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spaces/1acneusushi/gradio-2dmoleculeeditor/data/3 Ninjas Movies Free Download.md

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-<h1>How to Download and Watch 3 Ninjas Movies for Free</h1>
-<p>3 Ninjas is a series of action comedy family films that follow the adventures of three young brothers who are trained by their Japanese grandfather in the art of Ninjutsu. The films are 3 Ninjas (1992), 3 Ninjas Kick Back (1994), 3 Ninjas Knuckle Up (1995), and 3 Ninjas: High Noon at Mega Mountain (1998). The films feature Victor Wong as the grandfather and various actors as the brothers. The films are fun and entertaining for kids and adults alike. If you want to download and watch 3 Ninjas movies for free, here are some ways to do that.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Poetic Justice The Movie - A Masterpiece of African American Cinema by John Singleton.md

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-<h1>Poetic Justice: A Classic Movie with a Powerful Message</h1>
- <p>Have you ever watched a movie that touched your soul and made you think differently about life? If you have, then you might have seen Poetic Justice, a 1993 romantic drama film written and directed by John Singleton, starring Janet Jackson as the titular Justice, Tupac Shakur as Lucky, and Regina King as Justice's best friend Iesha. The movie follows Justice, a young poet who lost her boyfriend to gun violence, as she goes on a road trip from South Central L.A. to Oakland on a mail truck along with Iesha, Lucky and his co-worker Chicago. Along the way, she learns to cope with her grief, open up to love, and discover her true self.</p>
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- <h2>The Characters and Their Relationships</h2>
- <p>One of the strengths of Poetic Justice is its realistic and complex portrayal of its characters and their relationships. Each character has their own personality, background, motivation and flaw that make them human and relatable. They also have dynamic interactions that change and evolve throughout the movie.</p>
- <p>Justice is a young African American woman who works as a hairdresser at a local salon. She is also a talented poet who writes poems to express her feelings and thoughts. She is deeply depressed after witnessing the murder of her boyfriend Markell by gang members. She isolates herself from her friends and family, except for her cat Whiteboy. She has trust issues and low self-esteem, which prevent her from opening up to new people.</p>
- <p>Lucky is a young African American man who works as a postal worker at a mail truck company. He is also an aspiring musician who dreams of becoming a rap star. He has a daughter named Keisha whom he loves dearly. He had to take her away from her mother Angel, who was addicted to drugs and prostitution. He has anger issues and commitment problems, which prevent him from settling down with one woman.</p>
- <p>Iesha is Justice's best friend who works as a beautician at the same salon. She is also dating Chicago, Lucky's co-worker at the mail truck company. She is loud, outgoing, fun-loving and loyal. She likes to party and have fun with her friends. She has low standards and self-respect, which prevent her from leaving Chicago despite his abuse and infidelity.</p>
- <p>Chicago is Lucky's co-worker who drives the mail truck with him. He is also dating Iesha, Justice's best friend. He is arrogant, rude, violent and unfaithful. He likes to drink and gamble with his friends. He has no respect for women or himself, which prevent him from treating Iesha right or being faithful to her.</p>
- <p>The four characters go on a road trip together when Lucky's boss asks him to deliver mail to Oakland on his day off. He agrees on one condition: he can bring Iesha along so he can spend time with her. Iesha agrees on one condition: she can bring Justice along so she can cheer her up. Chicago agrees on one condition: he can drive the mail truck so he can be in charge.</p>
- <p>The road trip becomes an opportunity for them to get to know each other better, confront their issues, resolve their conflicts, develop their feelings and grow as individuals.</p>
- <h2>The Poetry and The Music</h2>
- <p>Another strength of Poetic Justice is its use of poetry and music to enhance its mood OK, here is the rest of the article: <h2>The Poetry and The Music</h2>
- <p>Another strength of Poetic Justice is its use of poetry and music to enhance its mood and message. The movie features poems written by Maya Angelou, who also plays Justice's aunt June in the movie. The poems are read by Janet Jackson in voice-over or to other characters in the movie. The poems reflect Justice's emotions and thoughts as she goes through her journey of healing and growth. They also convey themes such as love, loss, hope, courage, beauty and justice.</p>
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-<li>"And Still I Rise", which Justice reads to herself to express her resilience and strength.</li>
-<li>"Poetic Justice", which Justice reads at the end of the movie to express her gratitude and happiness.</li>
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- <p>The movie also features songs from various genres such as hip hop, R&B, soul, jazz and blues. The songs complement the poems and the mood of the movie. They also reflect the characters' personalities and feelings. Some of the songs that are featured in the movie are:</p>
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-<li>"Again", by Janet Jackson, which plays during the opening credits and at the end of the movie. It is a ballad that expresses Justice's longing for Markell and her hope for Lucky.</li>
-<li>"Definition of a Thug Nigga", by Tupac Shakur, which plays when Lucky is driving his car with Keisha. It is a rap song that expresses Lucky's anger and frustration with his life.</li>
-<li>"Nite & Day", by Al B. Sure!, which plays when Iesha and Chicago are making love in their motel room. It is an R&B song that expresses Iesha and Chicago's passion and lust.</li>
-<li>"My Funny Valentine", by Chaka Khan, which plays when Justice and Lucky are having dinner at Aunt June's house. It is a jazz song that expresses Justice and Lucky's attraction and romance.</li>
-<li>"Get It Up", by TLC, which plays when Iesha, Justice, Lucky and Chicago are dancing at a club in Oakland. It is a dance song that expresses Iesha, Justice, Lucky and Chicago's fun and excitement.</li>
-</ul>
- <h2>The Road Trip and The Scenery</h2>
- <p>A third strength of Poetic Justice is its depiction of the road trip and the scenery that the characters encounter along the way. The road trip serves as a metaphor for the characters' journey of self-discovery and transformation. The scenery serves as a contrast to their urban environment and a reflection of their inner states and feelings.</p>
- <p>The road trip begins in South Central L.A., where Justice, Lucky, Iesha and Chicago live. It is a place marked by poverty, violence, crime and despair. The characters face many challenges and dangers in their daily lives, such as gang shootings, drug dealers, police harassment and domestic abuse. They also face racism, sexism, classism and oppression from the society at large. They have little hope or opportunity for a better future.</p>
- <p>The road trip ends in Oakland, where Lucky has to deliver mail to his cousin Pete. It is a place marked by diversity, culture, art and activism. The characters encounter many people and places that inspire them and challenge them to think differently about themselves and their world. They also encounter beauty, joy, love and friendship along the way. They have more hope or opportunity for a better future.</p>
- <p>Some of the places that they visit along the way are:</p>
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-<li>The beach, where Justice reads her poem "Phenomenal Woman" to Lucky while they watch the sunset.</li>
-<li>The carnival, where Iesha wins a stuffed animal for Keisha while Chicago cheats on her with another woman.</li>
-<li>The diner, where Justice reads her poem "In a Time" to Lucky while they share their fears and dreams.</li>
-<li>The gas station, where Chicago gets into a fight with some bikers who insult Iesha.</li>
-<li>The motel, where Iesha confronts Chicago about his infidelity while Justice and Lucky make love for the first time.</li>
-<li>Aunt June's house, where Justice meets Maya Angelou who gives her advice on life and love while they have dinner with Lucky and his family.</li>
-<li>The club, where Iesha breaks up with Chicago while Justice dances with Lucky.</li>
-</ul>
- <h2>The Message and The Impact</h2>
- <p>A fourth strength of Poetic Justice is its message OK, here is the rest of the article: <h2>The Message and The Impact</h2>
- <p>A fourth strength of Poetic Justice is its message and its impact on the audience and the culture. The movie conveys a message of hope, healing, empowerment and justice for young African American women like Justice who face many challenges and obstacles in their lives. The movie also addresses issues such as racism, violence, love, friendship, family and identity that affect many people in the society.</p>
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-<li>A: You can watch Poetic Justice on Amazon Prime Video, iTunes, YouTube or other streaming platforms.</li>
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-<li>A: The poems in Poetic Justice were written by Maya Angelou, who also plays Justice's aunt June in the movie.</li>
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-<li>A: Poetic justice is a literary term that refers to a situation where someone gets what they deserve in a fitting or ironic way.</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Tally ERP 9 6.1.1 for Free and Enjoy Its Amazing Features and Benefits.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Egg Cracking Sound Effect Free Download __HOT__.md

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-<p>Ratchagan is a Tamil action thriller movie that was released in 1997. It was directed by Praveen Gandhi and starred Nagarjuna Akkineni and Sushmita Sen in the lead roles. The movie was a blockbuster hit and received positive reviews from critics and audiences alike. It was also dubbed in Hindi as Rakshakudu.</p>
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-<p>The plot of ratchagan tamil movie revolves around Ajay (Nagarjuna), a young man who works as a mechanic and lives with his mother (Lakshmi). He falls in love with Sonia (Sushmita), the daughter of a rich businessman named Chandra Shekar (Girish Karnad). However, Chandra Shekar does not approve of their relationship and tries to separate them by various means. He also hires a gangster named Raghu (Raghuvaran) to kill Ajay. Ajay has to fight against all odds to protect his love and his life.</p>
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-<tr><td>Sonia</td><td>The love interest of Ajay. She is the daughter of Chandra Shekar.</td></tr>
-<tr><td>Chandra Shekar</td><td>The antagonist of the movie. He is a rich businessman who opposes Ajay and Sonia's relationship.</td></tr>
-<tr><td>Raghu</td><td>The secondary antagonist of the movie. He is a gangster who works for Chandra Shekar.</td></tr>
-<tr><td>Ajay's mother</td><td>The supporting character of the movie. She is Ajay's mother who supports him in his struggles.</td></tr>
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-<p>Ratchagan tamil movie received positive reviews from critics and audiences alike. It was praised for its action sequences, music, cinematography, editing, and performances. It was also appreciated for its message of love and courage. Some of the reviews are:</p>
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-<li>"Ratchagan is a well-made action thriller that keeps you hooked till the end. Nagarjuna and Sushmita Sen make a great pair on screen. The music by A.R.Rahman is superb." - The Hindu</li>
-<li>"Ratchagan is a fast-paced entertainer that delivers on its promise. The direction by Praveen Gandhi is crisp and stylish. The action scenes are thrilling and realistic. The chemistry between Nagarjuna and Sushmita Sen is sizzling." - Rediff.com</li>
-<li>"Ratchagan is a must-watch for all action lovers. It has everything you need in a masala movie - romance, comedy, drama, suspense, and violence. Nagarjuna proves his mettle as an action hero once again. Sushmita Sen looks stunning and acts well." - India Today</li>
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spaces/1gistliPinn/ChatGPT4/Examples/Fast Gsm Omap 1.0.0.7.md

@@ -1,159 +0,0 @@
-<br />
-<h1>What is Fast GSM OMAP 1.0.0.7 and How to Use It</h1>
-
-<p>If you have a mobile phone that is locked to a specific network or region, you might be looking for a way to unlock it and use it with any SIM card you want. One of the tools that can help you with this task is Fast GSM OMAP 1.0.0.7, a software that can flash and repair mobile phones using the OMAP (Open Multimedia Applications Platform) technology.</p>
-<h2>fast gsm omap 1.0.0.7</h2><br /><p><b><b>Download File</b> >>> <a href="https://imgfil.com/2uxY1n">https://imgfil.com/2uxY1n</a></b></p><br /><br />
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-<p>In this article, we will explain what Fast GSM OMAP 1.0.0.7 is, how it works, what are its advantages and disadvantages, and how to download and use it safely and effectively.</p>
-
-<h2>What is Fast GSM OMAP 1.0.0.7?</h2>
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-<p>Fast GSM OMAP 1.0.0.7 is a software that can flash and repair mobile phones that use the OMAP technology, which is a type of system-on-chip (SoC) developed by Texas Instruments for multimedia applications such as smartphones, tablets, digital cameras, etc.</p>
-
-<p>Fast GSM OMAP 1.0.0.7 can unlock mobile phones that are locked to a specific network or region by changing their firmware or software, which is the program that controls the phone's functions and features.</p>
-<p></p>
-
-<p>Fast GSM OMAP 1.0.0.7 can also repair mobile phones that have software problems such as freezing, crashing, bootlooping, etc., by restoring their original or custom firmware.</p>
-
-<p>Fast GSM OMAP 1.0.0.7 supports many models of mobile phones that use the OMAP technology, such as Samsung, LG, Motorola, Nokia, Sony Ericsson, etc.</p>
-
-<h2>How does Fast GSM OMAP 1.0.0.7 work?</h2>
-
-<p>Fast GSM OMAP 1.0.0.7 works by connecting the mobile phone to a computer via a USB cable and using a special driver that allows the software to communicate with the phone's chipset.</p>
-
-<p>Fast GSM OMAP 1.0.0.7 then reads the phone's information and detects its model, firmware version, IMEI number, etc.</p>
-
-<p>Fast GSM OMAP 1.0.0.7 then allows the user to select the desired operation, such as unlocking, flashing, or repairing the phone.</p>
-
-<p>Fast GSM OMAP 1.0.0.7 then downloads the appropriate firmware file from its online database or from a local folder and writes it to the phone's memory.</p>
-
-<p>Fast GSM OMAP 1.0.0.7 then reboots the phone and completes the operation.</p>
-
-<h2>What are the advantages and disadvantages of Fast GSM OMAP 1.0.0.7?</h2>
-
-<p>Fast GSM OMAP 1.0.0.7 has some advantages and disadvantages that you should consider before using it.</p>
-
-<h3>Advantages</h3>
-
-<ul>
-<li>Fast GSM OMAP 1.0.0.7 is fast and easy to use, as it has a simple and user-friendly interface and does not require any technical skills or knowledge.</li>
-<li>Fast GSM OMAP 1.0.0.7 is free to download and use, as it does not require any registration or payment.</li>
-<li>Fast GSM OMAP 1.0</p>
-<h3>Disadvantages</h3>
-
-<ul>
-<li>Fast GSM OMAP 1.0.0.7 is not legal, as it violates the terms and conditions of the mobile phone manufacturers and network providers. Using Fast GSM OMAP 1.0.0.7 may result in legal actions, fines, or penalties.</li>
-<li>Fast GSM OMAP 1.0.0.7 is not safe, as it may contain viruses, malware, spyware, or other harmful components that may damage your computer or phone or compromise your privacy or security.</li>
-<li>Fast GSM OMAP 1.0.0.7 is not reliable, as it may not work properly or cause errors or problems on your phone such as bricking, losing data, invalidating warranty, etc.</li>
-<li>Fast GSM OMAP 1.0.0.7 is not ethical, as it supports piracy and hurts the revenue and reputation of the mobile phone developers and network providers who invest time and money to create and deliver quality products and services.</li>
-</ul>
-
-<h2>How to download and use Fast GSM OMAP 1.0.0.7?</h2>
-
-<p>If you still want to download and use Fast GSM OMAP 1.0.0.7 despite its disadvantages, you should follow these steps:</p>
-
-<ol>
-<li>Go to a website that offers Fast GSM OMAP 1.0.0.7 for download, such as https://new.c.mi.com/ng/post/38228/Fast_Gsm_Omap_1007_13_NEW or https://www.zedload.com/fastgsm-omap-1.0.0.7-crack-serial-download.html.</li>
-<li>Download the Fast GSM OMAP 1.0.0.7 zip file to your computer and extract it using a program like WinRAR or 7-Zip.</li>
-<li>Install the Fast GSM OMAP 1.0 driver on your computer by running the setup.exe file in the driver folder.</li>
-<li>Connect your phone to your computer via a USB cable and make sure it is detected by the Fast GSM OMAP 1.0 driver.</li>
-<li>Run the FastGSMOMAP.exe file in the main folder and enter your username and password if you have registered on the website.</li>
-<li>Select your phone model from the list and click on Read Info to get your phone's information.</li>
-<li>Select the operation you want to perform on your phone, such as Unlock, Flash, or Repair.</li>
-<li>Select the firmware file you want to use for your phone from the online database or from a local folder.</li>
-<li>Click on Start to begin the operation and wait for it to finish.</li>
-<li>Reboot your phone and check if it is unlocked or repaired successfully.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Fast GSM OMAP 1.0.0.7 is a software that can flash and repair mobile phones that use the OMAP technology by changing their firmware or software.</p>
-
-<p>Fast GSM OMAP 1.0
-<p>.0.7 can also unlock mobile phones that are locked to a specific network or region by removing the network lock code.</p>
-
-<p>However, Fast GSM OMAP 1.0.0.7 has some disadvantages, such as being illegal, unsafe, unreliable, and unethical.</p>
-
-<p>Therefore, we do not recommend using Fast GSM OMAP 1.0.0.7 for unlocking or repairing your phone, as it may cause more harm than good.</p>
-
-<p>Instead, we suggest that you use a legal and safe method to unlock or repair your phone, such as contacting your network provider or a professional service center.</p>
-
-<p>We hope that this article has helped you understand what Fast GSM OMAP 1.0.0.7 is and how to use it.</p>
-
-<p>If you have any questions or comments, please feel free to leave them below.</p>
-
-<p>Thank you for reading and have a nice day!</p>
-<h2>What are the alternatives to Fast GSM OMAP 1.0.0.7?</h2>
-
-<p>If you are looking for a different way to unlock or repair your phone that uses the OMAP technology, you may want to consider some of the alternatives to Fast GSM OMAP 1.0.0.7.</p>
-
-<p>Some of the alternatives to Fast GSM OMAP 1.0.0.7 are:</p>
-
-<ul>
-<li>Official unlock codes: You can request an official unlock code from your network provider or a third-party service that can provide you with a genuine code that can unlock your phone permanently and legally.</li>
-<li>Professional service: You can take your phone to a professional service center that can flash or repair your phone using specialized equipment and software that can fix your phone without damaging it or voiding its warranty.</li>
-<li>Custom ROMs: You can install a custom ROM on your phone that can replace the original firmware or software with a modified version that can enhance your phone's performance, features, and compatibility with different networks and regions.</li>
-</ul>
-
-<p>However, these alternatives may also have some drawbacks, such as being expensive, time-consuming, risky, or complicated.</p>
-
-<p>Therefore, you should weigh the pros and cons of each alternative and choose the one that suits your needs and preferences best.</p>
-
-<h2>What is OMAP technology and how does it work?</h2>
-
-<p>OMAP stands for Open Multimedia Applications Platform, which is a type of system-on-chip (SoC) developed by Texas Instruments for multimedia applications such as smartphones, tablets, digital cameras, etc.</p>
-
-<p>An SoC is a single chip that integrates various components such as a processor, a memory, a graphics unit, a modem, etc., that work together to perform various functions and tasks on a device.</p>
-
-<p>An OMAP SoC consists of two main parts: an applications processor and a modem processor.</p>
-
-<p>The applications processor is responsible for running the operating system and the user interface of the device, as well as handling the multimedia features such as audio, video, camera, gaming, etc.</p>
-
-<p>The modem processor is responsible for managing the wireless communication functions of the device, such as cellular, Wi-Fi, Bluetooth, GPS, etc.</p>
-
-<p>The OMAP SoC also supports various peripherals and interfaces such as USB, HDMI, SD card, etc., that allow the device to connect with other devices and accessories.</p>
-
-<p>The OMAP SoC is designed to provide high performance, low power consumption, and flexibility for different devices and applications.</p>
-<h2>What are the benefits of unlocking or repairing your phone with Fast GSM OMAP 1.0.0.7?</h2>
-
-<p>If you decide to use Fast GSM OMAP 1.0.0.7 to unlock or repair your phone that uses the OMAP technology, you may enjoy some benefits that can improve your user experience and satisfaction.</p>
-
-<p>Some of the benefits of unlocking or repairing your phone with Fast GSM OMAP 1.0.0.7 are:</p>
-
-<ul>
-<li>Freedom: You can use your phone with any SIM card from any network provider or region, without any restrictions or limitations.</li>
-<li>Savings: You can save money by choosing a cheaper or better plan from a different network provider or by avoiding roaming charges when traveling abroad.</li>
-<li>Compatibility: You can use your phone with different devices and accessories that may not be compatible with your original network or region.</li>
-<li>Performance: You can improve your phone's performance by updating or changing its firmware or software to a newer or better version that can fix bugs, glitches, errors, etc.</li>
-<li>Customization: You can customize your phone's appearance and functionality by installing different themes, wallpapers, icons, apps, etc., that may not be available or allowed on your original network or region.</li>
-</ul>
-
-<h2>What are the risks of unlocking or repairing your phone with Fast GSM OMAP 1.0.0.7?</h2>
-
-<p>If you decide to use Fast GSM OMAP 1.0.0.7 to unlock or repair your phone that uses the OMAP technology, you may also face some risks that can harm your device or yourself.</p>
-
-<p>Some of the risks of unlocking or repairing your phone with Fast GSM OMAP 1.0.0.7 are:</p>
-
-<ul>
-<li>Legal: You may violate the terms and conditions of your mobile phone manufacturer or network provider, which may result in legal actions, fines, or penalties.</li>
-<li>Safety: You may download viruses, malware, spyware, or other harmful components that may damage your computer or phone or compromise your privacy or security.</li>
-<li>Reliability: You may encounter errors or problems on your phone such as bricking, losing data, invalidating warranty, etc., that may require professional service or replacement.</li>
-<li>Ethics: You may support piracy and hurt the revenue and reputation of the mobile phone developers and network providers who invest time and money to create and deliver quality products and services.</li>
-</ul>
-
-<p>Therefore, you should be aware of the pros and cons of using Fast GSM OMAP 1.0.0.7 and make an informed decision based on your needs and preferences.</p>
-<h2>Conclusion</h2>
-
-<p>Fast GSM OMAP 1.0.0.7 is a software that can flash and repair mobile phones that use the OMAP technology by changing their firmware or software. It can also unlock mobile phones that are locked to a specific network or region by removing the network lock code.</p>
-
-<p>However, Fast GSM OMAP 1.0.0.7 has some disadvantages, such as being illegal, unsafe, unreliable, and unethical. Therefore, we do not recommend using Fast GSM OMAP 1.0.0.7 for unlocking or repairing your phone, as it may cause more harm than good.</p>
-
-<p>Instead, we suggest that you use a legal and safe method to unlock or repair your phone, such as contacting your network provider or a professional service center.</p>
-
-<p>We hope that this article has helped you understand what Fast GSM OMAP 1.0.0.7 is and how to use it.</p>
-
-<p>If you have any questions or comments, please feel free to leave them below.</p>
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-<p>Thank you for reading and have a nice day!</p> 3cee63e6c2<br />
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spaces/1nferno/Imdb_sentiment/app.py

@@ -1,30 +0,0 @@
-import gradio as gr
-from fastai.text.all import *
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-sample_reviews = ["""Top Gun (1986) made Tom Cruise a star, and now 36 years later he jumps back in role of Pete Mitchell AKA Maverick almost like he never left.Maverick never seems let his age slow him down, and still is cocky has ever, and is ordered to train a bunch of young pilots for a deadly mission, but sees a little bit of himself in them, and must get them working together has a team.
-Tom Cruise is great has Maverick, who is coming to terms with the past. Miles Teller and Glen Powell are also great, and not to mention Jennifer Connelly. But the flying scenes are what makes this movie, you feel like your flying with them. Feels has real has ever. A terrific sequel 36 years worth the wait."""
-,"""Brahmastra is good to watch for 3d only if you are in for visual treat attempted by Bollywood but that's all I have to say.In terms of storyline it lacks what Karthikeya 2 was able to achieve with its storyline and narration.
-Songs are good too but I am really amazed when I see that director took so long but the storyline after interval went boring.
-My verdict that Bollywood needs to come out of Love story mode and present more logical and reasoning along with joyful moments depiction in their movie.
-Movie is ok for 1 time watch for the visual treat whoch Ayan mukherji tried and for the efforts."""]
-
-
-title = "IMDB Reviews Sentiment Classifier"
-
-description = """A movie review sentiment classifier using the ULMFit ( A transfer learning approach ) on the AWD_LSTM Architecture, Achieved an accuracy of 94.7 %
-
-  Github : https://github.com/ferno9/IMDB_SentimentAnalysis"""
-
-learn = load_learner('export_sentiment_imdb.pkl')
-classes = ["Negative","Positive"]
-def predict(review):
-    _,_,preds = learn.predict(review)
-
-    return {classes[i] : float(preds[i]) for i in range(len(classes))}
-
-
-iface = gr.Interface(fn=predict, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Label(),examples=sample_reviews,title=title,description = description)
-iface.launch()

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Farming Simulator 14 for Free and Experience Realistic Farming on Your Device.md

