandidate]:
- """
- Return the applicable candidates from a list of candidates.
- """
- # Using None infers from the specifier instead.
- allow_prereleases = self._allow_all_prereleases or None
- specifier = self._specifier
- versions = {
- str(v)
- for v in specifier.filter(
- # We turn the version object into a str here because otherwise
- # when we're debundled but setuptools isn't, Python will see
- # packaging.version.Version and
- # pkg_resources._vendor.packaging.version.Version as different
- # types. This way we'll use a str as a common data interchange
- # format. If we stop using the pkg_resources provided specifier
- # and start using our own, we can drop the cast to str().
- (str(c.version) for c in candidates),
- prereleases=allow_prereleases,
- )
- }
-
- # Again, converting version to str to deal with debundling.
- applicable_candidates = [c for c in candidates if str(c.version) in versions]
-
- filtered_applicable_candidates = filter_unallowed_hashes(
- candidates=applicable_candidates,
- hashes=self._hashes,
- project_name=self._project_name,
- )
-
- return sorted(filtered_applicable_candidates, key=self._sort_key)
-
- def _sort_key(self, candidate: InstallationCandidate) -> CandidateSortingKey:
- """
- Function to pass as the `key` argument to a call to sorted() to sort
- InstallationCandidates by preference.
-
- Returns a tuple such that tuples sorting as greater using Python's
- default comparison operator are more preferred.
-
- The preference is as follows:
-
- First and foremost, candidates with allowed (matching) hashes are
- always preferred over candidates without matching hashes. This is
- because e.g. if the only candidate with an allowed hash is yanked,
- we still want to use that candidate.
-
- Second, excepting hash considerations, candidates that have been
- yanked (in the sense of PEP 592) are always less preferred than
- candidates that haven't been yanked. Then:
-
- If not finding wheels, they are sorted by version only.
- If finding wheels, then the sort order is by version, then:
- 1. existing installs
- 2. wheels ordered via Wheel.support_index_min(self._supported_tags)
- 3. source archives
- If prefer_binary was set, then all wheels are sorted above sources.
-
- Note: it was considered to embed this logic into the Link
- comparison operators, but then different sdist links
- with the same version, would have to be considered equal
- """
- valid_tags = self._supported_tags
- support_num = len(valid_tags)
- build_tag: BuildTag = ()
- binary_preference = 0
- link = candidate.link
- if link.is_wheel:
- # can raise InvalidWheelFilename
- wheel = Wheel(link.filename)
- try:
- pri = -(
- wheel.find_most_preferred_tag(
- valid_tags, self._wheel_tag_preferences
- )
- )
- except ValueError:
- raise UnsupportedWheel(
- "{} is not a supported wheel for this platform. It "
- "can't be sorted.".format(wheel.filename)
- )
- if self._prefer_binary:
- binary_preference = 1
- if wheel.build_tag is not None:
- match = re.match(r"^(\d+)(.*)$", wheel.build_tag)
- assert match is not None, "guaranteed by filename validation"
- build_tag_groups = match.groups()
- build_tag = (int(build_tag_groups[0]), build_tag_groups[1])
- else: # sdist
- pri = -(support_num)
- has_allowed_hash = int(link.is_hash_allowed(self._hashes))
- yank_value = -1 * int(link.is_yanked) # -1 for yanked.
- return (
- has_allowed_hash,
- yank_value,
- binary_preference,
- candidate.version,
- pri,
- build_tag,
- )
-
- def sort_best_candidate(
- self,
- candidates: List[InstallationCandidate],
- ) -> Optional[InstallationCandidate]:
- """
- Return the best candidate per the instance's sort order, or None if
- no candidate is acceptable.
- """
- if not candidates:
- return None
- best_candidate = max(candidates, key=self._sort_key)
- return best_candidate
-
- def compute_best_candidate(
- self,
- candidates: List[InstallationCandidate],
- ) -> BestCandidateResult:
- """
- Compute and return a `BestCandidateResult` instance.
- """
- applicable_candidates = self.get_applicable_candidates(candidates)
-
- best_candidate = self.sort_best_candidate(applicable_candidates)
-
- return BestCandidateResult(
- candidates,
- applicable_candidates=applicable_candidates,
- best_candidate=best_candidate,
- )
-
-
-class PackageFinder:
- """This finds packages.
-
- This is meant to match easy_install's technique for looking for
- packages, by reading pages and looking for appropriate links.
- """
-
- def __init__(
- self,
- link_collector: LinkCollector,
- target_python: TargetPython,
- allow_yanked: bool,
- format_control: Optional[FormatControl] = None,
- candidate_prefs: Optional[CandidatePreferences] = None,
- ignore_requires_python: Optional[bool] = None,
- ) -> None:
- """
- This constructor is primarily meant to be used by the create() class
- method and from tests.
-
- :param format_control: A FormatControl object, used to control
- the selection of source packages / binary packages when consulting
- the index and links.
- :param candidate_prefs: Options to use when creating a
- CandidateEvaluator object.
- """
- if candidate_prefs is None:
- candidate_prefs = CandidatePreferences()
-
- format_control = format_control or FormatControl(set(), set())
-
- self._allow_yanked = allow_yanked
- self._candidate_prefs = candidate_prefs
- self._ignore_requires_python = ignore_requires_python
- self._link_collector = link_collector
- self._target_python = target_python
-
- self.format_control = format_control
-
- # These are boring links that have already been logged somehow.
- self._logged_links: Set[Tuple[Link, LinkType, str]] = set()
-
- # Don't include an allow_yanked default value to make sure each call
- # site considers whether yanked releases are allowed. This also causes
- # that decision to be made explicit in the calling code, which helps
- # people when reading the code.
- @classmethod
- def create(
- cls,
- link_collector: LinkCollector,
- selection_prefs: SelectionPreferences,
- target_python: Optional[TargetPython] = None,
- ) -> "PackageFinder":
- """Create a PackageFinder.
-
- :param selection_prefs: The candidate selection preferences, as a
- SelectionPreferences object.
- :param target_python: The target Python interpreter to use when
- checking compatibility. If None (the default), a TargetPython
- object will be constructed from the running Python.
- """
- if target_python is None:
- target_python = TargetPython()
-
- candidate_prefs = CandidatePreferences(
- prefer_binary=selection_prefs.prefer_binary,
- allow_all_prereleases=selection_prefs.allow_all_prereleases,
- )
-
- return cls(
- candidate_prefs=candidate_prefs,
- link_collector=link_collector,
- target_python=target_python,
- allow_yanked=selection_prefs.allow_yanked,
- format_control=selection_prefs.format_control,
- ignore_requires_python=selection_prefs.ignore_requires_python,
- )
-
- @property
- def target_python(self) -> TargetPython:
- return self._target_python
-
- @property
- def search_scope(self) -> SearchScope:
- return self._link_collector.search_scope
-
- @search_scope.setter
- def search_scope(self, search_scope: SearchScope) -> None:
- self._link_collector.search_scope = search_scope
-
- @property
- def find_links(self) -> List[str]:
- return self._link_collector.find_links
-
- @property
- def index_urls(self) -> List[str]:
- return self.search_scope.index_urls
-
- @property
- def trusted_hosts(self) -> Iterable[str]:
- for host_port in self._link_collector.session.pip_trusted_origins:
- yield build_netloc(*host_port)
-
- @property
- def allow_all_prereleases(self) -> bool:
- return self._candidate_prefs.allow_all_prereleases
-
- def set_allow_all_prereleases(self) -> None:
- self._candidate_prefs.allow_all_prereleases = True
-
- @property
- def prefer_binary(self) -> bool:
- return self._candidate_prefs.prefer_binary
-
- def set_prefer_binary(self) -> None:
- self._candidate_prefs.prefer_binary = True
-
- def requires_python_skipped_reasons(self) -> List[str]:
- reasons = {
- detail
- for _, result, detail in self._logged_links
- if result == LinkType.requires_python_mismatch
- }
- return sorted(reasons)
-
- def make_link_evaluator(self, project_name: str) -> LinkEvaluator:
- canonical_name = canonicalize_name(project_name)
- formats = self.format_control.get_allowed_formats(canonical_name)
-
- return LinkEvaluator(
- project_name=project_name,
- canonical_name=canonical_name,
- formats=formats,
- target_python=self._target_python,
- allow_yanked=self._allow_yanked,
- ignore_requires_python=self._ignore_requires_python,
- )
-
- def _sort_links(self, links: Iterable[Link]) -> List[Link]:
- """
- Returns elements of links in order, non-egg links first, egg links
- second, while eliminating duplicates
- """
- eggs, no_eggs = [], []
- seen: Set[Link] = set()
- for link in links:
- if link not in seen:
- seen.add(link)
- if link.egg_fragment:
- eggs.append(link)
- else:
- no_eggs.append(link)
- return no_eggs + eggs
-
- def _log_skipped_link(self, link: Link, result: LinkType, detail: str) -> None:
- entry = (link, result, detail)
- if entry not in self._logged_links:
- # Put the link at the end so the reason is more visible and because
- # the link string is usually very long.
- logger.debug("Skipping link: %s: %s", detail, link)
- self._logged_links.add(entry)
-
- def get_install_candidate(
- self, link_evaluator: LinkEvaluator, link: Link
- ) -> Optional[InstallationCandidate]:
- """
- If the link is a candidate for install, convert it to an
- InstallationCandidate and return it. Otherwise, return None.
- """
- result, detail = link_evaluator.evaluate_link(link)
- if result != LinkType.candidate:
- self._log_skipped_link(link, result, detail)
- return None
-
- return InstallationCandidate(
- name=link_evaluator.project_name,
- link=link,
- version=detail,
- )
-
- def evaluate_links(
- self, link_evaluator: LinkEvaluator, links: Iterable[Link]
- ) -> List[InstallationCandidate]:
- """
- Convert links that are candidates to InstallationCandidate objects.
- """
- candidates = []
- for link in self._sort_links(links):
- candidate = self.get_install_candidate(link_evaluator, link)
- if candidate is not None:
- candidates.append(candidate)
-
- return candidates
-
- def process_project_url(
- self, project_url: Link, link_evaluator: LinkEvaluator
- ) -> List[InstallationCandidate]:
- logger.debug(
- "Fetching project page and analyzing links: %s",
- project_url,
- )
- index_response = self._link_collector.fetch_response(project_url)
- if index_response is None:
- return []
-
- page_links = list(parse_links(index_response))
-
- with indent_log():
- package_links = self.evaluate_links(
- link_evaluator,
- links=page_links,
- )
-
- return package_links
-
- @functools.lru_cache(maxsize=None)
- def find_all_candidates(self, project_name: str) -> List[InstallationCandidate]:
- """Find all available InstallationCandidate for project_name
-
- This checks index_urls and find_links.
- All versions found are returned as an InstallationCandidate list.
-
- See LinkEvaluator.evaluate_link() for details on which files
- are accepted.
- """
- link_evaluator = self.make_link_evaluator(project_name)
-
- collected_sources = self._link_collector.collect_sources(
- project_name=project_name,
- candidates_from_page=functools.partial(
- self.process_project_url,
- link_evaluator=link_evaluator,
- ),
- )
-
- page_candidates_it = itertools.chain.from_iterable(
- source.page_candidates()
- for sources in collected_sources
- for source in sources
- if source is not None
- )
- page_candidates = list(page_candidates_it)
-
- file_links_it = itertools.chain.from_iterable(
- source.file_links()
- for sources in collected_sources
- for source in sources
- if source is not None
- )
- file_candidates = self.evaluate_links(
- link_evaluator,
- sorted(file_links_it, reverse=True),
- )
-
- if logger.isEnabledFor(logging.DEBUG) and file_candidates:
- paths = []
- for candidate in file_candidates:
- assert candidate.link.url # we need to have a URL
- try:
- paths.append(candidate.link.file_path)
- except Exception:
- paths.append(candidate.link.url) # it's not a local file
-
- logger.debug("Local files found: %s", ", ".join(paths))
-
- # This is an intentional priority ordering
- return file_candidates + page_candidates
-
- def make_candidate_evaluator(
- self,
- project_name: str,
- specifier: Optional[specifiers.BaseSpecifier] = None,
- hashes: Optional[Hashes] = None,
- ) -> CandidateEvaluator:
- """Create a CandidateEvaluator object to use."""
- candidate_prefs = self._candidate_prefs
- return CandidateEvaluator.create(
- project_name=project_name,
- target_python=self._target_python,
- prefer_binary=candidate_prefs.prefer_binary,
- allow_all_prereleases=candidate_prefs.allow_all_prereleases,
- specifier=specifier,
- hashes=hashes,
- )
-
- @functools.lru_cache(maxsize=None)
- def find_best_candidate(
- self,
- project_name: str,
- specifier: Optional[specifiers.BaseSpecifier] = None,
- hashes: Optional[Hashes] = None,
- ) -> BestCandidateResult:
- """Find matches for the given project and specifier.
-
- :param specifier: An optional object implementing `filter`
- (e.g. `packaging.specifiers.SpecifierSet`) to filter applicable
- versions.
-
- :return: A `BestCandidateResult` instance.
- """
- candidates = self.find_all_candidates(project_name)
- candidate_evaluator = self.make_candidate_evaluator(
- project_name=project_name,
- specifier=specifier,
- hashes=hashes,
- )
- return candidate_evaluator.compute_best_candidate(candidates)
-
- def find_requirement(
- self, req: InstallRequirement, upgrade: bool
- ) -> Optional[InstallationCandidate]:
- """Try to find a Link matching req
-
- Expects req, an InstallRequirement and upgrade, a boolean
- Returns a InstallationCandidate if found,
- Raises DistributionNotFound or BestVersionAlreadyInstalled otherwise
- """
- hashes = req.hashes(trust_internet=False)
- best_candidate_result = self.find_best_candidate(
- req.name,
- specifier=req.specifier,
- hashes=hashes,
- )
- best_candidate = best_candidate_result.best_candidate
-
- installed_version: Optional[_BaseVersion] = None
- if req.satisfied_by is not None:
- installed_version = req.satisfied_by.version
-
- def _format_versions(cand_iter: Iterable[InstallationCandidate]) -> str:
- # This repeated parse_version and str() conversion is needed to
- # handle different vendoring sources from pip and pkg_resources.
- # If we stop using the pkg_resources provided specifier and start
- # using our own, we can drop the cast to str().
- return (
- ", ".join(
- sorted(
- {str(c.version) for c in cand_iter},
- key=parse_version,
- )
- )
- or "none"
- )
-
- if installed_version is None and best_candidate is None:
- logger.critical(
- "Could not find a version that satisfies the requirement %s "
- "(from versions: %s)",
- req,
- _format_versions(best_candidate_result.iter_all()),
- )
-
- raise DistributionNotFound(
- "No matching distribution found for {}".format(req)
- )
-
- def _should_install_candidate(
- candidate: Optional[InstallationCandidate],
- ) -> "TypeGuard[InstallationCandidate]":
- if installed_version is None:
- return True
- if best_candidate is None:
- return False
- return best_candidate.version > installed_version
-
- if not upgrade and installed_version is not None:
- if _should_install_candidate(best_candidate):
- logger.debug(
- "Existing installed version (%s) satisfies requirement "
- "(most up-to-date version is %s)",
- installed_version,
- best_candidate.version,
- )
- else:
- logger.debug(
- "Existing installed version (%s) is most up-to-date and "
- "satisfies requirement",
- installed_version,
- )
- return None
-
- if _should_install_candidate(best_candidate):
- logger.debug(
- "Using version %s (newest of versions: %s)",
- best_candidate.version,
- _format_versions(best_candidate_result.iter_applicable()),
- )
- return best_candidate
-
- # We have an existing version, and its the best version
- logger.debug(
- "Installed version (%s) is most up-to-date (past versions: %s)",
- installed_version,
- _format_versions(best_candidate_result.iter_applicable()),
- )
- raise BestVersionAlreadyInstalled
-
-
-def _find_name_version_sep(fragment: str, canonical_name: str) -> int:
- """Find the separator's index based on the package's canonical name.
-
- :param fragment: A + filename "fragment" (stem) or
- egg fragment.
- :param canonical_name: The package's canonical name.
-
- This function is needed since the canonicalized name does not necessarily
- have the same length as the egg info's name part. An example::
-
- >>> fragment = 'foo__bar-1.0'
- >>> canonical_name = 'foo-bar'
- >>> _find_name_version_sep(fragment, canonical_name)
- 8
- """
- # Project name and version must be separated by one single dash. Find all
- # occurrences of dashes; if the string in front of it matches the canonical
- # name, this is the one separating the name and version parts.
- for i, c in enumerate(fragment):
- if c != "-":
- continue
- if canonicalize_name(fragment[:i]) == canonical_name:
- return i
- raise ValueError(f"{fragment} does not match {canonical_name}")
-
-
-def _extract_version_from_fragment(fragment: str, canonical_name: str) -> Optional[str]:
- """Parse the version string from a + filename
- "fragment" (stem) or egg fragment.
-
- :param fragment: The string to parse. E.g. foo-2.1
- :param canonical_name: The canonicalized name of the package this
- belongs to.
- """
- try:
- version_start = _find_name_version_sep(fragment, canonical_name) + 1
- except ValueError:
- return None
- version = fragment[version_start:]
- if not version:
- return None
- return version
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py
deleted file mode 100644
index 2cba4b0708032d62b4c1278f99e5db87ed8d90fe..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import division
-
-from datetime import datetime
-from pip._vendor.cachecontrol.cache import BaseCache
-
-
-class RedisCache(BaseCache):
-
- def __init__(self, conn):
- self.conn = conn
-
- def get(self, key):
- return self.conn.get(key)
-
- def set(self, key, value, expires=None):
- if not expires:
- self.conn.set(key, value)
- elif isinstance(expires, datetime):
- expires = expires - datetime.utcnow()
- self.conn.setex(key, int(expires.total_seconds()), value)
- else:
- self.conn.setex(key, expires, value)
-
- def delete(self, key):
- self.conn.delete(key)
-
- def clear(self):
- """Helper for clearing all the keys in a database. Use with
- caution!"""
- for key in self.conn.keys():
- self.conn.delete(key)
-
- def close(self):
- """Redis uses connection pooling, no need to close the connection."""
- pass
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/bandwidth.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/bandwidth.py
deleted file mode 100644
index 9bac5885e1a22543e4f30b6819aab91bafc20db5..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/bandwidth.py
+++ /dev/null
@@ -1,439 +0,0 @@
-# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import threading
-import time
-
-
-class RequestExceededException(Exception):
- def __init__(self, requested_amt, retry_time):
- """Error when requested amount exceeds what is allowed
-
- The request that raised this error should be retried after waiting
- the time specified by ``retry_time``.
-
- :type requested_amt: int
- :param requested_amt: The originally requested byte amount
-
- :type retry_time: float
- :param retry_time: The length in time to wait to retry for the
- requested amount
- """
- self.requested_amt = requested_amt
- self.retry_time = retry_time
- msg = 'Request amount {} exceeded the amount available. Retry in {}'.format(
- requested_amt, retry_time
- )
- super().__init__(msg)
-
-
-class RequestToken:
- """A token to pass as an identifier when consuming from the LeakyBucket"""
-
- pass
-
-
-class TimeUtils:
- def time(self):
- """Get the current time back
-
- :rtype: float
- :returns: The current time in seconds
- """
- return time.time()
-
- def sleep(self, value):
- """Sleep for a designated time
-
- :type value: float
- :param value: The time to sleep for in seconds
- """
- return time.sleep(value)
-
-
-class BandwidthLimiter:
- def __init__(self, leaky_bucket, time_utils=None):
- """Limits bandwidth for shared S3 transfers
-
- :type leaky_bucket: LeakyBucket
- :param leaky_bucket: The leaky bucket to use limit bandwidth
-
- :type time_utils: TimeUtils
- :param time_utils: Time utility to use for interacting with time.
- """
- self._leaky_bucket = leaky_bucket
- self._time_utils = time_utils
- if time_utils is None:
- self._time_utils = TimeUtils()
-
- def get_bandwith_limited_stream(
- self, fileobj, transfer_coordinator, enabled=True
- ):
- """Wraps a fileobj in a bandwidth limited stream wrapper
-
- :type fileobj: file-like obj
- :param fileobj: The file-like obj to wrap
-
- :type transfer_coordinator: s3transfer.futures.TransferCoordinator
- param transfer_coordinator: The coordinator for the general transfer
- that the wrapped stream is a part of
-
- :type enabled: boolean
- :param enabled: Whether bandwidth limiting should be enabled to start
- """
- stream = BandwidthLimitedStream(
- fileobj, self._leaky_bucket, transfer_coordinator, self._time_utils
- )
- if not enabled:
- stream.disable_bandwidth_limiting()
- return stream
-
-
-class BandwidthLimitedStream:
- def __init__(
- self,
- fileobj,
- leaky_bucket,
- transfer_coordinator,
- time_utils=None,
- bytes_threshold=256 * 1024,
- ):
- """Limits bandwidth for reads on a wrapped stream
-
- :type fileobj: file-like object
- :param fileobj: The file like object to wrap
-
- :type leaky_bucket: LeakyBucket
- :param leaky_bucket: The leaky bucket to use to throttle reads on
- the stream
-
- :type transfer_coordinator: s3transfer.futures.TransferCoordinator
- param transfer_coordinator: The coordinator for the general transfer
- that the wrapped stream is a part of
-
- :type time_utils: TimeUtils
- :param time_utils: The time utility to use for interacting with time
- """
- self._fileobj = fileobj
- self._leaky_bucket = leaky_bucket
- self._transfer_coordinator = transfer_coordinator
- self._time_utils = time_utils
- if time_utils is None:
- self._time_utils = TimeUtils()
- self._bandwidth_limiting_enabled = True
- self._request_token = RequestToken()
- self._bytes_seen = 0
- self._bytes_threshold = bytes_threshold
-
- def enable_bandwidth_limiting(self):
- """Enable bandwidth limiting on reads to the stream"""
- self._bandwidth_limiting_enabled = True
-
- def disable_bandwidth_limiting(self):
- """Disable bandwidth limiting on reads to the stream"""
- self._bandwidth_limiting_enabled = False
-
- def read(self, amount):
- """Read a specified amount
-
- Reads will only be throttled if bandwidth limiting is enabled.
- """
- if not self._bandwidth_limiting_enabled:
- return self._fileobj.read(amount)
-
- # We do not want to be calling consume on every read as the read
- # amounts can be small causing the lock of the leaky bucket to
- # introduce noticeable overhead. So instead we keep track of
- # how many bytes we have seen and only call consume once we pass a
- # certain threshold.
- self._bytes_seen += amount
- if self._bytes_seen < self._bytes_threshold:
- return self._fileobj.read(amount)
-
- self._consume_through_leaky_bucket()
- return self._fileobj.read(amount)
-
- def _consume_through_leaky_bucket(self):
- # NOTE: If the read amount on the stream are high, it will result
- # in large bursty behavior as there is not an interface for partial
- # reads. However given the read's on this abstraction are at most 256KB
- # (via downloads), it reduces the burstiness to be small KB bursts at
- # worst.
- while not self._transfer_coordinator.exception:
- try:
- self._leaky_bucket.consume(
- self._bytes_seen, self._request_token
- )
- self._bytes_seen = 0
- return
- except RequestExceededException as e:
- self._time_utils.sleep(e.retry_time)
- else:
- raise self._transfer_coordinator.exception
-
- def signal_transferring(self):
- """Signal that data being read is being transferred to S3"""
- self.enable_bandwidth_limiting()
-
- def signal_not_transferring(self):
- """Signal that data being read is not being transferred to S3"""
- self.disable_bandwidth_limiting()
-
- def seek(self, where, whence=0):
- self._fileobj.seek(where, whence)
-
- def tell(self):
- return self._fileobj.tell()
-
- def close(self):
- if self._bandwidth_limiting_enabled and self._bytes_seen:
- # This handles the case where the file is small enough to never
- # trigger the threshold and thus is never subjugated to the
- # leaky bucket on read(). This specifically happens for small
- # uploads. So instead to account for those bytes, have
- # it go through the leaky bucket when the file gets closed.
- self._consume_through_leaky_bucket()
- self._fileobj.close()
-
- def __enter__(self):
- return self
-
- def __exit__(self, *args, **kwargs):
- self.close()
-
-
-class LeakyBucket:
- def __init__(
- self,
- max_rate,
- time_utils=None,
- rate_tracker=None,
- consumption_scheduler=None,
- ):
- """A leaky bucket abstraction to limit bandwidth consumption
-
- :type rate: int
- :type rate: The maximum rate to allow. This rate is in terms of
- bytes per second.
-
- :type time_utils: TimeUtils
- :param time_utils: The time utility to use for interacting with time
-
- :type rate_tracker: BandwidthRateTracker
- :param rate_tracker: Tracks bandwidth consumption
-
- :type consumption_scheduler: ConsumptionScheduler
- :param consumption_scheduler: Schedules consumption retries when
- necessary
- """
- self._max_rate = float(max_rate)
- self._time_utils = time_utils
- if time_utils is None:
- self._time_utils = TimeUtils()
- self._lock = threading.Lock()
- self._rate_tracker = rate_tracker
- if rate_tracker is None:
- self._rate_tracker = BandwidthRateTracker()
- self._consumption_scheduler = consumption_scheduler
- if consumption_scheduler is None:
- self._consumption_scheduler = ConsumptionScheduler()
-
- def consume(self, amt, request_token):
- """Consume an a requested amount
-
- :type amt: int
- :param amt: The amount of bytes to request to consume
-
- :type request_token: RequestToken
- :param request_token: The token associated to the consumption
- request that is used to identify the request. So if a
- RequestExceededException is raised the token should be used
- in subsequent retry consume() request.
-
- :raises RequestExceededException: If the consumption amount would
- exceed the maximum allocated bandwidth
-
- :rtype: int
- :returns: The amount consumed
- """
- with self._lock:
- time_now = self._time_utils.time()
- if self._consumption_scheduler.is_scheduled(request_token):
- return self._release_requested_amt_for_scheduled_request(
- amt, request_token, time_now
- )
- elif self._projected_to_exceed_max_rate(amt, time_now):
- self._raise_request_exceeded_exception(
- amt, request_token, time_now
- )
- else:
- return self._release_requested_amt(amt, time_now)
-
- def _projected_to_exceed_max_rate(self, amt, time_now):
- projected_rate = self._rate_tracker.get_projected_rate(amt, time_now)
- return projected_rate > self._max_rate
-
- def _release_requested_amt_for_scheduled_request(
- self, amt, request_token, time_now
- ):
- self._consumption_scheduler.process_scheduled_consumption(
- request_token
- )
- return self._release_requested_amt(amt, time_now)
-
- def _raise_request_exceeded_exception(self, amt, request_token, time_now):
- allocated_time = amt / float(self._max_rate)
- retry_time = self._consumption_scheduler.schedule_consumption(
- amt, request_token, allocated_time
- )
- raise RequestExceededException(
- requested_amt=amt, retry_time=retry_time
- )
-
- def _release_requested_amt(self, amt, time_now):
- self._rate_tracker.record_consumption_rate(amt, time_now)
- return amt
-
-
-class ConsumptionScheduler:
- def __init__(self):
- """Schedules when to consume a desired amount"""
- self._tokens_to_scheduled_consumption = {}
- self._total_wait = 0
-
- def is_scheduled(self, token):
- """Indicates if a consumption request has been scheduled
-
- :type token: RequestToken
- :param token: The token associated to the consumption
- request that is used to identify the request.
- """
- return token in self._tokens_to_scheduled_consumption
-
- def schedule_consumption(self, amt, token, time_to_consume):
- """Schedules a wait time to be able to consume an amount
-
- :type amt: int
- :param amt: The amount of bytes scheduled to be consumed
-
- :type token: RequestToken
- :param token: The token associated to the consumption
- request that is used to identify the request.
-
- :type time_to_consume: float
- :param time_to_consume: The desired time it should take for that
- specific request amount to be consumed in regardless of previously
- scheduled consumption requests
-
- :rtype: float
- :returns: The amount of time to wait for the specific request before
- actually consuming the specified amount.
- """
- self._total_wait += time_to_consume
- self._tokens_to_scheduled_consumption[token] = {
- 'wait_duration': self._total_wait,
- 'time_to_consume': time_to_consume,
- }
- return self._total_wait
-
- def process_scheduled_consumption(self, token):
- """Processes a scheduled consumption request that has completed
-
- :type token: RequestToken
- :param token: The token associated to the consumption
- request that is used to identify the request.
- """
- scheduled_retry = self._tokens_to_scheduled_consumption.pop(token)
- self._total_wait = max(
- self._total_wait - scheduled_retry['time_to_consume'], 0
- )
-
-
-class BandwidthRateTracker:
- def __init__(self, alpha=0.8):
- """Tracks the rate of bandwidth consumption
-
- :type a: float
- :param a: The constant to use in calculating the exponentional moving
- average of the bandwidth rate. Specifically it is used in the
- following calculation:
-
- current_rate = alpha * new_rate + (1 - alpha) * current_rate
-
- This value of this constant should be between 0 and 1.
- """
- self._alpha = alpha
- self._last_time = None
- self._current_rate = None
-
- @property
- def current_rate(self):
- """The current transfer rate
-
- :rtype: float
- :returns: The current tracked transfer rate
- """
- if self._last_time is None:
- return 0.0
- return self._current_rate
-
- def get_projected_rate(self, amt, time_at_consumption):
- """Get the projected rate using a provided amount and time
-
- :type amt: int
- :param amt: The proposed amount to consume
-
- :type time_at_consumption: float
- :param time_at_consumption: The proposed time to consume at
-
- :rtype: float
- :returns: The consumption rate if that amt and time were consumed
- """
- if self._last_time is None:
- return 0.0
- return self._calculate_exponential_moving_average_rate(
- amt, time_at_consumption
- )
-
- def record_consumption_rate(self, amt, time_at_consumption):
- """Record the consumption rate based off amount and time point
-
- :type amt: int
- :param amt: The amount that got consumed
-
- :type time_at_consumption: float
- :param time_at_consumption: The time at which the amount was consumed
- """
- if self._last_time is None:
- self._last_time = time_at_consumption
- self._current_rate = 0.0
- return
- self._current_rate = self._calculate_exponential_moving_average_rate(
- amt, time_at_consumption
- )
- self._last_time = time_at_consumption
-
- def _calculate_rate(self, amt, time_at_consumption):
- time_delta = time_at_consumption - self._last_time
- if time_delta <= 0:
- # While it is really unlikely to see this in an actual transfer,
- # we do not want to be returning back a negative rate or try to
- # divide the amount by zero. So instead return back an infinite
- # rate as the time delta is infinitesimally small.
- return float('inf')
- return amt / (time_delta)
-
- def _calculate_exponential_moving_average_rate(
- self, amt, time_at_consumption
- ):
- new_rate = self._calculate_rate(amt, time_at_consumption)
- return self._alpha * new_rate + (1 - self._alpha) * self._current_rate
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/py37compat.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/py37compat.py
deleted file mode 100644
index aa0c0a7fcd100886e3cd27b3076b6b30c4de1718..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/py37compat.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import sys
-
-
-def _pythonlib_compat():
- """
- On Python 3.7 and earlier, distutils would include the Python
- library. See pypa/distutils#9.
- """
- from distutils import sysconfig
-
- if not sysconfig.get_config_var('Py_ENABLED_SHARED'):
- return
-
- yield 'python{}.{}{}'.format(
- sys.hexversion >> 24,
- (sys.hexversion >> 16) & 0xFF,
- sysconfig.get_config_var('ABIFLAGS'),
- )
-
-
-def compose(f1, f2):
- return lambda *args, **kwargs: f1(f2(*args, **kwargs))
-
-
-pythonlib = (
- compose(list, _pythonlib_compat)
- if sys.version_info < (3, 8)
- and sys.platform != 'darwin'
- and sys.platform[:3] != 'aix'
- else list
-)
diff --git a/spaces/CALM/Dashboard/dashboard_utils/time_tracker.py b/spaces/CALM/Dashboard/dashboard_utils/time_tracker.py
deleted file mode 100644
index c9b27aa43e9ba92952c91c2aea0be1889b1f097f..0000000000000000000000000000000000000000
--- a/spaces/CALM/Dashboard/dashboard_utils/time_tracker.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from functools import wraps
-from time import time
-
-
-def simple_time_tracker(log_fun):
- def _simple_time_tracker(fn):
- @wraps(fn)
- def wrapped_fn(*args, **kwargs):
- start_time = time()
-
- try:
- result = fn(*args, **kwargs)
- finally:
- elapsed_time = time() - start_time
-
- # log the result
- log_fun(
- {
- "function_name": fn.__name__,
- "total_time": elapsed_time,
- }
- )
-
- return result
-
- return wrapped_fn
-
- return _simple_time_tracker
-
-
-def _log(message):
- print("[SimpleTimeTracker] {function_name} {total_time:.3f}".format(**message))
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/install.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/install.md
deleted file mode 100644
index 5f52b2be3c9650cfc3e16ffb8fa374d3fcbad371..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/install.md
+++ /dev/null
@@ -1 +0,0 @@
-../../INSTALL.md
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/color.cpp b/spaces/CVPR/LIVE/color.cpp
deleted file mode 100644
index 2a2e8abcee1dacefeaeb0268359737aec178bace..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/color.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-#include "color.h"
-
-void LinearGradient::copy_to(ptr stop_offsets,
- ptr stop_colors) const {
- float *o = stop_offsets.get();
- float *c = stop_colors.get();
- for (int i = 0; i < num_stops; i++) {
- o[i] = this->stop_offsets[i];
- }
- for (int i = 0; i < 4 * num_stops; i++) {
- c[i] = this->stop_colors[i];
- }
-}
-
-void RadialGradient::copy_to(ptr stop_offsets,
- ptr stop_colors) const {
- float *o = stop_offsets.get();
- float *c = stop_colors.get();
- for (int i = 0; i < num_stops; i++) {
- o[i] = this->stop_offsets[i];
- }
- for (int i = 0; i < 4 * num_stops; i++) {
- c[i] = this->stop_colors[i];
- }
-}
diff --git a/spaces/CVPR/LIVE/thrust/dependencies/cub/cmake/CubHeaderTesting.cmake b/spaces/CVPR/LIVE/thrust/dependencies/cub/cmake/CubHeaderTesting.cmake
deleted file mode 100644
index 45f20ce5f3b130a76e00bdbacfd7fc00784ba758..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/dependencies/cub/cmake/CubHeaderTesting.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-# For every public header, build a translation unit containing `#include `
-# to let the compiler try to figure out warnings in that header if it is not otherwise
-# included in tests, and also to verify if the headers are modular enough.
-# .inl files are not globbed for, because they are not supposed to be used as public
-# entrypoints.
-
-file(GLOB_RECURSE headers
- RELATIVE "${CUB_SOURCE_DIR}/cub"
- CONFIGURE_DEPENDS
- cub/*.cuh
-)
-
-set(headertest_srcs)
-foreach (header IN LISTS headers)
- set(headertest_src "headers/${header}.cu")
- configure_file("${CUB_SOURCE_DIR}/cmake/header_test.in" "${headertest_src}")
- list(APPEND headertest_srcs "${headertest_src}")
-endforeach()
-
-foreach(cub_target IN LISTS CUB_TARGETS)
- cub_get_target_property(config_prefix ${cub_target} PREFIX)
-
- set(headertest_target ${config_prefix}.headers)
- add_library(${headertest_target} OBJECT ${headertest_srcs})
- target_link_libraries(${headertest_target} PUBLIC ${cub_target})
- cub_clone_target_properties(${headertest_target} ${cub_target})
-
- add_dependencies(${config_prefix}.all ${headertest_target})
-endforeach()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/reduce.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/reduce.h
deleted file mode 100644
index 55e92acb9afc75787955a74808fb6cca96c45964..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/reduce.h
+++ /dev/null
@@ -1,73 +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.
- */
-
-
-/*! \file reduce.h
- * \brief Sequential implementation of reduce algorithm.
- */
-
-#pragma once
-
-#include
-#include
-#include
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-__thrust_exec_check_disable__
-template
-__host__ __device__
- OutputType reduce(sequential::execution_policy &,
- InputIterator begin,
- InputIterator end,
- OutputType init,
- BinaryFunction binary_op)
-{
- // wrap binary_op
- thrust::detail::wrapped_function<
- BinaryFunction,
- OutputType
- > wrapped_binary_op(binary_op);
-
- // initialize the result
- OutputType result = init;
-
- while(begin != end)
- {
- result = wrapped_binary_op(result, *begin);
- ++begin;
- } // end while
-
- return result;
-}
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/streams.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/streams.py
deleted file mode 100644
index 726b02326f66d37b9de1947cb78470479a7bc82b..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/streams.py
+++ /dev/null
@@ -1,660 +0,0 @@
-import asyncio
-import collections
-import warnings
-from typing import Awaitable, Callable, Deque, Generic, List, Optional, Tuple, TypeVar
-
-from .base_protocol import BaseProtocol
-from .helpers import BaseTimerContext, set_exception, set_result
-from .log import internal_logger
-from .typedefs import Final
-
-__all__ = (
- "EMPTY_PAYLOAD",
- "EofStream",
- "StreamReader",
- "DataQueue",
- "FlowControlDataQueue",
-)
-
-_T = TypeVar("_T")
-
-
-class EofStream(Exception):
- """eof stream indication."""
-
-
-class AsyncStreamIterator(Generic[_T]):
- def __init__(self, read_func: Callable[[], Awaitable[_T]]) -> None:
- self.read_func = read_func
-
- def __aiter__(self) -> "AsyncStreamIterator[_T]":
- return self
-
- async def __anext__(self) -> _T:
- try:
- rv = await self.read_func()
- except EofStream:
- raise StopAsyncIteration
- if rv == b"":
- raise StopAsyncIteration
- return rv
-
-
-class ChunkTupleAsyncStreamIterator:
- def __init__(self, stream: "StreamReader") -> None:
- self._stream = stream
-
- def __aiter__(self) -> "ChunkTupleAsyncStreamIterator":
- return self
-
- async def __anext__(self) -> Tuple[bytes, bool]:
- rv = await self._stream.readchunk()
- if rv == (b"", False):
- raise StopAsyncIteration
- return rv
-
-
-class AsyncStreamReaderMixin:
- def __aiter__(self) -> AsyncStreamIterator[bytes]:
- return AsyncStreamIterator(self.readline) # type: ignore[attr-defined]
-
- def iter_chunked(self, n: int) -> AsyncStreamIterator[bytes]:
- """Returns an asynchronous iterator that yields chunks of size n.
-
- Python-3.5 available for Python 3.5+ only
- """
- return AsyncStreamIterator(
- lambda: self.read(n) # type: ignore[attr-defined,no-any-return]
- )
-
- def iter_any(self) -> AsyncStreamIterator[bytes]:
- """Yield all available data as soon as it is received.
-
- Python-3.5 available for Python 3.5+ only
- """
- return AsyncStreamIterator(self.readany) # type: ignore[attr-defined]
-
- def iter_chunks(self) -> ChunkTupleAsyncStreamIterator:
- """Yield chunks of data as they are received by the server.
-
- The yielded objects are tuples
- of (bytes, bool) as returned by the StreamReader.readchunk method.
-
- Python-3.5 available for Python 3.5+ only
- """
- return ChunkTupleAsyncStreamIterator(self) # type: ignore[arg-type]
-
-
-class StreamReader(AsyncStreamReaderMixin):
- """An enhancement of asyncio.StreamReader.
-
- Supports asynchronous iteration by line, chunk or as available::
-
- async for line in reader:
- ...
- async for chunk in reader.iter_chunked(1024):
- ...
- async for slice in reader.iter_any():
- ...
-
- """
-
- total_bytes = 0
-
- def __init__(
- self,
- protocol: BaseProtocol,
- limit: int,
- *,
- timer: Optional[BaseTimerContext] = None,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- ) -> None:
- self._protocol = protocol
- self._low_water = limit
- self._high_water = limit * 2
- if loop is None:
- loop = asyncio.get_event_loop()
- self._loop = loop
- self._size = 0
- self._cursor = 0
- self._http_chunk_splits: Optional[List[int]] = None
- self._buffer: Deque[bytes] = collections.deque()
- self._buffer_offset = 0
- self._eof = False
- self._waiter: Optional[asyncio.Future[None]] = None
- self._eof_waiter: Optional[asyncio.Future[None]] = None
- self._exception: Optional[BaseException] = None
- self._timer = timer
- self._eof_callbacks: List[Callable[[], None]] = []
-
- def __repr__(self) -> str:
- info = [self.__class__.__name__]
- if self._size:
- info.append("%d bytes" % self._size)
- if self._eof:
- info.append("eof")
- if self._low_water != 2**16: # default limit
- info.append("low=%d high=%d" % (self._low_water, self._high_water))
- if self._waiter:
- info.append("w=%r" % self._waiter)
- if self._exception:
- info.append("e=%r" % self._exception)
- return "<%s>" % " ".join(info)
-
- def get_read_buffer_limits(self) -> Tuple[int, int]:
- return (self._low_water, self._high_water)
-
- def exception(self) -> Optional[BaseException]:
- return self._exception
-
- def set_exception(self, exc: BaseException) -> None:
- self._exception = exc
- self._eof_callbacks.clear()
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_exception(waiter, exc)
-
- waiter = self._eof_waiter
- if waiter is not None:
- self._eof_waiter = None
- set_exception(waiter, exc)
-
- def on_eof(self, callback: Callable[[], None]) -> None:
- if self._eof:
- try:
- callback()
- except Exception:
- internal_logger.exception("Exception in eof callback")
- else:
- self._eof_callbacks.append(callback)
-
- def feed_eof(self) -> None:
- self._eof = True
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- waiter = self._eof_waiter
- if waiter is not None:
- self._eof_waiter = None
- set_result(waiter, None)
-
- for cb in self._eof_callbacks:
- try:
- cb()
- except Exception:
- internal_logger.exception("Exception in eof callback")
-
- self._eof_callbacks.clear()
-
- def is_eof(self) -> bool:
- """Return True if 'feed_eof' was called."""
- return self._eof
-
- def at_eof(self) -> bool:
- """Return True if the buffer is empty and 'feed_eof' was called."""
- return self._eof and not self._buffer
-
- async def wait_eof(self) -> None:
- if self._eof:
- return
-
- assert self._eof_waiter is None
- self._eof_waiter = self._loop.create_future()
- try:
- await self._eof_waiter
- finally:
- self._eof_waiter = None
-
- def unread_data(self, data: bytes) -> None:
- """rollback reading some data from stream, inserting it to buffer head."""
- warnings.warn(
- "unread_data() is deprecated "
- "and will be removed in future releases (#3260)",
- DeprecationWarning,
- stacklevel=2,
- )
- if not data:
- return
-
- if self._buffer_offset:
- self._buffer[0] = self._buffer[0][self._buffer_offset :]
- self._buffer_offset = 0
- self._size += len(data)
- self._cursor -= len(data)
- self._buffer.appendleft(data)
- self._eof_counter = 0
-
- # TODO: size is ignored, remove the param later
- def feed_data(self, data: bytes, size: int = 0) -> None:
- assert not self._eof, "feed_data after feed_eof"
-
- if not data:
- return
-
- self._size += len(data)
- self._buffer.append(data)
- self.total_bytes += len(data)
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- if self._size > self._high_water and not self._protocol._reading_paused:
- self._protocol.pause_reading()
-
- def begin_http_chunk_receiving(self) -> None:
- if self._http_chunk_splits is None:
- if self.total_bytes:
- raise RuntimeError(
- "Called begin_http_chunk_receiving when" "some data was already fed"
- )
- self._http_chunk_splits = []
-
- def end_http_chunk_receiving(self) -> None:
- if self._http_chunk_splits is None:
- raise RuntimeError(
- "Called end_chunk_receiving without calling "
- "begin_chunk_receiving first"
- )
-
- # self._http_chunk_splits contains logical byte offsets from start of
- # the body transfer. Each offset is the offset of the end of a chunk.
- # "Logical" means bytes, accessible for a user.
- # If no chunks containig logical data were received, current position
- # is difinitely zero.
- pos = self._http_chunk_splits[-1] if self._http_chunk_splits else 0
-
- if self.total_bytes == pos:
- # We should not add empty chunks here. So we check for that.
- # Note, when chunked + gzip is used, we can receive a chunk
- # of compressed data, but that data may not be enough for gzip FSM
- # to yield any uncompressed data. That's why current position may
- # not change after receiving a chunk.
- return
-
- self._http_chunk_splits.append(self.total_bytes)
-
- # wake up readchunk when end of http chunk received
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- async def _wait(self, func_name: str) -> None:
- # StreamReader uses a future to link the protocol feed_data() method
- # to a read coroutine. Running two read coroutines at the same time
- # would have an unexpected behaviour. It would not possible to know
- # which coroutine would get the next data.
- if self._waiter is not None:
- raise RuntimeError(
- "%s() called while another coroutine is "
- "already waiting for incoming data" % func_name
- )
-
- waiter = self._waiter = self._loop.create_future()
- try:
- if self._timer:
- with self._timer:
- await waiter
- else:
- await waiter
- finally:
- self._waiter = None
-
- async def readline(self) -> bytes:
- return await self.readuntil()
-
- async def readuntil(self, separator: bytes = b"\n") -> bytes:
- seplen = len(separator)
- if seplen == 0:
- raise ValueError("Separator should be at least one-byte string")
-
- if self._exception is not None:
- raise self._exception
-
- chunk = b""
- chunk_size = 0
- not_enough = True
-
- while not_enough:
- while self._buffer and not_enough:
- offset = self._buffer_offset
- ichar = self._buffer[0].find(separator, offset) + 1
- # Read from current offset to found separator or to the end.
- data = self._read_nowait_chunk(ichar - offset if ichar else -1)
- chunk += data
- chunk_size += len(data)
- if ichar:
- not_enough = False
-
- if chunk_size > self._high_water:
- raise ValueError("Chunk too big")
-
- if self._eof:
- break
-
- if not_enough:
- await self._wait("readuntil")
-
- return chunk
-
- async def read(self, n: int = -1) -> bytes:
- if self._exception is not None:
- raise self._exception
-
- # migration problem; with DataQueue you have to catch
- # EofStream exception, so common way is to run payload.read() inside
- # infinite loop. what can cause real infinite loop with StreamReader
- # lets keep this code one major release.
- if __debug__:
- if self._eof and not self._buffer:
- self._eof_counter = getattr(self, "_eof_counter", 0) + 1
- if self._eof_counter > 5:
- internal_logger.warning(
- "Multiple access to StreamReader in eof state, "
- "might be infinite loop.",
- stack_info=True,
- )
-
- if not n:
- return b""
-
- if n < 0:
- # This used to just loop creating a new waiter hoping to
- # collect everything in self._buffer, but that would
- # deadlock if the subprocess sends more than self.limit
- # bytes. So just call self.readany() until EOF.
- blocks = []
- while True:
- block = await self.readany()
- if not block:
- break
- blocks.append(block)
- return b"".join(blocks)
-
- # TODO: should be `if` instead of `while`
- # because waiter maybe triggered on chunk end,
- # without feeding any data
- while not self._buffer and not self._eof:
- await self._wait("read")
-
- return self._read_nowait(n)
-
- async def readany(self) -> bytes:
- if self._exception is not None:
- raise self._exception
-
- # TODO: should be `if` instead of `while`
- # because waiter maybe triggered on chunk end,
- # without feeding any data
- while not self._buffer and not self._eof:
- await self._wait("readany")
-
- return self._read_nowait(-1)
-
- async def readchunk(self) -> Tuple[bytes, bool]:
- """Returns a tuple of (data, end_of_http_chunk).
-
- When chunked transfer
- encoding is used, end_of_http_chunk is a boolean indicating if the end
- of the data corresponds to the end of a HTTP chunk , otherwise it is
- always False.
- """
- while True:
- if self._exception is not None:
- raise self._exception
-
- while self._http_chunk_splits:
- pos = self._http_chunk_splits.pop(0)
- if pos == self._cursor:
- return (b"", True)
- if pos > self._cursor:
- return (self._read_nowait(pos - self._cursor), True)
- internal_logger.warning(
- "Skipping HTTP chunk end due to data "
- "consumption beyond chunk boundary"
- )
-
- if self._buffer:
- return (self._read_nowait_chunk(-1), False)
- # return (self._read_nowait(-1), False)
-
- if self._eof:
- # Special case for signifying EOF.
- # (b'', True) is not a final return value actually.
- return (b"", False)
-
- await self._wait("readchunk")
-
- async def readexactly(self, n: int) -> bytes:
- if self._exception is not None:
- raise self._exception
-
- blocks: List[bytes] = []
- while n > 0:
- block = await self.read(n)
- if not block:
- partial = b"".join(blocks)
- raise asyncio.IncompleteReadError(partial, len(partial) + n)
- blocks.append(block)
- n -= len(block)
-
- return b"".join(blocks)
-
- def read_nowait(self, n: int = -1) -> bytes:
- # default was changed to be consistent with .read(-1)
- #
- # I believe the most users don't know about the method and
- # they are not affected.
- if self._exception is not None:
- raise self._exception
-
- if self._waiter and not self._waiter.done():
- raise RuntimeError(
- "Called while some coroutine is waiting for incoming data."
- )
-
- return self._read_nowait(n)
-
- def _read_nowait_chunk(self, n: int) -> bytes:
- first_buffer = self._buffer[0]
- offset = self._buffer_offset
- if n != -1 and len(first_buffer) - offset > n:
- data = first_buffer[offset : offset + n]
- self._buffer_offset += n
-
- elif offset:
- self._buffer.popleft()
- data = first_buffer[offset:]
- self._buffer_offset = 0
-
- else:
- data = self._buffer.popleft()
-
- self._size -= len(data)
- self._cursor += len(data)
-
- chunk_splits = self._http_chunk_splits
- # Prevent memory leak: drop useless chunk splits
- while chunk_splits and chunk_splits[0] < self._cursor:
- chunk_splits.pop(0)
-
- if self._size < self._low_water and self._protocol._reading_paused:
- self._protocol.resume_reading()
- return data
-
- def _read_nowait(self, n: int) -> bytes:
- """Read not more than n bytes, or whole buffer if n == -1"""
- chunks = []
-
- while self._buffer:
- chunk = self._read_nowait_chunk(n)
- chunks.append(chunk)
- if n != -1:
- n -= len(chunk)
- if n == 0:
- break
-
- return b"".join(chunks) if chunks else b""
-
-
-class EmptyStreamReader(StreamReader): # lgtm [py/missing-call-to-init]
- def __init__(self) -> None:
- pass
-
- def exception(self) -> Optional[BaseException]:
- return None
-
- def set_exception(self, exc: BaseException) -> None:
- pass
-
- def on_eof(self, callback: Callable[[], None]) -> None:
- try:
- callback()
- except Exception:
- internal_logger.exception("Exception in eof callback")
-
- def feed_eof(self) -> None:
- pass
-
- def is_eof(self) -> bool:
- return True
-
- def at_eof(self) -> bool:
- return True
-
- async def wait_eof(self) -> None:
- return
-
- def feed_data(self, data: bytes, n: int = 0) -> None:
- pass
-
- async def readline(self) -> bytes:
- return b""
-
- async def read(self, n: int = -1) -> bytes:
- return b""
-
- # TODO add async def readuntil
-
- async def readany(self) -> bytes:
- return b""
-
- async def readchunk(self) -> Tuple[bytes, bool]:
- return (b"", True)
-
- async def readexactly(self, n: int) -> bytes:
- raise asyncio.IncompleteReadError(b"", n)
-
- def read_nowait(self, n: int = -1) -> bytes:
- return b""
-
-
-EMPTY_PAYLOAD: Final[StreamReader] = EmptyStreamReader()
-
-
-class DataQueue(Generic[_T]):
- """DataQueue is a general-purpose blocking queue with one reader."""
-
- def __init__(self, loop: asyncio.AbstractEventLoop) -> None:
- self._loop = loop
- self._eof = False
- self._waiter: Optional[asyncio.Future[None]] = None
- self._exception: Optional[BaseException] = None
- self._size = 0
- self._buffer: Deque[Tuple[_T, int]] = collections.deque()
-
- def __len__(self) -> int:
- return len(self._buffer)
-
- def is_eof(self) -> bool:
- return self._eof
-
- def at_eof(self) -> bool:
- return self._eof and not self._buffer
-
- def exception(self) -> Optional[BaseException]:
- return self._exception
-
- def set_exception(self, exc: BaseException) -> None:
- self._eof = True
- self._exception = exc
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_exception(waiter, exc)
-
- def feed_data(self, data: _T, size: int = 0) -> None:
- self._size += size
- self._buffer.append((data, size))
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- def feed_eof(self) -> None:
- self._eof = True
-
- waiter = self._waiter
- if waiter is not None:
- self._waiter = None
- set_result(waiter, None)
-
- async def read(self) -> _T:
- if not self._buffer and not self._eof:
- assert not self._waiter
- self._waiter = self._loop.create_future()
- try:
- await self._waiter
- except (asyncio.CancelledError, asyncio.TimeoutError):
- self._waiter = None
- raise
-
- if self._buffer:
- data, size = self._buffer.popleft()
- self._size -= size
- return data
- else:
- if self._exception is not None:
- raise self._exception
- else:
- raise EofStream
-
- def __aiter__(self) -> AsyncStreamIterator[_T]:
- return AsyncStreamIterator(self.read)
-
-
-class FlowControlDataQueue(DataQueue[_T]):
- """FlowControlDataQueue resumes and pauses an underlying stream.
-
- It is a destination for parsed data.
- """
-
- def __init__(
- self, protocol: BaseProtocol, limit: int, *, loop: asyncio.AbstractEventLoop
- ) -> None:
- super().__init__(loop=loop)
-
- self._protocol = protocol
- self._limit = limit * 2
-
- def feed_data(self, data: _T, size: int = 0) -> None:
- super().feed_data(data, size)
-
- if self._size > self._limit and not self._protocol._reading_paused:
- self._protocol.pause_reading()
-
- async def read(self) -> _T:
- try:
- return await super().read()
- finally:
- if self._size < self._limit and self._protocol._reading_paused:
- self._protocol.resume_reading()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attrs/validators.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attrs/validators.py
deleted file mode 100644
index ab2c9b3024714d3b1caeb2f0773a0274dfc10f01..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attrs/validators.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-from attr.validators import * # noqa
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_p_r_o_p.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_p_r_o_p.py
deleted file mode 100644
index aead9d72062e878d5e497f263a4f08eddbb048f6..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_p_r_o_p.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6prop.html
-class table__p_r_o_p(BaseTTXConverter):
- pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ModifyUpload.svelte_svelte_type_style_lang-d2acacf0.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ModifyUpload.svelte_svelte_type_style_lang-d2acacf0.js
deleted file mode 100644
index ea59a3c30d1a396de1e3dcd8e62be35a7e273f73..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/ModifyUpload.svelte_svelte_type_style_lang-d2acacf0.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function l(e,n,a){if(e==null)return null;if(typeof e=="string")return{name:"file_data",data:e};if(Array.isArray(e)){const s=[];for(const t of e)t===null?s.push(null):s.push(l(t,n,a));return s}else e.is_file&&(a==null?e.data=n+"/file="+e.name:e.data="/proxy="+a+"file="+e.name);return e}const r=e=>{const n=new FileReader;return n.readAsDataURL(e),new Promise(a=>{n.onloadend=()=>{a(n.result)}})};export{r as b,l as n};
-//# sourceMappingURL=ModifyUpload.svelte_svelte_type_style_lang-d2acacf0.js.map
diff --git a/spaces/Damstra/safety-hazard-classifier/app.py b/spaces/Damstra/safety-hazard-classifier/app.py
deleted file mode 100644
index daa40ec25b185e9f6abcf1b2aa143b0a4bda1f5a..0000000000000000000000000000000000000000
--- a/spaces/Damstra/safety-hazard-classifier/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-
-article = ''' '''
-
-examples = [
- ['''
- A large screen was being lifted into the plant when the crane operator observed someone walk directly under the suspended load. \
- The drop zone underneath the load had not been adequately barricaded
- '''
- ],
- ['''
- While building scaffold to access a pipe for repairs a worker was observed not wearing fall protection while the scaffold \
- was still being constructed. There was a potential for him to fall over 5 metres from the side of the plant
- '''
- ],
- ['''
- A worker was using a grinder in a confined space when he became dizzy from the fumes in the area and had to be helped out. \
- The gas monitor he was using was found to be faulty and when the area was assessed with another monitor there was an \
- unacceptably high level of CO2 in the area
- '''
- ],
- [
- '''
- Henry Winkler tripped over some rocks that had overflowed from the discharge chute of screen 1011. He had been walking past the screen \
- on the way to the control room. He suffered a bruised hip and broken wrist and had to be transported to Mackay Base Hospital \
- for treatment.
- '''
- ],
- [
- '''
- Susan Lauper experienced a mild electrical shock while operating an arc welder in the plant today. It was discovered that the earth \
- was not effectively grounded due to corrosion and buildup in the work area. She was treated according to site procedures and was \
- given a medical clearance to return to work by her GP later that day.
- '''
- ],
- [
- '''
- While conducting regular inspections of the ground floor, guarding was noticed to be missing on the main drive of pump 1330. \
- This pump had been serviced during the previous maintenance day and it appears that the guard was not replaced prior to startup \
- and was found nearby. This is a potential breach of isolation procedure and requires further investigation.
- '''
- ]
-]
-
-
-title = "Safety Hazard Classifier"
-description = "Using zero shot classification to determine which critical hazard an incident belongs to"
-
-classifier = pipeline("zero-shot-classification", model="Narsil/deberta-large-mnli-zero-cls")
-
-def predict(text):
- preds = classifier(text, candidate_labels=["electrical", "confined space", "unguarded machinery", \
- "spills and tripping hazards", "working from heights", "suspended loads", "machinery related"])
- return dict(zip(preds['labels'], preds['scores']))
-
-gradio_ui = gr.Interface(
- fn=predict,
- title=title,
- description=description,
- inputs=[
- gr.inputs.Textbox(lines=5, label="Paste some text here"),
- ],
- outputs=[
- gr.outputs.Label(num_top_classes=3)
- ],
- examples=examples,
- article=article
-
-)
-
-gradio_ui.launch(debug=True)
-
diff --git a/spaces/Derni/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/Derni/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/Derni/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/Detomo/ai-avatar-backend/Dockerfile b/spaces/Detomo/ai-avatar-backend/Dockerfile
deleted file mode 100644
index 785945879b4869f0e79e23dbc67621b11312cad7..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-avatar-backend/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-FROM node:16
-
-# Set the working directory inside the container
-WORKDIR /usr/src/app
-
-# Copy package.json and package-lock.json (if available) to the working directory
-COPY package*.json ./
-
-# Install application dependencies
-RUN npm install && npm install -g nodemon
-
-# Copy the rest of the application to the working directory
-COPY . .
-
-# Expose the port the app runs on
-EXPOSE 5000
-
-# Change permissions for the 'public' directory to allow write access
-RUN chmod -R 777 /usr/src/app/public
-
-# Command to run the application
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/DonDoesStuff/GPT3.5-voice/README.md b/spaces/DonDoesStuff/GPT3.5-voice/README.md
deleted file mode 100644
index e1e3e043d4128d9d03c26689283a7ae081f5f90d..0000000000000000000000000000000000000000
--- a/spaces/DonDoesStuff/GPT3.5-voice/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GPT3.5 Voice
-emoji: 📉
-colorFrom: indigo
-colorTo: yellow
-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/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py
deleted file mode 100644
index 5fa95018f961f1aaa8013befcae7471995eee505..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.py
+++ /dev/null
@@ -1,409 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-
-"""Custom PyTorch ops for efficient resampling of 2D images."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import traceback
-
-from .. import custom_ops
-from .. import misc
-from . import conv2d_gradfix
-
-# ----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-
-
-def _init():
- global _inited, _plugin
- if not _inited:
- sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
- sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
- try:
- _plugin = custom_ops.get_plugin(
- 'upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
- except:
- warnings.warn(
- 'Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
- return _plugin is not None
-
-
-def _parse_scaling(scaling):
- if isinstance(scaling, int):
- scaling = [scaling, scaling]
- assert isinstance(scaling, (list, tuple))
- assert all(isinstance(x, int) for x in scaling)
- sx, sy = scaling
- assert sx >= 1 and sy >= 1
- return sx, sy
-
-
-def _parse_padding(padding):
- if isinstance(padding, int):
- padding = [padding, padding]
- assert isinstance(padding, (list, tuple))
- assert all(isinstance(x, int) for x in padding)
- if len(padding) == 2:
- padx, pady = padding
- padding = [padx, padx, pady, pady]
- padx0, padx1, pady0, pady1 = padding
- return padx0, padx1, pady0, pady1
-
-
-def _get_filter_size(f):
- if f is None:
- return 1, 1
- assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
- fw = f.shape[-1]
- fh = f.shape[0]
- with misc.suppress_tracer_warnings():
- fw = int(fw)
- fh = int(fh)
- misc.assert_shape(f, [fh, fw][:f.ndim])
- assert fw >= 1 and fh >= 1
- return fw, fh
-
-# ----------------------------------------------------------------------------
-
-
-def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
- r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
-
- Args:
- f: Torch tensor, numpy array, or python list of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable),
- `[]` (impulse), or
- `None` (identity).
- device: Result device (default: cpu).
- normalize: Normalize the filter so that it retains the magnitude
- for constant input signal (DC)? (default: True).
- flip_filter: Flip the filter? (default: False).
- gain: Overall scaling factor for signal magnitude (default: 1).
- separable: Return a separable filter? (default: select automatically).
-
- Returns:
- Float32 tensor of the shape
- `[filter_height, filter_width]` (non-separable) or
- `[filter_taps]` (separable).
- """
- # Validate.
- if f is None:
- f = 1
- f = torch.as_tensor(f, dtype=torch.float32)
- assert f.ndim in [0, 1, 2]
- assert f.numel() > 0
- if f.ndim == 0:
- f = f[np.newaxis]
-
- # Separable?
- if separable is None:
- separable = (f.ndim == 1 and f.numel() >= 8)
- if f.ndim == 1 and not separable:
- f = f.ger(f)
- assert f.ndim == (1 if separable else 2)
-
- # Apply normalize, flip, gain, and device.
- if normalize:
- f /= f.sum()
- if flip_filter:
- f = f.flip(list(range(f.ndim)))
- f = f * (gain ** (f.ndim / 2))
- f = f.to(device=device)
- return f
-
-# ----------------------------------------------------------------------------
-
-
-def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
- r"""Pad, upsample, filter, and downsample a batch of 2D images.
-
- Performs the following sequence of operations for each channel:
-
- 1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
- 2. Pad the image with the specified number of zeros on each side (`padding`).
- Negative padding corresponds to cropping the image.
-
- 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
- so that the footprint of all output pixels lies within the input image.
-
- 4. Downsample the image by keeping every Nth pixel (`down`).
-
- This sequence of operations bears close resemblance to scipy.signal.upfirdn().
- The fused op is considerably more efficient than performing the same calculation
- using standard PyTorch ops. It supports gradients of arbitrary order.
-
- Args:
- x: Float32/float64/float16 input tensor of the shape
- `[batch_size, num_channels, in_height, in_width]`.
- f: Float32 FIR filter of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable), or
- `None` (identity).
- up: Integer upsampling factor. Can be a single int or a list/tuple
- `[x, y]` (default: 1).
- down: Integer downsampling factor. Can be a single int or a list/tuple
- `[x, y]` (default: 1).
- padding: Padding with respect to the upsampled image. Can be a single number
- or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- flip_filter: False = convolution, True = correlation (default: False).
- gain: Overall scaling factor for signal magnitude (default: 1).
- impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- assert isinstance(x, torch.Tensor)
- assert impl in ['ref', 'cuda']
- if impl == 'cuda' and x.device.type == 'cuda' and _init():
- return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
- return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
- """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
- """
- # Validate arguments.
- assert isinstance(x, torch.Tensor) and x.ndim == 4
- if f is None:
- f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
- assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
- assert f.dtype == torch.float32 and not f.requires_grad
- batch_size, num_channels, in_height, in_width = x.shape
- upx, upy = _parse_scaling(up)
- downx, downy = _parse_scaling(down)
- padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
- # Upsample by inserting zeros.
- x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
- x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
- x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
-
- # Pad or crop.
- x = torch.nn.functional.pad(
- x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
- x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0),
- max(-padx0, 0): x.shape[3] - max(-padx1, 0)]
-
- # Setup filter.
- f = f * (gain ** (f.ndim / 2))
- f = f.to(x.dtype)
- if not flip_filter:
- f = f.flip(list(range(f.ndim)))
-
- # Convolve with the filter.
- f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
- if f.ndim == 4:
- x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
- else:
- x = conv2d_gradfix.conv2d(
- input=x, weight=f.unsqueeze(2), groups=num_channels)
- x = conv2d_gradfix.conv2d(
- input=x, weight=f.unsqueeze(3), groups=num_channels)
-
- # Downsample by throwing away pixels.
- x = x[:, :, ::downy, ::downx]
- return x
-
-# ----------------------------------------------------------------------------
-
-
-_upfirdn2d_cuda_cache = dict()
-
-
-def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
- """Fast CUDA implementation of `upfirdn2d()` using custom ops.
- """
- # Parse arguments.
- upx, upy = _parse_scaling(up)
- downx, downy = _parse_scaling(down)
- padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
- # Lookup from cache.
- key = (upx, upy, downx, downy, padx0, padx1,
- pady0, pady1, flip_filter, gain)
- if key in _upfirdn2d_cuda_cache:
- return _upfirdn2d_cuda_cache[key]
-
- # Forward op.
- class Upfirdn2dCuda(torch.autograd.Function):
- @staticmethod
- def forward(ctx, x, f): # pylint: disable=arguments-differ
- assert isinstance(x, torch.Tensor) and x.ndim == 4
- if f is None:
- f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
- assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
- y = x
- if f.ndim == 2:
- y = _plugin.upfirdn2d(
- y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
- else:
- y = _plugin.upfirdn2d(y, f.unsqueeze(
- 0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
- y = _plugin.upfirdn2d(y, f.unsqueeze(
- 1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
- ctx.save_for_backward(f)
- ctx.x_shape = x.shape
- return y
-
- @staticmethod
- def backward(ctx, dy): # pylint: disable=arguments-differ
- f, = ctx.saved_tensors
- _, _, ih, iw = ctx.x_shape
- _, _, oh, ow = dy.shape
- fw, fh = _get_filter_size(f)
- p = [
- fw - padx0 - 1,
- iw * upx - ow * downx + padx0 - upx + 1,
- fh - pady0 - 1,
- ih * upy - oh * downy + pady0 - upy + 1,
- ]
- dx = None
- df = None
-
- if ctx.needs_input_grad[0]:
- dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(
- not flip_filter), gain=gain).apply(dy, f)
-
- assert not ctx.needs_input_grad[1]
- return dx, df
-
- # Add to cache.
- _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
- return Upfirdn2dCuda
-
-# ----------------------------------------------------------------------------
-
-
-def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
- r"""Filter a batch of 2D images using the given 2D FIR filter.
-
- By default, the result is padded so that its shape matches the input.
- User-specified padding is applied on top of that, with negative values
- indicating cropping. Pixels outside the image are assumed to be zero.
-
- Args:
- x: Float32/float64/float16 input tensor of the shape
- `[batch_size, num_channels, in_height, in_width]`.
- f: Float32 FIR filter of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable), or
- `None` (identity).
- padding: Padding with respect to the output. Can be a single number or a
- list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- flip_filter: False = convolution, True = correlation (default: False).
- gain: Overall scaling factor for signal magnitude (default: 1).
- impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- padx0, padx1, pady0, pady1 = _parse_padding(padding)
- fw, fh = _get_filter_size(f)
- p = [
- padx0 + fw // 2,
- padx1 + (fw - 1) // 2,
- pady0 + fh // 2,
- pady1 + (fh - 1) // 2,
- ]
- return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
- r"""Upsample a batch of 2D images using the given 2D FIR filter.
-
- By default, the result is padded so that its shape is a multiple of the input.
- User-specified padding is applied on top of that, with negative values
- indicating cropping. Pixels outside the image are assumed to be zero.
-
- Args:
- x: Float32/float64/float16 input tensor of the shape
- `[batch_size, num_channels, in_height, in_width]`.
- f: Float32 FIR filter of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable), or
- `None` (identity).
- up: Integer upsampling factor. Can be a single int or a list/tuple
- `[x, y]` (default: 1).
- padding: Padding with respect to the output. Can be a single number or a
- list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- flip_filter: False = convolution, True = correlation (default: False).
- gain: Overall scaling factor for signal magnitude (default: 1).
- impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- upx, upy = _parse_scaling(up)
- padx0, padx1, pady0, pady1 = _parse_padding(padding)
- fw, fh = _get_filter_size(f)
- p = [
- padx0 + (fw + upx - 1) // 2,
- padx1 + (fw - upx) // 2,
- pady0 + (fh + upy - 1) // 2,
- pady1 + (fh - upy) // 2,
- ]
- return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
- r"""Downsample a batch of 2D images using the given 2D FIR filter.
-
- By default, the result is padded so that its shape is a fraction of the input.
- User-specified padding is applied on top of that, with negative values
- indicating cropping. Pixels outside the image are assumed to be zero.
-
- Args:
- x: Float32/float64/float16 input tensor of the shape
- `[batch_size, num_channels, in_height, in_width]`.
- f: Float32 FIR filter of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable), or
- `None` (identity).
- down: Integer downsampling factor. Can be a single int or a list/tuple
- `[x, y]` (default: 1).
- padding: Padding with respect to the input. Can be a single number or a
- list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- flip_filter: False = convolution, True = correlation (default: False).
- gain: Overall scaling factor for signal magnitude (default: 1).
- impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- downx, downy = _parse_scaling(down)
- padx0, padx1, pady0, pady1 = _parse_padding(padding)
- fw, fh = _get_filter_size(f)
- p = [
- padx0 + (fw - downx + 1) // 2,
- padx1 + (fw - downx) // 2,
- pady0 + (fh - downy + 1) // 2,
- pady1 + (fh - downy) // 2,
- ]
- return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Ebost/animeganv2-self/app.py b/spaces/Ebost/animeganv2-self/app.py
deleted file mode 100644
index 6d5e92432b9038a866d5dbe4cdd3d548645dd850..0000000000000000000000000000000000000000
--- a/spaces/Ebost/animeganv2-self/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-os.system("git clone https://github.com/bryandlee/animegan2-pytorch")
-os.system("gdown https://drive.google.com/uc?id=1WK5Mdt6mwlcsqCZMHkCUSDJxN1UyFi0-")
-os.system("gdown https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU")
-import sys
-sys.path.append("animegan2-pytorch")
-
-import torch
-torch.set_grad_enabled(False)
-
-from model import Generator
-
-device = "cpu"
-
-model = Generator().eval().to(device)
-model.load_state_dict(torch.load("face_paint_512_v2_0.pt"))
-
-from PIL import Image
-from torchvision.transforms.functional import to_tensor, to_pil_image
-import gradio as gr
-
-def face2paint(
- img: Image.Image,
- size: int,
- side_by_side: bool = False,
-) -> Image.Image:
-
-
- input = to_tensor(img).unsqueeze(0) * 2 - 1
- output = model(input.to(device)).cpu()[0]
-
- if side_by_side:
- output = torch.cat([input[0], output], dim=2)
-
- output = (output * 0.5 + 0.5).clip(0, 1)
-
- return to_pil_image(output)
-
-
-
-
-import os
-import collections
-from typing import Union, List
-import numpy as np
-from PIL import Image
-
-
-import PIL.Image
-import PIL.ImageFile
-import numpy as np
-import scipy.ndimage
-
-
-import requests
-
-def inference(img):
- out = face2paint(img, 512)
- return out
-
-
-title = "Animeganv2"
-description = "Gradio demo for AnimeGanv2 Face Portrait v2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "
- """, every=3, elem_id="files"
- )
- download_btn = gr.Button("Download All Files")
-
- chat_history = gr.State([[None, None]])
- api = gr.State(None)
-
- def start(open_ai_key, ai_name, ai_role, top_5_goals):
- auto_api = AutoAPI(open_ai_key, ai_name, ai_role, top_5_goals)
- return gr.Column.update(visible=False), gr.Column.update(visible=True), auto_api
-
- def bot_response(chat, api):
- messages = []
- for message in api.get_chatbot_response():
- messages.append(message)
- chat[-1][1] = "\n".join(messages) + "..."
- yield chat
- chat[-1][1] = "\n".join(messages)
- yield chat
-
- def send_message(count, chat, api, message="Y"):
- if message != "Y":
- count = 1
- for i in range(count):
- chat.append([message, None])
- yield chat, count - i
- api.send_message(message)
- for updated_chat in bot_response(chat, api):
- yield updated_chat, count - i
-
- def activate_inputs():
- return {
- yes_btn: gr.Button.update(interactive=True),
- consecutive_yes: gr.Slider.update(interactive=True),
- custom_response: gr.Textbox.update(interactive=True),
- }
-
- def deactivate_inputs():
- return {
- yes_btn: gr.Button.update(interactive=False),
- consecutive_yes: gr.Slider.update(interactive=False),
- custom_response: gr.Textbox.update(interactive=False),
- }
-
- start_btn.click(
- start,
- [open_ai_key, ai_name, ai_role, top_5_goals],
- [setup_pane, main_pane, api],
- ).then(bot_response, [chat_history, api], chatbot).then(
- activate_inputs, None, [yes_btn, consecutive_yes, custom_response]
- )
-
- yes_btn.click(
- deactivate_inputs, None, [yes_btn, consecutive_yes, custom_response]
- ).then(
- send_message, [consecutive_yes, chat_history, api], [chatbot, consecutive_yes]
- ).then(
- activate_inputs, None, [yes_btn, consecutive_yes, custom_response]
- )
- custom_response.submit(
- deactivate_inputs, None, [yes_btn, consecutive_yes, custom_response]
- ).then(
- send_message,
- [consecutive_yes, chat_history, api, custom_response],
- [chatbot, consecutive_yes],
- ).then(
- activate_inputs, None, [yes_btn, consecutive_yes, custom_response]
- )
-
- def download_all_files():
- shutil.make_archive("outputs", "zip", OUTPUT_DIR)
-
- download_btn.click(download_all_files).then(None, _js=utils.DOWNLOAD_OUTPUTS_JS)
-
-app.queue(concurrency_count=20).launch(file_directories=[OUTPUT_DIR])
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/docs/train.md b/spaces/Mellow-ai/PhotoAI_Mellow/docs/train.md
deleted file mode 100644
index 1c94ea17ba18eee8890a675a51ab9371013a62c8..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/docs/train.md
+++ /dev/null
@@ -1,276 +0,0 @@
-# Train a ControlNet to Control SD
-
-You are here because you want to control SD in your own way, maybe you have an idea for your perfect research project, and you will annotate some data or have already annotated your own dataset automatically or manually. Herein, the control can be anything that can be converted to images, such as edges, keypoints, segments, etc.
-
-Before moving on to your own dataset, we highly recommend to first try the toy dataset, Fill50K, as a sanity check. This will help you get a "feeling" for the training. You will know how long it will take for the model to converge and whether your device will be able to complete the training in an acceptable amount of time. And what it "feels" like when the model converges.
-
-We hope that after you read this page, you will find that training a ControlNet is as easy as (or easier than) training a pix2pix.
-
-## Step 0 - Design your control
-
-Let us take a look at a very simple task to control SD to fill color in circles.
-
-![p](../github_page/t1.png)
-
-This is simple: we want to control SD to fill a circle with colors, and the prompt contains some description of our target.
-
-Stable diffusion is trained on billions of images, and it already knows what is "cyan", what is "circle", what is "pink", and what is "background".
-
-But it does not know the meaning of that "Control Image (Source Image)". Our target is to let it know.
-
-## Step 1 - Get a dataset
-
-Just download the Fill50K dataset from [our huggingface page](https://huggingface.co/lllyasviel/ControlNet) (training/fill50k.zip, the file is only 200M!). Make sure that the data is decompressed as
-
- ControlNet/training/fill50k/prompt.json
- ControlNet/training/fill50k/source/X.png
- ControlNet/training/fill50k/target/X.png
-
-In the folder "fill50k/source", you will have 50k images of circle lines.
-
-![p](../github_page/t2.png)
-
-In the folder "fill50k/target", you will have 50k images of filled circles.
-
-![p](../github_page/t3.png)
-
-In the "fill50k/prompt.json", you will have their filenames and prompts. Each prompt is like "a balabala color circle in some other color background."
-
-![p](../github_page/t4.png)
-
-## Step 2 - Load the dataset
-
-Then you need to write a simple script to read this dataset for pytorch. (In fact we have written it for you in "tutorial_dataset.py".)
-
-```python
-import json
-import cv2
-import numpy as np
-
-from torch.utils.data import Dataset
-
-
-class MyDataset(Dataset):
- def __init__(self):
- self.data = []
- with open('./training/fill50k/prompt.json', 'rt') as f:
- for line in f:
- self.data.append(json.loads(line))
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, idx):
- item = self.data[idx]
-
- source_filename = item['source']
- target_filename = item['target']
- prompt = item['prompt']
-
- source = cv2.imread('./training/fill50k/' + source_filename)
- target = cv2.imread('./training/fill50k/' + target_filename)
-
- # Do not forget that OpenCV read images in BGR order.
- source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB)
- target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)
-
- # Normalize source images to [0, 1].
- source = source.astype(np.float32) / 255.0
-
- # Normalize target images to [-1, 1].
- target = (target.astype(np.float32) / 127.5) - 1.0
-
- return dict(jpg=target, txt=prompt, hint=source)
-
-```
-
-This will make your dataset into an array-like object in python. You can test this dataset simply by accessing the array, like this
-
-```python
-from tutorial_dataset import MyDataset
-
-dataset = MyDataset()
-print(len(dataset))
-
-item = dataset[1234]
-jpg = item['jpg']
-txt = item['txt']
-hint = item['hint']
-print(txt)
-print(jpg.shape)
-print(hint.shape)
-
-```
-
-The outputs of this simple test on my machine are
-
- 50000
- burly wood circle with orange background
- (512, 512, 3)
- (512, 512, 3)
-
-And this code is in "tutorial_dataset_test.py".
-
-In this way, the dataset is an array-like object with 50000 items. Each item is a dict with three entry "jpg", "txt", and "hint". The "jpg" is the target image, the "hint" is the control image, and the "txt" is the prompt.
-
-Do not ask us why we use these three names - this is related to the dark history of a library called LDM.
-
-## Step 3 - What SD model do you want to control?
-
-Then you need to decide which Stable Diffusion Model you want to control. In this example, we will just use standard SD1.5. You can download it from the [official page of Stability](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main). You want the file ["v1-5-pruned.ckpt"](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main).
-
-(Or ["v2-1_512-ema-pruned.ckpt"](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/tree/main) if you are using SD2.)
-
-Then you need to attach a control net to the SD model. The architecture is
-
-![img](../github_page/sd.png)
-
-Note that all weights inside the ControlNet are also copied from SD so that no layer is trained from scratch, and you are still finetuning the entire model.
-
-We provide a simple script for you to achieve this easily. If your SD filename is "./models/v1-5-pruned.ckpt" and you want the script to save the processed model (SD+ControlNet) at location "./models/control_sd15_ini.ckpt", you can just run:
-
- python tool_add_control.py ./models/v1-5-pruned.ckpt ./models/control_sd15_ini.ckpt
-
-Or if you are using SD2:
-
- python tool_add_control_sd21.py ./models/v2-1_512-ema-pruned.ckpt ./models/control_sd21_ini.ckpt
-
-You may also use other filenames as long as the command is "python tool_add_control.py input_path output_path".
-
-This is the correct output from my machine:
-
-![img](../github_page/t5.png)
-
-## Step 4 - Train!
-
-Happy! We finally come to the most exciting part: training!
-
-The training code in "tutorial_train.py" is actually surprisingly simple:
-
-```python
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader
-from tutorial_dataset import MyDataset
-from cldm.logger import ImageLogger
-from cldm.model import create_model, load_state_dict
-
-
-# Configs
-resume_path = './models/control_sd15_ini.ckpt'
-batch_size = 4
-logger_freq = 300
-learning_rate = 1e-5
-sd_locked = True
-only_mid_control = False
-
-
-# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
-model = create_model('./models/cldm_v15.yaml').cpu()
-model.load_state_dict(load_state_dict(resume_path, location='cpu'))
-model.learning_rate = learning_rate
-model.sd_locked = sd_locked
-model.only_mid_control = only_mid_control
-
-
-# Misc
-dataset = MyDataset()
-dataloader = DataLoader(dataset, num_workers=0, batch_size=batch_size, shuffle=True)
-logger = ImageLogger(batch_frequency=logger_freq)
-trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger])
-
-
-# Train!
-trainer.fit(model, dataloader)
-
-```
-(or "tutorial_train_sd21.py" if you are using SD2)
-
-Thanks to our organized dataset pytorch object and the power of pytorch_lightning, the entire code is just super short.
-
-Now, you may take a look at [Pytorch Lightning Official DOC](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer) to find out how to enable many useful features like gradient accumulation, multiple GPU training, accelerated dataset loading, flexible checkpoint saving, etc. All these only need about one line of code. Great!
-
-Note that if you find OOM, perhaps you need to enable [Low VRAM mode](low_vram.md), and perhaps you also need to use smaller batch size and gradient accumulation. Or you may also want to use some “advanced” tricks like sliced attention or xformers. For example:
-
-```python
-# Configs
-batch_size = 1
-
-# Misc
-trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger], accumulate_grad_batches=4) # But this will be 4x slower
-```
-
-Note that training with 8 GB laptop GPU is challenging. We will need some GPU memory optimization at least as good as automatic1111’s UI. This may require expert modifications to the code.
-
-### Screenshots
-
-The training is fast. After 4000 steps (batch size 4, learning rate 1e-5, about 50 minutes on PCIE 40G), the results on my machine (in an output folder "image_log") is
-
-Control:
-
-![img](../github_page/t/ip.png)
-
-Prompt:
-
-![img](../github_page/t/t.png)
-
-Prediction:
-
-![img](../github_page/t/op.png)
-
-Ground Truth:
-
-![img](../github_page/t/gt.png)
-
-Note that the SD's capability is preserved. Even training on this super aligned dataset, it still draws some random textures and those snow decorations. (Besides, note that the ground truth looks a bit modified because it is converted from SD's latent image.)
-
-Larger batch size and longer training will further improve this. Adequate training will make the filling perfect.
-
-Of course, training SD to fill circles is meaningless, but this is a successful beginning of your story.
-
-Let us work together to control large models more and more.
-
-## Other options
-
-Beyond standard things, we also provide two important parameters "sd_locked" and "only_mid_control" that you need to know.
-
-### only_mid_control
-
-By default, only_mid_control is False. When it is True, you will train the below architecture.
-
-![img](../github_page/t6.png)
-
-This can be helpful when your computation power is limited and want to speed up the training, or when you want to facilitate the "global" context learning. Note that sometimes you may pause training, set it to True, resume training, and pause again, and set it again, and resume again.
-
-If your computation device is good, perhaps you do not need this. But I also know some artists are willing to train a model on their laptop for a month - in that case, perhaps this option can be useful.
-
-### sd_locked
-
-By default, sd_locked is True. When it is False, you will train the below architecture.
-
-![img](../github_page/t7.png)
-
-This will unlock some layers in SD and you will train them as a whole.
-
-This option is DANGEROUS! If your dataset is not good enough, this may downgrade the capability of your SD model.
-
-However, this option is also very useful when you are training on images with some specific style, or when you are training with special datasets (like medical dataset with X-ray images or geographic datasets with lots of Google Maps). You can understand this as simultaneously training the ControlNet and something like a DreamBooth.
-
-Also, if your dataset is large, you may want to end the training with a few thousands of steps with those layer unlocked. This usually improve the "problem-specific" solutions a little. You may try it yourself to feel the difference.
-
-Also, if you unlock some original layers, you may want a lower learning rate, like 2e-6.
-
-## More Consideration: Sudden Converge Phenomenon and Gradient Accumulation
-
-![img](../github_page/ex1.jpg)
-
-Because we use zero convolutions, the SD should always be able to predict meaningful images. (If it cannot, the training has already failed.)
-
-You will always find that at some iterations, the model "suddenly" be able to fit some training conditions. This means that you will get a basically usable model at about 3k to 7k steps (future training will improve it, but that model after the first "sudden converge" should be basically functional).
-
-Note that 3k to 7k steps is not very large, and you should consider larger batch size rather than more training steps. If you can observe the "sudden converge" at 3k step using batch size 4, then, rather than train it with 300k further steps, a better idea is to use 100× gradient accumulation to re-train that 3k steps with 100× batch size. Note that perhaps we should not do this *too* extremely (perhaps 100x accumulation is too extreme), but you should consider that, since "sudden converge" will *always* happen at that certain point, getting a better converge is more important.
-
-Because that "sudden converge" always happens, lets say "sudden converge" will happen at 3k step and our money can optimize 90k step, then we have two options: (1) train 3k steps, sudden converge, then train 87k steps. (2) 30x gradient accumulation, train 3k steps (90k real computation steps), then sudden converge.
-
-In my experiments, (2) is usually better than (1). However, in real cases, perhaps you may need to balance the steps before and after the "sudden converge" on your own to find a balance. The training after "sudden converge" is also important.
-
-But usually, if your logic batch size is already bigger than 256, then further extending the batch size is not very meaningful. In that case, perhaps a better idea is to train more steps. I tried some "common" logic batch size at 64 or 96 or 128 (by gradient accumulation), it seems that many complicated conditions can be solved very well already.
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/_base_/schedules/schedule_sgd_1200e.py b/spaces/Mountchicken/MAERec-Gradio/configs/textdet/_base_/schedules/schedule_sgd_1200e.py
deleted file mode 100644
index f8555e468bccaa6e5dbca23c9d2821164e21e516..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/_base_/schedules/schedule_sgd_1200e.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# optimizer
-optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001))
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1200, val_interval=20)
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
-# learning policy
-param_scheduler = [
- dict(type='PolyLR', power=0.9, eta_min=1e-7, end=1200),
-]
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/module_losses/textsnake_module_loss.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/module_losses/textsnake_module_loss.py
deleted file mode 100644
index 651a74755cf44e4103721b7416c6455bf0438f05..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/module_losses/textsnake_module_loss.py
+++ /dev/null
@@ -1,648 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Sequence, Tuple
-
-import cv2
-import numpy as np
-import torch
-from mmcv.image import impad, imrescale
-from mmdet.models.utils import multi_apply
-from numpy import ndarray
-from numpy.linalg import norm
-from torch import Tensor
-
-from mmocr.registry import MODELS
-from mmocr.structures import TextDetDataSample
-from .seg_based_module_loss import SegBasedModuleLoss
-
-
-@MODELS.register_module()
-class TextSnakeModuleLoss(SegBasedModuleLoss):
- """The class for implementing TextSnake loss. This is partially adapted
- from https://github.com/princewang1994/TextSnake.pytorch.
-
- TextSnake: `A Flexible Representation for Detecting Text of Arbitrary
- Shapes `_.
-
- Args:
- ohem_ratio (float): The negative/positive ratio in ohem.
- downsample_ratio (float): Downsample ratio. Defaults to 1.0. TODO:
- remove it.
- orientation_thr (float): The threshold for distinguishing between
- head edge and tail edge among the horizontal and vertical edges
- of a quadrangle.
- resample_step (float): The step of resampling.
- center_region_shrink_ratio (float): The shrink ratio of text center.
- loss_text (dict): The loss config used to calculate the text loss.
- loss_center (dict): The loss config used to calculate the center loss.
- loss_radius (dict): The loss config used to calculate the radius loss.
- loss_sin (dict): The loss config used to calculate the sin loss.
- loss_cos (dict): The loss config used to calculate the cos loss.
- """
-
- def __init__(
- self,
- ohem_ratio: float = 3.0,
- downsample_ratio: float = 1.0,
- orientation_thr: float = 2.0,
- resample_step: float = 4.0,
- center_region_shrink_ratio: float = 0.3,
- loss_text: Dict = dict(
- type='MaskedBalancedBCEWithLogitsLoss',
- fallback_negative_num=100,
- eps=1e-5),
- loss_center: Dict = dict(type='MaskedBCEWithLogitsLoss'),
- loss_radius: Dict = dict(type='MaskedSmoothL1Loss'),
- loss_sin: Dict = dict(type='MaskedSmoothL1Loss'),
- loss_cos: Dict = dict(type='MaskedSmoothL1Loss')
- ) -> None:
- super().__init__()
- self.ohem_ratio = ohem_ratio
- self.downsample_ratio = downsample_ratio
- self.orientation_thr = orientation_thr
- self.resample_step = resample_step
- self.center_region_shrink_ratio = center_region_shrink_ratio
- self.eps = 1e-8
- self.loss_text = MODELS.build(loss_text)
- self.loss_center = MODELS.build(loss_center)
- self.loss_radius = MODELS.build(loss_radius)
- self.loss_sin = MODELS.build(loss_sin)
- self.loss_cos = MODELS.build(loss_cos)
-
- def _batch_pad(self, masks: List[ndarray],
- target_sz: Tuple[int, int]) -> ndarray:
- """Pad the masks to the right and bottom side to the target size and
- pack them into a batch.
-
- Args:
- mask (list[ndarray]): The masks to be padded.
- target_sz (tuple(int, int)): The target tensor of size
- :math:`(H, W)`.
-
- Returns:
- ndarray: A batch of padded mask.
- """
- batch = []
- for mask in masks:
- # H x W
- mask_sz = mask.shape
- # left, top, right, bottom
- padding = (0, 0, target_sz[1] - mask_sz[1],
- target_sz[0] - mask_sz[0])
- padded_mask = impad(
- mask, padding=padding, padding_mode='constant', pad_val=0)
- batch.append(np.expand_dims(padded_mask, axis=0))
- return np.concatenate(batch)
-
- def forward(self, preds: Tensor,
- data_samples: Sequence[TextDetDataSample]) -> Dict:
- """
- Args:
- preds (Tensor): The prediction map of shape
- :math:`(N, 5, H, W)`, where each dimension is the map of
- "text_region", "center_region", "sin_map", "cos_map", and
- "radius_map" respectively.
- data_samples (list[TextDetDataSample]): The data samples.
-
- Returns:
- dict: A loss dict with ``loss_text``, ``loss_center``,
- ``loss_radius``, ``loss_sin`` and ``loss_cos``.
- """
-
- (gt_text_masks, gt_masks, gt_center_region_masks, gt_radius_maps,
- gt_sin_maps, gt_cos_maps) = self.get_targets(data_samples)
-
- pred_text_region = preds[:, 0, :, :]
- pred_center_region = preds[:, 1, :, :]
- pred_sin_map = preds[:, 2, :, :]
- pred_cos_map = preds[:, 3, :, :]
- pred_radius_map = preds[:, 4, :, :]
- feature_sz = preds.size()
- device = preds.device
-
- mapping = {
- 'gt_text_masks': gt_text_masks,
- 'gt_center_region_masks': gt_center_region_masks,
- 'gt_masks': gt_masks,
- 'gt_radius_maps': gt_radius_maps,
- 'gt_sin_maps': gt_sin_maps,
- 'gt_cos_maps': gt_cos_maps
- }
- gt = {}
- for key, value in mapping.items():
- gt[key] = value
- if abs(self.downsample_ratio - 1.0) < 1e-2:
- gt[key] = self._batch_pad(gt[key], feature_sz[2:])
- else:
- gt[key] = [
- imrescale(
- mask,
- scale=self.downsample_ratio,
- interpolation='nearest') for mask in gt[key]
- ]
- gt[key] = self._batch_pad(gt[key], feature_sz[2:])
- if key == 'gt_radius_maps':
- gt[key] *= self.downsample_ratio
- gt[key] = torch.from_numpy(gt[key]).float().to(device)
-
- scale = torch.sqrt(1.0 / (pred_sin_map**2 + pred_cos_map**2 + 1e-8))
- pred_sin_map = pred_sin_map * scale
- pred_cos_map = pred_cos_map * scale
-
- loss_text = self.loss_text(pred_text_region, gt['gt_text_masks'],
- gt['gt_masks'])
-
- text_mask = (gt['gt_text_masks'] * gt['gt_masks']).float()
- loss_center = self.loss_center(pred_center_region,
- gt['gt_center_region_masks'], text_mask)
-
- center_mask = (gt['gt_center_region_masks'] * gt['gt_masks']).float()
- map_sz = pred_radius_map.size()
- ones = torch.ones(map_sz, dtype=torch.float, device=device)
- loss_radius = self.loss_radius(
- pred_radius_map / (gt['gt_radius_maps'] + 1e-2), ones, center_mask)
- loss_sin = self.loss_sin(pred_sin_map, gt['gt_sin_maps'], center_mask)
- loss_cos = self.loss_cos(pred_cos_map, gt['gt_cos_maps'], center_mask)
-
- results = dict(
- loss_text=loss_text,
- loss_center=loss_center,
- loss_radius=loss_radius,
- loss_sin=loss_sin,
- loss_cos=loss_cos)
-
- return results
-
- 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(gt_text_masks, gt_masks, gt_center_region_masks,
- gt_radius_maps, gt_sin_maps, gt_cos_maps):
- A tuple of six lists of ndarrays as the targets.
- """
- return multi_apply(self._get_target_single, data_samples)
-
- 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(gt_text_mask, gt_mask, gt_center_region_mask, gt_radius_map,
- gt_sin_map, gt_cos_map):
- A tuple of six ndarrays as the targets of one prediction.
- """
-
- gt_instances = data_sample.gt_instances
- ignore_flags = gt_instances.ignored
-
- polygons = gt_instances[~ignore_flags].polygons
- ignored_polygons = gt_instances[ignore_flags].polygons
-
- gt_text_mask = self._generate_text_region_mask(data_sample.img_shape,
- polygons)
- gt_mask = self._generate_effective_mask(data_sample.img_shape,
- ignored_polygons)
-
- (gt_center_region_mask, gt_radius_map, gt_sin_map,
- gt_cos_map) = self._generate_center_mask_attrib_maps(
- data_sample.img_shape, polygons)
-
- return (gt_text_mask, gt_mask, gt_center_region_mask, gt_radius_map,
- gt_sin_map, gt_cos_map)
-
- def _generate_text_region_mask(self, img_size: Tuple[int, int],
- text_polys: List[ndarray]) -> ndarray:
- """Generate text center region mask and geometry attribute maps.
-
- Args:
- img_size (tuple): The image size (height, width).
- text_polys (list[ndarray]): The list of text polygons.
-
- Returns:
- text_region_mask (ndarray): The text region mask.
- """
-
- assert isinstance(img_size, tuple)
-
- text_region_mask = np.zeros(img_size, dtype=np.uint8)
-
- for poly in text_polys:
- polygon = np.array(poly, dtype=np.int32).reshape((1, -1, 2))
- cv2.fillPoly(text_region_mask, polygon, 1)
-
- return text_region_mask
-
- def _generate_center_mask_attrib_maps(
- self, img_size: Tuple[int, int], text_polys: List[ndarray]
- ) -> Tuple[ndarray, ndarray, ndarray, ndarray]:
- """Generate text center region mask and geometric attribute maps.
-
- Args:
- img_size (tuple(int, int)): The image size of (height, width).
- text_polys (list[ndarray]): The list of text polygons.
-
- Returns:
- Tuple(center_region_mask, radius_map, sin_map, cos_map):
-
- - center_region_mask (ndarray): The text center region mask.
- - radius_map (ndarray): The distance map from each pixel in text
- center region to top sideline.
- - sin_map (ndarray): The sin(theta) map where theta is the angle
- between vector (top point - bottom point) and vector (1, 0).
- - cos_map (ndarray): The cos(theta) map where theta is the angle
- between vector (top point - bottom point) and vector (1, 0).
- """
-
- assert isinstance(img_size, tuple)
-
- center_region_mask = np.zeros(img_size, np.uint8)
- radius_map = np.zeros(img_size, dtype=np.float32)
- sin_map = np.zeros(img_size, dtype=np.float32)
- cos_map = np.zeros(img_size, dtype=np.float32)
-
- for poly in text_polys:
- polygon_points = np.array(poly).reshape(-1, 2)
-
- n = len(polygon_points)
- keep_inds = []
- for i in range(n):
- if norm(polygon_points[i] -
- polygon_points[(i + 1) % n]) > 1e-5:
- keep_inds.append(i)
- polygon_points = polygon_points[keep_inds]
-
- _, _, top_line, bot_line = self._reorder_poly_edge(polygon_points)
- resampled_top_line, resampled_bot_line = self._resample_sidelines(
- top_line, bot_line, self.resample_step)
- resampled_bot_line = resampled_bot_line[::-1]
- center_line = (resampled_top_line + resampled_bot_line) / 2
-
- if self.vector_slope(center_line[-1] - center_line[0]) > 0.9:
- if (center_line[-1] - center_line[0])[1] < 0:
- center_line = center_line[::-1]
- resampled_top_line = resampled_top_line[::-1]
- resampled_bot_line = resampled_bot_line[::-1]
- else:
- if (center_line[-1] - center_line[0])[0] < 0:
- center_line = center_line[::-1]
- resampled_top_line = resampled_top_line[::-1]
- resampled_bot_line = resampled_bot_line[::-1]
-
- line_head_shrink_len = norm(resampled_top_line[0] -
- resampled_bot_line[0]) / 4.0
- line_tail_shrink_len = norm(resampled_top_line[-1] -
- resampled_bot_line[-1]) / 4.0
- head_shrink_num = int(line_head_shrink_len // self.resample_step)
- tail_shrink_num = int(line_tail_shrink_len // self.resample_step)
-
- if len(center_line) > head_shrink_num + tail_shrink_num + 2:
- center_line = center_line[head_shrink_num:len(center_line) -
- tail_shrink_num]
- resampled_top_line = resampled_top_line[
- head_shrink_num:len(resampled_top_line) - tail_shrink_num]
- resampled_bot_line = resampled_bot_line[
- head_shrink_num:len(resampled_bot_line) - tail_shrink_num]
-
- self._draw_center_region_maps(resampled_top_line,
- resampled_bot_line, center_line,
- center_region_mask, radius_map,
- sin_map, cos_map,
- self.center_region_shrink_ratio)
-
- return center_region_mask, radius_map, sin_map, cos_map
-
- def _reorder_poly_edge(self, points: ndarray
- ) -> Tuple[ndarray, ndarray, ndarray, ndarray]:
- """Get the respective points composing head edge, tail edge, top
- sideline and bottom sideline.
-
- Args:
- points (ndarray): The points composing a text polygon.
-
- Returns:
- Tuple(center_region_mask, radius_map, sin_map, cos_map):
-
- - head_edge (ndarray): The two points composing the head edge of
- text polygon.
- - tail_edge (ndarray): The two points composing the tail edge of
- text polygon.
- - top_sideline (ndarray): The points composing top curved sideline
- of text polygon.
- - bot_sideline (ndarray): The points composing bottom curved
- sideline of text polygon.
- """
-
- assert points.ndim == 2
- assert points.shape[0] >= 4
- assert points.shape[1] == 2
-
- head_inds, tail_inds = self._find_head_tail(points,
- self.orientation_thr)
- head_edge, tail_edge = points[head_inds], points[tail_inds]
-
- pad_points = np.vstack([points, points])
- if tail_inds[1] < 1:
- tail_inds[1] = len(points)
- sideline1 = pad_points[head_inds[1]:tail_inds[1]]
- sideline2 = pad_points[tail_inds[1]:(head_inds[1] + len(points))]
- sideline_mean_shift = np.mean(
- sideline1, axis=0) - np.mean(
- sideline2, axis=0)
-
- if sideline_mean_shift[1] > 0:
- top_sideline, bot_sideline = sideline2, sideline1
- else:
- top_sideline, bot_sideline = sideline1, sideline2
-
- return head_edge, tail_edge, top_sideline, bot_sideline
-
- def _find_head_tail(self, points: ndarray,
- orientation_thr: float) -> Tuple[List[int], List[int]]:
- """Find the head edge and tail edge of a text polygon.
-
- Args:
- points (ndarray): The points composing a text polygon.
- orientation_thr (float): The threshold for distinguishing between
- head edge and tail edge among the horizontal and vertical edges
- of a quadrangle.
-
- Returns:
- Tuple(head_inds, tail_inds):
-
- - head_inds (list[int]): The indexes of two points composing head
- edge.
- - tail_inds (list[int]): The indexes of two points composing tail
- edge.
- """
-
- assert points.ndim == 2
- assert points.shape[0] >= 4
- assert points.shape[1] == 2
- assert isinstance(orientation_thr, float)
-
- if len(points) > 4:
- pad_points = np.vstack([points, points[0]])
- edge_vec = pad_points[1:] - pad_points[:-1]
-
- theta_sum = []
- adjacent_vec_theta = []
- for i, edge_vec1 in enumerate(edge_vec):
- adjacent_ind = [x % len(edge_vec) for x in [i - 1, i + 1]]
- adjacent_edge_vec = edge_vec[adjacent_ind]
- temp_theta_sum = np.sum(
- self.vector_angle(edge_vec1, adjacent_edge_vec))
- temp_adjacent_theta = self.vector_angle(
- adjacent_edge_vec[0], adjacent_edge_vec[1])
- theta_sum.append(temp_theta_sum)
- adjacent_vec_theta.append(temp_adjacent_theta)
- theta_sum_score = np.array(theta_sum) / np.pi
- adjacent_theta_score = np.array(adjacent_vec_theta) / np.pi
- poly_center = np.mean(points, axis=0)
- edge_dist = np.maximum(
- norm(pad_points[1:] - poly_center, axis=-1),
- norm(pad_points[:-1] - poly_center, axis=-1))
- dist_score = edge_dist / (np.max(edge_dist) + self.eps)
- position_score = np.zeros(len(edge_vec))
- score = 0.5 * theta_sum_score + 0.15 * adjacent_theta_score
- score += 0.35 * dist_score
- if len(points) % 2 == 0:
- position_score[(len(score) // 2 - 1)] += 1
- position_score[-1] += 1
- score += 0.1 * position_score
- pad_score = np.concatenate([score, score])
- score_matrix = np.zeros((len(score), len(score) - 3))
- x = np.arange(len(score) - 3) / float(len(score) - 4)
- gaussian = 1. / (np.sqrt(2. * np.pi) * 0.5) * np.exp(-np.power(
- (x - 0.5) / 0.5, 2.) / 2)
- gaussian = gaussian / np.max(gaussian)
- for i in range(len(score)):
- score_matrix[i, :] = score[i] + pad_score[
- (i + 2):(i + len(score) - 1)] * gaussian * 0.3
-
- head_start, tail_increment = np.unravel_index(
- score_matrix.argmax(), score_matrix.shape)
- tail_start = (head_start + tail_increment + 2) % len(points)
- head_end = (head_start + 1) % len(points)
- tail_end = (tail_start + 1) % len(points)
-
- if head_end > tail_end:
- head_start, tail_start = tail_start, head_start
- head_end, tail_end = tail_end, head_end
- head_inds = [head_start, head_end]
- tail_inds = [tail_start, tail_end]
- else:
- if self.vector_slope(points[1] - points[0]) + self.vector_slope(
- points[3] - points[2]) < self.vector_slope(
- points[2] - points[1]) + self.vector_slope(points[0] -
- points[3]):
- horizontal_edge_inds = [[0, 1], [2, 3]]
- vertical_edge_inds = [[3, 0], [1, 2]]
- else:
- horizontal_edge_inds = [[3, 0], [1, 2]]
- vertical_edge_inds = [[0, 1], [2, 3]]
-
- vertical_len_sum = norm(points[vertical_edge_inds[0][0]] -
- points[vertical_edge_inds[0][1]]) + norm(
- points[vertical_edge_inds[1][0]] -
- points[vertical_edge_inds[1][1]])
- horizontal_len_sum = norm(
- points[horizontal_edge_inds[0][0]] -
- points[horizontal_edge_inds[0][1]]) + norm(
- points[horizontal_edge_inds[1][0]] -
- points[horizontal_edge_inds[1][1]])
-
- if vertical_len_sum > horizontal_len_sum * orientation_thr:
- head_inds = horizontal_edge_inds[0]
- tail_inds = horizontal_edge_inds[1]
- else:
- head_inds = vertical_edge_inds[0]
- tail_inds = vertical_edge_inds[1]
-
- return head_inds, tail_inds
-
- def _resample_line(self, line: ndarray, n: int) -> ndarray:
- """Resample n points on a line.
-
- Args:
- line (ndarray): The points composing a line.
- n (int): The resampled points number.
-
- Returns:
- resampled_line (ndarray): The points composing the resampled line.
- """
-
- assert line.ndim == 2
- assert line.shape[0] >= 2
- assert line.shape[1] == 2
- assert isinstance(n, int)
- assert n > 2
-
- edges_length, total_length = self._cal_curve_length(line)
- t_org = np.insert(np.cumsum(edges_length), 0, 0)
- unit_t = total_length / (n - 1)
- t_equidistant = np.arange(1, n - 1, dtype=np.float32) * unit_t
- edge_ind = 0
- points = [line[0]]
- for t in t_equidistant:
- while edge_ind < len(edges_length) - 1 and t > t_org[edge_ind + 1]:
- edge_ind += 1
- t_l, t_r = t_org[edge_ind], t_org[edge_ind + 1]
- weight = np.array([t_r - t, t - t_l], dtype=np.float32) / (
- t_r - t_l + self.eps)
- p_coords = np.dot(weight, line[[edge_ind, edge_ind + 1]])
- points.append(p_coords)
- points.append(line[-1])
- resampled_line = np.vstack(points)
-
- return resampled_line
-
- def _resample_sidelines(self, sideline1: ndarray, sideline2: ndarray,
- resample_step: float) -> Tuple[ndarray, ndarray]:
- """Resample two sidelines to be of the same points number according to
- step size.
-
- Args:
- sideline1 (ndarray): The points composing a sideline of a text
- polygon.
- sideline2 (ndarray): The points composing another sideline of a
- text polygon.
- resample_step (float): The resampled step size.
-
- Returns:
- Tuple(resampled_line1, resampled_line2):
-
- - resampled_line1 (ndarray): The resampled line 1.
- - resampled_line2 (ndarray): The resampled line 2.
- """
-
- assert sideline1.ndim == sideline2.ndim == 2
- assert sideline1.shape[1] == sideline2.shape[1] == 2
- assert sideline1.shape[0] >= 2
- assert sideline2.shape[0] >= 2
- assert isinstance(resample_step, float)
-
- _, length1 = self._cal_curve_length(sideline1)
- _, length2 = self._cal_curve_length(sideline2)
-
- avg_length = (length1 + length2) / 2
- resample_point_num = max(int(float(avg_length) / resample_step) + 1, 3)
-
- resampled_line1 = self._resample_line(sideline1, resample_point_num)
- resampled_line2 = self._resample_line(sideline2, resample_point_num)
-
- return resampled_line1, resampled_line2
-
- def _cal_curve_length(self, line: ndarray) -> Tuple[ndarray, float]:
- """Calculate the length of each edge on the discrete curve and the sum.
-
- Args:
- line (ndarray): The points composing a discrete curve.
-
- Returns:
- Tuple(edges_length, total_length):
-
- - edge_length (ndarray): The length of each edge on the
- discrete curve.
- - total_length (float): The total length of the discrete
- curve.
- """
-
- assert line.ndim == 2
- assert len(line) >= 2
-
- edges_length = np.sqrt((line[1:, 0] - line[:-1, 0])**2 +
- (line[1:, 1] - line[:-1, 1])**2)
- total_length = np.sum(edges_length)
- return edges_length, total_length
-
- def _draw_center_region_maps(self, top_line: ndarray, bot_line: ndarray,
- center_line: ndarray,
- center_region_mask: ndarray,
- radius_map: ndarray, sin_map: ndarray,
- cos_map: ndarray,
- region_shrink_ratio: float) -> None:
- """Draw attributes on text center region.
-
- Args:
- top_line (ndarray): The points composing top curved sideline of
- text polygon.
- bot_line (ndarray): The points composing bottom curved sideline
- of text polygon.
- center_line (ndarray): The points composing the center line of text
- instance.
- center_region_mask (ndarray): The text center region mask.
- radius_map (ndarray): The map where the distance from point to
- sidelines will be drawn on for each pixel in text center
- region.
- sin_map (ndarray): The map where vector_sin(theta) will be drawn
- on text center regions. Theta is the angle between tangent
- line and vector (1, 0).
- cos_map (ndarray): The map where vector_cos(theta) will be drawn on
- text center regions. Theta is the angle between tangent line
- and vector (1, 0).
- region_shrink_ratio (float): The shrink ratio of text center.
- """
-
- assert top_line.shape == bot_line.shape == center_line.shape
- assert (center_region_mask.shape == radius_map.shape == sin_map.shape
- == cos_map.shape)
- assert isinstance(region_shrink_ratio, float)
- for i in range(0, len(center_line) - 1):
-
- top_mid_point = (top_line[i] + top_line[i + 1]) / 2
- bot_mid_point = (bot_line[i] + bot_line[i + 1]) / 2
- radius = norm(top_mid_point - bot_mid_point) / 2
-
- text_direction = center_line[i + 1] - center_line[i]
- sin_theta = self.vector_sin(text_direction)
- cos_theta = self.vector_cos(text_direction)
-
- tl = center_line[i] + (top_line[i] -
- center_line[i]) * region_shrink_ratio
- tr = center_line[i + 1] + (
- top_line[i + 1] - center_line[i + 1]) * region_shrink_ratio
- br = center_line[i + 1] + (
- bot_line[i + 1] - center_line[i + 1]) * region_shrink_ratio
- bl = center_line[i] + (bot_line[i] -
- center_line[i]) * region_shrink_ratio
- current_center_box = np.vstack([tl, tr, br, bl]).astype(np.int32)
-
- cv2.fillPoly(center_region_mask, [current_center_box], color=1)
- cv2.fillPoly(sin_map, [current_center_box], color=sin_theta)
- cv2.fillPoly(cos_map, [current_center_box], color=cos_theta)
- cv2.fillPoly(radius_map, [current_center_box], color=radius)
-
- def vector_angle(self, vec1: ndarray, vec2: ndarray) -> ndarray:
- """Compute the angle between two vectors."""
- if vec1.ndim > 1:
- unit_vec1 = vec1 / (norm(vec1, axis=-1) + self.eps).reshape(
- (-1, 1))
- else:
- unit_vec1 = vec1 / (norm(vec1, axis=-1) + self.eps)
- if vec2.ndim > 1:
- unit_vec2 = vec2 / (norm(vec2, axis=-1) + self.eps).reshape(
- (-1, 1))
- else:
- unit_vec2 = vec2 / (norm(vec2, axis=-1) + self.eps)
- return np.arccos(
- np.clip(np.sum(unit_vec1 * unit_vec2, axis=-1), -1.0, 1.0))
-
- def vector_slope(self, vec: ndarray) -> float:
- """Compute the slope of a vector."""
- assert len(vec) == 2
- return abs(vec[1] / (vec[0] + self.eps))
-
- def vector_sin(self, vec: ndarray) -> float:
- """Compute the sin of the angle between vector and x-axis."""
- assert len(vec) == 2
- return vec[1] / (norm(vec) + self.eps)
-
- def vector_cos(self, vec: ndarray) -> float:
- """Compute the cos of the angle between vector and x-axis."""
- assert len(vec) == 2
- return vec[0] / (norm(vec) + self.eps)
diff --git a/spaces/NATSpeech/DiffSpeech/utils/audio/vad.py b/spaces/NATSpeech/DiffSpeech/utils/audio/vad.py
deleted file mode 100644
index cbe9c7a6417f234ae46e1754d6736b26e22b2427..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/utils/audio/vad.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from skimage.transform import resize
-import struct
-import webrtcvad
-from scipy.ndimage.morphology import binary_dilation
-import librosa
-import numpy as np
-import pyloudnorm as pyln
-import warnings
-
-warnings.filterwarnings("ignore", message="Possible clipped samples in output")
-
-int16_max = (2 ** 15) - 1
-
-
-def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12):
- """
- 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
- :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have.
- :return: the same waveform with silences trimmed away (length <= original wav length)
- """
-
- ## 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.
- sampling_rate = 16000
- wav_raw, sr = librosa.core.load(path, sr=sr)
-
- if norm:
- meter = pyln.Meter(sr) # create BS.1770 meter
- loudness = meter.integrated_loudness(wav_raw)
- wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0)
- if np.abs(wav_raw).max() > 1.0:
- wav_raw = wav_raw / np.abs(wav_raw).max()
-
- wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best')
-
- 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
-
- # 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)
- audio_mask = resize(audio_mask, (len(wav_raw),)) > 0
- if return_raw_wav:
- return wav_raw, audio_mask, sr
- return wav_raw[audio_mask], audio_mask, sr
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/utils/object_detection/shape_utils.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/utils/object_detection/shape_utils.py
deleted file mode 100644
index e30b62b7acc15b7f9f98b6c27b1a22efaf2998a8..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/utils/object_detection/shape_utils.py
+++ /dev/null
@@ -1,112 +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.
-# ==============================================================================
-
-"""Utils used to manipulate tensor shapes."""
-
-import tensorflow as tf
-
-
-def assert_shape_equal(shape_a, shape_b):
- """Asserts that shape_a and shape_b are equal.
-
- If the shapes are static, raises a ValueError when the shapes
- mismatch.
-
- If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
- mismatch.
-
- Args:
- shape_a: a list containing shape of the first tensor.
- shape_b: a list containing shape of the second tensor.
-
- Returns:
- Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
- when the shapes are dynamic.
-
- Raises:
- ValueError: When shapes are both static and unequal.
- """
- if (all(isinstance(dim, int) for dim in shape_a) and
- all(isinstance(dim, int) for dim in shape_b)):
- if shape_a != shape_b:
- raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
- else: return tf.no_op()
- else:
- return tf.assert_equal(shape_a, shape_b)
-
-
-def combined_static_and_dynamic_shape(tensor):
- """Returns a list containing static and dynamic values for the dimensions.
-
- Returns a list of static and dynamic values for shape dimensions. This is
- useful to preserve static shapes when available in reshape operation.
-
- Args:
- tensor: A tensor of any type.
-
- Returns:
- A list of size tensor.shape.ndims containing integers or a scalar tensor.
- """
- static_tensor_shape = tensor.shape.as_list()
- dynamic_tensor_shape = tf.shape(input=tensor)
- combined_shape = []
- for index, dim in enumerate(static_tensor_shape):
- if dim is not None:
- combined_shape.append(dim)
- else:
- combined_shape.append(dynamic_tensor_shape[index])
- return combined_shape
-
-
-def pad_or_clip_nd(tensor, output_shape):
- """Pad or Clip given tensor to the output shape.
-
- Args:
- tensor: Input tensor to pad or clip.
- output_shape: A list of integers / scalar tensors (or None for dynamic dim)
- representing the size to pad or clip each dimension of the input tensor.
-
- Returns:
- Input tensor padded and clipped to the output shape.
- """
- tensor_shape = tf.shape(input=tensor)
- clip_size = [
- tf.where(tensor_shape[i] - shape > 0, shape, -1)
- if shape is not None else -1 for i, shape in enumerate(output_shape)
- ]
- clipped_tensor = tf.slice(
- tensor,
- begin=tf.zeros(len(clip_size), dtype=tf.int32),
- size=clip_size)
-
- # Pad tensor if the shape of clipped tensor is smaller than the expected
- # shape.
- clipped_tensor_shape = tf.shape(input=clipped_tensor)
- trailing_paddings = [
- shape - clipped_tensor_shape[i] if shape is not None else 0
- for i, shape in enumerate(output_shape)
- ]
- paddings = tf.stack(
- [
- tf.zeros(len(trailing_paddings), dtype=tf.int32),
- trailing_paddings
- ],
- axis=1)
- padded_tensor = tf.pad(tensor=clipped_tensor, paddings=paddings)
- output_static_shape = [
- dim if not isinstance(dim, tf.Tensor) else None for dim in output_shape
- ]
- padded_tensor.set_shape(output_static_shape)
- return padded_tensor
diff --git a/spaces/Nee001/bing0/src/components/chat-notification.tsx b/spaces/Nee001/bing0/src/components/chat-notification.tsx
deleted file mode 100644
index 3474e522992c43a4d1d0eadcf205a9760d5b930b..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/src/components/chat-notification.tsx
+++ /dev/null
@@ -1,91 +0,0 @@
-import { useEffect } from 'react'
-import Image from 'next/image'
-
-import IconWarning from '@/assets/images/warning.svg'
-import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types'
-import { ExternalLink } from './external-link'
-import { useBing } from '@/lib/hooks/use-bing'
-
-export interface ChatNotificationProps extends Pick, 'bot'> {
- message?: ChatMessageModel
-}
-
-function getAction(error: ChatError, reset: () => void) {
- if (error.code === ErrorCode.THROTTLE_LIMIT) {
- reset()
- return (
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/TNR-5/zeroscope/README.md b/spaces/TNR-5/zeroscope/README.md
deleted file mode 100644
index a2495e840a48f6c2000755a443716b83c253be12..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/zeroscope/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: HedgehogAI with Zeroscope and SD
-emoji: 🦔 AI
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.36.1
-app_file: text2video.py
-pinned: true
----
-
-🦔 This is HedgehogAI with Zeroscope and SD from Demo! Try FREE text2video/text to video!
\ No newline at end of file
diff --git a/spaces/TaliaKorobkin/AIPairProgramming1/app.py b/spaces/TaliaKorobkin/AIPairProgramming1/app.py
deleted file mode 100644
index 30c217a7a71ac0d06306aeab343910af9347781a..0000000000000000000000000000000000000000
--- a/spaces/TaliaKorobkin/AIPairProgramming1/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import streamlit as st
-import time def main():
- st.title("File Upload and Display")
-
- # File upload
- uploaded_file = st.file_uploader("Upload a file")
-
- if uploaded_file is not None:
-
- # Display file contents using st.markdown()
- file_contents = uploaded_file.read().decode("utf-8")
- st.markdown("### File Contents:") st.markdown(f"```{file_contents}```")
-
- # Wait for 5 seconds
- time.sleep(5)
-
- # Show completed message
- st.success("File processing completed!")
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/utils.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/utils.py
deleted file mode 100644
index 472fafb4403efb9673d5cc724dafd9cf764aac5b..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-from contextlib import contextmanager
-from io import StringIO
-import sys
-import os
-
-
-class StreamTTY(StringIO):
- def isatty(self):
- return True
-
-class StreamNonTTY(StringIO):
- def isatty(self):
- return False
-
-@contextmanager
-def osname(name):
- orig = os.name
- os.name = name
- yield
- os.name = orig
-
-@contextmanager
-def replace_by(stream):
- orig_stdout = sys.stdout
- orig_stderr = sys.stderr
- sys.stdout = stream
- sys.stderr = stream
- yield
- sys.stdout = orig_stdout
- sys.stderr = orig_stderr
-
-@contextmanager
-def replace_original_by(stream):
- orig_stdout = sys.__stdout__
- orig_stderr = sys.__stderr__
- sys.__stdout__ = stream
- sys.__stderr__ = stream
- yield
- sys.__stdout__ = orig_stdout
- sys.__stderr__ = orig_stderr
-
-@contextmanager
-def pycharm():
- os.environ["PYCHARM_HOSTED"] = "1"
- non_tty = StreamNonTTY()
- with replace_by(non_tty), replace_original_by(non_tty):
- yield
- del os.environ["PYCHARM_HOSTED"]
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py
deleted file mode 100644
index b0d1ef37cbccbf20c0606fd1132bf58c26d91da0..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import os
-import subprocess
-import contextlib
-import functools
-import tempfile
-import shutil
-import operator
-import warnings
-
-
-@contextlib.contextmanager
-def pushd(dir):
- """
- >>> tmp_path = getfixture('tmp_path')
- >>> with pushd(tmp_path):
- ... assert os.getcwd() == os.fspath(tmp_path)
- >>> assert os.getcwd() != os.fspath(tmp_path)
- """
-
- orig = os.getcwd()
- os.chdir(dir)
- try:
- yield dir
- finally:
- os.chdir(orig)
-
-
-@contextlib.contextmanager
-def tarball_context(url, target_dir=None, runner=None, pushd=pushd):
- """
- Get a tarball, extract it, change to that directory, yield, then
- clean up.
- `runner` is the function to invoke commands.
- `pushd` is a context manager for changing the directory.
- """
- if target_dir is None:
- target_dir = os.path.basename(url).replace('.tar.gz', '').replace('.tgz', '')
- if runner is None:
- runner = functools.partial(subprocess.check_call, shell=True)
- else:
- warnings.warn("runner parameter is deprecated", DeprecationWarning)
- # In the tar command, use --strip-components=1 to strip the first path and
- # then
- # use -C to cause the files to be extracted to {target_dir}. This ensures
- # that we always know where the files were extracted.
- runner('mkdir {target_dir}'.format(**vars()))
- try:
- getter = 'wget {url} -O -'
- extract = 'tar x{compression} --strip-components=1 -C {target_dir}'
- cmd = ' | '.join((getter, extract))
- runner(cmd.format(compression=infer_compression(url), **vars()))
- with pushd(target_dir):
- yield target_dir
- finally:
- runner('rm -Rf {target_dir}'.format(**vars()))
-
-
-def infer_compression(url):
- """
- Given a URL or filename, infer the compression code for tar.
-
- >>> infer_compression('http://foo/bar.tar.gz')
- 'z'
- >>> infer_compression('http://foo/bar.tgz')
- 'z'
- >>> infer_compression('file.bz')
- 'j'
- >>> infer_compression('file.xz')
- 'J'
- """
- # cheat and just assume it's the last two characters
- compression_indicator = url[-2:]
- mapping = dict(gz='z', bz='j', xz='J')
- # Assume 'z' (gzip) if no match
- return mapping.get(compression_indicator, 'z')
-
-
-@contextlib.contextmanager
-def temp_dir(remover=shutil.rmtree):
- """
- Create a temporary directory context. Pass a custom remover
- to override the removal behavior.
-
- >>> import pathlib
- >>> with temp_dir() as the_dir:
- ... assert os.path.isdir(the_dir)
- ... _ = pathlib.Path(the_dir).joinpath('somefile').write_text('contents')
- >>> assert not os.path.exists(the_dir)
- """
- temp_dir = tempfile.mkdtemp()
- try:
- yield temp_dir
- finally:
- remover(temp_dir)
-
-
-@contextlib.contextmanager
-def repo_context(url, branch=None, quiet=True, dest_ctx=temp_dir):
- """
- Check out the repo indicated by url.
-
- If dest_ctx is supplied, it should be a context manager
- to yield the target directory for the check out.
- """
- exe = 'git' if 'git' in url else 'hg'
- with dest_ctx() as repo_dir:
- cmd = [exe, 'clone', url, repo_dir]
- if branch:
- cmd.extend(['--branch', branch])
- devnull = open(os.path.devnull, 'w')
- stdout = devnull if quiet else None
- subprocess.check_call(cmd, stdout=stdout)
- yield repo_dir
-
-
-@contextlib.contextmanager
-def null():
- """
- A null context suitable to stand in for a meaningful context.
-
- >>> with null() as value:
- ... assert value is None
- """
- yield
-
-
-class ExceptionTrap:
- """
- A context manager that will catch certain exceptions and provide an
- indication they occurred.
-
- >>> with ExceptionTrap() as trap:
- ... raise Exception()
- >>> bool(trap)
- True
-
- >>> with ExceptionTrap() as trap:
- ... pass
- >>> bool(trap)
- False
-
- >>> with ExceptionTrap(ValueError) as trap:
- ... raise ValueError("1 + 1 is not 3")
- >>> bool(trap)
- True
- >>> trap.value
- ValueError('1 + 1 is not 3')
- >>> trap.tb
-
-
- >>> with ExceptionTrap(ValueError) as trap:
- ... raise Exception()
- Traceback (most recent call last):
- ...
- Exception
-
- >>> bool(trap)
- False
- """
-
- exc_info = None, None, None
-
- def __init__(self, exceptions=(Exception,)):
- self.exceptions = exceptions
-
- def __enter__(self):
- return self
-
- @property
- def type(self):
- return self.exc_info[0]
-
- @property
- def value(self):
- return self.exc_info[1]
-
- @property
- def tb(self):
- return self.exc_info[2]
-
- def __exit__(self, *exc_info):
- type = exc_info[0]
- matches = type and issubclass(type, self.exceptions)
- if matches:
- self.exc_info = exc_info
- return matches
-
- def __bool__(self):
- return bool(self.type)
-
- def raises(self, func, *, _test=bool):
- """
- Wrap func and replace the result with the truth
- value of the trap (True if an exception occurred).
-
- First, give the decorator an alias to support Python 3.8
- Syntax.
-
- >>> raises = ExceptionTrap(ValueError).raises
-
- Now decorate a function that always fails.
-
- >>> @raises
- ... def fail():
- ... raise ValueError('failed')
- >>> fail()
- True
- """
-
- @functools.wraps(func)
- def wrapper(*args, **kwargs):
- with ExceptionTrap(self.exceptions) as trap:
- func(*args, **kwargs)
- return _test(trap)
-
- return wrapper
-
- def passes(self, func):
- """
- Wrap func and replace the result with the truth
- value of the trap (True if no exception).
-
- First, give the decorator an alias to support Python 3.8
- Syntax.
-
- >>> passes = ExceptionTrap(ValueError).passes
-
- Now decorate a function that always fails.
-
- >>> @passes
- ... def fail():
- ... raise ValueError('failed')
-
- >>> fail()
- False
- """
- return self.raises(func, _test=operator.not_)
-
-
-class suppress(contextlib.suppress, contextlib.ContextDecorator):
- """
- A version of contextlib.suppress with decorator support.
-
- >>> @suppress(KeyError)
- ... def key_error():
- ... {}['']
- >>> key_error()
- """
-
-
-class on_interrupt(contextlib.ContextDecorator):
- """
- Replace a KeyboardInterrupt with SystemExit(1)
-
- >>> def do_interrupt():
- ... raise KeyboardInterrupt()
- >>> on_interrupt('error')(do_interrupt)()
- Traceback (most recent call last):
- ...
- SystemExit: 1
- >>> on_interrupt('error', code=255)(do_interrupt)()
- Traceback (most recent call last):
- ...
- SystemExit: 255
- >>> on_interrupt('suppress')(do_interrupt)()
- >>> with __import__('pytest').raises(KeyboardInterrupt):
- ... on_interrupt('ignore')(do_interrupt)()
- """
-
- def __init__(
- self,
- action='error',
- # py3.7 compat
- # /,
- code=1,
- ):
- self.action = action
- self.code = code
-
- def __enter__(self):
- return self
-
- def __exit__(self, exctype, excinst, exctb):
- if exctype is not KeyboardInterrupt or self.action == 'ignore':
- return
- elif self.action == 'error':
- raise SystemExit(self.code) from excinst
- return self.action == 'suppress'
diff --git a/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-grid.min.css b/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-grid.min.css
deleted file mode 100644
index 16649a6a2d5239159f4b78a44f2fd76931927731..0000000000000000000000000000000000000000
--- a/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-grid.min.css
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * Bootstrap Grid v5.1.3 (https://getbootstrap.com/)
- * Copyright 2011-2021 The Bootstrap Authors
- * Copyright 2011-2021 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */:root{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-body-color-rgb:33,37,41;--bs-body-bg-rgb:255,255,255;--bs-font-sans-serif:system-ui,-apple-system,"Segoe 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--- a/spaces/TushDeMort/yolo/file.py
+++ /dev/null
@@ -1 +0,0 @@
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"""
\ No newline at end of file
diff --git a/spaces/TushDeMort/yolo/yolo.py b/spaces/TushDeMort/yolo/yolo.py
deleted file mode 100644
index f6205c0a3868d1d304e2b503fd9def8a7ac12099..0000000000000000000000000000000000000000
--- a/spaces/TushDeMort/yolo/yolo.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import cv2
-import numpy as np
-import torch
-from PIL import Image, ImageDraw, ImageFont
-from io import BytesIO
-
-from models.experimental import attempt_load
-from utils.general import non_max_suppression
-
-WEIGHTS = "yolov7.pt"
-DEVICE = "cpu"
-IMAGE_SIZE = 640
-
-CLASSES = [
- "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
- "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
- "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
- "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
- "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
- "sandwich", "orange", "broccoli", "car|rot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
- "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
- "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
- "teddy bear", "hair drier", "toothbrush"]
-
-allowed = ["person", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "book"]
-
-model = attempt_load(WEIGHTS, map_location=DEVICE)
-
-
-def predict(img, image_size=640):
-
- im_file = BytesIO(img) # convert image to file-like object
- img = Image.open(im_file)
-
- image = np.asarray(img)
-
- # Resize image to the inference size
- ori_h, ori_w = image.shape[:2]
- image = cv2.resize(image, (image_size, image_size))
-
- # Transform image from numpy to torch format
- image_pt = torch.from_numpy(image).permute(2, 0, 1).to(DEVICE)
- image_pt = image_pt.float() / 255.0
-
- # Infer
- with torch.no_grad():
- pred = model(image_pt[None], augment=False)[0]
-
- # NMS
- pred = non_max_suppression(pred)[0].cpu().numpy()
-
- # Resize boxes to the original image size
- pred[:, [0, 2]] *= ori_w / image_size
- pred[:, [1, 3]] *= ori_h / image_size
-
- return pred
-
-
-# print(predict(file_in_b64))
\ No newline at end of file
diff --git a/spaces/Vardaan08/TeamPredictor2/app.py b/spaces/Vardaan08/TeamPredictor2/app.py
deleted file mode 100644
index db5dcf3d31f99184527b594de5d457f8a065760a..0000000000000000000000000000000000000000
--- a/spaces/Vardaan08/TeamPredictor2/app.py
+++ /dev/null
@@ -1,172 +0,0 @@
-### ----------------------------- ###
-### libraries ###
-### ----------------------------- ###
-
-import gradio as gr
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn import metrics
-
-
-### ------------------------------ ###
-### data transformation ###
-### ------------------------------ ###
-
-# load dataset
-uncleaned_data = pd.read_csv('data.csv')
-
-# remove timestamp from dataset (always first column)
-uncleaned_data = uncleaned_data.iloc[: , 1:]
-data = pd.DataFrame()
-
-# keep track of which columns are categorical and what
-# those columns' value mappings are
-# structure: {colname1: {...}, colname2: {...} }
-cat_value_dicts = {}
-final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
-
-# for each column...
-for (colname, colval) in uncleaned_data.iteritems():
-
- # check if col is already a number; if so, add col directly
- # to new dataframe and skip to next column
- if isinstance(colval.values[0], (np.integer, float)):
- data[colname] = uncleaned_data[colname].copy()
- continue
-
- # structure: {0: "lilac", 1: "blue", ...}
- new_dict = {}
- val = 0 # first index per column
- transformed_col_vals = [] # new numeric datapoints
-
- # if not, for each item in that column...
- for (row, item) in enumerate(colval.values):
-
- # if item is not in this col's dict...
- if item not in new_dict:
- new_dict[item] = val
- val += 1
-
- # then add numerical value to transformed dataframe
- transformed_col_vals.append(new_dict[item])
-
- # reverse dictionary only for final col (0, 1) => (vals)
- if colname == final_colname:
- new_dict = {value : key for (key, value) in new_dict.items()}
-
- cat_value_dicts[colname] = new_dict
- data[colname] = transformed_col_vals
-
-
-### -------------------------------- ###
-### model training ###
-### -------------------------------- ###
-
-# select features and predicton; automatically selects last column as prediction
-cols = len(data.columns)
-num_features = cols - 1
-x = data.iloc[: , :num_features]
-y = data.iloc[: , num_features:]
-
-# split data into training and testing sets
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
-
-# instantiate the model (using default parameters)
-model = LogisticRegression()
-model.fit(x_train, y_train.values.ravel())
-y_pred = model.predict(x_test)
-
-
-### -------------------------------- ###
-### article generation ###
-### -------------------------------- ###
-# borrow file reading function from reader.py
-
-def get_feat():
- feats = [abs(x) for x in model.coef_[0]]
- max_val = max(feats)
- idx = feats.index(max_val)
- return data.columns[idx]
-
-acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%"
-most_imp_feat = get_feat()
-# info = get_article(acc, most_imp_feat)
-
-
-
-### ------------------------------- ###
-### interface creation ###
-### ------------------------------- ###
-
-
-# predictor for generic number of features
-def general_predictor(*args):
- features = []
-
- # transform categorical input
- for colname, arg in zip(data.columns, args):
- if (colname in cat_value_dicts):
- features.append(cat_value_dicts[colname][arg])
- else:
- features.append(arg)
-
- # predict single datapoint
- new_input = [features]
- result = model.predict(new_input)
- return cat_value_dicts[final_colname][result[0]]
-
-# add data labels to replace those lost via star-args
-
-
-block = gr.Blocks()
-
-with open('info.md') as f:
- with block:
- gr.Markdown(f.readline())
- gr.Markdown('Take the quiz to get a personalized recommendation using AI.')
-
- with gr.Row():
- with gr.Box():
- inputls = []
- for colname in data.columns:
- # skip last column
- if colname == final_colname:
- continue
-
- # access categories dict if data is categorical
- # otherwise, just use a number input
- if colname in cat_value_dicts:
- radio_options = list(cat_value_dicts[colname].keys())
- inputls.append(gr.Dropdown(radio_options, type="value", label=colname))
- else:
- # add numerical input
- inputls.append(gr.Number(label=colname))
- gr.Markdown(" ")
-
- submit = gr.Button("Click to see your personalized result!", variant="primary")
- gr.Markdown(" ")
- output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here")
-
- submit.click(fn=general_predictor, inputs=inputls, outputs=output)
- gr.Markdown(" ")
-
- with gr.Row():
- with gr.Box():
- gr.Markdown(f"
Accuracy:
{acc}")
- with gr.Box():
- gr.Markdown(f"
Most important feature:
{most_imp_feat}")
-
- gr.Markdown(" ")
-
- with gr.Box():
- gr.Markdown('''⭐ Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for that dataset. Model accuracy and most important feature can be helpful for understanding how the model works, but should not be considered absolute facts about the real world.''')
-
- with gr.Box():
- with open('info.md') as f:
- f.readline()
- gr.Markdown(f.read())
-
-# show the interface
-block.launch()
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/mlflow.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/mlflow.py
deleted file mode 100644
index b32467490bc9d148ef0249d0e2b5e0ab110e9db8..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/mlflow.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"A `Callback` that saves tracked metrics and notebook file into MLflow server."
-from ..torch_core import *
-from ..callback import *
-from ..basic_train import Learner, LearnerCallback
-#This is an optional dependency in fastai. Must install separately.
-try: import mlflow
-except: print("To use this tracker, please run 'pip install mlflow'")
-
-class MLFlowTracker(LearnerCallback):
- "A `TrackerCallback` that tracks the loss and metrics into MLFlow"
- def __init__(self, learn:Learner, exp_name: str, params: dict, nb_path: str, uri: str = "http://localhost:5000"):
- super().__init__(learn)
- self.learn,self.exp_name,self.params,self.nb_path,self.uri = learn,exp_name,params,nb_path,uri
- self.metrics_names = ['train_loss', 'valid_loss'] + [o.__name__ for o in learn.metrics]
-
- def on_train_begin(self, **kwargs: Any) -> None:
- "Prepare MLflow experiment and log params"
- self.client = mlflow.tracking.MlflowClient(self.uri)
- exp = self.client.get_experiment_by_name(self.exp_name)
- self.exp_id = self.client.create_experiment(self.exp_name) if exp is None else exp.experiment_id
- run = self.client.create_run(experiment_id=self.exp_id)
- self.run = run.info.run_uuid
- for k,v in self.params.items():
- self.client.log_param(run_id=self.run, key=k, value=v)
-
- def on_epoch_end(self, epoch, **kwargs:Any)->None:
- "Send loss and metrics values to MLFlow after each epoch"
- if kwargs['smooth_loss'] is None or kwargs["last_metrics"] is None: return
- metrics = [kwargs['smooth_loss']] + kwargs["last_metrics"]
- for name, val in zip(self.metrics_names, metrics):
- self.client.log_metric(self.run, name, np.float(val), step=epoch)
-
- def on_train_end(self, **kwargs: Any) -> None:
- "Store the notebook and stop run"
- self.client.log_artifact(run_id=self.run, local_path=self.nb_path)
- self.client.set_terminated(run_id=self.run)
diff --git a/spaces/Xeaser/rvc-tes/README.md b/spaces/Xeaser/rvc-tes/README.md
deleted file mode 100644
index f077cd85340c26ebfcb0857816d0f1f511408242..0000000000000000000000000000000000000000
--- a/spaces/Xeaser/rvc-tes/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ardha27/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XuebaoDingZhen/YOLOv50.0.1/data/scripts/get_imagenet.sh b/spaces/XuebaoDingZhen/YOLOv50.0.1/data/scripts/get_imagenet.sh
deleted file mode 100644
index 1df0fc7b66cc2555383a14b0704db7fe848e1af5..0000000000000000000000000000000000000000
--- a/spaces/XuebaoDingZhen/YOLOv50.0.1/data/scripts/get_imagenet.sh
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-# Download ILSVRC2012 ImageNet dataset https://image-net.org
-# Example usage: bash data/scripts/get_imagenet.sh
-# parent
-# ├── yolov5
-# └── datasets
-# └── imagenet ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
-if [ "$#" -gt 0 ]; then
- for opt in "$@"; do
- case "${opt}" in
- --train) train=true ;;
- --val) val=true ;;
- esac
- done
-else
- train=true
- val=true
-fi
-
-# Make dir
-d='../datasets/imagenet' # unzip directory
-mkdir -p $d && cd $d
-
-# Download/unzip train
-if [ "$train" == "true" ]; then
- wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
- mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
- tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
- find . -name "*.tar" | while read NAME; do
- mkdir -p "${NAME%.tar}"
- tar -xf "${NAME}" -C "${NAME%.tar}"
- rm -f "${NAME}"
- done
- cd ..
-fi
-
-# Download/unzip val
-if [ "$val" == "true" ]; then
- wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
- mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
- wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
-fi
-
-# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
-# rm train/n04266014/n04266014_10835.JPEG
-
-# TFRecords (optional)
-# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
diff --git a/spaces/XzJosh/JM-Bert-VITS2/monotonic_align/core.py b/spaces/XzJosh/JM-Bert-VITS2/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/JM-Bert-VITS2/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +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/Yan233th/so-vits-svc-models/modules/losses.py b/spaces/Yan233th/so-vits-svc-models/modules/losses.py
deleted file mode 100644
index cd21799eccde350c3aac0bdd661baf96ed220147..0000000000000000000000000000000000000000
--- a/spaces/Yan233th/so-vits-svc-models/modules/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import modules.commons as commons
-
-
-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):
- rl = rl.float().detach()
- gl = gl.float()
- 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):
- dr = dr.float()
- dg = dg.float()
- 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:
- dg = dg.float()
- l = torch.mean((1-dg)**2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
- #print(logs_p)
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/Yeno/text-to-3D/README.md b/spaces/Yeno/text-to-3D/README.md
deleted file mode 100644
index 243f6cf265f7fba001aa2f2065af966fbc9aca20..0000000000000000000000000000000000000000
--- a/spaces/Yeno/text-to-3D/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Point-e Demo
-emoji: 🐢
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-duplicated_from: AP123/text-to-3D
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/rotated_boxes.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/rotated_boxes.py
deleted file mode 100644
index 03f73b3bb99275931a887ad9b2d8c0ac9f412bf3..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/rotated_boxes.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from __future__ import absolute_import, division, print_function, unicode_literals
-import torch
-
-
-def pairwise_iou_rotated(boxes1, boxes2):
- """
- Return intersection-over-union (Jaccard index) of boxes.
-
- Both sets of boxes are expected to be in
- (x_center, y_center, width, height, angle) format.
-
- Arguments:
- boxes1 (Tensor[N, 5])
- boxes2 (Tensor[M, 5])
-
- Returns:
- iou (Tensor[N, M]): the NxM matrix containing the pairwise
- IoU values for every element in boxes1 and boxes2
- """
- return torch.ops.detectron2.box_iou_rotated(boxes1, boxes2)
diff --git a/spaces/YuAnthony/Audio-Caption/data_handling/test_data_loader.py b/spaces/YuAnthony/Audio-Caption/data_handling/test_data_loader.py
deleted file mode 100644
index 54d91bf0e4648322a7e24c7af239999fc44a6777..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/data_handling/test_data_loader.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from torch.utils.data.dataloader import DataLoader
-from functools import partial
-from .clotho_test_set import ClothoTestset
-from .collate_fn_test import clotho_collate_fn_test
-
-
-def get_test_data_loader(data_dir, batch_size, nb_t_steps_pad, shuffle, drop_last, input_pad_at='start', num_workers=0):
- dataset = ClothoTestset(data_dir)
-
- collate_fn = partial(
- clotho_collate_fn_test,
- nb_t_steps=nb_t_steps_pad,
- input_pad_at=input_pad_at)
-
- return DataLoader(
- dataset=dataset, batch_size=batch_size,
- shuffle=shuffle, num_workers=num_workers,
- drop_last=drop_last, collate_fn=collate_fn)
\ No newline at end of file
diff --git a/spaces/Yukki-Yui/moe-tts/README.md b/spaces/Yukki-Yui/moe-tts/README.md
deleted file mode 100644
index ded8d9b36de6908ee5e1244c98b0bb1cf8e3fa04..0000000000000000000000000000000000000000
--- a/spaces/Yukki-Yui/moe-tts/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Moe TTS
-emoji: 😊🎙️
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.6
-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/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/retinanet.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/retinanet.py
deleted file mode 100644
index 41378e8bc74bf9d5cbc7e3e6630bb1e6657049f9..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/detectors/retinanet.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class RetinaNet(SingleStageDetector):
- """Implementation of `RetinaNet `_"""
-
- def __init__(self,
- backbone,
- neck,
- bbox_head,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg,
- test_cfg, pretrained)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/points_in_boxes.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/points_in_boxes.py
deleted file mode 100644
index 4003173a53052161dbcd687a2fa1d755642fdab8..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/points_in_boxes.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'points_in_boxes_part_forward', 'points_in_boxes_cpu_forward',
- 'points_in_boxes_all_forward'
-])
-
-
-def points_in_boxes_part(points, boxes):
- """Find the box in which each point is (CUDA).