@@ -1,105 +0,0 @@
-<br />
-<h1>Farming Simulator 14 Download Za Darmo: How to Get the Best Farming Game for Free</h1>
-<p>If you are a fan of simulation games and farming, you might have heard of <strong>Farming Simulator 14</strong>, one of the most realistic and enjoyable farming games ever made. But did you know that you can download this game for free on your mobile device or PC? In this article, we will show you how to get Farming Simulator 14 download za darmo, which means "farming simulator 14 download for free" in Polish. We will also give you some tips and tricks to master this game and become a successful farmer.</p>
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- <h2>What is Farming Simulator 14 and Why You Should Play It</h2>
-<p>Farming Simulator 14 is a game developed by GIANTS Software, a company that specializes in creating realistic simulation games. In this game, you can start your own agricultural career and take control of your farm and its fields. You can plant, harvest, and sell various crops, such as wheat, canola, corn, or grass. You can also raise cows and sell their milk, or produce biogas from grass or chaff. You can use authentic machines from real agricultural manufacturers, such as Case IH, Deutz-Fahr, Lamborghini, Kuhn, Amazone, and Krone. You can also play with a friend in a local multiplayer mode using WiFi or Bluetooth.</p>
- <h3>The Features and Gameplay of Farming Simulator 14</h3>
-<p>Some of the features and gameplay elements of Farming Simulator 14 are:</p>
-<ul>
-<li>New highly detailed 3D graphics and a slick user interface that enhance your gaming experience.</li>
-<li>A dynamic market that changes according to supply and demand. You have to choose the best time and place to sell your crops or products.</li>
-<li>A variety of vehicles and equipment that you can buy or rent from the shop. You can also customize them with different colors or attachments.</li>
-<li>A large open world that you can explore and farm. You can buy new fields or expand your existing ones.</li>
-<li>A realistic physics engine that simulates the behavior of soil, crops, machines, and weather.</li>
-<li>A day-night cycle that affects the lighting and visibility of the game.</li>
-</ul>
- <h3>The Benefits of Playing Farming Simulator 14</h3>
-<p>Playing Farming Simulator 14 can be fun and relaxing, but it can also have some benefits for your mental health and skills. Some of the benefits are:</p>
-<ul>
-<li>It can improve your concentration and attention span. You have to pay attention to the details of your farm, such as the soil condition, the crop growth, the fuel level, or the weather forecast.</li>
-<li>It can enhance your problem-solving and decision-making abilities. You have to plan ahead and choose the best strategy for your farm, such as what crops to plant, when to harvest, or how to invest your money.</li>
-<li>It can boost your creativity and imagination. You can create your own farm according to your preferences and style. You can also experiment with different combinations of crops, machines, or products.</li>
-<li>It can reduce your stress and anxiety levels. You can enjoy the peaceful atmosphere of the countryside, listen to the sounds of nature, or watch the animals roam around.</li>
-</ul>
- <h2> as plowing, cultivating, sowing, or harvesting. This can save you time and effort, but it also costs you money. Here are some tips to hire and manage workers efficiently:</p>
-<ul>
-<li>You can hire a worker by pressing the "Hire Worker" button on the bottom right corner of the screen. You can also assign a worker to a specific vehicle or equipment by entering it and pressing the same button.</li>
-<li>You can see the status and cost of your hired workers on the map screen or by pressing the "Workers" button on the bottom left corner of the screen. You can also fire a worker by selecting it and pressing the "Fire Worker" button.</li>
-<li>You can save money by hiring workers only when you need them and by using them for simple tasks. For example, you can hire a worker to plow a field, but you can do the sowing yourself using a seeder with a fertilizer function.</li>
-<li>You can also save money by using workers that are already hired by other players in the multiplayer mode. You can join their farm and use their vehicles and equipment for free, as long as they allow you to do so.</li>
-</ul>
- <h3>How to Use Different Vehicles and Equipment</h3>
-<p>Farming Simulator 14 offers a wide range of vehicles and equipment that you can use for various purposes on your farm. However, some of them can be tricky to use or require some knowledge and skills. Here are some tips to use different vehicles and equipment effectively:</p>
-<ul>
-<li>You can switch between different vehicles or equipment by swiping left or right on the screen. You can also select a specific vehicle or equipment from the garage menu by pressing the "Garage" button on the bottom right corner of the screen.</li>
-<li>You can attach or detach different equipment to your vehicles by driving close to them and pressing the "Attach/Detach" button on the bottom right corner of the screen. You can also fold or unfold some equipment by pressing the same button.</li>
-<li>You can activate or deactivate different functions of your vehicles or equipment by pressing the "Function" button on the bottom right corner of the screen. For example, you can turn on or off the lights, the engine, or the cruise control of your vehicles, or you can lower or raise, turn on or off, or empty or fill your equipment.</li>
-<li>You can adjust the speed of your vehicles or equipment by using the slider on the right side of the screen. You can also use the brake pedal on the left side of the screen to stop or reverse your vehicles or equipment.</li>
-<li>You can steer your vehicles or equipment by using the steering wheel on the left side of the screen. You can also change the camera angle by tapping on the camera icon on the top right corner of the screen.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
-<p>Farming Simulator 14 is a great game for anyone who loves farming and simulation games. It offers a realistic and immersive experience of managing your own farm and harvesting crops using authentic machines. You can download Farming Simulator 14 za darmo, which means "farming simulator 14 download for free" in Polish, from different platforms, such as Android, iOS, or Windows. You can also use some tips and tricks to make more money, hire and manage workers, and use different vehicles and equipment in the game. We hope this article has helped you learn more about Farming Simulator 14 and how to get it for free. Happy farming!</p>
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- <h4>Q: How do I save my progress in Farming Simulator 14?</h4>
-<p>A: You can save your progress in Farming Simulator 14 by pressing the "Menu" button on the top left corner of the screen and selecting "Save Game". You can also enable auto-save from the settings menu by pressing the same button and selecting "Settings".</p>
- <h4>Q: How do I play Farming Simulator 14 with a friend?</h4>
-<p>A: You can play Farming Simulator 14 with a friend in a local multiplayer mode using WiFi or Bluetooth. To do so, you have to press the "Menu" button on the top left corner of the screen and selecting "Multiplayer". Then, you have to choose whether you want to host or join a game, and select the connection type (WiFi or Bluetooth). If you host a game, you have to create a farm name and a password, and wait for your friend to join. If you join a game, you have to enter the farm name and the password of your friend, and wait for the host to start the game.</p>
- <h4>Q: How do I buy new fields in Farming Simulator 14?</h4>
-<p>A: You can buy new fields in Farming Simulator 14 by driving close to them and pressing the "Buy Field" button on the bottom right corner of the screen. You can also see the price and size of each field on the map screen by pressing the "Map" button on the bottom left corner of the screen. Note that you can only buy fields that are not owned by other players in the multiplayer mode.</p>
- <h4>Q: How do I produce silage or mixed ration in Farming Simulator 14?</h4>
-<p>A: You can produce silage or mixed ration in Farming Simulator 14 by using a forage harvester or a mower to cut grass or chaff from your fields, then transporting it to the silo or the mixing station using a trailer or a loader wagon. To produce silage, you have to dump the grass or chaff into the silo and wait for it to ferment. To produce mixed ration, you have to dump the grass or chaff into the mixing station and add some straw and hay. You can use silage or mixed ration to feed your cows or sell them at the biogas plant.</p>
- <h4>Q: How do I refill my vehicles or equipment in Farming Simulator 14?</h4>
-<p>A: You can refill your vehicles or equipment in Farming Simulator 14 by driving close to the fuel station, the seed pallets, or the fertilizer tanks, and pressing the "Refill" button on the bottom right corner of the screen. You can also buy your own fuel trailer, seed big bag, or fertilizer big bag from the shop and use them to refill your vehicles or equipment anywhere on your farm.</p>
- <h4>Q: How do I customize my vehicles or equipment in Farming Simulator 14?</h4>
-<p>A: You can customize your vehicles or equipment in Farming Simulator 14 by pressing the "Garage" button on the bottom right corner of the screen and selecting the vehicle or equipment that you want to customize. You can change the color of your vehicles or equipment by pressing the "Color" button on the top right corner of the screen. You can also add different attachments to your vehicles or equipment by pressing the "Attachments" button on the top right corner of the screen.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Bingo Images for Free How to Find and Use Them for Your Projects.md

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-<h1>Bingo Images Free Download: How to Find and Use Them for Fun and Profit</h1>
-<h2>Introduction</h2>
-<p>Bingo is a popular game of chance that can be played for fun or profit. Whether you are a bingo enthusiast or a bingo organizer, you might be looking for some bingo images to spice up your game. Bingo images are graphic representations of the items that appear on the bingo cards or the call list. They can be numbers, words, pictures, or symbols that correspond to the theme or variation of the game.</p>
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-<p>But where can you find and download free bingo images? And how can you use them for different purposes? In this article, we will answer these questions and more. We will explore the history and trivia of bingo, the types and features of bingo images, the sources and resources for finding and creating them, and the uses and benefits of using them. By the end of this article, you will have a better understanding of how to find and use free bingo images for fun and profit.</p>
-<h2>Bingo Images: History and Trivia</h2>
-<h3>The origins of bingo and how it evolved over time</h3>
-<p>Bingo's origins can be traced back to 16th-century Italy, where a similar game called "Lo Giuoco del Lotto D'Italia" was played. The game spread to France in the late 1770s, where it was called "Le Lotto", a game played among wealthy Frenchmen. The Germans also played a version of the game in the 1800s, but they used it as an educational tool to teach children spelling, multiplication, and history.</p>
-<p>In the U.S., bingo was originally called "beano". It was a country fair game where a dealer would select numbered discs from a cigar box and players would mark their cards with beans. They yelled "beano" if they won. Edwin S. Lowe, a New York toy salesman, renamed it "bingo" after he overheard someone accidentally yell "bingo" instead of "beano". He hired a Columbia University math professor, Carl Leffler, who created more than 6,000 distinct bingo cards.</p>
-<p>In Great Britain, the game was initially called housey-housey. It's now known as bingo throughout the UK, but you might still hear the word "house" used to describe a bingo win.</p>
-<h <h3>The different names and variations of bingo around the world</h3>
-<p>Bingo is not only played in different countries, but also in different ways. Depending on the region, the game may have different names, rules, or themes. Here are some examples of the variations of bingo around the world:</p>
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-<ul>
-<li>In Australia, bingo is also known as "housie". The game is played with 90 balls and cards with 15 numbers each. The numbers are called by a caller who uses rhyming slang to announce them, such as "one little duck" for 2 or "legs eleven" for 11.</li>
-<li>In Canada, bingo is often played as a fundraiser for charities or community groups. The game is played with 75 balls and cards with 25 numbers each. The numbers are called by a caller who uses standard bingo lingo, such as "B-4" or "O-69".</li>
-<li>In Japan, bingo is also known as "bingo-kei". The game is played with 75 balls and cards with 25 numbers each. The numbers are called by a computerized voice that uses Japanese numerals, such as "ichi" for 1 or "juu-go" for 15.</li>
-<li>In Mexico, bingo is also known as "loteria". The game is played with 54 cards and boards with 16 images each. The images are drawn from Mexican culture and folklore, such as "el gallo" (the rooster) or "la sirena" (the mermaid). The images are called by a caller who uses riddles or poems to describe them.</li>
-<li>In Sweden, bingo is also known as "bingolotto". The game is played with 75 balls and cards with 25 numbers each. The numbers are called by a host who hosts a live TV show that features musical performances, quizzes, and prizes.</li>
-</ul>
-<h3>The interesting facts and statistics about bingo and its players</h3>
-<p>Bingo is not only a fun and exciting game, but also a fascinating phenomenon. Here are some interesting facts and statistics about bingo and its players: </p>
-<ul>
-<li>The largest bingo game ever recorded was in Bogota, Colombia in 2006. It involved 70,080 players and lasted for four hours.</li>
-<li>The largest online bingo game ever recorded was in Tokyo, Japan in 2010. It involved 493,824 players and lasted for five minutes.</li>
-<li>The longest bingo game ever recorded was in San Antonio, Texas in 2011. It lasted for 23 hours and raised $10,000 for charity.</li>
-<li>The most common winning bingo pattern is the four corners, followed by the diagonal line and the horizontal line.</li>
-<li>The most common bingo number called is 38, followed by 44 and 11.</li>
-<li>The average age of a bingo player is 47 years old, but the game attracts people of all ages and backgrounds.</li>
-<li>The majority of bingo players are women, but men are also becoming more interested in the game.</li>
-<li>The most popular reasons for playing bingo are socializing, having fun, and winning money.</li>
-</ul> <h2>Bingo Images: Types and Features</h2>
-<h3>The different types of bingo images, such as numbers, words, pictures, or symbols</h3>
-<p>Bingo images can be classified into different types, depending on the content and the theme of the game. The most common types of bingo images are:</p>
-<ul>
-<li>Numbers: These are the traditional bingo images that consist of numbers from 1 to 75 or 90, depending on the version of the game. They are usually arranged in columns according to the letters B, I, N, G, and O.</li>
-<li>Words: These are bingo images that consist of words that relate to a specific topic or category, such as animals, fruits, colors, or countries. They can be used to create themed bingo games or to teach vocabulary.</li>
-<li>Pictures: These are bingo images that consist of pictures that illustrate a concept or an object, such as shapes, flowers, flags, or faces. They can be used to create visual bingo games or to stimulate the imagination.</li>
-<li>Symbols: These are bingo images that consist of symbols that represent an idea or a value, such as emojis, icons, logos, or signs. They can be used to create modern bingo games or to convey a message.</li>
-</ul>
-<h3>The different features of bingo images, such as colors, sizes, shapes, or styles</h3>
-<p>Bingo images can also have different features that affect their appearance and their function. Some of the common features of bingo images are:</p>
-<ul>
-<li>Colors: These are the hues and shades that give bingo images their distinct look and mood. They can be used to create contrast, harmony, or emphasis in the game.</li>
-<li>Sizes: These are the dimensions and proportions that determine how big or small bingo images are. They can be used to adjust the level of difficulty, clarity, or detail in the game.</li>
-<li>Shapes: These are the forms and outlines that define the boundaries and edges of bingo images. They can be used to create variety, symmetry, or pattern in the game.</li>
-<li>Styles: These are the modes and manners that express the personality and tone of bingo images. They can be used to create fun, elegance, or professionalism in the game.</li>
-</ul>
-<h3>The different formats of bingo images, such as JPEG, PNG, PDF, or SVG</h3>
-<p>Bingo images can also have different formats that affect their quality and their performance. Some of the common formats of bingo images are:</p>
-<ul>
-<li>JPEG: This is a compressed image format that reduces the file size and preserves the color and detail of bingo images. It is suitable for web use and printing.</li>
-<li>PNG: This is a lossless image format that maintains the original quality and supports transparency of bingo images. It is suitable for web use and editing.</li>
-<li>PDF: This is a document format that embeds fonts, graphics, and layout of bingo images. It is suitable for printing and sharing.</li>
-<li>SVG: This is a vector image format that scales up or down without losing quality and allows interactivity of bingo images. It is suitable for web use and animation.</li>
-</ul> <h2>Bingo Images: Sources and Resources</h2>
-<h3>The best websites to find and download free bingo images</h3>
-<p>If you are looking for some free bingo images to use for your game, you might want to check out these websites that offer a variety of bingo images for different themes and purposes:</p>
-<ul>
-<li><a href="">Bingo Baker</a>: This is a website that allows you to create and print custom bingo cards with your own images or words. You can also browse and download thousands of pre-made bingo cards with different themes, such as animals, holidays, sports, or movies.</li>
-<li><a href="">Clipart Library</a>: This is a website that offers a collection of free clipart images that you can use for your bingo game. You can find images of numbers, letters, symbols, or objects that match your theme. You can also edit and resize the images to suit your needs.</li>
-<li><a href="">Pixabay</a>: This is a website that provides a large database of free stock photos and illustrations that you can use for your bingo game. You can search for images by keywords, categories, or colors. You can also download the images in different sizes and formats.</li>
-</ul>
-<h3>The best tools to create and customize your own bingo images</h3>
-<p>If you want to create and customize your own bingo images, you might want to use these tools that offer various features and options to help you design your perfect bingo image:</p>
-<ul>
-<li><a href="">Canva</a>: This is an online graphic design tool that allows you to create and edit stunning bingo images with ease. You can choose from hundreds of templates, fonts, icons, stickers, and backgrounds. You can also upload your own photos or logos and add filters, effects, or text.</li>
-<li><a href="">GIMP</a>: This is a free and open-source image editor that allows you to create and modify bingo images with advanced features. You can use tools such as brushes, layers, masks, gradients, or filters. You can also import and export images in various formats.</li>
-<li><a href="">Inkscape</a>: This is a free and open-source vector graphics editor that allows you to create and edit scalable bingo images with high quality. You can use tools such as paths, shapes, nodes, text, or clones. You can also import and export images in various formats.</li>
-</ul>
-<h3>The best tips and tricks to optimize your bingo images for quality and performance</h3>
-<p>If you want to optimize your bingo images for quality and performance, you might want to follow these tips and tricks that will help you enhance your bingo image experience:</p>
-<ul>
-<li>Choose the right format for your bingo image. Depending on your purpose and preference, you might want to use JPEG for web use and printing, PNG for web use and editing, PDF for printing and sharing, or SVG for web use and animation.</li>
-<li>Choose the right size for your bingo image. Depending on your device and resolution, you might want to use smaller sizes for faster loading and larger sizes for better clarity. You can also use tools such as <a href="">Image Resizer</a> or <a href="">Compress PNG/JPEG</a> to resize or compress your image without losing quality.</li>
-<li>Choose the right color for your bingo image. Depending on your theme and mood, you might want to use bright colors for fun and excitement, dark colors for mystery and suspense, or neutral colors for balance and harmony. You can also use tools such as <a href="">Color Picker</a> or <a href="">Color Scheme Generator</a> to find the perfect color combination for your image.</li>
-</ul> <h2>Bingo Images: Uses and Benefits</h2>
-<h3>How to play bingo online or offline using bingo images</h3>
-<p>One of the main uses of bingo images is to play bingo online or offline. Bingo is a simple and fun game that can be enjoyed by anyone, anywhere, anytime. Here are the basic steps to play bingo using bingo images:</p>
-<ol>
-<li>Get your bingo cards. You can either print them from a website, create them with a tool, or buy them from a store. Make sure you have enough cards for each player and that they have different combinations of bingo images.</li>
-<li>Get your bingo caller. You can either use a website, an app, a device, or a person to call out the bingo images randomly. Make sure you have a reliable and audible caller that can be heard by all players.</li>
-<li>Get your bingo markers. You can either use coins, chips, stickers, pens, or anything else to mark your bingo cards. Make sure you have enough markers for each player and that they are easy to use and remove.</li>
-<li>Start the game. The caller will announce the bingo images one by one and the players will mark their cards accordingly. The first player to mark a complete row, column, diagonal, or pattern of bingo images will shout "Bingo!" and win the game.</li>
-<li>Repeat the game. You can either play with the same cards or get new ones. You can also change the rules or the prizes to make the game more interesting and challenging.</li>
-</ol>
-<h3>How to create bingo cards using bingo images</h3>
-<p>Another use of bingo images is to create bingo cards for your own game. Bingo cards are the essential elements of the game that contain the bingo images that you need to mark. Creating your own bingo cards can be fun and creative, as you can customize them according to your preferences and needs. Here are some tips to create bingo cards using bingo images:</p>
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-<li>Choose your theme. You can either use a general theme, such as numbers or words, or a specific theme, such as animals or holidays. Your theme will determine the type and number of bingo images that you will use for your cards.</li>
-<li>Choose your layout. You can either use a standard layout, such as 5x5 or 3x9, or a custom layout, such as 4x4 or 6x6. Your layout will determine the size and shape of your cards and the number of bingo images that you will need for each card.</li>
-<li>Choose your images. You can either use existing images from a website or a tool, or create your own images with a tool or an editor. Your images should match your theme and your layout and should be clear and attractive.</li>
-<li>Print your cards. You can either use a website or a tool to print your cards directly, or save them as PDF files and print them later. Your cards should be printed on durable paper or cardstock and should be cut neatly and evenly.</li>
-<h3>How to promote bingo events using bingo images</h3>
-<p>A third use of bingo images is to promote bingo events for your business or organization. Bingo events are great ways to attract customers, raise funds, or build community. Using bingo images can help you advertise your event and generate interest and excitement among your target audience. Here are some ways to promote bingo events using bingo images:</p>
-<ul>
-<li>Create flyers or posters. You can use a tool or an editor to create eye-catching flyers or posters that feature your bingo images, along with your event details, such as date, time, location, prizes, and contact information. You can distribute them online or offline to reach potential participants.</li>
-<li>Create social media posts. You can use a tool or an editor to create engaging social media posts that showcase your bingo images, along with your event details, such as hashtags, links, testimonials, and calls to action. You can share them on various platforms, such as Facebook, Twitter, Instagram, or Pinterest, to increase your visibility and followers.</li>
-<li>Create email newsletters. You can use a tool or an editor to create informative email newsletters that highlight your bingo images, along with your event details, such as benefits, features, discounts, and reminders. You can send them to your subscribers or contacts to build trust and loyalty.</li>
-</ul> <h2>Conclusion</h2>
-<p>In conclusion, bingo images are useful and versatile resources that can enhance your bingo game experience. Whether you want to play bingo online or offline, create bingo cards, or promote bingo events, you can find and use free bingo images for fun and profit. You just need to know the history and trivia, the types and features, the sources and resources, and the uses and benefits of bingo images. We hope this article has helped you learn more about bingo images and how to find and use them. If you have any questions or comments, please feel free to contact us. Happy bingo!</p>
-<h2>FAQs</h2>
-<h4>What are the best websites to play bingo online using bingo images?</h4>
-<p>There are many websites that offer online bingo games using bingo images. Some of the best ones are:</p>
-<ul>
-<li><a href="">Bingo Blitz</a>: This is a website that offers free online bingo games with various themes, such as travel, cooking, or casino. You can play with friends, chat with other players, and collect bonuses and rewards.</li>
-<li><a href="">Bingo Bash</a>: This is a website that offers free online bingo games with different modes, such as classic, speed, or team. You can play with millions of players, join clubs, and win prizes and jackpots.</li>
-<li><a href="">Bingo Pop</a>: This is a website that offers free online bingo games with stunning graphics, animations, and sounds. You can play with live callers, unlock new levels, and earn coins and cherries.</li>
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-<h4>What are the best tools to create bingo cards using bingo images?</h4>
-<p>There are many tools that allow you to create bingo cards using bingo images. Some of the best ones are:</p>
-<ul>
-<li><a href="">Bingo Card Generator</a>: This is a tool that allows you to create custom bingo cards with your own images or words. You can choose from different sizes, colors, fonts, and layouts. You can also print or save your cards as PDF files.</li>
-<li><a href="">Bingo Card Maker</a>: This is a tool that allows you to create printable bingo cards with your own images or words. You can choose from different themes, such as animals, holidays, sports, or movies. You can also edit and preview your cards before printing.</li>
-<li><a href="">Bingo Card Creator</a>: This is a tool that allows you to create interactive bingo cards with your own images or words. You can choose from different formats, such as 3x3, 4x4, or 5x5. You can also play your cards online or share them with others.</li>
-</ul>
-<h4>What are the best ways to promote bingo events using bingo images?</h4>
-<p>There are many ways to promote bingo events using bingo images. Some of the best ones are:</p>
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-<li>Create a website or a landing page for your event. You can use a tool or an editor to create a professional and attractive website or landing page that features your bingo images, along with your event details, such as date, time, location, prizes, and registration form.</li>
-<li>Create a video or a podcast for your event. You can use a tool or an editor to create a captivating and informative video or podcast that showcases your bingo images, along with your event details, such as testimonials, benefits, features, and discounts.</li>
-<li>Create a blog or a newsletter for your event. You can use a tool or an editor to create a relevant and engaging blog or newsletter that highlights your bingo images, along with your event details, such as tips, tricks, stories, and reminders.</li>
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-<h1>Como baixar os filmes da série "Todo Mundo em Pânico" do Google Drive</h1>
- <p>Você gosta de filmes de terror? E de comédia? Que tal juntar os dois gêneros em uma série de filmes que fazem paródia dos maiores sucessos do cinema de horror? Essa é a proposta da série "Todo Mundo em Pânico", que já conta com cinco filmes lançados entre 2000 e 2013. Se você quer se divertir com as aventuras e desventuras dos personagens que enfrentam assassinos mascarados, fantasmas vingativos, alienígenas invasores e outras ameaças sobrenaturais, este artigo é para você. Aqui, você vai aprender como baixar os filmes da série "Todo Mundo em Pânico" do Google Drive, um serviço de armazenamento em nuvem gratuito e seguro que permite fazer upload e download de arquivos de forma fácil e rápida. Vamos lá?</p>
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- <p>Os filmes "Todo Mundo em Pânico" são uma sátira dos filmes de terror mais famosos e populares da história do cinema. Cada filme da série faz referência a vários filmes de horror, misturando cenas, personagens, situações e diálogos de forma irreverente e criativa. Por exemplo, o primeiro filme da série é uma paródia principalmente dos filmes "Pânico" e "Eu Sei o que Vocês Fizeram no Verão Passado", mas também inclui elementos de "O Sexto Sentido", "Matrix", "Os Suspeitos" e outros. O segundo filme é uma paródia principalmente dos filmes "A Casa Amaldiçoada" e "O Exorcista", mas também faz referência a "Poltergeist", "O Iluminado", "Missão Impossível 2" e outros. E assim por diante. Se você é fã de filmes de terror, vai se divertir reconhecendo as cenas e os personagens parodiados nos filmes "Todo Mundo em Pânico". E se você não é fã de filmes de terror, vai se divertir com as piadas e as situações absurdas que os filmes apresentam.</p>
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- <p>Outro motivo para assistir aos filmes "Todo Mundo em Pânico" é o elenco, formado por atores e atrizes que sabem fazer rir. A protagonista da série é Cindy Campbell, interpretada pela atriz Anna Faris, que mostra seu talento para a comédia em todas as cenas. Ela é acompanhada por outros personagens marcantes, como Brenda Meeks (Regina Hall), Ray Wilkins (Shawn Wayans), Shorty Meeks (Marlon Wayans), Bobby Prinze (Jon Abrahams), Doofy Gilmore (Dave Sheridan), Gail Hailstorm (Cheri Oteri), entre outros. Além disso, os filmes contam com participações especiais de atores e atrizes famosos, como Charlie Sheen, Pamela Anderson, Leslie Nielsen, Carmen Electra, Shaquille O'Neal, Dr. Phil, entre outros. Todos eles contribuem para tornar os filmes "Todo Mundo em Pânico" ainda mais engraçados e divertidos.</p>
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- <p>O último motivo para assistir aos filmes "Todo Mundo em Pânico" é o fato de que eles são uma franquia de sucesso e longa. Os filmes foram lançados entre 2000 e 2013, totalizando cinco obras. Juntos, eles arrecadaram mais de 800 milhões de dólares nas bilheterias mundiais, mostrando que o público gosta desse tipo de humor. Além disso, os filmes receberam críticas positivas de parte da imprensa especializada, que elogiou a criatividade, a originalidade e a irreverência da série. Se você gosta de acompanhar uma saga cinematográfica que mistura terror e comédia, os filmes "Todo Mundo em Pânico" são uma ótima opção para você.</p>
- <h2 >O que é o Google Drive e como usá-lo?</h2>
- <p>O Google Drive é um serviço de armazenamento em nuvem gratuito e seguro que permite que você guarde e acesse seus arquivos online, de qualquer lugar e a qualquer hora. Com o Google Drive, você pode enviar, compartilhar e baixar arquivos de diversos tipos, como documentos, fotos, vídeos, músicas, entre outros. Além disso, você pode usar o Google Drive para criar e editar arquivos usando os aplicativos do Google, como o Google Docs, o Google Sheets, o Google Slides, entre outros. Veja como usar o Google Drive a seguir:</p>
- <h3>Como criar uma conta no Google Drive</h3>
- <p>Para usar o Google Drive, você precisa ter uma conta do Google. Se você já tem uma conta do Gmail, do YouTube ou de qualquer outro serviço do Google, você já pode usar o Google Drive com o mesmo login e senha. Se você não tem uma conta do Google, você pode criar uma gratuitamente seguindo estes passos:</p>
- <ol>
-<li>Acesse o site do <a href="">Google Drive</a> e clique em "Ir para o Google Drive".</li>
-<li>Clique em "Criar conta" e escolha se quer criar uma conta para uso pessoal ou profissional.</li>
-<li>Preencha os dados solicitados, como nome, sobrenome, nome de usuário, senha, data de nascimento, gênero e número de telefone.</li>
-<li>Aceite os termos de serviço e a política de privacidade do Google e clique em "Próxima etapa".</li>
-<li>Verifique seu número de telefone por meio de um código enviado por SMS ou ligação.</li>
-<li>Pronto! Você já pode usar o Google Drive com sua nova conta do Google.</li>
-</ol>
- <h3>Como fazer upload e compartilhar arquivos no Google Drive</h3>
- <p>Depois de criar sua conta no Google Drive, você pode começar a enviar seus arquivos para a nuvem. Você pode fazer isso pelo computador ou pelo celular. Veja como:</p>
- <h4>Pelo computador</h4>
- <ul>
-<li>Acesse o site do <a href="">Google Drive</a> e faça login com sua conta do Google.</li>
-<li>Clique no botão "+ Novo" no canto superior esquerdo da tela e escolha se quer criar um novo arquivo usando os aplicativos do Google ou se quer fazer upload de um arquivo já existente no seu computador.</li>
-<li>Se você escolher fazer upload de um arquivo, você pode arrastar e soltar o arquivo na janela do Google Drive ou clicar em "Upload de arquivo" ou "Upload de pasta" e selecionar o arquivo ou a pasta que quer enviar.</li>
-<li>Aguarde o upload ser concluído e veja seu arquivo aparecer na lista do Google Drive.</li>
-<li>Para compartilhar seu arquivo com outras pessoas, clique com o botão direito sobre ele e selecione "Compartilhar". Você pode digitar os endereços de e-mail das pessoas com quem quer compartilhar ou gerar um link que pode ser copiado e colado em qualquer lugar. Você também pode definir o nível de acesso das pessoas ao seu arquivo: se elas podem apenas visualizar, comentar ou editar.</li>
-<li>Clique em "Concluído" para finalizar o compartilhamento.</li>
-</ul>
- <h4>Pelo celular</h4>
- <ul>
-<li>Baixe o aplicativo do <a href="">Google Drive</a> na loja de aplicativos do seu celular e faça login com sua conta do Google.</li>
-<li>Toque no botão "+", representado por um círculo vermelho com um sinal de mais branco, no canto inferior direito da tela e escolha se quer criar um novo arquivo usando os aplicativos do Google ou se quer fazer upload de um arquivo já existente no seu celular.</li>
-<li>Se você escolher fazer upload de um arquivo, você pode navegar pelas pastas do seu celular ou acessar a galeria de fotos e vídeos. Toque no arquivo que quer enviar e aguarde o upload ser concluído.</li>
-<li>Para compartilhar seu arquivo com outras pessoas, toque nos três pontos verticais ao lado do nome do arquivo e selecione "Compartilhar". Você pode digitar os endereços de e-mail das pessoas com quem quer compartilhar ou gerar um link que pode ser copiado e colado em qualquer lugar. Você também pode definir o nível de acesso das pessoas ao seu arquivo: se elas podem apenas visualizar, comentar ou editar.</li>
-<li>Toque em "Enviar" para finalizar o compartilhamento.</li>
-</ul>
- <h3>Como fazer download de arquivos do Google Drive</h3>
- <p>Se você quer baixar os filmes "Todo Mundo em Pânico" do Google Drive, você precisa saber como fazer download de arquivos desse serviço. Você também pode fazer isso pelo computador ou pelo celular. Veja como:</p>
- <h4>Pelo computador</h4>
- <ul>
-<li>Acesse o site do <a href="">Google Drive</a> e faça login com sua conta do Google.</li>
-<li>Localize o arquivo que quer baixar na lista do Google Drive e clique com o botão direito sobre ele.</li>
-<li>Selecione a opção "Fazer o download" e escolha a pasta de destino no seu computador.</li>
-<li>Aguarde o download ser concluído e abra o arquivo no seu computador.</li>
-<li>Se você tiver algum problema para fazer o download, verifique se você tem espaço suficiente no seu disco rígido, se sua conexão com a internet está estável e se seu navegador está atualizado. Você também pode tentar usar outro navegador ou desativar temporariamente seu antivírus ou firewall.</li>
-</ul>
- <h4>Pelo celular</h4>
- <ul>
-<li>Baixe o aplicativo do <a href="">Google Drive</a> na loja de aplicativos do seu celular e faça login com sua conta do Google.</li>
-<li>Localize o arquivo que quer baixar na lista do Google Drive e toque nos três pontos verticais ao lado do nome do arquivo.</li>
-<li>Selecione a opção "Fazer o download" e escolha a pasta de destino no seu celular.</li>
-<li>Aguarde o download ser concluído e abra o arquivo no seu celular.</li>
-<li>Se você tiver algum problema para fazer o download, verifique se você tem espaço suficiente na memória do seu celular, se sua conexão com a internet está estável e se seu aplicativo do Google Drive está atualizado. Você também pode tentar usar outro aplicativo para abrir o arquivo ou desativar temporariamente seu antivírus ou firewall.</li>
-</ul>
- <h2>Como baixar os filmes "Todo Mundo em Pânico" do Google Drive</h2>
- <p>Agora que você já sabe o que é o Google Drive e como usá-lo, vamos ao que interessa: como baixar os filmes "Todo Mundo em Pânico" desse serviço. Para isso, você vai precisar encontrar os links dos filmes no Google Drive, escolher o filme que deseja baixar e clicar no link, e fazer o download do filme para o seu dispositivo. Veja como fazer isso em detalhes:</p>
-<p>todo mundo em pânico 1 download drive<br />
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- <h3>Encontrar os links dos filmes no Google Drive</h3>
- <p>O primeiro passo para baixar os filmes "Todo Mundo em Pânico" do Google Drive é encontrar os links dos filmes nesse serviço. Você pode fazer isso usando um mecanismo de busca, como o próprio Bing, ou usando um site especializado em compartilhar links de filmes no Google Drive, como o <a href="">Drive Mega Filmes</a>. Para facilitar sua vida, nós fizemos uma pesquisa na web e encontramos os links dos cinco filmes da série no Google Drive. Veja a tabela abaixo:</p>
- <table>
-<tr><th>Filme</th><th>Ano</th><th>Link</th></tr>
-<tr><td>Todo Mundo em Pânico</td><td>2000</td><td><a href=""></a></td></tr>
-<tr><td>Todo Mundo em Pânico 2</td><td>2001</td><td><a href=""></a></td></tr>
-<tr><td>Todo Mundo em Pânico 3</td><td>2003</td><td><a href=""></a></td></tr>
-<tr><td>Todo Mundo em Pânico 4</td><td>2006</td><td><a href=""></a></td></tr>
-<tr><td>Todo Mundo em Pânico 5</td><td>2013</td><td><a href=""></a></td></tr>
-</table>
- <p>Antes de clicar nos links, é importante verificar se eles são conf iáveis e funcionais. Para isso, você pode verificar se os links têm o domínio "drive.google.com", se os links têm o ícone de um filme ou de um arquivo, se os links têm o nome do filme e o formato do arquivo, e se os links têm comentários ou avaliações de outros usuários. Se você tiver alguma dúvida sobre a confiabilidade de um link, não clique nele e procure outro. Lembre-se de que baixar arquivos de fontes desconhecidas pode trazer riscos para a segurança do seu dispositivo e para a sua privacidade.</p>
- <h3>Escolher o filme que deseja baixar e clicar no link</h3>
- <p>O segundo passo para baixar os filmes "Todo Mundo em Pânico" do Google Drive é escolher o filme que você quer assistir e clicar no link correspondente na tabela. Você pode escolher o filme que mais lhe interessa, seja pelo ano de lançamento, pelo tema, pelo elenco ou pela crítica. Você também pode assistir aos filmes na ordem cronológica, para acompanhar a evolução da série e das paródias. Depois de escolher o filme, basta clicar no link e você será redirecionado para a página do Google Drive que contém o arquivo do filme.</p>
- <h3>Fazer o download do filme para o seu dispositivo</h3>
- <p>O terceiro e último passo para baixar os filmes "Todo Mundo em Pânico" do Google Drive �� fazer o download do filme para o seu dispositivo, seja ele um computador ou um celular. Para isso, você deve seguir os passos que já explicamos na seção anterior sobre como fazer download de arquivos do Google Drive. Lembre-se de escolher uma pasta de destino para o seu arquivo e de verificar se você tem espaço suficiente na memória do seu dispositivo. Depois de fazer o download, você pode abrir o arquivo e assistir ao filme com o seu player de vídeo preferido.</p>
- <h2>Conclusão</h2>
- <p>Neste artigo, você aprendeu como baixar os filmes da série "Todo Mundo em Pânico" do Google Drive. Você viu por que assistir a esses filmes é uma ótima forma de se divertir com as paródias dos filmes de terror, com o elenco divertido e talentoso e com a franquia de sucesso e longa. Você também viu o que é o Google Drive e como usá-lo para fazer upload, compartilhar e baixar arquivos de forma fácil e rápida. E você viu como encontrar os links dos filmes no Google Drive, como escolher o filme que quer baixar e clicar no link, e como fazer o download do filme para o seu dispositivo. Agora, você está pronto para aproveitar os filmes "Todo Mundo em Pânico" no conforto da sua casa. Esperamos que você tenha gostado deste artigo e que ele tenha sido útil para você. Se você tiver alguma dúvida ou sugestão, deixe um comentário abaixo. E se você quiser ler mais artigos sobre filmes, séries, entretenimento e tecnologia, continue acompanhando o nosso site. Até a próxima!</p>
- <h2>FAQs</h2>
- <p>Aqui estão algumas perguntas frequentes sobre o tema deste artigo e suas respectivas respostas:</p>
- <h3>Quem são os diretores dos filmes "Todo Mundo em Pânico"?</h3>
- <p>Os diretores dos filmes "Todo Mundo em Pânico" são: Keenen Ivory Wayans (filmes 1 e 2), David Zucker (filmes 3 e 4) e Malcolm D. Lee (filme 5).</p>
- <h3>Quais são os filmes de terror parodiados nos filmes "Todo Mundo em Pânico"?</h3>
- <p>Os filmes de terror parodiados nos filmes "Todo Mundo em Pânico" são: Pânico, Eu Sei o que Vocês Fizeram no Verão Passado, O Sexto Sentido, Matrix, Os Suspeitos, A Casa Amaldiçoada, O Exorcista, Poltergeist, O Iluminado, Missão Impossível 2, O Chamado, Sinais, O Grito, Guerra dos Mundos, Jogos Mortais, O Massacre da Serra Elétrica, Atividade Paranormal, A Órfã, A Morte do Demônio, Mama, Cisne Negro, Planeta dos Macacos: A Origem, entre outros.</p>
- <h3>Como posso assistir aos filmes "Todo Mundo em Pânico" online?</h3>
- <p>Além de baixar os filmes "Todo Mundo em Pânico" do Google Drive, você também pode assistir aos filmes online, por meio de plataformas de streaming ou sites de filmes. Algumas das opções disponíveis são: Netflix, Amazon Prime Video, Telecine Play, Looke, Megabox, entre outros. Para assistir aos filmes online, você precisa ter uma conta e uma assinatura em alguma dessas plataformas ou sites, ou usar um período de teste grátis. Você também precisa ter uma boa conexão com a internet e um dispositivo compatível, como um computador, um celular, uma smart TV, entre outros.</p>
- <h3>Os filmes "Todo Mundo em Pânico" têm classificação indicativa?</h3>
- <p>Sim, os filmes "Todo Mundo em Pânico" têm classificação indicativa, que varia de acordo com o país e o órgão responsável pela avaliação. No Brasil, os filmes são classificados pelo Ministério da Justiça e têm as seguintes classificações: 14 anos (filmes 1, 2 e 3), 12 anos (filme 4) e 16 anos (filme 5). Nos Estados Unidos, os filmes são classificados pela MPAA (Motion Picture Association of America) e têm as seguintes classificações: R (filmes 1, 2 e 4), PG-13 (filme 3) e PG-13 (filme 5). As classificações indicam que os filmes contêm cenas de violência, sexo, drogas, linguagem imprópria e humor adulto.</p>
- <h3>Existe algum livro ou quadrinho baseado nos filmes "Todo Mundo em Pânico"?</h3>
- <p>Não, não existe nenhum livro ou quadrinho baseado nos filmes "Todo Mundo em Pânico". Os filmes são obras originais criadas pelos roteiristas e diretores da série. No entanto, existem alguns livros e quadrinhos que fazem paródia de filmes de terror, assim como os filmes "Todo Mundo em Pânico". Alguns exemplos são: "Scary Movie: A Novelization", de Michael Teitelbaum, baseado no primeiro filme da série; "The Walking Dead: The Official Cookbook and Survival Guide", de Lauren Wilson, baseado na série de TV e quadrinhos de zumbis; "The Simpsons Treehouse of Horror", uma série de quadrinhos que parodia vários filmes e histórias de terror; entre outros.</p> 197e85843d<br />
-<br />
-<br />