-
- Args:
- points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate
- boxes (torch.Tensor): [B, T, 7],
- num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz] in
- LiDAR/DEPTH coordinate, (x, y, z) is the bottom center
-
- Returns:
- box_idxs_of_pts (torch.Tensor): (B, M), default background = -1
- """
- assert points.shape[0] == boxes.shape[0], \
- 'Points and boxes should have the same batch size, ' \
- f'but got {points.shape[0]} and {boxes.shape[0]}'
- assert boxes.shape[2] == 7, \
- 'boxes dimension should be 7, ' \
- f'but got unexpected shape {boxes.shape[2]}'
- assert points.shape[2] == 3, \
- 'points dimension should be 3, ' \
- f'but got unexpected shape {points.shape[2]}'
- batch_size, num_points, _ = points.shape
-
- box_idxs_of_pts = points.new_zeros((batch_size, num_points),
- dtype=torch.int).fill_(-1)
-
- # If manually put the tensor 'points' or 'boxes' on a device
- # which is not the current device, some temporary variables
- # will be created on the current device in the cuda op,
- # and the output will be incorrect.
- # Therefore, we force the current device to be the same
- # as the device of the tensors if it was not.
- # Please refer to https://github.com/open-mmlab/mmdetection3d/issues/305
- # for the incorrect output before the fix.
- points_device = points.get_device()
- assert points_device == boxes.get_device(), \
- 'Points and boxes should be put on the same device'
- if torch.cuda.current_device() != points_device:
- torch.cuda.set_device(points_device)
-
- ext_module.points_in_boxes_part_forward(boxes.contiguous(),
- points.contiguous(),
- box_idxs_of_pts)
-
- return box_idxs_of_pts
-
-
-def points_in_boxes_cpu(points, boxes):
- """Find all boxes in which each point is (CPU). The CPU version of
- :meth:`points_in_boxes_all`.
-
- Args:
- points (torch.Tensor): [B, M, 3], [x, y, z] in
- LiDAR/DEPTH coordinate
- boxes (torch.Tensor): [B, T, 7],
- num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz],
- (x, y, z) is the bottom center.
-
- Returns:
- box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0.
- """
- assert points.shape[0] == boxes.shape[0], \
- 'Points and boxes should have the same batch size, ' \
- f'but got {points.shape[0]} and {boxes.shape[0]}'
- assert boxes.shape[2] == 7, \
- 'boxes dimension should be 7, ' \
- f'but got unexpected shape {boxes.shape[2]}'
- assert points.shape[2] == 3, \
- 'points dimension should be 3, ' \
- f'but got unexpected shape {points.shape[2]}'
- batch_size, num_points, _ = points.shape
- num_boxes = boxes.shape[1]
-
- point_indices = points.new_zeros((batch_size, num_boxes, num_points),
- dtype=torch.int)
- for b in range(batch_size):
- ext_module.points_in_boxes_cpu_forward(boxes[b].float().contiguous(),
- points[b].float().contiguous(),
- point_indices[b])
- point_indices = point_indices.transpose(1, 2)
-
- return point_indices
-
-
-def points_in_boxes_all(points, boxes):
- """Find all boxes in which each point is (CUDA).
-
- Args:
- points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate
- boxes (torch.Tensor): [B, T, 7],
- num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz],
- (x, y, z) is the bottom center.
-
- Returns:
- box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0.
- """
- assert boxes.shape[0] == points.shape[0], \
- 'Points and boxes should have the same batch size, ' \
- f'but got {boxes.shape[0]} and {boxes.shape[0]}'
- assert boxes.shape[2] == 7, \
- 'boxes dimension should be 7, ' \
- f'but got unexpected shape {boxes.shape[2]}'
- assert points.shape[2] == 3, \
- 'points dimension should be 3, ' \
- f'but got unexpected shape {points.shape[2]}'
- batch_size, num_points, _ = points.shape
- num_boxes = boxes.shape[1]
-
- box_idxs_of_pts = points.new_zeros((batch_size, num_points, num_boxes),
- dtype=torch.int).fill_(0)
-
- # Same reason as line 25-32
- points_device = points.get_device()
- assert points_device == boxes.get_device(), \
- 'Points and boxes should be put on the same device'
- if torch.cuda.current_device() != points_device:
- torch.cuda.set_device(points_device)
-
- ext_module.points_in_boxes_all_forward(boxes.contiguous(),
- points.contiguous(),
- box_idxs_of_pts)
-
- return box_idxs_of_pts
diff --git a/spaces/abrar-lohia/text-2-character-anim/VQTrans/dataset/prepare/download_smpl.sh b/spaces/abrar-lohia/text-2-character-anim/VQTrans/dataset/prepare/download_smpl.sh
deleted file mode 100644
index 411325b509e891d96b859bf28f7b983005ca360a..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/VQTrans/dataset/prepare/download_smpl.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-
-mkdir -p body_models
-cd body_models/
-
-echo -e "The smpl files will be stored in the 'body_models/smpl/' folder\n"
-gdown 1INYlGA76ak_cKGzvpOV2Pe6RkYTlXTW2
-rm -rf smpl
-
-unzip smpl.zip
-echo -e "Cleaning\n"
-rm smpl.zip
-
-echo -e "Downloading done!"
\ No newline at end of file
diff --git a/spaces/adirik/stylemc-demo/encoder4editing/utils/__init__.py b/spaces/adirik/stylemc-demo/encoder4editing/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ahmadprince007/HolyBot/code/templates/head.html b/spaces/ahmadprince007/HolyBot/code/templates/head.html
deleted file mode 100644
index d7e2e26ef9e852a782eb4f6abc7b9dbc9fa1e788..0000000000000000000000000000000000000000
--- a/spaces/ahmadprince007/HolyBot/code/templates/head.html
+++ /dev/null
@@ -1,15 +0,0 @@
-{% block head %}
-
- HolyBot
-
-
-
-
-
-
-
-
-
-
-
-{% endblock %}
diff --git a/spaces/aiditi/nvidia_denoiser/README.md b/spaces/aiditi/nvidia_denoiser/README.md
deleted file mode 100644
index 65eb2bc93a2b4c185e88e7efe1ad5e6a2395e23b..0000000000000000000000000000000000000000
--- a/spaces/aiditi/nvidia_denoiser/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: CleanUNet Denoiser by Nvidia
-emoji: 🔥
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
\ No newline at end of file
diff --git a/spaces/akhaliq/JoJoGAN/e4e/configs/data_configs.py b/spaces/akhaliq/JoJoGAN/e4e/configs/data_configs.py
deleted file mode 100644
index deccb0b1c266ad4b6abaef53d67ec1ed0ddbd462..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/configs/data_configs.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from configs import transforms_config
-from configs.paths_config import dataset_paths
-
-
-DATASETS = {
- 'ffhq_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['ffhq'],
- 'train_target_root': dataset_paths['ffhq'],
- 'test_source_root': dataset_paths['celeba_test'],
- 'test_target_root': dataset_paths['celeba_test'],
- },
- 'cars_encode': {
- 'transforms': transforms_config.CarsEncodeTransforms,
- 'train_source_root': dataset_paths['cars_train'],
- 'train_target_root': dataset_paths['cars_train'],
- 'test_source_root': dataset_paths['cars_test'],
- 'test_target_root': dataset_paths['cars_test'],
- },
- 'horse_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['horse_train'],
- 'train_target_root': dataset_paths['horse_train'],
- 'test_source_root': dataset_paths['horse_test'],
- 'test_target_root': dataset_paths['horse_test'],
- },
- 'church_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['church_train'],
- 'train_target_root': dataset_paths['church_train'],
- 'test_source_root': dataset_paths['church_test'],
- 'test_target_root': dataset_paths['church_test'],
- },
- 'cats_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['cats_train'],
- 'train_target_root': dataset_paths['cats_train'],
- 'test_source_root': dataset_paths['cats_test'],
- 'test_target_root': dataset_paths['cats_test'],
- }
-}
diff --git a/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/data_objects/random_cycler.py b/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/data_objects/random_cycler.py
deleted file mode 100644
index c405db6b27f46d874d8feb37e3f9c1e12c251109..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/data_objects/random_cycler.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import random
-
-class RandomCycler:
- """
- Creates an internal copy of a sequence and allows access to its items in a constrained random
- order. For a source sequence of n items and one or several consecutive queries of a total
- of m items, the following guarantees hold (one implies the other):
- - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
- - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
- """
-
- def __init__(self, source):
- if len(source) == 0:
- raise Exception("Can't create RandomCycler from an empty collection")
- self.all_items = list(source)
- self.next_items = []
-
- def sample(self, count: int):
- shuffle = lambda l: random.sample(l, len(l))
-
- out = []
- while count > 0:
- if count >= len(self.all_items):
- out.extend(shuffle(list(self.all_items)))
- count -= len(self.all_items)
- continue
- n = min(count, len(self.next_items))
- out.extend(self.next_items[:n])
- count -= n
- self.next_items = self.next_items[n:]
- if len(self.next_items) == 0:
- self.next_items = shuffle(list(self.all_items))
- return out
-
- def __next__(self):
- return self.sample(1)[0]
-
diff --git a/spaces/akhaliq/anything-v4.0/README.md b/spaces/akhaliq/anything-v4.0/README.md
deleted file mode 100644
index b537b084f509c262b63ef541a530e805531669e2..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/anything-v4.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Anything V4.0
-emoji: ⚡
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.1b1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/deeplab2/model/layers/positional_encodings_test.py b/spaces/akhaliq/deeplab2/model/layers/positional_encodings_test.py
deleted file mode 100644
index 05d78b55e42a2acab5dccdd49f00664d9aecf4cb..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/layers/positional_encodings_test.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-"""Tests for positional_encodings."""
-
-import tensorflow as tf
-
-from deeplab2.model.layers import positional_encodings
-
-
-class PositionalEncodingsTest(tf.test.TestCase):
-
- def test_compute_relative_distance_matrix_output_shape(self):
- output = positional_encodings._compute_relative_distance_matrix(33, 97)
- self.assertListEqual(output.get_shape().as_list(), [33, 97])
-
- def test_relative_positional_encoding_output_shape(self):
- layer = positional_encodings.RelativePositionalEncoding(
- 33, 97, 32, 8, 'rpe')
- output = layer(None)
- self.assertListEqual(output.get_shape().as_list(), [8, 33, 97, 32])
-
- def test_add_absolute_positional_encoding_1d_output_shape(self):
- layer = positional_encodings.AddAbsolutePositionalEncoding(
- 'ape1d', positional_encoding_type='1d')
- shape = [2, 5, 5, 3]
- output = layer(tf.zeros(shape))
- self.assertEqual(len(layer.get_weights()), 10)
- self.assertListEqual(output.get_shape().as_list(), shape)
-
- def test_add_absolute_positional_encoding_2d_output_shape(self):
- layer = positional_encodings.AddAbsolutePositionalEncoding(
- 'ape2d', positional_encoding_type='2d')
- shape = [2, 5, 5, 3]
- output = layer(tf.zeros(shape))
- self.assertEqual(len(layer.get_weights()), 5)
- self.assertListEqual(output.get_shape().as_list(), shape)
-
- def test_add_absolute_positional_encoding_none_output_shape(self):
- layer = positional_encodings.AddAbsolutePositionalEncoding(
- 'none', positional_encoding_type='none')
- shape = [2, 5, 5, 3]
- output = layer(tf.zeros(shape))
- self.assertEqual(len(layer.get_weights()), 0)
- self.assertListEqual(output.get_shape().as_list(), shape)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/akhaliq/neural-waveshaping-synthesis/neural_waveshaping_synthesis/utils/seed_all.py b/spaces/akhaliq/neural-waveshaping-synthesis/neural_waveshaping_synthesis/utils/seed_all.py
deleted file mode 100644
index 14dbef2d650ef86a2464c8c0e548c5327b52aafe..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/neural-waveshaping-synthesis/neural_waveshaping_synthesis/utils/seed_all.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import numpy as np
-import os
-import random
-import torch
-
-def seed_all(seed):
- np.random.seed(seed)
- os.environ['PYTHONHASHSEED'] = str(seed)
- random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.backends.cudnn.deterministic = True
\ No newline at end of file
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/_distutils_hack/__init__.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/_distutils_hack/__init__.py
deleted file mode 100644
index f707416286b22ddbdcf84f60b6ad38ded604bdfc..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/_distutils_hack/__init__.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import sys
-import os
-import re
-import importlib
-import warnings
-
-
-is_pypy = '__pypy__' in sys.builtin_module_names
-
-
-warnings.filterwarnings('ignore',
- r'.+ distutils\b.+ deprecated',
- DeprecationWarning)
-
-
-def warn_distutils_present():
- if 'distutils' not in sys.modules:
- return
- if is_pypy and sys.version_info < (3, 7):
- # PyPy for 3.6 unconditionally imports distutils, so bypass the warning
- # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
- return
- warnings.warn(
- "Distutils was imported before Setuptools, but importing Setuptools "
- "also replaces the `distutils` module in `sys.modules`. This may lead "
- "to undesirable behaviors or errors. To avoid these issues, avoid "
- "using distutils directly, ensure that setuptools is installed in the "
- "traditional way (e.g. not an editable install), and/or make sure "
- "that setuptools is always imported before distutils.")
-
-
-def clear_distutils():
- if 'distutils' not in sys.modules:
- return
- warnings.warn("Setuptools is replacing distutils.")
- mods = [name for name in sys.modules if re.match(r'distutils\b', name)]
- for name in mods:
- del sys.modules[name]
-
-
-def enabled():
- """
- Allow selection of distutils by environment variable.
- """
- which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'stdlib')
- return which == 'local'
-
-
-def ensure_local_distutils():
- clear_distutils()
-
- # With the DistutilsMetaFinder in place,
- # perform an import to cause distutils to be
- # loaded from setuptools._distutils. Ref #2906.
- add_shim()
- importlib.import_module('distutils')
- remove_shim()
-
- # check that submodules load as expected
- core = importlib.import_module('distutils.core')
- assert '_distutils' in core.__file__, core.__file__
-
-
-def do_override():
- """
- Ensure that the local copy of distutils is preferred over stdlib.
-
- See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
- for more motivation.
- """
- if enabled():
- warn_distutils_present()
- ensure_local_distutils()
-
-
-class DistutilsMetaFinder:
- def find_spec(self, fullname, path, target=None):
- if path is not None:
- return
-
- method_name = 'spec_for_{fullname}'.format(**locals())
- method = getattr(self, method_name, lambda: None)
- return method()
-
- def spec_for_distutils(self):
- import importlib.abc
- import importlib.util
-
- class DistutilsLoader(importlib.abc.Loader):
-
- def create_module(self, spec):
- return importlib.import_module('setuptools._distutils')
-
- def exec_module(self, module):
- pass
-
- return importlib.util.spec_from_loader('distutils', DistutilsLoader())
-
- def spec_for_pip(self):
- """
- Ensure stdlib distutils when running under pip.
- See pypa/pip#8761 for rationale.
- """
- if self.pip_imported_during_build():
- return
- clear_distutils()
- self.spec_for_distutils = lambda: None
-
- @staticmethod
- def pip_imported_during_build():
- """
- Detect if pip is being imported in a build script. Ref #2355.
- """
- import traceback
- return any(
- frame.f_globals['__file__'].endswith('setup.py')
- for frame, line in traceback.walk_stack(None)
- )
-
-
-DISTUTILS_FINDER = DistutilsMetaFinder()
-
-
-def add_shim():
- sys.meta_path.insert(0, DISTUTILS_FINDER)
-
-
-def remove_shim():
- try:
- sys.meta_path.remove(DISTUTILS_FINDER)
- except ValueError:
- pass
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/other.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/other.py
deleted file mode 100644
index 4fdf5e72baf9164f18cd9a0bd7ba43ca7d84e412..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/other.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""
- pygments.formatters.other
- ~~~~~~~~~~~~~~~~~~~~~~~~~
-
- Other formatters: NullFormatter, RawTokenFormatter.
-
- :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.util import get_choice_opt
-from pip._vendor.pygments.token import Token
-from pip._vendor.pygments.console import colorize
-
-__all__ = ['NullFormatter', 'RawTokenFormatter', 'TestcaseFormatter']
-
-
-class NullFormatter(Formatter):
- """
- Output the text unchanged without any formatting.
- """
- name = 'Text only'
- aliases = ['text', 'null']
- filenames = ['*.txt']
-
- def format(self, tokensource, outfile):
- enc = self.encoding
- for ttype, value in tokensource:
- if enc:
- outfile.write(value.encode(enc))
- else:
- outfile.write(value)
-
-
-class RawTokenFormatter(Formatter):
- r"""
- Format tokens as a raw representation for storing token streams.
-
- The format is ``tokentyperepr(tokenstring)\n``. The output can later
- be converted to a token stream with the `RawTokenLexer`, described in the
- :doc:`lexer list `.
-
- Only two options are accepted:
-
- `compress`
- If set to ``'gz'`` or ``'bz2'``, compress the output with the given
- compression algorithm after encoding (default: ``''``).
- `error_color`
- If set to a color name, highlight error tokens using that color. If
- set but with no value, defaults to ``'red'``.
-
- .. versionadded:: 0.11
-
- """
- name = 'Raw tokens'
- aliases = ['raw', 'tokens']
- filenames = ['*.raw']
-
- unicodeoutput = False
-
- def __init__(self, **options):
- Formatter.__init__(self, **options)
- # We ignore self.encoding if it is set, since it gets set for lexer
- # and formatter if given with -Oencoding on the command line.
- # The RawTokenFormatter outputs only ASCII. Override here.
- self.encoding = 'ascii' # let pygments.format() do the right thing
- self.compress = get_choice_opt(options, 'compress',
- ['', 'none', 'gz', 'bz2'], '')
- self.error_color = options.get('error_color', None)
- if self.error_color is True:
- self.error_color = 'red'
- if self.error_color is not None:
- try:
- colorize(self.error_color, '')
- except KeyError:
- raise ValueError("Invalid color %r specified" %
- self.error_color)
-
- def format(self, tokensource, outfile):
- try:
- outfile.write(b'')
- except TypeError:
- raise TypeError('The raw tokens formatter needs a binary '
- 'output file')
- if self.compress == 'gz':
- import gzip
- outfile = gzip.GzipFile('', 'wb', 9, outfile)
-
- write = outfile.write
- flush = outfile.close
- elif self.compress == 'bz2':
- import bz2
- compressor = bz2.BZ2Compressor(9)
-
- def write(text):
- outfile.write(compressor.compress(text))
-
- def flush():
- outfile.write(compressor.flush())
- outfile.flush()
- else:
- write = outfile.write
- flush = outfile.flush
-
- if self.error_color:
- for ttype, value in tokensource:
- line = b"%r\t%r\n" % (ttype, value)
- if ttype is Token.Error:
- write(colorize(self.error_color, line))
- else:
- write(line)
- else:
- for ttype, value in tokensource:
- write(b"%r\t%r\n" % (ttype, value))
- flush()
-
-
-TESTCASE_BEFORE = '''\
- def testNeedsName(lexer):
- fragment = %r
- tokens = [
-'''
-TESTCASE_AFTER = '''\
- ]
- assert list(lexer.get_tokens(fragment)) == tokens
-'''
-
-
-class TestcaseFormatter(Formatter):
- """
- Format tokens as appropriate for a new testcase.
-
- .. versionadded:: 2.0
- """
- name = 'Testcase'
- aliases = ['testcase']
-
- def __init__(self, **options):
- Formatter.__init__(self, **options)
- if self.encoding is not None and self.encoding != 'utf-8':
- raise ValueError("Only None and utf-8 are allowed encodings.")
-
- def format(self, tokensource, outfile):
- indentation = ' ' * 12
- rawbuf = []
- outbuf = []
- for ttype, value in tokensource:
- rawbuf.append(value)
- outbuf.append('%s(%s, %r),\n' % (indentation, ttype, value))
-
- before = TESTCASE_BEFORE % (''.join(rawbuf),)
- during = ''.join(outbuf)
- after = TESTCASE_AFTER
- if self.encoding is None:
- outfile.write(before + during + after)
- else:
- outfile.write(before.encode('utf-8'))
- outfile.write(during.encode('utf-8'))
- outfile.write(after.encode('utf-8'))
- outfile.flush()
diff --git a/spaces/ali-ghamdan/realesrgan-models/realesrgan/archs/srvgg_arch.py b/spaces/ali-ghamdan/realesrgan-models/realesrgan/archs/srvgg_arch.py
deleted file mode 100644
index 39460965c9c5ee9cd6eb41c50d33574cb8ba6e50..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/realesrgan-models/realesrgan/archs/srvgg_arch.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-
-
-@ARCH_REGISTRY.register()
-class SRVGGNetCompact(nn.Module):
- """A compact VGG-style network structure for super-resolution.
-
- It is a compact network structure, which performs upsampling in the last layer and no convolution is
- conducted on the HR feature space.
-
- Args:
- num_in_ch (int): Channel number of inputs. Default: 3.
- num_out_ch (int): Channel number of outputs. Default: 3.
- num_feat (int): Channel number of intermediate features. Default: 64.
- num_conv (int): Number of convolution layers in the body network. Default: 16.
- upscale (int): Upsampling factor. Default: 4.
- act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
- """
-
- def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
- super(SRVGGNetCompact, self).__init__()
- self.num_in_ch = num_in_ch
- self.num_out_ch = num_out_ch
- self.num_feat = num_feat
- self.num_conv = num_conv
- self.upscale = upscale
- self.act_type = act_type
-
- self.body = nn.ModuleList()
- # the first conv
- self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
- # the first activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the body structure
- for _ in range(num_conv):
- self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
- # activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the last conv
- self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
- # upsample
- self.upsampler = nn.PixelShuffle(upscale)
-
- def forward(self, x):
- out = x
- for i in range(0, len(self.body)):
- out = self.body[i](out)
-
- out = self.upsampler(out)
- # add the nearest upsampled image, so that the network learns the residual
- base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
- out += base
- return out
diff --git a/spaces/aliabd/SummerTime/pipeline/__init__.py b/spaces/aliabd/SummerTime/pipeline/__init__.py
deleted file mode 100644
index 103118449b5be6f39cb194bcc16b23fc9eb25b94..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/pipeline/__init__.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from model import SUPPORTED_SUMM_MODELS
-from model.base_model import SummModel
-from model.single_doc import LexRankModel
-
-from dataset.st_dataset import SummDataset
-from dataset.non_huggingface_datasets import ScisummnetDataset
-
-from typing import List, Tuple
-
-
-def get_lxr_train_set(dataset: SummDataset, size: int = 100) -> List[str]:
-
- """
- return some dummy summarization examples, in the format of a list of sources
- """
- subset = []
- for i in range(size):
- subset.append(next(iter(dataset.train_set)))
-
- src = list(
- map(
- lambda x: " ".join(x.source)
- if dataset.is_dialogue_based or dataset.is_multi_document
- else x.source[0]
- if isinstance(dataset, ScisummnetDataset)
- else x.source,
- subset,
- )
- )
-
- return src
-
-
-def assemble_model_pipeline(
- dataset: SummDataset, model_list: List[SummModel] = SUPPORTED_SUMM_MODELS
-) -> List[Tuple[SummModel, str]]:
-
- """
- Return initialized list of all model pipelines that match the summarization task of given dataset.
-
- :param SummDataset `dataset`: Dataset to retrieve model pipelines for.
- :param List[SummModel] `model_list`: List of candidate model classes (uninitialized). Defaults to `model.SUPPORTED_SUMM_MODELS`.
- :returns List of tuples, where each tuple contains an initialized model and the name of that model as `(model, name)`.
- """
-
- dataset = dataset if isinstance(dataset, SummDataset) else dataset()
-
- single_doc_model_list = list(
- filter(
- lambda model_cls: not (
- model_cls.is_dialogue_based
- or model_cls.is_query_based
- or model_cls.is_multi_document
- ),
- model_list,
- )
- )
- single_doc_model_instances = [
- model_cls(get_lxr_train_set(dataset))
- if model_cls == LexRankModel
- else model_cls()
- for model_cls in single_doc_model_list
- ]
-
- multi_doc_model_list = list(
- filter(lambda model_cls: model_cls.is_multi_document, model_list)
- )
-
- query_based_model_list = list(
- filter(lambda model_cls: model_cls.is_query_based, model_list)
- )
-
- dialogue_based_model_list = list(
- filter(lambda model_cls: model_cls.is_dialogue_based, model_list)
- )
- dialogue_based_model_instances = (
- [model_cls() for model_cls in dialogue_based_model_list]
- if dataset.is_dialogue_based
- else []
- )
-
- matching_models = []
- if dataset.is_query_based:
- if dataset.is_dialogue_based:
- for query_model_cls in query_based_model_list:
- for dialogue_model in dialogue_based_model_list:
- full_query_dialogue_model = query_model_cls(
- model_backend=dialogue_model
- )
- matching_models.append(
- (
- full_query_dialogue_model,
- f"{query_model_cls.model_name} ({dialogue_model.model_name})",
- )
- )
- else:
- for query_model_cls in query_based_model_list:
- for single_doc_model in single_doc_model_list:
- full_query_model = (
- query_model_cls(
- model_backend=single_doc_model,
- data=get_lxr_train_set(dataset),
- )
- if single_doc_model == LexRankModel
- else query_model_cls(model_backend=single_doc_model)
- )
- matching_models.append(
- (
- full_query_model,
- f"{query_model_cls.model_name} ({single_doc_model.model_name})",
- )
- )
- return matching_models
-
- if dataset.is_multi_document:
- for multi_doc_model_cls in multi_doc_model_list:
- for single_doc_model in single_doc_model_list:
- full_multi_doc_model = (
- multi_doc_model_cls(
- model_backend=single_doc_model, data=get_lxr_train_set(dataset)
- )
- if single_doc_model == LexRankModel
- else multi_doc_model_cls(model_backend=single_doc_model)
- )
- matching_models.append(
- (
- full_multi_doc_model,
- f"{multi_doc_model_cls.model_name} ({single_doc_model.model_name})",
- )
- )
- return matching_models
-
- if dataset.is_dialogue_based:
- return list(
- map(
- lambda db_model: (db_model, db_model.model_name),
- dialogue_based_model_instances,
- )
- )
-
- return list(
- map(lambda s_model: (s_model, s_model.model_name), single_doc_model_instances)
- )
diff --git a/spaces/aliabid94/AutoGPT/autogpt/commands/file_operations.py b/spaces/aliabid94/AutoGPT/autogpt/commands/file_operations.py
deleted file mode 100644
index ad145ec956dd9dafd39e09c2244d001cf5febd2f..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/commands/file_operations.py
+++ /dev/null
@@ -1,267 +0,0 @@
-"""File operations for AutoGPT"""
-from __future__ import annotations
-
-import os
-import os.path
-from typing import Generator
-
-import requests
-from colorama import Back, Fore
-from requests.adapters import HTTPAdapter, Retry
-
-from autogpt.spinner import Spinner
-from autogpt.utils import readable_file_size
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-LOG_FILE = "file_logger.txt"
-LOG_FILE_PATH = WORKSPACE_PATH / LOG_FILE
-
-
-def check_duplicate_operation(operation: str, filename: str) -> bool:
- """Check if the operation has already been performed on the given file
-
- Args:
- operation (str): The operation to check for
- filename (str): The name of the file to check for
-
- Returns:
- bool: True if the operation has already been performed on the file
- """
- log_content = read_file(LOG_FILE)
- log_entry = f"{operation}: {filename}\n"
- return log_entry in log_content
-
-
-def log_operation(operation: str, filename: str) -> None:
- """Log the file operation to the file_logger.txt
-
- Args:
- operation (str): The operation to log
- filename (str): The name of the file the operation was performed on
- """
- log_entry = f"{operation}: {filename}\n"
-
- # Create the log file if it doesn't exist
- if not os.path.exists(LOG_FILE_PATH):
- with open(LOG_FILE_PATH, "w", encoding="utf-8") as f:
- f.write("File Operation Logger ")
-
- append_to_file(LOG_FILE, log_entry, shouldLog=False)
-
-
-def split_file(
- content: str, max_length: int = 4000, overlap: int = 0
-) -> Generator[str, None, None]:
- """
- Split text into chunks of a specified maximum length with a specified overlap
- between chunks.
-
- :param content: The input text to be split into chunks
- :param max_length: The maximum length of each chunk,
- default is 4000 (about 1k token)
- :param overlap: The number of overlapping characters between chunks,
- default is no overlap
- :return: A generator yielding chunks of text
- """
- start = 0
- content_length = len(content)
-
- while start < content_length:
- end = start + max_length
- if end + overlap < content_length:
- chunk = content[start : end + overlap - 1]
- else:
- chunk = content[start:content_length]
-
- # Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
- if len(chunk) <= overlap:
- break
-
- yield chunk
- start += max_length - overlap
-
-
-def read_file(filename: str) -> str:
- """Read a file and return the contents
-
- Args:
- filename (str): The name of the file to read
-
- Returns:
- str: The contents of the file
- """
- try:
- filepath = path_in_workspace(filename)
- with open(filepath, "r", encoding="utf-8") as f:
- content = f.read()
- return content
- except Exception as e:
- return f"Error: {str(e)}"
-
-
-def ingest_file(
- filename: str, memory, max_length: int = 4000, overlap: int = 200
-) -> None:
- """
- Ingest a file by reading its content, splitting it into chunks with a specified
- maximum length and overlap, and adding the chunks to the memory storage.
-
- :param filename: The name of the file to ingest
- :param memory: An object with an add() method to store the chunks in memory
- :param max_length: The maximum length of each chunk, default is 4000
- :param overlap: The number of overlapping characters between chunks, default is 200
- """
- try:
- print(f"Working with file {filename}")
- content = read_file(filename)
- content_length = len(content)
- print(f"File length: {content_length} characters")
-
- chunks = list(split_file(content, max_length=max_length, overlap=overlap))
-
- num_chunks = len(chunks)
- for i, chunk in enumerate(chunks):
- print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
- memory_to_add = (
- f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
- )
-
- memory.add(memory_to_add)
-
- print(f"Done ingesting {num_chunks} chunks from {filename}.")
- except Exception as e:
- print(f"Error while ingesting file '{filename}': {str(e)}")
-
-
-def write_to_file(filename: str, text: str) -> str:
- """Write text to a file
-
- Args:
- filename (str): The name of the file to write to
- text (str): The text to write to the file
-
- Returns:
- str: A message indicating success or failure
- """
- if check_duplicate_operation("write", filename):
- return "Error: File has already been updated."
- try:
- filepath = path_in_workspace(filename)
- directory = os.path.dirname(filepath)
- if not os.path.exists(directory):
- os.makedirs(directory)
- with open(filepath, "w", encoding="utf-8") as f:
- f.write(text)
- log_operation("write", filename)
- return "File written to successfully."
- except Exception as e:
- return f"Error: {str(e)}"
-
-
-def append_to_file(filename: str, text: str, shouldLog: bool = True) -> str:
- """Append text to a file
-
- Args:
- filename (str): The name of the file to append to
- text (str): The text to append to the file
-
- Returns:
- str: A message indicating success or failure
- """
- try:
- filepath = path_in_workspace(filename)
- with open(filepath, "a") as f:
- f.write(text)
-
- if shouldLog:
- log_operation("append", filename)
-
- return "Text appended successfully."
- except Exception as e:
- return f"Error: {str(e)}"
-
-
-def delete_file(filename: str) -> str:
- """Delete a file
-
- Args:
- filename (str): The name of the file to delete
-
- Returns:
- str: A message indicating success or failure
- """
- if check_duplicate_operation("delete", filename):
- return "Error: File has already been deleted."
- try:
- filepath = path_in_workspace(filename)
- os.remove(filepath)
- log_operation("delete", filename)
- return "File deleted successfully."
- except Exception as e:
- return f"Error: {str(e)}"
-
-
-def search_files(directory: str) -> list[str]:
- """Search for files in a directory
-
- Args:
- directory (str): The directory to search in
-
- Returns:
- list[str]: A list of files found in the directory
- """
- found_files = []
-
- if directory in {"", "/"}:
- search_directory = WORKSPACE_PATH
- else:
- search_directory = path_in_workspace(directory)
-
- for root, _, files in os.walk(search_directory):
- for file in files:
- if file.startswith("."):
- continue
- relative_path = os.path.relpath(os.path.join(root, file), WORKSPACE_PATH)
- found_files.append(relative_path)
-
- return found_files
-
-
-def download_file(url, filename):
- """Downloads a file
- Args:
- url (str): URL of the file to download
- filename (str): Filename to save the file as
- """
- safe_filename = path_in_workspace(filename)
- try:
- message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
- with Spinner(message) as spinner:
- session = requests.Session()
- retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
- adapter = HTTPAdapter(max_retries=retry)
- session.mount("http://", adapter)
- session.mount("https://", adapter)
-
- total_size = 0
- downloaded_size = 0
-
- with session.get(url, allow_redirects=True, stream=True) as r:
- r.raise_for_status()
- total_size = int(r.headers.get("Content-Length", 0))
- downloaded_size = 0
-
- with open(safe_filename, "wb") as f:
- for chunk in r.iter_content(chunk_size=8192):
- f.write(chunk)
- downloaded_size += len(chunk)
-
- # Update the progress message
- progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
- spinner.update_message(f"{message} {progress}")
-
- return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
- except requests.HTTPError as e:
- return f"Got an HTTP Error whilst trying to download file: {e}"
- except Exception as e:
- return "Error: " + str(e)
diff --git a/spaces/anpigon/talktosayno/README.md b/spaces/anpigon/talktosayno/README.md
deleted file mode 100644
index 8b69276ef8c30a1d6b96103cc6fadad96f0b2f26..0000000000000000000000000000000000000000
--- a/spaces/anpigon/talktosayno/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: 세이노 챗봇
-emoji: 💬
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: JUNGU/talktosayno
----
-
-## 설치하기
-```sh
-git clone git@hf.co:spaces/anpigon/talktosayno
-cd talktosayno
-pip install -r requirements.txt
-```
-
-## PDF 학습하기
-1. `docs` 폴더를 생성하고, 학습할 PDF 파일을 `docs` 폴더에 넣습니다.
-2. 그 다음 `python ingest.py` 명령을 실행하여 문서를 학습시킵니다.
-
-## 실행하기
-```py
-python app.pyt
-```
-
-## 허깅페이스
-
-Settings > Variables and secrets 에 `OPENAI_API_KEY`를 등록합니다.
-![](https://i.imgur.com/CDq2rIL.png)
-
-허깅페이스 [Getting Started with Repositories](https://huggingface.co/docs/hub/repositories-getting-started) 문서를 참고합니다.
-
-
-
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/generate.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/generate.py
deleted file mode 100644
index 4203405bdf3a06330a655ebc6b58c5bd9dcccca6..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/generate.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import numpy as np
-import cv2
-from PIL import Image
-from .prompt import split_weighted_subprompts
-from .load_images import load_img, prepare_mask, check_mask_for_errors
-from .webui_sd_pipeline import get_webui_sd_pipeline
-from .animation import sample_from_cv2, sample_to_cv2
-from .rich import console
-#Webui
-import cv2
-from .animation import sample_from_cv2, sample_to_cv2
-from modules import processing, sd_models
-from modules.shared import opts, sd_model
-from modules.processing import process_images, StableDiffusionProcessingTxt2Img
-from .deforum_controlnet import is_controlnet_enabled, process_txt2img_with_controlnet, process_img2img_with_controlnet
-
-import math, json, itertools
-import requests
-
-def load_mask_latent(mask_input, shape):
- # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object
- # shape (list-like len(4)): shape of the image to match, usually latent_image.shape
-
- if isinstance(mask_input, str): # mask input is probably a file name
- if mask_input.startswith('http://') or mask_input.startswith('https://'):
- mask_image = Image.open(requests.get(mask_input, stream=True).raw).convert('RGBA')
- else:
- mask_image = Image.open(mask_input).convert('RGBA')
- elif isinstance(mask_input, Image.Image):
- mask_image = mask_input
- else:
- raise Exception("mask_input must be a PIL image or a file name")
-
- mask_w_h = (shape[-1], shape[-2])
- mask = mask_image.resize(mask_w_h, resample=Image.LANCZOS)
- mask = mask.convert("L")
- return mask
-
-def isJson(myjson):
- try:
- json.loads(myjson)
- except ValueError as e:
- return False
- return True
-
-# Add pairwise implementation here not to upgrade
-# the whole python to 3.10 just for one function
-def pairwise_repl(iterable):
- a, b = itertools.tee(iterable)
- next(b, None)
- return zip(a, b)
-
-def generate(args, anim_args, loop_args, controlnet_args, root, frame = 0, return_sample=False, sampler_name=None):
- assert args.prompt is not None
-
- # Setup the pipeline
- p = get_webui_sd_pipeline(args, root, frame)
- p.prompt, p.negative_prompt = split_weighted_subprompts(args.prompt, frame)
-
- if not args.use_init and args.strength > 0 and args.strength_0_no_init:
- print("\nNo init image, but strength > 0. Strength has been auto set to 0, since use_init is False.")
- print("If you want to force strength > 0 with no init, please set strength_0_no_init to False.\n")
- args.strength = 0
- processed = None
- mask_image = None
- init_image = None
- image_init0 = None
-
- if loop_args.use_looper:
- # TODO find out why we need to set this in the init tab
- if args.strength == 0:
- raise RuntimeError("Strength needs to be greater than 0 in Init tab and strength_0_no_init should *not* be checked")
- if args.seed_behavior != "schedule":
- raise RuntimeError("seed_behavior needs to be set to schedule in under 'Keyframes' tab --> 'Seed scheduling'")
- if not isJson(loop_args.imagesToKeyframe):
- raise RuntimeError("The images set for use with keyframe-guidance are not in a proper JSON format")
- args.strength = loop_args.imageStrength
- tweeningFrames = loop_args.tweeningFrameSchedule
- blendFactor = .07
- colorCorrectionFactor = loop_args.colorCorrectionFactor
- jsonImages = json.loads(loop_args.imagesToKeyframe)
- framesToImageSwapOn = list(map(int, list(jsonImages.keys())))
- # find which image to show
- frameToChoose = 0
- for swappingFrame in framesToImageSwapOn[1:]:
- frameToChoose += (frame >= int(swappingFrame))
-
- #find which frame to do our swapping on for tweening
- skipFrame = 25
- for fs, fe in pairwise_repl(framesToImageSwapOn):
- if fs <= frame <= fe:
- skipFrame = fe - fs
-
- if frame % skipFrame <= tweeningFrames: # number of tweening frames
- blendFactor = loop_args.blendFactorMax - loop_args.blendFactorSlope*math.cos((frame % tweeningFrames) / (tweeningFrames / 2))
- init_image2, _ = load_img(list(jsonImages.values())[frameToChoose],
- shape=(args.W, args.H),
- use_alpha_as_mask=args.use_alpha_as_mask)
- image_init0 = list(jsonImages.values())[0]
-
- else: # they passed in a single init image
- image_init0 = args.init_image
-
-
- available_samplers = {
- 'euler a':'Euler a',
- 'euler':'Euler',
- 'lms':'LMS',
- 'heun':'Heun',
- 'dpm2':'DPM2',
- 'dpm2 a':'DPM2 a',
- 'dpm++ 2s a':'DPM++ 2S a',
- 'dpm++ 2m':'DPM++ 2M',
- 'dpm++ sde':'DPM++ SDE',
- 'dpm fast':'DPM fast',
- 'dpm adaptive':'DPM adaptive',
- 'lms karras':'LMS Karras' ,
- 'dpm2 karras':'DPM2 Karras',
- 'dpm2 a karras':'DPM2 a Karras',
- 'dpm++ 2s a karras':'DPM++ 2S a Karras',
- 'dpm++ 2m karras':'DPM++ 2M Karras',
- 'dpm++ sde karras':'DPM++ SDE Karras'
- }
- if sampler_name is not None:
- if sampler_name in available_samplers.keys():
- args.sampler = available_samplers[sampler_name]
-
- if args.checkpoint is not None:
- info = sd_models.get_closet_checkpoint_match(args.checkpoint)
- if info is None:
- raise RuntimeError(f"Unknown checkpoint: {args.checkpoint}")
- sd_models.reload_model_weights(info=info)
-
- if args.init_sample is not None:
- # TODO: cleanup init_sample remains later
- img = args.init_sample
- init_image = img
- image_init0 = img
- if loop_args.use_looper and isJson(loop_args.imagesToKeyframe):
- init_image = Image.blend(init_image, init_image2, blendFactor)
- correction_colors = Image.blend(init_image, init_image2, colorCorrectionFactor)
- p.color_corrections = [processing.setup_color_correction(correction_colors)]
-
- # this is the first pass
- elif loop_args.use_looper or (args.use_init and ((args.init_image != None and args.init_image != ''))):
- init_image, mask_image = load_img(image_init0, # initial init image
- shape=(args.W, args.H),
- use_alpha_as_mask=args.use_alpha_as_mask)
-
- else:
-
- if anim_args.animation_mode != 'Interpolation':
- print(f"Not using an init image (doing pure txt2img)")
- p_txt = StableDiffusionProcessingTxt2Img(
- sd_model=sd_model,
- outpath_samples=root.tmp_deforum_run_duplicated_folder,
- outpath_grids=root.tmp_deforum_run_duplicated_folder,
- prompt=p.prompt,
- styles=p.styles,
- negative_prompt=p.negative_prompt,
- seed=p.seed,
- subseed=p.subseed,
- subseed_strength=p.subseed_strength,
- seed_resize_from_h=p.seed_resize_from_h,
- seed_resize_from_w=p.seed_resize_from_w,
- sampler_name=p.sampler_name,
- batch_size=p.batch_size,
- n_iter=p.n_iter,
- steps=p.steps,
- cfg_scale=p.cfg_scale,
- width=p.width,
- height=p.height,
- restore_faces=p.restore_faces,
- tiling=p.tiling,
- enable_hr=None,
- denoising_strength=None,
- )
- # print dynamic table to cli
- print_generate_table(args, anim_args, p_txt)
-
- if is_controlnet_enabled(controlnet_args):
- processed = process_txt2img_with_controlnet(p, args, anim_args, loop_args, controlnet_args, root, frame)
- else:
- processed = processing.process_images(p_txt)
-
- if processed is None:
- # Mask functions
- if args.use_mask:
- mask = args.mask_image
- #assign masking options to pipeline
- if mask is not None:
- p.inpainting_mask_invert = args.invert_mask
- p.inpainting_fill = args.fill
- p.inpaint_full_res= args.full_res_mask
- p.inpaint_full_res_padding = args.full_res_mask_padding
- else:
- mask = None
-
- assert not ( (mask is not None and args.use_mask and args.overlay_mask) and (args.init_sample is None and init_image is None)), "Need an init image when use_mask == True and overlay_mask == True"
-
- p.init_images = [init_image]
- p.image_mask = mask
- p.image_cfg_scale = args.pix2pix_img_cfg_scale
-
- # print dynamic table to cli
- print_generate_table(args, anim_args, p)
-
- if is_controlnet_enabled(controlnet_args):
- processed = process_img2img_with_controlnet(p, args, anim_args, loop_args, controlnet_args, root, frame)
- else:
- processed = processing.process_images(p)
-
- if root.initial_info == None:
- root.initial_seed = processed.seed
- root.initial_info = processed.info
-
- if root.first_frame == None:
- root.first_frame = processed.images[0]
-
- results = processed.images[0]
-
- return results
-
-def print_generate_table(args, anim_args, p):
- from rich.table import Table
- from rich import box
- table = Table(padding=0, box=box.ROUNDED)
- field_names = ["Steps", "CFG"]
- if anim_args.animation_mode != 'Interpolation':
- field_names.append("Denoise")
- field_names += ["Subseed", "Subs. str"] * (anim_args.enable_subseed_scheduling)
- field_names += ["Sampler"] * anim_args.enable_sampler_scheduling
- field_names += ["Checkpoint"] * anim_args.enable_checkpoint_scheduling
- for field_name in field_names:
- table.add_column(field_name, justify="center")
- rows = [str(p.steps), str(p.cfg_scale)]
- if anim_args.animation_mode != 'Interpolation':
- rows.append(str(p.denoising_strength))
- rows += [str(p.subseed), str(p.subseed_strength)] * (anim_args.enable_subseed_scheduling)
- rows += [p.sampler_name] * anim_args.enable_sampler_scheduling
- rows += [str(args.checkpoint)] * anim_args.enable_checkpoint_scheduling
- table.add_row(*rows)
-
- console.print(table)
\ No newline at end of file
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/bin/train_vocoder.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/bin/train_vocoder.py
deleted file mode 100644
index 32ecd7bdc3652b3683be846bdd9518e937aee904..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/bin/train_vocoder.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-from dataclasses import dataclass, field
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import load_config, register_config
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
-from TTS.vocoder.models import setup_model
-
-
-@dataclass
-class TrainVocoderArgs(TrainerArgs):
- config_path: str = field(default=None, metadata={"help": "Path to the config file."})
-
-
-def main():
- """Run `tts` model training directly by a `config.json` file."""
- # init trainer args
- train_args = TrainVocoderArgs()
- parser = train_args.init_argparse(arg_prefix="")
-
- # override trainer args from comman-line args
- args, config_overrides = parser.parse_known_args()
- train_args.parse_args(args)
-
- # load config.json and register
- if args.config_path or args.continue_path:
- if args.config_path:
- # init from a file
- config = load_config(args.config_path)
- if len(config_overrides) > 0:
- config.parse_known_args(config_overrides, relaxed_parser=True)
- elif args.continue_path:
- # continue from a prev experiment
- config = load_config(os.path.join(args.continue_path, "config.json"))
- if len(config_overrides) > 0:
- config.parse_known_args(config_overrides, relaxed_parser=True)
- else:
- # init from console args
- from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel
-
- config_base = BaseTrainingConfig()
- config_base.parse_known_args(config_overrides)
- config = register_config(config_base.model)()
-
- # load training samples
- if "feature_path" in config and config.feature_path:
- # load pre-computed features
- print(f" > Loading features from: {config.feature_path}")
- eval_samples, train_samples = load_wav_feat_data(config.data_path, config.feature_path, config.eval_split_size)
- else:
- # load data raw wav files
- eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
- # setup audio processor
- ap = AudioProcessor(**config.audio)
-
- # init the model from config
- model = setup_model(config)
-
- # init the trainer and 🚀
- trainer = Trainer(
- train_args,
- config,
- config.output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
- parse_command_line_args=False,
- )
- trainer.fit()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/layers/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/layers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/CommonTokenFactory.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/CommonTokenFactory.py
deleted file mode 100644
index 17296fab121579768c589162357d3a9d5c34c9d6..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/CommonTokenFactory.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-#
-
-#
-# This default implementation of {@link TokenFactory} creates
-# {@link CommonToken} objects.
-#
-from antlr4.Token import CommonToken
-
-class TokenFactory(object):
-
- pass
-
-class CommonTokenFactory(TokenFactory):
- #
- # The default {@link CommonTokenFactory} instance.
- #
- #
- # This token factory does not explicitly copy token text when constructing
- # tokens.
- #
- DEFAULT = None
-
- def __init__(self, copyText:bool=False):
- # Indicates whether {@link CommonToken#setText} should be called after
- # constructing tokens to explicitly set the text. This is useful for cases
- # where the input stream might not be able to provide arbitrary substrings
- # of text from the input after the lexer creates a token (e.g. the
- # implementation of {@link CharStream#getText} in
- # {@link UnbufferedCharStream} throws an
- # {@link UnsupportedOperationException}). Explicitly setting the token text
- # allows {@link Token#getText} to be called at any time regardless of the
- # input stream implementation.
- #
- #
- # The default value is {@code false} to avoid the performance and memory
- # overhead of copying text for every token unless explicitly requested.
- #
- self.copyText = copyText
-
- def create(self, source, type:int, text:str, channel:int, start:int, stop:int, line:int, column:int):
- t = CommonToken(source, type, channel, start, stop)
- t.line = line
- t.column = column
- if text is not None:
- t.text = text
- elif self.copyText and source[1] is not None:
- t.text = source[1].getText(start,stop)
- return t
-
- def createThin(self, type:int, text:str):
- t = CommonToken(type=type)
- t.text = text
- return t
-
-CommonTokenFactory.DEFAULT = CommonTokenFactory()
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/globals.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/globals.py
deleted file mode 100644
index 480058f10dd6a8205d1bff0b94de7ae347a7629a..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/click/globals.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import typing as t
-from threading import local
-
-if t.TYPE_CHECKING:
- import typing_extensions as te
- from .core import Context
-
-_local = local()
-
-
-@t.overload
-def get_current_context(silent: "te.Literal[False]" = False) -> "Context":
- ...
-
-
-@t.overload
-def get_current_context(silent: bool = ...) -> t.Optional["Context"]:
- ...
-
-
-def get_current_context(silent: bool = False) -> t.Optional["Context"]:
- """Returns the current click context. This can be used as a way to
- access the current context object from anywhere. This is a more implicit
- alternative to the :func:`pass_context` decorator. This function is
- primarily useful for helpers such as :func:`echo` which might be
- interested in changing its behavior based on the current context.
-
- To push the current context, :meth:`Context.scope` can be used.
-
- .. versionadded:: 5.0
-
- :param silent: if set to `True` the return value is `None` if no context
- is available. The default behavior is to raise a
- :exc:`RuntimeError`.
- """
- try:
- return t.cast("Context", _local.stack[-1])
- except (AttributeError, IndexError) as e:
- if not silent:
- raise RuntimeError("There is no active click context.") from e
-
- return None
-
-
-def push_context(ctx: "Context") -> None:
- """Pushes a new context to the current stack."""
- _local.__dict__.setdefault("stack", []).append(ctx)
-
-
-def pop_context() -> None:
- """Removes the top level from the stack."""
- _local.stack.pop()
-
-
-def resolve_color_default(color: t.Optional[bool] = None) -> t.Optional[bool]:
- """Internal helper to get the default value of the color flag. If a
- value is passed it's returned unchanged, otherwise it's looked up from
- the current context.
- """
- if color is not None:
- return color
-
- ctx = get_current_context(silent=True)
-
- if ctx is not None:
- return ctx.color
-
- return None
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/setup.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/setup.py
deleted file mode 100644
index a9a82296ea9c3a53f760c29c34020b5a90091a89..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/setup.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import setuptools
-
-with open("README.md", "r") as fh:
- long_description = fh.read()
-
-setuptools.setup(
- name="mmpt",
- version="0.0.1",
- author="Hu Xu, Po-yao Huang",
- author_email="huxu@fb.com",
- description="A package for multimodal pretraining.",
- long_description=long_description,
- long_description_content_type="text/markdown",
- url="https://github.com/pytorch/fairseq/examples/MMPT",
- packages=setuptools.find_packages(),
- install_requires=[
- ],
- classifiers=[
- "Programming Language :: Python :: 3",
- "License :: CC-BY-NC",
- "Operating System :: OS Independent",
- ],
- python_requires='>=3.6',
-)
diff --git a/spaces/asafAdge/Detic/detic/data/transforms/custom_augmentation_impl.py b/spaces/asafAdge/Detic/detic/data/transforms/custom_augmentation_impl.py
deleted file mode 100644
index 47bef39566ed741287ceb55fb98ec9b03ee30b63..0000000000000000000000000000000000000000
--- a/spaces/asafAdge/Detic/detic/data/transforms/custom_augmentation_impl.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py
-# Modified by Xingyi Zhou
-# The original code is under Apache-2.0 License
-import numpy as np
-import sys
-from fvcore.transforms.transform import (
- BlendTransform,
- CropTransform,
- HFlipTransform,
- NoOpTransform,
- Transform,
- VFlipTransform,
-)
-from PIL import Image
-
-from detectron2.data.transforms.augmentation import Augmentation
-from .custom_transform import EfficientDetResizeCropTransform
-
-__all__ = [
- "EfficientDetResizeCrop",
-]
-
-class EfficientDetResizeCrop(Augmentation):
- """
- Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
- If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
- """
-
- def __init__(
- self, size, scale, interp=Image.BILINEAR
- ):
- """
- """
- super().__init__()
- self.target_size = (size, size)
- self.scale = scale
- self.interp = interp
-
- def get_transform(self, img):
- # Select a random scale factor.
- scale_factor = np.random.uniform(*self.scale)
- scaled_target_height = scale_factor * self.target_size[0]
- scaled_target_width = scale_factor * self.target_size[1]
- # Recompute the accurate scale_factor using rounded scaled image size.
- width, height = img.shape[1], img.shape[0]
- img_scale_y = scaled_target_height / height
- img_scale_x = scaled_target_width / width
- img_scale = min(img_scale_y, img_scale_x)
-
- # Select non-zero random offset (x, y) if scaled image is larger than target size
- scaled_h = int(height * img_scale)
- scaled_w = int(width * img_scale)
- offset_y = scaled_h - self.target_size[0]
- offset_x = scaled_w - self.target_size[1]
- offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1))
- offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1))
- return EfficientDetResizeCropTransform(
- scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp)
diff --git a/spaces/austin/adr-detection/README.md b/spaces/austin/adr-detection/README.md
deleted file mode 100644
index adce31d1946b4545033f43d3cfdfa07e2577dd21..0000000000000000000000000000000000000000
--- a/spaces/austin/adr-detection/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Adr Detection
-emoji: 🌖
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/awacke1/GraphViz-Demo/README.md b/spaces/awacke1/GraphViz-Demo/README.md
deleted file mode 100644
index 603e60975303d5f74ac4a182711e40b7866998b6..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GraphViz-Demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 📊Graph NLP Language Visual🕸️
-emoji: 📊🕸️📊
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/Model-Easy-Button1-ZeroShotImageClassifier-Openai-clip-vit-large-patch14/README.md b/spaces/awacke1/Model-Easy-Button1-ZeroShotImageClassifier-Openai-clip-vit-large-patch14/README.md
deleted file mode 100644
index 84bac91060aed4f26e699fa21ad32fe050e8411b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Model-Easy-Button1-ZeroShotImageClassifier-Openai-clip-vit-large-patch14/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Model Easy Button1 ZeroShotImageClassifier Openai Clip Vit Large Patch14
-emoji: ⚡
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.21.0
-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/awacke1/StreamlitHeatmapAndCluster/README.md b/spaces/awacke1/StreamlitHeatmapAndCluster/README.md
deleted file mode 100644
index 55641335f69f4fe67ba75de8b8aaa17ad85d8cc4..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitHeatmapAndCluster/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: StreamlitHeatmapAndCluster
-emoji: 💻Heat
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.10.0
-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/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/app.py b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/app.py
deleted file mode 100644
index deff8fd93a396269d2700ef5b4fe139dff0acdbb..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import tensorflow as tf
-import cv2
-import numpy as np
-from glob import glob
-from models import Yolov4
-import gradio as gr
-model = Yolov4(weight_path="yolov4.weights", class_name_path='coco_classes.txt')
-def gradio_wrapper(img):
- global model
- #print(np.shape(img))
- results = model.predict(img)
- return results[0]
-demo = gr.Interface(
- gradio_wrapper,
- #gr.Image(source="webcam", streaming=True, flip=True),
- gr.Image(source="webcam", streaming=True),
- "image",
- live=True
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/ayushnoori/program-synthesis/setup_jupyter.sh b/spaces/ayushnoori/program-synthesis/setup_jupyter.sh
deleted file mode 100644
index e9391358e0818f621c3e498be0fe6a004b61423b..0000000000000000000000000000000000000000
--- a/spaces/ayushnoori/program-synthesis/setup_jupyter.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/bash
-conda deactivate
-source synthesis_env/bin/activate
-jupyter notebook --port=54321 --browser='none'
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/effects/AnaglyphEffect.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/effects/AnaglyphEffect.js
deleted file mode 100644
index 4ed842c802b5a954bafe33dae7690cd5c30c20a0..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/effects/AnaglyphEffect.js
+++ /dev/null
@@ -1,165 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- * @author marklundin / http://mark-lundin.com/
- * @author alteredq / http://alteredqualia.com/
- * @author tschw
- */
-
-THREE.AnaglyphEffect = function ( renderer, width, height ) {
-
- // Matrices generated with angler.js https://github.com/tschw/angler.js/
- // (in column-major element order, as accepted by WebGL)
-
- this.colorMatrixLeft = new THREE.Matrix3().fromArray( [
-
- 1.0671679973602295, -0.0016435992438346148, 0.0001777536963345483, // r out
- -0.028107794001698494, -0.00019593400065787137, -0.0002875397040043026, // g out
- -0.04279090091586113, 0.000015809757314855233, -0.00024287120322696865 // b out
-
- ] );
-
- // red green blue in
-
- this.colorMatrixRight = new THREE.Matrix3().fromArray( [
-
- -0.0355340838432312, -0.06440307199954987, 0.018319187685847282, // r out
- -0.10269022732973099, 0.8079727292060852, -0.04835830628871918, // g out
- 0.0001224992738571018, -0.009558862075209618, 0.567823588848114 // b out
-
- ] );
-
- var _camera = new THREE.OrthographicCamera( - 1, 1, 1, - 1, 0, 1 );
-
- var _scene = new THREE.Scene();
-
- var _stereo = new THREE.StereoCamera();
-
- var _params = { minFilter: THREE.LinearFilter, magFilter: THREE.NearestFilter, format: THREE.RGBAFormat };
-
- if ( width === undefined ) width = 512;
- if ( height === undefined ) height = 512;
-
- var _renderTargetL = new THREE.WebGLRenderTarget( width, height, _params );
- var _renderTargetR = new THREE.WebGLRenderTarget( width, height, _params );
-
- var _material = new THREE.ShaderMaterial( {
-
- uniforms: {
-
- "mapLeft": { value: _renderTargetL.texture },
- "mapRight": { value: _renderTargetR.texture },
-
- "colorMatrixLeft": { value: this.colorMatrixLeft },
- "colorMatrixRight": { value: this.colorMatrixRight }
-
- },
-
- vertexShader: [
-
- "varying vec2 vUv;",
-
- "void main() {",
-
- " vUv = vec2( uv.x, uv.y );",
- " gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );",
-
- "}"
-
- ].join( "\n" ),
-
- fragmentShader: [
-
- "uniform sampler2D mapLeft;",
- "uniform sampler2D mapRight;",
- "varying vec2 vUv;",
-
- "uniform mat3 colorMatrixLeft;",
- "uniform mat3 colorMatrixRight;",
-
- // These functions implement sRGB linearization and gamma correction
-
- "float lin( float c ) {",
- " return c <= 0.04045 ? c * 0.0773993808 :",
- " pow( c * 0.9478672986 + 0.0521327014, 2.4 );",
- "}",
-
- "vec4 lin( vec4 c ) {",
- " return vec4( lin( c.r ), lin( c.g ), lin( c.b ), c.a );",
- "}",
-
- "float dev( float c ) {",
- " return c <= 0.0031308 ? c * 12.92",
- " : pow( c, 0.41666 ) * 1.055 - 0.055;",
- "}",
-
-
- "void main() {",
-
- " vec2 uv = vUv;",
-
- " vec4 colorL = lin( texture2D( mapLeft, uv ) );",
- " vec4 colorR = lin( texture2D( mapRight, uv ) );",
-
- " vec3 color = clamp(",
- " colorMatrixLeft * colorL.rgb +",
- " colorMatrixRight * colorR.rgb, 0., 1. );",
-
- " gl_FragColor = vec4(",
- " dev( color.r ), dev( color.g ), dev( color.b ),",
- " max( colorL.a, colorR.a ) );",
-
- "}"
-
- ].join( "\n" )
-
- } );
-
- var _mesh = new THREE.Mesh( new THREE.PlaneBufferGeometry( 2, 2 ), _material );
- _scene.add( _mesh );
-
- this.setSize = function ( width, height ) {
-
- renderer.setSize( width, height );
-
- var pixelRatio = renderer.getPixelRatio();
-
- _renderTargetL.setSize( width * pixelRatio, height * pixelRatio );
- _renderTargetR.setSize( width * pixelRatio, height * pixelRatio );
-
- };
-
- this.render = function ( scene, camera ) {
-
- var currentRenderTarget = renderer.getRenderTarget();
-
- scene.updateMatrixWorld();
-
- if ( camera.parent === null ) camera.updateMatrixWorld();
-
- _stereo.update( camera );
-
- renderer.setRenderTarget( _renderTargetL );
- renderer.clear();
- renderer.render( scene, _stereo.cameraL );
-
- renderer.setRenderTarget( _renderTargetR );
- renderer.clear();
- renderer.render( scene, _stereo.cameraR );
-
- renderer.setRenderTarget( null );
- renderer.render( _scene, _camera );
-
- renderer.setRenderTarget( currentRenderTarget );
-
- };
-
- this.dispose = function () {
-
- if ( _renderTargetL ) _renderTargetL.dispose();
- if ( _renderTargetR ) _renderTargetR.dispose();
- if ( _mesh ) _mesh.geometry.dispose();
- if ( _material ) _material.dispose();
-
- };
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/EllipseCurve.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/EllipseCurve.d.ts
deleted file mode 100644
index fc02a18c2b969e7f805f5d42323243e3a171b24a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/EllipseCurve.d.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-import { Curve } from './../core/Curve';
-import { Vector2 } from '../../math/Vector2';
-
-export class EllipseCurve extends Curve {
- constructor(
- aX: number,
- aY: number,
- xRadius: number,
- yRadius: number,
- aStartAngle: number,
- aEndAngle: number,
- aClockwise: boolean,
- aRotation: number
- );
-
- aX: number;
- aY: number;
- xRadius: number;
- yRadius: number;
- aStartAngle: number;
- aEndAngle: number;
- aClockwise: boolean;
- aRotation: number;
-}
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220326233155.py b/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220326233155.py
deleted file mode 100644
index ee4ef237741a7083b5b9eb309cdf6ac39a1a79f1..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220326233155.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-#os.system("pip install gfpgan")
-
-#os.system("pip freeze")
-#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg')
-
-
-
-
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
-
-import warnings
-warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
- 'If you really want to use it, please modify the corresponding codes.')
-bg_upsampler = None
-
-
-
-# set up GFPGAN restorer
-restorer = GFPGANer(
- model_path='experiments/pretrained_models/GFPGANv1.3.pth',
- upscale=2,
- arch='clean',
- channel_multiplier=2,
- bg_upsampler=bg_upsampler)
-
-
-def inference(img):
- input_img = cv2.imread(img, cv2.IMREAD_COLOR)
- cropped_faces, restored_faces, restored_img = restorer.enhance(
- input_img, has_aligned=False, only_center_face=False, paste_back=True)
-
- return Image.fromarray(restored_faces[0][:,:,::-1])
-
-title = "GFP-GAN"
-description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "
"
-gr.Interface(
- inference,
- [gr.inputs.Image(type="filepath", label="Input")],
- gr.outputs.Image(type="pil", label="Output"),
- title=title,
- description=description,
- article=article,
- examples=[
- ['lincoln.jpg'],
- ['einstein.png'],
- ['edison.jpg'],
- ['Henry.jpg'],
- ['Frida.jpg']
- ]
- ).launch(enable_queue=True,cache_examples=True)
\ No newline at end of file
diff --git a/spaces/bigPear/digitalWDF/src/utils/.ipynb_checkpoints/config-checkpoint.py b/spaces/bigPear/digitalWDF/src/utils/.ipynb_checkpoints/config-checkpoint.py
deleted file mode 100644
index 849e0b57eb8f843e5eef26fc9a126f20211bf75c..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/src/utils/.ipynb_checkpoints/config-checkpoint.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import os
-import json
-from typing import Optional
-from dataclasses import dataclass, field
-
-
-CHATGLM_REPO_NAME = "THUDM/chatglm-6b"
-CHATGLM_LASTEST_HASH = "a8ede826cf1b62bd3c78bdfb3625c7c5d2048fbd"
-
-
-@dataclass
-class DatasetAttr:
-
- load_from: str
- dataset_name: Optional[str] = None
- file_name: Optional[str] = None
- file_sha1: Optional[str] = None
-
- def __post_init__(self):
- self.prompt_column = "instruction"
- self.query_column = "input"
- self.response_column = "output"
- self.history_column = None
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
- """
- model_name_or_path: Optional[str] = field(
- default=CHATGLM_REPO_NAME,
- metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}
- )
- config_name: Optional[str] = field(
- default=None,
- metadata={"help": "Pretrained config name or path if not the same as model_name."}
- )
- tokenizer_name: Optional[str] = field(
- default=None,
- metadata={"help": "Pretrained tokenizer name or path if not the same as model_name."}
- )
- cache_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Where to store the pretrained models downloaded from huggingface.co."}
- )
- use_fast_tokenizer: Optional[bool] = field(
- default=True,
- metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}
- )
- model_revision: Optional[str] = field(
- default=CHATGLM_LASTEST_HASH,
- metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}
- )
- use_auth_token: Optional[bool] = field(
- default=False,
- metadata={"help": "Will use the token generated when running `huggingface-cli login`."}
- )
- quantization_bit: Optional[int] = field(
- default=None,
- metadata={"help": "The number of bits to quantize the model."}
- )
- checkpoint_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Path to the directory containing the model checkpoints as well as the configurations."}
- )
- reward_model: Optional[str] = field(
- default=None,
- metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
- )
-
- def __post_init__(self):
- if self.checkpoint_dir is not None: # support merging lora weights
- self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and evaluation.
- """
- dataset: Optional[str] = field(
- default="alpaca_zh",
- metadata={"help": "The name of provided dataset(s) to use. Use comma to separate multiple datasets."}
- )
- dataset_dir: Optional[str] = field(
- default="data",
- metadata={"help": "The name of the folder containing datasets."}
- )
- split: Optional[str] = field(
- default="train",
- metadata={"help": "Which dataset split to use for training and evaluation."}
- )
- overwrite_cache: Optional[bool] = field(
- default=False,
- metadata={"help": "Overwrite the cached training and evaluation sets."}
- )
- preprocessing_num_workers: Optional[int] = field(
- default=None,
- metadata={"help": "The number of processes to use for the preprocessing."}
- )
- max_source_length: Optional[int] = field(
- default=512,
- metadata={"help": "The maximum total input sequence length after tokenization."}
- )
- max_target_length: Optional[int] = field(
- default=512,
- metadata={"help": "The maximum total output sequence length after tokenization."}
- )
- max_samples: Optional[int] = field(
- default=None,
- metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."}
- )
- num_beams: Optional[int] = field(
- default=None,
- metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"}
- )
- ignore_pad_token_for_loss: Optional[bool] = field(
- default=True,
- metadata={"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."}
- )
- source_prefix: Optional[str] = field(
- default=None,
- metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
- )
-
- def __post_init__(self): # support mixing multiple datasets
- dataset_names = [ds.strip() for ds in self.dataset.split(",")]
- dataset_info = json.load(open(os.path.join(self.dataset_dir, "dataset_info.json"), "r"))
-
- self.dataset_list = []
- for name in dataset_names:
- if name not in dataset_info:
- raise ValueError("Undefined dataset {} in dataset_info.json.".format(name))
-
- if "hf_hub_url" in dataset_info[name]:
- dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
- elif "script_url" in dataset_info[name]:
- dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
- else:
- dataset_attr = DatasetAttr(
- "file",
- file_name=dataset_info[name]["file_name"],
- file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
- )
-
- if "columns" in dataset_info[name]:
- dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
- dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
- dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
- dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
-
- self.dataset_list.append(dataset_attr)
-
-
-@dataclass
-class FinetuningArguments:
- """
- Arguments pertaining to which techniques we are going to fine-tuning with.
- """
- finetuning_type: Optional[str] = field(
- default="lora",
- metadata={"help": "Which fine-tuning method to use."}
- )
- num_layer_trainable: Optional[int] = field(
- default=3,
- metadata={"help": "Number of trainable layers for Freeze fine-tuning."}
- )
- name_module_trainable: Optional[str] = field(
- default="mlp",
- metadata={"help": "Name of trainable modules for Freeze fine-tuning."}
- )
- pre_seq_len: Optional[int] = field(
- default=16,
- metadata={"help": "Number of prefix tokens to use for P-tuning V2."}
- )
- prefix_projection: Optional[bool] = field(
- default=False,
- metadata={"help": "Whether to add a project layer for the prefix in P-tuning V2 or not."}
- )
- lora_rank: Optional[int] = field(
- default=8,
- metadata={"help": "The intrinsic dimension for LoRA fine-tuning."}
- )
- lora_alpha: Optional[float] = field(
- default=32.0,
- metadata={"help": "The scale factor for LoRA fine-tuning. (similar with the learning rate)"}
- )
- lora_dropout: Optional[float] = field(
- default=0.1,
- metadata={"help": "Dropout rate for the LoRA fine-tuning."}
- )
- lora_target: Optional[str] = field(
- default="query_key_value",
- metadata={"help": "Name(s) of target modules to apply LoRA. Use comma to separate multiple modules."}
- )
- resume_lora_training: Optional[bool] = field(
- default=True,
- metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
- )
- plot_loss: Optional[bool] = field(
- default=False,
- metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
- )
-
- def __post_init__(self):
- self.lora_target = [target.strip() for target in self.lora_target.split(",")] # support custom target modules of LoRA
-
- if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
- trainable_layer_ids = [27-k for k in range(self.num_layer_trainable)]
- else: # fine-tuning the first n layers if num_layer_trainable < 0
- trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]
- if self.name_module_trainable == "mlp":
- self.trainable_layers = ["layers.{:d}.mlp".format(idx) for idx in trainable_layer_ids]
- elif self.name_module_trainable == "qkv":
- self.trainable_layers = ["layers.{:d}.attention.query_key_value".format(idx) for idx in trainable_layer_ids]
-
- if self.finetuning_type not in ["none", "freeze", "p_tuning", "lora", "full"]:
- raise NotImplementedError("Invalid fine-tuning method.")
diff --git a/spaces/billusanda007/Shortlisted_Candidate_Email_Sender/app.py b/spaces/billusanda007/Shortlisted_Candidate_Email_Sender/app.py
deleted file mode 100644
index 596b1fa2759b7fdbf82188be417fa366c4647ed0..0000000000000000000000000000000000000000
--- a/spaces/billusanda007/Shortlisted_Candidate_Email_Sender/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-import streamlit as st
-import csv
-import json
-import pandas as pd
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-from langchain.agents import initialize_agent
-from langchain.agents.agent_toolkits import ZapierToolkit
-from langchain.utilities.zapier import ZapierNLAWrapper
-
-
-openai_api_key = os.environ.get('OPENAI_API_KEY')
-zapier_nla_api_key = os.environ.get('ZAPIER_API_KEY')
-
-
-# Set up Langchain components
-llm = ChatOpenAI(openai_api_key=openai_api_key, model_name="gpt-3.5-turbo")
-zapier = ZapierNLAWrapper(zapier_nla_api_key=zapier_nla_api_key)
-toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
-agent = initialize_agent(toolkit.get_tools(), llm, agent="zero-shot-react-description", verbose=True)
-
-# Streamlit UI
-st.title("HR's Shortlisted Candidates Email Sender")
-
-uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
-if uploaded_file is not None:
- df = pd.read_csv(uploaded_file)
- emails = df['Emails']
-
- json_data = []
- with uploaded_file as csv_file:
- csv_reader = csv.DictReader(csv_file)
- for row in csv_reader:
- json_data.append(row)
-
- short = json.dumps(json_data)
-
- message = f""" Here are the emails of the candidates selected: {emails}. NO NEED to CC anyone. Make sure you do not include "[Candidate's Email]" in the "To" section.
-
- Your task is to send personalized congratulatory emails to the selected candidates, informing them about their selection and the next steps in the hiring process.
-
- Please craft individual emails for each candidate, addressing them by their name and including specific details about their selection and the next steps. Your emails should be professional, concise, and well-written, demonstrating enthusiasm for their selection and providing clear instructions on what they need to do next.
-
- Please note that each email should be unique and tailored to the individual candidate. You should avoid using any generic or template language. Instead, personalize each email by mentioning specific qualifications, experiences, or accomplishments that stood out during the selection process. Additionally, feel free to include any relevant information about the company, team, or role that may be of interest to the candidate.
-
- You may consult the following JSON object to gain specific information about each candidate:
-
- {short}
-
- Ensure that the emails are error-free, have a professional tone, and are formatted correctly. Check the names and emails of the candidates to ensure accuracy before sending the emails.
-
- Your goal is to make each candidate feel appreciated, valued, and excited about the next steps in the hiring process.
-
- Make sure you have sent the emails to every eligible candidate selected.
- """
-
- st.text_area("Generated Message", message, height=300)
-
- if st.button("Send Emails"):
- agent.run(message)
- st.success("Emails sent successfully!")
- st.markdown("Click [here](https://huggingface.co/spaces/smallboy713102/Q-Maker) to visit the Q-Maker page.")
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Badges Of Fury (2013).rar __LINK__.md b/spaces/bioriAsaeru/text-to-voice/Badges Of Fury (2013).rar __LINK__.md
deleted file mode 100644
index 4fdcf0fb0b9026c6a092e8adb8ef59a8e6798b77..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Badges Of Fury (2013).rar __LINK__.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Badges of Fury is a 2013 Chinese-Hong Kong action comedy film directed by Wong Tsz-ming in his directorial debut. The film stars Jet Li and Wen Zhang in their ... 4d29de3e1b
-
-
-
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/models/loaders.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/models/loaders.py
deleted file mode 100644
index 9c7808a0588bd1a8084157b072bae42aa7efaf84..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/models/loaders.py
+++ /dev/null
@@ -1,141 +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 functions to load from the checkpoints.
-Each checkpoint is a torch.saved dict with the following keys:
-- 'xp.cfg': the hydra config as dumped during training. This should be used
- to rebuild the object using the audiocraft.models.builders functions,
-- 'model_best_state': a readily loadable best state for the model, including
- the conditioner. The model obtained from `xp.cfg` should be compatible
- with this state dict. In the case of a LM, the encodec model would not be
- bundled along but instead provided separately.
-
-Those functions also support loading from a remote location with the Torch Hub API.
-They also support overriding some parameters, in particular the device and dtype
-of the returned model.
-"""
-
-from pathlib import Path
-from huggingface_hub import hf_hub_download
-import typing as tp
-import os
-
-from omegaconf import OmegaConf, DictConfig
-import torch
-
-from . import builders
-from .encodec import CompressionModel
-
-
-def get_audiocraft_cache_dir() -> tp.Optional[str]:
- return os.environ.get('AUDIOCRAFT_CACHE_DIR', None)
-
-
-def _get_state_dict(
- file_or_url_or_id: tp.Union[Path, str],
- filename: tp.Optional[str] = None,
- device='cpu',
- cache_dir: tp.Optional[str] = None,
-):
- if cache_dir is None:
- cache_dir = get_audiocraft_cache_dir()
- # Return the state dict either from a file or url
- file_or_url_or_id = str(file_or_url_or_id)
- assert isinstance(file_or_url_or_id, str)
-
- if os.path.isfile(file_or_url_or_id):
- return torch.load(file_or_url_or_id, map_location=device)
-
- if os.path.isdir(file_or_url_or_id):
- file = f"{file_or_url_or_id}/{filename}"
- return torch.load(file, map_location=device)
-
- elif file_or_url_or_id.startswith('https://'):
- return torch.hub.load_state_dict_from_url(file_or_url_or_id, map_location=device, check_hash=True)
-
- else:
- assert filename is not None, "filename needs to be defined if using HF checkpoints"
-
- file = hf_hub_download(repo_id=file_or_url_or_id, filename=filename, cache_dir=cache_dir)
- return torch.load(file, map_location=device)
-
-
-def load_compression_model_ckpt(file_or_url_or_id: tp.Union[Path, str], cache_dir: tp.Optional[str] = None):
- return _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir)
-
-
-def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
- pkg = load_compression_model_ckpt(file_or_url_or_id, cache_dir=cache_dir)
- if 'pretrained' in pkg:
- return CompressionModel.get_pretrained(pkg['pretrained'], device=device)
- cfg = OmegaConf.create(pkg['xp.cfg'])
- cfg.device = str(device)
- model = builders.get_compression_model(cfg)
- model.load_state_dict(pkg['best_state'])
- model.eval()
- return model
-
-
-def load_lm_model_ckpt(file_or_url_or_id: tp.Union[Path, str], cache_dir: tp.Optional[str] = None):
- return _get_state_dict(file_or_url_or_id, filename="state_dict.bin", cache_dir=cache_dir)
-
-
-def _delete_param(cfg: DictConfig, full_name: str):
- parts = full_name.split('.')
- for part in parts[:-1]:
- if part in cfg:
- cfg = cfg[part]
- else:
- return
- OmegaConf.set_struct(cfg, False)
- if parts[-1] in cfg:
- del cfg[parts[-1]]
- OmegaConf.set_struct(cfg, True)
-
-
-def load_lm_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
- pkg = load_lm_model_ckpt(file_or_url_or_id, cache_dir=cache_dir)
- cfg = OmegaConf.create(pkg['xp.cfg'])
- cfg.device = str(device)
- if cfg.device == 'cpu':
- cfg.dtype = 'float32'
- else:
- cfg.dtype = 'float16'
- _delete_param(cfg, 'conditioners.self_wav.chroma_stem.cache_path')
- _delete_param(cfg, 'conditioners.args.merge_text_conditions_p')
- _delete_param(cfg, 'conditioners.args.drop_desc_p')
- model = builders.get_lm_model(cfg)
- model.load_state_dict(pkg['best_state'])
- model.eval()
- model.cfg = cfg
- return model
-
-
-def load_mbd_ckpt(file_or_url_or_id: tp.Union[Path, str], cache_dir: tp.Optional[str] = None):
- return _get_state_dict(file_or_url_or_id, filename="all_in_one.pt", cache_dir=cache_dir)
-
-
-def load_diffusion_models(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
- pkg = load_mbd_ckpt(file_or_url_or_id, cache_dir=cache_dir)
- models = []
- processors = []
- cfgs = []
- sample_rate = pkg['sample_rate']
- for i in range(pkg['n_bands']):
- cfg = pkg[i]['cfg']
- model = builders.get_diffusion_model(cfg)
- model_dict = pkg[i]['model_state']
- model.load_state_dict(model_dict)
- model.to(device)
- processor = builders.get_processor(cfg=cfg.processor, sample_rate=sample_rate)
- processor_dict = pkg[i]['processor_state']
- processor.load_state_dict(processor_dict)
- processor.to(device)
- models.append(model)
- processors.append(processor)
- cfgs.append(cfg)
- return models, processors, cfgs
diff --git a/spaces/brjathu/HMR2.0/hmr2/utils/__init__.py b/spaces/brjathu/HMR2.0/hmr2/utils/__init__.py
deleted file mode 100644
index 09e47cdf8cdb303432d64902fbe58b256273f88a..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/hmr2/utils/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import torch
-from typing import Any
-
-from .renderer import Renderer
-from .mesh_renderer import MeshRenderer
-from .skeleton_renderer import SkeletonRenderer
-from .pose_utils import eval_pose, Evaluator
-
-def recursive_to(x: Any, target: torch.device):
- """
- Recursively transfer a batch of data to the target device
- Args:
- x (Any): Batch of data.
- target (torch.device): Target device.
- Returns:
- Batch of data where all tensors are transfered to the target device.
- """
- if isinstance(x, dict):
- return {k: recursive_to(v, target) for k, v in x.items()}
- elif isinstance(x, torch.Tensor):
- return x.to(target)
- elif isinstance(x, list):
- return [recursive_to(i, target) for i in x]
- else:
- return x
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/payload_streamer.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/payload_streamer.py
deleted file mode 100644
index 9f8b8bc57cc22fc693da1646bf806c2a6ca8d797..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/payload_streamer.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""
-Payload implemenation for coroutines as data provider.
-
-As a simple case, you can upload data from file::
-
- @aiohttp.streamer
- async def file_sender(writer, file_name=None):
- with open(file_name, 'rb') as f:
- chunk = f.read(2**16)
- while chunk:
- await writer.write(chunk)
-
- chunk = f.read(2**16)
-
-Then you can use `file_sender` like this:
-
- async with session.post('http://httpbin.org/post',
- data=file_sender(file_name='huge_file')) as resp:
- print(await resp.text())
-
-..note:: Coroutine must accept `writer` as first argument
-
-"""
-
-import types
-import warnings
-from typing import Any, Awaitable, Callable, Dict, Tuple
-
-from .abc import AbstractStreamWriter
-from .payload import Payload, payload_type
-
-__all__ = ("streamer",)
-
-
-class _stream_wrapper:
- def __init__(
- self,
- coro: Callable[..., Awaitable[None]],
- args: Tuple[Any, ...],
- kwargs: Dict[str, Any],
- ) -> None:
- self.coro = types.coroutine(coro)
- self.args = args
- self.kwargs = kwargs
-
- async def __call__(self, writer: AbstractStreamWriter) -> None:
- await self.coro(writer, *self.args, **self.kwargs) # type: ignore[operator]
-
-
-class streamer:
- def __init__(self, coro: Callable[..., Awaitable[None]]) -> None:
- warnings.warn(
- "@streamer is deprecated, use async generators instead",
- DeprecationWarning,
- stacklevel=2,
- )
- self.coro = coro
-
- def __call__(self, *args: Any, **kwargs: Any) -> _stream_wrapper:
- return _stream_wrapper(self.coro, args, kwargs)
-
-
-@payload_type(_stream_wrapper)
-class StreamWrapperPayload(Payload):
- async def write(self, writer: AbstractStreamWriter) -> None:
- await self._value(writer)
-
-
-@payload_type(streamer)
-class StreamPayload(StreamWrapperPayload):
- def __init__(self, value: Any, *args: Any, **kwargs: Any) -> None:
- super().__init__(value(), *args, **kwargs)
-
- async def write(self, writer: AbstractStreamWriter) -> None:
- await self._value(writer)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/__init__.py
deleted file mode 100644
index bdd994b49294485c27610772f97f177741f5518f..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .utils.env import setup_environment
-
-setup_environment()
-
-
-# This line will be programatically read/write by setup.py.
-# Leave them at the bottom of this file and don't touch them.
-__version__ = "0.6"
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/datasets/dataset_type.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/datasets/dataset_type.py
deleted file mode 100644
index ed8f8f299af96847d9d16a77920429fe0195c526..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/datasets/dataset_type.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from enum import Enum
-
-
-class DatasetType(Enum):
- """
- Dataset type, mostly used for datasets that contain data to bootstrap models on
- """
-
- VIDEO_LIST = "video_list"
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/evaluation/d2_evaluator_adapter.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/evaluation/d2_evaluator_adapter.py
deleted file mode 100644
index 1fbc526059a191f9414231c1b21ed3e8b7b58580..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/evaluation/d2_evaluator_adapter.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from detectron2.data.catalog import Metadata
-from detectron2.evaluation import COCOEvaluator
-
-from densepose.data.datasets.coco import (
- get_contiguous_id_to_category_id_map,
- maybe_filter_categories_cocoapi,
-)
-
-
-def _maybe_add_iscrowd_annotations(cocoapi) -> None:
- for ann in cocoapi.dataset["annotations"]:
- if "iscrowd" not in ann:
- ann["iscrowd"] = 0
-
-
-class Detectron2COCOEvaluatorAdapter(COCOEvaluator):
- def __init__(
- self,
- dataset_name,
- output_dir=None,
- distributed=True,
- ):
- super().__init__(dataset_name, output_dir=output_dir, distributed=distributed)
- maybe_filter_categories_cocoapi(dataset_name, self._coco_api)
- _maybe_add_iscrowd_annotations(self._coco_api)
- # substitute category metadata to account for categories
- # that are mapped to the same contiguous id
- if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
- self._maybe_substitute_metadata()
-
- def _maybe_substitute_metadata(self):
- cont_id_2_cat_id = get_contiguous_id_to_category_id_map(self._metadata)
- cat_id_2_cont_id = self._metadata.thing_dataset_id_to_contiguous_id
- if len(cont_id_2_cat_id) == len(cat_id_2_cont_id):
- return
-
- cat_id_2_cont_id_injective = {}
- for cat_id, cont_id in cat_id_2_cont_id.items():
- if (cont_id in cont_id_2_cat_id) and (cont_id_2_cat_id[cont_id] == cat_id):
- cat_id_2_cont_id_injective[cat_id] = cont_id
-
- metadata_new = Metadata(name=self._metadata.name)
- for key, value in self._metadata.__dict__.items():
- if key == "thing_dataset_id_to_contiguous_id":
- setattr(metadata_new, key, cat_id_2_cont_id_injective)
- else:
- setattr(metadata_new, key, value)
- self._metadata = metadata_new
diff --git a/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-tpu/Dockerfile b/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-tpu/Dockerfile
deleted file mode 100644
index b61f4add51469b712eebbb0c26d84d6895d6caf2..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/docker/transformers-pytorch-tpu/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-FROM google/cloud-sdk:slim
-
-# Build args.
-ARG GITHUB_REF=refs/heads/main
-
-# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7
-# wheels available; see below.
-ENV PYTHON_VERSION=3.6
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
- build-essential \
- cmake \
- git \
- curl \
- ca-certificates
-
-# Install conda and python.
-# NOTE new Conda does not forward the exit status... https://github.com/conda/conda/issues/8385
-RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh && \
- chmod +x ~/miniconda.sh && \
- ~/miniconda.sh -b && \
- rm ~/miniconda.sh
-
-ENV PATH=/root/miniconda3/bin:$PATH
-
-RUN conda create -y --name container python=$PYTHON_VERSION
-
-# Run the rest of commands within the new conda env.
-# Use absolute path to appease Codefactor.
-SHELL ["/root/miniconda3/bin/conda", "run", "-n", "container", "/bin/bash", "-c"]
-RUN conda install -y python=$PYTHON_VERSION mkl
-
-RUN pip uninstall -y torch && \
- # Python 3.7 wheels are available. Replace cp36-cp36m with cp37-cp37m
- gsutil cp 'gs://tpu-pytorch/wheels/torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
- gsutil cp 'gs://tpu-pytorch/wheels/torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
- gsutil cp 'gs://tpu-pytorch/wheels/torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \
- pip install 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
- pip install 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
- pip install 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
- rm 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
- rm 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
- rm 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \
- apt-get install -y libomp5
-
-ENV LD_LIBRARY_PATH=root/miniconda3/envs/container/lib
-
-
-# Install huggingface/transformers at the current PR, plus dependencies.
-RUN git clone https://github.com/huggingface/transformers.git && \
- cd transformers && \
- git fetch origin $GITHUB_REF:CI && \
- git checkout CI && \
- cd .. && \
- pip install ./transformers && \
- pip install -r ./transformers/examples/pytorch/_test_requirements.txt && \
- pip install pytest
-
-RUN python -c "import torch_xla; print(torch_xla.__version__)"
-RUN python -c "import transformers as trf; print(trf.__version__)"
-RUN conda init bash
-COPY docker-entrypoint.sh /usr/local/bin/
-RUN chmod +x /usr/local/bin/docker-entrypoint.sh
-ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]
-CMD ["bash"]
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py
deleted file mode 100644
index 3c5bdb7b44507c1bf21c75b42c6c87b58e5c1650..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py
+++ /dev/null
@@ -1,636 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# 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.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
-text file or a dataset.
-
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=fill-mask
-"""
-import logging
-import os
-import sys
-import time
-from collections import defaultdict
-from dataclasses import dataclass, field
-
-# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import datasets
-import flax
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-from datasets import load_dataset
-from flax import jax_utils, traverse_util
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from tqdm import tqdm
-
-from transformers import (
- CONFIG_MAPPING,
- FLAX_MODEL_FOR_MASKED_LM_MAPPING,
- AutoConfig,
- AutoTokenizer,
- FlaxAutoModelForMaskedLM,
- HfArgumentParser,
- PreTrainedTokenizerBase,
- TensorType,
- TrainingArguments,
- is_tensorboard_available,
- set_seed,
-)
-
-
-if datasets.__version__ <= "1.8.0":
- raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming")
-
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
- """
-
- model_name_or_path: Optional[str] = field(
- default=None,
- metadata={
- "help": (
- "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
- )
- },
- )
- model_type: Optional[str] = field(
- default=None,
- metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
- )
- config_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
- )
- tokenizer_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
- )
- cache_dir: Optional[str] = field(
- default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
- )
- use_fast_tokenizer: bool = field(
- default=True,
- metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
- )
- dtype: Optional[str] = field(
- default="float32",
- metadata={
- "help": (
- "Floating-point format in which the model weights should be initialized and trained. Choose one of"
- " `[float32, float16, bfloat16]`."
- )
- },
- )
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
- """
-
- dataset_name: Optional[str] = field(
- default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
- )
- dataset_config_name: Optional[str] = field(
- default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
- )
- train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
- validation_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
- )
- train_ref_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
- )
- validation_ref_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
- )
- overwrite_cache: bool = field(
- default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
- )
- validation_split_percentage: Optional[int] = field(
- default=5,
- metadata={
- "help": "The percentage of the train set used as validation set in case there's no validation split"
- },
- )
- max_seq_length: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "The maximum total input sequence length after tokenization. Sequences longer "
- "than this will be truncated. Default to the max input length of the model."
- )
- },
- )
- preprocessing_num_workers: Optional[int] = field(
- default=None,
- metadata={"help": "The number of processes to use for the preprocessing."},
- )
- mlm_probability: float = field(
- default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
- )
- pad_to_max_length: bool = field(
- default=False,
- metadata={
- "help": (
- "Whether to pad all samples to `max_seq_length`. "
- "If False, will pad the samples dynamically when batching to the maximum length in the batch."
- )
- },
- )
- line_by_line: bool = field(
- default=False,
- metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
- )
- text_column_name: str = field(
- default="text", metadata={"help": "The name of the column to retrieve the training text."}
- )
- shuffle_buffer_size: int = field(
- default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
- )
- num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
- num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
-
- def __post_init__(self):
- if self.dataset_name is None and self.train_file is None and self.validation_file is None:
- raise ValueError("Need either a dataset name or a training/validation file.")
- else:
- if self.train_file is not None:
- extension = self.train_file.split(".")[-1]
- assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
- if self.validation_file is not None:
- extension = self.validation_file.split(".")[-1]
- assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorForLanguageModeling:
- """
- Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
- are not all of the same length.
-
- Args:
- tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
- The tokenizer used for encoding the data.
- mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
- The probability with which to (randomly) mask tokens in the input.
-
- .. note::
-
- For best performance, this data collator should be used with a dataset having items that are dictionaries or
- BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
- :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
- argument :obj:`return_special_tokens_mask=True`.
- """
-
- tokenizer: PreTrainedTokenizerBase
- mlm_probability: float = 0.15
-
- def __post_init__(self):
- if self.tokenizer.mask_token is None:
- raise ValueError(
- "This tokenizer does not have a mask token which is necessary for masked language modeling. "
- "You should pass `mlm=False` to train on causal language modeling instead."
- )
-
- def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:
- # Handle dict or lists with proper padding and conversion to tensor.
- batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY)
-
- # If special token mask has been preprocessed, pop it from the dict.
- special_tokens_mask = batch.pop("special_tokens_mask", None)
-
- batch["input_ids"], batch["labels"] = self.mask_tokens(
- batch["input_ids"], special_tokens_mask=special_tokens_mask
- )
- return batch
-
- def mask_tokens(
- self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
- ) -> Tuple[jnp.ndarray, jnp.ndarray]:
- """
- Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
- """
- labels = inputs.copy()
- # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
- probability_matrix = np.full(labels.shape, self.mlm_probability)
- special_tokens_mask = special_tokens_mask.astype("bool")
-
- probability_matrix[special_tokens_mask] = 0.0
- masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
- labels[~masked_indices] = -100 # We only compute loss on masked tokens
-
- # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
- indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
- inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
- # 10% of the time, we replace masked input tokens with random word
- indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
- indices_random &= masked_indices & ~indices_replaced
-
- random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
- inputs[indices_random] = random_words[indices_random]
-
- # The rest of the time (10% of the time) we keep the masked input tokens unchanged
- return inputs, labels
-
-
-def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray:
- num_samples = len(samples_idx)
- samples_to_remove = num_samples % batch_size
-
- if samples_to_remove != 0:
- samples_idx = samples_idx[:-samples_to_remove]
- sections_split = num_samples // batch_size
- batch_idx = np.split(samples_idx, sections_split)
- return batch_idx
-
-
-def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
- """
- The training iterator is advanced so that after groupifying the samples,
- `num_samples` of length `max_seq_length` are returned.
- """
- num_total_tokens = max_seq_length * num_samples
- samples = defaultdict(list)
-
- i = 0
- while i < num_total_tokens:
- tokenized_samples = next(train_iterator)
- i += len(tokenized_samples["input_ids"])
-
- # concatenate tokenized samples to list (excluding "id" and "text")
- samples = {
- k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"]
- }
-
- # Concatenated tokens are split to lists of length `max_seq_length`.
- # Note that remainedr of % max_seq_length are thrown away.
- def group_texts(examples):
- result = {
- k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
- for k, t in examples.items()
- }
- return result
-
- grouped_samples = group_texts(samples)
- return grouped_samples
-
-
-def write_train_metric(summary_writer, train_metrics, train_time, step):
- summary_writer.scalar("train_time", train_time, step)
-
- train_metrics = get_metrics(train_metrics)
- for key, vals in train_metrics.items():
- tag = f"train_{key}"
- for i, val in enumerate(vals):
- summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
- for metric_name, value in eval_metrics.items():
- summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-if __name__ == "__main__":
- # See all possible arguments in src/transformers/training_args.py
- # or by passing the --help flag to this script.
- # We now keep distinct sets of args, for a cleaner separation of concerns.
-
- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
- if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
- else:
- model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
- if (
- os.path.exists(training_args.output_dir)
- and os.listdir(training_args.output_dir)
- and training_args.do_train
- and not training_args.overwrite_output_dir
- ):
- raise ValueError(
- f"Output directory ({training_args.output_dir}) already exists and is not empty."
- "Use --overwrite_output_dir to overcome."
- )
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- level="INFO",
- datefmt="[%X]",
- )
-
- # Log on each process the small summary:
- logger = logging.getLogger(__name__)
- logger.warning(
- f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
- + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
- )
-
- # Set the verbosity to info of the Transformers logger (on main process only):
- logger.info(f"Training/evaluation parameters {training_args}")
-
- # Set seed before initializing model.
- set_seed(training_args.seed)
-
- # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
- # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
- # (the dataset will be downloaded automatically from the datasets Hub).
- #
- # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
- # 'text' is found. You can easily tweak this behavior (see below).
- if data_args.dataset_name is not None:
- # Downloading and loading a dataset from the hub.
- dataset = load_dataset(
- data_args.dataset_name,
- data_args.dataset_config_name,
- cache_dir=model_args.cache_dir,
- streaming=True,
- split="train",
- )
-
- if model_args.config_name:
- config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
- elif model_args.model_name_or_path:
- config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
- else:
- config = CONFIG_MAPPING[model_args.model_type]()
- logger.warning("You are instantiating a new config instance from scratch.")
-
- if model_args.tokenizer_name:
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
- )
- elif model_args.model_name_or_path:
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
- )
- else:
- raise ValueError(
- "You are instantiating a new tokenizer from scratch. This is not supported by this script."
- "You can do it from another script, save it, and load it from here, using --tokenizer_name."
- )
-
- # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
- # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
- # efficient when it receives the `special_tokens_mask`.
- def tokenize_function(examples):
- return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True)
-
- tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys()))
-
- shuffle_seed = training_args.seed
- tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
-
- has_tensorboard = is_tensorboard_available()
- if has_tensorboard and jax.process_index() == 0:
- try:
- from flax.metrics.tensorboard import SummaryWriter
- except ImportError as ie:
- has_tensorboard = False
- logger.warning(
- f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
- )
-
- summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
-
- # Data collator
- # This one will take care of randomly masking the tokens.
- data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
-
- # Initialize our training
- rng = jax.random.PRNGKey(training_args.seed)
- dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
- if model_args.model_name_or_path:
- model = FlaxAutoModelForMaskedLM.from_pretrained(
- model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
- )
- else:
- model = FlaxAutoModelForMaskedLM.from_config(
- config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
- )
-
- # Store some constant
- num_epochs = int(training_args.num_train_epochs)
- train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
- eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
-
- # define number steps per stream epoch
- num_train_steps = data_args.num_train_steps
-
- # Create learning rate schedule
- warmup_fn = optax.linear_schedule(
- init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
- )
- decay_fn = optax.linear_schedule(
- init_value=training_args.learning_rate,
- end_value=0,
- transition_steps=num_train_steps - training_args.warmup_steps,
- )
- linear_decay_lr_schedule_fn = optax.join_schedules(
- schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
- )
-
- # We use Optax's "masking" functionality to not apply weight decay
- # to bias and LayerNorm scale parameters. decay_mask_fn returns a
- # mask boolean with the same structure as the parameters.
- # The mask is True for parameters that should be decayed.
- # Note that this mask is specifically adapted for FlaxBERT-like models.
- # For other models, one should correct the layer norm parameter naming
- # accordingly.
- def decay_mask_fn(params):
- flat_params = traverse_util.flatten_dict(params)
- flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
- return traverse_util.unflatten_dict(flat_mask)
-
- # create adam optimizer
- adamw = optax.adamw(
- learning_rate=linear_decay_lr_schedule_fn,
- b1=training_args.adam_beta1,
- b2=training_args.adam_beta2,
- eps=training_args.adam_epsilon,
- weight_decay=training_args.weight_decay,
- mask=decay_mask_fn,
- )
-
- # Setup train state
- state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw)
-
- # Define gradient update step fn
- def train_step(state, batch, dropout_rng):
- dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
- def loss_fn(params):
- labels = batch.pop("labels")
-
- logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-
- # compute loss, ignore padded input tokens
- label_mask = jnp.where(labels > 0, 1.0, 0.0)
- loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
- # take average
- loss = loss.sum() / label_mask.sum()
-
- return loss
-
- grad_fn = jax.value_and_grad(loss_fn)
- loss, grad = grad_fn(state.params)
- grad = jax.lax.pmean(grad, "batch")
- new_state = state.apply_gradients(grads=grad)
-
- metrics = jax.lax.pmean(
- {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
- )
-
- return new_state, metrics, new_dropout_rng
-
- # Create parallel version of the train step
- p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
- # Define eval fn
- def eval_step(params, batch):
- labels = batch.pop("labels")
-
- logits = model(**batch, params=params, train=False)[0]
-
- # compute loss, ignore padded input tokens
- label_mask = jnp.where(labels > 0, 1.0, 0.0)
- loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
- # compute accuracy
- accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
-
- # summarize metrics
- metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
- metrics = jax.lax.psum(metrics, axis_name="batch")
-
- return metrics
-
- p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
- # Replicate the train state on each device
- state = jax_utils.replicate(state)
-
- train_time = 0
- train_start = time.time()
- train_metrics = []
- eval_metrics = []
-
- training_iter = iter(tokenized_datasets)
-
- max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
- eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
-
- steps = tqdm(range(num_train_steps), desc="Training...", position=0)
- for step in range(num_train_steps):
- # ======================== Training ================================
- try:
- samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
- except StopIteration:
- # Once the end of the dataset stream is reached, the training iterator
- # is reinitialized and reshuffled and a new eval dataset is randomly chosen.
- shuffle_seed += 1
- tokenized_datasets.set_epoch(shuffle_seed)
-
- training_iter = iter(tokenized_datasets)
-
- eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
- samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
-
- # process input samples
- model_inputs = data_collator(samples)
-
- # Model forward
- model_inputs = shard(model_inputs.data)
- state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
-
- train_metrics.append(train_metric)
-
- if step % training_args.logging_steps == 0 and step > 0:
- steps.write(
- f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
- f" {train_metric['learning_rate'].mean()})"
- )
- train_time += time.time() - train_start
- if has_tensorboard and jax.process_index() == 0:
- write_train_metric(summary_writer, train_metrics, train_time, step)
- train_metrics = []
-
- # ======================== Evaluating ==============================
- if step % training_args.eval_steps == 0 and step > 0:
- # Avoid using jax.numpy here in case of TPU training
- eval_samples_idx = np.arange(data_args.num_eval_samples)
- eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
-
- for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)):
- # process input samples
- batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()}
- model_inputs = data_collator(batch_eval_samples)
-
- # Model forward
- model_inputs = shard(model_inputs.data)
- metrics = p_eval_step(state.params, model_inputs)
- eval_metrics.append(metrics)
-
- # normalize eval metrics
- eval_metrics = get_metrics(eval_metrics)
- eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
- eval_normalizer = eval_metrics.pop("normalizer")
- eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
-
- # Update progress bar
- steps.desc = (
- f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:"
- f" {eval_metrics['accuracy']})"
- )
-
- if has_tensorboard and jax.process_index() == 0:
- write_eval_metric(summary_writer, eval_metrics, step)
- eval_metrics = []
-
- # save checkpoint after each epoch and push checkpoint to the hub
- if jax.process_index() == 0:
- params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
- model.save_pretrained(
- training_args.output_dir,
- params=params,
- push_to_hub=training_args.push_to_hub,
- commit_message=f"Saving weights and logs of step {step+1}",
- )
-
- # update tqdm bar
- steps.update(1)
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/commands/env.py b/spaces/chendl/compositional_test/transformers/src/transformers/commands/env.py
deleted file mode 100644
index aa0dccb579cc04551b97472293074cc1b1461db5..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/commands/env.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# Copyright 2020 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 importlib.util
-import platform
-from argparse import ArgumentParser
-
-import huggingface_hub
-
-from .. import __version__ as version
-from ..utils import is_flax_available, is_safetensors_available, is_tf_available, is_torch_available
-from . import BaseTransformersCLICommand
-
-
-def info_command_factory(_):
- return EnvironmentCommand()
-
-
-class EnvironmentCommand(BaseTransformersCLICommand):
- @staticmethod
- def register_subcommand(parser: ArgumentParser):
- download_parser = parser.add_parser("env")
- download_parser.set_defaults(func=info_command_factory)
-
- def run(self):
- safetensors_version = "not installed"
- if is_safetensors_available():
- import safetensors
-
- safetensors_version = safetensors.__version__
- elif importlib.util.find_spec("safetensors") is not None:
- import safetensors
-
- safetensors_version = f"{safetensors.__version__} but is ignored because of PyTorch version too old."
-
- pt_version = "not installed"
- pt_cuda_available = "NA"
- if is_torch_available():
- import torch
-
- pt_version = torch.__version__
- pt_cuda_available = torch.cuda.is_available()
-
- tf_version = "not installed"
- tf_cuda_available = "NA"
- if is_tf_available():
- import tensorflow as tf
-
- tf_version = tf.__version__
- try:
- # deprecated in v2.1
- tf_cuda_available = tf.test.is_gpu_available()
- except AttributeError:
- # returns list of devices, convert to bool
- tf_cuda_available = bool(tf.config.list_physical_devices("GPU"))
-
- flax_version = "not installed"
- jax_version = "not installed"
- jaxlib_version = "not installed"
- jax_backend = "NA"
- if is_flax_available():
- import flax
- import jax
- import jaxlib
-
- flax_version = flax.__version__
- jax_version = jax.__version__
- jaxlib_version = jaxlib.__version__
- jax_backend = jax.lib.xla_bridge.get_backend().platform
-
- info = {
- "`transformers` version": version,
- "Platform": platform.platform(),
- "Python version": platform.python_version(),
- "Huggingface_hub version": huggingface_hub.__version__,
- "Safetensors version": f"{safetensors_version}",
- "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
- "Tensorflow version (GPU?)": f"{tf_version} ({tf_cuda_available})",
- "Flax version (CPU?/GPU?/TPU?)": f"{flax_version} ({jax_backend})",
- "Jax version": f"{jax_version}",
- "JaxLib version": f"{jaxlib_version}",
- "Using GPU in script?": "",
- "Using distributed or parallel set-up in script?": "",
- }
-
- print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
- print(self.format_dict(info))
-
- return info
-
- @staticmethod
- def format_dict(d):
- return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
diff --git a/spaces/chrisjay/afro-speech/app.py b/spaces/chrisjay/afro-speech/app.py
deleted file mode 100644
index f853c13ac20fc8d34a9f75425e6aeb4873bbadd5..0000000000000000000000000000000000000000
--- a/spaces/chrisjay/afro-speech/app.py
+++ /dev/null
@@ -1,397 +0,0 @@
-import pycountry
-import os
-import csv
-import random
-import pandas as pd
-import numpy as np
-import gradio as gr
-from collections import Counter
-from article import ARTICLE
-from utils import *
-import matplotlib.pyplot as plt
-import scipy.io.wavfile as wavf
-from huggingface_hub import Repository, upload_file
-from inference import make_inference
-
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-NUMBER_DIR = './number'
-number_files = [f.name for f in os.scandir(NUMBER_DIR)]
-
-DEFAULT_LIST_OF_COUNTRIES = [country.name for country in pycountry.countries]
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/chrisjay/crowd-speech-africa"
-EMAILS_REPO_URL="https://huggingface.co/datasets/chrisjay/african-digits-recording-sprint-email"
-REPOSITORY_DIR = "data"
-LOCAL_DIR = 'data_local'
-os.makedirs(LOCAL_DIR,exist_ok=True)
-#DEFAULT_LANGS = {'Igbo':'ibo','Yoruba':'yor','Hausa':'hau'}
-
-GENDER = ['Choose Gender','Male','Female','Other','Prefer not to say']
-
-#------------------Work on Languages--------------------
-DEFAULT_LANGS = {}
-languages = read_json_lines('clean_languages.json')
-languages_lower=[l for l in languages]
-
-_ = [DEFAULT_LANGS.update({l['full'].lower():l['id'].lower()}) for l in languages_lower]
-#_ = [DEFAULT_LANGS.update({l_other.lower():[l['id'].lower()]}) for l in languages_lower for l_other in l['others'] if l_other.lower()!=l['full'].lower()]
-
-#------------------Work on Languages--------------------
-
-repo = Repository(
- local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-repo.git_pull()
-
-with open('app.css','r') as f:
- BLOCK_CSS = f.read()
-
-def save_record(language,text,record,number,age,gender,accent,number_history,current_number,country,email,done_recording):
- # set default
- number_history = number_history if number_history is not None else [0]
- current_number = current_number if current_number is not None else 0
- done_recording = done_recording if done_recording is not None else False
- #----
-
- # Save text and its corresponding record to flag
- speaker_metadata={}
- speaker_metadata['gender'] = gender if gender!=GENDER[0] else ''
- speaker_metadata['age'] = age if age !='' else ''
- speaker_metadata['accent'] = accent if accent!='' else ''
- default_record = None
- if not done_recording:
- if language!=None and language!='Choose language' and record is not None and number is not None:
- language = language.lower()
- lang_id = DEFAULT_LANGS[language]
- text =text.strip()
-
- # Write audio to file
- audio_name = get_unique_name()
- SAVE_FILE_DIR = os.path.join(LOCAL_DIR,audio_name)
- os.makedirs(SAVE_FILE_DIR,exist_ok=True)
- audio_output_filename = os.path.join(SAVE_FILE_DIR,'audio.wav')
- wavf.write(audio_output_filename,record[0],record[1])
-
- # Write metadata.json to file
- json_file_path = os.path.join(SAVE_FILE_DIR,'metadata.jsonl')
- metadata= {'id':audio_name,'file_name':'audio.wav',
- 'language_name':language,'language_id':lang_id,
- 'number':current_number, 'text':text,'frequency':record[0],
- 'age': speaker_metadata['age'],'gender': speaker_metadata['gender'],
- 'accent': speaker_metadata['accent'],
- 'country':country
- }
-
- dump_json(metadata,json_file_path)
-
- # Simply upload the audio file and metadata using the hub's upload_file
- # Upload the audio
- repo_audio_path = os.path.join(REPOSITORY_DIR,os.path.join(audio_name,'audio.wav'))
-
- _ = upload_file(path_or_fileobj = audio_output_filename,
- path_in_repo =repo_audio_path,
- repo_id='chrisjay/crowd-speech-africa',
- repo_type='dataset',
- token=HF_TOKEN
- )
-
- # Upload the metadata
- repo_json_path = os.path.join(REPOSITORY_DIR,os.path.join(audio_name,'metadata.jsonl'))
- _ = upload_file(path_or_fileobj = json_file_path,
- path_in_repo =repo_json_path,
- repo_id='chrisjay/crowd-speech-africa',
- repo_type='dataset',
- token=HF_TOKEN
- )
-
- output = f'Recording successfully saved! On to the next one...'
-
- # Choose the next number
- number_history.append(current_number)
- number_choices = [num for num in [i for i in range(10)] if num not in number_history]
- if number_choices!=[]:
- next_number = random.choice(number_choices)
-
- next_number_image = f'number/{next_number}.jpg'
- else:
- email_metadata_name = get_unique_name()
- EMAIL_SAVE_FILE = os.path.join(LOCAL_DIR,f"{email_metadata_name}.json")
- # Write metadata.json to file
- email_metadata = {'id':email_metadata_name,'email':email,
- 'language_name':language,'language_id':lang_id,
- 'age': speaker_metadata['age'],'gender': speaker_metadata['gender'],
- 'accent': speaker_metadata['accent'],
- 'country':country
- }
-
- dump_json(email_metadata,EMAIL_SAVE_FILE)
-
- # Upload the metadata
- repo_json_path = os.path.join('emails',f"{email_metadata_name}.json")
- _ = upload_file(path_or_fileobj = EMAIL_SAVE_FILE,
- path_in_repo =repo_json_path,
- repo_id='chrisjay/african-digits-recording-sprint-email',
- repo_type='dataset',
- token=HF_TOKEN
- )
- # Delete the email from local repo
- if os.path.exists(EMAIL_SAVE_FILE):
- os.remove(EMAIL_SAVE_FILE)
- #-------------------
- done_recording=True
- next_number = 0 # the default number
- next_number_image = f'number/best.gif'
- output = "You have finished all recording! You can reload to start again."
- output_string = "
"+output+"
"
- return output_string,next_number_image,number_history,next_number,done_recording,default_record
-
- if number is None:
- output = "Number must be specified!"
- if record is None:
- output="No recording found!"
- if language is None or language=='Choose language':
- output = 'Language must be specified!'
- output_string = "
"+output+"
"
-
- # return output_string, previous image and state
- return output_string, number,number_history,current_number,done_recording,default_record
- else:
-
- # Stop submitting recording (best.gif is displaying)
- output = '🙌 You have finished all recording! Thank You. You can reload to start again (maybe in another language).'
- output_string = "
"+output+"
"
- next_number = 0 # the default number
- next_number_image = f'number/best.gif'
- return output_string,next_number_image,number_history,next_number,done_recording,default_record
-
-
-def get_metadata_json(path):
- try:
- return read_json_lines(path)[0]
- except Exception:
- return []
-
-
-def plot_bar(value,name,x_name,y_name,title):
- fig, ax = plt.subplots(figsize=(10,4),tight_layout=True)
-
- ax.set(xlabel=x_name, ylabel=y_name,title=title)
-
- ax.barh(name, value)
-
-
- return ax.figure
-
-def get_metadata_of_dataset():
- repo.git_pull()
- REPOSITORY_DATA_DIR = os.path.join(REPOSITORY_DIR,'data')
- repo_recordings = [os.path.join(REPOSITORY_DATA_DIR,f.name) for f in os.scandir(REPOSITORY_DATA_DIR)] if os.path.isdir(REPOSITORY_DATA_DIR) else []
-
- audio_repo = [os.path.join(f,'audio.wav') for f in repo_recordings]
- audio_repo = [a.replace('data/data/','https://huggingface.co/datasets/chrisjay/crowd-speech-africa/resolve/main/data/') for a in audio_repo]
- metadata_all = [get_metadata_json(os.path.join(f,'metadata.jsonl')) for f in repo_recordings]
- metadata_all = [m for m in metadata_all if m!=[]]
- return metadata_all
-
-
-
-def display_records():
- repo.git_pull()
- REPOSITORY_DATA_DIR = os.path.join(REPOSITORY_DIR,'data')
- repo_recordings = [os.path.join(REPOSITORY_DATA_DIR,f.name) for f in os.scandir(REPOSITORY_DATA_DIR)] if os.path.isdir(REPOSITORY_DATA_DIR) else []
-
- audio_repo = [os.path.join(f,'audio.wav') for f in repo_recordings]
- audio_repo = [a.replace('data/data/','https://huggingface.co/datasets/chrisjay/crowd-speech-africa/resolve/main/data/') for a in audio_repo]
- metadata_repo = [read_json_lines(os.path.join(f,'metadata.jsonl'))[0] for f in repo_recordings]
- audios_all = audio_repo
- metadata_all = metadata_repo
-
-
- langs=[m['language_name'] for m in metadata_all]
- audios = [a for a in audios_all]
- texts = [m['text'] for m in metadata_all]
- numbers = [m['number'] for m in metadata_all]
-
-
- html = f"""
-
Hooray! We have collected {len(metadata_all)} samples!
-
-
-
language
-
audio
-
number
-
text
-
"""
- for lang, audio, text,num_ in zip(langs,audios,texts,numbers):
- html+= f"""
-
{lang}
-
-
{num_}
-
{text}
-
"""
- html+="
"
- return html
-
-
-# NUMBERS = [{'image':os.path.join(NUMBER_DIR,f),'number':int(f.split('.')[0])} for f in number_files]
-
-
-
-markdown = """
Africa Crowdsource Speech
-This is a platform to contribute to your African language by recording your voice
"""
-
-
-markdown="""
-# 🌍 African Digits Recording Sprint
-Existing speech recognition systems do not support ANY African languages, excluding African speakers from voice-enabled devices. Our voice is our identity!
-
-The purpose of this project is to show the effectiveness of community-based crowd-sourcing dataset curation in the development of technologies for African languages.
-
-We start with a simple digits dataset for African languages through crowd-sourcing. You can easily teach a model to recognise numbers in your language using this dataset.
-
-"""
-record_markdown = """
-> Record numbers 0-9 in your African language.
-
-1. Fill in your email. This is completely optional. We need this to track your progress for the prize.
-__Note:__ You should record all numbers shown till the end. It does not count if you stop mid-way.
-2. Choose your African language
-3. Fill in the speaker metadata (age, gender, accent). This is optional but important to build better speech models.
-4. You will see the image of a number __(this is the number you will record)__.
-5. Fill in the word of that number (optional). You can leave this blank.
-6. Click record and say the number in your African language.
-7. Click ‘Submit’. It will save your record and go to the next number.
-8. Repeat 4-7
-9. Leave a ❤ in the Space, if you found it fun.
-
-> Please Note: Record as many as times as possible (minimum of 20 and maximum of 200).
-"""
-
-
-PLOTS_FOR_GRADIO = []
-FUNCTIONS_FOR_GRADIO = []
-
-
-# Interface design begins
-block = gr.Blocks(css=BLOCK_CSS)
-with block:
- gr.Markdown(markdown)
- with gr.Tabs():
-
- with gr.TabItem('Record'):
- gr.Markdown(record_markdown)
- email = gr.inputs.Textbox(placeholder='your email',label="Email (Your email is not made public. We need it to consider you for the prize.)",default='')
-
- with gr.Row():
-
- language = gr.inputs.Dropdown(choices = sorted([lang_.title() for lang_ in list(DEFAULT_LANGS.keys())]),label="Choose language",default="Choose language")
- age = gr.inputs.Textbox(placeholder='e.g. 21',label="Your age (optional)",default='')
- gender = gr.inputs.Dropdown(choices=GENDER, type="value", default=None, label="Gender (optional)")
- accent = gr.inputs.Textbox(label="Accent (optional)",default='')
- country = gr.Dropdown(choices=[''] + sorted(DEFAULT_LIST_OF_COUNTRIES),type='value',default=None,label="Country you are recording from (optional)")
-
- number = gr.Image('number/0.jpg',image_mode="L")
- text = gr.inputs.Textbox(placeholder='e.g. `one` is `otu` in Igbo or `ọkan` in Yoruba',label="How is the number called in your language (optional)")
- record = gr.Audio(source="microphone",label='Record your voice')
-
- output_result = gr.outputs.HTML()
- state = gr.Variable()
- current_number = gr.Variable()
- done_recording = gr.Variable() # Signifies when to stop submitting records even if `submit`` is clicked
- save = gr.Button("Submit")
-
- save.click(save_record, inputs=[language,text,record,number,age,gender,accent,state,current_number,country,email,done_recording],outputs=[output_result,number,state,current_number,done_recording,record])
-
- with gr.TabItem('Dataset') as listen_tab:
-
- gr.Markdown("Statistics on the recordings contributed. You can find the dataset here.")
- display_html = gr.HTML("""
-
⌛ Please wait. Loading dashboard...
-
- """)
- plot = gr.Plot(type="matplotlib")
- metadata_all = get_metadata_of_dataset()
-
- def show_records():
- global PLOTS_FOR_GRADIO
- global FUNCTIONS_FOR_GRADIO
-
- assert len(PLOTS_FOR_GRADIO) == len(FUNCTIONS_FOR_GRADIO), f"Function output and gradio plots must be the same length! \n Found: function => {len(FUNCTIONS_FOR_GRADIO)} and gradio plots => {len(PLOTS_FOR_GRADIO)}."
- langs=[m['language_name'] for m in metadata_all]
- all_genders = [m['gender'] for m in metadata_all
- ]
- lang_dict = Counter(langs)
- lang_dict.update({'All others':0})
- all_langs = list(lang_dict.keys())
- langs_count = [lang_dict[k] for k in all_langs]
- plt_ = plot_bar(langs_count,all_langs,'Number of audio samples',"Language",'Distribution of audio samples over languages')
- html = f"""
-
Hooray! We have collected {len(metadata_all)} samples!
- """
- return [html,plt_]+FUNCTIONS_FOR_GRADIO
-
-
-
- languages = list(Counter([m['language_name'] for m in metadata_all]).keys())
- for language in languages:
- with gr.Row() as row_lang:
- metadata_for_language = [m for m in metadata_all if m['language_name']==language]
- gender_for_language = [m['gender'] for m in metadata_for_language]
- digits_for_language = [m['number'] for m in metadata_for_language]
- gender_for_language = [g if g!="" else 'Not given' for g in gender_for_language]
-
- digits_dict = Counter(digits_for_language)
- gender_dict = Counter(gender_for_language)
-
- digits_name_for_language = list(digits_dict.keys())
- digits_count_for_language = [digits_dict[k] for k in digits_name_for_language]
-
- gender_name_for_language = list(gender_dict.keys())
- gender_count_for_language = [gender_dict[k] for k in gender_name_for_language]
-
- plot_digits = gr.Plot(type="matplotlib")
- plot_gender = gr.Plot(type="matplotlib")
-
- PLOTS_FOR_GRADIO.append(plot_digits)
- PLOTS_FOR_GRADIO.append(plot_gender)
-
- def plot_metadata_for_language():
- plt_digits = plot_bar(digits_count_for_language,digits_name_for_language,'Number of audio samples',"Digit",f"Distribution of audio samples over digits for {language.upper()} ")
- plt_gender = plot_bar(gender_count_for_language,gender_name_for_language,'Number of audio samples',"Gender",f"Distribution of audio samples over gender for {language.upper()}")
- return plt_digits, plt_gender
-
- output_digits,ouput_gender = plot_metadata_for_language()
- FUNCTIONS_FOR_GRADIO.append(output_digits)
- FUNCTIONS_FOR_GRADIO.append(ouput_gender)
-
-
- #listen = gr.Button("Listen")
- listen_tab.select(show_records,inputs=[],outputs=[display_html,plot]+PLOTS_FOR_GRADIO)
-
- with gr.TabItem('Model') as listen_tab:
- # Dropdown to choose a language from any of the 6
- # When you choose, it will load the correponding model
- # And then one can record a voice and get the model prediction
-
- #Igbo - ibo
- #Oshiwambo - kua
- #Yoruba - yor
- #Oromo (although note all of these audios are from female) - gax
- #Shona (all male) - sna
- #Rundi (all male) - run
-
- gr.Markdown("""Here we are testing the models which we trained on the dataset collected.
-
- Choose a language from the dropdown, record your voice and select `See model's prediction`.""")
-
- language_choice = gr.Dropdown(["Choose language","Igbo", "Oshiwambo", "Yoruba","Oromo","Shona","Rundi","MULTILINGUAL"],label="Choose language",default="Choose language")
- input_audio = gr.Audio(source="microphone",label='Record your voice',type='filepath')
- output_pred = gr.Label(num_top_classes=5)
- submit = gr.Button("See model's prediction")
- submit.click(make_inference, inputs = [language_choice,input_audio], outputs = [output_pred])
-
- gr.Markdown(ARTICLE)
-
-block.launch()
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/tools/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/tools/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/ec.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/ec.py
deleted file mode 100644
index 9821bd193e2954a916e80140b832b958c8368914..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/ec.py
+++ /dev/null
@@ -1,328 +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 typing
-
-from cryptography.exceptions import (
- InvalidSignature,
- UnsupportedAlgorithm,
- _Reasons,
-)
-from cryptography.hazmat.backends.openssl.utils import (
- _calculate_digest_and_algorithm,
- _evp_pkey_derive,
-)
-from cryptography.hazmat.primitives import serialization
-from cryptography.hazmat.primitives.asymmetric import ec
-
-if typing.TYPE_CHECKING:
- from cryptography.hazmat.backends.openssl.backend import Backend
-
-
-def _check_signature_algorithm(
- signature_algorithm: ec.EllipticCurveSignatureAlgorithm,
-) -> None:
- if not isinstance(signature_algorithm, ec.ECDSA):
- raise UnsupportedAlgorithm(
- "Unsupported elliptic curve signature algorithm.",
- _Reasons.UNSUPPORTED_PUBLIC_KEY_ALGORITHM,
- )
-
-
-def _ec_key_curve_sn(backend: Backend, ec_key) -> str:
- group = backend._lib.EC_KEY_get0_group(ec_key)
- backend.openssl_assert(group != backend._ffi.NULL)
-
- nid = backend._lib.EC_GROUP_get_curve_name(group)
- # The following check is to find EC keys with unnamed curves and raise
- # an error for now.
- if nid == backend._lib.NID_undef:
- raise ValueError(
- "ECDSA keys with explicit parameters are unsupported at this time"
- )
-
- # This is like the above check, but it also catches the case where you
- # explicitly encoded a curve with the same parameters as a named curve.
- # Don't do that.
- if (
- not backend._lib.CRYPTOGRAPHY_IS_LIBRESSL
- and backend._lib.EC_GROUP_get_asn1_flag(group) == 0
- ):
- raise ValueError(
- "ECDSA keys with explicit parameters are unsupported at this time"
- )
-
- curve_name = backend._lib.OBJ_nid2sn(nid)
- backend.openssl_assert(curve_name != backend._ffi.NULL)
-
- sn = backend._ffi.string(curve_name).decode("ascii")
- return sn
-
-
-def _mark_asn1_named_ec_curve(backend: Backend, ec_cdata):
- """
- Set the named curve flag on the EC_KEY. This causes OpenSSL to
- serialize EC keys along with their curve OID which makes
- deserialization easier.
- """
-
- backend._lib.EC_KEY_set_asn1_flag(
- ec_cdata, backend._lib.OPENSSL_EC_NAMED_CURVE
- )
-
-
-def _check_key_infinity(backend: Backend, ec_cdata) -> None:
- point = backend._lib.EC_KEY_get0_public_key(ec_cdata)
- backend.openssl_assert(point != backend._ffi.NULL)
- group = backend._lib.EC_KEY_get0_group(ec_cdata)
- backend.openssl_assert(group != backend._ffi.NULL)
- if backend._lib.EC_POINT_is_at_infinity(group, point):
- raise ValueError(
- "Cannot load an EC public key where the point is at infinity"
- )
-
-
-def _sn_to_elliptic_curve(backend: Backend, sn: str) -> ec.EllipticCurve:
- try:
- return ec._CURVE_TYPES[sn]()
- except KeyError:
- raise UnsupportedAlgorithm(
- f"{sn} is not a supported elliptic curve",
- _Reasons.UNSUPPORTED_ELLIPTIC_CURVE,
- )
-
-
-def _ecdsa_sig_sign(
- backend: Backend, private_key: _EllipticCurvePrivateKey, data: bytes
-) -> bytes:
- max_size = backend._lib.ECDSA_size(private_key._ec_key)
- backend.openssl_assert(max_size > 0)
-
- sigbuf = backend._ffi.new("unsigned char[]", max_size)
- siglen_ptr = backend._ffi.new("unsigned int[]", 1)
- res = backend._lib.ECDSA_sign(
- 0, data, len(data), sigbuf, siglen_ptr, private_key._ec_key
- )
- backend.openssl_assert(res == 1)
- return backend._ffi.buffer(sigbuf)[: siglen_ptr[0]]
-
-
-def _ecdsa_sig_verify(
- backend: Backend,
- public_key: _EllipticCurvePublicKey,
- signature: bytes,
- data: bytes,
-) -> None:
- res = backend._lib.ECDSA_verify(
- 0, data, len(data), signature, len(signature), public_key._ec_key
- )
- if res != 1:
- backend._consume_errors()
- raise InvalidSignature
-
-
-class _EllipticCurvePrivateKey(ec.EllipticCurvePrivateKey):
- def __init__(self, backend: Backend, ec_key_cdata, evp_pkey):
- self._backend = backend
- self._ec_key = ec_key_cdata
- self._evp_pkey = evp_pkey
-
- sn = _ec_key_curve_sn(backend, ec_key_cdata)
- self._curve = _sn_to_elliptic_curve(backend, sn)
- _mark_asn1_named_ec_curve(backend, ec_key_cdata)
- _check_key_infinity(backend, ec_key_cdata)
-
- @property
- def curve(self) -> ec.EllipticCurve:
- return self._curve
-
- @property
- def key_size(self) -> int:
- return self.curve.key_size
-
- def exchange(
- self, algorithm: ec.ECDH, peer_public_key: ec.EllipticCurvePublicKey
- ) -> bytes:
- if not (
- self._backend.elliptic_curve_exchange_algorithm_supported(
- algorithm, self.curve
- )
- ):
- raise UnsupportedAlgorithm(
- "This backend does not support the ECDH algorithm.",
- _Reasons.UNSUPPORTED_EXCHANGE_ALGORITHM,
- )
-
- if peer_public_key.curve.name != self.curve.name:
- raise ValueError(
- "peer_public_key and self are not on the same curve"
- )
-
- return _evp_pkey_derive(self._backend, self._evp_pkey, peer_public_key)
-
- def public_key(self) -> ec.EllipticCurvePublicKey:
- group = self._backend._lib.EC_KEY_get0_group(self._ec_key)
- self._backend.openssl_assert(group != self._backend._ffi.NULL)
-
- curve_nid = self._backend._lib.EC_GROUP_get_curve_name(group)
- public_ec_key = self._backend._ec_key_new_by_curve_nid(curve_nid)
-
- point = self._backend._lib.EC_KEY_get0_public_key(self._ec_key)
- self._backend.openssl_assert(point != self._backend._ffi.NULL)
-
- res = self._backend._lib.EC_KEY_set_public_key(public_ec_key, point)
- self._backend.openssl_assert(res == 1)
-
- evp_pkey = self._backend._ec_cdata_to_evp_pkey(public_ec_key)
-
- return _EllipticCurvePublicKey(self._backend, public_ec_key, evp_pkey)
-
- def private_numbers(self) -> ec.EllipticCurvePrivateNumbers:
- bn = self._backend._lib.EC_KEY_get0_private_key(self._ec_key)
- private_value = self._backend._bn_to_int(bn)
- return ec.EllipticCurvePrivateNumbers(
- private_value=private_value,
- public_numbers=self.public_key().public_numbers(),
- )
-
- def private_bytes(
- self,
- encoding: serialization.Encoding,
- format: serialization.PrivateFormat,
- encryption_algorithm: serialization.KeySerializationEncryption,
- ) -> bytes:
- return self._backend._private_key_bytes(
- encoding,
- format,
- encryption_algorithm,
- self,
- self._evp_pkey,
- self._ec_key,
- )
-
- def sign(
- self,
- data: bytes,
- signature_algorithm: ec.EllipticCurveSignatureAlgorithm,
- ) -> bytes:
- _check_signature_algorithm(signature_algorithm)
- data, _ = _calculate_digest_and_algorithm(
- data,
- signature_algorithm.algorithm,
- )
- return _ecdsa_sig_sign(self._backend, self, data)
-
-
-class _EllipticCurvePublicKey(ec.EllipticCurvePublicKey):
- def __init__(self, backend: Backend, ec_key_cdata, evp_pkey):
- self._backend = backend
- self._ec_key = ec_key_cdata
- self._evp_pkey = evp_pkey
-
- sn = _ec_key_curve_sn(backend, ec_key_cdata)
- self._curve = _sn_to_elliptic_curve(backend, sn)
- _mark_asn1_named_ec_curve(backend, ec_key_cdata)
- _check_key_infinity(backend, ec_key_cdata)
-
- @property
- def curve(self) -> ec.EllipticCurve:
- return self._curve
-
- @property
- def key_size(self) -> int:
- return self.curve.key_size
-
- def __eq__(self, other: object) -> bool:
- if not isinstance(other, _EllipticCurvePublicKey):
- return NotImplemented
-
- return (
- self._backend._lib.EVP_PKEY_cmp(self._evp_pkey, other._evp_pkey)
- == 1
- )
-
- def public_numbers(self) -> ec.EllipticCurvePublicNumbers:
- group = self._backend._lib.EC_KEY_get0_group(self._ec_key)
- self._backend.openssl_assert(group != self._backend._ffi.NULL)
-
- point = self._backend._lib.EC_KEY_get0_public_key(self._ec_key)
- self._backend.openssl_assert(point != self._backend._ffi.NULL)
-
- with self._backend._tmp_bn_ctx() as bn_ctx:
- bn_x = self._backend._lib.BN_CTX_get(bn_ctx)
- bn_y = self._backend._lib.BN_CTX_get(bn_ctx)
-
- res = self._backend._lib.EC_POINT_get_affine_coordinates(
- group, point, bn_x, bn_y, bn_ctx
- )
- self._backend.openssl_assert(res == 1)
-
- x = self._backend._bn_to_int(bn_x)
- y = self._backend._bn_to_int(bn_y)
-
- return ec.EllipticCurvePublicNumbers(x=x, y=y, curve=self._curve)
-
- def _encode_point(self, format: serialization.PublicFormat) -> bytes:
- if format is serialization.PublicFormat.CompressedPoint:
- conversion = self._backend._lib.POINT_CONVERSION_COMPRESSED
- else:
- assert format is serialization.PublicFormat.UncompressedPoint
- conversion = self._backend._lib.POINT_CONVERSION_UNCOMPRESSED
-
- group = self._backend._lib.EC_KEY_get0_group(self._ec_key)
- self._backend.openssl_assert(group != self._backend._ffi.NULL)
- point = self._backend._lib.EC_KEY_get0_public_key(self._ec_key)
- self._backend.openssl_assert(point != self._backend._ffi.NULL)
- with self._backend._tmp_bn_ctx() as bn_ctx:
- buflen = self._backend._lib.EC_POINT_point2oct(
- group, point, conversion, self._backend._ffi.NULL, 0, bn_ctx
- )
- self._backend.openssl_assert(buflen > 0)
- buf = self._backend._ffi.new("char[]", buflen)
- res = self._backend._lib.EC_POINT_point2oct(
- group, point, conversion, buf, buflen, bn_ctx
- )
- self._backend.openssl_assert(buflen == res)
-
- return self._backend._ffi.buffer(buf)[:]
-
- def public_bytes(
- self,
- encoding: serialization.Encoding,
- format: serialization.PublicFormat,
- ) -> bytes:
- if (
- encoding is serialization.Encoding.X962
- or format is serialization.PublicFormat.CompressedPoint
- or format is serialization.PublicFormat.UncompressedPoint
- ):
- if encoding is not serialization.Encoding.X962 or format not in (
- serialization.PublicFormat.CompressedPoint,
- serialization.PublicFormat.UncompressedPoint,
- ):
- raise ValueError(
- "X962 encoding must be used with CompressedPoint or "
- "UncompressedPoint format"
- )
-
- return self._encode_point(format)
- else:
- return self._backend._public_key_bytes(
- encoding, format, self, self._evp_pkey, None
- )
-
- def verify(
- self,
- signature: bytes,
- data: bytes,
- signature_algorithm: ec.EllipticCurveSignatureAlgorithm,
- ) -> None:
- _check_signature_algorithm(signature_algorithm)
- data, _ = _calculate_digest_and_algorithm(
- data,
- signature_algorithm.algorithm,
- )
- _ecdsa_sig_verify(self._backend, self, signature, data)
diff --git a/spaces/chuanenlin/pdf2preview/utils.py b/spaces/chuanenlin/pdf2preview/utils.py
deleted file mode 100644
index ce0ea7171f0b7629f42767d4ebccfef60dd4abbf..0000000000000000000000000000000000000000
--- a/spaces/chuanenlin/pdf2preview/utils.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import urllib.request
-import os
-from pathlib import Path
-
-def download_file(download_url):
- response = urllib.request.urlopen(download_url)
- filename = Path(download_url).name
- file = open(filename, 'wb')
- file.write(response.read())
- file.close()
- return filename
-
-def remove_file(local_path):
- os.remove(local_path)
diff --git a/spaces/cihyFjudo/fairness-paper-search/Free Pics Nude Japanese Women With Big Asses Bent Over __EXCLUSIVE__.md b/spaces/cihyFjudo/fairness-paper-search/Free Pics Nude Japanese Women With Big Asses Bent Over __EXCLUSIVE__.md
deleted file mode 100644
index dbfacc5b9388c9d5494ecfc49fdbba57410b9af7..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Free Pics Nude Japanese Women With Big Asses Bent Over __EXCLUSIVE__.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
free pics nude japanese women with big asses bent over
-
-
-You can load the model and tokenizer directly from 🤗 [`transformers`](https://huggingface.co/docs/transformers/index):
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-16B-mono')
-model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-16B-mono')
-
-inputs = tokenizer("def hello_world():", return_tensors="pt")
-outputs = model(**inputs)
-```
\ No newline at end of file
diff --git a/spaces/codeslake/RefVSR/README.md b/spaces/codeslake/RefVSR/README.md
deleted file mode 100644
index eebb814ce6b31d90d65f75e25e77aeeba0e8f7b5..0000000000000000000000000000000000000000
--- a/spaces/codeslake/RefVSR/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RefVSR
-emoji: 🏃
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/colakin/video-generater/public/ffmpeg/compat/djgpp/math.c b/spaces/colakin/video-generater/public/ffmpeg/compat/djgpp/math.c
deleted file mode 100644
index 777b879e017e548e48a522e0a8708d262e7121f1..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/compat/djgpp/math.c
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * 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
-
-#define FUN(name, type, op) \
-type name(type x, type y) \
-{ \
- if (fpclassify(x) == FP_NAN) return y; \
- if (fpclassify(y) == FP_NAN) return x; \
- return x op y ? x : y; \
-}
-
-FUN(fmin, double, <)
-FUN(fmax, double, >)
-FUN(fminf, float, <)
-FUN(fmaxf, float, >)
-
-long double fmodl(long double x, long double y)
-{
- return fmod(x, y);
-}
-
-long double scalbnl(long double x, int exp)
-{
- return scalbn(x, exp);
-}
-
-long double copysignl(long double x, long double y)
-{
- return copysign(x, y);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Temple Run Oz APK - Fly in a Hot Air Balloon Explore Different Locations and Compete in Weekly Challenges.md b/spaces/congsaPfin/Manga-OCR/logs/Temple Run Oz APK - Fly in a Hot Air Balloon Explore Different Locations and Compete in Weekly Challenges.md
deleted file mode 100644
index e0ccda8a3fc49b2e54b840f209afef80c8610285..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Temple Run Oz APK - Fly in a Hot Air Balloon Explore Different Locations and Compete in Weekly Challenges.md
+++ /dev/null
@@ -1,155 +0,0 @@
-
-
Oz Temple Run APK Download: A Thrilling Running Game Experience
-
If you are a fan of endless runner games, you might have heard of Temple Run, one of the most popular games in this genre. But have you tried Oz Temple Run, a movie-themed version of the game that takes you to the magical land of Oz? If not, you are missing out on a lot of fun and excitement. In this article, we will tell you everything you need to know about Oz Temple Run APK download, how to play it, and why you should give it a try.
Oz Temple Run is a game developed by Disney and Imangi Studios, based on the film Oz the Great and Powerful. It is a spin-off of Temple Run 2, one of the most successful games in the Temple Run franchise. In this game, you play as Oz, a magician who travels to the land of Oz and has to outrun the evil flying baboons that chase him. Along the way, you will encounter various obstacles, collect coins, power-ups, and gems, and explore different locations inspired by the film.
-
A movie-themed endless runner game
-
Oz Temple Run is not just a simple reskin of Temple Run 2. It has its own unique features that make it stand out from other games in this genre. For example, it has a beautiful graphics and sound design that capture the essence of the film. You will see lush forests, dark graveyards, colorful balloons, and more as you run through the yellow brick road. You will also hear the voice of James Franco as Oz, as well as the music and sound effects from the film.
-
Inspired by Temple Run 2 and Oz the Great and Powerful
-
Oz Temple Run is also influenced by both Temple Run 2 and Oz the Great and Powerful. It uses the same core gameplay mechanics as Temple Run 2, such as swiping to turn, jump, and slide, tilting to move side to side, and double-tapping to activate power-ups. However, it also adds some new elements that are related to the film, such as flying in a hot air balloon, running on walls, and using magic wands. Moreover, it has some references to the film's characters and storylines, such as China Girl, Glinda, Wicked Witch, Emerald City, etc.
-
Features stunning environments, power-ups, and mini-games
-
Oz Temple Run is not just a simple running game. It also has some features that make it more interesting and challenging. For example, it has different environments that change as you run, such as Whimsie Woods, Dark Forest, Winkie Country, etc. Each environment has its own obstacles, scenery, and surprises that test your reflexes. Moreover, it has various power-ups that help you run faster, longer, or safer, such as Finley's Boost, Magic Magnet, Shield Charm, etc. Furthermore, it has some mini-games that give you more coins or gems, such as flying in a balloon or riding in a mine cart.
-
How to download Oz Temple Run APK?
-
If you want to play Oz Temple Run on your Android device, you will need to download its APK file from a reliable source. An APK file is an application package file that contains all the data and code needed to install an app on your device. However, before you download Oz Temple Run APK file from any website or link , you should check some requirements and compatibility issues, follow some steps to download and install it, and take some precautions to avoid malware and viruses.
-
Requirements and compatibility
-
Oz Temple Run APK file is compatible with Android devices that run on Android 4.0 or higher. It also requires at least 76 MB of free storage space on your device. Moreover, it may require some permissions to access your device's features, such as camera, microphone, location, etc. You should review these permissions before installing the app and grant them only if you trust the source of the APK file.
-
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-download game android populer 2023 :oz :temple :run :apk :popular :trending :downloaded
-
Steps to download and install
-
To download and install Oz Temple Run APK file on your device, you can follow these steps:
-
-
Go to a reputable website that offers Oz Temple Run APK file for download, such as [APKPure], [APKMirror], or [Uptodown].
-
Find the latest version of Oz Temple Run APK file and tap on the download button.
-
Wait for the download to finish and locate the APK file on your device's file manager.
-
Before installing the APK file, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-
Tap on the APK file and follow the instructions to install it on your device.
-
Once the installation is complete, you can launch the app and enjoy playing Oz Temple Run.
-
-
Tips to avoid malware and viruses
-
While downloading and installing Oz Temple Run APK file can be a quick and easy way to play the game, it also comes with some risks. Some websites or links may offer fake or modified APK files that contain malware or viruses that can harm your device or steal your personal information. To avoid these dangers, you should follow these tips:
-
-
Only download Oz Temple Run APK file from trusted and verified sources, such as the ones mentioned above.
-
Check the reviews and ratings of the website or link before downloading anything from it.
-
Scan the APK file with a reliable antivirus software before installing it on your device.
-
Do not grant any unnecessary or suspicious permissions to the app.
-
Delete the APK file from your device after installing it to save space and prevent accidental installation.
-
-
How to play Oz Temple Run?
-
Oz Temple Run is a fun and addictive game that will keep you entertained for hours. However, if you want to master the game and achieve high scores, you need to know how to play it properly. Here are some basic controls and gameplay tips, as well as some tricks and challenges that will make your game more exciting.
-
Basic controls and gameplay
-
Oz Temple Run is a simple game that anyone can play with just a few gestures. Here are the basic controls and gameplay rules:
-
-
Gestures
Actions
-
Swipe left or right
Turn left or right
-
Swipe up
Jump over obstacles
-
Swipe down
Slide under obstacles
-
Tilt left or right
Move side to side
-
Double-tap
Activate power-ups
-
-
The gameplay is simple: run as far as you can without hitting any obstacles or falling off the edge. The longer you run, the faster you go, and the more coins, power-ups, and gems you collect. You can use these items to upgrade your abilities, unlock new characters, or buy more lives. However, be careful of the flying baboons that will try to catch you. If they do, you will lose a life or have to start over.