spaces/4Taps/SadTalker/Dockerfile

@@ -1,59 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
-    apt-get upgrade -y && \
-    apt-get install -y --no-install-recommends \
-    git \
-    zip \
-    unzip \
-    git-lfs \
-    wget \
-    curl \
-    # ffmpeg \
-    ffmpeg \
-    x264 \
-    # python build dependencies \
-    build-essential \
-    libssl-dev \
-    zlib1g-dev \
-    libbz2-dev \
-    libreadline-dev \
-    libsqlite3-dev \
-    libncursesw5-dev \
-    xz-utils \
-    tk-dev \
-    libxml2-dev \
-    libxmlsec1-dev \
-    libffi-dev \
-    liblzma-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
-    pyenv global ${PYTHON_VERSION} && \
-    pyenv rehash && \
-    pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-ENV PYTHONPATH=${HOME}/app \
-    PYTHONUNBUFFERED=1 \
-    GRADIO_ALLOW_FLAGGING=never \
-    GRADIO_NUM_PORTS=1 \
-    GRADIO_SERVER_NAME=0.0.0.0 \
-    GRADIO_THEME=huggingface \
-    SYSTEM=spaces
-CMD ["python", "app.py"]

spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers.py

@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle

spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_61968KB.py

@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/A-Celsius/Caption-Generator/README.md

@@ -1,12 +0,0 @@
----
-title: Caption Generator
-emoji: 🦀
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/audiocraft/optim/inverse_sqrt_lr_scheduler.py

@@ -1,38 +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.
-
-import typing as tp
-
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
-
-
-class InverseSquareRootLRScheduler(_LRScheduler):
-    """Inverse square root LR scheduler.
-
-    Args:
-        optimizer (Optimizer): Torch optimizer.
-        warmup_steps (int): Number of warmup steps.
-        warmup_init_lr (tp.Optional[float]): Initial learning rate
-            during warmup phase. When not set, use the provided learning rate.
-    """
-    def __init__(self, optimizer: Optimizer, warmup_steps: int, warmup_init_lr: tp.Optional[float] = 0):
-        self.warmup_steps = warmup_steps
-        self.warmup_init_lr = warmup_init_lr
-        super().__init__(optimizer)
-
-    def _get_sched_lr(self, lr: float, step: int):
-        if step < self.warmup_steps:
-            warmup_init_lr = self.warmup_init_lr or 0
-            lr_step = (lr - warmup_init_lr) / self.warmup_steps
-            lr = warmup_init_lr + step * lr_step
-        else:
-            decay_factor = lr * self.warmup_steps**0.5
-            lr = decay_factor * step**-0.5
-        return lr
-
-    def get_lr(self):
-        return [self._get_sched_lr(base_lr, self._step_count) for base_lr in self.base_lrs]

spaces/AP123/IllusionDiffusion/app.py

@@ -1,281 +0,0 @@
-import torch
-import gradio as gr
-from gradio import processing_utils, utils
-from PIL import Image
-import random
-from diffusers import (
-    DiffusionPipeline,
-    AutoencoderKL,
-    StableDiffusionControlNetPipeline,
-    ControlNetModel,
-    StableDiffusionLatentUpscalePipeline,
-    StableDiffusionImg2ImgPipeline,
-    StableDiffusionControlNetImg2ImgPipeline,
-    DPMSolverMultistepScheduler,  # <-- Added import
-    EulerDiscreteScheduler  # <-- Added import
-)
-import time
-from share_btn import community_icon_html, loading_icon_html, share_js
-import user_history
-from illusion_style import css
-
-BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
-
-# Initialize both pipelines
-vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
-#init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", torch_dtype=torch.float16)
-controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)#, torch_dtype=torch.float16)
-main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    BASE_MODEL,
-    controlnet=controlnet,
-    vae=vae,
-    safety_checker=None,
-    torch_dtype=torch.float16,
-).to("cuda")
-
-#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
-#main_pipe.unet.to(memory_format=torch.channels_last)
-#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
-#model_id = "stabilityai/sd-x2-latent-upscaler"
-image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
-
-#image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True)
-#upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-#upscaler.to("cuda")
-
-
-# Sampler map
-SAMPLER_MAP = {
-    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
-    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
-}
-
-def center_crop_resize(img, output_size=(512, 512)):
-    width, height = img.size
-
-    # Calculate dimensions to crop to the center
-    new_dimension = min(width, height)
-    left = (width - new_dimension)/2
-    top = (height - new_dimension)/2
-    right = (width + new_dimension)/2
-    bottom = (height + new_dimension)/2
-
-    # Crop and resize
-    img = img.crop((left, top, right, bottom))
-    img = img.resize(output_size)
-
-    return img
-
-def common_upscale(samples, width, height, upscale_method, crop=False):
-        if crop == "center":
-            old_width = samples.shape[3]
-            old_height = samples.shape[2]
-            old_aspect = old_width / old_height
-            new_aspect = width / height
-            x = 0
-            y = 0
-            if old_aspect > new_aspect:
-                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
-            elif old_aspect < new_aspect:
-                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
-            s = samples[:,:,y:old_height-y,x:old_width-x]
-        else:
-            s = samples
-
-        return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
-
-def upscale(samples, upscale_method, scale_by):
-        #s = samples.copy()
-        width = round(samples["images"].shape[3] * scale_by)
-        height = round(samples["images"].shape[2] * scale_by)
-        s = common_upscale(samples["images"], width, height, upscale_method, "disabled")
-        return (s)
-
-def check_inputs(prompt: str, control_image: Image.Image):
-    if control_image is None:
-        raise gr.Error("Please select or upload an Input Illusion")
-    if prompt is None or prompt == "":
-        raise gr.Error("Prompt is required")
-
-def convert_to_pil(base64_image):
-    pil_image = processing_utils.decode_base64_to_image(base64_image)
-    return pil_image
-
-def convert_to_base64(pil_image):
-    base64_image = processing_utils.encode_pil_to_base64(pil_image)
-    return base64_image
-
-# Inference function
-def inference(
-    control_image: Image.Image,
-    prompt: str,
-    negative_prompt: str,
-    guidance_scale: float = 8.0,
-    controlnet_conditioning_scale: float = 1,
-    control_guidance_start: float = 1,    
-    control_guidance_end: float = 1,
-    upscaler_strength: float = 0.5,
-    seed: int = -1,
-    sampler = "DPM++ Karras SDE",
-    progress = gr.Progress(track_tqdm=True),
-    profile: gr.OAuthProfile | None = None,
-):
-    start_time = time.time()
-    start_time_struct = time.localtime(start_time)
-    start_time_formatted = time.strftime("%H:%M:%S", start_time_struct)
-    print(f"Inference started at {start_time_formatted}")
-    
-    # Generate the initial image
-    #init_image = init_pipe(prompt).images[0]
-
-    # Rest of your existing code
-    control_image_small = center_crop_resize(control_image)
-    control_image_large = center_crop_resize(control_image, (1024, 1024))
-
-    main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
-    my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
-    generator = torch.Generator(device="cuda").manual_seed(my_seed)
-    
-    out = main_pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=control_image_small,
-        guidance_scale=float(guidance_scale),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-        generator=generator,
-        control_guidance_start=float(control_guidance_start),
-        control_guidance_end=float(control_guidance_end),
-        num_inference_steps=15,
-        output_type="latent"
-    )
-    upscaled_latents = upscale(out, "nearest-exact", 2)
-    out_image = image_pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        control_image=control_image_large,        
-        image=upscaled_latents,
-        guidance_scale=float(guidance_scale),
-        generator=generator,
-        num_inference_steps=20,
-        strength=upscaler_strength,
-        control_guidance_start=float(control_guidance_start),
-        control_guidance_end=float(control_guidance_end),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale)
-    )
-    end_time = time.time()
-    end_time_struct = time.localtime(end_time)
-    end_time_formatted = time.strftime("%H:%M:%S", end_time_struct)
-    print(f"Inference ended at {end_time_formatted}, taking {end_time-start_time}s")
-
-    # Save image + metadata
-    user_history.save_image(
-        label=prompt,
-        image=out_image["images"][0],
-        profile=profile,
-        metadata={
-            "prompt": prompt,
-            "negative_prompt": negative_prompt,
-            "guidance_scale": guidance_scale,
-            "controlnet_conditioning_scale": controlnet_conditioning_scale,
-            "control_guidance_start": control_guidance_start,
-            "control_guidance_end": control_guidance_end,
-            "upscaler_strength": upscaler_strength,
-            "seed": seed,
-            "sampler": sampler,
-        },
-    )
-
-    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
-        
-with gr.Blocks() as app:
-    gr.Markdown(
-        '''
-        <center><h1>Illusion Diffusion HQ 🌀</h1></span>  
-        <span font-size:16px;">Generate stunning high quality illusion artwork with Stable Diffusion</span>  
-        </center>
- 
-        A space by AP [Follow me on Twitter](https://twitter.com/angrypenguinPNG) with big contributions from [multimodalart](https://twitter.com/multimodalart)
-
-        This project works by using [Monster Labs QR Control Net](https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster).
-        Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: [MrUgleh](https://twitter.com/MrUgleh) for discovering the workflow :)
-        '''
-    )
-    state_img_input = gr.State()
-    state_img_output = gr.State()
-    with gr.Row():
-        with gr.Column():
-            control_image = gr.Image(label="Input Illusion", type="pil", elem_id="control_image")
-            controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.8, label="Illusion strength", elem_id="illusion_strength", info="ControlNet conditioning scale")
-            gr.Examples(examples=["checkers.png", "checkers_mid.jpg", "pattern.png", "ultra_checkers.png", "spiral.jpeg", "funky.jpeg" ], inputs=control_image)
-            prompt = gr.Textbox(label="Prompt", elem_id="prompt", info="Type what you want to generate", placeholder="Medieval village scene with busy streets and castle in the distance")
-            negative_prompt = gr.Textbox(label="Negative Prompt", info="Type what you don't want to see", value="low quality", elem_id="negative_prompt")
-            with gr.Accordion(label="Advanced Options", open=False):
-                guidance_scale = gr.Slider(minimum=0.0, maximum=50.0, step=0.25, value=7.5, label="Guidance Scale")
-                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="Euler")
-                control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="Start of ControlNet")
-                control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="End of ControlNet")
-                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="Strength of the upscaler")
-                seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed", info="-1 means random seed")
-                used_seed = gr.Number(label="Last seed used",interactive=False)
-            run_btn = gr.Button("Run")
-        with gr.Column():
-            result_image = gr.Image(label="Illusion Diffusion Output", interactive=False, elem_id="output")
-            with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
-                community_icon = gr.HTML(community_icon_html)
-                loading_icon = gr.HTML(loading_icon_html)
-                share_button = gr.Button("Share to community", elem_id="share-btn")
-
-    prompt.submit(
-        check_inputs,
-        inputs=[prompt, control_image],
-        queue=False
-    ).success(
-        convert_to_pil,
-        inputs=[control_image],
-        outputs=[state_img_input],
-        queue=False,
-        preprocess=False,
-    ).success(
-        inference,
-        inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
-        outputs=[state_img_output, result_image, share_group, used_seed]
-    ).success(
-        convert_to_base64,
-        inputs=[state_img_output],
-        outputs=[result_image],
-        queue=False,
-        postprocess=False
-    )
-    run_btn.click(
-        check_inputs,
-        inputs=[prompt, control_image],
-        queue=False
-    ).success(
-        convert_to_pil,
-        inputs=[control_image],
-        outputs=[state_img_input],
-        queue=False,
-        preprocess=False,
-    ).success(
-        inference,
-        inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
-        outputs=[state_img_output, result_image, share_group, used_seed]
-    ).success(
-        convert_to_base64,
-        inputs=[state_img_output],
-        outputs=[result_image],
-        queue=False,
-        postprocess=False
-    )
-    share_button.click(None, [], [], _js=share_js)
-
-with gr.Blocks(css=css) as app_with_history:
-    with gr.Tab("Demo"):
-        app.render()
-    with gr.Tab("Past generations"):
-        user_history.render()
-
-app_with_history.queue(max_size=20,api_open=False )
-
-if __name__ == "__main__":
-    app_with_history.launch(max_threads=400)