-
Tips and tricks to score higher and run longer
-
If you want to improve your performance in Oz Temple Run, you need to follow some tips and tricks that will help you score higher and run longer. Here are some of them:
-
-
Avoid running on walls unless necessary. Running on walls will slow you down and make you more vulnerable to obstacles.
-
Use power-ups wisely. Power-ups can give you an edge in the game, but they also have a cooldown time. Use them when you need them most, such as when you are surrounded by baboons or when you see a lot of coins or gems.
-
Collect gems whenever possible . Gems are more valuable than coins, and they can be used to revive yourself if you die or to unlock new characters.
-
Watch out for the signs that indicate a change of environment. When you see a sign that says "Whimsie Woods" or "Dark Forest", for example, be prepared to face new obstacles and scenery.
-
Complete the objectives and achievements. These are tasks that challenge you to do something specific in the game, such as running a certain distance, collecting a certain amount of coins, or using a certain power-up. Completing them will give you extra coins, gems, or rewards.
-
-
Challenges and leaderboards to compete with others
-
Oz Temple Run is not just a solo game. You can also compete with other players around the world and see how you rank among them. Here are some ways to do that:
-
-
Connect your game to Facebook or Google Play Games. This will allow you to see your friends' scores and challenge them to beat your best runs.
-
Join the weekly challenges. These are special events that give you a chance to win exclusive prizes and rewards by completing certain goals in the game.
-
Check the global leaderboards. These are rankings that show the top players in the game based on their scores, distances, or achievements. You can compare your stats with theirs and try to climb up the ladder.
-
-
Why should you play Oz Temple Run?
-
Oz Temple Run is a game that offers a lot of fun and excitement for anyone who loves running games. But is it worth playing? Here are some pros and cons of the game, as well as a review and rating, and a conclusion and recommendation.
-
Pros and cons of the game
-
Oz Temple Run has many advantages and disadvantages that you should consider before playing it. Here are some of them:
-
-
Pros
Cons
-
It has amazing graphics and sound effects that immerse you in the world of Oz.
It can be repetitive and boring after a while, as it has no end or story.
-
It has various power-ups, mini-games, and environments that add variety and challenge to the game.
It can be frustrating and difficult at times, as it has many obstacles and enemies that can kill you easily.
-
It has social features that allow you to compete and interact with other players.
It can be addictive and time-consuming, as it makes you want to play more and more to beat your own or others' scores.
-
It is free to download and play, and it does not require an internet connection.
It has ads and in-app purchases that can be annoying and expensive.
-
-
Review and rating of the game
-
Oz Temple Run is a game that deserves a positive review and rating. It is a well-made game that combines the best elements of Temple Run 2 and Oz the Great and Powerful. It is a game that will appeal to fans of both the film and the genre. It is a game that will keep you entertained for hours with its stunning visuals, thrilling gameplay, and competitive features. It is a game that you should definitely try if you are looking for a running game experience like no other.
-
Oz Temple Run has an average rating of 4.4 out of 5 stars on Google Play Store, based on over 1 million reviews. It also has over 50 million downloads on the same platform. These numbers show how popular and well-liked the game is among its users.
-
Conclusion and recommendation
-
In conclusion, Oz Temple Run is a game that we highly recommend for anyone who loves running games or movies. It is a game that will take you on an adventure in the land of Oz, where you will run, jump, slide, fly, and use magic to escape from the evil baboons. It is a game that will challenge your skills, test your reflexes, and reward your efforts. It is a game that will make you feel like you are part of the film, with its amazing graphics and sound effects. It is a game that will make you have fun and enjoy yourself for hours.
-
If you want to play Oz Temple Run on your Android device, all you need to do is download its APK file from a reliable source, follow some steps to install it on your device, and follow some tips to play it safely and effectively. You can also check some tricks and challenges to make your game more exciting and competitive. You can also read some FAQs below to learn more about the game or solve some problems that you may encounter while playing it.
-
FAQs
-
Here are some frequently asked questions and answers about Oz Temple Run:
-
Q: How can I change the character in Oz Temple Run?
-
A: You can change the character in Oz Temple Run by tapping on the menu icon on the top left corner of the screen, then tapping on the character icon on the bottom left corner of the screen. You will see a list of characters that you can choose from, such as China Girl, Glinda, Wicked Witch, etc. Some characters are free, while others require gems to unlock. Tap on the character that you want to play as and confirm your choice.
-
Q: How can I get more coins and gems in Oz Temple Run?
-
A: You can get more coins and gems in Oz Temple Run by doing the following:
-
-
Run as far as you can and collect as many coins and gems as you can along the way.
-
Complete the objectives and achievements that give you extra coins or gems as rewards.
-
Play the mini-games that give you more coins or gems, such as flying in a balloon or riding in a mine cart.
-
Use power-ups that increase your coin or gem collection, such as Magic Magnet or Gem Bonus.
-
Watch ads or videos that offer you free coins or gems.
-
Purchase coins or gems with real money through in-app purchases.
-
-
Q: How can I fix Oz Temple Run if it crashes or freezes?
-
A: If Oz Temple Run crashes or freezes on your device, you can try these solutions:
-
-
Restart your device and launch the game again.
-
Clear the cache and data of the game from your device's settings.
-
Update the game to the latest version from Google Play Store.
-
Uninstall and reinstall the game from a trusted source.
-
Contact the developer of the game for support or feedback.
-
-
Q: How can I share my Oz Temple Run score with my friends?
-
A: You can share your Oz Temple Run score with your friends by doing the following:
-
-
Connect your game to Facebook or Google Play Games to see your friends' scores and challenge them to beat yours.
-
Take a screenshot of your score and share it on social media platforms, such as Instagram, Twitter, or WhatsApp.
-
Use the share button on the game's screen to send your score to your friends via email, SMS, or other apps.
-
-
Q: Is Oz Temple Run safe for kids?
-
A: Oz Temple Run is rated 7+ on Google Play Store, which means that it may contain mild violence or fear. The game is not very violent or scary, but it does have some scenes that may be frightening for younger children, such as flying baboons, fireballs, or dark forests. Parents should supervise their children while playing the game and set parental controls if needed. The game also has ads and in-app purchases that may be inappropriate or tempting for kids. Parents should monitor their children's spending and disable these features if necessary.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/datasets/ade.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/datasets/ade.py
deleted file mode 100644
index 5913e43775ed4920b6934c855eb5a37c54218ebf..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/datasets/ade.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class ADE20KDataset(CustomDataset):
- """ADE20K dataset.
-
- In segmentation map annotation for ADE20K, 0 stands for background, which
- is not included in 150 categories. ``reduce_zero_label`` is fixed to True.
- The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to
- '.png'.
- """
- CLASSES = (
- 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
- 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
- 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
- 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
- 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
- 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
- 'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
- 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
- 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
- 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
- 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
- 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
- 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
- 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
- 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
- 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
- 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
- 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
- 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
- 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
- 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
- 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
- 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
- 'clock', 'flag')
-
- PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
- [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
- [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
- [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
- [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
- [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
- [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
- [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
- [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
- [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
- [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
- [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
- [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
- [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
- [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
- [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
- [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
- [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
- [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
- [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
- [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
- [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
- [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
- [102, 255, 0], [92, 0, 255]]
-
- def __init__(self, **kwargs):
- super(ADE20KDataset, self).__init__(
- img_suffix='.jpg',
- seg_map_suffix='.png',
- reduce_zero_label=True,
- **kwargs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/structures/instances.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/structures/instances.py
deleted file mode 100644
index c9579bce2730f42e256c6eed99d9014d09304c99..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/structures/instances.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import warnings
-from typing import Any, Dict, List, Tuple, Union
-import torch
-
-
-class Instances:
- """
- This class represents a list of instances in an image.
- It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields".
- All fields must have the same ``__len__`` which is the number of instances.
-
- All other (non-field) attributes of this class are considered private:
- they must start with '_' and are not modifiable by a user.
-
- Some basic usage:
-
- 1. Set/get/check a field:
-
- .. code-block:: python
-
- instances.gt_boxes = Boxes(...)
- print(instances.pred_masks) # a tensor of shape (N, H, W)
- print('gt_masks' in instances)
-
- 2. ``len(instances)`` returns the number of instances
- 3. Indexing: ``instances[indices]`` will apply the indexing on all the fields
- and returns a new :class:`Instances`.
- Typically, ``indices`` is a integer vector of indices,
- or a binary mask of length ``num_instances``
-
- .. code-block:: python
-
- category_3_detections = instances[instances.pred_classes == 3]
- confident_detections = instances[instances.scores > 0.9]
- """
-
- def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
- """
- Args:
- image_size (height, width): the spatial size of the image.
- kwargs: fields to add to this `Instances`.
- """
- self._image_size = image_size
- self._fields: Dict[str, Any] = {}
- for k, v in kwargs.items():
- self.set(k, v)
-
- @property
- def image_size(self) -> Tuple[int, int]:
- """
- Returns:
- tuple: height, width
- """
- return self._image_size
-
- def __setattr__(self, name: str, val: Any) -> None:
- if name.startswith("_"):
- super().__setattr__(name, val)
- else:
- self.set(name, val)
-
- def __getattr__(self, name: str) -> Any:
- if name == "_fields" or name not in self._fields:
- raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
- return self._fields[name]
-
- def set(self, name: str, value: Any) -> None:
- """
- Set the field named `name` to `value`.
- The length of `value` must be the number of instances,
- and must agree with other existing fields in this object.
- """
- with warnings.catch_warnings(record=True):
- data_len = len(value)
- if len(self._fields):
- assert (
- len(self) == data_len
- ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self))
- self._fields[name] = value
-
- def has(self, name: str) -> bool:
- """
- Returns:
- bool: whether the field called `name` exists.
- """
- return name in self._fields
-
- def remove(self, name: str) -> None:
- """
- Remove the field called `name`.
- """
- del self._fields[name]
-
- def get(self, name: str) -> Any:
- """
- Returns the field called `name`.
- """
- return self._fields[name]
-
- def get_fields(self) -> Dict[str, Any]:
- """
- Returns:
- dict: a dict which maps names (str) to data of the fields
-
- Modifying the returned dict will modify this instance.
- """
- return self._fields
-
- # Tensor-like methods
- def to(self, *args: Any, **kwargs: Any) -> "Instances":
- """
- Returns:
- Instances: all fields are called with a `to(device)`, if the field has this method.
- """
- ret = Instances(self._image_size)
- for k, v in self._fields.items():
- if hasattr(v, "to"):
- v = v.to(*args, **kwargs)
- ret.set(k, v)
- return ret
-
- def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Instances":
- """
- Args:
- item: an index-like object and will be used to index all the fields.
-
- Returns:
- If `item` is a string, return the data in the corresponding field.
- Otherwise, returns an `Instances` where all fields are indexed by `item`.
- """
- if type(item) == int:
- if item >= len(self) or item < -len(self):
- raise IndexError("Instances index out of range!")
- else:
- item = slice(item, None, len(self))
-
- ret = Instances(self._image_size)
- for k, v in self._fields.items():
- ret.set(k, v[item])
- return ret
-
- def __len__(self) -> int:
- for v in self._fields.values():
- # use __len__ because len() has to be int and is not friendly to tracing
- return v.__len__()
- raise NotImplementedError("Empty Instances does not support __len__!")
-
- def __iter__(self):
- raise NotImplementedError("`Instances` object is not iterable!")
-
- @staticmethod
- def cat(instance_lists: List["Instances"]) -> "Instances":
- """
- Args:
- instance_lists (list[Instances])
-
- Returns:
- Instances
- """
- assert all(isinstance(i, Instances) for i in instance_lists)
- assert len(instance_lists) > 0
- if len(instance_lists) == 1:
- return instance_lists[0]
-
- image_size = instance_lists[0].image_size
- if not isinstance(image_size, torch.Tensor): # could be a tensor in tracing
- for i in instance_lists[1:]:
- assert i.image_size == image_size
- ret = Instances(image_size)
- for k in instance_lists[0]._fields.keys():
- values = [i.get(k) for i in instance_lists]
- v0 = values[0]
- if isinstance(v0, torch.Tensor):
- values = torch.cat(values, dim=0)
- elif isinstance(v0, list):
- values = list(itertools.chain(*values))
- elif hasattr(type(v0), "cat"):
- values = type(v0).cat(values)
- else:
- raise ValueError("Unsupported type {} for concatenation".format(type(v0)))
- ret.set(k, values)
- return ret
-
- def __str__(self) -> str:
- s = self.__class__.__name__ + "("
- s += "num_instances={}, ".format(len(self))
- s += "image_height={}, ".format(self._image_size[0])
- s += "image_width={}, ".format(self._image_size[1])
- s += "fields=[{}])".format(", ".join((f"{k}: {v}" for k, v in self._fields.items())))
- return s
-
- __repr__ = __str__
diff --git a/spaces/cpwan/RLOR-TSP/ppo.py b/spaces/cpwan/RLOR-TSP/ppo.py
deleted file mode 100644
index a814eab73799470d4f4f6cddae4b27045fa8efcc..0000000000000000000000000000000000000000
--- a/spaces/cpwan/RLOR-TSP/ppo.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Retrieved from https://github.com/vwxyzjn/cleanrl/blob/28fd178ca182bd83c75ed0d49d52e235ca6cdc88/cleanrl/ppo.py
-# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy
-
-import argparse
-import os
-import random
-import time
-from distutils.util import strtobool
-
-import gym
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.distributions.categorical import Categorical
-from torch.utils.tensorboard import SummaryWriter
-
-
-def parse_args():
- # fmt: off
- parser = argparse.ArgumentParser()
- parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
- help="the name of this experiment")
- parser.add_argument("--seed", type=int, default=1,
- help="seed of the experiment")
- parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
- help="if toggled, `torch.backends.cudnn.deterministic=False`")
- parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
- help="if toggled, cuda will be enabled by default")
- parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
- help="if toggled, this experiment will be tracked with Weights and Biases")
- parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
- help="the wandb's project name")
- parser.add_argument("--wandb-entity", type=str, default=None,
- help="the entity (team) of wandb's project")
- parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
- help="whether to capture videos of the agent performances (check out `videos` folder)")
-
- # Algorithm specific arguments
- parser.add_argument("--env-id", type=str, default="CartPole-v1",
- help="the id of the environment")
- parser.add_argument("--total-timesteps", type=int, default=500000,
- help="total timesteps of the experiments")
- parser.add_argument("--learning-rate", type=float, default=2.5e-4,
- help="the learning rate of the optimizer")
- parser.add_argument("--num-envs", type=int, default=4,
- help="the number of parallel game environments")
- parser.add_argument("--num-steps", type=int, default=128,
- help="the number of steps to run in each environment per policy rollout")
- parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
- help="Toggle learning rate annealing for policy and value networks")
- parser.add_argument("--gamma", type=float, default=0.99,
- help="the discount factor gamma")
- parser.add_argument("--gae-lambda", type=float, default=0.95,
- help="the lambda for the general advantage estimation")
- parser.add_argument("--num-minibatches", type=int, default=4,
- help="the number of mini-batches")
- parser.add_argument("--update-epochs", type=int, default=4,
- help="the K epochs to update the policy")
- parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
- help="Toggles advantages normalization")
- parser.add_argument("--clip-coef", type=float, default=0.2,
- help="the surrogate clipping coefficient")
- parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
- help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
- parser.add_argument("--ent-coef", type=float, default=0.01,
- help="coefficient of the entropy")
- parser.add_argument("--vf-coef", type=float, default=0.5,
- help="coefficient of the value function")
- parser.add_argument("--max-grad-norm", type=float, default=0.5,
- help="the maximum norm for the gradient clipping")
- parser.add_argument("--target-kl", type=float, default=None,
- help="the target KL divergence threshold")
- args = parser.parse_args()
- args.batch_size = int(args.num_envs * args.num_steps)
- args.minibatch_size = int(args.batch_size // args.num_minibatches)
- # fmt: on
- return args
-
-
-def make_env(env_id, seed, idx, capture_video, run_name):
- def thunk():
- env = gym.make(env_id)
- if capture_video:
- if idx == 0:
- env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
- env.action_space.seed(seed)
- env.observation_space.seed(seed)
- return env
-
- return thunk
-
-
-def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
- torch.nn.init.orthogonal_(layer.weight, std)
- torch.nn.init.constant_(layer.bias, bias_const)
- return layer
-
-
-class Agent(nn.Module):
- def __init__(self, envs):
- super().__init__()
- self.critic = nn.Sequential(
- layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
- nn.Tanh(),
- layer_init(nn.Linear(64, 64)),
- nn.Tanh(),
- layer_init(nn.Linear(64, 1), std=1.0),
- )
- self.actor = nn.Sequential(
- layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
- nn.Tanh(),
- layer_init(nn.Linear(64, 64)),
- nn.Tanh(),
- layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),
- )
-
- def get_value(self, x):
- return self.critic(x)
-
- def get_action_and_value(self, x, action=None):
- logits = self.actor(x)
- probs = Categorical(logits=logits)
- if action is None:
- action = probs.sample()
- return action, probs.log_prob(action), probs.entropy(), self.critic(x)
-
-
-if __name__ == "__main__":
- args = parse_args()
- run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
- if args.track:
- import wandb
-
- wandb.init(
- project=args.wandb_project_name,
- entity=args.wandb_entity,
- sync_tensorboard=True,
- config=vars(args),
- name=run_name,
- monitor_gym=True,
- save_code=True,
- )
- writer = SummaryWriter(f"runs/{run_name}")
- writer.add_text(
- "hyperparameters",
- "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
- )
-
- # TRY NOT TO MODIFY: seeding
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- torch.backends.cudnn.deterministic = args.torch_deterministic
-
- device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
-
- # env setup
- envs = gym.vector.SyncVectorEnv(
- [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
- )
- envs = gym.wrappers.RecordEpisodeStatistics(envs)
- assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
-
- agent = Agent(envs).to(device)
- optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
-
- # ALGO Logic: Storage setup
- obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
- actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
- logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
- rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
- terminateds = torch.zeros((args.num_steps, args.num_envs)).to(device)
- values = torch.zeros((args.num_steps, args.num_envs)).to(device)
-
- # TRY NOT TO MODIFY: start the game
- global_step = 0
- start_time = time.time()
- next_obs = torch.Tensor(envs.reset()[0]).to(device)
- next_terminated = torch.zeros(args.num_envs).to(device)
- num_updates = args.total_timesteps // args.batch_size
-
- for update in range(1, num_updates + 1):
- # Annealing the rate if instructed to do so.
- if args.anneal_lr:
- frac = 1.0 - (update - 1.0) / num_updates
- lrnow = frac * args.learning_rate
- optimizer.param_groups[0]["lr"] = lrnow
-
- for step in range(0, args.num_steps):
- global_step += 1 * args.num_envs
- obs[step] = next_obs
- terminateds[step] = next_terminated
-
- # ALGO LOGIC: action logic
- with torch.no_grad():
- action, logprob, _, value = agent.get_action_and_value(next_obs)
- values[step] = value.flatten()
- actions[step] = action
- logprobs[step] = logprob
-
- # TRY NOT TO MODIFY: execute the game and log data.
- next_obs, reward, terminated, _, info = envs.step(action.cpu().numpy())
- rewards[step] = torch.tensor(reward).to(device).view(-1)
- next_obs, next_terminated = torch.Tensor(next_obs).to(device), torch.Tensor(terminated).to(device)
-
- if "episode" in info:
- first_idx = info["_episode"].nonzero()[0][0]
- r = info["episode"]["r"][first_idx]
- l = info["episode"]["l"][first_idx]
- print(f"global_step={global_step}, episodic_return={r}")
- writer.add_scalar("charts/episodic_return", r, global_step)
- writer.add_scalar("charts/episodic_length", l, global_step)
-
- # bootstrap value if not terminated
- with torch.no_grad():
- next_value = agent.get_value(next_obs).reshape(1, -1)
- advantages = torch.zeros_like(rewards).to(device)
- lastgaelam = 0
- for t in reversed(range(args.num_steps)):
- if t == args.num_steps - 1:
- nextnonterminal = 1.0 - next_terminated
- nextvalues = next_value
- else:
- nextnonterminal = 1.0 - terminateds[t + 1]
- nextvalues = values[t + 1]
- delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
- advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
- returns = advantages + values
-
- # flatten the batch
- b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
- b_logprobs = logprobs.reshape(-1)
- b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
- b_advantages = advantages.reshape(-1)
- b_returns = returns.reshape(-1)
- b_values = values.reshape(-1)
-
- # Optimizing the policy and value network
- b_inds = np.arange(args.batch_size)
- clipfracs = []
- for epoch in range(args.update_epochs):
- np.random.shuffle(b_inds)
- for start in range(0, args.batch_size, args.minibatch_size):
- end = start + args.minibatch_size
- mb_inds = b_inds[start:end]
-
- _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds])
- logratio = newlogprob - b_logprobs[mb_inds]
- ratio = logratio.exp()
-
- with torch.no_grad():
- # calculate approx_kl http://joschu.net/blog/kl-approx.html
- old_approx_kl = (-logratio).mean()
- approx_kl = ((ratio - 1) - logratio).mean()
- clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
-
- mb_advantages = b_advantages[mb_inds]
- if args.norm_adv:
- mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
-
- # Policy loss
- pg_loss1 = -mb_advantages * ratio
- pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
- pg_loss = torch.max(pg_loss1, pg_loss2).mean()
-
- # Value loss
- newvalue = newvalue.view(-1)
- if args.clip_vloss:
- v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
- v_clipped = b_values[mb_inds] + torch.clamp(
- newvalue - b_values[mb_inds],
- -args.clip_coef,
- args.clip_coef,
- )
- v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
- v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
- v_loss = 0.5 * v_loss_max.mean()
- else:
- v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
-
- entropy_loss = entropy.mean()
- loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
-
- optimizer.zero_grad()
- loss.backward()
- nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
- optimizer.step()
-
- if args.target_kl is not None:
- if approx_kl > args.target_kl:
- break
-
- y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
- var_y = np.var(y_true)
- explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
-
- # TRY NOT TO MODIFY: record rewards for plotting purposes
- writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
- writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
- writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
- writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
- writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
- writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
- writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
- writer.add_scalar("losses/explained_variance", explained_var, global_step)
- print("SPS:", int(global_step / (time.time() - start_time)))
- writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
-
- envs.close()
- writer.close()
\ No newline at end of file
diff --git a/spaces/crytion/DeepNude/opencv_transform/dress_to_correct.py b/spaces/crytion/DeepNude/opencv_transform/dress_to_correct.py
deleted file mode 100644
index 33f4a33ef2b9350da4db658d9cc005875b52351a..0000000000000000000000000000000000000000
--- a/spaces/crytion/DeepNude/opencv_transform/dress_to_correct.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-
-# create_correct ===============================================================
-# return:
-# ( True/False), depending on the transformation process
-def create_correct(cv_dress):
-
- #Production dir:
- return correct_color(cv_dress, 5)
-
-# correct_color ==============================================================================
-# return:
-# image corrected
-def correct_color(img, percent):
-
- assert img.shape[2] == 3
- assert percent > 0 and percent < 100
-
- half_percent = percent / 200.0
-
- channels = cv2.split(img)
-
- out_channels = []
- for channel in channels:
- assert len(channel.shape) == 2
- # find the low and high precentile values (based on the input percentile)
- height, width = channel.shape
- vec_size = width * height
- flat = channel.reshape(vec_size)
-
- assert len(flat.shape) == 1
-
- flat = np.sort(flat)
-
- n_cols = flat.shape[0]
-
- low_val = flat[math.floor(n_cols * half_percent)]
- high_val = flat[math.ceil( n_cols * (1.0 - half_percent))]
-
- # saturate below the low percentile and above the high percentile
- thresholded = apply_threshold(channel, low_val, high_val)
- # scale the channel
- normalized = cv2.normalize(thresholded, thresholded.copy(), 0, 255, cv2.NORM_MINMAX)
- out_channels.append(normalized)
-
- return cv2.merge(out_channels)
-
-#Color correction utils
-def apply_threshold(matrix, low_value, high_value):
- low_mask = matrix < low_value
- matrix = apply_mask(matrix, low_mask, low_value)
-
- high_mask = matrix > high_value
- matrix = apply_mask(matrix, high_mask, high_value)
-
- return matrix
-
-#Color correction utils
-def apply_mask(matrix, mask, fill_value):
- masked = np.ma.array(matrix, mask=mask, fill_value=fill_value)
- return masked.filled()
diff --git a/spaces/cymic/VITS-Tokaiteio/data_utils.py b/spaces/cymic/VITS-Tokaiteio/data_utils.py
deleted file mode 100644
index 4855699d23d5dee36d4a12e875c7465265caac0f..0000000000000000000000000000000000000000
--- a/spaces/cymic/VITS-Tokaiteio/data_utils.py
+++ /dev/null
@@ -1,392 +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
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence, cleaned_text_to_sequence
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
- def __init__(self, audiopaths_and_text, hparams):
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
- self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_and_text)
- self._filter()
-
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_and_text_new = []
- lengths = []
- for audiopath, text in self.audiopaths_and_text:
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_and_text_new.append([audiopath, text])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.audiopaths_and_text = audiopaths_and_text_new
- self.lengths = lengths
-
- def get_audio_text_pair(self, audiopath_and_text):
- # separate filename and text
- audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- return (text, spec, wav)
-
- 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)
- return spec, audio_norm
-
- def get_text(self, text):
- if self.cleaned_text:
- text_norm = cleaned_text_to_sequence(text)
- else:
- text_norm = text_to_sequence(text, self.text_cleaners)
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def __getitem__(self, index):
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
- def __len__(self):
- return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text and aduio
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
-
-
-"""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_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- for audiopath, sid, text in self.audiopaths_sid_text:
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_sid_text_new.append([audiopath, sid, text])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- sid = self.get_sid(sid)
- return (text, spec, wav, sid)
-
- 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)
- return spec, audio_norm
-
- def get_text(self, text):
- if self.cleaned_text:
- text_norm = cleaned_text_to_sequence(text)
- else:
- text_norm = text_to_sequence(text, self.text_cleaners)
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i+1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/cymic/Waifu_Diffusion_Webui/javascript/edit-attention.js b/spaces/cymic/Waifu_Diffusion_Webui/javascript/edit-attention.js
deleted file mode 100644
index ee73eafffe34b5020b55d62eb19b0386fe46fefa..0000000000000000000000000000000000000000
--- a/spaces/cymic/Waifu_Diffusion_Webui/javascript/edit-attention.js
+++ /dev/null
@@ -1,41 +0,0 @@
-addEventListener('keydown', (event) => {
- let target = event.originalTarget;
- if (!target.hasAttribute("placeholder")) return;
- if (!target.placeholder.toLowerCase().includes("prompt")) return;
-
- let plus = "ArrowUp"
- let minus = "ArrowDown"
- if (event.key != plus && event.key != minus) return;
-
- selectionStart = target.selectionStart;
- selectionEnd = target.selectionEnd;
- if(selectionStart == selectionEnd) return;
-
- event.preventDefault();
-
- if (selectionStart == 0 || target.value[selectionStart - 1] != "(") {
- target.value = target.value.slice(0, selectionStart) +
- "(" + target.value.slice(selectionStart, selectionEnd) + ":1.0)" +
- target.value.slice(selectionEnd);
-
- target.focus();
- target.selectionStart = selectionStart + 1;
- target.selectionEnd = selectionEnd + 1;
-
- } else {
- end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
- weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
- if (event.key == minus) weight -= 0.1;
- if (event.key == plus) weight += 0.1;
-
- weight = parseFloat(weight.toPrecision(12));
-
- target.value = target.value.slice(0, selectionEnd + 1) +
- weight +
- target.value.slice(selectionEnd + 1 + end - 1);
-
- target.focus();
- target.selectionStart = selectionStart;
- target.selectionEnd = selectionEnd;
- }
-});
diff --git a/spaces/dawood/Kanye-AI/app.py b/spaces/dawood/Kanye-AI/app.py
deleted file mode 100644
index 12ac93726fd4a4a4d8cd354b63e6a30bfc547b67..0000000000000000000000000000000000000000
--- a/spaces/dawood/Kanye-AI/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import io
-import os
-
-os.system("wget -P hubert/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-model = Svc("logs/44k/G_199200.pth", "logs/44k/config.json", cluster_model_path="logs/44k/kmeans_10000.pt")
-
-def predict(input_audio, not_singing):
- 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 > 45:
- return "Please upload audio less than 45 seconds", 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)
- print(audio.shape)
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 16000, format="wav")
- out_audio, out_sr = model.infer("aimodel", 0, out_wav_path,
- cluster_infer_ratio=0,
- auto_predict_f0=not_singing,
- noice_scale=0.4
- )
- return (44100, out_audio.numpy())
-
-audio_input = gr.Audio(label="Upload Audio")
-not_singing = gr.Checkbox(label="Check this box if this audio is not singing", value=False)
-audio_output = gr.Audio(label="Output Audio")
-demo = gr.Interface(predict, inputs=[audio_input, not_singing], outputs=[audio_output])
-# app = gr.Blocks()
-# with app:
-# audio_input = gr.Audio(label="Upload Audio")
-# not_singing = gr.Checkbox(label="Check this box if this audio is not singing", value=False)
-# audio_output = gr.Audio(label="Output Audio")
-# submit_btn = gr.Button("Submit", variant="primary")
-# submit_btn.click(predict, [audio_input, not_singing], [audio_output], api_name="predict")
-
-demo.launch()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py
deleted file mode 100644
index 30a0ae626c26cc285e7e89e38180043239d9b0eb..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from typing import Optional
-
-from fastapi.concurrency import AsyncExitStack
-from starlette.types import ASGIApp, Receive, Scope, Send
-
-
-class AsyncExitStackMiddleware:
- def __init__(self, app: ASGIApp, context_name: str = "fastapi_astack") -> None:
- self.app = app
- self.context_name = context_name
-
- async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
- dependency_exception: Optional[Exception] = None
- async with AsyncExitStack() as stack:
- scope[self.context_name] = stack
- try:
- await self.app(scope, receive, send)
- except Exception as e:
- dependency_exception = e
- raise e
- if dependency_exception:
- # This exception was possibly handled by the dependency but it should
- # still bubble up so that the ServerErrorMiddleware can return a 500
- # or the ExceptionMiddleware can catch and handle any other exceptions
- raise dependency_exception
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/fontBuilder.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/fontBuilder.py
deleted file mode 100644
index dd57a0507d61465b1849ee4884e473351a004920..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/fontBuilder.py
+++ /dev/null
@@ -1,993 +0,0 @@
-__all__ = ["FontBuilder"]
-
-"""
-This module is *experimental*, meaning it still may evolve and change.
-
-The `FontBuilder` class is a convenient helper to construct working TTF or
-OTF fonts from scratch.
-
-Note that the various setup methods cannot be called in arbitrary order,
-due to various interdependencies between OpenType tables. Here is an order
-that works:
-
- fb = FontBuilder(...)
- fb.setupGlyphOrder(...)
- fb.setupCharacterMap(...)
- fb.setupGlyf(...) --or-- fb.setupCFF(...)
- fb.setupHorizontalMetrics(...)
- fb.setupHorizontalHeader()
- fb.setupNameTable(...)
- fb.setupOS2()
- fb.addOpenTypeFeatures(...)
- fb.setupPost()
- fb.save(...)
-
-Here is how to build a minimal TTF:
-
-```python
-from fontTools.fontBuilder import FontBuilder
-from fontTools.pens.ttGlyphPen import TTGlyphPen
-
-
-def drawTestGlyph(pen):
- pen.moveTo((100, 100))
- pen.lineTo((100, 1000))
- pen.qCurveTo((200, 900), (400, 900), (500, 1000))
- pen.lineTo((500, 100))
- pen.closePath()
-
-
-fb = FontBuilder(1024, isTTF=True)
-fb.setupGlyphOrder([".notdef", ".null", "space", "A", "a"])
-fb.setupCharacterMap({32: "space", 65: "A", 97: "a"})
-advanceWidths = {".notdef": 600, "space": 500, "A": 600, "a": 600, ".null": 0}
-
-familyName = "HelloTestFont"
-styleName = "TotallyNormal"
-version = "0.1"
-
-nameStrings = dict(
- familyName=dict(en=familyName, nl="HalloTestFont"),
- styleName=dict(en=styleName, nl="TotaalNormaal"),
- uniqueFontIdentifier="fontBuilder: " + familyName + "." + styleName,
- fullName=familyName + "-" + styleName,
- psName=familyName + "-" + styleName,
- version="Version " + version,
-)
-
-pen = TTGlyphPen(None)
-drawTestGlyph(pen)
-glyph = pen.glyph()
-glyphs = {".notdef": glyph, "space": glyph, "A": glyph, "a": glyph, ".null": glyph}
-fb.setupGlyf(glyphs)
-metrics = {}
-glyphTable = fb.font["glyf"]
-for gn, advanceWidth in advanceWidths.items():
- metrics[gn] = (advanceWidth, glyphTable[gn].xMin)
-fb.setupHorizontalMetrics(metrics)
-fb.setupHorizontalHeader(ascent=824, descent=-200)
-fb.setupNameTable(nameStrings)
-fb.setupOS2(sTypoAscender=824, usWinAscent=824, usWinDescent=200)
-fb.setupPost()
-fb.save("test.ttf")
-```
-
-And here's how to build a minimal OTF:
-
-```python
-from fontTools.fontBuilder import FontBuilder
-from fontTools.pens.t2CharStringPen import T2CharStringPen
-
-
-def drawTestGlyph(pen):
- pen.moveTo((100, 100))
- pen.lineTo((100, 1000))
- pen.curveTo((200, 900), (400, 900), (500, 1000))
- pen.lineTo((500, 100))
- pen.closePath()
-
-
-fb = FontBuilder(1024, isTTF=False)
-fb.setupGlyphOrder([".notdef", ".null", "space", "A", "a"])
-fb.setupCharacterMap({32: "space", 65: "A", 97: "a"})
-advanceWidths = {".notdef": 600, "space": 500, "A": 600, "a": 600, ".null": 0}
-
-familyName = "HelloTestFont"
-styleName = "TotallyNormal"
-version = "0.1"
-
-nameStrings = dict(
- familyName=dict(en=familyName, nl="HalloTestFont"),
- styleName=dict(en=styleName, nl="TotaalNormaal"),
- uniqueFontIdentifier="fontBuilder: " + familyName + "." + styleName,
- fullName=familyName + "-" + styleName,
- psName=familyName + "-" + styleName,
- version="Version " + version,
-)
-
-pen = T2CharStringPen(600, None)
-drawTestGlyph(pen)
-charString = pen.getCharString()
-charStrings = {
- ".notdef": charString,
- "space": charString,
- "A": charString,
- "a": charString,
- ".null": charString,
-}
-fb.setupCFF(nameStrings["psName"], {"FullName": nameStrings["psName"]}, charStrings, {})
-lsb = {gn: cs.calcBounds(None)[0] for gn, cs in charStrings.items()}
-metrics = {}
-for gn, advanceWidth in advanceWidths.items():
- metrics[gn] = (advanceWidth, lsb[gn])
-fb.setupHorizontalMetrics(metrics)
-fb.setupHorizontalHeader(ascent=824, descent=200)
-fb.setupNameTable(nameStrings)
-fb.setupOS2(sTypoAscender=824, usWinAscent=824, usWinDescent=200)
-fb.setupPost()
-fb.save("test.otf")
-```
-"""
-
-from .ttLib import TTFont, newTable
-from .ttLib.tables._c_m_a_p import cmap_classes
-from .ttLib.tables._g_l_y_f import flagCubic
-from .ttLib.tables.O_S_2f_2 import Panose
-from .misc.timeTools import timestampNow
-import struct
-from collections import OrderedDict
-
-
-_headDefaults = dict(
- tableVersion=1.0,
- fontRevision=1.0,
- checkSumAdjustment=0,
- magicNumber=0x5F0F3CF5,
- flags=0x0003,
- unitsPerEm=1000,
- created=0,
- modified=0,
- xMin=0,
- yMin=0,
- xMax=0,
- yMax=0,
- macStyle=0,
- lowestRecPPEM=3,
- fontDirectionHint=2,
- indexToLocFormat=0,
- glyphDataFormat=0,
-)
-
-_maxpDefaultsTTF = dict(
- tableVersion=0x00010000,
- numGlyphs=0,
- maxPoints=0,
- maxContours=0,
- maxCompositePoints=0,
- maxCompositeContours=0,
- maxZones=2,
- maxTwilightPoints=0,
- maxStorage=0,
- maxFunctionDefs=0,
- maxInstructionDefs=0,
- maxStackElements=0,
- maxSizeOfInstructions=0,
- maxComponentElements=0,
- maxComponentDepth=0,
-)
-_maxpDefaultsOTF = dict(
- tableVersion=0x00005000,
- numGlyphs=0,
-)
-
-_postDefaults = dict(
- formatType=3.0,
- italicAngle=0,
- underlinePosition=0,
- underlineThickness=0,
- isFixedPitch=0,
- minMemType42=0,
- maxMemType42=0,
- minMemType1=0,
- maxMemType1=0,
-)
-
-_hheaDefaults = dict(
- tableVersion=0x00010000,
- ascent=0,
- descent=0,
- lineGap=0,
- advanceWidthMax=0,
- minLeftSideBearing=0,
- minRightSideBearing=0,
- xMaxExtent=0,
- caretSlopeRise=1,
- caretSlopeRun=0,
- caretOffset=0,
- reserved0=0,
- reserved1=0,
- reserved2=0,
- reserved3=0,
- metricDataFormat=0,
- numberOfHMetrics=0,
-)
-
-_vheaDefaults = dict(
- tableVersion=0x00010000,
- ascent=0,
- descent=0,
- lineGap=0,
- advanceHeightMax=0,
- minTopSideBearing=0,
- minBottomSideBearing=0,
- yMaxExtent=0,
- caretSlopeRise=0,
- caretSlopeRun=0,
- reserved0=0,
- reserved1=0,
- reserved2=0,
- reserved3=0,
- reserved4=0,
- metricDataFormat=0,
- numberOfVMetrics=0,
-)
-
-_nameIDs = dict(
- copyright=0,
- familyName=1,
- styleName=2,
- uniqueFontIdentifier=3,
- fullName=4,
- version=5,
- psName=6,
- trademark=7,
- manufacturer=8,
- designer=9,
- description=10,
- vendorURL=11,
- designerURL=12,
- licenseDescription=13,
- licenseInfoURL=14,
- # reserved = 15,
- typographicFamily=16,
- typographicSubfamily=17,
- compatibleFullName=18,
- sampleText=19,
- postScriptCIDFindfontName=20,
- wwsFamilyName=21,
- wwsSubfamilyName=22,
- lightBackgroundPalette=23,
- darkBackgroundPalette=24,
- variationsPostScriptNamePrefix=25,
-)
-
-# to insert in setupNameTable doc string:
-# print("\n".join(("%s (nameID %s)" % (k, v)) for k, v in sorted(_nameIDs.items(), key=lambda x: x[1])))
-
-_panoseDefaults = Panose()
-
-_OS2Defaults = dict(
- version=3,
- xAvgCharWidth=0,
- usWeightClass=400,
- usWidthClass=5,
- fsType=0x0004, # default: Preview & Print embedding
- ySubscriptXSize=0,
- ySubscriptYSize=0,
- ySubscriptXOffset=0,
- ySubscriptYOffset=0,
- ySuperscriptXSize=0,
- ySuperscriptYSize=0,
- ySuperscriptXOffset=0,
- ySuperscriptYOffset=0,
- yStrikeoutSize=0,
- yStrikeoutPosition=0,
- sFamilyClass=0,
- panose=_panoseDefaults,
- ulUnicodeRange1=0,
- ulUnicodeRange2=0,
- ulUnicodeRange3=0,
- ulUnicodeRange4=0,
- achVendID="????",
- fsSelection=0,
- usFirstCharIndex=0,
- usLastCharIndex=0,
- sTypoAscender=0,
- sTypoDescender=0,
- sTypoLineGap=0,
- usWinAscent=0,
- usWinDescent=0,
- ulCodePageRange1=0,
- ulCodePageRange2=0,
- sxHeight=0,
- sCapHeight=0,
- usDefaultChar=0, # .notdef
- usBreakChar=32, # space
- usMaxContext=0,
- usLowerOpticalPointSize=0,
- usUpperOpticalPointSize=0,
-)
-
-
-class FontBuilder(object):
- def __init__(self, unitsPerEm=None, font=None, isTTF=True, glyphDataFormat=0):
- """Initialize a FontBuilder instance.
-
- If the `font` argument is not given, a new `TTFont` will be
- constructed, and `unitsPerEm` must be given. If `isTTF` is True,
- the font will be a glyf-based TTF; if `isTTF` is False it will be
- a CFF-based OTF.
-
- The `glyphDataFormat` argument corresponds to the `head` table field
- that defines the format of the TrueType `glyf` table (default=0).
- TrueType glyphs historically can only contain quadratic splines and static
- components, but there's a proposal to add support for cubic Bezier curves as well
- as variable composites/components at
- https://github.com/harfbuzz/boring-expansion-spec/blob/main/glyf1.md
- You can experiment with the new features by setting `glyphDataFormat` to 1.
- A ValueError is raised if `glyphDataFormat` is left at 0 but glyphs are added
- that contain cubic splines or varcomposites. This is to prevent accidentally
- creating fonts that are incompatible with existing TrueType implementations.
-
- If `font` is given, it must be a `TTFont` instance and `unitsPerEm`
- must _not_ be given. The `isTTF` and `glyphDataFormat` arguments will be ignored.
- """
- if font is None:
- self.font = TTFont(recalcTimestamp=False)
- self.isTTF = isTTF
- now = timestampNow()
- assert unitsPerEm is not None
- self.setupHead(
- unitsPerEm=unitsPerEm,
- created=now,
- modified=now,
- glyphDataFormat=glyphDataFormat,
- )
- self.setupMaxp()
- else:
- assert unitsPerEm is None
- self.font = font
- self.isTTF = "glyf" in font
-
- def save(self, file):
- """Save the font. The 'file' argument can be either a pathname or a
- writable file object.
- """
- self.font.save(file)
-
- def _initTableWithValues(self, tableTag, defaults, values):
- table = self.font[tableTag] = newTable(tableTag)
- for k, v in defaults.items():
- setattr(table, k, v)
- for k, v in values.items():
- setattr(table, k, v)
- return table
-
- def _updateTableWithValues(self, tableTag, values):
- table = self.font[tableTag]
- for k, v in values.items():
- setattr(table, k, v)
-
- def setupHead(self, **values):
- """Create a new `head` table and initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("head", _headDefaults, values)
-
- def updateHead(self, **values):
- """Update the head table with the fields and values passed as
- keyword arguments.
- """
- self._updateTableWithValues("head", values)
-
- def setupGlyphOrder(self, glyphOrder):
- """Set the glyph order for the font."""
- self.font.setGlyphOrder(glyphOrder)
-
- def setupCharacterMap(self, cmapping, uvs=None, allowFallback=False):
- """Build the `cmap` table for the font. The `cmapping` argument should
- be a dict mapping unicode code points as integers to glyph names.
-
- The `uvs` argument, when passed, must be a list of tuples, describing
- Unicode Variation Sequences. These tuples have three elements:
- (unicodeValue, variationSelector, glyphName)
- `unicodeValue` and `variationSelector` are integer code points.
- `glyphName` may be None, to indicate this is the default variation.
- Text processors will then use the cmap to find the glyph name.
- Each Unicode Variation Sequence should be an officially supported
- sequence, but this is not policed.
- """
- subTables = []
- highestUnicode = max(cmapping) if cmapping else 0
- if highestUnicode > 0xFFFF:
- cmapping_3_1 = dict((k, v) for k, v in cmapping.items() if k < 0x10000)
- subTable_3_10 = buildCmapSubTable(cmapping, 12, 3, 10)
- subTables.append(subTable_3_10)
- else:
- cmapping_3_1 = cmapping
- format = 4
- subTable_3_1 = buildCmapSubTable(cmapping_3_1, format, 3, 1)
- try:
- subTable_3_1.compile(self.font)
- except struct.error:
- # format 4 overflowed, fall back to format 12
- if not allowFallback:
- raise ValueError(
- "cmap format 4 subtable overflowed; sort glyph order by unicode to fix."
- )
- format = 12
- subTable_3_1 = buildCmapSubTable(cmapping_3_1, format, 3, 1)
- subTables.append(subTable_3_1)
- subTable_0_3 = buildCmapSubTable(cmapping_3_1, format, 0, 3)
- subTables.append(subTable_0_3)
-
- if uvs is not None:
- uvsDict = {}
- for unicodeValue, variationSelector, glyphName in uvs:
- if cmapping.get(unicodeValue) == glyphName:
- # this is a default variation
- glyphName = None
- if variationSelector not in uvsDict:
- uvsDict[variationSelector] = []
- uvsDict[variationSelector].append((unicodeValue, glyphName))
- uvsSubTable = buildCmapSubTable({}, 14, 0, 5)
- uvsSubTable.uvsDict = uvsDict
- subTables.append(uvsSubTable)
-
- self.font["cmap"] = newTable("cmap")
- self.font["cmap"].tableVersion = 0
- self.font["cmap"].tables = subTables
-
- def setupNameTable(self, nameStrings, windows=True, mac=True):
- """Create the `name` table for the font. The `nameStrings` argument must
- be a dict, mapping nameIDs or descriptive names for the nameIDs to name
- record values. A value is either a string, or a dict, mapping language codes
- to strings, to allow localized name table entries.
-
- By default, both Windows (platformID=3) and Macintosh (platformID=1) name
- records are added, unless any of `windows` or `mac` arguments is False.
-
- The following descriptive names are available for nameIDs:
-
- copyright (nameID 0)
- familyName (nameID 1)
- styleName (nameID 2)
- uniqueFontIdentifier (nameID 3)
- fullName (nameID 4)
- version (nameID 5)
- psName (nameID 6)
- trademark (nameID 7)
- manufacturer (nameID 8)
- designer (nameID 9)
- description (nameID 10)
- vendorURL (nameID 11)
- designerURL (nameID 12)
- licenseDescription (nameID 13)
- licenseInfoURL (nameID 14)
- typographicFamily (nameID 16)
- typographicSubfamily (nameID 17)
- compatibleFullName (nameID 18)
- sampleText (nameID 19)
- postScriptCIDFindfontName (nameID 20)
- wwsFamilyName (nameID 21)
- wwsSubfamilyName (nameID 22)
- lightBackgroundPalette (nameID 23)
- darkBackgroundPalette (nameID 24)
- variationsPostScriptNamePrefix (nameID 25)
- """
- nameTable = self.font["name"] = newTable("name")
- nameTable.names = []
-
- for nameName, nameValue in nameStrings.items():
- if isinstance(nameName, int):
- nameID = nameName
- else:
- nameID = _nameIDs[nameName]
- if isinstance(nameValue, str):
- nameValue = dict(en=nameValue)
- nameTable.addMultilingualName(
- nameValue, ttFont=self.font, nameID=nameID, windows=windows, mac=mac
- )
-
- def setupOS2(self, **values):
- """Create a new `OS/2` table and initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("OS/2", _OS2Defaults, values)
- if "xAvgCharWidth" not in values:
- assert (
- "hmtx" in self.font
- ), "the 'hmtx' table must be setup before the 'OS/2' table"
- self.font["OS/2"].recalcAvgCharWidth(self.font)
- if not (
- "ulUnicodeRange1" in values
- or "ulUnicodeRange2" in values
- or "ulUnicodeRange3" in values
- or "ulUnicodeRange3" in values
- ):
- assert (
- "cmap" in self.font
- ), "the 'cmap' table must be setup before the 'OS/2' table"
- self.font["OS/2"].recalcUnicodeRanges(self.font)
-
- def setupCFF(self, psName, fontInfo, charStringsDict, privateDict):
- from .cffLib import (
- CFFFontSet,
- TopDictIndex,
- TopDict,
- CharStrings,
- GlobalSubrsIndex,
- PrivateDict,
- )
-
- assert not self.isTTF
- self.font.sfntVersion = "OTTO"
- fontSet = CFFFontSet()
- fontSet.major = 1
- fontSet.minor = 0
- fontSet.otFont = self.font
- fontSet.fontNames = [psName]
- fontSet.topDictIndex = TopDictIndex()
-
- globalSubrs = GlobalSubrsIndex()
- fontSet.GlobalSubrs = globalSubrs
- private = PrivateDict()
- for key, value in privateDict.items():
- setattr(private, key, value)
- fdSelect = None
- fdArray = None
-
- topDict = TopDict()
- topDict.charset = self.font.getGlyphOrder()
- topDict.Private = private
- topDict.GlobalSubrs = fontSet.GlobalSubrs
- for key, value in fontInfo.items():
- setattr(topDict, key, value)
- if "FontMatrix" not in fontInfo:
- scale = 1 / self.font["head"].unitsPerEm
- topDict.FontMatrix = [scale, 0, 0, scale, 0, 0]
-
- charStrings = CharStrings(
- None, topDict.charset, globalSubrs, private, fdSelect, fdArray
- )
- for glyphName, charString in charStringsDict.items():
- charString.private = private
- charString.globalSubrs = globalSubrs
- charStrings[glyphName] = charString
- topDict.CharStrings = charStrings
-
- fontSet.topDictIndex.append(topDict)
-
- self.font["CFF "] = newTable("CFF ")
- self.font["CFF "].cff = fontSet
-
- def setupCFF2(self, charStringsDict, fdArrayList=None, regions=None):
- from .cffLib import (
- CFFFontSet,
- TopDictIndex,
- TopDict,
- CharStrings,
- GlobalSubrsIndex,
- PrivateDict,
- FDArrayIndex,
- FontDict,
- )
-
- assert not self.isTTF
- self.font.sfntVersion = "OTTO"
- fontSet = CFFFontSet()
- fontSet.major = 2
- fontSet.minor = 0
-
- cff2GetGlyphOrder = self.font.getGlyphOrder
- fontSet.topDictIndex = TopDictIndex(None, cff2GetGlyphOrder, None)
-
- globalSubrs = GlobalSubrsIndex()
- fontSet.GlobalSubrs = globalSubrs
-
- if fdArrayList is None:
- fdArrayList = [{}]
- fdSelect = None
- fdArray = FDArrayIndex()
- fdArray.strings = None
- fdArray.GlobalSubrs = globalSubrs
- for privateDict in fdArrayList:
- fontDict = FontDict()
- fontDict.setCFF2(True)
- private = PrivateDict()
- for key, value in privateDict.items():
- setattr(private, key, value)
- fontDict.Private = private
- fdArray.append(fontDict)
-
- topDict = TopDict()
- topDict.cff2GetGlyphOrder = cff2GetGlyphOrder
- topDict.FDArray = fdArray
- scale = 1 / self.font["head"].unitsPerEm
- topDict.FontMatrix = [scale, 0, 0, scale, 0, 0]
-
- private = fdArray[0].Private
- charStrings = CharStrings(None, None, globalSubrs, private, fdSelect, fdArray)
- for glyphName, charString in charStringsDict.items():
- charString.private = private
- charString.globalSubrs = globalSubrs
- charStrings[glyphName] = charString
- topDict.CharStrings = charStrings
-
- fontSet.topDictIndex.append(topDict)
-
- self.font["CFF2"] = newTable("CFF2")
- self.font["CFF2"].cff = fontSet
-
- if regions:
- self.setupCFF2Regions(regions)
-
- def setupCFF2Regions(self, regions):
- from .varLib.builder import buildVarRegionList, buildVarData, buildVarStore
- from .cffLib import VarStoreData
-
- assert "fvar" in self.font, "fvar must to be set up first"
- assert "CFF2" in self.font, "CFF2 must to be set up first"
- axisTags = [a.axisTag for a in self.font["fvar"].axes]
- varRegionList = buildVarRegionList(regions, axisTags)
- varData = buildVarData(list(range(len(regions))), None, optimize=False)
- varStore = buildVarStore(varRegionList, [varData])
- vstore = VarStoreData(otVarStore=varStore)
- topDict = self.font["CFF2"].cff.topDictIndex[0]
- topDict.VarStore = vstore
- for fontDict in topDict.FDArray:
- fontDict.Private.vstore = vstore
-
- def setupGlyf(self, glyphs, calcGlyphBounds=True, validateGlyphFormat=True):
- """Create the `glyf` table from a dict, that maps glyph names
- to `fontTools.ttLib.tables._g_l_y_f.Glyph` objects, for example
- as made by `fontTools.pens.ttGlyphPen.TTGlyphPen`.
-
- If `calcGlyphBounds` is True, the bounds of all glyphs will be
- calculated. Only pass False if your glyph objects already have
- their bounding box values set.
-
- If `validateGlyphFormat` is True, raise ValueError if any of the glyphs contains
- cubic curves or is a variable composite but head.glyphDataFormat=0.
- Set it to False to skip the check if you know in advance all the glyphs are
- compatible with the specified glyphDataFormat.
- """
- assert self.isTTF
-
- if validateGlyphFormat and self.font["head"].glyphDataFormat == 0:
- for name, g in glyphs.items():
- if g.isVarComposite():
- raise ValueError(
- f"Glyph {name!r} is a variable composite, but glyphDataFormat=0"
- )
- elif g.numberOfContours > 0 and any(f & flagCubic for f in g.flags):
- raise ValueError(
- f"Glyph {name!r} has cubic Bezier outlines, but glyphDataFormat=0; "
- "either convert to quadratics with cu2qu or set glyphDataFormat=1."
- )
-
- self.font["loca"] = newTable("loca")
- self.font["glyf"] = newTable("glyf")
- self.font["glyf"].glyphs = glyphs
- if hasattr(self.font, "glyphOrder"):
- self.font["glyf"].glyphOrder = self.font.glyphOrder
- if calcGlyphBounds:
- self.calcGlyphBounds()
-
- def setupFvar(self, axes, instances):
- """Adds an font variations table to the font.
-
- Args:
- axes (list): See below.
- instances (list): See below.
-
- ``axes`` should be a list of axes, with each axis either supplied as
- a py:class:`.designspaceLib.AxisDescriptor` object, or a tuple in the
- format ```tupletag, minValue, defaultValue, maxValue, name``.
- The ``name`` is either a string, or a dict, mapping language codes
- to strings, to allow localized name table entries.
-
- ```instances`` should be a list of instances, with each instance either
- supplied as a py:class:`.designspaceLib.InstanceDescriptor` object, or a
- dict with keys ``location`` (mapping of axis tags to float values),
- ``stylename`` and (optionally) ``postscriptfontname``.
- The ``stylename`` is either a string, or a dict, mapping language codes
- to strings, to allow localized name table entries.
- """
-
- addFvar(self.font, axes, instances)
-
- def setupAvar(self, axes, mappings=None):
- """Adds an axis variations table to the font.
-
- Args:
- axes (list): A list of py:class:`.designspaceLib.AxisDescriptor` objects.
- """
- from .varLib import _add_avar
-
- if "fvar" not in self.font:
- raise KeyError("'fvar' table is missing; can't add 'avar'.")
-
- axisTags = [axis.axisTag for axis in self.font["fvar"].axes]
- axes = OrderedDict(enumerate(axes)) # Only values are used
- _add_avar(self.font, axes, mappings, axisTags)
-
- def setupGvar(self, variations):
- gvar = self.font["gvar"] = newTable("gvar")
- gvar.version = 1
- gvar.reserved = 0
- gvar.variations = variations
-
- def calcGlyphBounds(self):
- """Calculate the bounding boxes of all glyphs in the `glyf` table.
- This is usually not called explicitly by client code.
- """
- glyphTable = self.font["glyf"]
- for glyph in glyphTable.glyphs.values():
- glyph.recalcBounds(glyphTable)
-
- def setupHorizontalMetrics(self, metrics):
- """Create a new `hmtx` table, for horizontal metrics.
-
- The `metrics` argument must be a dict, mapping glyph names to
- `(width, leftSidebearing)` tuples.
- """
- self.setupMetrics("hmtx", metrics)
-
- def setupVerticalMetrics(self, metrics):
- """Create a new `vmtx` table, for horizontal metrics.
-
- The `metrics` argument must be a dict, mapping glyph names to
- `(height, topSidebearing)` tuples.
- """
- self.setupMetrics("vmtx", metrics)
-
- def setupMetrics(self, tableTag, metrics):
- """See `setupHorizontalMetrics()` and `setupVerticalMetrics()`."""
- assert tableTag in ("hmtx", "vmtx")
- mtxTable = self.font[tableTag] = newTable(tableTag)
- roundedMetrics = {}
- for gn in metrics:
- w, lsb = metrics[gn]
- roundedMetrics[gn] = int(round(w)), int(round(lsb))
- mtxTable.metrics = roundedMetrics
-
- def setupHorizontalHeader(self, **values):
- """Create a new `hhea` table initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("hhea", _hheaDefaults, values)
-
- def setupVerticalHeader(self, **values):
- """Create a new `vhea` table initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("vhea", _vheaDefaults, values)
-
- def setupVerticalOrigins(self, verticalOrigins, defaultVerticalOrigin=None):
- """Create a new `VORG` table. The `verticalOrigins` argument must be
- a dict, mapping glyph names to vertical origin values.
-
- The `defaultVerticalOrigin` argument should be the most common vertical
- origin value. If omitted, this value will be derived from the actual
- values in the `verticalOrigins` argument.
- """
- if defaultVerticalOrigin is None:
- # find the most frequent vorg value
- bag = {}
- for gn in verticalOrigins:
- vorg = verticalOrigins[gn]
- if vorg not in bag:
- bag[vorg] = 1
- else:
- bag[vorg] += 1
- defaultVerticalOrigin = sorted(
- bag, key=lambda vorg: bag[vorg], reverse=True
- )[0]
- self._initTableWithValues(
- "VORG",
- {},
- dict(VOriginRecords={}, defaultVertOriginY=defaultVerticalOrigin),
- )
- vorgTable = self.font["VORG"]
- vorgTable.majorVersion = 1
- vorgTable.minorVersion = 0
- for gn in verticalOrigins:
- vorgTable[gn] = verticalOrigins[gn]
-
- def setupPost(self, keepGlyphNames=True, **values):
- """Create a new `post` table and initialize it with default values,
- which can be overridden by keyword arguments.
- """
- isCFF2 = "CFF2" in self.font
- postTable = self._initTableWithValues("post", _postDefaults, values)
- if (self.isTTF or isCFF2) and keepGlyphNames:
- postTable.formatType = 2.0
- postTable.extraNames = []
- postTable.mapping = {}
- else:
- postTable.formatType = 3.0
-
- def setupMaxp(self):
- """Create a new `maxp` table. This is called implicitly by FontBuilder
- itself and is usually not called by client code.
- """
- if self.isTTF:
- defaults = _maxpDefaultsTTF
- else:
- defaults = _maxpDefaultsOTF
- self._initTableWithValues("maxp", defaults, {})
-
- def setupDummyDSIG(self):
- """This adds an empty DSIG table to the font to make some MS applications
- happy. This does not properly sign the font.
- """
- values = dict(
- ulVersion=1,
- usFlag=0,
- usNumSigs=0,
- signatureRecords=[],
- )
- self._initTableWithValues("DSIG", {}, values)
-
- def addOpenTypeFeatures(self, features, filename=None, tables=None, debug=False):
- """Add OpenType features to the font from a string containing
- Feature File syntax.
-
- The `filename` argument is used in error messages and to determine
- where to look for "include" files.
-
- The optional `tables` argument can be a list of OTL tables tags to
- build, allowing the caller to only build selected OTL tables. See
- `fontTools.feaLib` for details.
-
- The optional `debug` argument controls whether to add source debugging
- information to the font in the `Debg` table.
- """
- from .feaLib.builder import addOpenTypeFeaturesFromString
-
- addOpenTypeFeaturesFromString(
- self.font, features, filename=filename, tables=tables, debug=debug
- )
-
- def addFeatureVariations(self, conditionalSubstitutions, featureTag="rvrn"):
- """Add conditional substitutions to a Variable Font.
-
- See `fontTools.varLib.featureVars.addFeatureVariations`.
- """
- from .varLib import featureVars
-
- if "fvar" not in self.font:
- raise KeyError("'fvar' table is missing; can't add FeatureVariations.")
-
- featureVars.addFeatureVariations(
- self.font, conditionalSubstitutions, featureTag=featureTag
- )
-
- def setupCOLR(
- self,
- colorLayers,
- version=None,
- varStore=None,
- varIndexMap=None,
- clipBoxes=None,
- allowLayerReuse=True,
- ):
- """Build new COLR table using color layers dictionary.
-
- Cf. `fontTools.colorLib.builder.buildCOLR`.
- """
- from fontTools.colorLib.builder import buildCOLR
-
- glyphMap = self.font.getReverseGlyphMap()
- self.font["COLR"] = buildCOLR(
- colorLayers,
- version=version,
- glyphMap=glyphMap,
- varStore=varStore,
- varIndexMap=varIndexMap,
- clipBoxes=clipBoxes,
- allowLayerReuse=allowLayerReuse,
- )
-
- def setupCPAL(
- self,
- palettes,
- paletteTypes=None,
- paletteLabels=None,
- paletteEntryLabels=None,
- ):
- """Build new CPAL table using list of palettes.
-
- Optionally build CPAL v1 table using paletteTypes, paletteLabels and
- paletteEntryLabels.
-
- Cf. `fontTools.colorLib.builder.buildCPAL`.
- """
- from fontTools.colorLib.builder import buildCPAL
-
- self.font["CPAL"] = buildCPAL(
- palettes,
- paletteTypes=paletteTypes,
- paletteLabels=paletteLabels,
- paletteEntryLabels=paletteEntryLabels,
- nameTable=self.font.get("name"),
- )
-
- def setupStat(self, axes, locations=None, elidedFallbackName=2):
- """Build a new 'STAT' table.
-
- See `fontTools.otlLib.builder.buildStatTable` for details about
- the arguments.
- """
- from .otlLib.builder import buildStatTable
-
- buildStatTable(self.font, axes, locations, elidedFallbackName)
-
-
-def buildCmapSubTable(cmapping, format, platformID, platEncID):
- subTable = cmap_classes[format](format)
- subTable.cmap = cmapping
- subTable.platformID = platformID
- subTable.platEncID = platEncID
- subTable.language = 0
- return subTable
-
-
-def addFvar(font, axes, instances):
- from .ttLib.tables._f_v_a_r import Axis, NamedInstance
-
- assert axes
-
- fvar = newTable("fvar")
- nameTable = font["name"]
-
- for axis_def in axes:
- axis = Axis()
-
- if isinstance(axis_def, tuple):
- (
- axis.axisTag,
- axis.minValue,
- axis.defaultValue,
- axis.maxValue,
- name,
- ) = axis_def
- else:
- (axis.axisTag, axis.minValue, axis.defaultValue, axis.maxValue, name) = (
- axis_def.tag,
- axis_def.minimum,
- axis_def.default,
- axis_def.maximum,
- axis_def.name,
- )
- if axis_def.hidden:
- axis.flags = 0x0001 # HIDDEN_AXIS
-
- if isinstance(name, str):
- name = dict(en=name)
-
- axis.axisNameID = nameTable.addMultilingualName(name, ttFont=font)
- fvar.axes.append(axis)
-
- for instance in instances:
- if isinstance(instance, dict):
- coordinates = instance["location"]
- name = instance["stylename"]
- psname = instance.get("postscriptfontname")
- else:
- coordinates = instance.location
- name = instance.localisedStyleName or instance.styleName
- psname = instance.postScriptFontName
-
- if isinstance(name, str):
- name = dict(en=name)
-
- inst = NamedInstance()
- inst.subfamilyNameID = nameTable.addMultilingualName(name, ttFont=font)
- if psname is not None:
- inst.postscriptNameID = nameTable.addName(psname)
- inst.coordinates = coordinates
- fvar.instances.append(inst)
-
- font["fvar"] = fvar
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/mtiLib/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/mtiLib/__init__.py
deleted file mode 100644
index dbedf275e3d3cfb2e8ec43eddd88b9d78ad53e15..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/mtiLib/__init__.py
+++ /dev/null
@@ -1,1402 +0,0 @@
-#!/usr/bin/python
-
-# FontDame-to-FontTools for OpenType Layout tables
-#
-# Source language spec is available at:
-# http://monotype.github.io/OpenType_Table_Source/otl_source.html
-# https://github.com/Monotype/OpenType_Table_Source/
-
-from fontTools import ttLib
-from fontTools.ttLib.tables._c_m_a_p import cmap_classes
-from fontTools.ttLib.tables import otTables as ot
-from fontTools.ttLib.tables.otBase import ValueRecord, valueRecordFormatDict
-from fontTools.otlLib import builder as otl
-from contextlib import contextmanager
-from fontTools.ttLib import newTable
-from fontTools.feaLib.lookupDebugInfo import LOOKUP_DEBUG_ENV_VAR, LOOKUP_DEBUG_INFO_KEY
-from operator import setitem
-import os
-import logging
-
-
-class MtiLibError(Exception):
- pass
-
-
-class ReferenceNotFoundError(MtiLibError):
- pass
-
-
-class FeatureNotFoundError(ReferenceNotFoundError):
- pass
-
-
-class LookupNotFoundError(ReferenceNotFoundError):
- pass
-
-
-log = logging.getLogger("fontTools.mtiLib")
-
-
-def makeGlyph(s):
- if s[:2] in ["U ", "u "]:
- return ttLib.TTFont._makeGlyphName(int(s[2:], 16))
- elif s[:2] == "# ":
- return "glyph%.5d" % int(s[2:])
- assert s.find(" ") < 0, "Space found in glyph name: %s" % s
- assert s, "Glyph name is empty"
- return s
-
-
-def makeGlyphs(l):
- return [makeGlyph(g) for g in l]
-
-
-def mapLookup(sym, mapping):
- # Lookups are addressed by name. So resolved them using a map if available.
- # Fallback to parsing as lookup index if a map isn't provided.
- if mapping is not None:
- try:
- idx = mapping[sym]
- except KeyError:
- raise LookupNotFoundError(sym)
- else:
- idx = int(sym)
- return idx
-
-
-def mapFeature(sym, mapping):
- # Features are referenced by index according the spec. So, if symbol is an
- # integer, use it directly. Otherwise look up in the map if provided.
- try:
- idx = int(sym)
- except ValueError:
- try:
- idx = mapping[sym]
- except KeyError:
- raise FeatureNotFoundError(sym)
- return idx
-
-
-def setReference(mapper, mapping, sym, setter, collection, key):
- try:
- mapped = mapper(sym, mapping)
- except ReferenceNotFoundError as e:
- try:
- if mapping is not None:
- mapping.addDeferredMapping(
- lambda ref: setter(collection, key, ref), sym, e
- )
- return
- except AttributeError:
- pass
- raise
- setter(collection, key, mapped)
-
-
-class DeferredMapping(dict):
- def __init__(self):
- self._deferredMappings = []
-
- def addDeferredMapping(self, setter, sym, e):
- log.debug("Adding deferred mapping for symbol '%s' %s", sym, type(e).__name__)
- self._deferredMappings.append((setter, sym, e))
-
- def applyDeferredMappings(self):
- for setter, sym, e in self._deferredMappings:
- log.debug(
- "Applying deferred mapping for symbol '%s' %s", sym, type(e).__name__
- )
- try:
- mapped = self[sym]
- except KeyError:
- raise e
- setter(mapped)
- log.debug("Set to %s", mapped)
- self._deferredMappings = []
-
-
-def parseScriptList(lines, featureMap=None):
- self = ot.ScriptList()
- records = []
- with lines.between("script table"):
- for line in lines:
- while len(line) < 4:
- line.append("")
- scriptTag, langSysTag, defaultFeature, features = line
- log.debug("Adding script %s language-system %s", scriptTag, langSysTag)
-
- langSys = ot.LangSys()
- langSys.LookupOrder = None
- if defaultFeature:
- setReference(
- mapFeature,
- featureMap,
- defaultFeature,
- setattr,
- langSys,
- "ReqFeatureIndex",
- )
- else:
- langSys.ReqFeatureIndex = 0xFFFF
- syms = stripSplitComma(features)
- langSys.FeatureIndex = theList = [3] * len(syms)
- for i, sym in enumerate(syms):
- setReference(mapFeature, featureMap, sym, setitem, theList, i)
- langSys.FeatureCount = len(langSys.FeatureIndex)
-
- script = [s for s in records if s.ScriptTag == scriptTag]
- if script:
- script = script[0].Script
- else:
- scriptRec = ot.ScriptRecord()
- scriptRec.ScriptTag = scriptTag + " " * (4 - len(scriptTag))
- scriptRec.Script = ot.Script()
- records.append(scriptRec)
- script = scriptRec.Script
- script.DefaultLangSys = None
- script.LangSysRecord = []
- script.LangSysCount = 0
-
- if langSysTag == "default":
- script.DefaultLangSys = langSys
- else:
- langSysRec = ot.LangSysRecord()
- langSysRec.LangSysTag = langSysTag + " " * (4 - len(langSysTag))
- langSysRec.LangSys = langSys
- script.LangSysRecord.append(langSysRec)
- script.LangSysCount = len(script.LangSysRecord)
-
- for script in records:
- script.Script.LangSysRecord = sorted(
- script.Script.LangSysRecord, key=lambda rec: rec.LangSysTag
- )
- self.ScriptRecord = sorted(records, key=lambda rec: rec.ScriptTag)
- self.ScriptCount = len(self.ScriptRecord)
- return self
-
-
-def parseFeatureList(lines, lookupMap=None, featureMap=None):
- self = ot.FeatureList()
- self.FeatureRecord = []
- with lines.between("feature table"):
- for line in lines:
- name, featureTag, lookups = line
- if featureMap is not None:
- assert name not in featureMap, "Duplicate feature name: %s" % name
- featureMap[name] = len(self.FeatureRecord)
- # If feature name is integer, make sure it matches its index.
- try:
- assert int(name) == len(self.FeatureRecord), "%d %d" % (
- name,
- len(self.FeatureRecord),
- )
- except ValueError:
- pass
- featureRec = ot.FeatureRecord()
- featureRec.FeatureTag = featureTag
- featureRec.Feature = ot.Feature()
- self.FeatureRecord.append(featureRec)
- feature = featureRec.Feature
- feature.FeatureParams = None
- syms = stripSplitComma(lookups)
- feature.LookupListIndex = theList = [None] * len(syms)
- for i, sym in enumerate(syms):
- setReference(mapLookup, lookupMap, sym, setitem, theList, i)
- feature.LookupCount = len(feature.LookupListIndex)
-
- self.FeatureCount = len(self.FeatureRecord)
- return self
-
-
-def parseLookupFlags(lines):
- flags = 0
- filterset = None
- allFlags = [
- "righttoleft",
- "ignorebaseglyphs",
- "ignoreligatures",
- "ignoremarks",
- "markattachmenttype",
- "markfiltertype",
- ]
- while lines.peeks()[0].lower() in allFlags:
- line = next(lines)
- flag = {
- "righttoleft": 0x0001,
- "ignorebaseglyphs": 0x0002,
- "ignoreligatures": 0x0004,
- "ignoremarks": 0x0008,
- }.get(line[0].lower())
- if flag:
- assert line[1].lower() in ["yes", "no"], line[1]
- if line[1].lower() == "yes":
- flags |= flag
- continue
- if line[0].lower() == "markattachmenttype":
- flags |= int(line[1]) << 8
- continue
- if line[0].lower() == "markfiltertype":
- flags |= 0x10
- filterset = int(line[1])
- return flags, filterset
-
-
-def parseSingleSubst(lines, font, _lookupMap=None):
- mapping = {}
- for line in lines:
- assert len(line) == 2, line
- line = makeGlyphs(line)
- mapping[line[0]] = line[1]
- return otl.buildSingleSubstSubtable(mapping)
-
-
-def parseMultiple(lines, font, _lookupMap=None):
- mapping = {}
- for line in lines:
- line = makeGlyphs(line)
- mapping[line[0]] = line[1:]
- return otl.buildMultipleSubstSubtable(mapping)
-
-
-def parseAlternate(lines, font, _lookupMap=None):
- mapping = {}
- for line in lines:
- line = makeGlyphs(line)
- mapping[line[0]] = line[1:]
- return otl.buildAlternateSubstSubtable(mapping)
-
-
-def parseLigature(lines, font, _lookupMap=None):
- mapping = {}
- for line in lines:
- assert len(line) >= 2, line
- line = makeGlyphs(line)
- mapping[tuple(line[1:])] = line[0]
- return otl.buildLigatureSubstSubtable(mapping)
-
-
-def parseSinglePos(lines, font, _lookupMap=None):
- values = {}
- for line in lines:
- assert len(line) == 3, line
- w = line[0].title().replace(" ", "")
- assert w in valueRecordFormatDict
- g = makeGlyph(line[1])
- v = int(line[2])
- if g not in values:
- values[g] = ValueRecord()
- assert not hasattr(values[g], w), (g, w)
- setattr(values[g], w, v)
- return otl.buildSinglePosSubtable(values, font.getReverseGlyphMap())
-
-
-def parsePair(lines, font, _lookupMap=None):
- self = ot.PairPos()
- self.ValueFormat1 = self.ValueFormat2 = 0
- typ = lines.peeks()[0].split()[0].lower()
- if typ in ("left", "right"):
- self.Format = 1
- values = {}
- for line in lines:
- assert len(line) == 4, line
- side = line[0].split()[0].lower()
- assert side in ("left", "right"), side
- what = line[0][len(side) :].title().replace(" ", "")
- mask = valueRecordFormatDict[what][0]
- glyph1, glyph2 = makeGlyphs(line[1:3])
- value = int(line[3])
- if not glyph1 in values:
- values[glyph1] = {}
- if not glyph2 in values[glyph1]:
- values[glyph1][glyph2] = (ValueRecord(), ValueRecord())
- rec2 = values[glyph1][glyph2]
- if side == "left":
- self.ValueFormat1 |= mask
- vr = rec2[0]
- else:
- self.ValueFormat2 |= mask
- vr = rec2[1]
- assert not hasattr(vr, what), (vr, what)
- setattr(vr, what, value)
- self.Coverage = makeCoverage(set(values.keys()), font)
- self.PairSet = []
- for glyph1 in self.Coverage.glyphs:
- values1 = values[glyph1]
- pairset = ot.PairSet()
- records = pairset.PairValueRecord = []
- for glyph2 in sorted(values1.keys(), key=font.getGlyphID):
- values2 = values1[glyph2]
- pair = ot.PairValueRecord()
- pair.SecondGlyph = glyph2
- pair.Value1 = values2[0]
- pair.Value2 = values2[1] if self.ValueFormat2 else None
- records.append(pair)
- pairset.PairValueCount = len(pairset.PairValueRecord)
- self.PairSet.append(pairset)
- self.PairSetCount = len(self.PairSet)
- elif typ.endswith("class"):
- self.Format = 2
- classDefs = [None, None]
- while lines.peeks()[0].endswith("class definition begin"):
- typ = lines.peek()[0][: -len("class definition begin")].lower()
- idx, klass = {
- "first": (0, ot.ClassDef1),
- "second": (1, ot.ClassDef2),
- }[typ]
- assert classDefs[idx] is None
- classDefs[idx] = parseClassDef(lines, font, klass=klass)
- self.ClassDef1, self.ClassDef2 = classDefs
- self.Class1Count, self.Class2Count = (
- 1 + max(c.classDefs.values()) for c in classDefs
- )
- self.Class1Record = [ot.Class1Record() for i in range(self.Class1Count)]
- for rec1 in self.Class1Record:
- rec1.Class2Record = [ot.Class2Record() for j in range(self.Class2Count)]
- for rec2 in rec1.Class2Record:
- rec2.Value1 = ValueRecord()
- rec2.Value2 = ValueRecord()
- for line in lines:
- assert len(line) == 4, line
- side = line[0].split()[0].lower()
- assert side in ("left", "right"), side
- what = line[0][len(side) :].title().replace(" ", "")
- mask = valueRecordFormatDict[what][0]
- class1, class2, value = (int(x) for x in line[1:4])
- rec2 = self.Class1Record[class1].Class2Record[class2]
- if side == "left":
- self.ValueFormat1 |= mask
- vr = rec2.Value1
- else:
- self.ValueFormat2 |= mask
- vr = rec2.Value2
- assert not hasattr(vr, what), (vr, what)
- setattr(vr, what, value)
- for rec1 in self.Class1Record:
- for rec2 in rec1.Class2Record:
- rec2.Value1 = ValueRecord(self.ValueFormat1, rec2.Value1)
- rec2.Value2 = (
- ValueRecord(self.ValueFormat2, rec2.Value2)
- if self.ValueFormat2
- else None
- )
-
- self.Coverage = makeCoverage(set(self.ClassDef1.classDefs.keys()), font)
- else:
- assert 0, typ
- return self
-
-
-def parseKernset(lines, font, _lookupMap=None):
- typ = lines.peeks()[0].split()[0].lower()
- if typ in ("left", "right"):
- with lines.until(
- ("firstclass definition begin", "secondclass definition begin")
- ):
- return parsePair(lines, font)
- return parsePair(lines, font)
-
-
-def makeAnchor(data, klass=ot.Anchor):
- assert len(data) <= 2
- anchor = klass()
- anchor.Format = 1
- anchor.XCoordinate, anchor.YCoordinate = intSplitComma(data[0])
- if len(data) > 1 and data[1] != "":
- anchor.Format = 2
- anchor.AnchorPoint = int(data[1])
- return anchor
-
-
-def parseCursive(lines, font, _lookupMap=None):
- records = {}
- for line in lines:
- assert len(line) in [3, 4], line
- idx, klass = {
- "entry": (0, ot.EntryAnchor),
- "exit": (1, ot.ExitAnchor),
- }[line[0]]
- glyph = makeGlyph(line[1])
- if glyph not in records:
- records[glyph] = [None, None]
- assert records[glyph][idx] is None, (glyph, idx)
- records[glyph][idx] = makeAnchor(line[2:], klass)
- return otl.buildCursivePosSubtable(records, font.getReverseGlyphMap())
-
-
-def makeMarkRecords(data, coverage, c):
- records = []
- for glyph in coverage.glyphs:
- klass, anchor = data[glyph]
- record = c.MarkRecordClass()
- record.Class = klass
- setattr(record, c.MarkAnchor, anchor)
- records.append(record)
- return records
-
-
-def makeBaseRecords(data, coverage, c, classCount):
- records = []
- idx = {}
- for glyph in coverage.glyphs:
- idx[glyph] = len(records)
- record = c.BaseRecordClass()
- anchors = [None] * classCount
- setattr(record, c.BaseAnchor, anchors)
- records.append(record)
- for (glyph, klass), anchor in data.items():
- record = records[idx[glyph]]
- anchors = getattr(record, c.BaseAnchor)
- assert anchors[klass] is None, (glyph, klass)
- anchors[klass] = anchor
- return records
-
-
-def makeLigatureRecords(data, coverage, c, classCount):
- records = [None] * len(coverage.glyphs)
- idx = {g: i for i, g in enumerate(coverage.glyphs)}
-
- for (glyph, klass, compIdx, compCount), anchor in data.items():
- record = records[idx[glyph]]
- if record is None:
- record = records[idx[glyph]] = ot.LigatureAttach()
- record.ComponentCount = compCount
- record.ComponentRecord = [ot.ComponentRecord() for i in range(compCount)]
- for compRec in record.ComponentRecord:
- compRec.LigatureAnchor = [None] * classCount
- assert record.ComponentCount == compCount, (
- glyph,
- record.ComponentCount,
- compCount,
- )
-
- anchors = record.ComponentRecord[compIdx - 1].LigatureAnchor
- assert anchors[klass] is None, (glyph, compIdx, klass)
- anchors[klass] = anchor
- return records
-
-
-def parseMarkToSomething(lines, font, c):
- self = c.Type()
- self.Format = 1
- markData = {}
- baseData = {}
- Data = {
- "mark": (markData, c.MarkAnchorClass),
- "base": (baseData, c.BaseAnchorClass),
- "ligature": (baseData, c.BaseAnchorClass),
- }
- maxKlass = 0
- for line in lines:
- typ = line[0]
- assert typ in ("mark", "base", "ligature")
- glyph = makeGlyph(line[1])
- data, anchorClass = Data[typ]
- extraItems = 2 if typ == "ligature" else 0
- extras = tuple(int(i) for i in line[2 : 2 + extraItems])
- klass = int(line[2 + extraItems])
- anchor = makeAnchor(line[3 + extraItems :], anchorClass)
- if typ == "mark":
- key, value = glyph, (klass, anchor)
- else:
- key, value = ((glyph, klass) + extras), anchor
- assert key not in data, key
- data[key] = value
- maxKlass = max(maxKlass, klass)
-
- # Mark
- markCoverage = makeCoverage(set(markData.keys()), font, c.MarkCoverageClass)
- markArray = c.MarkArrayClass()
- markRecords = makeMarkRecords(markData, markCoverage, c)
- setattr(markArray, c.MarkRecord, markRecords)
- setattr(markArray, c.MarkCount, len(markRecords))
- setattr(self, c.MarkCoverage, markCoverage)
- setattr(self, c.MarkArray, markArray)
- self.ClassCount = maxKlass + 1
-
- # Base
- self.classCount = 0 if not baseData else 1 + max(k[1] for k, v in baseData.items())
- baseCoverage = makeCoverage(
- set([k[0] for k in baseData.keys()]), font, c.BaseCoverageClass
- )
- baseArray = c.BaseArrayClass()
- if c.Base == "Ligature":
- baseRecords = makeLigatureRecords(baseData, baseCoverage, c, self.classCount)
- else:
- baseRecords = makeBaseRecords(baseData, baseCoverage, c, self.classCount)
- setattr(baseArray, c.BaseRecord, baseRecords)
- setattr(baseArray, c.BaseCount, len(baseRecords))
- setattr(self, c.BaseCoverage, baseCoverage)
- setattr(self, c.BaseArray, baseArray)
-
- return self
-
-
-class MarkHelper(object):
- def __init__(self):
- for Which in ("Mark", "Base"):
- for What in ("Coverage", "Array", "Count", "Record", "Anchor"):
- key = Which + What
- if Which == "Mark" and What in ("Count", "Record", "Anchor"):
- value = key
- else:
- value = getattr(self, Which) + What
- if value == "LigatureRecord":
- value = "LigatureAttach"
- setattr(self, key, value)
- if What != "Count":
- klass = getattr(ot, value)
- setattr(self, key + "Class", klass)
-
-
-class MarkToBaseHelper(MarkHelper):
- Mark = "Mark"
- Base = "Base"
- Type = ot.MarkBasePos
-
-
-class MarkToMarkHelper(MarkHelper):
- Mark = "Mark1"
- Base = "Mark2"
- Type = ot.MarkMarkPos
-
-
-class MarkToLigatureHelper(MarkHelper):
- Mark = "Mark"
- Base = "Ligature"
- Type = ot.MarkLigPos
-
-
-def parseMarkToBase(lines, font, _lookupMap=None):
- return parseMarkToSomething(lines, font, MarkToBaseHelper())
-
-
-def parseMarkToMark(lines, font, _lookupMap=None):
- return parseMarkToSomething(lines, font, MarkToMarkHelper())
-
-
-def parseMarkToLigature(lines, font, _lookupMap=None):
- return parseMarkToSomething(lines, font, MarkToLigatureHelper())
-
-
-def stripSplitComma(line):
- return [s.strip() for s in line.split(",")] if line else []
-
-
-def intSplitComma(line):
- return [int(i) for i in line.split(",")] if line else []
-
-
-# Copied from fontTools.subset
-class ContextHelper(object):
- def __init__(self, klassName, Format):
- if klassName.endswith("Subst"):
- Typ = "Sub"
- Type = "Subst"
- else:
- Typ = "Pos"
- Type = "Pos"
- if klassName.startswith("Chain"):
- Chain = "Chain"
- InputIdx = 1
- DataLen = 3
- else:
- Chain = ""
- InputIdx = 0
- DataLen = 1
- ChainTyp = Chain + Typ
-
- self.Typ = Typ
- self.Type = Type
- self.Chain = Chain
- self.ChainTyp = ChainTyp
- self.InputIdx = InputIdx
- self.DataLen = DataLen
-
- self.LookupRecord = Type + "LookupRecord"
-
- if Format == 1:
- Coverage = lambda r: r.Coverage
- ChainCoverage = lambda r: r.Coverage
- ContextData = lambda r: (None,)
- ChainContextData = lambda r: (None, None, None)
- SetContextData = None
- SetChainContextData = None
- RuleData = lambda r: (r.Input,)
- ChainRuleData = lambda r: (r.Backtrack, r.Input, r.LookAhead)
-
- def SetRuleData(r, d):
- (r.Input,) = d
- (r.GlyphCount,) = (len(x) + 1 for x in d)
-
- def ChainSetRuleData(r, d):
- (r.Backtrack, r.Input, r.LookAhead) = d
- (
- r.BacktrackGlyphCount,
- r.InputGlyphCount,
- r.LookAheadGlyphCount,
- ) = (len(d[0]), len(d[1]) + 1, len(d[2]))
-
- elif Format == 2:
- Coverage = lambda r: r.Coverage
- ChainCoverage = lambda r: r.Coverage
- ContextData = lambda r: (r.ClassDef,)
- ChainContextData = lambda r: (
- r.BacktrackClassDef,
- r.InputClassDef,
- r.LookAheadClassDef,
- )
-
- def SetContextData(r, d):
- (r.ClassDef,) = d
-
- def SetChainContextData(r, d):
- (r.BacktrackClassDef, r.InputClassDef, r.LookAheadClassDef) = d
-
- RuleData = lambda r: (r.Class,)
- ChainRuleData = lambda r: (r.Backtrack, r.Input, r.LookAhead)
-
- def SetRuleData(r, d):
- (r.Class,) = d
- (r.GlyphCount,) = (len(x) + 1 for x in d)
-
- def ChainSetRuleData(r, d):
- (r.Backtrack, r.Input, r.LookAhead) = d
- (
- r.BacktrackGlyphCount,
- r.InputGlyphCount,
- r.LookAheadGlyphCount,
- ) = (len(d[0]), len(d[1]) + 1, len(d[2]))
-
- elif Format == 3:
- Coverage = lambda r: r.Coverage[0]
- ChainCoverage = lambda r: r.InputCoverage[0]
- ContextData = None
- ChainContextData = None
- SetContextData = None
- SetChainContextData = None
- RuleData = lambda r: r.Coverage
- ChainRuleData = lambda r: (
- r.BacktrackCoverage + r.InputCoverage + r.LookAheadCoverage
- )
-
- def SetRuleData(r, d):
- (r.Coverage,) = d
- (r.GlyphCount,) = (len(x) for x in d)
-
- def ChainSetRuleData(r, d):
- (r.BacktrackCoverage, r.InputCoverage, r.LookAheadCoverage) = d
- (
- r.BacktrackGlyphCount,
- r.InputGlyphCount,
- r.LookAheadGlyphCount,
- ) = (len(x) for x in d)
-
- else:
- assert 0, "unknown format: %s" % Format
-
- if Chain:
- self.Coverage = ChainCoverage
- self.ContextData = ChainContextData
- self.SetContextData = SetChainContextData
- self.RuleData = ChainRuleData
- self.SetRuleData = ChainSetRuleData
- else:
- self.Coverage = Coverage
- self.ContextData = ContextData
- self.SetContextData = SetContextData
- self.RuleData = RuleData
- self.SetRuleData = SetRuleData
-
- if Format == 1:
- self.Rule = ChainTyp + "Rule"
- self.RuleCount = ChainTyp + "RuleCount"
- self.RuleSet = ChainTyp + "RuleSet"
- self.RuleSetCount = ChainTyp + "RuleSetCount"
- self.Intersect = lambda glyphs, c, r: [r] if r in glyphs else []
- elif Format == 2:
- self.Rule = ChainTyp + "ClassRule"
- self.RuleCount = ChainTyp + "ClassRuleCount"
- self.RuleSet = ChainTyp + "ClassSet"
- self.RuleSetCount = ChainTyp + "ClassSetCount"
- self.Intersect = lambda glyphs, c, r: (
- c.intersect_class(glyphs, r)
- if c
- else (set(glyphs) if r == 0 else set())
- )
-
- self.ClassDef = "InputClassDef" if Chain else "ClassDef"
- self.ClassDefIndex = 1 if Chain else 0
- self.Input = "Input" if Chain else "Class"
-
-
-def parseLookupRecords(items, klassName, lookupMap=None):
- klass = getattr(ot, klassName)
- lst = []
- for item in items:
- rec = klass()
- item = stripSplitComma(item)
- assert len(item) == 2, item
- idx = int(item[0])
- assert idx > 0, idx
- rec.SequenceIndex = idx - 1
- setReference(mapLookup, lookupMap, item[1], setattr, rec, "LookupListIndex")
- lst.append(rec)
- return lst
-
-
-def makeClassDef(classDefs, font, klass=ot.Coverage):
- if not classDefs:
- return None
- self = klass()
- self.classDefs = dict(classDefs)
- return self
-
-
-def parseClassDef(lines, font, klass=ot.ClassDef):
- classDefs = {}
- with lines.between("class definition"):
- for line in lines:
- glyph = makeGlyph(line[0])
- assert glyph not in classDefs, glyph
- classDefs[glyph] = int(line[1])
- return makeClassDef(classDefs, font, klass)
-
-
-def makeCoverage(glyphs, font, klass=ot.Coverage):
- if not glyphs:
- return None
- if isinstance(glyphs, set):
- glyphs = sorted(glyphs)
- coverage = klass()
- coverage.glyphs = sorted(set(glyphs), key=font.getGlyphID)
- return coverage
-
-
-def parseCoverage(lines, font, klass=ot.Coverage):
- glyphs = []
- with lines.between("coverage definition"):
- for line in lines:
- glyphs.append(makeGlyph(line[0]))
- return makeCoverage(glyphs, font, klass)
-
-
-def bucketizeRules(self, c, rules, bucketKeys):
- buckets = {}
- for seq, recs in rules:
- buckets.setdefault(seq[c.InputIdx][0], []).append(
- (tuple(s[1 if i == c.InputIdx else 0 :] for i, s in enumerate(seq)), recs)
- )
-
- rulesets = []
- for firstGlyph in bucketKeys:
- if firstGlyph not in buckets:
- rulesets.append(None)
- continue
- thisRules = []
- for seq, recs in buckets[firstGlyph]:
- rule = getattr(ot, c.Rule)()
- c.SetRuleData(rule, seq)
- setattr(rule, c.Type + "Count", len(recs))
- setattr(rule, c.LookupRecord, recs)
- thisRules.append(rule)
-
- ruleset = getattr(ot, c.RuleSet)()
- setattr(ruleset, c.Rule, thisRules)
- setattr(ruleset, c.RuleCount, len(thisRules))
- rulesets.append(ruleset)
-
- setattr(self, c.RuleSet, rulesets)
- setattr(self, c.RuleSetCount, len(rulesets))
-
-
-def parseContext(lines, font, Type, lookupMap=None):
- self = getattr(ot, Type)()
- typ = lines.peeks()[0].split()[0].lower()
- if typ == "glyph":
- self.Format = 1
- log.debug("Parsing %s format %s", Type, self.Format)
- c = ContextHelper(Type, self.Format)
- rules = []
- for line in lines:
- assert line[0].lower() == "glyph", line[0]
- while len(line) < 1 + c.DataLen:
- line.append("")
- seq = tuple(makeGlyphs(stripSplitComma(i)) for i in line[1 : 1 + c.DataLen])
- recs = parseLookupRecords(line[1 + c.DataLen :], c.LookupRecord, lookupMap)
- rules.append((seq, recs))
-
- firstGlyphs = set(seq[c.InputIdx][0] for seq, recs in rules)
- self.Coverage = makeCoverage(firstGlyphs, font)
- bucketizeRules(self, c, rules, self.Coverage.glyphs)
- elif typ.endswith("class"):
- self.Format = 2
- log.debug("Parsing %s format %s", Type, self.Format)
- c = ContextHelper(Type, self.Format)
- classDefs = [None] * c.DataLen
- while lines.peeks()[0].endswith("class definition begin"):
- typ = lines.peek()[0][: -len("class definition begin")].lower()
- idx, klass = {
- 1: {
- "": (0, ot.ClassDef),
- },
- 3: {
- "backtrack": (0, ot.BacktrackClassDef),
- "": (1, ot.InputClassDef),
- "lookahead": (2, ot.LookAheadClassDef),
- },
- }[c.DataLen][typ]
- assert classDefs[idx] is None, idx
- classDefs[idx] = parseClassDef(lines, font, klass=klass)
- c.SetContextData(self, classDefs)
- rules = []
- for line in lines:
- assert line[0].lower().startswith("class"), line[0]
- while len(line) < 1 + c.DataLen:
- line.append("")
- seq = tuple(intSplitComma(i) for i in line[1 : 1 + c.DataLen])
- recs = parseLookupRecords(line[1 + c.DataLen :], c.LookupRecord, lookupMap)
- rules.append((seq, recs))
- firstClasses = set(seq[c.InputIdx][0] for seq, recs in rules)
- firstGlyphs = set(
- g for g, c in classDefs[c.InputIdx].classDefs.items() if c in firstClasses
- )
- self.Coverage = makeCoverage(firstGlyphs, font)
- bucketizeRules(self, c, rules, range(max(firstClasses) + 1))
- elif typ.endswith("coverage"):
- self.Format = 3
- log.debug("Parsing %s format %s", Type, self.Format)
- c = ContextHelper(Type, self.Format)
- coverages = tuple([] for i in range(c.DataLen))
- while lines.peeks()[0].endswith("coverage definition begin"):
- typ = lines.peek()[0][: -len("coverage definition begin")].lower()
- idx, klass = {
- 1: {
- "": (0, ot.Coverage),
- },
- 3: {
- "backtrack": (0, ot.BacktrackCoverage),
- "input": (1, ot.InputCoverage),
- "lookahead": (2, ot.LookAheadCoverage),
- },
- }[c.DataLen][typ]
- coverages[idx].append(parseCoverage(lines, font, klass=klass))
- c.SetRuleData(self, coverages)
- lines = list(lines)
- assert len(lines) == 1
- line = lines[0]
- assert line[0].lower() == "coverage", line[0]
- recs = parseLookupRecords(line[1:], c.LookupRecord, lookupMap)
- setattr(self, c.Type + "Count", len(recs))
- setattr(self, c.LookupRecord, recs)
- else:
- assert 0, typ
- return self
-
-
-def parseContextSubst(lines, font, lookupMap=None):
- return parseContext(lines, font, "ContextSubst", lookupMap=lookupMap)
-
-
-def parseContextPos(lines, font, lookupMap=None):
- return parseContext(lines, font, "ContextPos", lookupMap=lookupMap)
-
-
-def parseChainedSubst(lines, font, lookupMap=None):
- return parseContext(lines, font, "ChainContextSubst", lookupMap=lookupMap)
-
-
-def parseChainedPos(lines, font, lookupMap=None):
- return parseContext(lines, font, "ChainContextPos", lookupMap=lookupMap)
-
-
-def parseReverseChainedSubst(lines, font, _lookupMap=None):
- self = ot.ReverseChainSingleSubst()
- self.Format = 1
- coverages = ([], [])
- while lines.peeks()[0].endswith("coverage definition begin"):
- typ = lines.peek()[0][: -len("coverage definition begin")].lower()
- idx, klass = {
- "backtrack": (0, ot.BacktrackCoverage),
- "lookahead": (1, ot.LookAheadCoverage),
- }[typ]
- coverages[idx].append(parseCoverage(lines, font, klass=klass))
- self.BacktrackCoverage = coverages[0]
- self.BacktrackGlyphCount = len(self.BacktrackCoverage)
- self.LookAheadCoverage = coverages[1]
- self.LookAheadGlyphCount = len(self.LookAheadCoverage)
- mapping = {}
- for line in lines:
- assert len(line) == 2, line
- line = makeGlyphs(line)
- mapping[line[0]] = line[1]
- self.Coverage = makeCoverage(set(mapping.keys()), font)
- self.Substitute = [mapping[k] for k in self.Coverage.glyphs]
- self.GlyphCount = len(self.Substitute)
- return self
-
-
-def parseLookup(lines, tableTag, font, lookupMap=None):
- line = lines.expect("lookup")
- _, name, typ = line
- log.debug("Parsing lookup type %s %s", typ, name)
- lookup = ot.Lookup()
- lookup.LookupFlag, filterset = parseLookupFlags(lines)
- if filterset is not None:
- lookup.MarkFilteringSet = filterset
- lookup.LookupType, parseLookupSubTable = {
- "GSUB": {
- "single": (1, parseSingleSubst),
- "multiple": (2, parseMultiple),
- "alternate": (3, parseAlternate),
- "ligature": (4, parseLigature),
- "context": (5, parseContextSubst),
- "chained": (6, parseChainedSubst),
- "reversechained": (8, parseReverseChainedSubst),
- },
- "GPOS": {
- "single": (1, parseSinglePos),
- "pair": (2, parsePair),
- "kernset": (2, parseKernset),
- "cursive": (3, parseCursive),
- "mark to base": (4, parseMarkToBase),
- "mark to ligature": (5, parseMarkToLigature),
- "mark to mark": (6, parseMarkToMark),
- "context": (7, parseContextPos),
- "chained": (8, parseChainedPos),
- },
- }[tableTag][typ]
-
- with lines.until("lookup end"):
- subtables = []
-
- while lines.peek():
- with lines.until(("% subtable", "subtable end")):
- while lines.peek():
- subtable = parseLookupSubTable(lines, font, lookupMap)
- assert lookup.LookupType == subtable.LookupType
- subtables.append(subtable)
- if lines.peeks()[0] in ("% subtable", "subtable end"):
- next(lines)
- lines.expect("lookup end")
-
- lookup.SubTable = subtables
- lookup.SubTableCount = len(lookup.SubTable)
- if lookup.SubTableCount == 0:
- # Remove this return when following is fixed:
- # https://github.com/fonttools/fonttools/issues/789
- return None
- return lookup
-
-
-def parseGSUBGPOS(lines, font, tableTag):
- container = ttLib.getTableClass(tableTag)()
- lookupMap = DeferredMapping()
- featureMap = DeferredMapping()
- assert tableTag in ("GSUB", "GPOS")
- log.debug("Parsing %s", tableTag)
- self = getattr(ot, tableTag)()
- self.Version = 0x00010000
- fields = {
- "script table begin": (
- "ScriptList",
- lambda lines: parseScriptList(lines, featureMap),
- ),
- "feature table begin": (
- "FeatureList",
- lambda lines: parseFeatureList(lines, lookupMap, featureMap),
- ),
- "lookup": ("LookupList", None),
- }
- for attr, parser in fields.values():
- setattr(self, attr, None)
- while lines.peek() is not None:
- typ = lines.peek()[0].lower()
- if typ not in fields:
- log.debug("Skipping %s", lines.peek())
- next(lines)
- continue
- attr, parser = fields[typ]
- if typ == "lookup":
- if self.LookupList is None:
- self.LookupList = ot.LookupList()
- self.LookupList.Lookup = []
- _, name, _ = lines.peek()
- lookup = parseLookup(lines, tableTag, font, lookupMap)
- if lookupMap is not None:
- assert name not in lookupMap, "Duplicate lookup name: %s" % name
- lookupMap[name] = len(self.LookupList.Lookup)
- else:
- assert int(name) == len(self.LookupList.Lookup), "%d %d" % (
- name,
- len(self.Lookup),
- )
- self.LookupList.Lookup.append(lookup)
- else:
- assert getattr(self, attr) is None, attr
- setattr(self, attr, parser(lines))
- if self.LookupList:
- self.LookupList.LookupCount = len(self.LookupList.Lookup)
- if lookupMap is not None:
- lookupMap.applyDeferredMappings()
- if os.environ.get(LOOKUP_DEBUG_ENV_VAR):
- if "Debg" not in font:
- font["Debg"] = newTable("Debg")
- font["Debg"].data = {}
- debug = (
- font["Debg"]
- .data.setdefault(LOOKUP_DEBUG_INFO_KEY, {})
- .setdefault(tableTag, {})
- )
- for name, lookup in lookupMap.items():
- debug[str(lookup)] = ["", name, ""]
-
- featureMap.applyDeferredMappings()
- container.table = self
- return container
-
-
-def parseGSUB(lines, font):
- return parseGSUBGPOS(lines, font, "GSUB")
-
-
-def parseGPOS(lines, font):
- return parseGSUBGPOS(lines, font, "GPOS")
-
-
-def parseAttachList(lines, font):
- points = {}
- with lines.between("attachment list"):
- for line in lines:
- glyph = makeGlyph(line[0])
- assert glyph not in points, glyph
- points[glyph] = [int(i) for i in line[1:]]
- return otl.buildAttachList(points, font.getReverseGlyphMap())
-
-
-def parseCaretList(lines, font):
- carets = {}
- with lines.between("carets"):
- for line in lines:
- glyph = makeGlyph(line[0])
- assert glyph not in carets, glyph
- num = int(line[1])
- thisCarets = [int(i) for i in line[2:]]
- assert num == len(thisCarets), line
- carets[glyph] = thisCarets
- return otl.buildLigCaretList(carets, {}, font.getReverseGlyphMap())
-
-
-def makeMarkFilteringSets(sets, font):
- self = ot.MarkGlyphSetsDef()
- self.MarkSetTableFormat = 1
- self.MarkSetCount = 1 + max(sets.keys())
- self.Coverage = [None] * self.MarkSetCount
- for k, v in sorted(sets.items()):
- self.Coverage[k] = makeCoverage(set(v), font)
- return self
-
-
-def parseMarkFilteringSets(lines, font):
- sets = {}
- with lines.between("set definition"):
- for line in lines:
- assert len(line) == 2, line
- glyph = makeGlyph(line[0])
- # TODO accept set names
- st = int(line[1])
- if st not in sets:
- sets[st] = []
- sets[st].append(glyph)
- return makeMarkFilteringSets(sets, font)
-
-
-def parseGDEF(lines, font):
- container = ttLib.getTableClass("GDEF")()
- log.debug("Parsing GDEF")
- self = ot.GDEF()
- fields = {
- "class definition begin": (
- "GlyphClassDef",
- lambda lines, font: parseClassDef(lines, font, klass=ot.GlyphClassDef),
- ),
- "attachment list begin": ("AttachList", parseAttachList),
- "carets begin": ("LigCaretList", parseCaretList),
- "mark attachment class definition begin": (
- "MarkAttachClassDef",
- lambda lines, font: parseClassDef(lines, font, klass=ot.MarkAttachClassDef),
- ),
- "markfilter set definition begin": ("MarkGlyphSetsDef", parseMarkFilteringSets),
- }
- for attr, parser in fields.values():
- setattr(self, attr, None)
- while lines.peek() is not None:
- typ = lines.peek()[0].lower()
- if typ not in fields:
- log.debug("Skipping %s", typ)
- next(lines)
- continue
- attr, parser = fields[typ]
- assert getattr(self, attr) is None, attr
- setattr(self, attr, parser(lines, font))
- self.Version = 0x00010000 if self.MarkGlyphSetsDef is None else 0x00010002
- container.table = self
- return container
-
-
-def parseCmap(lines, font):
- container = ttLib.getTableClass("cmap")()
- log.debug("Parsing cmap")
- tables = []
- while lines.peek() is not None:
- lines.expect("cmap subtable %d" % len(tables))
- platId, encId, fmt, lang = [
- parseCmapId(lines, field)
- for field in ("platformID", "encodingID", "format", "language")
- ]
- table = cmap_classes[fmt](fmt)
- table.platformID = platId
- table.platEncID = encId
- table.language = lang
- table.cmap = {}
- line = next(lines)
- while line[0] != "end subtable":
- table.cmap[int(line[0], 16)] = line[1]
- line = next(lines)
- tables.append(table)
- container.tableVersion = 0
- container.tables = tables
- return container
-
-
-def parseCmapId(lines, field):
- line = next(lines)
- assert field == line[0]
- return int(line[1])
-
-
-def parseTable(lines, font, tableTag=None):
- log.debug("Parsing table")
- line = lines.peeks()
- tag = None
- if line[0].split()[0] == "FontDame":
- tag = line[0].split()[1]
- elif " ".join(line[0].split()[:3]) == "Font Chef Table":
- tag = line[0].split()[3]
- if tag is not None:
- next(lines)
- tag = tag.ljust(4)
- if tableTag is None:
- tableTag = tag
- else:
- assert tableTag == tag, (tableTag, tag)
-
- assert (
- tableTag is not None
- ), "Don't know what table to parse and data doesn't specify"
-
- return {
- "GSUB": parseGSUB,
- "GPOS": parseGPOS,
- "GDEF": parseGDEF,
- "cmap": parseCmap,
- }[tableTag](lines, font)
-
-
-class Tokenizer(object):
- def __init__(self, f):
- # TODO BytesIO / StringIO as needed? also, figure out whether we work on bytes or unicode
- lines = iter(f)
- try:
- self.filename = f.name
- except:
- self.filename = None
- self.lines = iter(lines)
- self.line = ""
- self.lineno = 0
- self.stoppers = []
- self.buffer = None
-
- def __iter__(self):
- return self
-
- def _next_line(self):
- self.lineno += 1
- line = self.line = next(self.lines)
- line = [s.strip() for s in line.split("\t")]
- if len(line) == 1 and not line[0]:
- del line[0]
- if line and not line[-1]:
- log.warning("trailing tab found on line %d: %s" % (self.lineno, self.line))
- while line and not line[-1]:
- del line[-1]
- return line
-
- def _next_nonempty(self):
- while True:
- line = self._next_line()
- # Skip comments and empty lines
- if line and line[0] and (line[0][0] != "%" or line[0] == "% subtable"):
- return line
-
- def _next_buffered(self):
- if self.buffer:
- ret = self.buffer
- self.buffer = None
- return ret
- else:
- return self._next_nonempty()
-
- def __next__(self):
- line = self._next_buffered()
- if line[0].lower() in self.stoppers:
- self.buffer = line
- raise StopIteration
- return line
-
- def next(self):
- return self.__next__()
-
- def peek(self):
- if not self.buffer:
- try:
- self.buffer = self._next_nonempty()
- except StopIteration:
- return None
- if self.buffer[0].lower() in self.stoppers:
- return None
- return self.buffer
-
- def peeks(self):
- ret = self.peek()
- return ret if ret is not None else ("",)
-
- @contextmanager
- def between(self, tag):
- start = tag + " begin"
- end = tag + " end"
- self.expectendswith(start)
- self.stoppers.append(end)
- yield
- del self.stoppers[-1]
- self.expect(tag + " end")
-
- @contextmanager
- def until(self, tags):
- if type(tags) is not tuple:
- tags = (tags,)
- self.stoppers.extend(tags)
- yield
- del self.stoppers[-len(tags) :]
-
- def expect(self, s):
- line = next(self)
- tag = line[0].lower()
- assert tag == s, "Expected '%s', got '%s'" % (s, tag)
- return line
-
- def expectendswith(self, s):
- line = next(self)
- tag = line[0].lower()
- assert tag.endswith(s), "Expected '*%s', got '%s'" % (s, tag)
- return line
-
-
-def build(f, font, tableTag=None):
- """Convert a Monotype font layout file to an OpenType layout object
-
- A font object must be passed, but this may be a "dummy" font; it is only
- used for sorting glyph sets when making coverage tables and to hold the
- OpenType layout table while it is being built.
-
- Args:
- f: A file object.
- font (TTFont): A font object.
- tableTag (string): If provided, asserts that the file contains data for the
- given OpenType table.
-
- Returns:
- An object representing the table. (e.g. ``table_G_S_U_B_``)
- """
- lines = Tokenizer(f)
- return parseTable(lines, font, tableTag=tableTag)
-
-
-def main(args=None, font=None):
- """Convert a FontDame OTL file to TTX XML
-
- Writes XML output to stdout.
-
- Args:
- args: Command line arguments (``--font``, ``--table``, input files).
- """
- import sys
- from fontTools import configLogger
- from fontTools.misc.testTools import MockFont
-
- if args is None:
- args = sys.argv[1:]
-
- # configure the library logger (for >= WARNING)
- configLogger()
- # comment this out to enable debug messages from mtiLib's logger
- # log.setLevel(logging.DEBUG)
-
- import argparse
-
- parser = argparse.ArgumentParser(
- "fonttools mtiLib",
- description=main.__doc__,
- )
-
- parser.add_argument(
- "--font",
- "-f",
- metavar="FILE",
- dest="font",
- help="Input TTF files (used for glyph classes and sorting coverage tables)",
- )
- parser.add_argument(
- "--table",
- "-t",
- metavar="TABLE",
- dest="tableTag",
- help="Table to fill (sniffed from input file if not provided)",
- )
- parser.add_argument(
- "inputs", metavar="FILE", type=str, nargs="+", help="Input FontDame .txt files"
- )
-
- args = parser.parse_args(args)
-
- if font is None:
- if args.font:
- font = ttLib.TTFont(args.font)
- else:
- font = MockFont()
-
- for f in args.inputs:
- log.debug("Processing %s", f)
- with open(f, "rt", encoding="utf-8") as f:
- table = build(f, font, tableTag=args.tableTag)
- blob = table.compile(font) # Make sure it compiles
- decompiled = table.__class__()
- decompiled.decompile(blob, font) # Make sure it decompiles!
-
- # continue
- from fontTools.misc import xmlWriter
-
- tag = table.tableTag
- writer = xmlWriter.XMLWriter(sys.stdout)
- writer.begintag(tag)
- writer.newline()
- # table.toXML(writer, font)
- decompiled.toXML(writer, font)
- writer.endtag(tag)
- writer.newline()
-
-
-if __name__ == "__main__":
- import sys
-
- sys.exit(main())
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I__5.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I__5.py
deleted file mode 100644
index 5edc86a9cbc9a0b710cfc014a3910f671f791e54..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/T_S_I__5.py
+++ /dev/null
@@ -1,46 +0,0 @@
-""" TSI{0,1,2,3,5} are private tables used by Microsoft Visual TrueType (VTT)
-tool to store its hinting source data.
-
-TSI5 contains the VTT character groups.
-"""
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import sys
-import array
-
-
-class table_T_S_I__5(DefaultTable.DefaultTable):
- def decompile(self, data, ttFont):
- numGlyphs = ttFont["maxp"].numGlyphs
- assert len(data) == 2 * numGlyphs
- a = array.array("H")
- a.frombytes(data)
- if sys.byteorder != "big":
- a.byteswap()
- self.glyphGrouping = {}
- for i in range(numGlyphs):
- self.glyphGrouping[ttFont.getGlyphName(i)] = a[i]
-
- def compile(self, ttFont):
- glyphNames = ttFont.getGlyphOrder()
- a = array.array("H")
- for i in range(len(glyphNames)):
- a.append(self.glyphGrouping.get(glyphNames[i], 0))
- if sys.byteorder != "big":
- a.byteswap()
- return a.tobytes()
-
- def toXML(self, writer, ttFont):
- names = sorted(self.glyphGrouping.keys())
- for glyphName in names:
- writer.simpletag(
- "glyphgroup", name=glyphName, value=self.glyphGrouping[glyphName]
- )
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if not hasattr(self, "glyphGrouping"):
- self.glyphGrouping = {}
- if name != "glyphgroup":
- return
- self.glyphGrouping[attrs["name"]] = safeEval(attrs["value"])
diff --git a/spaces/dddmiku/vits-uma-genshin-honkai/README.md b/spaces/dddmiku/vits-uma-genshin-honkai/README.md
deleted file mode 100644
index 1c0aa069bfd980b6b45bb2bf62ff74bd9b0b61c2..0000000000000000000000000000000000000000
--- a/spaces/dddmiku/vits-uma-genshin-honkai/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-license: apache-2.0
-title: ' vits-uma-genshin-honkai'
-sdk: gradio
-sdk_version: 3.7
-emoji: 🐨
-colorTo: yellow
-pinned: false
-app_file: app.py
-duplicated_from: ikechan8370/vits-uma-genshin-honkai
----
diff --git a/spaces/deepklarity/poster2plot/app.py b/spaces/deepklarity/poster2plot/app.py
deleted file mode 100644
index 1fe3a63e58eea99c408e996d07437fb39afa5f0f..0000000000000000000000000000000000000000
--- a/spaces/deepklarity/poster2plot/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import re
-
-import torch
-import gradio as gr
-from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel
-
-# Pattern to ignore all the text after 2 or more full stops
-regex_pattern = "[.]{2,}"
-
-
-def post_process(text):
- try:
- text = text.strip()
- text = re.split(regex_pattern, text)[0]
- except Exception as e:
- print(e)
- pass
- return text
-
-
-def set_example_image(example: list) -> dict:
- return gr.Image.update(value=example[0])
-
-
-def predict(image, max_length=64, num_beams=4):
- pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
- pixel_values = pixel_values.to(device)
-
- with torch.no_grad():
- output_ids = model.generate(
- pixel_values,
- max_length=max_length,
- num_beams=num_beams,
- return_dict_in_generate=True,
- ).sequences
-
- preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
- pred = post_process(preds[0])
-
- return pred
-
-
-model_name_or_path = "deepklarity/poster2plot"
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Load model.
-
-model = VisionEncoderDecoderModel.from_pretrained(model_name_or_path)
-model.to(device)
-print("Loaded model")
-
-feature_extractor = AutoFeatureExtractor.from_pretrained(model.encoder.name_or_path)
-print("Loaded feature_extractor")
-
-tokenizer = AutoTokenizer.from_pretrained(model.decoder.name_or_path, use_fast=True)
-if model.decoder.name_or_path == "gpt2":
- tokenizer.pad_token = tokenizer.eos_token
-print("Loaded tokenizer")
-
-examples = [[f"examples/{filename}"] for filename in next(os.walk('examples'), (None, None, []))[2]]
-print(f"Loaded {len(examples)} example images")
-
-with gr.Blocks() as poster2plot:
- with gr.Column():
- with gr.Row():
- gr.Markdown("# Poster2Plot: Upload a Movie/T.V show poster to generate a plot")
- with gr.Row():
- with gr.Column():
- with gr.Row():
- input_image = gr.Image(label='Input Image', type='numpy')
- with gr.Row():
- submit_button = gr.Button(value="Submit", variant='primary')
- with gr.Column():
- plot = gr.Textbox(label="Plot")
- with gr.Row():
- example_images = gr.Dataset(components=[input_image], samples=examples)
- with gr.Row():
- gr.Markdown("Made by: [dk-crazydiv](https://twitter.com/kartik_godawat) and [dsr](https://twitter.com/dsr_ai)")
-
- submit_button.click(fn=predict, inputs=[input_image], outputs=[plot])
- example_images.click(fn=set_example_image, inputs=[example_images], outputs=example_images.components)
-
-poster2plot.launch()
diff --git a/spaces/denisp1/AR-VR-IOT-DEMO/index.html b/spaces/denisp1/AR-VR-IOT-DEMO/index.html
deleted file mode 100644
index f64aad6580cd12cbdbb0bcc0321ed7a6486d2a19..0000000000000000000000000000000000000000
--- a/spaces/denisp1/AR-VR-IOT-DEMO/index.html
+++ /dev/null
@@ -1,66 +0,0 @@
-
-
-
- Dynamic Lights - A-Frame
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/descript/vampnet/scripts/utils/split_long_audio_file.py b/spaces/descript/vampnet/scripts/utils/split_long_audio_file.py
deleted file mode 100644
index 8648b2612ebd4f1344357222dff5b430525091c5..0000000000000000000000000000000000000000
--- a/spaces/descript/vampnet/scripts/utils/split_long_audio_file.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from pathlib import Path
-import argbind
-
-import audiotools as at
-import tqdm
-
-
-@argbind.bind(without_prefix=True)
-def split_long_audio_file(
- file: str = None,
- max_chunk_size_s: int = 60*10
-):
- file = Path(file)
- output_dir = file.parent / file.stem
- output_dir.mkdir()
-
- sig = at.AudioSignal(file)
-
- # split into chunks
- for i, sig in tqdm.tqdm(enumerate(sig.windows(
- window_duration=max_chunk_size_s, hop_duration=max_chunk_size_s/2,
- preprocess=True))
- ):
- sig.write(output_dir / f"{i}.wav")
-
- print(f"wrote {len(list(output_dir.glob('*.wav')))} files to {output_dir}")
-
- return output_dir
-
-if __name__ == "__main__":
- args = argbind.parse_args()
-
- with argbind.scope(args):
- split_long_audio_file()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Ccna 41 Guia Completo De Estudo Download Pdf !!INSTALL!!.md b/spaces/diacanFperku/AutoGPT/Ccna 41 Guia Completo De Estudo Download Pdf !!INSTALL!!.md
deleted file mode 100644
index f21964795199dfb0cbec5c28584745bbe85a237e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ccna 41 Guia Completo De Estudo Download Pdf !!INSTALL!!.md
+++ /dev/null
@@ -1,19 +0,0 @@
-
-
How to Download CCNA 4.1 - Guia Completo de Estudo
-
If you are preparing for the Cisco Certified Network Associate (CCNA) exam, you may be interested in downloading CCNA 4.1 - Guia Completo de Estudo, a comprehensive study guide written by Marco Aurlio Filippetti. This book covers all the topics and objectives of the CCNA exam (640-802), with detailed explanations, examples, exercises, and labs. It also includes a simulated exam that you can access from the publisher's website.
CCNA 4.1 - Guia Completo de Estudo is available in PDF format, which you can read on your computer or mobile device. However, you may not be able to find it easily on the internet, as it is a copyrighted material. Therefore, you should only download it from authorized sources, such as the publisher's website or online bookstores.
-
To download CCNA 4.1 - Guia Completo de Estudo from the publisher's website, you need to visit www.visualbooks.com.br and search for the book title. You will see a link to buy the book online, which will redirect you to a secure payment platform. After completing the payment, you will receive an email with a link to download the PDF file.
-
To download CCNA 4.1 - Guia Completo de Estudo from online bookstores, you need to visit websites that sell digital books, such as www.amazon.com or www.kobo.com. You will need to create an account and provide your payment information. After purchasing the book, you will be able to download it to your device or read it online using their apps.
-
-
CCNA 4.1 - Guia Completo de Estudo is a valuable resource for anyone who wants to pass the CCNA exam and become a certified network professional. By downloading it legally, you will also support the author and the publisher who invested their time and effort to create this book.
-
-
Once you have downloaded CCNA 4.1 - Guia Completo de Estudo, you should start studying as soon as possible. The CCNA exam is not easy, and it requires a lot of preparation and practice. Here are some tips that can help you succeed in your certification journey:
-
-
Get to know the CCNA certification exam. You cannot take a successful exam if you donât know the exact requirements. Fortunately, the Cisco CCNA is well documented. First of all, this website gives the basic information and a very useful overview of concepts[^1^]. You should also review the exam topics and objectives, which you can find here. Finally, you should familiarize yourself with the exam format and question types, which you can see in this video.
-
Make a study plan and stick to it. The CCNA exam covers a lot of topics, and you need to allocate enough time to study them thoroughly. A good study plan should include the following elements: a list of topics to cover, a schedule of study sessions, a set of resources to use, and a method of tracking your progress and performance. You can use CCNA 4.1 - Guia Completo de Estudo as your main resource, but you should also supplement it with other materials, such as videos, online courses, practice tests, and labs.
-
Practice as much as you can. The CCNA exam is not only about theory, but also about practical skills. You need to be able to configure and troubleshoot network devices using Cisco IOS commands. Therefore, you should practice with real or simulated equipment as much as possible. You can use CCNA 4.1 - Guia Completo de Estudo's labs, which are designed for Dynamips, a software that emulates Cisco routers and switches. You can also use other tools, such as Packet Tracer, GNS3, or Boson NetSim.
-
-
By following these tips and using CCNA 4.1 - Guia Completo de Estudo as your guide, you will be well prepared for the CCNA exam. Remember that passing the exam is not only a matter of knowledge, but also of confidence and attitude. Believe in yourself and your abilities, and you will achieve your certification goal.
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\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Cyberlink Youcam 4 Free Download For Windows 7 [EXCLUSIVE].md b/spaces/diacanFperku/AutoGPT/Cyberlink Youcam 4 Free Download For Windows 7 [EXCLUSIVE].md
deleted file mode 100644
index ff1686e9fbbff4f8535e2293e13af4e734fa39b2..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Cyberlink Youcam 4 Free Download For Windows 7 [EXCLUSIVE].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
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diff --git a/spaces/diacanFperku/AutoGPT/F-16 Multirole Fighter No Cd Crack ((LINK)).md b/spaces/diacanFperku/AutoGPT/F-16 Multirole Fighter No Cd Crack ((LINK)).md
deleted file mode 100644
index a7ad25e2bf37987d0f4628fe016a3fee3f272a96..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/F-16 Multirole Fighter No Cd Crack ((LINK)).md
+++ /dev/null
@@ -1,6 +0,0 @@
-
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diff --git a/spaces/diacanFperku/AutoGPT/Mobily Dongle Software Free Downloadl.md b/spaces/diacanFperku/AutoGPT/Mobily Dongle Software Free Downloadl.md
deleted file mode 100644
index 5a8ad41d671760c6f40ae342f50552514e7044b4..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Mobily Dongle Software Free Downloadl.md
+++ /dev/null
@@ -1,35 +0,0 @@
-
-
How to Download Mobily Dongle Software for Free
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Mobily is a telecom operator in Saudi Arabia that offers its customers a comprehensive account management application for Windows 10 devices. The application allows users to manage their different services, view their bills, redeem Neqaty loyalty points, and report any network problems. The application also provides users with news and sports updates from Mobily 3lhawa.
Click on the "Visit Site" button to go to the Windows Store.
-
You must have an active Microsoft account to download the application. Sign in with your Microsoft account or create one if you don't have one.
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Click on the "Get" button to start the download process.
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Once the download is complete, you can launch the application and sign in with your Mobily account.
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If you are not a Mobily customer, you can still use the application to read about Mobily's products and services, but you will not be able to access your account or use any of the features.
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diff --git a/spaces/diacanFperku/AutoGPT/Mus3 Cinema 4d R16 Keygen Software Fixed.md b/spaces/diacanFperku/AutoGPT/Mus3 Cinema 4d R16 Keygen Software Fixed.md
deleted file mode 100644
index 615e3198623fe65a0af2b3257b5fb67d17d0f3c7..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Mus3 Cinema 4d R16 Keygen Software Fixed.md
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Autocad 2007 Keygen Kickass To [REPACK].md b/spaces/falterWliame/Face_Mask_Detection/Autocad 2007 Keygen Kickass To [REPACK].md
deleted file mode 100644
index 84c0047e7ecc5205b17e4e119bee390cba42bd5b..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Autocad 2007 Keygen Kickass To [REPACK].md
+++ /dev/null
@@ -1,42 +0,0 @@
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Filmeaguiadefogodubladodownload.md b/spaces/falterWliame/Face_Mask_Detection/Filmeaguiadefogodubladodownload.md
deleted file mode 100644
index 4481c0bd6d31859bdbbf898869bf053aa3e00c05..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Filmeaguiadefogodubladodownload.md
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Car Racing APK Challenge Your Friends and Compete Online.md b/spaces/fatiXbelha/sd/Car Racing APK Challenge Your Friends and Compete Online.md
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index b806d2f196481c65ff2e857981ca83261031f782..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Car Racing APK Challenge Your Friends and Compete Online.md
+++ /dev/null
@@ -1,124 +0,0 @@
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Racing Car APK Download: How to Enjoy the Thrill of Speed on Your Android Device
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If you are a fan of racing games, you might have wondered how to play them on your Android device without spending a dime. One way to do that is to download racing car apks, which are files that contain the installation package of a game. In this article, we will explain what racing car apks are, how to download them safely and legally, and what are the best racing car apks to download in 2023.
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What is a racing car apk?
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An apk, which stands for Android Package Kit, is a file format that is used to distribute and install applications on Android devices. A racing car apk is an apk that contains a racing game, such as Asphalt 8, Sports Car Racing, or Race Master 3D. By downloading a racing car apk, you can install and play the game on your device without going through the Google Play Store or paying for it.
There are several benefits of downloading racing car apks, such as:
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You can access games that are not available in your region or country.
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You can play games that are no longer supported or updated by the developers.
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You can enjoy games that have been modified or hacked to unlock features, levels, or resources.
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You can save money by playing games for free.
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The risks of downloading racing car apks
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However, downloading racing car apks also comes with some risks, such as:
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You might violate the intellectual property rights of the game developers or publishers.
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You might expose your device to viruses, malware, or spyware that can harm your data or privacy.
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You might experience compatibility issues, bugs, or crashes that can affect your gameplay or device performance.
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You might lose your progress or account if the game requires online verification or synchronization.
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How to download racing car apks safely and legally
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If you decide to download racing car apks, you should follow these steps to ensure your safety and legality:
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Check the source and the reviews
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Before downloading any apk file, you should check the source website and the reviews from other users. You should avoid websites that look suspicious, have pop-up ads, or ask for personal information. You should also read the reviews to see if the apk file is working, safe, and authentic. You can use websites like [APKCombo](^2^) or [APKPure] to find reliable and verified apk files.
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Scan the file for viruses and malware
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After downloading the apk file, you should scan it for viruses and malware using a reputable antivirus software. You should delete any file that is detected as harmful or infected. You should also enable the security settings on your device that prevent unauthorized installations from unknown sources.
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Install the apk and grant the permissions
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Once you have verified that the apk file is safe, you can install it on your device by tapping on it and following the instructions. You might need to grant some permissions to the app, such as access to your storage, camera, microphone, or location. You should only grant permissions that are necessary for the app to function properly. You can also revoke any permissions that you feel uncomfortable with later
The best racing car apks to download in 2023
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Now that you know how to download racing car apks safely and legally, you might be wondering which ones are worth your time and storage space. Here are some of the best racing car apks to download in 2023, based on their features, gameplay, and reviews.
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Asphalt 8: Airborne
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Asphalt 8: Airborne is one of the most popular and acclaimed racing games on Android, with over 500 million downloads and a 4.5-star rating on the Google Play Store. It offers high-flying arcade racing action with stunning graphics, realistic physics, and a huge variety of cars, tracks, and modes.
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Features and gameplay
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Asphalt 8: Airborne features over 300 licensed cars from top manufacturers, such as Ferrari, Lamborghini, Bugatti, and Porsche. You can customize your cars with different paint jobs, decals, and upgrades. You can also perform amazing stunts and tricks in the air, thanks to the game's new jumping mechanic that lets you launch off ramps and fly over obstacles. The game has 16 different locations with multiple routes and shortcuts, such as Tokyo, London, Barcelona, and Nevada. You can race in different modes, such as classic, elimination, infected, knockdown, and more. You can also compete online with up to 12 players in multiplayer or join a club to team up with other racers.
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Pros and cons
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Asphalt 8: Airborne is a great game for arcade racing fans who love speed, adrenaline, and variety. The game has a lot of content to keep you entertained for hours, and the graphics and sound are top-notch. However, the game also has some drawbacks, such as:
-
-
It requires a lot of storage space (around 2 GB) and a good internet connection.
-
It can be frustrating to progress without spending real money on in-app purchases.
-
It can be repetitive and unrealistic at times.
-
-
Sports Car Racing
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Sports Car Racing is another racing game that focuses on fast and furious cars. It has over 10 million downloads and a 4.4-star rating on the Google Play Store. It lets you drive some of the most exotic and powerful sports cars in the world in thrilling races across different cities.
-
Features and gameplay
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Sports Car Racing features over 80 sports cars from brands like Ferrari, Lamborghini, Pagani, Koenigsegg, and more. You can upgrade your cars with different parts and tune them to suit your driving style. You can also customize your cars with different colors, vinyls, and rims. The game has 16 cities to race in, such as New York, Dubai, Tokyo, Paris, and more. You can race in different modes, such as career, elimination, time trial, free run, and more. You can also challenge other players online in multiplayer or join a league to compete for glory.
-
Pros and cons
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Sports Car Racing is a fun game for sports car lovers who want to experience the thrill of driving some of the most expensive and rare cars in the world. The game has a lot of cars to choose from and a lot of modes to play. However, the game also has some flaws, such as:
-
-
It requires a lot of storage space (around 1 GB) and a good internet connection.
-
It can be hard to earn enough money to buy new cars or upgrades without spending real money on in-app purchases.
-
It can be buggy and laggy at times.
-
-
Race Master 3D
-
Race Master 3D is a new racing game that was released in 2022. It has over 5 million downloads and a 4.6-star rating on the Google Play Store. It offers simple but addictive racing gameplay with colorful graphics and crazy obstacles.
-
Features and gameplay
-
Race Master 3D features over 30 cars that you can unlock by winning races or spinning the wheel. You can also upgrade your cars with different engines, tires, nitros, and more. The game has over 300 levels with different themes and challenges. You have to race against other cars while avoiding or smashing through various obstacles, such as ramps, tunnels, loops, spikes, balls, lasers, rockets, and more. The game has easy controls: you just have to tap to accelerate or brake. The game also has daily rewards, achievements, leaderboards, and more.
Pros and cons
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Race Master 3D is a game for casual racing fans who want to have some fun and relax. The game has simple but addictive gameplay, colorful graphics, and a lot of levels to play. However, the game also has some drawbacks, such as:
-
-
It does not have realistic physics or graphics.
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It can be too easy or repetitive for some players.
-
It can have annoying ads or pop-ups.
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-
Conclusion
-
Racing car apks are a great way to enjoy the thrill of speed on your Android device without spending a dime. However, you should be careful and responsible when downloading them, as they can also pose some risks to your device or your legality. You should always check the source and the reviews, scan the file for viruses and malware, and install the apk and grant the permissions. You should also try some of the best racing car apks to download in 2023, such as Asphalt 8: Airborne, Sports Car Racing, or Race Master 3D. These games will provide you with hours of fun and excitement, as you drive some of the fastest and coolest cars in the world.
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FAQs
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Here are some of the frequently asked questions about racing car apks:
-
What is the difference between an apk and an app?
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An apk is a file format that contains the installation package of an app. An app is a software application that runs on your device. You can install an app from the Google Play Store or from an apk file.
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Are racing car apks legal?
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It depends on the source and the content of the apk file. Some racing car apks are legal, as they are distributed by the developers or publishers themselves, or they are free or open-source games. However, some racing car apks are illegal, as they are pirated, modified, or hacked versions of paid or protected games. You should always respect the intellectual property rights of the game developers or publishers and avoid downloading illegal racing car apks.
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Are racing car apks safe?
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Not all racing car apks are safe, as some of them might contain viruses, malware, or spyware that can harm your device or your privacy. You should always scan the apk file for any harmful or infected elements before installing it. You should also enable the security settings on your device that prevent unauthorized installations from unknown sources.
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How can I uninstall a racing car apk?
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You can uninstall a racing car apk like any other app on your device. You can go to your settings, find the app manager, select the app you want to uninstall, and tap on uninstall. You can also long-press on the app icon on your home screen and drag it to the uninstall option.
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What are some alternatives to racing car apks?
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If you don't want to download racing car apks, you can still enjoy racing games on your Android device by using other methods, such as:
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Downloading racing games from the Google Play Store that are free or paid.
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Streaming racing games from cloud gaming platforms that let you play games on your device without downloading them.
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Emulating racing games from other consoles or platforms that let you play games on your device with an emulator.
-
197e85843d
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Download and Install JTWhatsApp APK - The WhatsApp MOD with Anti Ban Feature.md b/spaces/fatiXbelha/sd/Download and Install JTWhatsApp APK - The WhatsApp MOD with Anti Ban Feature.md
deleted file mode 100644
index bc53e9ecb892a00592db9b2d31604080e2753e3f..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download and Install JTWhatsApp APK - The WhatsApp MOD with Anti Ban Feature.md
+++ /dev/null
@@ -1,130 +0,0 @@
-
-
What is JTWhatsApp APK and why you should use it
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WhatsApp is one of the most popular messaging apps in the world, with over 2 billion users. However, many people are not satisfied with the limited features and options that WhatsApp offers. That's why some developers have created modified versions of WhatsApp, also known as WhatsApp mods, that provide more functionality and customization.
One of the best WhatsApp mods is JTWhatsApp APK, which is developed by Jimtech. JTWhatsApp APK is a modified version of WhatsApp that comes with advanced features that are not available in the original app, such as anti-ban protection, privacy settings, theme options, media enhancements, and more.
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If you are looking for a way to spice up your WhatsApp experience, then you should definitely try JTWhatsApp APK. In this article, we will tell you everything you need to know about JTWhatsApp APK, including its features, how to download and install it, how to update it, how to backup and restore your chats, and its pros and cons. We will also answer some frequently asked questions about JTWhatsApp APK at the end.
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Features of JTWhatsApp APK
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JTWhatsApp APK has many features that make it stand out from other WhatsApp mods. Here are some of the most notable ones:
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Anti-ban protection: Unlike some other WhatsApp mods that may get your account banned by WhatsApp, JTWhatsApp APK has an anti-ban feature that prevents this from happening. You can use JTWhatsApp APK without worrying about losing your account or data.
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Privacy settings: You can control your privacy settings on JTWhatsApp APK, such as hiding your online status, blue ticks, typing indicator, recording indicator, and more. You can also enable a fingerprint lock or a pattern lock to secure your app from unauthorized access.
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Theme options: You can customize your JTWhatsApp APK interface with hundreds of themes that are available in the app. You can also create your own theme or import themes from other sources.
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Media enhancements: You can send and receive media files on JTWhatsApp APK without any size or quality limitations. You can also download status videos and photos from your contacts, and view deleted messages and media.
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And more: There are many other features that JTWhatsApp APK offers, such as group settings, chat settings, stickers, emojis, fonts, languages, and more. You can explore all the features by downloading and installing JTWhatsApp APK on your device.
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How to download and install JTWhatsApp APK
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To download and install JTWhatsApp APK on your device, you need to follow these simple steps:
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Go to the official website of JTWhatsApp and download the latest version of the app.
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Before installing the app, make sure you have enabled the option to install apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources.
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After downloading the app, locate it in your file manager and tap on it to start the installation process.
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Follow the instructions on the screen to complete the installation.
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Once the app is installed, open it and enter your phone number to verify your account.
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You can now enjoy all the features of JTWhatsApp APK on your device.
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How to update JTWhatsApp APK
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To update JTWhatsApp APK on your device, you need to follow these simple steps:
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Go to the official website of JTWhatsApp and download the latest version of the app.
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Before installing the app, make sure you have backed up your chats on JTWhatsApp APK. You can do this by going to Settings > Chats > Chat Backup.
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After downloading the app, locate it in your file manager and tap on it to start the installation process.
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Follow the instructions on the screen to complete the installation.
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Once the app is installed, open it and enter your phone number to verify your account.
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You can now restore your chats from the backup and enjoy the updated features of JTWhatsApp APK on your device.
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How to backup and restore your chats on JTWhatsApp APK
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To backup and restore your chats on JTWhatsApp APK, you need to follow these simple steps:
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To backup your chats, go to Settings > Chats > Chat Backup and tap on Backup. You can choose to backup your chats to your device storage or to Google Drive.
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To restore your chats, go to Settings > Chats > Chat Backup and tap on Restore. You can choose to restore your chats from your device storage or from Google Drive.
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You can also backup and restore your chats using a local backup file. To do this, go to your file manager and locate the folder named JTWhatsApp. Inside this folder, you will find a file named msgstore.db.crypt12. This is your local backup file. You can copy this file to another device or location for safekeeping.
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To restore your chats from a local backup file, copy the file to the same folder (JTWhatsApp) on your device. Then, open JTWhatsApp APK and verify your account. You will be prompted to restore your chats from the local backup file.
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Pros and cons of JTWhatsApp APK
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JTWhatsApp APK has many advantages over the original WhatsApp app, but it also has some drawbacks. Here are some of the pros and cons of JTWhatsApp APK:
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-
Pros
Cons
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More features and options than WhatsApp
Not available on Google Play Store or App Store
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Anti-ban protection and privacy settings
May not be compatible with some devices or Android versions
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Customizable interface and themes
May consume more battery and data than WhatsApp
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Media enhancements and status downloader
May not receive official updates or support from WhatsApp
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Free and easy to use
May contain some bugs or errors
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Frequently asked questions about JTWhatsApp APK
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Is JTWhatsApp APK safe to use?
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JTWhatsApp APK is safe to use as long as you download it from a trusted source, such as its official website. However, you should always be careful when using any modded app, as they may contain some risks or vulnerabilities that are not present in the original app. You should also scan the app with an antivirus software before installing it on your device.
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Can I use JTWhatsApp APK with my original WhatsApp account?
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Yes, you can use JTWhatsApp APK with your original WhatsApp account, as it uses the same phone number verification system as WhatsApp. However, you cannot use both apps at the same time on the same device, as they may conflict with each other. You can either uninstall WhatsApp before installing JTWhatsApp APK, or use a different device or phone number for each app.
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What are the differences between JTWhatsApp APK and other WhatsApp mods?
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JTWhatsApp APK is one of the many WhatsApp mods that are available online, such as GBWhatsApp, FMWhatsApp, YoWhatsApp, etc. Each mod has its own features and advantages, but they all share some common characteristics, such as anti-ban protection, privacy settings, theme options, media enhancements, etc. You can compare different mods and choose the one that suits your needs and preferences best.
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How can I customize my JTWhatsApp APK interface?
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You can customize your JTWhatsApp APK interface by applying different themes that are available in the app. You can also create your own theme or import themes from other sources. To access the theme options, go to Settings > JTMods > Themes. You can also change other aspects of your interface, such as fonts, colors, icons, wallpapers, etc., by going to Settings > JTMods > Universal Mods.
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How can I can contact the developer of JTWhatsApp APK?
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You can contact the developer of JTWhatsApp APK by visiting his official website, Jimtechs, where you can find his email address, social media accounts, and other information. You can also report any bugs or issues you encounter while using the app, or request any features or improvements you would like to see in the future versions of the app.
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Conclusion
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JTWhatsApp APK is a great alternative to the original WhatsApp app, as it offers more features and options that enhance your messaging experience. You can enjoy anti-ban protection, privacy settings, theme options, media enhancements, and more with JTWhatsApp APK. You can also customize your interface and download status videos and photos from your contacts. You can download and install JTWhatsApp APK on your device by following the simple steps we have provided in this article. You can also update, backup, and restore your chats on JTWhatsApp APK easily. However, you should also be aware of the pros and cons of using JTWhatsApp APK, and always download it from a trusted source. We hope this article has helped you learn more about JTWhatsApp APK and how to use it.
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Frequently asked questions about JTWhatsApp APK
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Q: How do I uninstall JTWhatsApp APK?
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A: To uninstall JTWhatsApp APK, go to Settings > Apps > JTWhatsApp and tap on Uninstall. You can also delete the folder named JTWhatsApp from your file manager.
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Q: Can I use JTWhatsApp APK on my PC or laptop?
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A: Yes, you can use JTWhatsApp APK on your PC or laptop by using an Android emulator, such as Bluestacks, NoxPlayer, or LDPlayer. You can download and install the emulator on your PC or laptop, and then follow the same steps as you would on your device to download and install JTWhatsApp APK.
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Q: How do I change my language on JTWhatsApp APK?
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A: To change your language on JTWhatsApp APK, go to Settings > JTMods > Language. You can choose from over 40 languages that are available in the app.
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Q: How do I hide my online status on JTWhatsApp APK?
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A: To hide your online status on JTWhatsApp APK, go to Settings > Account > Privacy > Online Status. You can choose to hide your online status from everyone, or from specific contacts.
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Q: How do I enable dark mode on JTWhatsApp APK?
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A: To enable dark mode on JTWhatsApp APK, go to Settings > Chats > Theme. You can choose from three options: Light, Dark, or System Default.
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401be4b1e0
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-
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/examples/classification/finetune_classification_bert-3.9B_afqmc.sh b/spaces/fclong/summary/fengshen/examples/classification/finetune_classification_bert-3.9B_afqmc.sh
deleted file mode 100644
index 9d36b627d6cc1b0a8de575138eec6a7529b31137..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/classification/finetune_classification_bert-3.9B_afqmc.sh
+++ /dev/null
@@ -1,146 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=afqmc # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=4 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=20 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:4 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc.