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/search/[id]/+server.ts

@@ -1,7 +0,0 @@
-import { collections } from "$lib/server/database";
-import { hashConv } from "$lib/utils/hashConv.js";
-import { error } from "@sveltejs/kit";
-
-export async function GET({ params, locals }) {
-	return new Response(JSON.stringify(""), { headers: { "Content-Type": "application/json" } });
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/Menu.js

@@ -1,210 +0,0 @@
-import Buttons from '../buttons/Buttons.js';
-import Methods from './methods/Methods.js';
-import CreateBackground from './methods/CreateBackground.js';
-import CreateButtons from './methods/CreateButtons.js';
-import GetViewport from '../../../plugins/utils/system/GetViewport.js';
-import MenuSetInteractive from './methods/MenuSetInteractive.js';
-import ParseEaseConfig from './methods/ParseEaseConfig.js';
-import GetEaseConfig from './methods/GetEaseConfig.js';
-import Expand from './methods/Expand.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class Menu extends Buttons {
-    constructor(scene, config) {
-        if (config === undefined) {
-            config = {};
-        }
-
-        // Orientation
-        if (!config.hasOwnProperty('orientation')) {
-            config.orientation = 1; // y
-        }
-
-        // Parent
-        var rootMenu = config._rootMenu;
-        var parentMenu = config._parentMenu;
-        var parentButton = config._parentButton;
-        // Popup, root menu can be static, sub-menus are always popup.
-        var popUp = GetValue(config, 'popup', true);
-        // Items
-        var items = GetValue(config, 'items', undefined);
-        // Background
-        var createBackgroundCallback = GetValue(config, 'createBackgroundCallback', undefined);
-        var createBackgroundCallbackScope = GetValue(config, 'createBackgroundCallbackScope', undefined);
-        config.background = CreateBackground(scene, items, createBackgroundCallback, createBackgroundCallbackScope);
-        // Buttons
-        var createButtonCallback = GetValue(config, 'createButtonCallback', undefined);
-        var createButtonCallbackScope = GetValue(config, 'createButtonCallbackScope', undefined);
-        config.buttons = CreateButtons(scene, items, createButtonCallback, createButtonCallbackScope);
-
-        super(scene, config);
-        this.type = 'rexMenu';
-
-        this.items = items;
-        this.root = (rootMenu === undefined) ? this : rootMenu;
-        this.isRoot = (this.root === this);
-        this.parentMenu = parentMenu;
-        this.parentButton = parentButton;
-        this.timer = undefined;
-
-        // Root menu
-        if (this.isRoot) {
-            this.isPopUpMode = popUp;
-            // Bounds
-            var bounds = config.bounds;
-            if (bounds === undefined) {
-                bounds = GetViewport(scene);
-            }
-            this.bounds = bounds;
-
-            // Side of submenu
-            this.subMenuSide = [
-                ((this.y < bounds.centerY) ? SUBMENU_DOWN : SUBMENU_UP),
-                ((this.x < bounds.centerX) ? SUBMENU_RIGHT : SUBMENU_LEFT)
-            ];
-            // Overwrite subMenuSide value if given
-            var subMenuSide = GetValue(config, 'subMenuSide', undefined);
-            if (subMenuSide !== undefined) {
-                if (typeof (subMenuSide) === 'string') {
-                    subMenuSide = SubMenuSideMode[subMenuSide];
-                }
-                this.subMenuSide[this.orientation] = subMenuSide;
-            }
-            // ToggleOrientation mode
-            this.toggleOrientation = GetValue(config, 'toggleOrientation', false);
-            // Expand mode
-            this.expandEventName = GetValue(config, 'expandEvent', 'button.click');
-            // Transition
-            this.easeIn = ParseEaseConfig(this, GetValue(config, 'easeIn', 0));
-            this.easeOut = ParseEaseConfig(this, GetValue(config, 'easeOut', 0));
-            this.setTransitInCallback(GetValue(config, 'transitIn'));
-            this.setTransitOutCallback(GetValue(config, 'transitOut'));
-            // Callbacks
-            this.createBackgroundCallback = createBackgroundCallback;
-            this.createBackgroundCallbackScope = createBackgroundCallbackScope;
-            this.createButtonCallback = createButtonCallback;
-            this.createButtonCallbackScope = createButtonCallbackScope;
-            // Children key
-            this.childrenKey = GetValue(config, 'childrenKey', 'children');
-            // Event flag
-            this._isPassedEvent = false;
-
-            // pointerdown-outside-collapse
-            this.pointerDownOutsideCollapsing = GetValue(config, 'pointerDownOutsideCollapsing', true);
-            if (this.pointerDownOutsideCollapsing) {
-                scene.input.on('pointerdown', this.onPointerDownOutside, this);
-            }
-
-        } else {  // Sub-menu
-
-        }
-
-        var originX = 0, originY = 0;
-        if (!this.root.easeIn.sameOrientation) {
-            var easeOrientation = GetEaseConfig(this.root.easeIn, this).orientation;
-            var menuOrientation = (parentMenu) ? parentMenu.orientation : this.orientation;
-            var subMenuSide = this.root.subMenuSide[menuOrientation];
-            if ((easeOrientation === 0) && (subMenuSide === SUBMENU_LEFT)) {
-                originX = 1;
-            }
-            if ((easeOrientation === 1) && (subMenuSide === SUBMENU_UP)) {
-                originY = 1;
-            }
-        }
-
-        if (popUp) {
-            this.setOrigin(originX, originY).layout();
-        }
-
-        // Sub-menu: 
-        // - scale to root's scale value
-        // - align to parent button
-        if (!this.isRoot) {
-            this.setScale(this.root.scaleX, this.root.scaleY);
-            var subMenuSide = this.root.subMenuSide[parentMenu.orientation];
-            switch (subMenuSide) {
-                case SUBMENU_LEFT: //Put submene at left side
-                    this.alignTop(parentButton.top).alignRight(parentButton.left);
-                    break;
-
-                case SUBMENU_RIGHT: //Put submene at right side
-                    this.alignTop(parentButton.top).alignLeft(parentButton.right);
-                    break;
-
-                case SUBMENU_UP: //Put submene at up side
-                    this.alignLeft(parentButton.left).alignBottom(parentButton.top);
-                    break;
-
-                case SUBMENU_DOWN: //Put submene at down side
-                    this.alignLeft(parentButton.left).alignTop(parentButton.bottom);
-                    break;
-            }
-        }
-
-        MenuSetInteractive(this);
-
-        if (popUp) {
-            this.pushIntoBounds(this.root.bounds);
-
-            // Expand this menu
-            Expand.call(this);
-        }
-
-    }
-
-    destroy(fromScene) {
-        //  This Game Object has already been destroyed
-        if (!this.scene || this.ignoreDestroy) {
-            return;
-        }
-
-        if (this.isRoot && this.pointerDownOutsideCollapsing) {
-            this.scene.input.off('pointerdown', this.onPointerDownOutside, this);
-        }
-
-        super.destroy(fromScene);
-        this.removeDelayCall();
-    }
-
-    isInTouching(pointer) {
-        if (super.isInTouching(pointer)) {
-            return true;
-        } else if (this.childrenMap.subMenu) {
-            return this.childrenMap.subMenu.isInTouching(pointer);
-        } else {
-            return false;
-        }
-    }
-
-    onPointerDownOutside(pointer) {
-        if (this.isInTouching(pointer)) {
-            return;
-        }
-
-        if (this.isPopUpMode) {
-            this.collapse();
-        } else {
-            this.collapseSubMenu();
-        }
-    }
-
-
-}
-
-const SUBMENU_LEFT = 2;
-const SUBMENU_RIGHT = 0;
-const SUBMENU_UP = 3;
-const SUBMENU_DOWN = 1;
-const SubMenuSideMode = {
-    up: SUBMENU_UP,
-    down: SUBMENU_DOWN,
-    left: SUBMENU_LEFT,
-    right: SUBMENU_RIGHT
-}
-
-Object.assign(
-    Menu.prototype,
-    Methods
-);
-export default Menu;

spaces/Agusbs98/automatic-ecg-diagnosis/tools/tools.py

@@ -1,124 +0,0 @@
-from libs import *
-import configVars
-import ecg_plot
-def remove_baseline_filter(sample_rate):
-    fc = 0.8  # [Hz], cutoff frequency
-    fst = 0.2  # [Hz], rejection band
-    rp = 0.5  # [dB], ripple in passband
-    rs = 40  # [dB], attenuation in rejection band
-    wn = fc / (sample_rate / 2)
-    wst = fst / (sample_rate / 2)
-
-    filterorder, aux = sgn.ellipord(wn, wst, rp, rs)
-    sos = sgn.iirfilter(filterorder, wn, rp, rs, btype='high', ftype='ellip', output='sos')
-
-    return sos
-
-reduced_leads = ['DI', 'DII', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
-all_leads = ['DI', 'DII', 'DIII', 'AVR', 'AVL', 'AVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
-
-def preprocess_ecg(ecg, sample_rate, leads, scale=1,
-                   use_all_leads=True, remove_baseline=False):
-    # Remove baseline
-    if remove_baseline:
-        sos = remove_baseline_filter(sample_rate)
-        ecg_nobaseline = sgn.sosfiltfilt(sos, ecg, padtype='constant', axis=-1)
-    else:
-        ecg_nobaseline = ecg
-
-    # Rescale
-    ecg_rescaled = scale * ecg_nobaseline
-    
-     # Resample
-    if sample_rate != 500:
-        ecg_resampled = sgn.resample_poly(ecg_rescaled, up=500, down=sample_rate, axis=-1)
-    else:
-        ecg_resampled = ecg_rescaled
-    length = len(ecg_resampled[0])
-    
-    # Add leads if needed
-    target_leads = all_leads if use_all_leads else reduced_leads
-    n_leads_target = len(target_leads)
-    l2p = dict(zip(target_leads, range(n_leads_target)))
-    ecg_targetleads = np.zeros([n_leads_target, length])
-    ecg_targetleads = ecg_rescaled
-    if n_leads_target >= leads and use_all_leads:
-        ecg_targetleads[l2p['DIII'], :] = ecg_targetleads[l2p['DII'], :] - ecg_targetleads[l2p['DI'], :]
-        ecg_targetleads[l2p['AVR'], :] = -(ecg_targetleads[l2p['DI'], :] + ecg_targetleads[l2p['DII'], :]) / 2
-        ecg_targetleads[l2p['AVL'], :] = (ecg_targetleads[l2p['DI'], :] - ecg_targetleads[l2p['DIII'], :]) / 2
-        ecg_targetleads[l2p['AVF'], :] = (ecg_targetleads[l2p['DII'], :] + ecg_targetleads[l2p['DIII'], :]) / 2
-
-    return ecg_targetleads
-
-
-def generateH5(input_file,out_file,new_freq=None,new_len=None,scale=1,sample_rate=None):
-    n = len(input_file)  # Get length
-    try:
-      h5f = h5py.File(f"{configVars.pathCasos}{out_file}", 'r+')
-      h5f.clear()
-    except:
-      h5f = h5py.File(f"{configVars.pathCasos}{out_file}", 'w')
-
-    # Resample
-    if new_freq is not None:
-        ecg_resampled = sgn.resample_poly(input_file, up=new_freq, down=sample_rate, axis=-1)
-    else:
-        ecg_resampled = input_file
-        new_freq = sample_rate
-    n_leads, length = ecg_resampled.shape
-    
-    # Rescale
-    ecg_rescaled = scale * ecg_resampled
-    
-    # Reshape
-    if new_len is None or new_len == length:
-        ecg_reshaped = ecg_rescaled
-    elif new_len > length:
-        ecg_reshaped = np.zeros([n_leads, new_len])
-        pad = (new_len - length) // 2
-        ecg_reshaped[..., pad:length+pad] = ecg_rescaled
-    else:
-        extra = (length - new_len) // 2
-        ecg_reshaped = ecg_rescaled[:, extra:new_len + extra]
-    
-    n_leads, n_samples = ecg_reshaped.shape
-    x = h5f.create_dataset('tracings', (1, n_samples, n_leads), dtype='f8')
-    x[0, :, :] = ecg_reshaped.T
-    h5f.close()
-    
-def LightX3ECG(
-    train_loaders, 
-    config,
-    save_ckp_dir, 
-):
-    model = torch.load(f"{save_ckp_dir}/best.ptl", map_location='cpu')
-    #model = torch.load(f"{save_ckp_dir}/best.ptl", map_location = "cuda")
-    model.to(torch.device('cpu'))
-    with torch.no_grad():
-        model.eval()
-        running_preds = []
-    
-        for ecgs in train_loaders["pred"]:  
-            ecgs = ecgs.cpu()
-            logits = model(ecgs)
-            preds = list(torch.max(logits, 1)[1].detach().cpu().numpy()) if not config["is_multilabel"] else list(torch.sigmoid(logits).detach().cpu().numpy())
-            running_preds.extend(preds)
-    
-    if config["is_multilabel"]:
-        running_preds =  np.array(running_preds)
-        optimal_thresholds = pd.read_csv(f"{configVars.pathThresholds}CPSC-2018/optimal_thresholds_best.csv")
-        preds = optimal_thresholds[optimal_thresholds["Threshold"]<=running_preds[0]]
-        preds = preds['Pred'].values.tolist()
-    else:   
-        enfermedades = ['AFIB','GSVT','SB','SR']
-        running_preds =  np.array(running_preds)
-        #running_preds=np.reshape(running_preds, (len(running_preds),-1))
-        preds = enfermedades[running_preds[0]]
-    return preds
-
-def ecgPlot(source,sample):
-    data = np.load(source)
-    #print(data)
-    xml_leads = ['DI', 'DII', 'DIII', 'AVR', 'AVL', 'AVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
-    ecg_plot.plot_12(data, sample_rate= sample,lead_index=xml_leads, title="Muestra")
-    ecg_plot.save_as_png("ecg")

spaces/Alfasign/diffusers-gallery/Dockerfile

@@ -1,2 +0,0 @@
-FROM nginxinc/nginx-unprivileged:alpine
-COPY . /usr/share/nginx/html

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/backbones/iresnet.py

@@ -1,187 +0,0 @@
-import torch
-from torch import nn
-
-__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=3,
-                     stride=stride,
-                     padding=dilation,
-                     groups=groups,
-                     bias=False,
-                     dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=1,
-                     stride=stride,
-                     bias=False)
-
-
-class IBasicBlock(nn.Module):
-    expansion = 1
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 groups=1, base_width=64, dilation=1):
-        super(IBasicBlock, self).__init__()
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
-        self.conv1 = conv3x3(inplanes, planes)
-        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
-        self.prelu = nn.PReLU(planes)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-        out = self.bn1(x)
-        out = self.conv1(out)
-        out = self.bn2(out)
-        out = self.prelu(out)
-        out = self.conv2(out)
-        out = self.bn3(out)
-        if self.downsample is not None:
-            identity = self.downsample(x)
-        out += identity
-        return out
-
-
-class IResNet(nn.Module):
-    fc_scale = 7 * 7
-    def __init__(self,
-                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
-        super(IResNet, self).__init__()
-        self.fp16 = fp16
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
-        self.prelu = nn.PReLU(self.inplanes)
-        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
-        self.layer2 = self._make_layer(block,
-                                       128,
-                                       layers[1],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block,
-                                       256,
-                                       layers[2],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block,
-                                       512,
-                                       layers[3],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
-        self.dropout = nn.Dropout(p=dropout, inplace=True)
-        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
-        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
-        nn.init.constant_(self.features.weight, 1.0)
-        self.features.weight.requires_grad = False
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.normal_(m.weight, 0, 0.1)
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, IBasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, self.groups,
-                  self.base_width, previous_dilation))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(
-                block(self.inplanes,
-                      planes,
-                      groups=self.groups,
-                      base_width=self.base_width,
-                      dilation=self.dilation))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        with torch.cuda.amp.autocast(self.fp16):
-            x = self.conv1(x)
-            x = self.bn1(x)
-            x = self.prelu(x)
-            x = self.layer1(x)
-            x = self.layer2(x)
-            x = self.layer3(x)
-            x = self.layer4(x)
-            x = self.bn2(x)
-            x = torch.flatten(x, 1)
-            x = self.dropout(x)
-        x = self.fc(x.float() if self.fp16 else x)
-        x = self.features(x)
-        return x
-
-
-def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
-    model = IResNet(block, layers, **kwargs)
-    if pretrained:
-        raise ValueError()
-    return model
-
-
-def iresnet18(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet34(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet50(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet100(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet200(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
-                    progress, **kwargs)
-