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-set -x -e
-echo "START TIME: $(date)"
-
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/gaoxinyu/cache/torch_extendsions
-
-BERT_NAME=bert-3.9B
-
-TASK=afqmc
-TEXTA_NAME=sentence1
-TEXTB_NAME=sentence2
-LABEL_NAME=label
-ID_NAME=id
-
-
-BATCH_SIZE=8
-VAL_BATCH_SIZE=32
-ZERO_STAGE=2
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/
-PRETRAINED_MODEL_PATH=/cognitive_comp/gaoxinyu/pretrained_model/$BERT_NAME/
-
-
-CHECKPOINT_PATH=/cognitive_comp/gaoxinyu/ln_model/fintune/ckpt/fengshen-finetune/$TASK/
-DEFAULT_ROOT_DIR=/cognitive_comp/gaoxinyu/ln_model/finetune/${BERT_NAME}-${TASK}
-OUTPUT_PATH=/cognitive_comp/gaoxinyu/ln_model/finetune/${BERT_NAME}-${TASK}/predict.json
-
-
-config_json="./ds_config.json"
-# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
-# reduce_bucket_size: hidden_size*hidden_size
-# stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size
-# stage3_param_persistence_threshold: 10 * hidden_size
-
-cat < $config_json
-{
- "train_micro_batch_size_per_gpu": $BATCH_SIZE,
- "steps_per_print": 1000,
- "gradient_clipping": 0.1,
- "zero_optimization": {
- "stage": 2
- },
- "optimizer": {
- "type": "Adam",
- "params": {
- "lr": 1e-7,
- "eps": 1e-12,
- "weight_decay": 1e-1
- }
- },
- "scheduler": {
- "type": "WarmupLR",
- "params":{
- "warmup_min_lr": 1e-8,
- "warmup_max_lr": 1e-6,
- "warmup_num_steps": 400,
- "warmup_type": "linear"
- }
- },
- "zero_allow_untested_optimizer": false,
- "fp16": {
- "enabled": true,
- "loss_scale": 0,
- "loss_scale_window": 1000,
- "hysteresis": 2,
- "min_loss_scale": 1
- },
- "activation_checkpointing": {
- "partition_activations": false,
- "contiguous_memory_optimization": false
- },
- "wall_clock_breakdown": false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-
-
-DATA_ARGS="\
- --data_dir $DATA_DIR \
- --train_data train.json \
- --valid_data dev.json \
- --test_data test.json \
- --train_batchsize $BATCH_SIZE \
- --valid_batchsize $VAL_BATCH_SIZE \
- --max_length 128 \
- --texta_name $TEXTA_NAME \
- --textb_name $TEXTB_NAME \
- --label_name $LABEL_NAME \
- --id_name $ID_NAME \
- "
-
-MODEL_ARGS="\
- --learning_rate 1e-5 \
- --weight_decay 1e-2 \
- --warmup 0.01 \
- --num_labels 2 \
- "
-
-MODEL_CHECKPOINT_ARGS="\
- --monitor val_acc \
- --save_top_k 3 \
- --mode max \
- --every_n_train_steps 0 \
- --save_weights_only True \
- --dirpath $CHECKPOINT_PATH \
- --filename model-{epoch:02d}-{val_acc:.4f} \
- "
-
-
-TRAINER_ARGS="\
- --max_epochs 67 \
- --gpus 4 \
- --num_nodes 1 \
- --strategy $STRATEGY \
- --gradient_clip_val 1.0 \
- --check_val_every_n_epoch 1 \
- --val_check_interval 100 \
- --precision 16 \
- --default_root_dir $DEFAULT_ROOT_DIR \
- "
-
-options=" \
- --pretrained_model_path $PRETRAINED_MODEL_PATH \
- --output_save_path $OUTPUT_PATH \
- $DATA_ARGS \
- $MODEL_ARGS \
- $MODEL_CHECKPOINT_ARGS \
- $TRAINER_ARGS \
- "
-
-DOCKER_PATH=/cognitive_comp/gaoxinyu/docker/pytorch21_06_py3_docker_image_v2.sif
-SCRIPT_PATH=/cognitive_comp/gaoxinyu/github/Fengshenbang-LM/fengshen/examples/classification/finetune_classification.py
-
-# python3 $SCRIPT_PATH $options
-srun -N 1 --job-name=afqmc --jobid=151522 --ntasks=4 --cpus-per-task=15 --gres=gpu:4 -o %x-%j.log singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $DOCKER_PATH python3 $SCRIPT_PATH $options
-
diff --git a/spaces/fclong/summary/fengshen/examples/deepVAE/vae_pl_module.py b/spaces/fclong/summary/fengshen/examples/deepVAE/vae_pl_module.py
deleted file mode 100644
index 15a7ebdf52983f5266cf446b2c9c83c994f7a4f7..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/deepVAE/vae_pl_module.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# coding=utf-8
-# Copyright 2022 IDEA-CCNL 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.
-""" PyTorch Della model. """
-
-import os
-import torch
-import numpy as np
-from fengshen.models.deepVAE.deep_vae import DeepVAE
-from pytorch_lightning.core.lightning import LightningModule
-from transformers.models.gpt2.configuration_gpt2 import GPT2Config
-from transformers.models.bert.tokenization_bert import BertTokenizer
-from fengshen.models.deepVAE.latent_connector import GPT2ForDecoderLatentConnector, GPT2ForEncoderLatentConnector
-from transformers.optimization import AdamW, get_linear_schedule_with_warmup
-
-
-class DeepVAEModule(LightningModule):
- @classmethod
- def add_module_specific_args(cls, parser):
- group = parser.add_argument_group('vae', 'configurations')
- group.add_argument("--checkpoint_path", type=str, default=None)
- group.add_argument("--gpt2_model_path", type=str)
- group.add_argument("--beta_kl_constraints_start", default=1, type=float,
- help="min beta for all the latent z posterior vs prior kl loss")
- group.add_argument("--beta_kl_constraints_stop", default=1, type=float,
- help="max beta for all the latent z posterior vs prior kl loss")
- group.add_argument("--beta_n_cycles", default=30, type=int,
- help="number of cycles for kl loss ratio within an epoch")
- group.add_argument("--freebit_kl_constraints", default=.1, type=float,
- help="free bit for all the latent z kl loss")
- group.add_argument("--latent_dim", default=256, type=int,
- help="latent dimension of deepVAE Z")
- group.add_argument("--learning_rate", default=5e-5, type=float,
- help="The initial learning rate for Adam.")
- group.add_argument("--weight_decay", default=0.0, type=float,
- help="Weight deay if we apply some.")
- group.add_argument("--adam_epsilon", default=1e-8, type=float,
- help="Epsilon for Adam optimizer.")
- group.add_argument("--max_grad_norm", default=1.0, type=float,
- help="Max gradient norm.")
- group.add_argument("--warmup_steps", default=0, type=int,
- help="Linear warmup over warmup_steps.")
- group.add_argument("--CVAE", action='store_true',
- help="specify this argument if finetuning CVAE, otherwise ignore this argument")
-
- return parser
-
- @classmethod
- def load_model(cls, args, labels_dict=None):
- checkpoint = torch.load(os.path.join(args.checkpoint_path, 'mp_rank_00_model_states.pt'))
-
- latent_dim = checkpoint['latent_dim'] if ('latent_dim' in checkpoint.keys()) else args.latent_dim
- labels_dict = checkpoint['label_dict'] if ('label_dict' in checkpoint.keys()) else labels_dict
-
- enc_config = GPT2Config.from_pretrained(args.gpt2_model_path)
- tokenizer = BertTokenizer.from_pretrained(args.gpt2_model_path)
- special_tokens_dict = {'bos_token': '', 'eos_token': ''}
- # special_tokens_dict = {'bos_token': '', 'eos_token': '', 'additional_special_tokens': ['', '']}
- tokenizer.add_special_tokens(special_tokens_dict)
- encoder_model = GPT2ForEncoderLatentConnector(config=enc_config)
- encoder_model.resize_token_embeddings(len(tokenizer))
-
- dec_config = GPT2Config.from_pretrained(args.gpt2_model_path)
- decoder_model = GPT2ForDecoderLatentConnector(config=dec_config, latent_dim=latent_dim)
- decoder_model.resize_token_embeddings(len(tokenizer))
-
- vae_model = DeepVAE(encoder_model, decoder_model, latent_dim=latent_dim,
- hidden_dim=enc_config.hidden_size, layer_num=enc_config.num_hidden_layers,
- pad_token_id=tokenizer.pad_token_id, unk_token_id=tokenizer.unk_token_id,
- bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id,
- CVAE=args.CVAE)
-
- # TODO: all the related params should be loaded here! Including latent_nets, posterior_nets, prior_nets, pooling, decoder.transformer.Wv, decoder.transformer.Wz
- anchor = 'module.model.'
- start = len(anchor)
- vae_dict = {key[start:]: val for key, val in checkpoint['module'].items() if anchor in key}
- # comment out if not initialized from VAE
- # if args.CVAE:
- # # manually load prior and posterior if initialize CVAE model for the first time because of dim mismatch
- # prior_post_dict = {key: vae_dict.pop(key) for key in list(vae_dict) if ('posterior_nets' in key or 'prior_nets' in key)}
- # for idx in range(enc_config.num_hidden_layers):
- # vae_model.posterior_nets[idx].weight.data[:, enc_config.hidden_size:] = prior_post_dict[f"posterior_nets.{idx}.weight"]
- # vae_model.prior_nets[idx].weight.data[:, enc_config.hidden_size:] = prior_post_dict[f"prior_nets.{idx}.weight"]
- # enc_wte_shape, dec_wte_shape = vae_dict['encoder.transformer.wte.weight'].shape[0], vae_dict['decoder.transformer.wte.weight'].shape[0]
- # vae_model.encoder.transformer.wte.weight.data[:enc_wte_shape, :] = vae_dict.pop('encoder.transformer.wte.weight')
- # vae_model.decoder.transformer.wte.weight.data[:dec_wte_shape, :] = vae_dict.pop('decoder.transformer.wte.weight')
- # vae_model.decoder.lm_head.weight.data[:dec_wte_shape, :] = vae_dict.pop('decoder.lm_head.weight')
- missing_keys, unexpected_keys = vae_model.load_state_dict(vae_dict, strict=False)
- print(f"Vae model loading process: missing keys {missing_keys}, unexpected keys {unexpected_keys}")
-
- return vae_model, tokenizer
-
- def __init__(
- self,
- args,
- train_steps=0,
- labels_dict=None
- ):
- super().__init__()
- # self.save_hyperparameters()
- self.args = args
-
- if args.checkpoint_path is not None:
- self.model, self.encoder_tokenizer, self.decoder_tokenizer, self.latent_dim, \
- self.labels_dict, self.args = DeepVAEModule.load_model(self.args, labels_dict=labels_dict)
- else:
- self.encoder_tokenizer = BertTokenizer.from_pretrained(self.args.encoder_model_path)
- encoder_config = GPT2Config.from_pretrained(self.args.encoder_model_path)
- special_tokens_dict = {'bos_token': '', 'eos_token': '', 'additional_special_tokens': ['', '']}
- self.encoder_tokenizer.add_special_tokens(special_tokens_dict)
- self.latent_dim = self.args.latent_dim
- encoder = GPT2ForEncoderLatentConnector.from_pretrained(self.args.encoder_model_path, config=encoder_config)
- # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
- encoder.resize_token_embeddings(len(self.encoder_tokenizer))
-
- self.decoder_tokenizer = BertTokenizer.from_pretrained(self.args.decoder_model_path)
- self.decoder_tokenizer.add_special_tokens(special_tokens_dict)
- decoder_config = GPT2Config.from_pretrained(self.args.decoder_model_path)
- self.labels_dict = labels_dict
- decoder = GPT2ForDecoderLatentConnector.from_pretrained(self.args.decoder_model_path, config=decoder_config,
- latent_dim=self.latent_dim)
-
- # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
- decoder.resize_token_embeddings(len(self.decoder_tokenizer))
- self.model = DeepVAE(encoder, decoder, latent_dim=self.args.latent_dim,
- hidden_dim=encoder_config.hidden_size, layer_num=encoder_config.num_hidden_layers,
- pad_token_id=self.decoder_tokenizer.pad_token_id, unk_token_id=self.decoder_tokenizer.unk_token_id,
- bos_token_id=self.decoder_tokenizer.bos_token_id, eos_token_id=self.decoder_tokenizer.eos_token_id,
- CVAE=args.CVAE)
-
- self.train_steps = train_steps
- # TODO: adjust the cyclic schedule
- self.beta_kl_constraints_list = self.get_cyclic_linear_beta_list(self.train_steps,
- start=args.beta_kl_constraints_start, stop=args.beta_kl_constraints_stop, n_cycle=args.beta_n_cycles)
- # self.mlm_probability_list = self.get_decoder_beta_list(self.train_steps,
- # start=0., stop=1., n_cycle=args.beta_n_cycles)
- # self.beta_kl_constraints_list = self.get_constant_ratio(self.train_steps, args.beta_kl_constraints)
- self.mlm_probability_list = self.get_constant_ratio(self.train_steps, 0.)
- # self.freebit_kl_constraints = args.freebit_kl_constraints
-
- def get_constant_ratio(self, n_steps, ratio):
- L = np.ones(n_steps)
- L *= ratio
- return L
-
- def get_decoder_beta_list(self, n_steps, start=0., stop=1.0, n_cycle=4):
- L = np.ones(n_steps)
- t_range = int(n_steps / n_cycle)
- for t_cur in range(n_steps):
- if t_cur > t_range:
- L[t_cur] = 0.
- else:
- ratio = t_cur / t_range
- value = stop - ratio * (stop-start)
- L[t_cur] = value
- return L
-
- def get_cyclic_linear_beta_list(self, n_steps, start=0.5, stop=1.0, n_cycle=4):
- L = np.ones(n_steps)
- t_range = int(n_steps / n_cycle)
- for t_cur in range(n_steps):
- loc = t_cur % t_range
- split_range = int(t_range * 0.25)
- if loc <= 2*split_range:
- value = start
- elif loc <= 3*split_range:
- ratio = (loc % split_range) / split_range
- value = ratio * (stop-start)
- else:
- value = stop
- L[t_cur] = value
- return L
-
- #####
- # Torch lightning
- #####
-
- def on_save_checkpoint(self, checkpoint) -> None:
- checkpoint['label_dict'] = self.labels_dict
- checkpoint['latent_dim'] = self.latent_dim
-
- def training_step(self, batch, batch_idx):
- if batch is None:
- loss = torch.Tensor([0.]).to(next(self.model.parameters()).device)
- loss.requires_grad = True
- return loss
- inputs, cond_inputs = batch, None
- if self.args.CVAE:
- inputs, cond_inputs = batch
-
- total_loss, rec_loss, total_kl_loss, layer_kl_loss = \
- self.model(inputs, self.beta_kl_constraints_list[batch_idx], cond_inputs)
- # the logging interval are set by the trainer_args log_every_n_steps
- for idx, pg in enumerate(self.optimizers().param_groups):
- self.log(f"learning_rate_{idx}", pg['lr'])
- unscaled_kl_constraint_loss = 0. if self.beta_kl_constraints_list[batch_idx] == 0. else total_kl_loss/self.beta_kl_constraints_list[batch_idx]
- self.log("total_loss", total_loss)
- self.log("total_kl_constraint_loss", total_kl_loss)
- self.log("unscaled_kl_constraint_loss", unscaled_kl_constraint_loss)
- self.log("beta_kl_constraints", self.beta_kl_constraints_list[batch_idx])
- self.log("beta_mlm_probability", self.mlm_probability_list[batch_idx])
- self.log("rec_loss", rec_loss)
- for idx, kl_loss in enumerate(layer_kl_loss):
- self.log(f"layer_{idx}_kl_loss", kl_loss.mean())
-
- return total_loss
-
- def training_step_end(self, batch_parts):
- pass
-
- def training_epoch_end(self, outputs):
- pass
-
- def validation_step(self, batch, batch_idx):
- if batch is None:
- loss = torch.Tensor([0.]).to(next(self.model.parameters()).device)
- loss.requires_grad = True
- return loss
- inputs, cond_inputs = batch, None
- if self.args.CVAE:
- inputs, cond_inputs = batch
-
- total_loss, rec_loss, total_kl_loss, layer_kl_loss = self.model(inputs, 1., cond_inputs)
- # the logging interval are set by the trainer_args log_every_n_steps
- self.log("val_total_loss", total_loss)
- self.log("val_kl_constraint_loss", total_kl_loss)
- self.log("val_recon_loss", rec_loss)
- for idx, kl_loss in enumerate(layer_kl_loss):
- self.log(f"layer_{idx}_kl_loss", kl_loss.mean())
- return total_loss
-
- def validation_epoch_end(self, outputs):
- pass
-
- def test_step(self, batch, batch_idx):
- if batch is None:
- loss = torch.Tensor([0.]).to(next(self.model.parameters()).device)
- loss.requires_grad = True
- return loss
- inputs, cond_inputs = batch, None
- if self.args.CVAE:
- inputs, cond_inputs = batch
- total_loss, rec_loss, total_kl_loss, layer_kl_loss = self.model(inputs, 1., cond_inputs)
- self.log("test_total_loss", total_loss)
- self.log("test_recon_loss", rec_loss)
- self.log("test_kl_constraint_loss", total_kl_loss)
- for idx, kl_loss in enumerate(layer_kl_loss):
- self.log(f"layer_{idx}_kl_loss", kl_loss.mean())
- return total_loss
-
- def configure_optimizers(self):
- no_decay = ['bias', 'LayerNorm.weight']
- optimizer_grouped_parameters = [
- {'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': self.args.weight_decay},
- {'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
- ]
-
- optimizer = AdamW(optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon)
- scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=self.train_steps)
-
- return {'optimizer': optimizer,
- 'lr_scheduler': {
- 'scheduler': scheduler,
- 'interval': 'step',
- 'frequency': 1
- }
- }
diff --git a/spaces/ferdmartin/DogBreedsApp/App/app.py b/spaces/ferdmartin/DogBreedsApp/App/app.py
deleted file mode 100644
index 1ae54ce903da961723533f82cd757152f612c2fc..0000000000000000000000000000000000000000
--- a/spaces/ferdmartin/DogBreedsApp/App/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!streamlit/bin/python
-import streamlit as st
-from pathlib import Path
-import pandas as pd
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-from io import BytesIO
-import json
-#from GDownload import download_file_from_google_drive
-
-@st.cache(allow_output_mutation=True)
-def load_model():
-# if selected_model == 'PVAN-Stanford':
-# model_location = '1-q1R5dLfIFW7BbzKuYTjolAoqpjVClsb'
-# save_dest = Path('saved_model')
-# save_dest.mkdir(exist_ok=True)
-# saved_model = Path("saved_model/FerNet_EfficientNet.h5")
-
-# elif selected_model == 'PVAN-Tsinghua':
- # model_location = '1-q1R5dLfIFW7BbzKuYTjolAoqpjVClsb'
- # save_dest = Path('saved_model')
- # save_dest.mkdir(exist_ok=True)
- # saved_model = Path("saved_model/FerNet_EfficientNet.h5")
-
- # if not saved_model.exists():
- # download_file_from_google_drive(model_location, saved_model)
- saved_model = str(Path().parent.absolute())+"/saved_models/FerNetEfficientNetB2"
- saved_model = tf.keras.models.load_model(saved_model)
- return saved_model
-
-@st.cache
-def load_classes():
- with open(str(Path().parent.absolute())+'/App/classes_dict.json') as classes:
- class_names = json.load(classes)
- return class_names
-
-def load_and_prep_image(filename, img_shape=260):
- #img = tf.io.read_file(filename)
- img = np.array(filename)#tf.io.decode_image(filename, channels=3)
- # Resize our image
- img = tf.image.resize(img, [img_shape,img_shape])
- # Scale
- return img # don't need to resclae images for EfficientNet models in Tensorflow
-
-if __name__ == '__main__':
-
- hide_st_style = """
-
- """
- st.markdown(hide_st_style, unsafe_allow_html=True)
-
- st.title("Dog Breeds Detector")
-
- options = ['PVAN-Stanford', 'PVAN-Tsinghua']
- selected_model = st.selectbox('Select a model to use (Default: PVAN-Stanford):', options)
-
- saved_model = load_model()
- class_names = load_classes()
-
- st.write("Choose any dog image and get the corresponding breed:")
-
- uploaded_image = st.file_uploader("Choose an image...")
-
- if uploaded_image:
- uploaded_image = Image.open(uploaded_image)
- # try:
- uploaded_image = uploaded_image.convert("RGB")
- membuf = BytesIO()
- uploaded_image.save(membuf, format="jpeg")
- uploaded_image = Image.open(membuf)
- # finally:
-
-
- image_for_the_model = load_and_prep_image(uploaded_image)
- prediction = saved_model.predict(tf.expand_dims(image_for_the_model, axis=0), verbose=0)
-
- top_k_proba, top_k_indices = tf.nn.top_k(prediction,k=5)
- top_5_classes = {top_n+1:class_names[str(top_k)] for top_n, top_k in enumerate(list(tf.squeeze(top_k_indices).numpy()))}
- top_k_proba = tf.squeeze(top_k_proba).numpy()
- top_5_classes = pd.DataFrame({"Top-k":top_5_classes.keys(), "Dog Breed": top_5_classes.values(), "Probability": top_k_proba})
- #top_5_classes.set_index("Top-k", inplace=True)
-
- print(tf.argmax(prediction, axis=1).numpy())
- predicted_breed = class_names[str(tf.argmax(prediction, axis=1).numpy()[0])]
- predicted_breed = ' '.join(predicted_breed.split('_'))
- predicted_breed = predicted_breed.title()
- st.header(f'This dog looks like a {predicted_breed}')
-
- col1, col2 = st.columns([1,2])
-
- col1.image(uploaded_image,use_column_width=True)
- col2.bar_chart(top_5_classes, x="Dog Breed", y="Probability")
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Beauty Plus Camera 2018 APK - The Ultimate Photo Editing Tool for Android.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Beauty Plus Camera 2018 APK - The Ultimate Photo Editing Tool for Android.md
deleted file mode 100644
index 574d25a4646ae82e465a4a72d8f4123a42e0ba00..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Beauty Plus Camera 2018 APK - The Ultimate Photo Editing Tool for Android.md
+++ /dev/null
@@ -1,131 +0,0 @@
-
-
Beauty Plus Camera Download 2018 APK: How to Get the Best Selfie App for Android
-
Do you want to take stunning selfies with your Android phone? Do you want to enhance your beauty and express your personality with filters, stickers, and effects? Do you want to share your selfies with your friends and followers on social media? If you answered yes to any of these questions, then you need to download Beauty Plus Camera 2018 APK, the best selfie app for Android.
-
What is Beauty Plus Camera?
-
Beauty Plus Camera is a powerful photo editor and selfie camera app that lets you take and edit beautiful selfies with ease. It has over 100 million downloads and 4.4 stars rating on Google Play Store. It is also available on APKCombo, a website that provides free and safe APK downloads for Android apps.
Beauty Plus Camera has many features that make it stand out from other selfie apps. Some of these features are:
-
-
Auto Retouch: This feature automatically detects and adjusts your skin tone, smoothness, blemishes, dark circles, and more. You can also customize the level of retouching according to your preference.
-
Makeup Effects: This feature lets you apply various makeup effects to your selfies, such as lipstick, blush, eyeliner, mascara, eyeshadow, and more. You can choose from different styles and colors to suit your mood and occasion.
-
Filters and Stickers: This feature lets you add fun and creative filters and stickers to your selfies, such as animal ears, glasses, hats, crowns, flowers, and more. You can also adjust the intensity and position of the filters and stickers.
-
AR Effects: This feature lets you use augmented reality effects to transform your selfies into different scenes and characters, such as a fairy tale princess, a superhero, a zombie, and more. You can also interact with the effects by tapping or swiping on the screen.
-
Cutout and Collage: This feature lets you cut out your selfies from the background and create collages with different layouts, backgrounds, frames, and stickers. You can also mix and match your selfies with other photos from your gallery or camera.
-
-
Benefits of Beauty Plus Camera
-
Beauty Plus Camera has many benefits that make it worth downloading. Some of these benefits are:
-
-
Easy to Use: Beauty Plus Camera has a simple and user-friendly interface that makes it easy to use for anyone. You can take a selfie with one tap, edit it with a few swipes, and share it with another tap.
-
High Quality: Beauty Plus Camera has a high-quality camera that captures every detail of your face and skin. It also has a smart algorithm that enhances your selfies without losing their naturalness.
-
Versatile: Beauty Plus Camera has a versatile range of features that let you create different kinds of selfies for different purposes. You can use it for casual selfies, professional selfies, artistic selfies, funny selfies, and more.
-
Social: Beauty Plus Camera has a social aspect that lets you share your selfies with your friends and followers on social media platforms such as Facebook, Instagram, Twitter, Snapchat, WhatsApp, and more. You can also discover and follow other users who use Beauty Plus Camera and see their selfies.
-
-How to Download and Install Beauty Plus Camera 2018 APK?
-
If you are interested in downloading and installing Beauty Plus Camera 2018 APK on your Android phone, you have two options. You can either download it from Google Play Store or from APKCombo. Here are the steps for both options:
-
Steps to Download and Install Beauty Plus Camera 2018 APK from Google Play Store
-
-
Open Google Play Store on your Android phone and search for "Beauty Plus Camera" in the search bar.
-
Select the app with the pink icon and the name "BeautyPlus - Easy Photo Editor & Selfie Camera" by Meitu (China) Limited.
-
Tap on the "Install" button and wait for the app to download and install on your phone.
-
Once the app is installed, you can open it and start using it.
-
-
Steps to Download and Install Beauty Plus Camera 2018 APK from APKCombo
-
-
Open your web browser on your Android phone and go to APKCombo.
-
Search for "Beauty Plus Camera" in the search bar and select the app with the same name and icon as above.
-
Scroll down to the "Download APK" section and choose the version that matches your Android device. For example, if you have an Android 8.0 device, choose the version 7.1.030.
-
Tap on the "Download APK" button and wait for the file to download on your phone.
-
Once the file is downloaded, go to your file manager and locate the file. Tap on it and allow the installation from unknown sources if prompted.
-
Wait for the app to install on your phone and then open it and start using it.
-
-
How to Use Beauty Plus Camera?
-
Now that you have downloaded and installed Beauty Plus Camera 2018 APK on your Android phone, you can start using it to take and edit amazing selfies. Here are some tips on how to use Beauty Plus Camera:
-
How to Take a Selfie with Beauty Plus Camera
-
-
Open Beauty Plus Camera and tap on the camera icon at the bottom center of the screen.
-
You can choose between the front or rear camera by tapping on the icon at the top right of the screen.
-
You can also choose between different modes such as photo, video, boomerang, or AR by swiping left or right on the screen.
-
You can adjust the zoom, flash, timer, grid, or mirror by tapping on the icons at the top left of the screen.
-
You can apply auto retouch, makeup effects, filters, or stickers by tapping on the icons at the bottom left of the screen. You can also swipe up or down on the screen to change the intensity of these effects.
-
When you are ready, tap on the shutter button at the bottom center of the screen to take a selfie. You can also use the volume button or gesture control to take a selfie.
-
-
How to Edit a Selfie with Beauty Plus Camera
-
-
After taking a selfie, you can edit it by tapping on the edit icon at the bottom right of the screen.
-
You can use various tools to edit your selfie, such as crop, rotate, adjust, beautify, smooth, reshape, slim, enlarge, whiten, acne, eye bag, red eye, brighten eye, eye color, eyelash, eyebrow, hair color, hairline, nose, mouth, teeth, smile, chin, face shape, earlobe, neck length, shoulder width, height stretch,
bust size, waist size, hip size, leg length, leg shape, arm length, arm shape, tattoo, mosaic, blur, text, frame, and more. You can access these tools by tapping on the icons at the bottom of the screen or swiping left or right on them.
-
You can also apply more filters or stickers by tapping on the icons at the top of the screen.
-
You can undo or redo your edits by tapping on the icons at the top right of the screen.
-
You can compare your edited selfie with your original selfie by tapping and holding on the screen.
-
When you are satisfied with your edits, tap on the save icon at the top right of the screen to save your selfie to your gallery or share it with others.
-
-
How to Share a Selfie with Beauty Plus Camera
-
-
After saving your selfie, you can share it with your friends and followers on social media platforms by tapping on the share icon at the bottom right of the screen.
-
You can choose from different options such as Facebook, Instagram, Twitter, Snapchat, WhatsApp, and more. You can also copy the link or download the image to your phone.
-
You can also add a caption, tag, location, or hashtag to your selfie before sharing it.
-
Once you have selected your option, tap on the send or post button to share your selfie with others.
-
-
Conclusion
-
Beauty Plus Camera 2018 APK is a great app for taking and editing selfies on your Android phone. It has many features and benefits that make it the best selfie app for Android. You can download and install it from Google Play Store or APKCombo. You can also use it to take, edit, and share selfies with ease. If you want to take stunning selfies with your Android phone, you should download Beauty Plus Camera 2018 APK today.
-
FAQs
-
Q: Is Beauty Plus Camera 2018 APK free?
-
A: Yes, Beauty Plus Camera 2018 APK is free to download and use. However, some features and effects may require in-app purchases or subscriptions.
-
Q: Is Beauty Plus Camera 2018 APK safe?
-
A: Yes, Beauty Plus Camera 2018 APK is safe to download and use. It does not contain any viruses or malware. However, you should always download it from trusted sources such as Google Play Store or APKCombo.
-
Q: Is Beauty Plus Camera 2018 APK compatible with my Android device?
-
A: Beauty Plus Camera 2018 APK is compatible with most Android devices that run on Android 4.4 or higher. However, some features and effects may not work on some devices due to hardware limitations.
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Q: How can I contact the developer of Beauty Plus Camera 2018 APK?
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A: You can contact the developer of Beauty Plus Camera 2018 APK by emailing them at support@beautyplus.com or visiting their website at https://www.beautyplus.com/.
-
Q: How can I give feedback or rate Beauty Plus Camera 2018 APK?
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A: You can give feedback or rate Beauty Plus Camera 2018 APK by leaving a review on Google Play Store or APKCombo. You can also follow them on social media platforms such as Facebook, Instagram, Twitter, YouTube, and more.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ms/readme.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/ms/readme.md
deleted file mode 100644
index 84a9974cccd81f9296b7d3c77f2b0d2765dfe181..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ms/readme.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# ms
-
-[![Build Status](https://travis-ci.org/zeit/ms.svg?branch=master)](https://travis-ci.org/zeit/ms)
-[![Slack Channel](http://zeit-slackin.now.sh/badge.svg)](https://zeit.chat/)
-
-Use this package to easily convert various time formats to milliseconds.
-
-## Examples
-
-```js
-ms('2 days') // 172800000
-ms('1d') // 86400000
-ms('10h') // 36000000
-ms('2.5 hrs') // 9000000
-ms('2h') // 7200000
-ms('1m') // 60000
-ms('5s') // 5000
-ms('1y') // 31557600000
-ms('100') // 100
-```
-
-### Convert from milliseconds
-
-```js
-ms(60000) // "1m"
-ms(2 * 60000) // "2m"
-ms(ms('10 hours')) // "10h"
-```
-
-### Time format written-out
-
-```js
-ms(60000, { long: true }) // "1 minute"
-ms(2 * 60000, { long: true }) // "2 minutes"
-ms(ms('10 hours'), { long: true }) // "10 hours"
-```
-
-## Features
-
-- Works both in [node](https://nodejs.org) and in the browser.
-- If a number is supplied to `ms`, a string with a unit is returned.
-- If a string that contains the number is supplied, it returns it as a number (e.g.: it returns `100` for `'100'`).
-- If you pass a string with a number and a valid unit, the number of equivalent ms is returned.
-
-## Caught a bug?
-
-1. [Fork](https://help.github.com/articles/fork-a-repo/) this repository to your own GitHub account and then [clone](https://help.github.com/articles/cloning-a-repository/) it to your local device
-2. Link the package to the global module directory: `npm link`
-3. Within the module you want to test your local development instance of ms, just link it to the dependencies: `npm link ms`. Instead of the default one from npm, node will now use your clone of ms!
-
-As always, you can run the tests using: `npm test`
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_1.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_1.py
deleted file mode 100644
index 7847f0e62d26f88cf61662720484b52ba8765ead..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_1.py
+++ /dev/null
@@ -1,28 +0,0 @@
-
-import re
-
-def is_spam(message):
- message = message.lower()
-
- spam_keywords = ["추천주", "적중", "지급", "퍼센트", "확인", "축하", "상한가", "월 체", "추친", "click", "오시는길",
- "텔레그램", "텔레그램 친추", "건설알미늄", "벳썸", "무제한 충전", "소니드", "더메티팜", "메이저 계열",
- "VIP 담당 에이전시", "다음주"]
-
- normal_keywords = ["친구", "오랜만", "여기로", "여기와라", "하이", "내일", "자료", "오키", "안녕", "나는 잘지내",
- "가정의 달 그린피", "손이아파"]
-
- url_pattern = re.compile(r"http\S+|www\..+\..+|bit\.ly\S+|https:\/\/me2\.kr\S+")
-
- # Check if message contains any URLs
- if url_pattern.search(message):
- return True
-
- # Check if message contains any spam keywords
- if any(spam_word in message for spam_word in spam_keywords):
- return True
-
- # Check if message contains any normal words
- if any(normal_word in message for normal_word in normal_keywords):
- return False
-
- return False
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_54.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_54.py
deleted file mode 100644
index 0a7bfd66e80c3a2f13884df6b006ca809949b488..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_54.py
+++ /dev/null
@@ -1,28 +0,0 @@
-
-import re
-
-def is_spam(message: str) -> bool:
- # Check for common spam words and phrases
- spam_words = ["추천주", "체험", "공시발표", "목표달성", "수익", "투자", "증권", "정보방", "국내식약처", "안정적인 수익", "클릭", "금전요구", "상한가", "연매출", "매출", "무료거부", "총 수익", "위험", "특집", "국내", "상품안내", "알려드린", "출신"]
-
- for word in spam_words:
- pattern = re.compile(word)
- if pattern.search(message):
- return True
-
- # Check for shortened URLs and suspicious links
- url_regex = r"(?Phttps?://\S*\.[\w]*(?=\s|\b))"
- urls = re.findall(url_regex, message)
- spam_urls = ["me2.kr", "bit.ly", "dokdo.in"]
- for url in urls:
- for spam_url in spam_urls:
- if spam_url in url:
- return True
-
- # Check for unusual numbers by looking for consecutive digits or percentage signs
- numbers_regex = r"\d{2,}|%"
- numbers = re.findall(numbers_regex, message)
- if numbers:
- return True
-
- return False
diff --git a/spaces/flynster/FeinbergQuizNotes/question_generation/prepare_data.py b/spaces/flynster/FeinbergQuizNotes/question_generation/prepare_data.py
deleted file mode 100644
index 765b7aad9eef38d994b055ec27ccf4ea62f08773..0000000000000000000000000000000000000000
--- a/spaces/flynster/FeinbergQuizNotes/question_generation/prepare_data.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import os
-import logging
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import torch
-import nlp
-from transformers import T5Tokenizer, BartTokenizer, HfArgumentParser
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
- """
- task: str = field(
- metadata={"help": "Which task 'qa', 'qg', 'e2e_qg', 'ans_ext', 'multi'. 'multi' means 'qa', 'qg', 'ans_ext' tasks"},
- )
- model_type: str = field(metadata={"help": "One of 't5', 'bart'"})
- dataset_path: Optional[str] = field(
- default="data/squad_multitask",
- metadata={"help": "Path for dataset directory"},
- )
- train_file_name: Optional[str] = field(
- default=None,
- metadata={"help": "name for cached train dataset"},
- )
- valid_file_name: Optional[str] = field(
- default=None,
- metadata={"help": "name for cached valid dataset"},
- )
- valid_for_qg_only: bool = field(
- default=False,
- metadata={"help": "For multitask dataset valid split should contain only qg task or all tasks."}
- )
- qg_format: Optional[str] = field(
- default='highlight_qg_format',
- metadata={"help": "How to format inputs for que generation, 'highlight_qg_format' or 'prepend_qg_format'"},
- )
- max_source_length: Optional[int] = field(
- default=512,
- metadata={"help": "Max input length for the source text"},
- )
- max_target_length: Optional[int] = field(
- default=32,
- metadata={"help": "Max input length for the target text"},
- )
-
-class DataProcessor:
- def __init__(self, tokenizer, model_type="t5", max_source_length=512, max_target_length=32):
- self.tokenizer = tokenizer
- self.max_source_length = max_source_length
- self.max_target_length = max_target_length
- self.model_type = model_type
- self.hl_token = ""
-
- if model_type == "t5":
- self.sep_token = ""
- elif model_type == "bart":
- self.sep_token = ""
- else:
- self.sep_token = "[SEP]"
-
- def process(self, dataset):
- if self.model_type == "t5":
- dataset = dataset.map(self._add_eos_examples)
-
- dataset = dataset.map(self._add_special_tokens)
- dataset = dataset.map(self._convert_to_features, batched=True)
-
- return dataset
-
- def _add_eos_examples(self, example):
- example['source_text'] = example['source_text'] + " "
- example['target_text'] = example['target_text'] + " "
- return example
-
- def _add_special_tokens(self, example):
- example['source_text'] = example['source_text'].replace("{hl_token}", self.hl_token)
- example['target_text'] = example['target_text'].replace("{sep_token}", self.sep_token)
- return example
-
- # tokenize the examples
- def _convert_to_features(self, example_batch):
- source_encoding = self.tokenizer.batch_encode_plus(
- example_batch['source_text'],
- max_length=self.max_source_length,
- padding='max_length',
- pad_to_max_length=True,
- truncation=True,
- )
- target_encoding = self.tokenizer.batch_encode_plus(
- example_batch['target_text'],
- max_length=self.max_target_length,
- padding='max_length',
- pad_to_max_length=True,
- truncation=True,
- )
-
- encodings = {
- 'source_ids': source_encoding['input_ids'],
- 'target_ids': target_encoding['input_ids'],
- 'attention_mask': source_encoding['attention_mask'],
- }
-
- return encodings
-
-
-def filter_qa(example):
- return example['task'] == 'qa'
-
-def filter_qg(example):
- return example['task'] == 'qg'
-
-def filter_e2e_qg(example):
- return example['task'] == 'e2e_qg'
-
-def filter_ans_ext(example):
- return example['task'] == 'ans_ext'
-
-def filter_multi(example):
- return example['task'] != 'e2e_qg'
-
-
-TASK_TO_FILTER_FN = {
- 'qa': filter_qa,
- 'qg': filter_qg,
- 'e2e_qg': filter_e2e_qg,
- 'ans_ext': filter_ans_ext,
- 'multi': filter_multi
-}
-
-
-def main():
- parser = HfArgumentParser((DataTrainingArguments,))
-
- data_args = parser.parse_args_into_dataclasses()[0]
-
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO
- )
-
- if data_args.model_type == 't5':
- tokenizer = T5Tokenizer.from_pretrained("t5-base")
- else:
- tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-
- tokenizer.add_tokens(['', ''])
-
- train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN)
- valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION)
-
- processor = DataProcessor(
- tokenizer,
- model_type=data_args.model_type,
- max_source_length=data_args.max_source_length,
- max_target_length=data_args.max_target_length
- )
-
- train_dataset = train_dataset.filter(TASK_TO_FILTER_FN[data_args.task])
- if data_args.task == 'multi' and data_args.valid_for_qg_only:
- logger.info("processing valid data only for qg task")
- valid_dataset = valid_dataset.filter(filter_qg)
- else:
- valid_dataset = valid_dataset.filter(TASK_TO_FILTER_FN[data_args.task])
-
-
- train_dataset = processor.process(train_dataset)
- valid_dataset = processor.process(valid_dataset)
-
- columns = ["source_ids", "target_ids", "attention_mask"]
- train_dataset.set_format(type='torch', columns=columns)
- valid_dataset.set_format(type='torch', columns=columns)
-
- if data_args.train_file_name is None:
- train_file_name = f"train_data_{data_args.task}_{data_args.qg_format}_{data_args.model_type}.pt"
- train_path = os.path.join("data", train_file_name)
-
- valid_file_name = f"valid_data_{data_args.task}_{data_args.qg_format}_{data_args.model_type}.pt"
- valid_path = os.path.join("data", valid_file_name)
- else:
- train_path = os.path.join("data", data_args.train_file_name)
- valid_path = os.path.join("data", data_args.valid_file_name)
-
- torch.save(train_dataset, train_path)
- logger.info(f"saved train dataset at {train_path}")
-
- torch.save(valid_dataset, valid_path)
- logger.info(f"saved validation dataset at {valid_path}")
-
- tokenizer_path = f"{data_args.model_type}_qg_tokenizer"
- if not os.path.exists(tokenizer_path):
- os.mkdir(tokenizer_path)
- tokenizer.save_pretrained(tokenizer_path)
- logger.info(f"saved tokenizer at {tokenizer_path}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/forklift-app/forklift-images/models.py b/spaces/forklift-app/forklift-images/models.py
deleted file mode 100644
index b20abef8773eeecbb4d65a557cee4e2e9504d1ab..0000000000000000000000000000000000000000
--- a/spaces/forklift-app/forklift-images/models.py
+++ /dev/null
@@ -1,458 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-from torchvision import models
-import torchvision
-import torch
-import copy
-class ForkliftFrameClassifier_V0(nn.Module):
- def __init__(self, n_classes = 2, dropout = 0.15):
- super(ForkliftFrameClassifier_V0, self).__init__()
- self.dropout = dropout
- # N x 3 x 480 x 640
-
- self.Conv1 = nn.Conv2d(3, 32, kernel_size=(8,8), stride=(3,5), padding=(3,1))
- self.Bn1 = nn.BatchNorm2d(32)
- # N x 32 x 160 x 127
-
- self.Conv2 = nn.Conv2d(32, 64, kernel_size=(8,8), stride=(5,5), padding=(0,0))
- self.Bn2 = nn.BatchNorm2d(64)
- # N x 64 x 31 x 24
- self.Maxpool1 = nn.MaxPool2d(kernel_size=(5,5), stride=(3,3), padding=(0,2))
- # N x 64 x 9 x 8
-
- self.Conv3 = nn.Conv2d(64, 64, kernel_size=(5,5), stride=(3,3), padding=(1,2))
- self.Bn3 = nn.BatchNorm2d(64)
- # N x 64 x 3 x 3
- self.Maxpool2 = nn.MaxPool2d(kernel_size=(3,3), stride=(1,1), padding=(0,0))
- # N x 64 x 1 x 1
-
- self.Linear1 = nn.Linear(64, 16)
-
- self.FC_out = nn.Linear(16, 1) if n_classes==2 else nn.Linear(64, n_classes)
-
-
- def forward(self, x):
- #print(x.shape)
- x = self.Conv1(x)
- x = self.Bn1(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Conv2(x)
- x = self.Bn2(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Maxpool1(x)
- #print(x.shape)
- x = self.Conv3(x)
- x = self.Bn3(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Maxpool2(x)
- #print(x.shape)
- x = x.reshape(x.shape[0], -1)
- #print(x.shape)
- x = self.Linear1(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.FC_out(x)
-
- return x
-
-
-class ForkliftFrameClassifier_V1(nn.Module):
- def __init__(self, n_classes = 2, dropout = 0.15):
- super(ForkliftFrameClassifier_V1, self).__init__()
- self.dropout = dropout
- # N x 3 x 240 x 240
-
- self.Conv1 = nn.Conv2d(3, 32, kernel_size=(5,5), stride=(3,3), padding=(1,1))
- self.Bn1 = nn.BatchNorm2d(32)
- # N x 32 x 80 x 80
-
- self.Conv2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=(3,3), padding=(1,1))
- self.Bn2 = nn.BatchNorm2d(64)
- # N x 64 x 26 x 26
- self.Maxpool1 = nn.MaxPool2d(kernel_size=(5,5), stride=(3,3), padding=(1,1))
- # N x 64 x 8 x 8
-
- self.Conv3 = nn.Conv2d(64, 32, kernel_size=(3,3), stride=(2,2), padding=(1,1))
- self.Bn3 = nn.BatchNorm2d(32)
- # N x 64 x 4 x 4
- self.Maxpool2 = nn.MaxPool2d(kernel_size=(4,4), stride=(1,1), padding=(0,0))
- # N x 64 x 1 x 1
-
- #self.Linear1 = nn.Linear(64, 16)
-
- self.FC_out = nn.Linear(32, 1) if n_classes==2 else nn.Linear(64, n_classes)
-
-
- def forward(self, x):
- #print(x.shape)
- #print(x.shape)
- x = self.Conv1(x)
- x = self.Bn1(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Conv2(x)
- x = self.Bn2(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Maxpool1(x)
- #print(x.shape)
- x = self.Conv3(x)
- x = self.Bn3(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Maxpool2(x)
- #print(x.shape)
- x = x.reshape(x.shape[0], -1)
- #print(x.shape)
- #x = self.Linear1(x)
- #x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.FC_out(x)
-
- return x
-
-
-class ForkliftFrameClassifier_V2(nn.Module):
- def __init__(self, n_classes = 2, dropout = 0.15):
- super(ForkliftFrameClassifier_V2, self).__init__()
- self.dropout = dropout
- # N x 3 x 150 x 150
-
- self.Conv1 = nn.Conv2d(3, 32, kernel_size=(5,5), stride=(3,3), padding=(1,1))
- self.Bn1 = nn.BatchNorm2d(32)
- # N x 32 x 50 x 50
-
- self.Conv2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=(3,3), padding=(1,1))
- self.Bn2 = nn.BatchNorm2d(64)
- # N x 64 x 16 x 16
- self.Maxpool1 = nn.MaxPool2d(kernel_size=(3,3), stride=(2,2), padding=(1,1))
- # N x 64 x 8 x 8
-
- self.Conv3 = nn.Conv2d(64, 32, kernel_size=(3,3), stride=(2,2), padding=(1,1))
- self.Bn3 = nn.BatchNorm2d(32)
- # N x 64 x 4 x 4
- self.Maxpool2 = nn.MaxPool2d(kernel_size=(4,4), stride=(1,1), padding=(0,0))
- # N x 64 x 1 x 1
-
- #self.Linear1 = nn.Linear(64, 16)
-
- self.FC_out = nn.Linear(32, 1) if n_classes==2 else nn.Linear(64, n_classes)
-
-
- def forward(self, x):
- #print(x.shape)
- #print(x.shape)
- x = self.Conv1(x)
- x = self.Bn1(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Conv2(x)
- x = self.Bn2(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Maxpool1(x)
- #print(x.shape)
- x = self.Conv3(x)
- x = self.Bn3(x)
- x = F.relu(x)
- x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.Maxpool2(x)
- #print(x.shape)
- x = x.reshape(x.shape[0], -1)
- #print(x.shape)
- #x = self.Linear1(x)
- #x = F.dropout(x,self.dropout)
- #print(x.shape)
- x = self.FC_out(x)
-
- return x
-
-
-class ForkliftFrameClassifier_PT1(nn.Module):
- def __init__(self, pretrained_model,n_out_last_layer, n_classes = 2):
- super(ForkliftFrameClassifier_PT1, self).__init__()
- self.pt_model = pretrained_model
- self.pt_model.classifier = nn.Linear(25088,1) if n_classes==2 else nn.Linear(n_out_last_layer, n_classes)
- for param in self.pt_model.classifier.parameters():
- param.requires_grad = False
-
- def forward(self, x):
- x = self.pt_model(x)
- return x
-
-
-class CNN_Feature_Extractor(nn.Module):
- def __init__(self):
- super(CNN_Feature_Extractor, self).__init__()
-
- self.conv1 = nn.Conv2d(3, 10, kernel_size=(5,5), stride=(3,3))
- self.conv2 = nn.Conv2d(10, 20, kernel_size=(5,5), stride=(2,2))
- self.conv3 = nn.Conv2d(20, 30, kernel_size=(5,5), stride=(2,2))
-
- def forward(self, i):
- x = i.view(-1, i.shape[2], i.shape[3], i.shape[4])
- x = F.relu(self.conv1(x))
- x = F.relu(self.conv2(x))
- x = F.relu(self.conv3(x))
- x = nn.AvgPool2d(3)(x)
- x = x.view(i.shape[0], i.shape[1], -1)
- return x
-
-class LSTM(nn.Module):
- def __init__(self, seq_len, hidden_size,out_size):
- super(LSTM, self).__init__()
- self.lstm = nn.LSTM(750, hidden_size)
- self.fc = nn.Linear(seq_len*hidden_size, out_size)
-
- def forward(self, x):
- x, _ = self.lstm(x)
- x = x.view(x.shape[0], -1)
- x = self.fc(x)
- return x
-
-
-class Full_LSTM(nn.Module):
- def __init__(self,seq_len = 15, hidden_size = 100, out_size = 512):
- super(Full_LSTM, self).__init__()
- self.net_cnn = CNN_Feature_Extractor()
- self.net_lstm = LSTM(seq_len, hidden_size, out_size)
- self.classifier = nn.Sequential(nn.Linear(out_size, 16),
- nn.Dropout(0.3),
- nn.ReLU(),
- nn.Linear(16,1))
-
- def forward(self, x):
- # x.size() == (B,L,C,H,W)
- # B : Batch size
- # L : Sequence Length = 15
- # C : Channels = 3
- # H : Heigth = 224
- # W : Width = 224
- x = self.net_cnn(x)
- x = self.net_lstm(x)
- x = self.classifier(x)
- return x
-
-class Full_CNN(nn.Module):
- def __init__(self):
- super(Full_CNN, self).__init__()
- self.model = torchvision.models.resnet18(pretrained=True)
- #for param in model.parameters():
- # param.requires_grad = False
- self.model.fc = nn.Sequential(nn.Linear(512, 16),
- nn.Dropout(0.3),
- nn.ReLU(),
- nn.Linear(16,1))
-
- def forward(self, x):
- # x.size() == (B,L,C,H,W)
- # B : Batch size
- # L : Sequence Length = 15
- # C : Channels = 3
- # H : Heigth = 224
- # W : Width = 224
-
- x = self.model(x[:,0,:])
-
- return x
-
-
-class Full_Model(nn.Module):
- def __init__(self,seq_len = 15, hidden_size = 100, classifier_size = 512, dropout = 0.4, cnn_model_path = None, lstm_model_path= None):
- super(Full_Model, self).__init__()
- self.CNN_Part = Full_CNN()
-
- if cnn_model_path is not None:
- self.CNN_Part.model.load_state_dict(torch.load(cnn_model_path)['model_state_dict'])
- self.CNN_classifier = copy.deepcopy(self.CNN_Part.model.fc)
- else:
- self.CNN_classifier = nn.Sequential(nn.Linear(classifier_size, 16),
- nn.Dropout(dropout),
- nn.ReLU(),
- nn.Linear(16,1))
-
-
- self.CNN_Part.model.fc = nn.Sequential(nn.Linear(classifier_size, classifier_size),
- nn.Dropout(dropout),
- nn.ReLU())
-
-
-
- self.LSTM_Part = Full_LSTM(seq_len, hidden_size, classifier_size)
-
- if lstm_model_path is not None:
- self.LSTM_Part.load_state_dict(torch.load(lstm_model_path)['model_state_dict'])
- self.LSTM_classifier = copy.deepcopy(self.LSTM_Part.classifier)
- else:
- self.LSTM_classifier = nn.Sequential(nn.Linear(classifier_size, 16),
- nn.Dropout(dropout),
- nn.ReLU(),
- nn.Linear(16,1))
-
- self.Finalclassifier = nn.Sequential(nn.Linear(classifier_size, 16),
- nn.Dropout(dropout),
- nn.ReLU(),
- nn.Linear(16,1))
-
-
-
- def forward(self, x):
- # x.size() == (B,L,C,H,W)
- # B : Batch size
- # L : Sequence Length = 15
- # C : Channels = 3
- # H : Heigth = 224
- # W : Width = 224
- cnn_out = self.CNN_Part(x)
-
- # xcnn : (B,512)
- lstm_out = self.LSTM_Part.net_cnn(x)
- lstm_out = self.LSTM_Part.net_lstm(lstm_out)
- # xlstm : (B,512)
-
- out = cnn_out + lstm_out
-
- cnn_out = self.CNN_classifier(cnn_out)
- lstm_out = self.LSTM_classifier(lstm_out)
- out = self.Finalclassifier(out)
-
- return (cnn_out, lstm_out, out)
-
-
-# Se establece que el modelo final consista en cargar un modelo Full, pero considerando solamente la salida y el
-# forward de la componente LSTM. Esto se hace así (Cargar inclusive los pesos de LSTM) por si en el futuro se decidiera que estos pesos pueden ser útiles.
-class Final_CNN_Model(nn.Module):
- def __init__(self,seq_len = 15, hidden_size = 100, classifier_size = 512, dropout = 0.4, cnn_model_path = None, lstm_model_path= None):
- super(Final_CNN_Model, self).__init__()
- self.CNN_Part = Full_CNN()
-
- if cnn_model_path is not None:
- self.CNN_Part.model.load_state_dict(torch.load(cnn_model_path)['model_state_dict'])
- self.CNN_classifier = copy.deepcopy(self.CNN_Part.model.fc)
- else:
- self.CNN_classifier = nn.Sequential(nn.Linear(classifier_size, 16),
- nn.Dropout(dropout),
- nn.ReLU(),
- nn.Linear(16,1))
-
-
- self.CNN_Part.model.fc = nn.Sequential(nn.Linear(classifier_size, classifier_size),
- nn.Dropout(dropout),
- nn.ReLU())
-
-
-
- self.LSTM_Part = Full_LSTM(seq_len, hidden_size, classifier_size)
-
- if lstm_model_path is not None:
- self.LSTM_Part.load_state_dict(torch.load(lstm_model_path)['model_state_dict'])
- self.LSTM_classifier = copy.deepcopy(self.LSTM_Part.classifier)
- else:
- self.LSTM_classifier = nn.Sequential(nn.Linear(classifier_size, 16),
- nn.Dropout(dropout),
- nn.ReLU(),
- nn.Linear(16,1))
-
- self.Finalclassifier = nn.Sequential(nn.Linear(classifier_size, 16),
- nn.Dropout(dropout),
- nn.ReLU(),
- nn.Linear(16,1))
-
-
-
- def forward(self, x):
- # x.size() == (B,L,C,H,W)
- # B : Batch size
- # L : Sequence Length = 15
- # C : Channels = 3
- # H : Heigth = 224
- # W : Width = 224
- cnn_out = self.CNN_Part(x)
-
- cnn_out = self.CNN_classifier(cnn_out)
-
- return cnn_out
-
-
-# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-# torch.cuda.empty_cache()
-# x1 = torch.rand((64, 15, 3, 224 , 224))
-# model = Full_Model(cnn_model_path = 'Best_model_4.pt', lstm_model_path= 'Best_model_10.pt')
-
-# model.to(device)
-# x1 = x1.to(device)
-# out1 = model(x1)
-
-
-
-
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diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/tax_calculator/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/tax_calculator/run.py
deleted file mode 100644
index aebb841b0e84d011df42429eee1592b868acd30d..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/tax_calculator/run.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gradio as gr
-
-
-def tax_calculator(income, marital_status, assets):
- tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
- total_deductible = sum(assets["Cost"])
- taxable_income = income - total_deductible
-
- total_tax = 0
- for bracket, rate in tax_brackets:
- if taxable_income > bracket:
- total_tax += (taxable_income - bracket) * rate / 100
-
- if marital_status == "Married":
- total_tax *= 0.75
- elif marital_status == "Divorced":
- total_tax *= 0.8
-
- return round(total_tax)
-
-
-demo = gr.Interface(
- tax_calculator,
- [
- "number",
- gr.Radio(["Single", "Married", "Divorced"]),
- gr.Dataframe(
- headers=["Item", "Cost"],
- datatype=["str", "number"],
- label="Assets Purchased this Year",
- ),
- ],
- "number",
- examples=[
- [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
- [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
- ],
-)
-
-if __name__ == "__main__":
- demo.launch()
diff --git a/spaces/friedrichor/friedrichor-stable-diffusion-2-1-realistic/README.md b/spaces/friedrichor/friedrichor-stable-diffusion-2-1-realistic/README.md
deleted file mode 100644
index 0a819dbef8e1765fb3e6b8ad0d0463bbba64a647..0000000000000000000000000000000000000000
--- a/spaces/friedrichor/friedrichor-stable-diffusion-2-1-realistic/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Friedrichor Stable Diffusion 2 1 Realistic
-emoji: 😻
-colorFrom: indigo
-colorTo: indigo
-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/g4f/freegpt-webui/g4f/Provider/__init__.py b/spaces/g4f/freegpt-webui/g4f/Provider/__init__.py
deleted file mode 100644
index b01b310b39580351ebf76708b8e36de8bfd7fe1b..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from . import Provider
-from .Providers import (
- Aichat,
- Bard,
- Better,
- Bing,
- ChatgptAi,
- ChatgptLogin,
- ChatgptLogin,
- DeepAi,
- Dfehub,
- Easychat,
- Ezcht,
- Fakeopen,
- Forefront,
- GetGpt,
- Gravityengine,
- H2o,
- hteyun,
- Liaobots,
- Lockchat,
- Mishalsgpt,
- Phind,
- Theb,
- Vercel,
- Weuseing,
- Xiaor,
- Yqcloud,
- You,
-)
-
-Palm = Bard
diff --git a/spaces/gestiodinamica/recon_caras/app.py b/spaces/gestiodinamica/recon_caras/app.py
deleted file mode 100644
index a0785f61bfa4b1036d2bcfea841974d0f9b4d400..0000000000000000000000000000000000000000
--- a/spaces/gestiodinamica/recon_caras/app.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-José Carlos Machicao
-GestioDinámica
-Fecha de producción: 2021_10
-Fecha de actualización 2022_07
-Ubicación Original: PythonScripts/gdmk_facerecog/
-"""
-
-import streamlit as st
-import face_recognition
-from PIL import Image
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import datetime
-import base64
-from io import BytesIO
-from pyxlsb import open_workbook as open_xlsb
-
-# BODY
-st.image('gdmk.png', width=150)
-
-st.subheader('Aplicativos de Reconocimiento Facial')
-st.title('Comparación de Identidades')
-nom_oper = st.text_input('Nombre de Operador(a):')
-st.subheader('**Carga de archivo PKL con Tensores Preentrenados**')
-up_base = st.file_uploader('Cargue Archivo Base Recibido de su Supervisor: ')
-
-if up_base is not None:
-
- base = pd.read_pickle(up_base)
- st.write(base.shape)
- st.write(base.lista)
-
-# 2. CARGA DE FOTOS EVENTO
-st.subheader('**Carga de Archivos de Imagen Conteniendo Caras**')
-
-up_evento = st.file_uploader('Elija pantallazos del evento: ', accept_multiple_files=True)
-
-if len(up_evento)==0:
-
- mensaje='Todavía no se han cargado imágenes.'
- st.write(mensaje)
-
-else:
- mensaje='Confirmación, se han cargado '+str(len(up_evento))+' imágenes.'
- st.write(mensaje)
-
- caras_embed = []
-
- for j, pic in enumerate(up_evento):
- ima = face_recognition.load_image_file(pic)
- face_locs = face_recognition.face_locations(ima)
- face_enco = face_recognition.face_encodings(ima)
-
- for k, facex in enumerate(face_enco):
- top, right, bottom, left = face_locs[k]
- face_frame = ima[top:bottom, left:right]
- pil_image = Image.fromarray(face_frame)
- pil_image_100 = pil_image.resize((100,100))
- rotulox = 'nom_' + str(j) + '_' + str(k) + '.jpg'
- #pil_image_100.save(rotulox)
- caras_embed.append([rotulox, facex, pil_image_100])
-
- caras_evento_code_df = pd.DataFrame(caras_embed)
- caras_evento_code_df.columns = ['rotulo', 'face_embed', 'image']
-
- # 3. COMPARACIÓN
-
- st.subheader('**Procedimiento: Comparación**')
-
- codesx = base.codigos
- lista_fotos = base.lista
-
- resultados = []
- for face in caras_evento_code_df.face_embed:
- matches = face_recognition.compare_faces(list(codesx), face)
- timestamp = datetime.datetime.now()
- try:
- indice = int(np.where(matches)[0])
- halla = lista_fotos[indice]
- resultados.append([timestamp, halla])
- except:
- resultados.append([timestamp, 'Desconocido'])
-
- timestamp = str(datetime.datetime.now()).replace(':','-')
-
- resultados_df = pd.DataFrame(resultados)
- resultados_df.columns = ['timestamp', 'nombre']
- resultados_df['arch_evento'] = caras_evento_code_df.rotulo
- resultados_df['imagenes'] = caras_evento_code_df.image
-
- st.dataframe(resultados_df.drop(['imagenes'], axis=1))
- asistentes = resultados_df.nombre
-
- asistencia = []
- for nom in base.lista:
- if nom in list(asistentes):
- asistencia.append([nom, 'Asistió'])
- else:
- asistencia.append([nom, 'No Asistió'])
- asist_df = pd.DataFrame(asistencia)
- st.dataframe(asist_df)
-
- def convierte_excel(df):
- output = BytesIO()
- writer = pd.ExcelWriter(output, engine='xlsxwriter')
- df.to_excel(writer, index=False, sheet_name='data_extraida')
- workbook = writer.book
- worksheet = writer.sheets['data_extraida']
- format1 = workbook.add_format({'num_format': '0.00'})
- worksheet.set_column('A:A', None, format1)
- writer.save()
- processed_data = output.getvalue()
- writer.close()
- return processed_data
-
- csv = asist_df.to_csv(index=False)
- st.download_button(label='📩 Descargar CSV', data=csv,
- file_name = 'df_'+timestamp+'.csv')
-
- n_fig = 10
- fig, ax = plt.subplots(1, n_fig, figsize=(21, 2))
- for i in range(n_fig):
- if i > len(resultados_df)-1:
- img = Image.open('void.jpg')
- ax[i].imshow(img)
- ax[i].axis('off')
- else:
- img = resultados_df.imagenes.iloc[i]
- ax[i].imshow(img)
- ax[i].set_title(str(resultados_df.nombre.iloc[i]))
- ax[i].axis('off')
-
- #timestamp = str(datetime.datetime.now()).replace(':','-')
- #plt.savefig('resultados_'+timestamp+'.jpg')
- #st.image(Image.open('resultados_'+timestamp+'.jpg'), width=800)
- st.pyplot(fig)
-
- #st.write('Se ha guardado los archivos en el folder resultados.')
- #resultados_df.to_csv('resultados/verificacion.csv')
- #asist_df.to_csv('resultados/asistencia.csv')
-
- c1, c2, c3 = st.columns(3)
- with c1:
- st.write(' ')
- with c2:
- st.write(' ')
- with c3:
- st.write(' ')
- st.image('gdmk.png', width=100, caption='Designed and Powered by GestioDinámica')
\ No newline at end of file
diff --git a/spaces/glassofwine/glassofwine-DialoGPT-medium-johanwine/app.py b/spaces/glassofwine/glassofwine-DialoGPT-medium-johanwine/app.py
deleted file mode 100644
index 008e43f573991d790e1966bd30643130f54edd67..0000000000000000000000000000000000000000
--- a/spaces/glassofwine/glassofwine-DialoGPT-medium-johanwine/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/glassofwine/DialoGPT-medium-johanwine").launch()
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Game Video Crocore Rj082581 UPDATED.md b/spaces/gotiQspiryo/whisper-ui/examples/Game Video Crocore Rj082581 UPDATED.md
deleted file mode 100644
index f78a766d6918f70c39041a14ba9d2f166e86be13..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Game Video Crocore Rj082581 UPDATED.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Unofficial subreddit for the discussion of Rockstar Games and its products.. glsfctZ EXTRA ... ba1888a4a6. Game Video Crocore Rj082581 1fdad05405
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Marvel.Ultimate.Alliance.2.Update.v20160804-CODEX CODEX The Latest Update for the Best-Selling Game.md b/spaces/gotiQspiryo/whisper-ui/examples/Marvel.Ultimate.Alliance.2.Update.v20160804-CODEX CODEX The Latest Update for the Best-Selling Game.md
deleted file mode 100644
index 9147c186307734521d1085b14997acbd71830923..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Marvel.Ultimate.Alliance.2.Update.v20160804-CODEX CODEX The Latest Update for the Best-Selling Game.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
this ccsa r77 certification exam tests your skills in understanding and implementing check point’s products and security configurations. the exam consists of 120 multiple-choice questions, with each question having 4 possible answers. during the exam, you will have 30 minutes to answer all the questions. on average, you should spend 2 to 3 hours for the exam.
-
in this ccsa course you will learn how to install, configure, and manage check point’s nsas. you will learn how to install and configure the software for the product, how to configure the product hardware and how to configure the product network settings. you will learn how to use the check point management console to configure and monitor the product. the lab is a step by step lab for the nsas.
the ccsa r80 exam will ask you to be familiar with check point network security products. you will know how to install and configure the following components:
security gateway
management database
management client
ips (intrusion prevention system)
check point os (gaia)
log server
security database
network connection manager
network connection manager database
network load balancer
hardware components
ssh
site-to-site vpn
multiple vpn
vpn gateways
optional products
-
this training includes a setup process for the check point ccsa r80. the setup process includes: installing the management software, configuring the firewall, defining the ip address of the management server and initializing the system.
-
-is designed to meet the needs of users with no previous knowledge of the . To reduce the noise in the game, there is a built-in program interface for you to choose the audio device that you want to use, including the default soundcard. If you do not want to change your settings, just press the button in the interface, the audio device will automatically be changed to the default. Get to know the game's configuration and sound quality settings, and then you can easily set them to the way you want.The information about the audio devices can be found in the following page:
-
-This application allows you to run *without* changing the audio device by default. The information about the audio devices can be found in the following page:
-
-The application currently supports the following audio devices and playback formats:
-
-• Autoamt MIDI output
-
-• Audigy2 Output
-
-• Line In output
-
-• Portable Out (HDMI, AV, Composite, S-Video)
-
-• Mics/Line In
-
-• S3 Screen Saver (HDMI)
-
-• Sennheiser HD 670 (Lavalier/Mic)
-
-• Sony Hi-MD MDR-X1 (Mic)
-
-• SoundMAX HD (Mic)
-
-• TEAC UD-M50x (Mic)
-
-• Yamaha U3 (Mic)
-
-• Line In (LINE-IN)
-
-• Line In (MIC/LINE-IN)
-
-• Line In (Microphone)
-
-• Line In (Loopback)
-
-• Line In (Mic/Line-In+Loopback)
-
-• Line In (Microphone+Loopback)
-
-• Line In (Digital)
-
-• Line In (Mic)
-
-• Line In (Mic+Loopback)
-
-• Line In (Line-In+Loopback)
-
-• Line In (Line-In 4fefd39f24
-
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Dp Technology Esprit 2011 Crack Full.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Dp Technology Esprit 2011 Crack Full.md
deleted file mode 100644
index 082cf4bfe9e56346517fce2f9e0bccc4c4c17fba..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Dp Technology Esprit 2011 Crack Full.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
- )
-}
diff --git a/spaces/ml-energy/leaderboard/spitfight/colosseum/controller/controller.py b/spaces/ml-energy/leaderboard/spitfight/colosseum/controller/controller.py
deleted file mode 100644
index cdf7cfbb55459ed581e00448f6f5e9c7592757da..0000000000000000000000000000000000000000
--- a/spaces/ml-energy/leaderboard/spitfight/colosseum/controller/controller.py
+++ /dev/null
@@ -1,280 +0,0 @@
-from __future__ import annotations
-
-import json
-import asyncio
-from datetime import datetime
-from typing import AsyncGenerator, Literal, Optional, TYPE_CHECKING
-
-import aiohttp
-from pytz import timezone
-from pydantic import BaseModel, Field
-
-from spitfight.log import get_logger
-from spitfight.utils import BoundedExpiringDict, TokenGenerationBuffer, create_task
-from spitfight.colosseum.controller.worker import WorkerService
-from spitfight.prompt import apply_model_characteristics
-
-if TYPE_CHECKING:
- from spitfight.colosseum.controller.router import ControllerConfig
-
-controller_logger = get_logger(__name__)
-request_logger = get_logger("colosseum_requests")
-
-
-def now() -> datetime:
- return datetime.now(tz=timezone("US/Eastern"))
-
-
-# Internal states
-# The two "chose_*" stages are both the result of voting on a response.
-# A normal user will sequentially go through either
-# "prompted" -> "chose_less_energy_response", or
-# "prompted" -> "chose_more_energy_response" -> "voted_energy"
-UserStage = Literal[
- "prompted",
- "chose_less_energy_response",
- "chose_more_energy_response",
- "voted_energy",
-]
-
-
-class RequestState(BaseModel):
- """Models the state of a Colosseum play.
-
- This model is also serialized as is and logged.
- """
- request_id: str
- model_names: list[str]
- raw_prompt: str
- model_preference: str
- responses: list[str] = ["UNSET", "UNSET"]
- model_prompts: list[str] = ["UNSET", "UNSET"]
- energy_consumptions: list[float] = [0.0, 0.0]
- response_victory_index: Optional[Literal[0, 1]] = None
- extra_energy_was_worth: Optional[bool] = None
-
- # The time when the user's stage changed.
- timestamp: datetime = Field(default_factory=now)
- # The user's current stage.
- user_stage: UserStage = "prompted"
- # When the the user is not going through the aforementioned stages,
- # the user's stage transition is recorded here.
- abnormal_stage_change: list[tuple[UserStage, UserStage]] = []
-
- def set_response_and_energy(self, model_index: Literal[0, 1], response: str, energy_consumption: float) -> None:
- self.timestamp = now()
- self.energy_consumptions[model_index] = energy_consumption
- self.responses[model_index] = response
-
- def set_response_vote(self, victory_index: Literal[0, 1]) -> None:
- self.timestamp = now()
-
- # Next stage depends on the user's vote.
- energy_a, energy_b = self.energy_consumptions
- if (victory_index == 0 and energy_a <= energy_b) or (victory_index == 1 and energy_a >= energy_b):
- next_stage = "chose_less_energy_response"
- else:
- next_stage = "chose_more_energy_response"
-
- # Detect abnormal stage change.
- if self.user_stage != "prompted":
- self.abnormal_stage_change.append((self.user_stage, next_stage))
-
- self.user_stage = next_stage
- self.response_victory_index = victory_index
-
- def set_energy_vote(self, is_worth: bool) -> None:
- self.timestamp = now()
-
- # Detect abnormal stage change.
- if self.user_stage != "chose_more_energy_response":
- self.abnormal_stage_change.append((self.user_stage, "voted_energy"))
-
- self.user_stage = "voted_energy"
- self.extra_energy_was_worth = is_worth
-
-
-class GenerationConfig(BaseModel):
- """Configuration for generation of prompts."""
- max_new_tokens: int
- do_sample: bool
- temperature: float
- repetition_penalty: float
- top_k: int
- top_p: float
-
-
-class Controller:
- def __init__(
- self,
- background_task_interval: int,
- max_num_req_states: int,
- req_state_expiration_time: int,
- worker_service: WorkerService,
- generation_config: GenerationConfig,
- ):
- self.request_states: BoundedExpiringDict[str, RequestState] = \
- BoundedExpiringDict(max_num_req_states, req_state_expiration_time)
- self.worker_service = worker_service
-
- self.generation_config = generation_config
-
- self.background_task_handle = create_task(
- self._background_task(background_task_interval),
- )
-
- def shutdown(self) -> None:
- """Shutdown the controller."""
- self.background_task_handle.cancel()
-
- async def _background_task(self, heartbeat_interval: int) -> None:
- """Periodically check if dead workers are alive again and do request state GC."""
- while True:
- await asyncio.sleep(heartbeat_interval)
-
- await self.worker_service.check_workers()
-
- prev_num_req_states = len(self.request_states)
- self.request_states.cleanup()
- controller_logger.info(
- "Request state garbage collection done: Removed %d reqeusts",
- prev_num_req_states - len(self.request_states),
- )
-
- def get_available_models(self) -> list[str]:
- """Return the names of available models."""
- return [
- worker.model_name
- for worker in self.worker_service.workers
- if worker.status == "up"
- ]
-
- def response_vote(self, request_id: str, victory_index: Literal[0, 1]) -> RequestState | None:
- """Record the user's response vote and return the new state."""
- if (state := self.request_states.get(request_id)) is not None:
- state.set_response_vote(victory_index)
- # Pop the state from the dict if the user has voted on energy.
- if state.user_stage == "chose_less_energy_response":
- self.request_states.pop(request_id)
- request_logger.info(state.json())
- return state
- return None
-
- def energy_vote(self, request_id: str, is_worth: bool) -> RequestState | None:
- """Record the user's energy vote and return the new state."""
- # Pop the state from the dict, since this is the last step in any case.
- if (state := self.request_states.pop(request_id)) is not None:
- state.set_energy_vote(is_worth)
- request_logger.info(state.json())
- return state
- return None
-
- async def prompt(
- self,
- request_id: str,
- prompt: str,
- model_index: Literal[0, 1],
- model_preference: str,
- ) -> AsyncGenerator[bytes, None]:
- # This method is called twice for the same request, once for each model.
- # If it's the first time this method is called, assign models to the request.
- if request_id not in self.request_states:
- workers = self.worker_service.choose_based_on_preference(model_preference)
- model_names = [worker.model_name for worker in workers]
- self.request_states[request_id] = RequestState(
- request_id=request_id,
- raw_prompt=prompt,
- model_names=model_names,
- model_preference=model_preference,
- )
- request_state = self.request_states[request_id]
- model_name = request_state.model_names[model_index]
- try:
- worker = self.worker_service.get_worker(model_name)
- except KeyError:
- controller_logger.error("Worker %s not found.", model_name)
- raise
- except RuntimeError:
- controller_logger.error("Worker %s is dead.", model_name)
- raise
-
- # Models have different prompt formatting requirements and stopping criteria.
- prompt, stop_str, stop_token_ids = apply_model_characteristics(
- prompt=prompt,
- model_name=worker.model_id,
- )
- request_state.model_prompts[model_index] = prompt
-
- # Request the model worker to stream the response to the user's prompt.
- response = ""
- energy = 0.0
- client = worker.get_client()
- buffer = TokenGenerationBuffer(stop_str=stop_str)
- try:
- async for resp in client.generate_stream(
- prompt=prompt,
- stop_sequences=[stop_str] if stop_str is not None else None,
- **self.generation_config.dict(),
- ):
- # Even special tokens consume energy when they're generated.
- energy += resp.token.energy
-
- # Stop tokens usually don't overlap with (human-readable) stop sequences.
- # if resp.token.special or resp.token.id in stop_token_ids:
- if resp.token.id in stop_token_ids:
- # If the buffer is not empty (i.e., we had partial stop_str matches),
- # just yield it to the user.
- if (chunk := buffer.token_buffer):
- response += chunk
- yield json.dumps(chunk).encode() + b"\0"
- break
-
- # Skip special tokens.
- if resp.token.special:
- continue
-
- # The buffer automatically handles `stop_str` partial and full matches.
- buffer.append(resp.token.text)
- if (chunk := buffer.pop()) is not None:
- response += chunk
- yield json.dumps(chunk).encode() + b"\0"
- elif buffer.matched_stop_str:
- break
- except aiohttp.ClientConnectorError:
- worker.status = "down"
- controller_logger.error(
- "Problem talking to %s. Aborting and setting worker status to down",
- repr(worker),
- )
- raise
- except Exception:
- yield json.dumps(buffer.token_buffer).encode() + b"\0"
- raise
- finally:
- request_state.set_response_and_energy(model_index, response, energy)
- request_logger.info(request_state.json())
-
-
-CONTROLLER: Controller | None = None
-
-def init_global_controller(config: ControllerConfig) -> None:
- global CONTROLLER
- CONTROLLER = Controller(
- background_task_interval=config.background_task_interval,
- max_num_req_states=config.max_num_req_states,
- req_state_expiration_time=config.req_state_expiration_time,
- worker_service=WorkerService(config.compose_files),
- generation_config=GenerationConfig(
- max_new_tokens=config.max_new_tokens,
- do_sample=config.do_sample,
- temperature=config.temperature,
- repetition_penalty=config.repetition_penalty,
- top_k=config.top_k,
- top_p=config.top_p,
- ),
- )
-
-def get_global_controller() -> Controller:
- global CONTROLLER
- assert CONTROLLER is not None
- return CONTROLLER
diff --git a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/backbone/__init__.py b/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/backbone/__init__.py
deleted file mode 100644
index 49f9003b7a688f5396170dd89c26ef335a2c201f..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/backbone/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
diff --git a/spaces/mrm8488/PromptSource/README.md b/spaces/mrm8488/PromptSource/README.md
deleted file mode 100644
index ed8c67268293bc15c5936766fe726f030de8d33b..0000000000000000000000000000000000000000
--- a/spaces/mrm8488/PromptSource/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: PromptSource
-emoji: 💩
-colorFrom: indigo
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/mrneuralnet/P-DFD/trainer/abstract_trainer.py b/spaces/mrneuralnet/P-DFD/trainer/abstract_trainer.py
deleted file mode 100644
index 4e1354a94ec55db2fcad0d951ecaca2f7b804fbe..0000000000000000000000000000000000000000
--- a/spaces/mrneuralnet/P-DFD/trainer/abstract_trainer.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import torch
-import random
-from collections import OrderedDict
-from torchvision.utils import make_grid
-
-LEGAL_METRIC = ['Acc', 'AUC', 'LogLoss']
-
-
-class AbstractTrainer(object):
- def __init__(self, config, stage="Train"):
- feasible_stage = ["Train", "Test"]
- if stage not in feasible_stage:
- raise ValueError(f"stage should be in {feasible_stage}, but found '{stage}'")
-
- self.config = config
- model_cfg = config.get("model", None)
- data_cfg = config.get("data", None)
- config_cfg = config.get("config", None)
-
- self.model_name = model_cfg.pop("name")
-
- self.gpu = None
- self.dir = None
- self.debug = None
- self.device = None
- self.resume = None
- self.local_rank = None
- self.num_classes = None
-
- self.best_metric = 0.0
- self.best_step = 1
- self.start_step = 1
-
- self._initiated_settings(model_cfg, data_cfg, config_cfg)
-
- if stage == 'Train':
- self._train_settings(model_cfg, data_cfg, config_cfg)
- if stage == 'Test':
- self._test_settings(model_cfg, data_cfg, config_cfg)
-
- def _initiated_settings(self, model_cfg, data_cfg, config_cfg):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def _train_settings(self, model_cfg, data_cfg, config_cfg):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def _test_settings(self, model_cfg, data_cfg, config_cfg):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def _save_ckpt(self, step, best=False):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def _load_ckpt(self, best=False, train=False):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def to_device(self, items):
- return [obj.to(self.device) for obj in items]
-
- @staticmethod
- def fixed_randomness():
- random.seed(0)
- torch.manual_seed(0)
- torch.cuda.manual_seed(0)
- torch.cuda.manual_seed_all(0)
-
- def train(self):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def validate(self, epoch, step, timer, writer):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def test(self):
- raise NotImplementedError("Not implemented in abstract class.")
-
- def plot_figure(self, images, pred, gt, nrow, categories=None, show=True):
- import matplotlib.pyplot as plt
- plot = make_grid(
- images, nrow, padding=4, normalize=True, scale_each=True, pad_value=1)
- if self.num_classes == 1:
- pred = (pred >= 0.5).cpu().numpy()
- else:
- pred = pred.argmax(1).cpu().numpy()
- gt = gt.cpu().numpy()
- if categories is not None:
- pred = [categories[i] for i in pred]
- gt = [categories[i] for i in gt]
- plot = plot.permute([1, 2, 0])
- plot = plot.cpu().numpy()
- ret = plt.figure()
- plt.imshow(plot)
- plt.title("pred: %s\ngt: %s" % (pred, gt))
- plt.axis("off")
- if show:
- plt.savefig(os.path.join(self.dir, "test_image.png"), dpi=300)
- plt.show()
- plt.close()
- else:
- plt.close()
- return ret
diff --git a/spaces/mrrandom123/Book_recommendation/setup.sh b/spaces/mrrandom123/Book_recommendation/setup.sh
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mscsasem3/CHAAT/README.md b/spaces/mscsasem3/CHAAT/README.md
deleted file mode 100644
index b8e7e30714c19c93ce494ec16d779702c0daf449..0000000000000000000000000000000000000000
--- a/spaces/mscsasem3/CHAAT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CHAAT
-emoji: 🌍
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mshukor/UnIVAL/fairseq/.github/ISSUE_TEMPLATE/documentation.md b/spaces/mshukor/UnIVAL/fairseq/.github/ISSUE_TEMPLATE/documentation.md
deleted file mode 100644
index 3a6e2e9ea4bb71102122c17ff53051eb3770cb5e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/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/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py b/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py
deleted file mode 100644
index 36c85d1e2f60487494a92207feb4685e78db8aa2..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py
+++ /dev/null
@@ -1,16 +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 sys
-
-
-def main():
- for line in sys.stdin:
- print(line.replace(" ", "").replace("|", " ").strip())
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/mshukor/UnIVAL/run_scripts/refcoco/scaling_best/unival_refcocog.sh b/spaces/mshukor/UnIVAL/run_scripts/refcoco/scaling_best/unival_refcocog.sh
deleted file mode 100644
index 5ac1a2d1ce50904ea71ae63751f496f41c2133a9..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/refcoco/scaling_best/unival_refcocog.sh
+++ /dev/null
@@ -1,160 +0,0 @@
-#!/usr/bin/env
-
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-
-
-exp_name=unival_refcocog
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-new_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs
-save_dir=${new_base_log_dir}/ofa/checkpoints/refcocog/${exp_name}
-
-
-log_dir=${save_dir}
-
-
-mkdir -p $log_dir $save_dir
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-image_dir=${base_data_dir}
-
-data_dir=${base_data_dir}/ofa/refcocog_data
-data=${data_dir}/refcocog_train_1.tsv,${data_dir}/refcocog_train_2.tsv,${data_dir}/refcocog_train_3.tsv,${data_dir}/refcocog_train_4.tsv,${data_dir}/refcocog_train_5.tsv,${data_dir}/refcocog_train_6.tsv,${data_dir}/refcocog_train_7.tsv,${data_dir}/refcocog_train_8.tsv,${data_dir}/refcocog_train_9.tsv,${data_dir}/refcocog_train_10.tsv,${data_dir}/refcocog_val.tsv
-
-restore_file=${base_log_dir}/ofa/checkpoints/pretrain/unival_s2_hs/checkpoint1.pt
-
-selected_cols=0,4,2,3
-
-task=refcoco
-arch=unival_base
-pretrained_model=
-
-criterion=adjust_label_smoothed_cross_entropy
-label_smoothing=0.1
-lr=5e-5
-max_epoch=10
-warmup_ratio=0.06
-batch_size=8
-update_freq=4
-resnet_drop_path_rate=0.0
-encoder_drop_path_rate=0.1
-decoder_drop_path_rate=0.1
-dropout=0.1
-attention_dropout=0.0
-max_src_length=80
-max_tgt_length=20
-num_bins=1000
-patch_image_size=512
-
-
-image_encoder_name=timm_resnet #vit_base_patch16_224
-resnet_type=resnet101
-
-save_interval=1
-validate_interval_updates=2000
-save_interval_updates=0
-
-sample_patch_num='--sample-patch-num=784' # ''
-
-
-echo "max_epoch "${max_epoch}
-echo "lr "${lr}
-echo "patch_image_size "${patch_image_size}
-
-log_file=${log_dir}/${max_epoch}"_"${lr}"_"${patch_image_size}".log"
-save_path=${save_dir}/${max_epoch}"_"${lr}"_"${patch_image_size}
-mkdir -p $save_path
-
-acc_thresh=0.5
-
-python3 -m torch.distributed.launch \
- --nnodes=${NUM_NODES} \
- --nproc_per_node=${GPUS_PER_NODE} \
- --master_port=${MASTER_PORT} \
- --node_rank=${RANK} \
- --master_addr=${MASTER_ADDR} \
- --use_env ${ofa_dir}/train.py \
- $data \
- --selected-cols=${selected_cols} \
- --bpe-dir=${bpe_dir} \
- --user-dir=${user_dir} \
- --restore-file=${restore_file} \
- --reset-optimizer --reset-dataloader --reset-meters \
- --save-dir=${save_path} \
- --task=${task} \
- --arch=${arch} \
- --criterion=${criterion} \
- --label-smoothing=${label_smoothing} \
- --batch-size=${batch_size} \
- --update-freq=${update_freq} \
- --encoder-normalize-before \
- --decoder-normalize-before \
- --share-decoder-input-output-embed \
- --share-all-embeddings \
- --layernorm-embedding \
- --patch-layernorm-embedding \
- --code-layernorm-embedding \
- --resnet-drop-path-rate=${resnet_drop_path_rate} \
- --encoder-drop-path-rate=${encoder_drop_path_rate} \
- --decoder-drop-path-rate=${decoder_drop_path_rate} \
- --dropout=${dropout} \
- --attention-dropout=${attention_dropout} \
- --weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \
- --lr-scheduler=polynomial_decay --lr=${lr} \
- --max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \
- --log-format=simple --log-interval=10 \
- --fixed-validation-seed=7 \
- --no-epoch-checkpoints --keep-best-checkpoints=1 \
- --save-interval=${save_interval} --validate-interval=1 \
- --save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \
- --eval-acc \
- --eval-args='{"beam":5,"min_len":4,"max_len_a":0,"max_len_b":4}' \
- --best-checkpoint-metric=score --maximize-best-checkpoint-metric \
- --max-src-length=${max_src_length} \
- --max-tgt-length=${max_tgt_length} \
- --find-unused-parameters \
- --add-type-embedding \
- --scale-attn \
- --scale-fc \
- --scale-heads \
- --disable-entangle \
- --num-bins=${num_bins} \
- --patch-image-size=${patch_image_size} \
- --fp16 \
- --fp16-scale-window=512 \
- --num-workers=0 \
- --image-dir=${image_dir} \
- ${sample_patch_num} \
- --acc-thresh=${acc_thresh} \
- --image-encoder-name=${image_encoder_name} \
- --strict
diff --git a/spaces/mshukor/UnIVAL/run_scripts/vqa/eval/video/eval_video_qa.sh b/spaces/mshukor/UnIVAL/run_scripts/vqa/eval/video/eval_video_qa.sh
deleted file mode 100644
index d6097b1d4c9548e11068e95801aabcc67342c70a..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/vqa/eval/video/eval_video_qa.sh
+++ /dev/null
@@ -1,120 +0,0 @@
-#!/usr/bin/env bash
-
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-num_workers=0
-
-
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-
-
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-
-data_dir=${base_data_dir}/ofa/video_data/vqa_data
-
-# val or test or fullval
-split=test
-read_from_img_path=True
-image_dir=${base_data_dir}
-
-data=${data_dir}/msrvtt_qa_4k_test.tsv
-
-ans2label_file=${base_data_dir}/ofa/video_data/vqa_data/msrvtt_trainval_4k_ans2label.pkl
-
-
-
-selected_cols=0,5,2,3,4
-valid_batch_size=40
-
-eval_ema='--ema-eval'
-zero_shot=''
-new_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs
-
-
-# model_name=unival_s2_hs
-# path=/work/NAT/gda2204/mshukor/logs/ofa/checkpoints/pretrain/unival_s2_hs/checkpoint1.pt
-# zero_shot='--zero-shot'
-# eval_ema=''
-
-
-# model_name=avg_postfuse_vidvqacap
-# path=/lus/scratch/NAT/gda2204/SHARED/logs/ofa/pretrained_models/average_models/avg_postfuse_vidvqacap.pt
-# eval_ema=''
-
-
-echo ${path}
-result_path=${new_base_log_dir}/ofa/results/vqa/${exp_name}_${split}
-mkdir ${result_path}
-
-num_frames=8
-patch_frame_size=384
-
-python3 -m torch.distributed.launch \
- --nnodes=${NUM_NODES} \
- --nproc_per_node=${GPUS_PER_NODE} \
- --master_port=${MASTER_PORT} \
- --node_rank=${RANK} \
- --master_addr=${MASTER_ADDR} \
- --use_env ${ofa_dir}/evaluate.py \
- ${data} \
- --path=${path} \
- --user-dir=${user_dir} \
- --task=video_vqa_gen \
- --batch-size=16 \
- --log-format=simple --log-interval=10 \
- --seed=7 \
- --gen-subset=${split} \
- --results-path=${result_path} \
- --fp16 \
- --beam-search-vqa-eval \
- --beam=5 \
- --temperature=1.0 \
- --unnormalized \
- --num-workers=0 \
- --model-overrides="{\"data\":\"${data}\",\"bpe_dir\":\"${bpe_dir}\",\"selected_cols\":\"${selected_cols}\",\"ans2label_file\":\"${ans2label_file}\",\"valid_batch_size\":\"${valid_batch_size}\"}" \
- --image-dir=${image_dir} \
- --read-from-img-path \
- ${zero_shot} \
- --patch-frame-size=${patch_frame_size} \
- --num-frames=${num_frames} \
- ${eval_ema} \
- --prompt-type='prev_output'
- # --prompt-type='none' \
-
-
- # --ema-eval \
-
-
diff --git a/spaces/mthsk/sovits-models-misc/vdecoder/hifigan/models.py b/spaces/mthsk/sovits-models-misc/vdecoder/hifigan/models.py
deleted file mode 100644
index 9747301f350bb269e62601017fe4633ce271b27e..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-models-misc/vdecoder/hifigan/models.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-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
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
- config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
- with open(config_file) as f:
- data = f.read()
-
- global h
- json_config = json.loads(data)
- h = AttrDict(json_config)
-
- generator = Generator(h).to(device)
-
- cp_dict = torch.load(model_path)
- generator.load_state_dict(cp_dict['generator'])
- generator.eval()
- generator.remove_weight_norm()
- del cp_dict
- return generator, h
-
-
-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)
-
-
-def padDiff(x):
- return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
-
-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
- self.flag_for_pulse = flag_for_pulse
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = (f0 > self.voiced_threshold).type(torch.float32)
- return uv
-
- def _f02sine(self, f0_values):
- """ f0_values: (batchsize, length, dim)
- where dim indicates fundamental tone and overtones
- """
- # convert to F0 in rad. The interger part n can be ignored
- # because 2 * np.pi * n doesn't affect phase
- rad_values = (f0_values / self.sampling_rate) % 1
-
- # initial phase noise (no noise for fundamental component)
- rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
- device=f0_values.device)
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
- # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
- if not self.flag_for_pulse:
- # for normal case
-
- # To prevent torch.cumsum numerical overflow,
- # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
- # Buffer tmp_over_one_idx indicates the time step to add -1.
- # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
- tmp_over_one = torch.cumsum(rad_values, 1) % 1
- tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
- sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
- * 2 * np.pi)
- else:
- # If necessary, make sure that the first time step of every
- # voiced segments is sin(pi) or cos(0)
- # This is used for pulse-train generation
-
- # identify the last time step in unvoiced segments
- uv = self._f02uv(f0_values)
- uv_1 = torch.roll(uv, shifts=-1, dims=1)
- uv_1[:, -1, :] = 1
- u_loc = (uv < 1) * (uv_1 > 0)
-
- # get the instantanouse phase
- tmp_cumsum = torch.cumsum(rad_values, dim=1)
- # different batch needs to be processed differently
- for idx in range(f0_values.shape[0]):
- temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
- temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
- # stores the accumulation of i.phase within
- # each voiced segments
- tmp_cumsum[idx, :, :] = 0
- tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
- # rad_values - tmp_cumsum: remove the accumulation of i.phase
- # within the previous voiced segment.
- i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
- # get the sines
- sines = torch.cos(i_phase * 2 * np.pi)
- return sines
-
- def forward(self, f0):
- """ 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_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
- device=f0.device)
- # fundamental component
- fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
-
- # generate sine waveforms
- sine_waves = self._f02sine(fn) * self.sine_amp
-
- # generate uv signal
- # uv = torch.ones(f0.shape)
- # uv = uv * (f0 > self.voiced_threshold)
- uv = self._f02uv(f0)
-
- # noise: for unvoiced should be similar to sine_amp
- # std = self.sine_amp/3 -> max value ~ self.sine_amp
- # . for voiced regions is self.noise_std
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
-
- # first: set the unvoiced part to 0 by uv
- # then: additive noise
- 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):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
-
- # 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):
- """
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- """
- # source for harmonic branch
- sine_wavs, uv, _ = self.l_sin_gen(x)
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-
- # source for noise branch, in the same shape as uv
- noise = torch.randn_like(uv) * self.sine_amp / 3
- return sine_merge, noise, uv
-
-
-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.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=h["sampling_rate"],
- harmonic_num=8)
- self.noise_convs = nn.ModuleList()
- self.conv_pre = weight_norm(Conv1d(h["inter_channels"], 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"])):
- c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
- 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)))
- if i + 1 < len(h["upsample_rates"]): #
- stride_f0 = np.prod(h["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 = 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)
- self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
-
- def forward(self, x, f0, g=None):
- # print(1,x.shape,f0.shape,f0[:, None].shape)
- f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
- # print(2,f0.shape)
- har_source, noi_source, uv = self.m_source(f0)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- x = x + self.cond(g)
- # print(124,x.shape,har_source.shape)
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, LRELU_SLOPE)
- # print(3,x.shape)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- # print(4,x_source.shape,har_source.shape,x.shape)
- 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):
- print('Removing weight norm...')
- 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, periods=None):
- super(MultiPeriodDiscriminator, self).__init__()
- self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
- self.discriminators = nn.ModuleList()
- for period in self.periods:
- self.discriminators.append(DiscriminatorP(period))
-
- 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/muhammadayman/gradio-demo/app.py b/spaces/muhammadayman/gradio-demo/app.py
deleted file mode 100644
index 8f275422a78341d342d190054e08943abc99b730..0000000000000000000000000000000000000000
--- a/spaces/muhammadayman/gradio-demo/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import sys
-import gradio as gr
-from transformers import AutoTokenizer
-import torch
-
-
-tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
-model = torch.load("helsinki_fineTuned.pt", map_location=torch.device('cpu'))
-model.eval()
-
-
-def translate_gradio(input):
- tokenized_text = tokenizer.prepare_seq2seq_batch([input], return_tensors='pt')
- encode = model.generate(**tokenized_text)
- text_ar = tokenizer.batch_decode(encode,skip_special_tokens=True)[0]
- return text_ar
-translate_interface = gr.Interface(fn = translate_gradio,
- allow_flagging = True,
-
- title = 'Translating "English Data Science" content into Arabic',
- inputs=gr.inputs.Textbox(lines = 7, label = 'english content'),
- outputs="text",
- examples = [['In the last few years the RNN-based architectures have shown the best performance in machine translation problems, but still they have some problems that had to be solved. First, they have a difficulty to cope with long-range dependencies (also LSTM when it has to deal with really long sentences). Secondly, each hidden state depends on the previous one which impossible to parallelize and makes it inefficient on GPUs.']]
- )
-translate_interface.launch(inline = False)
\ No newline at end of file
diff --git a/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder/train.py b/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder/train.py
deleted file mode 100644
index 6dc2f892e1fc134b311e2c9ee42250a2d3713547..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/Real-Time-Voice-Cloning/vocoder/train.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from vocoder.models.fatchord_version import WaveRNN
-from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder
-from vocoder.distribution import discretized_mix_logistic_loss
-from vocoder.display import stream, simple_table
-from vocoder.gen_wavernn import gen_testset
-from torch.utils.data import DataLoader
-from pathlib import Path
-from torch import optim
-import torch.nn.functional as F
-import vocoder.hparams as hp
-import numpy as np
-import time
-import torch
-import platform
-
-def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_truth: bool,
- save_every: int, backup_every: int, force_restart: bool):
- # Check to make sure the hop length is correctly factorised
- assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length
-
- # Instantiate the model
- print("Initializing the model...")
- model = WaveRNN(
- rnn_dims=hp.voc_rnn_dims,
- fc_dims=hp.voc_fc_dims,
- bits=hp.bits,
- pad=hp.voc_pad,
- upsample_factors=hp.voc_upsample_factors,
- feat_dims=hp.num_mels,
- compute_dims=hp.voc_compute_dims,
- res_out_dims=hp.voc_res_out_dims,
- res_blocks=hp.voc_res_blocks,
- hop_length=hp.hop_length,
- sample_rate=hp.sample_rate,
- mode=hp.voc_mode
- )
-
- if torch.cuda.is_available():
- model = model.cuda()
- device = torch.device('cuda')
- else:
- device = torch.device('cpu')
-
- # Initialize the optimizer
- optimizer = optim.Adam(model.parameters())
- for p in optimizer.param_groups:
- p["lr"] = hp.voc_lr
- loss_func = F.cross_entropy if model.mode == "RAW" else discretized_mix_logistic_loss
-
- # Load the weights
- model_dir = models_dir.joinpath(run_id)
- model_dir.mkdir(exist_ok=True)
- weights_fpath = model_dir.joinpath(run_id + ".pt")
- if force_restart or not weights_fpath.exists():
- print("\nStarting the training of WaveRNN from scratch\n")
- model.save(weights_fpath, optimizer)
- else:
- print("\nLoading weights at %s" % weights_fpath)
- model.load(weights_fpath, optimizer)
- print("WaveRNN weights loaded from step %d" % model.step)
-
- # Initialize the dataset
- metadata_fpath = syn_dir.joinpath("train.txt") if ground_truth else \
- voc_dir.joinpath("synthesized.txt")
- mel_dir = syn_dir.joinpath("mels") if ground_truth else voc_dir.joinpath("mels_gta")
- wav_dir = syn_dir.joinpath("audio")
- dataset = VocoderDataset(metadata_fpath, mel_dir, wav_dir)
- test_loader = DataLoader(dataset,
- batch_size=1,
- shuffle=True,
- pin_memory=True)
-
- # Begin the training
- simple_table([('Batch size', hp.voc_batch_size),
- ('LR', hp.voc_lr),
- ('Sequence Len', hp.voc_seq_len)])
-
- for epoch in range(1, 350):
- data_loader = DataLoader(dataset,
- collate_fn=collate_vocoder,
- batch_size=hp.voc_batch_size,
- num_workers=2 if platform.system() != "Windows" else 0,
- shuffle=True,
- pin_memory=True)
- start = time.time()
- running_loss = 0.
-
- for i, (x, y, m) in enumerate(data_loader, 1):
- if torch.cuda.is_available():
- x, m, y = x.cuda(), m.cuda(), y.cuda()
-
- # Forward pass
- y_hat = model(x, m)
- if model.mode == 'RAW':
- y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
- elif model.mode == 'MOL':
- y = y.float()
- y = y.unsqueeze(-1)
-
- # Backward pass
- loss = loss_func(y_hat, y)
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
- running_loss += loss.item()
- speed = i / (time.time() - start)
- avg_loss = running_loss / i
-
- step = model.get_step()
- k = step // 1000
-
- if backup_every != 0 and step % backup_every == 0 :
- model.checkpoint(model_dir, optimizer)
-
- if save_every != 0 and step % save_every == 0 :
- model.save(weights_fpath, optimizer)
-
- msg = f"| Epoch: {epoch} ({i}/{len(data_loader)}) | " \
- f"Loss: {avg_loss:.4f} | {speed:.1f} " \
- f"steps/s | Step: {k}k | "
- stream(msg)
-
-
- gen_testset(model, test_loader, hp.voc_gen_at_checkpoint, hp.voc_gen_batched,
- hp.voc_target, hp.voc_overlap, model_dir)
- print("")
diff --git a/spaces/mygyasir/deep-voice-cloning/scripts/train.py b/spaces/mygyasir/deep-voice-cloning/scripts/train.py
deleted file mode 100644
index 0e22d6120ba6d44219d80a8ac6b9e7ce7c4f45c1..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/deep-voice-cloning/scripts/train.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import argparse
-import json
-import os
-from pathlib import Path
-
-import torch
-from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer
-
-from deep_voice_cloning.cloning.model import CloningModel
-from deep_voice_cloning.transcriber.model import TranscriberModel
-from deep_voice_cloning.data.collator import TTSDataCollatorWithPadding
-from deep_voice_cloning.data.dataset import get_cloning_dataset
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--lang", type=str, default=None, help="Language of speech samples")
- parser.add_argument("--audio_path", type=str, default=None, help="Path to training audio file")
- parser.add_argument("--output_dir", type=str, default=None, help="Path to output directory for trained model")
- args = parser.parse_args()
-
- with open(os.path.join(os.path.dirname(__file__), "training_config.json")) as f:
- training_config = json.load(f)
-
- if args.lang is not None:
- training_config['lang'] = args.lang
- if args.audio_path is not None:
- training_config['audio_path'] = Path(args.audio_path)
- if args.output_dir is not None:
- training_config['output_dir'] = Path(args.output_dir)
-
- transcriber_model = TranscriberModel(lang=training_config['lang'])
- cloning_model = CloningModel(lang=training_config['lang'])
-
- dataset = get_cloning_dataset(training_config['audio_path'], transcriber_model, cloning_model)
- data_collator = TTSDataCollatorWithPadding(processor=cloning_model.processor, model=cloning_model.model)
-
- training_args = Seq2SeqTrainingArguments(
- output_dir=training_config["output_dir"],
- per_device_train_batch_size=training_config['batch_size'],
- gradient_accumulation_steps=2,
- overwrite_output_dir=True,
- learning_rate=training_config['learning_rate'],
- warmup_steps=training_config['warmup_steps'],
- max_steps=training_config['max_steps'],
- gradient_checkpointing=True,
- fp16=transcriber_model.device == torch.device("cuda"),
- evaluation_strategy="steps",
- per_device_eval_batch_size=8,
- save_strategy="no",
- eval_steps=100,
- logging_steps=20,
- load_best_model_at_end=False,
- greater_is_better=False,
- label_names=["labels"],
- )
-
- trainer = Seq2SeqTrainer(
- args=training_args,
- model=cloning_model.model,
- train_dataset=dataset,
- eval_dataset=dataset,
- data_collator=data_collator,
- tokenizer=cloning_model.processor.tokenizer,
- )
-
- trainer.train()
- cloning_model.save_pretrained(Path(training_config["output_dir"]) /
- Path(cloning_model.config['model_path'].replace('/', '_')
- + '_' + Path(training_config['audio_path']).stem)
- )
diff --git a/spaces/nakas/MusicGenDemucs/audiocraft/data/__init__.py b/spaces/nakas/MusicGenDemucs/audiocraft/data/__init__.py
deleted file mode 100644
index 708a3dcead8dda89374a021177481dacae9f7fe9..0000000000000000000000000000000000000000
--- a/spaces/nakas/MusicGenDemucs/audiocraft/data/__init__.py
+++ /dev/null
@@ -1,8 +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.
-
-# flake8: noqa
-from . import audio, audio_dataset
diff --git a/spaces/nanom/syntactic_tree/css/style.css b/spaces/nanom/syntactic_tree/css/style.css
deleted file mode 100644
index c8f106f67262fd93dc84ff511553535f83b39f5d..0000000000000000000000000000000000000000
--- a/spaces/nanom/syntactic_tree/css/style.css
+++ /dev/null
@@ -1,167 +0,0 @@
-.container {
- max-width: 90%;
- margin: auto;
-}
-
-h1, h2, h3, h4, h5, h6 {
- margin-top: 0;
- margin-bottom: 0.5rem;
-}
-
-h1, h2, h3, h4, h5, h6,
-.h1, .h2, .h3, .h4, .h5, .h6 {
- margin-bottom: 0.5rem;
- font-weight: 500;
- line-height: 1.2;
-}
-
-h1, .h1 {
- font-size: 2.5rem;
-}
-
-h2, .h2 {
- font-size: 2rem;
-}
-
-h3, .h3 {
- font-size: 1.75rem;
-}
-
-h4, .h4 {
- font-size: 1.5rem;
-}
-
-h5, .h5 {
- font-size: 1.25rem;
-}
-
-h6, .h6 {
- font-size: 1rem;
-}
-
-.no-outline {
- border: 0px none;
-}
-
-.alert {
- position: relative;
- padding: 0.75rem 1.25rem;
- margin-bottom: 1rem;
- border: 1px solid transparent;
- border-radius: 0.25rem;
-}
-
-.alert-primary {
- color: #004085;
- background-color: #cce5ff;
- border-color: #b8daff;
-}
-
-.alert-secondary {
- color: #383d41;
- background-color: #e2e3e5;
- border-color: #d6d8db;
-}
-
-.alert-success {
- color: #155724;
- background-color: #d4edda;
- border-color: #c3e6cb;
-}
-
-.alert-info {
- color: #0c5460;
- background-color: #d1ecf1;
- border-color: #bee5eb;
-}
-
-.alert-warning {
- color: #856404;
- background-color: #fff3cd;
- border-color: #ffeeba;
-}
-
-.alert-danger {
- color: #721c24;
- background-color: #f8d7da;
- border-color: #f5c6cb;
-}
-
-.alert-light {
- color: #818182;
- background-color: #fefefe;
- border-color: #fdfdfe;
-}
-
-.alert-dark {
- color: #1b1e21;
- background-color: #d6d8d9;
- border-color: #c6c8ca;
-}
-
-.btn {
- display: inline-block;
- font-weight: 400;
- color: #212529;
- text-align: center;
- vertical-align: middle;
- -webkit-user-select: none;
- -moz-user-select: none;
- -ms-user-select: none;
- user-select: none;
- background-color: transparent;
- border: 1px solid transparent;
- padding: 0.375rem 0.75rem;
- font-size: 1rem;
- line-height: 1.5;
- border-radius: 0.25rem;
- transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;
-}
-
-.btn-primary {
- color: #fff;
- background-color: #007bff;
- border-color: #007bff;
-}
-
-.btn-secondary {
- color: #fff;
- background-color: #6c757d;
- border-color: #6c757d;
-}
-
-.btn-success {
- color: #fff;
- background-color: #28a745;
- border-color: #28a745;
-}
-
-.btn-info {
- color: #fff;
- background-color: #17a2b8;
- border-color: #17a2b8;
-}
-
-.btn-warning {
- color: #212529;
- background-color: #ffc107;
- border-color: #ffc107;
-}
-
-.btn-danger {
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Emv Chip Reader Writer Software Downloadgolkesl: What You Need to Know
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If you are interested in learning and studying EMV, the global standard for chip-based debit and credit card transactions, you may have heard of Emv Chip Reader Writer Software Downloadgolkesl. This is a software that allows you to read, write, duplicate, delete, and manipulate any EMV protocol, such as 201, 206, 226, or any other. It also supports contact and contactless bank cards, SDA, DDA, CDA, and session key calculation.
But what exactly is Emv Chip Reader Writer Software Downloadgolkesl? How can you download and install it? How can you use it effectively? And where can you find more information and support for it? In this article, we will answer all these questions and more. We will also provide you with some tips and tricks to make the most out of this software. So, let's get started!
-
What is Emv Chip Reader Writer Software?
-
Emv Chip Reader Writer Software is a software that enables you to read, write, duplicate, delete, and manipulate any EMV protocol. EMV stands for Europay, Mastercard, and Visa, the three companies that developed the standard for chip-based debit and credit card transactions. EMV protocols are the rules and specifications that define how EMV cards and terminals communicate with each other.
-
Emv Chip Reader Writer Software is designed for learning and studying EMV. It is not intended for illegal or fraudulent purposes. It can only be used in a test environment, not in a live environment. It can help you understand how EMV works, how to prepare data for EMV cards, how to manage keys for EMV cards and terminals, how to test and certify EMV cards and terminals, and more.
-
The Benefits of Emv Chip Reader Writer Software
-
Emv Chip Reader Writer Software has many benefits for anyone who wants to learn and study EMV. Some of these benefits are:
-
-
-
It supports contact and contactless bank cards. You can use it with any type of EMV card, whether it has a chip that needs to be inserted into a terminal or a chip that can be tapped on a terminal.
-
It supports SDA, DDA, CDA, and session key calculation. These are different methods of authenticating EMV cards and terminals. SDA stands for Static Data Authentication, DDA stands for Dynamic Data Authentication, CDA stands for Combined Data Authentication, and session key calculation is a way of generating temporary keys for each transaction.
-
It has a full KMS functionality. KMS stands for Key Management System. It is a system that manages the keys that are used for encrypting and decrypting data between EMV cards and terminals. Emv Chip Reader Writer Software can emulate CA (Certificate Authority), issuer (the bank that issues the card), bank card (the card itself), terminal (the device that accepts the card), and acquirer ( the entity that processes the transaction) roles. It can generate, import, export, and delete keys for each role.
-
It has a user-friendly interface. It has a graphical user interface (GUI) that is easy to navigate and use. It also has a command-line interface (CLI) that allows you to execute commands directly. You can switch between the GUI and the CLI as you wish.
-
It has a comprehensive documentation. It comes with a manual that explains how to use the software, how to configure the settings, how to troubleshoot common problems, and more. It also has a help section that provides answers to frequently asked questions and tips for using the software.
-
-
The Challenges of Emv Chip Reader Writer Software
-
Emv Chip Reader Writer Software also has some challenges that you need to be aware of before using it. Some of these challenges are:
-
-
It requires a compatible hardware device. You cannot use Emv Chip Reader Writer Software without a hardware device that can read and write EMV cards. You need to purchase a device that is compatible with the software, such as an ACR122U NFC Reader/Writer, an ACR38 Smart Card Reader, or an OMNIKEY 3121 Smart Card Reader.
-
It requires a valid license key. You cannot use Emv Chip Reader Writer Software without a valid license key that is registered to your name and email address. You need to purchase a license key from the official website of the software, which costs $149 for one year or $249 for lifetime access.
-
It requires some technical knowledge and skills. You cannot use Emv Chip Reader Writer Software without some basic knowledge and skills in EMV, cryptography, programming, and networking. You need to understand how EMV works, how to prepare data for EMV cards, how to manage keys for EMV cards and terminals, how to test and certify EMV cards and terminals, and more.
-
-
How to Download and Install Emv Chip Reader Writer Software?
-
If you have decided to use Emv Chip Reader Writer Software, you need to download and install it on your computer. Here are the steps for doing so:
-
The Requirements for Emv Chip Reader Writer Software
-
Before you download and install Emv Chip Reader Writer Software, you need to make sure that your computer meets the minimum requirements for running the software. These are:
-
-
A Windows operating system (Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10)
A valid license key (purchased from the official website of the software)
-
-
The Steps for Downloading and Installing Emv Chip Reader Writer Software
-
After you have checked that your computer meets the requirements for Emv Chip Reader Writer Software, you can follow these steps to download and install it:
-
-
Go to the official website of Emv Chip Reader Writer Software at https://emvchipreaderwriter.com/
-
Click on the "Download" button on the homepage
-
Enter your name and email address in the form that appears
-
Check your email inbox for a confirmation email from Emv Chip Reader Writer Software
-
Click on the link in the confirmation email to download the software file (EmvChipReaderWriter.zip)
-
Extract the zip file to a folder on your computer
-
Open the folder and double-click on the setup file (EmvChipReaderWriter.exe)
-
Follow the instructions on the screen to install the software on your computer
-
Enter your license key when prompted by the software
-
Restart your computer after the installation is complete
-
-
Congratulations! You have successfully downloaded and installed Emv Chip Reader Writer Software on your computer. You are now ready to use it!
-
How to Use Emv Chip Reader Writer Software?
-
Now that you have downloaded and installed Emv Chip Reader Writer Software on your computer, you may be wondering how to use it effectively. In this section, we will show you some of the features of Emv Chip Reader Writer Software and some tips and tricks for using it.
-
The Features of Emv Chip Reader Writer Software
-
Emv Chip Reader Writer Software has many features that allow you to read, write, duplicate, delete, and manipulate any EMV protocol. Some of these features are:
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-
It can read any EMV card and display the card data on the screen. You can see the card number, expiry date, service code, cardholder name, track 2 equivalent data, application identifier, application label, application priority indicator, issuer country code, issuer identification number, and more.
-
It can write any EMV card and modify the card data as you wish. You can change the card number, expiry date, service code, cardholder name, track 2 equivalent data, application identifier, application label, application priority indicator, issuer country code, issuer identification number, and more.
-
It can duplicate any EMV card and create a copy of it. You can use the copy for testing or backup purposes.
-
It can delete any EMV card and erase all the card data from it. You can use this feature to reset or dispose of a card.
-
It can manipulate any EMV protocol and customize it as you wish. You can change the protocol type, version, format, length, value, and more.
-
-
The Tips and Tricks for Using Emv Chip Reader Writer Software
-
Emv Chip Reader Writer Software is a powerful tool that can help you learn and study EMV. However, it also requires some caution and care when using it. Here are some tips and tricks for using Emv Chip Reader Writer Software safely and effectively:
-
-
Always use Emv Chip Reader Writer Software in a test environment, not in a live environment. Do not use it with real cards or terminals that are connected to a network or a bank. You may violate the law or cause damage to the cards or terminals if you do so.
-
Always backup your original card data before writing or modifying it. You can use the duplicate feature to create a copy of your original card data and store it in a safe place. This way, you can restore your original card data if something goes wrong.
-
Always check your license key validity before using Emv Chip Reader Writer Software. Your license key may expire after one year or after a certain number of uses. You can check your license key validity by clicking on the "About" button on the software interface. You can renew your license key by purchasing a new one from the official website of the software.
-
Always update your software to the latest version when available. The software may have new features, bug fixes, or security patches that can improve your user experience and performance. You can update your software by clicking on the "Update" button on the software interface. You can also check for updates manually by visiting the official website of the software.
-
Always read the manual and the help section before using Emv Chip Reader Writer Software. The manual and the help section provide detailed instructions on how to use the software, how to configure the settings, how to troubleshoot common problems, and more. You can access the manual and the help section by clicking on the "Manual" and "Help" buttons on the software interface.
-
-
Where to Find More Information and Support for Emv Chip Reader Writer Software?
-
If you want to find more information and support for Emv Chip Reader Writer Software, you have several options available. Some of these options are:
-
The Best Websites for Emv Chip Reader Writer Software
-
The best websites for Emv Chip Reader Writer Software are:
-
-
The official website of Emv Chip Reader Writer Software at https://emvchipreaderwriter.com/. This is where you can purchase a license key, download the software file, check for updates, read the manual and the help section, contact customer support, and more.
-
The official blog of Emv Chip Reader Writer Software at https://emvchipreaderwriter.com/blog/. This is where you can find news, articles, tutorials, reviews, tips, tricks, and more related to Emv Chip Reader Writer Software and EMV in general.
-
The official YouTube channel of Emv Chip Reader Writer Software at https://www.youtube.com/channel/UCwQx9yZfYi8t1L7xK0nJ4Og. This is where you can watch videos, demos, tutorials, reviews, tips, tricks, and more related to Emv Chip Reader Writer Software and EMV in general.
-
-
The Best Forums and Communities for Emv Chip Reader Writer Software
-
The best forums and communities for Emv Chip Reader Writer Software are:
-
-
The official forum of Emv Chip Reader Writer Software at https://emvchipreaderwriter.com/forum/. This is where you can ask questions, share experiences, give feedback, request features, report bugs, and more related to Emv Chip Reader Writer Software and EMV in general.
-
The Reddit community of Emv Chip Reader Writer Software at https://www.reddit.com/r/EmvChipReaderWriter/. This is where you can join discussions, post comments, vote on posts, and more related to Emv Chip Reader Writer Software and EMV in general.
-
The Stack Overflow community of Emv Chip Reader Writer Software at https://stackoverflow.com/questions/tagged/emv-chip-reader-writer. This is where you can find answers, ask questions, provide solutions, and more related to Emv Chip Reader Writer Software and EMV in general.
-
-
Conclusion
-
Emv Chip Reader Writer Software Downloadgolkesl is a software that allows you to read, write, duplicate, delete, and manipulate any EMV protocol. It is designed for learning and studying EMV, the global standard for chip-based debit and credit card transactions. It supports contact and contactless bank cards, SDA, DDA, CDA, and session key calculation. It has a full KMS functionality. It has a user-friendly interface. It has a comprehensive documentation.
-
However, Emv Chip Reader Writer Software also has some challenges that you need to be aware of before using it. It requires a compatible hardware device. It requires a valid license key. It requires some technical knowledge and skills. It can only be used in a test environment, not in a live environment.
-
If you want to use Emv Chip Reader Writer Software effectively, you need to download and install it on your computer. You also need to use some tips and tricks for using it safely and efficiently. You also need to find more information and support for it from various sources.
-
Summary of the Main Points
-
In this article, we have covered the following main points:
-
-
What is Emv Chip Reader Writer Software?
-
How to download and install Emv Chip Reader Writer Software?
-
How to use Emv Chip Reader Writer Software?
-
Where to find more information and support for Emv Chip Reader Writer Software?
-
-
Call to Action
-
If you are interested in learning and studying EMV, Emv Chip Reader Writer Software Downloadgolkesl is a software that you should try. It can help you understand how EMV works, how to prepare data for EMV cards, how to manage keys for EMV cards and terminals, how to test and certify EMV cards and terminals, and more.
-
To get started with Emv Chip Reader Writer Software Downloadgolkesl, you need to visit the official website of the software at https://emvchipreaderwriter.com/ and purchase a license key. Then, you need to download the software file and install it on your computer. Then, you need to connect your compatible hardware device and start using the software.
-
If you have any questions or problems with Emv Chip Reader Writer Software Downloadgolkesl, you can contact customer support at support@emvchipreaderwriter.com or visit the official forum of the software at https://emvchipreaderwriter.com/forum/. You can also find more information and support from the official blog, YouTube channel, Reddit community, and Stack Overflow community of the software.
-
So, what are you waiting for? Get your license key today and start learning and studying EMV with Emv Chip Reader Writer Software Downloadgolkesl!
-
FAQs
-
Here are some frequently asked questions about Emv Chip Reader Writer Software Downloadgolkesl:
-
-
What is the difference between EMV chip reader writer software downloadgolkesl and other EMV chip reader writer software?
-
Emv Chip Reader Writer Software Downloadgolkesl is different from other EMV chip reader writer software in several ways. Some of these ways are:
-
-
It supports contact and contactless bank cards, while other software may only support one or the other.
-
It supports SDA, DDA, CDA, and session key calculation, while other software may only support one or some of these methods.
-
It has a full KMS functionality, while other software may have limited or no KMS functionality.
-
It has a user-friendly interface, while other software may have a complex or outdated interface.
-
It has a comprehensive documentation, while other software may have poor or no documentation.
-
-
Therefore, Emv Chip Reader Writer Software Downloadgolkesl is more advanced, versatile, and convenient than other EMV chip reader writer software.
-
Is Emv Chip Reader Writer Software Downloadgolkesl legal?
-
Emv Chip Reader Writer Software Downloadgolkesl is legal as long as you use it for learning and studying EMV in a test environment. It is not legal if you use it for illegal or fraudulent purposes in a live environment. You may violate the law or cause damage to the cards or terminals if you do so.
-
Emv Chip Reader Writer Software Downloadgolkesl is not affiliated with or endorsed by any official EMV organization or company. It is an independent software that is developed and maintained by a team of EMV enthusiasts and experts. It is not responsible for any misuse or abuse of the software by the users.
-
How can I get a refund for Emv Chip Reader Writer Software Downloadgolkesl?
-
If you are not satisfied with Emv Chip Reader Writer Software Downloadgolkesl, you can request a refund within 30 days of your purchase. You need to contact customer support at support@emvchipreaderwriter.com and provide your name, email address, license key, and reason for requesting a refund. You will receive a confirmation email from customer support within 24 hours. You will then receive your refund within 7 days of your confirmation email.
-
Please note that you can only request a refund once per license key. You cannot request a refund if you have used the software for more than 30 days or if you have violated the terms and conditions of the software.
-
How can I contact customer support for Emv Chip Reader Writer Software Downloadgolkesl?
-
If you have any questions or problems with Emv Chip Reader Writer Software Downloadgolkesl, you can contact customer support at support@emvchipreaderwriter.com. You can also visit the official forum of the software at https://emvchipreaderwriter.com/forum/ and post your question or problem there. You will receive a reply from customer support or other users within 24 hours.
-
Please provide as much information as possible when contacting customer support or posting on the forum. This will help customer support or other users to understand your issue and provide you with a solution faster.
-
What are some alternatives to Emv Chip Reader Writer Software Downloadgolkesl?
-
If you are looking for some alternatives to Emv Chip Reader Writer Software Downloadgolkesl, you can try some of these software:
-
-
EMV Reader/Writer v8.6: This is another software that allows you to read, write, duplicate, delete, and manipulate any EMV protocol. It supports contact and contactless bank cards, SDA, DDA, CDA, and session key calculation. It has a user-friendly interface and a comprehensive documentation. It costs $199 for lifetime access.
-
EMV Smart Card Editor: This is a software that allows you to edit the data of any EMV smart card. It supports contact and contactless bank cards, SDA, DDA, CDA, and session key calculation. It has a simple interface and a basic documentation. It costs $99 for lifetime access.
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EMV Studio: This is a software that allows you to create and test EMV applications. It supports contact and contactless bank cards, SDA, DDA, CDA, and session key calculation. It has a complex interface and an advanced documentation. It costs $499 for lifetime access.
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You can compare these software with Emv Chip Reader Writer Software Downloadgolkesl and choose the one that suits your needs and budget best.
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This is the end of the article on Emv Chip Reader Writer Software Downloadgolkesl. I hope you have enjoyed reading it and learned something new from it. Thank you for your attention!
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Nokia 2690 Rm 635 1070 Full Flash Files Free Download.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Nokia 2690 Rm 635 1070 Full Flash Files Free Download.md
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-
Nokia 2690 Rm 635 1070 Full Flash Files Free Download
-
If you are looking for a way to upgrade, downgrade, or repair your Nokia 2690 phone, you might be interested in downloading and installing the flash files for this model. Flash files are the firmware or software that run on your phone and control its functions and features. By flashing your phone, you can change its operating system, fix any software issues, improve its performance, and customize its appearance.
-
Nokia 2690 Rm 635 1070 Full Flash Files Free Download
In this article, we will show you how to download Nokia 2690 Rm 635 1070 flash files for free, how to use a flash tool to install them on your phone, and how to troubleshoot common problems after flashing. We will also answer some frequently asked questions about flashing your Nokia 2690 phone. Let's get started!
-
Introduction
-
What are flash files and why do you need them?
-
Flash files are the files that contain the firmware or software of your phone. They are usually in ZIP or RAR format and have extensions like .bin, .mcu, .ppm, .cnt, etc. They are stored in the internal memory or ROM of your phone and can be accessed by a flash tool or a computer.
-
You might need to flash your phone for various reasons, such as:
-
-
To upgrade or downgrade your phone's operating system or firmware version.
-
To fix any software issues, bugs, errors, or glitches that affect your phone's functionality.
-
To improve your phone's performance, speed, battery life, security, and stability.
-
To customize your phone's appearance, settings, features, and apps.
-
To remove any unwanted or pre-installed apps, bloatware, or malware from your phone.
-
To unlock or root your phone and gain more control over its settings and features.
-
To restore your phone to factory settings or stock firmware if you have modified it.
-
-
What are the benefits of flashing your Nokia 2690 phone?
-
Flashing your Nokia 2690 phone can have many benefits, such as:
-
-
You can enjoy the latest features and updates of the operating system or firmware that you flash.
-
You can fix any software issues that prevent your phone from working properly.
-
You can enhance your phone's performance, speed, battery life, security, and stability.
-
You can customize your phone's appearance, settings, features, and apps according to your preferences.
-
You can remove any unwanted or pre-installed apps, bloatware, or malware that slow down your phone or compromise your privacy.
-
You can unlock or root your phone and access more settings and features that are normally restricted or hidden.
-
You can restore your phone to factory settings or stock firmware if you have messed up with it or want to sell it.
-
-
How to download Nokia 2690 Rm 635 1070 flash files for free
-
What are the requirements and precautions before flashing?
-
Before you flash your Nokia 2690 phone, you need to make sure that you have the following requirements and precautions:
-
-
-
A Nokia 2690 phone with a good battery charge (at least 50%).
-
A USB cable to connect your phone to your PC.
-
A PC with Windows operating system and enough free space.
-
A flash tool that supports Nokia 2690 Rm 635 1070 model, such as Nokia Care Suite, Phoenix Service Software, ATF Box, UFS Box, etc.
-
The official and tested flash files for Nokia 2690 Rm 635 1070 model, which you can download from the links below.
-
A backup of your data, such as contacts, messages, photos, videos, etc., as flashing will erase everything from your phone.
-
A good internet connection to download the flash files and the flash tool.
-
A basic knowledge of how to use the flash tool and follow the instructions carefully.
-
-
Where to find the official and tested flash files for Nokia 2690 Rm 635 1070?
-
There are many websites that claim to offer free flash files for Nokia 2690 Rm 635 1070 model, but not all of them are reliable or safe. Some of them may contain viruses, malware, or corrupted files that can damage your phone or PC. Therefore, you should always download the flash files from the official or trusted sources, such as:
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The official Nokia website, where you can find the latest firmware updates for your phone model. You can visit this link and enter your phone's product code to check if there is any available update. If there is, you can download it directly from the website or use the Nokia Software Updater tool to install it on your phone.
-
The official Nokia Care Suite, which is a software that allows you to manage and service your Nokia devices. You can download it from this link and install it on your PC. Then, you can use it to download the flash files for your phone model from the online servers. You can also use it to flash your phone using the USB cable.
-
The trusted third-party websites, such as GSM Hosting Forum, Firmware File, Flash File Host, etc., where you can find the tested and verified flash files for Nokia 2690 Rm 635 1070 model. You can download them from the links provided by the users or moderators of these websites. However, you should always scan them with an antivirus software before using them and use them at your own risk.
-
-
How to use a flash tool to install the flash files on your phone?
-
Once you have downloaded the flash files for your phone model, you need to use a flash tool to install them on your phone. There are different flash tools that support Nokia 2690 Rm 635 1070 model, such as Nokia Care Suite, Phoenix Service Software, ATF Box, UFS Box, etc. Each of them has its own features and interface, but the general steps are similar. Here is an example of how to use Phoenix Service Software to flash your phone:
-
-
Download and install Phoenix Service Software from this link on your PC.
-
Extract the downloaded flash files to a folder on your PC.
-
Run Phoenix Service Software as administrator and click on File > Open Product.
-
Select RM-635 from the list and click OK.
-
Click on Flashing > Firmware Update.
-
Click on Browse button next to Product Code and select the product code of your phone from the list. You can find it on the label under the battery of your phone.
-
Click on Refurbish button and wait for the process to start.
-
Connect your phone to your PC using the USB cable when prompted. Make sure that your phone is switched off and has enough battery charge.
-
Wait for the flashing process to complete. It may take several minutes depending on the size of the flash files and the speed of your PC and internet connection.
When the flashing process is done, you will see a message saying "Firmware updating succeeded". Click OK and disconnect your phone from your PC.
-
Switch on your phone and check if everything is working fine. You may need to set up your phone again as flashing will erase all your data and settings.
-
-
Congratulations! You have successfully flashed your Nokia 2690 phone using Phoenix Service Software. You can now enjoy the new features and improvements of the flash files that you installed.
-
How to troubleshoot common problems after flashing your Nokia 2690 phone
-
What to do if your phone is stuck on bootloop or logo screen?
-
Sometimes, after flashing your phone, you may encounter a problem where your phone is stuck on bootloop or logo screen and does not start normally. This can happen due to various reasons, such as incompatible or corrupted flash files, incomplete or interrupted flashing process, faulty hardware, etc. To fix this problem, you can try the following solutions:
-
-
Reflash your phone with the same or different flash files using the same or different flash tool. Make sure that you follow the instructions carefully and use the correct flash files and flash tool for your phone model.
-
Hard reset your phone by pressing and holding the power button and the volume down button for 10 seconds. This will erase all your data and settings and restore your phone to factory settings.
-
Use a recovery tool or mode to restore your phone to a previous backup or firmware version. You can use tools like Nokia Recovery Tool, Nokia Software Recovery Tool, Nokia Suite, etc., or access the recovery mode by pressing and holding the power button and the home button for 10 seconds.
-
Contact a professional technician or service center to check your phone's hardware and software and fix any issues.
-
-
What to do if your phone is not recognized by your PC or flash tool?
-
Another common problem that you may face after flashing your phone is that your phone is not recognized by your PC or flash tool when you connect it using the USB cable. This can happen due to various reasons, such as faulty or loose USB cable, damaged or dirty USB port, outdated or missing drivers, incompatible or corrupted flash tool, etc. To fix this problem, you can try the following solutions:
-
-
Change or replace the USB cable with a new or different one. Make sure that it is compatible with your phone and PC.
-
Clean or repair the USB port of your phone and PC. Make sure that there is no dust, dirt, moisture, or damage that can affect the connection.
-
Update or reinstall the drivers of your phone and PC. You can use tools like Nokia Connectivity Cable Driver, Nokia PC Suite, Nokia USB Driver, etc., to download and install the latest drivers for your phone and PC.
-
Update or reinstall the flash tool that you are using. Make sure that it is compatible with your phone model and firmware version.
-
Restart your phone and PC and try connecting them again.
-
-
What to do if your phone has network, camera, or battery issues after flashing?
-
Sometimes, after flashing your phone, you may notice that some of its functions or features are not working properly, such as network, camera, battery, etc. This can happen due to various reasons, such as incompatible or corrupted flash files, incorrect settings, hardware problems, etc. To fix this problem, you can try the following solutions:
-
-
Check if you have flashed the correct flash files for your phone model and firmware version. If not, reflash your phone with the right flash files using the right flash tool.
-
Check if you have configured the settings of your phone correctly. For example, check if you have selected the right network mode, APN settings, camera settings, battery saver mode, etc.
-
Calibrate or replace the battery of your phone. You can calibrate the battery by draining it completely and then charging it fully without interruption. You can replace the battery with a new or original one if it is damaged or old.
-
Clean or repair the camera lens of your phone. Make sure that there is no dust, dirt, moisture, or damage that can affect the quality of the photos or videos.
-
Contact a professional technician or service center to check your phone's hardware and software and fix any issues.
-
-
Conclusion
-
Summary of the main points and tips
-
In this article, we have shown you how to download Nokia 2690 Rm 635 1070 flash files for free, how to use a flash tool to install them on your phone, and how to troubleshoot common problems after flashing. We have also answered some frequently asked questions about flashing your Nokia 2690 phone. Here are some of the main points and tips that we have covered:
-
-
Flash files are the firmware or software that run on your phone and control its functions and features. By flashing your phone, you can change its operating system, fix any software issues, improve its performance, and customize its appearance.
-
Flashing your phone can have many benefits, such as enjoying the latest features and updates, fixing any software issues, enhancing your phone's performance, speed, battery life, security, and stability, customizing your phone's appearance, settings, features, and apps, removing any unwanted or pre-installed apps, bloatware, or malware, unlocking or rooting your phone and gaining more control over its settings and features, and restoring your phone to factory settings or stock firmware.
-
Before you flash your phone, you need to make sure that you have the following requirements and precautions: a Nokia 2690 phone with a good battery charge (at least 50%), a USB cable to connect your phone to your PC, a PC with Windows operating system and enough free space, a flash tool that supports Nokia 2690 Rm 635 1070 model, such as Nokia Care Suite, Phoenix Service Software, ATF Box, UFS Box, etc., the official and tested flash files for Nokia 2690 Rm 635 1070 model, which you can download from the official Nokia website, the official Nokia Care Suite, or the trusted third-party websites, a backup of your data, such as contacts, messages, photos, videos, etc., as flashing will erase everything from your phone, a good internet connection to download the flash files and the flash tool, and a basic knowledge of how to use the flash tool and follow the instructions carefully.
-
To flash your phone using a flash tool, you need to follow these general steps: download and install the flash tool on your PC, extract the downloaded flash files to a folder on your PC, run the flash tool as administrator and select your phone model from the list, click on Flashing > Firmware Update and select the product code of your phone from the list, click on Refurbish button and wait for the process to start, connect your phone to your PC using the USB cable when prompted, wait for the flashing process to complete, and disconnect your phone from your PC and switch it on.
-
After flashing your phone, you may encounter some common problems, such as your phone being stuck on bootloop or logo screen, your phone not being recognized by your PC or flash tool, or your phone having network, camera, or battery issues. To fix these problems, you can try some solutions, such as reflashing your phone with the same or different flash files using the same or different flash tool, hard resetting your phone by pressing and holding the power button and the volume down button for 10 seconds, using a recovery tool or mode to restore your phone to a previous backup or firmware version, or contacting a professional technician or service center to check your phone's hardware and software and fix any issues.
-
-
FAQs
-
Q1. Is flashing your Nokia 2690 phone safe and legal?
-
A1. Flashing your Nokia 2690 phone is generally safe and legal if you follow the instructions carefully and use the official or tested flash files and flash tool for your phone model. However, flashing your phone may void your warranty, expose your phone to security risks, or cause some functionality issues. Therefore, you should always backup your data before flashing and flash your phone at your own risk.
-
Q2. How long does it take to flash your Nokia 2690 phone?
-
A2. The time it takes to flash your Nokia 2690 phone depends on several factors, such as the size of the flash files, the speed of your PC and internet connection, the type of flash tool you use, etc. Generally, it may take from a few minutes to an hour to flash your phone.
-
Q3. How to backup your data before flashing your Nokia 2690 phone?
-
A3. You can backup your data before flashing your Nokia 2690 phone using various methods, such as:
-
-
Using the backup and restore feature of your phone. You can go to Settings > Backup and Restore and select the data that you want to backup. You can save the backup file on your phone's memory card or on a cloud service like Google Drive.
-
Using a PC software like Nokia PC Suite, Nokia Suite, Nokia Ovi Suite, etc. You can download and install the software on your PC and connect your phone to it using the USB cable. Then, you can use the software to backup your data on your PC or on a cloud service like Microsoft OneDrive.
-
Using a third-party app like Super Backup & Restore, Titanium Backup, Helium Backup, etc. You can download and install the app on your phone and use it to backup your data on your phone's memory card or on a cloud service like Dropbox.
-
-
Q4. How to restore your phone to factory settings after flashing?
-
A4. You can restore your phone to factory settings after flashing using various methods, such as:
-
-
Using the hard reset method. You can press and hold the power button and the volume down button for 10 seconds until you see a menu on the screen. Then, you can use the volume buttons to navigate and the power button to select Factory Reset option.
-
Using the recovery mode method. You can press and hold the power button and the home button for 10 seconds until you see a menu on the screen. Then, you can use the volume buttons to navigate and the power button to select Wipe Data/Factory Reset option.
-
Using the settings menu method. You can go to Settings > Backup and Restore > Factory Data Reset and confirm your choice.
-
-
Q5. How to contact customer support if you have any issues with flashing your Nokia 2690 phone?
-
A5. If you have any issues with flashing your Nokia 2690 phone, you can contact the customer support of Nokia or the flash tool that you are using. You can find their contact details on their official websites or in their user manuals. You can also visit their online forums or communities and ask for help from other users or experts. Here are some of the links that you can use to contact customer support:
-
-
Nokia customer support: https://www.nokia.com/phones/en_int/support
-
Nokia Care Suite customer support: https://www.nokia.com/phones/en_int/nokia-care-suite
-
Phoenix Service Software customer support: https://www.phoenix-service-software.com/contact-us
I hope this article has helped you to download Nokia 2690 Rm 635 1070 flash files for free, flash your phone, and troubleshoot any problems. If you have any questions or feedback, please leave a comment below. Thank you for reading!
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-
-
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/tests/__init__.py b/spaces/nikitaPDL2023/assignment4/detectron2/tests/__init__.py
deleted file mode 100644
index 9020c2df23e2af280b7bb168b996ae9eaf312eb8..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/spaces/nooji/GenieOnHuggingFaceSpaces/app.jl b/spaces/nooji/GenieOnHuggingFaceSpaces/app.jl
deleted file mode 100644
index 1fc5c0d97379629330976de17c7f91420840f926..0000000000000000000000000000000000000000
--- a/spaces/nooji/GenieOnHuggingFaceSpaces/app.jl
+++ /dev/null
@@ -1,30 +0,0 @@
-module App
-using Stipple
-
-@reactive mutable struct Name <: ReactiveModel
- name::R{String} = "World"
-end
-
-function ui(model)
- page( model, class="container", [
- h1([
- "Hello "
- span("", @text(:name))
- " from Genie.jl!"
- ])
-
- p([
- "What is your name? "
- input("", placeholder="Type your name", @bind(:name))
- ])
- ]
- )
-end
-
-route("/") do
- model = Name |> init
- html(ui(model), context = @__MODULE__)
-end
-
-end
-#up() # or `up(open_browser = true)` to automatically open a browser window/tab when launching the app
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/gadgets/__init__.py b/spaces/omlab/vlchecklist_demo/models/vilt/gadgets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/openskyml/starchat-playground/README.md b/spaces/openskyml/starchat-playground/README.md
deleted file mode 100644
index f824bed76793effa4d3a4d3a4872fb63ed702b53..0000000000000000000000000000000000000000
--- a/spaces/openskyml/starchat-playground/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: StarChat
-emoji: ⭐️
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-models: [HuggingFaceH4/starchat-beta, HuggingFaceH4/starchat-alpha, bigscience/bloom]
-datasets: [openskyml/starchat-dialogues]
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_p\303\251k_fr_aggregate.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_p\303\251k_fr_aggregate.html"
deleted file mode 100644
index d5110b59f022560830b0eec0478e7c1267af5f1c..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_p\303\251k_fr_aggregate.html"
+++ /dev/null
@@ -1,46 +0,0 @@
- 0th instance:
-
-
-
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/shap-e.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/shap-e.md
deleted file mode 100644
index b74a652582ecd7d534ebc412b64c4bf25c4a2183..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/shap-e.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Shap-E
-
-[[open-in-colab]]
-
-Shap-E is a conditional model for generating 3D assets which could be used for video game development, interior design, and architecture. It is trained on a large dataset of 3D assets, and post-processed to render more views of each object and produce 16K instead of 4K point clouds. The Shap-E model is trained in two steps:
-
-1. a encoder accepts the point clouds and rendered views of a 3D asset and outputs the parameters of implicit functions that represent the asset
-2. a diffusion model is trained on the latents produced by the encoder to generate either neural radiance fields (NeRFs) or a textured 3D mesh, making it easier to render and use the 3D asset in downstream applications
-
-This guide will show you how to use Shap-E to start generating your own 3D assets!
-
-Before you begin, make sure you have the following libraries installed:
-
-```py
-# uncomment to install the necessary libraries in Colab
-#!pip install diffusers transformers accelerate safetensors trimesh
-```
-
-## Text-to-3D
-
-To generate a gif of a 3D object, pass a text prompt to the [`ShapEPipeline`]. The pipeline generates a list of image frames which are used to create the 3D object.
-
-```py
-import torch
-from diffusers import ShapEPipeline
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-pipe = ShapEPipeline.from_pretrained("openai/shap-e", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-pipe = pipe.to(device)
-
-guidance_scale = 15.0
-prompt = ["A firecracker", "A birthday cupcake"]
-
-images = pipe(
- prompt,
- guidance_scale=guidance_scale,
- num_inference_steps=64,
- frame_size=256,
-).images
-```
-
-Now use the [`~utils.export_to_gif`] function to turn the list of image frames into a gif of the 3D object.
-
-```py
-from diffusers.utils import export_to_gif
-
-export_to_gif(images[0], "firecracker_3d.gif")
-export_to_gif(images[1], "cake_3d.gif")
-```
-
-
-
-
- firecracker
-
-
-
- cupcake
-
-
-
-## Image-to-3D
-
-To generate a 3D object from another image, use the [`ShapEImg2ImgPipeline`]. You can use an existing image or generate an entirely new one. Let's use the the [Kandinsky 2.1](../api/pipelines/kandinsky) model to generate a new image.
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-prior_pipeline = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
-pipeline = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
-
-prompt = "A cheeseburger, white background"
-
-image_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=1.0).to_tuple()
-image = pipeline(
- prompt,
- image_embeds=image_embeds,
- negative_image_embeds=negative_image_embeds,
-).images[0]
-
-image.save("burger.png")
-```
-
-Pass the cheeseburger to the [`ShapEImg2ImgPipeline`] to generate a 3D representation of it.
-
-```py
-from PIL import Image
-from diffusers.utils import export_to_gif
-
-pipe = ShapEImg2ImgPipeline.from_pretrained("openai/shap-e-img2img", torch_dtype=torch.float16, variant="fp16").to("cuda")
-
-guidance_scale = 3.0
-image = Image.open("burger.png").resize((256, 256))
-
-images = pipe(
- image,
- guidance_scale=guidance_scale,
- num_inference_steps=64,
- frame_size=256,
-).images
-
-gif_path = export_to_gif(images[0], "burger_3d.gif")
-```
-
-
-
-
- cheeseburger
-
-
-
- 3D cheeseburger
-
-
-
-## Generate mesh
-
-Shap-E is a flexible model that can also generate textured mesh outputs to be rendered for downstream applications. In this example, you'll convert the output into a `glb` file because the 🤗 Datasets library supports mesh visualization of `glb` files which can be rendered by the [Dataset viewer](https://huggingface.co/docs/hub/datasets-viewer#dataset-preview).
-
-You can generate mesh outputs for both the [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`] by specifying the `output_type` parameter as `"mesh"`:
-
-```py
-import torch
-from diffusers import ShapEPipeline
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-pipe = ShapEPipeline.from_pretrained("openai/shap-e", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-pipe = pipe.to(device)
-
-guidance_scale = 15.0
-prompt = "A birthday cupcake"
-
-images = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=64, frame_size=256, output_type="mesh").images
-```
-
-Use the [`~utils.export_to_ply`] function to save the mesh output as a `ply` file:
-
-
-
-You can optionally save the mesh output as an `obj` file with the [`~utils.export_to_obj`] function. The ability to save the mesh output in a variety of formats makes it more flexible for downstream usage!
-
-
-
-```py
-from diffusers.utils import export_to_ply
-
-ply_path = export_to_ply(images[0], "3d_cake.ply")
-print(f"saved to folder: {ply_path}")
-```
-
-Then you can convert the `ply` file to a `glb` file with the trimesh library:
-
-```py
-import trimesh
-
-mesh = trimesh.load("3d_cake.ply")
-mesh.export("3d_cake.glb", file_type="glb")
-```
-
-By default, the mesh output is focused from the bottom viewpoint but you can change the default viewpoint by applying a rotation transform:
-
-```py
-import trimesh
-import numpy as np
-
-mesh = trimesh.load("3d_cake.ply")
-rot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0])
-mesh = mesh.apply_transform(rot)
-mesh.export("3d_cake.glb", file_type="glb")
-```
-
-Upload the mesh file to your dataset repository to visualize it with the Dataset viewer!
-
-