spaces/Amrrs/DragGan-Inversion/PTI/README.md

@@ -1,230 +0,0 @@
-# PTI: Pivotal Tuning for Latent-based editing of Real Images     (ACM TOG 2022)
-
-<!-- > Recently, a surge of advanced facial editing techniques have been proposed
-that leverage the generative power of a pre-trained StyleGAN. To successfully
-edit an image this way, one must first project (or invert) the image into
-the pre-trained generator’s domain. As it turns out, however, StyleGAN’s
-latent space induces an inherent tradeoff between distortion and editability,
-i.e. between maintaining the original appearance and convincingly altering
-some of its attributes. Practically, this means it is still challenging to
-apply ID-preserving facial latent-space editing to faces which are out of the
-generator’s domain. In this paper, we present an approach to bridge this
-gap. Our technique slightly alters the generator, so that an out-of-domain
-image is faithfully mapped into an in-domain latent code. The key idea is
-pivotal tuning — a brief training process that preserves the editing quality
-of an in-domain latent region, while changing its portrayed identity and
-appearance. In Pivotal Tuning Inversion (PTI), an initial inverted latent code
-serves as a pivot, around which the generator is fined-tuned. At the same
-time, a regularization term keeps nearby identities intact, to locally contain
-the effect. This surgical training process ends up altering appearance features
-that represent mostly identity, without affecting editing capabilities.
-To supplement this, we further show that pivotal tuning can also adjust the
-generator to accommodate a multitude of faces, while introducing negligible
-distortion on the rest of the domain. We validate our technique through
-inversion and editing metrics, and show preferable scores to state-of-the-art
-methods. We further qualitatively demonstrate our technique by applying
-advanced edits (such as pose, age, or expression) to numerous images of
-well-known and recognizable identities. Finally, we demonstrate resilience
-to harder cases, including heavy make-up, elaborate hairstyles and/or headwear,
-which otherwise could not have been successfully inverted and edited
-by state-of-the-art methods. -->
-
-<a href="https://arxiv.org/abs/2106.05744"><img src="https://img.shields.io/badge/arXiv-2008.00951-b31b1b.svg"></a>
-<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>  
-Inference Notebook: <a href="https://colab.research.google.com/github/danielroich/PTI/blob/main/notebooks/inference_playground.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" height=20></a>  
-
-<p align="center">
-<img src="docs/teaser.jpg"/>  
-<br>
-Pivotal Tuning Inversion (PTI) enables employing off-the-shelf latent based
-semantic editing techniques on real images using StyleGAN. 
-PTI excels in identity preserving edits, portrayed through recognizable figures —
-Serena Williams and Robert Downey Jr. (top), and in handling faces which
-are clearly out-of-domain, e.g., due to heavy makeup (bottom).
-</br>
-</p>
-
-## Description   
-Official Implementation of our PTI paper + code for evaluation metrics. PTI introduces an optimization mechanizem for solving the StyleGAN inversion task.
-Providing near-perfect reconstruction results while maintaining the high editing abilitis of the native StyleGAN latent space W. For more details, see <a href="https://arxiv.org/abs/2106.05744"><img src="https://img.shields.io/badge/arXiv-2008.00951-b31b1b.svg"></a>
-
-## Recent Updates
-**2021.07.01**: Fixed files download phase in the inference notebook. Which might caused the notebook not to run smoothly.
-
-**2021.06.29**: Added support for CPU. In order to run PTI on CPU please change `device` parameter under `configs/global_config.py` to "cpu" instead of "cuda".
-
-**2021.06.25** : Adding mohawk edit using StyleCLIP+PTI in inference notebook.
-	      Updating documentation in inference notebook due to Google Drive rate limit reached.
-	      Currently, Google Drive does not allow to download the pretrined models using Colab automatically. Manual intervention might be needed.
-
-## Getting Started
-### Prerequisites
-- Linux or macOS
-- NVIDIA GPU + CUDA CuDNN (Not mandatory bur recommended)
-- Python 3
-
-### Installation
-- Dependencies:  
-	1. lpips
-	2. wandb
-	3. pytorch
-	4. torchvision
-	5. matplotlib
-	6. dlib
-- All dependencies can be installed using *pip install* and the package name
-
-## Pretrained Models
-Please download the pretrained models from the following links.
-
-### Auxiliary Models
-We provide various auxiliary models needed for PTI inversion task.  
-This includes the StyleGAN generator and pre-trained models used for loss computation.
-| Path | Description
-| :--- | :----------
-|[FFHQ StyleGAN](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl) | StyleGAN2-ada model trained on FFHQ with 1024x1024 output resolution.
-|[Dlib alignment](https://drive.google.com/file/d/1HKmjg6iXsWr4aFPuU0gBXPGR83wqMzq7/view?usp=sharing) | Dlib alignment used for images preproccessing.
-|[FFHQ e4e encoder](https://drive.google.com/file/d/1ALC5CLA89Ouw40TwvxcwebhzWXM5YSCm/view?usp=sharing) | Pretrained e4e encoder. Used for StyleCLIP editing.
-
-Note: The StyleGAN model is used directly from the official [stylegan2-ada-pytorch implementation](https://github.com/NVlabs/stylegan2-ada-pytorch).
-For StyleCLIP pretrained mappers, please see [StyleCLIP's official routes](https://github.com/orpatashnik/StyleCLIP/blob/main/utils.py)
-
-
-By default, we assume that all auxiliary models are downloaded and saved to the directory `pretrained_models`. 
-However, you may use your own paths by changing the necessary values in `configs/path_configs.py`. 
-
-
-## Inversion
-### Preparing your Data
-In order to invert a real image and edit it you should first align and crop it to the correct size. To do so you should perform *One* of the following steps: 
-1. Run `notebooks/align_data.ipynb` and change the "images_path" variable to the raw images path
-2. Run `utils/align_data.py` and change the "images_path" variable to the raw images path
-
-
-### Weights And Biases
-The project supports [Weights And Biases](https://wandb.ai/home) framework for experiment tracking. For the inversion task it enables visualization of the losses progression and the generator intermediate results during the initial inversion and the *Pivotal Tuning*(PT) procedure.
-
-The log frequency can be adjusted using the parameters defined at `configs/global_config.py` under the "Logs" subsection.
-
-There is no no need to have an account. However, in order to use the features provided by Weights and Biases you first have to register on their site.
-
-
-### Running PTI
-The main training script is `scripts/run_pti.py`. The script receives aligned and cropped images from paths configured in the "Input info" subscetion in
- `configs/paths_config.py`. 
-Results are saved to directories found at "Dirs for output files" under `configs/paths_config.py`. This includes inversion latent codes and tuned generators. 
-The hyperparametrs for the inversion task can be found at  `configs/hyperparameters.py`. They are intilized to the default values used in the paper. 
-
-## Editing
-By default, we assume that all auxiliary edit directions are downloaded and saved to the directory `editings`. 
-However, you may use your own paths by changing the necessary values in `configs/path_configs.py` under "Edit directions" subsection.
-
-Example of editing code can be found at `scripts/latent_editor_wrapper.py`
-
-## Inference Notebooks
-To help visualize the results of PTI we provide a Jupyter notebook found in `notebooks/inference_playground.ipynb`.   
-The notebook will download the pretrained models and run inference on a sample image found online or 
-on images of your choosing. It is recommended to run this in [Google Colab](https://colab.research.google.com/github/danielroich/PTI/blob/main/notebooks/inference_playground.ipynb).
-
-The notebook demonstrates how to:
-- Invert an image using PTI
-- Visualise the inversion and use the PTI output
-- Edit the image after PTI using InterfaceGAN and StyleCLIP
-- Compare to other inversion methods
-
-## Evaluation
-Currently the repository supports qualitative evaluation for reconstruction of: PTI, SG2 (*W Space*), e4e, SG2Plus (*W+ Space*). 
-As well as editing using InterfaceGAN and GANSpace for the same inversion methods.
-To run the evaluation please see `evaluation/qualitative_edit_comparison.py`. Examples of the evaluation scripts are:
-
-<p align="center">
-<img src="docs/model_rec.jpg"/>  
-<br>
-Reconsturction comparison between different methods. The images order is: Original image, W+ inversion, e4e inversion, W inversion, PTI inversion
-</br>  
-</p>
-
-<p align="center">
-<img src="docs/stern_rotation.jpg"/>  
-<br>
-InterfaceGAN pose edit comparison between different methods. The images order is: Original, W+, e4e, W, PTI
-</br>  
-</p>
-
-<p align="center">
-<img src="docs/tyron_original.jpg" width="220" height="220"/>  
-<img src="docs/tyron_edit.jpg" width="220" height="220"/>
-<br>
-Image per edit or several edits without comparison
-</br>  
-</p>
-
-###  Coming Soon - Quantitative evaluation and StyleCLIP qualitative evaluation
-
-## Repository structure
-| Path | Description <img width=200>
-| :--- | :---
-| &boxvr;&nbsp; configs | Folder containing configs defining Hyperparameters, paths and logging
-| &boxvr;&nbsp; criteria | Folder containing various loss and regularization criterias for the optimization
-| &boxvr;&nbsp; dnnlib | Folder containing internal utils for StyleGAN2-ada
-| &boxvr;&nbsp; docs | Folder containing the latent space edit directions
-| &boxvr;&nbsp; editings | Folder containing images displayed in the README
-| &boxvr;&nbsp; environment | Folder containing Anaconda environment used in our experiments
-| &boxvr;&nbsp; licenses | Folder containing licenses of the open source projects used in this repository
-| &boxvr;&nbsp; models | Folder containing models used in different editing techniques and first phase inversion
-| &boxvr;&nbsp; notebooks | Folder with jupyter notebooks to demonstrate the usage of PTI end-to-end
-| &boxvr;&nbsp; scripts | Folder with running scripts for inversion, editing and metric computations
-| &boxvr;&nbsp; torch_utils | Folder containing internal utils for StyleGAN2-ada
-| &boxvr;&nbsp; training | Folder containing the core training logic of PTI
-| &boxvr;&nbsp; utils | Folder with various utility functions
-
-
-## Credits
-**StyleGAN2-ada model and implementation:**  
-https://github.com/NVlabs/stylegan2-ada-pytorch
-Copyright © 2021, NVIDIA Corporation.  
-Nvidia Source Code License https://nvlabs.github.io/stylegan2-ada-pytorch/license.html
-
-**LPIPS model and implementation:**  
-https://github.com/richzhang/PerceptualSimilarity  
-Copyright (c) 2020, Sou Uchida  
-License (BSD 2-Clause) https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE
-
-**e4e model and implementation:**   
-https://github.com/omertov/encoder4editing
-Copyright (c) 2021 omertov  
-License (MIT) https://github.com/omertov/encoder4editing/blob/main/LICENSE
-
-**StyleCLIP model and implementation:**   
-https://github.com/orpatashnik/StyleCLIP
-Copyright (c) 2021 orpatashnik  
-License (MIT) https://github.com/orpatashnik/StyleCLIP/blob/main/LICENSE
-
-**InterfaceGAN implementation:**   
-https://github.com/genforce/interfacegan
-Copyright (c) 2020 genforce  
-License (MIT) https://github.com/genforce/interfacegan/blob/master/LICENSE
-
-**GANSpace implementation:**   
-https://github.com/harskish/ganspace
-Copyright (c) 2020 harkish  
-License (Apache License 2.0) https://github.com/harskish/ganspace/blob/master/LICENSE
-
-
-## Acknowledgments
-This repository structure is based on [encoder4editing](https://github.com/omertov/encoder4editing) and [ReStyle](https://github.com/yuval-alaluf/restyle-encoder) repositories
-
-## Contact
-For any inquiry please contact us at our email addresses: [email protected] or [email protected]
-
-
-## Citation
-If you use this code for your research, please cite:
-```
-@article{roich2021pivotal,
-  title={Pivotal Tuning for Latent-based Editing of Real Images},
-  author={Roich, Daniel and Mokady, Ron and Bermano, Amit H and Cohen-Or, Daniel},
-  publisher = {Association for Computing Machinery},
-  journal={ACM Trans. Graph.},
-  year={2021}
-}
-```

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py

@@ -1,418 +0,0 @@
-# Copyright (c) 2020, 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 TensorFlow ops for efficient resampling of 2D images."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-
-def _get_plugin():
-    return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-#----------------------------------------------------------------------------
-
-def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'):
-    r"""Pad, upsample, FIR filter, and downsample a batch of 2D images.
-
-    Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]`
-    and performs the following operations for each image, batched across
-    `majorDim` and `minorDim`:
-
-    1. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`).
-
-    2. Pad the image with zeros by the specified number of pixels on each side
-       (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value
-       corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the
-       image so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by throwing away pixels (`downx`, `downy`).
-
-    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 TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:      Input tensor of the shape `[majorDim, inH, inW, minorDim]`.
-        k:      2D FIR filter of the shape `[firH, firW]`.
-        upx:    Integer upsampling factor along the X-axis (default: 1).
-        upy:    Integer upsampling factor along the Y-axis (default: 1).
-        downx:  Integer downsampling factor along the X-axis (default: 1).
-        downy:  Integer downsampling factor along the Y-axis (default: 1).
-        padx0:  Number of pixels to pad on the left side (default: 0).
-        padx1:  Number of pixels to pad on the right side (default: 0).
-        pady0:  Number of pixels to pad on the top side (default: 0).
-        pady1:  Number of pixels to pad on the bottom side (default: 0).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`.
-    """
-
-    impl_dict = {
-        'ref':  _upfirdn_2d_ref,
-        'cuda': _upfirdn_2d_cuda,
-    }
-    return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
-
-#----------------------------------------------------------------------------
-
-def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    assert x.shape.rank == 4
-    inH = x.shape[1].value
-    inW = x.shape[2].value
-    minorDim = _shape(x, 3)
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    # Upsample (insert zeros).
-    x = tf.reshape(x, [-1, inH, 1, inW, 1, minorDim])
-    x = tf.pad(x, [[0, 0], [0, 0], [0, upy - 1], [0, 0], [0, upx - 1], [0, 0]])
-    x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim])
-
-    # Pad (crop if negative).
-    x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], [max(padx0, 0), max(padx1, 0)], [0, 0]])
-    x = x[:, max(-pady0, 0) : x.shape[1].value - max(-pady1, 0), max(-padx0, 0) : x.shape[2].value - max(-padx1, 0), :]
-
-    # Convolve with filter.
-    x = tf.transpose(x, [0, 3, 1, 2])
-    x = tf.reshape(x, [-1, 1, inH * upy + pady0 + pady1, inW * upx + padx0 + padx1])
-    w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype)
-    x = tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='VALID', data_format='NCHW')
-    x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1])
-    x = tf.transpose(x, [0, 2, 3, 1])
-
-    # Downsample (throw away pixels).
-    return x[:, ::downy, ::downx, :]
-
-#----------------------------------------------------------------------------
-
-def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Fast CUDA implementation of `upfirdn_2d()` using custom ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    majorDim, inH, inW, minorDim = x.shape.as_list()
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    outW = (inW * upx + padx0 + padx1 - kernelW) // downx + 1
-    outH = (inH * upy + pady0 + pady1 - kernelH) // downy + 1
-    assert outW >= 1 and outH >= 1
-
-    cuda_op = _get_plugin().up_fir_dn2d
-    kc = tf.constant(k, dtype=x.dtype)
-    gkc = tf.constant(k[::-1, ::-1], dtype=x.dtype)
-    gpadx0 = kernelW - padx0 - 1
-    gpady0 = kernelH - pady0 - 1
-    gpadx1 = inW * upx - outW * downx + padx0 - upx + 1
-    gpady1 = inH * upy - outH * downy + pady0 - upy + 1
-
-    @tf.custom_gradient
-    def func(x):
-        y = cuda_op(x=x, k=kc, upx=int(upx), upy=int(upy), downx=int(downx), downy=int(downy), padx0=int(padx0), padx1=int(padx1), pady0=int(pady0), pady1=int(pady1))
-        y.set_shape([majorDim, outH, outW, minorDim])
-        @tf.custom_gradient
-        def grad(dy):
-            dx = cuda_op(x=dy, k=gkc, upx=int(downx), upy=int(downy), downx=int(upx), downy=int(upy), padx0=int(gpadx0), padx1=int(gpadx1), pady0=int(gpady0), pady1=int(gpady1))
-            dx.set_shape([majorDim, inH, inW, minorDim])
-            return dx, func
-        return y, grad
-    return func(x)
-
-#----------------------------------------------------------------------------
-
-def filter_2d(x, k, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Filter a batch of 2D images with the given FIR filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and filters each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-
-    assert isinstance(padding, int)
-    k = _FilterKernel(k=k, gain=gain)
-    assert k.w == k.h
-    pad0 = k.w // 2 + padding
-    pad1 = (k.w - 1) // 2 + padding
-    return _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample_2d(x, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Upsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and upsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the upsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-    k = _FilterKernel(k if k is not None else [1] * factor, gain * (factor ** 2))
-    assert k.w == k.h
-    pad0 = (k.w + factor - 1) // 2 + padding
-    pad1 = (k.w - factor) // 2 + padding
-    return _simple_upfirdn_2d(x, k, up=factor, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def downsample_2d(x, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Downsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and downsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the downsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-    k = _FilterKernel(k if k is not None else [1] * factor, gain)
-    assert k.w == k.h
-    pad0 = (k.w - factor + 1) // 2 + padding * factor
-    pad1 = (k.w - factor) // 2 + padding * factor
-    return _simple_upfirdn_2d(x, k, down=factor, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample_conv_2d(x, w, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-
-    # Check weight shape.
-    w = tf.convert_to_tensor(w)
-    ch, cw, _inC, _outC = w.shape.as_list()
-    inC = _shape(w, 2)
-    outC = _shape(w, 3)
-    assert cw == ch
-
-    # Fast path for 1x1 convolution.
-    if cw == 1 and ch == 1:
-        x = tf.nn.conv2d(x, w, data_format=data_format, strides=[1,1,1,1], padding='VALID')
-        x = upsample_2d(x, k, factor=factor, gain=gain, padding=padding, data_format=data_format, impl=impl)
-        return x
-
-    # Setup filter kernel.
-    k = _FilterKernel(k if k is not None else [1] * factor, gain * (factor ** 2))
-    assert k.w == k.h
-
-    # Determine data dimensions.
-    if data_format == 'NCHW':
-        stride = [1, 1, factor, factor]
-        output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + ch, (_shape(x, 3) - 1) * factor + cw]
-        num_groups = _shape(x, 1) // inC
-    else:
-        stride = [1, factor, factor, 1]
-        output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + ch, (_shape(x, 2) - 1) * factor + cw, outC]
-        num_groups = _shape(x, 3) // inC
-
-    # Transpose weights.
-    w = tf.reshape(w, [ch, cw, inC, num_groups, -1])
-    w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
-    w = tf.reshape(w, [ch, cw, -1, num_groups * inC])
-
-    # Execute.
-    x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format)
-    pad0 = (k.w + factor - cw) // 2 + padding
-    pad1 = (k.w - factor - cw + 3) // 2 + padding
-    return _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def conv_downsample_2d(x, w, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-
-    # Check weight shape.
-    w = tf.convert_to_tensor(w)
-    ch, cw, _inC, _outC = w.shape.as_list()
-    assert cw == ch
-
-    # Fast path for 1x1 convolution.
-    if cw == 1 and ch == 1:
-        x = downsample_2d(x, k, factor=factor, gain=gain, padding=padding, data_format=data_format, impl=impl)
-        x = tf.nn.conv2d(x, w, data_format=data_format, strides=[1,1,1,1], padding='VALID')
-        return x
-
-    # Setup filter kernel.
-    k = _FilterKernel(k if k is not None else [1] * factor, gain)
-    assert k.w == k.h
-
-    # Determine stride.
-    if data_format == 'NCHW':
-        s = [1, 1, factor, factor]
-    else:
-        s = [1, factor, factor, 1]
-
-    # Execute.
-    pad0 = (k.w - factor + cw) // 2 + padding * factor
-    pad1 = (k.w - factor + cw - 1) // 2 + padding * factor
-    x = _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-    return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
-
-#----------------------------------------------------------------------------
-# Internal helpers.
-
-class _FilterKernel:
-    def __init__(self, k, gain=1):
-        k = np.asarray(k, dtype=np.float32)
-        k /= np.sum(k)
-
-        # Separable.
-        if k.ndim == 1 and k.size >= 8:
-            self.w = k.size
-            self.h = k.size
-            self.kx = k[np.newaxis, :]
-            self.ky = k[:, np.newaxis] * gain
-            self.kxy = None
-
-        # Non-separable.
-        else:
-            if k.ndim == 1:
-                k = np.outer(k, k)
-            assert k.ndim == 2
-            self.w = k.shape[1]
-            self.h = k.shape[0]
-            self.kx = None
-            self.ky = None
-            self.kxy = k * gain
-
-def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'):
-    assert isinstance(k, _FilterKernel)
-    assert data_format in ['NCHW', 'NHWC']
-    assert x.shape.rank == 4
-    y = x
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1])
-    if k.kx is not None:
-        y = upfirdn_2d(y, k.kx, upx=up, downx=down, padx0=pad0, padx1=pad1, impl=impl)
-    if k.ky is not None:
-        y = upfirdn_2d(y, k.ky, upy=up, downy=down, pady0=pad0, pady1=pad1, impl=impl)
-    if k.kxy is not None:
-        y = upfirdn_2d(y, k.kxy, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl)
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)])
-    return y
-
-def _shape(tf_expr, dim_idx):
-    if tf_expr.shape.rank is not None:
-        dim = tf_expr.shape[dim_idx].value
-        if dim is not None:
-            return dim
-    return tf.shape(tf_expr)[dim_idx]
-
-#----------------------------------------------------------------------------

spaces/Amrrs/DragGan-Inversion/torch_utils/ops/filtered_lrelu.py

@@ -1,307 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import numpy as np
-import torch
-import warnings
-
-from .. import custom_ops
-from .. import misc
-from . import upfirdn2d
-from . import bias_act
-
-# ----------------------------------------------------------------------------
-
-_plugin = None
-
-
-def _init():
-    global _plugin
-    if _plugin is None:
-        _plugin = custom_ops.get_plugin(
-            module_name='filtered_lrelu_plugin',
-            sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu',
-                     'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'],
-            headers=['filtered_lrelu.h', 'filtered_lrelu.cu'],
-            source_dir=os.path.dirname(__file__),
-            extra_cuda_cflags=['--use_fast_math',
-                               '--allow-unsupported-compiler'],
-        )
-    return True
-
-
-def _get_filter_size(f):
-    if f is None:
-        return 1, 1
-    assert isinstance(f, torch.Tensor)
-    assert 1 <= f.ndim <= 2
-    return f.shape[-1], f.shape[0]  # width, height
-
-
-def _parse_padding(padding):
-    if isinstance(padding, int):
-        padding = [padding, padding]
-    assert isinstance(padding, (list, tuple))
-    assert all(isinstance(x, (int, np.integer)) for x in padding)
-    padding = [int(x) for x in padding]
-    if len(padding) == 2:
-        px, py = padding
-        padding = [px, px, py, py]
-    px0, px1, py0, py1 = padding
-    return px0, px1, py0, py1
-
-# ----------------------------------------------------------------------------
-
-
-def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'):
-    r"""Filtered leaky ReLU for a batch of 2D images.
-
-    Performs the following sequence of operations for each channel:
-
-    1. Add channel-specific bias if provided (`b`).
-
-    2. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
-    3. Pad the image with the specified number of zeros on each side (`padding`).
-       Negative padding corresponds to cropping the image.
-
-    4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it
-       so that the footprint of all output pixels lies within the input image.
-
-    5. Multiply each value by the provided gain factor (`gain`).
-
-    6. Apply leaky ReLU activation function to each value.
-
-    7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided.
-
-    8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking
-       it so that the footprint of all output pixels lies within the input image.
-
-    9. Downsample the image by keeping every Nth pixel (`down`).
-
-    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/float16/float64 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        fu:          Float32 upsampling FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        fd:          Float32 downsampling FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        b:           Bias vector, or `None` to disable. Must be a 1D tensor of the same type
-                     as `x`. The length of vector must must match the channel dimension of `x`.
-        up:          Integer upsampling factor (default: 1).
-        down:        Integer downsampling factor. (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).
-        gain:        Overall scaling factor for signal magnitude (default: sqrt(2)).
-        slope:       Slope on the negative side of leaky ReLU (default: 0.2).
-        clamp:       Maximum magnitude for leaky ReLU output (default: None).
-        flip_filter: False = convolution, True = correlation (default: False).
-        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 _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0)
-    return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter)
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
-    """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using
-    existing `upfirdn2n()` and `bias_act()` ops.
-    """
-    assert isinstance(x, torch.Tensor) and x.ndim == 4
-    fu_w, fu_h = _get_filter_size(fu)
-    fd_w, fd_h = _get_filter_size(fd)
-    if b is not None:
-        assert isinstance(b, torch.Tensor) and b.dtype == x.dtype
-        misc.assert_shape(b, [x.shape[1]])
-    assert isinstance(up, int) and up >= 1
-    assert isinstance(down, int) and down >= 1
-    px0, px1, py0, py1 = _parse_padding(padding)
-    assert gain == float(gain) and gain > 0
-    assert slope == float(slope) and slope >= 0
-    assert clamp is None or (clamp == float(clamp) and clamp >= 0)
-
-    # Calculate output size.
-    batch_size, channels, in_h, in_w = x.shape
-    in_dtype = x.dtype
-    out_w = (in_w * up + (px0 + px1) - (fu_w - 1) -
-             (fd_w - 1) + (down - 1)) // down
-    out_h = (in_h * up + (py0 + py1) - (fu_h - 1) -
-             (fd_h - 1) + (down - 1)) // down
-
-    # Compute using existing ops.
-    x = bias_act.bias_act(x=x, b=b)  # Apply bias.
-    # Upsample.
-    x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[
-                            px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter)
-    # Bias, leaky ReLU, clamp.
-    x = bias_act.bias_act(x=x, act='lrelu', alpha=slope,
-                          gain=gain, clamp=clamp)
-    # Downsample.
-    x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter)
-
-    # Check output shape & dtype.
-    misc.assert_shape(x, [batch_size, channels, out_h, out_w])
-    assert x.dtype == in_dtype
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-_filtered_lrelu_cuda_cache = dict()
-
-
-def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
-    """Fast CUDA implementation of `filtered_lrelu()` using custom ops.
-    """
-    assert isinstance(up, int) and up >= 1
-    assert isinstance(down, int) and down >= 1
-    px0, px1, py0, py1 = _parse_padding(padding)
-    assert gain == float(gain) and gain > 0
-    gain = float(gain)
-    assert slope == float(slope) and slope >= 0
-    slope = float(slope)
-    assert clamp is None or (clamp == float(clamp) and clamp >= 0)
-    clamp = float(clamp if clamp is not None else 'inf')
-
-    # Lookup from cache.
-    key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter)
-    if key in _filtered_lrelu_cuda_cache:
-        return _filtered_lrelu_cuda_cache[key]
-
-    # Forward op.
-    class FilteredLReluCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, fu, fd, b, si, sx, sy):  # pylint: disable=arguments-differ
-            assert isinstance(x, torch.Tensor) and x.ndim == 4
-
-            # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable).
-            if fu is None:
-                fu = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            if fd is None:
-                fd = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            assert 1 <= fu.ndim <= 2
-            assert 1 <= fd.ndim <= 2
-
-            # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1.
-            if up == 1 and fu.ndim == 1 and fu.shape[0] == 1:
-                fu = fu.square()[None]
-            if down == 1 and fd.ndim == 1 and fd.shape[0] == 1:
-                fd = fd.square()[None]
-
-            # Missing sign input tensor.
-            if si is None:
-                si = torch.empty([0])
-
-            # Missing bias tensor.
-            if b is None:
-                b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device)
-
-            # Construct internal sign tensor only if gradients are needed.
-            write_signs = (si.numel() == 0) and (
-                x.requires_grad or b.requires_grad)
-
-            # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout.
-            strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1]
-            if any(a < b for a, b in zip(strides[:-1], strides[1:])):
-                warnings.warn(
-                    "low-performance memory layout detected in filtered_lrelu input", RuntimeWarning)
-
-            # Call C++/Cuda plugin if datatype is supported.
-            if x.dtype in [torch.float16, torch.float32]:
-                if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device):
-                    warnings.warn(
-                        "filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning)
-                y, so, return_code = _plugin.filtered_lrelu(
-                    x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs)
-            else:
-                return_code = -1
-
-            # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because
-            # only the bit-packed sign tensor is retained for gradient computation.
-            if return_code < 0:
-                warnings.warn(
-                    "filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning)
-
-                y = x.add(b.unsqueeze(-1).unsqueeze(-1))  # Add bias.
-                # Upsample.
-                y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[
-                                        px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter)
-                # Activation function and sign handling. Modifies y in-place.
-                so = _plugin.filtered_lrelu_act_(
-                    y, si, sx, sy, gain, slope, clamp, write_signs)
-                # Downsample.
-                y = upfirdn2d.upfirdn2d(
-                    x=y, f=fd, down=down, flip_filter=flip_filter)
-
-            # Prepare for gradient computation.
-            ctx.save_for_backward(fu, fd, (si if si.numel() else so))
-            ctx.x_shape = x.shape
-            ctx.y_shape = y.shape
-            ctx.s_ofs = sx, sy
-            return y
-
-        @staticmethod
-        def backward(ctx, dy):  # pylint: disable=arguments-differ
-            fu, fd, si = ctx.saved_tensors
-            _, _, xh, xw = ctx.x_shape
-            _, _, yh, yw = ctx.y_shape
-            sx, sy = ctx.s_ofs
-            dx = None  # 0
-            dfu = None
-            assert not ctx.needs_input_grad[1]
-            dfd = None
-            assert not ctx.needs_input_grad[2]
-            db = None  # 3
-            dsi = None
-            assert not ctx.needs_input_grad[4]
-            dsx = None
-            assert not ctx.needs_input_grad[5]
-            dsy = None
-            assert not ctx.needs_input_grad[6]
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]:
-                pp = [
-                    (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0,
-                    xw * up - yw * down + px0 - (up - 1),
-                    (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0,
-                    xh * up - yh * down + py0 - (up - 1),
-                ]
-                gg = gain * (up ** 2) / (down ** 2)
-                ff = (not flip_filter)
-                sx = sx - (fu.shape[-1] - 1) + px0
-                sy = sy - (fu.shape[0] - 1) + py0
-                dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope,
-                                          clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy)
-
-            if ctx.needs_input_grad[3]:
-                db = dx.sum([0, 2, 3])
-
-            return dx, dfu, dfd, db, dsi, dsx, dsy
-
-    # Add to cache.
-    _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda
-    return FilteredLReluCuda
-
-# ----------------------------------------------------------------------------

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/stylegan_ops/fused_act.py

@@ -1,85 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-module_path = os.path.dirname(__file__)
-fused = load(
-    'fused',
-    sources=[
-        os.path.join(module_path, 'fused_bias_act.cpp'),
-        os.path.join(module_path, 'fused_bias_act_kernel.cu'),
-    ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused.fused_bias_act(
-            grad_output, empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused.fused_bias_act(
-            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
-        )
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.negative_slope, ctx.scale
-        )
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
-    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)

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

@@ -1,219 +0,0 @@
-import cv2
-import numpy as np
-import math
-import time
-from scipy.ndimage.filters import gaussian_filter
-import matplotlib.pyplot as plt
-import matplotlib
-import torch
-from torchvision import transforms
-
-from . import util
-from .model import bodypose_model
-
-class Body(object):
-    def __init__(self, model_path):
-        self.model = bodypose_model()
-        if torch.cuda.is_available():
-            self.model = self.model.cuda()
-            print('cuda')
-        model_dict = util.transfer(self.model, torch.load(model_path))
-        self.model.load_state_dict(model_dict)
-        self.model.eval()
-
-    def __call__(self, oriImg):
-        # scale_search = [0.5, 1.0, 1.5, 2.0]
-        scale_search = [0.5]
-        boxsize = 368
-        stride = 8
-        padValue = 128
-        thre1 = 0.1
-        thre2 = 0.05
-        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
-        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
-        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
-
-        for m in range(len(multiplier)):
-            scale = multiplier[m]
-            imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
-            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
-            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
-            im = np.ascontiguousarray(im)
-
-            data = torch.from_numpy(im).float()
-            if torch.cuda.is_available():
-                data = data.cuda()
-            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
-            with torch.no_grad():
-                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
-            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
-            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
-
-            # extract outputs, resize, and remove padding
-            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
-            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
-            heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
-            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
-            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
-            paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
-            paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
-            paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
-            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
-            paf_avg += + paf / len(multiplier)
-
-        all_peaks = []
-        peak_counter = 0
-
-        for part in range(18):
-            map_ori = heatmap_avg[:, :, part]
-            one_heatmap = gaussian_filter(map_ori, sigma=3)
-
-            map_left = np.zeros(one_heatmap.shape)
-            map_left[1:, :] = one_heatmap[:-1, :]
-            map_right = np.zeros(one_heatmap.shape)
-            map_right[:-1, :] = one_heatmap[1:, :]
-            map_up = np.zeros(one_heatmap.shape)
-            map_up[:, 1:] = one_heatmap[:, :-1]
-            map_down = np.zeros(one_heatmap.shape)
-            map_down[:, :-1] = one_heatmap[:, 1:]
-
-            peaks_binary = np.logical_and.reduce(
-                (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
-            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]))  # note reverse
-            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
-            peak_id = range(peak_counter, peak_counter + len(peaks))
-            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
-
-            all_peaks.append(peaks_with_score_and_id)
-            peak_counter += len(peaks)
-
-        # find connection in the specified sequence, center 29 is in the position 15
-        limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
-                   [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
-                   [1, 16], [16, 18], [3, 17], [6, 18]]
-        # the middle joints heatmap correpondence
-        mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
-                  [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
-                  [55, 56], [37, 38], [45, 46]]
-
-        connection_all = []
-        special_k = []
-        mid_num = 10
-
-        for k in range(len(mapIdx)):
-            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
-            candA = all_peaks[limbSeq[k][0] - 1]
-            candB = all_peaks[limbSeq[k][1] - 1]
-            nA = len(candA)
-            nB = len(candB)
-            indexA, indexB = limbSeq[k]
-            if (nA != 0 and nB != 0):
-                connection_candidate = []
-                for i in range(nA):
-                    for j in range(nB):
-                        vec = np.subtract(candB[j][:2], candA[i][:2])
-                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
-                        norm = max(0.001, norm)
-                        vec = np.divide(vec, norm)
-
-                        startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
-                                            np.linspace(candA[i][1], candB[j][1], num=mid_num)))
-
-                        vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
-                                          for I in range(len(startend))])
-                        vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
-                                          for I in range(len(startend))])
-
-                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
-                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
-                            0.5 * oriImg.shape[0] / norm - 1, 0)
-                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
-                        criterion2 = score_with_dist_prior > 0
-                        if criterion1 and criterion2:
-                            connection_candidate.append(
-                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
-
-                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
-                connection = np.zeros((0, 5))
-                for c in range(len(connection_candidate)):
-                    i, j, s = connection_candidate[c][0:3]
-                    if (i not in connection[:, 3] and j not in connection[:, 4]):
-                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
-                        if (len(connection) >= min(nA, nB)):
-                            break
-
-                connection_all.append(connection)
-            else:
-                special_k.append(k)
-                connection_all.append([])
-
-        # last number in each row is the total parts number of that person
-        # the second last number in each row is the score of the overall configuration
-        subset = -1 * np.ones((0, 20))
-        candidate = np.array([item for sublist in all_peaks for item in sublist])
-
-        for k in range(len(mapIdx)):
-            if k not in special_k:
-                partAs = connection_all[k][:, 0]
-                partBs = connection_all[k][:, 1]
-                indexA, indexB = np.array(limbSeq[k]) - 1
-
-                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
-                    found = 0
-                    subset_idx = [-1, -1]
-                    for j in range(len(subset)):  # 1:size(subset,1):
-                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
-                            subset_idx[found] = j
-                            found += 1
-
-                    if found == 1:
-                        j = subset_idx[0]
-                        if subset[j][indexB] != partBs[i]:
-                            subset[j][indexB] = partBs[i]
-                            subset[j][-1] += 1
-                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-                    elif found == 2:  # if found 2 and disjoint, merge them
-                        j1, j2 = subset_idx
-                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
-                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
-                            subset[j1][:-2] += (subset[j2][:-2] + 1)
-                            subset[j1][-2:] += subset[j2][-2:]
-                            subset[j1][-2] += connection_all[k][i][2]
-                            subset = np.delete(subset, j2, 0)
-                        else:  # as like found == 1
-                            subset[j1][indexB] = partBs[i]
-                            subset[j1][-1] += 1
-                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-
-                    # if find no partA in the subset, create a new subset
-                    elif not found and k < 17:
-                        row = -1 * np.ones(20)
-                        row[indexA] = partAs[i]
-                        row[indexB] = partBs[i]
-                        row[-1] = 2
-                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
-                        subset = np.vstack([subset, row])
-        # delete some rows of subset which has few parts occur
-        deleteIdx = []
-        for i in range(len(subset)):
-            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
-                deleteIdx.append(i)
-        subset = np.delete(subset, deleteIdx, axis=0)
-
-        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
-        # candidate: x, y, score, id
-        return candidate, subset
-
-if __name__ == "__main__":
-    body_estimation = Body('../model/body_pose_model.pth')
-
-    test_image = '../images/ski.jpg'
-    oriImg = cv2.imread(test_image)  # B,G,R order
-    candidate, subset = body_estimation(oriImg)
-    canvas = util.draw_bodypose(oriImg, candidate, subset)
-    plt.imshow(canvas[:, :, [2, 1, 0]])
-    plt.show()

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/iou3d.py

@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
-    'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
-    """Calculate boxes IoU in the Bird's Eye View.
-
-    Args:
-        boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
-        boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
-    Returns:
-        ans_iou (torch.Tensor): IoU result with shape (M, N).
-    """
-    ans_iou = boxes_a.new_zeros(
-        torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
-    ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
-                                           boxes_b.contiguous(), ans_iou)
-
-    return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
-    """NMS function GPU implementation (for BEV boxes). The overlap of two
-    boxes for IoU calculation is defined as the exact overlapping area of the
-    two boxes. In this function, one can also set ``pre_max_size`` and
-    ``post_max_size``.
-
-    Args:
-        boxes (torch.Tensor): Input boxes with the shape of [N, 5]
-            ([x1, y1, x2, y2, ry]).
-        scores (torch.Tensor): Scores of boxes with the shape of [N].
-        thresh (float): Overlap threshold of NMS.
-        pre_max_size (int, optional): Max size of boxes before NMS.
-            Default: None.
-        post_max_size (int, optional): Max size of boxes after NMS.
-            Default: None.
-
-    Returns:
-        torch.Tensor: Indexes after NMS.
-    """
-    assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
-    order = scores.sort(0, descending=True)[1]
-
-    if pre_max_size is not None:
-        order = order[:pre_max_size]
-    boxes = boxes[order].contiguous()
-
-    keep = torch.zeros(boxes.size(0), dtype=torch.long)
-    num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
-    keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
-    if post_max_size is not None:
-        keep = keep[:post_max_size]
-    return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
-    """Normal NMS function GPU implementation (for BEV boxes). The overlap of
-    two boxes for IoU calculation is defined as the exact overlapping area of
-    the two boxes WITH their yaw angle set to 0.
-
-    Args:
-        boxes (torch.Tensor): Input boxes with shape (N, 5).
-        scores (torch.Tensor): Scores of predicted boxes with shape (N).
-        thresh (float): Overlap threshold of NMS.
-
-    Returns:
-        torch.Tensor: Remaining indices with scores in descending order.
-    """
-    assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
-    order = scores.sort(0, descending=True)[1]
-
-    boxes = boxes[order].contiguous()
-
-    keep = torch.zeros(boxes.size(0), dtype=torch.long)
-    num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
-    return order[keep[:num_out].cuda(boxes.device)].contiguous()

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/logger/dvclive.py

@@ -1,58 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ...dist_utils import master_only
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class DvcliveLoggerHook(LoggerHook):
-    """Class to log metrics with dvclive.
-
-    It requires `dvclive`_ to be installed.
-
-    Args:
-        path (str): Directory where dvclive will write TSV log files.
-        interval (int): Logging interval (every k iterations).
-            Default 10.
-        ignore_last (bool): Ignore the log of last iterations in each epoch
-            if less than `interval`.
-            Default: True.
-        reset_flag (bool): Whether to clear the output buffer after logging.
-            Default: True.
-        by_epoch (bool): Whether EpochBasedRunner is used.
-            Default: True.
-
-    .. _dvclive:
-        https://dvc.org/doc/dvclive
-    """
-
-    def __init__(self,
-                 path,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=True,
-                 by_epoch=True):
-
-        super(DvcliveLoggerHook, self).__init__(interval, ignore_last,
-                                                reset_flag, by_epoch)
-        self.path = path
-        self.import_dvclive()
-
-    def import_dvclive(self):
-        try:
-            import dvclive
-        except ImportError:
-            raise ImportError(
-                'Please run "pip install dvclive" to install dvclive')
-        self.dvclive = dvclive
-
-    @master_only
-    def before_run(self, runner):
-        self.dvclive.init(self.path)
-
-    @master_only
-    def log(self, runner):
-        tags = self.get_loggable_tags(runner)
-        if tags:
-            for k, v in tags.items():
-                self.dvclive.log(k, v, step=self.get_iter(runner))

spaces/AquaSuisei/ChatGPTXE/README.md

@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.24.1
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/_device.py

@@ -1,30 +0,0 @@
-import os
-from enum import Enum
-from .device_id import DeviceId
-
-#NOTE:  This must be called first before any torch imports in order to work properly!
-
-class DeviceException(Exception):
-    pass
-
-class _Device:
-    def __init__(self):
-        self.set(DeviceId.CPU)
-
-    def is_gpu(self):
-        ''' Returns `True` if the current device is GPU, `False` otherwise. '''
-        return self.current() is not DeviceId.CPU
-  
-    def current(self):
-        return self._current_device
-
-    def set(self, device:DeviceId):     
-        if device == DeviceId.CPU:
-            os.environ['CUDA_VISIBLE_DEVICES']=''
-        else:
-            os.environ['CUDA_VISIBLE_DEVICES']=str(device.value)
-            import torch
-            torch.backends.cudnn.benchmark=False
-        
-        self._current_device = device    
-        return device

spaces/ArkanDash/rvc-models/README.md

@@ -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

spaces/Artrajz/vits-simple-api/vits/vits.py

@@ -1,255 +0,0 @@
-import librosa
-import re
-import numpy as np
-import torch
-from torch import no_grad, LongTensor, inference_mode, FloatTensor
-import utils
-from contants import ModelType
-from utils import get_hparams_from_file, lang_dict
-from utils.sentence import sentence_split_and_markup
-from vits import commons
-from vits.mel_processing import spectrogram_torch
-from vits.text import text_to_sequence
-from vits.models import SynthesizerTrn
-
-
-class VITS:
-    def __init__(self, model, config, additional_model=None, model_type=None, device=torch.device("cpu"), **kwargs):
-        self.model_type = model_type
-        self.hps_ms = get_hparams_from_file(config) if isinstance(config, str) else config
-        self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
-        self.n_symbols = len(getattr(self.hps_ms, 'symbols', []))
-        self.speakers = getattr(self.hps_ms, 'speakers', ['0'])
-        if not isinstance(self.speakers, list):
-            self.speakers = [item[0] for item in sorted(list(self.speakers.items()), key=lambda x: x[1])]
-        self.use_f0 = getattr(self.hps_ms.data, 'use_f0', False)
-        self.emotion_embedding = getattr(self.hps_ms.data, 'emotion_embedding',
-                                         getattr(self.hps_ms.model, 'emotion_embedding', False))
-        self.bert_embedding = getattr(self.hps_ms.data, 'bert_embedding',
-                                      getattr(self.hps_ms.model, 'bert_embedding', False))
-        self.hps_ms.model.emotion_embedding = self.emotion_embedding
-        self.hps_ms.model.bert_embedding = self.bert_embedding
-
-        self.net_g_ms = SynthesizerTrn(
-            self.n_symbols,
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            n_speakers=self.n_speakers,
-            **self.hps_ms.model)
-        _ = self.net_g_ms.eval()
-        self.device = device
-
-        key = getattr(self.hps_ms.data, "text_cleaners", ["none"])[0]
-        self.lang = lang_dict.get(key, ["unknown"])
-
-        # load model
-        self.load_model(model, additional_model)
-
-    def load_model(self, model, additional_model=None):
-        utils.load_checkpoint(model, self.net_g_ms)
-        self.net_g_ms.to(self.device)
-        if self.model_type == ModelType.HUBERT_VITS:
-            self.hubert = additional_model
-        elif self.model_type == ModelType.W2V2_VITS:
-            self.emotion_reference = additional_model
-
-    def get_cleaned_text(self, text, hps, cleaned=False):
-        if cleaned:
-            text_norm = text_to_sequence(text, hps.symbols, [])
-        else:
-            if self.bert_embedding:
-                text_norm, char_embed = text_to_sequence(text, hps.symbols, hps.data.text_cleaners,
-                                                         bert_embedding=self.bert_embedding)
-                text_norm = LongTensor(text_norm)
-                return text_norm, char_embed
-            else:
-                text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-        if hps.data.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = LongTensor(text_norm)
-        return text_norm
-
-    def get_cleaner(self):
-        return getattr(self.hps_ms.data, 'text_cleaners', [None])[0]
-
-    def get_speakers(self, escape=False):
-        return self.speakers
-
-    @property
-    def sampling_rate(self):
-        return self.hps_ms.data.sampling_rate
-
-    def infer(self, params):
-        with no_grad():
-            x_tst = params.get("stn_tst").unsqueeze(0).to(self.device)
-            x_tst_lengths = LongTensor([params.get("stn_tst").size(0)]).to(self.device)
-            x_tst_prosody = torch.FloatTensor(params.get("char_embeds")).unsqueeze(0).to(
-                self.device) if self.bert_embedding else None
-            sid = params.get("sid").to(self.device)
-            emotion = params.get("emotion").to(self.device) if self.emotion_embedding else None
-
-            audio = self.net_g_ms.infer(x=x_tst,
-                                        x_lengths=x_tst_lengths,
-                                        sid=sid,
-                                        noise_scale=params.get("noise_scale"),
-                                        noise_scale_w=params.get("noise_scale_w"),
-                                        length_scale=params.get("length_scale"),
-                                        emotion_embedding=emotion,
-                                        bert=x_tst_prosody)[0][0, 0].data.float().cpu().numpy()
-
-        torch.cuda.empty_cache()
-
-        return audio
-
-    def get_infer_param(self, length_scale, noise_scale, noise_scale_w, text=None, speaker_id=None, audio_path=None,
-                        emotion=None, cleaned=False, f0_scale=1):
-        emo = None
-        char_embeds = None
-        if self.model_type != ModelType.HUBERT_VITS:
-            if self.bert_embedding:
-                stn_tst, char_embeds = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
-            else:
-                stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
-            sid = LongTensor([speaker_id])
-
-        if self.model_type == ModelType.W2V2_VITS:
-            # if emotion_reference.endswith('.npy'):
-            #     emotion = np.load(emotion_reference)
-            #     emotion = FloatTensor(emotion).unsqueeze(0)
-            # else:
-            #     audio16000, sampling_rate = librosa.load(
-            #         emotion_reference, sr=16000, mono=True)
-            #     emotion = self.w2v2(audio16000, sampling_rate)[
-            #         'hidden_states']
-            #     emotion_reference = re.sub(
-            #         r'\..*$', '', emotion_reference)
-            #     np.save(emotion_reference, emotion.squeeze(0))
-            #     emotion = FloatTensor(emotion)
-            emo = torch.FloatTensor(self.emotion_reference[emotion]).unsqueeze(0)
-
-
-        elif self.model_type == ModelType.HUBERT_VITS:
-            if self.use_f0:
-                audio, sampling_rate = librosa.load(audio_path, sr=self.hps_ms.data.sampling_rate, mono=True)
-                audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            else:
-                audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True)
-
-            with inference_mode():
-                units = self.hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy()
-                if self.use_f0:
-                    f0 = librosa.pyin(audio,
-                                      sr=sampling_rate,
-                                      fmin=librosa.note_to_hz('C0'),
-                                      fmax=librosa.note_to_hz('C7'),
-                                      frame_length=1780)[0]
-                    target_length = len(units[:, 0])
-                    f0 = np.nan_to_num(np.interp(np.arange(0, len(f0) * target_length, len(f0)) / target_length,
-                                                 np.arange(0, len(f0)), f0)) * f0_scale
-                    units[:, 0] = f0 / 10
-
-            stn_tst = FloatTensor(units)
-            sid = LongTensor([speaker_id])
-        params = {"length_scale": length_scale, "noise_scale": noise_scale,
-                  "noise_scale_w": noise_scale_w, "stn_tst": stn_tst,
-                  "sid": sid, "emotion": emo, "char_embeds": char_embeds}
-
-        return params
-
-    def get_tasks(self, voice):
-        text = voice.get("text", None)
-        speaker_id = voice.get("id", 0)
-        length = voice.get("length", 1)
-        noise = voice.get("noise", 0.667)
-        noisew = voice.get("noisew", 0.8)
-        max = voice.get("max", 50)
-        lang = voice.get("lang", "auto")
-        speaker_lang = voice.get("speaker_lang", None)
-        audio_path = voice.get("audio_path", None)
-        emotion = voice.get("emotion", 0)
-
-        # 去除所有多余的空白字符
-        if text is not None: text = re.sub(r'\s+', ' ', text).strip()
-
-        tasks = []
-        if self.model_type == ModelType.VITS:
-            sentence_list = sentence_split_and_markup(text, max, lang, speaker_lang)
-            for sentence in sentence_list:
-                params = self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length,
-                                              noise_scale=noise, noise_scale_w=noisew)
-                tasks.append(params)
-
-        elif self.model_type == ModelType.HUBERT_VITS:
-            params = self.get_infer_param(speaker_id=speaker_id, length_scale=length, noise_scale=noise,
-                                          noise_scale_w=noisew, audio_path=audio_path)
-            tasks.append(params)
-
-        elif self.model_type == ModelType.W2V2_VITS:
-            sentence_list = sentence_split_and_markup(text, max, lang, speaker_lang)
-            for sentence in sentence_list:
-                params = self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length,
-                                              noise_scale=noise, noise_scale_w=noisew, emotion=emotion)
-                tasks.append(params)
-        else:
-            raise ValueError(f"Unsupported model type: {self.model_type}")
-
-        return tasks
-
-    def get_audio(self, voice, auto_break=False):
-        tasks = self.get_tasks(voice)
-        # 停顿0.75s，避免语音分段合成再拼接后的连接突兀
-        brk = np.zeros(int(0.75 * self.sampling_rate), dtype=np.int16)
-
-        audios = []
-        num_tasks = len(tasks)
-
-        for i, task in enumerate(tasks):
-            if auto_break and i < num_tasks - 1:
-                chunk = np.concatenate((self.infer(task), brk), axis=0)
-            else:
-                chunk = self.infer(task)
-            audios.append(chunk)
-
-        audio = np.concatenate(audios, axis=0)
-        return audio
-
-    def get_stream_audio(self, voice, auto_break=False):
-        tasks = self.get_tasks(voice)
-
-        brk = np.zeros(int(0.75 * 22050), dtype=np.int16)
-
-        for task in tasks:
-            if auto_break:
-                chunk = np.concatenate((self.infer(task), brk), axis=0)
-            else:
-                chunk = self.infer(task)
-
-            yield chunk
-
-    def voice_conversion(self, voice):
-        audio_path = voice.get("audio_path")
-        original_id = voice.get("original_id")
-        target_id = voice.get("target_id")
-
-        audio = utils.load_audio_to_torch(
-            audio_path, self.hps_ms.data.sampling_rate)
-
-        y = audio.unsqueeze(0)
-
-        spec = spectrogram_torch(y, self.hps_ms.data.filter_length,
-                                 self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length,
-                                 self.hps_ms.data.win_length,
-                                 center=False)
-        spec_lengths = LongTensor([spec.size(-1)])
-        sid_src = LongTensor([original_id])
-
-        with no_grad():
-            sid_tgt = LongTensor([target_id])
-            audio = self.net_g_ms.voice_conversion(spec.to(self.device),
-                                                   spec_lengths.to(self.device),
-                                                   sid_src=sid_src.to(self.device),
-                                                   sid_tgt=sid_tgt.to(self.device))[0][0, 0].data.cpu().float().numpy()
-
-        torch.cuda.empty_cache()
-
-        return audio

spaces/Awesimo/jojogan/README.md

@@ -1,38 +0,0 @@
----
-title: JoJoGAN
-emoji: 🌍
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.1
-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.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h

@@ -1,35 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-#include <torch/types.h>
-
-namespace detectron2 {
-
-at::Tensor box_iou_rotated_cpu(
-    const at::Tensor& boxes1,
-    const at::Tensor& boxes2);
-
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-at::Tensor box_iou_rotated_cuda(
-    const at::Tensor& boxes1,
-    const at::Tensor& boxes2);
-#endif
-
-// Interface for Python
-// inline is needed to prevent multiple function definitions when this header is
-// included by different cpps
-inline at::Tensor box_iou_rotated(
-    const at::Tensor& boxes1,
-    const at::Tensor& boxes2) {
-  assert(boxes1.device().is_cuda() == boxes2.device().is_cuda());
-  if (boxes1.device().is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    return box_iou_rotated_cuda(boxes1.contiguous(), boxes2.contiguous());
-#else
-    AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
-  }
-
-  return box_iou_rotated_cpu(boxes1.contiguous(), boxes2.contiguous());
-}
-
-} // namespace detectron2

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/panoptic_fpn.py

@@ -1,266 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-from typing import Dict, List
-import torch
-from torch import nn
-
-from detectron2.config import configurable
-from detectron2.structures import ImageList
-
-from ..postprocessing import detector_postprocess, sem_seg_postprocess
-from .build import META_ARCH_REGISTRY
-from .rcnn import GeneralizedRCNN
-from .semantic_seg import build_sem_seg_head
-
-__all__ = ["PanopticFPN"]
-
-
-@META_ARCH_REGISTRY.register()
-class PanopticFPN(GeneralizedRCNN):
-    """
-    Implement the paper :paper:`PanopticFPN`.
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        sem_seg_head: nn.Module,
-        combine_overlap_thresh: float = 0.5,
-        combine_stuff_area_thresh: float = 4096,
-        combine_instances_score_thresh: float = 0.5,
-        **kwargs,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            sem_seg_head: a module for the semantic segmentation head.
-            combine_overlap_thresh: combine masks into one instances if
-                they have enough overlap
-            combine_stuff_area_thresh: ignore stuff areas smaller than this threshold
-            combine_instances_score_thresh: ignore instances whose score is
-                smaller than this threshold
-
-        Other arguments are the same as :class:`GeneralizedRCNN`.
-        """
-        super().__init__(**kwargs)
-        self.sem_seg_head = sem_seg_head
-        # options when combining instance & semantic outputs
-        self.combine_overlap_thresh = combine_overlap_thresh
-        self.combine_stuff_area_thresh = combine_stuff_area_thresh
-        self.combine_instances_score_thresh = combine_instances_score_thresh
-
-    @classmethod
-    def from_config(cls, cfg):
-        ret = super().from_config(cfg)
-        ret.update(
-            {
-                "combine_overlap_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH,
-                "combine_stuff_area_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT,
-                "combine_instances_score_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH,  # noqa
-            }
-        )
-        ret["sem_seg_head"] = build_sem_seg_head(cfg, ret["backbone"].output_shape())
-        logger = logging.getLogger(__name__)
-        if not cfg.MODEL.PANOPTIC_FPN.COMBINE.ENABLED:
-            logger.warning(
-                "PANOPTIC_FPN.COMBINED.ENABLED is no longer used. "
-                " model.inference(do_postprocess=) should be used to toggle postprocessing."
-            )
-        if cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT != 1.0:
-            w = cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT
-            logger.warning(
-                "PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT should be replaced by weights on each ROI head."
-            )
-
-            def update_weight(x):
-                if isinstance(x, dict):
-                    return {k: v * w for k, v in x.items()}
-                else:
-                    return x * w
-
-            roi_heads = ret["roi_heads"]
-            roi_heads.box_predictor.loss_weight = update_weight(roi_heads.box_predictor.loss_weight)
-            roi_heads.mask_head.loss_weight = update_weight(roi_heads.mask_head.loss_weight)
-        return ret
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
-                Each item in the list contains the inputs for one image.
-
-                For now, each item in the list is a dict that contains:
-
-                * "image": Tensor, image in (C, H, W) format.
-                * "instances": Instances
-                * "sem_seg": semantic segmentation ground truth.
-                * Other information that's included in the original dicts, such as:
-                  "height", "width" (int): the output resolution of the model, used in inference.
-                  See :meth:`postprocess` for details.
-
-        Returns:
-            list[dict]:
-                each dict has the results for one image. The dict contains the following keys:
-
-                * "instances": see :meth:`GeneralizedRCNN.forward` for its format.
-                * "sem_seg": see :meth:`SemanticSegmentor.forward` for its format.
-                * "panoptic_seg": See the return value of
-                  :func:`combine_semantic_and_instance_outputs` for its format.
-        """
-        if not self.training:
-            return self.inference(batched_inputs)
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-
-        assert "sem_seg" in batched_inputs[0]
-        gt_sem_seg = [x["sem_seg"].to(self.device) for x in batched_inputs]
-        gt_sem_seg = ImageList.from_tensors(
-            gt_sem_seg, self.backbone.size_divisibility, self.sem_seg_head.ignore_value
-        ).tensor
-        sem_seg_results, sem_seg_losses = self.sem_seg_head(features, gt_sem_seg)
-
-        gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-        proposals, proposal_losses = self.proposal_generator(images, features, gt_instances)
-        detector_results, detector_losses = self.roi_heads(
-            images, features, proposals, gt_instances
-        )
-
-        losses = sem_seg_losses
-        losses.update(proposal_losses)
-        losses.update(detector_losses)
-        return losses
-
-    def inference(self, batched_inputs: List[Dict[str, torch.Tensor]], do_postprocess: bool = True):
-        """
-        Run inference on the given inputs.
-
-        Args:
-            batched_inputs (list[dict]): same as in :meth:`forward`
-            do_postprocess (bool): whether to apply post-processing on the outputs.
-
-        Returns:
-            When do_postprocess=True, see docs in :meth:`forward`.
-            Otherwise, returns a (list[Instances], list[Tensor]) that contains
-            the raw detector outputs, and raw semantic segmentation outputs.
-        """
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-        sem_seg_results, sem_seg_losses = self.sem_seg_head(features, None)
-        proposals, _ = self.proposal_generator(images, features, None)
-        detector_results, _ = self.roi_heads(images, features, proposals, None)
-
-        if do_postprocess:
-            processed_results = []
-            for sem_seg_result, detector_result, input_per_image, image_size in zip(
-                sem_seg_results, detector_results, batched_inputs, images.image_sizes
-            ):
-                height = input_per_image.get("height", image_size[0])
-                width = input_per_image.get("width", image_size[1])
-                sem_seg_r = sem_seg_postprocess(sem_seg_result, image_size, height, width)
-                detector_r = detector_postprocess(detector_result, height, width)
-
-                processed_results.append({"sem_seg": sem_seg_r, "instances": detector_r})
-
-                panoptic_r = combine_semantic_and_instance_outputs(
-                    detector_r,
-                    sem_seg_r.argmax(dim=0),
-                    self.combine_overlap_thresh,
-                    self.combine_stuff_area_thresh,
-                    self.combine_instances_score_thresh,
-                )
-                processed_results[-1]["panoptic_seg"] = panoptic_r
-            return processed_results
-        else:
-            return detector_results, sem_seg_results
-
-
-def combine_semantic_and_instance_outputs(
-    instance_results,
-    semantic_results,
-    overlap_threshold,
-    stuff_area_thresh,
-    instances_score_thresh,
-):
-    """
-    Implement a simple combining logic following
-    "combine_semantic_and_instance_predictions.py" in panopticapi
-    to produce panoptic segmentation outputs.
-
-    Args:
-        instance_results: output of :func:`detector_postprocess`.
-        semantic_results: an (H, W) tensor, each element is the contiguous semantic
-            category id
-
-    Returns:
-        panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
-        segments_info (list[dict]): Describe each segment in `panoptic_seg`.
-            Each dict contains keys "id", "category_id", "isthing".
-    """
-    panoptic_seg = torch.zeros_like(semantic_results, dtype=torch.int32)
-
-    # sort instance outputs by scores
-    sorted_inds = torch.argsort(-instance_results.scores)
-
-    current_segment_id = 0
-    segments_info = []
-
-    instance_masks = instance_results.pred_masks.to(dtype=torch.bool, device=panoptic_seg.device)
-
-    # Add instances one-by-one, check for overlaps with existing ones
-    for inst_id in sorted_inds:
-        score = instance_results.scores[inst_id].item()
-        if score < instances_score_thresh:
-            break
-        mask = instance_masks[inst_id]  # H,W
-        mask_area = mask.sum().item()
-
-        if mask_area == 0:
-            continue
-
-        intersect = (mask > 0) & (panoptic_seg > 0)
-        intersect_area = intersect.sum().item()
-
-        if intersect_area * 1.0 / mask_area > overlap_threshold:
-            continue
-
-        if intersect_area > 0:
-            mask = mask & (panoptic_seg == 0)
-
-        current_segment_id += 1
-        panoptic_seg[mask] = current_segment_id
-        segments_info.append(
-            {
-                "id": current_segment_id,
-                "isthing": True,
-                "score": score,
-                "category_id": instance_results.pred_classes[inst_id].item(),
-                "instance_id": inst_id.item(),
-            }
-        )
-
-    # Add semantic results to remaining empty areas
-    semantic_labels = torch.unique(semantic_results).cpu().tolist()
-    for semantic_label in semantic_labels:
-        if semantic_label == 0:  # 0 is a special "thing" class
-            continue
-        mask = (semantic_results == semantic_label) & (panoptic_seg == 0)
-        mask_area = mask.sum().item()
-        if mask_area < stuff_area_thresh:
-            continue
-
-        current_segment_id += 1
-        panoptic_seg[mask] = current_segment_id
-        segments_info.append(
-            {
-                "id": current_segment_id,
-                "isthing": False,
-                "category_id": semantic_label,
-                "area": mask_area,
-            }
-        )
-
-    return panoptic_seg, segments_info

spaces/Banbri/zcvzcv/src/lib/generateSeed.ts

@@ -1,3 +0,0 @@
-export function generateSeed() {
-  return Math.floor(Math.random() * Math.pow(2, 31));
-}

spaces/Benson/text-generation/Examples/Clash Royale Mod Apk Raja Apk.md

@@ -1,85 +0,0 @@
-<br />
-<h1>Choque Royale Mod APK Raja APK: La guía definitiva</h1>
-<p>Si eres un fan de los juegos de estrategia en tiempo real, debes haber oído hablar de Clash Royale, uno de los juegos más populares y adictivos en dispositivos móviles. ¿Pero sabías que hay una manera de disfrutar el juego aún más con recursos ilimitados, tarjetas personalizadas y otras características increíbles? Sí, estamos hablando de Clash Royale Mod APK Raja APK, una versión modificada del juego que le da una ventaja sobre sus oponentes. En este artículo, le diremos todo lo que necesita saber acerca de este apk mod, incluyendo sus beneficios, guía de instalación, y descargo de responsabilidad. ¡Sigue leyendo para saber más! </p>
- <h2>¿Qué es Clash Royale? </h2>
-<p>Clash Royale es un juego multijugador en tiempo real desarrollado por Supercell, los creadores de Clash of Clans. Cuenta con tus personajes favoritos de Clash y mucho más. En este juego, tienes que recoger y actualizar las cartas que representan las tropas, hechizos y defensas del universo Clash. También tienes que construir tu propia baraja de batalla y usarla para luchar contra otros jugadores en partidas rápidas. El objetivo es destruir las torres de tu enemigo y ganar coronas que se pueden utilizar para desbloquear cofres con recompensas. También puedes unirte o formar un clan con otros jugadores para compartir cartas y participar en guerras de clanes para obtener premios más grandes. </p>
-<h2>clash royale mod apk raja apk</h2><br /><p><b><b>Download File</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6JaN">https://bltlly.com/2v6JaN</a></b></p><br /><br />
- <h3>Características de Clash Royale</h3>
-<p>Clash Royale tiene muchas características que lo convierten en un juego emocionante y divertido para jugar. Algunas de ellas son:</p>
-<ul>
-<li>Gana cofres para desbloquear recompensas, recoger nuevas cartas de gran alcance y actualizar los existentes. </li>
-<li>Destruye las torres del oponente y gana coronas para ganar cofres épicos. </li>
-<li>Construye y mejora tu colección de cartas con la familia Clash Royale junto con docenas de tus tropas, hechizos y defensas Clash favoritas. </li>
-<li>Ábrete camino a la cima en diferentes arenas y ligas. </li>
-<li>Compite en eventos estacionales y desafíos que ponen a prueba tus habilidades. </li>
-
-</ul>
- <h3>Cómo jugar Clash Royale</h3>
-<p>Jugar a Clash Royale es fácil y divertido. Estos son los pasos básicos a seguir:</p>
-<ol>
-<li>Descargar e instalar el juego desde la Google Play Store o la App Store.</li>
-<li>Cree o inicie sesión en su cuenta de Supercell. </li>
-<li>Completa el tutorial para aprender los fundamentos del juego. </li>
-<li>Iniciar un partido tocando el botón de batalla. </li>
-<li>Selecciona cuatro cartas de tu mazo para usarlas en la batalla. </li>
-<li>Arrastra y suelta las cartas en la arena para desplegar tus unidades. </li>
-<li>Usa elixir sabiamente para administrar tus recursos. </li>
-<li>Apunta a las torres del enemigo y trata de destruirlas antes de que destruyan la tuya. </li>
-<li>Ganar coronas y cofres por cada victoria. </li>
-<li>Abrir cofres para obtener recompensas como cartas, oro, gemas, etc.</li>
-<li>Actualiza tus tarjetas con oro para hacerlas más fuertes. </li>
-<li>Crea o únete a un clan para chatear, donar, solicitar e intercambiar cartas con otros jugadores. </li>
-<li>Participa en guerras de clanes, torneos, eventos y desafíos para más diversión y recompensas. </li>
-</ol>
- <h2>¿Qué es Clash Royale Mod APK Raja APK? </h2>
-<p>Clash Royale Mod APK Raja APK es una versión modificada del juego original que ofrece algunas características adicionales que no están disponibles en la versión oficial. También se conoce como CR mod apk o CR hack apk. Es desarrollado por Raja APK, un sitio web que proporciona varios apks mod para diferentes juegos y aplicaciones. Algunas de las características que ofrece este apk mod son:</p>
- <h3>Beneficios <h3>Beneficios de choque Royale Mod APK Raja APK</h3>
-<p>Algunos de los beneficios que se pueden disfrutar mediante el uso de Clash Royale Mod APK Raja APK son:</p>
-<ul>
-<li>Recursos ilimitados: Puede obtener oro ilimitado, gemas, elixir y elixir oscuro para actualizar sus tarjetas, comprar cofres y desbloquear nuevas funciones. </li>
-<li>Tarjetas personalizadas: Puedes crear tus propias tarjetas con estadísticas, habilidades y diseños personalizados. También puedes usar cartas de otros juegos de Supercell como Brawl Stars, Boom Beach y Hay Day.</li>
-
-<li>No hay anuncios: Puedes disfrutar del juego sin anuncios molestos o pop-ups. </li>
-<li>No hay raíz: No es necesario rootear el dispositivo para utilizar este apk mod. Funciona tanto en dispositivos arraigados y no arraigados. </li>
-</ul>
- <h3>Cómo descargar e instalar Clash Royale Mod APK Raja APK</h3>
-<p>Para descargar e instalar Clash Royale Mod APK Raja APK, es necesario seguir estos sencillos pasos:</p>
-<ol>
-<li>Ir a la página web oficial de Raja APK y encontrar el archivo Clash Royale Mod APK Raja APK. También puede utilizar este enlace: . </li>
-<li>Descargue el archivo en su dispositivo. Asegúrese de tener suficiente espacio de almacenamiento y una conexión a Internet estable. </li>
-<li>Habilita la instalación de fuentes desconocidas en tu dispositivo. Para hacer esto, ve a Configuración > Seguridad > Fuentes desconocidas y cámbiala. </li>
-<li>Localice el archivo descargado en su dispositivo y toque en él para iniciar el proceso de instalación. </li>
-<li>Siga las instrucciones en la pantalla y espere a que se complete la instalación. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h2>Descargo de responsabilidad y los riesgos de usar Clash Royale Mod APK Raja APK</h2>
-<p>Aunque Clash Royale Mod APK Raja APK puede sonar tentador y divertido, también viene con algunos riesgos y desventajas que usted debe ser consciente de antes de usarlo. Estos son algunos de ellos:</p>
-<p></p>
- <h3>Cuestiones legales y prohibiciones</h3>
-<p>Usando Clash Royale Mod APK Raja APK está en contra de los términos de servicio de Supercell, el desarrollador de Clash Royale. Esto significa que usted está violando sus reglas y reglamentos mediante el uso de una versión modificada de su juego. Esto puede dar lugar a acciones legales o prohibiciones de sus servidores. Si le pillan usando este mod apk, puede perder su cuenta, progreso y compras. También puede enfrentar consecuencias legales como multas o demandas. </p>
- <h3>Malware y virus</h3>
-
- <h3>Pérdida de datos y corrupción</h3>
-<p>Usando Clash Royale Mod APK Raja APK también puede causar pérdida de datos o corrupción en su dispositivo o juego. Algunos apks mod pueden no ser compatibles con su dispositivo o versión del juego, lo que puede conducir a errores, fallos o fallos. Esto puede resultar en la pérdida de los datos del juego o la corrupción de los archivos. Para evitar esto, siempre debe hacer una copia de seguridad de sus datos antes de usar cualquier apk mod. También debe actualizar su juego y mod apk regularmente para asegurar un rendimiento suave. </p>
- <h2>Conclusión</h2>
-<p>Clash Royale Mod APK Raja APK es una versión modificada de Clash Royale que ofrece recursos ilimitados, tarjetas personalizadas, servidor privado, sin anuncios, y sin características de raíz. Es una gran manera de disfrutar del juego con más libertad y diversión. Sin embargo, también viene con algunos riesgos y desventajas, como problemas legales, prohibiciones, malware, virus, pérdida de datos y corrupción. Por lo tanto, usted debe utilizar a su propio riesgo y discreción. Esperamos que este artículo le ha dado toda la información que necesita acerca de este apk mod. Si tiene alguna pregunta o comentario, háganoslo saber en los comentarios a continuación. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Clash Royale Mod APK Raja APK:</p>
- <h4>Q: Es Clash Royale Mod APK Raja APK seguro de usar? </h4>
-<p>A: Clash Royale Mod APK Raja APK es seguro de usar si lo descarga de una fuente de confianza como Raja APK. Sin embargo, siempre debe escanear el archivo en busca de virus antes de instalarlo en su dispositivo. También debe hacer una copia de seguridad de sus datos antes de usarlo para evitar cualquier pérdida de datos o corrupción. </ <p>Q: ¿Cómo puedo actualizar Clash Royale Mod APK Raja APK? </p>
-<p>A: Para actualizar Clash Royale Mod APK Raja APK, es necesario visitar el sitio web oficial de Raja APK y descargar la última versión del archivo. También puede utilizar este enlace: . Entonces, es necesario desinstalar la versión anterior de la apk mod e instalar el nuevo. Es posible que necesite habilitar la instalación de fuentes desconocidas de nuevo si se le solicita. </p>
-
-<p>A: Sí, puedes jugar Clash Royale Mod APK Raja APK con tus amigos que también utilizan el mismo mod apk. Puede unirse o crear un clan con ellos y chatear, donar, solicitar e intercambiar tarjetas. También puede luchar con ellos en el servidor privado. Sin embargo, no puedes jugar con tus amigos que usan la versión oficial del juego, ya que están en un servidor diferente. </p>
- <h4>Q: ¿Puedo usar Clash Royale Mod APK Raja APK en dispositivos iOS? </h4>
-<p>A: No, Clash Royale Mod APK Raja APK solo es compatible con dispositivos Android. No funciona en dispositivos iOS como iPhones o iPads. Si desea utilizar un apk mod en dispositivos iOS, es necesario encontrar uno diferente que está diseñado para iOS. </p>
- <h4>Q: ¿Cuáles son algunas alternativas a Clash Royale Mod APK Raja APK? </h4>
-<p>A: Algunas alternativas a Clash Royale Mod APK Raja APK son:</p>
-<ul>
-<li>Choque Royale Mod APK Null’s Royale: Este es otro popular mod apk que ofrece recursos ilimitados, tarjetas personalizadas, servidor privado, y más. </li>
-<li>Choque Royale Mod APK Master Royale: Este es un apk mod que ofrece gemas ilimitadas, monedas, cofres y tarjetas. También tiene un servidor privado y mods personalizados. </li>
-<li>Clash Royale Mod APK PlenixRoyale: Este es un apk mod que ofrece gemas ilimitadas, oro, elixir y elixir oscuro. También tiene un servidor privado y tarjetas personalizadas. </li>
-</ul>
- <h4>Q: ¿Dónde puedo encontrar más información sobre Clash Royale Mod APK Raja APK? </h4>
-<p>A: Usted puede encontrar más información sobre Clash Royale Mod APK Raja APK en el sitio web oficial de Raja APK. También puede visitar sus páginas de redes sociales o contactarlos por correo electrónico o teléfono para cualquier consulta o retroalimentación. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/PRIVACY.md

@@ -1,35 +0,0 @@
-## Privacy
-
-> Last updated: May 2nd, 2023
-
-In this `v0.1` of BetterChat, users are not authenticated in any way, i.e. this app doesn't have access to your HF user account even if you're logged in to huggingface.co. The app is only using an anonymous session cookie. ❗️ Warning ❗️ this means if you switch browsers or clear cookies, you will currently lose your conversations.
-
-By default, your conversations are shared with the model's authors (for the `v0.1` model, to <a target="_blank" href="https://open-assistant.io/dashboard">Open Assistant</a>) to improve their training data and model over time. Model authors are the custodians of the data collected by their model, even if it's hosted on our platform.
-
-If you disable data sharing in your settings, your conversations will not be used for any downstream usage (including for research or model training purposes), and they will only be stored to let you access past conversations. You can click on the Delete icon to delete any past conversation at any moment.
-
-🗓 Please also consult huggingface.co's main privacy policy at https://huggingface.co/privacy. To exercise any of your legal privacy rights, please send an email to [email protected].
-
-## About available LLMs
-
-The goal of this app is to showcase that it is now (April 2023) possible to build an open source alternative to ChatGPT. 💪
-
-For now, it's running OpenAssistant's [latest LLaMA based model](https://huggingface.co/OpenAssistant/oasst-sft-6-llama-30b-xor) (which is one of the current best open source chat models), but the plan in the longer-term is to expose all good-quality chat models from the Hub.
-
-We are not affiliated with Open Assistant, but if you want to contribute to the training data for the next generation of open models, please consider contributing to https://open-assistant.io/ ❤️
-
-## Technical details
-
-This app is running in a [Space](https://huggingface.co/docs/hub/spaces-overview), which entails that the code for this UI is open source: https://huggingface.co/spaces/huggingchat/chat-ui/tree/main.
-The inference backend is running [text-generation-inference](https://github.com/huggingface/text-generation-inference) on HuggingFace's Inference API infrastructure.
-
-It is therefore possible to deploy a copy of this app to a Space and customize it (swap model, add some UI elements, or store user messages according to your own Terms and conditions)
-
-We welcome any feedback on this app: please participate to the public discussion at https://huggingface.co/spaces/huggingchat/chat-ui/discussions
-
-<a target="_blank" href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-a-discussion-xl.svg" title="open a discussion"></a>
-
-## Coming soon
-
-- LLM watermarking
-- User setting to share conversations with model authors (done ✅)

spaces/BetterAPI/BetterChat/src/routes/conversation/[id]/+page.server.ts

@@ -1,33 +0,0 @@
-import type { PageServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import { ObjectId } from "mongodb";
-import { error } from "@sveltejs/kit";
-
-export const load: PageServerLoad = async (event) => {
-	// todo: add validation on params.id
-	const conversation = await collections.conversations.findOne({
-		_id: new ObjectId(event.params.id),
-		sessionId: event.locals.sessionId,
-	});
-
-	if (!conversation) {
-		const conversationExists =
-			(await collections.conversations.countDocuments({
-				_id: new ObjectId(event.params.id),
-			})) !== 0;
-
-		if (conversationExists) {
-			throw error(
-				403,
-				"You don't have access to this conversation. If someone gave you this link, ask them to use the 'share' feature instead."
-			);
-		}
-
-		throw error(404, "Conversation not found.");
-	}
-
-	return {
-		messages: conversation.messages,
-		title: conversation.title,
-	};
-};

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_itertools.py

@@ -1,23 +0,0 @@
-from setuptools.extern.more_itertools import consume  # noqa: F401
-
-
-# copied from jaraco.itertools 6.1
-def ensure_unique(iterable, key=lambda x: x):
-    """
-    Wrap an iterable to raise a ValueError if non-unique values are encountered.
-
-    >>> list(ensure_unique('abc'))
-    ['a', 'b', 'c']
-    >>> consume(ensure_unique('abca'))
-    Traceback (most recent call last):
-    ...
-    ValueError: Duplicate element 'a' encountered.
-    """
-    seen = set()
-    seen_add = seen.add
-    for element in iterable:
-        k = key(element)
-        if k in seen:
-            raise ValueError(f"Duplicate element {element!r} encountered.")
-        seen_add(k)
-        yield element

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/util/response.py

@@ -1,107 +0,0 @@
-from __future__ import absolute_import
-
-from email.errors import MultipartInvariantViolationDefect, StartBoundaryNotFoundDefect
-
-from ..exceptions import HeaderParsingError
-from ..packages.six.moves import http_client as httplib
-
-
-def is_fp_closed(obj):
-    """
-    Checks whether a given file-like object is closed.
-
-    :param obj:
-        The file-like object to check.
-    """
-
-    try:
-        # Check `isclosed()` first, in case Python3 doesn't set `closed`.
-        # GH Issue #928
-        return obj.isclosed()
-    except AttributeError:
-        pass
-
-    try:
-        # Check via the official file-like-object way.
-        return obj.closed
-    except AttributeError:
-        pass
-
-    try:
-        # Check if the object is a container for another file-like object that
-        # gets released on exhaustion (e.g. HTTPResponse).
-        return obj.fp is None
-    except AttributeError:
-        pass
-
-    raise ValueError("Unable to determine whether fp is closed.")
-
-
-def assert_header_parsing(headers):
-    """
-    Asserts whether all headers have been successfully parsed.
-    Extracts encountered errors from the result of parsing headers.
-
-    Only works on Python 3.
-
-    :param http.client.HTTPMessage headers: Headers to verify.
-
-    :raises urllib3.exceptions.HeaderParsingError:
-        If parsing errors are found.
-    """
-
-    # This will fail silently if we pass in the wrong kind of parameter.
-    # To make debugging easier add an explicit check.
-    if not isinstance(headers, httplib.HTTPMessage):
-        raise TypeError("expected httplib.Message, got {0}.".format(type(headers)))
-
-    defects = getattr(headers, "defects", None)
-    get_payload = getattr(headers, "get_payload", None)
-
-    unparsed_data = None
-    if get_payload:
-        # get_payload is actually email.message.Message.get_payload;
-        # we're only interested in the result if it's not a multipart message
-        if not headers.is_multipart():
-            payload = get_payload()
-
-            if isinstance(payload, (bytes, str)):
-                unparsed_data = payload
-    if defects:
-        # httplib is assuming a response body is available
-        # when parsing headers even when httplib only sends
-        # header data to parse_headers() This results in
-        # defects on multipart responses in particular.
-        # See: https://github.com/urllib3/urllib3/issues/800
-
-        # So we ignore the following defects:
-        # - StartBoundaryNotFoundDefect:
-        #     The claimed start boundary was never found.
-        # - MultipartInvariantViolationDefect:
-        #     A message claimed to be a multipart but no subparts were found.
-        defects = [
-            defect
-            for defect in defects
-            if not isinstance(
-                defect, (StartBoundaryNotFoundDefect, MultipartInvariantViolationDefect)
-            )
-        ]
-
-    if defects or unparsed_data:
-        raise HeaderParsingError(defects=defects, unparsed_data=unparsed_data)
-
-
-def is_response_to_head(response):
-    """
-    Checks whether the request of a response has been a HEAD-request.
-    Handles the quirks of AppEngine.
-
-    :param http.client.HTTPResponse response:
-        Response to check if the originating request
-        used 'HEAD' as a method.
-    """
-    # FIXME: Can we do this somehow without accessing private httplib _method?
-    method = response._method
-    if isinstance(method, int):  # Platform-specific: Appengine
-        return method == 3
-    return method.upper() == "HEAD"

spaces/CVPR/LIVE/matrix.h

@@ -1,544 +0,0 @@
-#pragma once
-
-#include "diffvg.h"
-#include "vector.h"
-#include <iostream>
-
-template <typename T>
-struct TMatrix3x3 {
-    DEVICE
-    TMatrix3x3() {
-        for (int i = 0; i < 3; i++) {
-            for (int j = 0; j < 3; j++) {
-                data[i][j] = T(0);
-            }
-        }
-    }
-
-    template <typename T2>
-    DEVICE
-    TMatrix3x3(T2 *arr) {
-    	data[0][0] = arr[0];
-    	data[0][1] = arr[1];
-    	data[0][2] = arr[2];
-    	data[1][0] = arr[3];
-    	data[1][1] = arr[4];
-    	data[1][2] = arr[5];
-    	data[2][0] = arr[6];
-    	data[2][1] = arr[7];
-    	data[2][2] = arr[8];
-    }
-    DEVICE
-    TMatrix3x3(T v00, T v01, T v02,
-               T v10, T v11, T v12,
-               T v20, T v21, T v22) {
-        data[0][0] = v00;
-        data[0][1] = v01;
-        data[0][2] = v02;
-        data[1][0] = v10;
-        data[1][1] = v11;
-        data[1][2] = v12;
-        data[2][0] = v20;
-        data[2][1] = v21;
-        data[2][2] = v22;
-    }
-
-    DEVICE
-    const T& operator()(int i, int j) const {
-        return data[i][j];
-    }
-    DEVICE
-    T& operator()(int i, int j) {
-        return data[i][j];
-    }
-    DEVICE
-    static TMatrix3x3<T> identity() {
-        TMatrix3x3<T> m(1, 0, 0,
-                        0, 1, 0,
-                        0, 0, 1);
-        return m;
-    }
-
-    T data[3][3];
-};
-
-using Matrix3x3 = TMatrix3x3<Real>;
-using Matrix3x3f = TMatrix3x3<float>;
-
-template <typename T>
-struct TMatrix4x4 {
-    DEVICE TMatrix4x4() {
-        for (int i = 0; i < 4; i++) {
-            for (int j = 0; j < 4; j++) {
-                data[i][j] = T(0);
-            }
-        }
-    }
-
-    template <typename T2>
-    DEVICE TMatrix4x4(const T2 *arr) {
-        for (int i = 0; i < 4; i++) {
-            for (int j = 0; j < 4; j++) {
-                data[i][j] = (T)arr[i * 4 + j];
-            }
-        }
-    }
-
-    template <typename T2>
-    DEVICE TMatrix4x4(const TMatrix4x4<T2> &m) {
-        for (int i = 0; i < 4; i++) {
-            for (int j = 0; j < 4; j++) {
-                data[i][j] = T(m.data[i][j]);
-            }
-        }
-    }
-
-    template <typename T2>
-    DEVICE TMatrix4x4(T2 v00, T2 v01, T2 v02, T2 v03,
-                      T2 v10, T2 v11, T2 v12, T2 v13,
-                      T2 v20, T2 v21, T2 v22, T2 v23,
-                      T2 v30, T2 v31, T2 v32, T2 v33) {
-        data[0][0] = (T)v00;
-        data[0][1] = (T)v01;
-        data[0][2] = (T)v02;
-        data[0][3] = (T)v03;
-        data[1][0] = (T)v10;
-        data[1][1] = (T)v11;
-        data[1][2] = (T)v12;
-        data[1][3] = (T)v13;
-        data[2][0] = (T)v20;
-        data[2][1] = (T)v21;
-        data[2][2] = (T)v22;
-        data[2][3] = (T)v23;
-        data[3][0] = (T)v30;
-        data[3][1] = (T)v31;
-        data[3][2] = (T)v32;
-        data[3][3] = (T)v33;
-    }
-
-    DEVICE
-    const T& operator()(int i, int j) const {
-        return data[i][j];
-    }
-
-    DEVICE
-    T& operator()(int i, int j) {
-        return data[i][j];
-    }
-
-    DEVICE
-    static TMatrix4x4<T> identity() {
-        TMatrix4x4<T> m(1, 0, 0, 0,
-                        0, 1, 0, 0,
-                        0, 0, 1, 0,
-                        0, 0, 0, 1);
-        return m;
-    }
-
-    T data[4][4];
-};
-
-using Matrix4x4 = TMatrix4x4<Real>;
-using Matrix4x4f = TMatrix4x4<float>;
-
-template <typename T0, typename T1>
-DEVICE
-inline auto operator+(const TMatrix3x3<T0> &m0, const TMatrix3x3<T1> &m1) -> TMatrix3x3<decltype(m0(0, 0) + m1(0, 0))> {
-    TMatrix3x3<decltype(m0(0, 0) + m1(0, 0))> m;
-    for (int i = 0; i < 3; i++) {
-        for (int j = 0; j < 3; j++) {
-            m(i, j) = m0(i, j) + m1(i, j);
-        }
-    }
-    return m;
-}
-
-template <typename T0, typename T1>
-DEVICE
-inline auto operator-(const TMatrix3x3<T0> &m0, const TMatrix3x3<T1> &m1) -> TMatrix3x3<decltype(m0(0, 0) - m1(0, 0))> {
-    TMatrix3x3<decltype(m0(0, 0) - m1(0, 0))> m;
-    for (int i = 0; i < 3; i++) {
-        for (int j = 0; j < 3; j++) {
-            m(i, j) = m0(i, j) - m1(i, j);
-        }
-    }
-    return m;
-}
-
-template <typename T>
-DEVICE
-inline auto operator*(const TMatrix3x3<T> &m0, const TMatrix3x3<T> &m1) -> TMatrix3x3<T> {
-    TMatrix3x3<T> ret;
-    for (int i = 0; i < 3; i++) {
-        for (int j = 0; j < 3; j++) {
-            ret(i, j) = T(0);
-            for (int k = 0; k < 3; k++) {
-                ret(i, j) += m0(i, k) * m1(k, j);
-            }
-        }
-    }
-    return ret;
-}
-
-template <typename T>
-DEVICE
-inline auto operator*(const TVector3<T> &v, const TMatrix3x3<T> &m) -> TVector3<T> {
-    TVector3<T> ret;
-    for (int i = 0; i < 3; i++) {
-        ret[i] = T(0);
-        for (int j = 0; j < 3; j++) {
-            ret[i] += v[j] * m(j, i);
-        }
-    }
-    return ret;
-}
-
-template <typename T>
-DEVICE
-inline auto operator*(const TMatrix3x3<T> &m, const TVector3<T> &v) -> TVector3<T> {
-    TVector3<T> ret;
-    for (int i = 0; i < 3; i++) {
-        ret[i] = 0.f;
-        for (int j = 0; j < 3; j++) {
-            ret[i] += m(i, j) * v[j];
-        }
-    }
-    return ret;
-}
-
-template <typename T>
-DEVICE
-inline auto inverse(const TMatrix3x3<T> &m) -> TMatrix3x3<T> {
-    // computes the inverse of a matrix m
-    auto det = m(0, 0) * (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) -
-               m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) +
-               m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0));
-
-    auto invdet = 1 / det;
-
-    auto m_inv = TMatrix3x3<T>{};
-    m_inv(0, 0) = (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) * invdet;
-    m_inv(0, 1) = (m(0, 2) * m(2, 1) - m(0, 1) * m(2, 2)) * invdet;
-    m_inv(0, 2) = (m(0, 1) * m(1, 2) - m(0, 2) * m(1, 1)) * invdet;
-    m_inv(1, 0) = (m(1, 2) * m(2, 0) - m(1, 0) * m(2, 2)) * invdet;
-    m_inv(1, 1) = (m(0, 0) * m(2, 2) - m(0, 2) * m(2, 0)) * invdet;
-    m_inv(1, 2) = (m(1, 0) * m(0, 2) - m(0, 0) * m(1, 2)) * invdet;
-    m_inv(2, 0) = (m(1, 0) * m(2, 1) - m(2, 0) * m(1, 1)) * invdet;
-    m_inv(2, 1) = (m(2, 0) * m(0, 1) - m(0, 0) * m(2, 1)) * invdet;
-    m_inv(2, 2) = (m(0, 0) * m(1, 1) - m(1, 0) * m(0, 1)) * invdet;
-    return m_inv;
-}
-
-template <typename T0, typename T1>
-DEVICE
-inline auto operator+(const TMatrix4x4<T0> &m0, const TMatrix4x4<T1> &m1) -> TMatrix4x4<decltype(m0(0, 0) + m1(0, 0))> {
-    TMatrix4x4<decltype(m0(0, 0) + m1(0, 0))> m;
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            m(i, j) = m0(i, j) + m1(i, j);
-        }
-    }
-    return m;
-}
-
-template <typename T>
-DEVICE
-TMatrix3x3<T> transpose(const TMatrix3x3<T> &m) {
-    return TMatrix3x3<T>(m(0, 0), m(1, 0), m(2, 0),
-                         m(0, 1), m(1, 1), m(2, 1),
-                         m(0, 2), m(1, 2), m(2, 2));
-}
-
-template <typename T>
-DEVICE
-TMatrix4x4<T> transpose(const TMatrix4x4<T> &m) {
-    return TMatrix4x4<T>(m(0, 0), m(1, 0), m(2, 0), m(3, 0),
-                         m(0, 1), m(1, 1), m(2, 1), m(3, 1),
-                         m(0, 2), m(1, 2), m(2, 2), m(3, 2),
-                         m(0, 3), m(1, 3), m(2, 3), m(3, 3));
-}
-
-template <typename T>
-DEVICE
-inline TMatrix3x3<T> operator-(const TMatrix3x3<T> &m0) {
-    TMatrix3x3<T> m;
-    for (int i = 0; i < 3; i++) {
-        for (int j = 0; j < 3; j++) {
-            m(i, j) = -m0(i, j);
-        }
-    }
-    return m;
-}
-
-template <typename T>
-DEVICE
-inline TMatrix4x4<T> operator-(const TMatrix4x4<T> &m0) {
-    TMatrix4x4<T> m;
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            m(i, j) = -m0(i, j);
-        }
-    }
-    return m;
-}
-
-template <typename T>
-DEVICE
-inline TMatrix4x4<T> operator-(const TMatrix4x4<T> &m0, const TMatrix4x4<T> &m1) {
-    TMatrix4x4<T> m;
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            m(i, j) = m0(i, j) - m1(i, j);
-        }
-    }
-    return m;
-}
-
-template <typename T>
-DEVICE
-inline TMatrix3x3<T>& operator+=(TMatrix3x3<T> &m0, const TMatrix3x3<T> &m1) {
-    for (int i = 0; i < 3; i++) {
-        for (int j = 0; j < 3; j++) {
-            m0(i, j) += m1(i, j);
-        }
-    }
-    return m0;
-}
-
-template <typename T>
-DEVICE
-inline TMatrix4x4<T>& operator+=(TMatrix4x4<T> &m0, const TMatrix4x4<T> &m1) {
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            m0(i, j) += m1(i, j);
-        }
-    }
-    return m0;
-}
-
-template <typename T>
-DEVICE
-inline TMatrix4x4<T>& operator-=(TMatrix4x4<T> &m0, const TMatrix4x4<T> &m1) {
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            m0(i, j) -= m1(i, j);
-        }
-    }
-    return m0;
-}
-
-template <typename T>
-DEVICE
-inline TMatrix4x4<T> operator*(const TMatrix4x4<T> &m0, const TMatrix4x4<T> &m1) {
-    TMatrix4x4<T> m;
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            for (int k = 0; k < 4; k++) {
-                m(i, j) += m0(i, k) * m1(k, j);
-            }
-        }
-    }
-    return m;
-}
-
-template <typename T>
-DEVICE
-TMatrix4x4<T> inverse(const TMatrix4x4<T> &m) {
-    // https://stackoverflow.com/questions/1148309/inverting-a-4x4-matrix
-    TMatrix4x4<T> inv;
-
-    inv(0, 0) = m(1, 1) * m(2, 2) * m(3, 3) -
-                m(1, 1) * m(2, 3) * m(3, 2) -
-                m(2, 1) * m(1, 2) * m(3, 3) +
-                m(2, 1) * m(1, 3) * m(3, 2) +
-                m(3, 1) * m(1, 2) * m(2, 3) -
-                m(3, 1) * m(1, 3) * m(2, 2);
-
-    inv(1, 0) = -m(1, 0) * m(2, 2) * m(3, 3) +
-                 m(1, 0) * m(2, 3) * m(3, 2) +
-                 m(2, 0) * m(1, 2) * m(3, 3) -
-                 m(2, 0) * m(1, 3) * m(3, 2) -
-                 m(3, 0) * m(1, 2) * m(2, 3) +
-                 m(3, 0) * m(1, 3) * m(2, 2);
-
-    inv(2, 0) = m(1, 0) * m(2, 1) * m(3, 3) -
-                m(1, 0) * m(2, 3) * m(3, 1) -
-                m(2, 0) * m(1, 1) * m(3, 3) +
-                m(2, 0) * m(1, 3) * m(3, 1) +
-                m(3, 0) * m(1, 1) * m(2, 3) -
-                m(3, 0) * m(1, 3) * m(2, 1);
-
-    inv(3, 0) = -m(1, 0) * m(2, 1) * m(3, 2) +
-                 m(1, 0) * m(2, 2) * m(3, 1) +
-                 m(2, 0) * m(1, 1) * m(3, 2) -
-                 m(2, 0) * m(1, 2) * m(3, 1) -
-                 m(3, 0) * m(1, 1) * m(2, 2) +
-                 m(3, 0) * m(1, 2) * m(2, 1);
-
-    inv(0, 1) = -m(0, 1) * m(2, 2) * m(3, 3) +
-                 m(0, 1) * m(2, 3) * m(3, 2) +
-                 m(2, 1) * m(0, 2) * m(3, 3) -
-                 m(2, 1) * m(0, 3) * m(3, 2) -
-                 m(3, 1) * m(0, 2) * m(2, 3) +
-                 m(3, 1) * m(0, 3) * m(2, 2);
-
-    inv(1, 1) = m(0, 0) * m(2, 2) * m(3, 3) -
-                m(0, 0) * m(2, 3) * m(3, 2) -
-                m(2, 0) * m(0, 2) * m(3, 3) +
-                m(2, 0) * m(0, 3) * m(3, 2) +
-                m(3, 0) * m(0, 2) * m(2, 3) -
-                m(3, 0) * m(0, 3) * m(2, 2);
-
-    inv(2, 1) = -m(0, 0) * m(2, 1) * m(3, 3) +
-                 m(0, 0) * m(2, 3) * m(3, 1) +
-                 m(2, 0) * m(0, 1) * m(3, 3) -
-                 m(2, 0) * m(0, 3) * m(3, 1) -
-                 m(3, 0) * m(0, 1) * m(2, 3) +
-                 m(3, 0) * m(0, 3) * m(2, 1);
-
-    inv(3, 1) = m(0, 0) * m(2, 1) * m(3, 2) -
-                m(0, 0) * m(2, 2) * m(3, 1) -
-                m(2, 0) * m(0, 1) * m(3, 2) +
-                m(2, 0) * m(0, 2) * m(3, 1) +
-                m(3, 0) * m(0, 1) * m(2, 2) -
-                m(3, 0) * m(0, 2) * m(2, 1);
-
-    inv(0, 2) = m(0, 1) * m(1, 2) * m(3, 3) -
-                m(0, 1) * m(1, 3) * m(3, 2) -
-                m(1, 1) * m(0, 2) * m(3, 3) +
-                m(1, 1) * m(0, 3) * m(3, 2) +
-                m(3, 1) * m(0, 2) * m(1, 3) -
-                m(3, 1) * m(0, 3) * m(1, 2);
-
-    inv(1, 2) = -m(0, 0) * m(1, 2) * m(3, 3) +
-                 m(0, 0) * m(1, 3) * m(3, 2) +
-                 m(1, 0) * m(0, 2) * m(3, 3) -
-                 m(1, 0) * m(0, 3) * m(3, 2) -
-                 m(3, 0) * m(0, 2) * m(1, 3) +
-                 m(3, 0) * m(0, 3) * m(1, 2);
-
-    inv(2, 2) = m(0, 0) * m(1, 1) * m(3, 3) -
-                m(0, 0) * m(1, 3) * m(3, 1) -
-                m(1, 0) * m(0, 1) * m(3, 3) +
-                m(1, 0) * m(0, 3) * m(3, 1) +
-                m(3, 0) * m(0, 1) * m(1, 3) -
-                m(3, 0) * m(0, 3) * m(1, 1);
-
-    inv(3, 2) = -m(0, 0) * m(1, 1) * m(3, 2) +
-                 m(0, 0) * m(1, 2) * m(3, 1) +
-                 m(1, 0) * m(0, 1) * m(3, 2) -
-                 m(1, 0) * m(0, 2) * m(3, 1) -
-                 m(3, 0) * m(0, 1) * m(1, 2) +
-                 m(3, 0) * m(0, 2) * m(1, 1);
-
-    inv(0, 3) = -m(0, 1) * m(1, 2) * m(2, 3) +
-                 m(0, 1) * m(1, 3) * m(2, 2) +
-                 m(1, 1) * m(0, 2) * m(2, 3) -
-                 m(1, 1) * m(0, 3) * m(2, 2) -
-                 m(2, 1) * m(0, 2) * m(1, 3) +
-                 m(2, 1) * m(0, 3) * m(1, 2);
-
-    inv(1, 3) = m(0, 0) * m(1, 2) * m(2, 3) -
-                m(0, 0) * m(1, 3) * m(2, 2) -
-                m(1, 0) * m(0, 2) * m(2, 3) +
-                m(1, 0) * m(0, 3) * m(2, 2) +
-                m(2, 0) * m(0, 2) * m(1, 3) -
-                m(2, 0) * m(0, 3) * m(1, 2);
-
-    inv(2, 3) = -m(0, 0) * m(1, 1) * m(2, 3) +
-                 m(0, 0) * m(1, 3) * m(2, 1) +
-                 m(1, 0) * m(0, 1) * m(2, 3) -
-                 m(1, 0) * m(0, 3) * m(2, 1) -
-                 m(2, 0) * m(0, 1) * m(1, 3) +
-                 m(2, 0) * m(0, 3) * m(1, 1);
-
-    inv(3, 3) = m(0, 0) * m(1, 1) * m(2, 2) -
-                m(0, 0) * m(1, 2) * m(2, 1) -
-                m(1, 0) * m(0, 1) * m(2, 2) +
-                m(1, 0) * m(0, 2) * m(2, 1) +
-                m(2, 0) * m(0, 1) * m(1, 2) -
-                m(2, 0) * m(0, 2) * m(1, 1);
-
-    auto det = m(0, 0) * inv(0, 0) +
-               m(0, 1) * inv(1, 0) +
-               m(0, 2) * inv(2, 0) +
-               m(0, 3) * inv(3, 0);
-
-    if (det == 0) {
-        return TMatrix4x4<T>{};
-    }
-
-    auto inv_det = 1.0 / det;
-
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            inv(i, j) *= inv_det;
-        }
-    }
-
-    return inv;
-}
-
-template <typename T>
-inline std::ostream& operator<<(std::ostream &os, const TMatrix3x3<T> &m) {
-    for (int i = 0; i < 3; i++) {
-        for (int j = 0; j < 3; j++) {
-            os << m(i, j) << " ";
-        }
-        os << std::endl;
-    }
-    return os;
-}
-
-template <typename T>
-inline std::ostream& operator<<(std::ostream &os, const TMatrix4x4<T> &m) {
-    for (int i = 0; i < 4; i++) {
-        for (int j = 0; j < 4; j++) {
-            os << m(i, j) << " ";
-        }
-        os << std::endl;
-    }
-    return os;
-}
-
-template <typename T>
-DEVICE
-TVector2<T> xform_pt(const TMatrix3x3<T> &m, const TVector2<T> &pt) {
-    TVector3<T> t{m(0, 0) * pt[0] + m(0, 1) * pt[1] + m(0, 2),
-                  m(1, 0) * pt[0] + m(1, 1) * pt[1] + m(1, 2),
-                  m(2, 0) * pt[0] + m(2, 1) * pt[1] + m(2, 2)};
-    return TVector2<T>{t[0] / t[2], t[1] / t[2]};
-}
-
-template <typename T>
-DEVICE
-void d_xform_pt(const TMatrix3x3<T> &m, const TVector2<T> &pt,
-                const TVector2<T> &d_out,
-                TMatrix3x3<T> &d_m,
-                TVector2<T> &d_pt) {
-    TVector3<T> t{m(0, 0) * pt[0] + m(0, 1) * pt[1] + m(0, 2),
-                  m(1, 0) * pt[0] + m(1, 1) * pt[1] + m(1, 2),
-                  m(2, 0) * pt[0] + m(2, 1) * pt[1] + m(2, 2)};
-    auto out = TVector2<T>{t[0] / t[2], t[1] / t[2]};
-    TVector3<T> d_t{d_out[0] / t[2],
-                    d_out[1] / t[2],
-                    -(d_out[0] * out[0] + d_out[1] * out[1]) / t[2]};
-    d_m(0, 0) += d_t[0] * pt[0];
-    d_m(0, 1) += d_t[0] * pt[1];
-    d_m(0, 2) += d_t[0];
-    d_m(1, 0) += d_t[1] * pt[0];
-    d_m(1, 1) += d_t[1] * pt[1];
-    d_m(1, 2) += d_t[1];
-    d_m(2, 0) += d_t[2] * pt[0];
-    d_m(2, 1) += d_t[2] * pt[1];
-    d_m(2, 2) += d_t[2];
-    d_pt[0] += d_t[0] * m(0, 0) + d_t[1] * m(1, 0) + d_t[2] * m(2, 0);
-    d_pt[1] += d_t[0] * m(0, 1) + d_t[1] * m(1, 1) + d_t[2] * m(2, 1);
-}
-
-template <typename T>
-DEVICE
-TVector2<T> xform_normal(const TMatrix3x3<T> &m_inv, const TVector2<T> &n) {
-    return normalize(TVector2<T>{m_inv(0, 0) * n[0] + m_inv(1, 0) * n[1],
-                                 m_inv(0, 1) * n[0] + m_inv(1, 1) * n[1]});
-}

spaces/Callimethee/Imagine-CR/README.md

@@ -1,13 +0,0 @@
----
-title: Imagine CR
-emoji: 🎲
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Generate your own Critical Role moments with this neural network!

spaces/CatNika/New_Cat_Proxy/README.md

@@ -1,10 +0,0 @@
----
-title: NikaProxy
-emoji: 🏆
-colorFrom: yellow
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Chris4K/llms_compare/Arthashastra Book In Urdu Free [NEW] Download.md

@@ -1,80 +0,0 @@
-## Arthashastra Book In Urdu Free Download
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-
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-- **Rajamandala**: This is a theory of foreign relations that divides the world into circles of friendly and hostile states. The king should seek to expand his influence by forming alliances with the friendly states and weakening the hostile ones.
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spaces/ClassCat/ViT-ImageNet-Classification/README.md

@@ -1,12 +0,0 @@
----
-title: ViT ImageNet Classification
-emoji: 🔥
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CompVis/stable-diffusion-license/README.md

@@ -1,10 +0,0 @@
----
-title: License
-emoji: ⚖️
-colorFrom: red
-colorTo: indigo
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference