diff --git a/spaces/101-5/gpt4free/g4f/.v1/unfinished/gptbz/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/unfinished/gptbz/__init__.py
deleted file mode 100644
index e95d5716960fdf1862744534f1a22522efd864a8..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/unfinished/gptbz/__init__.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from json import dumps, loads
-
-import websockets
-
-
-# Define the asynchronous function to test the WebSocket connection
-
-
-async def test():
- # Establish a WebSocket connection with the specified URL
- async with websockets.connect('wss://chatgpt.func.icu/conversation+ws') as wss:
-
- # Prepare the message payload as a JSON object
- payload = {
- 'content_type': 'text',
- 'engine': 'chat-gpt',
- 'parts': ['hello world'],
- 'options': {}
- }
-
- # Send the payload to the WebSocket server
- await wss.send(dumps(obj=payload, separators=(',', ':')))
-
- # Initialize a variable to track the end of the conversation
- ended = None
-
- # Continuously receive and process messages until the conversation ends
- while not ended:
- try:
- # Receive and parse the JSON response from the server
- response = await wss.recv()
- json_response = loads(response)
-
- # Print the entire JSON response
- print(json_response)
-
- # Check for the end of the conversation
- ended = json_response.get('eof')
-
- # If the conversation has not ended, print the received message
- if not ended:
- print(json_response['content']['parts'][0])
-
- # Handle cases when the connection is closed by the server
- except websockets.ConnectionClosed:
- break
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Aermod View Crackl A Comprehensive Review of the Features and Functions of the Air Dispersion Model.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Aermod View Crackl A Comprehensive Review of the Features and Functions of the Air Dispersion Model.md
deleted file mode 100644
index 441662e5b5568246e8e7044aeb72aec884b7d9ec..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Aermod View Crackl A Comprehensive Review of the Features and Functions of the Air Dispersion Model.md
+++ /dev/null
@@ -1,91 +0,0 @@
-
-
Aermod View Crack: What Is It and How to Use It?
-
If you are interested in air dispersion modeling, you may have heard of Aermod View, a powerful software package that includes integrated models such as AERMOD, ISCST3 and SC-PRIME. But what if you don't have a license for this software? Can you still use it for free? In this article, we will explain what Aermod View Crack is, how to download and install it, and how to use it for your modeling projects.
-
Introduction
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Aermod View is a software developed by Lakes Environmental that allows you to perform air dispersion modeling using the U.S. EPA's preferred regulatory models. Air dispersion modeling is a technique that simulates how pollutants disperse in the atmosphere from various sources, such as industrial plants, vehicles, fires, etc. Air dispersion modeling can help you assess the impact of emissions on human health and the environment, as well as design mitigation strategies.
A crack is a modified version of a software that bypasses its license protection and allows you to use it without paying for it. Some people use cracks to access expensive or unavailable software for personal or professional purposes. However, using cracks also involves some risks, such as legal issues, malware infections, errors, or poor performance.
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Using Aermod View Crack can have some benefits, such as saving money, accessing advanced features, or learning new skills. However, it can also have some drawbacks, such as violating intellectual property rights, exposing your computer to viruses or hackers, compromising your modeling results, or missing out on updates or support.
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How to Download and Install Aermod View Crack
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If you decide to use Aermod View Crack, you need to be careful about where you download it from and how you install it on your computer. Here are some steps you can follow:
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Find a reliable source for the crack file. You can search online for websites that offer cracks for various software, but make sure they are trustworthy and have positive reviews from other users. For example, you can check out wannacrack.com or downloadly.net for Aermod View Crack .
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Download the crack file and extract it using a tool like WinRAR or 7-Zip. You should get a folder with nine files inside.
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Install Aermod View on your computer using the setup file provided by Lakes Environmental. You can download it from their official website weblakes.com . You can choose either the 32-bit or the 64-bit version depending on your operating system.
-
After installation, copy and replace the files in the crack folder into the software installation folder. The default location is C:\Program Files (x86)\Lakes Environmental\AERMOD View\.
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Avoid antivirus issues and errors by adding the software installation folder to your antivirus exclusion list. Some antiviruses may mistakenly detect and delete some crack files as viruses. You can also disable your antivirus temporarily while installing or running the crack.
-
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How to Use Aermod View Crack
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Once you have installed Aermod View Crack successfully, you can start using it for your air dispersion modeling projects. Here are some tips on how to use it:
-
-
Create a new project by selecting File | New Project from the menu bar. You can enter a project name, description, location, time zone, units, etc.
-
Import your data by selecting File | Import Data from the menu bar. You can import meteorological data, terrain data, source data, receptor data, building data, etc. from various file formats.
-
Use the graphical user interface (GUI) to edit your data and view your model domain. You can use tools like resources (to add or modify sources), building (to add or modify buildings), receiver (to add or modify receptors), etc.
-
Use the 3D visualization tools to see your model domain in three dimensions. You can use tools like zoom (to zoom in or out), rotate (to rotate the view), pan (to move the view), etc.
-
Output your results by selecting Run | Run Model from the menu bar. You can choose which model to run (AERMOD, ISCST3 or SC-PRIME) and set various options such as output type (concentration or deposition), averaging period (hourly, daily or annual), pollutant type (gas or particle), etc.
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You can also output your results to Google Earth or other formats by selecting File | Export Data from the menu bar. This feature allows you to see where pollutants affect the surrounding areas in a more realistic way.
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Conclusion
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In conclusion, Aermod View Crack is a modified version of Aermod View that allows you to use it without paying for a license. It can be useful for learning or testing purposes, but it also involves some risks and limitations. If you want to use Aermod View Crack, you need to download it from a reliable source, install it carefully on your computer, and use it wisely for your modeling projects.
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If you want to learn more about air dispersion modeling or get professional support for your projects, we recommend you to visit weblakes.com, where you can find more information about Aermod View and other software products by Lakes Environmental . You can also contact them for licensing options or technical assistance.
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FAQs
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What are the system requirements for Aermod View Crack? The system requirements for Aermod View Crack are similar to those for Aermod View. You need a Windows operating system (Windows 10, Windows 8 & 8.1 or Windows 7), an Intel Pentium 4 processor (or equivalent) or higher, at least 2 GB of available hard disk space and at least 1 GB of RAM .
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Is Aermod View Crack legal and safe? Aermod View Crack is not legal nor safe. It violates the intellectual property rights of Lakes Environmental and exposes your computer to potential malware infections or hacking attacks. It may also compromise your modeling results or cause errors or poor performance.
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What are the differences between AERMOD, ISCST3 and SC-PRIME models? AERMOD is the state-of-the-science model based on planetary boundary layer theory that accounts for complex terrain effects and urban dispersion scenarios . ISCST3 is an older model based on Gaussian plume theory that assumes flat terrain and rural dispersion scenarios . SC-PRIME is an enhanced version of ISCST3 that incorporates building downwash effects .
-purchase a license for Aermod View .
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Where can I learn more about air dispersion modeling? You can learn more about air dispersion modeling by visiting weblakes.com/resources, where you can find tutorials, webinars, articles, newsletters and other resources on air dispersion modeling and related topics . You can also join online forums or communities where you can interact with other modelers and experts.
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bartender 10.1 Crack With Product Key Free Download 2020 LINK.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bartender 10.1 Crack With Product Key Free Download 2020 LINK.md
deleted file mode 100644
index cb87cb45f08bc35170194232142b8fe3a562c7d4..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bartender 10.1 Crack With Product Key Free Download 2020 LINK.md
+++ /dev/null
@@ -1,16 +0,0 @@
-
-
Bartender 10.1 Crack With Product Key Free Download 2020
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If you are looking for a way to design and print barcodes, labels, cards, and RFID tags, you may have heard of Bartender 10.1, a powerful and versatile software from Seagull Scientific. However, you may also be deterred by its high price tag, which can range from $265 to $795 depending on the edition. That's why some people resort to using a crack and a product key to get the software for free.
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Bartender 10.1 Crack With Product Key Free Download 2020
But is it really worth it to use a cracked version of Bartender 10.1? What are the risks and consequences of doing so? And are there any better alternatives to get the software legally and safely? In this article, we will answer these questions and more, so you can make an informed decision.
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What is Bartender 10.1?
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Bartender 10.1 is a software that allows you to create and print professional-quality barcodes, labels, cards, and RFID tags for any industry or application. You can use it to design custom layouts, import data from various sources, automate printing processes, manage printer settings, and integrate with other systems.
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Bartender 10.1 has four editions: Basic, Professional, Automation, and Enterprise Automation. Each edition has different features and capabilities, depending on your needs and budget. For example, the Basic edition only supports one user and one printer, while the Enterprise Automation edition supports unlimited users and printers, as well as advanced features such as web printing, database connectivity, centralized management, and security.
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Bartender 10.1 is compatible with Windows XP, Vista, 7, 8, 8.1, and 10 operating systems. It also supports over 3,200 printer models from more than 100 manufacturers. You can download a free 30-day trial version of Bartender 10.1 from the official website or purchase a license from an authorized reseller.
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What is a crack and a product key?
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A crack is a modified version of a software that bypasses its licensing restrictions and allows unauthorized use or distribution. A product key is a unique code that activates a software license and verifies its authenticity. A crack may include a product key generator, which is a program that produces valid product keys for a software without paying for them.
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Some people use cracks and product keys to get paid software for free or share them with others who don't have a license. They may download them from websites that offer cracked software or use peer-to-peer networks to exchange them. They may also use tools such as Magical Jelly Bean KeyFinder or command prompt to find their existing product keys and reuse them on different devices.
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However, using cracks and product keys is illegal and unethical, as it violates the software's terms of service and infringes on its intellectual property rights. It also exposes users to various risks such as malware infections, data loss, performance issues, legal penalties, and more.
b2dd77e56b
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Bug [EXCLUSIVE].md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Bug [EXCLUSIVE].md
deleted file mode 100644
index ab7c656c987245bc94075a5d890ddcff2a816624..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Bug [EXCLUSIVE].md
+++ /dev/null
@@ -1,28 +0,0 @@
-
-
Crack Bug: A Horrifying Hallucination of Cocaine Abuse
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Have you ever heard of crack bug? It is a terrifying phenomenon that affects some people who use cocaine, especially crack cocaine or methamphetamine. Crack bug is a type of tactile hallucination that makes the user feel like there are bugs crawling on or under their skin. They may scratch, cut, or burn themselves to get rid of the imaginary insects, causing severe injuries and infections.
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Crack bug is also known as coke bug, meth mite, or amphetamite. It is a sign of cocaine-induced psychosis, a condition that occurs when a person uses too much cocaine for too long and loses touch with reality. Cocaine psychosis can also cause paranoia, delusions, aggression, and violence.
In this article, we will explain what causes crack bug, how to recognize it, and how to treat it. We will also provide some resources for people who want to quit cocaine and recover from its harmful effects.
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What Causes Crack Bug?
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Crack bug is caused by a combination of factors that affect the brain and the body of a cocaine user. Some of these factors are:
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Cocaine's effects on the nervous system: Cocaine is a powerful stimulant that increases the levels of dopamine, a neurotransmitter that regulates mood, pleasure, and reward. Dopamine also plays a role in sensory perception and motor control. When cocaine floods the brain with dopamine, it disrupts the normal functioning of these systems and causes abnormal sensations and movements.
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Cocaine's effects on the skin: Cocaine also increases the body temperature and blood flow to the skin, which results in increased sweating. Sweat contains an enzyme that further increases the blood flow to the skin. When the sweat evaporates, it removes the protective oil that coats the skin and prevents dryness and irritation. The combined effects of sweating, oil loss, and dehydration create a sensation similar to a bug crawling on or under the skin.
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Cocaine's effects on hygiene: Many people who use cocaine excessively neglect their personal hygiene and health. They may skip showering, brushing their teeth, changing their clothes, or washing their bedding. They may also live in dirty or infested environments. Being physically dirty can worsen the itching and scratching caused by crack bug.
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Cocaine's effects on mental health: Cocaine can also trigger or exacerbate mental health problems such as anxiety, depression, bipolar disorder, schizophrenia, or post-traumatic stress disorder (PTSD). These conditions can affect how a person perceives and interprets reality and can contribute to hallucinations and delusions.
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How to Recognize Crack Bug?
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Crack bug is not a common or normal side effect of cocaine use. It is a sign of severe cocaine abuse and addiction that requires immediate medical attention. Some of the symptoms and signs of crack bug are:
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Intense itching: The person feels an unbearable itch on their skin that does not go away with scratching or applying creams or lotions.
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Scratching: The person scratches their skin compulsively and excessively, sometimes until they bleed or create open wounds.
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Cutting: The person uses knives, scissors, needles, or other sharp objects to cut their skin in an attempt to remove the bugs.
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Burning: The person uses fire, cigarettes, lighters, or other hot objects to burn their skin in an attempt to kill the bugs.
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Picking: The person picks at their skin with their fingers or tweezers to extract the bugs.
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Hallucinating: The person sees, hears, feels, smells, or tastes bugs that are not there. They may also have other hallucinations such as voices, shadows, or figures.
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Deluding: The person believes that they are infested with bugs and that no one can help them. They may also have other delusions such as being persecuted, controlled, or spied on by others.
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Isolating: The person avoids social contact and stays in their home or room for long periods of time.
- ddb901b051
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dilwale Movie 2015 English Subtitles 30 How to Download and Watch the Full HD Version.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dilwale Movie 2015 English Subtitles 30 How to Download and Watch the Full HD Version.md
deleted file mode 100644
index e629e3067d30b25e5914f76938c01fe966bff2ac..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dilwale Movie 2015 English Subtitles 30 How to Download and Watch the Full HD Version.md
+++ /dev/null
@@ -1,102 +0,0 @@
-
-
Dilwale Movie 2015 English Subtitles 30
-
Dilwale is a Hindi-language action comedy film that was released in 2015. It is directed by Rohit Shetty and stars Shah Rukh Khan, Kajol, Varun Dhawan, and Kriti Sanon in the lead roles. The film is about two estranged lovers who reunite after 15 years with the help of their younger siblings. Dilwale was a huge commercial success, grossing over ₹376 crore (US$58.61 million) worldwide. It was also well-received by the international audiences, especially in countries like Malaysia, Indonesia, Pakistan, and UAE. However, it also faced some criticism for its weak plot, unrealistic scenes, and negative portrayal of Bulgaria.
-
Introduction
-
Dilwale is a film that combines romance, comedy, action, and drama in a typical Bollywood style. It is a story of two lovers who are separated by their rival families, but fate brings them together again after many years. It is also a story of two siblings who fall in love with each other, unaware of their past connection. Dilwale is a film that showcases the star power of Shah Rukh Khan and Kajol, who are considered as one of the most iconic on-screen couples in Indian cinema. It is also a film that introduces Varun Dhawan and Kriti Sanon as a fresh pair, who add some humor and charm to the film. Dilwale is a film that appeals to the masses with its entertaining elements, such as stunning locations, catchy songs, thrilling stunts, and hilarious dialogues.
The film begins with a flashback to Bulgaria in 2000, where Raj (Shah Rukh Khan) and Meera (Kajol) meet during a car chase between their rival gangs. Raj is the son of Randhir Bakshi (Vinod Khanna), a crime boss who has a feud with Dev Malik (Kabir Bedi), Meera's father. Raj and Meera fall in love at first sight, but soon discover their true identities. They decide to elope, but their plan goes wrong when Meera shoots Raj on her birthday, believing that he has betrayed her. Raj survives the gunshot, but vows to never forgive Meera.
-
The present story of Veer and Ishita
-
The film then shifts to Goa in 2015, where Veer (Varun Dhawan), Raj's younger brother, works as a car mechanic with his friends Sidhu (Varun Sharma) and Oscar (Sanjay Mishra). Veer meets Ishita (Kriti Sanon), Meera's younger sister, who has come to India for her studies. They fall in love with each other, but face trouble from King (Boman Irani), a drug lord who wants to marry Ishita. Veer also gets into a fight with King's men, who try to harass Ishita. Raj intervenes and saves Veer from King's wrath. Raj reveals himself to be Kaali, a notorious gangster who has been hiding his identity for 15 years. He also tells Veer that he had a girlfriend named Meera in Bulgaria, who broke his heart.
-
Veer decides to reunite Raj and Meera with Ishita's help. They trick Raj and Meera into meeting each other at various places, hoping that they will reconcile their differences. However, Raj and Meera still hold grudges against each other, and refuse to accept their love. Meanwhile, Dev Malik arrives in Goa with his henchman Shetty (Mukesh Tiwari), looking for Kaali. He learns that Kaali is none other than Raj Bakshi, his old enemy. He also finds out that Veer and Ishita are his grandchildren.
-
Dev Malik kidnaps Veer and Ishita, and threatens to kill them unless Raj surrenders himself. Raj agrees to meet Dev Malik at an abandoned factory, where he confronts him and his men. He also meets Meera there, who reveals that she never wanted to shoot him in Bulgaria. She says that she was forced by her father to do so, as he had planted a bomb in her car that would explode if she did not pull the trigger. She also says that she still loves him, and asks for his forgiveness.
-
Raj forgives Meera, and they embrace each other. They then fight against Dev Malik's gang together with Veer and Ishita's help. They manage to defeat them all, but Dev Malik escapes with a gun. He shoots at Raj from behind, but Meera comes in front of him and takes the bullet instead. She falls down bleeding profusely.
-
Raj rushes to her side, and begs her not to die. He confesses his love for her again, and says that he cannot live without her. Meera smiles weakly at him, and says that she loves him too. She then closes her eyes.
-
The film ends with a voice-over by Raj saying that Meera survived the gunshot wound miraculously due to their true love. He says that they got married soon after that incident along with Veer and Ishita. He says that they are living happily ever after as Dilwale (bravehearts).
-
Analysis of the movie
-
The strengths of the movie
-
One of the main strengths of Dilwale is the chemistry between Shah Rukh Khan and Kajol, who have worked together in several hit films before such as Dilwale Dulhania Le Jayenge, Kuch Kuch Hota Hai , Kabhi Khushi Kabhie Gham , My Name Is Khan , etc . They share an amazing rapport on screen , which makes their scenes more believable and emotional . They also look good together , despite their age difference . Their fans were eagerly waiting for their reunion after five years , which was one of the reasons for Dilwale 's popularity .
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Another strength of Dilwale is the comedy scenes involving Varun Dhawan , Varun Sharma , Johnny Lever , Sanjay Mishra , Boman Irani , etc . They provide some comic relief in between the serious drama , which makes the film more enjoyable . They also have some funny dialogues , which make the audience laugh out loud . Varun Dhawan especially impresses with his comic timing , expressions , and dance moves . He also has a good chemistry with Kriti Sanon , who plays his love interest .
-
A third strength of Dilwale is the action sequences and cinematography . The film has some spectacular stunts , such as car chases , explosions , gunfights , etc . which keep the audience on the edge of their seats . The film also has some beautiful locations , such as Goa , Bulgaria , Iceland , etc . which add to its visual appeal . The film has been shot well by Dudley , who captures the scenic beauty as well as the thrill of the action scenes .
-
A fourth strength of Dilwale is the music and songs by Pritam . The film has some catchy songs , such as Gerua , Janam Janam , Tukur Tukur , Manma Emotion Jaage , etc . which are liked by many people . The songs are also picturized well on the actors , especially Gerua , which features Shah Rukh Khan and Here is the continuation of the article. videos, and songs of the film on YouTube. However, if you are looking for a more engaging and original film, you may want to skip Dilwale and watch something else.
-
FAQs
-
-
-
Question
-
Answer
-
-
-
Who are the singers of Gerua song?
-
The singers of Gerua song are Arijit Singh and Antara Mitra.
-
-
-
Where was Dilwale shot?
-
Dilwale was shot in various locations, such as Goa, Bulgaria, Iceland, Hyderabad, etc.
-
-
-
What is the meaning of Dilwale?
-
Dilwale means bravehearts or people who have a big heart.
-
-
-
How many awards did Dilwale win?
-
Dilwale won 11 awards and received 12 nominations in various categories.
-
-
-
Who played the role of King in Dilwale?
-
The role of King in Dilwale was played by Boman Irani.
-
-
- 0a6ba089eb
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Half-Life 2 Non Steam Crack and Join the Millions of Fans Worldwide.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Half-Life 2 Non Steam Crack and Join the Millions of Fans Worldwide.md
deleted file mode 100644
index 895caa3b7c5decc20eaa45e694549dba0d65d22c..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Half-Life 2 Non Steam Crack and Join the Millions of Fans Worldwide.md
+++ /dev/null
@@ -1,233 +0,0 @@
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How to Download Half-Life 2 for Free on PC (Latest Version)
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Are you a fan of first-person shooter games? Do you want to experience one of the most acclaimed games of all time? If so, you might be interested in downloading Half-Life 2 for free on your PC.
Half-Life 2 is a sequel to the legendary Half-Life, which was released in 1998 by Valve. It is a game that combines action, adventure, horror, sci-fi, and puzzle elements in a immersive story-driven experience. You play as Gordon Freeman, a scientist who wakes up in a dystopian future where Earth is under the control of an alien empire called the Combine. You join a resistance movement that aims to overthrow the Combine and restore freedom to humanity.
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Half-Life 2 was released in 2004 and received universal acclaim from critics and players alike. It won over 50 Game of the Year awards and is considered one of the best games ever made. It has sold over 12 million copies worldwide and spawned two episodic sequels, Half-Life 2: Episode One and Half-Life 2: Episode Two.
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However, if you want to play Half-Life 2 on your PC, you normally need to buy it from Steam, which is an online platform that distributes digital games. Steam requires you to create an account, pay for the game, download it, install it, and launch it through Steam. This can be inconvenient for some people who prefer to play offline or without Steam.
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That's why some people look for a non-steam crack for Half-Life 2. A non-steam crack is a modified version of the game that allows you to play it without Steam or internet connection required. You can download it for free from various websites that offer cracked games.
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However, before you proceed, you should be aware of some legal and ethical issues regarding downloading cracked games. First of all, downloading cracked games is illegal in most countries, as it violates the copyright laws that protect the game developers. Second, downloading cracked games can be risky for your PC, as they may contain viruses, malware, or spyware that can harm your system or steal your data. Third, downloading cracked games can be unfair for the game developers, as they lose revenue and recognition for their hard work.
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Therefore, we do not endorse or encourage downloading cracked games. We are only providing this information for educational purposes only. We recommend that you buy Half-Life 2 from Steam if you can afford it and support Valve. However, if you still want to download Half-Life 2 for free on your PC (latest version), we will show you how to do it in this article.
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Features and Gameplay of Half-Life 2
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Before we show you how to download Half-Life 2 for free on your PC (latest version), let's take a look at some of its features and gameplay aspects that make it such a great game.
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Game Plot
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The plot of Half-Life 2 is set several years after the events of Half-Life. Gordon Freeman, who was put into stasis by a mysterious figure known as G-Man at the end of Half-Life, is awakened by G-Man and transported to City 17, a metropolis under the rule of the Combine.
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The Combine is an interdimensional empire that invaded Earth after Gordon opened a portal to their world at Black Mesa Research Facility in Half-Life. The Combine has enslaved most of humanity using brutal methods such as suppression fields, brainwashing, torture, and genetic modification.
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Gordon joins a resistance movement led by his former colleagues from Black Mesa, such as Alyx Vance, Barney Calhoun, Isaac Kleiner, Eli Vance, Judith Mossman, and others. They plan to use an ancient alien device called the Lambda Core to teleport Gordon to Borealis, a research vessel that contains a powerful weapon that can destroy the Combine.
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Along his journey, Gordon faces various enemies such as Combine soldiers, zombies, headcrabs, antlions, hunters, striders, gunships, helicopters, advisors, and more. He also meets various allies such as Vortigaunts (alien slaves who rebelled against the Combine), Dog (Alyx's robotic pet), Father Grigori (a mad priest who lives in Ravenholm), Resistance fighters (human rebels who fight against the Combine), Citizens (oppressed humans who live in City 17), etc.
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The game has several memorable moments such as escaping from City 17 with Alyx using an airboat; fighting zombies in Ravenholm with a gravity gun; driving across Highway 17 with a buggy; assaulting Nova Prospekt with antlions; rescuing Eli Vance from prison; defending White Forest base from striders; infiltrating Citadel with Alyx; confronting Dr. Breen (the human leader who betrayed humanity); escaping from G-Man's control; etc.
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The game also has two episodic sequels that continue Gordon's story after he defeats Dr. Breen at Citadel. In Episode One, Gordon escapes from City 17 with Alyx before it explodes due to a dark energy reactor meltdown; they also discover that G-Man has been captured by Combine advisors. In Episode Two, Gordon travels across Eastern Europe with Alyx; they reach White Forest base where they launch a rocket that closes Portal Storms caused by Citadel's explosion; they also encounter Hunters (new Combine enemies) and Advisors (the true leaders of Combine); they also learn about Borealis' location from Judith Mossman; however, Alyx gets mortally wounded by an Advisor at White Forest base; Gordon gets contacted by G-Man who offers him a choice: either to accept a new assignment or to refuse and face the consequences. The game ends with a cliffhanger, leaving Gordon's fate unknown. [1]
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Game Mechanics
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The game mechanics of Half-Life 2 are similar to those of Half-Life, but with several improvements and additions. The game is a first-person shooter that requires the player to use various weapons, items, and environmental objects to combat enemies and solve puzzles. The game also features a physics engine that allows realistic interactions with the environment and objects.
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Some of the weapons and items that Gordon can use include:
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The crowbar: Gordon's signature melee weapon that can break wooden crates and smash enemies.
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The pistol: A basic firearm that has a laser sight and a secondary fire mode that fires a powerful bullet.
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The submachine gun: A rapid-fire weapon that has a grenade launcher as a secondary fire mode.
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The shotgun: A powerful close-range weapon that can fire two shots at once as a secondary fire mode.
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The crossbow: A silent weapon that can fire heated metal bolts that can pin enemies to walls or objects.
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The pulse rifle: An energy-based weapon that can fire plasma balls as a secondary fire mode that can disintegrate enemies or create shockwaves.
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The rocket launcher: A weapon that can fire rockets that can be guided by a laser pointer.
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The gravity gun: A unique weapon that can manipulate objects using gravity. It can pick up, throw, or catch objects, or use them as shields or projectiles. It can also be upgraded to manipulate organic matter and larger objects.
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The pheropods: A device that can control antlions, which are insect-like creatures that can attack enemies or dig tunnels.
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Some of the enemies that Gordon will encounter include:
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Headcrabs: Small parasitic creatures that can latch onto human heads and turn them into zombies.
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Zombies: Humans who have been infected by headcrabs and turned into mindless creatures that can attack with claws or throw objects.
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Combine soldiers: Human soldiers who have been augmented by the Combine and equipped with advanced weapons and armor.
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Combine elites: Elite versions of Combine soldiers who have more health and better weapons.
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Combine snipers: Combine soldiers who are hidden in high places and use sniper rifles to shoot at Gordon.
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Combine turrets: Automated turrets that can detect and shoot at Gordon with machine guns or rockets.
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Combine gunships: Flying vehicles that can shoot at Gordon with pulse cannons or missiles.
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Combine striders: Giant tripod-like vehicles that can walk on any terrain and shoot at Gordon with pulse cannons or warp cannons.
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Combine hunters: Small tripod-like vehicles that can chase Gordon and shoot at him with flechettes or explosives.
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Combine advisors: Large worm-like creatures that are the true leaders of the Combine. They have telekinetic and telepathic abilities and are protected by a metal shell.
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Antlions: Insect-like creatures that live underground and attack anything that disturbs the sand. They can be controlled by pheropods.
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Barnacles: Ceiling-dwelling creatures that use their tongues to catch and eat anything below them.
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Some of the puzzles and physics-based challenges that Gordon will face include:
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Using the gravity gun to move objects, create paths, activate switches, or defeat enemies.
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Using the rocket launcher to guide rockets through obstacles or windows to hit targets.
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Using the crossbow to pin enemies or objects to walls or ceilings.
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Using the pheropods to command antlions to attack enemies or dig tunnels.
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Using vehicles such as an airboat, a buggy, or a jalopy to traverse different terrains and avoid hazards.
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Using environmental objects such as barrels, crates, saw blades, magnets, explosives, etc. to create diversions, traps, or weapons.
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Game Graphics and Sound
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The game graphics of Half-Life 2 are impressive for its time and still hold up well today. The game uses the Source engine, which allows for realistic lighting, shadows, textures, animations, physics, facial expressions, etc. The game also features various graphical effects such as water reflections, particle effects, motion blur, etc. The game has a distinct visual style that combines realistic environments with sci-fi elements. The game has various settings such as urban areas, industrial facilities, rural landscapes, coastal regions, arctic zones, etc. The game also has various weather effects such as rain, snow, fog, etc. The game also has various cinematic sequences that enhance the immersion and storytelling of the game.
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The game sound of Half-Life 2 is also excellent for its time and still sounds great today. The game uses the Source engine's sound system, which allows for realistic sound effects, ambient noises, music, voice acting, etc. The game also features various sound effects such as gunfire, explosions, footsteps, screams, etc. The game also features various ambient noises such as wind, water, machines, animals, etc. The game also features various music tracks that suit the mood and atmosphere of each scene. The game also features various voice actors who deliver their lines with emotion and personality. Some of the notable voice actors include Robert Guillaume (Eli Vance), Merle Dandridge (Alyx Vance), Louis Gossett Jr. (Vortigaunt), Robert Culp (Dr. Breen), Michelle Forbes (Judith Mossman), Jim French (Father Grigori), John Patrick Lowrie (G-Man), etc.
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System Requirements for Half-Life 2
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If you want to download Half-Life 2 for free on your PC (latest version), you need to make sure your PC meets the minimum or recommended system requirements for the game. Here are the system requirements for Half-Life 2 according to Steam:
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Minimum Requirements
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OS:
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Windows XP/Vista/7/8/10
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Processor:
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1.7 GHz
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Memory:
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512 MB RAM
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Graphics:
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DirectX® 8.1 level Graphics Card (Requires support for SSE)
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Storage:
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7 GB available space
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Sound Card:
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DirectX® Compatible Sound Card
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Recommended Requirements
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OS:
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Windows XP/Vista/7/8/10
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Processor:
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Pentium 4 3 GHz or AMD Athlon 64 3000+
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Memory:
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1 GB RAM
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Graphics:
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DirectX® 9 level Graphics Card (256 MB)
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Storage:
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7 GB available space
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Sound Card:
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DirectX® Compatible Sound Card
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How to Download Half-Life 2 for Free on PC (Latest Version)
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If you have checked your system requirements and decided to download Half-Life 2 for free on your PC (latest version), you need to follow these steps carefully. We will show you how to download the Half-Life 2 Complete Edition by AR-81, which is a non-steam crack that includes all the games in the Half-Life 2 series. Here are the steps:
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Step 1: Download the Half-Life 2 Complete Edition by AR-81 Torrent File
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The first step is to download the torrent file of the Half-Life 2 Complete Edition by AR-81. A torrent file is a small file that contains information about the files and folders that you want to download. You can use a torrent file to download large files faster and easier using a torrent client software.
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You can download the torrent file of the Half-Life 2 Complete Edition by AR-81 from this link. This link will take you to a website called DODI Repack, which is one of the websites that offer cracked games. You can also find other websites that offer cracked games by searching on Google or other search engines.
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Once you are on the DODI Repack website, you will see a page with some information about the Half-Life 2 Complete Edition by AR-81. You will also see some download options such as Cloud MailRu, Google Drive, MEGA.io, etc. You can choose any of these options, but we recommend using the Torrent option, which is usually faster and more reliable.
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To download the torrent file, you need to click on the Torrent option and then click on the Download button. You will then see a pop-up window that asks you to save the torrent file on your PC. You can choose any location on your PC to save the torrent file, but we recommend saving it on your desktop for easy access.
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Step 2: Install a Torrent Client Software on Your PC
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The second step is to install a torrent client software on your PC. A torrent client software is a program that allows you to open and use torrent files to download files and folders from other users who have the same torrent file. You can think of it as a peer-to-peer network that shares files and folders.
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There are many torrent client software options available for free on the internet. Some of the popular ones include uTorrent, BitTorrent, qBittorrent, etc. You can choose any of these options, but we recommend using uTorrent, which is one of the most widely used and user-friendly torrent client software.
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To install uTorrent on your PC, you need to go to this link. This link will take you to the official website of uTorrent, where you can download and install uTorrent for free. You will see a page with some information about uTorrent and a button that says Download uTorrent Web.
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To download uTorrent, you need to click on the Download uTorrent Web button and then click on Save File. You will then see a pop-up window that asks you to save the uTorrent installer file on your PC. You can choose any location on your PC to save the uTorrent installer file, but we recommend saving it on your desktop for easy access.
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To install uTorrent, you need to double-click on the uTorrent installer file that you have saved on your desktop. You will then see a window that asks you to agree to the terms and conditions of uTorrent. You need to click on Agree and then click on Next. You will then see another window that asks you to choose some installation options such as creating shortcuts, launching uTorrent at startup, etc. You can choose any of these options according to your preference and then click on Next. You will then see another window that asks you to install some additional software such as antivirus or browser extensions. We recommend unchecking these options as they are not necessary and may slow down your PC or cause unwanted changes. You can then click on Next and then click on Finish. You have now successfully installed uTorrent on your PC.
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Step 3: Open the Torrent File with Your Torrent Client Software and Start Downloading
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The third step is to open the torrent file of the Half-Life 2 Complete Edition by AR-81 with your torrent client software and start downloading. To do this, you need to double-click on the torrent file that you have saved on your desktop. You will then see a window that asks you to choose some download options such as the destination folder, the files to download, the bandwidth limit, etc. You can choose any of these options according to your preference and then click on OK. You will then see another window that shows you the progress of the download, such as the speed, the time remaining, the peers, the seeds, etc.
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The download process may take some time depending on your internet connection speed and the availability of peers and seeds. You can see how much time is left by looking at the ETA (estimated time of arrival) indicator. You can also pause or resume the download at any time by clicking on the Pause or Resume buttons. You can also stop or delete the download by clicking on the Stop or Delete buttons.
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Once the download is complete, you will see a message that says Seeding or Finished. This means that you have successfully downloaded all the files and folders of the Half-Life 2 Complete Edition by AR-81. You can now close the torrent client software and proceed to the next step.
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Step 4: Extract the Downloaded Files with WinRAR or 7-Zip Software
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The fourth step is to extract the downloaded files with WinRAR or 7-Zip software. WinRAR or 7-Zip software are programs that allow you to compress or decompress files and folders using various formats such as ZIP, RAR, 7Z, etc. You can use WinRAR or 7-Zip software to extract the files and folders of the Half-Life 2 Complete Edition by AR-81 from a compressed format to a normal format.
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You can download WinRAR or 7-Zip software for free from these links . These links will take you to the official websites of WinRAR or 7-Zip software, where you can download and install WinRAR or 7-Zip software for free. You will see a page with some information about WinRAR or 7-Zip software and a button that says Download WinRAR or Download 7-Zip.
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To download WinRAR or 7-Zip software, you need to click on the Download WinRAR or Download 7-Zip button and then click on Save File. You will then see a pop-up window that asks you to save the WinRAR or 7-Zip installer file on your PC. You can choose any location on your PC to save the WinRAR or 7-Zip installer file, but we recommend saving it on your desktop for easy access.
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To install WinRAR or 7-Zip software, you need to double-click on the WinRAR or 7-Zip installer file that you have saved on your desktop. You will then see a window that asks you to agree to the terms and conditions of WinRAR or 7-Zip software. You need to click on I Agree and then click on Install. You will then see another window that shows you the progress of the installation. You can then click on Finish. You have now successfully installed WinRAR or 7-Zip software on your PC.
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To extract the downloaded files with WinRAR or 7-Zip software, you need to go to the destination folder where you have downloaded the files and folders of the Half-Life 2 Complete Edition by AR-81. You will see a file named Half-Life_2_Complete_Edition_[DODI_Repack].rar (or .zip). This is a compressed file that contains all the files and folders of the Half-Life 2 Complete Edition by AR-81. You need to extract this file to get access to the normal files and folders of the game.
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To extract the compressed file with WinRAR software, you need to right-click on the file and select Extract Here or Extract to Half-Life_2_Complete_Edition_[DODI_Repack]. You will then see a window that shows you the progress of the extraction. You can then click on OK. You have now successfully extracted the compressed file with WinRAR software.
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To extract the compressed file with 7-Zip software, you need to right-click on the file and select 7-Zip > Extract Here or 7-Zip > Extract to Half-Life_2_Complete_Edition_[DODI_Repack]. You will then see a window that shows you the progress of the extraction. You can then click on OK. You have now successfully extracted the compressed file with 7-Zip software.
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After extracting the compressed file, you will see a folder named Half-Life_2_Complete_Edition_[DODI_Repack] on your destination folder. This folder contains all the files and folders of the Half-Life 2 Complete Edition by AR-81. You can now proceed to the next step.
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Step 5: Run the Setup.exe File and Follow the Installation Instructions
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The fifth and final step is to run the setup.exe file and follow the installation instructions. The setup.exe file is a program that allows you to install the Half-Life 2 Complete Edition by AR-81 on your PC. You can customize some installation options and language settings during the installation process.
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To run the setup.exe file, you need to go to the folder named Half-Life_2_Complete_Edition_[DODI_Repack] that you have extracted on your destination folder. You will see a file named Setup.exe inside this folder. You need to double-click on this file to launch it. You will then see a window that asks you to choose a language for the installation. You can choose any language from the list and then click on OK.
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You will then see another window that shows you some information about the Half-Life 2 Complete Edition by AR-81 and a button that says Install. You need to click on this button to start the installation process. You will then see another window that asks you to choose a destination folder for the installation. You can choose any location on your PC to install the game, but we recommend installing it on C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods for convenience and compatibility reasons. You can then click on Next.
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You will then see another window that asks you to choose some installation options such as creating shortcuts, launching at startup, etc. You can choose any of these options according to your preference and then click on Next. You will then see another window that asks you to choose a language for the game. You can choose any language from the list and then click on Next.
-
You will then see another window that shows you the progress of the installation. The installation process may take some time depending on your PC's performance and disk space. You can see how much time is left by looking at the ETA (estimated time of arrival) indicator. You can also cancel or pause the installation at any time by clicking on the Cancel or Pause buttons.
-
Once the installation is complete, you will see a message that says Installation Complete. You can then click on Finish. You have now successfully installed the Half-Life 2 Complete Edition by AR-81 on your PC.
-
Step 6: Run the Game from Your Desktop Shortcut or Steam Library and Enjoy
-
The sixth and final step is to run the game from your desktop shortcut or Steam library and enjoy. The game will automatically create a desktop shortcut and a Steam library entry for your convenience. You can use either of these options to launch the game.
-
To run the game from your desktop shortcut, you need to double-click on the icon named Half-Life 2 Complete Edition by AR-81 that you will see on your desktop. You will then see a window that shows you some information about the game and a button that says Play. You need to click on this button to start the game.
-
To run the game from your Steam library, you need to open Steam and go to your library. You will see a list of games that you have installed on your PC. You need to find and select the game named Half-Life 2 Complete Edition by AR-81. You will then see a page with some information about the game and a button that says Play. You need to click on this button to start the game.
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Once you have launched the game, you will see a menu with some options such as New Game, Load Game, Options, etc. You can choose any of these options according to your preference and start playing the game. You can also customize some game settings such as video, audio, keyboard, mouse, etc. from the Options menu.
-
You have now successfully downloaded and installed Half-Life 2 for free on your PC (latest version). Enjoy playing one of the best FPS games of all time with enhanced graphics and features.
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How to Use Launch Options for Half-Life 2
-
If you want to optimize your gaming experience or tweak some settings for Half-Life 2, you can use launch options. Launch options are command-line parameters that you can add to the game's executable file or Steam shortcut to modify some aspects of the game's behavior. For example, you can use launch options to force a specific resolution, enable developer mode, disable intro videos, etc.
-
There are many launch options available for Half-Life 2 and other Source engine games. You can find a list of them here. However, not all of them may work for your system or game version, so use them at your own risk. Some of them may also conflict with each other or cause performance issues or crashes.
-
To use launch options for Half-Life 2, you need to follow these steps:
-
Step 1: Find the Game's Executable File or Steam Shortcut
-
The first step is to find the game's executable file or Steam shortcut. The executable file is a program that runs the game when you double-click on it. The Steam shortcut is a link that launches the game through Steam when you double-click on it.
-
If you have installed the game using the setup.exe file from Step 5 above, you will find the executable file in C:\\Program Files (x86)\\Steam\\steamapps\\sourcemods\\Half-Life_2_Complete_Edition_[DODI_Repack]. The file name is hl2.exe.
-
If you have installed the game using Steam, you will find the Steam shortcut in C:\\Program Files (x86)\\Steam\\steamapps\\common\\Half-Life 2. The file name is hl2.exe - Shortcut.
-
Step 2: Right-Click on the Executable File or Steam Shortcut and Select Properties
-
The second step is to right-click on the executable file or Steam shortcut and select Properties. This will open a window that shows you some information about the file or shortcut and some options to modify it.
-
If you have right-clicked on the executable file, you will see a window with four tabs: General, Compatibility, Security and Details. You need to select the Compatibility tab. You will see some options to run the file in compatibility mode for different versions of Windows. You can ignore these options unless you have some issues running the game on your system.
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If you have right-clicked on the Steam shortcut, you will see a window with three tabs: General, Web Document and Security. You need to select the Web Document tab. You will see some options to change the URL or icon of the shortcut. You can ignore these options unless you want to customize your shortcut.
-
Step 3: Add Launch Options to the Target Field
-
The third step is to add launch options to the target field. The target field is a text box that shows you the path of the file or shortcut. You can add launch options to this field by typing them after a space at the end of the path.
-
If you have right-clicked on the executable file, you will see a target field that looks something like this:
You can add launch options to these fields by typing them after a space at the end of the path. For example, if you want to force a specific resolution of 1920x1080 and disable intro videos, you can add these launch options:
- -w 1920 -h 1080 -novid
-
Your target field will then look something like this:
You can add as many launch options as you want, but make sure to separate them with spaces and use quotation marks if they contain spaces themselves. For example, if you want to enable developer mode and set a custom console key, you can add these launch options:
- -dev -console -consolekey \"`\"
-
Your target field will then look something like this:
Step 4: Click on Apply and OK to Save Your Changes
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The fourth and final step is to click on Apply and OK to save your changes. This will close the window and apply your launch options to the file or shortcut. You can now run the game with your customized settings by double-clicking on the file or shortcut.
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You can also change or remove your launch options at any time by repeating these steps and editing or deleting them from the target field.
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You have now successfully used launch options for Half-Life 2. Enjoy playing the game with your preferred settings and features.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Chess Play and Learn MOD APK The Ultimate Chess App for Beginners and Experts.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Chess Play and Learn MOD APK The Ultimate Chess App for Beginners and Experts.md
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index 6348895c8e06153dbed8cf7b801f4fd58da79d1a..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,192 +0,0 @@
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Chess App Mod APK: How to Download and Play the Best Chess Games on Your Android Device
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Chess is one of the oldest and most popular games in the world. It is a game of strategy, logic, and skill that can challenge your mind and improve your cognitive abilities. Whether you are a beginner or a master, chess can offer you endless hours of fun and entertainment.
But what if you want to play chess on your Android device without any limitations or restrictions? What if you want to access premium features, unlimited puzzles, online tournaments, and more for free? That's where Chess App Mod APK comes in.
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What is Chess App Mod APK?
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Chess App Mod APK is a modified version of the original chess app that allows you to enjoy the best chess games on your Android device without any ads, in-app purchases, or subscriptions. You can download and install Chess App Mod APK from various sources on the internet and play chess offline or online with millions of players around the world.
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There are many reasons why you should use Chess App Mod APK instead of the regular chess app. Here are some of them:
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You can save money by not paying for any premium features, puzzles, or memberships.
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You can save space by not downloading any extra files or updates.
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How to Download and Install Chess App Mod APK
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Downloading and installing Chess App Mod APK is easy and simple. Just follow these steps:
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Find a reliable source that offers Chess App Mod APK for free. You can search on Google or use any of the links below:
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[Chess - Play and Learn MOD APK 4.5.15 (Premium Unlocked) for Android - APKdone](^1^)
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Download the Chess App Mod APK file to your device.
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Go to your device settings and enable the installation of apps from unknown sources.
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Locate the downloaded file and tap on it to install it.
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Launch the app and enjoy playing chess with all the modded features.
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Chess - Play and Learn
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This is one of the most popular and comprehensive chess apps on the market. It offers everything you need to learn, practice, and play chess at any level. You can play online or offline with friends or computer opponents, join clubs and tournaments, watch live games and videos, solve puzzles and lessons, and much more. With Chess App Mod APK, you can unlock all the premium features, such as unlimited puzzles, advanced analysis, custom themes, and more.
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Features
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Play online or offline with friends or computer opponents of different skill levels.
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Join clubs and tournaments to compete with other players and win prizes.
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Watch live games and videos from top players and coaches.
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Solve puzzles and lessons to improve your skills and tactics.
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Analyze your games and moves with powerful tools and engines.
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Customize your app with different themes, boards, pieces, sounds, and more.
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Pros and Cons
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Pros
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Cons
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Offers a variety of features and modes for all levels of players.
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Can be overwhelming for beginners or casual players.
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Has a large and active community of players and clubs.
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Can be laggy or buggy at times due to high traffic or server issues.
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Provides high-quality content and resources for learning and improving.
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Can be expensive or limited without the premium subscription or modded version.
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Lichess - Free Online Chess
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This is another popular and reputable chess app that offers a free and open-source platform for playing and learning chess. It has a simple and elegant design that focuses on the game itself. You can play online or offline with friends or computer opponents, join tournaments and events, watch broadcasts and streams, solve puzzles and studies, and more. With Chess App Mod APK, you can access all the features without any ads or donations.
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Features
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Play online or offline with friends or computer opponents of different skill levels.
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Join tournaments and events to compete with other players and win trophies.
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Watch broadcasts and streams from top players and commentators.
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Solve puzzles and studies to improve your skills and tactics.
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Analyze your games and moves with powerful tools and engines.
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Customize your app with different themes, boards, pieces, sounds, and more.
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Pros and Cons
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Pros
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Cons
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Offers a free and open-source platform for playing and learning chess.
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Lacks some features and modes that other apps offer.
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Has a simple and elegant design that focuses on the game itself.
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Can be boring or bland for some users who prefer more customization or variety.
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Provides high-quality content and resources for learning and improving.
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Can be challenging or intimidating for some users who face strong opponents or puzzles.
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Chess Tactics Pro - Puzzles
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This is a specialized chess app that focuses on solving chess puzzles. It has a large database of puzzles that are categorized by difficulty, theme, rating, and source. You can solve puzzles to improve your skills and tactics, earn points and badges, track your progress and statistics, challenge yourself with daily puzzles, and more. With Chess App Mod APK, you can unlock all the puzzles without any ads or limitations.
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Features
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Solve puzzles to improve your skills and tactics.
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Earn points and badges to measure your performance and achievements.
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Track your progress and statistics to see your strengths and weaknesses.
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Challenge yourself with daily puzzles to test your knowledge and skills.
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Analyze your solutions and moves with powerful tools and engines.
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Pros and Cons
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Pros
Cons
Offers a large database of puzzles that are categorized by difficulty, theme, rating, and source.
Lacks other features and modes that other apps offer.
Has a simple and elegant design that focuses on the puzzles.
Can be boring or bland for some users who prefer more customization or variety.
Provides high-quality content and resources for learning and improving.
Can be challenging or intimidating for some users who face difficult or complex puzzles.
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What are the Benefits and Challenges of Playing Chess?
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Playing chess is not only fun and entertaining, but also beneficial and challenging. It can help you develop various skills and abilities that can improve your life and well-being. However, it can also pose some difficulties and drawbacks that can affect your mood and health. Here are some of the benefits and challenges of playing chess:
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Benefits of Playing Chess
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Playing chess can have many positive effects on your mind and body. Here are some of them:
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Develops perspective
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Playing chess can help you develop perspective, which is the ability to see things from different angles and viewpoints. This can help you understand yourself and others better, as well as solve problems and conflicts more effectively.
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Improves memory
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Playing chess can help you improve your memory, which is the ability to store and recall information. This can help you learn faster and easier, as well as remember things longer and clearer.
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Deepens focus
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Playing chess can help you deepen your focus, which is the ability to concentrate on one thing and ignore distractions. This can help you perform better and faster, as well as achieve your goals and tasks more efficiently.
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Elevates creativity
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Playing chess can help you elevate your creativity, which is the ability to generate new and original ideas. This can help you express yourself and others better, as well as create something unique and valuable.
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Boosts planning skills
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Playing chess can help you boost your planning skills, which is the ability to organize and execute a series of actions. This can help you manage your time and resources better, as well as anticipate and prevent potential problems.
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Increases self-awareness
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Playing chess can help you increase your self-awareness, which is the ability to recognize and understand your own emotions, thoughts, and behaviors. This can help you control your impulses and reactions better, as well as improve your self-esteem and confidence.
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Protects against dementia
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Playing chess can help you protect against dementia, which is a condition that causes a decline in mental functions. This can help you maintain your cognitive abilities and prevent memory loss, confusion, and Alzheimer's disease.
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Helps ADHD
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Playing chess can help you with ADHD, which is a disorder that causes hyperactivity, impulsivity, and inattention. This can help you calm down and focus better, as well as reduce stress and anxiety.
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Reduces panic attack symptoms
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Playing chess can help you reduce panic attack symptoms, which are sudden episodes of intense fear and discomfort. This can help you relax and breathe better, as well as cope with negative thoughts and emotions.
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Challenges of Playing Chess
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Playing chess can also have some negative effects on your mind and body. Here are some of them:
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Requires time and dedication
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Playing chess requires time and dedication, which means that you have to spend a lot of hours practicing and studying the game. This can interfere with your other responsibilities and activities, such as work, school, family, or friends.
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Can be frustrating and stressful
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Playing chess can be frustrating and stressful, which means that you have to deal with a lot of pressure and expectations. This can affect your mood and performance, as well as cause anger, frustration, or depression.
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Can be addictive and isolating
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Playing chess can be addictive and isolating, which means that you can become obsessed and dependent on the game. This can affect your social and personal life, as well as cause loneliness, isolation, or withdrawal.
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Conclusion
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Chess is a wonderful game that can offer you many benefits and challenges. It can help you develop your perspective, memory, focus, creativity, planning skills, self-awareness, and more. It can also help you protect against dementia, ADHD, and panic attack symptoms. However, it can also require time and dedication, be frustrating and stressful, and be addictive and isolating. Therefore, you should play chess with moderation and balance, and enjoy it as a hobby and not a necessity.
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If you want to play chess on your Android device without any limitations or restrictions, you should use Chess App Mod APK. This is a modified version of the original chess app that allows you to access premium features, unlimited puzzles, online tournaments, and more for free. You can download and install Chess App Mod APK from various sources on the internet and play chess offline or online with millions of players around the world.
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Some of the best chess games that you can play with Chess App Mod APK are Chess - Play and Learn, Lichess - Free Online Chess, and Chess Tactics Pro - Puzzles. These are popular and comprehensive chess apps that offer a variety of features and modes for all levels of players. You can play online or offline with friends or computer opponents, join clubs and tournaments, watch live games and videos, solve puzzles and lessons, analyze your games and moves, customize your app settings, preferences, and appearance, and more.
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Chess is a game of strategy, logic, and skill that can challenge your mind and improve your cognitive abilities. Whether you are a beginner or a master, chess can offer you endless hours of fun and entertainment. So what are you waiting for? Download Chess App Mod APK today and start playing the best chess games on your Android device!
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FAQs
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Here are some frequently asked questions about Chess App Mod APK:
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Is Chess App Mod APK safe to use?
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Yes, Chess App Mod APK is safe to use as long as you download it from a reliable source that does not contain any viruses or malware. However, you should always be careful when installing apps from unknown sources and scan them with an antivirus before opening them.
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Is Chess App Mod APK legal to use?
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No, Chess App Mod APK is not legal to use as it violates the terms and conditions of the original chess app developers. By using Chess App Mod APK, you are bypassing the security measures and revenue streams of the original app. Therefore, you should use Chess App Mod APK at your own risk and discretion.
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Is Chess App Mod APK compatible with all Android devices?
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No, Chess App Mod APK may not be compatible with all Android devices as it depends on the specifications and requirements of each device. Some devices may not support the modded features or run the app smoothly. Therefore, you should check the compatibility of your device before downloading and installing Chess App Mod APK.
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How do I update Chess App Mod APK?
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To update Chess App Mod APK, you have to download the latest version of the modded app from the same source that you downloaded it from before. Then, you have to uninstall the previous version of the app from your device and install the new version. Alternatively, you can check if the app has an auto-update feature that allows you to update it within the app itself.
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How do I uninstall Chess App Mod APK?
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To uninstall Chess App Mod APK , you have to go to your device settings and find the app in the list of installed apps. Then, you have to tap on the app and select the uninstall option. Alternatively, you can long-press the app icon on your home screen and drag it to the uninstall option.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Anime Stories with Gacha Life on PC - Free Download 1.1.4.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Anime Stories with Gacha Life on PC - Free Download 1.1.4.md
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How to Download and Play Gacha Life on PC
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If you are a fan of anime, casual games, and character customization, you might have heard of Gacha Life. This popular game lets you create your own anime-styled characters and dress them up in your favorite fashion outfits. You can also interact with other characters, play mini-games, and create your own scenes and stories. But did you know that you can also play Gacha Life on your PC? In this article, we will show you how to download and play Gacha Life on PC, and why it is a better option than playing on your mobile device.
Gacha Life is a casual game developed by Lunime, a studio that specializes in anime-themed games. The game was released in October 2018 for Android and iOS devices, and has since gained over 50 million downloads and a 4.5-star rating on Google Play. The game is suitable for players of all ages, as it does not contain any violence or inappropriate content.
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A creative platform for character customization and storytelling
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The main attraction of Gacha Life is the character customization feature. You can create your own anime characters from scratch, or use the preset ones. You can choose from hundreds of hairstyles, clothes, accessories, weapons, pets, and more. You can also change the colors, sizes, positions, and expressions of each item. You can create up to 20 characters and save them in your studio.
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But that's not all. You can also use your characters to create your own scenes and stories. You can choose from different backgrounds, poses, props, and dialogue bubbles. You can also adjust the camera angle, zoom, and lighting. You can make your characters talk to each other, or to you. You can create comics, skits, movies, or anything you can imagine. You can also share your creations with other players online, or download them to your device.
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Why play Gacha Life on PC?
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Enjoy a larger and clearer display
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One of the advantages of playing Gacha Life on PC is that you can enjoy a larger and clearer display than on your mobile device. This means that you can see more details of your characters and scenes, and appreciate the colorful graphics better. You can also avoid eye strain and battery drain from staring at a small screen for too long.
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Use keyboard and mouse controls
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Another benefit of playing Gacha Life on PC is that you can use keyboard and mouse controls instead of touch controls. This can make the game easier and more comfortable to play, especially if you are used to playing games on PC. You can also customize the key mappings to suit your preferences.
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Access more features and updates
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A third reason to play Gacha Life on PC is that you can access more features and updates than on your mobile device. For example, you can play the full version of Gacha Life on PC, which includes more modes such as online chat, leaderboards, battle mode, gacha club, gacha resort, etc. You can also get the latest updates faster than on your mobile device.
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How to download and play Gacha Life on PC?
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Download an Android emulator
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To play Gacha Life on PC, you will need - Mini-games: These are fun and simple games that you can play to earn gems and gacha tickets. You can play games such as Bex's Festival, Duck & Dodge, Phantom's Remix, and more. You can also challenge other players online and compete for high scores. - Chat mode: This is where you can chat with other players online. You can join different chat rooms, or create your own. You can also send private messages, friend requests, and gifts to other players. You can also use emojis, stickers, and filters to express yourself.
Collect gems and gacha tickets
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Gems and gacha tickets are the main currencies in Gacha Life. You can use them to get items for your characters, or to unlock more features and modes. Here are some ways to get gems and gacha tickets:
- - Play mini-games: This is the easiest and most fun way to earn gems and gacha tickets. You can play any of the mini-games and get rewards based on your performance. You can also get bonus rewards for playing daily or for achieving certain milestones. - Watch ads: This is another simple way to get gems and gacha tickets. You can watch short video ads from the game's menu or from certain locations in life mode. You can get 5 gems or 1 gacha ticket per ad, and you can watch up to 50 ads per day. - Complete tasks: This is a more challenging way to get gems and gacha tickets. You can complete tasks from the task menu or from certain characters in life mode. The tasks vary from creating characters, scenes, stories, to answering trivia questions, playing mini-games, etc. You can get different amounts of gems and gacha tickets depending on the difficulty of the task. - Buy with real money: This is the last resort if you want to get more gems and gacha tickets. You can buy them with real money from the shop menu or from certain locations in life mode. The prices range from $0.99 to $19.99, and you can get different packages of gems and gacha tickets.
Share your creations with others
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Gacha Life is not only a game, but also a community of creative people. You can share your creations with others online, or download them to your device. Here are some ways to share your creations:
- - Share online: This is where you can upload your scenes and stories to the game's server, and let other players see them. You can also browse other players' creations, rate them, comment on them, or report them if they are inappropriate. You can also join contests and events where you can submit your creations and win prizes. - Download to device: This is where you can save your scenes and stories as images or videos on your device. You can also edit them with other apps, or share them on social media platforms such as Facebook, Instagram, YouTube, etc.
Conclusion
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Gacha Life is a fun and creative game that lets you create your own anime characters and stories. You can also play mini-games, chat with other players, and explore different modes and features. But if you want to enjoy the game more, you should try playing it on PC. By playing Gacha Life on PC, you can have a larger and clearer display, use keyboard and mouse controls, access more features and updates, and have a smoother gaming experience.
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To play Gacha Life on PC, you just need to download an Android emulator such as BlueStacks, NoxPlayer, or MEmu, install Gacha Life from Google Play or APK file, launch the game and start your adventure. You can also follow our tips and tricks to make the most out of Gacha Life on PC.
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So what are you waiting for? Download Gacha Life on PC today and unleash your creativity!
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FAQs
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Here are some frequently asked questions about Gacha Life on PC:
- - Q: Is Gacha Life free to play on PC? - A: Yes, Gacha Life is free to play on PC. You just need to download an Android emulator and install Gacha Life from Google Play or APK file. - Q: Is Gacha Life safe to play on PC? - A: Yes, Gacha Life is safe to play on PC. However, you should be careful when chatting with other players online, as some of them may be rude or inappropriate. You should also avoid downloading any unofficial or modded versions of Gacha Life, as they may contain viruses or malware. - Q: How do I update Gacha Life on PC? - A: To update Gacha Life on PC, you need to update the Android emulator first. Then, you need to update Gacha Life from Google Play or To update Gacha Life on PC, you need to update the Android emulator first. Then, you need to update Gacha Life from Google Play or from the APK file. Here are the steps for both methods: - To update the Android emulator, open the emulator's settings and check for updates. If there are any available updates, download and install them. You may need to restart the emulator for the changes to take effect. - To update Gacha Life from Google Play, open the Google Play Store app from the emulator's home screen and search for Gacha Life. If there is an update available, you will see an Update button next to the game. Click on it and wait for the download and installation to complete. - To update Gacha Life from the APK file, download the latest version of Gacha Life from a trusted source, such as APKPure or APKMirror. Then, drag and drop the APK file onto the emulator's window, or use the Install APK button from the emulator's toolbar. The emulator will automatically update Gacha Life on your PC. - Q: How do I transfer my Gacha Life data from mobile to PC? - A: To transfer your Gacha Life data from mobile to PC, you need to use a cloud service such as Google Drive or Dropbox. Here are the steps to do so: - On your mobile device, open Gacha Life and go to the settings menu. Tap on the Backup Data button and choose a cloud service to upload your data. You may need to sign in with your account and grant permission for Gacha Life to access your cloud storage. - On your PC, open the Android emulator and launch Gacha Life. Go to the settings menu and tap on the Restore Data button. Choose the same cloud service that you used to backup your data on your mobile device. You may need to sign in with your account and grant permission for Gacha Life to access your cloud storage. - Wait for the data transfer to complete and enjoy playing Gacha Life on PC with your previous progress. - Q: How do I play Gacha Life offline on PC? - A: To play Gacha Life offline on PC, you need to download and install Gacha Life from an APK file instead of Google Play. Here are the steps to do so: - Download the APK file of Gacha Life from a trusted source, such as APKPure or APKMirror. - Drag and drop the APK file onto the emulator's window, or use the Install APK button from the emulator's toolbar. The emulator will automatically install Gacha Life on your PC. - Launch Gacha Life from the emulator's home screen or app drawer. You can play Gacha Life offline on PC without an internet connection. 197e85843d
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diff --git a/spaces/1phancelerku/anime-remove-background/Download 11th Hour by Betty Bayo - The Best of Kenyan Music.md b/spaces/1phancelerku/anime-remove-background/Download 11th Hour by Betty Bayo - The Best of Kenyan Music.md
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Download 11th Hour by Betty Bayo
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If you are looking for a gospel song that will inspire you and uplift your spirit, you should download 11th Hour by Betty Bayo. This song is a testimony of how God can turn your situation around in the last minute, when all hope seems to be lost. In this article, we will tell you more about Betty Bayo, the meaning and production of 11th Hour, and how you can download it legally and easily.
Betty Bayo is a Kenyan gospel singer, songwriter, and entrepreneur. She was born in 1985 in Kiambu County, Kenya. She started singing at a young age in her church choir and later joined a gospel group called Still Alive. She released her first solo album, 11th Hour, in 2008 and became a household name in the Kenyan gospel music scene.
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Her musical journey
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Betty Bayo has released six albums so far: 11th Hour (2008), Siyabonga (2010), Jemedari (2012), Anabadilisha (2014), Nimebadilika (2016), and Ndimwega (2018). She has also collaborated with other gospel artists such as Daddy Owen, Loise Kim, Jane Muthoni, and Phyllis Mbuthia. Some of her popular songs include Siyabonga, Jemedari, Anabadilisha, Ndimwega, and of course, 11th Hour. She has won several awards for her music, such as Groove Awards, Mwafaka Awards, and Kikuyu Music Awards.
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Her personal life
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Betty Bayo is a mother of two children, a boy and a girl. She was married to Pastor Victor Kanyari, the founder of Salvation Healing Ministry, but they divorced in 2015 after a scandal involving his fake miracles. She has since moved on and is currently dating an unnamed man. She is also a businesswoman who owns a beauty salon and a clothing line.
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What is 11th Hour?
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11th Hour is the title track of Betty Bayo's debut album. It is a song that expresses her gratitude to God for saving her from a difficult situation in her life. She sings about how God intervened in her life when she was about to give up and restored her joy and peace.
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The meaning of the song
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The song is based on the biblical parable of the workers in the vineyard (Matthew 20:1-16). In this parable, Jesus tells a story of a landowner who hired workers at different times of the day to work in his vineyard. He agreed to pay them all the same wage at the end of the day. However, when he paid them, the workers who were hired first complained that they received the same amount as those who were hired last. The landowner replied that he was not unfair to them and that he had the right to do what he wanted with his money.
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The message of this parable is that God's grace is not based on our merit or performance, but on his generosity and sovereignty. He can bless anyone at any time, regardless of their background or circumstances. He can also change anyone's situation at the last minute, even when it seems impossible or hopeless. This is what Betty Bayo experienced in her life and what she wants to share with her listeners through this song.
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The production of the song
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The song was produced by John Nyika, a renowned Kenyan music producer who has worked with other gospel artists such as Mercy Masika, Gloria Muliro, Eunice Njeri, and Reuben Kigame. He is known for his quality sound and creativity. He used a blend of African and contemporary beats to create a catchy and uplifting tune for 11th Hour. He also added some background vocals and harmonies to enhance the song's message and mood.
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The reception of the song
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The song was released in 2008 and became an instant hit among gospel music fans in Kenya and beyond. It received massive airplay on radio and TV stations, as well as online platforms. It also topped various charts and playlists, such as Groove Awards, Mdundo, and Skiza. The song has over 2 million views on YouTube and over 1 million streams on Spotify. It has also been covered by several other artists, such as Alice Kimanzi, Evelyn Wanjiru, and Ruth Wamuyu. The song has touched many lives and inspired many testimonies of God's faithfulness and power.
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How to download 11th Hour by Betty Bayo?
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If you want to enjoy this song on your device, you need to download it legally and easily. Here are some of the benefits of downloading the song, the legal ways to do it, and the steps to follow.
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The benefits of downloading the song
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Downloading 11th Hour by Betty Bayo has several advantages, such as:
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You can listen to the song offline, without worrying about internet connection or data charges.
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You can add the song to your personal playlist and customize it according to your preference.
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You can share the song with your friends and family via Bluetooth, WhatsApp, or other apps.
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You can support the artist financially and morally by paying for her work and appreciating her talent.
-
You can access the song anytime, anywhere, and on any device that supports MP3 format.
-
-
The legal ways to download the song
-
There are many illegal ways to download 11th Hour by Betty Bayo, such as using pirated websites, apps, or software. However, these methods are not only unethical but also risky. You may expose your device to viruses, malware, or hackers. You may also face legal consequences for violating the artist's intellectual property rights. Therefore, you should avoid these methods and use only the legal ways to download the song. Some of the legal ways are:
-
-
Buying the song from online platforms such as iTunes, Amazon Music, or Google Play Music.
-
Subscribing to streaming services such as Spotify, Apple Music, or Deezer.
-
Downloading the song from authorized websites such as Mdundo, Boomplay, or Skiza.
-
-
The steps to download the song
-
The steps to download 11th Hour by Betty Bayo may vary depending on the platform you choose. However, here are some general steps that you can follow:
-
-
Go to the platform's website or app and search for 11th Hour by Betty Bayo.
-
Select the song from the results and click on the download button.
-
Choose the quality and format of the song that you want to download.
-
Confirm your payment method if required and complete the transaction.
-
Wait for the download to finish and enjoy the song on your device.
-
-
Conclusion
-
Summary of the main points
-
In conclusion, 11th Hour by Betty Bayo is a gospel song that will inspire you and uplift your spirit. It is a testimony of how God can turn your situation around in the last minute, when all hope seems to be lost. Betty Bayo is a Kenyan gospel singer who has released six albums and won several awards for her music. She wrote this song based on her personal experience and the biblical parable of the workers in the vineyard. The song was produced by John Nyika and received a positive reception from gospel music fans. You can download this song legally and easily from various online platforms and enjoy its benefits.
-
Call to action
-
If you have not downloaded 11th Hour by Betty Bayo yet, what are you waiting for? This song will bless you and encourage you in your faith journey. It will remind you that God is always with you and that he can do miracles in your life. Download this song today and share it with others who need to hear this message. You will not regret it!
-
Frequently Asked Questions
-
-
Q: Who is Betty Bayo?
-
A: Betty Bayo is a Kenyan gospel singer, songwriter, and entrepreneur. She released her first solo album, 11th Hour, in 2008 and became a household name in the Kenyan gospel music scene.
-
Q: What is the meaning of 11th Hour?
-
A: 11th Hour is a song that expresses Betty Bayo's gratitude to God for saving her from a difficult situation in her life. She sings about how God intervened in her life when she was about to give up and restored her joy and peace. The song is based on the biblical parable of the workers in the vineyard (Matthew 20:1-16), which teaches that God's grace is not based on our merit or performance, but on his generosity and sovereignty.
-
Q: How can I download 11th Hour by Betty Bayo?
-
A: You can download 11th Hour by Betty Bayo legally and easily from various online platforms, such as iTunes, Amazon Music, Google Play Music, Spotify, Apple Music, Deezer, Mdundo, Boomplay, or Skiza. You just need to go to the platform's website or app, search for the song, select it, click on the download button, choose the quality and format of the song, confirm your payment method if required, and wait for the download to finish.
-
Q: Why should I download 11th Hour by Betty Bayo?
-
A: You should download 11th Hour by Betty Bayo because it is a gospel song that will inspire you and uplift your spirit. It will remind you that God can turn your situation around in the last minute, when all hope seems to be lost. It will also allow you to listen to the song offline, add it to your personal playlist, share it with your friends and family, support the artist financially and morally, and access it anytime, anywhere, and on any device that supports MP3 format.
-
Q: Where can I find more information about Betty Bayo and her music?
-
A: You can find more information about Betty Bayo and her music on her official website (www.bettybayo.com), her Facebook page (Betty Bayo Official), her Instagram account (@bettybayo_official_page), her YouTube channel (Betty Bayo Official), or her Wikipedia page (Betty Bayo).
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Quiz Results and Share Them with Your Students Colleagues or Clients.md b/spaces/1phancelerku/anime-remove-background/Download Quiz Results and Share Them with Your Students Colleagues or Clients.md
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-
How to Download Quiz Results from Different Platforms
-
Quiz results are the data that show how well your participants performed on your quizzes. They can include information such as scores, answers, time spent, feedback, and more. Quiz results are useful for teachers, students, and professionals who want to assess learning outcomes, measure progress, identify gaps, provide feedback, and improve performance.
-
Downloading quiz results can help you gain more insights into your quizzes and your participants. You can export quiz results to different formats such as CSV, XLSX, or PDF files. You can then use these files for more in-depth analysis and charting. You can also share these files with others who may be interested in your quizzes or your participants. For example, you can share quiz results with your colleagues, students, clients, or stakeholders.
In this article, we will show you how to download quiz results from five different platforms: Microsoft Forms, myQuiz, Quizizz, ProProfs, and Canvas. These platforms are popular tools for creating online quizzes for various purposes. We will provide step-by-step guides on how to download quiz results from each platform. We will also include screenshots of the pages where you can find the download options.
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How to Download Quiz Results from Microsoft Forms
-
Microsoft Forms is a web-based tool that allows you to create quizzes and surveys easily. You can use Microsoft Forms to create quizzes for education, training, feedback, research, or fun. You can add different types of questions such as multiple choice, text, rating, date, ranking, likert scale, net promoter score (NPS), file upload, etc. You can also add images or videos to your questions. You can customize your quizzes with themes or logos. You can share your quizzes with anyone via email or link. You can also embed your quizzes on your website or blog.
-
To download quiz results from Microsoft Forms:
-
-
In Microsoft Forms, open the quiz for which you want to download the results.
-
Click on the Responses tab to see the quiz statistics and responses.
-
Click on the More options button (three dots) in the upper right corner of the page.
-
Select Open in Excel to export the quiz results as an XLSX file. You can also select Print summary to export the quiz results as a PDF file.
-
Save the file to your preferred location or print it as needed.
-
-
Here is a screenshot of the quiz statistics page and the download options:
-
-
How to Download Quiz Results from myQuiz
-
myQuiz is a web-based tool that allows you to host interactive trivia quizzes at events. You can use myQuiz to create quizzes for entertainment, education, marketing, or gamification. You can add different types of questions such as multiple choice, true/false, image, video, audio, etc. You can also add timers, leaderboards, prizes, and chat features to your quizzes. You can share your quizzes with your audience via QR code or link. You can also integrate your quizzes with Zoom, YouTube, Twitch, or Facebook Live.
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-
To download quiz results from myQuiz:
-
-
In myQuiz, open the quiz for which you want to download the results.
-
Click on the Reports tab to see the quiz statistics and responses.
-
Click on the Download button in the upper right corner of the page.
-
Select Excel to export the quiz results as an XLSX file.
-
Save the file to your preferred location.
-
-
Here is a screenshot of the report page and the download button:
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-
How to Download Quiz Results from Quizizz
-
Quizizz is a web-based tool that allows you to create online quizzes, lessons, activities, and homework. You can use Quizizz to create quizzes for education, training, feedback, or fun. You can add different types of questions such as multiple choice, fill in the blanks, check boxes, poll, etc. You can also add images, gifs, memes, equations, or audio to your questions. You can customize your quizzes with themes, music, timers, leaderboards, etc. You can share your quizzes with your participants via email or link. You can also assign your quizzes as homework or live sessions.
-
To download quiz results from Quizizz:
-
-
In Quizizz, open the quiz for which you want to download the results.
-
Click on the Reports tab to see the quiz statistics and responses.
-
Click on the Responses tab to see the individual responses of each participant.
-
Click on the Download button in the upper right corner of the page.
-
Select CSV to export the quiz results as a CSV file.
-
Save the file to your preferred location.
-
-
Here is a screenshot of the responses tab and the download button:
-
How to Download Quiz Results from ProProfs
-
ProProfs is a web-based tool that allows you to create online quizzes, courses, assessments, and surveys. You can use ProProfs to create quizzes for education, business, certification, or fun. You can add different types of questions such as multiple choice, true/false, matching, short answer, essay, etc. You can also add images, videos, documents, or audio to your questions. You can customize your quizzes with themes, logos, certificates, etc. You can share your quizzes with your participants via email or link. You can also embed your quizzes on your website or blog.
-
To download quiz results from ProProfs:
-
-
In ProProfs, open the quiz for which you want to download the results.
-
Click on the Reports and Stats tab to see the quiz statistics and responses.
-
Click on the Download icon in the upper right corner of the page.
-
Select CSV or PDF to export the quiz results as a CSV or PDF file.
-
Save the file to your preferred location.
-
-
Here is a screenshot of the reports and stats page and the download icon:
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-
How to Download Quiz Results from Canvas
-
Canvas is a web-based tool that allows you to create online courses, assignments, quizzes, and discussions. You can use Canvas to create quizzes for education, training, or assessment. You can add different types of questions such as multiple choice, true/false, fill in the blank, multiple answers, numerical answer, formula, matching, etc. You can also add images, videos, equations, or files to your questions. You can customize your quizzes with settings such as time limit, shuffle answers, show feedback, etc. You can share your quizzes with your students via email or link. You can also grade your quizzes automatically or manually.
-
To download quiz results from Canvas:
-
-
In Canvas, open the course for which you want to download the quiz results.
-
Click on the Quizzes tab to see the list of quizzes in the course.
-
Click on the quiz for which you want to download the results.
-
Click on the Quiz Statistics button in the upper right corner of the page.
-
Click on the Student Analysis button in the upper right corner of the page.
-
Select CSV to export the quiz results as a CSV file.
-
Save the file to your preferred location.
-
-
Here is a screenshot of the quiz statistics page and the student analysis button:
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-
Conclusion
-
In this article, we have shown you how to download quiz results from five different platforms: Microsoft Forms, myQuiz, Quizizz, ProProfs, and Canvas. These platforms are great tools for creating online quizzes for various purposes. By downloading quiz results from these platforms, you can gain more insights into your quizzes and your participants. You can use the downloaded data for more in-depth analysis and charting. You can also share the downloaded data with others who may be interested in your quizzes or your participants.
-
Here are some tips on how to use the downloaded data for analysis, sharing, and feedback:
-
-
Use spreadsheet software such as Excel or Google Sheets to open and manipulate the CSV or XLSX files. You can sort, filter, pivot, or chart the data as you wish.
-
Use PDF software such as Adobe Acrobat or Foxit Reader to open and view the PDF files. You can print or email the files as needed.
-
Use online tools such as Datawrapper or Infogram to create interactive visualizations from your data. You can embed these visualizations on your website or blog.
-
Use online tools such as Google Drive or Dropbox to store and share your files with others. You can set permissions and access levels for different users.
-
Use online tools such as SurveyMonkey or Typeform to collect feedback from your participants on your quizzes. You can ask them about their experience, satisfaction, learning outcomes, suggestions, etc.
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-
We hope you found this article helpful and informative. If you have any questions or comments about downloading quiz results from different platforms, please feel free to leave them below. We would love to hear from you!
-
FAQs
-
Q
Q: How can I download quiz results from other platforms?
-
A: The process of downloading quiz results from other platforms may vary depending on the platform and the quiz settings. However, some common steps are:
-
-
Open the platform and log in to your account.
-
Find the quiz for which you want to download the results.
-
Look for a tab or a button that says something like "Results", "Reports", "Statistics", "Analysis", etc.
-
Select the format that you want to download the results in, such as CSV, XLSX, PDF, etc.
-
Save the file to your preferred location.
-
-
If you are not sure how to download quiz results from a specific platform, you can check the platform's help center or contact their support team for assistance.
-
Q: How can I create online quizzes with different platforms?
-
A: Creating online quizzes with different platforms is easy and fun. You can use any of the platforms mentioned in this article or explore other options online. Some common steps are:
-
-
Open the platform and sign up for an account.
-
Choose a template or start from scratch to create your quiz.
-
Add questions and answers to your quiz. You can choose from different types of questions and add multimedia elements if you wish.
-
Customize your quiz with settings such as title, theme, timer, feedback, etc.
-
Publish and share your quiz with your participants via email or link. You can also embed your quiz on your website or blog.
-
-
If you need more guidance on how to create online quizzes with different platforms, you can check the platform's tutorials or blogs for tips and examples.
-
Q: How can I improve my quizzes and my participants' performance?
-
A: Improving your quizzes and your participants' performance is an ongoing process that requires feedback and analysis. You can use the downloaded quiz results to help you with this process. Some tips are:
-
-
Analyze the quiz results to see how well your participants performed on your quiz. Look for patterns, trends, outliers, strengths, and weaknesses.
-
Compare the quiz results with your learning objectives or expectations. See if your quiz aligned with your goals and measured what you intended to measure.
-
Evaluate the quality and difficulty of your questions and answers. See if your questions were clear, relevant, accurate, and fair. See if your answers were correct, comprehensive, and distractive.
-
Provide feedback to your participants on their performance. Praise their achievements, point out their errors, explain their misconceptions, and suggest ways to improve.
-
Revise your quiz based on the feedback and analysis. Modify or delete questions that were too easy or too hard, add or remove options, update or correct information, etc.
-
-
If you want more advice on how to improve your quizzes and your participants' performance, you can check online resources such as books, articles, podcasts, or courses on quiz design and assessment.
-
Q: What are some of the advantages and disadvantages of online quizzes?
-
A: Online quizzes have many advantages and disadvantages compared to traditional paper-based quizzes. Some of them are:
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-
Advantages
Disadvantages
-
- Online quizzes are easy and fast to create, administer, grade, and analyze.
- Online quizzes may require internet access, devices, software, or accounts that some participants may not have or prefer.
-
- Online quizzes can be more interactive and engaging with multimedia elements, gamification features, adaptive learning paths, etc.
- Online quizzes may pose technical issues such as glitches, errors, crashes, or compatibility problems that may affect the quiz experience or results.
-
- Online quizzes can reach more participants across different locations and time zones.
- Online quizzes may reduce the social interaction and collaboration among participants and between participants and instructors.
-
- Online quizzes can provide instant feedback and results to participants and instructors.
- Online quizzes may increase the risk of cheating or plagiarism among participants who may use unauthorized sources or methods to complete the quiz.
-
- Online quizzes can be more flexible and customizable with different settings, formats, options, etc.
- Online quizzes may be less reliable or valid than paper-based quizzes due to factors such as question quality, scoring criteria, participant motivation, etc.
-
-
The advantages and disadvantages of online quizzes may vary depending on the context, purpose, [user](# audience, and design of the quiz. You should weigh the pros and cons of online quizzes before deciding to use them for your needs.
-
Q: What are some of the best practices for creating online quizzes?
-
A: Creating online quizzes can be a fun and rewarding experience, but it also requires some planning and preparation. Here are some of the best practices for creating online quizzes:
-
-
Define your learning objectives and outcomes. What do you want your participants to learn, know, or do after taking your quiz?
-
Choose the right platform and format for your quiz. What tool or software will you use to create and deliver your quiz? What type of questions and answers will you use?
-
Write clear and concise questions and answers. What are you asking your participants and what are you expecting them to answer? How can you avoid ambiguity, confusion, or bias in your questions and answers?
-
Test and review your quiz. How can you ensure that your quiz is working properly and accurately? How can you get feedback from others on your quiz?
-
Analyze and improve your quiz. How can you measure the effectiveness and impact of your quiz? How can you use the quiz results to improve your quiz or your participants' performance?
-
-
If you want more guidance on how to create online quizzes, you can check online resources such as books, articles, podcasts, or courses on quiz design and development.
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diff --git a/spaces/1phancelerku/anime-remove-background/Epson L3110 Printer Driver Download - How to Install and Update.md b/spaces/1phancelerku/anime-remove-background/Epson L3110 Printer Driver Download - How to Install and Update.md
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How to Download Driver L3110 for Windows 11/10/8/7
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If you own an Epson L3110 printer, you might be wondering how to download driver L3110 for your Windows PC. In this article, we will show you what driver L3110 is, why you need it, how to download and install it, and how to fix common driver issues and errors.
Driver L3110 is a software component that enables your Epson L3110 printer to communicate with your Windows PC.
-
A driver is a program that allows your computer and your hardware devices to interact with each other. Without a driver, your computer won't be able to recognize or use your printer properly.
-
Driver L3110 is the specific driver for Epson L3110 printer, which is an all-in-one ink tank printer that offers high-quality printing, scanning, and copying functions. It also supports wireless connectivity, borderless printing, and low-cost ink refilling.
-
You need to install and update Driver L3110 to ensure the optimal performance and functionality of your printer.
-
To use your Epson L3110 printer on your Windows PC, you need to install driver L3110 first. This will allow your PC to detect and operate your printer correctly.
-
Moreover, you also need to update driver L3110 regularly to keep it compatible with the latest Windows updates and software applications. Updating driver L3110 can also fix bugs, improve stability, enhance security, and add new features for your printer.Benefits of Updating Driver L3110
-
Updating Driver L3110 can boost your system performance, stability, and security.
-
By updating driver L3110, you can ensure that your printer and your PC are working in harmony. This can improve the speed, efficiency, and reliability of your printing tasks. It can also prevent system crashes, freezes, or errors that may occur due to outdated or incompatible drivers.
-
Furthermore, updating driver L3110 can also protect your PC and your printer from potential threats or attacks. Newer drivers often come with patches or fixes that address security vulnerabilities or bugs that may compromise your system or your data.
-
How to download driver l3110 for Windows 11
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-Download driver l3110 print from Windows or Mac application
-
Updating Driver L3110 can also unlock new features and enhancements for your printer.
-
Another benefit of updating driver L3110 is that you can enjoy the latest features and improvements that Epson has to offer for your printer. For example, you may be able to access new printing modes, settings, or options that can enhance your printing quality and experience. You may also be able to use new functions or tools that can make your printing tasks easier and more convenient.
-
Updating Driver L3110 can prevent common driver issues and errors that may affect your printing quality and experience.
-
Finally, updating driver L3110 can also help you avoid or resolve common driver issues and errors that may hamper your printing quality and experience. Some of these issues and errors include:
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-
Printer not recognized or detected by PC
-
Printer not responding or working properly
-
Printer showing offline or unavailable status
-
Printer printing blank pages or incorrect colors
-
Printer scanning or copying errors
-
Printer driver installation or update failures
-
-
By updating driver L3110, you can ensure that your printer is running smoothly and efficiently on your PC.
-
How to Download and Install Driver L3110
-
Method 1: Use Bit Driver Updater to Automatically Update Driver L3110 (Recommended)
-
Bit Driver Updater is a reliable and easy-to-use tool that can scan, download, and install the latest drivers for your devices with one click.
-
If you want to save time and hassle in updating driver L3110, we recommend using Bit Driver Updater. This is a powerful and user-friendly software that can automatically scan your PC for outdated, missing, or faulty drivers and update them with the most compatible and up-to-date versions. It can also backup and restore your drivers, optimize your system performance, and provide 24/7 technical support.
-
Steps to use Bit Driver Updater to update Driver L3110
-
-
Download and install Bit Driver Updater from its official website here.
-
Launch the program and click on the Scan button to start scanning your PC for driver issues.
-
Wait for the scan to complete and then review the scan results. You will see a list of drivers that need to be updated, including driver L3110.
-
Click on the Update Now button next to driver L3110 to download and install the latest version of the driver. Alternatively, you can click on the Update All button to update all the drivers on your PC at once.
-
Restart your PC to apply the changes and enjoy your updated printer.
-
Method 2: Manually Download and Install Driver L3110 from Epson's Official Website
-
You can also download and install the latest version of Driver L3110 from Epson's official website by following these steps
-
Another way to update driver L3110 is to manually download and install it from Epson's official website. This method requires you to have some technical knowledge and patience, as you need to find the correct driver for your printer model and Windows version. You also need to follow the installation instructions carefully to avoid any errors or issues.
-
Steps to manually download and install driver L3110 from Epson's official website
-
-
Go to Epson's official website here and enter your printer model (L3110) in the search box. Then, click on the search icon or press Enter.
-
On the product page, click on the Downloads tab and select your Windows version from the drop-down menu.
-
Under the Drivers section, click on the Download button next to Printer Driver. This will download the driver file to your PC.
-
Once the download is complete, open the file and follow the on-screen instructions to install the driver. You may need to agree to the license agreement, select your connection type, and connect your printer to your PC during the installation process.
-
After the installation is finished, restart your PC and test your printer to see if it works properly.
-
-
Method 3: Update Driver L3110 in Device Manager
-
Another way to update Driver L3110 is to use the built-in Device Manager in Windows by following these steps
-
The Device Manager is a Windows utility that allows you to manage and update your hardware devices and their drivers. You can use it to update driver L3110 by searching for it online or browsing for it on your PC. However, this method may not always find the latest or most compatible driver for your printer, so it is not recommended as the first option.
-
Steps to update driver L3110 in Device Manager
-
-
Press Windows + R keys on your keyboard to open the Run dialog box. Then, type devmgmt.msc and click OK or press Enter. This will open the Device Manager window.
-
Expand the Printers or Print queues category and right-click on your Epson L3110 printer. Then, select Update driver from the context menu.
-
You will see two options: Search automatically for updated driver software and Browse my computer for driver software. You can choose either one depending on your preference.
-
If you choose Search automatically for updated driver software, Windows will search online for the best available driver for your printer and install it automatically. You may need an internet connection for this option.
-
If you choose Browse my computer for driver software, you will need to locate the driver file that you downloaded from Epson's website or another source. Then, follow the instructions to install it manually.
-
After the update is done, restart your PC and check if your printer is working correctly.
-
Click on Update & Security and select Windows Update from the left pane.
-
Click on Check for updates and wait for Windows to scan for available updates.
-
If there are any driver updates available, including driver L3110, you will see them under the Optional updates section. Click on View optional updates and select the driver updates you want to install.
-
Click on Download and install and wait for Windows to download and install the driver updates.
-
Restart your PC and see if your printer is working properly.
-
-
Conclusion
-
In this article, we have shown you how to download driver L3110 for your Epson L3110 printer on your Windows PC. We have also explained what driver L3110 is, why you need it, and how to fix common driver issues and errors. We hope that this article has been helpful and informative for you.
-
To summarize, here are the main points of the article:
-
-
Driver L3110 is a software component that enables your Epson L3110 printer to communicate with your Windows PC.
-
You need to install and update driver L3110 to ensure the optimal performance and functionality of your printer.
-
Updating driver L3110 can boost your system performance, stability, security, as well as unlock new features and enhancements for your printer.
-
You can download and install driver L3110 using one of these methods: Bit Driver Updater (recommended), Epson's official website, or Device Manager.
-
You can fix common driver issues and errors using one of these solutions: Windows Troubleshooter, reinstalling driver L3110 in Device Manager, or Windows Update.
-
-
If you have any questions or feedback about this article, please feel free to leave a comment below. We would love to hear from you. Thank you for reading and happy printing!
-
FAQs
-
Q1. What are the system requirements for Driver L3110?
-
A1. The system requirements for driver L3110 are as follows:
-
-
Operating System
Supported Versions
-
Windows 11
All versions
-
Windows 10
All versions
-
Windows 8/8.1
All versions
-
Windows 7
All versions
-
-
Q2. How often should I update Driver L3110?
-
A2. You should update driver L3110 whenever there is a new version available from Epson or Windows. This will ensure that your printer is always compatible and up-to-date with the latest system updates and software applications. You can use Bit Driver Updater to automatically check for and update driver L3110 with one click.
-
Q3. What are the advantages of using Bit Driver Updater over other methods?
-
A3. Bit Driver Updater is a reliable and easy-to-use tool that can scan, download, and install the latest drivers for your devices with one click. It has several advantages over other methods, such as:
-
-
It saves you time and hassle in finding and installing the correct drivers for your devices.
-
It ensures that you get the most compatible and up-to-date drivers for your devices.
-
It backs up and restores your drivers in case of any issues or errors.
-
It optimizes your system performance, stability, and security by keeping your drivers updated.
-
It provides 24/7 technical support and a 60-day money-back guarantee.
-
-
Q4. How can I contact Epson's customer support if I need further assistance with Driver L3110?
-
A4. If you need further assistance with driver L3110 or your Epson L3110 printer, you can contact Epson's customer support by visiting their website here. You can also find more information about Epson L3110 printer and its features on their website.
-
Q5. Where can I find more information about Epson L3110 printer and its features?
-
A5. You can find more information about Epson L3110 printer and its features on Epson's official website here. You can also read user reviews, watch videos, or download manuals and guides for your printer on their website.
""")
-
- with gr.Row():
-
- with gr.Column():
- text_output = gr.Textbox(
- label="Prettified text prompt",
- lines=4,
- elem_id="translated"
- )
- with gr.Row():
- diffuse_btn = gr.Button(value="Generate!")
- with gr.Column(elem_id="generated-gallery"):
- sd_output = gr.Gallery().style(grid=2, height="auto")
- with gr.Group(elem_id="share-btn-container"):
- community_icon = gr.HTML(community_icon_html, visible=False)
- loading_icon = gr.HTML(loading_icon_html, visible=False)
- share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
- diffuse_btn.click(get_images,
- inputs = [
- text_output
- ],
- outputs = [sd_output, community_icon, loading_icon, share_button]
- )
- share_button.click(None, [], [], _js=share_js)
-
-
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/How to Deploy b7c4f3fd308944af8ba4637ec40fa4f9.md b/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/How to Deploy b7c4f3fd308944af8ba4637ec40fa4f9.md
deleted file mode 100644
index 69874950c1102905d6d90100eaf5c1e4786f5cbd..0000000000000000000000000000000000000000
--- a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/How to Deploy b7c4f3fd308944af8ba4637ec40fa4f9.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# How to Deploy
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Guides and Processes
-
-
-
-# 1. Ping in Slack #deploys channel
-
-Let everyone know you're about to start a deploy.
-
-# 2. SSH into the deployment server
-
-
-
-```bash
-ssh deployments.acmecorp.com
-```
-
-- You can create `inline code snippets` with the shortcut `cmd/ctrl + e`.
-
-# 3. Run the following commands
-
-```bash
-acme deploy --prod
-```
\ No newline at end of file
diff --git a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Smart Domain 3b0daf8bb0d740439426cfab214f1fa6.md b/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Smart Domain 3b0daf8bb0d740439426cfab214f1fa6.md
deleted file mode 100644
index 4fc505bfa159c12cfdc90f0bba43d5741aca6a62..0000000000000000000000000000000000000000
--- a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Smart Domain 3b0daf8bb0d740439426cfab214f1fa6.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# Smart Domain
-
-Smart Domain是一种新的软件架构模式，主要用于实现领域驱动设计（DDD）。相较于传统的分层架构，Smart Domain更加注重从业务领域的角度去划分层次，并在每一层次上都使用领域专家和开发人员共同理解的通用语言进行交流，从而建立起完整的领域模型。与此同时，Smart Domain还摒弃了服务层的概念，将原本在服务层实现的逻辑下沉到领域模型中，从而让领域模型更加丰满和清晰。
-
-总之，Smart Domain是一种新的软件架构模式，通过从业务领域的角度去划分层次和使用通用语言建立领域模型，以及将服务层逻辑下沉到领域模型中，提高了领域驱动设计的实现效果。
-
-## SmartDomain 分层
-1. Domain层
-Domain层是整个系统的核心，主要定义业务相关的实体（Entity）和表示实体之间关联关系的关联对象。该层定义的实体和关联对象是业务模型的核心，反映了整个系统的业务规则和逻辑。同时，该层仅提供接口或抽象类定义，不包含具体实现。这样做可以避免业务逻辑和实现细节的混淆，也方便后续的扩展和维护。
-2. 集成层
-集成层主要负责Domain层中关系对象的实现，该层会依赖数据库、Http接口访问进行功能实现。该层的职责是将Domain层的抽象定义转换成具体实现，包括数据持久化、网络通信等。该层还可以处理一些跨域访问和数据加密解密等底层功能。
-3. API层
-API层是整个系统的入口，基于Domain层的Entity或关联对象提供的能力，提供外部访问的RestFul接口。该层负责处理外部请求，将请求参数转换成业务对象并调用Domain层的业务逻辑实现，返回结果给客户端。该层还可以对请求参数进行验证、安全过滤等操作，保证系统的安全性和可靠性。
\ No newline at end of file
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/adversarial/losses.py b/spaces/AIConsultant/MusicGen/audiocraft/adversarial/losses.py
deleted file mode 100644
index be293e739bdc2d91273f30fb789befe7c8b49a43..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/adversarial/losses.py
+++ /dev/null
@@ -1,228 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Utility module to handle adversarial losses without requiring to mess up the main training loop.
-"""
-
-import typing as tp
-
-import flashy
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-ADVERSARIAL_LOSSES = ['mse', 'hinge', 'hinge2']
-
-
-AdvLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor], torch.Tensor]]
-FeatLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor, torch.Tensor], torch.Tensor]]
-
-
-class AdversarialLoss(nn.Module):
- """Adversary training wrapper.
-
- Args:
- adversary (nn.Module): The adversary module will be used to estimate the logits given the fake and real samples.
- We assume here the adversary output is ``Tuple[List[torch.Tensor], List[List[torch.Tensor]]]``
- where the first item is a list of logits and the second item is a list of feature maps.
- optimizer (torch.optim.Optimizer): Optimizer used for training the given module.
- loss (AdvLossType): Loss function for generator training.
- loss_real (AdvLossType): Loss function for adversarial training on logits from real samples.
- loss_fake (AdvLossType): Loss function for adversarial training on logits from fake samples.
- loss_feat (FeatLossType): Feature matching loss function for generator training.
- normalize (bool): Whether to normalize by number of sub-discriminators.
-
- Example of usage:
- adv_loss = AdversarialLoss(adversaries, optimizer, loss, loss_real, loss_fake)
- for real in loader:
- noise = torch.randn(...)
- fake = model(noise)
- adv_loss.train_adv(fake, real)
- loss, _ = adv_loss(fake, real)
- loss.backward()
- """
- def __init__(self,
- adversary: nn.Module,
- optimizer: torch.optim.Optimizer,
- loss: AdvLossType,
- loss_real: AdvLossType,
- loss_fake: AdvLossType,
- loss_feat: tp.Optional[FeatLossType] = None,
- normalize: bool = True):
- super().__init__()
- self.adversary: nn.Module = adversary
- flashy.distrib.broadcast_model(self.adversary)
- self.optimizer = optimizer
- self.loss = loss
- self.loss_real = loss_real
- self.loss_fake = loss_fake
- self.loss_feat = loss_feat
- self.normalize = normalize
-
- def _save_to_state_dict(self, destination, prefix, keep_vars):
- # Add the optimizer state dict inside our own.
- super()._save_to_state_dict(destination, prefix, keep_vars)
- destination[prefix + 'optimizer'] = self.optimizer.state_dict()
- return destination
-
- def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
- # Load optimizer state.
- self.optimizer.load_state_dict(state_dict.pop(prefix + 'optimizer'))
- super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
- def get_adversary_pred(self, x):
- """Run adversary model, validating expected output format."""
- logits, fmaps = self.adversary(x)
- assert isinstance(logits, list) and all([isinstance(t, torch.Tensor) for t in logits]), \
- f'Expecting a list of tensors as logits but {type(logits)} found.'
- assert isinstance(fmaps, list), f'Expecting a list of features maps but {type(fmaps)} found.'
- for fmap in fmaps:
- assert isinstance(fmap, list) and all([isinstance(f, torch.Tensor) for f in fmap]), \
- f'Expecting a list of tensors as feature maps but {type(fmap)} found.'
- return logits, fmaps
-
- def train_adv(self, fake: torch.Tensor, real: torch.Tensor) -> torch.Tensor:
- """Train the adversary with the given fake and real example.
-
- We assume the adversary output is the following format: Tuple[List[torch.Tensor], List[List[torch.Tensor]]].
- The first item being the logits and second item being a list of feature maps for each sub-discriminator.
-
- This will automatically synchronize gradients (with `flashy.distrib.eager_sync_model`)
- and call the optimizer.
- """
- loss = torch.tensor(0., device=fake.device)
- all_logits_fake_is_fake, _ = self.get_adversary_pred(fake.detach())
- all_logits_real_is_fake, _ = self.get_adversary_pred(real.detach())
- n_sub_adversaries = len(all_logits_fake_is_fake)
- for logit_fake_is_fake, logit_real_is_fake in zip(all_logits_fake_is_fake, all_logits_real_is_fake):
- loss += self.loss_fake(logit_fake_is_fake) + self.loss_real(logit_real_is_fake)
-
- if self.normalize:
- loss /= n_sub_adversaries
-
- self.optimizer.zero_grad()
- with flashy.distrib.eager_sync_model(self.adversary):
- loss.backward()
- self.optimizer.step()
-
- return loss
-
- def forward(self, fake: torch.Tensor, real: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]:
- """Return the loss for the generator, i.e. trying to fool the adversary,
- and feature matching loss if provided.
- """
- adv = torch.tensor(0., device=fake.device)
- feat = torch.tensor(0., device=fake.device)
- with flashy.utils.readonly(self.adversary):
- all_logits_fake_is_fake, all_fmap_fake = self.get_adversary_pred(fake)
- all_logits_real_is_fake, all_fmap_real = self.get_adversary_pred(real)
- n_sub_adversaries = len(all_logits_fake_is_fake)
- for logit_fake_is_fake in all_logits_fake_is_fake:
- adv += self.loss(logit_fake_is_fake)
- if self.loss_feat:
- for fmap_fake, fmap_real in zip(all_fmap_fake, all_fmap_real):
- feat += self.loss_feat(fmap_fake, fmap_real)
-
- if self.normalize:
- adv /= n_sub_adversaries
- feat /= n_sub_adversaries
-
- return adv, feat
-
-
-def get_adv_criterion(loss_type: str) -> tp.Callable:
- assert loss_type in ADVERSARIAL_LOSSES
- if loss_type == 'mse':
- return mse_loss
- elif loss_type == 'hinge':
- return hinge_loss
- elif loss_type == 'hinge2':
- return hinge2_loss
- raise ValueError('Unsupported loss')
-
-
-def get_fake_criterion(loss_type: str) -> tp.Callable:
- assert loss_type in ADVERSARIAL_LOSSES
- if loss_type == 'mse':
- return mse_fake_loss
- elif loss_type in ['hinge', 'hinge2']:
- return hinge_fake_loss
- raise ValueError('Unsupported loss')
-
-
-def get_real_criterion(loss_type: str) -> tp.Callable:
- assert loss_type in ADVERSARIAL_LOSSES
- if loss_type == 'mse':
- return mse_real_loss
- elif loss_type in ['hinge', 'hinge2']:
- return hinge_real_loss
- raise ValueError('Unsupported loss')
-
-
-def mse_real_loss(x: torch.Tensor) -> torch.Tensor:
- return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x))
-
-
-def mse_fake_loss(x: torch.Tensor) -> torch.Tensor:
- return F.mse_loss(x, torch.tensor(0., device=x.device).expand_as(x))
-
-
-def hinge_real_loss(x: torch.Tensor) -> torch.Tensor:
- return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-def hinge_fake_loss(x: torch.Tensor) -> torch.Tensor:
- return -torch.mean(torch.min(-x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-def mse_loss(x: torch.Tensor) -> torch.Tensor:
- if x.numel() == 0:
- return torch.tensor([0.0], device=x.device)
- return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x))
-
-
-def hinge_loss(x: torch.Tensor) -> torch.Tensor:
- if x.numel() == 0:
- return torch.tensor([0.0], device=x.device)
- return -x.mean()
-
-
-def hinge2_loss(x: torch.Tensor) -> torch.Tensor:
- if x.numel() == 0:
- return torch.tensor([0.0])
- return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x)))
-
-
-class FeatureMatchingLoss(nn.Module):
- """Feature matching loss for adversarial training.
-
- Args:
- loss (nn.Module): Loss to use for feature matching (default=torch.nn.L1).
- normalize (bool): Whether to normalize the loss.
- by number of feature maps.
- """
- def __init__(self, loss: nn.Module = torch.nn.L1Loss(), normalize: bool = True):
- super().__init__()
- self.loss = loss
- self.normalize = normalize
-
- def forward(self, fmap_fake: tp.List[torch.Tensor], fmap_real: tp.List[torch.Tensor]) -> torch.Tensor:
- assert len(fmap_fake) == len(fmap_real) and len(fmap_fake) > 0
- feat_loss = torch.tensor(0., device=fmap_fake[0].device)
- feat_scale = torch.tensor(0., device=fmap_fake[0].device)
- n_fmaps = 0
- for (feat_fake, feat_real) in zip(fmap_fake, fmap_real):
- assert feat_fake.shape == feat_real.shape
- n_fmaps += 1
- feat_loss += self.loss(feat_fake, feat_real)
- feat_scale += torch.mean(torch.abs(feat_real))
-
- if self.normalize:
- feat_loss /= n_fmaps
-
- return feat_loss
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/__init__.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/__init__.py
deleted file mode 100644
index e477f51116a3157781b1aefefbaf32fe4d4bd1f0..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .causal_conv import * # NOQA
-from .pqmf import * # NOQA
-from .residual_block import * # NOQA
-from modules.parallel_wavegan.layers.residual_stack import * # NOQA
-from .upsample import * # NOQA
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/fs_adv_aishell.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/fs_adv_aishell.py
deleted file mode 100644
index 9956f8211ceaec80e42761caea5aa2d5160a4fac..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/fs_adv_aishell.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import numpy as np
-
-from text_to_speech.modules.tts.syntaspeech.multi_window_disc import Discriminator
-from tasks.tts.fs import FastSpeechTask
-from text_to_speech.modules.tts.fs import FastSpeech
-
-from text_to_speech.utils.audio.align import mel2token_to_dur
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.nn.model_utils import num_params
-from text_to_speech.utils.commons.tensor_utils import tensors_to_scalars
-from text_to_speech.utils.audio.pitch.utils import denorm_f0, norm_f0
-from text_to_speech.utils.audio.pitch_extractors import get_pitch
-from text_to_speech.utils.metrics.dtw import dtw as DTW
-
-from text_to_speech.utils.plot.plot import spec_to_figure
-from text_to_speech.utils.text.text_encoder import build_token_encoder
-
-
-class FastSpeechAdvTask(FastSpeechTask):
- def __init__(self):
- super().__init__()
- self.build_disc_model()
- self.mse_loss_fn = torch.nn.MSELoss()
-
- def build_tts_model(self):
- dict_size = len(self.token_encoder)
- self.model = FastSpeech(dict_size, hparams)
- self.gen_params = [p for p in self.model.parameters() if p.requires_grad]
- self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)]
- self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)]
- self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)]
- self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ]
- self.use_bert = True if len(self.bert_params) > 0 else False
-
-
- def build_disc_model(self):
- disc_win_num = hparams['disc_win_num']
- h = hparams['mel_disc_hidden_size']
- self.mel_disc = Discriminator(
- time_lengths=[32, 64, 128][:disc_win_num],
- freq_length=80, hidden_size=h, kernel=(3, 3)
- )
- self.disc_params = list(self.mel_disc.parameters())
-
- def _training_step(self, sample, batch_idx, optimizer_idx):
- loss_output = {}
- loss_weights = {}
- disc_start = self.global_step >= hparams["disc_start_steps"] and hparams['lambda_mel_adv'] > 0
- if optimizer_idx == 0:
- #######################
- # Generator #
- #######################
- loss_output, model_out = self.run_model(sample, infer=False)
- self.model_out_gt = self.model_out = \
- {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)}
- if disc_start:
- mel_p = model_out['mel_out']
- if hasattr(self.model, 'out2mel'):
- mel_p = self.model.out2mel(mel_p)
- o_ = self.mel_disc(mel_p)
- p_, pc_ = o_['y'], o_['y_c']
- if p_ is not None:
- loss_output['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size()))
- loss_weights['a'] = hparams['lambda_mel_adv']
- if pc_ is not None:
- loss_output['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size()))
- loss_weights['ac'] = hparams['lambda_mel_adv']
- else:
- #######################
- # Discriminator #
- #######################
- if disc_start and self.global_step % hparams['disc_interval'] == 0:
- model_out = self.model_out_gt
- mel_g = sample['mels']
- mel_p = model_out['mel_out']
- o = self.mel_disc(mel_g)
- p, pc = o['y'], o['y_c']
- o_ = self.mel_disc(mel_p)
- p_, pc_ = o_['y'], o_['y_c']
- if p_ is not None:
- loss_output["r"] = self.mse_loss_fn(p, p.new_ones(p.size()))
- loss_output["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size()))
- if pc_ is not None:
- loss_output["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size()))
- loss_output["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size()))
- else:
- return None
- total_loss = sum([loss_weights.get(k, 1) * v for k, v in loss_output.items() if isinstance(v, torch.Tensor) and v.requires_grad])
- loss_output['batch_size'] = sample['txt_tokens'].size()[0]
- return total_loss, loss_output
-
- def validation_start(self):
- self.vocoder = None
-
- def validation_step(self, sample, batch_idx):
- outputs = {}
- outputs['losses'] = {}
- outputs['losses'], model_out = self.run_model(sample)
- outputs['total_loss'] = sum(outputs['losses'].values())
- outputs['nsamples'] = sample['nsamples']
- outputs = tensors_to_scalars(outputs)
- if self.global_step % hparams['valid_infer_interval'] == 0 \
- and batch_idx < hparams['num_valid_plots']:
- valid_results = self.save_valid_result(sample, batch_idx, model_out)
- # wav_gt = valid_results['wav_gt']
- mel_gt = valid_results['mel_gt']
- # wav_pred = valid_results['wav_pred']
- mel_pred = valid_results['mel_pred']
- # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
- # f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
- # manhattan_distance = lambda x, y: np.abs(x - y)
- # dist, cost, acc, path = DTW(f0_pred_, f0_gt_, manhattan_distance)
- # outputs['losses']['f0_dtw'] = dist / len(f0_gt_)
- return outputs
-
- def save_valid_result(self, sample, batch_idx, model_out):
- sr = hparams['audio_sample_rate']
- f0_gt = None
- mel_out = model_out['mel_out']
- if sample.get('f0') is not None:
- f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu())
- self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt)
-
- # if self.global_step > 0:
- if self.vocoder is not None:
- wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt)
- self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr)
- # with gt duration
- model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True)
- dur_info = self.get_plot_dur_info(sample, model_out)
- del dur_info['dur_pred']
- if self.vocoder is not None:
- wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
- self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr)
- self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_gdur_{batch_idx}',
- dur_info=dur_info, f0s=f0_gt)
-
- # with pred duration
- if not hparams['use_gt_dur']:
- model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False)
- dur_info = self.get_plot_dur_info(sample, model_out)
- self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}',
- dur_info=dur_info, f0s=f0_gt)
- if self.vocoder is not None:
- wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
- self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr)
- # gt wav
- mel_gt = sample['mels'][0].cpu()
- if self.vocoder is not None:
- wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
- if self.global_step <= hparams['valid_infer_interval']:
- self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr)
-
- # add attn plot
- # if self.global_step > 0 and hparams['dur_level'] == 'word':
- # self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step)
-
- return {'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()}
- # return {'wav_gt': wav_gt, 'wav_pred': wav_pred, 'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()}
-
-
- def get_plot_dur_info(self, sample, model_out):
- # if hparams['dur_level'] == 'word':
- # T_txt = sample['word_lengths'].max()
- # dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0]
- # dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
- # txt = sample['ph_words'][0].split(" ")
- # else:
- T_txt = sample['txt_tokens'].shape[1]
- dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0]
- dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
- txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy())
- txt = txt.split(" ")
- return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt}
-
- def build_optimizer(self, model):
-
- optimizer_gen = torch.optim.AdamW(
- self.gen_params,
- lr=hparams['lr'],
- betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
- weight_decay=hparams['weight_decay'])
-
- optimizer_disc = torch.optim.AdamW(
- self.disc_params,
- lr=hparams['disc_lr'],
- betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
- **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None
-
- return [optimizer_gen, optimizer_disc]
-
- def build_scheduler(self, optimizer):
- return [
- FastSpeechTask.build_scheduler(self, optimizer[0]), # Generator Scheduler
- torch.optim.lr_scheduler.StepLR(optimizer=optimizer[1], # Discriminator Scheduler
- **hparams["discriminator_scheduler_params"]),
- ]
-
- def on_before_optimization(self, opt_idx):
- if opt_idx == 0:
- nn.utils.clip_grad_norm_(self.dp_params, hparams['clip_grad_norm'])
- if self.use_bert:
- nn.utils.clip_grad_norm_(self.bert_params, hparams['clip_grad_norm'])
- nn.utils.clip_grad_norm_(self.gen_params_except_bert_and_dp, hparams['clip_grad_norm'])
- else:
- nn.utils.clip_grad_norm_(self.gen_params_except_dp, hparams['clip_grad_norm'])
- else:
- nn.utils.clip_grad_norm_(self.disc_params, hparams["clip_grad_norm"])
-
- def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
- if self.scheduler is not None:
- self.scheduler[0].step(self.global_step // hparams['accumulate_grad_batches'])
- self.scheduler[1].step(self.global_step // hparams['accumulate_grad_batches'])
-
- ############
- # infer
- ############
- def test_start(self):
- super().test_start()
- if hparams.get('save_attn', False):
- os.makedirs(f'{self.gen_dir}/attn', exist_ok=True)
- self.model.store_inverse_all()
-
- def test_step(self, sample, batch_idx):
- assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
- outputs = self.run_model(sample, infer=True)
- text = sample['text'][0]
- item_name = sample['item_name'][0]
- tokens = sample['txt_tokens'][0].cpu().numpy()
- mel_gt = sample['mels'][0].cpu().numpy()
- mel_pred = outputs['mel_out'][0].cpu().numpy()
- mel2ph = sample['mel2ph'][0].cpu().numpy()
- mel2ph_pred = None
- str_phs = self.token_encoder.decode(tokens, strip_padding=True)
- base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]'
- if text is not None:
- base_fn += text.replace(":", "$3A")[:80]
- base_fn = base_fn.replace(' ', '_')
- gen_dir = self.gen_dir
- wav_pred = self.vocoder.spec2wav(mel_pred)
- self.saving_result_pool.add_job(self.save_result, args=[
- wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred])
- if hparams['save_gt']:
- wav_gt = self.vocoder.spec2wav(mel_gt)
- self.saving_result_pool.add_job(self.save_result, args=[
- wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph])
- if hparams.get('save_attn', False):
- attn = outputs['attn'][0].cpu().numpy()
- np.save(f'{gen_dir}/attn/{item_name}.npy', attn)
- # save f0 for pitch dtw
- f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
- f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
- np.save(f'{gen_dir}/f0/{item_name}.npy', f0_pred_)
- np.save(f'{gen_dir}/f0/{item_name}_gt.npy', f0_gt_)
-
- print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
- return {
- 'item_name': item_name,
- 'text': text,
- 'ph_tokens': self.token_encoder.decode(tokens.tolist()),
- 'wav_fn_pred': base_fn % 'P',
- 'wav_fn_gt': base_fn % 'G',
- }
diff --git a/spaces/AIWaves/Debate/src/agents/State.py b/spaces/AIWaves/Debate/src/agents/State.py
deleted file mode 100644
index fa4b050eb09fba46a9a9431f39ac281d2abca016..0000000000000000000000000000000000000000
--- a/spaces/AIWaves/Debate/src/agents/State.py
+++ /dev/null
@@ -1,142 +0,0 @@
-from Component import *
-
-
-class State:
- """
- Sub-scenes of role activities, responsible for storing the tasks that each role needs to do
- """
- def __init__(self, **kwargs):
- self.next_states = {}
- self.name = kwargs["name"]
-
- self.environment_prompt = (
- kwargs["environment_prompt"] if "environment_prompt" in kwargs else ""
- )
-
- self.roles = kwargs["roles"] if "roles" in kwargs else (list(kwargs["agent_states"].keys()) if "agent_states" in kwargs else [0])
- if len(self.roles) == 0:
- self.roles = [0]
- self.begin_role = (
- kwargs["begin_role"] if "begin_role" in kwargs else self.roles[0]
- )
- self.begin_query = kwargs["begin_query"] if "begin_query" in kwargs else None
-
- self.is_begin = True
-
- self.summary_prompt = (
- kwargs["summary_prompt"] if "summary_prompt" in kwargs else None
- )
- self.current_role = self.begin_role
- self.components = (
- self.init_components(kwargs["agent_states"])
- if "agent_states" in kwargs
- else {}
- )
- self.index = (
- self.roles.index(self.begin_role) if self.begin_role in self.roles else 0
- )
- self.chat_nums = 0
-
- def init_components(self, agent_states_dict: dict):
- agent_states = {}
- for role, components in agent_states_dict.items():
- component_dict = {}
- for component, component_args in components.items():
- if component:
- # "role" "style"
- if component == "style":
- component_dict["style"] = StyleComponent(component_args["role"])
-
- # "task"
- elif component == "task":
- component_dict["task"] = TaskComponent(component_args["task"])
-
- # "rule"
- elif component == "rule":
- component_dict["rule"] = RuleComponent(component_args["rule"])
-
- # "demonstration"
- elif component == "demonstrations":
- component_dict["demonstrations"] = DemonstrationComponent(
- component_args["demonstrations"]
- )
-
- # "output"
- elif component == "output":
- component_dict["output"] = OutputComponent(
- component_args["output"]
- )
-
- elif component == "last":
- component_dict["last"] = LastComponent(
- component_args["last_prompt"]
- )
-
- # "demonstrations"
- elif component == "cot":
- component_dict["cot"] = CoTComponent(
- component_args["demonstrations"]
- )
- elif component == "CustomizeComponent":
- component_dict["CustomizeComponent"] = CustomizeComponent(
- component_args["template"], component_args["keywords"]
- )
-
- elif component == "system" :
- component_dict["system"] = SystemComponent(
- component_args["system_prompt"]
- )
-
- # =================================================================================#
-
- # "output"
- elif component == "StaticComponent":
- component_dict["StaticComponent"] = StaticComponent(
- component_args["output"]
- )
-
- # "top_k" "type" "knowledge_base" "system_prompt" "last_prompt"
- elif component == "KnowledgeBaseComponent":
- component_dict["tool"] = KnowledgeBaseComponent(
- component_args["top_k"],
- component_args["type"],
- component_args["knowledge_path"],
- )
-
- elif component == "CategoryRequirementsComponent":
- component_dict[
- "CategoryRequirementsComponent"
- ] = CategoryRequirementsComponent(
- component_args["information_path"]
- )
-
- elif component == "FunctionComponent":
- component_dict["FunctionComponent"] = FunctionComponent(component_args[""])
- # "short_memory_extract_words" "long_memory_extract_words" "system_prompt" "last_prompt"
- elif component == "ExtractComponent":
- component_dict["ExtractComponent"] = ExtractComponent(
- component_args["extract_words"],
- component_args["system_prompt"],
- component_args["last_prompt"],
- )
- elif component == "WebSearchComponent":
- component_dict["WebSearchComponent"] = WebSearchComponent(
- component_args["engine_name"], component_args["api"]
- )
- elif component == "WebCrawlComponent":
- component_dict["WebCrawlComponent"] = WebCrawlComponent(
- component_args["name"]
- )
-
- elif component == "CodeComponent":
- component_dict["CodeComponent"] = CodeComponent(
- component_args["file_name"], component_args["keyword"]
- )
-
- # ====================================================
- else:
- continue
-
- agent_states[role] = component_dict
-
- return agent_states
diff --git a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Zeabur.py b/spaces/ASJMO/freegpt/g4f/Provider/Providers/Zeabur.py
deleted file mode 100644
index e412720bd9a0c88860f6ea8a657cb0a24bcce63f..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Zeabur.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import os
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = "https://gptleg.zeabur.app"
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-0301',
- 'gpt-3.5-turbo-16k', 'gpt-4', 'gpt-4-0613']
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- headers = {
- 'Authority': 'chat.dfehub.com',
- 'Content-Type': 'application/json',
- 'Method': 'POST',
- 'Path': '/api/openai/v1/chat/completions',
- 'Scheme': 'https',
- 'Accept': 'text/event-stream',
- 'Accept-Language': 'pt-BR,pt;q=0.9,en-US;q=0.8,en;q=0.7,zh-CN;q=0.6,zh;q=0.5',
- 'Content-Type': 'application/json',
- 'Origin': 'https://gptleg.zeabur.app',
- 'Referer': 'https://gptleg.zeabur.app/',
- 'Sec-Ch-Ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
- 'Sec-Ch-Ua-Mobile': '?0',
- 'Sec-Ch-Ua-Platform': '"Windows"',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
- 'X-Requested-With': 'XMLHttpRequest',
- }
-
- data = {
- 'model': model,
- 'temperature': 0.7,
- 'max_tokens': '16000',
- 'presence_penalty': 0,
- 'messages': messages,
- }
-
- response = requests.post(url + '/api/openai/v1/chat/completions',
- headers=headers, json=data, stream=stream)
-
- yield response.json()['choices'][0]['message']['content']
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/$types.d.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/$types.d.ts
deleted file mode 100644
index 849c7db387cf4bf3079b16e7b86d5d5ae60a4774..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/$types.d.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = { }
-type RouteId = '/';
-type MaybeWithVoid = {} extends T ? T | void : T;
-export type RequiredKeys = { [K in keyof T]-?: {} extends { [P in K]: T[K] } ? never : K; }[keyof T];
-type OutputDataShape = MaybeWithVoid> & Partial> & Record>
-type EnsureDefined = T extends null | undefined ? {} : T;
-type OptionalUnion, A extends keyof U = U extends U ? keyof U : never> = U extends unknown ? { [P in Exclude]?: never } & U : never;
-export type Snapshot = Kit.Snapshot;
-type PageParentData = EnsureDefined;
-type LayoutRouteId = RouteId | "/" | "/conversation/[id]" | "/conversations" | "/login" | "/login/callback" | "/logout" | "/privacy" | "/r/[id]" | "/settings" | null
-type LayoutParams = RouteParams & { id?: string }
-type LayoutServerParentData = EnsureDefined<{}>;
-type LayoutParentData = EnsureDefined<{}>;
-
-export type PageServerData = null;
-export type PageData = Expand;
-export type LayoutServerLoad = OutputDataShape> = Kit.ServerLoad;
-export type LayoutServerLoadEvent = Parameters[0];
-export type LayoutServerData = Expand>>>>>;
-export type LayoutData = Expand & EnsureDefined>;
-export type RequestEvent = Kit.RequestEvent;
\ No newline at end of file
diff --git a/spaces/Adapter/CoAdapter/ldm/modules/encoders/modules.py b/spaces/Adapter/CoAdapter/ldm/modules/encoders/modules.py
deleted file mode 100644
index d59229ac1c97980e811e3b808f3431311c4f3b7d..0000000000000000000000000000000000000000
--- a/spaces/Adapter/CoAdapter/ldm/modules/encoders/modules.py
+++ /dev/null
@@ -1,441 +0,0 @@
-import torch
-import torch.nn as nn
-import math
-from torch.utils.checkpoint import checkpoint
-
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, CLIPModel
-
-import open_clip
-import re
-from ldm.util import default, count_params
-
-
-class AbstractEncoder(nn.Module):
- def __init__(self):
- super().__init__()
-
- def encode(self, *args, **kwargs):
- raise NotImplementedError
-
-
-class IdentityEncoder(AbstractEncoder):
-
- def encode(self, x):
- return x
-
-
-class ClassEmbedder(nn.Module):
- def __init__(self, embed_dim, n_classes=1000, key='class'):
- super().__init__()
- self.key = key
- self.embedding = nn.Embedding(n_classes, embed_dim)
-
- def forward(self, batch, key=None):
- if key is None:
- key = self.key
- # this is for use in crossattn
- c = batch[key][:, None]
- c = self.embedding(c)
- return c
-
-
-class FrozenT5Embedder(AbstractEncoder):
- """Uses the T5 transformer encoder for text"""
- def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
- super().__init__()
- self.tokenizer = T5Tokenizer.from_pretrained(version)
- self.transformer = T5EncoderModel.from_pretrained(version)
- self.device = device
- self.max_length = max_length # TODO: typical value?
- if freeze:
- self.freeze()
-
- def freeze(self):
- self.transformer = self.transformer.eval()
- #self.train = disabled_train
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, text):
- batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
- return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- tokens = batch_encoding["input_ids"].to(self.device)
- outputs = self.transformer(input_ids=tokens)
-
- z = outputs.last_hidden_state
- return z
-
- def encode(self, text):
- return self(text)
-
-
-class FrozenCLIPEmbedder(AbstractEncoder):
- """Uses the CLIP transformer encoder for text (from huggingface)"""
- def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
- freeze=True, layer="last"): # clip-vit-base-patch32
- super().__init__()
- self.tokenizer = CLIPTokenizer.from_pretrained(version)
- self.transformer = CLIPModel.from_pretrained(version).text_model
- self.device = device
- self.max_length = max_length
- if freeze:
- self.freeze()
- self.layer = layer
-
- def freeze(self):
- self.transformer = self.transformer.eval()
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, text):
- batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
- return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- tokens = batch_encoding["input_ids"].to(self.device)
- outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer != 'last')
-
- if self.layer == 'penultimate':
- z = outputs.hidden_states[-2]
- z = self.transformer.final_layer_norm(z)
- else:
- z = outputs.last_hidden_state
- return z
-
- def encode(self, text):
- return self(text)
-
-
-class FrozenOpenCLIPEmbedder(AbstractEncoder):
- """
- Uses the OpenCLIP transformer encoder for text
- """
- LAYERS = [
- #"pooled",
- "last",
- "penultimate"
- ]
- def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
- freeze=True, layer="last"):
- super().__init__()
- assert layer in self.LAYERS
- model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
- del model.visual
- self.model = model
-
- self.device = device
- self.max_length = max_length
- if freeze:
- self.freeze()
- self.layer = layer
- if self.layer == "last":
- self.layer_idx = 0
- elif self.layer == "penultimate":
- self.layer_idx = 1
- else:
- raise NotImplementedError()
-
- def freeze(self):
- self.model = self.model.eval()
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, text):
- tokens = open_clip.tokenize(text)
- z = self.encode_with_transformer(tokens.to(self.device))
- return z
-
- def encode_with_transformer(self, text):
- x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
- x = x + self.model.positional_embedding
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.model.ln_final(x)
- return x
-
- def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
- for i, r in enumerate(self.model.transformer.resblocks):
- if i == len(self.model.transformer.resblocks) - self.layer_idx:
- break
- if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
- x = checkpoint(r, x, attn_mask)
- else:
- x = r(x, attn_mask=attn_mask)
- return x
-
- def encode(self, text):
- return self(text)
-
-
-class FrozenCLIPT5Encoder(AbstractEncoder):
- def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
- clip_max_length=77, t5_max_length=77):
- super().__init__()
- self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
- self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
- print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
- f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
-
- def encode(self, text):
- return self(text)
-
- def forward(self, text):
- clip_z = self.clip_encoder.encode(text)
- t5_z = self.t5_encoder.encode(text)
- return [clip_z, t5_z]
-
-
-# code from sd-webui
-re_attention = re.compile(r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""", re.X)
-
-
-def parse_prompt_attention(text):
- """
- Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
- Accepted tokens are:
- (abc) - increases attention to abc by a multiplier of 1.1
- (abc:3.12) - increases attention to abc by a multiplier of 3.12
- [abc] - decreases attention to abc by a multiplier of 1.1
- \( - literal character '('
- \[ - literal character '['
- \) - literal character ')'
- \] - literal character ']'
- \\ - literal character '\'
- anything else - just text
-
- >>> parse_prompt_attention('normal text')
- [['normal text', 1.0]]
- >>> parse_prompt_attention('an (important) word')
- [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
- >>> parse_prompt_attention('(unbalanced')
- [['unbalanced', 1.1]]
- >>> parse_prompt_attention('\(literal\]')
- [['(literal]', 1.0]]
- >>> parse_prompt_attention('(unnecessary)(parens)')
- [['unnecessaryparens', 1.1]]
- >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
- [['a ', 1.0],
- ['house', 1.5730000000000004],
- [' ', 1.1],
- ['on', 1.0],
- [' a ', 1.1],
- ['hill', 0.55],
- [', sun, ', 1.1],
- ['sky', 1.4641000000000006],
- ['.', 1.1]]
- """
-
- res = []
- round_brackets = []
- square_brackets = []
-
- round_bracket_multiplier = 1.1
- square_bracket_multiplier = 1 / 1.1
-
- def multiply_range(start_position, multiplier):
- for p in range(start_position, len(res)):
- res[p][1] *= multiplier
-
- for m in re_attention.finditer(text):
- text = m.group(0)
- weight = m.group(1)
-
- if text.startswith('\\'):
- res.append([text[1:], 1.0])
- elif text == '(':
- round_brackets.append(len(res))
- elif text == '[':
- square_brackets.append(len(res))
- elif weight is not None and len(round_brackets) > 0:
- multiply_range(round_brackets.pop(), float(weight))
- elif text == ')' and len(round_brackets) > 0:
- multiply_range(round_brackets.pop(), round_bracket_multiplier)
- elif text == ']' and len(square_brackets) > 0:
- multiply_range(square_brackets.pop(), square_bracket_multiplier)
- else:
- res.append([text, 1.0])
-
- for pos in round_brackets:
- multiply_range(pos, round_bracket_multiplier)
-
- for pos in square_brackets:
- multiply_range(pos, square_bracket_multiplier)
-
- if len(res) == 0:
- res = [["", 1.0]]
-
- # merge runs of identical weights
- i = 0
- while i + 1 < len(res):
- if res[i][1] == res[i + 1][1]:
- res[i][0] += res[i + 1][0]
- res.pop(i + 1)
- else:
- i += 1
-
- return res
-
-class WebUIFrozenCLIPEmebedder(AbstractEncoder):
- def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", freeze=True, layer="penultimate"):
- super(WebUIFrozenCLIPEmebedder, self).__init__()
- self.tokenizer = CLIPTokenizer.from_pretrained(version)
- self.transformer = CLIPModel.from_pretrained(version).text_model
- self.device = device
- self.layer = layer
- if freeze:
- self.freeze()
-
- self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0]
- self.comma_padding_backtrack = 20
-
- def freeze(self):
- self.transformer = self.transformer.eval()
- for param in self.parameters():
- param.requires_grad = False
-
- def tokenize(self, texts):
- tokenized = self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
- return tokenized
-
- def encode_with_transformers(self, tokens):
- outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer!='last')
-
- if self.layer == 'penultimate':
- z = outputs.hidden_states[-2]
- z = self.transformer.final_layer_norm(z)
- else:
- z = outputs.last_hidden_state
-
- return z
-
- def tokenize_line(self, line):
- parsed = parse_prompt_attention(line)
- # print(parsed)
-
- tokenized = self.tokenize([text for text, _ in parsed])
-
- remade_tokens = []
- multipliers = []
- last_comma = -1
-
- for tokens, (text, weight) in zip(tokenized, parsed):
- i = 0
- while i < len(tokens):
- token = tokens[i]
-
- if token == self.comma_token:
- last_comma = len(remade_tokens)
- elif self.comma_padding_backtrack != 0 and max(len(remade_tokens),
- 1) % 75 == 0 and last_comma != -1 and len(
- remade_tokens) - last_comma <= self.comma_padding_backtrack:
- last_comma += 1
- reloc_tokens = remade_tokens[last_comma:]
- reloc_mults = multipliers[last_comma:]
-
- remade_tokens = remade_tokens[:last_comma]
- length = len(remade_tokens)
-
- rem = int(math.ceil(length / 75)) * 75 - length
- remade_tokens += [self.tokenizer.eos_token_id] * rem + reloc_tokens
- multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-
- remade_tokens.append(token)
- multipliers.append(weight)
- i += 1
-
- token_count = len(remade_tokens)
- prompt_target_length = math.ceil(max(token_count, 1) / 75) * 75
- tokens_to_add = prompt_target_length - len(remade_tokens)
-
- remade_tokens = remade_tokens + [self.tokenizer.eos_token_id] * tokens_to_add
- multipliers = multipliers + [1.0] * tokens_to_add
-
- return remade_tokens, multipliers, token_count
-
- def process_text(self, texts):
- remade_batch_tokens = []
- token_count = 0
-
- cache = {}
- batch_multipliers = []
- for line in texts:
- if line in cache:
- remade_tokens, multipliers = cache[line]
- else:
- remade_tokens, multipliers, current_token_count = self.tokenize_line(line)
- token_count = max(current_token_count, token_count)
-
- cache[line] = (remade_tokens, multipliers)
-
- remade_batch_tokens.append(remade_tokens)
- batch_multipliers.append(multipliers)
-
- return batch_multipliers, remade_batch_tokens, token_count
-
- def process_tokens(self, remade_batch_tokens, batch_multipliers):
- remade_batch_tokens = [[self.tokenizer.bos_token_id] + x[:75] + [self.tokenizer.eos_token_id] for x in remade_batch_tokens]
- batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-
- tokens = torch.asarray(remade_batch_tokens).to(self.device)
-
- z = self.encode_with_transformers(tokens)
-
- # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
- batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
- batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(self.device)
- original_mean = z.mean()
- z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
- new_mean = z.mean()
- z *= original_mean / new_mean
-
- return z
-
- def forward(self, text):
- batch_multipliers, remade_batch_tokens, token_count = self.process_text(text)
-
- z = None
- i = 0
- while max(map(len, remade_batch_tokens)) != 0:
- rem_tokens = [x[75:] for x in remade_batch_tokens]
- rem_multipliers = [x[75:] for x in batch_multipliers]
-
- tokens = []
- multipliers = []
- for j in range(len(remade_batch_tokens)):
- if len(remade_batch_tokens[j]) > 0:
- tokens.append(remade_batch_tokens[j][:75])
- multipliers.append(batch_multipliers[j][:75])
- else:
- tokens.append([self.tokenizer.eos_token_id] * 75)
- multipliers.append([1.0] * 75)
-
- z1 = self.process_tokens(tokens, multipliers)
- z = z1 if z is None else torch.cat((z, z1), axis=-2)
-
- remade_batch_tokens = rem_tokens
- batch_multipliers = rem_multipliers
- i += 1
-
- return z
-
- def encode(self, text):
- return self(text)
-
-
-
-if __name__ == "__main__":
- model = FrozenCLIPEmbedder()
- count_params(model, verbose=True)
diff --git a/spaces/AkitoP/umamusume_bert_vits2/text/japanese_bert.py b/spaces/AkitoP/umamusume_bert_vits2/text/japanese_bert.py
deleted file mode 100644
index 308081af3b997c5ef61ce368009019a7b77ebe09..0000000000000000000000000000000000000000
--- a/spaces/AkitoP/umamusume_bert_vits2/text/japanese_bert.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-import sys
-import os
-from text.japanese import text2sep_kata
-tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
-
-models = dict()
-
-
-def get_bert_feature(text, word2ph, device=None):
- sep_text,_ = text2sep_kata(text)
- sep_tokens = [tokenizer.tokenize(t) for t in sep_text]
- sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens]
- sep_ids = [2]+[item for sublist in sep_ids for item in sublist]+[3]
- return get_bert_feature_with_token(sep_ids, word2ph, device)
-
-
-# def get_bert_feature(text, word2ph, device=None):
-# if (
-# sys.platform == "darwin"
-# and torch.backends.mps.is_available()
-# and device == "cpu"
-# ):
-# device = "mps"
-# if not device:
-# device = "cuda"
-# if device not in models.keys():
-# models[device] = AutoModelForMaskedLM.from_pretrained(
-# "cl-tohoku/bert-base-japanese-v3"
-# ).to(device)
-# with torch.no_grad():
-# inputs = tokenizer(text, return_tensors="pt")
-# for i in inputs:
-# inputs[i] = inputs[i].to(device)
-# res = models[device](**inputs, output_hidden_states=True)
-# res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-# assert inputs["input_ids"].shape[-1] == len(word2ph)
-# word2phone = word2ph
-# phone_level_feature = []
-# for i in range(len(word2phone)):
-# repeat_feature = res[i].repeat(word2phone[i], 1)
-# phone_level_feature.append(repeat_feature)
-
-# phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-# return phone_level_feature.T
-
-def get_bert_feature_with_token(tokens, word2ph, device=None):
- if (
- sys.platform == "darwin"
- and torch.backends.mps.is_available()
- and device == "cpu"
- ):
- device = "mps"
- if not device:
- device = "cuda"
- if device not in models.keys():
- models[device] = AutoModelForMaskedLM.from_pretrained(
- "./bert/bert-base-japanese-v3"
- ).to(device)
- with torch.no_grad():
- inputs = torch.tensor(tokens).to(device).unsqueeze(0)
- token_type_ids = torch.zeros_like(inputs).to(device)
- attention_mask = torch.ones_like(inputs).to(device)
- inputs = {"input_ids": inputs, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
-
-
- # for i in inputs:
- # inputs[i] = inputs[i].to(device)
- res = models[device](**inputs, output_hidden_states=True)
- res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
- assert inputs["input_ids"].shape[-1] == len(word2ph)
- word2phone = word2ph
- phone_level_feature = []
- for i in range(len(word2phone)):
- repeat_feature = res[i].repeat(word2phone[i], 1)
- phone_level_feature.append(repeat_feature)
-
- phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
- return phone_level_feature.T
-
-
-if __name__ == "__main__":
- print(get_bert_feature("観覧車",[4,2]))
- pass
\ No newline at end of file
diff --git a/spaces/AlekseyKorshuk/gai-project/modules/utils.py b/spaces/AlekseyKorshuk/gai-project/modules/utils.py
deleted file mode 100644
index 30ea1ed06d2b90a8ae78695a28117b131045ca90..0000000000000000000000000000000000000000
--- a/spaces/AlekseyKorshuk/gai-project/modules/utils.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import json
-
-import config
-
-
-def get_bot_config(bot_name):
- folder = os.path.join(config.RESOURCE_DIR, 'bot_configs')
- path = os.path.join(folder, f'{bot_name}.json')
- return load_json(path)
-
-
-def get_bot_names():
- folder = os.path.join(config.RESOURCE_DIR, 'bot_configs')
- out = [name.split('.json')[0] for name in os.listdir(folder)]
- return out
-
-
-def load_json(path):
- with open(path, 'r') as f:
- return json.load(f)
-
-
-def get_bot_picture_html(bot_config):
- model_html = f"""
-
-
-
-
-
-
{bot_config['name']}
-
- """
- return model_html
diff --git a/spaces/Alfasign/dIFFU/index.html b/spaces/Alfasign/dIFFU/index.html
deleted file mode 100644
index 6250c2958a7186a4e64f21c02b0359ff5ecd7e97..0000000000000000000000000000000000000000
--- a/spaces/Alfasign/dIFFU/index.html
+++ /dev/null
@@ -1,16 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/An-619/FastSAM/app_gradio.py b/spaces/An-619/FastSAM/app_gradio.py
deleted file mode 100644
index 098bf6486fd21a16027c9a58114e76248fe1bef8..0000000000000000000000000000000000000000
--- a/spaces/An-619/FastSAM/app_gradio.py
+++ /dev/null
@@ -1,374 +0,0 @@
-from ultralytics import YOLO
-import gradio as gr
-import torch
-from utils.tools_gradio import fast_process
-from utils.tools import format_results, box_prompt, point_prompt, text_prompt
-from PIL import ImageDraw
-import numpy as np
-
-# Load the pre-trained model
-model = YOLO('./weights/FastSAM.pt')
-
-device = torch.device(
- "cuda"
- if torch.cuda.is_available()
- else "mps"
- if torch.backends.mps.is_available()
- else "cpu"
-)
-
-# Description
-title = "
🏃 Fast Segment Anything 🤗
"
-
-news = """ # 📖 News
- 🔥 2023/07/14: Add a "wider result" button in text mode (Thanks for [gaoxinge](https://github.com/CASIA-IVA-Lab/FastSAM/pull/95)).
-
- 🔥 2023/06/29: Support the text mode (Thanks for [gaoxinge](https://github.com/CASIA-IVA-Lab/FastSAM/pull/47)).
-
- 🔥 2023/06/26: Support the points mode. (Better and faster interaction will come soon!)
-
- 🔥 2023/06/24: Add the 'Advanced options" in Everything mode to get a more detailed adjustment.
- """
-
-description_e = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it.
-
- 🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon.
-
- ⌛️ It takes about 6~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded.
-
- 🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked.
-
- 📣 You can also obtain the segmentation results of any Image through this Colab: [](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing)
-
- 😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant.
-
- 🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM)
-
- """
-
-description_p = """ # 🎯 Instructions for points mode
- This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it.
-
- 1. Upload an image or choose an example.
-
- 2. Choose the point label ('Add mask' means a positive point. 'Remove' Area means a negative point that is not segmented).
-
- 3. Add points one by one on the image.
-
- 4. Click the 'Segment with points prompt' button to get the segmentation results.
-
- **5. If you get Error, click the 'Clear points' button and try again may help.**
-
- """
-
-examples = [["examples/sa_8776.jpg"], ["examples/sa_414.jpg"], ["examples/sa_1309.jpg"], ["examples/sa_11025.jpg"],
- ["examples/sa_561.jpg"], ["examples/sa_192.jpg"], ["examples/sa_10039.jpg"], ["examples/sa_862.jpg"]]
-
-default_example = examples[0]
-
-css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
-
-
-def segment_everything(
- input,
- input_size=1024,
- iou_threshold=0.7,
- conf_threshold=0.25,
- better_quality=False,
- withContours=True,
- use_retina=True,
- text="",
- wider=False,
- mask_random_color=True,
-):
- input_size = int(input_size) # 确保 imgsz 是整数
- # Thanks for the suggestion by hysts in HuggingFace.
- w, h = input.size
- scale = input_size / max(w, h)
- new_w = int(w * scale)
- new_h = int(h * scale)
- input = input.resize((new_w, new_h))
-
- results = model(input,
- device=device,
- retina_masks=True,
- iou=iou_threshold,
- conf=conf_threshold,
- imgsz=input_size,)
-
- if len(text) > 0:
- results = format_results(results[0], 0)
- annotations, _ = text_prompt(results, text, input, device=device, wider=wider)
- annotations = np.array([annotations])
- else:
- annotations = results[0].masks.data
-
- fig = fast_process(annotations=annotations,
- image=input,
- device=device,
- scale=(1024 // input_size),
- better_quality=better_quality,
- mask_random_color=mask_random_color,
- bbox=None,
- use_retina=use_retina,
- withContours=withContours,)
- return fig
-
-
-def segment_with_points(
- input,
- input_size=1024,
- iou_threshold=0.7,
- conf_threshold=0.25,
- better_quality=False,
- withContours=True,
- use_retina=True,
- mask_random_color=True,
-):
- global global_points
- global global_point_label
-
- input_size = int(input_size) # 确保 imgsz 是整数
- # Thanks for the suggestion by hysts in HuggingFace.
- w, h = input.size
- scale = input_size / max(w, h)
- new_w = int(w * scale)
- new_h = int(h * scale)
- input = input.resize((new_w, new_h))
-
- scaled_points = [[int(x * scale) for x in point] for point in global_points]
-
- results = model(input,
- device=device,
- retina_masks=True,
- iou=iou_threshold,
- conf=conf_threshold,
- imgsz=input_size,)
-
- results = format_results(results[0], 0)
- annotations, _ = point_prompt(results, scaled_points, global_point_label, new_h, new_w)
- annotations = np.array([annotations])
-
- fig = fast_process(annotations=annotations,
- image=input,
- device=device,
- scale=(1024 // input_size),
- better_quality=better_quality,
- mask_random_color=mask_random_color,
- bbox=None,
- use_retina=use_retina,
- withContours=withContours,)
-
- global_points = []
- global_point_label = []
- return fig, None
-
-
-def get_points_with_draw(image, label, evt: gr.SelectData):
- global global_points
- global global_point_label
-
- x, y = evt.index[0], evt.index[1]
- point_radius, point_color = 15, (255, 255, 0) if label == 'Add Mask' else (255, 0, 255)
- global_points.append([x, y])
- global_point_label.append(1 if label == 'Add Mask' else 0)
-
- print(x, y, label == 'Add Mask')
-
- # 创建一个可以在图像上绘图的对象
- draw = ImageDraw.Draw(image)
- draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
- return image
-
-
-cond_img_e = gr.Image(label="Input", value=default_example[0], type='pil')
-cond_img_p = gr.Image(label="Input with points", value=default_example[0], type='pil')
-cond_img_t = gr.Image(label="Input with text", value="examples/dogs.jpg", type='pil')
-
-segm_img_e = gr.Image(label="Segmented Image", interactive=False, type='pil')
-segm_img_p = gr.Image(label="Segmented Image with points", interactive=False, type='pil')
-segm_img_t = gr.Image(label="Segmented Image with text", interactive=False, type='pil')
-
-global_points = []
-global_point_label = []
-
-input_size_slider = gr.components.Slider(minimum=512,
- maximum=1024,
- value=1024,
- step=64,
- label='Input_size',
- info='Our model was trained on a size of 1024')
-
-with gr.Blocks(css=css, title='Fast Segment Anything') as demo:
- with gr.Row():
- with gr.Column(scale=1):
- # Title
- gr.Markdown(title)
-
- with gr.Column(scale=1):
- # News
- gr.Markdown(news)
-
- with gr.Tab("Everything mode"):
- # Images
- with gr.Row(variant="panel"):
- with gr.Column(scale=1):
- cond_img_e.render()
-
- with gr.Column(scale=1):
- segm_img_e.render()
-
- # Submit & Clear
- with gr.Row():
- with gr.Column():
- input_size_slider.render()
-
- with gr.Row():
- contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
-
- with gr.Column():
- segment_btn_e = gr.Button("Segment Everything", variant='primary')
- clear_btn_e = gr.Button("Clear", variant="secondary")
-
- gr.Markdown("Try some of the examples below ⬇️")
- gr.Examples(examples=examples,
- inputs=[cond_img_e],
- outputs=segm_img_e,
- fn=segment_everything,
- cache_examples=True,
- examples_per_page=4)
-
- with gr.Column():
- with gr.Accordion("Advanced options", open=False):
- iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations')
- conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
- with gr.Row():
- mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
- with gr.Column():
- retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
-
- # Description
- gr.Markdown(description_e)
-
- segment_btn_e.click(segment_everything,
- inputs=[
- cond_img_e,
- input_size_slider,
- iou_threshold,
- conf_threshold,
- mor_check,
- contour_check,
- retina_check,
- ],
- outputs=segm_img_e)
-
- with gr.Tab("Points mode"):
- # Images
- with gr.Row(variant="panel"):
- with gr.Column(scale=1):
- cond_img_p.render()
-
- with gr.Column(scale=1):
- segm_img_p.render()
-
- # Submit & Clear
- with gr.Row():
- with gr.Column():
- with gr.Row():
- add_or_remove = gr.Radio(["Add Mask", "Remove Area"], value="Add Mask", label="Point_label (foreground/background)")
-
- with gr.Column():
- segment_btn_p = gr.Button("Segment with points prompt", variant='primary')
- clear_btn_p = gr.Button("Clear points", variant='secondary')
-
- gr.Markdown("Try some of the examples below ⬇️")
- gr.Examples(examples=examples,
- inputs=[cond_img_p],
- # outputs=segm_img_p,
- # fn=segment_with_points,
- # cache_examples=True,
- examples_per_page=4)
-
- with gr.Column():
- # Description
- gr.Markdown(description_p)
-
- cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p)
-
- segment_btn_p.click(segment_with_points,
- inputs=[cond_img_p],
- outputs=[segm_img_p, cond_img_p])
-
- with gr.Tab("Text mode"):
- # Images
- with gr.Row(variant="panel"):
- with gr.Column(scale=1):
- cond_img_t.render()
-
- with gr.Column(scale=1):
- segm_img_t.render()
-
- # Submit & Clear
- with gr.Row():
- with gr.Column():
- input_size_slider_t = gr.components.Slider(minimum=512,
- maximum=1024,
- value=1024,
- step=64,
- label='Input_size',
- info='Our model was trained on a size of 1024')
- with gr.Row():
- with gr.Column():
- contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
- text_box = gr.Textbox(label="text prompt", value="a black dog")
-
- with gr.Column():
- segment_btn_t = gr.Button("Segment with text", variant='primary')
- clear_btn_t = gr.Button("Clear", variant="secondary")
-
- gr.Markdown("Try some of the examples below ⬇️")
- gr.Examples(examples=[["examples/dogs.jpg"], ["examples/fruits.jpg"], ["examples/flowers.jpg"]],
- inputs=[cond_img_t],
- # outputs=segm_img_e,
- # fn=segment_everything,
- # cache_examples=True,
- examples_per_page=4)
-
- with gr.Column():
- with gr.Accordion("Advanced options", open=False):
- iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations')
- conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
- with gr.Row():
- mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
- retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
- wider_check = gr.Checkbox(value=False, label='wider', info='wider result')
-
- # Description
- gr.Markdown(description_e)
-
- segment_btn_t.click(segment_everything,
- inputs=[
- cond_img_t,
- input_size_slider_t,
- iou_threshold,
- conf_threshold,
- mor_check,
- contour_check,
- retina_check,
- text_box,
- wider_check,
- ],
- outputs=segm_img_t)
-
- def clear():
- return None, None
-
- def clear_text():
- return None, None, None
-
- clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e])
- clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])
- clear_btn_t.click(clear_text, outputs=[cond_img_p, segm_img_p, text_box])
-
-demo.queue()
-demo.launch()
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/ethical_guidelines.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/ethical_guidelines.md
deleted file mode 100644
index 100a92152f000d6d2f05055735a385c6391152ce..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/ethical_guidelines.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# 🧨 Diffusers’ Ethical Guidelines
-
-## Preamble
-
-[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training.
-
-Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library.
-
-The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups.
-We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback.
-
-
-## Scope
-
-The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns.
-
-
-## Ethical guidelines
-
-The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question.
-
-- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions.
-
-- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent.
-
-- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent.
-
-- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community.
-
-- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library.
-
-- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers.
-
-
-## Examples of implementations: Safety features and Mechanisms
-
-The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us.
-
-- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project.
-
-- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations.
-
-- **Encouraging safety in deployment**
-
- - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
-
- - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker.
-
-- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use.
-
-- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use.
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/hf_utils.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/hf_utils.py
deleted file mode 100644
index 269eb5e9755b1af33e677977cb657f34689e11d5..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/hf_utils.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from huggingface_hub import get_hf_file_metadata, hf_hub_url, hf_hub_download, scan_cache_dir, whoami, list_models
-
-
-def get_my_model_names(token):
-
- try:
- author = whoami(token=token)
- model_infos = list_models(author=author["name"], use_auth_token=token)
- return [model.modelId for model in model_infos], None
-
- except Exception as e:
- return [], e
-
-def download_file(repo_id: str, filename: str, token: str):
- """Download a file from a repo on the Hugging Face Hub.
-
- Returns:
- file_path (:obj:`str`): The path to the downloaded file.
- revision (:obj:`str`): The commit hash of the file.
- """
-
- md = get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename=filename), token=token)
- revision = md.commit_hash
-
- file_path = hf_hub_download(repo_id=repo_id, filename=filename, revision=revision, token=token)
-
- return file_path, revision
-
-def delete_file(revision: str):
- """Delete a file from local cache.
-
- Args:
- revision (:obj:`str`): The commit hash of the file.
- Returns:
- None
- """
- scan_cache_dir().delete_revisions(revision).execute()
-
-def get_pr_url(api, repo_id, title):
- try:
- discussions = api.get_repo_discussions(repo_id=repo_id)
- except Exception:
- return None
- for discussion in discussions:
- if (
- discussion.status == "open"
- and discussion.is_pull_request
- and discussion.title == title
- ):
- return f"https://huggingface.co/{repo_id}/discussions/{discussion.num}"
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
deleted file mode 100644
index 0471fe86eb50b0fd644f10d77ab0ea7e150c95cf..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,36 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://detectron2/resnet50_caffe',
- backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
-# use caffe img_norm
-img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/mse_loss.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/mse_loss.py
deleted file mode 100644
index 68d05752a245548862f4c9919448d4fb8dc1b8ca..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/mse_loss.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@weighted_loss
-def mse_loss(pred, target):
- """Warpper of mse loss."""
- return F.mse_loss(pred, target, reduction='none')
-
-
-@LOSSES.register_module()
-class MSELoss(nn.Module):
- """MSELoss.
-
- Args:
- reduction (str, optional): The method that reduces the loss to a
- scalar. Options are "none", "mean" and "sum".
- loss_weight (float, optional): The weight of the loss. Defaults to 1.0
- """
-
- def __init__(self, reduction='mean', loss_weight=1.0):
- super().__init__()
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self, pred, target, weight=None, avg_factor=None):
- """Forward function of loss.
-
- Args:
- pred (torch.Tensor): The prediction.
- target (torch.Tensor): The learning target of the prediction.
- weight (torch.Tensor, optional): Weight of the loss for each
- prediction. Defaults to None.
- avg_factor (int, optional): Average factor that is used to average
- the loss. Defaults to None.
-
- Returns:
- torch.Tensor: The calculated loss
- """
- loss = self.loss_weight * mse_loss(
- pred,
- target,
- weight,
- reduction=self.reduction,
- avg_factor=avg_factor)
- return loss
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py
deleted file mode 100644
index 2aa6033eec17a30aeb68c0fdd218d8f0d41157e8..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init
-from mmcv.runner import auto_fp16, force_fp32
-
-from mmdet.models.builder import HEADS
-
-
-@HEADS.register_module()
-class FusedSemanticHead(nn.Module):
- r"""Multi-level fused semantic segmentation head.
-
- .. code-block:: none
-
- in_1 -> 1x1 conv ---
- |
- in_2 -> 1x1 conv -- |
- ||
- in_3 -> 1x1 conv - ||
- ||| /-> 1x1 conv (mask prediction)
- in_4 -> 1x1 conv -----> 3x3 convs (*4)
- | \-> 1x1 conv (feature)
- in_5 -> 1x1 conv ---
- """ # noqa: W605
-
- def __init__(self,
- num_ins,
- fusion_level,
- num_convs=4,
- in_channels=256,
- conv_out_channels=256,
- num_classes=183,
- ignore_label=255,
- loss_weight=0.2,
- conv_cfg=None,
- norm_cfg=None):
- super(FusedSemanticHead, self).__init__()
- self.num_ins = num_ins
- self.fusion_level = fusion_level
- self.num_convs = num_convs
- self.in_channels = in_channels
- self.conv_out_channels = conv_out_channels
- self.num_classes = num_classes
- self.ignore_label = ignore_label
- self.loss_weight = loss_weight
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.fp16_enabled = False
-
- self.lateral_convs = nn.ModuleList()
- for i in range(self.num_ins):
- self.lateral_convs.append(
- ConvModule(
- self.in_channels,
- self.in_channels,
- 1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- inplace=False))
-
- self.convs = nn.ModuleList()
- for i in range(self.num_convs):
- in_channels = self.in_channels if i == 0 else conv_out_channels
- self.convs.append(
- ConvModule(
- in_channels,
- conv_out_channels,
- 3,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- self.conv_embedding = ConvModule(
- conv_out_channels,
- conv_out_channels,
- 1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg)
- self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1)
-
- self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label)
-
- def init_weights(self):
- kaiming_init(self.conv_logits)
-
- @auto_fp16()
- def forward(self, feats):
- x = self.lateral_convs[self.fusion_level](feats[self.fusion_level])
- fused_size = tuple(x.shape[-2:])
- for i, feat in enumerate(feats):
- if i != self.fusion_level:
- feat = F.interpolate(
- feat, size=fused_size, mode='bilinear', align_corners=True)
- x += self.lateral_convs[i](feat)
-
- for i in range(self.num_convs):
- x = self.convs[i](x)
-
- mask_pred = self.conv_logits(x)
- x = self.conv_embedding(x)
- return mask_pred, x
-
- @force_fp32(apply_to=('mask_pred', ))
- def loss(self, mask_pred, labels):
- labels = labels.squeeze(1).long()
- loss_semantic_seg = self.criterion(mask_pred, labels)
- loss_semantic_seg *= self.loss_weight
- return loss_semantic_seg
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/Training_PRO/matplotgraph.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/Training_PRO/matplotgraph.py
deleted file mode 100644
index 5e607526925445134fc1715a1fab6bb4af99112d..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/Training_PRO/matplotgraph.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import json
-
-def create_graph(lora_path, lora_name):
- try:
- import matplotlib.pyplot as plt
- from matplotlib.ticker import ScalarFormatter
-
- peft_model_path = f'{lora_path}/training_graph.json'
- image_model_path = f'{lora_path}/training_graph.png'
- # Check if the JSON file exists
- if os.path.exists(peft_model_path):
- # Load data from JSON file
- with open(peft_model_path, 'r') as file:
- data = json.load(file)
- # Extract x, y1, and y2 values
- x = [item['epoch'] for item in data]
- y1 = [item['learning_rate'] for item in data]
- y2 = [item['loss'] for item in data]
-
- # Create the line chart
- fig, ax1 = plt.subplots(figsize=(10, 6))
-
-
- # Plot y1 (learning rate) on the first y-axis
- ax1.plot(x, y1, 'b-', label='Learning Rate')
- ax1.set_xlabel('Epoch')
- ax1.set_ylabel('Learning Rate', color='b')
- ax1.tick_params('y', colors='b')
-
- # Create a second y-axis
- ax2 = ax1.twinx()
-
- # Plot y2 (loss) on the second y-axis
- ax2.plot(x, y2, 'r-', label='Loss')
- ax2.set_ylabel('Loss', color='r')
- ax2.tick_params('y', colors='r')
-
- # Set the y-axis formatter to display numbers in scientific notation
- ax1.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
- ax1.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
-
- # Add grid
- ax1.grid(True)
-
- # Combine the legends for both plots
- lines, labels = ax1.get_legend_handles_labels()
- lines2, labels2 = ax2.get_legend_handles_labels()
- ax2.legend(lines + lines2, labels + labels2, loc='best')
-
- # Set the title
- plt.title(f'{lora_name} LR and Loss vs Epoch')
-
- # Save the chart as an image
- plt.savefig(image_model_path)
-
- print(f"Graph saved in {image_model_path}")
- else:
- print(f"File 'training_graph.json' does not exist in the {lora_path}")
-
- except ImportError:
- print("matplotlib is not installed. Please install matplotlib to create PNG graphs")
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/midas_net_custom.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/midas_net_custom.py
deleted file mode 100644
index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/midas_net_custom.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
- """Network for monocular depth estimation.
- """
-
- def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
- blocks={'expand': True}):
- """Init.
-
- Args:
- path (str, optional): Path to saved model. Defaults to None.
- features (int, optional): Number of features. Defaults to 256.
- backbone (str, optional): Backbone network for encoder. Defaults to resnet50
- """
- print("Loading weights: ", path)
-
- super(MidasNet_small, self).__init__()
-
- use_pretrained = False if path else True
-
- self.channels_last = channels_last
- self.blocks = blocks
- self.backbone = backbone
-
- self.groups = 1
-
- features1=features
- features2=features
- features3=features
- features4=features
- self.expand = False
- if "expand" in self.blocks and self.blocks['expand'] == True:
- self.expand = True
- features1=features
- features2=features*2
- features3=features*4
- features4=features*8
-
- self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-
- self.scratch.activation = nn.ReLU(False)
-
- self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
- self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
- self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
- self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-
- self.scratch.output_conv = nn.Sequential(
- nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
- Interpolate(scale_factor=2, mode="bilinear"),
- nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
- self.scratch.activation,
- nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
- nn.ReLU(True) if non_negative else nn.Identity(),
- nn.Identity(),
- )
-
- if path:
- self.load(path)
-
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input data (image)
-
- Returns:
- tensor: depth
- """
- if self.channels_last==True:
- print("self.channels_last = ", self.channels_last)
- x.contiguous(memory_format=torch.channels_last)
-
-
- layer_1 = self.pretrained.layer1(x)
- layer_2 = self.pretrained.layer2(layer_1)
- layer_3 = self.pretrained.layer3(layer_2)
- layer_4 = self.pretrained.layer4(layer_3)
-
- layer_1_rn = self.scratch.layer1_rn(layer_1)
- layer_2_rn = self.scratch.layer2_rn(layer_2)
- layer_3_rn = self.scratch.layer3_rn(layer_3)
- layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
- path_4 = self.scratch.refinenet4(layer_4_rn)
- path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
- path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
- path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
- out = self.scratch.output_conv(path_1)
-
- return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
- prev_previous_type = nn.Identity()
- prev_previous_name = ''
- previous_type = nn.Identity()
- previous_name = ''
- for name, module in m.named_modules():
- if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
- # print("FUSED ", prev_previous_name, previous_name, name)
- torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
- elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
- # print("FUSED ", prev_previous_name, previous_name)
- torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
- # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
- # print("FUSED ", previous_name, name)
- # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
- prev_previous_type = previous_type
- prev_previous_name = previous_name
- previous_type = type(module)
- previous_name = name
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/openpose/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/openpose/__init__.py
deleted file mode 100644
index 92e530fd6913a92b1e624d3e334252bcfdba902f..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/openpose/__init__.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Openpose
-# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
-# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
-# 3rd Edited by ControlNet
-
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-import torch
-import numpy as np
-from . import util
-from .body import Body
-from .hand import Hand
-from annotator.util import annotator_ckpts_path
-
-
-body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
-hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
-
-
-class OpenposeDetector:
- def __init__(self):
- body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth")
- hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth")
-
- if not os.path.exists(hand_modelpath):
- from basicsr.utils.download_util import load_file_from_url
- load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
- load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
-
- self.body_estimation = Body(body_modelpath)
- self.hand_estimation = Hand(hand_modelpath)
-
- def __call__(self, oriImg, hand=False):
- oriImg = oriImg[:, :, ::-1].copy()
- with torch.no_grad():
- candidate, subset = self.body_estimation(oriImg)
- canvas = np.zeros_like(oriImg)
- canvas = util.draw_bodypose(canvas, candidate, subset)
- if hand:
- hands_list = util.handDetect(candidate, subset, oriImg)
- all_hand_peaks = []
- for x, y, w, is_left in hands_list:
- peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
- peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
- peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
- all_hand_peaks.append(peaks)
- canvas = util.draw_handpose(canvas, all_hand_peaks)
- return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py
deleted file mode 100644
index 3f23b6717d53ad29f02dd15046802a2631a5076b..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './pascal_voc12.py'
-# dataset settings
-data = dict(
- train=dict(
- ann_dir=['SegmentationClass', 'SegmentationClassAug'],
- split=[
- 'ImageSets/Segmentation/train.txt',
- 'ImageSets/Segmentation/aug.txt'
- ]))
diff --git a/spaces/ApathyINC/CustomGPT/README.md b/spaces/ApathyINC/CustomGPT/README.md
deleted file mode 100644
index 76e8f6584524cfd1907fe10fe483784bd986d052..0000000000000000000000000000000000000000
--- a/spaces/ApathyINC/CustomGPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Test
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Apex-X/Tm/roop/globals.py b/spaces/Apex-X/Tm/roop/globals.py
deleted file mode 100644
index 77fd391db235b878ce1f91765596bd76adb06697..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/Tm/roop/globals.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from typing import List
-
-source_path = None
-target_path = None
-output_path = None
-frame_processors: List[str] = []
-keep_fps = None
-keep_audio = None
-keep_frames = None
-many_faces = None
-video_encoder = None
-video_quality = None
-max_memory = None
-execution_providers: List[str] = []
-execution_threads = None
-headless = None
-log_level = 'error'
diff --git a/spaces/Arcypojeb/NeuralServer/README.md b/spaces/Arcypojeb/NeuralServer/README.md
deleted file mode 100644
index 8da0804ecc3f5c0f00ccf4d61d7de4d053e702c7..0000000000000000000000000000000000000000
--- a/spaces/Arcypojeb/NeuralServer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: NeuralServer
-emoji: 🦀
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ariharasudhan/YoloV5/utils/autobatch.py b/spaces/Ariharasudhan/YoloV5/utils/autobatch.py
deleted file mode 100644
index bdeb91c3d2bd15e53eb65715228932d3e87e0989..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/utils/autobatch.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-batch utils
-"""
-
-from copy import deepcopy
-
-import numpy as np
-import torch
-
-from utils.general import LOGGER, colorstr
-from utils.torch_utils import profile
-
-
-def check_train_batch_size(model, imgsz=640, amp=True):
- # Check YOLOv5 training batch size
- with torch.cuda.amp.autocast(amp):
- return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
-
-
-def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
- # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
- # Usage:
- # import torch
- # from utils.autobatch import autobatch
- # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
- # print(autobatch(model))
-
- # Check device
- prefix = colorstr('AutoBatch: ')
- LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
- device = next(model.parameters()).device # get model device
- if device.type == 'cpu':
- LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
- return batch_size
- if torch.backends.cudnn.benchmark:
- LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
- return batch_size
-
- # Inspect CUDA memory
- gb = 1 << 30 # bytes to GiB (1024 ** 3)
- d = str(device).upper() # 'CUDA:0'
- properties = torch.cuda.get_device_properties(device) # device properties
- t = properties.total_memory / gb # GiB total
- r = torch.cuda.memory_reserved(device) / gb # GiB reserved
- a = torch.cuda.memory_allocated(device) / gb # GiB allocated
- f = t - (r + a) # GiB free
- LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
-
- # Profile batch sizes
- batch_sizes = [1, 2, 4, 8, 16]
- try:
- img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
- results = profile(img, model, n=3, device=device)
- except Exception as e:
- LOGGER.warning(f'{prefix}{e}')
-
- # Fit a solution
- y = [x[2] for x in results if x] # memory [2]
- p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
- b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
- if None in results: # some sizes failed
- i = results.index(None) # first fail index
- if b >= batch_sizes[i]: # y intercept above failure point
- b = batch_sizes[max(i - 1, 0)] # select prior safe point
- if b < 1 or b > 1024: # b outside of safe range
- b = batch_size
- LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
-
- fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
- LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
- return b
diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/csrc/vision.cpp b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/csrc/vision.cpp
deleted file mode 100644
index c1f2c50c82909bbd5492c163d634af77a3ba1781..0000000000000000000000000000000000000000
--- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/csrc/vision.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
- std::ostringstream oss;
-
- // copied from
- // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
- auto printCudaStyleVersion = [&](int v) {
- oss << (v / 1000) << "." << (v / 10 % 100);
- if (v % 10 != 0) {
- oss << "." << (v % 10);
- }
- };
- printCudaStyleVersion(get_cudart_version());
- return oss.str();
-#else
- return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
- std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
- { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
- {
- ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
- << __clang_patchlevel__;
- }
-#endif
-
-#if defined(_MSC_VER)
- { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
- return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
- m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/build_tracker.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/build_tracker.py
deleted file mode 100644
index 6621549b8449130d2d01ebac0a3649d8b70c4f91..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/build_tracker.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import contextlib
-import hashlib
-import logging
-import os
-from types import TracebackType
-from typing import Dict, Generator, Optional, Set, Type, Union
-
-from pip._internal.models.link import Link
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-@contextlib.contextmanager
-def update_env_context_manager(**changes: str) -> Generator[None, None, None]:
- target = os.environ
-
- # Save values from the target and change them.
- non_existent_marker = object()
- saved_values: Dict[str, Union[object, str]] = {}
- for name, new_value in changes.items():
- try:
- saved_values[name] = target[name]
- except KeyError:
- saved_values[name] = non_existent_marker
- target[name] = new_value
-
- try:
- yield
- finally:
- # Restore original values in the target.
- for name, original_value in saved_values.items():
- if original_value is non_existent_marker:
- del target[name]
- else:
- assert isinstance(original_value, str) # for mypy
- target[name] = original_value
-
-
-@contextlib.contextmanager
-def get_build_tracker() -> Generator["BuildTracker", None, None]:
- root = os.environ.get("PIP_BUILD_TRACKER")
- with contextlib.ExitStack() as ctx:
- if root is None:
- root = ctx.enter_context(TempDirectory(kind="build-tracker")).path
- ctx.enter_context(update_env_context_manager(PIP_BUILD_TRACKER=root))
- logger.debug("Initialized build tracking at %s", root)
-
- with BuildTracker(root) as tracker:
- yield tracker
-
-
-class BuildTracker:
- def __init__(self, root: str) -> None:
- self._root = root
- self._entries: Set[InstallRequirement] = set()
- logger.debug("Created build tracker: %s", self._root)
-
- def __enter__(self) -> "BuildTracker":
- logger.debug("Entered build tracker: %s", self._root)
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.cleanup()
-
- def _entry_path(self, link: Link) -> str:
- hashed = hashlib.sha224(link.url_without_fragment.encode()).hexdigest()
- return os.path.join(self._root, hashed)
-
- def add(self, req: InstallRequirement) -> None:
- """Add an InstallRequirement to build tracking."""
-
- assert req.link
- # Get the file to write information about this requirement.
- entry_path = self._entry_path(req.link)
-
- # Try reading from the file. If it exists and can be read from, a build
- # is already in progress, so a LookupError is raised.
- try:
- with open(entry_path) as fp:
- contents = fp.read()
- except FileNotFoundError:
- pass
- else:
- message = "{} is already being built: {}".format(req.link, contents)
- raise LookupError(message)
-
- # If we're here, req should really not be building already.
- assert req not in self._entries
-
- # Start tracking this requirement.
- with open(entry_path, "w", encoding="utf-8") as fp:
- fp.write(str(req))
- self._entries.add(req)
-
- logger.debug("Added %s to build tracker %r", req, self._root)
-
- def remove(self, req: InstallRequirement) -> None:
- """Remove an InstallRequirement from build tracking."""
-
- assert req.link
- # Delete the created file and the corresponding entries.
- os.unlink(self._entry_path(req.link))
- self._entries.remove(req)
-
- logger.debug("Removed %s from build tracker %r", req, self._root)
-
- def cleanup(self) -> None:
- for req in set(self._entries):
- self.remove(req)
-
- logger.debug("Removed build tracker: %r", self._root)
-
- @contextlib.contextmanager
- def track(self, req: InstallRequirement) -> Generator[None, None, None]:
- self.add(req)
- yield
- self.remove(req)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/utils.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/utils.py
deleted file mode 100644
index bab11b80c60f10a4f3bccb12eb5b17c48a449767..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/utils.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidWheelFilename(ValueError):
- """
- An invalid wheel filename was found, users should refer to PEP 427.
- """
-
-
-class InvalidSdistFilename(ValueError):
- """
- An invalid sdist filename was found, users should refer to the packaging user guide.
- """
-
-
-_canonicalize_regex = re.compile(r"[-_.]+")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str) -> NormalizedName:
- # This is taken from PEP 503.
- value = _canonicalize_regex.sub("-", name).lower()
- return cast(NormalizedName, value)
-
-
-def canonicalize_version(version: Union[Version, str]) -> str:
- """
- This is very similar to Version.__str__, but has one subtle difference
- with the way it handles the release segment.
- """
- if isinstance(version, str):
- try:
- parsed = Version(version)
- except InvalidVersion:
- # Legacy versions cannot be normalized
- return version
- else:
- parsed = version
-
- parts = []
-
- # Epoch
- if parsed.epoch != 0:
- parts.append(f"{parsed.epoch}!")
-
- # Release segment
- # NB: This strips trailing '.0's to normalize
- parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
-
- # Pre-release
- if parsed.pre is not None:
- parts.append("".join(str(x) for x in parsed.pre))
-
- # Post-release
- if parsed.post is not None:
- parts.append(f".post{parsed.post}")
-
- # Development release
- if parsed.dev is not None:
- parts.append(f".dev{parsed.dev}")
-
- # Local version segment
- if parsed.local is not None:
- parts.append(f"+{parsed.local}")
-
- return "".join(parts)
-
-
-def parse_wheel_filename(
- filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
- if not filename.endswith(".whl"):
- raise InvalidWheelFilename(
- f"Invalid wheel filename (extension must be '.whl'): {filename}"
- )
-
- filename = filename[:-4]
- dashes = filename.count("-")
- if dashes not in (4, 5):
- raise InvalidWheelFilename(
- f"Invalid wheel filename (wrong number of parts): {filename}"
- )
-
- parts = filename.split("-", dashes - 2)
- name_part = parts[0]
- # See PEP 427 for the rules on escaping the project name
- if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
- raise InvalidWheelFilename(f"Invalid project name: {filename}")
- name = canonicalize_name(name_part)
- version = Version(parts[1])
- if dashes == 5:
- build_part = parts[2]
- build_match = _build_tag_regex.match(build_part)
- if build_match is None:
- raise InvalidWheelFilename(
- f"Invalid build number: {build_part} in '{filename}'"
- )
- build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
- else:
- build = ()
- tags = parse_tag(parts[-1])
- return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
- if filename.endswith(".tar.gz"):
- file_stem = filename[: -len(".tar.gz")]
- elif filename.endswith(".zip"):
- file_stem = filename[: -len(".zip")]
- else:
- raise InvalidSdistFilename(
- f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
- f" {filename}"
- )
-
- # We are requiring a PEP 440 version, which cannot contain dashes,
- # so we split on the last dash.
- name_part, sep, version_part = file_stem.rpartition("-")
- if not sep:
- raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
- name = canonicalize_name(name_part)
- version = Version(version_part)
- return (name, version)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/version.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/version.py
deleted file mode 100644
index e29e265750fbccfbd072d1541e376aa150724be2..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/version.py
+++ /dev/null
@@ -1,358 +0,0 @@
-#
-# distutils/version.py
-#
-# Implements multiple version numbering conventions for the
-# Python Module Distribution Utilities.
-#
-# $Id$
-#
-
-"""Provides classes to represent module version numbers (one class for
-each style of version numbering). There are currently two such classes
-implemented: StrictVersion and LooseVersion.
-
-Every version number class implements the following interface:
- * the 'parse' method takes a string and parses it to some internal
- representation; if the string is an invalid version number,
- 'parse' raises a ValueError exception
- * the class constructor takes an optional string argument which,
- if supplied, is passed to 'parse'
- * __str__ reconstructs the string that was passed to 'parse' (or
- an equivalent string -- ie. one that will generate an equivalent
- version number instance)
- * __repr__ generates Python code to recreate the version number instance
- * _cmp compares the current instance with either another instance
- of the same class or a string (which will be parsed to an instance
- of the same class, thus must follow the same rules)
-"""
-
-import re
-import warnings
-import contextlib
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
- with warnings.catch_warnings(record=True) as ctx:
- warnings.filterwarnings(
- action='default',
- category=DeprecationWarning,
- message="distutils Version classes are deprecated.",
- )
- yield ctx
-
-
-class Version:
- """Abstract base class for version numbering classes. Just provides
- constructor (__init__) and reproducer (__repr__), because those
- seem to be the same for all version numbering classes; and route
- rich comparisons to _cmp.
- """
-
- def __init__(self, vstring=None):
- if vstring:
- self.parse(vstring)
- warnings.warn(
- "distutils Version classes are deprecated. "
- "Use packaging.version instead.",
- DeprecationWarning,
- stacklevel=2,
- )
-
- def __repr__(self):
- return "{} ('{}')".format(self.__class__.__name__, str(self))
-
- def __eq__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c == 0
-
- def __lt__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c < 0
-
- def __le__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c <= 0
-
- def __gt__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c > 0
-
- def __ge__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c >= 0
-
-
-# Interface for version-number classes -- must be implemented
-# by the following classes (the concrete ones -- Version should
-# be treated as an abstract class).
-# __init__ (string) - create and take same action as 'parse'
-# (string parameter is optional)
-# parse (string) - convert a string representation to whatever
-# internal representation is appropriate for
-# this style of version numbering
-# __str__ (self) - convert back to a string; should be very similar
-# (if not identical to) the string supplied to parse
-# __repr__ (self) - generate Python code to recreate
-# the instance
-# _cmp (self, other) - compare two version numbers ('other' may
-# be an unparsed version string, or another
-# instance of your version class)
-
-
-class StrictVersion(Version):
-
- """Version numbering for anal retentives and software idealists.
- Implements the standard interface for version number classes as
- described above. A version number consists of two or three
- dot-separated numeric components, with an optional "pre-release" tag
- on the end. The pre-release tag consists of the letter 'a' or 'b'
- followed by a number. If the numeric components of two version
- numbers are equal, then one with a pre-release tag will always
- be deemed earlier (lesser) than one without.
-
- The following are valid version numbers (shown in the order that
- would be obtained by sorting according to the supplied cmp function):
-
- 0.4 0.4.0 (these two are equivalent)
- 0.4.1
- 0.5a1
- 0.5b3
- 0.5
- 0.9.6
- 1.0
- 1.0.4a3
- 1.0.4b1
- 1.0.4
-
- The following are examples of invalid version numbers:
-
- 1
- 2.7.2.2
- 1.3.a4
- 1.3pl1
- 1.3c4
-
- The rationale for this version numbering system will be explained
- in the distutils documentation.
- """
-
- version_re = re.compile(
- r'^(\d+) \. (\d+) (\. (\d+))? ([ab](\d+))?$', re.VERBOSE | re.ASCII
- )
-
- def parse(self, vstring):
- match = self.version_re.match(vstring)
- if not match:
- raise ValueError("invalid version number '%s'" % vstring)
-
- (major, minor, patch, prerelease, prerelease_num) = match.group(1, 2, 4, 5, 6)
-
- if patch:
- self.version = tuple(map(int, [major, minor, patch]))
- else:
- self.version = tuple(map(int, [major, minor])) + (0,)
-
- if prerelease:
- self.prerelease = (prerelease[0], int(prerelease_num))
- else:
- self.prerelease = None
-
- def __str__(self):
-
- if self.version[2] == 0:
- vstring = '.'.join(map(str, self.version[0:2]))
- else:
- vstring = '.'.join(map(str, self.version))
-
- if self.prerelease:
- vstring = vstring + self.prerelease[0] + str(self.prerelease[1])
-
- return vstring
-
- def _cmp(self, other): # noqa: C901
- if isinstance(other, str):
- with suppress_known_deprecation():
- other = StrictVersion(other)
- elif not isinstance(other, StrictVersion):
- return NotImplemented
-
- if self.version != other.version:
- # numeric versions don't match
- # prerelease stuff doesn't matter
- if self.version < other.version:
- return -1
- else:
- return 1
-
- # have to compare prerelease
- # case 1: neither has prerelease; they're equal
- # case 2: self has prerelease, other doesn't; other is greater
- # case 3: self doesn't have prerelease, other does: self is greater
- # case 4: both have prerelease: must compare them!
-
- if not self.prerelease and not other.prerelease:
- return 0
- elif self.prerelease and not other.prerelease:
- return -1
- elif not self.prerelease and other.prerelease:
- return 1
- elif self.prerelease and other.prerelease:
- if self.prerelease == other.prerelease:
- return 0
- elif self.prerelease < other.prerelease:
- return -1
- else:
- return 1
- else:
- assert False, "never get here"
-
-
-# end class StrictVersion
-
-
-# The rules according to Greg Stein:
-# 1) a version number has 1 or more numbers separated by a period or by
-# sequences of letters. If only periods, then these are compared
-# left-to-right to determine an ordering.
-# 2) sequences of letters are part of the tuple for comparison and are
-# compared lexicographically
-# 3) recognize the numeric components may have leading zeroes
-#
-# The LooseVersion class below implements these rules: a version number
-# string is split up into a tuple of integer and string components, and
-# comparison is a simple tuple comparison. This means that version
-# numbers behave in a predictable and obvious way, but a way that might
-# not necessarily be how people *want* version numbers to behave. There
-# wouldn't be a problem if people could stick to purely numeric version
-# numbers: just split on period and compare the numbers as tuples.
-# However, people insist on putting letters into their version numbers;
-# the most common purpose seems to be:
-# - indicating a "pre-release" version
-# ('alpha', 'beta', 'a', 'b', 'pre', 'p')
-# - indicating a post-release patch ('p', 'pl', 'patch')
-# but of course this can't cover all version number schemes, and there's
-# no way to know what a programmer means without asking him.
-#
-# The problem is what to do with letters (and other non-numeric
-# characters) in a version number. The current implementation does the
-# obvious and predictable thing: keep them as strings and compare
-# lexically within a tuple comparison. This has the desired effect if
-# an appended letter sequence implies something "post-release":
-# eg. "0.99" < "0.99pl14" < "1.0", and "5.001" < "5.001m" < "5.002".
-#
-# However, if letters in a version number imply a pre-release version,
-# the "obvious" thing isn't correct. Eg. you would expect that
-# "1.5.1" < "1.5.2a2" < "1.5.2", but under the tuple/lexical comparison
-# implemented here, this just isn't so.
-#
-# Two possible solutions come to mind. The first is to tie the
-# comparison algorithm to a particular set of semantic rules, as has
-# been done in the StrictVersion class above. This works great as long
-# as everyone can go along with bondage and discipline. Hopefully a
-# (large) subset of Python module programmers will agree that the
-# particular flavour of bondage and discipline provided by StrictVersion
-# provides enough benefit to be worth using, and will submit their
-# version numbering scheme to its domination. The free-thinking
-# anarchists in the lot will never give in, though, and something needs
-# to be done to accommodate them.
-#
-# Perhaps a "moderately strict" version class could be implemented that
-# lets almost anything slide (syntactically), and makes some heuristic
-# assumptions about non-digits in version number strings. This could
-# sink into special-case-hell, though; if I was as talented and
-# idiosyncratic as Larry Wall, I'd go ahead and implement a class that
-# somehow knows that "1.2.1" < "1.2.2a2" < "1.2.2" < "1.2.2pl3", and is
-# just as happy dealing with things like "2g6" and "1.13++". I don't
-# think I'm smart enough to do it right though.
-#
-# In any case, I've coded the test suite for this module (see
-# ../test/test_version.py) specifically to fail on things like comparing
-# "1.2a2" and "1.2". That's not because the *code* is doing anything
-# wrong, it's because the simple, obvious design doesn't match my
-# complicated, hairy expectations for real-world version numbers. It
-# would be a snap to fix the test suite to say, "Yep, LooseVersion does
-# the Right Thing" (ie. the code matches the conception). But I'd rather
-# have a conception that matches common notions about version numbers.
-
-
-class LooseVersion(Version):
-
- """Version numbering for anarchists and software realists.
- Implements the standard interface for version number classes as
- described above. A version number consists of a series of numbers,
- separated by either periods or strings of letters. When comparing
- version numbers, the numeric components will be compared
- numerically, and the alphabetic components lexically. The following
- are all valid version numbers, in no particular order:
-
- 1.5.1
- 1.5.2b2
- 161
- 3.10a
- 8.02
- 3.4j
- 1996.07.12
- 3.2.pl0
- 3.1.1.6
- 2g6
- 11g
- 0.960923
- 2.2beta29
- 1.13++
- 5.5.kw
- 2.0b1pl0
-
- In fact, there is no such thing as an invalid version number under
- this scheme; the rules for comparison are simple and predictable,
- but may not always give the results you want (for some definition
- of "want").
- """
-
- component_re = re.compile(r'(\d+ | [a-z]+ | \.)', re.VERBOSE)
-
- def parse(self, vstring):
- # I've given up on thinking I can reconstruct the version string
- # from the parsed tuple -- so I just store the string here for
- # use by __str__
- self.vstring = vstring
- components = [x for x in self.component_re.split(vstring) if x and x != '.']
- for i, obj in enumerate(components):
- try:
- components[i] = int(obj)
- except ValueError:
- pass
-
- self.version = components
-
- def __str__(self):
- return self.vstring
-
- def __repr__(self):
- return "LooseVersion ('%s')" % str(self)
-
- def _cmp(self, other):
- if isinstance(other, str):
- other = LooseVersion(other)
- elif not isinstance(other, LooseVersion):
- return NotImplemented
-
- if self.version == other.version:
- return 0
- if self.version < other.version:
- return -1
- if self.version > other.version:
- return 1
-
-
-# end class LooseVersion
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py
deleted file mode 100644
index 7a19db71562ef47569dc7f77ec616af85447f0ec..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import logging
-import numpy as np
-import os
-from collections import OrderedDict
-import PIL.Image as Image
-import pycocotools.mask as mask_util
-import torch
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.utils.comm import all_gather, is_main_process, synchronize
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class SemSegEvaluator(DatasetEvaluator):
- """
- Evaluate semantic segmentation metrics.
- """
-
- def __init__(
- self,
- dataset_name,
- distributed=True,
- output_dir=None,
- *,
- num_classes=None,
- ignore_label=None,
- ):
- """
- Args:
- dataset_name (str): name of the dataset to be evaluated.
- distributed (bool): if True, will collect results from all ranks for evaluation.
- Otherwise, will evaluate the results in the current process.
- output_dir (str): an output directory to dump results.
- num_classes, ignore_label: deprecated argument
- """
- self._logger = logging.getLogger(__name__)
- if num_classes is not None:
- self._logger.warn(
- "SemSegEvaluator(num_classes) is deprecated! It should be obtained from metadata."
- )
- if ignore_label is not None:
- self._logger.warn(
- "SemSegEvaluator(ignore_label) is deprecated! It should be obtained from metadata."
- )
- self._dataset_name = dataset_name
- self._distributed = distributed
- self._output_dir = output_dir
-
- self._cpu_device = torch.device("cpu")
-
- self.input_file_to_gt_file = {
- dataset_record["file_name"]: dataset_record["sem_seg_file_name"]
- for dataset_record in DatasetCatalog.get(dataset_name)
- }
-
- meta = MetadataCatalog.get(dataset_name)
- # Dict that maps contiguous training ids to COCO category ids
- try:
- c2d = meta.stuff_dataset_id_to_contiguous_id
- self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()}
- except AttributeError:
- self._contiguous_id_to_dataset_id = None
- self._class_names = meta.stuff_classes
- self._num_classes = len(meta.stuff_classes)
- if num_classes is not None:
- assert self._num_classes == num_classes, f"{self._num_classes} != {num_classes}"
- self._ignore_label = ignore_label if ignore_label is not None else meta.ignore_label
-
- def reset(self):
- self._conf_matrix = np.zeros((self._num_classes + 1, self._num_classes + 1), dtype=np.int64)
- self._predictions = []
-
- def process(self, inputs, outputs):
- """
- Args:
- inputs: the inputs to a model.
- It is a list of dicts. Each dict corresponds to an image and
- contains keys like "height", "width", "file_name".
- outputs: the outputs of a model. It is either list of semantic segmentation predictions
- (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic
- segmentation prediction in the same format.
- """
- for input, output in zip(inputs, outputs):
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device)
- pred = np.array(output, dtype=np.int)
- with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f:
- gt = np.array(Image.open(f), dtype=np.int)
-
- gt[gt == self._ignore_label] = self._num_classes
-
- self._conf_matrix += np.bincount(
- (self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1),
- minlength=self._conf_matrix.size,
- ).reshape(self._conf_matrix.shape)
-
- self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"]))
-
- def evaluate(self):
- """
- Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval):
-
- * Mean intersection-over-union averaged across classes (mIoU)
- * Frequency Weighted IoU (fwIoU)
- * Mean pixel accuracy averaged across classes (mACC)
- * Pixel Accuracy (pACC)
- """
- if self._distributed:
- synchronize()
- conf_matrix_list = all_gather(self._conf_matrix)
- self._predictions = all_gather(self._predictions)
- self._predictions = list(itertools.chain(*self._predictions))
- if not is_main_process():
- return
-
- self._conf_matrix = np.zeros_like(self._conf_matrix)
- for conf_matrix in conf_matrix_list:
- self._conf_matrix += conf_matrix
-
- if self._output_dir:
- PathManager.mkdirs(self._output_dir)
- file_path = os.path.join(self._output_dir, "sem_seg_predictions.json")
- with PathManager.open(file_path, "w") as f:
- f.write(json.dumps(self._predictions))
-
- acc = np.full(self._num_classes, np.nan, dtype=np.float)
- iou = np.full(self._num_classes, np.nan, dtype=np.float)
- tp = self._conf_matrix.diagonal()[:-1].astype(np.float)
- pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float)
- class_weights = pos_gt / np.sum(pos_gt)
- pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float)
- acc_valid = pos_gt > 0
- acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
- iou_valid = (pos_gt + pos_pred) > 0
- union = pos_gt + pos_pred - tp
- iou[acc_valid] = tp[acc_valid] / union[acc_valid]
- macc = np.sum(acc[acc_valid]) / np.sum(acc_valid)
- miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
- fiou = np.sum(iou[acc_valid] * class_weights[acc_valid])
- pacc = np.sum(tp) / np.sum(pos_gt)
-
- res = {}
- res["mIoU"] = 100 * miou
- res["fwIoU"] = 100 * fiou
- for i, name in enumerate(self._class_names):
- res["IoU-{}".format(name)] = 100 * iou[i]
- res["mACC"] = 100 * macc
- res["pACC"] = 100 * pacc
- for i, name in enumerate(self._class_names):
- res["ACC-{}".format(name)] = 100 * acc[i]
-
- if self._output_dir:
- file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth")
- with PathManager.open(file_path, "wb") as f:
- torch.save(res, f)
- results = OrderedDict({"sem_seg": res})
- self._logger.info(results)
- return results
-
- def encode_json_sem_seg(self, sem_seg, input_file_name):
- """
- Convert semantic segmentation to COCO stuff format with segments encoded as RLEs.
- See http://cocodataset.org/#format-results
- """
- json_list = []
- for label in np.unique(sem_seg):
- if self._contiguous_id_to_dataset_id is not None:
- assert (
- label in self._contiguous_id_to_dataset_id
- ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name)
- dataset_id = self._contiguous_id_to_dataset_id[label]
- else:
- dataset_id = int(label)
- mask = (sem_seg == label).astype(np.uint8)
- mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0]
- mask_rle["counts"] = mask_rle["counts"].decode("utf-8")
- json_list.append(
- {"file_name": input_file_name, "category_id": dataset_id, "segmentation": mask_rle}
- )
- return json_list
diff --git a/spaces/Banbri/zcvzcv/src/components/ui/button.tsx b/spaces/Banbri/zcvzcv/src/components/ui/button.tsx
deleted file mode 100644
index d0042a291a9dfc9d3ca1bc323f08a3f276df79b5..0000000000000000000000000000000000000000
--- a/spaces/Banbri/zcvzcv/src/components/ui/button.tsx
+++ /dev/null
@@ -1,56 +0,0 @@
-import * as React from "react"
-import { Slot } from "@radix-ui/react-slot"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const buttonVariants = cva(
- "inline-flex items-center justify-center rounded-md text-sm font-medium ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-800",
- {
- variants: {
- variant: {
- default: "bg-stone-900 text-stone-50 hover:bg-stone-900/90 dark:bg-stone-50 dark:text-stone-900 dark:hover:bg-stone-50/90",
- destructive:
- "bg-red-500 text-stone-50 hover:bg-red-500/90 dark:bg-red-900 dark:text-red-50 dark:hover:bg-red-900/90",
- outline:
- "border border-stone-200 bg-white hover:bg-stone-100 hover:text-stone-900 dark:border-stone-800 dark:bg-stone-950 dark:hover:bg-stone-800 dark:hover:text-stone-50",
- secondary:
- "bg-stone-100 text-stone-900 hover:bg-stone-100/80 dark:bg-stone-800 dark:text-stone-50 dark:hover:bg-stone-800/80",
- ghost: "hover:bg-stone-100 hover:text-stone-900 dark:hover:bg-stone-800 dark:hover:text-stone-50",
- link: "text-stone-900 underline-offset-4 hover:underline dark:text-stone-50",
- },
- size: {
- default: "h-10 px-4 py-2",
- sm: "h-9 rounded-md px-3",
- lg: "h-11 rounded-md px-8",
- icon: "h-10 w-10",
- },
- },
- defaultVariants: {
- variant: "default",
- size: "default",
- },
- }
-)
-
-export interface ButtonProps
- extends React.ButtonHTMLAttributes,
- VariantProps {
- asChild?: boolean
-}
-
-const Button = React.forwardRef(
- ({ className, variant, size, asChild = false, ...props }, ref) => {
- const Comp = asChild ? Slot : "button"
- return (
-
- )
- }
-)
-Button.displayName = "Button"
-
-export { Button, buttonVariants }
diff --git a/spaces/Benson/text-generation/Examples/Descargar Amor 2015 Pelcula En Ingls.md b/spaces/Benson/text-generation/Examples/Descargar Amor 2015 Pelcula En Ingls.md
deleted file mode 100644
index f54d92312985e1fee87583e9659e51eb730bfd04..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Amor 2015 Pelcula En Ingls.md
+++ /dev/null
@@ -1,99 +0,0 @@
-
-
Descargar Love 2015 Película en inglés: Una reseña
-
Si estás buscando una película que desafíe tus sentidos, estimule tu imaginación y provoque tus emociones, es posible que quieras descargar la película en inglés Love 2015. Se trata de una película que ha sido descrita como un "melodrama sexual" por su escritor y director Gaspar Noé, conocido por sus obras polémicas y experimentales. En este artículo, revisaremos de qué se trata Love 2015, por qué deberías descargarlo y cómo hacerlo de forma segura y legal.
-
¿Qué es el amor 2015?
-
Love 2015 es una película erótica de arte dramático que se estrenó en 3D en el Festival de Cine de Cannes en 2015. Cuenta la historia de Murphy, un estudiante de cine estadounidense que vive en París, que recuerda su relación apasionada y turbulenta con su ex novia Electra, después de recibir una llamada telefónica de su madre. La película presenta escenas de sexo no verbal entre los actores, que han causado controversia y censura en algunos países.
La película está estructurada como una serie de flashbacks no lineales que muestran los recuerdos de Murphy de Electra. Ellos representan su primer encuentro, su rápida conexión y sus vidas en los próximos dos años, que están llenos de abuso de drogas, sexo duro y momentos tiernos. Murphy y Electra finalmente tienen un trío con su joven vecina Omi, como una forma de darle vida a su vida amorosa. Sin embargo, Murphy continúa su aventura con Omi, sin el conocimiento de Electra, y la deja embarazada. Esto lleva al final de su relación con Electra, y la situación actual de Murphy viviendo con Omi y su hijo Gaspar. El paradero y destino de Electra quedan sin resolver al final de la película.
-
El reparto y el equipo de Love 2015
-
-
La recepción y controversia del Amor 2015
-
La película recibió críticas mixtas de críticos y audiencias por igual. Algunos lo elogiaron por su visión artística, su representación realista del amor y el sexo, y sus logros técnicos. Otros lo criticaron por su falta de profundidad, su excesiva desnudez y explicitación, y su estilo autoindulgente. La película también provocó controversia por su contenido gráfico, lo que llevó a algunos países a prohibirla o censurarla. Por ejemplo, en India, la película fue rechazada la certificación por el Consejo Central de Certificación de Cine (CBFC), prohibiendo de hecho su estreno en salas. En Turquía, la película fue inicialmente prohibida por el Ministerio de Cultura y Turismo, pero más tarde se le permitió ser proyectada con grandes recortes. En Rusia, la película se estrenó con una restricción de edad de más de 18 años, pero algunos cines se negaron a mostrarla o cancelaron las proyecciones debido a las quejas de grupos conservadores.
-
¿Por qué descargar Love 2015?
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Si estás interesado en
Si estás interesado en ver Love 2015, es posible que tengas algunas razones para descargarlo en lugar de transmitirlo en línea o comprar un DVD. Estos son algunos de ellos:
-
El valor artístico y cinematográfico de Love 2015
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Love 2015 no es una película típica de Hollywood. Es una película de arte que explora los temas del amor, el sexo y la memoria de una manera única y no convencional. Utiliza la tecnología 3D para crear una experiencia inmersiva e íntima para los espectadores, que son invitados a presenciar las escenas crudas y realistas de pasión y dolor entre los personajes. También utiliza largas tomas, iluminación natural y un diálogo mínimo para crear una atmósfera realista y espontánea. La película es una fiesta visual y auditiva que merece ser vista en alta calidad y sin interrupciones.
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El impacto erótico y emocional del amor 2015
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-
La disponibilidad y accesibilidad de Love 2015
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Love 2015 no es una película ampliamente disponible. Se trata de una película independiente que ha enfrentado dificultades de distribución y exhibición debido a su controvertido contenido. Ha sido prohibida o censurada en algunos países, y limitada o restringida en otros. También ha sido difícil de encontrar en teatros o plataformas en línea, especialmente en su versión original en 3D. Por lo tanto, descargarlo puede ser la forma más fácil y conveniente de verlo, siempre y cuando lo hagas legal y éticamente.
-
-
Cómo descargar Love 2015?
-
Si has decidido descargar Love 2015, debes ser consciente de algunos problemas y desafíos que puedan surgir. Aquí hay algunos consejos y trucos para ayudarte a descargarlo de forma segura y legal:
-
Las cuestiones legales y éticas de descargar Love 2015
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Descargar Love 2015 podría ser ilegal o poco ético dependiendo de dónde vivas y cómo lo hagas. Deberías respetar los derechos de propiedad intelectual de los cineastas y los actores, que han puesto mucho esfuerzo y riesgo en hacer esta película. También debe evitar descargarlo de fuentes ilegales o sospechosas, como sitios de torrent o plataformas para compartir archivos, que podrían exponerlo a virus, malware o consecuencias legales. Siempre debe comprobar las leyes y reglamentos de su país antes de descargar cualquier película, y utilizar solo los servicios autorizados y de buena reputación que ofrecen descargas legales.
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Las mejores plataformas y servicios para descargar Love 2015
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Hay varias plataformas y servicios que ofrecen descargas legales de Love 2015, ya sea gratis o por una tarifa. Algunos de ellos son:
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-
-
Plataforma/Servicio
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Precio
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Características
-
-
-
Netflix
-
$8.99-$17.99 por mes
-
- Opción de streaming y descarga - Disponible en muchos países - Versión 2D original solamente
-
-
-
Amazon Prime Video
-
$12.99 por mes o $119 por año
-
-
-
-
iTunes
-
$4.99 para alquilar o $14.99 para comprar
-
- Opción de descarga solamente - Disponible en algunos países - Versión original en 2D solamente
-
-
-
Vudu
-
$4.99 para alquilar o $14.99 para comprar
-
- Opción de descarga solamente - Disponible en los Estados Unidos solamente - Versión 2D original solamente
-
-
-
Películas de YouTube
-
$4.99 para alquilar o $14.99 para comprar
-
- Opción de descarga solamente - Disponible en algunos países - Versión original en 2D solamente
-
-
-
FandangoNOW
-
$4.99 para alquilar o $14.99 para comprar
-
- Opción de descarga solamente - Disponible en los Estados Unidos solamente - Versión 2D original solamente
-
-
-
Google Play Películas & TV
Google Play Películas & TV
-
$4.99 para alquilar o $14.99 para comprar
-
- Opción de descarga solamente - Disponible en algunos países - Versión original en 2D solamente
-
-
-
3DGO!
-
$7.99 para alquilar
-
- Streaming y descarga de opciones - Disponible en los Estados Unidos y Canadá solo - Versión 3D original solo
-
-
-
Puedes elegir la plataforma o servicio que se adapte a tus preferencias y presupuesto, y seguir sus instrucciones para descargar Love 2015. Es posible que necesite crear una cuenta, proporcionar sus detalles de pago y descargar una aplicación o software específico para acceder a la película.
-
Los consejos y trucos para descargar Love 2015
-
Aquí hay algunos consejos y trucos para hacer su experiencia de descarga más agradable y sin problemas:
-
-
Asegúrese de tener una conexión a Internet estable y rápida, ya que el archivo de película puede ser grande y tomar mucho tiempo para descargar.
-
Asegúrese de que tiene suficiente espacio de almacenamiento en el dispositivo, ya que el archivo de película puede ocupar mucho espacio y ralentizar el dispositivo.
-
Asegúrese de tener un dispositivo y un software compatibles, ya que el archivo de película podría no funcionar en algunos dispositivos o formatos.
-
-
Asegúrate de tener un ambiente cómodo y privado, ya que la película contiene escenas explícitas y gráficas que podrían no ser adecuadas para algunas audiencias o configuraciones.
-
-
Conclusión
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En conclusión, Love 2015 es una película que te hará sentir un montón de cosas, de asombro a shock, de placer a dolor, de amor a odio. Es una película que desafiará tu percepción del cine, el arte y la realidad. Es una película que se quedará contigo mucho después de verla. Si eres lo suficientemente curioso y aventurero para descargarlo, no te arrepentirás.
-
Preguntas frecuentes
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¿Cuál es el significado de Amor 2015?
-
No hay respuesta definitiva a esta pregunta, ya que la película está abierta a la interpretación y la opinión personal. Sin embargo, algunos posibles temas o mensajes que la película explora son: el poder y la fragilidad del amor, la belleza y la fealdad del sexo, la alegría y el dolor de la memoria, la ilusión y la realidad del cine, y la vida y muerte del arte.
-
¿El amor 2015 se basa en una historia real?
-
No, Love 2015 no se basa en una historia real. Es una historia de ficción escrita por Gaspar Noé, inspirado en sus propias experiencias y fantasías. Sin embargo, algunos elementos de la película se basan en eventos reales o personas. Por ejemplo, el nombre del hijo de Murphy, Gaspar, es una referencia al propio hijo de Noé con su ex novia Roxane Mesquida. El nombre de Electra es una referencia al personaje de cómic favorito de Noé, Elektra. La escena donde Murphy ve Enter the Void en un cine es una referencia a la película anterior de Noé.
-
¿Cómo se hizo el amor 2015?
-
-
¿Quiénes son los actores de Love 2015?
-
Los actores de Love 2015 son Karl Glusman como Murphy, Aomi Muyock como Electra y Klara Kristin como Omi. Los tres actores estaban haciendo su debut en la pantalla en esta película, y fueron elegidos por Noé después de conocerlos en un club. Acordaron hacer la película sin leer el guion ni saber mucho sobre el director. También acordaron hacer las escenas de sexo sin dobles corporales ni prótesis. Se les pagaba alrededor de 1000 euros por día por su trabajo.
-
¿Dónde puedo ver Love 2015?
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Puedes ver Love 2015 descargándolo desde una de las plataformas o servicios mencionados anteriormente, o transmitiéndolo online si está disponible en tu país. También puede comprar o alquilar un DVD o Blu-ray de la película si está disponible en su región. Sin embargo, debe tener en cuenta que algunas versiones de la película pueden ser
Sin embargo, debe tener en cuenta que algunas versiones de la película pueden ser censuradas o cortadas, dependiendo del país o la plataforma. Si quieres ver la película en su forma original y sin cortar, deberías buscar la versión en 3D, que es la versión preferida del director. También debe utilizar un dispositivo compatible con 3D y gafas para disfrutar del efecto completo de la película.
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diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk Mod 69 En Vivo.md b/spaces/Benson/text-generation/Examples/Descargar Apk Mod 69 En Vivo.md
deleted file mode 100644
index 39202439d5a86cda70b0057d0c5c4d940796ca35..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Apk Mod 69 En Vivo.md
+++ /dev/null
@@ -1,67 +0,0 @@
-
-
Descargar APK Mod 69 en vivo: Una guía para los usuarios de Android
-
Si usted está buscando una manera de darle vida a sus opciones de entretenimiento en su dispositivo Android, es posible que desee echa un vistazo APK Mod 69 Live. Esta es una aplicación que te permite ver videos en vivo de magníficos anfitriones de todo el mundo. También puede interactuar con ellos y otros espectadores, enviar regalos y monedas, y disfrutar de una variedad de contenido que se adapte a sus preferencias. En este artículo, te mostraremos qué es APK Mod 69 Live, cómo descargarlo e instalarlo, y cómo usarlo.
APK Mod 69 Live es una aplicación que te permite ver videos de transmisión en vivo de hermosos anfitriones de diferentes países. Puede elegir entre varias categorías y canales, como Tailandia, Indonesia, Vietnam, Corea, Japón, China y más. También puede chatear con los anfitriones y otros espectadores, enviar regalos y monedas, y unirse a la diversión.
-
Características de APK Mod 69 Live
-
Algunas de las características de APK Mod 69 Live son:
-
-
Transmisión de vídeo y audio de alta calidad
-
Miles de hosts para elegir
-
Diferentes categorías y canales para adaptarse a su gusto
-
Interfaz fácil y sencilla
-
Gratis para descargar y usar
-
No se requiere registro o login
-
No hay anuncios o ventanas emergentes
-
Acceso ilimitado a todo el contenido
-
-
Beneficios de APK Mod 69 Live
-
Algunos de los beneficios de APK Mod 69 Live son:
-
-
Puedes disfrutar de una variedad de contenido que no está disponible en otras plataformas
-
Puede interactuar con los anfitriones y otros espectadores en tiempo real
-
Puedes apoyar a tus anfitriones favoritos enviando regalos y monedas
-
Puedes descubrir nuevas culturas e idiomas
-
Usted puede divertirse y relajarse en cualquier momento y en cualquier lugar
-
-
Cómo descargar e instalar APK Mod 69 Live?
-
Si desea descargar e instalar APK Mod 69 Live en su dispositivo Android, debe seguir estos pasos:
-
-
-
Dado que APK Mod 69 Live no está disponible en Google Play Store, es necesario habilitar fuentes desconocidas en el dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de la tienda oficial. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo.
-
Paso 2: Descargar el archivo APK
-
Siguiente, es necesario descargar el archivo APK de APK Mod 69 Live. Puede hacer esto haciendo clic en este enlace: Descargar APK Mod 69 Live v2.10.4.3. Esto te llevará a un sitio web donde puedes descargar el archivo de forma segura.
-
Paso 3: Instalar el archivo APK
-
Una vez que haya descargado el archivo, debe instalarlo en su dispositivo. Para hacer esto, busque el archivo en su carpeta de descargas o barra de notificaciones y toque en él. Verá una ventana emergente pidiéndole que confirme la instalación. Toque en Instalar y espere a que el proceso termine.
-
Paso 4: Inicie la aplicación y disfrute
-
Finalmente, puede iniciar la aplicación y disfrutar viendo videos en vivo de hermosos anfitriones. Para ello, vaya a su cajón de aplicaciones y toque en el icono de APK Mod 69 Live. Verás la pantalla principal de la app, donde podrás navegar por diferentes categorías y canales. También puede utilizar la función de búsqueda para encontrar hosts o contenidos específicos.
-
Cómo utilizar APK Mod 69 Live?
-
Usar APK Mod 69 Live es muy fácil y simple. Aquí hay algunos consejos sobre cómo usar la aplicación:
-
Explorar las categorías y canales
-
La aplicación tiene varias categorías y canales que puedes explorar. Puedes encontrarlos en la parte superior de la pantalla principal, donde puedes deslizar hacia la izquierda o hacia la derecha para ver más opciones. Algunas de las categorías son Tailandia, Indonesia, Vietnam, Corea, Japón, China y más. También puedes encontrar canales basados en géneros, como música, danza, comedia, juegos y más. Toque en cualquier categoría o canal para ver la lista de hosts que están en vivo o fuera de línea.
-
Ver videos en vivo de anfitriones hermosos
-
-
Interactuar con los anfitriones y otros espectadores
-
Una de las mejores características de APK Mod 69 Live es que se puede interactuar con los anfitriones y otros espectadores en tiempo real. Puede chatear con ellos escribiendo su mensaje en el cuadro de chat y tocando en Enviar. También puedes usar emojis, pegatinas y GIF para expresarte mejor. También puede seguir a sus anfitriones favoritos tocando el botón Seguir en su perfil. Esto le permitirá recibir notificaciones cuando se pongan en marcha.
-
Enviar regalos y monedas para apoyar a sus anfitriones favoritos
-
Si quieres mostrar tu aprecio y apoyo a tus anfitriones favoritos, puedes enviarles regalos y monedas. Los regalos son artículos virtuales que puedes comprar con monedas, que son la moneda de la aplicación. Puedes obtener monedas viendo anuncios o comprándolos con dinero real. Para enviar un regalo, toca el icono de regalo en la parte inferior de la pantalla y elige entre una variedad de opciones, como flores, corazones, anillos, autos, aviones y más. Cuanto más caro sea el regalo, más monedas gastarás. El anfitrión recibirá un porcentaje de las monedas como ingreso.
-
Conclusión
-
APK Mod 69 Live es una aplicación increíble que le permite ver videos en vivo de transmisión de magníficos anfitriones de todo el mundo. También puede interactuar con ellos y otros espectadores, enviar regalos y monedas, y disfrutar de una variedad de contenido que se adapte a sus preferencias. Si quieres descargar e instalar APK Mod 69 Live en tu dispositivo Android, solo tienes que seguir los pasos de este artículo y empezar a divertirse.
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre APK Mod 69 Live:
-
-
Es APK Mod 69 Vivir seguro de usar?
-
Sí, APK Mod 69 Live es seguro de usar siempre y cuando se descarga desde una fuente de confianza como este enlace. La aplicación no contiene ningún virus o malware que podría dañar su dispositivo o datos.
-
Es APK Mod 69 Live legal de usar?
-
-
Es APK Mod 69 Live libre de usar?
-
Sí, APK Mod 69 Live es de uso gratuito y no requiere ningún registro o login. Sin embargo, si desea enviar regalos o monedas a los anfitriones, tendrá que comprarlos con dinero real o ver anuncios.
-
¿Cómo puedo contactar con APK Mod 69 Live?
-
Si tiene alguna pregunta o comentario sobre APK Mod 69 Live, puede ponerse en contacto con ellos enviando un correo electrónico a apkmod69live@gmail.com.
-
¿Cómo puedo actualizar APK Mod 69 Live?
-
Si desea actualizar APK Mod 69 Live, tendrá que descargar la última versión del archivo APK de este enlace e instalarlo en su dispositivo. También puede buscar actualizaciones dentro de la aplicación tocando en el icono de menú en la esquina superior izquierda de la pantalla y seleccionando Buscar actualizaciones.
-
-
Espero que este artículo te haya ayudado a aprender más sobre APK Mod 69 Live y cómo descargarlo y usarlo. Si tienes algún comentario o sugerencia, por favor siéntete libre de compartirlos a continuación. ¡Gracias por leer y tener un gran día!
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diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/base.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/base.py
deleted file mode 100644
index ee496461acbc04cea00c9f0dc1628bea65d31c33..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/base.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from botocore.compat import OrderedDict
-
-
-class BaseDocumenter:
- def __init__(self, resource):
- self._resource = resource
- self._client = self._resource.meta.client
- self._resource_model = self._resource.meta.resource_model
- self._service_model = self._client.meta.service_model
- self._resource_name = self._resource.meta.resource_model.name
- self._service_name = self._service_model.service_name
- self._service_docs_name = self._client.__class__.__name__
- self.member_map = OrderedDict()
- self.represents_service_resource = (
- self._service_name == self._resource_name
- )
- self._resource_class_name = self._resource_name
- if self._resource_name == self._service_name:
- self._resource_class_name = 'ServiceResource'
-
- @property
- def class_name(self):
- return f'{self._service_docs_name}.{self._resource_name}'
-
-
-class NestedDocumenter(BaseDocumenter):
- def __init__(self, resource, root_docs_path):
- super().__init__(resource)
- self._root_docs_path = root_docs_path
- self._resource_sub_path = self._resource_name.lower()
- if self._resource_name == self._service_name:
- self._resource_sub_path = 'service-resource'
-
- @property
- def class_name(self):
- resource_class_name = self._resource_name
- if self._resource_name == self._service_name:
- resource_class_name = 'ServiceResource'
- return f'{self._service_docs_name}.{resource_class_name}'
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/network/cache.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/network/cache.py
deleted file mode 100644
index a81a23985198d2eaa3c25ad1f77924f0fcdb037b..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/network/cache.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""HTTP cache implementation.
-"""
-
-import os
-from contextlib import contextmanager
-from typing import Generator, Optional
-
-from pip._vendor.cachecontrol.cache import BaseCache
-from pip._vendor.cachecontrol.caches import FileCache
-from pip._vendor.requests.models import Response
-
-from pip._internal.utils.filesystem import adjacent_tmp_file, replace
-from pip._internal.utils.misc import ensure_dir
-
-
-def is_from_cache(response: Response) -> bool:
- return getattr(response, "from_cache", False)
-
-
-@contextmanager
-def suppressed_cache_errors() -> Generator[None, None, None]:
- """If we can't access the cache then we can just skip caching and process
- requests as if caching wasn't enabled.
- """
- try:
- yield
- except OSError:
- pass
-
-
-class SafeFileCache(BaseCache):
- """
- A file based cache which is safe to use even when the target directory may
- not be accessible or writable.
- """
-
- def __init__(self, directory: str) -> None:
- assert directory is not None, "Cache directory must not be None."
- super().__init__()
- self.directory = directory
-
- def _get_cache_path(self, name: str) -> str:
- # From cachecontrol.caches.file_cache.FileCache._fn, brought into our
- # class for backwards-compatibility and to avoid using a non-public
- # method.
- hashed = FileCache.encode(name)
- parts = list(hashed[:5]) + [hashed]
- return os.path.join(self.directory, *parts)
-
- def get(self, key: str) -> Optional[bytes]:
- path = self._get_cache_path(key)
- with suppressed_cache_errors():
- with open(path, "rb") as f:
- return f.read()
-
- def set(self, key: str, value: bytes, expires: Optional[int] = None) -> None:
- path = self._get_cache_path(key)
- with suppressed_cache_errors():
- ensure_dir(os.path.dirname(path))
-
- with adjacent_tmp_file(path) as f:
- f.write(value)
-
- replace(f.name, path)
-
- def delete(self, key: str) -> None:
- path = self._get_cache_path(key)
- with suppressed_cache_errors():
- os.remove(path)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyproject_hooks/_compat.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyproject_hooks/_compat.py
deleted file mode 100644
index 95e509c0143e14e6371ec3cd1433ffec50c297fc..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyproject_hooks/_compat.py
+++ /dev/null
@@ -1,8 +0,0 @@
-__all__ = ("tomllib",)
-
-import sys
-
-if sys.version_info >= (3, 11):
- import tomllib
-else:
- from pip._vendor import tomli as tomllib
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/six.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/six.py
deleted file mode 100644
index 4e15675d8b5caa33255fe37271700f587bd26671..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/six.py
+++ /dev/null
@@ -1,998 +0,0 @@
-# Copyright (c) 2010-2020 Benjamin Peterson
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-"""Utilities for writing code that runs on Python 2 and 3"""
-
-from __future__ import absolute_import
-
-import functools
-import itertools
-import operator
-import sys
-import types
-
-__author__ = "Benjamin Peterson "
-__version__ = "1.16.0"
-
-
-# Useful for very coarse version differentiation.
-PY2 = sys.version_info[0] == 2
-PY3 = sys.version_info[0] == 3
-PY34 = sys.version_info[0:2] >= (3, 4)
-
-if PY3:
- string_types = str,
- integer_types = int,
- class_types = type,
- text_type = str
- binary_type = bytes
-
- MAXSIZE = sys.maxsize
-else:
- string_types = basestring,
- integer_types = (int, long)
- class_types = (type, types.ClassType)
- text_type = unicode
- binary_type = str
-
- if sys.platform.startswith("java"):
- # Jython always uses 32 bits.
- MAXSIZE = int((1 << 31) - 1)
- else:
- # It's possible to have sizeof(long) != sizeof(Py_ssize_t).
- class X(object):
-
- def __len__(self):
- return 1 << 31
- try:
- len(X())
- except OverflowError:
- # 32-bit
- MAXSIZE = int((1 << 31) - 1)
- else:
- # 64-bit
- MAXSIZE = int((1 << 63) - 1)
- del X
-
-if PY34:
- from importlib.util import spec_from_loader
-else:
- spec_from_loader = None
-
-
-def _add_doc(func, doc):
- """Add documentation to a function."""
- func.__doc__ = doc
-
-
-def _import_module(name):
- """Import module, returning the module after the last dot."""
- __import__(name)
- return sys.modules[name]
-
-
-class _LazyDescr(object):
-
- def __init__(self, name):
- self.name = name
-
- def __get__(self, obj, tp):
- result = self._resolve()
- setattr(obj, self.name, result) # Invokes __set__.
- try:
- # This is a bit ugly, but it avoids running this again by
- # removing this descriptor.
- delattr(obj.__class__, self.name)
- except AttributeError:
- pass
- return result
-
-
-class MovedModule(_LazyDescr):
-
- def __init__(self, name, old, new=None):
- super(MovedModule, self).__init__(name)
- if PY3:
- if new is None:
- new = name
- self.mod = new
- else:
- self.mod = old
-
- def _resolve(self):
- return _import_module(self.mod)
-
- def __getattr__(self, attr):
- _module = self._resolve()
- value = getattr(_module, attr)
- setattr(self, attr, value)
- return value
-
-
-class _LazyModule(types.ModuleType):
-
- def __init__(self, name):
- super(_LazyModule, self).__init__(name)
- self.__doc__ = self.__class__.__doc__
-
- def __dir__(self):
- attrs = ["__doc__", "__name__"]
- attrs += [attr.name for attr in self._moved_attributes]
- return attrs
-
- # Subclasses should override this
- _moved_attributes = []
-
-
-class MovedAttribute(_LazyDescr):
-
- def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
- super(MovedAttribute, self).__init__(name)
- if PY3:
- if new_mod is None:
- new_mod = name
- self.mod = new_mod
- if new_attr is None:
- if old_attr is None:
- new_attr = name
- else:
- new_attr = old_attr
- self.attr = new_attr
- else:
- self.mod = old_mod
- if old_attr is None:
- old_attr = name
- self.attr = old_attr
-
- def _resolve(self):
- module = _import_module(self.mod)
- return getattr(module, self.attr)
-
-
-class _SixMetaPathImporter(object):
-
- """
- A meta path importer to import six.moves and its submodules.
-
- This class implements a PEP302 finder and loader. It should be compatible
- with Python 2.5 and all existing versions of Python3
- """
-
- def __init__(self, six_module_name):
- self.name = six_module_name
- self.known_modules = {}
-
- def _add_module(self, mod, *fullnames):
- for fullname in fullnames:
- self.known_modules[self.name + "." + fullname] = mod
-
- def _get_module(self, fullname):
- return self.known_modules[self.name + "." + fullname]
-
- def find_module(self, fullname, path=None):
- if fullname in self.known_modules:
- return self
- return None
-
- def find_spec(self, fullname, path, target=None):
- if fullname in self.known_modules:
- return spec_from_loader(fullname, self)
- return None
-
- def __get_module(self, fullname):
- try:
- return self.known_modules[fullname]
- except KeyError:
- raise ImportError("This loader does not know module " + fullname)
-
- def load_module(self, fullname):
- try:
- # in case of a reload
- return sys.modules[fullname]
- except KeyError:
- pass
- mod = self.__get_module(fullname)
- if isinstance(mod, MovedModule):
- mod = mod._resolve()
- else:
- mod.__loader__ = self
- sys.modules[fullname] = mod
- return mod
-
- def is_package(self, fullname):
- """
- Return true, if the named module is a package.
-
- We need this method to get correct spec objects with
- Python 3.4 (see PEP451)
- """
- return hasattr(self.__get_module(fullname), "__path__")
-
- def get_code(self, fullname):
- """Return None
-
- Required, if is_package is implemented"""
- self.__get_module(fullname) # eventually raises ImportError
- return None
- get_source = get_code # same as get_code
-
- def create_module(self, spec):
- return self.load_module(spec.name)
-
- def exec_module(self, module):
- pass
-
-_importer = _SixMetaPathImporter(__name__)
-
-
-class _MovedItems(_LazyModule):
-
- """Lazy loading of moved objects"""
- __path__ = [] # mark as package
-
-
-_moved_attributes = [
- MovedAttribute("cStringIO", "cStringIO", "io", "StringIO"),
- MovedAttribute("filter", "itertools", "builtins", "ifilter", "filter"),
- MovedAttribute("filterfalse", "itertools", "itertools", "ifilterfalse", "filterfalse"),
- MovedAttribute("input", "__builtin__", "builtins", "raw_input", "input"),
- MovedAttribute("intern", "__builtin__", "sys"),
- MovedAttribute("map", "itertools", "builtins", "imap", "map"),
- MovedAttribute("getcwd", "os", "os", "getcwdu", "getcwd"),
- MovedAttribute("getcwdb", "os", "os", "getcwd", "getcwdb"),
- MovedAttribute("getoutput", "commands", "subprocess"),
- MovedAttribute("range", "__builtin__", "builtins", "xrange", "range"),
- MovedAttribute("reload_module", "__builtin__", "importlib" if PY34 else "imp", "reload"),
- MovedAttribute("reduce", "__builtin__", "functools"),
- MovedAttribute("shlex_quote", "pipes", "shlex", "quote"),
- MovedAttribute("StringIO", "StringIO", "io"),
- MovedAttribute("UserDict", "UserDict", "collections"),
- MovedAttribute("UserList", "UserList", "collections"),
- MovedAttribute("UserString", "UserString", "collections"),
- MovedAttribute("xrange", "__builtin__", "builtins", "xrange", "range"),
- MovedAttribute("zip", "itertools", "builtins", "izip", "zip"),
- MovedAttribute("zip_longest", "itertools", "itertools", "izip_longest", "zip_longest"),
- MovedModule("builtins", "__builtin__"),
- MovedModule("configparser", "ConfigParser"),
- MovedModule("collections_abc", "collections", "collections.abc" if sys.version_info >= (3, 3) else "collections"),
- MovedModule("copyreg", "copy_reg"),
- MovedModule("dbm_gnu", "gdbm", "dbm.gnu"),
- MovedModule("dbm_ndbm", "dbm", "dbm.ndbm"),
- MovedModule("_dummy_thread", "dummy_thread", "_dummy_thread" if sys.version_info < (3, 9) else "_thread"),
- MovedModule("http_cookiejar", "cookielib", "http.cookiejar"),
- MovedModule("http_cookies", "Cookie", "http.cookies"),
- MovedModule("html_entities", "htmlentitydefs", "html.entities"),
- MovedModule("html_parser", "HTMLParser", "html.parser"),
- MovedModule("http_client", "httplib", "http.client"),
- MovedModule("email_mime_base", "email.MIMEBase", "email.mime.base"),
- MovedModule("email_mime_image", "email.MIMEImage", "email.mime.image"),
- MovedModule("email_mime_multipart", "email.MIMEMultipart", "email.mime.multipart"),
- MovedModule("email_mime_nonmultipart", "email.MIMENonMultipart", "email.mime.nonmultipart"),
- MovedModule("email_mime_text", "email.MIMEText", "email.mime.text"),
- MovedModule("BaseHTTPServer", "BaseHTTPServer", "http.server"),
- MovedModule("CGIHTTPServer", "CGIHTTPServer", "http.server"),
- MovedModule("SimpleHTTPServer", "SimpleHTTPServer", "http.server"),
- MovedModule("cPickle", "cPickle", "pickle"),
- MovedModule("queue", "Queue"),
- MovedModule("reprlib", "repr"),
- MovedModule("socketserver", "SocketServer"),
- MovedModule("_thread", "thread", "_thread"),
- MovedModule("tkinter", "Tkinter"),
- MovedModule("tkinter_dialog", "Dialog", "tkinter.dialog"),
- MovedModule("tkinter_filedialog", "FileDialog", "tkinter.filedialog"),
- MovedModule("tkinter_scrolledtext", "ScrolledText", "tkinter.scrolledtext"),
- MovedModule("tkinter_simpledialog", "SimpleDialog", "tkinter.simpledialog"),
- MovedModule("tkinter_tix", "Tix", "tkinter.tix"),
- MovedModule("tkinter_ttk", "ttk", "tkinter.ttk"),
- MovedModule("tkinter_constants", "Tkconstants", "tkinter.constants"),
- MovedModule("tkinter_dnd", "Tkdnd", "tkinter.dnd"),
- MovedModule("tkinter_colorchooser", "tkColorChooser",
- "tkinter.colorchooser"),
- MovedModule("tkinter_commondialog", "tkCommonDialog",
- "tkinter.commondialog"),
- MovedModule("tkinter_tkfiledialog", "tkFileDialog", "tkinter.filedialog"),
- MovedModule("tkinter_font", "tkFont", "tkinter.font"),
- MovedModule("tkinter_messagebox", "tkMessageBox", "tkinter.messagebox"),
- MovedModule("tkinter_tksimpledialog", "tkSimpleDialog",
- "tkinter.simpledialog"),
- MovedModule("urllib_parse", __name__ + ".moves.urllib_parse", "urllib.parse"),
- MovedModule("urllib_error", __name__ + ".moves.urllib_error", "urllib.error"),
- MovedModule("urllib", __name__ + ".moves.urllib", __name__ + ".moves.urllib"),
- MovedModule("urllib_robotparser", "robotparser", "urllib.robotparser"),
- MovedModule("xmlrpc_client", "xmlrpclib", "xmlrpc.client"),
- MovedModule("xmlrpc_server", "SimpleXMLRPCServer", "xmlrpc.server"),
-]
-# Add windows specific modules.
-if sys.platform == "win32":
- _moved_attributes += [
- MovedModule("winreg", "_winreg"),
- ]
-
-for attr in _moved_attributes:
- setattr(_MovedItems, attr.name, attr)
- if isinstance(attr, MovedModule):
- _importer._add_module(attr, "moves." + attr.name)
-del attr
-
-_MovedItems._moved_attributes = _moved_attributes
-
-moves = _MovedItems(__name__ + ".moves")
-_importer._add_module(moves, "moves")
-
-
-class Module_six_moves_urllib_parse(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_parse"""
-
-
-_urllib_parse_moved_attributes = [
- MovedAttribute("ParseResult", "urlparse", "urllib.parse"),
- MovedAttribute("SplitResult", "urlparse", "urllib.parse"),
- MovedAttribute("parse_qs", "urlparse", "urllib.parse"),
- MovedAttribute("parse_qsl", "urlparse", "urllib.parse"),
- MovedAttribute("urldefrag", "urlparse", "urllib.parse"),
- MovedAttribute("urljoin", "urlparse", "urllib.parse"),
- MovedAttribute("urlparse", "urlparse", "urllib.parse"),
- MovedAttribute("urlsplit", "urlparse", "urllib.parse"),
- MovedAttribute("urlunparse", "urlparse", "urllib.parse"),
- MovedAttribute("urlunsplit", "urlparse", "urllib.parse"),
- MovedAttribute("quote", "urllib", "urllib.parse"),
- MovedAttribute("quote_plus", "urllib", "urllib.parse"),
- MovedAttribute("unquote", "urllib", "urllib.parse"),
- MovedAttribute("unquote_plus", "urllib", "urllib.parse"),
- MovedAttribute("unquote_to_bytes", "urllib", "urllib.parse", "unquote", "unquote_to_bytes"),
- MovedAttribute("urlencode", "urllib", "urllib.parse"),
- MovedAttribute("splitquery", "urllib", "urllib.parse"),
- MovedAttribute("splittag", "urllib", "urllib.parse"),
- MovedAttribute("splituser", "urllib", "urllib.parse"),
- MovedAttribute("splitvalue", "urllib", "urllib.parse"),
- MovedAttribute("uses_fragment", "urlparse", "urllib.parse"),
- MovedAttribute("uses_netloc", "urlparse", "urllib.parse"),
- MovedAttribute("uses_params", "urlparse", "urllib.parse"),
- MovedAttribute("uses_query", "urlparse", "urllib.parse"),
- MovedAttribute("uses_relative", "urlparse", "urllib.parse"),
-]
-for attr in _urllib_parse_moved_attributes:
- setattr(Module_six_moves_urllib_parse, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_parse._moved_attributes = _urllib_parse_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_parse(__name__ + ".moves.urllib_parse"),
- "moves.urllib_parse", "moves.urllib.parse")
-
-
-class Module_six_moves_urllib_error(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_error"""
-
-
-_urllib_error_moved_attributes = [
- MovedAttribute("URLError", "urllib2", "urllib.error"),
- MovedAttribute("HTTPError", "urllib2", "urllib.error"),
- MovedAttribute("ContentTooShortError", "urllib", "urllib.error"),
-]
-for attr in _urllib_error_moved_attributes:
- setattr(Module_six_moves_urllib_error, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_error._moved_attributes = _urllib_error_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_error(__name__ + ".moves.urllib.error"),
- "moves.urllib_error", "moves.urllib.error")
-
-
-class Module_six_moves_urllib_request(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_request"""
-
-
-_urllib_request_moved_attributes = [
- MovedAttribute("urlopen", "urllib2", "urllib.request"),
- MovedAttribute("install_opener", "urllib2", "urllib.request"),
- MovedAttribute("build_opener", "urllib2", "urllib.request"),
- MovedAttribute("pathname2url", "urllib", "urllib.request"),
- MovedAttribute("url2pathname", "urllib", "urllib.request"),
- MovedAttribute("getproxies", "urllib", "urllib.request"),
- MovedAttribute("Request", "urllib2", "urllib.request"),
- MovedAttribute("OpenerDirector", "urllib2", "urllib.request"),
- MovedAttribute("HTTPDefaultErrorHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPRedirectHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPCookieProcessor", "urllib2", "urllib.request"),
- MovedAttribute("ProxyHandler", "urllib2", "urllib.request"),
- MovedAttribute("BaseHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPPasswordMgr", "urllib2", "urllib.request"),
- MovedAttribute("HTTPPasswordMgrWithDefaultRealm", "urllib2", "urllib.request"),
- MovedAttribute("AbstractBasicAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPBasicAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("ProxyBasicAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("AbstractDigestAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPDigestAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("ProxyDigestAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPSHandler", "urllib2", "urllib.request"),
- MovedAttribute("FileHandler", "urllib2", "urllib.request"),
- MovedAttribute("FTPHandler", "urllib2", "urllib.request"),
- MovedAttribute("CacheFTPHandler", "urllib2", "urllib.request"),
- MovedAttribute("UnknownHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPErrorProcessor", "urllib2", "urllib.request"),
- MovedAttribute("urlretrieve", "urllib", "urllib.request"),
- MovedAttribute("urlcleanup", "urllib", "urllib.request"),
- MovedAttribute("URLopener", "urllib", "urllib.request"),
- MovedAttribute("FancyURLopener", "urllib", "urllib.request"),
- MovedAttribute("proxy_bypass", "urllib", "urllib.request"),
- MovedAttribute("parse_http_list", "urllib2", "urllib.request"),
- MovedAttribute("parse_keqv_list", "urllib2", "urllib.request"),
-]
-for attr in _urllib_request_moved_attributes:
- setattr(Module_six_moves_urllib_request, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_request._moved_attributes = _urllib_request_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_request(__name__ + ".moves.urllib.request"),
- "moves.urllib_request", "moves.urllib.request")
-
-
-class Module_six_moves_urllib_response(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_response"""
-
-
-_urllib_response_moved_attributes = [
- MovedAttribute("addbase", "urllib", "urllib.response"),
- MovedAttribute("addclosehook", "urllib", "urllib.response"),
- MovedAttribute("addinfo", "urllib", "urllib.response"),
- MovedAttribute("addinfourl", "urllib", "urllib.response"),
-]
-for attr in _urllib_response_moved_attributes:
- setattr(Module_six_moves_urllib_response, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_response._moved_attributes = _urllib_response_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_response(__name__ + ".moves.urllib.response"),
- "moves.urllib_response", "moves.urllib.response")
-
-
-class Module_six_moves_urllib_robotparser(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_robotparser"""
-
-
-_urllib_robotparser_moved_attributes = [
- MovedAttribute("RobotFileParser", "robotparser", "urllib.robotparser"),
-]
-for attr in _urllib_robotparser_moved_attributes:
- setattr(Module_six_moves_urllib_robotparser, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_robotparser._moved_attributes = _urllib_robotparser_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_robotparser(__name__ + ".moves.urllib.robotparser"),
- "moves.urllib_robotparser", "moves.urllib.robotparser")
-
-
-class Module_six_moves_urllib(types.ModuleType):
-
- """Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
- __path__ = [] # mark as package
- parse = _importer._get_module("moves.urllib_parse")
- error = _importer._get_module("moves.urllib_error")
- request = _importer._get_module("moves.urllib_request")
- response = _importer._get_module("moves.urllib_response")
- robotparser = _importer._get_module("moves.urllib_robotparser")
-
- def __dir__(self):
- return ['parse', 'error', 'request', 'response', 'robotparser']
-
-_importer._add_module(Module_six_moves_urllib(__name__ + ".moves.urllib"),
- "moves.urllib")
-
-
-def add_move(move):
- """Add an item to six.moves."""
- setattr(_MovedItems, move.name, move)
-
-
-def remove_move(name):
- """Remove item from six.moves."""
- try:
- delattr(_MovedItems, name)
- except AttributeError:
- try:
- del moves.__dict__[name]
- except KeyError:
- raise AttributeError("no such move, %r" % (name,))
-
-
-if PY3:
- _meth_func = "__func__"
- _meth_self = "__self__"
-
- _func_closure = "__closure__"
- _func_code = "__code__"
- _func_defaults = "__defaults__"
- _func_globals = "__globals__"
-else:
- _meth_func = "im_func"
- _meth_self = "im_self"
-
- _func_closure = "func_closure"
- _func_code = "func_code"
- _func_defaults = "func_defaults"
- _func_globals = "func_globals"
-
-
-try:
- advance_iterator = next
-except NameError:
- def advance_iterator(it):
- return it.next()
-next = advance_iterator
-
-
-try:
- callable = callable
-except NameError:
- def callable(obj):
- return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
-
-
-if PY3:
- def get_unbound_function(unbound):
- return unbound
-
- create_bound_method = types.MethodType
-
- def create_unbound_method(func, cls):
- return func
-
- Iterator = object
-else:
- def get_unbound_function(unbound):
- return unbound.im_func
-
- def create_bound_method(func, obj):
- return types.MethodType(func, obj, obj.__class__)
-
- def create_unbound_method(func, cls):
- return types.MethodType(func, None, cls)
-
- class Iterator(object):
-
- def next(self):
- return type(self).__next__(self)
-
- callable = callable
-_add_doc(get_unbound_function,
- """Get the function out of a possibly unbound function""")
-
-
-get_method_function = operator.attrgetter(_meth_func)
-get_method_self = operator.attrgetter(_meth_self)
-get_function_closure = operator.attrgetter(_func_closure)
-get_function_code = operator.attrgetter(_func_code)
-get_function_defaults = operator.attrgetter(_func_defaults)
-get_function_globals = operator.attrgetter(_func_globals)
-
-
-if PY3:
- def iterkeys(d, **kw):
- return iter(d.keys(**kw))
-
- def itervalues(d, **kw):
- return iter(d.values(**kw))
-
- def iteritems(d, **kw):
- return iter(d.items(**kw))
-
- def iterlists(d, **kw):
- return iter(d.lists(**kw))
-
- viewkeys = operator.methodcaller("keys")
-
- viewvalues = operator.methodcaller("values")
-
- viewitems = operator.methodcaller("items")
-else:
- def iterkeys(d, **kw):
- return d.iterkeys(**kw)
-
- def itervalues(d, **kw):
- return d.itervalues(**kw)
-
- def iteritems(d, **kw):
- return d.iteritems(**kw)
-
- def iterlists(d, **kw):
- return d.iterlists(**kw)
-
- viewkeys = operator.methodcaller("viewkeys")
-
- viewvalues = operator.methodcaller("viewvalues")
-
- viewitems = operator.methodcaller("viewitems")
-
-_add_doc(iterkeys, "Return an iterator over the keys of a dictionary.")
-_add_doc(itervalues, "Return an iterator over the values of a dictionary.")
-_add_doc(iteritems,
- "Return an iterator over the (key, value) pairs of a dictionary.")
-_add_doc(iterlists,
- "Return an iterator over the (key, [values]) pairs of a dictionary.")
-
-
-if PY3:
- def b(s):
- return s.encode("latin-1")
-
- def u(s):
- return s
- unichr = chr
- import struct
- int2byte = struct.Struct(">B").pack
- del struct
- byte2int = operator.itemgetter(0)
- indexbytes = operator.getitem
- iterbytes = iter
- import io
- StringIO = io.StringIO
- BytesIO = io.BytesIO
- del io
- _assertCountEqual = "assertCountEqual"
- if sys.version_info[1] <= 1:
- _assertRaisesRegex = "assertRaisesRegexp"
- _assertRegex = "assertRegexpMatches"
- _assertNotRegex = "assertNotRegexpMatches"
- else:
- _assertRaisesRegex = "assertRaisesRegex"
- _assertRegex = "assertRegex"
- _assertNotRegex = "assertNotRegex"
-else:
- def b(s):
- return s
- # Workaround for standalone backslash
-
- def u(s):
- return unicode(s.replace(r'\\', r'\\\\'), "unicode_escape")
- unichr = unichr
- int2byte = chr
-
- def byte2int(bs):
- return ord(bs[0])
-
- def indexbytes(buf, i):
- return ord(buf[i])
- iterbytes = functools.partial(itertools.imap, ord)
- import StringIO
- StringIO = BytesIO = StringIO.StringIO
- _assertCountEqual = "assertItemsEqual"
- _assertRaisesRegex = "assertRaisesRegexp"
- _assertRegex = "assertRegexpMatches"
- _assertNotRegex = "assertNotRegexpMatches"
-_add_doc(b, """Byte literal""")
-_add_doc(u, """Text literal""")
-
-
-def assertCountEqual(self, *args, **kwargs):
- return getattr(self, _assertCountEqual)(*args, **kwargs)
-
-
-def assertRaisesRegex(self, *args, **kwargs):
- return getattr(self, _assertRaisesRegex)(*args, **kwargs)
-
-
-def assertRegex(self, *args, **kwargs):
- return getattr(self, _assertRegex)(*args, **kwargs)
-
-
-def assertNotRegex(self, *args, **kwargs):
- return getattr(self, _assertNotRegex)(*args, **kwargs)
-
-
-if PY3:
- exec_ = getattr(moves.builtins, "exec")
-
- def reraise(tp, value, tb=None):
- try:
- if value is None:
- value = tp()
- if value.__traceback__ is not tb:
- raise value.with_traceback(tb)
- raise value
- finally:
- value = None
- tb = None
-
-else:
- def exec_(_code_, _globs_=None, _locs_=None):
- """Execute code in a namespace."""
- if _globs_ is None:
- frame = sys._getframe(1)
- _globs_ = frame.f_globals
- if _locs_ is None:
- _locs_ = frame.f_locals
- del frame
- elif _locs_ is None:
- _locs_ = _globs_
- exec("""exec _code_ in _globs_, _locs_""")
-
- exec_("""def reraise(tp, value, tb=None):
- try:
- raise tp, value, tb
- finally:
- tb = None
-""")
-
-
-if sys.version_info[:2] > (3,):
- exec_("""def raise_from(value, from_value):
- try:
- raise value from from_value
- finally:
- value = None
-""")
-else:
- def raise_from(value, from_value):
- raise value
-
-
-print_ = getattr(moves.builtins, "print", None)
-if print_ is None:
- def print_(*args, **kwargs):
- """The new-style print function for Python 2.4 and 2.5."""
- fp = kwargs.pop("file", sys.stdout)
- if fp is None:
- return
-
- def write(data):
- if not isinstance(data, basestring):
- data = str(data)
- # If the file has an encoding, encode unicode with it.
- if (isinstance(fp, file) and
- isinstance(data, unicode) and
- fp.encoding is not None):
- errors = getattr(fp, "errors", None)
- if errors is None:
- errors = "strict"
- data = data.encode(fp.encoding, errors)
- fp.write(data)
- want_unicode = False
- sep = kwargs.pop("sep", None)
- if sep is not None:
- if isinstance(sep, unicode):
- want_unicode = True
- elif not isinstance(sep, str):
- raise TypeError("sep must be None or a string")
- end = kwargs.pop("end", None)
- if end is not None:
- if isinstance(end, unicode):
- want_unicode = True
- elif not isinstance(end, str):
- raise TypeError("end must be None or a string")
- if kwargs:
- raise TypeError("invalid keyword arguments to print()")
- if not want_unicode:
- for arg in args:
- if isinstance(arg, unicode):
- want_unicode = True
- break
- if want_unicode:
- newline = unicode("\n")
- space = unicode(" ")
- else:
- newline = "\n"
- space = " "
- if sep is None:
- sep = space
- if end is None:
- end = newline
- for i, arg in enumerate(args):
- if i:
- write(sep)
- write(arg)
- write(end)
-if sys.version_info[:2] < (3, 3):
- _print = print_
-
- def print_(*args, **kwargs):
- fp = kwargs.get("file", sys.stdout)
- flush = kwargs.pop("flush", False)
- _print(*args, **kwargs)
- if flush and fp is not None:
- fp.flush()
-
-_add_doc(reraise, """Reraise an exception.""")
-
-if sys.version_info[0:2] < (3, 4):
- # This does exactly the same what the :func:`py3:functools.update_wrapper`
- # function does on Python versions after 3.2. It sets the ``__wrapped__``
- # attribute on ``wrapper`` object and it doesn't raise an error if any of
- # the attributes mentioned in ``assigned`` and ``updated`` are missing on
- # ``wrapped`` object.
- def _update_wrapper(wrapper, wrapped,
- assigned=functools.WRAPPER_ASSIGNMENTS,
- updated=functools.WRAPPER_UPDATES):
- for attr in assigned:
- try:
- value = getattr(wrapped, attr)
- except AttributeError:
- continue
- else:
- setattr(wrapper, attr, value)
- for attr in updated:
- getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
- wrapper.__wrapped__ = wrapped
- return wrapper
- _update_wrapper.__doc__ = functools.update_wrapper.__doc__
-
- def wraps(wrapped, assigned=functools.WRAPPER_ASSIGNMENTS,
- updated=functools.WRAPPER_UPDATES):
- return functools.partial(_update_wrapper, wrapped=wrapped,
- assigned=assigned, updated=updated)
- wraps.__doc__ = functools.wraps.__doc__
-
-else:
- wraps = functools.wraps
-
-
-def with_metaclass(meta, *bases):
- """Create a base class with a metaclass."""
- # This requires a bit of explanation: the basic idea is to make a dummy
- # metaclass for one level of class instantiation that replaces itself with
- # the actual metaclass.
- class metaclass(type):
-
- def __new__(cls, name, this_bases, d):
- if sys.version_info[:2] >= (3, 7):
- # This version introduced PEP 560 that requires a bit
- # of extra care (we mimic what is done by __build_class__).
- resolved_bases = types.resolve_bases(bases)
- if resolved_bases is not bases:
- d['__orig_bases__'] = bases
- else:
- resolved_bases = bases
- return meta(name, resolved_bases, d)
-
- @classmethod
- def __prepare__(cls, name, this_bases):
- return meta.__prepare__(name, bases)
- return type.__new__(metaclass, 'temporary_class', (), {})
-
-
-def add_metaclass(metaclass):
- """Class decorator for creating a class with a metaclass."""
- def wrapper(cls):
- orig_vars = cls.__dict__.copy()
- slots = orig_vars.get('__slots__')
- if slots is not None:
- if isinstance(slots, str):
- slots = [slots]
- for slots_var in slots:
- orig_vars.pop(slots_var)
- orig_vars.pop('__dict__', None)
- orig_vars.pop('__weakref__', None)
- if hasattr(cls, '__qualname__'):
- orig_vars['__qualname__'] = cls.__qualname__
- return metaclass(cls.__name__, cls.__bases__, orig_vars)
- return wrapper
-
-
-def ensure_binary(s, encoding='utf-8', errors='strict'):
- """Coerce **s** to six.binary_type.
-
- For Python 2:
- - `unicode` -> encoded to `str`
- - `str` -> `str`
-
- For Python 3:
- - `str` -> encoded to `bytes`
- - `bytes` -> `bytes`
- """
- if isinstance(s, binary_type):
- return s
- if isinstance(s, text_type):
- return s.encode(encoding, errors)
- raise TypeError("not expecting type '%s'" % type(s))
-
-
-def ensure_str(s, encoding='utf-8', errors='strict'):
- """Coerce *s* to `str`.
-
- For Python 2:
- - `unicode` -> encoded to `str`
- - `str` -> `str`
-
- For Python 3:
- - `str` -> `str`
- - `bytes` -> decoded to `str`
- """
- # Optimization: Fast return for the common case.
- if type(s) is str:
- return s
- if PY2 and isinstance(s, text_type):
- return s.encode(encoding, errors)
- elif PY3 and isinstance(s, binary_type):
- return s.decode(encoding, errors)
- elif not isinstance(s, (text_type, binary_type)):
- raise TypeError("not expecting type '%s'" % type(s))
- return s
-
-
-def ensure_text(s, encoding='utf-8', errors='strict'):
- """Coerce *s* to six.text_type.
-
- For Python 2:
- - `unicode` -> `unicode`
- - `str` -> `unicode`
-
- For Python 3:
- - `str` -> `str`
- - `bytes` -> decoded to `str`
- """
- if isinstance(s, binary_type):
- return s.decode(encoding, errors)
- elif isinstance(s, text_type):
- return s
- else:
- raise TypeError("not expecting type '%s'" % type(s))
-
-
-def python_2_unicode_compatible(klass):
- """
- A class decorator that defines __unicode__ and __str__ methods under Python 2.
- Under Python 3 it does nothing.
-
- To support Python 2 and 3 with a single code base, define a __str__ method
- returning text and apply this decorator to the class.
- """
- if PY2:
- if '__str__' not in klass.__dict__:
- raise ValueError("@python_2_unicode_compatible cannot be applied "
- "to %s because it doesn't define __str__()." %
- klass.__name__)
- klass.__unicode__ = klass.__str__
- klass.__str__ = lambda self: self.__unicode__().encode('utf-8')
- return klass
-
-
-# Complete the moves implementation.
-# This code is at the end of this module to speed up module loading.
-# Turn this module into a package.
-__path__ = [] # required for PEP 302 and PEP 451
-__package__ = __name__ # see PEP 366 @ReservedAssignment
-if globals().get("__spec__") is not None:
- __spec__.submodule_search_locations = [] # PEP 451 @UndefinedVariable
-# Remove other six meta path importers, since they cause problems. This can
-# happen if six is removed from sys.modules and then reloaded. (Setuptools does
-# this for some reason.)
-if sys.meta_path:
- for i, importer in enumerate(sys.meta_path):
- # Here's some real nastiness: Another "instance" of the six module might
- # be floating around. Therefore, we can't use isinstance() to check for
- # the six meta path importer, since the other six instance will have
- # inserted an importer with different class.
- if (type(importer).__name__ == "_SixMetaPathImporter" and
- importer.name == __name__):
- del sys.meta_path[i]
- break
- del i, importer
-# Finally, add the importer to the meta path import hook.
-sys.meta_path.append(_importer)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/install.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/install.py
deleted file mode 100644
index 55fdb124e8966a859b2655a8e99a9186c8755ba7..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/install.py
+++ /dev/null
@@ -1,139 +0,0 @@
-from distutils.errors import DistutilsArgError
-import inspect
-import glob
-import warnings
-import platform
-import distutils.command.install as orig
-
-import setuptools
-
-# Prior to numpy 1.9, NumPy relies on the '_install' name, so provide it for
-# now. See https://github.com/pypa/setuptools/issues/199/
-_install = orig.install
-
-
-class install(orig.install):
- """Use easy_install to install the package, w/dependencies"""
-
- user_options = orig.install.user_options + [
- ('old-and-unmanageable', None, "Try not to use this!"),
- ('single-version-externally-managed', None,
- "used by system package builders to create 'flat' eggs"),
- ]
- boolean_options = orig.install.boolean_options + [
- 'old-and-unmanageable', 'single-version-externally-managed',
- ]
- new_commands = [
- ('install_egg_info', lambda self: True),
- ('install_scripts', lambda self: True),
- ]
- _nc = dict(new_commands)
-
- def initialize_options(self):
-
- warnings.warn(
- "setup.py install is deprecated. "
- "Use build and pip and other standards-based tools.",
- setuptools.SetuptoolsDeprecationWarning,
- )
-
- orig.install.initialize_options(self)
- self.old_and_unmanageable = None
- self.single_version_externally_managed = None
-
- def finalize_options(self):
- orig.install.finalize_options(self)
- if self.root:
- self.single_version_externally_managed = True
- elif self.single_version_externally_managed:
- if not self.root and not self.record:
- raise DistutilsArgError(
- "You must specify --record or --root when building system"
- " packages"
- )
-
- def handle_extra_path(self):
- if self.root or self.single_version_externally_managed:
- # explicit backward-compatibility mode, allow extra_path to work
- return orig.install.handle_extra_path(self)
-
- # Ignore extra_path when installing an egg (or being run by another
- # command without --root or --single-version-externally-managed
- self.path_file = None
- self.extra_dirs = ''
-
- def run(self):
- # Explicit request for old-style install? Just do it
- if self.old_and_unmanageable or self.single_version_externally_managed:
- return orig.install.run(self)
-
- if not self._called_from_setup(inspect.currentframe()):
- # Run in backward-compatibility mode to support bdist_* commands.
- orig.install.run(self)
- else:
- self.do_egg_install()
-
- @staticmethod
- def _called_from_setup(run_frame):
- """
- Attempt to detect whether run() was called from setup() or by another
- command. If called by setup(), the parent caller will be the
- 'run_command' method in 'distutils.dist', and *its* caller will be
- the 'run_commands' method. If called any other way, the
- immediate caller *might* be 'run_command', but it won't have been
- called by 'run_commands'. Return True in that case or if a call stack
- is unavailable. Return False otherwise.
- """
- if run_frame is None:
- msg = "Call stack not available. bdist_* commands may fail."
- warnings.warn(msg)
- if platform.python_implementation() == 'IronPython':
- msg = "For best results, pass -X:Frames to enable call stack."
- warnings.warn(msg)
- return True
-
- frames = inspect.getouterframes(run_frame)
- for frame in frames[2:4]:
- caller, = frame[:1]
- info = inspect.getframeinfo(caller)
- caller_module = caller.f_globals.get('__name__', '')
-
- if caller_module == "setuptools.dist" and info.function == "run_command":
- # Starting from v61.0.0 setuptools overwrites dist.run_command
- continue
-
- return (
- caller_module == 'distutils.dist'
- and info.function == 'run_commands'
- )
-
- def do_egg_install(self):
-
- easy_install = self.distribution.get_command_class('easy_install')
-
- cmd = easy_install(
- self.distribution, args="x", root=self.root, record=self.record,
- )
- cmd.ensure_finalized() # finalize before bdist_egg munges install cmd
- cmd.always_copy_from = '.' # make sure local-dir eggs get installed
-
- # pick up setup-dir .egg files only: no .egg-info
- cmd.package_index.scan(glob.glob('*.egg'))
-
- self.run_command('bdist_egg')
- args = [self.distribution.get_command_obj('bdist_egg').egg_output]
-
- if setuptools.bootstrap_install_from:
- # Bootstrap self-installation of setuptools
- args.insert(0, setuptools.bootstrap_install_from)
-
- cmd.args = args
- cmd.run(show_deprecation=False)
- setuptools.bootstrap_install_from = None
-
-
-# XXX Python 3.1 doesn't see _nc if this is inside the class
-install.sub_commands = (
- [cmd for cmd in orig.install.sub_commands if cmd[0] not in install._nc] +
- install.new_commands
-)
diff --git a/spaces/Branon/TurboKeys/greeting.md b/spaces/Branon/TurboKeys/greeting.md
deleted file mode 100644
index 7e5bad6210f9fb4ea81d9ff1d3a4805560964e6f..0000000000000000000000000000000000000000
--- a/spaces/Branon/TurboKeys/greeting.md
+++ /dev/null
@@ -1 +0,0 @@
-Pass is my greatest enemy.
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/for_each.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/for_each.h
deleted file mode 100644
index dfe5329b84ed273e60dacab576a559e351d26c42..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/for_each.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-template
- RandomAccessIterator for_each(execution_policy &exec,
- RandomAccessIterator first,
- RandomAccessIterator last,
- UnaryFunction f);
-
-template
- RandomAccessIterator for_each_n(execution_policy &exec,
- RandomAccessIterator first,
- Size n,
- UnaryFunction f);
-
-} // end namespace detail
-} // end namespace tbb
-} // end namespace system
-} // end namespace thrust
-
-#include
-
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/update.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/update.js
deleted file mode 100644
index bbc1655c0582857d37233d174f90c3da826525b5..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/update.js
+++ /dev/null
@@ -1,218 +0,0 @@
-import plugin from '../../../lib/plugins/plugin.js'
-import lodash from 'lodash'
-import { createRequire } from 'module'
-import { Restart } from '../../other/restart.js'
-import common from '../../../lib/common/common.js'
-
-const require = createRequire(import.meta.url)
-const { exec, execSync } = require('child_process')
-
-let uping = false
-
-export class update extends plugin {
- constructor() {
- super({
- name: '[ws-plugin] 插件更新',
- dsc: '[ws-plugin] 插件更新',
- event: 'message',
- priority: 1,
- rule: [
- {
- reg: '^#ws(插件)?(强制)?更新$',
- fnc: 'update'
- },
- {
- reg: '^#ws(插件)?更新日志$',
- fnc: 'updateLog'
- }
- ]
- })
- this.typeName = 'ws-plugin'
- }
-
- async update() {
- if (!this.e.isMaster) return false
- if (uping) {
- await this.reply('已有命令更新中..请勿重复操作')
- return
- }
- /** 获取插件 */
- let plugin = 'ws-plugin'
-
- /** 检查git安装 */
- if (!await this.checkGit()) return
-
- /** 执行更新 */
- await this.runUpdate(plugin)
-
- /** 是否需要重启 */
- if (this.isUp) {
- // await this.reply('即将执行重启，以应用更新')
- setTimeout(() => this.restart(), 2000)
- }
- }
-
- restart() {
- new Restart(this.e).restart()
- }
-
- async checkGit() {
- let ret = await execSync('git --version', { encoding: 'utf-8' })
- if (!ret || !ret.includes('git version')) {
- await this.reply('请先安装git')
- return false
- }
-
- return true
- }
-
- async runUpdate(plugin = '') {
- this.isNowUp = false
-
- let cm = `cd "plugins/${plugin}" && git pull --no-rebase`
-
- let type = '更新'
- if (this.e.msg.includes('强制')) {
- type = '强制更新'
- cm = `cd "plugins/${plugin}" && git reset --hard && git pull --rebase --allow-unrelated-histories`
- }
-
- this.oldCommitId = await this.getcommitId(plugin)
-
- await this.reply(`开始执行${type}操作...`)
- uping = true
- let ret = await this.execSync(cm)
- uping = false
-
- if (ret.error) {
- logger.mark(`${this.e.logFnc} 更新失败：${this.typeName}`)
- this.gitErr(ret.error, ret.stdout)
- return false
- }
-
- let time = await this.getTime(plugin)
-
- if (/Already up|已经是最新/g.test(ret.stdout)) {
- await this.reply(`${this.typeName}已经是最新\n最后更新时间：${time}`)
- } else {
- await this.reply(`${this.typeName}更新成功\n更新时间：${time}`)
- this.isUp = true
- let log = await this.getLog(plugin)
- await this.reply(log)
- }
-
- logger.mark(`${this.e.logFnc} 最后更新时间：${time}`)
-
- return true
- }
-
- async gitErr(err, stdout) {
- let msg = '更新失败！'
- let errMsg = err.toString()
- stdout = stdout.toString()
-
- if (errMsg.includes('Timed out')) {
- let remote = errMsg.match(/'(.+?)'/g)[0].replace(/'/g, '')
- await this.reply(msg + `\n连接超时：${remote}`)
- return
- }
-
- if (/Failed to connect|unable to access/g.test(errMsg)) {
- let remote = errMsg.match(/'(.+?)'/g)[0].replace(/'/g, '')
- await this.reply(msg + `\n连接失败：${remote}`)
- return
- }
-
- if (errMsg.includes('be overwritten by merge')) {
- await this.reply(msg + `存在冲突：\n${errMsg}\n` + '请解决冲突后再更新，或者执行#ws强制更新，放弃本地修改')
- return
- }
-
- if (stdout.includes('CONFLICT')) {
- await this.reply([msg + '存在冲突\n', errMsg, stdout, '\n请解决冲突后再更新，或者执行#ws强制更新，放弃本地修改'])
- return
- }
-
- await this.reply([errMsg, stdout])
- }
-
- async getcommitId(plugin = '') {
- let cm = `cd "plugins/${plugin}" && git rev-parse --short HEAD`
-
- let commitId = await execSync(cm, { encoding: 'utf-8' })
- commitId = lodash.trim(commitId)
-
- return commitId
- }
-
- async getTime(plugin = '') {
- let cm = `cd "plugins/${plugin}" && git log -1 --pretty=format:"%cd" --date=format:"%F %T"`
-
- let time = ''
- try {
- time = await execSync(cm, { encoding: 'utf-8' })
- time = lodash.trim(time)
- } catch (error) {
- logger.error(error.toString())
- time = '获取时间失败'
- }
-
- return time
- }
-
- async execSync(cmd) {
- return new Promise((resolve, reject) => {
- exec(cmd, { windowsHide: true }, (error, stdout, stderr) => {
- resolve({ error, stdout, stderr })
- })
- })
- }
-
- async getLog(plugin = '') {
- let cm = 'git log -100 --pretty="%h||[%cd] %s" --date=format:"%F %T"'
- if (plugin) cm = `cd "plugins/${plugin}" && ${cm}`
-
- let logAll
- try {
- logAll = await execSync(cm, { encoding: 'utf-8' })
- } catch (error) {
- logger.error(error.toString())
- await this.reply(error.toString())
- }
-
- if (!logAll) return false
-
- logAll = logAll.trim().split('\n')
-
- let log = []
- for (let str of logAll) {
- str = str.split('||')
- if (str[0] == this.oldCommitId) break
- if (str[1].includes('Merge branch')) continue
- log.push(str[1])
- }
- let line = log.length
- log = log.join('\n\n')
-
- if (log.length <= 0) return ''
-
- let end = ''
- try {
- cm = 'git config -l'
- if (plugin) cm = `cd "plugins/${plugin}" && ${cm}`
- end = await execSync(cm, { encoding: 'utf-8' })
- end = end.match(/remote\..*\.url=.+/g).join('\n\n').replace(/remote\..*\.url=/g, '')
- } catch (error) {
- logger.error(error.toString())
- await this.reply(error.toString())
- }
-
- return common.makeForwardMsg(this.e, [log, end], `${plugin || 'ws-plugin'} 更新日志，共${line}条`)
- }
-
- async updateLog() {
- let log = await this.getLog('ws-plugin')
- await this.reply(log)
- }
-
-}
\ No newline at end of file
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/learn/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/learn/__init__.py
deleted file mode 100644
index a3c2fad58b5d39e8f781fc6bc6dac7d03f0c3377..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/learn/__init__.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def learn(images: List[BuildImage], texts: List[str], args):
- text = texts[0] if texts else "偷学群友数理基础"
- frame = BuildImage.open(img_dir / "0.png")
- try:
- frame.draw_text(
- (100, 1360, frame.width - 100, 1730),
- text,
- max_fontsize=350,
- min_fontsize=200,
- weight="bold",
- )
- except ValueError:
- raise TextOverLength(text)
-
- def make(img: BuildImage) -> BuildImage:
- img = img.convert("RGBA").resize((1751, 1347), keep_ratio=True)
- return frame.copy().paste(img, (1440, 0), alpha=True)
-
- return make_jpg_or_gif(images[0], make)
-
-
-add_meme(
- "learn",
- learn,
- min_images=1,
- max_images=1,
- min_texts=0,
- max_texts=1,
- default_texts=["偷学群友数理基础"],
- keywords=["偷学"],
-)
diff --git a/spaces/ClassCat/YOLOS-Object-Detection/README.md b/spaces/ClassCat/YOLOS-Object-Detection/README.md
deleted file mode 100644
index 6fb0acfe1a0d0bfc9c38fc4c1804d307c49ed430..0000000000000000000000000000000000000000
--- a/spaces/ClassCat/YOLOS-Object-Detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: YOLOS Object Detection
-emoji: 🚀
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CofAI/chat.v1/Dockerfile b/spaces/CofAI/chat.v1/Dockerfile
deleted file mode 100644
index 8a3000e46b2706d40cae6585898ee88696022bde..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.v1/Dockerfile
+++ /dev/null
@@ -1,133 +0,0 @@
-# Use the official Python 3.9 image as the base image
-FROM python:3.9
-
-# Expose the port
-EXPOSE 7860
-
-# Keeps Python from generating .pyc files in the container
-ENV PYTHONDONTWRITEBYTECODE=1
-
-# Turns off buffering for easier container logging
-ENV PYTHONUNBUFFERED=1
-
-# Set the PYNGROK_CONFIG environment variable
-ENV PYNGROK_CONFIG /tmp/pyngrok.yml
-
-# Set the NGROK_PATH environment variable to a writable location
-ENV NGROK_PATH /tmp/ngrok
-
-# Copy requirements.txt into the container
-COPY requirements.txt .
-
-# RUN apt-get update
-# RUN apt-get install -y wget
-# RUN wget -q https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
-# RUN apt-get install ./google-chrome-stable_current_amd64.deb -y
-
-
-# RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add -
-# RUN sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list'
-# RUN apt-get -y update
-# RUN apt-get install -y google-chrome-stable
-
-# # install chromedriver
-# RUN apt-get install -yqq unzip
-# RUN wget -O /tmp/chromedriver.zip http://chromedriver.storage.googleapis.com/`curl -sS chromedriver.storage.googleapis.com/LATEST_RELEASE`/chromedriver_linux64.zip
-# RUN unzip /tmp/chromedriver.zip chromedriver -d /usr/local/bin/
-
-
-
-
-
-# RUN apt install wget -y
-# RUN wget https://github.com/mozilla/geckodriver/releases/download/v0.32.0/geckodriver-v0.32.0-linux64.tar.gz
-# RUN tar -xzvf geckodriver-v0.32.0-linux64.tar.gz -C /usr/local/bin
-# RUN chmod +x /usr/local/bin/geckodriver
-# RUN geckodriver -V
-
-
-# RUN apt install firefox-esr -y
-# RUN apt-get install firefox-geckodriver
-
-# Upgrade pip and install the required packages
-RUN pip install --upgrade pip && \
- pip install -r requirements.txt
-
-# Install sudo and create the necessary directories before copying the files
-RUN apt-get update && \
- apt-get install -y sudo && \
- mkdir -p /code/image
-
-# Creates a non-root user with an explicit UID and adds permission to access the /code folder
-RUN adduser -u 5678 --disabled-password --gecos "" appuser && \
- usermod -aG sudo appuser && \
- usermod -aG root appuser && \
- chown -R appuser:appuser /code
-
-# Create the pyngrok bin directory and set the ownership and permissions for appuser
-RUN mkdir -p /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
- chown -R appuser:appuser /usr/local/lib/python3.9/site-packages/pyngrok/bin && \
- chmod -R 777 /usr/local/lib/python3.9/site-packages/pyngrok/bin
-
-RUN mkdir -p /.ngrok2 && \
- chown -R appuser:appuser /.ngrok2 && \
- chmod -R 777 /.ngrok2
-
-RUN apt-get update && \
- apt-get install -y curl
-
-RUN echo "deb http://deb.debian.org/debian/ unstable main contrib non-free" >> /etc/apt/sources.list.d/debian.list
-
-
-# RUN apt install firefox-esr && \
-# apt install geckodriver
-
-# Set the working directory and copy the files
-WORKDIR /code
-
-# Set the ownership and permissions for the /code directory and its contents
-RUN chown -R appuser:appuser /code && \
- chmod -R 777 /code
-
-COPY . /code
-
-# RUN chown -R appuser:appuser /code/data.csv && \
-# chmod -R 777 /code/data.csv
-
-# Copy the pyngrok.yml configuration file
-COPY pyngrok.yml /tmp/pyngrok.yml
-
-# Set the TRANSFORMERS_CACHE environment variable to a cache directory inside /tmp
-ENV TRANSFORMERS_CACHE /tmp/transformers_cache
-ENV TORCH_HOME /tmp/torch_cache
-
-USER appuser
-
-
-RUN git clone https://github.com/rphrp1985/gpt4f
-# WORKDIR /gpt4f
-# COPY . /gpt4f
-# RUN cd gpt4f
-# RUN ls
-
-# cp -R / /root/dest-folder
-RUN cp -R gpt4f/* /code
-RUN ls
-CMD python run.py
-
-
-
-
-
-
-
-# Start the application using pyngrok
-# CMD python main.py
-# Get the public IP address and display it
-# RUN curl -s https://api.ipify.org | xargs echo "Public IP:"
-RUN pip install gunicorn
-
-# Start the Uvicorn server
-# ENTRYPOINT ["python", "main.py"]
-# CMD ["sh", "-c", "python main.py & sleep infinity"]
-CMD ["gunicorn", "--bind", "0.0.0.0:7860","run:app"]
\ No newline at end of file
diff --git a/spaces/CofAI/chat/g4f/Provider/Providers/Theb.py b/spaces/CofAI/chat/g4f/Provider/Providers/Theb.py
deleted file mode 100644
index aa43ebc55d74ffaa722fe008424fce97c622a323..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/g4f/Provider/Providers/Theb.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://theb.ai'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
- path = os.path.dirname(os.path.realpath(__file__))
- config = json.dumps({
- 'messages': messages,
- 'model': model}, separators=(',', ':'))
-
- cmd = ['python3', f'{path}/helpers/theb.py', config]
-
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
- for line in iter(p.stdout.readline, b''):
- yield line.decode('utf-8')
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Cropinky/esrgan/app.py b/spaces/Cropinky/esrgan/app.py
deleted file mode 100644
index 0b8f22303486ffd4f7ed6669713178cf6e5612eb..0000000000000000000000000000000000000000
--- a/spaces/Cropinky/esrgan/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-import numpy as np
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.download_util import load_file_from_url
-from realesrgan import RealESRGANer
-import gc
-import os
-import torch
-from PIL import Image
-
-def upscale_image(img: np.ndarray) -> np.ndarray:
- upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- upsampler = RealESRGANer(
- scale=4,
- model_path=os.path.join('weights', 'RealESRGAN_x4plus.pth'),
- dni_weight=None,
- model=upscale_model,
- tile=0,
- tile_pad=10,
- pre_pad=0,
- half=False,
- )
- output, _ = upsampler.enhance(img, outscale=4)
-
- #cleanup
- torch.cuda.empty_cache()
- del(upsampler)
- del(upscale_model)
- gc.collect()
- return Image.fromarray(output)
-
-with gr.Blocks() as demo:
- #gr.Markdown("Flip text or image files using this demo.")
- with gr.Tab("Upscale Image"):
- with gr.Row():
- image_input = gr.Image()
- image_output = gr.Image()
- image_button = gr.Button("Upscale")
- gr.Markdown("this is running on a CPU so it's gonna be VERY slow")
-
- gr.Markdown("Real esrgan implementation from [this repo](https://github.com/xinntao/Real-ESRGAN)")
- gr.Markdown("The idea behind this space was that you can clone it and use this model locally on your GPU")
- image_button.click(upscale_image, inputs=image_input, outputs=image_output)
-
-if __name__ == "__main__":
- demo.launch(max_threads = 16)
\ No newline at end of file
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/detector/__init__.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/detector/__init__.py
deleted file mode 100644
index ff421e281e16e6623bab2551b242ea003d1f2166..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/detector/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from .detectors import build_detection_model
diff --git a/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/tasks/base_task.py b/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/tasks/base_task.py
deleted file mode 100644
index d91aa346df45ed58b7d2425a59dd21f3ae8ef93c..0000000000000000000000000000000000000000
--- a/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/tasks/base_task.py
+++ /dev/null
@@ -1,286 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import logging
-import os
-
-import torch
-import torch.distributed as dist
-from video_llama.common.dist_utils import get_rank, get_world_size, is_main_process, is_dist_avail_and_initialized
-from video_llama.common.logger import MetricLogger, SmoothedValue
-from video_llama.common.registry import registry
-from video_llama.datasets.data_utils import prepare_sample
-
-
-class BaseTask:
- def __init__(self, **kwargs):
- super().__init__()
-
- self.inst_id_key = "instance_id"
-
- @classmethod
- def setup_task(cls, **kwargs):
- return cls()
-
- def build_model(self, cfg):
- model_config = cfg.model_cfg
-
- model_cls = registry.get_model_class(model_config.arch)
- return model_cls.from_config(model_config)
-
- def build_datasets(self, cfg):
- """
- Build a dictionary of datasets, keyed by split 'train', 'valid', 'test'.
- Download dataset and annotations automatically if not exist.
-
- Args:
- cfg (common.config.Config): _description_
-
- Returns:
- dict: Dictionary of torch.utils.data.Dataset objects by split.
- """
-
- datasets = dict()
-
- datasets_config = cfg.datasets_cfg
-
- assert len(datasets_config) > 0, "At least one dataset has to be specified."
-
- for name in datasets_config:
- dataset_config = datasets_config[name]
-
- builder = registry.get_builder_class(name)(dataset_config)
- dataset = builder.build_datasets()
-
- dataset['train'].name = name
- if 'sample_ratio' in dataset_config:
- dataset['train'].sample_ratio = dataset_config.sample_ratio
-
- datasets[name] = dataset
-
- return datasets
-
- def train_step(self, model, samples):
- loss = model(samples)["loss"]
- return loss
-
- def valid_step(self, model, samples):
- raise NotImplementedError
-
- def before_evaluation(self, model, dataset, **kwargs):
- model.before_evaluation(dataset=dataset, task_type=type(self))
-
- def after_evaluation(self, **kwargs):
- pass
-
- def inference_step(self):
- raise NotImplementedError
-
- def evaluation(self, model, data_loader, cuda_enabled=True):
- metric_logger = MetricLogger(delimiter=" ")
- header = "Evaluation"
- # TODO make it configurable
- print_freq = 10
-
- results = []
-
- for samples in metric_logger.log_every(data_loader, print_freq, header):
- samples = prepare_sample(samples, cuda_enabled=cuda_enabled)
-
- eval_output = self.valid_step(model=model, samples=samples)
- results.extend(eval_output)
-
- if is_dist_avail_and_initialized():
- dist.barrier()
-
- return results
-
- def train_epoch(
- self,
- epoch,
- model,
- data_loader,
- optimizer,
- lr_scheduler,
- scaler=None,
- cuda_enabled=False,
- log_freq=50,
- accum_grad_iters=1,
- ):
- return self._train_inner_loop(
- epoch=epoch,
- iters_per_epoch=lr_scheduler.iters_per_epoch,
- model=model,
- data_loader=data_loader,
- optimizer=optimizer,
- scaler=scaler,
- lr_scheduler=lr_scheduler,
- log_freq=log_freq,
- cuda_enabled=cuda_enabled,
- accum_grad_iters=accum_grad_iters,
- )
-
- def train_iters(
- self,
- epoch,
- start_iters,
- iters_per_inner_epoch,
- model,
- data_loader,
- optimizer,
- lr_scheduler,
- scaler=None,
- cuda_enabled=False,
- log_freq=50,
- accum_grad_iters=1,
- ):
- return self._train_inner_loop(
- epoch=epoch,
- start_iters=start_iters,
- iters_per_epoch=iters_per_inner_epoch,
- model=model,
- data_loader=data_loader,
- optimizer=optimizer,
- scaler=scaler,
- lr_scheduler=lr_scheduler,
- log_freq=log_freq,
- cuda_enabled=cuda_enabled,
- accum_grad_iters=accum_grad_iters,
- )
-
- def _train_inner_loop(
- self,
- epoch,
- iters_per_epoch,
- model,
- data_loader,
- optimizer,
- lr_scheduler,
- scaler=None,
- start_iters=None,
- log_freq=50,
- cuda_enabled=False,
- accum_grad_iters=1,
- ):
- """
- An inner training loop compatible with both epoch-based and iter-based training.
-
- When using epoch-based, training stops after one epoch; when using iter-based,
- training stops after #iters_per_epoch iterations.
- """
- use_amp = scaler is not None
-
- if not hasattr(data_loader, "__next__"):
- # convert to iterator if not already
- data_loader = iter(data_loader)
-
- metric_logger = MetricLogger(delimiter=" ")
- metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6f}"))
- metric_logger.add_meter("loss", SmoothedValue(window_size=1, fmt="{value:.4f}"))
-
- # if iter-based runner, schedule lr based on inner epoch.
- logging.info(
- "Start training epoch {}, {} iters per inner epoch.".format(
- epoch, iters_per_epoch
- )
- )
- header = "Train: data epoch: [{}]".format(epoch)
- if start_iters is None:
- # epoch-based runner
- inner_epoch = epoch
- else:
- # In iter-based runner, we schedule the learning rate based on iterations.
- inner_epoch = start_iters // iters_per_epoch
- header = header + "; inner epoch [{}]".format(inner_epoch)
-
- for i in metric_logger.log_every(range(iters_per_epoch), log_freq, header):
- # if using iter-based runner, we stop after iters_per_epoch iterations.
- if i >= iters_per_epoch:
- break
-
- samples = next(data_loader)
-
- samples = prepare_sample(samples, cuda_enabled=cuda_enabled)
- samples.update(
- {
- "epoch": inner_epoch,
- "num_iters_per_epoch": iters_per_epoch,
- "iters": i,
- }
- )
-
- lr_scheduler.step(cur_epoch=inner_epoch, cur_step=i)
-
- with torch.cuda.amp.autocast(enabled=use_amp):
- loss = self.train_step(model=model, samples=samples)
-
- # after_train_step()
- if use_amp:
- scaler.scale(loss).backward()
- else:
- loss.backward()
-
- # update gradients every accum_grad_iters iterations
- if (i + 1) % accum_grad_iters == 0:
- if use_amp:
- scaler.step(optimizer)
- scaler.update()
- else:
- optimizer.step()
- optimizer.zero_grad()
-
- metric_logger.update(loss=loss.item())
- metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-
- # after train_epoch()
- # gather the stats from all processes
- metric_logger.synchronize_between_processes()
- logging.info("Averaged stats: " + str(metric_logger.global_avg()))
- return {
- k: "{:.3f}".format(meter.global_avg)
- for k, meter in metric_logger.meters.items()
- }
-
- @staticmethod
- def save_result(result, result_dir, filename, remove_duplicate=""):
- import json
-
- result_file = os.path.join(
- result_dir, "%s_rank%d.json" % (filename, get_rank())
- )
- final_result_file = os.path.join(result_dir, "%s.json" % filename)
-
- json.dump(result, open(result_file, "w"))
-
- if is_dist_avail_and_initialized():
- dist.barrier()
-
- if is_main_process():
- logging.warning("rank %d starts merging results." % get_rank())
- # combine results from all processes
- result = []
-
- for rank in range(get_world_size()):
- result_file = os.path.join(
- result_dir, "%s_rank%d.json" % (filename, rank)
- )
- res = json.load(open(result_file, "r"))
- result += res
-
- if remove_duplicate:
- result_new = []
- id_list = []
- for res in result:
- if res[remove_duplicate] not in id_list:
- id_list.append(res[remove_duplicate])
- result_new.append(res)
- result = result_new
-
- json.dump(result, open(final_result_file, "w"))
- print("result file saved to %s" % final_result_file)
-
- return final_result_file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/to_thread.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/to_thread.py
deleted file mode 100644
index 9315d1ecf16eee45cd129ce17e48041a7f82348a..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/to_thread.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from __future__ import annotations
-
-from typing import Callable, TypeVar
-from warnings import warn
-
-from ._core._eventloop import get_asynclib
-from .abc import CapacityLimiter
-
-T_Retval = TypeVar("T_Retval")
-
-
-async def run_sync(
- func: Callable[..., T_Retval],
- *args: object,
- cancellable: bool = False,
- limiter: CapacityLimiter | None = None,
-) -> T_Retval:
- """
- Call the given function with the given arguments in a worker thread.
-
- If the ``cancellable`` option is enabled and the task waiting for its completion is cancelled,
- the thread will still run its course but its return value (or any raised exception) will be
- ignored.
-
- :param func: a callable
- :param args: positional arguments for the callable
- :param cancellable: ``True`` to allow cancellation of the operation
- :param limiter: capacity limiter to use to limit the total amount of threads running
- (if omitted, the default limiter is used)
- :return: an awaitable that yields the return value of the function.
-
- """
- return await get_asynclib().run_sync_in_worker_thread(
- func, *args, cancellable=cancellable, limiter=limiter
- )
-
-
-async def run_sync_in_worker_thread(
- func: Callable[..., T_Retval],
- *args: object,
- cancellable: bool = False,
- limiter: CapacityLimiter | None = None,
-) -> T_Retval:
- warn(
- "run_sync_in_worker_thread() has been deprecated, use anyio.to_thread.run_sync() instead",
- DeprecationWarning,
- )
- return await run_sync(func, *args, cancellable=cancellable, limiter=limiter)
-
-
-def current_default_thread_limiter() -> CapacityLimiter:
- """
- Return the capacity limiter that is used by default to limit the number of concurrent threads.
-
- :return: a capacity limiter object
-
- """
- return get_asynclib().current_default_thread_limiter()
-
-
-def current_default_worker_thread_limiter() -> CapacityLimiter:
- warn(
- "current_default_worker_thread_limiter() has been deprecated, "
- "use anyio.to_thread.current_default_thread_limiter() instead",
- DeprecationWarning,
- )
- return current_default_thread_limiter()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/version.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/version.py
deleted file mode 100644
index 1579fac7ec8302773a07ea2e760b5c14ee7786e4..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/version.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# file generated by setuptools_scm
-# don't change, don't track in version control
-__version__ = version = '3.12.2'
-__version_tuple__ = version_tuple = (3, 12, 2)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/models.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/models.py
deleted file mode 100644
index 954cf87bfa6a6485aaf32052604dc76ef7cd2853..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/models.py
+++ /dev/null
@@ -1,584 +0,0 @@
-"""Variation fonts interpolation models."""
-
-__all__ = [
- "normalizeValue",
- "normalizeLocation",
- "supportScalar",
- "VariationModel",
-]
-
-from fontTools.misc.roundTools import noRound
-from .errors import VariationModelError
-
-
-def nonNone(lst):
- return [l for l in lst if l is not None]
-
-
-def allNone(lst):
- return all(l is None for l in lst)
-
-
-def allEqualTo(ref, lst, mapper=None):
- if mapper is None:
- return all(ref == item for item in lst)
-
- mapped = mapper(ref)
- return all(mapped == mapper(item) for item in lst)
-
-
-def allEqual(lst, mapper=None):
- if not lst:
- return True
- it = iter(lst)
- try:
- first = next(it)
- except StopIteration:
- return True
- return allEqualTo(first, it, mapper=mapper)
-
-
-def subList(truth, lst):
- assert len(truth) == len(lst)
- return [l for l, t in zip(lst, truth) if t]
-
-
-def normalizeValue(v, triple, extrapolate=False):
- """Normalizes value based on a min/default/max triple.
-
- >>> normalizeValue(400, (100, 400, 900))
- 0.0
- >>> normalizeValue(100, (100, 400, 900))
- -1.0
- >>> normalizeValue(650, (100, 400, 900))
- 0.5
- """
- lower, default, upper = triple
- if not (lower <= default <= upper):
- raise ValueError(
- f"Invalid axis values, must be minimum, default, maximum: "
- f"{lower:3.3f}, {default:3.3f}, {upper:3.3f}"
- )
- if not extrapolate:
- v = max(min(v, upper), lower)
-
- if v == default or lower == upper:
- return 0.0
-
- if (v < default and lower != default) or (v > default and upper == default):
- return (v - default) / (default - lower)
- else:
- assert (v > default and upper != default) or (
- v < default and lower == default
- ), f"Ooops... v={v}, triple=({lower}, {default}, {upper})"
- return (v - default) / (upper - default)
-
-
-def normalizeLocation(location, axes, extrapolate=False):
- """Normalizes location based on axis min/default/max values from axes.
-
- >>> axes = {"wght": (100, 400, 900)}
- >>> normalizeLocation({"wght": 400}, axes)
- {'wght': 0.0}
- >>> normalizeLocation({"wght": 100}, axes)
- {'wght': -1.0}
- >>> normalizeLocation({"wght": 900}, axes)
- {'wght': 1.0}
- >>> normalizeLocation({"wght": 650}, axes)
- {'wght': 0.5}
- >>> normalizeLocation({"wght": 1000}, axes)
- {'wght': 1.0}
- >>> normalizeLocation({"wght": 0}, axes)
- {'wght': -1.0}
- >>> axes = {"wght": (0, 0, 1000)}
- >>> normalizeLocation({"wght": 0}, axes)
- {'wght': 0.0}
- >>> normalizeLocation({"wght": -1}, axes)
- {'wght': 0.0}
- >>> normalizeLocation({"wght": 1000}, axes)
- {'wght': 1.0}
- >>> normalizeLocation({"wght": 500}, axes)
- {'wght': 0.5}
- >>> normalizeLocation({"wght": 1001}, axes)
- {'wght': 1.0}
- >>> axes = {"wght": (0, 1000, 1000)}
- >>> normalizeLocation({"wght": 0}, axes)
- {'wght': -1.0}
- >>> normalizeLocation({"wght": -1}, axes)
- {'wght': -1.0}
- >>> normalizeLocation({"wght": 500}, axes)
- {'wght': -0.5}
- >>> normalizeLocation({"wght": 1000}, axes)
- {'wght': 0.0}
- >>> normalizeLocation({"wght": 1001}, axes)
- {'wght': 0.0}
- """
- out = {}
- for tag, triple in axes.items():
- v = location.get(tag, triple[1])
- out[tag] = normalizeValue(v, triple, extrapolate=extrapolate)
- return out
-
-
-def supportScalar(location, support, ot=True, extrapolate=False, axisRanges=None):
- """Returns the scalar multiplier at location, for a master
- with support. If ot is True, then a peak value of zero
- for support of an axis means "axis does not participate". That
- is how OpenType Variation Font technology works.
-
- If extrapolate is True, axisRanges must be a dict that maps axis
- names to (axisMin, axisMax) tuples.
-
- >>> supportScalar({}, {})
- 1.0
- >>> supportScalar({'wght':.2}, {})
- 1.0
- >>> supportScalar({'wght':.2}, {'wght':(0,2,3)})
- 0.1
- >>> supportScalar({'wght':2.5}, {'wght':(0,2,4)})
- 0.75
- >>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
- 0.75
- >>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}, ot=False)
- 0.375
- >>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
- 0.75
- >>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
- 0.75
- >>> supportScalar({'wght':3}, {'wght':(0,1,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
- -1.0
- >>> supportScalar({'wght':-1}, {'wght':(0,1,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
- -1.0
- >>> supportScalar({'wght':3}, {'wght':(0,2,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
- 1.5
- >>> supportScalar({'wght':-1}, {'wght':(0,2,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
- -0.5
- """
- if extrapolate and axisRanges is None:
- raise TypeError("axisRanges must be passed when extrapolate is True")
- scalar = 1.0
- for axis, (lower, peak, upper) in support.items():
- if ot:
- # OpenType-specific case handling
- if peak == 0.0:
- continue
- if lower > peak or peak > upper:
- continue
- if lower < 0.0 and upper > 0.0:
- continue
- v = location.get(axis, 0.0)
- else:
- assert axis in location
- v = location[axis]
- if v == peak:
- continue
-
- if extrapolate:
- axisMin, axisMax = axisRanges[axis]
- if v < axisMin and lower <= axisMin:
- if peak <= axisMin and peak < upper:
- scalar *= (v - upper) / (peak - upper)
- continue
- elif axisMin < peak:
- scalar *= (v - lower) / (peak - lower)
- continue
- elif axisMax < v and axisMax <= upper:
- if axisMax <= peak and lower < peak:
- scalar *= (v - lower) / (peak - lower)
- continue
- elif peak < axisMax:
- scalar *= (v - upper) / (peak - upper)
- continue
-
- if v <= lower or upper <= v:
- scalar = 0.0
- break
-
- if v < peak:
- scalar *= (v - lower) / (peak - lower)
- else: # v > peak
- scalar *= (v - upper) / (peak - upper)
- return scalar
-
-
-class VariationModel(object):
- """Locations must have the base master at the origin (ie. 0).
-
- If the extrapolate argument is set to True, then values are extrapolated
- outside the axis range.
-
- >>> from pprint import pprint
- >>> locations = [ \
- {'wght':100}, \
- {'wght':-100}, \
- {'wght':-180}, \
- {'wdth':+.3}, \
- {'wght':+120,'wdth':.3}, \
- {'wght':+120,'wdth':.2}, \
- {}, \
- {'wght':+180,'wdth':.3}, \
- {'wght':+180}, \
- ]
- >>> model = VariationModel(locations, axisOrder=['wght'])
- >>> pprint(model.locations)
- [{},
- {'wght': -100},
- {'wght': -180},
- {'wght': 100},
- {'wght': 180},
- {'wdth': 0.3},
- {'wdth': 0.3, 'wght': 180},
- {'wdth': 0.3, 'wght': 120},
- {'wdth': 0.2, 'wght': 120}]
- >>> pprint(model.deltaWeights)
- [{},
- {0: 1.0},
- {0: 1.0},
- {0: 1.0},
- {0: 1.0},
- {0: 1.0},
- {0: 1.0, 4: 1.0, 5: 1.0},
- {0: 1.0, 3: 0.75, 4: 0.25, 5: 1.0, 6: 0.6666666666666666},
- {0: 1.0,
- 3: 0.75,
- 4: 0.25,
- 5: 0.6666666666666667,
- 6: 0.4444444444444445,
- 7: 0.6666666666666667}]
- """
-
- def __init__(self, locations, axisOrder=None, extrapolate=False):
-
- if len(set(tuple(sorted(l.items())) for l in locations)) != len(locations):
- raise VariationModelError("Locations must be unique.")
-
- self.origLocations = locations
- self.axisOrder = axisOrder if axisOrder is not None else []
- self.extrapolate = extrapolate
- self.axisRanges = self.computeAxisRanges(locations) if extrapolate else None
-
- locations = [{k: v for k, v in loc.items() if v != 0.0} for loc in locations]
- keyFunc = self.getMasterLocationsSortKeyFunc(
- locations, axisOrder=self.axisOrder
- )
- self.locations = sorted(locations, key=keyFunc)
-
- # Mapping from user's master order to our master order
- self.mapping = [self.locations.index(l) for l in locations]
- self.reverseMapping = [locations.index(l) for l in self.locations]
-
- self._computeMasterSupports()
- self._subModels = {}
-
- def getSubModel(self, items):
- if None not in items:
- return self, items
- key = tuple(v is not None for v in items)
- subModel = self._subModels.get(key)
- if subModel is None:
- subModel = VariationModel(subList(key, self.origLocations), self.axisOrder)
- self._subModels[key] = subModel
- return subModel, subList(key, items)
-
- @staticmethod
- def computeAxisRanges(locations):
- axisRanges = {}
- allAxes = {axis for loc in locations for axis in loc.keys()}
- for loc in locations:
- for axis in allAxes:
- value = loc.get(axis, 0)
- axisMin, axisMax = axisRanges.get(axis, (value, value))
- axisRanges[axis] = min(value, axisMin), max(value, axisMax)
- return axisRanges
-
- @staticmethod
- def getMasterLocationsSortKeyFunc(locations, axisOrder=[]):
- if {} not in locations:
- raise VariationModelError("Base master not found.")
- axisPoints = {}
- for loc in locations:
- if len(loc) != 1:
- continue
- axis = next(iter(loc))
- value = loc[axis]
- if axis not in axisPoints:
- axisPoints[axis] = {0.0}
- assert (
- value not in axisPoints[axis]
- ), 'Value "%s" in axisPoints["%s"] --> %s' % (value, axis, axisPoints)
- axisPoints[axis].add(value)
-
- def getKey(axisPoints, axisOrder):
- def sign(v):
- return -1 if v < 0 else +1 if v > 0 else 0
-
- def key(loc):
- rank = len(loc)
- onPointAxes = [
- axis
- for axis, value in loc.items()
- if axis in axisPoints and value in axisPoints[axis]
- ]
- orderedAxes = [axis for axis in axisOrder if axis in loc]
- orderedAxes.extend(
- [axis for axis in sorted(loc.keys()) if axis not in axisOrder]
- )
- return (
- rank, # First, order by increasing rank
- -len(onPointAxes), # Next, by decreasing number of onPoint axes
- tuple(
- axisOrder.index(axis) if axis in axisOrder else 0x10000
- for axis in orderedAxes
- ), # Next, by known axes
- tuple(orderedAxes), # Next, by all axes
- tuple(
- sign(loc[axis]) for axis in orderedAxes
- ), # Next, by signs of axis values
- tuple(
- abs(loc[axis]) for axis in orderedAxes
- ), # Next, by absolute value of axis values
- )
-
- return key
-
- ret = getKey(axisPoints, axisOrder)
- return ret
-
- def reorderMasters(self, master_list, mapping):
- # For changing the master data order without
- # recomputing supports and deltaWeights.
- new_list = [master_list[idx] for idx in mapping]
- self.origLocations = [self.origLocations[idx] for idx in mapping]
- locations = [
- {k: v for k, v in loc.items() if v != 0.0} for loc in self.origLocations
- ]
- self.mapping = [self.locations.index(l) for l in locations]
- self.reverseMapping = [locations.index(l) for l in self.locations]
- self._subModels = {}
- return new_list
-
- def _computeMasterSupports(self):
- self.supports = []
- regions = self._locationsToRegions()
- for i, region in enumerate(regions):
- locAxes = set(region.keys())
- # Walk over previous masters now
- for prev_region in regions[:i]:
- # Master with extra axes do not participte
- if set(prev_region.keys()) != locAxes:
- continue
- # If it's NOT in the current box, it does not participate
- relevant = True
- for axis, (lower, peak, upper) in region.items():
- if not (
- prev_region[axis][1] == peak
- or lower < prev_region[axis][1] < upper
- ):
- relevant = False
- break
- if not relevant:
- continue
-
- # Split the box for new master; split in whatever direction
- # that has largest range ratio.
- #
- # For symmetry, we actually cut across multiple axes
- # if they have the largest, equal, ratio.
- # https://github.com/fonttools/fonttools/commit/7ee81c8821671157968b097f3e55309a1faa511e#commitcomment-31054804
-
- bestAxes = {}
- bestRatio = -1
- for axis in prev_region.keys():
- val = prev_region[axis][1]
- assert axis in region
- lower, locV, upper = region[axis]
- newLower, newUpper = lower, upper
- if val < locV:
- newLower = val
- ratio = (val - locV) / (lower - locV)
- elif locV < val:
- newUpper = val
- ratio = (val - locV) / (upper - locV)
- else: # val == locV
- # Can't split box in this direction.
- continue
- if ratio > bestRatio:
- bestAxes = {}
- bestRatio = ratio
- if ratio == bestRatio:
- bestAxes[axis] = (newLower, locV, newUpper)
-
- for axis, triple in bestAxes.items():
- region[axis] = triple
- self.supports.append(region)
- self._computeDeltaWeights()
-
- def _locationsToRegions(self):
- locations = self.locations
- # Compute min/max across each axis, use it as total range.
- # TODO Take this as input from outside?
- minV = {}
- maxV = {}
- for l in locations:
- for k, v in l.items():
- minV[k] = min(v, minV.get(k, v))
- maxV[k] = max(v, maxV.get(k, v))
-
- regions = []
- for loc in locations:
- region = {}
- for axis, locV in loc.items():
- if locV > 0:
- region[axis] = (0, locV, maxV[axis])
- else:
- region[axis] = (minV[axis], locV, 0)
- regions.append(region)
- return regions
-
- def _computeDeltaWeights(self):
- self.deltaWeights = []
- for i, loc in enumerate(self.locations):
- deltaWeight = {}
- # Walk over previous masters now, populate deltaWeight
- for j, support in enumerate(self.supports[:i]):
- scalar = supportScalar(loc, support)
- if scalar:
- deltaWeight[j] = scalar
- self.deltaWeights.append(deltaWeight)
-
- def getDeltas(self, masterValues, *, round=noRound):
- assert len(masterValues) == len(self.deltaWeights)
- mapping = self.reverseMapping
- out = []
- for i, weights in enumerate(self.deltaWeights):
- delta = masterValues[mapping[i]]
- for j, weight in weights.items():
- if weight == 1:
- delta -= out[j]
- else:
- delta -= out[j] * weight
- out.append(round(delta))
- return out
-
- def getDeltasAndSupports(self, items, *, round=noRound):
- model, items = self.getSubModel(items)
- return model.getDeltas(items, round=round), model.supports
-
- def getScalars(self, loc):
- return [
- supportScalar(
- loc, support, extrapolate=self.extrapolate, axisRanges=self.axisRanges
- )
- for support in self.supports
- ]
-
- @staticmethod
- def interpolateFromDeltasAndScalars(deltas, scalars):
- v = None
- assert len(deltas) == len(scalars)
- for delta, scalar in zip(deltas, scalars):
- if not scalar:
- continue
- contribution = delta * scalar
- if v is None:
- v = contribution
- else:
- v += contribution
- return v
-
- def interpolateFromDeltas(self, loc, deltas):
- scalars = self.getScalars(loc)
- return self.interpolateFromDeltasAndScalars(deltas, scalars)
-
- def interpolateFromMasters(self, loc, masterValues, *, round=noRound):
- deltas = self.getDeltas(masterValues, round=round)
- return self.interpolateFromDeltas(loc, deltas)
-
- def interpolateFromMastersAndScalars(self, masterValues, scalars, *, round=noRound):
- deltas = self.getDeltas(masterValues, round=round)
- return self.interpolateFromDeltasAndScalars(deltas, scalars)
-
-
-def piecewiseLinearMap(v, mapping):
- keys = mapping.keys()
- if not keys:
- return v
- if v in keys:
- return mapping[v]
- k = min(keys)
- if v < k:
- return v + mapping[k] - k
- k = max(keys)
- if v > k:
- return v + mapping[k] - k
- # Interpolate
- a = max(k for k in keys if k < v)
- b = min(k for k in keys if k > v)
- va = mapping[a]
- vb = mapping[b]
- return va + (vb - va) * (v - a) / (b - a)
-
-
-def main(args=None):
- """Normalize locations on a given designspace"""
- from fontTools import configLogger
- import argparse
-
- parser = argparse.ArgumentParser(
- "fonttools varLib.models",
- description=main.__doc__,
- )
- parser.add_argument(
- "--loglevel",
- metavar="LEVEL",
- default="INFO",
- help="Logging level (defaults to INFO)",
- )
-
- group = parser.add_mutually_exclusive_group(required=True)
- group.add_argument("-d", "--designspace", metavar="DESIGNSPACE", type=str)
- group.add_argument(
- "-l",
- "--locations",
- metavar="LOCATION",
- nargs="+",
- help="Master locations as comma-separate coordinates. One must be all zeros.",
- )
-
- args = parser.parse_args(args)
-
- configLogger(level=args.loglevel)
- from pprint import pprint
-
- if args.designspace:
- from fontTools.designspaceLib import DesignSpaceDocument
-
- doc = DesignSpaceDocument()
- doc.read(args.designspace)
- locs = [s.location for s in doc.sources]
- print("Original locations:")
- pprint(locs)
- doc.normalize()
- print("Normalized locations:")
- locs = [s.location for s in doc.sources]
- pprint(locs)
- else:
- axes = [chr(c) for c in range(ord("A"), ord("Z") + 1)]
- locs = [
- dict(zip(axes, (float(v) for v in s.split(",")))) for s in args.locations
- ]
-
- model = VariationModel(locs)
- print("Sorted locations:")
- pprint(model.locations)
- print("Supports:")
- pprint(model.supports)
-
-
-if __name__ == "__main__":
- import doctest, sys
-
- if len(sys.argv) > 1:
- sys.exit(main())
-
- sys.exit(doctest.testmod().failed)
diff --git a/spaces/Dana19/outfit_color_guide/README.md b/spaces/Dana19/outfit_color_guide/README.md
deleted file mode 100644
index a7cdd79fb01d65a5ad41ce2ebea7689eedcf77aa..0000000000000000000000000000000000000000
--- a/spaces/Dana19/outfit_color_guide/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Outfit Color Guide
-emoji: 😻
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/non_leaking.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/non_leaking.py
deleted file mode 100644
index 4e044f98e836ae2c011ea91246b304d5ab1a1422..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/non_leaking.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import math
-
-import torch
-from torch.nn import functional as F
-
-
-def translate_mat(t_x, t_y):
- batch = t_x.shape[0]
-
- mat = torch.eye(3).unsqueeze(0).repeat(batch, 1, 1)
- translate = torch.stack((t_x, t_y), 1)
- mat[:, :2, 2] = translate
-
- return mat
-
-
-def rotate_mat(theta):
- batch = theta.shape[0]
-
- mat = torch.eye(3).unsqueeze(0).repeat(batch, 1, 1)
- sin_t = torch.sin(theta)
- cos_t = torch.cos(theta)
- rot = torch.stack((cos_t, -sin_t, sin_t, cos_t), 1).view(batch, 2, 2)
- mat[:, :2, :2] = rot
-
- return mat
-
-
-def scale_mat(s_x, s_y):
- batch = s_x.shape[0]
-
- mat = torch.eye(3).unsqueeze(0).repeat(batch, 1, 1)
- mat[:, 0, 0] = s_x
- mat[:, 1, 1] = s_y
-
- return mat
-
-
-def lognormal_sample(size, mean=0, std=1):
- return torch.empty(size).log_normal_(mean=mean, std=std)
-
-
-def category_sample(size, categories):
- category = torch.tensor(categories)
- sample = torch.randint(high=len(categories), size=(size,))
-
- return category[sample]
-
-
-def uniform_sample(size, low, high):
- return torch.empty(size).uniform_(low, high)
-
-
-def normal_sample(size, mean=0, std=1):
- return torch.empty(size).normal_(mean, std)
-
-
-def bernoulli_sample(size, p):
- return torch.empty(size).bernoulli_(p)
-
-
-def random_affine_apply(p, transform, prev, eye):
- size = transform.shape[0]
- select = bernoulli_sample(size, p).view(size, 1, 1)
- select_transform = select * transform + (1 - select) * eye
-
- return select_transform @ prev
-
-
-def sample_affine(p, size, height, width):
- G = torch.eye(3).unsqueeze(0).repeat(size, 1, 1)
- eye = G
-
- # flip
- param = category_sample(size, (0, 1))
- Gc = scale_mat(1 - 2.0 * param, torch.ones(size))
- G = random_affine_apply(p, Gc, G, eye)
- # print('flip', G, scale_mat(1 - 2.0 * param, torch.ones(size)), sep='\n')
-
- # 90 rotate
- param = category_sample(size, (0, 3))
- Gc = rotate_mat(-math.pi / 2 * param)
- G = random_affine_apply(p, Gc, G, eye)
- # print('90 rotate', G, rotate_mat(-math.pi / 2 * param), sep='\n')
-
- # integer translate
- param = uniform_sample(size, -0.125, 0.125)
- param_height = torch.round(param * height) / height
- param_width = torch.round(param * width) / width
- Gc = translate_mat(param_width, param_height)
- G = random_affine_apply(p, Gc, G, eye)
- # print('integer translate', G, translate_mat(param_width, param_height), sep='\n')
-
- # isotropic scale
- param = lognormal_sample(size, std=0.2 * math.log(2))
- Gc = scale_mat(param, param)
- G = random_affine_apply(p, Gc, G, eye)
- # print('isotropic scale', G, scale_mat(param, param), sep='\n')
-
- p_rot = 1 - math.sqrt(1 - p)
-
- # pre-rotate
- param = uniform_sample(size, -math.pi, math.pi)
- Gc = rotate_mat(-param)
- G = random_affine_apply(p_rot, Gc, G, eye)
- # print('pre-rotate', G, rotate_mat(-param), sep='\n')
-
- # anisotropic scale
- param = lognormal_sample(size, std=0.2 * math.log(2))
- Gc = scale_mat(param, 1 / param)
- G = random_affine_apply(p, Gc, G, eye)
- # print('anisotropic scale', G, scale_mat(param, 1 / param), sep='\n')
-
- # post-rotate
- param = uniform_sample(size, -math.pi, math.pi)
- Gc = rotate_mat(-param)
- G = random_affine_apply(p_rot, Gc, G, eye)
- # print('post-rotate', G, rotate_mat(-param), sep='\n')
-
- # fractional translate
- param = normal_sample(size, std=0.125)
- Gc = translate_mat(param, param)
- G = random_affine_apply(p, Gc, G, eye)
- # print('fractional translate', G, translate_mat(param, param), sep='\n')
-
- return G
-
-
-def apply_affine(img, G):
- grid = F.affine_grid(
- torch.inverse(G).to(img)[:, :2, :], img.shape, align_corners=False
- )
- img_affine = F.grid_sample(
- img, grid, mode="bilinear", align_corners=False, padding_mode="reflection"
- )
-
- return img_affine
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py b/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py
deleted file mode 100644
index 79991b0497d3d92f25194a31668b9568048163f8..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py
+++ /dev/null
@@ -1,211 +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 bias and activation."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-from ...util import EasyDict
-
-def _get_plugin():
- return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
- 'linear': EasyDict(func=lambda x, **_: x, def_alpha=None, def_gain=1.0, cuda_idx=1, ref='y', zero_2nd_grad=True),
- 'relu': EasyDict(func=lambda x, **_: tf.nn.relu(x), def_alpha=None, def_gain=np.sqrt(2), cuda_idx=2, ref='y', zero_2nd_grad=True),
- 'lrelu': EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', zero_2nd_grad=True),
- 'tanh': EasyDict(func=lambda x, **_: tf.nn.tanh(x), def_alpha=None, def_gain=1.0, cuda_idx=4, ref='y', zero_2nd_grad=False),
- 'sigmoid': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x), def_alpha=None, def_gain=1.0, cuda_idx=5, ref='y', zero_2nd_grad=False),
- 'elu': EasyDict(func=lambda x, **_: tf.nn.elu(x), def_alpha=None, def_gain=1.0, cuda_idx=6, ref='y', zero_2nd_grad=False),
- 'selu': EasyDict(func=lambda x, **_: tf.nn.selu(x), def_alpha=None, def_gain=1.0, cuda_idx=7, ref='y', zero_2nd_grad=False),
- 'softplus': EasyDict(func=lambda x, **_: tf.nn.softplus(x), def_alpha=None, def_gain=1.0, cuda_idx=8, ref='y', zero_2nd_grad=False),
- 'swish': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x) * x, def_alpha=None, def_gain=np.sqrt(2), cuda_idx=9, ref='x', zero_2nd_grad=False),
-}
-
-#----------------------------------------------------------------------------
-
-def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
- r"""Fused bias and activation function.
-
- Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
- and scales the result by `gain`. Each of the steps is optional. In most cases,
- the fused op is considerably more efficient than performing the same calculation
- using standard TensorFlow ops. It supports first and second order gradients,
- but not third order gradients.
-
- Args:
- x: Input activation tensor. Can have any shape, but if `b` is defined, the
- dimension corresponding to `axis`, as well as the rank, must be known.
- b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
- as `x`. The shape must be known, and it must match the dimension of `x`
- corresponding to `axis`.
- axis: The dimension in `x` corresponding to the elements of `b`.
- The value of `axis` is ignored if `b` is not specified.
- act: Name of the activation function to evaluate, or `"linear"` to disable.
- Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
- See `activation_funcs` for a full list. `None` is not allowed.
- alpha: Shape parameter for the activation function, or `None` to use the default.
- gain: Scaling factor for the output tensor, or `None` to use default.
- See `activation_funcs` for the default scaling of each activation function.
- If unsure, consider specifying `1.0`.
- clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
- the clamping (default).
- impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
- Returns:
- Tensor of the same shape and datatype as `x`.
- """
-
- impl_dict = {
- 'ref': _fused_bias_act_ref,
- 'cuda': _fused_bias_act_cuda,
- }
- return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-def _fused_bias_act_ref(x, b, axis, act, alpha, gain, clamp):
- """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops."""
-
- # Validate arguments.
- x = tf.convert_to_tensor(x)
- b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype)
- act_spec = activation_funcs[act]
- assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis])
- assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
- if alpha is None:
- alpha = act_spec.def_alpha
- if gain is None:
- gain = act_spec.def_gain
-
- # Add bias.
- if b.shape[0] != 0:
- x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)])
-
- # Evaluate activation function.
- x = act_spec.func(x, alpha=alpha)
-
- # Scale by gain.
- if gain != 1:
- x *= gain
-
- # Clamp.
- if clamp is not None:
- clamp = np.asarray(clamp, dtype=x.dtype.name)
- assert clamp.shape == () and clamp >= 0
- x = tf.clip_by_value(x, -clamp, clamp)
- return x
-
-#----------------------------------------------------------------------------
-
-def _fused_bias_act_cuda(x, b, axis, act, alpha, gain, clamp):
- """Fast CUDA implementation of `fused_bias_act()` using custom ops."""
-
- # Validate arguments.
- x = tf.convert_to_tensor(x)
- empty_tensor = tf.constant([], dtype=x.dtype)
- b = tf.convert_to_tensor(b) if b is not None else empty_tensor
- act_spec = activation_funcs[act]
- assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis])
- assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
- if alpha is None:
- alpha = act_spec.def_alpha
- if gain is None:
- gain = act_spec.def_gain
-
- # Special cases.
- if act == 'linear' and b is None and gain == 1.0:
- return x
- if act_spec.cuda_idx is None:
- return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
- # CUDA op.
- cuda_op = _get_plugin().fused_bias_act
- cuda_kwargs = dict(axis=int(axis), act=int(act_spec.cuda_idx), gain=float(gain))
- if alpha is not None:
- cuda_kwargs['alpha'] = float(alpha)
- if clamp is not None:
- clamp = np.asarray(clamp, dtype=x.dtype.name)
- assert clamp.shape == () and clamp >= 0
- cuda_kwargs['clamp'] = float(clamp.astype(np.float32))
- def ref(tensor, name):
- return tensor if act_spec.ref == name else empty_tensor
-
- # Forward pass: y = func(x, b).
- def func_y(x, b):
- y = cuda_op(x=x, b=b, xref=empty_tensor, yref=empty_tensor, grad=0, **cuda_kwargs)
- y.set_shape(x.shape)
- return y
-
- # Backward pass: dx, db = grad(dy, x, y)
- def grad_dx(dy, x, y):
- dx = cuda_op(x=dy, b=empty_tensor, xref=ref(x,'x'), yref=ref(y,'y'), grad=1, **cuda_kwargs)
- dx.set_shape(x.shape)
- return dx
- def grad_db(dx):
- if b.shape[0] == 0:
- return empty_tensor
- db = dx
- if axis < x.shape.rank - 1:
- db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank)))
- if axis > 0:
- db = tf.reduce_sum(db, list(range(axis)))
- db.set_shape(b.shape)
- return db
-
- # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y)
- def grad2_d_dy(d_dx, d_db, x, y):
- d_dy = cuda_op(x=d_dx, b=d_db, xref=ref(x,'x'), yref=ref(y,'y'), grad=1, **cuda_kwargs)
- d_dy.set_shape(x.shape)
- return d_dy
- def grad2_d_x(d_dx, d_db, x, y):
- d_x = cuda_op(x=d_dx, b=d_db, xref=ref(x,'x'), yref=ref(y,'y'), grad=2, **cuda_kwargs)
- d_x.set_shape(x.shape)
- return d_x
-
- # Fast version for piecewise-linear activation funcs.
- @tf.custom_gradient
- def func_zero_2nd_grad(x, b):
- y = func_y(x, b)
- @tf.custom_gradient
- def grad(dy):
- dx = grad_dx(dy, x, y)
- db = grad_db(dx)
- def grad2(d_dx, d_db):
- d_dy = grad2_d_dy(d_dx, d_db, x, y)
- return d_dy
- return (dx, db), grad2
- return y, grad
-
- # Slow version for general activation funcs.
- @tf.custom_gradient
- def func_nonzero_2nd_grad(x, b):
- y = func_y(x, b)
- def grad_wrap(dy):
- @tf.custom_gradient
- def grad_impl(dy, x):
- dx = grad_dx(dy, x, y)
- db = grad_db(dx)
- def grad2(d_dx, d_db):
- d_dy = grad2_d_dy(d_dx, d_db, x, y)
- d_x = grad2_d_x(d_dx, d_db, x, y)
- return d_dy, d_x
- return (dx, db), grad2
- return grad_impl(dy, x)
- return y, grad_wrap
-
- # Which version to use?
- if act_spec.zero_2nd_grad:
- return func_zero_2nd_grad(x, b)
- return func_nonzero_2nd_grad(x, b)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/ECCV2022/storydalle/dalle/utils/utils.py b/spaces/ECCV2022/storydalle/dalle/utils/utils.py
deleted file mode 100644
index 1d3636cc02adc61dddbf551f929b38bab4b017fc..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/storydalle/dalle/utils/utils.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Minimal DALL-E
-# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------
-
-import os
-import random
-import urllib
-import hashlib
-import tarfile
-import torch
-import numpy as np
-from tqdm import tqdm
-import matplotlib.pyplot as plt
-
-
-def set_seed(seed: int):
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
-
-
-def download(url: str, root: str) -> str:
- os.makedirs(root, exist_ok=True)
- filename = os.path.basename(url)
- pathname = filename[:-len('.tar.gz')]
-
- expected_md5 = url.split("/")[-2]
- download_target = os.path.join(root, filename)
- result_path = os.path.join(root, pathname)
-
- if os.path.isfile(download_target) and (os.path.exists(result_path) and not os.path.isfile(result_path)):
- return result_path
-
- with urllib.request.urlopen(url) as source, open(download_target, 'wb') as output:
- with tqdm(total=int(source.info().get('Content-Length')), ncols=80, unit='iB', unit_scale=True,
- unit_divisor=1024) as loop:
- while True:
- buffer = source.read(8192)
- if not buffer:
- break
-
- output.write(buffer)
- loop.update(len(buffer))
-
- if hashlib.md5(open(download_target, 'rb').read()).hexdigest() != expected_md5:
- raise RuntimeError(f'Model has been downloaded but the md5 checksum does not not match')
-
- with tarfile.open(download_target, 'r:gz') as f:
- pbar = tqdm(f.getmembers(), total=len(f.getmembers()))
- for member in pbar:
- pbar.set_description(f'extracting: {member.name} (size:{member.size // (1024 * 1024)}MB)')
- f.extract(member=member, path=root)
-
- return result_path
-
-
-def realpath_url_or_path(url_or_path: str, root: str = None) -> str:
- if urllib.parse.urlparse(url_or_path).scheme in ('http', 'https'):
- return download(url_or_path, root)
- return url_or_path
-
-
-def images_to_numpy(tensor):
- generated = tensor.data.cpu().numpy().transpose(1,2,0)
- generated[generated < -1] = -1
- generated[generated > 1] = 1
- generated = (generated + 1) / 2 * 255
- return generated.astype('uint8')
-
-
-def save_image(ground_truth, images, out_dir, batch_idx):
-
- for i, im in enumerate(images):
- if len(im.shape) == 3:
- plt.imsave(os.path.join(out_dir, 'test_%s_%s.png' % (batch_idx, i)), im)
- else:
- bs = im.shape[0]
- # plt.imsave()
- for j in range(bs):
- plt.imsave(os.path.join(out_dir, 'test_%s_%s_%s.png' % (batch_idx, i, j)), im[j])
-
-
- # print("Ground truth Images shape: ", ground_truth.shape, len(images))
-
- # images = vutils.make_grid(images, nrow=ground_truth.shape[0])
- # images = images_to_numpy(images)
- #
- # if ground_truth is not None:
- # ground_truth = vutils.make_grid(ground_truth, 5)
- # ground_truth = images_to_numpy(ground_truth)
- # print("Ground Truth shape, Generated Images shape: ", ground_truth.shape, images.shape)
- # images = np.concatenate([ground_truth, images], axis=0)
- #
- # output = Image.fromarray(images)
- # output.save('%s/fake_samples_epoch_%03d.png' % (out_dir, batch_idx))
-
- # if texts is not None:
- # fid = open('%s/fake_samples_epoch_%03d_%03d.txt' % (image_dir, epoch, idx), 'w')
- # for idx in range(images.shape[0]):
- # fid.write(str(idx) + '--------------------------------------------------------\n')
- # for i in range(len(texts)):
- # fid.write(texts[i][idx] + '\n')
- # fid.write('\n\n')
- # fid.close()
- return
diff --git a/spaces/EsoCode/text-generation-webui/extensions/api/script.py b/spaces/EsoCode/text-generation-webui/extensions/api/script.py
deleted file mode 100644
index 5d1b1a68c4418d51d716ec2a2b06e80ec2f4d27a..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/extensions/api/script.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import extensions.api.blocking_api as blocking_api
-import extensions.api.streaming_api as streaming_api
-from modules import shared
-
-
-def setup():
- blocking_api.start_server(shared.args.api_blocking_port, share=shared.args.public_api)
- streaming_api.start_server(shared.args.api_streaming_port, share=shared.args.public_api)
diff --git a/spaces/EuroPython2022/Step-By-Step-With-Bloom/README.md b/spaces/EuroPython2022/Step-By-Step-With-Bloom/README.md
deleted file mode 100644
index 3b1d9ad717c0730d0f6ec1a6ebdf8435d735e367..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Step-By-Step-With-Bloom/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Step By Step With Bloom
-emoji: 🌸
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ForBo7/FloodDetector/app.py b/spaces/ForBo7/FloodDetector/app.py
deleted file mode 100644
index 85ffd5cf833acfbd6847979e380041cf5242cdc8..0000000000000000000000000000000000000000
--- a/spaces/ForBo7/FloodDetector/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['learner', 'categories', 'image', 'label', 'examples', 'title', 'description', 'article', 'interface',
- 'classify_image']
-
-# %% app.ipynb 2
-import gradio as gr
-from fastai.vision.all import *
-
-# %% app.ipynb 5
-learner = load_learner('model/flood_classifier.pkl')
-
-# %% app.ipynb 8
-categories = 'Not Flooded', 'Flooded',
-
-def classify_image(image):
- prediction, index, probabilities = learner.predict(image)
- return dict(zip(categories, map(float, probabilities)))
-
-# %% app.ipynb 11
-image = gr.Image()
-label = gr.Label()
-examples = [str(image_path) for image_path in Path('images/example_images')
-.rglob('*.jpeg')]
-
-title = 'Flood Classifier'
-description = "An image classifier that can tell whether an image is flooded " \
- "or not. Works well with images that have a top-down/aeiral " \
- "view of the land below." \
- " This model was trained on the ResNet18 architecture and the " \
- "fastai library." \
- " Check out the associated blog post with the link below!"
-article = """
-
- )
-}
diff --git a/spaces/TNR-5/Search/index.html b/spaces/TNR-5/Search/index.html
deleted file mode 100644
index 79c4eda1bcbeba96cfd5c02832bd6e5559453d58..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/Search/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
- Поисковик
-
-
-
-
-
diff --git a/spaces/TandCAcceptMe/face-swap-docker/chain_img_processor/batchimage.py b/spaces/TandCAcceptMe/face-swap-docker/chain_img_processor/batchimage.py
deleted file mode 100644
index 9a4185eb664b50f913a895fa3a2f9b2085998919..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/chain_img_processor/batchimage.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from typing import Any, List, Callable
-import psutil
-import os
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from .image import ChainImgProcessor
-from tqdm import tqdm
-import cv2
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
- queue: Queue[str] = Queue()
- for frame_path in temp_frame_paths:
- queue.put(frame_path)
- return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
- queues = []
- for _ in range(queue_per_future):
- if not queue.empty():
- queues.append(queue.get())
- return queues
-
-
-
-class ChainBatchImageProcessor(ChainImgProcessor):
- chain = None
- func_params_gen = None
- num_threads = 1
-
- def __init__(self):
- ChainImgProcessor.__init__(self)
-
-
- def init_with_plugins(self):
- self.init_plugins(["core"])
- self.display_init_info()
-
- init_on_start_arr = self.init_on_start.split(",")
- for proc_id in init_on_start_arr:
- self.init_processor(proc_id)
-
- def update_progress(self, progress: Any = None) -> None:
- process = psutil.Process(os.getpid())
- memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
- progress.set_postfix({
- 'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
- 'execution_threads': self.num_threads
- })
- progress.refresh()
- progress.update(1)
-
-
- def process_frames(self, source_files: List[str], target_files: List[str], current_files, update: Callable[[], None]) -> None:
- for f in current_files:
- temp_frame = cv2.imread(f)
- if temp_frame is not None:
- if self.func_params_gen:
- params = self.func_params_gen(None, temp_frame)
- else:
- params = {}
- resimg, _ = self.run_chain(temp_frame, params, self.chain)
- if resimg is not None:
- i = source_files.index(f)
- cv2.imwrite(target_files[i], resimg)
- if update:
- update()
-
-
- def run_batch_chain(self, source_files, target_files, threads:int = 1, chain = None, params_frame_gen_func = None):
- self.chain = chain
- self.func_params_gen = params_frame_gen_func
- progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
- total = len(source_files)
- self.num_threads = threads
- with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
- with ThreadPoolExecutor(max_workers=threads) as executor:
- futures = []
- queue = create_queue(source_files)
- queue_per_future = max(len(source_files) // threads, 1)
- while not queue.empty():
- future = executor.submit(self.process_frames, source_files, target_files, pick_queue(queue, queue_per_future), lambda: self.update_progress(progress))
- futures.append(future)
- for future in as_completed(futures):
- future.result()
-
diff --git a/spaces/Toaster496/openaccess-ai-collective-manticore-13b/README.md b/spaces/Toaster496/openaccess-ai-collective-manticore-13b/README.md
deleted file mode 100644
index 41f59bd90652867ff84ef79656d3c901afc6557e..0000000000000000000000000000000000000000
--- a/spaces/Toaster496/openaccess-ai-collective-manticore-13b/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Openaccess Ai Collective Manticore 13b
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TornikeO/dreambooth-training/train_dreambooth.py b/spaces/TornikeO/dreambooth-training/train_dreambooth.py
deleted file mode 100644
index a496382fbc895961b9902c33a9d5cc926d4fcc8d..0000000000000000000000000000000000000000
--- a/spaces/TornikeO/dreambooth-training/train_dreambooth.py
+++ /dev/null
@@ -1,881 +0,0 @@
-import argparse
-import itertools
-import math
-import os
-from pathlib import Path
-from typing import Optional
-import subprocess
-import sys
-import gc
-import random
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from huggingface_hub import HfFolder, Repository, whoami
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-
-logger = get_logger(__name__)
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description="Simple example of a training script.")
- parser.add_argument(
- "--pretrained_model_name_or_path",
- type=str,
- default=None,
- #required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--tokenizer_name",
- type=str,
- default=None,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--instance_data_dir",
- type=str,
- default=None,
- #required=True,
- help="A folder containing the training data of instance images.",
- )
- parser.add_argument(
- "--class_data_dir",
- type=str,
- default=None,
- #required=False,
- help="A folder containing the training data of class images.",
- )
- parser.add_argument(
- "--instance_prompt",
- type=str,
- default=None,
- help="The prompt with identifier specifying the instance",
- )
- parser.add_argument(
- "--class_prompt",
- type=str,
- default="",
- help="The prompt to specify images in the same class as provided instance images.",
- )
- parser.add_argument(
- "--with_prior_preservation",
- default=False,
- action="store_true",
- help="Flag to add prior preservation loss.",
- )
- parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
- parser.add_argument(
- "--num_class_images",
- type=int,
- default=100,
- help=(
- "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
- " sampled with class_prompt."
- ),
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default="",
- help="The output directory where the model predictions and checkpoints will be written.",
- )
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--resolution",
- type=int,
- default=512,
- help=(
- "The resolution for input images, all the images in the train/validation dataset will be resized to this"
- " resolution"
- ),
- )
- parser.add_argument(
- "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
- )
- parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
- parser.add_argument(
- "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
- )
- parser.add_argument(
- "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
- )
- parser.add_argument("--num_train_epochs", type=int, default=1)
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=None,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--gradient_checkpointing",
- action="store_true",
- help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=5e-6,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument(
- "--scale_lr",
- action="store_true",
- default=False,
- help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
- )
- parser.add_argument(
- "--lr_scheduler",
- type=str,
- default="constant",
- help=(
- 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
- ' "constant", "constant_with_warmup"]'
- ),
- )
- parser.add_argument(
- "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument(
- "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
- )
- parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
- parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
- parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
- parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--hub_model_id",
- type=str,
- default=None,
- help="The name of the repository to keep in sync with the local `output_dir`.",
- )
- parser.add_argument(
- "--logging_dir",
- type=str,
- default="logs",
- help=(
- "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
- " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
- ),
- )
- parser.add_argument(
- "--mixed_precision",
- type=str,
- default="no",
- choices=["no", "fp16", "bf16"],
- help=(
- "Whether to use mixed precision. Choose"
- "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
- "and an Nvidia Ampere GPU."
- ),
- )
-
- parser.add_argument(
- "--save_n_steps",
- type=int,
- default=1,
- help=("Save the model every n global_steps"),
- )
-
-
- parser.add_argument(
- "--save_starting_step",
- type=int,
- default=1,
- help=("The step from which it starts saving intermediary checkpoints"),
- )
-
- parser.add_argument(
- "--stop_text_encoder_training",
- type=int,
- default=1000000,
- help=("The step at which the text_encoder is no longer trained"),
- )
-
-
- parser.add_argument(
- "--image_captions_filename",
- action="store_true",
- help="Get captions from filename",
- )
-
-
- parser.add_argument(
- "--dump_only_text_encoder",
- action="store_true",
- default=False,
- help="Dump only text encoder",
- )
-
- parser.add_argument(
- "--train_only_unet",
- action="store_true",
- default=False,
- help="Train only the unet",
- )
-
- parser.add_argument(
- "--cache_latents",
- action="store_true",
- default=False,
- help="Train only the unet",
- )
-
- parser.add_argument(
- "--Session_dir",
- type=str,
- default="",
- help="Current session directory",
- )
-
-
-
-
- parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
- args = parser.parse_args()
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- #if args.instance_data_dir is None:
- # raise ValueError("You must specify a train data directory.")
-
- #if args.with_prior_preservation:
- # if args.class_data_dir is None:
- # raise ValueError("You must specify a data directory for class images.")
- # if args.class_prompt is None:
- # raise ValueError("You must specify prompt for class images.")
-
- return args
-
-
-class DreamBoothDataset(Dataset):
- """
- A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
- It pre-processes the images and the tokenizes prompts.
- """
-
- def __init__(
- self,
- instance_data_root,
- instance_prompt,
- tokenizer,
- args,
- class_data_root=None,
- class_prompt=None,
- size=512,
- center_crop=False,
- ):
- self.size = size
- self.center_crop = center_crop
- self.tokenizer = tokenizer
- self.image_captions_filename = None
-
- self.instance_data_root = Path(instance_data_root)
- if not self.instance_data_root.exists():
- raise ValueError("Instance images root doesn't exists.")
-
- self.instance_images_path = list(Path(instance_data_root).iterdir())
- self.num_instance_images = len(self.instance_images_path)
- self.instance_prompt = instance_prompt
- self._length = self.num_instance_images
-
- if args.image_captions_filename:
- self.image_captions_filename = True
-
- if class_data_root is not None:
- self.class_data_root = Path(class_data_root)
- self.class_data_root.mkdir(parents=True, exist_ok=True)
- self.class_images_path = list(self.class_data_root.iterdir())
- random.shuffle(self.class_images_path)
- self.num_class_images = len(self.class_images_path)
- self._length = max(self.num_class_images, self.num_instance_images)
- self.class_prompt = class_prompt
- else:
- self.class_data_root = None
-
- self.image_transforms = transforms.Compose(
- [
- transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
- transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
- transforms.ToTensor(),
- transforms.Normalize([0.5], [0.5]),
- ]
- )
-
- def __len__(self):
- return self._length
-
- def __getitem__(self, index):
- example = {}
- path = self.instance_images_path[index % self.num_instance_images]
- instance_image = Image.open(path)
- if not instance_image.mode == "RGB":
- instance_image = instance_image.convert("RGB")
-
- instance_prompt = self.instance_prompt
-
- if self.image_captions_filename:
- filename = Path(path).stem
- pt=''.join([i for i in filename if not i.isdigit()])
- pt=pt.replace("_"," ")
- pt=pt.replace("(","")
- pt=pt.replace(")","")
- pt=pt.replace("-","")
- instance_prompt = pt
- sys.stdout.write(" [0;32m" +instance_prompt+" [0m")
- sys.stdout.flush()
-
-
- example["instance_images"] = self.image_transforms(instance_image)
- example["instance_prompt_ids"] = self.tokenizer(
- instance_prompt,
- padding="do_not_pad",
- truncation=True,
- max_length=self.tokenizer.model_max_length,
- ).input_ids
-
- if self.class_data_root:
- class_image = Image.open(self.class_images_path[index % self.num_class_images])
- if not class_image.mode == "RGB":
- class_image = class_image.convert("RGB")
- example["class_images"] = self.image_transforms(class_image)
- example["class_prompt_ids"] = self.tokenizer(
- self.class_prompt,
- padding="do_not_pad",
- truncation=True,
- max_length=self.tokenizer.model_max_length,
- ).input_ids
-
- return example
-
-
-
-class PromptDataset(Dataset):
- "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
- def __init__(self, prompt, num_samples):
- self.prompt = prompt
- self.num_samples = num_samples
-
- def __len__(self):
- return self.num_samples
-
- def __getitem__(self, index):
- example = {}
- example["prompt"] = self.prompt
- example["index"] = index
- return example
-
-class LatentsDataset(Dataset):
- def __init__(self, latents_cache, text_encoder_cache):
- self.latents_cache = latents_cache
- self.text_encoder_cache = text_encoder_cache
-
- def __len__(self):
- return len(self.latents_cache)
-
- def __getitem__(self, index):
- return self.latents_cache[index], self.text_encoder_cache[index]
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
- if token is None:
- token = HfFolder.get_token()
- if organization is None:
- username = whoami(token)["name"]
- return f"{username}/{model_id}"
- else:
- return f"{organization}/{model_id}"
-
-def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
- """
- Starts from base starting dict and then adds the remaining key values from updater replacing the values from
- the first starting/base dict with the second updater dict.
-
- For later: how does d = {**d1, **d2} replace collision?
-
- :param starting_dict:
- :param updater_dict:
- :return:
- """
- new_dict: dict = starting_dict.copy() # start with keys and values of starting_dict
- new_dict.update(updater_dict) # modifies starting_dict with keys and values of updater_dict
- return new_dict
-
-def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
- """
-
- ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
- :param args1:
- :param args2:
- :return:
- """
- # - the merged args
- # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
- merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
- args = argparse.Namespace(**merged_key_values_for_namespace)
- return args
-
-def run_training(args_imported):
- args_default = parse_args()
- args = merge_args(args_default, args_imported)
- print(args)
- logging_dir = Path(args.output_dir, args.logging_dir)
- i=args.save_starting_step
- accelerator = Accelerator(
- gradient_accumulation_steps=args.gradient_accumulation_steps,
- mixed_precision=args.mixed_precision,
- log_with="tensorboard",
- logging_dir=logging_dir,
- )
-
- # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
- # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
- # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
- if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
- raise ValueError(
- "Gradient accumulation is not supported when training the text encoder in distributed training. "
- "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
- )
-
- if args.seed is not None:
- set_seed(args.seed)
-
- if args.with_prior_preservation:
- class_images_dir = Path(args.class_data_dir)
- if not class_images_dir.exists():
- class_images_dir.mkdir(parents=True)
- cur_class_images = len(list(class_images_dir.iterdir()))
-
- if cur_class_images < args.num_class_images:
- torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path, torch_dtype=torch_dtype
- )
- pipeline.set_progress_bar_config(disable=True)
-
- num_new_images = args.num_class_images - cur_class_images
- logger.info(f"Number of class images to sample: {num_new_images}.")
-
- sample_dataset = PromptDataset(args.class_prompt, num_new_images)
- sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
- sample_dataloader = accelerator.prepare(sample_dataloader)
- pipeline.to(accelerator.device)
-
- for example in tqdm(
- sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
- ):
- with torch.autocast("cuda"):
- images = pipeline(example["prompt"]).images
-
- for i, image in enumerate(images):
- image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
-
- del pipeline
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
- else:
- repo_name = args.hub_model_id
- repo = Repository(args.output_dir, clone_from=repo_name)
-
- with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
- if "step_*" not in gitignore:
- gitignore.write("step_*\n")
- if "epoch_*" not in gitignore:
- gitignore.write("epoch_*\n")
- elif args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
-
- # Load the tokenizer
- if args.tokenizer_name:
- tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
- elif args.pretrained_model_name_or_path:
- tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
- # Load models and create wrapper for stable diffusion
- if args.train_only_unet:
- if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
- text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
- elif os.path.exists(str(args.output_dir+"/text_encoder")):
- text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
- else:
- text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
- else:
- text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
- vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
- unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-
- vae.requires_grad_(False)
- if not args.train_text_encoder:
- text_encoder.requires_grad_(False)
-
- if args.gradient_checkpointing:
- unet.enable_gradient_checkpointing()
- if args.train_text_encoder:
- text_encoder.gradient_checkpointing_enable()
-
- if args.scale_lr:
- args.learning_rate = (
- args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
- )
-
- # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
- if args.use_8bit_adam:
- try:
- import bitsandbytes as bnb
- except ImportError:
- raise ImportError(
- "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
- )
-
- optimizer_class = bnb.optim.AdamW8bit
- else:
- optimizer_class = torch.optim.AdamW
-
- params_to_optimize = (
- itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
- )
- optimizer = optimizer_class(
- params_to_optimize,
- lr=args.learning_rate,
- betas=(args.adam_beta1, args.adam_beta2),
- weight_decay=args.adam_weight_decay,
- eps=args.adam_epsilon,
- )
-
- noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
-
- train_dataset = DreamBoothDataset(
- instance_data_root=args.instance_data_dir,
- instance_prompt=args.instance_prompt,
- class_data_root=args.class_data_dir if args.with_prior_preservation else None,
- class_prompt=args.class_prompt,
- tokenizer=tokenizer,
- size=args.resolution,
- center_crop=args.center_crop,
- args=args,
- )
-
- def collate_fn(examples):
- input_ids = [example["instance_prompt_ids"] for example in examples]
- pixel_values = [example["instance_images"] for example in examples]
-
- # Concat class and instance examples for prior preservation.
- # We do this to avoid doing two forward passes.
- if args.with_prior_preservation:
- input_ids += [example["class_prompt_ids"] for example in examples]
- pixel_values += [example["class_images"] for example in examples]
-
- pixel_values = torch.stack(pixel_values)
- pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
- input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
-
- batch = {
- "input_ids": input_ids,
- "pixel_values": pixel_values,
- }
- return batch
-
- train_dataloader = torch.utils.data.DataLoader(
- train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
- )
-
- # Scheduler and math around the number of training steps.
- overrode_max_train_steps = False
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if args.max_train_steps is None:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- overrode_max_train_steps = True
-
- lr_scheduler = get_scheduler(
- args.lr_scheduler,
- optimizer=optimizer,
- num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
- num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
- )
-
- if args.train_text_encoder:
- unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- unet, text_encoder, optimizer, train_dataloader, lr_scheduler
- )
- else:
- unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- unet, optimizer, train_dataloader, lr_scheduler
- )
-
- weight_dtype = torch.float32
- if args.mixed_precision == "fp16":
- weight_dtype = torch.float16
- elif args.mixed_precision == "bf16":
- weight_dtype = torch.bfloat16
-
- # Move text_encode and vae to gpu.
- # For mixed precision training we cast the text_encoder and vae weights to half-precision
- # as these models are only used for inference, keeping weights in full precision is not required.
- vae.to(accelerator.device, dtype=weight_dtype)
- if not args.train_text_encoder:
- text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-
- if args.cache_latents:
- latents_cache = []
- text_encoder_cache = []
- for batch in tqdm(train_dataloader, desc="Caching latents"):
- with torch.no_grad():
- batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype)
- batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True)
- latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
- if args.train_text_encoder:
- text_encoder_cache.append(batch["input_ids"])
- else:
- text_encoder_cache.append(text_encoder(batch["input_ids"])[0])
- train_dataset = LatentsDataset(latents_cache, text_encoder_cache)
- train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True)
-
- del vae
- #if not args.train_text_encoder:
- # del text_encoder
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if overrode_max_train_steps:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- # Afterwards we recalculate our number of training epochs
- args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
- # We need to initialize the trackers we use, and also store our configuration.
- # The trackers initializes automatically on the main process.
- if accelerator.is_main_process:
- accelerator.init_trackers("dreambooth", config=vars(args))
-
- def bar(prg):
- br='|'+'█' * prg + ' ' * (25-prg)+'|'
- return br
-
- # Train!
- total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(train_dataset)}")
- logger.info(f" Num batches each epoch = {len(train_dataloader)}")
- logger.info(f" Num Epochs = {args.num_train_epochs}")
- logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {args.max_train_steps}")
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
- global_step = 0
-
- for epoch in range(args.num_train_epochs):
- unet.train()
- if args.train_text_encoder:
- text_encoder.train()
- for step, batch in enumerate(train_dataloader):
- with accelerator.accumulate(unet):
- # Convert images to latent space
- with torch.no_grad():
- if args.cache_latents:
- latents_dist = batch[0][0]
- else:
- latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
- latents = latents_dist.sample() * 0.18215
-
- # Sample noise that we'll add to the latents
- noise = torch.randn_like(latents)
- bsz = latents.shape[0]
- # Sample a random timestep for each image
- timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
- timesteps = timesteps.long()
-
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
- # Get the text embedding for conditioning
- if(args.cache_latents):
- if args.train_text_encoder:
- encoder_hidden_states = text_encoder(batch[0][1])[0]
- else:
- encoder_hidden_states = batch[0][1]
- else:
- encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
- # Predict the noise residual
- model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
- # Get the target for loss depending on the prediction type
- if noise_scheduler.config.prediction_type == "epsilon":
- target = noise
- elif noise_scheduler.config.prediction_type == "v_prediction":
- target = noise_scheduler.get_velocity(latents, noise, timesteps)
- else:
- raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
- if args.with_prior_preservation:
- # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
- model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
- target, target_prior = torch.chunk(target, 2, dim=0)
-
- # Compute instance loss
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
- # Compute prior loss
- prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
- # Add the prior loss to the instance loss.
- loss = loss + args.prior_loss_weight * prior_loss
- else:
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
- accelerator.backward(loss)
- if accelerator.sync_gradients:
- params_to_clip = (
- itertools.chain(unet.parameters(), text_encoder.parameters())
- if args.train_text_encoder
- else unet.parameters()
- )
- accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad()
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- progress_bar.update(1)
- global_step += 1
-
- fll=round((global_step*100)/args.max_train_steps)
- fll=round(fll/4)
- pr=bar(fll)
-
- logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
- progress_bar.set_description_str("Progress:"+pr)
- accelerator.log(logs, step=global_step)
-
- if global_step >= args.max_train_steps:
- break
-
- if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
- if accelerator.is_main_process:
- print(" [0;32m" +" Freezing the text_encoder ..."+" [0m")
- frz_dir=args.output_dir + "/text_encoder_frozen"
- if os.path.exists(frz_dir):
- subprocess.call('rm -r '+ frz_dir, shell=True)
- os.mkdir(frz_dir)
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- )
- pipeline.text_encoder.save_pretrained(frz_dir)
-
- if args.save_n_steps >= 200:
- if global_step < args.max_train_steps and global_step+1==i:
- ckpt_name = "_step_" + str(global_step+1)
- save_dir = Path(args.output_dir+ckpt_name)
- save_dir=str(save_dir)
- save_dir=save_dir.replace(" ", "_")
- if not os.path.exists(save_dir):
- os.mkdir(save_dir)
- inst=save_dir[16:]
- inst=inst.replace(" ", "_")
- print(" [1;32mSAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
- # Create the pipeline using the trained modules and save it.
- if accelerator.is_main_process:
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- )
- pipeline.save_pretrained(save_dir)
- frz_dir=args.output_dir + "/text_encoder_frozen"
- if args.train_text_encoder and os.path.exists(frz_dir):
- subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
- subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)
- chkpth=args.Session_dir+"/"+inst+".ckpt"
- subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
- subprocess.call('rm -r '+ save_dir, shell=True)
- i=i+args.save_n_steps
-
- accelerator.wait_for_everyone()
-
- # Create the pipeline using using the trained modules and save it.
- if accelerator.is_main_process:
- if args.dump_only_text_encoder:
- txt_dir=args.output_dir + "/text_encoder_trained"
- if not os.path.exists(txt_dir):
- os.mkdir(txt_dir)
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- )
- pipeline.text_encoder.save_pretrained(txt_dir)
-
- elif args.train_only_unet:
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- )
- pipeline.save_pretrained(args.output_dir)
- txt_dir=args.output_dir + "/text_encoder_trained"
- subprocess.call('rm -r '+txt_dir, shell=True)
-
- else:
- pipeline = StableDiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- )
- frz_dir=args.output_dir + "/text_encoder_frozen"
- pipeline.save_pretrained(args.output_dir)
- if args.train_text_encoder and os.path.exists(frz_dir):
- subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
- subprocess.call('rm -r '+ frz_dir, shell=True)
-
- if args.push_to_hub:
- repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
- accelerator.end_training()
- del pipeline
- torch.cuda.empty_cache()
- gc.collect()
-if __name__ == "__main__":
- pass
- #main()
-
diff --git a/spaces/Unggi/title_extraction_bart_logical/app.py b/spaces/Unggi/title_extraction_bart_logical/app.py
deleted file mode 100644
index 58c69f0c3c62e7a921faaa4a35fd979cd6293b95..0000000000000000000000000000000000000000
--- a/spaces/Unggi/title_extraction_bart_logical/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import gradio as gr
-
-# make function using import pip to install torch
-import pip
-pip.main(['install', 'torch'])
-pip.main(['install', 'transformers'])
-
-import torch
-import transformers
-
-
-# saved_model
-def load_model(model_path):
- saved_data = torch.load(
- model_path,
- map_location="cpu"
- )
-
- bart_best = saved_data["model"]
- train_config = saved_data["config"]
- tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
-
- ## Load weights.
- model = transformers.BartForConditionalGeneration.from_pretrained('gogamza/kobart-base-v1')
- model.load_state_dict(bart_best)
-
- return model, tokenizer
-
-
-# main
-def inference(prompt):
- model_path = "./kobart-model-logical.pth"
-
- model, tokenizer = load_model(
- model_path=model_path
- )
-
- input_ids = tokenizer.encode(prompt)
- input_ids = torch.tensor(input_ids)
- input_ids = input_ids.unsqueeze(0)
- output = model.generate(input_ids)
- output = tokenizer.decode(output[0], skip_special_tokens=True)
-
- return output
-
-
-demo = gr.Interface(
- fn=inference,
- inputs="text",
- outputs="text", #return 값
- examples=[
- "강원도 양양군에서 신청한 설악산국립공원 케이블카 사업이 7가지 부대조건을 지키는 조건으로 2015년 8월 28일 환경부 산하 국립공원위원회의 심의를 통과하였다. 제113차 국립공원위원회에서 내려진 이 결정으로 설악 오색-끝청 하단을 연결하는 3,492km의 케이블카과 건립될 예정이며, 설악산 대청봉과 1.4km 떨어진 지점까지 케이블카를 이용하여 접근할 수 있게 되었다. 사업비는 총 460억이며, 국비 50%, 군비 35%, 도비 15%로 결정되었다. 이 사업은 총 세차례에 걸친 반려와 부결 등을 통해 기획에 수정되어가다 결국 사업 타당성과 적정성을 향상시켰다는 평가를 받으며 확정되었다. 그러나 많은 시민단체와 환경단체는 이와 같은 결정에도 불구하고 국립공원의 환경파괴를 이유로 여전히 이를 반대하고 있으며, ‘산으로 간 4대강 사업’이라는 비난과 함께 국립공원이 유원지화 되는 비극을 막자고 주장한다. 편의성과 경제성의 논리와 함께 환경보존의 논리가 부딪히는 설악산국립공원의 케이블카는 건설되어야 하는가."
- ]
- ).launch() # launch(share=True)를 설정하면 외부에서 접속 가능한 링크가 생성됨
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Vishnu-sai-teja/Dog-vs-Cats-2/app.py b/spaces/Vishnu-sai-teja/Dog-vs-Cats-2/app.py
deleted file mode 100644
index d4ad3d5e072da7e564496cc22480ebeac63881de..0000000000000000000000000000000000000000
--- a/spaces/Vishnu-sai-teja/Dog-vs-Cats-2/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python
-
-# In[1]:
-
-import gradio as gr
-#|export
-from fastai.vision.all import *
-
-
-# Check Labels of your data
-def is_cat(x):
- return x[0].isupper()
-
-
-# In[2]:
-
-# Create a python image library (PIL) Image object
-im = PILImage.create("doggy.jpg")
-# Resize the image object
-im.thumbnail((192, 192))
-print(im)
-
-# In[3]:
-
-#|export
-# Import our model
-learn = load_learner("model.pkl")
-
-# In[4]:
-
-# Predict with our data
-learn.predict(im)
-
-#
-
-# learn.predict *output*
-# ----
-# **True/false / Categeory / Probabiliry belonging to the diff Categories**
-
-# In[5]:
-
-#|export
-# Creating a Gradio Interface
-# Note : The function Zip only creates a iterator which is further used
-categories = ("Dog", "Cat")
-
-
-def classify_images(img):
- pred, index, prob = learn.predict(img)
- return dict(zip(categories, map(float, prob)))
-
-
-# In[6]:
-
-classify_images(im)
-
-# In[9]:
-
-#|export
-# Create the gradio interface
-
-# 1 - Gradio Input FORMAT
-images = gr.inputs.Image(shape=(192, 192))
-
-# 2 - Gradio Output FORMAT
-labels = gr.outputs.Label()
-
-# 3 - Give some exmpale for the interface
-examples = ["cat.jpg", "bird.jpg", "doggy.jpg"]
-
-# 4 - Build the gradle interface (funciton,input,putput,examples)
-interface = gr.Interface(classify_images, images, labels, examples)
-
-# 5 - Launch the interface
-interface.launch(inline=False)
-
-# Export the model to the python app
-# ----
-#
-
-#
-# * 1 - Write **#|export** on all the cells you want to export
-#
-
-# **Note - always remember to add requirements.txt in your space**
-
-# In[8]:
-
-# Export Notebook to python Script -- nbdev --
-from notebook2script import convert_notebook
-
-convert_notebook("Lesson-2.ipynb", "app.py")
-
-# In[ ]:
-
-# In[ ]:
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/app.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/app.py
deleted file mode 100644
index aa1fa88d59193af62db70947145e8b652de447e6..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-"""
-Copyright (c) Meta Platforms, Inc. and affiliates.
-All rights reserved.
-
-This source code is licensed under the license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-from tempfile import NamedTemporaryFile
-import torch
-import gradio as gr
-import os
-from audiocraft.models import MusicGen
-
-from audiocraft.data.audio import audio_write
-
-
-MODEL = None
-IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ['SPACE_ID']
-
-def load_model(version):
- print("Loading model", version)
- return MusicGen.get_pretrained(version)
-
-
-def predict(model, text, melody, duration, topk, topp, temperature, cfg_coef):
- global MODEL
- topk = int(topk)
- if MODEL is None or MODEL.name != model:
- MODEL = load_model(model)
-
- if duration > MODEL.lm.cfg.dataset.segment_duration:
- raise gr.Error("MusicGen currently supports durations of up to 30 seconds!")
- MODEL.set_generation_params(
- use_sampling=True,
- top_k=topk,
- top_p=topp,
- temperature=temperature,
- cfg_coef=cfg_coef,
- duration=duration,
- )
-
- if melody:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
- print(melody.shape)
- if melody.dim() == 2:
- melody = melody[None]
- melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
- output = MODEL.generate_with_chroma(
- descriptions=[text],
- melody_wavs=melody,
- melody_sample_rate=sr,
- progress=False
- )
- else:
- output = MODEL.generate(descriptions=[text], progress=False)
-
- output = output.detach().cpu().float()[0]
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False)
- waveform_video = gr.make_waveform(file.name)
- return waveform_video
-
-
-with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
- """
- )
- if IS_SHARED_SPACE:
- gr.Markdown("""
- ⚠ This Space doesn't work in this shared UI ⚠
-
-
-
- to use it privately, or use the public demo
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Input Text", interactive=True)
- melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
- with gr.Row():
- submit = gr.Button("Submit" if not IS_SHARED_SPACE else "Duplicate the Space to generate", interactive=not IS_SHARED_SPACE)
- with gr.Row():
- model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Column():
- output = gr.Video(label="Generated Music")
- submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
- gr.Examples(
- fn=predict,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- "melody"
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- "melody"
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- "medium"
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
- "./assets/bach.mp3",
- "melody"
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- "medium",
- ],
- ],
- inputs=[text, melody, model],
- outputs=[output]
- )
- gr.Markdown(
- """
- ### More details
-
- The model will generate a short music extract based on the description you provided.
- You can generate up to 30 seconds of audio.
-
- We present 4 model variations:
- 1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
- 2. Small -- a 300M transformer decoder conditioned on text only.
- 3. Medium -- a 1.5B transformer decoder conditioned on text only.
- 4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
-
- When using `melody`, ou can optionaly provide a reference audio from
- which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """
- )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/X1A/UniPoll/app.py b/spaces/X1A/UniPoll/app.py
deleted file mode 100644
index 4e4bcb0afb26e3218887038a70a584c8f91b054d..0000000000000000000000000000000000000000
--- a/spaces/X1A/UniPoll/app.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import gc
-import sys
-import torch
-import argparse
-import gradio as gr
-from typing import List, Tuple
-from transformers import AutoConfig
-from transformers.models.mt5.modeling_mt5 import MT5ForConditionalGeneration
-from utils import T5PegasusTokenizer
-
-try:
- from loguru import logger as logging
- logging.add(sys.stderr, filter="my_module")
-except ImportError:
- import logging
-
-import time
-class TimerDecorator:
- def __init__(self, func) :
- self.func = func
-
- def __call__(self, *args, **kwargs) :
- start_time = time.time()
- result = self.func(*args, **kwargs)
- end_time = time.time()
- t = end_time - start_time
- logging.info(f"Function `{self.func.__name__}` took {round(t, 2)} s to run.")
- return result
-
-@TimerDecorator
-def load_model(model_path, device="cpu"):
- logging.info(f"Loading model from {model_path}")
- config = AutoConfig.from_pretrained(model_path)
- tokenizer = T5PegasusTokenizer.from_pretrained(model_path)
- model = MT5ForConditionalGeneration.from_pretrained(model_path, config=config)
- if device != "cpu":
- model.to(device)
- logging.info("Done.")
- return model, tokenizer
-
-def wrap_prompt(
- post, comments,
- prompt="生成 和 <choices>: [SEP] {post} [SEP] {comments}"
- ):
- if not comments or comments == "":
- logging.info("No comments input, comments will be ignored.")
- prompt = prompt.replace(" [SEP] {comments}", "")
- prompt = prompt.format(post=post)
- else:
- prompt = prompt.format(post=post, comments=comments)
- logging.info(f"Wrapped prompt: {prompt}")
- return prompt
-
-@TimerDecorator
-def generate(query, model, tokenizer, num_beams=4, device="cpu"):
- logging.info("Generating output...")
- tokens = tokenizer(query, return_tensors="pt")["input_ids"]
- if device != "cpu":
- tokens = tokens.to(device)
- output = model.generate(tokens, num_beams=num_beams, max_length=100)
- output_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
- logging.info("Done.")
- return output_text
-
-def post_process(raw_output: str) -> Tuple[str, str]:
- def same_title_choices(raw_output):
- # return the same raw output as title and choices
- # if no <title> or <choices> in raw_output
- raw_output = raw_output.replace("<title>", "")
- raw_output = raw_output.replace("<choices>", "")
- return raw_output.strip(), [raw_output.strip()]
-
- def split_choices(choices_str: str) -> List[str]:
- choices = choices_str.split("<c>")
- choices = [choice.strip() for choice in choices]
- return choices
-
- # extract title and choices from raw_output
- # e.g. raw_output = "<title> 你 觉得 线 上 复试 公平 吗 <choices> 公平 <c> 不 公平"
- if "<title>" in raw_output and "<choices>" in raw_output:
- index1 = raw_output.index("<title>")
- index2 = raw_output.index("<choices>")
- if index1 > index2:
- logging.debug(f"idx1>idx2, same title and choices will be used.\nraw_output: {raw_output}")
- return same_title_choices(raw_output)
- title = raw_output[index1+7: index2].strip() # "你 觉得 线 上 复试 公平 吗"
- choices_str = raw_output[index2+9:].strip() # "公平 <c> 不 公平"
- choices = split_choices(choices_str) # ["公平", "不 公平"]
- else:
- logging.debug(f"missing title/choices, same title and choices will be used.\nraw_output: {raw_output}")
- title, choices = same_title_choices(raw_output)
-
- def remove_blank(string):
- return string.replace(" ", "")
-
- title = remove_blank(title)
- choices = [remove_blank(choice) for choice in choices]
- return title, choices
-
-def parse_args():
- parser = argparse.ArgumentParser(description="Demo")
- parser.add_argument("--model_path", type=str, default="./UniPoll-t5/best_model", help="path to the model.")
- parser.add_argument("--device", type=str, default="cpu", help="specify the device to load the model, e.g. 'cpu', 'cuda:0'.")
- parser.add_argument(
- "--options",
- nargs="+",
- help="override some settings in the used config, the key-value pair "
- "in xxx=yyy format will be merged into config file (deprecate), "
- "change to --cfg-options instead.",
- )
- args = parser.parse_args()
- return args
-
-
-if __name__ == "__main__":
- args = parse_args()
-
- logging.info('Initializing Model...')
- # prepare the model
- model, tokenizer = load_model(args.model_path, args.device)
-
- def submit(post, comments, num_beams):
- try:
- logging.info("Received post input: {}".format(post))
- if comments:
- logging.info("Received comments input: {}".format(comments))
-
- query = wrap_prompt(post, comments)
- raw_output = generate(
- query, model, tokenizer, num_beams, args.device)
- title, choices = post_process(raw_output) # post process
- logging.info(f"Raw output: {raw_output}")
- logging.info(f"Processed title: {title}")
- logging.info(f"Processed choices: {choices}")
- # return title, choices, raw_output
- return title, choices
- except Exception as e:
- return "An error occurred: {}".format(str(e)), "An error occurred: {}".format(str(e))
- finally:
- gc.collect()
- torch.cuda.empty_cache()
-
- examples = [
- ["#哪吒，大鱼海棠重映#动画电影《哪吒之魔童降世》、《大鱼海棠》，以及雷佳音、佟丽娅主演的 《超时空同居》确定将重映。据最新数据显示，3月24日全国复工影院495家，复工率4.36%，单日票房2.7万元。", "我在人间贩卖黄昏，只为收集世间温柔，去见你。谢谢你的分享，来看看你。我的微博，随时恭候你的到..."],
- ["#线上复试是否能保障公平＃ 高考延期惹的祸，考研线上复试，那还能保证公平吗？", "这个世界上本来就没有绝对的公平。你可以说一个倒数第一考了第一，但考上了他也还是啥都不会。也可以说他会利用一切机会达到目的，反正结果就是人家考的好，你还找不出来证据。线上考试，平时考倒数的人进了年级前十。平时考试有水分，线上之后，那不就是在水里考？"],
- ["#断亲现象为何如此流行#？所谓“断亲”指的是当代年轻人懒于、疏于、不屑于跟亲戚交往、联系、互动，日常音信全无，哪怕在逢年过节期间，宁可独来独往，也不愿意走亲戚，甚至将此作为一种时尚生活方式来推崇。", ""]
- ]
-
- description = """This is the demo of UniPoll. Please input post and comments. <div style='display:flex; gap: 0.25rem; '><a href='https://uni-poll.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/X1AOX1A/UniPoll'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://arxiv.org/abs/2306.06851'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
-"""
-
- demo = gr.Interface(
- fn=submit,
- inputs=[gr.Textbox(lines=1, label="Social Media Post", placeholder="Input post here..."),
- gr.Textbox(lines=1, label="Social Media Comments (Optional)", placeholder="Input comments here..."),
- gr.Number(value=4, label="Number of Beams", precision=0),
- ],
- outputs=[gr.Textbox(lines=1, label="Generated Poll Question", placeholder="Generated poll question will be shown here"),
- gr.Textbox(lines=1, label="Generated Poll Choices", placeholder="Generated poll choices will be shown here"),
- ], # question, choices
- title="Demo of UniPoll",
- description=description,
- allow_flagging="never",
- examples=examples,
- )
-
- demo.queue(max_size=10)
- demo.launch()
-
-# python app.py --model_path "./UniPoll-t5/best_model" --device "cpu"
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/models/awd_lstm.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/models/awd_lstm.py
deleted file mode 100644
index 012df64256cd13de10ac3913a32adef4f5b070cf..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/models/awd_lstm.py
+++ /dev/null
@@ -1,261 +0,0 @@
-from ...torch_core import *
-from ...layers import *
-from ...train import ClassificationInterpretation
-from ...basic_train import *
-from ...basic_data import *
-from ..data import TextClasDataBunch
-import matplotlib.cm as cm
-
-__all__ = ['EmbeddingDropout', 'LinearDecoder', 'AWD_LSTM', 'RNNDropout',
- 'SequentialRNN', 'WeightDropout', 'dropout_mask', 'awd_lstm_lm_split', 'awd_lstm_clas_split',
- 'awd_lstm_lm_config', 'awd_lstm_clas_config', 'TextClassificationInterpretation']
-
-def dropout_mask(x:Tensor, sz:Collection[int], p:float):
- "Return a dropout mask of the same type as `x`, size `sz`, with probability `p` to cancel an element."
- return x.new(*sz).bernoulli_(1-p).div_(1-p)
-
-class RNNDropout(Module):
- "Dropout with probability `p` that is consistent on the seq_len dimension."
-
- def __init__(self, p:float=0.5): self.p=p
-
- def forward(self, x:Tensor)->Tensor:
- if not self.training or self.p == 0.: return x
- m = dropout_mask(x.data, (x.size(0), 1, x.size(2)), self.p)
- return x * m
-
-class WeightDropout(Module):
- "A module that warps another layer in which some weights will be replaced by 0 during training."
-
- def __init__(self, module:nn.Module, weight_p:float, layer_names:Collection[str]=['weight_hh_l0']):
- self.module,self.weight_p,self.layer_names = module,weight_p,layer_names
- for layer in self.layer_names:
- #Makes a copy of the weights of the selected layers.
- w = getattr(self.module, layer)
- self.register_parameter(f'{layer}_raw', nn.Parameter(w.data))
- self.module._parameters[layer] = F.dropout(w, p=self.weight_p, training=False)
-
- def _setweights(self):
- "Apply dropout to the raw weights."
- for layer in self.layer_names:
- raw_w = getattr(self, f'{layer}_raw')
- self.module._parameters[layer] = F.dropout(raw_w, p=self.weight_p, training=self.training)
-
- def forward(self, *args:ArgStar):
- self._setweights()
- with warnings.catch_warnings():
- #To avoid the warning that comes because the weights aren't flattened.
- warnings.simplefilter("ignore")
- return self.module.forward(*args)
-
- def reset(self):
- for layer in self.layer_names:
- raw_w = getattr(self, f'{layer}_raw')
- self.module._parameters[layer] = F.dropout(raw_w, p=self.weight_p, training=False)
- if hasattr(self.module, 'reset'): self.module.reset()
-
-class EmbeddingDropout(Module):
- "Apply dropout with probabily `embed_p` to an embedding layer `emb`."
-
- def __init__(self, emb:nn.Module, embed_p:float):
- self.emb,self.embed_p = emb,embed_p
- self.pad_idx = self.emb.padding_idx
- if self.pad_idx is None: self.pad_idx = -1
-
- def forward(self, words:LongTensor, scale:Optional[float]=None)->Tensor:
- if self.training and self.embed_p != 0:
- size = (self.emb.weight.size(0),1)
- mask = dropout_mask(self.emb.weight.data, size, self.embed_p)
- masked_embed = self.emb.weight * mask
- else: masked_embed = self.emb.weight
- if scale: masked_embed.mul_(scale)
- return F.embedding(words, masked_embed, self.pad_idx, self.emb.max_norm,
- self.emb.norm_type, self.emb.scale_grad_by_freq, self.emb.sparse)
-
-class AWD_LSTM(Module):
- "AWD-LSTM/QRNN inspired by https://arxiv.org/abs/1708.02182."
-
- initrange=0.1
-
- def __init__(self, vocab_sz:int, emb_sz:int, n_hid:int, n_layers:int, pad_token:int=1, hidden_p:float=0.2,
- input_p:float=0.6, embed_p:float=0.1, weight_p:float=0.5, qrnn:bool=False, bidir:bool=False):
- self.bs,self.qrnn,self.emb_sz,self.n_hid,self.n_layers = 1,qrnn,emb_sz,n_hid,n_layers
- self.n_dir = 2 if bidir else 1
- self.encoder = nn.Embedding(vocab_sz, emb_sz, padding_idx=pad_token)
- self.encoder_dp = EmbeddingDropout(self.encoder, embed_p)
- if self.qrnn:
- #Using QRNN requires an installation of cuda
- from .qrnn import QRNN
- self.rnns = [QRNN(emb_sz if l == 0 else n_hid, (n_hid if l != n_layers - 1 else emb_sz)//self.n_dir, 1,
- save_prev_x=True, zoneout=0, window=2 if l == 0 else 1, output_gate=True, bidirectional=bidir)
- for l in range(n_layers)]
- for rnn in self.rnns:
- rnn.layers[0].linear = WeightDropout(rnn.layers[0].linear, weight_p, layer_names=['weight'])
- else:
- self.rnns = [nn.LSTM(emb_sz if l == 0 else n_hid, (n_hid if l != n_layers - 1 else emb_sz)//self.n_dir, 1,
- batch_first=True, bidirectional=bidir) for l in range(n_layers)]
- self.rnns = [WeightDropout(rnn, weight_p) for rnn in self.rnns]
- self.rnns = nn.ModuleList(self.rnns)
- self.encoder.weight.data.uniform_(-self.initrange, self.initrange)
- self.input_dp = RNNDropout(input_p)
- self.hidden_dps = nn.ModuleList([RNNDropout(hidden_p) for l in range(n_layers)])
-
- def forward(self, input:Tensor, from_embeddings:bool=False)->Tuple[Tensor,Tensor]:
- if from_embeddings: bs,sl,es = input.size()
- else: bs,sl = input.size()
- if bs!=self.bs:
- self.bs=bs
- self.reset()
- raw_output = self.input_dp(input if from_embeddings else self.encoder_dp(input))
- new_hidden,raw_outputs,outputs = [],[],[]
- for l, (rnn,hid_dp) in enumerate(zip(self.rnns, self.hidden_dps)):
- raw_output, new_h = rnn(raw_output, self.hidden[l])
- new_hidden.append(new_h)
- raw_outputs.append(raw_output)
- if l != self.n_layers - 1: raw_output = hid_dp(raw_output)
- outputs.append(raw_output)
- self.hidden = to_detach(new_hidden, cpu=False)
- return raw_outputs, outputs
-
- def _one_hidden(self, l:int)->Tensor:
- "Return one hidden state."
- nh = (self.n_hid if l != self.n_layers - 1 else self.emb_sz) // self.n_dir
- return one_param(self).new(self.n_dir, self.bs, nh).zero_()
-
- def select_hidden(self, idxs):
- if self.qrnn: self.hidden = [h[:,idxs,:] for h in self.hidden]
- else: self.hidden = [(h[0][:,idxs,:],h[1][:,idxs,:]) for h in self.hidden]
- self.bs = len(idxs)
-
- def reset(self):
- "Reset the hidden states."
- [r.reset() for r in self.rnns if hasattr(r, 'reset')]
- if self.qrnn: self.hidden = [self._one_hidden(l) for l in range(self.n_layers)]
- else: self.hidden = [(self._one_hidden(l), self._one_hidden(l)) for l in range(self.n_layers)]
-
-class LinearDecoder(Module):
- "To go on top of a RNNCore module and create a Language Model."
- initrange=0.1
-
- def __init__(self, n_out:int, n_hid:int, output_p:float, tie_encoder:nn.Module=None, bias:bool=True):
- self.decoder = nn.Linear(n_hid, n_out, bias=bias)
- self.decoder.weight.data.uniform_(-self.initrange, self.initrange)
- self.output_dp = RNNDropout(output_p)
- if bias: self.decoder.bias.data.zero_()
- if tie_encoder: self.decoder.weight = tie_encoder.weight
-
- def forward(self, input:Tuple[Tensor,Tensor])->Tuple[Tensor,Tensor,Tensor]:
- raw_outputs, outputs = input
- output = self.output_dp(outputs[-1])
- decoded = self.decoder(output)
- return decoded, raw_outputs, outputs
-
-class SequentialRNN(nn.Sequential):
- "A sequential module that passes the reset call to its children."
- def reset(self):
- for c in self.children():
- if hasattr(c, 'reset'): c.reset()
-
-def awd_lstm_lm_split(model:nn.Module) -> List[nn.Module]:
- "Split a RNN `model` in groups for differential learning rates."
- groups = [[rnn, dp] for rnn, dp in zip(model[0].rnns, model[0].hidden_dps)]
- return groups + [[model[0].encoder, model[0].encoder_dp, model[1]]]
-
-def awd_lstm_clas_split(model:nn.Module) -> List[nn.Module]:
- "Split a RNN `model` in groups for differential learning rates."
- groups = [[model[0].module.encoder, model[0].module.encoder_dp]]
- groups += [[rnn, dp] for rnn, dp in zip(model[0].module.rnns, model[0].module.hidden_dps)]
- return groups + [[model[1]]]
-
-awd_lstm_lm_config = dict(emb_sz=400, n_hid=1152, n_layers=3, pad_token=1, qrnn=False, bidir=False, output_p=0.1,
- hidden_p=0.15, input_p=0.25, embed_p=0.02, weight_p=0.2, tie_weights=True, out_bias=True)
-
-awd_lstm_clas_config = dict(emb_sz=400, n_hid=1152, n_layers=3, pad_token=1, qrnn=False, bidir=False, output_p=0.4,
- hidden_p=0.3, input_p=0.4, embed_p=0.05, weight_p=0.5)
-
-def value2rgba(x:float, cmap:Callable=cm.RdYlGn, alpha_mult:float=1.0)->Tuple:
- "Convert a value `x` from 0 to 1 (inclusive) to an RGBA tuple according to `cmap` times transparency `alpha_mult`."
- c = cmap(x)
- rgb = (np.array(c[:-1]) * 255).astype(int)
- a = c[-1] * alpha_mult
- return tuple(rgb.tolist() + [a])
-
-def piece_attn_html(pieces:List[str], attns:List[float], sep:str=' ', **kwargs)->str:
- html_code,spans = ['<span style="font-family: monospace;">'], []
- for p, a in zip(pieces, attns):
- p = html.escape(p)
- c = str(value2rgba(a, alpha_mult=0.5, **kwargs))
- spans.append(f'<span title="{a:.3f}" style="background-color: rgba{c};">{p}</span>')
- html_code.append(sep.join(spans))
- html_code.append('</span>')
- return ''.join(html_code)
-
-def show_piece_attn(*args, **kwargs):
- from IPython.display import display, HTML
- display(HTML(piece_attn_html(*args, **kwargs)))
-
-def _eval_dropouts(mod):
- module_name = mod.__class__.__name__
- if 'Dropout' in module_name or 'BatchNorm' in module_name: mod.training = False
- for module in mod.children(): _eval_dropouts(module)
-
-class TextClassificationInterpretation(ClassificationInterpretation):
- """Provides an interpretation of classification based on input sensitivity.
- This was designed for AWD-LSTM only for the moment, because Transformer already has its own attentional model.
- """
-
- def __init__(self, learn: Learner, preds: Tensor, y_true: Tensor, losses: Tensor, ds_type: DatasetType = DatasetType.Valid):
- super().__init__(learn,preds,y_true,losses,ds_type)
- self.model = learn.model
-
- def intrinsic_attention(self, text:str, class_id:int=None):
- """Calculate the intrinsic attention of the input w.r.t to an output `class_id`, or the classification given by the model if `None`.
- For reference, see the Sequential Jacobian session at https://www.cs.toronto.edu/~graves/preprint.pdf
- """
- self.model.train()
- _eval_dropouts(self.model)
- self.model.zero_grad()
- self.model.reset()
- ids = self.data.one_item(text)[0]
- emb = self.model[0].module.encoder(ids).detach().requires_grad_(True)
- lstm_output = self.model[0].module(emb, from_embeddings=True)
- self.model.eval()
- cl = self.model[1](lstm_output + (torch.zeros_like(ids).byte(),))[0].softmax(dim=-1)
- if class_id is None: class_id = cl.argmax()
- cl[0][class_id].backward()
- attn = emb.grad.squeeze().abs().sum(dim=-1)
- attn /= attn.max()
- tokens = self.data.single_ds.reconstruct(ids[0])
- return tokens, attn
-
- def html_intrinsic_attention(self, text:str, class_id:int=None, **kwargs)->str:
- text, attn = self.intrinsic_attention(text, class_id)
- return piece_attn_html(text.text.split(), to_np(attn), **kwargs)
-
- def show_intrinsic_attention(self, text:str, class_id:int=None, **kwargs)->None:
- text, attn = self.intrinsic_attention(text, class_id)
- show_piece_attn(text.text.split(), to_np(attn), **kwargs)
-
- def show_top_losses(self, k:int, max_len:int=70)->None:
- """
- Create a tabulation showing the first `k` texts in top_losses along with their prediction, actual,loss, and probability of
- actual class. `max_len` is the maximum number of tokens displayed.
- """
- from IPython.display import display, HTML
- items = []
- tl_val,tl_idx = self.top_losses()
- for i,idx in enumerate(tl_idx):
- if k <= 0: break
- k -= 1
- tx,cl = self.data.dl(self.ds_type).dataset[idx]
- cl = cl.data
- classes = self.data.classes
- txt = ' '.join(tx.text.split(' ')[:max_len]) if max_len is not None else tx.text
- tmp = [txt, f'{classes[self.pred_class[idx]]}', f'{classes[cl]}', f'{self.losses[idx]:.2f}',
- f'{self.preds[idx][cl]:.2f}']
- items.append(tmp)
- items = np.array(items)
- names = ['Text', 'Prediction', 'Actual', 'Loss', 'Probability']
- df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
- with pd.option_context('display.max_colwidth', -1):
- display(HTML(df.to_html(index=False)))
diff --git a/spaces/Xenova/sponsorblock-ml/src/train_classifier.py b/spaces/Xenova/sponsorblock-ml/src/train_classifier.py
deleted file mode 100644
index 1e2eaca809ffa6820d0e25f8155b011bd9def0f3..0000000000000000000000000000000000000000
--- a/spaces/Xenova/sponsorblock-ml/src/train_classifier.py
+++ /dev/null
@@ -1,182 +0,0 @@
-
-""" Finetuning the library models for sequence classification."""
-
-import logging
-import os
-import sys
-from dataclasses import dataclass
-from typing import Optional
-
-import datasets
-import numpy as np
-
-import transformers
-from transformers import (
- DataCollatorWithPadding,
- EvalPrediction,
- HfArgumentParser,
- Trainer,
- TrainingArguments,
- set_seed,
-)
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-from shared import (
- CATEGORIES,
- DatasetArguments,
- prepare_datasets,
- load_datasets,
- CustomTrainingArguments,
- train_from_checkpoint,
- get_last_checkpoint
-)
-from model import get_model_tokenizer, ModelArguments
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version('4.17.0')
-require_version('datasets>=1.8.0', 'To fix: pip install -r requirements.txt')
-
-os.environ['WANDB_DISABLED'] = 'true'
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ClassifierTrainingArguments(CustomTrainingArguments, TrainingArguments):
- pass
-
-
-@dataclass
-class ClassifierDatasetArguments(DatasetArguments):
- def __post_init__(self):
- self.train_file = self.c_train_file
- self.validation_file = self.c_validation_file
- self.test_file = self.c_test_file
-
-
-def main():
- # See all possible arguments in src/transformers/training_args.py
- # or by passing the --help flag to this script.
- # We now keep distinct sets of args, for a cleaner separation of concerns.
-
- hf_parser = HfArgumentParser((
- ModelArguments,
- ClassifierDatasetArguments,
- ClassifierTrainingArguments
- ))
- model_args, dataset_args, training_args = hf_parser.parse_args_into_dataclasses()
-
- # Setup logging
- logging.basicConfig(
- format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
- datefmt='%m/%d/%Y %H:%M:%S',
- handlers=[logging.StreamHandler(sys.stdout)],
- )
-
- log_level = training_args.get_process_log_level()
- logger.setLevel(log_level)
- datasets.utils.logging.set_verbosity(log_level)
- transformers.utils.logging.set_verbosity(log_level)
- transformers.utils.logging.enable_default_handler()
- transformers.utils.logging.enable_explicit_format()
-
- # Log on each process the small summary:
- logger.warning(
- f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
- + f'distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}'
- )
- logger.info(f'Training/evaluation parameters {training_args}')
-
- # Detecting last checkpoint.
- last_checkpoint = get_last_checkpoint(training_args)
-
- # Set seed before initializing model.
- set_seed(training_args.seed)
-
- # Loading a dataset from your local files.
- # CSV/JSON training and evaluation files are needed.
- raw_datasets = load_datasets(dataset_args)
-
- # See more about loading any type of standard or custom dataset at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- config_args = {
- 'num_labels': len(CATEGORIES),
- 'id2label': {k: str(v).upper() for k, v in enumerate(CATEGORIES)},
- 'label2id': {str(v).upper(): k for k, v in enumerate(CATEGORIES)}
- }
- model, tokenizer = get_model_tokenizer(
- model_args, training_args, config_args=config_args, model_type='classifier')
-
- if training_args.max_seq_length > tokenizer.model_max_length:
- logger.warning(
- f'The max_seq_length passed ({training_args.max_seq_length}) is larger than the maximum length for the'
- f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
- )
- max_seq_length = min(training_args.max_seq_length,
- tokenizer.model_max_length)
-
- def preprocess_function(examples):
- # Tokenize the texts
- result = tokenizer(
- examples['text'], padding='max_length', max_length=max_seq_length, truncation=True)
- result['label'] = examples['label']
- return result
-
- train_dataset, eval_dataset, predict_dataset = prepare_datasets(
- raw_datasets, dataset_args, training_args, preprocess_function)
-
- # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
- # predictions and label_ids field) and has to return a dictionary string to float.
- def compute_metrics(p: EvalPrediction):
- preds = p.predictions[0] if isinstance(
- p.predictions, tuple) else p.predictions
- preds = np.argmax(preds, axis=1)
- return {'accuracy': (preds == p.label_ids).astype(np.float32).mean().item()}
-
- # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
- # we already did the padding.
- if training_args.fp16:
- data_collator = DataCollatorWithPadding(
- tokenizer, pad_to_multiple_of=8)
- else:
- data_collator = None
-
- # Initialize our Trainer
- trainer = Trainer(
- model=model,
- args=training_args,
- train_dataset=train_dataset,
- eval_dataset=eval_dataset,
- compute_metrics=compute_metrics,
- tokenizer=tokenizer,
- data_collator=data_collator,
- )
-
- # Training
- train_result = train_from_checkpoint(
- trainer, last_checkpoint, training_args)
-
- metrics = train_result.metrics
- max_train_samples = (
- training_args.max_train_samples if training_args.max_train_samples is not None else len(
- train_dataset)
- )
- metrics['train_samples'] = min(max_train_samples, len(train_dataset))
-
- trainer.save_model() # Saves the tokenizer too for easy upload
-
- trainer.log_metrics('train', metrics)
- trainer.save_metrics('train', metrics)
- trainer.save_state()
-
- kwargs = {'finetuned_from': model_args.model_name_or_path,
- 'tasks': 'text-classification'}
- if training_args.push_to_hub:
- trainer.push_to_hub(**kwargs)
- else:
- trainer.create_model_card(**kwargs)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/XzJosh/Jianmo-Bert-VITS2/resample.py b/spaces/XzJosh/Jianmo-Bert-VITS2/resample.py
deleted file mode 100644
index 2ed1685654a371c5722168e9987809b05b1cb224..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jianmo-Bert-VITS2/resample.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-
-import soundfile
-from scipy.io import wavfile
-from tqdm import tqdm
-
-
-def process(item):
- spkdir, wav_name, args = item
- speaker = spkdir.replace("\\", "/").split("/")[-1]
- wav_path = os.path.join(args.in_dir, speaker, wav_name)
- if os.path.exists(wav_path) and '.wav' in wav_path:
- os.makedirs(os.path.join(args.out_dir, speaker), exist_ok=True)
- wav, sr = librosa.load(wav_path, sr=args.sr)
- soundfile.write(
- os.path.join(args.out_dir, speaker, wav_name),
- wav,
- sr
- )
-
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--sr", type=int, default=44100, help="sampling rate")
- parser.add_argument("--in_dir", type=str, default="./raw", help="path to source dir")
- parser.add_argument("--out_dir", type=str, default="./dataset", help="path to target dir")
- args = parser.parse_args()
- # processs = 8
- processs = cpu_count()-2 if cpu_count() >4 else 1
- pool = Pool(processes=processs)
-
- for speaker in os.listdir(args.in_dir):
- spk_dir = os.path.join(args.in_dir, speaker)
- if os.path.isdir(spk_dir):
- print(spk_dir)
- for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
- pass
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/utils.py b/spaces/XzJosh/Taffy-Bert-VITS2/utils.py
deleted file mode 100644
index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- elif optimizer is None and not skip_optimizer:
- #else: #Disable this line if Infer ,and enable the line upper
- new_opt_dict = optimizer.state_dict()
- new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
- new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
- new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
- optimizer.load_state_dict(new_opt_dict)
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- #assert "emb_g" not in k
- # print("load", k)
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
- except:
- print("error, %s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
- print("load ")
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL",
- help='Model name')
- parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint")
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- hparams.cont = args.cont
- return hparams
-
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- import re
- ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
- name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
- time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
- sort_key = time_key if sort_by_time else name_key
- x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
- key=sort_key)
- to_del = [os.path.join(path_to_models, fn) for fn in
- (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
- del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
- del_routine = lambda x: [os.remove(x), del_info(x)]
- rs = [del_routine(fn) for fn in to_del]
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/Yiqin/ChatVID/model/fastchat/serve/huggingface_api.py b/spaces/Yiqin/ChatVID/model/fastchat/serve/huggingface_api.py
deleted file mode 100644
index 9dd4ea466d72345670163c94874ae0a377e874b6..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/fastchat/serve/huggingface_api.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""
-Usage:
-python3 -m fastchat.serve.huggingface_api --model ~/model_weights/vicuna-7b/
-"""
-import argparse
-import json
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-from fastchat.conversation import get_default_conv_template, compute_skip_echo_len
-from fastchat.serve.inference import load_model
-
-
-@torch.inference_mode()
-def main(args):
- model, tokenizer = load_model(
- args.model_path,
- args.device,
- args.num_gpus,
- args.max_gpu_memory,
- args.load_8bit,
- debug=args.debug,
- )
-
- msg = args.message
-
- conv = get_default_conv_template(args.model_path).copy()
- conv.append_message(conv.roles[0], msg)
- conv.append_message(conv.roles[1], None)
- prompt = conv.get_prompt()
-
- inputs = tokenizer([prompt])
- output_ids = model.generate(
- torch.as_tensor(inputs.input_ids).cuda(),
- do_sample=True,
- temperature=0.7,
- max_new_tokens=1024,
- )
- outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
- skip_echo_len = compute_skip_echo_len(args.model_path, conv, prompt)
- outputs = outputs[skip_echo_len:]
-
- print(f"{conv.roles[0]}: {msg}")
- print(f"{conv.roles[1]}: {outputs}")
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--model-path",
- type=str,
- default="facebook/opt-350m",
- help="The path to the weights",
- )
- parser.add_argument(
- "--device", type=str, choices=["cpu", "cuda", "mps"], default="cuda"
- )
- parser.add_argument("--num-gpus", type=str, default="1")
- parser.add_argument(
- "--max-gpu-memory",
- type=str,
- help="The maximum memory per gpu. Use a string like '13Gib'",
- )
- parser.add_argument(
- "--load-8bit", action="store_true", help="Use 8-bit quantization."
- )
- parser.add_argument(
- "--conv-template", type=str, default=None, help="Conversation prompt template."
- )
- parser.add_argument("--temperature", type=float, default=0.7)
- parser.add_argument("--max-new-tokens", type=int, default=512)
- parser.add_argument("--debug", action="store_true")
- parser.add_argument("--message", type=str, default="Hello! Who are you?")
- args = parser.parse_args()
-
- main(args)
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/compat.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/compat.py
deleted file mode 100644
index 11a08c439bf14defd880e37a938fab8a08e68eeb..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/compat.py
+++ /dev/null
@@ -1,229 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Backward compatibility of configs.
-
-Instructions to bump version:
-+ It's not needed to bump version if new keys are added.
- It's only needed when backward-incompatible changes happen
- (i.e., some existing keys disappear, or the meaning of a key changes)
-+ To bump version, do the following:
- 1. Increment _C.VERSION in defaults.py
- 2. Add a converter in this file.
-
- Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X,
- and a function "downgrade" which in-place downgrades config from X to X-1
-
- In each function, VERSION is left unchanged.
-
- Each converter assumes that its input has the relevant keys
- (i.e., the input is not a partial config).
- 3. Run the tests (test_config.py) to make sure the upgrade & downgrade
- functions are consistent.
-"""
-
-import logging
-from typing import List, Optional, Tuple
-
-from .config import CfgNode as CN
-from .defaults import _C
-
-__all__ = ["upgrade_config", "downgrade_config"]
-
-
-def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN:
- """
- Upgrade a config from its current version to a newer version.
-
- Args:
- cfg (CfgNode):
- to_version (int): defaults to the latest version.
- """
- cfg = cfg.clone()
- if to_version is None:
- to_version = _C.VERSION
-
- assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format(
- cfg.VERSION, to_version
- )
- for k in range(cfg.VERSION, to_version):
- converter = globals()["ConverterV" + str(k + 1)]
- converter.upgrade(cfg)
- cfg.VERSION = k + 1
- return cfg
-
-
-def downgrade_config(cfg: CN, to_version: int) -> CN:
- """
- Downgrade a config from its current version to an older version.
-
- Args:
- cfg (CfgNode):
- to_version (int):
-
- Note:
- A general downgrade of arbitrary configs is not always possible due to the
- different functionalities in different versions.
- The purpose of downgrade is only to recover the defaults in old versions,
- allowing it to load an old partial yaml config.
- Therefore, the implementation only needs to fill in the default values
- in the old version when a general downgrade is not possible.
- """
- cfg = cfg.clone()
- assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format(
- cfg.VERSION, to_version
- )
- for k in range(cfg.VERSION, to_version, -1):
- converter = globals()["ConverterV" + str(k)]
- converter.downgrade(cfg)
- cfg.VERSION = k - 1
- return cfg
-
-
-def guess_version(cfg: CN, filename: str) -> int:
- """
- Guess the version of a partial config where the VERSION field is not specified.
- Returns the version, or the latest if cannot make a guess.
-
- This makes it easier for users to migrate.
- """
- logger = logging.getLogger(__name__)
-
- def _has(name: str) -> bool:
- cur = cfg
- for n in name.split("."):
- if n not in cur:
- return False
- cur = cur[n]
- return True
-
- # Most users' partial configs have "MODEL.WEIGHT", so guess on it
- ret = None
- if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"):
- ret = 1
-
- if ret is not None:
- logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret))
- else:
- ret = _C.VERSION
- logger.warning(
- "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format(
- filename, ret
- )
- )
- return ret
-
-
-def _rename(cfg: CN, old: str, new: str) -> None:
- old_keys = old.split(".")
- new_keys = new.split(".")
-
- def _set(key_seq: List[str], val: str) -> None:
- cur = cfg
- for k in key_seq[:-1]:
- if k not in cur:
- cur[k] = CN()
- cur = cur[k]
- cur[key_seq[-1]] = val
-
- def _get(key_seq: List[str]) -> CN:
- cur = cfg
- for k in key_seq:
- cur = cur[k]
- return cur
-
- def _del(key_seq: List[str]) -> None:
- cur = cfg
- for k in key_seq[:-1]:
- cur = cur[k]
- del cur[key_seq[-1]]
- if len(cur) == 0 and len(key_seq) > 1:
- _del(key_seq[:-1])
-
- _set(new_keys, _get(old_keys))
- _del(old_keys)
-
-
-class _RenameConverter:
- """
- A converter that handles simple rename.
- """
-
- RENAME: List[Tuple[str, str]] = [] # list of tuples of (old name, new name)
-
- @classmethod
- def upgrade(cls, cfg: CN) -> None:
- for old, new in cls.RENAME:
- _rename(cfg, old, new)
-
- @classmethod
- def downgrade(cls, cfg: CN) -> None:
- for old, new in cls.RENAME[::-1]:
- _rename(cfg, new, old)
-
-
-class ConverterV1(_RenameConverter):
- RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")]
-
-
-class ConverterV2(_RenameConverter):
- """
- A large bulk of rename, before public release.
- """
-
- RENAME = [
- ("MODEL.WEIGHT", "MODEL.WEIGHTS"),
- ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"),
- ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"),
- ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"),
- ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"),
- (
- "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD",
- "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH",
- ),
- (
- "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT",
- "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT",
- ),
- (
- "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD",
- "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH",
- ),
- ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"),
- ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"),
- ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"),
- ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"),
- ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"),
- ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"),
- ("TEST.AUG_ON", "TEST.AUG.ENABLED"),
- ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"),
- ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"),
- ("TEST.AUG_FLIP", "TEST.AUG.FLIP"),
- ]
-
- @classmethod
- def upgrade(cls, cfg: CN) -> None:
- super().upgrade(cfg)
-
- if cfg.MODEL.META_ARCHITECTURE == "RetinaNet":
- _rename(
- cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS"
- )
- _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
- del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"]
- del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"]
- else:
- _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS")
- _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
- del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"]
- del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"]
- del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"]
-
- @classmethod
- def downgrade(cls, cfg: CN) -> None:
- super().downgrade(cfg)
-
- _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS")
- _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES")
- cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS
- cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES
- cfg.MODEL.RETINANET.ANCHOR_STRIDES = [] # this is not used anywhere in any version
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/encodec.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/encodec.py
deleted file mode 100644
index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,302 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-import typing as tp
-
-from einops import rearrange
-import torch
-from torch import nn
-
-from .. import quantization as qt
-
-
-class CompressionModel(ABC, nn.Module):
-
- @abstractmethod
- def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
- ...
-
- @abstractmethod
- def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- """See `EncodecModel.encode`"""
- ...
-
- @abstractmethod
- def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
- """See `EncodecModel.decode`"""
- ...
-
- @property
- @abstractmethod
- def channels(self) -> int:
- ...
-
- @property
- @abstractmethod
- def frame_rate(self) -> int:
- ...
-
- @property
- @abstractmethod
- def sample_rate(self) -> int:
- ...
-
- @property
- @abstractmethod
- def cardinality(self) -> int:
- ...
-
- @property
- @abstractmethod
- def num_codebooks(self) -> int:
- ...
-
- @property
- @abstractmethod
- def total_codebooks(self) -> int:
- ...
-
- @abstractmethod
- def set_num_codebooks(self, n: int):
- """Set the active number of codebooks used by the quantizer.
- """
- ...
-
-
-class EncodecModel(CompressionModel):
- """Encodec model operating on the raw waveform.
-
- Args:
- encoder (nn.Module): Encoder network.
- decoder (nn.Module): Decoder network.
- quantizer (qt.BaseQuantizer): Quantizer network.
- frame_rate (int): Frame rate for the latent representation.
- sample_rate (int): Audio sample rate.
- channels (int): Number of audio channels.
- causal (bool): Whether to use a causal version of the model.
- renormalize (bool): Whether to renormalize the audio before running the model.
- """
- # we need assignement to override the property in the abstract class,
- # I couldn't find a better way...
- frame_rate: int = 0
- sample_rate: int = 0
- channels: int = 0
-
- def __init__(self,
- encoder: nn.Module,
- decoder: nn.Module,
- quantizer: qt.BaseQuantizer,
- frame_rate: int,
- sample_rate: int,
- channels: int,
- causal: bool = False,
- renormalize: bool = False):
- super().__init__()
- self.encoder = encoder
- self.decoder = decoder
- self.quantizer = quantizer
- self.frame_rate = frame_rate
- self.sample_rate = sample_rate
- self.channels = channels
- self.renormalize = renormalize
- self.causal = causal
- if self.causal:
- # we force disabling here to avoid handling linear overlap of segments
- # as supported in original EnCodec codebase.
- assert not self.renormalize, 'Causal model does not support renormalize'
-
- @property
- def total_codebooks(self):
- """Total number of quantizer codebooks available.
- """
- return self.quantizer.total_codebooks
-
- @property
- def num_codebooks(self):
- """Active number of codebooks used by the quantizer.
- """
- return self.quantizer.num_codebooks
-
- def set_num_codebooks(self, n: int):
- """Set the active number of codebooks used by the quantizer.
- """
- self.quantizer.set_num_codebooks(n)
-
- @property
- def cardinality(self):
- """Cardinality of each codebook.
- """
- return self.quantizer.bins
-
- def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- scale: tp.Optional[torch.Tensor]
- if self.renormalize:
- mono = x.mean(dim=1, keepdim=True)
- volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
- scale = 1e-8 + volume
- x = x / scale
- scale = scale.view(-1, 1)
- else:
- scale = None
- return x, scale
-
- def postprocess(self,
- x: torch.Tensor,
- scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
- if scale is not None:
- assert self.renormalize
- x = x * scale.view(-1, 1, 1)
- return x
-
- def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
- assert x.dim() == 3
- length = x.shape[-1]
- x, scale = self.preprocess(x)
-
- emb = self.encoder(x)
- q_res = self.quantizer(emb, self.frame_rate)
- out = self.decoder(q_res.x)
-
- # remove extra padding added by the encoder and decoder
- assert out.shape[-1] >= length, (out.shape[-1], length)
- out = out[..., :length]
-
- q_res.x = self.postprocess(out, scale)
-
- return q_res
-
- def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- """Encode the given input tensor to quantized representation along with scale parameter.
-
- Args:
- x (torch.Tensor): Float tensor of shape [B, C, T]
-
- Returns:
- codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
- codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
- scale a float tensor containing the scale for audio renormalizealization.
- """
- assert x.dim() == 3
- x, scale = self.preprocess(x)
- emb = self.encoder(x)
- codes = self.quantizer.encode(emb)
- return codes, scale
-
- def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
- """Decode the given codes to a reconstructed representation, using the scale to perform
- audio denormalization if needed.
-
- Args:
- codes (torch.Tensor): Int tensor of shape [B, K, T]
- scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
-
- Returns:
- out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
- """
- emb = self.quantizer.decode(codes)
- out = self.decoder(emb)
- out = self.postprocess(out, scale)
- # out contains extra padding added by the encoder and decoder
- return out
-
-
-class FlattenedCompressionModel(CompressionModel):
- """Wraps a CompressionModel and flatten its codebooks, e.g.
- instead of returning [B, K, T], return [B, S, T * (K // S)] with
- S the number of codebooks per step, and `K // S` the number of 'virtual steps'
- for each real time step.
-
- Args:
- model (CompressionModel): compression model to wrap.
- codebooks_per_step (int): number of codebooks to keep per step,
- this must divide the number of codebooks provided by the wrapped model.
- extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
- if each codebook has a cardinality N, then the first codebook will
- use the range [0, N - 1], and the second [N, 2 N - 1] etc.
- On decoding, this can lead to potentially invalid sequences.
- Any invalid entry will be silently remapped to the proper range
- with a modulo.
- """
- def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
- extend_cardinality: bool = True):
- super().__init__()
- self.model = model
- self.codebooks_per_step = codebooks_per_step
- self.extend_cardinality = extend_cardinality
-
- @property
- def total_codebooks(self):
- return self.model.total_codebooks
-
- @property
- def num_codebooks(self):
- """Active number of codebooks used by the quantizer.
-
- ..Warning:: this reports the number of codebooks after the flattening
- of the codebooks!
- """
- assert self.model.num_codebooks % self.codebooks_per_step == 0
- return self.codebooks_per_step
-
- def set_num_codebooks(self, n: int):
- """Set the active number of codebooks used by the quantizer.
-
- ..Warning:: this sets the number of codebooks **before** the flattening
- of the codebooks.
- """
- assert n % self.codebooks_per_step == 0
- self.model.set_num_codebooks(n)
-
- @property
- def num_virtual_steps(self) -> int:
- """Return the number of virtual steps, e.g. one real step
- will be split into that many steps.
- """
- return self.model.num_codebooks // self.codebooks_per_step
-
- @property
- def frame_rate(self) -> int:
- return self.model.frame_rate * self.num_virtual_steps
-
- @property
- def sample_rate(self) -> int:
- return self.model.sample_rate
-
- @property
- def channels(self) -> int:
- return self.model.channels
-
- @property
- def cardinality(self):
- """Cardinality of each codebook.
- """
- if self.extend_cardinality:
- return self.model.cardinality * self.num_virtual_steps
- else:
- return self.model.cardinality
-
- def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
- raise NotImplementedError("Not supported, use encode and decode.")
-
- def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- indices, scales = self.model.encode(x)
- B, K, T = indices.shape
- indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
- if self.extend_cardinality:
- for virtual_step in range(1, self.num_virtual_steps):
- indices[..., virtual_step] += self.model.cardinality * virtual_step
- indices = rearrange(indices, 'b k t v -> b k (t v)')
- return (indices, scales)
-
- def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
- B, K, T = codes.shape
- assert T % self.num_virtual_steps == 0
- codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
- # We silently ignore potential errors from the LM when
- # using extend_cardinality.
- codes = codes % self.model.cardinality
- return self.model.decode(codes, scale)
diff --git a/spaces/a-v-bely/russian-task-generator/utilities_language_w2v/rus_main_workflow_w2v.py b/spaces/a-v-bely/russian-task-generator/utilities_language_w2v/rus_main_workflow_w2v.py
deleted file mode 100644
index b72a2688d43bcf8694cd9ba95671aeb6c84ce3f2..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/russian-task-generator/utilities_language_w2v/rus_main_workflow_w2v.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import datetime
-from io import StringIO
-from random import sample
-from collections import defaultdict
-from streamlit import progress as st_progress
-from streamlit.elements import WIDGETS as ST_WIDGETS
-from utilities_language_general.rus_constants import st
-from utilities_language_w2v.rus_sentence_w2v import TASK
-from utilities_language_general.rus_constants import load_w2v
-from utilities_language_general.rus_utils import prepare_tasks
-from utilities_language_w2v.rus_sentence_w2v import SENTENCE
-import utilities_language_general.rus_constants as esp_constants
-from utilities_language_general.rus_utils import prepare_target_words
-from utilities_language_general.rus_constants import w2v_model1_path
-from utilities_language_general.rus_constants import w2v_model2_path
-from utilities_language_general.rus_utils import compute_frequency_dict
-from streamlit.runtime.uploaded_file_manager import UploadedFile
-from utilities_language_general.rus_constants import BAD_USER_TARGET_WORDS
-
-
-def main_workflow_w2v(
- file: UploadedFile or None,
- text: str,
- logs: ST_WIDGETS,
- logs_d: ST_WIDGETS,
- progress: st_progress,
- progress_d: st_progress,
- progress_s: st_progress,
- level: str,
- tw_mode_automatic_mode: str,
- target_words: str,
- num_distractors: int,
- save_name: str,
- model_name: str,
- global_bad_target_words=BAD_USER_TARGET_WORDS):
- """
- This is the main course of the program.
- All processes and changes take place here.
- Partially works with the interface, displaying the success messages and download buttons.
-
- :param file: user's file to generate tasks in
- :param text: user's text input to generate tasks in
- :param logs: widget to output logs to
- :param logs_d: show how many distractors already processed
- :param progress: progress bar
- :param progress_d: distractors progress bar
- :param progress_s: sentences progress bar
- :param target_words: how target words are chosen: by user or automatically
- :param tw_mode_automatic_mode:
- :param level: user's specification of CEFR level of text
- :param num_distractors: how many distractors does the user want the task to contain
- :param save_name: user specifies name to save file in cloud
- :param global_bad_target_words:global_bad_target_words
- :param model_name
- :return: Dictionary with output data: filename, amount_mode, text_with_gaps, tasks_as_list, correct_answers,
- student_out, teacher_out, total_out, original_text
- """
- # Clear bad target_words each time
- global_bad_target_words = []
-
- # Define main global variables
- logs.write()
- GLOBAL_DISTRACTORS = set()
- MAX_FREQUENCY = 0
-
- # Define which model is used for distractor generation
- if model_name == 'Модель-1':
- mask_filler = load_w2v(w2v_model1_path)
- else:
- mask_filler = load_w2v(w2v_model2_path)
-
- # Get input text
- if file is not None:
- stringio = StringIO(file.getvalue().decode("utf-8"))
- current_text = stringio.read()
- elif text != '':
- current_text = text
- else:
- esp_constants.st.warning('Вы и текст не вставили, и файл не выбрали 😢')
- current_text = ''
- esp_constants.st.stop()
-
- # Process target words
- if tw_mode_automatic_mode == 'Самостоятельно':
- if target_words == '':
- esp_constants.st.warning('Вы не ввели целевые слова')
- esp_constants.st.stop()
- # Cannot make up paradigm, so only USER_TARGET_WORDS is used
- USER_TARGET_WORDS = prepare_target_words(target_words)
- tw_mode_automatic_mode = False
- else:
- USER_TARGET_WORDS = None
- tw_mode_automatic_mode = True
-
- # Text preprocessing
- original_text = current_text
- current_text = current_text.replace('.', '. ').replace('. . .', '...').replace(' ', ' ').replace('…', '...') \
- .replace('…', '...').replace('—', '-').replace('\u2014', '-').replace('—', '-').replace('-\n', '') \
- .replace('\n', '%^&*')
- current_text_sentences = [sent.text.strip() for sent in esp_constants.nlp(current_text).sents]
- logs.success('Получили Ваш текст!')
- progress.progress(10)
-
- # Compute frequency dict
- FREQ_DICT = compute_frequency_dict(current_text)
-
- # Get maximum frequency (top 5% barrier)
- _frequency_barrier_percent = 0.05
- for j, tp in enumerate(FREQ_DICT.items()):
- if j < len(FREQ_DICT) * _frequency_barrier_percent:
- MAX_FREQUENCY = tp[1]
- MAX_FREQUENCY = 3 if MAX_FREQUENCY < 3 else MAX_FREQUENCY
- logs.success("Посчитали немного статистики!")
- progress.progress(15)
-
- # Choose necessary language minimum according to user's input
- if level == 'A1':
- target_minimum = esp_constants.a1_target_set
- distractor_minimum = esp_constants.a1_distractor_set
- elif level == 'A2':
- target_minimum = esp_constants.a2_target_set
- distractor_minimum = esp_constants.a2_distractor_set
- elif level == 'B1':
- target_minimum = esp_constants.b1_target_set
- distractor_minimum = esp_constants.b1_distractor_set
- elif level == 'B2':
- target_minimum = esp_constants.b2_target_set
- distractor_minimum = esp_constants.b2_distractor_set
- elif level == 'C1':
- target_minimum = esp_constants.c1_target_set
- distractor_minimum = esp_constants.c1_distractor_set
- elif level == 'C2':
- target_minimum = esp_constants.c2_target_set
- distractor_minimum = esp_constants.c2_distractor_set
- elif level == 'Без уровня':
- target_minimum = None
- distractor_minimum = None
- else:
- target_minimum = None
- distractor_minimum = None
- logs.error('Вы не выбрали языковой уровень!')
- st.stop()
-
- # Start generation process
- workflow = [SENTENCE(original=sent.strip(), n_sentence=num, max_num_distractors=num_distractors)
- for num, sent in enumerate(current_text_sentences)]
- logs.success("Запускаем процесс генерации заданий!")
- progress.progress(20)
-
- for sentence in workflow:
- sentence.lemmatize_sentence()
-
- for sentence in workflow:
- sentence.bind_phrases()
- logs.success("Подготовили предложения для дальнейшей работы!")
- progress.progress(30)
-
- for j, sentence in enumerate(workflow):
- sentence.search_target_words(model=mask_filler,
- target_words_automatic_mode=tw_mode_automatic_mode,
- target_minimum=target_minimum,
- user_target_words=USER_TARGET_WORDS,
- frequency_dict=FREQ_DICT)
- progress.progress(int(30 + (j * (30 / len(workflow)))))
- progress_s.progress(60)
- DUPLICATE_TARGET_WORDS = defaultdict(list)
- for sentence in workflow:
- for target_word in sentence.target_words:
- DUPLICATE_TARGET_WORDS[target_word['lemma']].append(target_word)
- RESULT_TW = []
- for tw_lemma, tw_data in DUPLICATE_TARGET_WORDS.items():
- RESULT_TW.append(sample(tw_data, 1)[0])
- for sentence in workflow:
- for target_word in sentence.target_words:
- if target_word not in RESULT_TW:
- global_bad_target_words.append(target_word['original_text'])
- sentence.target_words.remove(target_word)
- progress_s.progress(65)
- logs.success('Выбрали слова-пропуски!')
-
- for sentence in workflow:
- sentence.attach_distractors_to_target_word(model=mask_filler,
- global_distractors=GLOBAL_DISTRACTORS,
- distractor_minimum=distractor_minimum,
- level_name=level,
- max_frequency=MAX_FREQUENCY,
- progress=progress_d,
- logs=logs_d)
- progress_s.progress(70)
- logs.success('Подобрали неправильные варианты!')
- for sentence in workflow:
- sentence.inflect_distractors()
- progress_s.progress(80)
- logs.success('Просклоняли и проспрягали неправильные варианты!')
-
- for sentence in workflow:
- sentence.filter_target_words(target_words_automatic_mode=tw_mode_automatic_mode)
-
- for sentence in workflow:
- sentence.sample_distractors(num_distractors=num_distractors)
- progress_s.progress(90)
- logs.success('Отобрали лучшие задания!')
-
- RESULT_TASKS = []
- for sentence in workflow:
- for target_word in sentence.target_words:
- task = TASK(task_data=target_word)
- RESULT_TASKS.append(task)
- del workflow
-
- # Compute number of final tasks
- if len(RESULT_TASKS) >= 20:
- NUMBER_TASKS = 20
- else:
- if len(RESULT_TASKS) >= 15:
- NUMBER_TASKS = 15
- else:
- if len(RESULT_TASKS) >= 10:
- NUMBER_TASKS = 10
- else:
- NUMBER_TASKS = len(RESULT_TASKS)
- RESULT_TASKS = sample(RESULT_TASKS, NUMBER_TASKS)
- RESULT_TASKS = sorted(RESULT_TASKS, key=lambda t: (t.sentence_number, t.position_in_sentence))
-
- for task in RESULT_TASKS:
- task.compile_task(max_num_distractors=num_distractors)
-
- TEXT_WITH_GAPS = []
- VARIANTS = []
- tasks_counter = 1
- for i, sentence in enumerate(current_text_sentences):
- for task in filter(lambda t: t.sentence_number == i, RESULT_TASKS):
- sentence = sentence.replace(task.original_text, f'__________({tasks_counter})')
- VARIANTS.append(task.variants)
- tasks_counter += 1
- TEXT_WITH_GAPS.append(sentence)
- del RESULT_TASKS
-
- TEXT_WITH_GAPS = ' '.join([sentence for sentence in TEXT_WITH_GAPS]).replace('%^&*', '\n')
- PREPARED_TASKS = prepare_tasks(VARIANTS)
- STUDENT_OUT = f'{TEXT_WITH_GAPS}\n\n{"=" * 70}\n\n{PREPARED_TASKS["TASKS_STUDENT"]}'
- TEACHER_OUT = f'{TEXT_WITH_GAPS}\n\n{"=" * 70}\n\n{PREPARED_TASKS["TASKS_TEACHER"]}\n\n{"=" * 70}\n\n' \
- f'{PREPARED_TASKS["KEYS_ONLY"]}'
- TOTAL_OUT = f'{original_text}\n\n{"$" * 70}\n\n{STUDENT_OUT}\n\n{"=" * 70}\n\n{PREPARED_TASKS["TASKS_TEACHER"]}' \
- f'\n\n{"$" * 70}\n\n{PREPARED_TASKS["KEYS_ONLY"]}'
- logs.success('Сейчас все будет готово!')
- progress_s.progress(90)
- save_name = save_name if save_name != '' else f'{str(datetime.datetime.now())[:-7]}_{original_text[:20]}'
- out = {
- 'name': save_name,
- 'STUDENT_OUT': STUDENT_OUT,
- 'TEACHER_OUT': TEACHER_OUT,
- 'TEXT_WITH_GAPS': TEXT_WITH_GAPS,
- 'TASKS_ONLY': PREPARED_TASKS["RAW_TASKS"],
- 'KEYS_ONLY': PREPARED_TASKS["KEYS_ONLY"],
- 'KEYS_ONLY_RAW': PREPARED_TASKS["RAW_KEYS_ONLY"],
- 'TOTAL_OUT': TOTAL_OUT,
- 'ORIGINAL': original_text,
- 'BAD_USER_TARGET_WORDS': sorted(set(global_bad_target_words))
- }
- return out
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/evaluation/class_names.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/evaluation/class_names.py
deleted file mode 100644
index c2487c2ee2d010c40db0e1c2b51c91b194e84dc7..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/evaluation/class_names.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import mmcv
-
-
-def wider_face_classes():
- return ['face']
-
-
-def voc_classes():
- return [
- 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
- 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
- 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
- ]
-
-
-def imagenet_det_classes():
- return [
- 'accordion', 'airplane', 'ant', 'antelope', 'apple', 'armadillo',
- 'artichoke', 'axe', 'baby_bed', 'backpack', 'bagel', 'balance_beam',
- 'banana', 'band_aid', 'banjo', 'baseball', 'basketball', 'bathing_cap',
- 'beaker', 'bear', 'bee', 'bell_pepper', 'bench', 'bicycle', 'binder',
- 'bird', 'bookshelf', 'bow_tie', 'bow', 'bowl', 'brassiere', 'burrito',
- 'bus', 'butterfly', 'camel', 'can_opener', 'car', 'cart', 'cattle',
- 'cello', 'centipede', 'chain_saw', 'chair', 'chime', 'cocktail_shaker',
- 'coffee_maker', 'computer_keyboard', 'computer_mouse', 'corkscrew',
- 'cream', 'croquet_ball', 'crutch', 'cucumber', 'cup_or_mug', 'diaper',
- 'digital_clock', 'dishwasher', 'dog', 'domestic_cat', 'dragonfly',
- 'drum', 'dumbbell', 'electric_fan', 'elephant', 'face_powder', 'fig',
- 'filing_cabinet', 'flower_pot', 'flute', 'fox', 'french_horn', 'frog',
- 'frying_pan', 'giant_panda', 'goldfish', 'golf_ball', 'golfcart',
- 'guacamole', 'guitar', 'hair_dryer', 'hair_spray', 'hamburger',
- 'hammer', 'hamster', 'harmonica', 'harp', 'hat_with_a_wide_brim',
- 'head_cabbage', 'helmet', 'hippopotamus', 'horizontal_bar', 'horse',
- 'hotdog', 'iPod', 'isopod', 'jellyfish', 'koala_bear', 'ladle',
- 'ladybug', 'lamp', 'laptop', 'lemon', 'lion', 'lipstick', 'lizard',
- 'lobster', 'maillot', 'maraca', 'microphone', 'microwave', 'milk_can',
- 'miniskirt', 'monkey', 'motorcycle', 'mushroom', 'nail', 'neck_brace',
- 'oboe', 'orange', 'otter', 'pencil_box', 'pencil_sharpener', 'perfume',
- 'person', 'piano', 'pineapple', 'ping-pong_ball', 'pitcher', 'pizza',
- 'plastic_bag', 'plate_rack', 'pomegranate', 'popsicle', 'porcupine',
- 'power_drill', 'pretzel', 'printer', 'puck', 'punching_bag', 'purse',
- 'rabbit', 'racket', 'ray', 'red_panda', 'refrigerator',
- 'remote_control', 'rubber_eraser', 'rugby_ball', 'ruler',
- 'salt_or_pepper_shaker', 'saxophone', 'scorpion', 'screwdriver',
- 'seal', 'sheep', 'ski', 'skunk', 'snail', 'snake', 'snowmobile',
- 'snowplow', 'soap_dispenser', 'soccer_ball', 'sofa', 'spatula',
- 'squirrel', 'starfish', 'stethoscope', 'stove', 'strainer',
- 'strawberry', 'stretcher', 'sunglasses', 'swimming_trunks', 'swine',
- 'syringe', 'table', 'tape_player', 'tennis_ball', 'tick', 'tie',
- 'tiger', 'toaster', 'traffic_light', 'train', 'trombone', 'trumpet',
- 'turtle', 'tv_or_monitor', 'unicycle', 'vacuum', 'violin',
- 'volleyball', 'waffle_iron', 'washer', 'water_bottle', 'watercraft',
- 'whale', 'wine_bottle', 'zebra'
- ]
-
-
-def imagenet_vid_classes():
- return [
- 'airplane', 'antelope', 'bear', 'bicycle', 'bird', 'bus', 'car',
- 'cattle', 'dog', 'domestic_cat', 'elephant', 'fox', 'giant_panda',
- 'hamster', 'horse', 'lion', 'lizard', 'monkey', 'motorcycle', 'rabbit',
- 'red_panda', 'sheep', 'snake', 'squirrel', 'tiger', 'train', 'turtle',
- 'watercraft', 'whale', 'zebra'
- ]
-
-
-def coco_classes():
- return [
- 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
- 'truck', 'boat', 'traffic_light', 'fire_hydrant', 'stop_sign',
- 'parking_meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
- 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
- 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
- 'sports_ball', 'kite', 'baseball_bat', 'baseball_glove', 'skateboard',
- 'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup', 'fork',
- 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
- 'broccoli', 'carrot', 'hot_dog', 'pizza', 'donut', 'cake', 'chair',
- 'couch', 'potted_plant', 'bed', 'dining_table', 'toilet', 'tv',
- 'laptop', 'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
- 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
- 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush'
- ]
-
-
-def cityscapes_classes():
- return [
- 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
- 'bicycle'
- ]
-
-
-dataset_aliases = {
- 'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'],
- 'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'],
- 'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'],
- 'coco': ['coco', 'mscoco', 'ms_coco'],
- 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WIDERFace'],
- 'cityscapes': ['cityscapes']
-}
-
-
-def get_classes(dataset):
- """Get class names of a dataset."""
- alias2name = {}
- for name, aliases in dataset_aliases.items():
- for alias in aliases:
- alias2name[alias] = name
-
- if mmcv.is_str(dataset):
- if dataset in alias2name:
- labels = eval(alias2name[dataset] + '_classes()')
- else:
- raise ValueError(f'Unrecognized dataset: {dataset}')
- else:
- raise TypeError(f'dataset must a str, but got {type(dataset)}')
- return labels
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/grid_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/grid_head.py
deleted file mode 100644
index 83058cbdda934ebfc3a76088e1820848ac01b78b..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/grid_head.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init, normal_init
-
-from mmdet.models.builder import HEADS, build_loss
-
-
-@HEADS.register_module()
-class GridHead(nn.Module):
-
- def __init__(self,
- grid_points=9,
- num_convs=8,
- roi_feat_size=14,
- in_channels=256,
- conv_kernel_size=3,
- point_feat_channels=64,
- deconv_kernel_size=4,
- class_agnostic=False,
- loss_grid=dict(
- type='CrossEntropyLoss', use_sigmoid=True,
- loss_weight=15),
- conv_cfg=None,
- norm_cfg=dict(type='GN', num_groups=36)):
- super(GridHead, self).__init__()
- self.grid_points = grid_points
- self.num_convs = num_convs
- self.roi_feat_size = roi_feat_size
- self.in_channels = in_channels
- self.conv_kernel_size = conv_kernel_size
- self.point_feat_channels = point_feat_channels
- self.conv_out_channels = self.point_feat_channels * self.grid_points
- self.class_agnostic = class_agnostic
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN':
- assert self.conv_out_channels % norm_cfg['num_groups'] == 0
-
- assert self.grid_points >= 4
- self.grid_size = int(np.sqrt(self.grid_points))
- if self.grid_size * self.grid_size != self.grid_points:
- raise ValueError('grid_points must be a square number')
-
- # the predicted heatmap is half of whole_map_size
- if not isinstance(self.roi_feat_size, int):
- raise ValueError('Only square RoIs are supporeted in Grid R-CNN')
- self.whole_map_size = self.roi_feat_size * 4
-
- # compute point-wise sub-regions
- self.sub_regions = self.calc_sub_regions()
-
- self.convs = []
- for i in range(self.num_convs):
- in_channels = (
- self.in_channels if i == 0 else self.conv_out_channels)
- stride = 2 if i == 0 else 1
- padding = (self.conv_kernel_size - 1) // 2
- self.convs.append(
- ConvModule(
- in_channels,
- self.conv_out_channels,
- self.conv_kernel_size,
- stride=stride,
- padding=padding,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=True))
- self.convs = nn.Sequential(*self.convs)
-
- self.deconv1 = nn.ConvTranspose2d(
- self.conv_out_channels,
- self.conv_out_channels,
- kernel_size=deconv_kernel_size,
- stride=2,
- padding=(deconv_kernel_size - 2) // 2,
- groups=grid_points)
- self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels)
- self.deconv2 = nn.ConvTranspose2d(
- self.conv_out_channels,
- grid_points,
- kernel_size=deconv_kernel_size,
- stride=2,
- padding=(deconv_kernel_size - 2) // 2,
- groups=grid_points)
-
- # find the 4-neighbor of each grid point
- self.neighbor_points = []
- grid_size = self.grid_size
- for i in range(grid_size): # i-th column
- for j in range(grid_size): # j-th row
- neighbors = []
- if i > 0: # left: (i - 1, j)
- neighbors.append((i - 1) * grid_size + j)
- if j > 0: # up: (i, j - 1)
- neighbors.append(i * grid_size + j - 1)
- if j < grid_size - 1: # down: (i, j + 1)
- neighbors.append(i * grid_size + j + 1)
- if i < grid_size - 1: # right: (i + 1, j)
- neighbors.append((i + 1) * grid_size + j)
- self.neighbor_points.append(tuple(neighbors))
- # total edges in the grid
- self.num_edges = sum([len(p) for p in self.neighbor_points])
-
- self.forder_trans = nn.ModuleList() # first-order feature transition
- self.sorder_trans = nn.ModuleList() # second-order feature transition
- for neighbors in self.neighbor_points:
- fo_trans = nn.ModuleList()
- so_trans = nn.ModuleList()
- for _ in range(len(neighbors)):
- # each transition module consists of a 5x5 depth-wise conv and
- # 1x1 conv.
- fo_trans.append(
- nn.Sequential(
- nn.Conv2d(
- self.point_feat_channels,
- self.point_feat_channels,
- 5,
- stride=1,
- padding=2,
- groups=self.point_feat_channels),
- nn.Conv2d(self.point_feat_channels,
- self.point_feat_channels, 1)))
- so_trans.append(
- nn.Sequential(
- nn.Conv2d(
- self.point_feat_channels,
- self.point_feat_channels,
- 5,
- 1,
- 2,
- groups=self.point_feat_channels),
- nn.Conv2d(self.point_feat_channels,
- self.point_feat_channels, 1)))
- self.forder_trans.append(fo_trans)
- self.sorder_trans.append(so_trans)
-
- self.loss_grid = build_loss(loss_grid)
-
- def init_weights(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
- # TODO: compare mode = "fan_in" or "fan_out"
- kaiming_init(m)
- for m in self.modules():
- if isinstance(m, nn.ConvTranspose2d):
- normal_init(m, std=0.001)
- nn.init.constant_(self.deconv2.bias, -np.log(0.99 / 0.01))
-
- def forward(self, x):
- assert x.shape[-1] == x.shape[-2] == self.roi_feat_size
- # RoI feature transformation, downsample 2x
- x = self.convs(x)
-
- c = self.point_feat_channels
- # first-order fusion
- x_fo = [None for _ in range(self.grid_points)]
- for i, points in enumerate(self.neighbor_points):
- x_fo[i] = x[:, i * c:(i + 1) * c]
- for j, point_idx in enumerate(points):
- x_fo[i] = x_fo[i] + self.forder_trans[i][j](
- x[:, point_idx * c:(point_idx + 1) * c])
-
- # second-order fusion
- x_so = [None for _ in range(self.grid_points)]
- for i, points in enumerate(self.neighbor_points):
- x_so[i] = x[:, i * c:(i + 1) * c]
- for j, point_idx in enumerate(points):
- x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx])
-
- # predicted heatmap with fused features
- x2 = torch.cat(x_so, dim=1)
- x2 = self.deconv1(x2)
- x2 = F.relu(self.norm1(x2), inplace=True)
- heatmap = self.deconv2(x2)
-
- # predicted heatmap with original features (applicable during training)
- if self.training:
- x1 = x
- x1 = self.deconv1(x1)
- x1 = F.relu(self.norm1(x1), inplace=True)
- heatmap_unfused = self.deconv2(x1)
- else:
- heatmap_unfused = heatmap
-
- return dict(fused=heatmap, unfused=heatmap_unfused)
-
- def calc_sub_regions(self):
- """Compute point specific representation regions.
-
- See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details.
- """
- # to make it consistent with the original implementation, half_size
- # is computed as 2 * quarter_size, which is smaller
- half_size = self.whole_map_size // 4 * 2
- sub_regions = []
- for i in range(self.grid_points):
- x_idx = i // self.grid_size
- y_idx = i % self.grid_size
- if x_idx == 0:
- sub_x1 = 0
- elif x_idx == self.grid_size - 1:
- sub_x1 = half_size
- else:
- ratio = x_idx / (self.grid_size - 1) - 0.25
- sub_x1 = max(int(ratio * self.whole_map_size), 0)
-
- if y_idx == 0:
- sub_y1 = 0
- elif y_idx == self.grid_size - 1:
- sub_y1 = half_size
- else:
- ratio = y_idx / (self.grid_size - 1) - 0.25
- sub_y1 = max(int(ratio * self.whole_map_size), 0)
- sub_regions.append(
- (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size))
- return sub_regions
-
- def get_targets(self, sampling_results, rcnn_train_cfg):
- # mix all samples (across images) together.
- pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results],
- dim=0).cpu()
- pos_gt_bboxes = torch.cat(
- [res.pos_gt_bboxes for res in sampling_results], dim=0).cpu()
- assert pos_bboxes.shape == pos_gt_bboxes.shape
-
- # expand pos_bboxes to 2x of original size
- x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
- y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
- x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
- y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
- pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
- pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1)
- pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1)
-
- num_rois = pos_bboxes.shape[0]
- map_size = self.whole_map_size
- # this is not the final target shape
- targets = torch.zeros((num_rois, self.grid_points, map_size, map_size),
- dtype=torch.float)
-
- # pre-compute interpolation factors for all grid points.
- # the first item is the factor of x-dim, and the second is y-dim.
- # for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1)
- factors = []
- for j in range(self.grid_points):
- x_idx = j // self.grid_size
- y_idx = j % self.grid_size
- factors.append((1 - x_idx / (self.grid_size - 1),
- 1 - y_idx / (self.grid_size - 1)))
-
- radius = rcnn_train_cfg.pos_radius
- radius2 = radius**2
- for i in range(num_rois):
- # ignore small bboxes
- if (pos_bbox_ws[i] <= self.grid_size
- or pos_bbox_hs[i] <= self.grid_size):
- continue
- # for each grid point, mark a small circle as positive
- for j in range(self.grid_points):
- factor_x, factor_y = factors[j]
- gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + (
- 1 - factor_x) * pos_gt_bboxes[i, 2]
- gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + (
- 1 - factor_y) * pos_gt_bboxes[i, 3]
-
- cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] *
- map_size)
- cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] *
- map_size)
-
- for x in range(cx - radius, cx + radius + 1):
- for y in range(cy - radius, cy + radius + 1):
- if x >= 0 and x < map_size and y >= 0 and y < map_size:
- if (x - cx)**2 + (y - cy)**2 <= radius2:
- targets[i, j, y, x] = 1
- # reduce the target heatmap size by a half
- # proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688).
- sub_targets = []
- for i in range(self.grid_points):
- sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i]
- sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2])
- sub_targets = torch.cat(sub_targets, dim=1)
- sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device)
- return sub_targets
-
- def loss(self, grid_pred, grid_targets):
- loss_fused = self.loss_grid(grid_pred['fused'], grid_targets)
- loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets)
- loss_grid = loss_fused + loss_unfused
- return dict(loss_grid=loss_grid)
-
- def get_bboxes(self, det_bboxes, grid_pred, img_metas):
- # TODO: refactoring
- assert det_bboxes.shape[0] == grid_pred.shape[0]
- det_bboxes = det_bboxes.cpu()
- cls_scores = det_bboxes[:, [4]]
- det_bboxes = det_bboxes[:, :4]
- grid_pred = grid_pred.sigmoid().cpu()
-
- R, c, h, w = grid_pred.shape
- half_size = self.whole_map_size // 4 * 2
- assert h == w == half_size
- assert c == self.grid_points
-
- # find the point with max scores in the half-sized heatmap
- grid_pred = grid_pred.view(R * c, h * w)
- pred_scores, pred_position = grid_pred.max(dim=1)
- xs = pred_position % w
- ys = pred_position // w
-
- # get the position in the whole heatmap instead of half-sized heatmap
- for i in range(self.grid_points):
- xs[i::self.grid_points] += self.sub_regions[i][0]
- ys[i::self.grid_points] += self.sub_regions[i][1]
-
- # reshape to (num_rois, grid_points)
- pred_scores, xs, ys = tuple(
- map(lambda x: x.view(R, c), [pred_scores, xs, ys]))
-
- # get expanded pos_bboxes
- widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1)
- heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1)
- x1 = (det_bboxes[:, 0, None] - widths / 2)
- y1 = (det_bboxes[:, 1, None] - heights / 2)
- # map the grid point to the absolute coordinates
- abs_xs = (xs.float() + 0.5) / w * widths + x1
- abs_ys = (ys.float() + 0.5) / h * heights + y1
-
- # get the grid points indices that fall on the bbox boundaries
- x1_inds = [i for i in range(self.grid_size)]
- y1_inds = [i * self.grid_size for i in range(self.grid_size)]
- x2_inds = [
- self.grid_points - self.grid_size + i
- for i in range(self.grid_size)
- ]
- y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)]
-
- # voting of all grid points on some boundary
- bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, x1_inds].sum(dim=1, keepdim=True))
- bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, y1_inds].sum(dim=1, keepdim=True))
- bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, x2_inds].sum(dim=1, keepdim=True))
- bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, y2_inds].sum(dim=1, keepdim=True))
-
- bbox_res = torch.cat(
- [bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1)
- bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1])
- bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0])
-
- return bbox_res
diff --git a/spaces/aj-data/AP2223_P1/app.py b/spaces/aj-data/AP2223_P1/app.py
deleted file mode 100644
index 3e63f8df6714b7a5310d4559475f4c06b52a3d6f..0000000000000000000000000000000000000000
--- a/spaces/aj-data/AP2223_P1/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from huggingface_hub import from_pretrained_fastai
-import gradio as gr
-from fastai.vision.all import *
-
-
-
-# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
-repo_id = "aj-data/Practica01_01"
-
-learner = from_pretrained_fastai(repo_id)
-labels = learner.dls.vocab
-
-# Definimos una función que se encarga de llevar a cabo las predicciones
-def predict(img):
- #img = PILImage.create(img)
- pred,pred_idx,probs = learner.predict(img)
- return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-# Creamos la interfaz y la lanzamos.
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['06be1092a062.png','8c0d05233238.png']).launch(share=False)
\ No newline at end of file
diff --git a/spaces/akhaliq/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan/README.md b/spaces/akhaliq/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan/README.md
deleted file mode 100644
index e49f6b7336bf125a7527866f2b5f4532d79f46da..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: conformer_fastspeech2_hifigan
-emoji: 🏢
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/alamin655/websurfx/public/static/settings.js b/spaces/alamin655/websurfx/public/static/settings.js
deleted file mode 100644
index 42b8a4be7062398b44bde185555a05a08b12af33..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/public/static/settings.js
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * This function handles the toggling of selections of all upstream search engines
- * options in the settings page under the tab engines.
- */
-function toggleAllSelection() {
- document
- .querySelectorAll('.engine')
- .forEach(
- (engine_checkbox) =>
- (engine_checkbox.checked =
- document.querySelector('.select_all').checked)
- )
-}
-
-/**
- * This function adds the functionality to sidebar buttons to only show settings
- * related to that tab.
- * @param {HTMLElement} current_tab - The current tab that was clicked.
- */
-function setActiveTab(current_tab) {
- // Remove the active class from all tabs and buttons
- document
- .querySelectorAll('.tab')
- .forEach((tab) => tab.classList.remove('active'))
- document
- .querySelectorAll('.btn')
- .forEach((tab) => tab.classList.remove('active'))
-
- // Add the active class to the current tab and its corresponding settings
- current_tab.classList.add('active')
- document
- .querySelector(`.${current_tab.innerText.toLowerCase().replace(' ', '_')}`)
- .classList.add('active')
-}
-
-/**
- * This function adds the functionality to save all the user selected preferences
- * to be saved in a cookie on the users machine.
- */
-function setClientSettings() {
- // Create an object to store the user's preferences
- let cookie_dictionary = new Object()
-
- // Loop through all select tags and add their values to the cookie dictionary
- document.querySelectorAll('select').forEach((select_tag) => {
- if (select_tag.name === 'themes') {
- cookie_dictionary['theme'] = select_tag.value
- } else if (select_tag.name === 'colorschemes') {
- cookie_dictionary['colorscheme'] = select_tag.value
- }
- })
-
- // Loop through all engine checkboxes and add their values to the cookie dictionary
- let engines = []
- document.querySelectorAll('.engine').forEach((engine_checkbox) => {
- if (engine_checkbox.checked === true) {
- engines.push(engine_checkbox.parentNode.parentNode.innerText.trim())
- }
- })
- cookie_dictionary['engines'] = engines
-
- // Set the expiration date for the cookie to 1 year from the current date
- let expiration_date = new Date()
- expiration_date.setFullYear(expiration_date.getFullYear() + 1)
-
- // Save the cookie to the user's machine
- document.cookie = `appCookie=${JSON.stringify(
- cookie_dictionary
- )}; expires=${expiration_date.toUTCString()}`
-
- // Display a success message to the user
- document.querySelector('.message').innerText =
- '✅ The settings have been saved sucessfully!!'
-
- // Clear the success message after 10 seconds
- setTimeout(() => {
- document.querySelector('.message').innerText = ''
- }, 10000)
-}
-
-/**
- * This functions gets the saved cookies if it is present on the user's machine If it
- * is available then it is parsed and converted to an object which is then used to
- * retrieve the preferences that the user had selected previously and is then loaded in the
- * website otherwise the function does nothing and the default server side settings are loaded.
- */
-function getClientSettings() {
- // Get the appCookie from the user's machine
- let cookie = decodeURIComponent(document.cookie)
-
- // If the cookie is not empty, parse it and use it to set the user's preferences
- if (cookie !== '') {
- let cookie_value = decodeURIComponent(document.cookie)
- .split(';')
- .map((item) => item.split('='))
- .reduce((acc, [_, v]) => (acc = JSON.parse(v)) && acc, {})
-
- // Loop through all link tags and update their href values to match the user's preferences
- let links = Array.from(document.querySelectorAll('link')).forEach(
- (item) => {
- if (item.href.includes('static/themes')) {
- item.href = `static/themes/${cookie_value['theme']}.css`
- } else if (item.href.includes('static/colorschemes')) {
- item.href = `static/colorschemes/${cookie_value['colorscheme']}.css`
- }
- }
- )
- }
-}
diff --git a/spaces/alex-mindspace/gpt-agents/README.md b/spaces/alex-mindspace/gpt-agents/README.md
deleted file mode 100644
index 8045e15408c9a4b78ad830bd6cb2e524f965e891..0000000000000000000000000000000000000000
--- a/spaces/alex-mindspace/gpt-agents/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Swarm Agents
-emoji: 👁
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alex42t/EssayChecker/features.py b/spaces/alex42t/EssayChecker/features.py
deleted file mode 100644
index b9c9f8e1fa550d08937fe486083fc71fc5b2af23..0000000000000000000000000000000000000000
--- a/spaces/alex42t/EssayChecker/features.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import numpy as np
-import pandas as pd
-import language_tool_python
-import readability
-import enchant
-from enchant.checker import SpellChecker
-from collections import OrderedDict
-import nltk
-nltk.download('punkt')
-from nltk.tokenize import word_tokenize
-
-
-class FeatureGenerator:
- def __init__(self):
- self.spell_checker = SpellChecker('en_US')
- self.enchant_dict = enchant.Dict("en_US")
-
- common_words_1k_filename = './1-1000.txt'
- with open(common_words_1k_filename) as f:
- self.common_words_1k = set(x.strip() for x in f.readlines())
-
- common_words_filename_10k = './google-10000-english-no-swears.txt'
- with open(common_words_filename_10k) as f:
- self.common_words_10k = set(x.strip() for x in f.readlines())
-
- # make sure common_words are a subset as well
- self.common_words_10k.update(self.common_words_1k)
-
- profanity_filename = './profanity.txt'
- with open(profanity_filename) as f:
- self.profanity_set = set(x.strip() for x in f.readlines())
-
- words_freq_filename = "./count_1w.txt"
- self.words_freq = pd.read_csv(words_freq_filename,
- names=['word', 'freq'],
- sep='\t',
- header=None,
- dtype={'word': str, 'freq': int},
- keep_default_na=False,
- na_values=[''])
-
- self.words_freq = self.words_freq.set_index('word')
-
- self.language_tool = language_tool_python.LanguageTool('en-US')
-
-
- def text_preprocess(self, text: str):
- '''
- Transform text to be processed by readability
-
- :param text: input text
-
- :return: str preprocessed text
- '''
- text = text.strip()
-
- # new paragraph starts with \n\n
- # readability also requires each sentence to end with \n
- paragraphs = [p.strip()\
- .replace('. ', '.\n')\
- .replace('? ', '?\n')\
- .replace('! ', '!\n') for p in text.split('\n\n')]
-
- return "\n\n".join(paragraphs)
-
-
- def misspelled_count(self, text: str):
- '''
- Get count out misspelled words by enchant SpellChecker
-
- :param text: input text
-
- :return: count of misspelled words in text
- '''
- self.spell_checker.set_text(text)
- return len(list(self.spell_checker))
-
- def flatten_readability(self, r: OrderedDict):
- '''
- Flatten readability output by adding prefixes
-
- :param r: OrderedDict of readability output
-
- :return: dict
- '''
- out = {}
- for k, group in r.items():
- prefix = {'readability grades': '',
- 'sentence info': '',
- 'word usage': 'wu_',
- 'sentence beginnings': 'sb_'}[k]
- for var_name, value in group.items():
- out[prefix + var_name] = value
- return out
-
- def get_noncommon_words_count(self, text, common_words_dict):
- # first, tokenize the text
- # second, iterate over tokens and see whether
- # a. it is a word
- # b. not in common_words
- # c. correctly spelled
- # d. does not have underscore '_'
-
- def is_noncommon_word(w):
- return len(w) > 2 and\
- w not in common_words_dict and\
- '_' not in w and\
- self.enchant_dict.check(w)
-
- return sum(is_noncommon_word(x) for x in word_tokenize(text.lower()))
-
-
- def get_noncommon_words_count_1k(self, text):
- return self.get_noncommon_words_count(text, self.common_words_1k)
-
- def get_noncommon_words_count_10k(self, text):
- return self.get_noncommon_words_count(text, self.common_words_10k)
-
-
-
- def get_profanity_count(self, text):
- return sum(x in self.profanity_set for x in word_tokenize(text.lower()))
-
- def get_uncommon_words_counts(self, text):
- word_freq_thresholds = np.array([1e8, 1e7, 1e6, 1e5], dtype=int)
- counts = np.zeros(len(word_freq_thresholds), dtype=int)
- for w in word_tokenize(text):
- if len(w) < 3 or '_' in w or not self.enchant_dict.check(w) or w not in self.words_freq.index:
- continue
- w_freq = self.words_freq.loc[w].values[0]
- counts += (word_freq_thresholds > w_freq)
- return counts
-
-
-
- # I have commented out categories giving 0 columns in train dataset
- # SEMANTICS is mostly 0, we drop it as well
- LT_categories = ['CASING',
- #'COLLOQUIALISMS',
- 'COMPOUNDING',
- 'CONFUSED_WORDS',
- #'FALSE_FRIENDS',
- #'GENDER_NEUTRALITY',
- 'GRAMMAR',
- 'MISC',
- 'PUNCTUATION',
- 'REDUNDANCY',
- #'REGIONALISMS',
- #'REPETITIONS',
- #'REPETITIONS_STYLE',
- #'SEMANTICS',
- 'STYLE',
- 'TYPOGRAPHY',
- 'TYPOS',
- 'TOTAL',
- ]
- def get_LT_features(self, text):
- '''
- Generates LanguateTool features: each category count and a total number
- '''
- matches = self.language_tool.check(text)
- cat_counts = [sum(m.category == cat for m in matches) for cat in self.LT_categories[:-1]]
- return cat_counts + [len(matches)]
-
-
- def generate_features(self, df: pd.DataFrame):
- '''
- Generate features from a dataframe with `full_text` column containing english text
-
- :param df: input dataframe
-
- :return: pd.DataFrame with features and possibly updated `full_text` column
- '''
-
- res_df = df.copy()
- #res_df['full_text'] = res_df['full_text'].apply(self.text_preprocess)
-
-
-
- features_df = res_df[['full_text']].apply(lambda row:
- self.flatten_readability(
- readability.getmeasures(
- self.text_preprocess(row[0]), lang='en')),
- axis='columns',
- result_type='expand')
-
- features_df['text_len'] = res_df['full_text'].apply(lambda x: len(x))
-
- features_df['misspelled'] = res_df['full_text'].apply(self.misspelled_count)
- features_df['noncommon_words_1k'] = res_df['full_text'].apply(self.get_noncommon_words_count_1k)
- features_df['noncommon_words_10k'] = res_df['full_text'].apply(self.get_noncommon_words_count_10k)
- features_df['profanity_count'] = res_df['full_text'].apply(self.get_profanity_count)
- features_df[['uwc1e8', 'uwc1e7', 'uwc1e6', 'uwc1e5']] =\
- res_df[['full_text']].apply(lambda x: self.get_uncommon_words_counts(x[0]),
- axis='columns',
- result_type='expand')
-
- # Generate ratio features
- words_ratio_features = ['wordtypes',
- 'long_words',
- 'complex_words',
- 'complex_words_dc',
- 'wu_tobeverb',
- 'wu_auxverb',
- 'wu_conjunction',
- 'wu_pronoun',
- 'wu_preposition',
- 'wu_nominalization',
- 'misspelled',
- 'noncommon_words_1k',
- 'noncommon_words_10k',
- 'uwc1e8',
- 'uwc1e7',
- 'uwc1e6',
- 'uwc1e5',
- ]
- features_df[[x + "_ratio" for x in words_ratio_features]] = features_df[words_ratio_features]\
- .div(features_df['words'], axis=0)
- sentences_ratio_features = ['sb_pronoun',
- 'sb_interrogative',
- 'sb_article',
- 'sb_subordination',
- 'sb_conjunction',
- 'sb_preposition',
- ]
- features_df[[x + "_ratio" for x in sentences_ratio_features]] = features_df[sentences_ratio_features]\
- .div(features_df['sentences'], axis=0)
-
-
- features_df[['LT_' + x for x in self.LT_categories]] = res_df[['full_text']]\
- .apply(lambda x: self.get_LT_features(x[0]),
- axis=1,
- result_type='expand')
-
- features_df[['LT_' + x + '_ratio' for x in self.LT_categories]] = features_df[['LT_' + x for x in self.LT_categories]]\
- .div(features_df['words'], axis=0)
-
- features_df = features_df.sort_index(axis=1)
- return pd.concat([res_df, features_df], axis='columns')
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/__init__.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/__init__.py
deleted file mode 100644
index 44becd68439b37acb37166ff4bcfed11ab7f2b48..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-from .file_cache import FileCache # noqa
-from .redis_cache import RedisCache # noqa
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/progress_bar.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/progress_bar.py
deleted file mode 100644
index 1797b5f786eb0d5adb2f5d1e3e5ee0e2b4541eaf..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/progress_bar.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import math
-from functools import lru_cache
-from time import monotonic
-from typing import Iterable, List, Optional
-
-from .color import Color, blend_rgb
-from .color_triplet import ColorTriplet
-from .console import Console, ConsoleOptions, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import Style, StyleType
-
-# Number of characters before 'pulse' animation repeats
-PULSE_SIZE = 20
-
-
-class ProgressBar(JupyterMixin):
- """Renders a (progress) bar. Used by rich.progress.
-
- Args:
- total (float, optional): Number of steps in the bar. Defaults to 100.
- completed (float, optional): Number of steps completed. Defaults to 0.
- width (int, optional): Width of the bar, or ``None`` for maximum width. Defaults to None.
- pulse (bool, optional): Enable pulse effect. Defaults to False.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.done".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- animation_time (Optional[float], optional): Time in seconds to use for animation, or None to use system time.
- """
-
- def __init__(
- self,
- total: float = 100.0,
- completed: float = 0,
- width: Optional[int] = None,
- pulse: bool = False,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- animation_time: Optional[float] = None,
- ):
- self.total = total
- self.completed = completed
- self.width = width
- self.pulse = pulse
- self.style = style
- self.complete_style = complete_style
- self.finished_style = finished_style
- self.pulse_style = pulse_style
- self.animation_time = animation_time
-
- self._pulse_segments: Optional[List[Segment]] = None
-
- def __repr__(self) -> str:
- return f"<Bar {self.completed!r} of {self.total!r}>"
-
- @property
- def percentage_completed(self) -> float:
- """Calculate percentage complete."""
- completed = (self.completed / self.total) * 100.0
- completed = min(100, max(0.0, completed))
- return completed
-
- @lru_cache(maxsize=16)
- def _get_pulse_segments(
- self,
- fore_style: Style,
- back_style: Style,
- color_system: str,
- no_color: bool,
- ascii: bool = False,
- ) -> List[Segment]:
- """Get a list of segments to render a pulse animation.
-
- Returns:
- List[Segment]: A list of segments, one segment per character.
- """
- bar = "-" if ascii else "━"
- segments: List[Segment] = []
- if color_system not in ("standard", "eight_bit", "truecolor") or no_color:
- segments += [Segment(bar, fore_style)] * (PULSE_SIZE // 2)
- segments += [Segment(" " if no_color else bar, back_style)] * (
- PULSE_SIZE - (PULSE_SIZE // 2)
- )
- return segments
-
- append = segments.append
- fore_color = (
- fore_style.color.get_truecolor()
- if fore_style.color
- else ColorTriplet(255, 0, 255)
- )
- back_color = (
- back_style.color.get_truecolor()
- if back_style.color
- else ColorTriplet(0, 0, 0)
- )
- cos = math.cos
- pi = math.pi
- _Segment = Segment
- _Style = Style
- from_triplet = Color.from_triplet
-
- for index in range(PULSE_SIZE):
- position = index / PULSE_SIZE
- fade = 0.5 + cos((position * pi * 2)) / 2.0
- color = blend_rgb(fore_color, back_color, cross_fade=fade)
- append(_Segment(bar, _Style(color=from_triplet(color))))
- return segments
-
- def update(self, completed: float, total: Optional[float] = None) -> None:
- """Update progress with new values.
-
- Args:
- completed (float): Number of steps completed.
- total (float, optional): Total number of steps, or ``None`` to not change. Defaults to None.
- """
- self.completed = completed
- self.total = total if total is not None else self.total
-
- def _render_pulse(
- self, console: Console, width: int, ascii: bool = False
- ) -> Iterable[Segment]:
- """Renders the pulse animation.
-
- Args:
- console (Console): Console instance.
- width (int): Width in characters of pulse animation.
-
- Returns:
- RenderResult: [description]
-
- Yields:
- Iterator[Segment]: Segments to render pulse
- """
- fore_style = console.get_style(self.pulse_style, default="white")
- back_style = console.get_style(self.style, default="black")
-
- pulse_segments = self._get_pulse_segments(
- fore_style, back_style, console.color_system, console.no_color, ascii=ascii
- )
- segment_count = len(pulse_segments)
- current_time = (
- monotonic() if self.animation_time is None else self.animation_time
- )
- segments = pulse_segments * (int(width / segment_count) + 2)
- offset = int(-current_time * 15) % segment_count
- segments = segments[offset : offset + width]
- yield from segments
-
- def __rich_console__(
- self, console: Console, options: ConsoleOptions
- ) -> RenderResult:
-
- width = min(self.width or options.max_width, options.max_width)
- ascii = options.legacy_windows or options.ascii_only
- if self.pulse:
- yield from self._render_pulse(console, width, ascii=ascii)
- return
-
- completed = min(self.total, max(0, self.completed))
-
- bar = "-" if ascii else "━"
- half_bar_right = " " if ascii else "╸"
- half_bar_left = " " if ascii else "╺"
- complete_halves = (
- int(width * 2 * completed / self.total) if self.total else width * 2
- )
- bar_count = complete_halves // 2
- half_bar_count = complete_halves % 2
- style = console.get_style(self.style)
- complete_style = console.get_style(
- self.complete_style if self.completed < self.total else self.finished_style
- )
- _Segment = Segment
- if bar_count:
- yield _Segment(bar * bar_count, complete_style)
- if half_bar_count:
- yield _Segment(half_bar_right * half_bar_count, complete_style)
-
- if not console.no_color:
- remaining_bars = width - bar_count - half_bar_count
- if remaining_bars and console.color_system is not None:
- if not half_bar_count and bar_count:
- yield _Segment(half_bar_left, style)
- remaining_bars -= 1
- if remaining_bars:
- yield _Segment(bar * remaining_bars, style)
-
- def __rich_measure__(
- self, console: Console, options: ConsoleOptions
- ) -> Measurement:
- return (
- Measurement(self.width, self.width)
- if self.width is not None
- else Measurement(4, options.max_width)
- )
-
-
-if __name__ == "__main__": # pragma: no cover
- console = Console()
- bar = ProgressBar(width=50, total=100)
-
- import time
-
- console.show_cursor(False)
- for n in range(0, 101, 1):
- bar.update(n)
- console.print(bar)
- console.file.write("\r")
- time.sleep(0.05)
- console.show_cursor(True)
- console.print()
diff --git a/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/UBAR_code/reader.py b/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/UBAR_code/reader.py
deleted file mode 100644
index bb59fe89c553de748d5090e315d6e797eec85a3c..0000000000000000000000000000000000000000
--- a/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/UBAR_code/reader.py
+++ /dev/null
@@ -1,1263 +0,0 @@
-import csv
-import json
-import logging
-import os
-import random
-from collections import OrderedDict
-from copy import deepcopy
-
-import numpy as np
-import spacy
-from transformers import GPT2Tokenizer
-
-from . import ontology
-from . import utils
-from .config import global_config as cfg
-from .db_ops import MultiWozDB
-
-# from config21 import global_config as cfg
-
-
-class _ReaderBase(object):
- def __init__(self):
- self.train, self.dev, self.test = [], [], []
- self.vocab = None
- self.db = None
- self.set_stats = {}
-
- def _bucket_by_turn(self, encoded_data):
- turn_bucket = {}
- for dial in encoded_data:
- turn_len = len(dial)
- if turn_len not in turn_bucket:
- turn_bucket[turn_len] = []
- turn_bucket[turn_len].append(dial)
- del_l = []
- for k in turn_bucket:
- if k >= 5:
- del_l.append(k)
- logging.debug("bucket %d instance %d" % (k, len(turn_bucket[k])))
- # for k in del_l:
- # turn_bucket.pop(k)
- return OrderedDict(sorted(turn_bucket.items(), key=lambda i: i[0]))
-
- def _construct_mini_batch(self, data):
- all_batches = []
- batch = []
- for dial in data:
- batch.append(dial)
- if len(batch) == cfg.batch_size:
- # print('batch size: %d, batch num +1'%(len(batch)))
- all_batches.append(batch)
- batch = []
- # if remainder > 1/2 batch_size, just put them in the previous batch, otherwise form a new batch
- # print('last batch size: %d, batch num +1'%(len(batch)))
- if (len(batch) % len(cfg.cuda_device)) != 0:
- batch = batch[: -(len(batch) % len(cfg.cuda_device))]
- if len(batch) > 0.5 * cfg.batch_size:
- all_batches.append(batch)
- elif len(all_batches):
- all_batches[-1].extend(batch)
- else:
- all_batches.append(batch)
- return all_batches
-
- def transpose_batch(self, batch):
- dial_batch = []
- turn_num = len(batch[0])
- for turn in range(turn_num):
- turn_l = {}
- for dial in batch:
- this_turn = dial[turn]
- for k in this_turn:
- if k not in turn_l:
- turn_l[k] = []
- turn_l[k].append(this_turn[k])
- dial_batch.append(turn_l)
- return dial_batch
-
- def inverse_transpose_turn(self, turn_list):
- """
- eval, one dialog at a time
- """
- dialogs = {}
- turn_num = len(turn_list)
- dial_id = turn_list[0]["dial_id"]
- dialogs[dial_id] = []
- for turn_idx in range(turn_num):
- dial_turn = {}
- turn = turn_list[turn_idx]
- for key, value in turn.items():
- if key == "dial_id":
- continue
- if key == "pointer" and self.db is not None:
- turn_domain = turn["turn_domain"][-1]
- value = self.db.pointerBack(value, turn_domain)
- dial_turn[key] = value
- dialogs[dial_id].append(dial_turn)
- return dialogs
-
- def inverse_transpose_batch(self, turn_batch_list):
- """
- :param turn_batch_list: list of transpose dial batch
- """
- dialogs = {}
- total_turn_num = len(turn_batch_list)
- # initialize
- for idx_in_batch, dial_id in enumerate(turn_batch_list[0]["dial_id"]):
- dialogs[dial_id] = []
- for turn_n in range(total_turn_num):
- dial_turn = {}
- turn_batch = turn_batch_list[turn_n]
- for key, v_list in turn_batch.items():
- if key == "dial_id":
- continue
- value = v_list[idx_in_batch]
- if key == "pointer" and self.db is not None:
- turn_domain = turn_batch["turn_domain"][idx_in_batch][-1]
- value = self.db.pointerBack(value, turn_domain)
- dial_turn[key] = value
- dialogs[dial_id].append(dial_turn)
- return dialogs
-
- def get_eval_data(self, set_name="dev"):
- name_to_set = {"train": self.train, "test": self.test, "dev": self.dev}
- dial = name_to_set[set_name]
-
- if set_name not in self.set_stats:
- self.set_stats[set_name] = {}
- num_turns = 0
- num_dials = len(dial)
- for d in dial:
- num_turns += len(d)
-
- self.set_stats[set_name]["num_turns"] = num_turns
- self.set_stats[set_name]["num_dials"] = num_dials
-
- return dial
-
- def get_batches(self, set_name):
- """
- compute dataset stats.
- """
- global dia_count
- log_str = ""
- name_to_set = {"train": self.train, "test": self.test, "dev": self.dev}
- dial = name_to_set[set_name]
- if cfg.low_resource and set_name == "train":
- # dial = random.sample(dial, int(len(dial)*0.01))
- dial = random.sample(dial, 100)
- logging.info("Low Resource setting, finetuning size: {}".format(len(dial)))
- turn_bucket = self._bucket_by_turn(dial)
- # self._shuffle_turn_bucket(turn_bucket)
- all_batches = []
-
- if set_name not in self.set_stats:
- self.set_stats[set_name] = {}
- num_training_steps = 0
- num_turns = 0
- num_dials = 0
-
- for k in turn_bucket:
- if set_name != "test" and k == 1 or k >= 17:
- continue
- batches = self._construct_mini_batch(turn_bucket[k])
- log_str += "turn num:%d, dial num: %d, batch num: %d last batch len: %d\n" % (
- k,
- len(turn_bucket[k]),
- len(batches),
- len(batches[-1]),
- )
- # print("turn num:%d, dial num:v%d, batch num: %d, "%(k, len(turn_bucket[k]), len(batches)))
- num_training_steps += k * len(batches)
- num_turns += k * len(turn_bucket[k])
- num_dials += len(turn_bucket[k])
- all_batches += batches
- log_str += "total batch num: %d\n" % len(all_batches)
- # print('total batch num: %d'%len(all_batches))
- # print('dialog count: %d'%dia_count)
- # return all_batches
-
- # log stats
- # logging.info(log_str)
- # cfg.num_training_steps = num_training_steps * cfg.epoch_num
- self.set_stats[set_name]["num_training_steps_per_epoch"] = num_training_steps
- self.set_stats[set_name]["num_turns"] = num_turns
- self.set_stats[set_name]["num_dials"] = num_dials
-
- if set_name == "train":
- random.shuffle(all_batches)
- return all_batches
-
- def get_nontranspose_data_iterator(self, all_batches):
- for i, batch in enumerate(all_batches):
- yield batch
-
- def get_data_iterator(self, all_batches):
- for i, batch in enumerate(all_batches):
- yield self.transpose_batch(batch)
-
- def save_result(self, write_mode, results, field, write_title=False):
- with open(cfg.result_path, write_mode) as rf:
- if write_title:
- rf.write(write_title + "\n")
- writer = csv.DictWriter(rf, fieldnames=field)
- writer.writeheader()
- writer.writerows(results)
- return None
-
- def save_result_report(self, results):
- # if 'joint_goal' in results[0]:
- # with open(cfg.result_path[:-4] + '_report_dst.txt', 'w') as rf:
- # rf.write('joint goal\tslot_acc\tslot_f1\tact_f1\n')
- # for res in results:
- # a,b,c,d = res['joint_goal'], res['slot_acc'], res['slot_f1'], res['act_f1']
- # rf.write('%2.1f\t%2.1f\t%2.1f\t%2.1f\n'%(a,b,c,d))
- # elif 'joint_goal_delex' in results[0]:
- # with open(cfg.result_path[:-4] + '_report_bsdx.txt', 'w') as rf:
- # rf.write('joint goal\tslot_acc\tslot_f1\tact_f1\n')
- # for res in results:
- # a,b,c,d = res['joint_goal_delex'], res['slot_acc_delex'], res['slot_f1_delex'], res['act_f1']
- # rf.write('%2.1f\t%2.1f\t%2.1f\t%2.1f\n'%(a,b,c,d))
- ctr_save_path = cfg.result_path[:-4] + "_report_ctr%s.csv" % cfg.seed
- write_title = False if os.path.exists(ctr_save_path) else True
- if cfg.aspn_decode_mode == "greedy":
- setting = ""
- elif cfg.aspn_decode_mode == "beam":
- setting = "width=%s" % str(cfg.beam_width)
- if cfg.beam_diverse_param > 0:
- setting += ", penalty=%s" % str(cfg.beam_diverse_param)
- elif cfg.aspn_decode_mode == "topk_sampling":
- setting = "topk=%s" % str(cfg.topk_num)
- elif cfg.aspn_decode_mode == "nucleur_sampling":
- setting = "p=%s" % str(cfg.nucleur_p)
- res = {
- "exp": cfg.eval_load_path,
- "true_bspn": cfg.use_true_curr_bspn,
- "true_aspn": cfg.use_true_curr_aspn,
- "decode": cfg.aspn_decode_mode,
- "param": setting,
- "nbest": cfg.nbest,
- "selection_sheme": cfg.act_selection_scheme,
- "match": results[0]["match"],
- "success": results[0]["success"],
- "bleu": results[0]["bleu"],
- "act_f1": results[0]["act_f1"],
- "avg_act_num": results[0]["avg_act_num"],
- "avg_diverse": results[0]["avg_diverse_score"],
- }
- with open(ctr_save_path, "a") as rf:
- writer = csv.DictWriter(rf, fieldnames=list(res.keys()))
- if write_title:
- writer.writeheader()
- writer.writerows([res])
-
-
-class MultiWozReader(_ReaderBase):
- def __init__(self, tokenizer):
- super().__init__()
- # self.nlp = spacy.load("en_core_web_sm")
-
- self.db = MultiWozDB(cfg.dbs)
- self.vocab_size = self._build_vocab()
-
- # self.tokenizer = GPT2Tokenizer.from_pretrained(cfg.gpt_path) # add special tokens later
- self.tokenizer = tokenizer
- if cfg.mode == "train":
- self.add_sepcial_tokens()
-
- self.domain_files = json.loads(open(cfg.domain_file_path, "r").read())
- self.slot_value_set = json.loads(open(cfg.slot_value_set_path, "r").read())
- if cfg.multi_acts_training:
- self.multi_acts = json.loads(open(cfg.multi_acts_path, "r").read())
-
- test_list = [test_list.strip().lower() for test_list in open(cfg.test_list, "r").readlines()]
- dev_list = [dev_list.strip().lower() for dev_list in open(cfg.dev_list, "r").readlines()]
- self.dev_files, self.test_files = {}, {}
- for fn in test_list:
- self.test_files[fn.replace(".json", "")] = 1
- for fn in dev_list:
- self.dev_files[fn.replace(".json", "")] = 1
-
- # for domain expanse aka. Cross domain
- self.exp_files = {}
- # if 'all' not in cfg.exp_domains:
- # for domain in cfg.exp_domains:
- # fn_list = self.domain_files.get(domain)
- # if not fn_list:
- # raise ValueError(
- # '[%s] is an invalid experiment setting' % domain)
- # for fn in fn_list:
- # self.exp_files[fn.replace('.json', '')] = 1
- all_domains_list = list(self.domain_files.keys())
- if "all" not in cfg.exp_domains:
- domains = self.get_exp_domains(cfg.exp_domains, all_domains_list)
- logging.info(domains)
- for domain in domains:
- fn_list = self.domain_files.get(domain)
- if not fn_list:
- raise ValueError("[%s] is an invalid experiment setting" % domain)
- for fn in fn_list:
- self.exp_files[fn.replace(".json", "")] = 1
- #
-
- # self._load_data()
-
- if cfg.limit_bspn_vocab:
- self.bspn_masks = self._construct_bspn_constraint()
- if cfg.limit_aspn_vocab:
- self.aspn_masks = self._construct_aspn_constraint()
-
- self.multi_acts_record = None
-
- def get_exp_domains(self, exp_domains, all_domains_list):
- if "hotel" in exp_domains:
- if "except" in exp_domains:
- # ['except', 'hotel']
- domains = [d for d in all_domains_list if "hotel" not in d and "multi" not in d]
- else:
- # ['hotel']
- domains = ["hotel_single", "hotel_multi"]
- if "train" in exp_domains:
- if "except" in exp_domains:
- # ['except', 'train']
- domains = [d for d in all_domains_list if "train" not in d and "multi" not in d]
- else:
- # ['train']
- domains = ["train_single", "train_multi"]
- if "attraction" in exp_domains:
- if "except" in exp_domains:
- # ['except', 'attraction']
- domains = [d for d in all_domains_list if "attraction" not in d and "multi" not in d]
- else:
- # ['attraction']
- domains = ["attraction_single", "attraction_multi"]
- if "restaurant" in exp_domains:
- if "except" in exp_domains:
- # ['except', 'restaurant']
- domains = [d for d in all_domains_list if "restaurant" not in d and "multi" not in d]
- else:
- # ['restaurant']
- domains = ["restaurant_single", "restaurant_multi"]
- if "taxi" in exp_domains:
- if "except" in exp_domains:
- # ['except', 'taxi']
- domains = [d for d in all_domains_list if "taxi" not in d and "multi" not in d]
- else:
- # ['taxi']
- domains = ["taxi_single", "taxi_multi"]
- return domains
-
- def add_sepcial_tokens(self):
- """
- add special tokens to gpt tokenizer
- serves a similar role of Vocab.construt()
- make a dict of special tokens
- """
- special_tokens = []
- for word in ontology.all_domains + ["general"]:
- word = "[" + word + "]"
- special_tokens.append(word)
- for word in ontology.all_acts:
- word = "[" + word + "]"
- special_tokens.append(word)
- # for word in ontology.all_slots:
- # to be determine whether slot should be [slot]
- # if slot, tokenizer having trouble decoding.
- # special_tokens.append(word)
- for word in self.vocab._word2idx.keys():
- if word.startswith("[value_") and word.endswith("]"):
- special_tokens.append(word)
- special_tokens.extend(ontology.special_tokens)
-
- special_tokens_dict = {"additional_special_tokens": special_tokens}
- self.tokenizer.add_special_tokens(special_tokens_dict)
- logging.info("Added special tokens to gpt tokenizer.")
-
- cfg.pad_id = self.tokenizer.encode("<pad>")[0]
-
- def _build_vocab(self):
- self.vocab = utils.Vocab(cfg.vocab_size)
- vp = cfg.vocab_path_train if cfg.mode == "train" or cfg.vocab_path_eval is None else cfg.vocab_path_eval
- # vp = cfg.vocab_path+'.json.freq.json'
- self.vocab.load_vocab(vp)
- return self.vocab.vocab_size
-
- def _construct_bspn_constraint(self):
- bspn_masks = {}
- valid_domains = [
- "restaurant",
- "hotel",
- "attraction",
- "train",
- "taxi",
- "hospital",
- ]
- all_dom_codes = [self.vocab.encode("[" + d + "]") for d in valid_domains]
- all_slot_codes = [self.vocab.encode(s) for s in ontology.all_slots]
- bspn_masks[self.vocab.encode("<go_b>")] = all_dom_codes + [
- self.vocab.encode("<eos_b>"),
- 0,
- ]
- bspn_masks[self.vocab.encode("<eos_b>")] = [self.vocab.encode("<pad>")]
- bspn_masks[self.vocab.encode("<pad>")] = [self.vocab.encode("<pad>")]
- for domain, slot_values in self.slot_value_set.items():
- if domain == "police":
- continue
- dom_code = self.vocab.encode("[" + domain + "]")
- bspn_masks[dom_code] = []
- for slot, values in slot_values.items():
- slot_code = self.vocab.encode(slot)
- if slot_code not in bspn_masks:
- bspn_masks[slot_code] = []
- if slot_code not in bspn_masks[dom_code]:
- bspn_masks[dom_code].append(slot_code)
- for value in values:
- for idx, v in enumerate(value.split()):
- if not self.vocab.has_word(v):
- continue
- v_code = self.vocab.encode(v)
- if v_code not in bspn_masks:
- # print(self.vocab._word2idx)
- bspn_masks[v_code] = []
- if idx == 0 and v_code not in bspn_masks[slot_code]:
- bspn_masks[slot_code].append(v_code)
- if idx == (len(value.split()) - 1):
- for w in all_dom_codes + all_slot_codes:
- if self.vocab.encode("<eos_b>") not in bspn_masks[v_code]:
- bspn_masks[v_code].append(self.vocab.encode("<eos_b>"))
- if w not in bspn_masks[v_code]:
- bspn_masks[v_code].append(w)
- break
- if not self.vocab.has_word(value.split()[idx + 1]):
- continue
- next_v_code = self.vocab.encode(value.split()[idx + 1])
- if next_v_code not in bspn_masks[v_code]:
- bspn_masks[v_code].append(next_v_code)
- bspn_masks[self.vocab.encode("<unk>")] = list(bspn_masks.keys())
-
- with open("data/processed/multi-woz-processed/bspn_masks.txt", "w") as f:
- for i, j in bspn_masks.items():
- f.write(self.vocab.decode(i) + ": " + " ".join([self.vocab.decode(int(m)) for m in j]) + "\n")
- return bspn_masks
-
- def _construct_aspn_constraint(self):
- aspn_masks = {}
- aspn_masks = {}
- all_dom_codes = [self.vocab.encode("[" + d + "]") for d in ontology.dialog_acts.keys()]
- all_act_codes = [self.vocab.encode("[" + a + "]") for a in ontology.dialog_act_params]
- all_slot_codes = [self.vocab.encode(s) for s in ontology.dialog_act_all_slots]
- aspn_masks[self.vocab.encode("<go_a>")] = all_dom_codes + [
- self.vocab.encode("<eos_a>"),
- 0,
- ]
- aspn_masks[self.vocab.encode("<eos_a>")] = [self.vocab.encode("<pad>")]
- aspn_masks[self.vocab.encode("<pad>")] = [self.vocab.encode("<pad>")]
- # for d in all_dom_codes:
- # aspn_masks[d] = all_act_codes
- for a in all_act_codes:
- aspn_masks[a] = all_dom_codes + all_slot_codes + [self.vocab.encode("<eos_a>")]
- for domain, acts in ontology.dialog_acts.items():
- dom_code = self.vocab.encode("[" + domain + "]")
- aspn_masks[dom_code] = []
- for a in acts:
- act_code = self.vocab.encode("[" + a + "]")
- if act_code not in aspn_masks[dom_code]:
- aspn_masks[dom_code].append(act_code)
- # for a, slots in ontology.dialog_act_params.items():
- # act_code = self.vocab.encode('['+a+']')
- # slot_codes = [self.vocab.encode(s) for s in slots]
- # aspn_masks[act_code] = all_dom_codes + slot_codes + [self.vocab.encode('<eos_a>')]
- for s in all_slot_codes:
- aspn_masks[s] = all_dom_codes + all_slot_codes + [self.vocab.encode("<eos_a>")]
- aspn_masks[self.vocab.encode("<unk>")] = list(aspn_masks.keys())
-
- with open("processed/multi-woz-processed/aspn_masks.txt", "w") as f:
- for i, j in aspn_masks.items():
- f.write(self.vocab.decode(i) + ": " + " ".join([self.vocab.decode(int(m)) for m in j]) + "\n")
- return aspn_masks
-
- def _load_data(self, save_temp=True):
- """
- load processed data and encode, or load already encoded data
- """
- if save_temp: # save encoded data
- if "all" in cfg.exp_domains:
- encoded_file = os.path.join(cfg.data_path, "new_db_se_blank_encoded.data.json")
- # encoded: no sos, se_encoded: sos and eos
- # db: add db results every turn
- else:
- xdomain_dir = "./models/UBAR/experiments_Xdomain/data"
- if not os.path.exists(xdomain_dir):
- os.makedirs(xdomain_dir)
- encoded_file = os.path.join(
- xdomain_dir,
- "{}-encoded.data.json".format("-".join(cfg.exp_domains)),
- )
-
- if os.path.exists(encoded_file):
- logging.info("Reading encoded data from {}".format(encoded_file))
- # self.data = json.loads(open(cfg.data_path + cfg.data_file, "r", encoding="utf-8").read().lower())
- encoded_data = json.loads(open(encoded_file, "r", encoding="utf-8").read())
- self.train = encoded_data["train"]
- self.dev = encoded_data["dev"]
- self.test = encoded_data["test"]
- else:
- logging.info("Encoding data now and save the encoded data in {}".format(encoded_file))
- # not exists, encode data and save
- # self.data = json.loads(open(cfg.data_path + cfg.data_file, "r", encoding="utf-8").read().lower())
- self.train, self.dev, self.test = [], [], []
- for fn, dial in self.data.items():
- if ".json" in fn:
- fn = fn.replace(".json", "")
- if "all" in cfg.exp_domains or self.exp_files.get(fn):
- if self.dev_files.get(fn):
- self.dev.append(self._get_encoded_data(fn, dial))
- elif self.test_files.get(fn):
- self.test.append(self._get_encoded_data(fn, dial))
- else:
- self.train.append(self._get_encoded_data(fn, dial))
-
- # save encoded data
- encoded_data = {"train": self.train, "dev": self.dev, "test": self.test}
- json.dump(encoded_data, open(encoded_file, "w"), indent=2)
-
- else: # directly read processed data and encode
- self.data = json.loads(open(cfg.data_path + cfg.data_file, "r", encoding="utf-8").read().lower())
- self.train, self.dev, self.test = [], [], []
- for fn, dial in self.data.items():
- if ".json" in fn:
- fn = fn.replace(".json", "")
- if "all" in cfg.exp_domains or self.exp_files.get(fn):
- if self.dev_files.get(fn):
- self.dev.append(self._get_encoded_data(fn, dial))
- elif self.test_files.get(fn):
- self.test.append(self._get_encoded_data(fn, dial))
- else:
- self.train.append(self._get_encoded_data(fn, dial))
- # if save_temp:
- # json.dump(self.test, open(
- # 'data/multi-woz-analysis/test.encoded.json', 'w'), indent=2)
- # self.vocab.save_vocab('data/multi-woz-analysis/vocab_temp')
-
- random.shuffle(self.train)
- # random.shuffle(self.dev)
- # random.shuffle(self.test)
- logging.info("train size:{}, dev size:{}, test size:{}".format(len(self.train), len(self.dev), len(self.test)))
-
- def _get_encoded_data(self, fn, dial):
- encoded_dial = []
- for idx, t in enumerate(dial["log"]): # tokenize to list of ids
- enc = {}
- enc["dial_id"] = fn
-
- # enc['user'] = self.vocab.sentence_encode(t['user'].split() + ['<eos_u>'])
- # enc['usdx'] = self.vocab.sentence_encode(t['user_delex'].split() + ['<eos_u>'])
- # enc['resp'] = self.vocab.sentence_encode(t['resp'].split() + ['<eos_r>'])
- # enc['bspn'] = self.vocab.sentence_encode(t['constraint'].split() + ['<eos_b>'])
- # enc['bsdx'] = self.vocab.sentence_encode(t['cons_delex'].split() + ['<eos_b>'])
- # enc['aspn'] = self.vocab.sentence_encode(t['sys_act'].split() + ['<eos_a>'])
- # enc['dspn'] = self.vocab.sentence_encode(t['turn_domain'].split() + ['<eos_d>'])
-
- # use gpt tokenizer directly tokenize word list, prone to encode unknown words to |endoftext|
- # enc['user'] = self.tokenizer.encode(
- # t['user'].split() + ['<eos_u>'])
- # enc['usdx'] = self.tokenizer.encode(
- # t['user_delex'].split() + ['<eos_u>'])
- # enc['resp'] = self.tokenizer.encode(
- # t['resp'].split() + ['<eos_r>'])
- # enc['bspn'] = self.tokenizer.encode(
- # t['constraint'].split() + ['<eos_b>'])
- # enc['bsdx'] = self.tokenizer.encode(
- # t['cons_delex'].split() + ['<eos_b>'])
- # enc['aspn'] = self.tokenizer.encode(
- # t['sys_act'].split() + ['<eos_a>'])
- # enc['dspn'] = self.tokenizer.encode(
- # t['turn_domain'].split() + ['<eos_d>'])
-
- # gpt use bpe to encode strings, very very slow. ~9min
- # in tokenization_utils.encode I find encode can pad_to_max_length, and reutrn tensor
- enc["user"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_u> " + t["user"] + " <eos_u>")
- )
- enc["usdx"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_u> " + t["user"] + " <eos_u>")
- )
- enc["resp"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_r> " + t["resp"] + " <eos_r>")
- )
- enc["bspn"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_b> " + t["constraint"] + " <eos_b>")
- )
- enc["bsdx"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_b> " + t["cons_delex"] + " <eos_b>")
- )
- enc["aspn"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_a> " + t["sys_act"] + " <eos_a>")
- )
- enc["dspn"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_d> " + t["turn_domain"] + " <eos_d>")
- )
-
- enc["pointer"] = [int(i) for i in t["pointer"].split(",")]
- enc["turn_domain"] = t["turn_domain"].split()
- enc["turn_num"] = t["turn_num"]
- if cfg.multi_acts_training:
- enc["aspn_aug"] = []
- if fn in self.multi_acts:
- turn_ma = self.multi_acts[fn].get(str(idx), {})
- for act_type, act_spans in turn_ma.items():
- enc["aspn_aug"].append([self.tokenizer.encode(a.split() + ["<eos_a>"]) for a in act_spans])
-
- # add db results to enc, at every turn
- db_pointer = self.bspan_to_DBpointer(t["constraint"], t["turn_domain"].split())
- # db_tokens = ['<sos_db>', '<eos_db>', '[db_nores]', '[db_0]', '[db_1]', '[db_2]', '[db_3]']
- enc["db"] = self.tokenizer.convert_tokens_to_ids(
- self.tokenizer.tokenize("<sos_db> " + db_pointer + " <eos_db>")
- )
-
- encoded_dial.append(enc)
- return encoded_dial
-
- def bspan_to_constraint_dict(self, bspan, bspn_mode="bspn"):
- bspan = bspan.split() if isinstance(bspan, str) else bspan
- constraint_dict = {}
- domain = None
- conslen = len(bspan)
- for idx, cons in enumerate(bspan):
- cons = self.vocab.decode(cons) if type(cons) is not str else cons
- if cons == "<eos_b>":
- break
- if "[" in cons:
- if cons[1:-1] not in ontology.all_domains:
- continue
- domain = cons[1:-1]
- elif cons in ontology.get_slot:
- if domain is None:
- continue
- if cons == "people":
- # handle confusion of value name "people's portraits..." and slot people
- try:
- ns = bspan[idx + 1]
- ns = self.vocab.decode(ns) if type(ns) is not str else ns
- if ns == "'s":
- continue
- except Exception:
- continue
- if not constraint_dict.get(domain):
- constraint_dict[domain] = {}
- if bspn_mode == "bsdx":
- constraint_dict[domain][cons] = 1
- continue
- vidx = idx + 1
- if vidx == conslen:
- break
- vt_collect = []
- vt = bspan[vidx]
- vt = self.vocab.decode(vt) if type(vt) is not str else vt
- while vidx < conslen and vt != "<eos_b>" and "[" not in vt and vt not in ontology.get_slot:
- vt_collect.append(vt)
- vidx += 1
- if vidx == conslen:
- break
- vt = bspan[vidx]
- vt = self.vocab.decode(vt) if type(vt) is not str else vt
- if vt_collect:
- constraint_dict[domain][cons] = " ".join(vt_collect)
-
- return constraint_dict
-
- def bspan_to_DBpointer(self, bspan, turn_domain):
- constraint_dict = self.bspan_to_constraint_dict(bspan)
- # print(constraint_dict)
- matnums = self.db.get_match_num(constraint_dict)
- match_dom = turn_domain[0] if len(turn_domain) == 1 else turn_domain[1]
- match_dom = match_dom[1:-1] if match_dom.startswith("[") else match_dom
- match = matnums[match_dom]
- # vector = self.db.addDBPointer(match_dom, match)
- vector = self.db.addDBIndicator(match_dom, match)
- return vector
-
- def aspan_to_act_list(self, aspan):
- aspan = aspan.split() if isinstance(aspan, str) else aspan
- acts = []
- domain = None
- conslen = len(aspan)
- for idx, cons in enumerate(aspan):
- cons = self.vocab.decode(cons) if type(cons) is not str else cons
- if cons == "<eos_a>":
- break
- if "[" in cons and cons[1:-1] in ontology.dialog_acts:
- domain = cons[1:-1]
-
- elif "[" in cons and cons[1:-1] in ontology.dialog_act_params:
- if domain is None:
- continue
- vidx = idx + 1
- if vidx == conslen:
- acts.append(domain + "-" + cons[1:-1] + "-none")
- break
- vt = aspan[vidx]
- vt = self.vocab.decode(vt) if type(vt) is not str else vt
- no_param_act = True
- while vidx < conslen and vt != "<eos_a>" and "[" not in vt:
- no_param_act = False
- acts.append(domain + "-" + cons[1:-1] + "-" + vt)
- vidx += 1
- if vidx == conslen:
- break
- vt = aspan[vidx]
- vt = self.vocab.decode(vt) if type(vt) is not str else vt
- if no_param_act:
- acts.append(domain + "-" + cons[1:-1] + "-none")
-
- return acts
-
- def dspan_to_domain(self, dspan):
- domains = {}
- dspan = dspan.split() if isinstance(dspan, str) else dspan
- for d in dspan:
- dom = self.vocab.decode(d) if type(d) is not str else d
- if dom != "<eos_d>":
- domains[dom] = 1
- else:
- break
- return domains
-
- def convert_turn_eval(self, turn, pv_turn, first_turn=False):
- """
- input: [all previous ubar, U_t, B_t, A_t] predict R_t
- firts turn: [U_t, B_t, A_t] predict R_t
-
- regarding the context, all previous ubar is too slow, try the previous ubar
- """
- inputs = {}
-
- context_list = []
- # predict_list = []
- prompt = ""
- if cfg.use_true_curr_bspn:
- if cfg.use_true_curr_aspn: # only predict resp
- context_list = ["user", "bspn", "db", "aspn"]
- # context_list = ['user','aspn'] # predict resp based on current aspn and bspn
- # predict_list = ['resp']
- prompt = "<sos_r>"
- else: # predicted aspn
- context_list = ["user", "bspn", "db"]
- # predict_list = ['aspn', 'resp']
- prompt = "<sos_a>"
- else: # predict bspn aspn resp. db are not predicted. this part tbd.
- context_list = ["user"]
- # predict_list = ['bspn', 'db','aspn', 'resp']
- prompt = "<sos_b>"
-
- if first_turn:
- context = []
- for c in context_list:
- context += turn[c]
-
- inputs["context"] = context + self.tokenizer.encode([prompt])
- inputs["labels"] = context
- # e43 with BABAU
- # inputs['labels'] = []
-
- else:
- context = []
- for c in context_list:
- context += turn[c]
-
- pv_context = pv_turn["labels"] + pv_turn["bspn"] + pv_turn["db"] + pv_turn["aspn"] + pv_turn["resp"]
- # e43 with BABAU
- # pv_context = pv_turn['labels'] + pv_turn['bspn'] + pv_turn['db'] + pv_turn['aspn']
-
- # prompt response, add sos_r
- inputs["context"] = pv_context + context + self.tokenizer.encode([prompt])
- # context just the current turn
- # inputs['context'] = context + self.tokenizer.encode([prompt])
- # context just the current action
-
- if cfg.use_all_previous_context:
- inputs["labels"] = pv_context + context # use all previous ubar history
- else:
- inputs["labels"] = context # use previous trun
-
- if len(inputs["context"]) > 900:
- print("len exceeds 900")
- inputs["context"] = inputs["context"][-900:]
-
- return inputs
-
- def convert_batch_session(self, dial_batch):
- """
- convert the whole session for training
- concat [U_0, B_0, A_0, R_0, ... , U_n, B_n, A_n, R_n]
-
- try: [user, bspn, aspn, resp]
- or
- try: [user, bspn, db, aspn, resp]
- """
- inputs = {}
- contexts = []
- cell_list = ["user", "bspn", "db", "aspn", "resp"]
- for idx, dial in enumerate(dial_batch):
- context = []
- for turn_num, turn in enumerate(dial):
- for cell in cell_list:
- context.extend(turn[cell])
- contexts.append(context)
-
- inputs["contexts"] = contexts
- inputs["contexts_np"], inputs["lengths"] = utils.padSeqs_gpt(inputs["contexts"], cfg.pad_id)
- return inputs
-
- def convert_batch_gpt(self, turn_batch, pv_batch, first_turn=False):
- """
- convert the current and the last turn
- concat [U_{t-1}, B_{t-1}, A_{t-1}, R_{t-1}, U_t, B_t, A_t, R_t]
- firts turn: [U_t, B_t, A_t, R_t]
- try: [usdx, bspn, aspn, resp]
-
- """
- inputs = {}
- if first_turn:
- contexts = []
- batch_zipped = zip(
- turn_batch["usdx"],
- turn_batch["bspn"],
- turn_batch["aspn"],
- turn_batch["resp"],
- )
- for u, b, a, r in batch_zipped:
- context = u + b + a + r
- contexts.append(context)
- inputs["contexts"] = contexts
- # padSeqs to make [UBAR] the same length
- inputs["contexts_np"], inputs["lengths"] = utils.padSeqs_gpt(inputs["contexts"], cfg.pad_id)
- else:
- contexts = []
- batch_zipped = zip(
- pv_batch["pv_usdx"],
- pv_batch["pv_bspn"],
- pv_batch["pv_aspn"],
- pv_batch["pv_resp"],
- turn_batch["usdx"],
- turn_batch["bspn"],
- turn_batch["aspn"],
- turn_batch["resp"],
- )
- for pu, pb, pa, pr, u, b, a, r in batch_zipped:
- context = pu + pb + pa + pr + u + b + a + r
- contexts.append(context)
- inputs["contexts"] = contexts
- contexts_np, lengths = utils.padSeqs_gpt(inputs["contexts"], cfg.pad_id)
- inputs["contexts_np"] = contexts_np
- inputs["lengths"] = lengths
- return inputs
-
- def convert_batch(self, py_batch, py_prev, first_turn=False):
- inputs = {}
- if first_turn:
- for item, py_list in py_prev.items():
- batch_size = len(py_batch["user"])
- inputs[item + "_np"] = np.array([[1]] * batch_size)
- inputs[item + "_unk_np"] = np.array([[1]] * batch_size)
- else:
- for item, py_list in py_prev.items():
- if py_list is None:
- continue
- if not cfg.enable_aspn and "aspn" in item:
- continue
- if not cfg.enable_bspn and "bspn" in item:
- continue
- if not cfg.enable_dspn and "dspn" in item:
- continue
- prev_np = utils.padSeqs(py_list, truncated=cfg.truncated, trunc_method="pre")
- inputs[item + "_np"] = prev_np
- if item in ["pv_resp", "pv_bspn"]:
- inputs[item + "_unk_np"] = deepcopy(inputs[item + "_np"])
- # <unk>, restrict vocab size to 3k, map ids>3k to <unk>
- inputs[item + "_unk_np"][inputs[item + "_unk_np"] >= self.vocab_size] = 2
- else:
- inputs[item + "_unk_np"] = inputs[item + "_np"]
-
- for item in ["user", "usdx", "resp", "bspn", "aspn", "bsdx", "dspn"]:
- if not cfg.enable_aspn and item == "aspn":
- continue
- if not cfg.enable_bspn and item == "bspn":
- continue
-
- if not cfg.enable_dspn and item == "dspn":
- continue
- py_list = py_batch[item]
- trunc_method = "post" if item == "resp" else "pre"
- # max_length = cfg.max_nl_length if item in ['user', 'usdx', 'resp'] else cfg.max_span_length
- inputs[item + "_np"] = utils.padSeqs(py_list, truncated=cfg.truncated, trunc_method=trunc_method)
- if item in ["user", "usdx", "resp", "bspn"]:
- inputs[item + "_unk_np"] = deepcopy(inputs[item + "_np"])
- inputs[item + "_unk_np"][inputs[item + "_unk_np"] >= self.vocab_size] = 2 # <unk>
- else:
- inputs[item + "_unk_np"] = inputs[item + "_np"]
-
- if cfg.multi_acts_training and cfg.mode == "train":
- inputs["aspn_bidx"], multi_aspn = [], []
- for bidx, aspn_type_list in enumerate(py_batch["aspn_aug"]):
- if aspn_type_list:
- for aspn_list in aspn_type_list:
- random.shuffle(aspn_list)
- # choose one random act span in each act type
- aspn = aspn_list[0]
- multi_aspn.append(aspn)
- inputs["aspn_bidx"].append(bidx)
- if cfg.multi_act_sampling_num > 1:
- for i in range(cfg.multi_act_sampling_num):
- if len(aspn_list) >= i + 2:
- # choose one random act span in each act type
- aspn = aspn_list[i + 1]
- multi_aspn.append(aspn)
- inputs["aspn_bidx"].append(bidx)
-
- if multi_aspn:
- inputs["aspn_aug_np"] = utils.padSeqs(multi_aspn, truncated=cfg.truncated, trunc_method="pre")
- # [all available aspn num in the batch, T]
- inputs["aspn_aug_unk_np"] = inputs["aspn_aug_np"]
-
- inputs["db_np"] = np.array(py_batch["pointer"])
- inputs["turn_domain"] = py_batch["turn_domain"]
-
- return inputs
-
- def wrap_result_lm(self, result_dict, eos_syntax=None):
- results = []
- eos_syntax = ontology.eos_tokens if not eos_syntax else eos_syntax
- sos_syntax = ontology.sos_tokens
- # ground truth bs, as, ds.. generate response
- field = [
- "dial_id",
- "turn_num",
- "user",
- "bspn_gen",
- "bsdx",
- "resp_gen",
- "resp",
- "aspn_gen",
- "aspn",
- "dspn_gen",
- "dspn",
- "bspn",
- "pointer",
- ]
-
- for dial_id, turns in result_dict.items():
- entry = {"dial_id": dial_id, "trun_num": len(turns)}
- for f in field[2:]:
- entry[f] = "" # ???
- results.append(entry)
- for turn_idx, turn in enumerate(turns):
- entry = {"dial_id": dial_id}
- for key in field:
- if key in ["dial_id"]:
- continue
- v = turn.get(key, "")
- if key == "turn_domain":
- v = " ".join(v)
-
- if key in eos_syntax and v != "":
- # remove eos tokens
- v = self.tokenizer.decode(v)
- v = v.split()
- # remove eos/sos in span
- if eos_syntax[key] in v:
- v.remove(eos_syntax[key])
- if sos_syntax[key] in v:
- v.remove(sos_syntax[key])
- # if key != 'resp_gen':
- # # remove eos/sos in span
- # if eos_syntax[key] in v:
- # v.remove(eos_syntax[key])
- # if sos_syntax[key] in v:
- # v.remove(sos_syntax[key])
- # else: # 'resp_gen'
- # sos_index = 0
- # eos_index = -1
- # if sos_syntax[key] in v:
- # sos_index = v.index(sos_syntax[key])
- # if eos_syntax[key] in v:
- # eos_index = v.index(eos_syntax[key])
- # else:
- # pass # take too long
- # # no <eos_r> found, stop at any eos_tokens
- # # for i in range(sos_index+1, len(v)):
- # # if v[i] in sos_syntax.values() or v[i] in eos_syntax.values():
- # # eos_index = i
- # v = v[sos_index+1: eos_index]
-
- # v = self.tokenizer.convert_tokens_to_string(v)
- v = " ".join(v)
- else:
- pass # v = v
- entry[key] = v
-
- results.append(entry)
-
- return results, field
-
- def wrap_result(self, result_dict, eos_syntax=None):
- decode_fn = self.vocab.sentence_decode
- results = []
- eos_syntax = ontology.eos_tokens if not eos_syntax else eos_syntax
-
- if cfg.bspn_mode == "bspn":
- field = [
- "dial_id",
- "turn_num",
- "user",
- "bspn_gen",
- "bspn",
- "resp_gen",
- "resp",
- "aspn_gen",
- "aspn",
- "dspn_gen",
- "dspn",
- "pointer",
- ]
- elif not cfg.enable_dst: # this
- field = [
- "dial_id",
- "turn_num",
- "user",
- "bsdx_gen",
- "bsdx",
- "resp_gen",
- "resp",
- "aspn_gen",
- "aspn",
- "dspn_gen",
- "dspn",
- "bspn",
- "pointer",
- ]
- else:
- field = [
- "dial_id",
- "turn_num",
- "user",
- "bsdx_gen",
- "bsdx",
- "resp_gen",
- "resp",
- "aspn_gen",
- "aspn",
- "dspn_gen",
- "dspn",
- "bspn_gen",
- "bspn",
- "pointer",
- ]
- if self.multi_acts_record is not None:
- field.insert(7, "multi_act_gen")
-
- for dial_id, turns in result_dict.items():
- entry = {"dial_id": dial_id, "turn_num": len(turns)}
- for prop in field[2:]:
- entry[prop] = ""
- results.append(entry)
- for turn_no, turn in enumerate(turns):
- entry = {"dial_id": dial_id}
- for key in field:
- if key in ["dial_id"]:
- continue
- v = turn.get(key, "")
- if key == "turn_domain":
- v = " ".join(v)
- entry[key] = decode_fn(v, eos=eos_syntax[key]) if key in eos_syntax and v != "" else v
- results.append(entry)
- return results, field
-
- def restore(self, resp, domain, constraint_dict, mat_ents):
- restored = resp
-
- restored = restored.replace("[value_reference]", "53022")
- restored = restored.replace("[value_car]", "BMW")
-
- # restored.replace('[value_phone]', '830-430-6666')
- for d in domain:
- constraint = constraint_dict.get(d, None)
- if constraint:
- if "stay" in constraint:
- restored = restored.replace("[value_stay]", constraint["stay"])
- if "day" in constraint:
- restored = restored.replace("[value_day]", constraint["day"])
- if "people" in constraint:
- restored = restored.replace("[value_people]", constraint["people"])
- if "time" in constraint:
- restored = restored.replace("[value_time]", constraint["time"])
- if "type" in constraint:
- restored = restored.replace("[value_type]", constraint["type"])
- if d in mat_ents and len(mat_ents[d]) == 0:
- for s in constraint:
- if s == "pricerange" and d in ["hotel", "restaurant"] and "price]" in restored:
- restored = restored.replace("[value_price]", constraint["pricerange"])
- if s + "]" in restored:
- restored = restored.replace("[value_%s]" % s, constraint[s])
-
- if "[value_choice" in restored and mat_ents.get(d):
- restored = restored.replace("[value_choice]", str(len(mat_ents[d])))
- if "[value_choice" in restored:
- restored = restored.replace("[value_choice]", "3")
-
- # restored.replace('[value_car]', 'BMW')
-
- try:
- ent = mat_ents.get(domain[-1], [])
- if ent:
- ent = ent[0]
-
- for t in restored.split():
- if "[value" in t:
- slot = t[7:-1]
- if ent.get(slot):
- if domain[-1] == "hotel" and slot == "price":
- slot = "pricerange"
- restored = restored.replace(t, ent[slot])
- elif slot == "price":
- if ent.get("pricerange"):
- restored = restored.replace(t, ent["pricerange"])
- else:
- print(restored, domain)
- except Exception:
- print(resp)
- print(restored)
- quit()
-
- restored = restored.replace("[value_phone]", "62781111")
- restored = restored.replace("[value_postcode]", "CG9566")
- restored = restored.replace("[value_address]", "Parkside, Cambridge")
-
- # if '[value_' in restored:
-
- # print(domain)
- # # print(mat_ents)
- # print(resp)
- # print(restored)
- return restored
-
- def record_utterance(self, result_dict):
- decode_fn = self.vocab.sentence_decode
-
- ordered_dial = {}
- for dial_id, turns in result_dict.items():
- diverse = 0
- turn_count = 0
- for turn_no, turn in enumerate(turns):
- act_collect = {}
- act_type_collect = {}
- slot_score = 0
- for i in range(cfg.nbest):
- aspn = decode_fn(turn["multi_act"][i], eos=ontology.eos_tokens["aspn"])
- pred_acts = self.aspan_to_act_list(" ".join(aspn))
- act_type = ""
- for act in pred_acts:
- d, a, s = act.split("-")
- if d + "-" + a not in act_collect:
- act_collect[d + "-" + a] = {s: 1}
- slot_score += 1
- act_type += d + "-" + a + ";"
- elif s not in act_collect:
- act_collect[d + "-" + a][s] = 1
- slot_score += 1
- act_type_collect[act_type] = 1
- turn_count += 1
- diverse += len(act_collect) * 3 + slot_score
- ordered_dial[dial_id] = diverse / turn_count
-
- ordered_dial = sorted(ordered_dial.keys(), key=lambda x: -ordered_dial[x])
-
- dialog_record = {}
-
- with open(cfg.eval_load_path + "/dialogue_record.csv", "w") as rf:
- writer = csv.writer(rf)
-
- for dial_id in ordered_dial:
- dialog_record[dial_id] = []
- turns = result_dict[dial_id]
- writer.writerow([dial_id])
- for turn_no, turn in enumerate(turns):
- user = decode_fn(turn["user"], eos=ontology.eos_tokens["user"])
- bspn = decode_fn(turn["bspn"], eos=ontology.eos_tokens["bspn"])
- aspn = decode_fn(turn["aspn"], eos=ontology.eos_tokens["aspn"])
- resp = decode_fn(turn["resp"], eos=ontology.eos_tokens["resp"])
- constraint_dict = self.bspan_to_constraint_dict(bspn)
- # print(constraint_dict)
- mat_ents = self.db.get_match_num(constraint_dict, True)
- domain = [i[1:-1] for i in self.dspan_to_domain(turn["dspn"]).keys()]
- restored = self.restore(resp, domain, constraint_dict, mat_ents)
- writer.writerow([turn_no, user, turn["pointer"], domain, restored, resp])
- turn_record = {
- "user": user,
- "bspn": bspn,
- "aspn": aspn,
- "dom": domain,
- "resp": resp,
- "resp_res": restored,
- }
-
- resp_col = []
- aspn_col = []
- resp_restore_col = []
- for i in range(cfg.nbest):
- aspn = decode_fn(turn["multi_act"][i], eos=ontology.eos_tokens["aspn"])
- resp = decode_fn(turn["multi_resp"][i], eos=ontology.eos_tokens["resp"])
-
- restored = self.restore(resp, domain, constraint_dict, mat_ents)
- resp_col.append(resp)
- resp_restore_col.append(restored)
- aspn_col.append(aspn)
-
- zipped = list(zip(resp_restore_col, resp_col, aspn_col))
- zipped.sort(key=lambda s: len(s[0]))
- resp_restore_col = list(list(zip(*zipped))[0])
- aspn_col = list(list(zip(*zipped))[2])
- resp_col = list(list(zip(*zipped))[1])
- turn_record["aspn_col"] = aspn_col
- turn_record["resp_col"] = resp_col
- turn_record["resp_res_col"] = resp_restore_col
- for i in range(cfg.nbest):
- # aspn = decode_fn(turn['multi_act'][i], eos=ontology.eos_tokens['aspn'])
- resp = resp_col[i]
- aspn = aspn_col[i]
- resp_restore = resp_restore_col[i]
-
- writer.writerow(["", resp_restore, resp, aspn])
-
- dialog_record[dial_id].append(turn_record)
-
- # json.dump(dialog_record, open(cfg.eval_load_path + '/resultdict.json','w'))
-
-
-if __name__ == "__main__":
- reader = MultiWozReader(GPT2Tokenizer)
- # for aspan in ["[general] [bye] [welcome] <eos_a>","[train] [inform] trainid destination \
- # arrive leave [offerbook] [general] [reqmore] <eos_a>",]:
- # act = reader.aspan_to_constraint_dict(aspan.split())
- # print('！！！')
- # print(act)
-
- for bspan in [
- "[taxi] destination golden house departure broughton house gallery arrive 19:30 [attraction]"
- + " type museum name whipple museum of the history of science people 5 day monday",
- "[taxi] destination golden house departure broughton house gallery arrive 19:30 [attraction]"
- + " type museum name whipple museum of the history of science people 5 day monday <eos_b>",
- ]:
- encoded = reader.vocab.sentence_encode(bspan.split())
- print(encoded)
- cons = reader.bspan_to_constraint_dict(encoded, bspn_mode="bspn")
- print(cons)
- for bspan in [
- "[taxi] destination departure leave [hotel] name [attraction] name people day",
- "[taxi] destination departure leave [hotel] name [attraction] name people day <eos_b>",
- ]:
- encoded = reader.vocab.sentence_encode(bspan.split())
- print(encoded)
- cons = reader.bspan_to_constraint_dict(encoded, bspn_mode="bsdx")
- print(cons)
diff --git a/spaces/allknowingroger/Image-Models-Test5/README.md b/spaces/allknowingroger/Image-Models-Test5/README.md
deleted file mode 100644
index 77c1e93a18a4949e06fc805327946e542536f2c3..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test5/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test4
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/alsrbdni/remove-from-photo-background-removal/app.py b/spaces/alsrbdni/remove-from-photo-background-removal/app.py
deleted file mode 100644
index ecd4623cf824a563a8d374cd412feea01cc73152..0000000000000000000000000000000000000000
--- a/spaces/alsrbdni/remove-from-photo-background-removal/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import cv2
-import gradio as gr
-import os
-from PIL import Image
-import numpy as np
-import torch
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
-import gdown
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings("ignore")
-
-os.system("git clone https://github.com/xuebinqin/DIS")
-os.system("mv DIS/IS-Net/* .")
-
-# project imports
-from data_loader_cache import normalize, im_reader, im_preprocess
-from models import *
-
-#Helpers
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-# Download official weights
-if not os.path.exists("saved_models"):
- os.mkdir("saved_models")
- MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"
- gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
-
-class GOSNormalize(object):
- '''
- Normalize the Image using torch.transforms
- '''
- def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
- self.mean = mean
- self.std = std
-
- def __call__(self,image):
- image = normalize(image,self.mean,self.std)
- return image
-
-
-transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
-
-def load_image(im_path, hypar):
- im = im_reader(im_path)
- im, im_shp = im_preprocess(im, hypar["cache_size"])
- im = torch.divide(im,255.0)
- shape = torch.from_numpy(np.array(im_shp))
- return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
-
-
-def build_model(hypar,device):
- net = hypar["model"]#GOSNETINC(3,1)
-
- # convert to half precision
- if(hypar["model_digit"]=="half"):
- net.half()
- for layer in net.modules():
- if isinstance(layer, nn.BatchNorm2d):
- layer.float()
-
- net.to(device)
-
- if(hypar["restore_model"]!=""):
- net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
- net.to(device)
- net.eval()
- return net
-
-
-def predict(net, inputs_val, shapes_val, hypar, device):
- '''
- Given an Image, predict the mask
- '''
- net.eval()
-
- if(hypar["model_digit"]=="full"):
- inputs_val = inputs_val.type(torch.FloatTensor)
- else:
- inputs_val = inputs_val.type(torch.HalfTensor)
-
-
- inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
-
- ds_val = net(inputs_val_v)[0] # list of 6 results
-
- pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W # we want the first one which is the most accurate prediction
-
- ## recover the prediction spatial size to the orignal image size
- pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
-
- ma = torch.max(pred_val)
- mi = torch.min(pred_val)
- pred_val = (pred_val-mi)/(ma-mi) # max = 1
-
- if device == 'cuda': torch.cuda.empty_cache()
- return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
-
-# Set Parameters
-hypar = {} # paramters for inferencing
-
-
-hypar["model_path"] ="./saved_models" ## load trained weights from this path
-hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
-hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
-
-## choose floating point accuracy --
-hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
-hypar["seed"] = 0
-
-hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
-
-## data augmentation parameters ---
-hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-
-hypar["model"] = ISNetDIS()
-
- # Build Model
-net = build_model(hypar, device)
-
-
-def inference(image: Image):
- image_path = image
-
- image_tensor, orig_size = load_image(image_path, hypar)
- mask = predict(net, image_tensor, orig_size, hypar, device)
-
- pil_mask = Image.fromarray(mask).convert('L')
- im_rgb = Image.open(image).convert("RGB")
-
- im_rgba = im_rgb.copy()
- im_rgba.putalpha(pil_mask)
-
- return [im_rgba,pil_mask]
-
-
-title = ""
-description = ""
-article = ""
-
-interface = gr.Interface(
- fn=inference,
- inputs=gr.Image(type='filepath'),
- outputs=["image","image"],
- examples=[['robot.png'], ['ship.png']],
- title=title,
- description=description,
- article=article,
- allow_flagging='never',
- theme="default",
- cache_examples=False,
- css="footer {visibility: hidden}"
- ).launch(enable_queue=True, debug=True)
diff --git a/spaces/amber0097/amberSign/README.md b/spaces/amber0097/amberSign/README.md
deleted file mode 100644
index 5829bef6e3caee0ad212304a356cd3bbbb2db612..0000000000000000000000000000000000000000
--- a/spaces/amber0097/amberSign/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: QSign
-emoji: 💻
-colorFrom: gray
-colorTo: gray
-sdk: docker
-pinned: false
-duplicated_from: amber0097/QSign
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/amin2809/rvc-models2023/README.md b/spaces/amin2809/rvc-models2023/README.md
deleted file mode 100644
index 56936f1df15477c0ae2fdcfe59a77c175e1905d8..0000000000000000000000000000000000000000
--- a/spaces/amin2809/rvc-models2023/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: zomehwh/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/antreyes/stabilityai-stable-diffusion-2/README.md b/spaces/antreyes/stabilityai-stable-diffusion-2/README.md
deleted file mode 100644
index 201b8e8a5ec02d04d711c71964ea3ec804d0822f..0000000000000000000000000000000000000000
--- a/spaces/antreyes/stabilityai-stable-diffusion-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stabilityai Stable Diffusion 2
-emoji: 💩
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/masking.py b/spaces/aodianyun/stable-diffusion-webui/modules/masking.py
deleted file mode 100644
index e3fc7acc901e96cb1075ccee5aa56243ce6810c4..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/masking.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from PIL import Image, ImageFilter, ImageOps
-
-
-def get_crop_region(mask, pad=0):
- """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
- For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
-
- h, w = mask.shape
-
- crop_left = 0
- for i in range(w):
- if not (mask[:, i] == 0).all():
- break
- crop_left += 1
-
- crop_right = 0
- for i in reversed(range(w)):
- if not (mask[:, i] == 0).all():
- break
- crop_right += 1
-
- crop_top = 0
- for i in range(h):
- if not (mask[i] == 0).all():
- break
- crop_top += 1
-
- crop_bottom = 0
- for i in reversed(range(h)):
- if not (mask[i] == 0).all():
- break
- crop_bottom += 1
-
- return (
- int(max(crop_left-pad, 0)),
- int(max(crop_top-pad, 0)),
- int(min(w - crop_right + pad, w)),
- int(min(h - crop_bottom + pad, h))
- )
-
-
-def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
- """expands crop region get_crop_region() to match the ratio of the image the region will processed in; returns expanded region
- for example, if user drew mask in a 128x32 region, and the dimensions for processing are 512x512, the region will be expanded to 128x128."""
-
- x1, y1, x2, y2 = crop_region
-
- ratio_crop_region = (x2 - x1) / (y2 - y1)
- ratio_processing = processing_width / processing_height
-
- if ratio_crop_region > ratio_processing:
- desired_height = (x2 - x1) / ratio_processing
- desired_height_diff = int(desired_height - (y2-y1))
- y1 -= desired_height_diff//2
- y2 += desired_height_diff - desired_height_diff//2
- if y2 >= image_height:
- diff = y2 - image_height
- y2 -= diff
- y1 -= diff
- if y1 < 0:
- y2 -= y1
- y1 -= y1
- if y2 >= image_height:
- y2 = image_height
- else:
- desired_width = (y2 - y1) * ratio_processing
- desired_width_diff = int(desired_width - (x2-x1))
- x1 -= desired_width_diff//2
- x2 += desired_width_diff - desired_width_diff//2
- if x2 >= image_width:
- diff = x2 - image_width
- x2 -= diff
- x1 -= diff
- if x1 < 0:
- x2 -= x1
- x1 -= x1
- if x2 >= image_width:
- x2 = image_width
-
- return x1, y1, x2, y2
-
-
-def fill(image, mask):
- """fills masked regions with colors from image using blur. Not extremely effective."""
-
- image_mod = Image.new('RGBA', (image.width, image.height))
-
- image_masked = Image.new('RGBa', (image.width, image.height))
- image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
-
- image_masked = image_masked.convert('RGBa')
-
- for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
- blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
- for _ in range(repeats):
- image_mod.alpha_composite(blurred)
-
- return image_mod.convert("RGB")
-
diff --git a/spaces/ardha27/rvc-models/README.md b/spaces/ardha27/rvc-models/README.md
deleted file mode 100644
index 56936f1df15477c0ae2fdcfe59a77c175e1905d8..0000000000000000000000000000000000000000
--- a/spaces/ardha27/rvc-models/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: zomehwh/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arpm01/financial-summarization/README.md b/spaces/arpm01/financial-summarization/README.md
deleted file mode 100644
index 251d65070868c4fa352f0f51abae45f5cd8c2239..0000000000000000000000000000000000000000
--- a/spaces/arpm01/financial-summarization/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CoE197
-emoji: 📉
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 2.8.12
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/async_timeout/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/async_timeout/__init__.py
deleted file mode 100644
index 179d1b0a01bd896e9d1cb752a07be0f4422e0146..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/async_timeout/__init__.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import asyncio
-import enum
-import sys
-import warnings
-from types import TracebackType
-from typing import Any, Optional, Type
-
-
-if sys.version_info >= (3, 8):
- from typing import final
-else:
- from typing_extensions import final
-
-
-__version__ = "4.0.2"
-
-
-__all__ = ("timeout", "timeout_at", "Timeout")
-
-
-def timeout(delay: Optional[float]) -> "Timeout":
- """timeout context manager.
-
- Useful in cases when you want to apply timeout logic around block
- of code or in cases when asyncio.wait_for is not suitable. For example:
-
- >>> async with timeout(0.001):
- ... async with aiohttp.get('https://github.com') as r:
- ... await r.text()
-
-
- delay - value in seconds or None to disable timeout logic
- """
- loop = _get_running_loop()
- if delay is not None:
- deadline = loop.time() + delay # type: Optional[float]
- else:
- deadline = None
- return Timeout(deadline, loop)
-
-
-def timeout_at(deadline: Optional[float]) -> "Timeout":
- """Schedule the timeout at absolute time.
-
- deadline argument points on the time in the same clock system
- as loop.time().
-
- Please note: it is not POSIX time but a time with
- undefined starting base, e.g. the time of the system power on.
-
- >>> async with timeout_at(loop.time() + 10):
- ... async with aiohttp.get('https://github.com') as r:
- ... await r.text()
-
-
- """
- loop = _get_running_loop()
- return Timeout(deadline, loop)
-
-
-class _State(enum.Enum):
- INIT = "INIT"
- ENTER = "ENTER"
- TIMEOUT = "TIMEOUT"
- EXIT = "EXIT"
-
-
-@final
-class Timeout:
- # Internal class, please don't instantiate it directly
- # Use timeout() and timeout_at() public factories instead.
- #
- # Implementation note: `async with timeout()` is preferred
- # over `with timeout()`.
- # While technically the Timeout class implementation
- # doesn't need to be async at all,
- # the `async with` statement explicitly points that
- # the context manager should be used from async function context.
- #
- # This design allows to avoid many silly misusages.
- #
- # TimeoutError is raised immadiatelly when scheduled
- # if the deadline is passed.
- # The purpose is to time out as sson as possible
- # without waiting for the next await expression.
-
- __slots__ = ("_deadline", "_loop", "_state", "_timeout_handler")
-
- def __init__(
- self, deadline: Optional[float], loop: asyncio.AbstractEventLoop
- ) -> None:
- self._loop = loop
- self._state = _State.INIT
-
- self._timeout_handler = None # type: Optional[asyncio.Handle]
- if deadline is None:
- self._deadline = None # type: Optional[float]
- else:
- self.update(deadline)
-
- def __enter__(self) -> "Timeout":
- warnings.warn(
- "with timeout() is deprecated, use async with timeout() instead",
- DeprecationWarning,
- stacklevel=2,
- )
- self._do_enter()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> Optional[bool]:
- self._do_exit(exc_type)
- return None
-
- async def __aenter__(self) -> "Timeout":
- self._do_enter()
- return self
-
- async def __aexit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> Optional[bool]:
- self._do_exit(exc_type)
- return None
-
- @property
- def expired(self) -> bool:
- """Is timeout expired during execution?"""
- return self._state == _State.TIMEOUT
-
- @property
- def deadline(self) -> Optional[float]:
- return self._deadline
-
- def reject(self) -> None:
- """Reject scheduled timeout if any."""
- # cancel is maybe better name but
- # task.cancel() raises CancelledError in asyncio world.
- if self._state not in (_State.INIT, _State.ENTER):
- raise RuntimeError(f"invalid state {self._state.value}")
- self._reject()
-
- def _reject(self) -> None:
- if self._timeout_handler is not None:
- self._timeout_handler.cancel()
- self._timeout_handler = None
-
- def shift(self, delay: float) -> None:
- """Advance timeout on delay seconds.
-
- The delay can be negative.
-
- Raise RuntimeError if shift is called when deadline is not scheduled
- """
- deadline = self._deadline
- if deadline is None:
- raise RuntimeError("cannot shift timeout if deadline is not scheduled")
- self.update(deadline + delay)
-
- def update(self, deadline: float) -> None:
- """Set deadline to absolute value.
-
- deadline argument points on the time in the same clock system
- as loop.time().
-
- If new deadline is in the past the timeout is raised immediatelly.
-
- Please note: it is not POSIX time but a time with
- undefined starting base, e.g. the time of the system power on.
- """
- if self._state == _State.EXIT:
- raise RuntimeError("cannot reschedule after exit from context manager")
- if self._state == _State.TIMEOUT:
- raise RuntimeError("cannot reschedule expired timeout")
- if self._timeout_handler is not None:
- self._timeout_handler.cancel()
- self._deadline = deadline
- if self._state != _State.INIT:
- self._reschedule()
-
- def _reschedule(self) -> None:
- assert self._state == _State.ENTER
- deadline = self._deadline
- if deadline is None:
- return
-
- now = self._loop.time()
- if self._timeout_handler is not None:
- self._timeout_handler.cancel()
-
- task = _current_task(self._loop)
- if deadline <= now:
- self._timeout_handler = self._loop.call_soon(self._on_timeout, task)
- else:
- self._timeout_handler = self._loop.call_at(deadline, self._on_timeout, task)
-
- def _do_enter(self) -> None:
- if self._state != _State.INIT:
- raise RuntimeError(f"invalid state {self._state.value}")
- self._state = _State.ENTER
- self._reschedule()
-
- def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None:
- if exc_type is asyncio.CancelledError and self._state == _State.TIMEOUT:
- self._timeout_handler = None
- raise asyncio.TimeoutError
- # timeout has not expired
- self._state = _State.EXIT
- self._reject()
- return None
-
- def _on_timeout(self, task: "asyncio.Task[None]") -> None:
- task.cancel()
- self._state = _State.TIMEOUT
- # drop the reference early
- self._timeout_handler = None
-
-
-if sys.version_info >= (3, 7):
-
- def _current_task(loop: asyncio.AbstractEventLoop) -> "Optional[asyncio.Task[Any]]":
- return asyncio.current_task(loop=loop)
-
-else:
-
- def _current_task(loop: asyncio.AbstractEventLoop) -> "Optional[asyncio.Task[Any]]":
- return asyncio.Task.current_task(loop=loop)
-
-
-if sys.version_info >= (3, 7):
-
- def _get_running_loop() -> asyncio.AbstractEventLoop:
- return asyncio.get_running_loop()
-
-else:
-
- def _get_running_loop() -> asyncio.AbstractEventLoop:
- loop = asyncio.get_event_loop()
- if not loop.is_running():
- raise RuntimeError("no running event loop")
- return loop
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/chunk.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/chunk.py
deleted file mode 100644
index 19390335b648db1ea0de737a2ed88a834228f033..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/contourpy/chunk.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import math
-
-
-def calc_chunk_sizes(chunk_size, chunk_count, total_chunk_count, ny, nx):
- """Calculate chunk sizes.
-
- Args:
- chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same
- size in both directions if only one is specified.
- chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the
- same count in both irections if only one is specified.
- total_chunk_count (int, optional): Total number of chunks.
- ny (int): Number of grid points in y-direction.
- nx (int): Number of grid points in x-direction.
-
- Return:
- tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size).
-
- Note:
- A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be
- specified.
- """
- if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1:
- raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set")
-
- if total_chunk_count is not None:
- max_chunk_count = (nx-1)*(ny-1)
- total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count)
- if total_chunk_count == 1:
- chunk_size = 0
- elif total_chunk_count == max_chunk_count:
- chunk_size = (1, 1)
- else:
- factors = two_factors(total_chunk_count)
- if ny > nx:
- chunk_count = factors
- else:
- chunk_count = (factors[1], factors[0])
-
- if chunk_count is not None:
- if isinstance(chunk_count, tuple):
- y_chunk_count, x_chunk_count = chunk_count
- else:
- y_chunk_count = x_chunk_count = chunk_count
- x_chunk_count = min(max(x_chunk_count, 1), nx-1)
- y_chunk_count = min(max(y_chunk_count, 1), ny-1)
- chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count))
-
- if chunk_size is None:
- y_chunk_size = x_chunk_size = 0
- elif isinstance(chunk_size, tuple):
- y_chunk_size, x_chunk_size = chunk_size
- else:
- y_chunk_size = x_chunk_size = chunk_size
-
- if x_chunk_size < 0 or y_chunk_size < 0:
- raise ValueError("chunk_size cannot be negative")
-
- return y_chunk_size, x_chunk_size
-
-
-def two_factors(n):
- """Split an integer into two integer factors.
-
- The two factors will be as close as possible to the sqrt of n, and are returned in decreasing
- order. Worst case returns (n, 1).
-
- Args:
- n (int): The integer to factorize.
-
- Return:
- tuple(int, int): The two factors of n, in decreasing order.
- """
- i = math.ceil(math.sqrt(n))
- while n % i != 0:
- i -= 1
- j = n // i
- if i > j:
- return i, j
- else:
- return j, i
diff --git a/spaces/aryadytm/paraphrase/src/st_style.py b/spaces/aryadytm/paraphrase/src/st_style.py
deleted file mode 100644
index 5d2bc9e635c9744f77cbdb9998a4ff4c2a37c431..0000000000000000000000000000000000000000
--- a/spaces/aryadytm/paraphrase/src/st_style.py
+++ /dev/null
@@ -1,42 +0,0 @@
-button_style = """
-<style>
-div.stButton > button:first-child {
- background-color: rgb(255, 75, 75);
- color: rgb(255, 255, 255);
-}
-div.stButton > button:hover {
- background-color: rgb(255, 75, 75);
- color: rgb(255, 255, 255);
-}
-div.stButton > button:active {
- background-color: rgb(255, 75, 75);
- color: rgb(255, 255, 255);
-}
-div.stButton > button:focus {
- background-color: rgb(255, 75, 75);
- color: rgb(255, 255, 255);
-}
-.css-1cpxqw2:focus:not(:active) {
- background-color: rgb(255, 75, 75);
- border-color: rgb(255, 75, 75);
- color: rgb(255, 255, 255);
-}
-"""
-
-style = """
-<style>
-#MainMenu {
- visibility: hidden;
-}
-footer {
- visibility: hidden;
-}
-header {
- visibility: hidden;
-}
-</style>
-"""
-
-
-def apply_prod_style(st):
- return st.markdown(style, unsafe_allow_html=True)
\ No newline at end of file
diff --git a/spaces/avivdm1/AutoGPT/CODE_OF_CONDUCT.md b/spaces/avivdm1/AutoGPT/CODE_OF_CONDUCT.md
deleted file mode 100644
index d2331b4c60b9fb27f06953273355dcf53b8d4321..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Code of Conduct for auto-gpt
-
-## 1. Purpose
-
-The purpose of this Code of Conduct is to provide guidelines for contributors to the auto-gpt project on GitHub. We aim to create a positive and inclusive environment where all participants can contribute and collaborate effectively. By participating in this project, you agree to abide by this Code of Conduct.
-
-## 2. Scope
-
-This Code of Conduct applies to all contributors, maintainers, and users of the auto-gpt project. It extends to all project spaces, including but not limited to issues, pull requests, code reviews, comments, and other forms of communication within the project.
-
-## 3. Our Standards
-
-We encourage the following behavior:
-
-* Being respectful and considerate to others
-* Actively seeking diverse perspectives
-* Providing constructive feedback and assistance
-* Demonstrating empathy and understanding
-
-We discourage the following behavior:
-
-* Harassment or discrimination of any kind
-* Disrespectful, offensive, or inappropriate language or content
-* Personal attacks or insults
-* Unwarranted criticism or negativity
-
-## 4. Reporting and Enforcement
-
-If you witness or experience any violations of this Code of Conduct, please report them to the project maintainers by email or other appropriate means. The maintainers will investigate and take appropriate action, which may include warnings, temporary or permanent bans, or other measures as necessary.
-
-Maintainers are responsible for ensuring compliance with this Code of Conduct and may take action to address any violations.
-
-## 5. Acknowledgements
-
-This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html).
-
-## 6. Contact
-
-If you have any questions or concerns, please contact the project maintainers.
-
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/webui-user.sh b/spaces/awaawawawa/iurf7irfuyytruyyugb/webui-user.sh
deleted file mode 100644
index 30646f5c30731ff6622709daaec6277935a7c27e..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/webui-user.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/bin/bash
-#########################################################
-# Uncomment and change the variables below to your need:#
-#########################################################
-
-# Install directory without trailing slash
-#install_dir="/home/$(whoami)"
-
-# Name of the subdirectory
-#clone_dir="stable-diffusion-webui"
-
-# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
-export COMMANDLINE_ARGS=""
-
-# python3 executable
-#python_cmd="python3"
-
-# git executable
-#export GIT="git"
-
-# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-#venv_dir="venv"
-
-# script to launch to start the app
-#export LAUNCH_SCRIPT="launch.py"
-
-# install command for torch
-#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113"
-
-# Requirements file to use for stable-diffusion-webui
-#export REQS_FILE="requirements_versions.txt"
-
-# Fixed git repos
-#export K_DIFFUSION_PACKAGE=""
-#export GFPGAN_PACKAGE=""
-
-# Fixed git commits
-#export STABLE_DIFFUSION_COMMIT_HASH=""
-#export TAMING_TRANSFORMERS_COMMIT_HASH=""
-#export CODEFORMER_COMMIT_HASH=""
-#export BLIP_COMMIT_HASH=""
-
-###########################################
diff --git a/spaces/awacke1/HTML5.3D.Flight.with.Gravity/style.css b/spaces/awacke1/HTML5.3D.Flight.with.Gravity/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/HTML5.3D.Flight.with.Gravity/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/awen666/web-ui/_next/static/chunks/539bbe41.64229b30659f90aa.js b/spaces/awen666/web-ui/_next/static/chunks/539bbe41.64229b30659f90aa.js
deleted file mode 100644
index 5874c95b74caeb368f908d327f9975426dbbaae5..0000000000000000000000000000000000000000
--- a/spaces/awen666/web-ui/_next/static/chunks/539bbe41.64229b30659f90aa.js
+++ /dev/null
@@ -1 +0,0 @@
-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[173],{1033:function(t,a,c){c.d(a,{$NG:function(){return h},y9X:function(){return r}});var n=c(83270);function r(t){return(0,n.w_)({tag:"svg",attr:{viewBox:"0 0 1024 1024"},child:[{tag:"path",attr:{d:"M811.4 418.7C765.6 297.9 648.9 212 512.2 212S258.8 297.8 213 418.6C127.3 441.1 64 519.1 64 612c0 110.5 89.5 200 199.9 200h496.2C870.5 812 960 722.5 960 612c0-92.7-63.1-170.7-148.6-193.3z"}}]})(t)}function h(t){return(0,n.w_)({tag:"svg",attr:{viewBox:"0 0 1024 1024"},child:[{tag:"path",attr:{d:"M854.6 288.7c6 6 9.4 14.1 9.4 22.6V928c0 17.7-14.3 32-32 32H192c-17.7 0-32-14.3-32-32V96c0-17.7 14.3-32 32-32h424.7c8.5 0 16.7 3.4 22.7 9.4l215.2 215.3zM790.2 326L602 137.8V326h188.2zM426.13 600.93l59.11 132.97a16 16 0 0 0 14.62 9.5h24.06a16 16 0 0 0 14.63-9.51l59.1-133.35V758a16 16 0 0 0 16.01 16H641a16 16 0 0 0 16-16V486a16 16 0 0 0-16-16h-34.75a16 16 0 0 0-14.67 9.62L512.1 662.2l-79.48-182.59a16 16 0 0 0-14.67-9.61H383a16 16 0 0 0-16 16v272a16 16 0 0 0 16 16h27.13a16 16 0 0 0 16-16V600.93z"}}]})(t)}}}]);
\ No newline at end of file
diff --git a/spaces/azizalto/vanilla-ml-algorithms/README.md b/spaces/azizalto/vanilla-ml-algorithms/README.md
deleted file mode 100644
index 49b018e329a0663e94b20b1498a0980ba9f6b2b7..0000000000000000000000000000000000000000
--- a/spaces/azizalto/vanilla-ml-algorithms/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Vanilla Ml Algorithms
-emoji: 🔥
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/utils.py b/spaces/azusarang/so-vits-svc-models-ba_P/utils.py
deleted file mode 100644
index 0e3c1702d8160f8d348e8b07596a9d172da624eb..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/utils.py
+++ /dev/null
@@ -1,446 +0,0 @@
-import os
-import glob
-import re
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import warnings
-import random
-import functools
-
-import librosa
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.WARN)
-logger = logging
-
-f0_bin = 256
-f0_max = 1100.0
-f0_min = 50.0
-f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-def normalize_f0(f0, x_mask, uv, random_scale=True):
- # calculate means based on x_mask
- uv_sum = torch.sum(uv, dim=1, keepdim=True)
- uv_sum[uv_sum == 0] = 9999
- means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
-
- if random_scale:
- factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
- else:
- factor = torch.ones(f0.shape[0], 1).to(f0.device)
- # normalize f0 based on means and factor
- f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
- if torch.isnan(f0_norm).any():
- exit(0)
- return f0_norm * x_mask
-
-def plot_data_to_numpy(x, y):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- plt.plot(x)
- plt.plot(y)
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def f0_to_coarse(f0):
- is_torch = isinstance(f0, torch.Tensor)
- f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
- f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int)
- assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
- return f0_coarse
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-def get_f0_predictor(f0_predictor,hop_length,sampling_rate,**kargs):
- if f0_predictor == "pm":
- from modules.F0Predictor.PMF0Predictor import PMF0Predictor
- f0_predictor_object = PMF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
- elif f0_predictor == "crepe":
- from modules.F0Predictor.CrepeF0Predictor import CrepeF0Predictor
- f0_predictor_object = CrepeF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate,device=kargs["device"],threshold=kargs["threshold"])
- elif f0_predictor == "harvest":
- from modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
- f0_predictor_object = HarvestF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
- elif f0_predictor == "dio":
- from modules.F0Predictor.DioF0Predictor import DioF0Predictor
- f0_predictor_object = DioF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
- else:
- raise Exception("Unknown f0 predictor")
- return f0_predictor_object
-
-def get_speech_encoder(speech_encoder,device=None,**kargs):
- if speech_encoder == "vec768l12":
- from vencoder.ContentVec768L12 import ContentVec768L12
- speech_encoder_object = ContentVec768L12(device = device)
- elif speech_encoder == "vec256l9":
- from vencoder.ContentVec256L9 import ContentVec256L9
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec256l9-onnx":
- from vencoder.ContentVec256L9_Onnx import ContentVec256L9_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec256l12-onnx":
- from vencoder.ContentVec256L12_Onnx import ContentVec256L12_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec768l9-onnx":
- from vencoder.ContentVec768L9_Onnx import ContentVec768L9_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec768l12-onnx":
- from vencoder.ContentVec768L12_Onnx import ContentVec768L12_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "hubertsoft-onnx":
- from vencoder.HubertSoft_Onnx import HubertSoft_Onnx
- speech_encoder_object = HubertSoft(device = device)
- elif speech_encoder == "hubertsoft":
- from vencoder.HubertSoft import HubertSoft
- speech_encoder_object = HubertSoft(device = device)
- elif speech_encoder == "whisper-ppg":
- from vencoder.WhisperPPG import WhisperPPG
- speech_encoder_object = WhisperPPG(device = device)
- else:
- raise Exception("Unknown speech encoder")
- return speech_encoder_object
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- # assert "dec" in k or "disc" in k
- # print("load", k)
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
- except:
- print("error, %s is not in the checkpoint" % k)
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- print("load ")
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
- name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
- time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
- sort_key = time_key if sort_by_time else name_key
- x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
- to_del = [os.path.join(path_to_models, fn) for fn in
- (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
- del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
- del_routine = lambda x: [os.remove(x), del_info(x)]
- rs = [del_routine(fn) for fn in to_del]
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/config.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-def repeat_expand_2d(content, target_len):
- # content : [h, t]
-
- src_len = content.shape[-1]
- target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
- temp = torch.arange(src_len+1) * target_len / src_len
- current_pos = 0
- for i in range(target_len):
- if i < temp[current_pos+1]:
- target[:, i] = content[:, current_pos]
- else:
- current_pos += 1
- target[:, i] = content[:, current_pos]
-
- return target
-
-
-def mix_model(model_paths,mix_rate,mode):
- mix_rate = torch.FloatTensor(mix_rate)/100
- model_tem = torch.load(model_paths[0])
- models = [torch.load(path)["model"] for path in model_paths]
- if mode == 0:
- mix_rate = F.softmax(mix_rate,dim=0)
- for k in model_tem["model"].keys():
- model_tem["model"][k] = torch.zeros_like(model_tem["model"][k])
- for i,model in enumerate(models):
- model_tem["model"][k] += model[k]*mix_rate[i]
- torch.save(model_tem,os.path.join(os.path.curdir,"output.pth"))
- return os.path.join(os.path.curdir,"output.pth")
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
-
- def get(self,index):
- return self.__dict__.get(index)
-
-class Volume_Extractor:
- def __init__(self, hop_size = 512):
- self.hop_size = hop_size
-
- def extract(self, audio): # audio: 2d tensor array
- if not isinstance(audio,torch.Tensor):
- audio = torch.Tensor(audio)
- n_frames = int(audio.size(-1) // self.hop_size)
- audio2 = audio ** 2
- audio2 = torch.nn.functional.pad(audio2, (int(self.hop_size // 2), int((self.hop_size + 1) // 2)), mode = 'reflect')
- volume = torch.FloatTensor([torch.mean(audio2[:,int(n * self.hop_size) : int((n + 1) * self.hop_size)]) for n in range(n_frames)])
- volume = torch.sqrt(volume)
- return volume
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Box2.js b/spaces/banana-projects/web3d/node_modules/three/src/math/Box2.js
deleted file mode 100644
index 0010260cf220924f8555e1753de408764c635881..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/math/Box2.js
+++ /dev/null
@@ -1,244 +0,0 @@
-import { Vector2 } from './Vector2.js';
-
-/**
- * @author bhouston / http://clara.io
- */
-
-function Box2( min, max ) {
-
- this.min = ( min !== undefined ) ? min : new Vector2( + Infinity, + Infinity );
- this.max = ( max !== undefined ) ? max : new Vector2( - Infinity, - Infinity );
-
-}
-
-Object.assign( Box2.prototype, {
-
- set: function ( min, max ) {
-
- this.min.copy( min );
- this.max.copy( max );
-
- return this;
-
- },
-
- setFromPoints: function ( points ) {
-
- this.makeEmpty();
-
- for ( var i = 0, il = points.length; i < il; i ++ ) {
-
- this.expandByPoint( points[ i ] );
-
- }
-
- return this;
-
- },
-
- setFromCenterAndSize: function () {
-
- var v1 = new Vector2();
-
- return function setFromCenterAndSize( center, size ) {
-
- var halfSize = v1.copy( size ).multiplyScalar( 0.5 );
- this.min.copy( center ).sub( halfSize );
- this.max.copy( center ).add( halfSize );
-
- return this;
-
- };
-
- }(),
-
- clone: function () {
-
- return new this.constructor().copy( this );
-
- },
-
- copy: function ( box ) {
-
- this.min.copy( box.min );
- this.max.copy( box.max );
-
- return this;
-
- },
-
- makeEmpty: function () {
-
- this.min.x = this.min.y = + Infinity;
- this.max.x = this.max.y = - Infinity;
-
- return this;
-
- },
-
- isEmpty: function () {
-
- // this is a more robust check for empty than ( volume <= 0 ) because volume can get positive with two negative axes
-
- return ( this.max.x < this.min.x ) || ( this.max.y < this.min.y );
-
- },
-
- getCenter: function ( target ) {
-
- if ( target === undefined ) {
-
- console.warn( 'THREE.Box2: .getCenter() target is now required' );
- target = new Vector2();
-
- }
-
- return this.isEmpty() ? target.set( 0, 0 ) : target.addVectors( this.min, this.max ).multiplyScalar( 0.5 );
-
- },
-
- getSize: function ( target ) {
-
- if ( target === undefined ) {
-
- console.warn( 'THREE.Box2: .getSize() target is now required' );
- target = new Vector2();
-
- }
-
- return this.isEmpty() ? target.set( 0, 0 ) : target.subVectors( this.max, this.min );
-
- },
-
- expandByPoint: function ( point ) {
-
- this.min.min( point );
- this.max.max( point );
-
- return this;
-
- },
-
- expandByVector: function ( vector ) {
-
- this.min.sub( vector );
- this.max.add( vector );
-
- return this;
-
- },
-
- expandByScalar: function ( scalar ) {
-
- this.min.addScalar( - scalar );
- this.max.addScalar( scalar );
-
- return this;
-
- },
-
- containsPoint: function ( point ) {
-
- return point.x < this.min.x || point.x > this.max.x ||
- point.y < this.min.y || point.y > this.max.y ? false : true;
-
- },
-
- containsBox: function ( box ) {
-
- return this.min.x <= box.min.x && box.max.x <= this.max.x &&
- this.min.y <= box.min.y && box.max.y <= this.max.y;
-
- },
-
- getParameter: function ( point, target ) {
-
- // This can potentially have a divide by zero if the box
- // has a size dimension of 0.
-
- if ( target === undefined ) {
-
- console.warn( 'THREE.Box2: .getParameter() target is now required' );
- target = new Vector2();
-
- }
-
- return target.set(
- ( point.x - this.min.x ) / ( this.max.x - this.min.x ),
- ( point.y - this.min.y ) / ( this.max.y - this.min.y )
- );
-
- },
-
- intersectsBox: function ( box ) {
-
- // using 4 splitting planes to rule out intersections
-
- return box.max.x < this.min.x || box.min.x > this.max.x ||
- box.max.y < this.min.y || box.min.y > this.max.y ? false : true;
-
- },
-
- clampPoint: function ( point, target ) {
-
- if ( target === undefined ) {
-
- console.warn( 'THREE.Box2: .clampPoint() target is now required' );
- target = new Vector2();
-
- }
-
- return target.copy( point ).clamp( this.min, this.max );
-
- },
-
- distanceToPoint: function () {
-
- var v1 = new Vector2();
-
- return function distanceToPoint( point ) {
-
- var clampedPoint = v1.copy( point ).clamp( this.min, this.max );
- return clampedPoint.sub( point ).length();
-
- };
-
- }(),
-
- intersect: function ( box ) {
-
- this.min.max( box.min );
- this.max.min( box.max );
-
- return this;
-
- },
-
- union: function ( box ) {
-
- this.min.min( box.min );
- this.max.max( box.max );
-
- return this;
-
- },
-
- translate: function ( offset ) {
-
- this.min.add( offset );
- this.max.add( offset );
-
- return this;
-
- },
-
- equals: function ( box ) {
-
- return box.min.equals( this.min ) && box.max.equals( this.max );
-
- }
-
-} );
-
-
-export { Box2 };
diff --git a/spaces/betterme/mestreamlit/pages/1003_streamlit_authenticator.py b/spaces/betterme/mestreamlit/pages/1003_streamlit_authenticator.py
deleted file mode 100644
index 2302892f5df9dc10459d9109faa647e2eec7f689..0000000000000000000000000000000000000000
--- a/spaces/betterme/mestreamlit/pages/1003_streamlit_authenticator.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from meutils.pipe import *
-
-import streamlit as st
-import streamlit_authenticator as stauth
-from streamlit_authenticator import Authenticate
-
-from streamlit_option_menu import option_menu
-
-# https://github.com/mkhorasani/Streamlit-Authenticator
-
-st.set_page_config(page_title="Platform tester", page_icon=":rainbow:", layout="wide", initial_sidebar_state="auto")
-# 如下代码数据，可以来自数据库
-hashed_passwords = stauth.Hasher(['123', '456']).generate()
-
-_ = f"""
-credentials:
- usernames:
- nesc:
- email: nesc@gmail.com
- name: nesc
- password: {hashed_passwords[0]} # To be replaced with hashed password
-
- jsmith:
- email: jsmith@gmail.com
- name: John Smith
- password: 123 # To be replaced with hashed password
- rbriggs:
- email: rbriggs@gmail.com
- name: Rebecca Briggs
- password: 456 # To be replaced with hashed password
-cookie:
- expiry_days: 30
- key: some_signature_key
- name: some_cookie_name
-preauthorized:
- emails:
- - melsby@gmail.com
-"""
-
-config = yaml.load(_)
-
-authenticator = Authenticate(
- config['credentials'],
- config['cookie']['name'],
- config['cookie']['key'],
- config['cookie']['expiry_days'],
- config['preauthorized']
-)
-
-name, authentication_status, username = authenticator.login('Login', 'main')
-if authentication_status:
- authenticator.logout('Logout', 'main')
- st.write(f'Welcome *{name}*')
- st.title('Some content')
-elif authentication_status == False:
- st.error('Username/password is incorrect')
-elif authentication_status == None:
- st.warning('Please enter your username and password')
-
-
-if authentication_status:
- try:
- if authenticator.reset_password(username, 'Reset password'):
- st.success('Password modified successfully')
- except Exception as e:
- st.error(e)
-
-
-try:
- if authenticator.register_user('Register user', preauthorization=False):
- st.success('User registered successfully')
-except Exception as e:
- st.error(e)
-
-
-try:
- username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
- if username_forgot_pw:
- st.success('New password sent securely')
- # Random password to be transferred to user securely
- elif username_forgot_pw == False:
- st.error('Username not found')
-except Exception as e:
- st.error(e)
\ No newline at end of file
diff --git a/spaces/bilgeyucel/prompt-lemmatizer/README.md b/spaces/bilgeyucel/prompt-lemmatizer/README.md
deleted file mode 100644
index 0f09fdbe3aeeec6c94078e6a260c0bb17bae20b9..0000000000000000000000000000000000000000
--- a/spaces/bilgeyucel/prompt-lemmatizer/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: Prompt Lemmatizer
-emoji: 🐢
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-# Prompt Lemmatizer
-
-### Try it out on [🤗 Spaces](https://huggingface.co/spaces/bilgeyucel/prompt-lemmatizer)
-
-##### A simple app to compare the effect of lemmatization on prompts
-
-This is a demo just for fun 🥳 Feel free to open a PR for improvements!
-
-It's been built with [Haystack](https://haystack.deepset.ai) using the [`PromptNode`](https://docs.haystack.deepset.ai/docs/prompt_node).
-
-## To learn more about the PromptNode
-
-Check out our tutorial on the PromptNode and how to create your own templates [here](https://haystack.deepset.ai/tutorials/21_customizing_promptnode)
-
-## Installation and Running
-1. Install requirements:
-`pip install -r requirements.txt`
-2. Run the gradio app:
-`python app.py`
-
-This will start up the app on `http://127.0.0.1:7860`.
-
-#### The Haystack Community is on [Discord](https://discord.gg/haystack)
diff --git a/spaces/bioriAsaeru/text-to-voice/Alfred Kubin The Other Side A Vision of Evil and Apocalypse in Ebook Edition.md b/spaces/bioriAsaeru/text-to-voice/Alfred Kubin The Other Side A Vision of Evil and Apocalypse in Ebook Edition.md
deleted file mode 100644
index 6e48e163af4a37151cda313c7d0daef313286eac..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Alfred Kubin The Other Side A Vision of Evil and Apocalypse in Ebook Edition.md
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/BEST Edraw Max 9.4 Crack License Key [Torrent] Full Download 2020 Tips and Tricks.md b/spaces/bioriAsaeru/text-to-voice/BEST Edraw Max 9.4 Crack License Key [Torrent] Full Download 2020 Tips and Tricks.md
deleted file mode 100644
index b2aea0d1de3d824c690654e45ef852f69ef27e39..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/BEST Edraw Max 9.4 Crack License Key [Torrent] Full Download 2020 Tips and Tricks.md
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/Black Mesa (v1.02) Repack By Godmode On Torrent PORTABLE.md b/spaces/bioriAsaeru/text-to-voice/Black Mesa (v1.02) Repack By Godmode On Torrent PORTABLE.md
deleted file mode 100644
index f407077df9f36aef95b6d5bf174a26ae5e89f271..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Black Mesa (v1.02) Repack By Godmode On Torrent PORTABLE.md
+++ /dev/null
@@ -1,61 +0,0 @@
-<br />
-<h1>Black Mesa (v1.02) Repack by Godmode_On: How to Download and Play the Ultimate Half-Life Remake</h1>
-<p>If you are a fan of the classic first-person shooter game Half-Life, you may have heard of Black Mesa. Black Mesa is a fan-made remake of Half-Life, using the Source engine to recreate the original game with improved graphics, sound, gameplay, and story. Black Mesa is not a simple port of Half-Life, but a complete overhaul that adds new features, characters, environments, and challenges to the game. Black Mesa is one of the most ambitious and successful fan projects ever made, and it has received critical acclaim from both fans and critics.</p>
-<p>However, Black Mesa is not an official product of Valve Software, the developer of Half-Life. Therefore, you cannot buy or download Black Mesa from Steam or any other official platform. You need to find a reliable source to download Black Mesa and install it on your PC. One of the best sources for this purpose is Godmode_On, a repacker who has created a custom installer for Black Mesa that makes it easy and convenient to download and play the game.</p>
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-<p>In this article, we will show you how to download and install Black Mesa (v1.02) Repack by Godmode_On, how to use it to play Black Mesa, and what are the benefits and features of this repack.</p>
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-<p>To download and install Black Mesa (v1.02) Repack by Godmode_On, you need to follow these steps:</p>
-<ol>
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-<h2>How to use Black Mesa (v1.02) Repack by Godmode_On?</h2>
-<p>To use Black Mesa (v1.02) Repack by Godmode_On, you need to follow these steps:</p>
-<ol>
-<li>Launch Black Mesa from any shortcut on your desktop or start menu.</li>
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-</ol>
-<h2>What are the benefits and features of Black Mesa (v1.02) Repack by Godmode_On?</h2>
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-<ul>
-<li>It has a small installer file size compared to other sources.</li>
-<li>It is a full-rip repack that does not remove anything from the original content.</li>
-<li>It is compatible with future updates of Black Mesa.</li>
-<li>It is a complete standalone repack that does not require Steam or internet connection to play.</li>
-<li>It has a custom multifunctional installer/uninstaller with autorun components that makes it easy and convenient to install and uninstall.</li>
-</ul>
-
-<p>These are just some of the benefits and features of Black Mesa (v1.02) Repack by Godmode_On . There are many more features and improvements that you can discover and enjoy by downloading and installing Black Mesa (v1.02) Repack by Godmode_On . You will be amazed by the quality and performance of this repack .</p>
-
-<h2>Conclusion</h2>
-
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-<p>We hope this article was helpful and informative for you . If you have any questions or problems regarding this repack or Black Mesa , feel free to leave a comment below or contact us through our website . We will be happy to assist you with any issue you may have .</p>
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-
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\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Get Your Copy of HOT Metal Hammer 1990 Pdf Download Now The Ultimate Magazine for Metal Fans.md b/spaces/bioriAsaeru/text-to-voice/Get Your Copy of HOT Metal Hammer 1990 Pdf Download Now The Ultimate Magazine for Metal Fans.md
deleted file mode 100644
index 70d91599c87c2778c45ead61cbff1e37dcb06843..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Get Your Copy of HOT Metal Hammer 1990 Pdf Download Now The Ultimate Magazine for Metal Fans.md
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>This quote, representing similar statements by other German metal label employees, follows the general trend of independent labels to position themselves against the established music industry (see Strachan 2007). Furthermore, similar to Rolf-Ulrich Kaiser engaging independent producers in the krautrock era (Reetze 2020, p. 279), Steamhammer/SPV preferred not to operate recording studios and instead employed freelance specialist rock and metal producers like Kalle Trapp with his Karo recording studio (Herbst 2019).</p>
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diff --git a/spaces/bradarrML/stablediffusion-infinity/process.py b/spaces/bradarrML/stablediffusion-infinity/process.py
deleted file mode 100644
index 5db1495ac8098c0260f5fdf5a60ca35a043b461c..0000000000000000000000000000000000000000
--- a/spaces/bradarrML/stablediffusion-infinity/process.py
+++ /dev/null
@@ -1,395 +0,0 @@
-"""
-https://github.com/Trinkle23897/Fast-Poisson-Image-Editing
-MIT License
-
-Copyright (c) 2022 Jiayi Weng
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-import os
-from abc import ABC, abstractmethod
-from typing import Any, Optional, Tuple
-
-import numpy as np
-
-from fpie import np_solver
-
-import scipy
-import scipy.signal
-
-CPU_COUNT = os.cpu_count() or 1
-DEFAULT_BACKEND = "numpy"
-ALL_BACKEND = ["numpy"]
-
-try:
- from fpie import numba_solver
- ALL_BACKEND += ["numba"]
- DEFAULT_BACKEND = "numba"
-except ImportError:
- numba_solver = None # type: ignore
-
-try:
- from fpie import taichi_solver
- ALL_BACKEND += ["taichi-cpu", "taichi-gpu"]
- DEFAULT_BACKEND = "taichi-cpu"
-except ImportError:
- taichi_solver = None # type: ignore
-
-# try:
-# from fpie import core_gcc # type: ignore
-# DEFAULT_BACKEND = "gcc"
-# ALL_BACKEND.append("gcc")
-# except ImportError:
-# core_gcc = None
-
-# try:
-# from fpie import core_openmp # type: ignore
-# DEFAULT_BACKEND = "openmp"
-# ALL_BACKEND.append("openmp")
-# except ImportError:
-# core_openmp = None
-
-# try:
-# from mpi4py import MPI
-
-# from fpie import core_mpi # type: ignore
-# ALL_BACKEND.append("mpi")
-# except ImportError:
-# MPI = None # type: ignore
-# core_mpi = None
-
-try:
- from fpie import core_cuda # type: ignore
- DEFAULT_BACKEND = "cuda"
- ALL_BACKEND.append("cuda")
-except ImportError:
- core_cuda = None
-
-
-class BaseProcessor(ABC):
- """API definition for processor class."""
-
- def __init__(
- self, gradient: str, rank: int, backend: str, core: Optional[Any]
- ):
- if core is None:
- error_msg = {
- "numpy":
- "Please run `pip install numpy`.",
- "numba":
- "Please run `pip install numba`.",
- "gcc":
- "Please install cmake and gcc in your operating system.",
- "openmp":
- "Please make sure your gcc is compatible with `-fopenmp` option.",
- "mpi":
- "Please install MPI and run `pip install mpi4py`.",
- "cuda":
- "Please make sure nvcc and cuda-related libraries are available.",
- "taichi":
- "Please run `pip install taichi`.",
- }
- print(error_msg[backend.split("-")[0]])
-
- raise AssertionError(f"Invalid backend {backend}.")
-
- self.gradient = gradient
- self.rank = rank
- self.backend = backend
- self.core = core
- self.root = rank == 0
-
- def mixgrad(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
- if self.gradient == "src":
- return a
- if self.gradient == "avg":
- return (a + b) / 2
- # mix gradient, see Equ. 12 in PIE paper
- mask = np.abs(a) < np.abs(b)
- a[mask] = b[mask]
- return a
-
- @abstractmethod
- def reset(
- self,
- src: np.ndarray,
- mask: np.ndarray,
- tgt: np.ndarray,
- mask_on_src: Tuple[int, int],
- mask_on_tgt: Tuple[int, int],
- ) -> int:
- pass
-
- def sync(self) -> None:
- self.core.sync()
-
- @abstractmethod
- def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
- pass
-
-
-class EquProcessor(BaseProcessor):
- """PIE Jacobi equation processor."""
-
- def __init__(
- self,
- gradient: str = "max",
- backend: str = DEFAULT_BACKEND,
- n_cpu: int = CPU_COUNT,
- min_interval: int = 100,
- block_size: int = 1024,
- ):
- core: Optional[Any] = None
- rank = 0
-
- if backend == "numpy":
- core = np_solver.EquSolver()
- elif backend == "numba" and numba_solver is not None:
- core = numba_solver.EquSolver()
- elif backend == "gcc":
- core = core_gcc.EquSolver()
- elif backend == "openmp" and core_openmp is not None:
- core = core_openmp.EquSolver(n_cpu)
- elif backend == "mpi" and core_mpi is not None:
- core = core_mpi.EquSolver(min_interval)
- rank = MPI.COMM_WORLD.Get_rank()
- elif backend == "cuda" and core_cuda is not None:
- core = core_cuda.EquSolver(block_size)
- elif backend.startswith("taichi") and taichi_solver is not None:
- core = taichi_solver.EquSolver(backend, n_cpu, block_size)
-
- super().__init__(gradient, rank, backend, core)
-
- def mask2index(
- self, mask: np.ndarray
- ) -> Tuple[np.ndarray, int, np.ndarray, np.ndarray]:
- x, y = np.nonzero(mask)
- max_id = x.shape[0] + 1
- index = np.zeros((max_id, 3))
- ids = self.core.partition(mask)
- ids[mask == 0] = 0 # reserve id=0 for constant
- index = ids[x, y].argsort()
- return ids, max_id, x[index], y[index]
-
- def reset(
- self,
- src: np.ndarray,
- mask: np.ndarray,
- tgt: np.ndarray,
- mask_on_src: Tuple[int, int],
- mask_on_tgt: Tuple[int, int],
- ) -> int:
- assert self.root
- # check validity
- # assert 0 <= mask_on_src[0] and 0 <= mask_on_src[1]
- # assert mask_on_src[0] + mask.shape[0] <= src.shape[0]
- # assert mask_on_src[1] + mask.shape[1] <= src.shape[1]
- # assert mask_on_tgt[0] + mask.shape[0] <= tgt.shape[0]
- # assert mask_on_tgt[1] + mask.shape[1] <= tgt.shape[1]
-
- if len(mask.shape) == 3:
- mask = mask.mean(-1)
- mask = (mask >= 128).astype(np.int32)
-
- # zero-out edge
- mask[0] = 0
- mask[-1] = 0
- mask[:, 0] = 0
- mask[:, -1] = 0
-
- x, y = np.nonzero(mask)
- x0, x1 = x.min() - 1, x.max() + 2
- y0, y1 = y.min() - 1, y.max() + 2
- mask_on_src = (x0 + mask_on_src[0], y0 + mask_on_src[1])
- mask_on_tgt = (x0 + mask_on_tgt[0], y0 + mask_on_tgt[1])
- mask = mask[x0:x1, y0:y1]
- ids, max_id, index_x, index_y = self.mask2index(mask)
-
- src_x, src_y = index_x + mask_on_src[0], index_y + mask_on_src[1]
- tgt_x, tgt_y = index_x + mask_on_tgt[0], index_y + mask_on_tgt[1]
-
- src_C = src[src_x, src_y].astype(np.float32)
- src_U = src[src_x - 1, src_y].astype(np.float32)
- src_D = src[src_x + 1, src_y].astype(np.float32)
- src_L = src[src_x, src_y - 1].astype(np.float32)
- src_R = src[src_x, src_y + 1].astype(np.float32)
- tgt_C = tgt[tgt_x, tgt_y].astype(np.float32)
- tgt_U = tgt[tgt_x - 1, tgt_y].astype(np.float32)
- tgt_D = tgt[tgt_x + 1, tgt_y].astype(np.float32)
- tgt_L = tgt[tgt_x, tgt_y - 1].astype(np.float32)
- tgt_R = tgt[tgt_x, tgt_y + 1].astype(np.float32)
-
- grad = self.mixgrad(src_C - src_L, tgt_C - tgt_L) \
- + self.mixgrad(src_C - src_R, tgt_C - tgt_R) \
- + self.mixgrad(src_C - src_U, tgt_C - tgt_U) \
- + self.mixgrad(src_C - src_D, tgt_C - tgt_D)
-
- A = np.zeros((max_id, 4), np.int32)
- X = np.zeros((max_id, 3), np.float32)
- B = np.zeros((max_id, 3), np.float32)
-
- X[1:] = tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1]]
- # four-way
- A[1:, 0] = ids[index_x - 1, index_y]
- A[1:, 1] = ids[index_x + 1, index_y]
- A[1:, 2] = ids[index_x, index_y - 1]
- A[1:, 3] = ids[index_x, index_y + 1]
- B[1:] = grad
- m = (mask[index_x - 1, index_y] == 0).astype(float).reshape(-1, 1)
- B[1:] += m * tgt[index_x + mask_on_tgt[0] - 1, index_y + mask_on_tgt[1]]
- m = (mask[index_x, index_y - 1] == 0).astype(float).reshape(-1, 1)
- B[1:] += m * tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1] - 1]
- m = (mask[index_x, index_y + 1] == 0).astype(float).reshape(-1, 1)
- B[1:] += m * tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1] + 1]
- m = (mask[index_x + 1, index_y] == 0).astype(float).reshape(-1, 1)
- B[1:] += m * tgt[index_x + mask_on_tgt[0] + 1, index_y + mask_on_tgt[1]]
-
- self.tgt = tgt.copy()
- self.tgt_index = (index_x + mask_on_tgt[0], index_y + mask_on_tgt[1])
- self.core.reset(max_id, A, X, B)
- return max_id
-
- def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
- result = self.core.step(iteration)
- if self.root:
- x, err = result
- self.tgt[self.tgt_index] = x[1:]
- return self.tgt, err
- return None
-
-
-class GridProcessor(BaseProcessor):
- """PIE grid processor."""
-
- def __init__(
- self,
- gradient: str = "max",
- backend: str = DEFAULT_BACKEND,
- n_cpu: int = CPU_COUNT,
- min_interval: int = 100,
- block_size: int = 1024,
- grid_x: int = 8,
- grid_y: int = 8,
- ):
- core: Optional[Any] = None
- rank = 0
-
- if backend == "numpy":
- core = np_solver.GridSolver()
- elif backend == "numba" and numba_solver is not None:
- core = numba_solver.GridSolver()
- elif backend == "gcc":
- core = core_gcc.GridSolver(grid_x, grid_y)
- elif backend == "openmp" and core_openmp is not None:
- core = core_openmp.GridSolver(grid_x, grid_y, n_cpu)
- elif backend == "mpi" and core_mpi is not None:
- core = core_mpi.GridSolver(min_interval)
- rank = MPI.COMM_WORLD.Get_rank()
- elif backend == "cuda" and core_cuda is not None:
- core = core_cuda.GridSolver(grid_x, grid_y)
- elif backend.startswith("taichi") and taichi_solver is not None:
- core = taichi_solver.GridSolver(
- grid_x, grid_y, backend, n_cpu, block_size
- )
-
- super().__init__(gradient, rank, backend, core)
-
- def reset(
- self,
- src: np.ndarray,
- mask: np.ndarray,
- tgt: np.ndarray,
- mask_on_src: Tuple[int, int],
- mask_on_tgt: Tuple[int, int],
- ) -> int:
- assert self.root
- # check validity
- # assert 0 <= mask_on_src[0] and 0 <= mask_on_src[1]
- # assert mask_on_src[0] + mask.shape[0] <= src.shape[0]
- # assert mask_on_src[1] + mask.shape[1] <= src.shape[1]
- # assert mask_on_tgt[0] + mask.shape[0] <= tgt.shape[0]
- # assert mask_on_tgt[1] + mask.shape[1] <= tgt.shape[1]
-
- if len(mask.shape) == 3:
- mask = mask.mean(-1)
- mask = (mask >= 128).astype(np.int32)
-
- # zero-out edge
- mask[0] = 0
- mask[-1] = 0
- mask[:, 0] = 0
- mask[:, -1] = 0
-
- x, y = np.nonzero(mask)
- x0, x1 = x.min() - 1, x.max() + 2
- y0, y1 = y.min() - 1, y.max() + 2
- mask = mask[x0:x1, y0:y1]
- max_id = np.prod(mask.shape)
-
- src_crop = src[mask_on_src[0] + x0:mask_on_src[0] + x1,
- mask_on_src[1] + y0:mask_on_src[1] + y1].astype(np.float32)
- tgt_crop = tgt[mask_on_tgt[0] + x0:mask_on_tgt[0] + x1,
- mask_on_tgt[1] + y0:mask_on_tgt[1] + y1].astype(np.float32)
- grad = np.zeros([*mask.shape, 3], np.float32)
- grad[1:] += self.mixgrad(
- src_crop[1:] - src_crop[:-1], tgt_crop[1:] - tgt_crop[:-1]
- )
- grad[:-1] += self.mixgrad(
- src_crop[:-1] - src_crop[1:], tgt_crop[:-1] - tgt_crop[1:]
- )
- grad[:, 1:] += self.mixgrad(
- src_crop[:, 1:] - src_crop[:, :-1], tgt_crop[:, 1:] - tgt_crop[:, :-1]
- )
- grad[:, :-1] += self.mixgrad(
- src_crop[:, :-1] - src_crop[:, 1:], tgt_crop[:, :-1] - tgt_crop[:, 1:]
- )
-
- grad[mask == 0] = 0
- if True:
- kernel = [[1] * 3 for _ in range(3)]
- nmask = mask.copy()
- nmask[nmask > 0] = 1
- res = scipy.signal.convolve2d(
- nmask, kernel, mode="same", boundary="fill", fillvalue=1
- )
- res[nmask < 1] = 0
- res[res == 9] = 0
- res[res > 0] = 1
- grad[res>0]=0
- # ylst, xlst = res.nonzero()
- # for y, x in zip(ylst, xlst):
- # grad[y,x]=0
- # for yi in range(-1,2):
- # for xi in range(-1,2):
- # grad[y+yi,x+xi]=0
- self.x0 = mask_on_tgt[0] + x0
- self.x1 = mask_on_tgt[0] + x1
- self.y0 = mask_on_tgt[1] + y0
- self.y1 = mask_on_tgt[1] + y1
- self.tgt = tgt.copy()
- self.core.reset(max_id, mask, tgt_crop, grad)
- return max_id
-
- def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
- result = self.core.step(iteration)
- if self.root:
- tgt, err = result
- self.tgt[self.x0:self.x1, self.y0:self.y1] = tgt
- return self.tgt, err
- return None
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_vitdet_l_100ep.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_vitdet_l_100ep.py
deleted file mode 100644
index ebaf526ab7735309d5f50527136ad6207ce9d58b..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_vitdet_l_100ep.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.data.detection_utils import get_fed_loss_cls_weights
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.roi_heads import FastRCNNOutputLayers, FastRCNNConvFCHead, CascadeROIHeads
-
-from .mask_rcnn_vitdet_l_100ep import (
- dataloader,
- lr_multiplier,
- model,
- optimizer,
- train,
-)
-
-# arguments that don't exist for Cascade R-CNN
-[model.roi_heads.pop(k) for k in ["box_head", "box_predictor", "proposal_matcher"]]
-
-model.roi_heads.update(
- _target_=CascadeROIHeads,
- num_classes=1203,
- box_heads=[
- L(FastRCNNConvFCHead)(
- input_shape=ShapeSpec(channels=256, height=7, width=7),
- conv_dims=[256, 256, 256, 256],
- fc_dims=[1024],
- conv_norm="LN",
- )
- for _ in range(3)
- ],
- box_predictors=[
- L(FastRCNNOutputLayers)(
- input_shape=ShapeSpec(channels=1024),
- box2box_transform=L(Box2BoxTransform)(weights=(w1, w1, w2, w2)),
- num_classes="${...num_classes}",
- test_score_thresh=0.02,
- test_topk_per_image=300,
- cls_agnostic_bbox_reg=True,
- use_sigmoid_ce=True,
- use_fed_loss=True,
- get_fed_loss_cls_weights=lambda: get_fed_loss_cls_weights(
- dataloader.train.dataset.names, 0.5
- ),
- )
- for (w1, w2) in [(10, 5), (20, 10), (30, 15)]
- ],
- proposal_matchers=[
- L(Matcher)(thresholds=[th], labels=[0, 1], allow_low_quality_matches=False)
- for th in [0.5, 0.6, 0.7]
- ],
-)
diff --git a/spaces/bubbletea98/Neo4J_Integration/README.md b/spaces/bubbletea98/Neo4J_Integration/README.md
deleted file mode 100644
index 85b8cc392c845b6605090952053eef5af9eac793..0000000000000000000000000000000000000000
--- a/spaces/bubbletea98/Neo4J_Integration/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Neo4J_Integration
-emoji: 🔥
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/c1ybaby/bingAI/Dockerfile b/spaces/c1ybaby/bingAI/Dockerfile
deleted file mode 100644
index a29476a811614c1bcf1d2e01667d69d47ae524ab..0000000000000000000000000000000000000000
--- a/spaces/c1ybaby/bingAI/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="1JpY37GJMlgNQmhnT4QsZvo41wwaI9W9qL9xzzBGP3Co5uyeTXZLpv5UIIspNLWN1LoXoFJqQ_4_ibjIU6fFFG8hJ3WoBHZ9916YCmo0yhs5lcTsCxING8wwBnW2FNh3VZy47-qYL8Tso2ykQ4beDLM_JgZMOVmcDoMzY1HSJU70CCmUfIpOMANYdZyVM71HyQGS6gEja1tuxuPaoJibTSw"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/caffeinum/VToonify/app.py b/spaces/caffeinum/VToonify/app.py
deleted file mode 100644
index 7b12905c70c5964cf166ee555908be664ae7c92f..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/app.py
+++ /dev/null
@@ -1,291 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import pathlib
-import torch
-import gradio as gr
-
-from vtoonify_model import Model
-
-def parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser()
- parser.add_argument('--device', type=str, default='cpu')
- parser.add_argument('--theme', type=str)
- parser.add_argument('--share', action='store_true')
- parser.add_argument('--port', type=int)
- parser.add_argument('--disable-queue',
- dest='enable_queue',
- action='store_false')
- return parser.parse_args()
-
-DESCRIPTION = '''
-<div align=center>
-<h1 style="font-weight: 900; margin-bottom: 7px;">
- Portrait Style Transfer with <a href="https://github.com/williamyang1991/VToonify">VToonify</a>
-</h1>
-<p>For faster inference without waiting in queue, you may duplicate the space and use the GPU setting.
-<br/>
-<a href="https://huggingface.co/spaces/PKUWilliamYang/VToonify?duplicate=true">
-<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-<p/>
-<video id="video" width=50% controls="" preload="none" poster="https://repository-images.githubusercontent.com/534480768/53715b0f-a2df-4daa-969c-0e74c102d339">
-<source id="mp4" src="https://user-images.githubusercontent.com/18130694/189483939-0fc4a358-fb34-43cc-811a-b22adb820d57.mp4
-" type="video/mp4">
-</videos>
-</div>
-'''
-FOOTER = '<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=williamyang1991/VToonify" /></div>'
-
-ARTICLE = r"""
-If VToonify is helpful, please help to ⭐ the <a href='https://github.com/williamyang1991/VToonify' target='_blank'>Github Repo</a>. Thanks!
-[](https://github.com/williamyang1991/VToonify)
----
-📝 **Citation**
-If our work is useful for your research, please consider citing:
-```bibtex
-@article{yang2022Vtoonify,
- title={VToonify: Controllable High-Resolution Portrait Video Style Transfer},
- author={Yang, Shuai and Jiang, Liming and Liu, Ziwei and Loy, Chen Change},
- journal={ACM Transactions on Graphics (TOG)},
- volume={41},
- number={6},
- articleno={203},
- pages={1--15},
- year={2022},
- publisher={ACM New York, NY, USA},
- doi={10.1145/3550454.3555437},
-}
-```
-
-📋 **License**
-This project is licensed under <a rel="license" href="https://github.com/williamyang1991/VToonify/blob/main/LICENSE.md">S-Lab License 1.0</a>.
-Redistribution and use for non-commercial purposes should follow this license.
-
-📧 **Contact**
-If you have any questions, please feel free to reach me out at <b>williamyang@pku.edu.cn</b>.
-"""
-
-def update_slider(choice: str) -> dict:
- if type(choice) == str and choice.endswith('-d'):
- return gr.Slider.update(maximum=1, minimum=0, value=0.5)
- else:
- return gr.Slider.update(maximum=0.5, minimum=0.5, value=0.5)
-
-def set_example_image(example: list) -> dict:
- return gr.Image.update(value=example[0])
-
-def set_example_video(example: list) -> dict:
- return gr.Video.update(value=example[0]),
-
-sample_video = ['./vtoonify/data/529_2.mp4','./vtoonify/data/7154235.mp4','./vtoonify/data/651.mp4','./vtoonify/data/908.mp4']
-sample_vid = gr.Video(label='Video file') #for displaying the example
-example_videos = gr.components.Dataset(components=[sample_vid], samples=[[path] for path in sample_video], type='values', label='Video Examples')
-
-def main():
- args = parse_args()
- args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
- print('*** Now using %s.'%(args.device))
- model = Model(device=args.device)
-
- with gr.Blocks(theme=args.theme, css='style.css') as demo:
-
- gr.Markdown(DESCRIPTION)
-
- with gr.Box():
- gr.Markdown('''## Step 1(Select Style)
- - Select **Style Type**.
- - Type with `-d` means it supports style degree adjustment.
- - Type without `-d` usually has better toonification quality.
-
- ''')
- with gr.Row():
- with gr.Column():
- gr.Markdown('''Select Style Type''')
- with gr.Row():
- style_type = gr.Radio(label='Style Type',
- choices=['cartoon1','cartoon1-d','cartoon2-d','cartoon3-d',
- 'cartoon4','cartoon4-d','cartoon5-d','comic1-d',
- 'comic2-d','arcane1','arcane1-d','arcane2', 'arcane2-d',
- 'caricature1','caricature2','pixar','pixar-d',
- 'illustration1-d', 'illustration2-d', 'illustration3-d', 'illustration4-d', 'illustration5-d',
- ]
- )
- exstyle = gr.Variable()
- with gr.Row():
- loadmodel_button = gr.Button('Load Model')
- with gr.Row():
- load_info = gr.Textbox(label='Process Information', interactive=False, value='No model loaded.')
- with gr.Column():
- gr.Markdown('''Reference Styles
- ''')
-
-
- with gr.Box():
- gr.Markdown('''## Step 2 (Preprocess Input Image / Video)
- - Drop an image/video containing a near-frontal face to the **Input Image**/**Input Video**.
- - Hit the **Rescale Image**/**Rescale First Frame** button.
- - Rescale the input to make it best fit the model.
- - The final image result will be based on this **Rescaled Face**. Use padding parameters to adjust the background space.
- - **<font color=red>Solution to [Error: no face detected!]</font>**: VToonify uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. You can try several times or use other images until a face is detected, then switch back to the original image.
- - For video input, further hit the **Rescale Video** button.
- - The final video result will be based on this **Rescaled Video**. To avoid overload, video is cut to at most **100/300** frames for CPU/GPU, respectively.
-
- ''')
- with gr.Row():
- with gr.Box():
- with gr.Column():
- gr.Markdown('''Choose the padding parameters.
- ''')
- with gr.Row():
- top = gr.Slider(128,
- 256,
- value=200,
- step=8,
- label='top')
- with gr.Row():
- bottom = gr.Slider(128,
- 256,
- value=200,
- step=8,
- label='bottom')
- with gr.Row():
- left = gr.Slider(128,
- 256,
- value=200,
- step=8,
- label='left')
- with gr.Row():
- right = gr.Slider(128,
- 256,
- value=200,
- step=8,
- label='right')
- with gr.Box():
- with gr.Column():
- gr.Markdown('''Input''')
- with gr.Row():
- input_image = gr.Image(label='Input Image',
- type='filepath')
- with gr.Row():
- preprocess_image_button = gr.Button('Rescale Image')
- with gr.Row():
- input_video = gr.Video(label='Input Video',
- mirror_webcam=False,
- type='filepath')
- with gr.Row():
- preprocess_video0_button = gr.Button('Rescale First Frame')
- preprocess_video1_button = gr.Button('Rescale Video')
-
- with gr.Box():
- with gr.Column():
- gr.Markdown('''View''')
- with gr.Row():
- input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
- with gr.Row():
- aligned_face = gr.Image(label='Rescaled Face',
- type='numpy',
- interactive=False)
- instyle = gr.Variable()
- with gr.Row():
- aligned_video = gr.Video(label='Rescaled Video',
- type='mp4',
- interactive=False)
- with gr.Row():
- with gr.Column():
- paths = ['./vtoonify/data/pexels-andrea-piacquadio-733872.jpg','./vtoonify/data/i5R8hbZFDdc.jpg','./vtoonify/data/yRpe13BHdKw.jpg','./vtoonify/data/ILip77SbmOE.jpg','./vtoonify/data/077436.jpg','./vtoonify/data/081680.jpg']
- example_images = gr.Dataset(components=[input_image],
- samples=[[path] for path in paths],
- label='Image Examples')
- with gr.Column():
- #example_videos = gr.Dataset(components=[input_video], samples=[['./vtoonify/data/529.mp4']], type='values')
- #to render video example on mouse hover/click
- example_videos.render()
- #to load sample video into input_video upon clicking on it
- def load_examples(video):
- #print("****** inside load_example() ******")
- #print("in_video is : ", video[0])
- return video[0]
-
- example_videos.click(load_examples, example_videos, input_video)
-
- with gr.Box():
- gr.Markdown('''## Step 3 (Generate Style Transferred Image/Video)''')
- with gr.Row():
- with gr.Column():
- gr.Markdown('''
-
- - Adjust **Style Degree**.
- - Hit **Toonify!** to toonify one frame. Hit **VToonify!** to toonify full video.
- - Estimated time on 1600x1440 video of 300 frames: 1 hour (CPU); 2 mins (GPU)
- ''')
- style_degree = gr.Slider(0,
- 1,
- value=0.5,
- step=0.05,
- label='Style Degree')
- with gr.Column():
- gr.Markdown('''
- ''')
- with gr.Row():
- output_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.')
- with gr.Row():
- with gr.Column():
- with gr.Row():
- result_face = gr.Image(label='Result Image',
- type='numpy',
- interactive=False)
- with gr.Row():
- toonify_button = gr.Button('Toonify!')
- with gr.Column():
- with gr.Row():
- result_video = gr.Video(label='Result Video',
- type='mp4',
- interactive=False)
- with gr.Row():
- vtoonify_button = gr.Button('VToonify!')
-
- gr.Markdown(ARTICLE)
- gr.Markdown(FOOTER)
-
- loadmodel_button.click(fn=model.load_model,
- inputs=[style_type],
- outputs=[exstyle, load_info])
-
-
- style_type.change(fn=update_slider,
- inputs=style_type,
- outputs=style_degree)
-
- preprocess_image_button.click(fn=model.detect_and_align_image,
- inputs=[input_image, top, bottom, left, right],
- outputs=[aligned_face, instyle, input_info])
- preprocess_video0_button.click(fn=model.detect_and_align_video,
- inputs=[input_video, top, bottom, left, right],
- outputs=[aligned_face, instyle, input_info])
- preprocess_video1_button.click(fn=model.detect_and_align_full_video,
- inputs=[input_video, top, bottom, left, right],
- outputs=[aligned_video, instyle, input_info])
-
- toonify_button.click(fn=model.image_toonify,
- inputs=[aligned_face, instyle, exstyle, style_degree, style_type],
- outputs=[result_face, output_info])
- vtoonify_button.click(fn=model.video_tooniy,
- inputs=[aligned_video, instyle, exstyle, style_degree, style_type],
- outputs=[result_video, output_info])
-
-
- example_images.click(fn=set_example_image,
- inputs=example_images,
- outputs=example_images.components)
-
- demo.launch(
- enable_queue=args.enable_queue,
- server_port=args.port,
- share=args.share,
- )
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/carlgira/dreambooth-image-editor/app.py b/spaces/carlgira/dreambooth-image-editor/app.py
deleted file mode 100644
index 0bff8eb609038c6d3e30f79f20fae7cf86c087da..0000000000000000000000000000000000000000
--- a/spaces/carlgira/dreambooth-image-editor/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import cv2
-import numpy as np
-import os
-import torch
-from PIL import Image
-from diffusers import StableDiffusionInpaintPipeline
-
-
-auth_token = os.environ.get("READ_TOKEN") or True
-
-pipe = None
-def preview(image, state):
- h, w = image.shape[:2]
- scale_percent = 512 / max([w, h])
-
- width = int(w * scale_percent)
- height = int(h * scale_percent)
- dim = (width, height)
- resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
- yoff = round((512-height)/2)
- xoff = round((512-width)/2)
-
- final_image = np.zeros((512, 512, 3), dtype=np.uint8)
- final_image.fill(120)
- final_image[yoff:yoff+height, xoff:xoff+width, :] = resized
-
- mask_image = np.zeros((512, 512, 3), dtype=np.uint8)
- mask_image.fill(255)
- mask_image[yoff:yoff+height, xoff:xoff+width, :] = 0
- state.clear()
- state.append(mask_image)
- state.append([yoff, xoff, height, width])
- state.append(resized)
-
- return final_image, state
-
-
-def sd_inpaint(image, prompt, state):
- global pipe
- mask = state[0]
- yoff, xoff, height, width = state[1]
- orig_image = state[2]
-
- device = "cuda" if torch.cuda.is_available() else "cpu"
-
- if device == "cuda":
- if pipe is None:
- pipe = StableDiffusionInpaintPipeline.from_pretrained(
- "runwayml/stable-diffusion-inpainting",
- revision="fp16",
- torch_dtype=torch.float16,
- use_auth_token=auth_token
- ).to(device)
- output = pipe(prompt=prompt, image=Image.fromarray(image), mask_image=Image.fromarray(mask)).images[0]
- else:
- output = image
-
- result = np.array(output)
- result[yoff:yoff+height, xoff:xoff+width, :] = orig_image
- result = Image.fromarray(result)
-
- return result
-
-
-with gr.Blocks(title='Dreambooth Image Editing and Stable Diffusion Inpainting') as demo:
- state = gr.State([])
- gr.Markdown("# Dreambooth Image Editing and Stable Diffusion Inpainting")
- gr.Markdown("It's difficult to get a good image to use for dreambooth, I do not have many photograhps of myself alone and it's very slow to edit the images (crop the selection, scale it to 512x512 and solve the problem of the background somehow)")
- gr.Markdown("This app uses a combination of image selection, automatic scaling, and stable diffusion inpainting to speed that process. Follow the next instructions:")
- gr.Markdown("""- Upload an image
-- Use the select tool to select the area you want to use for dreambooth
-- The image will be resized to 512x512 and fill the rest of with a gray background
-- Then click the Inpaint button to use stable diffusion to inpaint the background
-- Save the image and use it for dreambooth
- """)
- with gr.Row():
- with gr.Column():
- img_ctr = gr.Image(tool='select')
- with gr.Column():
- output = gr.Image(label="Selection with mask (512x512)")
- with gr.Row():
- greet_btn = gr.Button("Selection")
- with gr.Row():
- sd_prompt = gr.Textbox(lines=2, label="Stable diffusion prompt")
- with gr.Row():
- final_image = gr.Image(label='Generated Image (512x512)')
- with gr.Row():
- stab_btn = gr.Button("Inpaint")
- with gr.Row():
- gr.Examples([
- ['one.png', 'in the office'],
- ], inputs=[img_ctr, sd_prompt])
- gr.Examples([
- ['two.png', 'in the office'],
- ], inputs=[img_ctr, sd_prompt])
-
- greet_btn.click(fn=preview, inputs=[img_ctr, state], outputs=[output, state])
- stab_btn.click(fn=sd_inpaint, inputs=[output, sd_prompt, state], outputs=final_image)
-
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/bertscore_eval.py b/spaces/chendl/compositional_test/multimodal/bertscore_eval.py
deleted file mode 100644
index c64a68866c05f32687a461eb4e2aa30d32cdabd5..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/bertscore_eval.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from open_flamingo.eval.vqa_metric import compute_vqa_accuracy
-import sys
-import json
-from bert_score import BERTScorer
-from tqdm.contrib.concurrent import process_map
-from tqdm import tqdm
-import random
-import time
-NUM_GPU = 128
-
-def single_job(args):
- data, refs, idx = args
- success = False
- while not success:
- try:
- time.sleep(random.random()*10)
- scorer = BERTScorer(
- lang="en",
- rescale_with_baseline=True,
- # model_type="microsoft/deberta-xlarge-mnli",
- model_type="bert-base-uncased",
- batch_size=4096,
- device=f"cuda:{idx % 6}"
- )
- success = True
- except:
- time.sleep(random.random()*5)
- for i, d in enumerate(tqdm(data, disable=(idx != 0))):
- if d["answer"] == "":
- continue
- cands = [d["answer"]] * len(refs)
- P, R, F1 = scorer.score(cands, refs, verbose=False)
- d["answer"] = refs[F1.argmax()]
- data[i] = d
- return data
-
-
-if __name__ == "__main__":
- if sys.argv[1] == "vqav2":
- question_json_path = "/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/task/open_flamingo/vqav2/v2_OpenEnded_mscoco_val2014_questions.json"
- annotation_json_path = "/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/task/open_flamingo/vqav2/v2_mscoco_val2014_annotations.json"
- else:
- raise NotImplementedError
- answer_list = json.load(open("answer_list.json"))
- data = json.load(open(sys.argv[2]))
- cands = []
- refs = []
- data_parts = []
- for i in range(NUM_GPU):
- data_parts.append([[], answer_list, i])
- for i, d in enumerate(data):
- data_parts[i % NUM_GPU][0].append(d)
- datas = process_map(single_job, data_parts, max_workers=NUM_GPU, disable=True)
- all_data = []
- for data in datas:
- all_data.extend(data)
- json.dump(all_data, open("temp_result", "w"))
- acc = compute_vqa_accuracy(
- result_json_path="temp_result",
- question_json_path=question_json_path,
- annotation_json_path=annotation_json_path,
- )
- print(acc)
diff --git a/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/run_mlm_flax.py b/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/run_mlm_flax.py
deleted file mode 100644
index ae289b84708f2cb672edc7dceacc6fb84307ea06..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/run_mlm_flax.py
+++ /dev/null
@@ -1,894 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Team All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
-text file or a dataset.
-
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=fill-mask
-"""
-import json
-import logging
-import math
-import os
-import sys
-import time
-from dataclasses import asdict, dataclass, field
-from enum import Enum
-from itertools import chain
-
-# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import flax
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-from datasets import load_dataset
-from flax import jax_utils, traverse_util
-from flax.jax_utils import pad_shard_unpad
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
-from tqdm import tqdm
-
-from transformers import (
- CONFIG_MAPPING,
- FLAX_MODEL_FOR_MASKED_LM_MAPPING,
- AutoConfig,
- AutoTokenizer,
- FlaxAutoModelForMaskedLM,
- HfArgumentParser,
- PreTrainedTokenizerBase,
- TensorType,
- is_tensorboard_available,
- set_seed,
-)
-from transformers.utils import get_full_repo_name, send_example_telemetry
-
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class TrainingArguments:
- output_dir: str = field(
- metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
- )
- overwrite_output_dir: bool = field(
- default=False,
- metadata={
- "help": (
- "Overwrite the content of the output directory. "
- "Use this to continue training if output_dir points to a checkpoint directory."
- )
- },
- )
- do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
- do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
- per_device_train_batch_size: int = field(
- default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
- )
- per_device_eval_batch_size: int = field(
- default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
- )
- learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
- weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
- adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
- adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
- adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
- adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
- num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
- warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
- logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
- save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
- eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
- seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
- push_to_hub: bool = field(
- default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
- )
- hub_model_id: str = field(
- default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
- )
- hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
- gradient_checkpointing: bool = field(
- default=False,
- metadata={
- "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
- },
- )
-
- def __post_init__(self):
- if self.output_dir is not None:
- self.output_dir = os.path.expanduser(self.output_dir)
-
- def to_dict(self):
- """
- Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
- the token values by removing their value.
- """
- d = asdict(self)
- for k, v in d.items():
- if isinstance(v, Enum):
- d[k] = v.value
- if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
- d[k] = [x.value for x in v]
- if k.endswith("_token"):
- d[k] = f"<{k.upper()}>"
- return d
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
- """
-
- model_name_or_path: Optional[str] = field(
- default=None,
- metadata={
- "help": (
- "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
- )
- },
- )
- model_type: Optional[str] = field(
- default=None,
- metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
- )
- config_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
- )
- tokenizer_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
- )
- cache_dir: Optional[str] = field(
- default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
- )
- use_fast_tokenizer: bool = field(
- default=True,
- metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
- )
- dtype: Optional[str] = field(
- default="float32",
- metadata={
- "help": (
- "Floating-point format in which the model weights should be initialized and trained. Choose one of"
- " `[float32, float16, bfloat16]`."
- )
- },
- )
- use_auth_token: bool = field(
- default=False,
- metadata={
- "help": (
- "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
- "with private models)."
- )
- },
- )
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
- """
-
- dataset_name: Optional[str] = field(
- default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
- )
- dataset_config_name: Optional[str] = field(
- default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
- )
- train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
- validation_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
- )
- train_ref_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
- )
- validation_ref_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
- )
- overwrite_cache: bool = field(
- default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
- )
- validation_split_percentage: Optional[int] = field(
- default=5,
- metadata={
- "help": "The percentage of the train set used as validation set in case there's no validation split"
- },
- )
- max_seq_length: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "The maximum total input sequence length after tokenization. Sequences longer "
- "than this will be truncated. Default to the max input length of the model."
- )
- },
- )
- preprocessing_num_workers: Optional[int] = field(
- default=None,
- metadata={"help": "The number of processes to use for the preprocessing."},
- )
- mlm_probability: float = field(
- default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
- )
- pad_to_max_length: bool = field(
- default=False,
- metadata={
- "help": (
- "Whether to pad all samples to `max_seq_length`. "
- "If False, will pad the samples dynamically when batching to the maximum length in the batch."
- )
- },
- )
- line_by_line: bool = field(
- default=False,
- metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
- )
-
- def __post_init__(self):
- if self.dataset_name is None and self.train_file is None and self.validation_file is None:
- raise ValueError("Need either a dataset name or a training/validation file.")
- else:
- if self.train_file is not None:
- extension = self.train_file.split(".")[-1]
- assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
- if self.validation_file is not None:
- extension = self.validation_file.split(".")[-1]
- assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorForLanguageModeling:
- """
- Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
- are not all of the same length.
-
- Args:
- tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
- The tokenizer used for encoding the data.
- mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
- The probability with which to (randomly) mask tokens in the input.
-
- .. note::
-
- For best performance, this data collator should be used with a dataset having items that are dictionaries or
- BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
- :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
- argument :obj:`return_special_tokens_mask=True`.
- """
-
- tokenizer: PreTrainedTokenizerBase
- mlm_probability: float = 0.15
-
- def __post_init__(self):
- if self.tokenizer.mask_token is None:
- raise ValueError(
- "This tokenizer does not have a mask token which is necessary for masked language modeling. "
- "You should pass `mlm=False` to train on causal language modeling instead."
- )
-
- def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
- # Handle dict or lists with proper padding and conversion to tensor.
- batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
-
- # If special token mask has been preprocessed, pop it from the dict.
- special_tokens_mask = batch.pop("special_tokens_mask", None)
-
- batch["input_ids"], batch["labels"] = self.mask_tokens(
- batch["input_ids"], special_tokens_mask=special_tokens_mask
- )
- return batch
-
- def mask_tokens(
- self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
- ) -> Tuple[np.ndarray, np.ndarray]:
- """
- Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
- """
- labels = inputs.copy()
- # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
- probability_matrix = np.full(labels.shape, self.mlm_probability)
- special_tokens_mask = special_tokens_mask.astype("bool")
-
- probability_matrix[special_tokens_mask] = 0.0
- masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
- labels[~masked_indices] = -100 # We only compute loss on masked tokens
-
- # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
- indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
- inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
- # 10% of the time, we replace masked input tokens with random word
- indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
- indices_random &= masked_indices & ~indices_replaced
-
- random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
- inputs[indices_random] = random_words[indices_random]
-
- # The rest of the time (10% of the time) we keep the masked input tokens unchanged
- return inputs, labels
-
-
-def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:
- """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
- the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned."""
- num_samples = len(samples_idx)
- if drop_last:
- samples_to_remove = num_samples % batch_size
- if samples_to_remove != 0:
- samples_idx = samples_idx[:-samples_to_remove]
- sections_split = num_samples // batch_size
- samples_idx = samples_idx.reshape((sections_split, batch_size))
- else:
- sections_split = math.ceil(num_samples / batch_size)
- samples_idx = np.array_split(samples_idx, sections_split)
- return samples_idx
-
-
-def write_train_metric(summary_writer, train_metrics, train_time, step):
- summary_writer.scalar("train_time", train_time, step)
-
- train_metrics = get_metrics(train_metrics)
- for key, vals in train_metrics.items():
- tag = f"train_{key}"
- for i, val in enumerate(vals):
- summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
- for metric_name, value in eval_metrics.items():
- summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-def main():
- # See all possible arguments in src/transformers/training_args.py
- # or by passing the --help flag to this script.
- # We now keep distinct sets of args, for a cleaner separation of concerns.
-
- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
- if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
- else:
- model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
- # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
- # information sent is the one passed as arguments along with your Python/PyTorch versions.
- send_example_telemetry("run_mlm", model_args, data_args, framework="flax")
-
- if (
- os.path.exists(training_args.output_dir)
- and os.listdir(training_args.output_dir)
- and training_args.do_train
- and not training_args.overwrite_output_dir
- ):
- raise ValueError(
- f"Output directory ({training_args.output_dir}) already exists and is not empty."
- "Use --overwrite_output_dir to overcome."
- )
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- level=logging.INFO,
- datefmt="[%X]",
- )
-
- # Log on each process the small summary:
- logger = logging.getLogger(__name__)
-
- # Set the verbosity to info of the Transformers logger (on main process only):
- logger.info(f"Training/evaluation parameters {training_args}")
-
- # Set seed before initializing model.
- set_seed(training_args.seed)
-
- # Handle the repository creation
- if training_args.push_to_hub:
- if training_args.hub_model_id is None:
- repo_name = get_full_repo_name(
- Path(training_args.output_dir).absolute().name, token=training_args.hub_token
- )
- else:
- repo_name = training_args.hub_model_id
- create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
- repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
-
- # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
- # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
- # (the dataset will be downloaded automatically from the datasets Hub).
- #
- # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
- # 'text' is found. You can easily tweak this behavior (see below).
- #
- # In distributed training, the load_dataset function guarantees that only one local process can concurrently
- # download the dataset.
- if data_args.dataset_name is not None:
- # Downloading and loading a dataset from the hub.
- datasets = load_dataset(
- data_args.dataset_name,
- data_args.dataset_config_name,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
-
- if "validation" not in datasets.keys():
- datasets["validation"] = load_dataset(
- data_args.dataset_name,
- data_args.dataset_config_name,
- split=f"train[:{data_args.validation_split_percentage}%]",
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- datasets["train"] = load_dataset(
- data_args.dataset_name,
- data_args.dataset_config_name,
- split=f"train[{data_args.validation_split_percentage}%:]",
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- data_files = {}
- if data_args.train_file is not None:
- data_files["train"] = data_args.train_file
- if data_args.validation_file is not None:
- data_files["validation"] = data_args.validation_file
- extension = data_args.train_file.split(".")[-1]
- if extension == "txt":
- extension = "text"
- datasets = load_dataset(
- extension,
- data_files=data_files,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
-
- if "validation" not in datasets.keys():
- datasets["validation"] = load_dataset(
- extension,
- data_files=data_files,
- split=f"train[:{data_args.validation_split_percentage}%]",
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- datasets["train"] = load_dataset(
- extension,
- data_files=data_files,
- split=f"train[{data_args.validation_split_percentage}%:]",
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- # Load pretrained model and tokenizer
-
- # Distributed training:
- # The .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- if model_args.config_name:
- config = AutoConfig.from_pretrained(
- model_args.config_name,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- elif model_args.model_name_or_path:
- config = AutoConfig.from_pretrained(
- model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- config = CONFIG_MAPPING[model_args.model_type]()
- logger.warning("You are instantiating a new config instance from scratch.")
-
- if model_args.tokenizer_name:
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.tokenizer_name,
- cache_dir=model_args.cache_dir,
- use_fast=model_args.use_fast_tokenizer,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- elif model_args.model_name_or_path:
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- use_fast=model_args.use_fast_tokenizer,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- raise ValueError(
- "You are instantiating a new tokenizer from scratch. This is not supported by this script."
- "You can do it from another script, save it, and load it from here, using --tokenizer_name."
- )
-
- # Preprocessing the datasets.
- # First we tokenize all the texts.
- if training_args.do_train:
- column_names = datasets["train"].column_names
- else:
- column_names = datasets["validation"].column_names
- text_column_name = "text" if "text" in column_names else column_names[0]
-
- max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
- if data_args.line_by_line:
- # When using line_by_line, we just tokenize each nonempty line.
- padding = "max_length" if data_args.pad_to_max_length else False
-
- def tokenize_function(examples):
- # Remove empty lines
- examples = [line for line in examples if len(line) > 0 and not line.isspace()]
- return tokenizer(
- examples,
- return_special_tokens_mask=True,
- padding=padding,
- truncation=True,
- max_length=max_seq_length,
- )
-
- tokenized_datasets = datasets.map(
- tokenize_function,
- input_columns=[text_column_name],
- batched=True,
- num_proc=data_args.preprocessing_num_workers,
- remove_columns=column_names,
- load_from_cache_file=not data_args.overwrite_cache,
- )
-
- else:
- # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
- # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
- # efficient when it receives the `special_tokens_mask`.
- def tokenize_function(examples):
- return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
-
- tokenized_datasets = datasets.map(
- tokenize_function,
- batched=True,
- num_proc=data_args.preprocessing_num_workers,
- remove_columns=column_names,
- load_from_cache_file=not data_args.overwrite_cache,
- )
-
- # Main data processing function that will concatenate all texts from our dataset and generate chunks of
- # max_seq_length.
- def group_texts(examples):
- # Concatenate all texts.
- concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
- total_length = len(concatenated_examples[list(examples.keys())[0]])
- # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
- # customize this part to your needs.
- if total_length >= max_seq_length:
- total_length = (total_length // max_seq_length) * max_seq_length
- # Split by chunks of max_len.
- result = {
- k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
- for k, t in concatenated_examples.items()
- }
- return result
-
- # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
- # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
- # might be slower to preprocess.
- #
- # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
- # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
- tokenized_datasets = tokenized_datasets.map(
- group_texts,
- batched=True,
- num_proc=data_args.preprocessing_num_workers,
- load_from_cache_file=not data_args.overwrite_cache,
- )
-
- # Enable tensorboard only on the master node
- has_tensorboard = is_tensorboard_available()
- if has_tensorboard and jax.process_index() == 0:
- try:
- from flax.metrics.tensorboard import SummaryWriter
-
- summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
- except ImportError as ie:
- has_tensorboard = False
- logger.warning(
- f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
- )
- else:
- logger.warning(
- "Unable to display metrics through TensorBoard because the package is not installed: "
- "Please run pip install tensorboard to enable."
- )
-
- # Data collator
- # This one will take care of randomly masking the tokens.
- data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
-
- # Initialize our training
- rng = jax.random.PRNGKey(training_args.seed)
- dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
- if model_args.model_name_or_path:
- model = FlaxAutoModelForMaskedLM.from_pretrained(
- model_args.model_name_or_path,
- config=config,
- seed=training_args.seed,
- dtype=getattr(jnp, model_args.dtype),
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- model = FlaxAutoModelForMaskedLM.from_config(
- config,
- seed=training_args.seed,
- dtype=getattr(jnp, model_args.dtype),
- )
-
- if training_args.gradient_checkpointing:
- model.enable_gradient_checkpointing()
-
- # Store some constant
- num_epochs = int(training_args.num_train_epochs)
- train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
- per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
- eval_batch_size = per_device_eval_batch_size * jax.device_count()
-
- num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
-
- # Create learning rate schedule
- warmup_fn = optax.linear_schedule(
- init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
- )
- decay_fn = optax.linear_schedule(
- init_value=training_args.learning_rate,
- end_value=0,
- transition_steps=num_train_steps - training_args.warmup_steps,
- )
- linear_decay_lr_schedule_fn = optax.join_schedules(
- schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
- )
-
- # We use Optax's "masking" functionality to not apply weight decay
- # to bias and LayerNorm scale parameters. decay_mask_fn returns a
- # mask boolean with the same structure as the parameters.
- # The mask is True for parameters that should be decayed.
- def decay_mask_fn(params):
- flat_params = traverse_util.flatten_dict(params)
- # find out all LayerNorm parameters
- layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
- layer_norm_named_params = {
- layer[-2:]
- for layer_norm_name in layer_norm_candidates
- for layer in flat_params.keys()
- if layer_norm_name in "".join(layer).lower()
- }
- flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
- return traverse_util.unflatten_dict(flat_mask)
-
- # create adam optimizer
- if training_args.adafactor:
- # We use the default parameters here to initialize adafactor,
- # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
- optimizer = optax.adafactor(
- learning_rate=linear_decay_lr_schedule_fn,
- )
- else:
- optimizer = optax.adamw(
- learning_rate=linear_decay_lr_schedule_fn,
- b1=training_args.adam_beta1,
- b2=training_args.adam_beta2,
- eps=training_args.adam_epsilon,
- weight_decay=training_args.weight_decay,
- mask=decay_mask_fn,
- )
-
- # Setup train state
- state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer)
-
- # Define gradient update step fn
- def train_step(state, batch, dropout_rng):
- dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
- def loss_fn(params):
- labels = batch.pop("labels")
-
- logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-
- # compute loss, ignore padded input tokens
- label_mask = jnp.where(labels > 0, 1.0, 0.0)
- loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
- # take average
- loss = loss.sum()
- num_labels = label_mask.sum()
-
- return loss, num_labels
-
- grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
- (loss, num_labels), grad = grad_fn(state.params)
- num_labels = jax.lax.psum(num_labels, "batch")
-
- # true loss = total loss / total samples
- loss = jax.lax.psum(loss, "batch")
- loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
- # true grad = total grad / total samples
- grad = jax.lax.psum(grad, "batch")
- grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
- new_state = state.apply_gradients(grads=grad)
-
- metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
-
- return new_state, metrics, new_dropout_rng
-
- # Create parallel version of the train step
- p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
- # Define eval fn
- def eval_step(params, batch):
- labels = batch.pop("labels")
-
- logits = model(**batch, params=params, train=False)[0]
-
- # compute loss, ignore padded input tokens
- label_mask = jnp.where(labels > 0, 1.0, 0.0)
- loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
- # compute accuracy
- accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
-
- # summarize metrics
- metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
- metrics = jax.lax.psum(metrics, axis_name="batch")
-
- return metrics
-
- p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
- # Replicate the train state on each device
- state = jax_utils.replicate(state)
-
- train_time = 0
- epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
- for epoch in epochs:
- # ======================== Training ================================
- train_start = time.time()
- train_metrics = []
-
- # Create sampling rng
- rng, input_rng = jax.random.split(rng)
-
- # Generate an epoch by shuffling sampling indices from the train dataset
- num_train_samples = len(tokenized_datasets["train"])
- # Avoid using jax.numpy here in case of TPU training
- train_samples_idx = np.random.permutation(np.arange(num_train_samples))
- train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
-
- # Gather the indexes for creating the batch and do a training step
- for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
- samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
- model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
- # Model forward
- model_inputs = shard(model_inputs.data)
- state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
- train_metrics.append(train_metric)
-
- cur_step = epoch * (num_train_samples // train_batch_size) + step
-
- if cur_step % training_args.logging_steps == 0 and cur_step > 0:
- # Save metrics
- train_metric = jax_utils.unreplicate(train_metric)
- train_time += time.time() - train_start
- if has_tensorboard and jax.process_index() == 0:
- write_train_metric(summary_writer, train_metrics, train_time, cur_step)
-
- epochs.write(
- f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:"
- f" {train_metric['learning_rate']})"
- )
-
- train_metrics = []
-
- if cur_step % training_args.eval_steps == 0 and cur_step > 0:
- # ======================== Evaluating ==============================
- num_eval_samples = len(tokenized_datasets["validation"])
- # Avoid using jax.numpy here in case of TPU training
- eval_samples_idx = np.arange(num_eval_samples)
- eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
-
- eval_metrics = []
- for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
- samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
- model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
- # Model forward
- metrics = pad_shard_unpad(p_eval_step, static_return=True)(
- state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
- )
- eval_metrics.append(metrics)
-
- # normalize eval metrics
- eval_metrics = get_metrics(eval_metrics)
- eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
- eval_normalizer = eval_metrics.pop("normalizer")
- eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
-
- # Update progress bar
- epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
-
- # Save metrics
- if has_tensorboard and jax.process_index() == 0:
- write_eval_metric(summary_writer, eval_metrics, cur_step)
-
- if cur_step % training_args.save_steps == 0 and cur_step > 0:
- # save checkpoint after each epoch and push checkpoint to the hub
- if jax.process_index() == 0:
- params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
- model.save_pretrained(training_args.output_dir, params=params)
- tokenizer.save_pretrained(training_args.output_dir)
- if training_args.push_to_hub:
- repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
-
- # Eval after training
- if training_args.do_eval:
- num_eval_samples = len(tokenized_datasets["validation"])
- # Avoid using jax.numpy here in case of TPU training
- eval_samples_idx = np.arange(num_eval_samples)
- eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
-
- eval_metrics = []
- for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
- samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
- model_inputs = data_collator(samples, pad_to_multiple_of=16)
-
- # Model forward
- metrics = pad_shard_unpad(p_eval_step, static_return=True)(
- state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
- )
- eval_metrics.append(metrics)
-
- # normalize eval metrics
- eval_metrics = get_metrics(eval_metrics)
- eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
- eval_normalizer = eval_metrics.pop("normalizer")
- eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
-
- try:
- perplexity = math.exp(eval_metrics["loss"])
- except OverflowError:
- perplexity = float("inf")
- eval_metrics["perplexity"] = perplexity
-
- if jax.process_index() == 0:
- eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
- path = os.path.join(training_args.output_dir, "eval_results.json")
- with open(path, "w") as f:
- json.dump(eval_metrics, f, indent=4, sort_keys=True)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chendl/compositional_test/transformers/examples/pytorch/translation/run_translation_no_trainer.py b/spaces/chendl/compositional_test/transformers/examples/pytorch/translation/run_translation_no_trainer.py
deleted file mode 100644
index b4db30eff2343bf2c1dcdf071c9345f22fff172c..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/pytorch/translation/run_translation_no_trainer.py
+++ /dev/null
@@ -1,753 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning a 🤗 Transformers model on text translation.
-"""
-# You can also adapt this script on your own text translation task. Pointers for this are left as comments.
-
-import argparse
-import json
-import logging
-import math
-import os
-import random
-from pathlib import Path
-
-import datasets
-import evaluate
-import numpy as np
-import torch
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
-import transformers
-from transformers import (
- CONFIG_MAPPING,
- MODEL_MAPPING,
- AutoConfig,
- AutoModelForSeq2SeqLM,
- AutoTokenizer,
- DataCollatorForSeq2Seq,
- MBartTokenizer,
- MBartTokenizerFast,
- SchedulerType,
- default_data_collator,
- get_scheduler,
-)
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.28.0")
-
-logger = get_logger(__name__)
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
-
-# You should update this to your particular problem to have better documentation of `model_type`
-MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-# Parsing input arguments
-def parse_args():
- parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
- parser.add_argument(
- "--dataset_name",
- type=str,
- default=None,
- help="The name of the dataset to use (via the datasets library).",
- )
-
- parser.add_argument(
- "--predict_with_generate",
- type=bool,
- default=True,
- help="",
- )
- parser.add_argument(
- "--dataset_config_name",
- type=str,
- default=None,
- help="The configuration name of the dataset to use (via the datasets library).",
- )
- parser.add_argument(
- "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
- )
-
- parser.add_argument(
- "--num_beams",
- type=int,
- default=None,
- help=(
- "Number of beams to use for evaluation. This argument will be "
- "passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
- ),
- )
-
- parser.add_argument(
- "--max_source_length",
- type=int,
- default=1024,
- help=(
- "The maximum total input sequence length after "
- "tokenization.Sequences longer than this will be truncated, sequences shorter will be padded."
- ),
- )
- parser.add_argument(
- "--max_target_length",
- type=int,
- default=128,
- help=(
- "The maximum total sequence length for target text after "
- "tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
- "during ``evaluate`` and ``predict``."
- ),
- )
- parser.add_argument(
- "--val_max_target_length",
- type=int,
- default=None,
- help=(
- "The maximum total sequence length for validation "
- "target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be "
- "padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` "
- "param of ``model.generate``, which is used during ``evaluate`` and ``predict``."
- ),
- )
- parser.add_argument(
- "--pad_to_max_length",
- type=bool,
- default=False,
- help=(
- "Whether to pad all samples to model maximum sentence "
- "length. If False, will pad the samples dynamically when batching to the maximum length in the batch. More"
- "efficient on GPU but very bad for TPU."
- ),
- )
- parser.add_argument(
- "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
- )
- parser.add_argument(
- "--ignore_pad_token_for_loss",
- type=bool,
- default=True,
- help="Whether to ignore the tokens corresponding to padded labels in the loss computation or not.",
- )
- parser.add_argument("--source_lang", type=str, default=None, help="Source language id for translation.")
- parser.add_argument("--target_lang", type=str, default=None, help="Target language id for translation.")
- parser.add_argument(
- "--source_prefix",
- type=str,
- default=None,
- help="A prefix to add before every source text (useful for T5 models).",
- )
- parser.add_argument(
- "--preprocessing_num_workers",
- type=int,
- default=None,
- help="The number of processes to use for the preprocessing.",
- )
- parser.add_argument(
- "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
- )
- parser.add_argument(
- "--max_length",
- type=int,
- default=128,
- help=(
- "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
- " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
- ),
- )
- parser.add_argument(
- "--model_name_or_path",
- type=str,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- required=False,
- )
- parser.add_argument(
- "--config_name",
- type=str,
- default=None,
- help="Pretrained config name or path if not the same as model_name",
- )
- parser.add_argument(
- "--tokenizer_name",
- type=str,
- default=None,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--use_slow_tokenizer",
- action="store_true",
- help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
- )
- parser.add_argument(
- "--per_device_train_batch_size",
- type=int,
- default=8,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--per_device_eval_batch_size",
- type=int,
- default=8,
- help="Batch size (per device) for the evaluation dataloader.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=5e-5,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
- parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=None,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--lr_scheduler_type",
- type=SchedulerType,
- default="linear",
- help="The scheduler type to use.",
- choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
- )
- parser.add_argument(
- "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--model_type",
- type=str,
- default=None,
- help="Model type to use if training from scratch.",
- choices=MODEL_TYPES,
- )
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument(
- "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
- )
- parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--checkpointing_steps",
- type=str,
- default=None,
- help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
- )
- parser.add_argument(
- "--resume_from_checkpoint",
- type=str,
- default=None,
- help="If the training should continue from a checkpoint folder.",
- )
- parser.add_argument(
- "--with_tracking",
- action="store_true",
- help="Whether to enable experiment trackers for logging.",
- )
- parser.add_argument(
- "--report_to",
- type=str,
- default="all",
- help=(
- 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
- ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
- "Only applicable when `--with_tracking` is passed."
- ),
- )
- args = parser.parse_args()
-
- # Sanity checks
-
- if args.dataset_name is None and args.train_file is None and args.validation_file is None:
- raise ValueError("Need either a task name or a training/validation file.")
-
- if args.train_file is not None:
- extension = args.train_file.split(".")[-1]
- assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
- if args.validation_file is not None:
- extension = args.validation_file.split(".")[-1]
- assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-
- if args.push_to_hub:
- assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
-
- return args
-
-
-def main():
- # Parse the arguments
- args = parse_args()
-
- # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
- # information sent is the one passed as arguments along with your Python/PyTorch versions.
- send_example_telemetry("run_translation_no_trainer", args)
-
- # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
- # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
- # in the environment
- accelerator = (
- Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator()
- )
-
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger.info(accelerator.state, main_process_only=False)
- if accelerator.is_local_main_process:
- datasets.utils.logging.set_verbosity_warning()
- transformers.utils.logging.set_verbosity_info()
- else:
- datasets.utils.logging.set_verbosity_error()
- transformers.utils.logging.set_verbosity_error()
-
- # If passed along, set the training seed now.
- if args.seed is not None:
- set_seed(args.seed)
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
- else:
- repo_name = args.hub_model_id
- create_repo(repo_name, exist_ok=True, token=args.hub_token)
- repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
-
- with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
- if "step_*" not in gitignore:
- gitignore.write("step_*\n")
- if "epoch_*" not in gitignore:
- gitignore.write("epoch_*\n")
- elif args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
- accelerator.wait_for_everyone()
-
- # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
- # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
- # (the dataset will be downloaded automatically from the datasets Hub).
- #
- # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
- # 'text' is found. You can easily tweak this behavior (see below).
- #
- # In distributed training, the load_dataset function guarantee that only one local process can concurrently
- # download the dataset.
- if args.dataset_name is not None:
- # Downloading and loading a dataset from the hub.
- raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
- else:
- data_files = {}
- if args.train_file is not None:
- data_files["train"] = args.train_file
- if args.validation_file is not None:
- data_files["validation"] = args.validation_file
- extension = args.train_file.split(".")[-1]
- raw_datasets = load_dataset(extension, data_files=data_files)
- # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- # Load pretrained model and tokenizer
- #
- # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- if args.config_name:
- config = AutoConfig.from_pretrained(args.config_name)
- elif args.model_name_or_path:
- config = AutoConfig.from_pretrained(args.model_name_or_path)
- else:
- config = CONFIG_MAPPING[args.model_type]()
- logger.warning("You are instantiating a new config instance from scratch.")
-
- if args.tokenizer_name:
- tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
- elif args.model_name_or_path:
- tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
- else:
- raise ValueError(
- "You are instantiating a new tokenizer from scratch. This is not supported by this script."
- "You can do it from another script, save it, and load it from here, using --tokenizer_name."
- )
-
- if args.model_name_or_path:
- model = AutoModelForSeq2SeqLM.from_pretrained(
- args.model_name_or_path,
- from_tf=bool(".ckpt" in args.model_name_or_path),
- config=config,
- )
- else:
- logger.info("Training new model from scratch")
- model = AutoModelForSeq2SeqLM.from_config(config)
-
- # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
- # on a small vocab and want a smaller embedding size, remove this test.
- embedding_size = model.get_input_embeddings().weight.shape[0]
- if len(tokenizer) > embedding_size:
- model.resize_token_embeddings(len(tokenizer))
-
- # Set decoder_start_token_id
- if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
- assert (
- args.target_lang is not None and args.source_lang is not None
- ), "mBart requires --target_lang and --source_lang"
- if isinstance(tokenizer, MBartTokenizer):
- model.config.decoder_start_token_id = tokenizer.lang_code_to_id[args.target_lang]
- else:
- model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(args.target_lang)
-
- if model.config.decoder_start_token_id is None:
- raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
-
- prefix = args.source_prefix if args.source_prefix is not None else ""
-
- # Preprocessing the datasets.
- # First we tokenize all the texts.
- column_names = raw_datasets["train"].column_names
-
- # For translation we set the codes of our source and target languages (only useful for mBART, the others will
- # ignore those attributes).
- if isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
- if args.source_lang is not None:
- tokenizer.src_lang = args.source_lang
- if args.target_lang is not None:
- tokenizer.tgt_lang = args.target_lang
-
- # Get the language codes for input/target.
- source_lang = args.source_lang.split("_")[0]
- target_lang = args.target_lang.split("_")[0]
-
- padding = "max_length" if args.pad_to_max_length else False
-
- # Temporarily set max_target_length for training.
- max_target_length = args.max_target_length
- padding = "max_length" if args.pad_to_max_length else False
-
- def preprocess_function(examples):
- inputs = [ex[source_lang] for ex in examples["translation"]]
- targets = [ex[target_lang] for ex in examples["translation"]]
- inputs = [prefix + inp for inp in inputs]
- model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=padding, truncation=True)
-
- # Tokenize targets with the `text_target` keyword argument
- labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
-
- # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
- # padding in the loss.
- if padding == "max_length" and args.ignore_pad_token_for_loss:
- labels["input_ids"] = [
- [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
- ]
-
- model_inputs["labels"] = labels["input_ids"]
- return model_inputs
-
- with accelerator.main_process_first():
- processed_datasets = raw_datasets.map(
- preprocess_function,
- batched=True,
- num_proc=args.preprocessing_num_workers,
- remove_columns=column_names,
- load_from_cache_file=not args.overwrite_cache,
- desc="Running tokenizer on dataset",
- )
-
- train_dataset = processed_datasets["train"]
- eval_dataset = processed_datasets["validation"]
-
- # Log a few random samples from the training set:
- for index in random.sample(range(len(train_dataset)), 3):
- logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
-
- # DataLoaders creation:
- label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id
- if args.pad_to_max_length:
- # If padding was already done ot max length, we use the default data collator that will just convert everything
- # to tensors.
- data_collator = default_data_collator
- else:
- # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
- # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
- # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
- data_collator = DataCollatorForSeq2Seq(
- tokenizer,
- model=model,
- label_pad_token_id=label_pad_token_id,
- pad_to_multiple_of=8 if accelerator.use_fp16 else None,
- )
-
- train_dataloader = DataLoader(
- train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
- )
- eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
-
- # Optimizer
- # Split weights in two groups, one with weight decay and the other not.
- no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
- "weight_decay": args.weight_decay,
- },
- {
- "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
- "weight_decay": 0.0,
- },
- ]
- optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
-
- # Scheduler and math around the number of training steps.
- overrode_max_train_steps = False
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if args.max_train_steps is None:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- overrode_max_train_steps = True
-
- lr_scheduler = get_scheduler(
- name=args.lr_scheduler_type,
- optimizer=optimizer,
- num_warmup_steps=args.num_warmup_steps,
- num_training_steps=args.max_train_steps,
- )
-
- # Prepare everything with our `accelerator`.
- model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
- model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
- )
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if overrode_max_train_steps:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- # Afterwards we recalculate our number of training epochs
- args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
- # Figure out how many steps we should save the Accelerator states
- checkpointing_steps = args.checkpointing_steps
- if checkpointing_steps is not None and checkpointing_steps.isdigit():
- checkpointing_steps = int(checkpointing_steps)
-
- # We need to initialize the trackers we use, and also store our configuration.
- # We initialize the trackers only on main process because `accelerator.log`
- # only logs on main process and we don't want empty logs/runs on other processes.
- if args.with_tracking:
- if accelerator.is_main_process:
- experiment_config = vars(args)
- # TensorBoard cannot log Enums, need the raw value
- experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
- accelerator.init_trackers("translation_no_trainer", experiment_config)
-
- metric = evaluate.load("sacrebleu")
-
- def postprocess_text(preds, labels):
- preds = [pred.strip() for pred in preds]
- labels = [[label.strip()] for label in labels]
-
- return preds, labels
-
- # Train!
- total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(train_dataset)}")
- logger.info(f" Num Epochs = {args.num_train_epochs}")
- logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {args.max_train_steps}")
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
- completed_steps = 0
- starting_epoch = 0
-
- # Potentially load in the weights and states from a previous save
- if args.resume_from_checkpoint:
- if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
- accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
- accelerator.load_state(args.resume_from_checkpoint)
- path = os.path.basename(args.resume_from_checkpoint)
- else:
- # Get the most recent checkpoint
- dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
- dirs.sort(key=os.path.getctime)
- path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
- # Extract `epoch_{i}` or `step_{i}`
- training_difference = os.path.splitext(path)[0]
-
- if "epoch" in training_difference:
- starting_epoch = int(training_difference.replace("epoch_", "")) + 1
- resume_step = None
- else:
- # need to multiply `gradient_accumulation_steps` to reflect real steps
- resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
- starting_epoch = resume_step // len(train_dataloader)
- resume_step -= starting_epoch * len(train_dataloader)
-
- # update the progress_bar if load from checkpoint
- progress_bar.update(starting_epoch * num_update_steps_per_epoch)
- completed_steps = starting_epoch * num_update_steps_per_epoch
-
- for epoch in range(starting_epoch, args.num_train_epochs):
- model.train()
- if args.with_tracking:
- total_loss = 0
- for step, batch in enumerate(train_dataloader):
- # We need to skip steps until we reach the resumed step
- if args.resume_from_checkpoint and epoch == starting_epoch:
- if resume_step is not None and step < resume_step:
- if step % args.gradient_accumulation_steps == 0:
- progress_bar.update(1)
- completed_steps += 1
- continue
- outputs = model(**batch)
- loss = outputs.loss
- # We keep track of the loss at each epoch
- if args.with_tracking:
- total_loss += loss.detach().float()
- loss = loss / args.gradient_accumulation_steps
- accelerator.backward(loss)
- if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad()
- progress_bar.update(1)
- completed_steps += 1
-
- if isinstance(checkpointing_steps, int):
- if completed_steps % checkpointing_steps == 0:
- output_dir = f"step_{completed_steps }"
- if args.output_dir is not None:
- output_dir = os.path.join(args.output_dir, output_dir)
- accelerator.save_state(output_dir)
-
- if completed_steps >= args.max_train_steps:
- break
-
- model.eval()
-
- if args.val_max_target_length is None:
- args.val_max_target_length = args.max_target_length
-
- gen_kwargs = {
- "max_length": args.val_max_target_length if args is not None else config.max_length,
- "num_beams": args.num_beams,
- }
- samples_seen = 0
- for step, batch in enumerate(eval_dataloader):
- with torch.no_grad():
- generated_tokens = accelerator.unwrap_model(model).generate(
- batch["input_ids"],
- attention_mask=batch["attention_mask"],
- **gen_kwargs,
- )
-
- generated_tokens = accelerator.pad_across_processes(
- generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
- )
- labels = batch["labels"]
- if not args.pad_to_max_length:
- # If we did not pad to max length, we need to pad the labels too
- labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id)
-
- generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()
- labels = accelerator.gather(labels).cpu().numpy()
-
- if args.ignore_pad_token_for_loss:
- # Replace -100 in the labels as we can't decode them.
- labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
-
- decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
- decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
-
- decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
-
- # If we are in a multiprocess environment, the last batch has duplicates
- if accelerator.num_processes > 1:
- if step == len(eval_dataloader) - 1:
- decoded_preds = decoded_preds[: len(eval_dataloader.dataset) - samples_seen]
- decoded_labels = decoded_labels[: len(eval_dataloader.dataset) - samples_seen]
- else:
- samples_seen += len(decoded_labels)
-
- metric.add_batch(predictions=decoded_preds, references=decoded_labels)
- eval_metric = metric.compute()
- logger.info({"bleu": eval_metric["score"]})
-
- if args.with_tracking:
- accelerator.log(
- {
- "bleu": eval_metric["score"],
- "train_loss": total_loss.item() / len(train_dataloader),
- "epoch": epoch,
- "step": completed_steps,
- },
- step=completed_steps,
- )
-
- if args.push_to_hub and epoch < args.num_train_epochs - 1:
- accelerator.wait_for_everyone()
- unwrapped_model = accelerator.unwrap_model(model)
- unwrapped_model.save_pretrained(
- args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
- )
- if accelerator.is_main_process:
- tokenizer.save_pretrained(args.output_dir)
- repo.push_to_hub(
- commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
- )
-
- if args.checkpointing_steps == "epoch":
- output_dir = f"epoch_{epoch}"
- if args.output_dir is not None:
- output_dir = os.path.join(args.output_dir, output_dir)
- accelerator.save_state(output_dir)
-
- if args.with_tracking:
- accelerator.end_training()
-
- if args.output_dir is not None:
- accelerator.wait_for_everyone()
- unwrapped_model = accelerator.unwrap_model(model)
- unwrapped_model.save_pretrained(
- args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
- )
- if accelerator.is_main_process:
- tokenizer.save_pretrained(args.output_dir)
- if args.push_to_hub:
- repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
- with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
- json.dump({"eval_bleu": eval_metric["score"]}, f)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/src/modeling_highway_bert.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/src/modeling_highway_bert.py
deleted file mode 100644
index 2a881decbbd529ace899cec986d1d3cd73848b88..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/src/modeling_highway_bert.py
+++ /dev/null
@@ -1,399 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
-from transformers.models.bert.modeling_bert import (
- BERT_INPUTS_DOCSTRING,
- BERT_START_DOCSTRING,
- BertEmbeddings,
- BertLayer,
- BertPooler,
- BertPreTrainedModel,
-)
-
-
-def entropy(x):
- """Calculate entropy of a pre-softmax logit Tensor"""
- exp_x = torch.exp(x)
- A = torch.sum(exp_x, dim=1) # sum of exp(x_i)
- B = torch.sum(x * exp_x, dim=1) # sum of x_i * exp(x_i)
- return torch.log(A) - B / A
-
-
-class DeeBertEncoder(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.output_attentions = config.output_attentions
- self.output_hidden_states = config.output_hidden_states
- self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
- self.highway = nn.ModuleList([BertHighway(config) for _ in range(config.num_hidden_layers)])
-
- self.early_exit_entropy = [-1 for _ in range(config.num_hidden_layers)]
-
- def set_early_exit_entropy(self, x):
- if (type(x) is float) or (type(x) is int):
- for i in range(len(self.early_exit_entropy)):
- self.early_exit_entropy[i] = x
- else:
- self.early_exit_entropy = x
-
- def init_highway_pooler(self, pooler):
- loaded_model = pooler.state_dict()
- for highway in self.highway:
- for name, param in highway.pooler.state_dict().items():
- param.copy_(loaded_model[name])
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- head_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- ):
- all_hidden_states = ()
- all_attentions = ()
- all_highway_exits = ()
- for i, layer_module in enumerate(self.layer):
- if self.output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- layer_outputs = layer_module(
- hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
- )
- hidden_states = layer_outputs[0]
-
- if self.output_attentions:
- all_attentions = all_attentions + (layer_outputs[1],)
-
- current_outputs = (hidden_states,)
- if self.output_hidden_states:
- current_outputs = current_outputs + (all_hidden_states,)
- if self.output_attentions:
- current_outputs = current_outputs + (all_attentions,)
-
- highway_exit = self.highway[i](current_outputs)
- # logits, pooled_output
-
- if not self.training:
- highway_logits = highway_exit[0]
- highway_entropy = entropy(highway_logits)
- highway_exit = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
- all_highway_exits = all_highway_exits + (highway_exit,)
-
- if highway_entropy < self.early_exit_entropy[i]:
- new_output = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
- raise HighwayException(new_output, i + 1)
- else:
- all_highway_exits = all_highway_exits + (highway_exit,)
-
- # Add last layer
- if self.output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- outputs = (hidden_states,)
- if self.output_hidden_states:
- outputs = outputs + (all_hidden_states,)
- if self.output_attentions:
- outputs = outputs + (all_attentions,)
-
- outputs = outputs + (all_highway_exits,)
- return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
-
-
-@add_start_docstrings(
- "The Bert Model transformer with early exiting (DeeBERT). ",
- BERT_START_DOCSTRING,
-)
-class DeeBertModel(BertPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.config = config
-
- self.embeddings = BertEmbeddings(config)
- self.encoder = DeeBertEncoder(config)
- self.pooler = BertPooler(config)
-
- self.init_weights()
-
- def init_highway_pooler(self):
- self.encoder.init_highway_pooler(self.pooler)
-
- def get_input_embeddings(self):
- return self.embeddings.word_embeddings
-
- def set_input_embeddings(self, value):
- self.embeddings.word_embeddings = value
-
- def _prune_heads(self, heads_to_prune):
- """Prunes heads of the model.
- heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
- See base class PreTrainedModel
- """
- for layer, heads in heads_to_prune.items():
- self.encoder.layer[layer].attention.prune_heads(heads)
-
- @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- ):
- r"""
- Return:
- :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
- last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
- Sequence of hidden-states at the output of the last layer of the model.
- pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
- Last layer hidden-state of the first token of the sequence (classification token)
- further processed by a Linear layer and a Tanh activation function. The Linear
- layer weights are trained from the next sentence prediction (classification)
- objective during pre-training.
-
- This output is usually *not* a good summary
- of the semantic content of the input, you're often better with averaging or pooling
- the sequence of hidden-states for the whole input sequence.
- hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
- Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
- of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
- Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
- :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
- Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
- heads.
- highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
- Tuple of each early exit's results (total length: number of layers)
- Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
- """
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
- elif input_ids is not None:
- input_shape = input_ids.size()
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- raise ValueError("You have to specify either input_ids or inputs_embeds")
-
- device = input_ids.device if input_ids is not None else inputs_embeds.device
-
- if attention_mask is None:
- attention_mask = torch.ones(input_shape, device=device)
- if encoder_attention_mask is None:
- encoder_attention_mask = torch.ones(input_shape, device=device)
- if token_type_ids is None:
- token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
- # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
- # ourselves in which case we just need to make it broadcastable to all heads.
- extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
-
- # If a 2D ou 3D attention mask is provided for the cross-attention
- # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
- if encoder_attention_mask.dim() == 3:
- encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
- if encoder_attention_mask.dim() == 2:
- encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
-
- encoder_extended_attention_mask = encoder_extended_attention_mask.to(
- dtype=next(self.parameters()).dtype
- ) # fp16 compatibility
- encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
-
- # Prepare head mask if needed
- # 1.0 in head_mask indicate we keep the head
- # attention_probs has shape bsz x n_heads x N x N
- # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
- # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
- head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
- embedding_output = self.embeddings(
- input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
- )
- encoder_outputs = self.encoder(
- embedding_output,
- attention_mask=extended_attention_mask,
- head_mask=head_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_extended_attention_mask,
- )
- sequence_output = encoder_outputs[0]
- pooled_output = self.pooler(sequence_output)
-
- outputs = (
- sequence_output,
- pooled_output,
- ) + encoder_outputs[
- 1:
- ] # add hidden_states and attentions if they are here
- return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
-
-
-class HighwayException(Exception):
- def __init__(self, message, exit_layer):
- self.message = message
- self.exit_layer = exit_layer # start from 1!
-
-
-class BertHighway(nn.Module):
- """A module to provide a shortcut
- from (the output of one non-final BertLayer in BertEncoder) to (cross-entropy computation in BertForSequenceClassification)
- """
-
- def __init__(self, config):
- super().__init__()
- self.pooler = BertPooler(config)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
- def forward(self, encoder_outputs):
- # Pooler
- pooler_input = encoder_outputs[0]
- pooler_output = self.pooler(pooler_input)
- # "return" pooler_output
-
- # BertModel
- bmodel_output = (pooler_input, pooler_output) + encoder_outputs[1:]
- # "return" bmodel_output
-
- # Dropout and classification
- pooled_output = bmodel_output[1]
-
- pooled_output = self.dropout(pooled_output)
- logits = self.classifier(pooled_output)
-
- return logits, pooled_output
-
-
-@add_start_docstrings(
- """Bert Model (with early exiting - DeeBERT) with a classifier on top,
- also takes care of multi-layer training. """,
- BERT_START_DOCSTRING,
-)
-class DeeBertForSequenceClassification(BertPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
- self.num_layers = config.num_hidden_layers
-
- self.bert = DeeBertModel(config)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
-
- self.init_weights()
-
- @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- output_layer=-1,
- train_highway=False,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
- Labels for computing the sequence classification/regression loss.
- Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
- If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
- If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
- Returns:
- :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
- loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
- Classification (or regression if config.num_labels==1) loss.
- logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
- Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
- of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
- Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
- :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
- Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
- heads.
- highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
- Tuple of each early exit's results (total length: number of layers)
- Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
- """
-
- exit_layer = self.num_layers
- try:
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- )
- # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
-
- pooled_output = outputs[1]
-
- pooled_output = self.dropout(pooled_output)
- logits = self.classifier(pooled_output)
- outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
- except HighwayException as e:
- outputs = e.message
- exit_layer = e.exit_layer
- logits = outputs[0]
-
- if not self.training:
- original_entropy = entropy(logits)
- highway_entropy = []
- highway_logits_all = []
- if labels is not None:
- if self.num_labels == 1:
- # We are doing regression
- loss_fct = MSELoss()
- loss = loss_fct(logits.view(-1), labels.view(-1))
- else:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
- # work with highway exits
- highway_losses = []
- for highway_exit in outputs[-1]:
- highway_logits = highway_exit[0]
- if not self.training:
- highway_logits_all.append(highway_logits)
- highway_entropy.append(highway_exit[2])
- if self.num_labels == 1:
- # We are doing regression
- loss_fct = MSELoss()
- highway_loss = loss_fct(highway_logits.view(-1), labels.view(-1))
- else:
- loss_fct = CrossEntropyLoss()
- highway_loss = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
- highway_losses.append(highway_loss)
-
- if train_highway:
- outputs = (sum(highway_losses[:-1]),) + outputs
- # exclude the final highway, of course
- else:
- outputs = (loss,) + outputs
- if not self.training:
- outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
- if output_layer >= 0:
- outputs = (
- (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
- ) # use the highway of the last layer
-
- return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/models/auto/__init__.py b/spaces/chendl/compositional_test/transformers/src/transformers/models/auto/__init__.py
deleted file mode 100644
index 4eccfded5b6a6ab462391b230976161bc4edd9bf..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/models/auto/__init__.py
+++ /dev/null
@@ -1,355 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import TYPE_CHECKING
-
-from ...utils import (
- OptionalDependencyNotAvailable,
- _LazyModule,
- is_flax_available,
- is_tf_available,
- is_torch_available,
-)
-
-
-_import_structure = {
- "auto_factory": ["get_values"],
- "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
- "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
- "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
- "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
- "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
-}
-
-try:
- if not is_torch_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["modeling_auto"] = [
- "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
- "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
- "MODEL_FOR_BACKBONE_MAPPING",
- "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
- "MODEL_FOR_CAUSAL_LM_MAPPING",
- "MODEL_FOR_CTC_MAPPING",
- "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
- "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
- "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
- "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
- "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
- "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
- "MODEL_FOR_MASKED_LM_MAPPING",
- "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
- "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
- "MODEL_FOR_OBJECT_DETECTION_MAPPING",
- "MODEL_FOR_PRETRAINING_MAPPING",
- "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
- "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
- "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
- "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
- "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
- "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
- "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
- "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
- "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
- "MODEL_FOR_VISION_2_SEQ_MAPPING",
- "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
- "MODEL_MAPPING",
- "MODEL_WITH_LM_HEAD_MAPPING",
- "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
- "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
- "AutoModel",
- "AutoBackbone",
- "AutoModelForAudioClassification",
- "AutoModelForAudioFrameClassification",
- "AutoModelForAudioXVector",
- "AutoModelForCausalLM",
- "AutoModelForCTC",
- "AutoModelForDepthEstimation",
- "AutoModelForImageClassification",
- "AutoModelForImageSegmentation",
- "AutoModelForInstanceSegmentation",
- "AutoModelForMaskedImageModeling",
- "AutoModelForMaskedLM",
- "AutoModelForMultipleChoice",
- "AutoModelForNextSentencePrediction",
- "AutoModelForObjectDetection",
- "AutoModelForPreTraining",
- "AutoModelForQuestionAnswering",
- "AutoModelForSemanticSegmentation",
- "AutoModelForSeq2SeqLM",
- "AutoModelForSequenceClassification",
- "AutoModelForSpeechSeq2Seq",
- "AutoModelForTableQuestionAnswering",
- "AutoModelForTokenClassification",
- "AutoModelForUniversalSegmentation",
- "AutoModelForVideoClassification",
- "AutoModelForVision2Seq",
- "AutoModelForVisualQuestionAnswering",
- "AutoModelForDocumentQuestionAnswering",
- "AutoModelWithLMHead",
- "AutoModelForZeroShotImageClassification",
- "AutoModelForZeroShotObjectDetection",
- ]
-
-try:
- if not is_tf_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["modeling_tf_auto"] = [
- "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
- "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
- "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
- "TF_MODEL_FOR_MASKED_LM_MAPPING",
- "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
- "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
- "TF_MODEL_FOR_PRETRAINING_MAPPING",
- "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
- "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
- "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
- "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
- "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
- "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
- "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
- "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
- "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
- "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
- "TF_MODEL_MAPPING",
- "TF_MODEL_WITH_LM_HEAD_MAPPING",
- "TFAutoModel",
- "TFAutoModelForCausalLM",
- "TFAutoModelForImageClassification",
- "TFAutoModelForMaskedLM",
- "TFAutoModelForMultipleChoice",
- "TFAutoModelForNextSentencePrediction",
- "TFAutoModelForPreTraining",
- "TFAutoModelForDocumentQuestionAnswering",
- "TFAutoModelForQuestionAnswering",
- "TFAutoModelForSemanticSegmentation",
- "TFAutoModelForSeq2SeqLM",
- "TFAutoModelForSequenceClassification",
- "TFAutoModelForSpeechSeq2Seq",
- "TFAutoModelForTableQuestionAnswering",
- "TFAutoModelForTokenClassification",
- "TFAutoModelForVision2Seq",
- "TFAutoModelForZeroShotImageClassification",
- "TFAutoModelWithLMHead",
- ]
-
-try:
- if not is_flax_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["modeling_flax_auto"] = [
- "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
- "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
- "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
- "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
- "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
- "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
- "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
- "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
- "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
- "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
- "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
- "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
- "FLAX_MODEL_MAPPING",
- "FlaxAutoModel",
- "FlaxAutoModelForCausalLM",
- "FlaxAutoModelForImageClassification",
- "FlaxAutoModelForMaskedLM",
- "FlaxAutoModelForMultipleChoice",
- "FlaxAutoModelForNextSentencePrediction",
- "FlaxAutoModelForPreTraining",
- "FlaxAutoModelForQuestionAnswering",
- "FlaxAutoModelForSeq2SeqLM",
- "FlaxAutoModelForSequenceClassification",
- "FlaxAutoModelForSpeechSeq2Seq",
- "FlaxAutoModelForTokenClassification",
- "FlaxAutoModelForVision2Seq",
- ]
-
-
-if TYPE_CHECKING:
- from .auto_factory import get_values
- from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
- from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
- from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
- from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
- from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
-
- try:
- if not is_torch_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .modeling_auto import (
- MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
- MODEL_FOR_AUDIO_XVECTOR_MAPPING,
- MODEL_FOR_BACKBONE_MAPPING,
- MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
- MODEL_FOR_CAUSAL_LM_MAPPING,
- MODEL_FOR_CTC_MAPPING,
- MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
- MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
- MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
- MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
- MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
- MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
- MODEL_FOR_MASKED_LM_MAPPING,
- MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
- MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
- MODEL_FOR_OBJECT_DETECTION_MAPPING,
- MODEL_FOR_PRETRAINING_MAPPING,
- MODEL_FOR_QUESTION_ANSWERING_MAPPING,
- MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
- MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
- MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
- MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
- MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
- MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
- MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
- MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
- MODEL_FOR_VISION_2_SEQ_MAPPING,
- MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
- MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
- MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
- MODEL_MAPPING,
- MODEL_WITH_LM_HEAD_MAPPING,
- AutoBackbone,
- AutoModel,
- AutoModelForAudioClassification,
- AutoModelForAudioFrameClassification,
- AutoModelForAudioXVector,
- AutoModelForCausalLM,
- AutoModelForCTC,
- AutoModelForDepthEstimation,
- AutoModelForDocumentQuestionAnswering,
- AutoModelForImageClassification,
- AutoModelForImageSegmentation,
- AutoModelForInstanceSegmentation,
- AutoModelForMaskedImageModeling,
- AutoModelForMaskedLM,
- AutoModelForMultipleChoice,
- AutoModelForNextSentencePrediction,
- AutoModelForObjectDetection,
- AutoModelForPreTraining,
- AutoModelForQuestionAnswering,
- AutoModelForSemanticSegmentation,
- AutoModelForSeq2SeqLM,
- AutoModelForSequenceClassification,
- AutoModelForSpeechSeq2Seq,
- AutoModelForTableQuestionAnswering,
- AutoModelForTokenClassification,
- AutoModelForUniversalSegmentation,
- AutoModelForVideoClassification,
- AutoModelForVision2Seq,
- AutoModelForVisualQuestionAnswering,
- AutoModelForZeroShotImageClassification,
- AutoModelForZeroShotObjectDetection,
- AutoModelWithLMHead,
- )
-
- try:
- if not is_tf_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .modeling_tf_auto import (
- TF_MODEL_FOR_CAUSAL_LM_MAPPING,
- TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
- TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
- TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
- TF_MODEL_FOR_MASKED_LM_MAPPING,
- TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
- TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
- TF_MODEL_FOR_PRETRAINING_MAPPING,
- TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
- TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
- TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
- TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
- TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
- TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
- TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
- TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
- TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
- TF_MODEL_MAPPING,
- TF_MODEL_WITH_LM_HEAD_MAPPING,
- TFAutoModel,
- TFAutoModelForCausalLM,
- TFAutoModelForDocumentQuestionAnswering,
- TFAutoModelForImageClassification,
- TFAutoModelForMaskedLM,
- TFAutoModelForMultipleChoice,
- TFAutoModelForNextSentencePrediction,
- TFAutoModelForPreTraining,
- TFAutoModelForQuestionAnswering,
- TFAutoModelForSemanticSegmentation,
- TFAutoModelForSeq2SeqLM,
- TFAutoModelForSequenceClassification,
- TFAutoModelForSpeechSeq2Seq,
- TFAutoModelForTableQuestionAnswering,
- TFAutoModelForTokenClassification,
- TFAutoModelForVision2Seq,
- TFAutoModelForZeroShotImageClassification,
- TFAutoModelWithLMHead,
- )
-
- try:
- if not is_flax_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .modeling_flax_auto import (
- FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
- FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
- FLAX_MODEL_FOR_MASKED_LM_MAPPING,
- FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
- FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
- FLAX_MODEL_FOR_PRETRAINING_MAPPING,
- FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
- FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
- FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
- FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
- FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
- FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
- FLAX_MODEL_MAPPING,
- FlaxAutoModel,
- FlaxAutoModelForCausalLM,
- FlaxAutoModelForImageClassification,
- FlaxAutoModelForMaskedLM,
- FlaxAutoModelForMultipleChoice,
- FlaxAutoModelForNextSentencePrediction,
- FlaxAutoModelForPreTraining,
- FlaxAutoModelForQuestionAnswering,
- FlaxAutoModelForSeq2SeqLM,
- FlaxAutoModelForSequenceClassification,
- FlaxAutoModelForSpeechSeq2Seq,
- FlaxAutoModelForTokenClassification,
- FlaxAutoModelForVision2Seq,
- )
-
-else:
- import sys
-
- sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/_core/_streams.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/_core/_streams.py
deleted file mode 100644
index 54ea2b2bafd321a4f88dfa6fd19993213eec8105..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/_core/_streams.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import math
-from typing import Any, TypeVar, overload
-
-from ..streams.memory import (
- MemoryObjectReceiveStream,
- MemoryObjectSendStream,
- MemoryObjectStreamState,
-)
-
-T_Item = TypeVar("T_Item")
-
-
-@overload
-def create_memory_object_stream(
- max_buffer_size: float = ...,
-) -> tuple[MemoryObjectSendStream[Any], MemoryObjectReceiveStream[Any]]:
- ...
-
-
-@overload
-def create_memory_object_stream(
- max_buffer_size: float = ..., item_type: type[T_Item] = ...
-) -> tuple[MemoryObjectSendStream[T_Item], MemoryObjectReceiveStream[T_Item]]:
- ...
-
-
-def create_memory_object_stream(
- max_buffer_size: float = 0, item_type: type[T_Item] | None = None
-) -> tuple[MemoryObjectSendStream[Any], MemoryObjectReceiveStream[Any]]:
- """
- Create a memory object stream.
-
- :param max_buffer_size: number of items held in the buffer until ``send()`` starts blocking
- :param item_type: type of item, for marking the streams with the right generic type for
- static typing (not used at run time)
- :return: a tuple of (send stream, receive stream)
-
- """
- if max_buffer_size != math.inf and not isinstance(max_buffer_size, int):
- raise ValueError("max_buffer_size must be either an integer or math.inf")
- if max_buffer_size < 0:
- raise ValueError("max_buffer_size cannot be negative")
-
- state: MemoryObjectStreamState = MemoryObjectStreamState(max_buffer_size)
- return MemoryObjectSendStream(state), MemoryObjectReceiveStream(state)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/psOperators.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/psOperators.py
deleted file mode 100644
index d0ef432f5243e5ed0c8fa5b02f4c147dfcb032c2..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/psOperators.py
+++ /dev/null
@@ -1,574 +0,0 @@
-_accessstrings = {0: "", 1: "readonly", 2: "executeonly", 3: "noaccess"}
-
-
-class ps_object(object):
-
- literal = 1
- access = 0
- value = None
-
- def __init__(self, value):
- self.value = value
- self.type = self.__class__.__name__[3:] + "type"
-
- def __repr__(self):
- return "<%s %s>" % (self.__class__.__name__[3:], repr(self.value))
-
-
-class ps_operator(ps_object):
-
- literal = 0
-
- def __init__(self, name, function):
- self.name = name
- self.function = function
- self.type = self.__class__.__name__[3:] + "type"
-
- def __repr__(self):
- return "<operator %s>" % self.name
-
-
-class ps_procedure(ps_object):
- literal = 0
-
- def __repr__(self):
- return "<procedure>"
-
- def __str__(self):
- psstring = "{"
- for i in range(len(self.value)):
- if i:
- psstring = psstring + " " + str(self.value[i])
- else:
- psstring = psstring + str(self.value[i])
- return psstring + "}"
-
-
-class ps_name(ps_object):
- literal = 0
-
- def __str__(self):
- if self.literal:
- return "/" + self.value
- else:
- return self.value
-
-
-class ps_literal(ps_object):
- def __str__(self):
- return "/" + self.value
-
-
-class ps_array(ps_object):
- def __str__(self):
- psstring = "["
- for i in range(len(self.value)):
- item = self.value[i]
- access = _accessstrings[item.access]
- if access:
- access = " " + access
- if i:
- psstring = psstring + " " + str(item) + access
- else:
- psstring = psstring + str(item) + access
- return psstring + "]"
-
- def __repr__(self):
- return "<array>"
-
-
-_type1_pre_eexec_order = [
- "FontInfo",
- "FontName",
- "Encoding",
- "PaintType",
- "FontType",
- "FontMatrix",
- "FontBBox",
- "UniqueID",
- "Metrics",
- "StrokeWidth",
-]
-
-_type1_fontinfo_order = [
- "version",
- "Notice",
- "FullName",
- "FamilyName",
- "Weight",
- "ItalicAngle",
- "isFixedPitch",
- "UnderlinePosition",
- "UnderlineThickness",
-]
-
-_type1_post_eexec_order = ["Private", "CharStrings", "FID"]
-
-
-def _type1_item_repr(key, value):
- psstring = ""
- access = _accessstrings[value.access]
- if access:
- access = access + " "
- if key == "CharStrings":
- psstring = psstring + "/%s %s def\n" % (
- key,
- _type1_CharString_repr(value.value),
- )
- elif key == "Encoding":
- psstring = psstring + _type1_Encoding_repr(value, access)
- else:
- psstring = psstring + "/%s %s %sdef\n" % (str(key), str(value), access)
- return psstring
-
-
-def _type1_Encoding_repr(encoding, access):
- encoding = encoding.value
- psstring = "/Encoding 256 array\n0 1 255 {1 index exch /.notdef put} for\n"
- for i in range(256):
- name = encoding[i].value
- if name != ".notdef":
- psstring = psstring + "dup %d /%s put\n" % (i, name)
- return psstring + access + "def\n"
-
-
-def _type1_CharString_repr(charstrings):
- items = sorted(charstrings.items())
- return "xxx"
-
-
-class ps_font(ps_object):
- def __str__(self):
- psstring = "%d dict dup begin\n" % len(self.value)
- for key in _type1_pre_eexec_order:
- try:
- value = self.value[key]
- except KeyError:
- pass
- else:
- psstring = psstring + _type1_item_repr(key, value)
- items = sorted(self.value.items())
- for key, value in items:
- if key not in _type1_pre_eexec_order + _type1_post_eexec_order:
- psstring = psstring + _type1_item_repr(key, value)
- psstring = psstring + "currentdict end\ncurrentfile eexec\ndup "
- for key in _type1_post_eexec_order:
- try:
- value = self.value[key]
- except KeyError:
- pass
- else:
- psstring = psstring + _type1_item_repr(key, value)
- return (
- psstring
- + "dup/FontName get exch definefont pop\nmark currentfile closefile\n"
- + 8 * (64 * "0" + "\n")
- + "cleartomark"
- + "\n"
- )
-
- def __repr__(self):
- return "<font>"
-
-
-class ps_file(ps_object):
- pass
-
-
-class ps_dict(ps_object):
- def __str__(self):
- psstring = "%d dict dup begin\n" % len(self.value)
- items = sorted(self.value.items())
- for key, value in items:
- access = _accessstrings[value.access]
- if access:
- access = access + " "
- psstring = psstring + "/%s %s %sdef\n" % (str(key), str(value), access)
- return psstring + "end "
-
- def __repr__(self):
- return "<dict>"
-
-
-class ps_mark(ps_object):
- def __init__(self):
- self.value = "mark"
- self.type = self.__class__.__name__[3:] + "type"
-
-
-class ps_procmark(ps_object):
- def __init__(self):
- self.value = "procmark"
- self.type = self.__class__.__name__[3:] + "type"
-
-
-class ps_null(ps_object):
- def __init__(self):
- self.type = self.__class__.__name__[3:] + "type"
-
-
-class ps_boolean(ps_object):
- def __str__(self):
- if self.value:
- return "true"
- else:
- return "false"
-
-
-class ps_string(ps_object):
- def __str__(self):
- return "(%s)" % repr(self.value)[1:-1]
-
-
-class ps_integer(ps_object):
- def __str__(self):
- return repr(self.value)
-
-
-class ps_real(ps_object):
- def __str__(self):
- return repr(self.value)
-
-
-class PSOperators(object):
- def ps_def(self):
- obj = self.pop()
- name = self.pop()
- self.dictstack[-1][name.value] = obj
-
- def ps_bind(self):
- proc = self.pop("proceduretype")
- self.proc_bind(proc)
- self.push(proc)
-
- def proc_bind(self, proc):
- for i in range(len(proc.value)):
- item = proc.value[i]
- if item.type == "proceduretype":
- self.proc_bind(item)
- else:
- if not item.literal:
- try:
- obj = self.resolve_name(item.value)
- except:
- pass
- else:
- if obj.type == "operatortype":
- proc.value[i] = obj
-
- def ps_exch(self):
- if len(self.stack) < 2:
- raise RuntimeError("stack underflow")
- obj1 = self.pop()
- obj2 = self.pop()
- self.push(obj1)
- self.push(obj2)
-
- def ps_dup(self):
- if not self.stack:
- raise RuntimeError("stack underflow")
- self.push(self.stack[-1])
-
- def ps_exec(self):
- obj = self.pop()
- if obj.type == "proceduretype":
- self.call_procedure(obj)
- else:
- self.handle_object(obj)
-
- def ps_count(self):
- self.push(ps_integer(len(self.stack)))
-
- def ps_eq(self):
- any1 = self.pop()
- any2 = self.pop()
- self.push(ps_boolean(any1.value == any2.value))
-
- def ps_ne(self):
- any1 = self.pop()
- any2 = self.pop()
- self.push(ps_boolean(any1.value != any2.value))
-
- def ps_cvx(self):
- obj = self.pop()
- obj.literal = 0
- self.push(obj)
-
- def ps_matrix(self):
- matrix = [
- ps_real(1.0),
- ps_integer(0),
- ps_integer(0),
- ps_real(1.0),
- ps_integer(0),
- ps_integer(0),
- ]
- self.push(ps_array(matrix))
-
- def ps_string(self):
- num = self.pop("integertype").value
- self.push(ps_string("\0" * num))
-
- def ps_type(self):
- obj = self.pop()
- self.push(ps_string(obj.type))
-
- def ps_store(self):
- value = self.pop()
- key = self.pop()
- name = key.value
- for i in range(len(self.dictstack) - 1, -1, -1):
- if name in self.dictstack[i]:
- self.dictstack[i][name] = value
- break
- self.dictstack[-1][name] = value
-
- def ps_where(self):
- name = self.pop()
- # XXX
- self.push(ps_boolean(0))
-
- def ps_systemdict(self):
- self.push(ps_dict(self.dictstack[0]))
-
- def ps_userdict(self):
- self.push(ps_dict(self.dictstack[1]))
-
- def ps_currentdict(self):
- self.push(ps_dict(self.dictstack[-1]))
-
- def ps_currentfile(self):
- self.push(ps_file(self.tokenizer))
-
- def ps_eexec(self):
- f = self.pop("filetype").value
- f.starteexec()
-
- def ps_closefile(self):
- f = self.pop("filetype").value
- f.skipwhite()
- f.stopeexec()
-
- def ps_cleartomark(self):
- obj = self.pop()
- while obj != self.mark:
- obj = self.pop()
-
- def ps_readstring(self, ps_boolean=ps_boolean, len=len):
- s = self.pop("stringtype")
- oldstr = s.value
- f = self.pop("filetype")
- # pad = file.value.read(1)
- # for StringIO, this is faster
- f.value.pos = f.value.pos + 1
- newstr = f.value.read(len(oldstr))
- s.value = newstr
- self.push(s)
- self.push(ps_boolean(len(oldstr) == len(newstr)))
-
- def ps_known(self):
- key = self.pop()
- d = self.pop("dicttype", "fonttype")
- self.push(ps_boolean(key.value in d.value))
-
- def ps_if(self):
- proc = self.pop("proceduretype")
- if self.pop("booleantype").value:
- self.call_procedure(proc)
-
- def ps_ifelse(self):
- proc2 = self.pop("proceduretype")
- proc1 = self.pop("proceduretype")
- if self.pop("booleantype").value:
- self.call_procedure(proc1)
- else:
- self.call_procedure(proc2)
-
- def ps_readonly(self):
- obj = self.pop()
- if obj.access < 1:
- obj.access = 1
- self.push(obj)
-
- def ps_executeonly(self):
- obj = self.pop()
- if obj.access < 2:
- obj.access = 2
- self.push(obj)
-
- def ps_noaccess(self):
- obj = self.pop()
- if obj.access < 3:
- obj.access = 3
- self.push(obj)
-
- def ps_not(self):
- obj = self.pop("booleantype", "integertype")
- if obj.type == "booleantype":
- self.push(ps_boolean(not obj.value))
- else:
- self.push(ps_integer(~obj.value))
-
- def ps_print(self):
- str = self.pop("stringtype")
- print("PS output --->", str.value)
-
- def ps_anchorsearch(self):
- seek = self.pop("stringtype")
- s = self.pop("stringtype")
- seeklen = len(seek.value)
- if s.value[:seeklen] == seek.value:
- self.push(ps_string(s.value[seeklen:]))
- self.push(seek)
- self.push(ps_boolean(1))
- else:
- self.push(s)
- self.push(ps_boolean(0))
-
- def ps_array(self):
- num = self.pop("integertype")
- array = ps_array([None] * num.value)
- self.push(array)
-
- def ps_astore(self):
- array = self.pop("arraytype")
- for i in range(len(array.value) - 1, -1, -1):
- array.value[i] = self.pop()
- self.push(array)
-
- def ps_load(self):
- name = self.pop()
- self.push(self.resolve_name(name.value))
-
- def ps_put(self):
- obj1 = self.pop()
- obj2 = self.pop()
- obj3 = self.pop("arraytype", "dicttype", "stringtype", "proceduretype")
- tp = obj3.type
- if tp == "arraytype" or tp == "proceduretype":
- obj3.value[obj2.value] = obj1
- elif tp == "dicttype":
- obj3.value[obj2.value] = obj1
- elif tp == "stringtype":
- index = obj2.value
- obj3.value = obj3.value[:index] + chr(obj1.value) + obj3.value[index + 1 :]
-
- def ps_get(self):
- obj1 = self.pop()
- if obj1.value == "Encoding":
- pass
- obj2 = self.pop(
- "arraytype", "dicttype", "stringtype", "proceduretype", "fonttype"
- )
- tp = obj2.type
- if tp in ("arraytype", "proceduretype"):
- self.push(obj2.value[obj1.value])
- elif tp in ("dicttype", "fonttype"):
- self.push(obj2.value[obj1.value])
- elif tp == "stringtype":
- self.push(ps_integer(ord(obj2.value[obj1.value])))
- else:
- assert False, "shouldn't get here"
-
- def ps_getinterval(self):
- obj1 = self.pop("integertype")
- obj2 = self.pop("integertype")
- obj3 = self.pop("arraytype", "stringtype")
- tp = obj3.type
- if tp == "arraytype":
- self.push(ps_array(obj3.value[obj2.value : obj2.value + obj1.value]))
- elif tp == "stringtype":
- self.push(ps_string(obj3.value[obj2.value : obj2.value + obj1.value]))
-
- def ps_putinterval(self):
- obj1 = self.pop("arraytype", "stringtype")
- obj2 = self.pop("integertype")
- obj3 = self.pop("arraytype", "stringtype")
- tp = obj3.type
- if tp == "arraytype":
- obj3.value[obj2.value : obj2.value + len(obj1.value)] = obj1.value
- elif tp == "stringtype":
- newstr = obj3.value[: obj2.value]
- newstr = newstr + obj1.value
- newstr = newstr + obj3.value[obj2.value + len(obj1.value) :]
- obj3.value = newstr
-
- def ps_cvn(self):
- self.push(ps_name(self.pop("stringtype").value))
-
- def ps_index(self):
- n = self.pop("integertype").value
- if n < 0:
- raise RuntimeError("index may not be negative")
- self.push(self.stack[-1 - n])
-
- def ps_for(self):
- proc = self.pop("proceduretype")
- limit = self.pop("integertype", "realtype").value
- increment = self.pop("integertype", "realtype").value
- i = self.pop("integertype", "realtype").value
- while 1:
- if increment > 0:
- if i > limit:
- break
- else:
- if i < limit:
- break
- if type(i) == type(0.0):
- self.push(ps_real(i))
- else:
- self.push(ps_integer(i))
- self.call_procedure(proc)
- i = i + increment
-
- def ps_forall(self):
- proc = self.pop("proceduretype")
- obj = self.pop("arraytype", "stringtype", "dicttype")
- tp = obj.type
- if tp == "arraytype":
- for item in obj.value:
- self.push(item)
- self.call_procedure(proc)
- elif tp == "stringtype":
- for item in obj.value:
- self.push(ps_integer(ord(item)))
- self.call_procedure(proc)
- elif tp == "dicttype":
- for key, value in obj.value.items():
- self.push(ps_name(key))
- self.push(value)
- self.call_procedure(proc)
-
- def ps_definefont(self):
- font = self.pop("dicttype")
- name = self.pop()
- font = ps_font(font.value)
- self.dictstack[0]["FontDirectory"].value[name.value] = font
- self.push(font)
-
- def ps_findfont(self):
- name = self.pop()
- font = self.dictstack[0]["FontDirectory"].value[name.value]
- self.push(font)
-
- def ps_pop(self):
- self.pop()
-
- def ps_dict(self):
- self.pop("integertype")
- self.push(ps_dict({}))
-
- def ps_begin(self):
- self.dictstack.append(self.pop("dicttype").value)
-
- def ps_end(self):
- if len(self.dictstack) > 2:
- del self.dictstack[-1]
- else:
- raise RuntimeError("dictstack underflow")
-
-
-notdef = ".notdef"
-from fontTools.encodings.StandardEncoding import StandardEncoding
-
-ps_StandardEncoding = list(map(ps_name, StandardEncoding))
diff --git a/spaces/cihyFjudo/fairness-paper-search/Baixar gratis tratado da pediatria Nelson portugues um guia prtico e essencial para os pais e cuidadores.md b/spaces/cihyFjudo/fairness-paper-search/Baixar gratis tratado da pediatria Nelson portugues um guia prtico e essencial para os pais e cuidadores.md
deleted file mode 100644
index 8289996e91a5e947e9d95c22105eed0f6dc03127..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Baixar gratis tratado da pediatria Nelson portugues um guia prtico e essencial para os pais e cuidadores.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>baixargratistratadodapediatrianelsonportugues</h2><br /><p><b><b>Download</b> ❤❤❤ <a href="https://tinurli.com/2uwkiY">https://tinurli.com/2uwkiY</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/MaxxAudio Pro Dell Download Windows Enhance Your Audio Experience.md b/spaces/cihyFjudo/fairness-paper-search/MaxxAudio Pro Dell Download Windows Enhance Your Audio Experience.md
deleted file mode 100644
index 145535e03bb05f04ffdc4764dada9a45108774e5..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/MaxxAudio Pro Dell Download Windows Enhance Your Audio Experience.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>I tried to search the internet, but I could not find any result on how to solve this. Then I saw that in the url of an Business store application the name and Store ID are used. So I tried to do the same with the Waves Maxxaudio application. I took the Name and Store IDwaves-maxxaudio-pro-for-dell-2019/9pjppgg8tgvgAnd add it to the bussinessstore url like this: -us/store/details/waves-maxxaudio-pro-for-dell-2019/9pjppgg8tgvgAnd that did the trick:</p>
-<h2>maxxaudio pro dell download windows</h2><br /><p><b><b>DOWNLOAD</b> ✒ <a href="https://tinurli.com/2uwjIL">https://tinurli.com/2uwjIL</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/PATCHED NI LabVIEW 2011 plus Toolkits and Device Drivers Explore the Possibilities of LabVIEW and NI Device Drivers for Data Acquisition and Control.md b/spaces/cihyFjudo/fairness-paper-search/PATCHED NI LabVIEW 2011 plus Toolkits and Device Drivers Explore the Possibilities of LabVIEW and NI Device Drivers for Data Acquisition and Control.md
deleted file mode 100644
index e98c26ec7c36a36643a5a0989dca8482e3f7dce0..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/PATCHED NI LabVIEW 2011 plus Toolkits and Device Drivers Explore the Possibilities of LabVIEW and NI Device Drivers for Data Acquisition and Control.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>PATCHED NI LabVIEW 2011 plus Toolkits and Device Drivers</h2><br /><p><b><b>Download Zip</b> ✒ ✒ ✒ <a href="https://tinurli.com/2uwijP">https://tinurli.com/2uwijP</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cleanmaster/so-vits-svc-akagi/mel_processing.py b/spaces/cleanmaster/so-vits-svc-akagi/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/so-vits-svc-akagi/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/client_reqrep.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/client_reqrep.py
deleted file mode 100644
index 28b8a28d0d80a3c374de204d25ab460427b3154c..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/client_reqrep.py
+++ /dev/null
@@ -1,1134 +0,0 @@
-import asyncio
-import codecs
-import functools
-import io
-import re
-import sys
-import traceback
-import warnings
-from hashlib import md5, sha1, sha256
-from http.cookies import CookieError, Morsel, SimpleCookie
-from types import MappingProxyType, TracebackType
-from typing import (
- TYPE_CHECKING,
- Any,
- Dict,
- Iterable,
- List,
- Mapping,
- Optional,
- Tuple,
- Type,
- Union,
- cast,
-)
-
-import attr
-from multidict import CIMultiDict, CIMultiDictProxy, MultiDict, MultiDictProxy
-from yarl import URL
-
-from . import hdrs, helpers, http, multipart, payload
-from .abc import AbstractStreamWriter
-from .client_exceptions import (
- ClientConnectionError,
- ClientOSError,
- ClientResponseError,
- ContentTypeError,
- InvalidURL,
- ServerFingerprintMismatch,
-)
-from .formdata import FormData
-from .helpers import (
- PY_36,
- BaseTimerContext,
- BasicAuth,
- HeadersMixin,
- TimerNoop,
- noop,
- reify,
- set_result,
-)
-from .http import SERVER_SOFTWARE, HttpVersion10, HttpVersion11, StreamWriter
-from .log import client_logger
-from .streams import StreamReader
-from .typedefs import (
- DEFAULT_JSON_DECODER,
- JSONDecoder,
- LooseCookies,
- LooseHeaders,
- RawHeaders,
-)
-
-try:
- import ssl
- from ssl import SSLContext
-except ImportError: # pragma: no cover
- ssl = None # type: ignore[assignment]
- SSLContext = object # type: ignore[misc,assignment]
-
-try:
- import cchardet as chardet
-except ImportError: # pragma: no cover
- import charset_normalizer as chardet # type: ignore[no-redef]
-
-
-__all__ = ("ClientRequest", "ClientResponse", "RequestInfo", "Fingerprint")
-
-
-if TYPE_CHECKING: # pragma: no cover
- from .client import ClientSession
- from .connector import Connection
- from .tracing import Trace
-
-
-json_re = re.compile(r"^application/(?:[\w.+-]+?\+)?json")
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class ContentDisposition:
- type: Optional[str]
- parameters: "MappingProxyType[str, str]"
- filename: Optional[str]
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class RequestInfo:
- url: URL
- method: str
- headers: "CIMultiDictProxy[str]"
- real_url: URL = attr.ib()
-
- @real_url.default
- def real_url_default(self) -> URL:
- return self.url
-
-
-class Fingerprint:
- HASHFUNC_BY_DIGESTLEN = {
- 16: md5,
- 20: sha1,
- 32: sha256,
- }
-
- def __init__(self, fingerprint: bytes) -> None:
- digestlen = len(fingerprint)
- hashfunc = self.HASHFUNC_BY_DIGESTLEN.get(digestlen)
- if not hashfunc:
- raise ValueError("fingerprint has invalid length")
- elif hashfunc is md5 or hashfunc is sha1:
- raise ValueError(
- "md5 and sha1 are insecure and " "not supported. Use sha256."
- )
- self._hashfunc = hashfunc
- self._fingerprint = fingerprint
-
- @property
- def fingerprint(self) -> bytes:
- return self._fingerprint
-
- def check(self, transport: asyncio.Transport) -> None:
- if not transport.get_extra_info("sslcontext"):
- return
- sslobj = transport.get_extra_info("ssl_object")
- cert = sslobj.getpeercert(binary_form=True)
- got = self._hashfunc(cert).digest()
- if got != self._fingerprint:
- host, port, *_ = transport.get_extra_info("peername")
- raise ServerFingerprintMismatch(self._fingerprint, got, host, port)
-
-
-if ssl is not None:
- SSL_ALLOWED_TYPES = (ssl.SSLContext, bool, Fingerprint, type(None))
-else: # pragma: no cover
- SSL_ALLOWED_TYPES = type(None)
-
-
-def _merge_ssl_params(
- ssl: Union["SSLContext", bool, Fingerprint, None],
- verify_ssl: Optional[bool],
- ssl_context: Optional["SSLContext"],
- fingerprint: Optional[bytes],
-) -> Union["SSLContext", bool, Fingerprint, None]:
- if verify_ssl is not None and not verify_ssl:
- warnings.warn(
- "verify_ssl is deprecated, use ssl=False instead",
- DeprecationWarning,
- stacklevel=3,
- )
- if ssl is not None:
- raise ValueError(
- "verify_ssl, ssl_context, fingerprint and ssl "
- "parameters are mutually exclusive"
- )
- else:
- ssl = False
- if ssl_context is not None:
- warnings.warn(
- "ssl_context is deprecated, use ssl=context instead",
- DeprecationWarning,
- stacklevel=3,
- )
- if ssl is not None:
- raise ValueError(
- "verify_ssl, ssl_context, fingerprint and ssl "
- "parameters are mutually exclusive"
- )
- else:
- ssl = ssl_context
- if fingerprint is not None:
- warnings.warn(
- "fingerprint is deprecated, " "use ssl=Fingerprint(fingerprint) instead",
- DeprecationWarning,
- stacklevel=3,
- )
- if ssl is not None:
- raise ValueError(
- "verify_ssl, ssl_context, fingerprint and ssl "
- "parameters are mutually exclusive"
- )
- else:
- ssl = Fingerprint(fingerprint)
- if not isinstance(ssl, SSL_ALLOWED_TYPES):
- raise TypeError(
- "ssl should be SSLContext, bool, Fingerprint or None, "
- "got {!r} instead.".format(ssl)
- )
- return ssl
-
-
-@attr.s(auto_attribs=True, slots=True, frozen=True)
-class ConnectionKey:
- # the key should contain an information about used proxy / TLS
- # to prevent reusing wrong connections from a pool
- host: str
- port: Optional[int]
- is_ssl: bool
- ssl: Union[SSLContext, None, bool, Fingerprint]
- proxy: Optional[URL]
- proxy_auth: Optional[BasicAuth]
- proxy_headers_hash: Optional[int] # hash(CIMultiDict)
-
-
-def _is_expected_content_type(
- response_content_type: str, expected_content_type: str
-) -> bool:
- if expected_content_type == "application/json":
- return json_re.match(response_content_type) is not None
- return expected_content_type in response_content_type
-
-
-class ClientRequest:
- GET_METHODS = {
- hdrs.METH_GET,
- hdrs.METH_HEAD,
- hdrs.METH_OPTIONS,
- hdrs.METH_TRACE,
- }
- POST_METHODS = {hdrs.METH_PATCH, hdrs.METH_POST, hdrs.METH_PUT}
- ALL_METHODS = GET_METHODS.union(POST_METHODS).union({hdrs.METH_DELETE})
-
- DEFAULT_HEADERS = {
- hdrs.ACCEPT: "*/*",
- hdrs.ACCEPT_ENCODING: "gzip, deflate",
- }
-
- body = b""
- auth = None
- response = None
-
- _writer = None # async task for streaming data
- _continue = None # waiter future for '100 Continue' response
-
- # N.B.
- # Adding __del__ method with self._writer closing doesn't make sense
- # because _writer is instance method, thus it keeps a reference to self.
- # Until writer has finished finalizer will not be called.
-
- def __init__(
- self,
- method: str,
- url: URL,
- *,
- params: Optional[Mapping[str, str]] = None,
- headers: Optional[LooseHeaders] = None,
- skip_auto_headers: Iterable[str] = frozenset(),
- data: Any = None,
- cookies: Optional[LooseCookies] = None,
- auth: Optional[BasicAuth] = None,
- version: http.HttpVersion = http.HttpVersion11,
- compress: Optional[str] = None,
- chunked: Optional[bool] = None,
- expect100: bool = False,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- response_class: Optional[Type["ClientResponse"]] = None,
- proxy: Optional[URL] = None,
- proxy_auth: Optional[BasicAuth] = None,
- timer: Optional[BaseTimerContext] = None,
- session: Optional["ClientSession"] = None,
- ssl: Union[SSLContext, bool, Fingerprint, None] = None,
- proxy_headers: Optional[LooseHeaders] = None,
- traces: Optional[List["Trace"]] = None,
- ):
-
- if loop is None:
- loop = asyncio.get_event_loop()
-
- assert isinstance(url, URL), url
- assert isinstance(proxy, (URL, type(None))), proxy
- # FIXME: session is None in tests only, need to fix tests
- # assert session is not None
- self._session = cast("ClientSession", session)
- if params:
- q = MultiDict(url.query)
- url2 = url.with_query(params)
- q.extend(url2.query)
- url = url.with_query(q)
- self.original_url = url
- self.url = url.with_fragment(None)
- self.method = method.upper()
- self.chunked = chunked
- self.compress = compress
- self.loop = loop
- self.length = None
- if response_class is None:
- real_response_class = ClientResponse
- else:
- real_response_class = response_class
- self.response_class: Type[ClientResponse] = real_response_class
- self._timer = timer if timer is not None else TimerNoop()
- self._ssl = ssl
-
- if loop.get_debug():
- self._source_traceback = traceback.extract_stack(sys._getframe(1))
-
- self.update_version(version)
- self.update_host(url)
- self.update_headers(headers)
- self.update_auto_headers(skip_auto_headers)
- self.update_cookies(cookies)
- self.update_content_encoding(data)
- self.update_auth(auth)
- self.update_proxy(proxy, proxy_auth, proxy_headers)
-
- self.update_body_from_data(data)
- if data is not None or self.method not in self.GET_METHODS:
- self.update_transfer_encoding()
- self.update_expect_continue(expect100)
- if traces is None:
- traces = []
- self._traces = traces
-
- def is_ssl(self) -> bool:
- return self.url.scheme in ("https", "wss")
-
- @property
- def ssl(self) -> Union["SSLContext", None, bool, Fingerprint]:
- return self._ssl
-
- @property
- def connection_key(self) -> ConnectionKey:
- proxy_headers = self.proxy_headers
- if proxy_headers:
- h: Optional[int] = hash(tuple((k, v) for k, v in proxy_headers.items()))
- else:
- h = None
- return ConnectionKey(
- self.host,
- self.port,
- self.is_ssl(),
- self.ssl,
- self.proxy,
- self.proxy_auth,
- h,
- )
-
- @property
- def host(self) -> str:
- ret = self.url.raw_host
- assert ret is not None
- return ret
-
- @property
- def port(self) -> Optional[int]:
- return self.url.port
-
- @property
- def request_info(self) -> RequestInfo:
- headers: CIMultiDictProxy[str] = CIMultiDictProxy(self.headers)
- return RequestInfo(self.url, self.method, headers, self.original_url)
-
- def update_host(self, url: URL) -> None:
- """Update destination host, port and connection type (ssl)."""
- # get host/port
- if not url.raw_host:
- raise InvalidURL(url)
-
- # basic auth info
- username, password = url.user, url.password
- if username:
- self.auth = helpers.BasicAuth(username, password or "")
-
- def update_version(self, version: Union[http.HttpVersion, str]) -> None:
- """Convert request version to two elements tuple.
-
- parser HTTP version '1.1' => (1, 1)
- """
- if isinstance(version, str):
- v = [part.strip() for part in version.split(".", 1)]
- try:
- version = http.HttpVersion(int(v[0]), int(v[1]))
- except ValueError:
- raise ValueError(
- f"Can not parse http version number: {version}"
- ) from None
- self.version = version
-
- def update_headers(self, headers: Optional[LooseHeaders]) -> None:
- """Update request headers."""
- self.headers: CIMultiDict[str] = CIMultiDict()
-
- # add host
- netloc = cast(str, self.url.raw_host)
- if helpers.is_ipv6_address(netloc):
- netloc = f"[{netloc}]"
- if self.url.port is not None and not self.url.is_default_port():
- netloc += ":" + str(self.url.port)
- self.headers[hdrs.HOST] = netloc
-
- if headers:
- if isinstance(headers, (dict, MultiDictProxy, MultiDict)):
- headers = headers.items() # type: ignore[assignment]
-
- for key, value in headers: # type: ignore[misc]
- # A special case for Host header
- if key.lower() == "host":
- self.headers[key] = value
- else:
- self.headers.add(key, value)
-
- def update_auto_headers(self, skip_auto_headers: Iterable[str]) -> None:
- self.skip_auto_headers = CIMultiDict(
- (hdr, None) for hdr in sorted(skip_auto_headers)
- )
- used_headers = self.headers.copy()
- used_headers.extend(self.skip_auto_headers) # type: ignore[arg-type]
-
- for hdr, val in self.DEFAULT_HEADERS.items():
- if hdr not in used_headers:
- self.headers.add(hdr, val)
-
- if hdrs.USER_AGENT not in used_headers:
- self.headers[hdrs.USER_AGENT] = SERVER_SOFTWARE
-
- def update_cookies(self, cookies: Optional[LooseCookies]) -> None:
- """Update request cookies header."""
- if not cookies:
- return
-
- c: SimpleCookie[str] = SimpleCookie()
- if hdrs.COOKIE in self.headers:
- c.load(self.headers.get(hdrs.COOKIE, ""))
- del self.headers[hdrs.COOKIE]
-
- if isinstance(cookies, Mapping):
- iter_cookies = cookies.items()
- else:
- iter_cookies = cookies # type: ignore[assignment]
- for name, value in iter_cookies:
- if isinstance(value, Morsel):
- # Preserve coded_value
- mrsl_val = value.get(value.key, Morsel())
- mrsl_val.set(value.key, value.value, value.coded_value)
- c[name] = mrsl_val
- else:
- c[name] = value # type: ignore[assignment]
-
- self.headers[hdrs.COOKIE] = c.output(header="", sep=";").strip()
-
- def update_content_encoding(self, data: Any) -> None:
- """Set request content encoding."""
- if data is None:
- return
-
- enc = self.headers.get(hdrs.CONTENT_ENCODING, "").lower()
- if enc:
- if self.compress:
- raise ValueError(
- "compress can not be set " "if Content-Encoding header is set"
- )
- elif self.compress:
- if not isinstance(self.compress, str):
- self.compress = "deflate"
- self.headers[hdrs.CONTENT_ENCODING] = self.compress
- self.chunked = True # enable chunked, no need to deal with length
-
- def update_transfer_encoding(self) -> None:
- """Analyze transfer-encoding header."""
- te = self.headers.get(hdrs.TRANSFER_ENCODING, "").lower()
-
- if "chunked" in te:
- if self.chunked:
- raise ValueError(
- "chunked can not be set "
- 'if "Transfer-Encoding: chunked" header is set'
- )
-
- elif self.chunked:
- if hdrs.CONTENT_LENGTH in self.headers:
- raise ValueError(
- "chunked can not be set " "if Content-Length header is set"
- )
-
- self.headers[hdrs.TRANSFER_ENCODING] = "chunked"
- else:
- if hdrs.CONTENT_LENGTH not in self.headers:
- self.headers[hdrs.CONTENT_LENGTH] = str(len(self.body))
-
- def update_auth(self, auth: Optional[BasicAuth]) -> None:
- """Set basic auth."""
- if auth is None:
- auth = self.auth
- if auth is None:
- return
-
- if not isinstance(auth, helpers.BasicAuth):
- raise TypeError("BasicAuth() tuple is required instead")
-
- self.headers[hdrs.AUTHORIZATION] = auth.encode()
-
- def update_body_from_data(self, body: Any) -> None:
- if body is None:
- return
-
- # FormData
- if isinstance(body, FormData):
- body = body()
-
- try:
- body = payload.PAYLOAD_REGISTRY.get(body, disposition=None)
- except payload.LookupError:
- body = FormData(body)()
-
- self.body = body
-
- # enable chunked encoding if needed
- if not self.chunked:
- if hdrs.CONTENT_LENGTH not in self.headers:
- size = body.size
- if size is None:
- self.chunked = True
- else:
- if hdrs.CONTENT_LENGTH not in self.headers:
- self.headers[hdrs.CONTENT_LENGTH] = str(size)
-
- # copy payload headers
- assert body.headers
- for (key, value) in body.headers.items():
- if key in self.headers:
- continue
- if key in self.skip_auto_headers:
- continue
- self.headers[key] = value
-
- def update_expect_continue(self, expect: bool = False) -> None:
- if expect:
- self.headers[hdrs.EXPECT] = "100-continue"
- elif self.headers.get(hdrs.EXPECT, "").lower() == "100-continue":
- expect = True
-
- if expect:
- self._continue = self.loop.create_future()
-
- def update_proxy(
- self,
- proxy: Optional[URL],
- proxy_auth: Optional[BasicAuth],
- proxy_headers: Optional[LooseHeaders],
- ) -> None:
- if proxy_auth and not isinstance(proxy_auth, helpers.BasicAuth):
- raise ValueError("proxy_auth must be None or BasicAuth() tuple")
- self.proxy = proxy
- self.proxy_auth = proxy_auth
- self.proxy_headers = proxy_headers
-
- def keep_alive(self) -> bool:
- if self.version < HttpVersion10:
- # keep alive not supported at all
- return False
- if self.version == HttpVersion10:
- if self.headers.get(hdrs.CONNECTION) == "keep-alive":
- return True
- else: # no headers means we close for Http 1.0
- return False
- elif self.headers.get(hdrs.CONNECTION) == "close":
- return False
-
- return True
-
- async def write_bytes(
- self, writer: AbstractStreamWriter, conn: "Connection"
- ) -> None:
- """Support coroutines that yields bytes objects."""
- # 100 response
- if self._continue is not None:
- await writer.drain()
- await self._continue
-
- protocol = conn.protocol
- assert protocol is not None
- try:
- if isinstance(self.body, payload.Payload):
- await self.body.write(writer)
- else:
- if isinstance(self.body, (bytes, bytearray)):
- self.body = (self.body,) # type: ignore[assignment]
-
- for chunk in self.body:
- await writer.write(chunk) # type: ignore[arg-type]
-
- await writer.write_eof()
- except OSError as exc:
- if exc.errno is None and isinstance(exc, asyncio.TimeoutError):
- protocol.set_exception(exc)
- else:
- new_exc = ClientOSError(
- exc.errno, "Can not write request body for %s" % self.url
- )
- new_exc.__context__ = exc
- new_exc.__cause__ = exc
- protocol.set_exception(new_exc)
- except asyncio.CancelledError as exc:
- if not conn.closed:
- protocol.set_exception(exc)
- except Exception as exc:
- protocol.set_exception(exc)
- finally:
- self._writer = None
-
- async def send(self, conn: "Connection") -> "ClientResponse":
- # Specify request target:
- # - CONNECT request must send authority form URI
- # - not CONNECT proxy must send absolute form URI
- # - most common is origin form URI
- if self.method == hdrs.METH_CONNECT:
- connect_host = self.url.raw_host
- assert connect_host is not None
- if helpers.is_ipv6_address(connect_host):
- connect_host = f"[{connect_host}]"
- path = f"{connect_host}:{self.url.port}"
- elif self.proxy and not self.is_ssl():
- path = str(self.url)
- else:
- path = self.url.raw_path
- if self.url.raw_query_string:
- path += "?" + self.url.raw_query_string
-
- protocol = conn.protocol
- assert protocol is not None
- writer = StreamWriter(
- protocol,
- self.loop,
- on_chunk_sent=functools.partial(
- self._on_chunk_request_sent, self.method, self.url
- ),
- on_headers_sent=functools.partial(
- self._on_headers_request_sent, self.method, self.url
- ),
- )
-
- if self.compress:
- writer.enable_compression(self.compress)
-
- if self.chunked is not None:
- writer.enable_chunking()
-
- # set default content-type
- if (
- self.method in self.POST_METHODS
- and hdrs.CONTENT_TYPE not in self.skip_auto_headers
- and hdrs.CONTENT_TYPE not in self.headers
- ):
- self.headers[hdrs.CONTENT_TYPE] = "application/octet-stream"
-
- # set the connection header
- connection = self.headers.get(hdrs.CONNECTION)
- if not connection:
- if self.keep_alive():
- if self.version == HttpVersion10:
- connection = "keep-alive"
- else:
- if self.version == HttpVersion11:
- connection = "close"
-
- if connection is not None:
- self.headers[hdrs.CONNECTION] = connection
-
- # status + headers
- status_line = "{0} {1} HTTP/{2[0]}.{2[1]}".format(
- self.method, path, self.version
- )
- await writer.write_headers(status_line, self.headers)
-
- self._writer = self.loop.create_task(self.write_bytes(writer, conn))
-
- response_class = self.response_class
- assert response_class is not None
- self.response = response_class(
- self.method,
- self.original_url,
- writer=self._writer,
- continue100=self._continue,
- timer=self._timer,
- request_info=self.request_info,
- traces=self._traces,
- loop=self.loop,
- session=self._session,
- )
- return self.response
-
- async def close(self) -> None:
- if self._writer is not None:
- try:
- await self._writer
- finally:
- self._writer = None
-
- def terminate(self) -> None:
- if self._writer is not None:
- if not self.loop.is_closed():
- self._writer.cancel()
- self._writer = None
-
- async def _on_chunk_request_sent(self, method: str, url: URL, chunk: bytes) -> None:
- for trace in self._traces:
- await trace.send_request_chunk_sent(method, url, chunk)
-
- async def _on_headers_request_sent(
- self, method: str, url: URL, headers: "CIMultiDict[str]"
- ) -> None:
- for trace in self._traces:
- await trace.send_request_headers(method, url, headers)
-
-
-class ClientResponse(HeadersMixin):
-
- # from the Status-Line of the response
- version = None # HTTP-Version
- status: int = None # type: ignore[assignment] # Status-Code
- reason = None # Reason-Phrase
-
- content: StreamReader = None # type: ignore[assignment] # Payload stream
- _headers: "CIMultiDictProxy[str]" = None # type: ignore[assignment]
- _raw_headers: RawHeaders = None # type: ignore[assignment] # Response raw headers
-
- _connection = None # current connection
- _source_traceback = None
- # setted up by ClientRequest after ClientResponse object creation
- # post-init stage allows to not change ctor signature
- _closed = True # to allow __del__ for non-initialized properly response
- _released = False
-
- def __init__(
- self,
- method: str,
- url: URL,
- *,
- writer: "asyncio.Task[None]",
- continue100: Optional["asyncio.Future[bool]"],
- timer: BaseTimerContext,
- request_info: RequestInfo,
- traces: List["Trace"],
- loop: asyncio.AbstractEventLoop,
- session: "ClientSession",
- ) -> None:
- assert isinstance(url, URL)
-
- self.method = method
- self.cookies: SimpleCookie[str] = SimpleCookie()
-
- self._real_url = url
- self._url = url.with_fragment(None)
- self._body: Any = None
- self._writer: Optional[asyncio.Task[None]] = writer
- self._continue = continue100 # None by default
- self._closed = True
- self._history: Tuple[ClientResponse, ...] = ()
- self._request_info = request_info
- self._timer = timer if timer is not None else TimerNoop()
- self._cache: Dict[str, Any] = {}
- self._traces = traces
- self._loop = loop
- # store a reference to session #1985
- self._session: Optional[ClientSession] = session
- if loop.get_debug():
- self._source_traceback = traceback.extract_stack(sys._getframe(1))
-
- @reify
- def url(self) -> URL:
- return self._url
-
- @reify
- def url_obj(self) -> URL:
- warnings.warn("Deprecated, use .url #1654", DeprecationWarning, stacklevel=2)
- return self._url
-
- @reify
- def real_url(self) -> URL:
- return self._real_url
-
- @reify
- def host(self) -> str:
- assert self._url.host is not None
- return self._url.host
-
- @reify
- def headers(self) -> "CIMultiDictProxy[str]":
- return self._headers
-
- @reify
- def raw_headers(self) -> RawHeaders:
- return self._raw_headers
-
- @reify
- def request_info(self) -> RequestInfo:
- return self._request_info
-
- @reify
- def content_disposition(self) -> Optional[ContentDisposition]:
- raw = self._headers.get(hdrs.CONTENT_DISPOSITION)
- if raw is None:
- return None
- disposition_type, params_dct = multipart.parse_content_disposition(raw)
- params = MappingProxyType(params_dct)
- filename = multipart.content_disposition_filename(params)
- return ContentDisposition(disposition_type, params, filename)
-
- def __del__(self, _warnings: Any = warnings) -> None:
- if self._closed:
- return
-
- if self._connection is not None:
- self._connection.release()
- self._cleanup_writer()
-
- if self._loop.get_debug():
- if PY_36:
- kwargs = {"source": self}
- else:
- kwargs = {}
- _warnings.warn(f"Unclosed response {self!r}", ResourceWarning, **kwargs)
- context = {"client_response": self, "message": "Unclosed response"}
- if self._source_traceback:
- context["source_traceback"] = self._source_traceback
- self._loop.call_exception_handler(context)
-
- def __repr__(self) -> str:
- out = io.StringIO()
- ascii_encodable_url = str(self.url)
- if self.reason:
- ascii_encodable_reason = self.reason.encode(
- "ascii", "backslashreplace"
- ).decode("ascii")
- else:
- ascii_encodable_reason = self.reason
- print(
- "<ClientResponse({}) [{} {}]>".format(
- ascii_encodable_url, self.status, ascii_encodable_reason
- ),
- file=out,
- )
- print(self.headers, file=out)
- return out.getvalue()
-
- @property
- def connection(self) -> Optional["Connection"]:
- return self._connection
-
- @reify
- def history(self) -> Tuple["ClientResponse", ...]:
- """A sequence of of responses, if redirects occurred."""
- return self._history
-
- @reify
- def links(self) -> "MultiDictProxy[MultiDictProxy[Union[str, URL]]]":
- links_str = ", ".join(self.headers.getall("link", []))
-
- if not links_str:
- return MultiDictProxy(MultiDict())
-
- links: MultiDict[MultiDictProxy[Union[str, URL]]] = MultiDict()
-
- for val in re.split(r",(?=\s*<)", links_str):
- match = re.match(r"\s*<(.*)>(.*)", val)
- if match is None: # pragma: no cover
- # the check exists to suppress mypy error
- continue
- url, params_str = match.groups()
- params = params_str.split(";")[1:]
-
- link: MultiDict[Union[str, URL]] = MultiDict()
-
- for param in params:
- match = re.match(r"^\s*(\S*)\s*=\s*(['\"]?)(.*?)(\2)\s*$", param, re.M)
- if match is None: # pragma: no cover
- # the check exists to suppress mypy error
- continue
- key, _, value, _ = match.groups()
-
- link.add(key, value)
-
- key = link.get("rel", url) # type: ignore[assignment]
-
- link.add("url", self.url.join(URL(url)))
-
- links.add(key, MultiDictProxy(link))
-
- return MultiDictProxy(links)
-
- async def start(self, connection: "Connection") -> "ClientResponse":
- """Start response processing."""
- self._closed = False
- self._protocol = connection.protocol
- self._connection = connection
-
- with self._timer:
- while True:
- # read response
- try:
- protocol = self._protocol
- message, payload = await protocol.read() # type: ignore[union-attr]
- except http.HttpProcessingError as exc:
- raise ClientResponseError(
- self.request_info,
- self.history,
- status=exc.code,
- message=exc.message,
- headers=exc.headers,
- ) from exc
-
- if message.code < 100 or message.code > 199 or message.code == 101:
- break
-
- if self._continue is not None:
- set_result(self._continue, True)
- self._continue = None
-
- # payload eof handler
- payload.on_eof(self._response_eof)
-
- # response status
- self.version = message.version
- self.status = message.code
- self.reason = message.reason
-
- # headers
- self._headers = message.headers # type is CIMultiDictProxy
- self._raw_headers = message.raw_headers # type is Tuple[bytes, bytes]
-
- # payload
- self.content = payload
-
- # cookies
- for hdr in self.headers.getall(hdrs.SET_COOKIE, ()):
- try:
- self.cookies.load(hdr)
- except CookieError as exc:
- client_logger.warning("Can not load response cookies: %s", exc)
- return self
-
- def _response_eof(self) -> None:
- if self._closed:
- return
-
- if self._connection is not None:
- # websocket, protocol could be None because
- # connection could be detached
- if (
- self._connection.protocol is not None
- and self._connection.protocol.upgraded
- ):
- return
-
- self._connection.release()
- self._connection = None
-
- self._closed = True
- self._cleanup_writer()
-
- @property
- def closed(self) -> bool:
- return self._closed
-
- def close(self) -> None:
- if not self._released:
- self._notify_content()
- if self._closed:
- return
-
- self._closed = True
- if self._loop is None or self._loop.is_closed():
- return
-
- if self._connection is not None:
- self._connection.close()
- self._connection = None
- self._cleanup_writer()
-
- def release(self) -> Any:
- if not self._released:
- self._notify_content()
- if self._closed:
- return noop()
-
- self._closed = True
- if self._connection is not None:
- self._connection.release()
- self._connection = None
-
- self._cleanup_writer()
- return noop()
-
- @property
- def ok(self) -> bool:
- """Returns ``True`` if ``status`` is less than ``400``, ``False`` if not.
-
- This is **not** a check for ``200 OK`` but a check that the response
- status is under 400.
- """
- return 400 > self.status
-
- def raise_for_status(self) -> None:
- if not self.ok:
- # reason should always be not None for a started response
- assert self.reason is not None
- self.release()
- raise ClientResponseError(
- self.request_info,
- self.history,
- status=self.status,
- message=self.reason,
- headers=self.headers,
- )
-
- def _cleanup_writer(self) -> None:
- if self._writer is not None:
- self._writer.cancel()
- self._writer = None
- self._session = None
-
- def _notify_content(self) -> None:
- content = self.content
- if content and content.exception() is None:
- content.set_exception(ClientConnectionError("Connection closed"))
- self._released = True
-
- async def wait_for_close(self) -> None:
- if self._writer is not None:
- try:
- await self._writer
- finally:
- self._writer = None
- self.release()
-
- async def read(self) -> bytes:
- """Read response payload."""
- if self._body is None:
- try:
- self._body = await self.content.read()
- for trace in self._traces:
- await trace.send_response_chunk_received(
- self.method, self.url, self._body
- )
- except BaseException:
- self.close()
- raise
- elif self._released:
- raise ClientConnectionError("Connection closed")
-
- return self._body # type: ignore[no-any-return]
-
- def get_encoding(self) -> str:
- ctype = self.headers.get(hdrs.CONTENT_TYPE, "").lower()
- mimetype = helpers.parse_mimetype(ctype)
-
- encoding = mimetype.parameters.get("charset")
- if encoding:
- try:
- codecs.lookup(encoding)
- except LookupError:
- encoding = None
- if not encoding:
- if mimetype.type == "application" and (
- mimetype.subtype == "json" or mimetype.subtype == "rdap"
- ):
- # RFC 7159 states that the default encoding is UTF-8.
- # RFC 7483 defines application/rdap+json
- encoding = "utf-8"
- elif self._body is None:
- raise RuntimeError(
- "Cannot guess the encoding of " "a not yet read body"
- )
- else:
- encoding = chardet.detect(self._body)["encoding"]
- if not encoding:
- encoding = "utf-8"
-
- return encoding
-
- async def text(self, encoding: Optional[str] = None, errors: str = "strict") -> str:
- """Read response payload and decode."""
- if self._body is None:
- await self.read()
-
- if encoding is None:
- encoding = self.get_encoding()
-
- return self._body.decode( # type: ignore[no-any-return,union-attr]
- encoding, errors=errors
- )
-
- async def json(
- self,
- *,
- encoding: Optional[str] = None,
- loads: JSONDecoder = DEFAULT_JSON_DECODER,
- content_type: Optional[str] = "application/json",
- ) -> Any:
- """Read and decodes JSON response."""
- if self._body is None:
- await self.read()
-
- if content_type:
- ctype = self.headers.get(hdrs.CONTENT_TYPE, "").lower()
- if not _is_expected_content_type(ctype, content_type):
- raise ContentTypeError(
- self.request_info,
- self.history,
- message=(
- "Attempt to decode JSON with " "unexpected mimetype: %s" % ctype
- ),
- headers=self.headers,
- )
-
- stripped = self._body.strip() # type: ignore[union-attr]
- if not stripped:
- return None
-
- if encoding is None:
- encoding = self.get_encoding()
-
- return loads(stripped.decode(encoding))
-
- async def __aenter__(self) -> "ClientResponse":
- return self
-
- async def __aexit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- # similar to _RequestContextManager, we do not need to check
- # for exceptions, response object can close connection
- # if state is broken
- self.release()
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/attr/setters.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/attr/setters.py
deleted file mode 100644
index 12ed6750df35b96e2ccde24a9752dca22929188d..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/attr/setters.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-Commonly used hooks for on_setattr.
-"""
-
-
-from . import _config
-from .exceptions import FrozenAttributeError
-
-
-def pipe(*setters):
- """
- Run all *setters* and return the return value of the last one.
-
- .. versionadded:: 20.1.0
- """
-
- def wrapped_pipe(instance, attrib, new_value):
- rv = new_value
-
- for setter in setters:
- rv = setter(instance, attrib, rv)
-
- return rv
-
- return wrapped_pipe
-
-
-def frozen(_, __, ___):
- """
- Prevent an attribute to be modified.
-
- .. versionadded:: 20.1.0
- """
- raise FrozenAttributeError()
-
-
-def validate(instance, attrib, new_value):
- """
- Run *attrib*'s validator on *new_value* if it has one.
-
- .. versionadded:: 20.1.0
- """
- if _config._run_validators is False:
- return new_value
-
- v = attrib.validator
- if not v:
- return new_value
-
- v(instance, attrib, new_value)
-
- return new_value
-
-
-def convert(instance, attrib, new_value):
- """
- Run *attrib*'s converter -- if it has one -- on *new_value* and return the
- result.
-
- .. versionadded:: 20.1.0
- """
- c = attrib.converter
- if c:
- return c(new_value)
-
- return new_value
-
-
-# Sentinel for disabling class-wide *on_setattr* hooks for certain attributes.
-# autodata stopped working, so the docstring is inlined in the API docs.
-NO_OP = object()
diff --git a/spaces/cncn102/bingo1/src/components/button-scroll-to-bottom.tsx b/spaces/cncn102/bingo1/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
- const isAtBottom = useAtBottom()
-
- return (
- <Button
- variant="outline"
- size="icon"
- className={cn(
- 'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
- isAtBottom ? 'opacity-0' : 'opacity-100',
- className
- )}
- onClick={() =>
- window.scrollTo({
- top: document.body.offsetHeight,
- behavior: 'smooth'
- })
- }
- {...props}
- >
- <IconArrowDown />
- <span className="sr-only">Scroll to bottom</span>
- </Button>
- )
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dpx.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dpx.c
deleted file mode 100644
index 31e4a3f82c44b55ccb7bb56fe98bef499ca46279..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dpx.c
+++ /dev/null
@@ -1,772 +0,0 @@
-/*
- * DPX (.dpx) image decoder
- * Copyright (c) 2009 Jimmy Christensen
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/avstring.h"
-#include "libavutil/intreadwrite.h"
-#include "libavutil/intfloat.h"
-#include "libavutil/imgutils.h"
-#include "libavutil/timecode.h"
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-enum DPX_TRC {
- DPX_TRC_USER_DEFINED = 0,
- DPX_TRC_PRINTING_DENSITY = 1,
- DPX_TRC_LINEAR = 2,
- DPX_TRC_LOGARITHMIC = 3,
- DPX_TRC_UNSPECIFIED_VIDEO = 4,
- DPX_TRC_SMPTE_274 = 5,
- DPX_TRC_ITU_R_709_4 = 6,
- DPX_TRC_ITU_R_601_625 = 7,
- DPX_TRC_ITU_R_601_525 = 8,
- DPX_TRC_SMPTE_170 = 9,
- DPX_TRC_ITU_R_624_4_PAL = 10,
- DPX_TRC_Z_LINEAR = 11,
- DPX_TRC_Z_HOMOGENEOUS = 12,
-};
-
-enum DPX_COL_SPEC {
- DPX_COL_SPEC_USER_DEFINED = 0,
- DPX_COL_SPEC_PRINTING_DENSITY = 1,
- /* 2 = N/A */
- /* 3 = N/A */
- DPX_COL_SPEC_UNSPECIFIED_VIDEO = 4,
- DPX_COL_SPEC_SMPTE_274 = 5,
- DPX_COL_SPEC_ITU_R_709_4 = 6,
- DPX_COL_SPEC_ITU_R_601_625 = 7,
- DPX_COL_SPEC_ITU_R_601_525 = 8,
- DPX_COL_SPEC_SMPTE_170 = 9,
- DPX_COL_SPEC_ITU_R_624_4_PAL = 10,
- /* 11 = N/A */
- /* 12 = N/A */
-};
-
-static unsigned int read16(const uint8_t **ptr, int is_big)
-{
- unsigned int temp;
- if (is_big) {
- temp = AV_RB16(*ptr);
- } else {
- temp = AV_RL16(*ptr);
- }
- *ptr += 2;
- return temp;
-}
-
-static unsigned int read32(const uint8_t **ptr, int is_big)
-{
- unsigned int temp;
- if (is_big) {
- temp = AV_RB32(*ptr);
- } else {
- temp = AV_RL32(*ptr);
- }
- *ptr += 4;
- return temp;
-}
-
-static uint16_t read10in32_gray(const uint8_t **ptr, uint32_t *lbuf,
- int *n_datum, int is_big, int shift)
-{
- uint16_t temp;
-
- if (*n_datum)
- (*n_datum)--;
- else {
- *lbuf = read32(ptr, is_big);
- *n_datum = 2;
- }
-
- temp = *lbuf >> shift & 0x3FF;
- *lbuf = *lbuf >> 10;
-
- return temp;
-}
-
-static uint16_t read10in32(const uint8_t **ptr, uint32_t *lbuf,
- int *n_datum, int is_big, int shift)
-{
- if (*n_datum)
- (*n_datum)--;
- else {
- *lbuf = read32(ptr, is_big);
- *n_datum = 2;
- }
-
- *lbuf = *lbuf << 10 | *lbuf >> shift & 0x3FFFFF;
-
- return *lbuf & 0x3FF;
-}
-
-static uint16_t read12in32(const uint8_t **ptr, uint32_t *lbuf,
- int *n_datum, int is_big)
-{
- if (*n_datum)
- (*n_datum)--;
- else {
- *lbuf = read32(ptr, is_big);
- *n_datum = 7;
- }
-
- switch (*n_datum){
- case 7: return *lbuf & 0xFFF;
- case 6: return (*lbuf >> 12) & 0xFFF;
- case 5: {
- uint32_t c = *lbuf >> 24;
- *lbuf = read32(ptr, is_big);
- c |= *lbuf << 8;
- return c & 0xFFF;
- }
- case 4: return (*lbuf >> 4) & 0xFFF;
- case 3: return (*lbuf >> 16) & 0xFFF;
- case 2: {
- uint32_t c = *lbuf >> 28;
- *lbuf = read32(ptr, is_big);
- c |= *lbuf << 4;
- return c & 0xFFF;
- }
- case 1: return (*lbuf >> 8) & 0xFFF;
- default: return *lbuf >> 20;
- }
-}
-
-static int decode_frame(AVCodecContext *avctx, AVFrame *p,
- int *got_frame, AVPacket *avpkt)
-{
- const uint8_t *buf = avpkt->data;
- int buf_size = avpkt->size;
- uint8_t *ptr[AV_NUM_DATA_POINTERS];
- uint32_t header_version, version = 0;
- char creator[101] = { 0 };
- char input_device[33] = { 0 };
-
- unsigned int offset;
- int magic_num, endian;
- int x, y, stride, i, j, ret;
- int w, h, bits_per_color, descriptor, elements, packing;
- int yuv, color_trc, color_spec;
- int encoding, need_align = 0, unpadded_10bit = 0;
-
- unsigned int rgbBuffer = 0;
- int n_datum = 0;
-
- if (avpkt->size <= 1634) {
- av_log(avctx, AV_LOG_ERROR, "Packet too small for DPX header\n");
- return AVERROR_INVALIDDATA;
- }
-
- magic_num = AV_RB32(buf);
- buf += 4;
-
- /* Check if the files "magic number" is "SDPX" which means it uses
- * big-endian or XPDS which is for little-endian files */
- if (magic_num == AV_RL32("SDPX")) {
- endian = 0;
- } else if (magic_num == AV_RB32("SDPX")) {
- endian = 1;
- } else {
- av_log(avctx, AV_LOG_ERROR, "DPX marker not found\n");
- return AVERROR_INVALIDDATA;
- }
-
- offset = read32(&buf, endian);
- if (avpkt->size <= offset) {
- av_log(avctx, AV_LOG_ERROR, "Invalid data start offset\n");
- return AVERROR_INVALIDDATA;
- }
-
- header_version = read32(&buf, 0);
- if (header_version == MKTAG('V','1','.','0'))
- version = 1;
- if (header_version == MKTAG('V','2','.','0'))
- version = 2;
- if (!version)
- av_log(avctx, AV_LOG_WARNING, "Unknown header format version %s.\n",
- av_fourcc2str(header_version));
-
- // Check encryption
- buf = avpkt->data + 660;
- ret = read32(&buf, endian);
- if (ret != 0xFFFFFFFF) {
- avpriv_report_missing_feature(avctx, "Encryption");
- av_log(avctx, AV_LOG_WARNING, "The image is encrypted and may "
- "not properly decode.\n");
- }
-
- // Need to end in 0x304 offset from start of file
- buf = avpkt->data + 0x304;
- w = read32(&buf, endian);
- h = read32(&buf, endian);
-
- if ((ret = ff_set_dimensions(avctx, w, h)) < 0)
- return ret;
-
- // Need to end in 0x320 to read the descriptor
- buf += 20;
- descriptor = buf[0];
- color_trc = buf[1];
- color_spec = buf[2];
-
- // Need to end in 0x323 to read the bits per color
- buf += 3;
- avctx->bits_per_raw_sample =
- bits_per_color = buf[0];
- buf++;
- packing = read16(&buf, endian);
- encoding = read16(&buf, endian);
-
- if (encoding) {
- avpriv_report_missing_feature(avctx, "Encoding %d", encoding);
- return AVERROR_PATCHWELCOME;
- }
-
- if (bits_per_color > 31)
- return AVERROR_INVALIDDATA;
-
- buf += 820;
- avctx->sample_aspect_ratio.num = read32(&buf, endian);
- avctx->sample_aspect_ratio.den = read32(&buf, endian);
- if (avctx->sample_aspect_ratio.num > 0 && avctx->sample_aspect_ratio.den > 0)
- av_reduce(&avctx->sample_aspect_ratio.num, &avctx->sample_aspect_ratio.den,
- avctx->sample_aspect_ratio.num, avctx->sample_aspect_ratio.den,
- 0x10000);
- else
- avctx->sample_aspect_ratio = (AVRational){ 0, 1 };
-
- /* preferred frame rate from Motion-picture film header */
- if (offset >= 1724 + 4) {
- buf = avpkt->data + 1724;
- i = read32(&buf, endian);
- if(i && i != 0xFFFFFFFF) {
- AVRational q = av_d2q(av_int2float(i), 4096);
- if (q.num > 0 && q.den > 0)
- avctx->framerate = q;
- }
- }
-
- /* alternative frame rate from television header */
- if (offset >= 1940 + 4 &&
- !(avctx->framerate.num && avctx->framerate.den)) {
- buf = avpkt->data + 1940;
- i = read32(&buf, endian);
- if(i && i != 0xFFFFFFFF) {
- AVRational q = av_d2q(av_int2float(i), 4096);
- if (q.num > 0 && q.den > 0)
- avctx->framerate = q;
- }
- }
-
- /* SMPTE TC from television header */
- if (offset >= 1920 + 4) {
- uint32_t tc;
- uint32_t *tc_sd;
- char tcbuf[AV_TIMECODE_STR_SIZE];
-
- buf = avpkt->data + 1920;
- // read32 to native endian, av_bswap32 to opposite of native for
- // compatibility with av_timecode_make_smpte_tc_string2 etc
- tc = av_bswap32(read32(&buf, endian));
-
- if (i != 0xFFFFFFFF) {
- AVFrameSideData *tcside =
- av_frame_new_side_data(p, AV_FRAME_DATA_S12M_TIMECODE,
- sizeof(uint32_t) * 4);
- if (!tcside)
- return AVERROR(ENOMEM);
-
- tc_sd = (uint32_t*)tcside->data;
- tc_sd[0] = 1;
- tc_sd[1] = tc;
-
- av_timecode_make_smpte_tc_string2(tcbuf, avctx->framerate,
- tc_sd[1], 0, 0);
- av_dict_set(&p->metadata, "timecode", tcbuf, 0);
- }
- }
-
- /* color range from television header */
- if (offset >= 1964 + 4) {
- buf = avpkt->data + 1952;
- i = read32(&buf, endian);
-
- buf = avpkt->data + 1964;
- j = read32(&buf, endian);
-
- if (i != 0xFFFFFFFF && j != 0xFFFFFFFF) {
- float minCV, maxCV;
- minCV = av_int2float(i);
- maxCV = av_int2float(j);
- if (bits_per_color >= 1 &&
- minCV == 0.0f && maxCV == ((1U<<bits_per_color) - 1)) {
- avctx->color_range = AVCOL_RANGE_JPEG;
- } else if (bits_per_color >= 8 &&
- minCV == (1 <<(bits_per_color - 4)) &&
- maxCV == (235<<(bits_per_color - 8))) {
- avctx->color_range = AVCOL_RANGE_MPEG;
- }
- }
- }
-
- switch (descriptor) {
- case 1: // R
- case 2: // G
- case 3: // B
- case 4: // A
- case 6: // Y
- elements = 1;
- yuv = 1;
- break;
- case 50: // RGB
- elements = 3;
- yuv = 0;
- break;
- case 52: // ABGR
- case 51: // RGBA
- elements = 4;
- yuv = 0;
- break;
- case 100: // UYVY422
- elements = 2;
- yuv = 1;
- break;
- case 102: // UYV444
- elements = 3;
- yuv = 1;
- break;
- case 103: // UYVA4444
- elements = 4;
- yuv = 1;
- break;
- default:
- avpriv_report_missing_feature(avctx, "Descriptor %d", descriptor);
- return AVERROR_PATCHWELCOME;
- }
-
- switch (bits_per_color) {
- case 8:
- stride = avctx->width * elements;
- break;
- case 10:
- if (!packing) {
- av_log(avctx, AV_LOG_ERROR, "Packing to 32bit required\n");
- return -1;
- }
- stride = (avctx->width * elements + 2) / 3 * 4;
- break;
- case 12:
- stride = avctx->width * elements;
- if (packing) {
- stride *= 2;
- } else {
- stride *= 3;
- if (stride % 8) {
- stride /= 8;
- stride++;
- stride *= 8;
- }
- stride /= 2;
- }
- break;
- case 16:
- stride = 2 * avctx->width * elements;
- break;
- case 32:
- stride = 4 * avctx->width * elements;
- break;
- case 1:
- case 64:
- avpriv_report_missing_feature(avctx, "Depth %d", bits_per_color);
- return AVERROR_PATCHWELCOME;
- default:
- return AVERROR_INVALIDDATA;
- }
-
- switch (color_trc) {
- case DPX_TRC_LINEAR:
- avctx->color_trc = AVCOL_TRC_LINEAR;
- break;
- case DPX_TRC_SMPTE_274:
- case DPX_TRC_ITU_R_709_4:
- avctx->color_trc = AVCOL_TRC_BT709;
- break;
- case DPX_TRC_ITU_R_601_625:
- case DPX_TRC_ITU_R_601_525:
- case DPX_TRC_SMPTE_170:
- avctx->color_trc = AVCOL_TRC_SMPTE170M;
- break;
- case DPX_TRC_ITU_R_624_4_PAL:
- avctx->color_trc = AVCOL_TRC_GAMMA28;
- break;
- case DPX_TRC_USER_DEFINED:
- case DPX_TRC_UNSPECIFIED_VIDEO:
- /* Nothing to do */
- break;
- default:
- av_log(avctx, AV_LOG_VERBOSE, "Cannot map DPX transfer characteristic "
- "%d to color_trc.\n", color_trc);
- break;
- }
-
- switch (color_spec) {
- case DPX_COL_SPEC_SMPTE_274:
- case DPX_COL_SPEC_ITU_R_709_4:
- avctx->color_primaries = AVCOL_PRI_BT709;
- break;
- case DPX_COL_SPEC_ITU_R_601_625:
- case DPX_COL_SPEC_ITU_R_624_4_PAL:
- avctx->color_primaries = AVCOL_PRI_BT470BG;
- break;
- case DPX_COL_SPEC_ITU_R_601_525:
- case DPX_COL_SPEC_SMPTE_170:
- avctx->color_primaries = AVCOL_PRI_SMPTE170M;
- break;
- case DPX_COL_SPEC_USER_DEFINED:
- case DPX_COL_SPEC_UNSPECIFIED_VIDEO:
- /* Nothing to do */
- break;
- default:
- av_log(avctx, AV_LOG_VERBOSE, "Cannot map DPX color specification "
- "%d to color_primaries.\n", color_spec);
- break;
- }
-
- if (yuv) {
- switch (color_spec) {
- case DPX_COL_SPEC_SMPTE_274:
- case DPX_COL_SPEC_ITU_R_709_4:
- avctx->colorspace = AVCOL_SPC_BT709;
- break;
- case DPX_COL_SPEC_ITU_R_601_625:
- case DPX_COL_SPEC_ITU_R_624_4_PAL:
- avctx->colorspace = AVCOL_SPC_BT470BG;
- break;
- case DPX_COL_SPEC_ITU_R_601_525:
- case DPX_COL_SPEC_SMPTE_170:
- avctx->colorspace = AVCOL_SPC_SMPTE170M;
- break;
- case DPX_COL_SPEC_USER_DEFINED:
- case DPX_COL_SPEC_UNSPECIFIED_VIDEO:
- /* Nothing to do */
- break;
- default:
- av_log(avctx, AV_LOG_INFO, "Cannot map DPX color specification "
- "%d to colorspace.\n", color_spec);
- break;
- }
- } else {
- avctx->colorspace = AVCOL_SPC_RGB;
- }
-
- av_strlcpy(creator, avpkt->data + 160, 100);
- creator[100] = '\0';
- av_dict_set(&p->metadata, "Creator", creator, 0);
-
- av_strlcpy(input_device, avpkt->data + 1556, 32);
- input_device[32] = '\0';
- av_dict_set(&p->metadata, "Input Device", input_device, 0);
-
- // Some devices do not pad 10bit samples to whole 32bit words per row
- if (!memcmp(input_device, "Scanity", 7) ||
- !memcmp(creator, "Lasergraphics Inc.", 18)) {
- if (bits_per_color == 10)
- unpadded_10bit = 1;
- }
-
- // Table 3c: Runs will always break at scan line boundaries. Packing
- // will always break to the next 32-bit word at scan-line boundaries.
- // Unfortunately, the encoder produced invalid files, so attempt
- // to detect it
- // Also handle special case with unpadded content
- need_align = FFALIGN(stride, 4);
- if (need_align*avctx->height + (int64_t)offset > avpkt->size &&
- (!unpadded_10bit || (avctx->width * avctx->height * elements + 2) / 3 * 4 + (int64_t)offset > avpkt->size)) {
- // Alignment seems unappliable, try without
- if (stride*avctx->height + (int64_t)offset > avpkt->size || unpadded_10bit) {
- av_log(avctx, AV_LOG_ERROR, "Overread buffer. Invalid header?\n");
- return AVERROR_INVALIDDATA;
- } else {
- av_log(avctx, AV_LOG_INFO, "Decoding DPX without scanline "
- "alignment.\n");
- need_align = 0;
- }
- } else {
- need_align -= stride;
- stride = FFALIGN(stride, 4);
- }
-
- switch (1000 * descriptor + 10 * bits_per_color + endian) {
- case 1081:
- case 1080:
- case 2081:
- case 2080:
- case 3081:
- case 3080:
- case 4081:
- case 4080:
- case 6081:
- case 6080:
- avctx->pix_fmt = AV_PIX_FMT_GRAY8;
- break;
- case 6121:
- case 6120:
- avctx->pix_fmt = AV_PIX_FMT_GRAY12;
- break;
- case 1320:
- case 2320:
- case 3320:
- case 4320:
- case 6320:
- avctx->pix_fmt = AV_PIX_FMT_GRAYF32LE;
- break;
- case 1321:
- case 2321:
- case 3321:
- case 4321:
- case 6321:
- avctx->pix_fmt = AV_PIX_FMT_GRAYF32BE;
- break;
- case 50081:
- case 50080:
- avctx->pix_fmt = AV_PIX_FMT_RGB24;
- break;
- case 52081:
- case 52080:
- avctx->pix_fmt = AV_PIX_FMT_ABGR;
- break;
- case 51081:
- case 51080:
- avctx->pix_fmt = AV_PIX_FMT_RGBA;
- break;
- case 50100:
- case 50101:
- avctx->pix_fmt = AV_PIX_FMT_GBRP10;
- break;
- case 51100:
- case 51101:
- avctx->pix_fmt = AV_PIX_FMT_GBRAP10;
- break;
- case 50120:
- case 50121:
- avctx->pix_fmt = AV_PIX_FMT_GBRP12;
- break;
- case 51120:
- case 51121:
- avctx->pix_fmt = AV_PIX_FMT_GBRAP12;
- break;
- case 6100:
- case 6101:
- avctx->pix_fmt = AV_PIX_FMT_GRAY10;
- break;
- case 6161:
- avctx->pix_fmt = AV_PIX_FMT_GRAY16BE;
- break;
- case 6160:
- avctx->pix_fmt = AV_PIX_FMT_GRAY16LE;
- break;
- case 50161:
- avctx->pix_fmt = AV_PIX_FMT_RGB48BE;
- break;
- case 50160:
- avctx->pix_fmt = AV_PIX_FMT_RGB48LE;
- break;
- case 51161:
- avctx->pix_fmt = AV_PIX_FMT_RGBA64BE;
- break;
- case 51160:
- avctx->pix_fmt = AV_PIX_FMT_RGBA64LE;
- break;
- case 50320:
- avctx->pix_fmt = AV_PIX_FMT_GBRPF32LE;
- break;
- case 50321:
- avctx->pix_fmt = AV_PIX_FMT_GBRPF32BE;
- break;
- case 51320:
- avctx->pix_fmt = AV_PIX_FMT_GBRAPF32LE;
- break;
- case 51321:
- avctx->pix_fmt = AV_PIX_FMT_GBRAPF32BE;
- break;
- case 100081:
- avctx->pix_fmt = AV_PIX_FMT_UYVY422;
- break;
- case 102081:
- avctx->pix_fmt = AV_PIX_FMT_YUV444P;
- break;
- case 103081:
- avctx->pix_fmt = AV_PIX_FMT_YUVA444P;
- break;
- default:
- av_log(avctx, AV_LOG_ERROR, "Unsupported format %d\n",
- 1000 * descriptor + 10 * bits_per_color + endian);
- return AVERROR_PATCHWELCOME;
- }
-
- ff_set_sar(avctx, avctx->sample_aspect_ratio);
-
- if ((ret = ff_get_buffer(avctx, p, 0)) < 0)
- return ret;
-
- // Move pointer to offset from start of file
- buf = avpkt->data + offset;
-
- for (i=0; i<AV_NUM_DATA_POINTERS; i++)
- ptr[i] = p->data[i];
-
- switch (bits_per_color) {
- case 10:
- for (x = 0; x < avctx->height; x++) {
- uint16_t *dst[4] = {(uint16_t*)ptr[0],
- (uint16_t*)ptr[1],
- (uint16_t*)ptr[2],
- (uint16_t*)ptr[3]};
- int shift = elements > 1 ? packing == 1 ? 22 : 20 : packing == 1 ? 2 : 0;
- for (y = 0; y < avctx->width; y++) {
- if (elements >= 3)
- *dst[2]++ = read10in32(&buf, &rgbBuffer,
- &n_datum, endian, shift);
- if (elements == 1)
- *dst[0]++ = read10in32_gray(&buf, &rgbBuffer,
- &n_datum, endian, shift);
- else
- *dst[0]++ = read10in32(&buf, &rgbBuffer,
- &n_datum, endian, shift);
- if (elements >= 2)
- *dst[1]++ = read10in32(&buf, &rgbBuffer,
- &n_datum, endian, shift);
- if (elements == 4)
- *dst[3]++ =
- read10in32(&buf, &rgbBuffer,
- &n_datum, endian, shift);
- }
- if (!unpadded_10bit)
- n_datum = 0;
- for (i = 0; i < elements; i++)
- ptr[i] += p->linesize[i];
- }
- break;
- case 12:
- for (x = 0; x < avctx->height; x++) {
- uint16_t *dst[4] = {(uint16_t*)ptr[0],
- (uint16_t*)ptr[1],
- (uint16_t*)ptr[2],
- (uint16_t*)ptr[3]};
- int shift = packing == 1 ? 4 : 0;
- for (y = 0; y < avctx->width; y++) {
- if (packing) {
- if (elements >= 3)
- *dst[2]++ = read16(&buf, endian) >> shift & 0xFFF;
- *dst[0]++ = read16(&buf, endian) >> shift & 0xFFF;
- if (elements >= 2)
- *dst[1]++ = read16(&buf, endian) >> shift & 0xFFF;
- if (elements == 4)
- *dst[3]++ = read16(&buf, endian) >> shift & 0xFFF;
- } else {
- if (elements >= 3)
- *dst[2]++ = read12in32(&buf, &rgbBuffer,
- &n_datum, endian);
- *dst[0]++ = read12in32(&buf, &rgbBuffer,
- &n_datum, endian);
- if (elements >= 2)
- *dst[1]++ = read12in32(&buf, &rgbBuffer,
- &n_datum, endian);
- if (elements == 4)
- *dst[3]++ = read12in32(&buf, &rgbBuffer,
- &n_datum, endian);
- }
- }
- n_datum = 0;
- for (i = 0; i < elements; i++)
- ptr[i] += p->linesize[i];
- // Jump to next aligned position
- buf += need_align;
- }
- break;
- case 32:
- if (elements == 1) {
- av_image_copy_plane(ptr[0], p->linesize[0],
- buf, stride,
- elements * avctx->width * 4, avctx->height);
- } else {
- for (y = 0; y < avctx->height; y++) {
- ptr[0] = p->data[0] + y * p->linesize[0];
- ptr[1] = p->data[1] + y * p->linesize[1];
- ptr[2] = p->data[2] + y * p->linesize[2];
- ptr[3] = p->data[3] + y * p->linesize[3];
- for (x = 0; x < avctx->width; x++) {
- AV_WN32(ptr[2], AV_RN32(buf));
- AV_WN32(ptr[0], AV_RN32(buf + 4));
- AV_WN32(ptr[1], AV_RN32(buf + 8));
- if (avctx->pix_fmt == AV_PIX_FMT_GBRAPF32BE ||
- avctx->pix_fmt == AV_PIX_FMT_GBRAPF32LE) {
- AV_WN32(ptr[3], AV_RN32(buf + 12));
- buf += 4;
- ptr[3] += 4;
- }
-
- buf += 12;
- ptr[2] += 4;
- ptr[0] += 4;
- ptr[1] += 4;
- }
- }
- }
- break;
- case 16:
- elements *= 2;
- case 8:
- if ( avctx->pix_fmt == AV_PIX_FMT_YUVA444P
- || avctx->pix_fmt == AV_PIX_FMT_YUV444P) {
- for (x = 0; x < avctx->height; x++) {
- ptr[0] = p->data[0] + x * p->linesize[0];
- ptr[1] = p->data[1] + x * p->linesize[1];
- ptr[2] = p->data[2] + x * p->linesize[2];
- ptr[3] = p->data[3] + x * p->linesize[3];
- for (y = 0; y < avctx->width; y++) {
- *ptr[1]++ = *buf++;
- *ptr[0]++ = *buf++;
- *ptr[2]++ = *buf++;
- if (avctx->pix_fmt == AV_PIX_FMT_YUVA444P)
- *ptr[3]++ = *buf++;
- }
- }
- } else {
- av_image_copy_plane(ptr[0], p->linesize[0],
- buf, stride,
- elements * avctx->width, avctx->height);
- }
- break;
- }
-
- *got_frame = 1;
-
- return buf_size;
-}
-
-const FFCodec ff_dpx_decoder = {
- .p.name = "dpx",
- CODEC_LONG_NAME("DPX (Digital Picture Exchange) image"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_DPX,
- FF_CODEC_DECODE_CB(decode_frame),
- .p.capabilities = AV_CODEC_CAP_DR1,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h261.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h261.c
deleted file mode 100644
index 8e0e13459a1b53c573e631bf4615a7f2f8840efb..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h261.c
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * H.261 common code
- * Copyright (c) 2002-2004 Michael Niedermayer <michaelni@gmx.at>
- * Copyright (c) 2004 Maarten Daniels
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * H.261 codec
- */
-
-#include "h261.h"
-#include "mpegvideo.h"
-
-#define IS_FIL(a) ((a) & MB_TYPE_H261_FIL)
-
-static void h261_loop_filter(uint8_t *src, ptrdiff_t stride)
-{
- int x, y;
- int temp[64];
-
- for (x = 0; x < 8; x++) {
- temp[x] = 4 * src[x];
- temp[x + 7 * 8] = 4 * src[x + 7 * stride];
- }
- for (y = 1; y < 7; y++) {
- for (x = 0; x < 8; x++) {
- ptrdiff_t xy = y * stride + x;
- ptrdiff_t yz = y * 8 + x;
- temp[yz] = src[xy - stride] + 2 * src[xy] + src[xy + stride];
- }
- }
-
- for (y = 0; y < 8; y++) {
- src[y * stride] = (temp[y * 8] + 2) >> 2;
- src[y * stride + 7] = (temp[y * 8 + 7] + 2) >> 2;
- for (x = 1; x < 7; x++) {
- ptrdiff_t xy = y * stride + x;
- ptrdiff_t yz = y * 8 + x;
- src[xy] = (temp[yz - 1] + 2 * temp[yz] + temp[yz + 1] + 8) >> 4;
- }
- }
-}
-
-void ff_h261_loop_filter(MpegEncContext *s)
-{
- H261Context *const h = s->private_ctx;
- const ptrdiff_t linesize = s->linesize;
- const ptrdiff_t uvlinesize = s->uvlinesize;
- uint8_t *dest_y = s->dest[0];
- uint8_t *dest_cb = s->dest[1];
- uint8_t *dest_cr = s->dest[2];
-
- if (!(IS_FIL(h->mtype)))
- return;
-
- h261_loop_filter(dest_y, linesize);
- h261_loop_filter(dest_y + 8, linesize);
- h261_loop_filter(dest_y + 8 * linesize, linesize);
- h261_loop_filter(dest_y + 8 * linesize + 8, linesize);
- h261_loop_filter(dest_cb, uvlinesize);
- h261_loop_filter(dest_cr, uvlinesize);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Android APK Oyun ve Uygulama ndirmenin En yi Yolu APKPure.md b/spaces/congsaPfin/Manga-OCR/logs/Android APK Oyun ve Uygulama ndirmenin En yi Yolu APKPure.md
deleted file mode 100644
index 1a4a7cc65b170baedb83875a13c265fc94633317..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Android APK Oyun ve Uygulama ndirmenin En yi Yolu APKPure.md
+++ /dev/null
@@ -1,144 +0,0 @@
-<br />
-<h1>APKPure Indir: What Is It and How to Use It?</h1>
- <p>If you are an Android user, you may have heard of <strong>APKPure</strong>, a website and an app that lets you download Android apps and games that are not available on Google Play Store. But what exactly is APKPure and how can you use it? In this article, we will answer these questions and more. We will also look at the pros and cons of using APKPure, as well as some alternatives that you can try.</p>
- <h2>What is APKPure?</h2>
- <p>APKPure is a website and an app that allows you to download Android apps and games in apk or xapk format. These are files that contain the installation package of an app or game, which you can install on your device without using Google Play Store. This way, you can access apps and games that are not available in your region, or that have been discontinued or restricted by Google.</p>
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- <ul>
-<li><strong>A wide variety of apps and games</strong>: You can find thousands of apps and games on APKPure, including popular ones like Facebook, WhatsApp, Instagram, PUBG Mobile, Minecraft, etc. You can also find older versions of apps and games, which may have features that are no longer available in the latest versions. Moreover, you can find region-locked apps and games, which are not accessible in your country due to legal or licensing issues.</li>
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-<li><strong>Open source and large user base</strong>: APKPure is an open source project, which means that anyone can inspect its code or contribute to its development. It also has a large user base, which means that you can find reviews, ratings, comments, and suggestions from other users. You can also join the APKPure community and share your feedback or request new apps and games.</li>
-</ul>
- <h2>How to use APKPure?</h2>
- <p>Using APKPure is simple and straightforward. Here are the steps that you need to follow:</p>
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- <ol>
-<li>Go to the official website of APKPure: <a href="">https://apkpure.com</a></li>
-<li>Tap on the "Download APKPure" button and wait for the apk file to be downloaded.</li>
-<li>Once the download is complete, tap on the apk file and allow the installation of unknown sources if prompted.</li>
-<li>Follow the on-screen instructions and install the APKPure app on your device.</li>
-</ol>
- <h3>How to browse and download apps and games from APKPure website or app</h3>
- <ol>
-<li>Open the APKPure website or app and search for the app or game that you want to download. You can also browse by categories, regions, editors' choices, etc.</li>
-<li>Tap on the app or game that you want to download and read its description, screenshots, reviews, etc.</li>
-<li>Tap on the "Download APK" or "Download XAPK" button and wait for the file to be downloaded.</li>
-<li>Once the download is complete, tap on the file and install it on your device. You may need to enable unknown sources if prompted.</li>
-</ol>
- <h3>How to update apps and games using APKPure app</h3>
- <ol>
-<li>Open the APKPure app and tap on the "Me" icon at the bottom right corner.</li>
-<li>Tap on the "Update" option and you will see a list of apps and games that have new updates available.</li>
-<li>Tap on the "Update" button next to the app or game that you want to update and wait for the file to be downloaded.</li>
-<li>Once the download is complete, tap on the file and install it on your device. You may need to enable unknown sources if prompted.</li>
-</ol>
- <h2>What are the pros and cons of APKPure?</h2>
- <p>Like any other app store, APKPure has its own advantages and disadvantages. Here are some of them:</p>
- <h3>Pros of APKPure</h3>
- <ul>
-<li><strong>APKPure has a wide variety of apps and games</strong>: You can find thousands of apps and games on APKPure, including popular ones like Facebook, WhatsApp, Instagram, PUBG Mobile, Minecraft, etc. You can also find older versions of apps and games, which may have features that are no longer available in the latest versions. Moreover, you can find region-locked apps and games, which are not accessible in your country due to legal or licensing issues.</li>
-<li><strong>APKPure is fast, safe, and easy to use</strong>: You can download apps and games from APKPure with one click, without any registration or verification. You can also update your apps and games using the APKPure app, which notifies you when new updates are available. Moreover, APKPure claims that it verifies the digital signatures of all the apps and games that it hosts, to ensure that they are genuine and free from malware.</li>
-<li><strong>APKPure is open source and has a large user base</strong>: APKPure is an open source project, which means that anyone can inspect its code or contribute to its development. It also has a large user base, which means that you can find reviews, ratings, comments, and suggestions from other users. You can also join the APKPure community and share your feedback or request new apps and games.</li>
-</ul>
- <h3>Cons of APKPure</h3>
- <ul>
-<li><strong>APKPure may contain some illegal or malicious apps</strong>: Although APKPure claims that it verifies all the apps and games that it hosts, there is still a possibility that some of them may contain illegal or malicious content. For example, some apps may violate intellectual property rights or privacy laws. Some apps may also contain viruses, spyware, adware, or other harmful software. Therefore, you should always be careful when downloading apps from unknown sources and scan them with a reliable antivirus program before installing them.</li>
-<li><strong>APKPure may not have the latest versions or updates of some apps</strong>: Because APKPure is not affiliated with Google or any other official app store, it may not have access to the latest versions or updates of some apps. For example, some apps may require Google Play Services or other Google components to function properly. Some apps may also have bugs or compatibility issues with some devices or operating systems. Therefore, you should always check the version and update date of the apps that you download from APKPure and compare them with the official sources.</li>
-<li><strong>APKPure may not be compatible with some devices or operating systems</strong>: Because APKPure is a third-party app store, it may not be compatible with some devices or operating systems. For example, some devices may have restrictions or limitations on installing apps from unknown sources. Some operating systems may also have different requirements or specifications for running apps. Therefore, you should always check the compatibility and requirements of the apps that you download from APKPure and make sure that they match your device and operating system.</li>
-</ul>
- <h2>What are some alternatives to APKPure?</h2>
- <p>If you are looking for some alternatives to APKPure, you can try the following app stores that also let you download Android apps and games in apk or xapk format:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-</tr>
-<tr>
-<td><strong>F-Droid</strong></td>
-<td>F-Droid is a free and open source app store for Android that only hosts apps that are free of charge, free of ads, and free of tracking. It also has a strict policy of respecting user privacy and security. You can find many useful and unique apps on F-Droid that are not available on other app stores.</td>
-</tr>
-<tr>
-<td><strong>Google Play Store</strong></td>
-<td>Google Play Store is the official app store for Android devices that is pre-installed on most of them. It has millions of apps and games that are verified and updated by Google. It also offers many features and services, such as Google Play Protect, Google Play Games, Google Play Books, etc. However, Google Play Store may not be available in some regions or countries, or may have some restrictions or limitations on some apps and games.</td>
-</tr>
-<tr>
-<td><strong>Aurora Store</strong></td>
-<td>Aurora Store is a third-party app store that lets you access Google Play Store anonymously. It does not require any Google account or services to function. It also lets you download apps and games that are not available in your region or country, or that are incompatible with your device. However, Aurora Store may not be as stable or reliable as Google Play Store, and may have some bugs or errors.</td>
-</tr>
-<tr>
-<td><strong>Aptoide</strong></td>
-<td>Aptoide is a community-driven app store for Android that lets you create your own app store or browse other users' app stores. It has millions of apps and games that are uploaded and updated by users. It also has a social network feature that lets you follow other users, rate and review apps, etc. However, Aptoide may contain some illegal or malicious apps, and may not have the latest versions or updates of some apps.</td>
-</tr>
-<tr>
-<td><strong>APKMirror</strong></td>
-<td>APKMirror is a website that hosts apk files of Android apps and games. It does not have an app version, but you can access it through any web browser. It has a large collection of apps and games that are updated regularly by users. It also has a simple and user-friendly interface that lets you search and download apps easily. However, APKMirror does not have any verification or security system for the apps and games that it hosts, so you should always be careful when downloading from it.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>APKPure is a website and an app that lets you download Android apps and games in apk or xapk format. It offers many features and benefits for Android users, such as a wide variety of apps and games, fast, safe, and easy to use service, open source and large user base project, etc. However, it also has some drawbacks, such as possible illegal or malicious apps, outdated versions or updates of some apps, compatibility issues with some devices or operating systems, etc. Therefore, you should always be cautious when using APKPure or any other third-party app store, and make sure that you download genuine and safe apps from reliable sources.</p>
- <h2>FAQs</h2>
- <h3>Q1. Is APKPure legal?</h3>
- <p>A1. APKPure is legal as long as it does not host or distribute any apps or games that violate intellectual property rights or other laws. However, some apps or games on APKPure may be illegal in your region or country due to legal or licensing issues. Therefore, you should always check the legality of the apps or games that you download from APKPure before using them.</p>
- <h3>Q2. Is APKPure safe to use?</h3>
- <p>A2. APKPure claims that it verifies the digital signatures of all the apps and games that it hosts, to ensure that they are genuine and free from malware. However, there is still a possibility that some apps or games on APKPure may contain viruses, spyware, adware, or other harmful software. Therefore, you should always scan the apps or games that you download from APKPure with a reliable antivirus program before installing them. You should also avoid downloading apps or games from unknown or untrusted sources.</p>
- <h3>Q3. How can I find out if an app on APKPure is genuine or not?</h3>
- <p>A3. One way to find out if an app on APKPure is genuine or not is to compare its digital signature with the official source. A digital signature is a code that identifies the developer and the authenticity of an app. You can check the digital signature of an app on APKPure by tapping on the "Details" option and looking for the "Signature" section. You can then compare it with the digital signature of the official app on Google Play Store or other app stores. If they match, then the app is likely to be genuine. If they do not match, then the app may be fake or modified.</p>
- <h3>Q4. How can I uninstall APKPure app from my device?</h3>
- <p>A4. To uninstall APKPure app from your device, you can follow these steps:</p>
- <ol>
-<li>Go to your device settings and tap on the "Apps" or "Applications" option.</li>
-<li>Find and tap on the "APKPure" app and tap on the "Uninstall" button.</li>
-<li>Confirm your action and wait for the app to be uninstalled.</li>
-</ol>
- <h3>Q5. How can I contact APKPure support team?</h3>
- <p>A5. If you have any questions, feedback, or issues regarding APKPure, you can contact its support team by using one of these methods:</p>
- <ul>
-<li>Email: <a href="mailto:support@apkpure.com">support@apkpure.com</a></li>
-<li>Facebook: <a href="https://www.facebook.com/apkpure">https://www.facebook.com/apkpure</a></li>
-<li>Twitter: <a href="https://twitter.com/apkpure">@apkpure</a></li>
-<li>Reddit: <a href="https://www.reddit.com/r/APKPure/">https://www.reddit.com/r/APKPure/</a></li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Permainan Bubble Shooter Gratis Game Santai dan Asyik untuk Semua Umur.md b/spaces/congsaPfin/Manga-OCR/logs/Download Permainan Bubble Shooter Gratis Game Santai dan Asyik untuk Semua Umur.md
deleted file mode 100644
index b313144a65b6e90ea39aefd515fb30ccfb6097c7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Permainan Bubble Shooter Gratis Game Santai dan Asyik untuk Semua Umur.md
+++ /dev/null
@@ -1,129 +0,0 @@
-<br />
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-<p>Bubble Shooter adalah sebuah permainan yang terinspirasi dari permainan arkade klasik yang dirilis oleh Taito pada tahun 1994 bernama Puzzle Bobble (di AS dan negara-negara Barat lainnya disebut Bust a Move). Permainan dan IP Bubble Shooter dimiliki oleh Ilyon Dynamics, setelah mereka mengakuisisi dari Absolutist, yang merilis permainan aslinya pada tahun 2002. Permainan ini kemudian dipindahkan ke iOS pada tahun 2010, dan dipindahkan ke Android pada tahun 2012. Pada tahun 2015, Absolutist menjual IP Bubble Shooter ke Ilyon Dynamics LTD, yang memperluas merek ini di ponsel dan ke platform baru seperti Facebook Messenger dan platform eSports mobile Skillz. Versi khusus juga dikembangkan dan diterbitkan dengan Suku Seminole Florida menggunakan merek Hard Rock Cafe mereka.</p>
- <h3>Sejarah permainan Bubble Shooter</h3>
-<p>Permainan bubble shooter pertama kali dirilis oleh Taito, dengan nama Puzzle Bobble. Permainan ini adalah sebuah permainan arkade yang mengharuskan pemain untuk menembakkan gelembung berwarna ke atas layar, dengan tujuan untuk membuat kelompok tiga atau lebih gelembung berwarna sama agar meledak. Permainan ini sangat populer di Jepang dan di seluruh dunia, dan menjadi salah satu permainan arkade terlaris sepanjang masa.</p>
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-download permainan dan instalasi aplikasi Bubble Shooter (Free) dari Microsoft Store id-ID[^3^]</p>
-<p>Pada tahun 2002, Absolutist membuat sebuah remake flash untuk web dengan nama Bubble Shooter. Permainan ini menjadi semakin populer di kalangan penggemar permainan online, karena sederhana namun menantang. Beberapa studio permainan lainnya juga membuat versi mereka sendiri dari permainan ini. Di bubbleshooter.net kami memiliki semua versi terbaik yang bisa dimainkan di situs web kami.</p>
- <h3>Cara bermain Bubble Shooter</h3>
-<p>Saat Anda memulai Bubble Shooter, Anda akan melihat banyak gele <p>mbung berwarna di atas layar, dan sebuah meriam di bagian bawah layar. Anda bisa menggerakkan meriam ke kiri dan kanan dengan menggunakan mouse atau jari Anda. Anda juga bisa melihat warna gelembung yang akan ditembakkan oleh meriam di bagian bawah layar. Untuk menembakkan gelembung, Anda cukup mengklik atau mengetuk layar.</p>
-<p>Tujuan dari permainan ini adalah untuk menghapus semua gelembung dari layar dengan menembakkan kelompok tiga atau lebih gelembung berwarna sama. Jika Anda berhasil membuat kelompok seperti itu, gelembung tersebut akan meledak dan jatuh ke bawah. Jika tidak, gelembung tersebut akan menempel di atas layar. Jika Anda menembakkan gelembung ke tempat yang kosong, Anda akan kehilangan satu nyawa. Jika Anda kehabisan nyawa, permainan berakhir.</p>
-<p>Anda juga harus memperhatikan bar waktu di bagian atas layar. Bar ini akan berkurang seiring waktu, dan jika habis, gelembung baru akan ditambahkan ke atas layar. Ini akan membuat permainan semakin sulit, karena Anda harus bergerak lebih cepat dan lebih akurat. Jika gelembung mencapai bagian bawah layar, permainan juga berakhir.</p>
- <h3>Fitur-fitur permainan Bubble Shooter</h3>
-<p>Bubble Shooter adalah permainan yang sangat sederhana, namun juga memiliki beberapa fitur yang membuatnya lebih menarik dan bervariasi. Beberapa fitur tersebut adalah:</p>
-<ul>
-<li>Berbagai mode permainan: Anda bisa memilih mode klasik, arcade, atau puzzle untuk menyesuaikan tingkat kesulitan dan gaya permainan Anda.</li>
-<li>Berbagai tema dan latar belakang: Anda bisa mengganti tampilan permainan dengan memilih tema dan latar belakang yang berbeda-beda. Ada tema hewan, buah-buahan, Halloween, Natal, dan banyak lagi.</li>
-<li>Berbagai jenis gelembung: Ada beberapa jenis gelembung yang memiliki efek khusus jika ditembakkan. Misalnya, ada gelembung bom yang bisa meledakkan sekelilingnya, gelembung pelangi yang bisa berubah warna sesuai dengan warna yang ditembakkan, dan gelembung api yang bisa membakar gelembung lainnya.</li>
-<li>Berbagai level dan tantangan: Ada ratusan level yang bisa Anda mainkan, dengan tingkat kesulitan yang berbeda-beda. Setiap level memiliki tujuan dan batasan tertentu yang harus Anda capai untuk mendapatkan bintang. Anda juga bisa menghadapi tantangan harian atau mingguan untuk mendapatkan hadiah khusus.</li>
-<li>Berbagai bonus dan power-up: Anda bisa mendapatkan bonus dan power-up yang bisa membantu Anda dalam bermain. Misalnya, ada bonus nyawa tambahan, bonus waktu tambahan, bonus skor tambahan, dan power-up seperti meriam ganda, meriam laser, meriam magnetik, dan lain-lain.</li>
-</ul>
- <h3>Tips dan trik untuk menguasai Bubble Shooter</h3>
-<p>Meskipun Bubble Shooter adalah permainan yang mudah dipelajari, tetapi tidak mudah untuk dikuasai. Ada beberapa tips dan trik yang bisa Anda gunakan untuk meningkatkan keterampilan dan skor Anda dalam bermain Bubble Shooter. Berikut adalah beberapa di antaranya:</p>
-<ul>
-<li>Arahkan meriam ke sudut: Jika Anda mengarahkan meriam ke sudut atas layar, Anda bisa membuat sudut pantulan yang lebih besar untuk menembakkan gelembung ke tempat yang sulit dijangkau. Ini bisa membantu Anda untuk menghancurkan lebih banyak gelembung dalam satu tembakan.</li>
-<li>Gunakan dinding sebagai pantulan: Jika Anda tidak bisa menembakkan gelembung secara langsung ke kelompok yang ingin Anda hancurkan, Anda bisa menggunakan dinding sebagai pantulan. Dengan begitu, Anda bisa mengubah arah tembakan Anda tanpa mengubah posisi meriam Anda.</li>
-<li>Perhatikan warna gelembung selanjutnya: Di bagian bawah layar, Anda bisa melihat warna gelembung yang akan ditembakkan selanjutnya oleh meriam. Ini bisa membantu Anda untuk merencanakan strategi Anda dan mencocokkan warna yang tepat. Jika Anda tidak bisa menggunakan warna gelembung selanjutnya, Anda bisa menukarnya dengan menekan tombol swap di bagian bawah layar.</li>
-<li>Targetkan gelembung yang menggantung: Gelembung yang menggantung adalah gelembung yang tidak terhubung dengan gelembung lainnya di atas layar. Jika Anda menghancurkan gelembung yang menggantung, Anda bisa membuat semua gelembung di bawahnya jatuh sekaligus. Ini bisa memberikan Anda skor yang lebih tinggi dan menghemat waktu.</li>
-<li>Gunakan bonus dan power-up dengan bijak: Bonus dan power-up bisa sangat membantu Anda dalam bermain, tetapi jangan terlalu bergantung pada mereka. Gunakan bonus dan power-up hanya jika Anda benar-benar membutuhkannya, atau jika Anda ingin mendapatkan skor yang sangat tinggi. Jangan sia-siakan bonus dan power-up untuk tembakan yang mudah atau tidak penting.</li>
-</ul>
- <h2>Dimana bisa download permainan Bubble Shooter gratis?</h2>
-<p>Bubble Shooter adalah permainan yang sangat populer dan tersedia di berbagai platform. Anda bisa download permainan Bubble Shooter gratis di PC atau ponsel Anda dengan mudah. Berikut adalah beberapa cara untuk download permainan Bubble Shooter gratis:</p>
- <h3>Google Play Store</h3>
-<p>Jika Anda memiliki ponsel Android, Anda bisa download permainan Bubble Shooter gratis di Google Play Store. Ada banyak versi dari Bubble Shooter yang bisa Anda pilih, seperti Bubble Shooter Classic, Bubble Shooter 2021, Bubble Shooter Legend, dan lain-lain. Anda bisa mencari nama permainan yang Anda inginkan di kotak pencarian Google Play Store, atau mengikuti link berikut untuk melihat daftar permainan Bubble Shooter gratis.</p>
- <h3>Microsoft Store</h3>
-<p>Jika Anda memiliki PC Windows 10, Anda bisa download permainan Bubble Shooter gratis di Microsoft Store. Ada beberapa versi dari Bubble Shooter yang bisa Anda pilih, seperti Bubble Witch 3 Saga, Bubble Pop! Puzzle Game Legend, Bubble Island 2, dan lain-lain. Anda bisa mencari nama permainan yang Anda inginkan di kotak pencarian Microsoft Store, atau mengikuti link berikut untuk melihat daftar permainan Bubble Shooter gratis.</p>
- <h3>Situs web online</h3>
-<p>Jika Anda tidak ingin download permainan Bubble Shooter ke PC atau ponsel Anda, Anda juga bisa bermain secara online di situs web tertentu. Ada banyak situs web yang menawarkan permainan Bubble Shooter gratis tanpa perlu instalasi atau registrasi. Beberapa situs web tersebut adalah:</p>
-<ul>
-<li>bubbleshooter.net: Situs web ini adalah salah satu situs web terbaik untuk bermain Bubble Shooter online. Situs web ini memiliki semua versi terbaik dari Bubble Shooter yang bisa dimainkan secara gratis dan tanpa batas waktu. Anda juga bisa melihat skor tertinggi dan tips bermain di situs web ini.</li>
-<li>bubble-shooter.co.id: Situs web ini adalah situs web khusus untuk penggemar permainan Bubble Shooter di Indonesia. Situs web ini memiliki banyak versi dari Bubble Shooter yang bisa dimainkan secara gratis dan tanpa iklan mengganggu. Anda juga bisa berbagi pengalaman bermain dengan pemain lain di situs web ini.</li>
-<li>bubble-shooter.com: Situs web ini adalah situs web lain yang menawarkan permainan Bubble Shooter online secara gratis. Situs web ini memiliki beberapa versi dari Bubble Shooter yang bisa dimainkan dengan grafis dan suara yang bagus. Anda juga bisa memilih tingkat kesulitan dan mode permainan yang Anda inginkan di situs web ini.</li>
-</ul> <h2>Apa manfaat bermain Bubble Shooter?</h2>
-<p>Bubble Shooter bukan hanya permainan yang menyenangkan, tetapi juga memiliki banyak manfaat bagi kesehatan dan kecerdasan Anda. Berikut adalah beberapa manfaat yang bisa Anda dapatkan dari bermain Bubble Shooter:</p>
- <h3>Mengasah otak dan keterampilan strategi</h3>
-<p>Bermain Bubble Shooter bisa melatih otak Anda untuk berpikir cepat dan tepat. Anda harus memperhatikan warna, posisi, dan gerakan gelembung untuk menentukan tembakan terbaik. Anda juga harus merencanakan langkah-langkah Anda beberapa langkah ke depan untuk mengantisipasi hasil dari tembakan Anda. Ini bisa meningkatkan kemampuan Anda dalam memecahkan masalah, membuat keputusan, dan berpikir kritis.</p>
- <h3>Meningkatkan konsentrasi dan koordinasi mata-tangan</h3>
-<p>Bermain Bubble Shooter juga bisa meningkatkan konsentrasi dan koordinasi mata-tangan Anda. Anda harus fokus pada layar dan menggerakkan meriam dengan akurat untuk menembakkan gelembung. Anda juga harus menghindari gangguan dari luar yang bisa mengganggu permainan Anda. Ini bisa membantu Anda untuk lebih fokus dan terampil dalam melakukan tugas-tugas lainnya.</p>
- <h3>Menghilangkan stres dan membawa kesenangan</h3>
-<p>Bermain Bubble Shooter juga bisa menghilangkan stres dan membawa kesenangan bagi Anda. Permainan ini memiliki warna-warna cerah, suara-suara menyenangkan, dan efek-efek menarik yang bisa membuat Anda merasa bahagia. Permainan ini juga bisa membuat Anda lupa akan masalah-masalah yang sedang Anda hadapi, dan memberikan Anda rasa puas dan percaya diri ketika Anda berhasil menyelesaikan level atau mendapatkan skor tinggi.</p>
- <h2>Kesimpulan</h2>
-<p>Bubble Shooter adalah permainan arkade yang sangat populer dan menyenangkan yang bisa dimainkan di PC atau ponsel Anda. Permainan ini mengharuskan Anda untuk menembakkan gelembung berwarna ke atas layar, dengan tujuan untuk menghancurkan sebanyak mungkin gelembung dengan menembakkan kelompok gelembung yang berwarna sama dengan amunisi meriam Anda.</p>
-<p>Anda bisa download permainan Bubble Shooter gratis di Google Play Store, Microsoft Store, atau situs web online tertentu. Anda juga bisa mendapatkan banyak manfaat dari bermain Bubble Shooter, seperti mengasah otak dan keterampilan strategi, meningkatkan konsentrasi dan koordinasi mata-tangan, serta menghilangkan stres dan membawa kesenangan.</p>
-<p>Jadi, tunggu apa lagi? Download permainan Bubble Shooter gratis sekarang juga, dan nikmati keseruan dan tantangannya!</p>
- <h2>FAQ</h2>
-<p>Berikut adalah beberapa pertanyaan yang sering diajukan tentang permainan Bubble Shooter:</p>
-<ul>
-<li><strong>Apakah Bubble Shooter cocok untuk semua usia?</strong></li>
-<li>Ya, Bubble Shooter adalah permainan yang cocok untuk semua usia. Permainan ini tidak mengandung unsur-unsur kekerasan, seksualitas, atau bahasa kasar yang tidak pantas. Permainan ini juga mudah dipelajari dan dimainkan oleh siapa saja.</li>
-<li><strong>Apakah Bubble Shooter membutuhkan koneksi internet?</strong></li>
-<li>Tidak, Bubble Shooter tidak membutuhkan koneksi internet untuk dimainkan. Namun, jika Anda ingin mendapatkan fitur-fitur tambahan seperti bonus harian, tantangan mingguan, atau berbagi skor dengan teman-teman Anda, maka Anda membutuhkan koneksi internet.</li>
-<li><strong>Apakah Bubble Shooter memiliki batas level?</strong></li>
-<li>Tidak, Bubble Shooter tidak memiliki batas level. Anda bisa terus bermain sebanyak yang Anda mau tanpa khawatir kehabisan level. Setiap level memiliki susunan gelembung yang berbeda-beda, sehingga permainan tidak akan pernah membosankan.</li>
-<li><strong>Apakah Bubble Shooter memiliki iklan?</strong></li>
-<li>Ya, Bubble Shooter memiliki iklan yang muncul sesekali di layar. Namun, i klan Anda bisa menghapus iklan dengan membeli versi premium dari permainan, atau dengan menonton video pendek yang ditawarkan oleh pengembang permainan.</li>
-<li><strong>Apakah Bubble Shooter memiliki fitur multiplayer?</strong></li>
-<li>Ya, Bubble Shooter memiliki fitur multiplayer yang memungkinkan Anda untuk bermain bersama dengan teman-teman Anda secara online. Anda bisa mengundang teman-teman Anda untuk bergabung dengan Anda di ruang permainan khusus, atau berkompetisi dengan pemain lain dari seluruh dunia di papan peringkat global. Anda juga bisa berpartisipasi dalam turnamen dan liga yang diselenggarakan oleh platform eSports mobile Skillz.</li>
-</ul></p> 197e85843d<br />
-<br />
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-<h1>Download Quran with English Translation for PC</h1>
-<p>If you are looking for a way to read, listen, and understand the Quran on your computer, you might be interested in downloading Quran with English translation for PC. In this article, we will explain what the Quran is and why it is important, what are the benefits of reading the Quran with English translation, and how to download Quran with English translation for PC. We will also review some of the best Quran software for PC that you can choose from.</p>
-<h2>Introduction</h2>
-<h3>What is the Quran and why is it important?</h3>
-<p>The Quran is the holy book of Islam, revealed by God to Prophet Muhammad (peace be upon him) through the angel Gabriel over a period of 23 years. The Quran contains guidance, wisdom, and commands for all aspects of life, such as faith, worship, morality, law, and history. The Quran is considered to be the final and complete revelation from God to humanity, and the primary source of Islamic teachings and beliefs.</p>
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-<p>The Quran is important for Muslims because it is the word of God, and the ultimate authority for their religion. Muslims believe that the Quran is a miracle, and that it contains no errors or contradictions. Muslims also believe that the Quran has many benefits and blessings for those who recite, memorize, study, and act upon it. The Quran is a source of guidance, healing, mercy, light, and peace for Muslims and non-Muslims alike.</p>
-<h3>What are the benefits of reading the Quran with English translation?</h3>
-<p>Reading the Quran with English translation can help you to understand the meaning and message of the Quran better. Although the original Arabic language of the Quran is rich and eloquent, not everyone can comprehend it fully. Therefore, reading the Quran with English translation can help you to appreciate the beauty and wisdom of the Quran in your own language.</p>
-<p>Reading the Quran with English translation can also help you to increase your knowledge, faith, and spirituality. The Quran contains many lessons, stories, parables, and examples that can inspire you to learn more about Islam and its teachings. The Quran can also strengthen your faith in God and His oneness, power, mercy, and justice. The Quran can also enhance your spirituality by reminding you of your purpose in life, your accountability before God, and your ultimate destiny in the hereafter.</p>
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-<h3>How to download Quran with English translation for PC?</h3>
-<p>Downloading Quran with English translation for PC is easy and convenient. You just need to find a reliable and user-friendly Quran software that suits your needs and preferences. There are many options available online, but we will review some of the best ones in the next section. Once you have chosen a Quran software, you can download it from its official website or from a trusted source. You can then install it on your PC and start reading or listening to the Quran with English translation.</p>
-<h2>Best Quran Software for PC</h2>
-<h3>Quran.com</h3>
-<h4>Features</h4>
-<ul>
-<li>Quran.com is a website that allows you to read, listen, search, download, and share the Quran online.</li>
-<li>Quran.com offers various translations of the Quran in different languages, including English.</li>
-<li>Quran.com also provides audio recitations of the Quran by different reciters.</li>
-<li>Quran.com has a simple and elegant interface that makes it easy to navigate and use.</li>
-<li>Quran.com also has a mobile app that you can download on your smartphone or tablet.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>Pros <ul>
-<li>Pros <ul>
- <li>Quran.com is free and accessible to anyone with an internet connection.</li>
- <li>Quran.com has a large collection of translations and recitations of the Quran.</li>
- <li>Quran.com allows you to customize your reading and listening experience by choosing the font size, color, translation, reciter, and mode.</li>
- <li>Quran.com also has other features such as bookmarks, notes, word-by-word analysis, tafsir (explanation), and dua (supplication).</li>
- </ul>
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-<li>Cons <ul>
- <li>Quran.com requires an internet connection to function properly.</li>
- <li>Quran.com may not have all the translations or recitations that you prefer or need.</li>
- <li>Quran.com may have some technical issues or errors that affect its performance or accuracy.</li>
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-<h3>Quran with translation - English - Quran listen and download</h3>
-<h4>Features</h4>
-<ul>
-<li>Quran with translation - English - Quran listen and download is a software that allows you to read, listen, and download the Quran with English translation on your PC.</li>
-<li>Quran with translation - English - Quran listen and download offers a clear and easy-to-read translation of the Quran by Dr. Mustafa Khattab.</li>
-<li>Quran with translation - English - Quran listen and download also provides audio recitations of the Quran by different reciters.</li>
-<li>Quran with translation - English - Quran listen and download has a simple and user-friendly interface that makes it easy to navigate and use.</li>
-<li>Quran with translation - English - Quran listen and download also has a mobile app that you can download on your smartphone or tablet.</li>
-</ul>
-<h4>Pros and cons</h4>
-<ul>
-<li>Pros <ul>
- <li>Quran with translation - English - Quran listen and download is free and does not require an internet connection to function.</li>
- <li>Quran with translation - English - Quran listen and download has a high-quality translation of the Quran that is faithful to the original meaning and context.</li>
- <li>Quran with translation - English - Quran listen and download allows you to download the Quran with translation and recitation for offline use.</li>
- <li>Quran with translation - English - Quran listen and download also has other features such as night mode, search, zoom, share, and feedback.</li>
- </ul>
-</li>
-<li>Cons <ul>
- <li>Quran with translation - English - Quran listen and download may not have all the translations or recitations that you prefer or need.</li>
- <li>Quran with translation - English - Quran listen and download may have some technical issues or errors that affect its performance or accuracy.</li>
- </ul>
-</li>
-</ul>
- <h3>Quran in English - Clear and Easy to Read</h3>
- <h4>Features</h4>
- <ul>
- <li>Quran in English - Clear and Easy to Read is a software that allows you to read the Quran in English on your PC.</li>
- <li>Quran in English - Clear and Easy to Read offers a modern and accessible translation of the Quran by Talal Itani.</li>
- <li>Quran in English - Clear and Easy to Read also provides audio recitations of the Quran by different reciters.</li>
- <li>Quran in English - Clear and Easy to Read has a simple and elegant interface that makes it easy to navigate and use.</li>
- <li>Quran in English - Clear and Easy to Read also has a mobile app that you can download on your smartphone or tablet.</li>
- </ul>
- <h4>Pros and cons</h4>
- <ul>
- <li>Pros <ul>
- <li>Quran in English - Clear and Easy to Read is free and does not require an internet connection to function.</li>
- <li>Quran in English - Clear and Easy to Read has a clear and easy-to-read translation of the Quran that is suitable for beginners and non-Muslims.</li>
- <li>Quran in English - Clear and Easy to Read allows you to download the Quran with translation and recitation for offline use.</li>
- <li>Quran in English - Clear and Easy to Read also has other features such as bookmarks, notes, highlights, search, share, and feedback.</li>
- </ul>
- </li>
- <li>Cons <ul>
- <li>Quran in English - Clear and Easy to Read may not have all the translations or recitations that you prefer or need.</li>
- <li>Quran in English - Clear and Easy to Read may have some technical issues or errors that affect its performance or accuracy.</li>
-</ul>
-</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, downloading Quran with English translation for PC is a great way to read, listen, and understand the Quran on your computer. The Quran is the holy book of Islam, and it has many benefits and blessings for those who recite, memorize, study, and act upon it. There are many Quran software for PC that you can choose from, but we have reviewed some of the best ones in this article. We hope that this article has helped you to find the best Quran software for PC that suits your needs and preferences.</p>
- <p>If you are interested in downloading Quran with English translation for PC, you can visit the official websites or trusted sources of the Quran software that we have mentioned in this article. You can also download their mobile apps on your smartphone or tablet. We hope that you will enjoy reading and listening to the Quran with English translation on your PC.</p>
- <p>Thank you for reading this article. If you have any questions or feedback, please feel free to contact us. We would love to hear from you.</p>
- <h3>FAQs</h3>
- <ul>
-<li>Q: What is the best translation of the Quran in English?</li>
-<li>A: There is no definitive answer to this question, as different translations may have different strengths and weaknesses, and different readers may have different preferences and needs. However, some of the most popular and widely accepted translations of the Quran in English are by Abdullah Yusuf Ali, Muhammad Marmaduke Pickthall, Muhammad Asad, Saheeh International, Dr. Mustafa Khattab, and Talal Itani.</li>
-<li>Q: How can I improve my understanding of the Quran?</li>
-<li>A: Besides reading the Quran with translation, you can also improve your understanding of the Quran by learning Arabic, studying the tafsir (explanation) of the Quran, listening to lectures and podcasts on the Quran, joining online or offline Quran classes or circles, and asking questions to scholars or experts on the Quran.</li>
-<li>Q: How can I memorize the Quran?</li>
-<li>A: Memorizing the Quran is a noble and rewarding goal, but it requires dedication, discipline, and consistency. Some of the tips that can help you to memorize the Quran are: choosing a suitable time and place for memorization, starting with short and easy surahs (chapters), repeating and revising regularly, listening to audio recitations of the Quran, using mnemonics and associations, seeking help from a teacher or a partner, and making dua (supplication) to God for help and guidance.</li>
-<li>Q: How can I download audio recitations of the Quran?</li>
-<li>A: You can download audio recitations of the Quran from various websites or apps that offer this service. Some of the examples are: quranicaudio.com, mp3quran.net, everyayah.com, quranexplorer.com, and quran.com. You can also download audio recitations of the Quran from the Quran software that we have reviewed in this article.</li>
-<li>Q: How can I share the Quran with others?</li>
-<li>A: You can share the Quran with others by giving them copies of the Quran or its translation, inviting them to read or listen to the Quran online or offline, discussing the meanings and messages of the Quran with them, sending them verses or quotes from the Quran via email or social media, and supporting or donating to organizations or projects that distribute or promote the Quran.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
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-<br />
-<h1>Factory 2 Mod APK: A Fun and Creative Simulation Game</h1>
-<p>Do you love building and managing your own factories? Do you want to unleash your creativity and design your own products? If yes, then you should try Factory 2, a simulation game that lets you create and run your own factory empire. In this game, you can produce various items, from simple tools to complex machines, and sell them to the market. You can also upgrade your factory, hire workers, research new technologies, and compete with other players. Factory 2 is a fun and addictive game that will keep you entertained for hours.</p>
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-<p>But what if you want to enjoy the game without any limitations or restrictions? What if you want to access all the features and items without spending real money? Well, there is a way to do that. You can download Factory 2 Mod APK, a modified version of the game that gives you unlimited resources, unlocked items, and more. In this article, we will tell you what Factory 2 is, what are its features, why you should download Factory 2 Mod APK, and how to do it. So, let's get started.</p>
- <h2>What is Factory 2?</h2>
-<p>Factory 2 is a simulation game developed by Lion Studios, a popular game studio that has created many hit games like Happy Glass, Mr Bullet, and Ink Inc. Factory 2 was released in March 2021 and has gained over 10 million downloads on Google Play Store. The game has a rating of 4.4 out of 5 stars and has received positive reviews from the users.</p>
- <h3>The gameplay of Factory 2</h3>
-<p>The gameplay of Factory 2 is simple and easy to understand. You start with a small factory and a few machines. Your goal is to produce different items and sell them to the market. You can choose from hundreds of products, such as hammers, nails, screws, wrenches, drills, saws, knives, scissors, pens, pencils, erasers, rulers, calculators, clocks, watches, cameras, phones, laptops, tablets, TVs, radios, speakers, headphones, keyboards, mice, printers, scanners, copiers, fax machines, projectors, monitors, gaming consoles, controllers, joysticks, VR headsets, drones, robots, cars, bikes, planes, helicopters, rockets, satellites, and more.</p>
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-<p>To produce an item, you need to select the machines that are required for its production. For example, to make a hammer, you need a metal press machine and a handle machine. You can drag and drop the machines on the conveyor belt and connect them with wires. Then you need to tap on the start button to begin the production process. You can watch as the raw materials are transformed into finished products.</p>
-<p>Once you have produced enough items, you can sell them to the market and earn money. You can use the money to upgrade your factory and machines. You can also buy new machines and unlock new products. Upgrading your factory will increase your production speed and efficiency. You can also hire workers to help you run your factory. Workers can perform various tasks such as collecting raw materials from the warehouse, operating machines, maintaining machines, delivering products to the market, and researching new technologies.</p>
- <h3>The features of Factory 2</h3>
-<p>Factory 2 is a game that offers many features that make it fun and enjoyable. Some of the features are:</p>
-<ul>
-<li><b>Creative gameplay:</b> You can create your own products by combining different machines and materials. You can also customize your products by changing their colors, shapes, sizes, and designs.</li>
-<li><b>Hundreds of products:</b> You can choose from hundreds of products to produce and sell. You can also discover new products by researching new technologies.</li>
-<li><b>Realistic graphics:</b> The game has realistic graphics that show the details of the machines, products, and factory environment.</li>
-<li><b>Friendly interface:</b> The game has a friendly interface that makes it easy to play and control. You can drag and drop the machines, tap on the buttons, and swipe on the screen to interact with the game.</li>
-<li><b>Competitive mode:</b> You can compete with other players in the online mode. You can compare your factory performance, product quality, and market share with others. You can also chat with other players and share tips and tricks.</li>
-<li><b>Achievements and rewards:</b> You can earn achievements and rewards by completing various tasks and challenges. You can also collect trophies and medals by reaching certain milestones.</li>
-</ul>
- <h2>Why download Factory 2 Mod APK?</h2>
-<p>Factory 2 is a free game that you can download from Google Play Store. However, the game has some limitations and restrictions that may affect your gaming experience. For example, you need to watch ads to get extra money or speed up the production process. You also need to spend real money to buy premium items or unlock new features. Moreover, some of the products and machines are locked and require a lot of time and effort to unlock them.</p>
-<p>If you want to enjoy the game without any limitations or restrictions, you should download Factory 2 Mod APK. Factory 2 Mod APK is a modified version of the game that gives you unlimited resources, unlocked items, and more. With Factory 2 Mod APK, you can:</p>
-<ul>
-<li><b>Get unlimited money:</b> You can get unlimited money to buy and upgrade anything you want. You don't need to watch ads or spend real money to get more money.</li>
-<li><b>Get unlimited gems:</b> You can get unlimited gems to buy premium items or unlock new features. You don't need to complete tasks or achievements to get more gems.</li>
-<li><b>Get unlimited energy:</b> You can get unlimited energy to run your factory without any interruption. You don't need to wait for the energy to refill or use energy boosters.</li>
-<li><b>Unlock all products and machines:</b> You can unlock all the products and machines without any requirement. You don't need to research new technologies or reach certain levels to unlock them.</li>
-<li><b>Remove ads:</b> You can remove all the ads from the game and enjoy a smooth and uninterrupted gaming experience.</li>
-</ul>
- <h3>The benefits of Factory 2 Mod APK</h3>
-<p>The benefits of Factory 2 Mod APK are obvious. By downloading Factory 2 Mod APK, you can enjoy the game with more freedom and fun. You can create and run your own factory empire without any limitation or restriction. You can also explore more features and items that are not available in the original game. Moreover, you can save your time and money by getting unlimited resources and unlocked items for free.</p>
- <h3>How to download and install Factory 2 Mod APK</h3>
-<p>If you are interested in downloading Factory 2 Mod APK, you can follow these simple steps:</p>
-<ol>
-<li><b>Download the APK file:</b> You can download the APK file from a trusted source such as [APKPure] or [APKMirror]. Make sure you download the latest version of the mod that is compatible with your device.</li>
-<li><b>Enable unknown sources:</b> Before installing the APK file, you need to enable unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li><b>Install the APK file:</b> After enabling unknown sources, locate the downloaded APK file on your device and tap on it to install it.</li>
-<li><b>Launch the game:</b> Once the installation is complete, you can launch the game from your app drawer or home screen.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Factory 2 is a simulation game that lets you create and run your own factory empire. You can produce hundreds of products, upgrade your factory, hire workers, research new technologies, and compete with other players. Factory 2 is a fun and addictive game that will keep you entertained for hours.</p>
-<p>If you want to enjoy the game without any limitations or restrictions, you should download Factory 2 Mod APK. Factory 2 Mod APK is a modified version of the game that gives you unlimited resources, unlocked items, and more. With Factory 2 Mod APK, you can create and run your own factory empire without any limitation or restriction.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Factory 2 Mod APK:</p>
- <h4>Q: Is Factory 2 Mod APK safe to download?</h4>
-<p>A: Yes, Factory 2 Mod APK is safe to download as long as you download it from a trusted source such as [ APKPure] or [APKMirror]. These sources are reliable and verified by millions of users. However, you should always scan the APK file with an antivirus software before installing it to ensure its safety.</p>
- <h4>Q: Does Factory 2 Mod APK require root access?</h4>
-<p>A: No, Factory 2 Mod APK does not require root access to work. You can install and play the game on any Android device without rooting it.</p>
- <h4>Q: Can I play Factory 2 Mod APK online?</h4>
-<p>A: Yes, you can play Factory 2 Mod APK online with other players. However, you may face some issues or errors while connecting to the server or syncing your data. This is because the mod is not supported by the official game developers. Therefore, we recommend you to play the game offline or use a VPN to avoid any problems.</p>
- <h4>Q: Can I update Factory 2 Mod APK?</h4>
-<p>A: Yes, you can update Factory 2 Mod APK whenever a new version is available. However, you need to download and install the new version manually from the same source you downloaded the previous version. You cannot update the game from Google Play Store or any other app store.</p>
- <h4>Q: Will I lose my progress if I uninstall Factory 2 Mod APK?</h4>
-<p>A: No, you will not lose your progress if you uninstall Factory 2 Mod APK. Your game data is stored on your device and not on the server. Therefore, you can reinstall the game anytime and resume your game from where you left off.</p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Ritim Ykl - WhatsApp ndirin ve Ritim Paylan.md b/spaces/congsaPfin/Manga-OCR/logs/Ritim Ykl - WhatsApp ndirin ve Ritim Paylan.md
deleted file mode 100644
index 9173bad8d49dfe2af24a21bbddf87f06e0acc0d9..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Ritim Ykl - WhatsApp ndirin ve Ritim Paylan.md
+++ /dev/null
@@ -1,45 +0,0 @@
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-<h1>Ritim Yüklə: How to Download Free Music Online</h1> | | H2: Introduction | <p>If you love listening to music, you might be wondering how to download free music online. There are many websites and apps that offer free music downloads, but not all of them are legal or safe. In this article, we will show you how to download free music online using Ritim Yüklə, a popular website that offers a wide range of songs in different genres and languages. We will also explain the benefits and risks of downloading free music online, and answer some frequently asked questions about Ritim Yüklə.</p>
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However, you should also be aware of the legal and safety issues that may arise from downloading pirated music. Ritim Yüklə is a website that offers a wide range of songs in different genres and languages for free download, but you should use it responsibly and respectfully. We hope this article has helped you understand how to use Ritim Yüklə safely and legally. If you have any questions, feel free to check out our FAQs below or contact us for more information. | <p>Downloading free music online can be a great way to enjoy your favorite songs without spending money or relying on an internet connection. However, you should also be aware of the legal and safety issues that may arise from downloading pirated music. Ritim Yüklə is a website that offers a wide range of songs in different genres and languages for free download, but you should use it responsibly and respectfully. We hope this article has helped you understand how to use Ritim Yüklə safely and legally. 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You should also respect the rights of the artists and record labels and only download music that is legally available or authorized.</h4> | | H3: Q2: Is Ritim Yüklə safe? | <h3>Q2: Is Ritim Yüklə safe?</h3> | | H4: A2: Ritim Yüklə is not safe in some cases where the files may contain viruses or malware that can harm your device or steal your personal information. You should use a reliable antivirus software and a VPN service when using Ritim Yüklə. You should also scan the downloaded files before opening them and delete any suspicious or unwanted files. | <h4>A2: Ritim Yüklə is not safe in some cases where the files may contain viruses or malware that can harm your device or steal your personal information. You should use a reliable antivirus software and a VPN service when using Ritim Yüklə. You should also scan the downloaded files before opening them and delete any suspicious or unwanted files.</h4> | | H3: Q3: How to download music from Ritim Yüklə? | <h3>Q3: How to download music from Ritim Yüklə?</h3> | | Outline | Article | | --- | --- | | H4: A3: To download music from Ritim Yüklə, you need to follow these steps:<br><ol><li>Go to ritimyukle.com and search for the song you want to download by artist, album, title, or genre, or browse through the latest or most downloaded songs on the homepage.</li><li>Click on the download button next to the song and choose from different quality options (such as 128 kbps or 320 kbps).</li><li>Wait for the download to complete and enjoy your music.</li></ol> | <h4>A3: To download music from Ritim Yüklə, you need to follow these steps:<br><ol><li>Go to ritimyukle.com and search for the song you want to download by artist, album, title, or genre, or browse through the latest or most downloaded songs on the homepage.</li><li>Click on the download button next to the song and choose from different quality options (such as 128 kbps or 320 kbps).</li><li>Wait for the download to complete and enjoy your music.</li></ol></h4> | | H3: Q4: How to create and share playlists on Ritim Yüklə? | <h3>Q4: How to create and share playlists on Ritim Yüklə?</h3> | | H4: A4: To create and share playlists on Ritim Yüklə, you need to follow these steps:<br><ol><li>Go to ritimyukle.com and log in with your email or social media account. 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If you don't have an account, you can sign up for free.</li><li>Click on the playlist icon on the top right corner of the website and choose "Create Playlist".</li><li>Give your playlist a name and a description and add songs from Ritim Yüklə or your device.</li><li>Click on "Save Playlist" and choose whether you want to make it public or private.</li><li>To share your playlist, click on the share icon next to your playlist and copy the link or choose from different social media platforms.</li></ol></h4> | | H3: Q5: How to contact Ritim Yüklə? | <h3>Q5: How to contact Ritim Yüklə?</h3> | | H4: A5: To contact Ritim Yüklə, you can use one of the following methods:<br><ul><li>Email: You can send an email to ritimyukle@gmail.com with your name, subject, and message. You can also attach any files or screenshots if needed.</li><li>Phone: You can call +994 12 345 6789 from Monday to Friday between 9 am and 5 pm (GMT+4).</li><li>Social media: You can follow Ritim Yüklə on Facebook, Twitter, Instagram, YouTube, or Telegram and send them a direct message or comment on their posts.</li></ul> | <h4>A5: To contact Ritim Yüklə, you can use one of the following methods:<br><ul><li>Email: You can send an email to ritimyukle@gmail.com with your name, subject, and message. You can also attach any files or screenshots if needed.</li><li>Phone: You can call +994 12 345 6789 from Monday to Friday between 9 am and 5 pm (GMT+4).</li><li>Social media: You can follow Ritim Yüklə on Facebook, Twitter, Instagram, YouTube, or Telegram and send them a direct message or comment on their posts.</li></ul></h4> |</p> 197e85843d<br />
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-<h1>How to Download Instagram Stories Online</h1>
-<p>Instagram Stories are one of the most popular features of the social media platform. They allow users to share short videos or photos that disappear after 24 hours, creating a sense of urgency and exclusivity. But what if you want to save some of these stories for later, repost them, or use them for other purposes? In this article, we will show you how to download Instagram stories online using two different websites: StorySaver.net and Inflact.</p>
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-<h2>What are Instagram Stories and Why Download Them?</h2>
-<h3>Instagram Stories are short videos or photos that disappear after 24 hours</h3>
-<p>Instagram Stories are a way for users to share their daily moments with their followers without cluttering their main feed. They can add filters, stickers, text, music, and other effects to make their stories more fun and engaging. Users can also view the stories of the accounts they follow, as well as some suggested stories from other users they might like.</p>
-<h3>Downloading Instagram Stories can help you save your favorite moments, repost them, or use them for other purposes</h3>
-<p>While Instagram Stories are meant to be ephemeral, sometimes you might want to keep them for longer than 24 hours. Maybe you saw a story that inspired you, made you laugh, or taught you something new. Maybe you want to repost a story from someone else, or use it for your own content creation. Or maybe you just want to have a backup of your own stories in case you lose them or delete them by mistake.</p>
-<p>Whatever the reason, downloading Instagram stories online can help you achieve your goals. You can save them to your device, share them with others, edit them, or use them however you want. And the best part is, you can do it without installing any apps or software, just by using your web browser.</p>
-<h2>How to Download Instagram Stories Online with StorySaver.net</h2>
-<h3>StorySaver.net is a free and easy-to-use website that lets you download Instagram Stories and Highlights</h3>
-<p>One of the websites that you can use to download Instagram stories online is StorySaver.net. This website allows you to download not only the current stories of any public account, but also their highlights, which are collections of stories that they have saved on their profile. StorySaver.net is free, fast, and simple to use. You don't need to register, log in, or provide any personal information. You just need the username of the account you want to download from.</p>
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-<h3>How to use StorySaver.net to download Instagram Stories and Highlights</h3>
-<h4>Step 1: Enter the Instagram username of the account you want to download from</h4>
-<p>To start downloading Instagram stories online with StorySaver.net, go to <a href="(^1^)">https://www.storysaver.net/</a>. On the homepage, you will see a search box where you can enter the Instagram username of the account you want to download from. For example, if you want to download the stories of <a href="">https://www.instagram.com/natgeo/</a>, you can enter "natgeo" in the search box. Then, click on the "Search" button or press the "Enter" key on your keyboard.</p>
-<h4>Step 2: Select the current story or highlight you want to download</h4>
-<p>After you enter the username, you will see a list of the current stories and highlights of that account. You can scroll down to see more options, or use the search bar at the top to filter by keywords. To download a story or highlight, simply click on it. You will see a preview of the story or highlight on the right side of the screen, along with some information such as the number of views, likes, and comments.</p>
-<h4>Step 3: Click on the "Save as" button to save the story or highlight to your device</h4>
-<p>Once you have selected the story or highlight you want to download, you can click on the "Save as" button at the bottom right corner of the screen. This will open a pop-up window where you can choose the format and quality of the download. You can choose between MP4 (video) or JPG (image) formats, and between high, medium, or low quality. Then, click on the "Download" button to start the download process. The story or highlight will be saved to your device's default download folder.</p>
-<h2>How to Download Instagram Stories Online with Inflact</h2>
-<h3>Inflact is another website that allows you to download Instagram Stories and Highlights for free</h3>
-<p>Another website that you can use to download Instagram stories online is Inflact. This website also lets you download the current stories and highlights of any public account, as well as their profile picture, posts, IGTV videos, and reels. Inflact is also free, fast, and easy to use. You don't need to sign up, log in, or provide any personal information. You just need the username or the URL of the account you want to download from.</p>
-<h3>How to use Inflact to download Instagram Stories and Highlights</h3>
-<h4>Step 1: Paste the username or the URL of the account you want to download from</h4>
-<p>To start downloading Instagram stories online with Inflact, go to <a href="">https://inflact.com/downloader/instagram/stories/</a>. On this page, you will see a text box where you can paste the username or the URL of the account you want to download from. For example, if you want to download the stories of <a href="">https://www.instagram.com/buzzfeedtasty/</a>, you can paste "buzzfeedtasty" or "https://www.instagram.com/buzzfeedtasty/" in the text box. Then, click on the "Download" button or press the "Enter" key on your keyboard.</p>
-<h4>Step 2: Click on the "Download" button to see the available stories and highlights</h4>
-<p>After you paste the username or the URL, you will see a list of the available stories and highlights of that account. You can scroll down to see more options, or use the tabs at the top to switch between stories, highlights, profile picture, posts, IGTV videos, and reels. To download a story or highlight, simply click on it. You will see a preview of the story or highlight on the right side of the screen, along with some information such as the date, time, and duration.</p>
-<h4>Step 3: Click on the "Download" button below each story or highlight to save it to your device</h4>
-<p>Once you have selected the story or highlight you want to download, you can click on the "Download" button below it. This will open a new tab where you can see the story or highlight in full size. You can right-click on it and choose "Save image as" or "Save video as" to save it to your device. Alternatively, you can also click on the "Download all" button at the bottom of the screen to download all the stories or highlights of that account at once.</p>
-<h2>Conclusion and FAQs</h2>
-<p>In this article, we have shown you how to download Instagram stories online using two different websites: StorySaver.net and Inflact. Both websites are free, fast, and easy to use, and allow you to download not only the current stories, but also the highlights of any public account. You can use these websites to save your favorite moments, repost them, or use them for other purposes. However, please be respectful of the privacy and rights of the original creators, and do not use their content without their permission or for illegal purposes.</p>
-<p>Here are some frequently asked questions about downloading Instagram stories online:</p>
-<h4>Q: Can I download Instagram stories online from private accounts?</h4>
-<p>A: No, you cannot download Instagram stories online from private accounts. These websites only work for public accounts that anyone can view. If you want to download stories from a private account, you need to follow that account and use a third-party app or extension that can access your Instagram data.</p>
-<h4>Q: Can I download Instagram stories online anonymously?</h4>
-<p>A: Yes, you can download Instagram stories online anonymously. These websites do not require you to log in or provide any personal information. They also do not notify the original creators that you have downloaded their stories or highlights.</p>
-<h4>Q: Can I download Instagram stories online in bulk?</h4>
-<p>A: Yes, you can download Instagram stories online in bulk. With StorySaver.net, you can select multiple stories or highlights and click on the "Save as" button to download them all at once. With Inflact, you can click on the "Download all" button to download all the stories or highlights of an account at once.</p>
-<h4>Q: Can I download Instagram stories online in high quality?</h4>
-<p>A: Yes, you can download Instagram stories online in high quality. With StorySaver.net, you can choose between high, medium, or low quality for your downloads. With Inflact, you can download the stories or highlights in their original quality.</p>
-<h4>Q: Can I download Instagram stories online on any device?</h4>
-<p>A: Yes, you can download Instagram stories online on any device. These websites are compatible with any web browser and any operating system. You can use them on your computer, laptop, tablet, or smartphone.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Stumble Guys Rainbow A Massive Multiplayer Party Game with 17 Unique Levels.md b/spaces/congsaPfin/Manga-OCR/logs/Stumble Guys Rainbow A Massive Multiplayer Party Game with 17 Unique Levels.md
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-<h1>Download Stumble Guys Rainbow: A Guide to the Ultimate Party Game</h1>
-<p>If you are looking for a fun and addictive game that will make you laugh and scream, then you should download Stumble Guys Rainbow. This game is a colorful and chaotic battle royale party game that will test your skills and luck as you race with up to 32 players online through crazy obstacle courses. But that's not all, you can also collect amazing cards featuring your favorite Stumblers and show them off to your friends. In this article, we will tell you everything you need to know about Stumble Guys Rainbow, including how to download it, how to play it, and how to collect the cards.</p>
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-<h2>What is Stumble Guys Rainbow?</h2>
-<h3>A colorful and chaotic battle royale game</h3>
-<p>Stumble Guys Rainbow is a spin-off of the popular game Stumble Guys, which is an online battle royale party game. In this game, you can choose from hundreds of different outfits and skins to customize your character, and then join a match with up to 32 players online. Your goal is to run, jump, dash, slide, and avoid obstacles as you try to reach the finish line before everyone else. The game has 17 unique obstacle courses that will challenge your reflexes and coordination, as well as different modes such as solo, duo, squad, and custom. You can also chat with other players, make friends, and join clans. The game is full of fun and laughter, as you will witness hilarious moments and epic fails along the way.</p>
-<h3>A limited edition card collection</h3>
-<p>Stumble Guys Rainbow is also a limited edition card collection that showcases your favorite Stumblers in a rainbow theme. The card collection has 162 cards in total, including 128 standard cards, 24 legendary gold cards, 10 ruby special cards, and 16 limited rainbow edition cards. The cards feature characters such as Mr. Stumble, the epic Megalodon, the legendary Leonidas, the special Ruby Cupid, and many more. You can collect the cards by buying packs or by playing the game. The cards have different benefits such as boosting your stats, unlocking new outfits and skins, and giving you special abilities. You can also trade the cards with other players or display them in a binder.</p>
-<h2>How to download Stumble Guys Rainbow?</h2>
-<h3>For Android and iOS devices</h3>
-<p>If you want to download Stumble Guys Rainbow on your Android or iOS device, you can do so by following these simple steps:</p>
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-<li>Go to the Google Play Store or the App Store on your device.</li>
-<li>Search for "Stumble Guys" and tap on the game icon.</li>
-<li>Tap on "Install" or "Get" and wait for the game to download.</li>
-<li>Open the game and enjoy!</li>
-</ol>
-<p>Note that you need an internet connection to play the game online.</p>
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-<p>If you want to download Stumble Guys Rainbow on your PC or Mac computer, you can do so by using an emulator. An emulator is a software that allows you to run Android or iOS apps on your computer. Here are the steps to download Stumble Guys Rainbow using an emulator:</p>
-<ol>
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-<li>Open the emulator and sign in with your Google account or create a new one.</li>
-<li>Go to the Google Play Store or the App Store on the emulator and search for "Stumble Guys".</li>
-<li>Tap on the game icon and then tap on "Install" and wait for the game to download.</li>
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-<h2>How to play Stumble Guys Rainbow?</h2>
-<h3>The basics of the game</h3>
-<p>Stumble Guys Rainbow is a very easy game to play, but it can also be very challenging. Here are the basic controls and rules of the game:</p>
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-<li>To move your character, you can use the joystick on the left side of the screen. You can also swipe on the screen to change the camera angle.</li>
-<li>To jump, you can tap on the button on the right side of the screen. You can also double tap to dash forward.</li>
-<li>To slide, you can hold the jump button while moving. This can help you avoid some obstacles or go under them.</li>
-<li>To interact with some objects, such as buttons, levers, or doors, you can tap on them when you are near them.</li>
-<li>To chat with other players, you can tap on the chat icon on the top right corner of the screen. You can also use emojis and stickers to express yourself.</li>
-<li>The game has 17 different obstacle courses that are randomly selected for each match. Each course has three rounds, and each round has a different number of players that can qualify. The last round is always a final showdown where only one player can win.</li>
-<li>You can earn coins and gems by playing the game, which you can use to buy outfits, skins, and card packs. You can also earn trophies by winning matches, which can increase your rank and unlock new rewards.</li>
-</ul>
-<h3>The different modes and maps</h3>
-<p>Stumble Guys Rainbow has four different modes that you can choose from:</p>
-<ul>
-<li>Solo: This is the default mode where you play alone against 31 other players online. You can join a random match or create your own with a code.</li>
-<li>Duo: This is a mode where you can team up with a friend or a random player online. You and your partner will share the same outfit and skin, and you will have to work together to reach the finish line. You can also chat with your partner using voice chat.</li>
-<li>Squad: This is a mode where you can form a squad of four players online. You and your squad members will have different outfits and skins, but you will have the same color. You will have to cooperate with your squad to beat the other squads. You can also chat with your squad using voice chat.</li>
-<li>Custom: This is a mode where you can create your own match with your own rules. You can invite up to 31 friends or random players online, and you can choose the number of rounds, the maps, and the difficulty level. You can also chat with everyone using voice chat.</li>
-</ul>
-<p>Stumble Guys Rainbow has 17 different maps that are randomly selected for each match. Each map has its own theme, obstacles, and hazards that will make your race more fun and challenging. Here are some examples of the maps:</p>
-<ul>
-<li>Candyland: A sweet map filled with candy, chocolate, and ice cream. Watch out for the giant lollipops, candy canes, and gummy bears that will try to knock you off.</li>
-<li>Pirate Bay: A pirate-themed map with ships, cannons, and treasure chests. Beware of the sharks, crocodiles, and octopuses that will try to bite you.</li>
-<li>Space Station: A futuristic map with rockets, lasers, and robots. Avoid the asteroids, meteors, and aliens that will try to blast you.</li>
-<li>Jungle Fever: A tropical map with trees, vines, and fruits. Dodge the monkeys, snakes, and spiders that will try to catch you.</li>
-<li>Nightmare: A spooky map with ghosts, zombies, and skeletons. Escape from the haunted house, graveyard, and dungeon that will try to scare you.</li>
-</ul> <h3>The best tips and tricks</h3>
-<p>Stumble Guys Rainbow is a game that requires both skill and luck, but there are some tips and tricks that can help you improve your performance and increase your chances of winning. Here are some of them:</p>
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-<li>Be aware of your surroundings. Pay attention to the obstacles, hazards, and other players that can affect your race. Use the camera angle to see what's ahead and plan your moves accordingly.</li>
-<li>Be flexible and adaptable. Don't stick to one path or strategy, as the game is unpredictable and dynamic. Try different routes, shortcuts, and tactics to find the best way to reach the finish line.</li>
-<li>Be smart and strategic. Use your dash and slide abilities wisely, as they can help you overcome some obstacles or gain an advantage over other players. However, don't overuse them, as they can also make you lose control or waste time.</li>
-<li>Be aggressive and assertive. Don't be afraid to push, bump, or grab other players, as they can slow you down or block your way. However, don't be too rude or reckless, as they can also retaliate or report you.</li>
-<li>Be patient and persistent. Don't give up or rage quit if you fail or fall behind, as the game is full of surprises and opportunities. Keep trying and learning from your mistakes, and you will eventually succeed.</li>
-</ul>
-<h2>How to collect Stumble Guys Rainbow cards?</h2>
-<h3>The types and rarities of the cards</h3>
-<p>Stumble Guys Rainbow cards are collectible items that feature your favorite Stumblers in a rainbow theme. The cards have different types and rarities, which affect their appearance and value. Here are the types and rarities of the cards:</p>
-<table>
-<tr>
-<th>Type</th>
-<th>Rarity</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Standard</td>
-<td>Common</td>
-<td>The most basic and common cards that have a white border and a plain background.</td>
-</tr>
-<tr>
-<td>Gold</td>
-<td>Legendary</td>
-<td>The most rare and valuable cards that have a gold border and a shiny background.</td>
-</tr>
-<tr>
-<td>Ruby</td>
-<td>Special</td>
-<td>The most unique and special cards that have a ruby border and a heart-shaped background.</td>
-</tr>
-<tr>
-<td>Rainbow</td>
-<td>Limited</td>
-<td>The most exclusive and limited cards that have a rainbow border and a rainbow background.</td>
-</tr>
-</table> <h3>The ways to get the cards</h3>
-<p>Stumble Guys Rainbow cards are not easy to get, as they are very rare and limited. However, there are some ways that you can try to get them, such as:</p>
-<ul>
-<li>Buying card packs. You can buy card packs using coins or gems, which you can earn by playing the game or by watching ads. Each card pack contains four random cards, and you can choose from different types of packs, such as standard, gold, ruby, or rainbow. The more expensive the pack, the higher the chance of getting rare cards.</li>
-<li>Playing the game. You can also get cards by playing the game and completing certain tasks or achievements. For example, you can get cards by winning matches, reaching a certain rank, joining a clan, or participating in events. The more you play, the more cards you can get.</li>
-<li>Trading with other players. You can also get cards by trading with other players online. You can offer your own cards or coins or gems in exchange for the cards you want. However, be careful and fair when trading, as some players may try to scam you or rip you off.</li>
-</ul>
-<h3>The benefits of the cards</h3>
-<p>Stumble Guys Rainbow cards are not just for collecting and showing off, they also have some benefits that can help you in the game, such as:</p>
-<ul>
-<li>Boosting your stats. Each card has a number that represents its power level, which can range from 1 to 10. The higher the power level, the more it can boost your stats, such as speed, stamina, agility, and luck. You can equip up to four cards at a time to increase your performance in the game.</li>
-<li>Unlocking new outfits and skins. Each card also has a picture that represents its character, which can be a Stumbler or an outfit or a skin. If you collect enough cards of the same character, you can unlock that character as an outfit or a skin that you can use in the game.</li>
-<li>Giving you special abilities. Some cards also have special abilities that can give you an edge in the game. For example, some cards can make you invincible for a short time, some cards can make you fly for a short distance, and some cards can make you explode and knock out other players.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Stumble Guys Rainbow is a game that will make you have a blast with your friends and other players online. It is a colorful and chaotic battle royale party game that will test your skills and luck as you race through crazy obstacle courses. It is also a limited edition card collection that will let you collect amazing cards featuring your favorite Stumblers in a rainbow theme. You can download Stumble Guys Rainbow on your Android or iOS device or on your PC or Mac computer using an emulator. You can also play Stumble Guys Rainbow in different modes and maps, and use different tips and tricks to win. You can also collect Stumble Guys Rainbow cards by buying packs, playing the game, or trading with other players, and use them to boost your stats, unlock new outfits and skins, and give you special abilities.</p>
-<p>If you are looking for a fun and addictive game that will make you laugh and scream, then you should download Stumble Guys Rainbow today!</p>
- <h2>FAQs</h2>
-<h4>Q: Is Stumble Guys Rainbow free to play?</h4>
-<p>A: Yes, Stumble Guys Rainbow is free to play, but it also has some in-app purchases that can enhance your gaming experience.</p>
-<h4>Q: How long is Stumble Guys Rainbow available?</h4>
-<p>A: Stumble Guys Rainbow is a limited edition game that is only available for a short period of time. The exact date of its end is not announced yet, but it is expected to be soon.</p>
-<h4>Q: Can I play Stumble Guys Rainbow offline?</h4>
-<p>A: No, Stumble Guys Rainbow is an online game that requires an internet connection to play.</p>
-<h4>Q: Can I play Stumble Guys Rainbow with my friends?</h4>
-<p>A: Yes, Stumble Guys Rainbow is a multiplayer game that allows you to play with your friends online. You can team up with them in duo or squad mode, or invite them to your custom match.</p>
-<h4>Q: How many cards are there in Stumble Guys Rainbow?</h4>
-<p>A: There are 162 cards in total in Stumble Guys Rainbow, including 128 standard cards, 24 legendary gold cards, 10 ruby special cards, and 16 limited rainbow edition cards. You can see the complete list of the cards in the game menu.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/The ultimate guide to happymod-multilang-2-7-5 (1).apk - Everything you need to know.md b/spaces/congsaPfin/Manga-OCR/logs/The ultimate guide to happymod-multilang-2-7-5 (1).apk - Everything you need to know.md
deleted file mode 100644
index 35c92a4ee289e35710aff7e461f806d293e4ab46..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/The ultimate guide to happymod-multilang-2-7-5 (1).apk - Everything you need to know.md
+++ /dev/null
@@ -1,154 +0,0 @@
-<br />
-<h1>What is happymod-multilang-2-7-5 (1).apk and why you should download it</h1>
- <p>If you are a fan of modded apps and games, you might have heard of HappyMod, the biggest mod apk downloader. But do you know what happymod-multilang-2-7-5 (1).apk is and why you should download it? In this article, we will tell you everything you need to know about this amazing app and how you can use it to enjoy unlimited fun and entertainment with your favorite apps and games.</p>
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- <p>HappyMod is an app store that provides thousands of modded apps and games for Android devices. Modded apps and games are modified versions of the original ones that have extra features, unlocked content, unlimited resources, or other enhancements that make them more fun, easy, or convenient to use. For example, you can find mods that give you unlimited money, free shopping, god mode, high damage, premium access, or ad-free experience in various apps and games.</p>
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- <p>HappyMod is not just a simple app store. It has many features and benefits that make it stand out from other mod apk stores. Here are some of them:</p>
- <ul>
-<li>HappyMod provides many versions of mod for one game or app, so you can choose the one that suits your preferences or device compatibility.</li>
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- <p>HappyMod has a huge collection of mods for various apps and games. You can find mods for almost any genre or category you can think of. Here are some examples:</p>
- <ul>
-<li>Action: Among Us, PUBG Mobile, Garena Free Fire, Call of Duty Mobile, etc.</li>
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-<h2>What is happymod-multilang-2-7-5 (1).apk and how to download it</h2>
- <p>Now that you know what HappyMod is and how it works, you might be wondering what happymod-multilang-2-7-5 (1).apk is and how to download it. Well, happymod-multilang-2-7-5 (1).apk is the latest version of HappyMod that supports multiple languages, including English, Spanish, Portuguese, Indonesian, Arabic, and more. This means that you can use HappyMod in your preferred language and enjoy a better user experience.</p>
- <h3>The details and specifications of happymod-multilang-2-7-5 (1).apk</h3>
- <p>Here are some of the details and specifications of happymod-multilang-2-7-5 (1).apk that you should know before downloading it:</p>
- <ul>
-<li>File name: happymod-multilang-2-7-5 (1).apk</li>
-<li>File size: 9.4 MB</li>
-<li>Version: 2.7.5</li>
-<li>Updated: June 22, 2023</li>
-<li>Developer: AIO Team</li>
-<li>Requirements: Android 4.1 or higher</li>
-<li>Permissions: Access network state, internet, read external storage, write external storage, etc.</li>
-</ul>
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- <p>If you want to download and install happymod-multilang-2-7-5 (1).apk on your Android device, you need to follow these steps and instructions:</p>
- <ol>
-<li>Go to the official website of HappyMod or click on this link to download happymod-multilang-2-7-5 (1).apk.</li>
-<li>Once the download is complete, locate the apk file in your device's file manager and tap on it to start the installation process.</li>
-<li>If you see a warning message that says "Install blocked", go to your device's settings and enable "Unknown sources" or "Allow from this source" option.</li>
-<li>Follow the on-screen prompts and accept the terms and conditions to complete the installation.</li>
-<li>Launch HappyMod from your app drawer and choose your preferred language from the menu.</li>
-<li>Congratulations! You have successfully installed happymod-multilang-2-7-5 (1).apk on your device. Now you can start downloading and enjoying modded apps and games.</li>
-</ol>
- <h2>How to use happymod-multilang-2-7-5 (1).apk to download and enjoy modded apps and games</h2>
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-<li>Enjoy the modded features and have fun!</li>
-</ul>
- <h3>The precautions and safety measures to avoid any risks or issues with modded apps and games</h3>
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- <ul>
-<li>Download mods only from trusted sources like HappyMod. Do not download mods from unknown or suspicious websites or links.</li>
-<li>Backup your original app or game data before installing a mod. You can use apps like Titanium Backup or Helium Backup for this purpose.</li>
-<h2>Conclusion</h2>
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- <table>
-<tr>
-<th>Mod apk store</th>
-<th>Language support</th>
-<th>Mod collection</th>
-<th>User community</th>
-<th>Quality assurance</th>
-<th>Download speed</th>
-</tr>
-<tr>
-<td>HappyMod</td>
-<td>Multiple languages</td>
-<td>Thousands of mods</td>
-<td>Large and active</td>
-<td>High</td>
-<td>Fast</td>
-</tr>
-<tr>
-<td>ACMarket</td>
-<td>English only</td>
-<td>Hundreds of mods</td>
-<td>Small and inactive</td>
-<td>Low</td>
-<td>Slow</td>
-</tr>
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-<td>Aptoide</td>
-<td>Multiple languages</td>
-<td>Few mods</td>
-<td>Medium and moderate</td>
-<td>Medium</td>
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-<tr>
-<td>Mobilism</td>
-<td>English only</td>
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-<td>Medium and moderate</td>
-<td>Medium</td ><td>Slow</td ></tr>
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-<td>Lucky Patcher</td ><td>Multiple languages</td ><td>No mods, only patches</td ><td>N/A</td ><td>N/A</td ><td>N/A</td ></tr ></table>
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diff --git a/spaces/contluForse/HuggingGPT/assets/Durood e Tanjeena PDF Free Download A Complete Guide to the Darood of Salvation.md b/spaces/contluForse/HuggingGPT/assets/Durood e Tanjeena PDF Free Download A Complete Guide to the Darood of Salvation.md
deleted file mode 100644
index 3d675bbe320bf8514b37e8600a40b52feae8f54e..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Durood e Tanjeena PDF Free Download A Complete Guide to the Darood of Salvation.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>duroodetanjeenapdffreedownload</h2><br /><p><b><b>Download File</b> › <a href="https://ssurll.com/2uzyFh">https://ssurll.com/2uzyFh</a></b></p><br /><br />
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/densenet.py b/spaces/cooelf/Multimodal-CoT/timm/models/densenet.py
deleted file mode 100644
index 38a19727874bfb08cf20281ec8e85b8cc7c60688..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/densenet.py
+++ /dev/null
@@ -1,387 +0,0 @@
-"""Pytorch Densenet implementation w/ tweaks
-This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
-fixed kwargs passthrough and addition of dynamic global avg/max pool.
-"""
-import re
-from collections import OrderedDict
-from functools import partial
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as cp
-from torch.jit.annotations import List
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import BatchNormAct2d, create_norm_act, BlurPool2d, create_classifier
-from .registry import register_model
-
-__all__ = ['DenseNet']
-
-
-def _cfg(url=''):
- return {
- 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
- 'crop_pct': 0.875, 'interpolation': 'bicubic',
- 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
- 'first_conv': 'features.conv0', 'classifier': 'classifier',
- }
-
-
-default_cfgs = {
- 'densenet121': _cfg(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenet121_ra-50efcf5c.pth'),
- 'densenet121d': _cfg(url=''),
- 'densenetblur121d': _cfg(
- url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenetblur121d_ra-100dcfbc.pth'),
- 'densenet169': _cfg(url='https://download.pytorch.org/models/densenet169-b2777c0a.pth'),
- 'densenet201': _cfg(url='https://download.pytorch.org/models/densenet201-c1103571.pth'),
- 'densenet161': _cfg(url='https://download.pytorch.org/models/densenet161-8d451a50.pth'),
- 'densenet264': _cfg(url=''),
- 'densenet264d_iabn': _cfg(url=''),
- 'tv_densenet121': _cfg(url='https://download.pytorch.org/models/densenet121-a639ec97.pth'),
-}
-
-
-class DenseLayer(nn.Module):
- def __init__(self, num_input_features, growth_rate, bn_size, norm_layer=BatchNormAct2d,
- drop_rate=0., memory_efficient=False):
- super(DenseLayer, self).__init__()
- self.add_module('norm1', norm_layer(num_input_features)),
- self.add_module('conv1', nn.Conv2d(
- num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)),
- self.add_module('norm2', norm_layer(bn_size * growth_rate)),
- self.add_module('conv2', nn.Conv2d(
- bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),
- self.drop_rate = float(drop_rate)
- self.memory_efficient = memory_efficient
-
- def bottleneck_fn(self, xs):
- # type: (List[torch.Tensor]) -> torch.Tensor
- concated_features = torch.cat(xs, 1)
- bottleneck_output = self.conv1(self.norm1(concated_features)) # noqa: T484
- return bottleneck_output
-
- # todo: rewrite when torchscript supports any
- def any_requires_grad(self, x):
- # type: (List[torch.Tensor]) -> bool
- for tensor in x:
- if tensor.requires_grad:
- return True
- return False
-
- @torch.jit.unused # noqa: T484
- def call_checkpoint_bottleneck(self, x):
- # type: (List[torch.Tensor]) -> torch.Tensor
- def closure(*xs):
- return self.bottleneck_fn(xs)
-
- return cp.checkpoint(closure, *x)
-
- @torch.jit._overload_method # noqa: F811
- def forward(self, x):
- # type: (List[torch.Tensor]) -> (torch.Tensor)
- pass
-
- @torch.jit._overload_method # noqa: F811
- def forward(self, x):
- # type: (torch.Tensor) -> (torch.Tensor)
- pass
-
- # torchscript does not yet support *args, so we overload method
- # allowing it to take either a List[Tensor] or single Tensor
- def forward(self, x): # noqa: F811
- if isinstance(x, torch.Tensor):
- prev_features = [x]
- else:
- prev_features = x
-
- if self.memory_efficient and self.any_requires_grad(prev_features):
- if torch.jit.is_scripting():
- raise Exception("Memory Efficient not supported in JIT")
- bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
- else:
- bottleneck_output = self.bottleneck_fn(prev_features)
-
- new_features = self.conv2(self.norm2(bottleneck_output))
- if self.drop_rate > 0:
- new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
- return new_features
-
-
-class DenseBlock(nn.ModuleDict):
- _version = 2
-
- def __init__(self, num_layers, num_input_features, bn_size, growth_rate, norm_layer=nn.ReLU,
- drop_rate=0., memory_efficient=False):
- super(DenseBlock, self).__init__()
- for i in range(num_layers):
- layer = DenseLayer(
- num_input_features + i * growth_rate,
- growth_rate=growth_rate,
- bn_size=bn_size,
- norm_layer=norm_layer,
- drop_rate=drop_rate,
- memory_efficient=memory_efficient,
- )
- self.add_module('denselayer%d' % (i + 1), layer)
-
- def forward(self, init_features):
- features = [init_features]
- for name, layer in self.items():
- new_features = layer(features)
- features.append(new_features)
- return torch.cat(features, 1)
-
-
-class DenseTransition(nn.Sequential):
- def __init__(self, num_input_features, num_output_features, norm_layer=nn.BatchNorm2d, aa_layer=None):
- super(DenseTransition, self).__init__()
- self.add_module('norm', norm_layer(num_input_features))
- self.add_module('conv', nn.Conv2d(
- num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
- if aa_layer is not None:
- self.add_module('pool', aa_layer(num_output_features, stride=2))
- else:
- self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
-
-
-class DenseNet(nn.Module):
- r"""Densenet-BC model class, based on
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
-
- Args:
- growth_rate (int) - how many filters to add each layer (`k` in paper)
- block_config (list of 4 ints) - how many layers in each pooling block
- bn_size (int) - multiplicative factor for number of bottle neck layers
- (i.e. bn_size * k features in the bottleneck layer)
- drop_rate (float) - dropout rate after each dense layer
- num_classes (int) - number of classification classes
- memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
- but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
- """
-
- def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), bn_size=4, stem_type='',
- num_classes=1000, in_chans=3, global_pool='avg',
- norm_layer=BatchNormAct2d, aa_layer=None, drop_rate=0, memory_efficient=False,
- aa_stem_only=True):
- self.num_classes = num_classes
- self.drop_rate = drop_rate
- super(DenseNet, self).__init__()
-
- # Stem
- deep_stem = 'deep' in stem_type # 3x3 deep stem
- num_init_features = growth_rate * 2
- if aa_layer is None:
- stem_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- else:
- stem_pool = nn.Sequential(*[
- nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
- aa_layer(channels=num_init_features, stride=2)])
- if deep_stem:
- stem_chs_1 = stem_chs_2 = growth_rate
- if 'tiered' in stem_type:
- stem_chs_1 = 3 * (growth_rate // 4)
- stem_chs_2 = num_init_features if 'narrow' in stem_type else 6 * (growth_rate // 4)
- self.features = nn.Sequential(OrderedDict([
- ('conv0', nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False)),
- ('norm0', norm_layer(stem_chs_1)),
- ('conv1', nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False)),
- ('norm1', norm_layer(stem_chs_2)),
- ('conv2', nn.Conv2d(stem_chs_2, num_init_features, 3, stride=1, padding=1, bias=False)),
- ('norm2', norm_layer(num_init_features)),
- ('pool0', stem_pool),
- ]))
- else:
- self.features = nn.Sequential(OrderedDict([
- ('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
- ('norm0', norm_layer(num_init_features)),
- ('pool0', stem_pool),
- ]))
- self.feature_info = [
- dict(num_chs=num_init_features, reduction=2, module=f'features.norm{2 if deep_stem else 0}')]
- current_stride = 4
-
- # DenseBlocks
- num_features = num_init_features
- for i, num_layers in enumerate(block_config):
- block = DenseBlock(
- num_layers=num_layers,
- num_input_features=num_features,
- bn_size=bn_size,
- growth_rate=growth_rate,
- norm_layer=norm_layer,
- drop_rate=drop_rate,
- memory_efficient=memory_efficient
- )
- module_name = f'denseblock{(i + 1)}'
- self.features.add_module(module_name, block)
- num_features = num_features + num_layers * growth_rate
- transition_aa_layer = None if aa_stem_only else aa_layer
- if i != len(block_config) - 1:
- self.feature_info += [
- dict(num_chs=num_features, reduction=current_stride, module='features.' + module_name)]
- current_stride *= 2
- trans = DenseTransition(
- num_input_features=num_features, num_output_features=num_features // 2,
- norm_layer=norm_layer, aa_layer=transition_aa_layer)
- self.features.add_module(f'transition{i + 1}', trans)
- num_features = num_features // 2
-
- # Final batch norm
- self.features.add_module('norm5', norm_layer(num_features))
-
- self.feature_info += [dict(num_chs=num_features, reduction=current_stride, module='features.norm5')]
- self.num_features = num_features
-
- # Linear layer
- self.global_pool, self.classifier = create_classifier(
- self.num_features, self.num_classes, pool_type=global_pool)
-
- # Official init from torch repo.
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight)
- elif isinstance(m, nn.BatchNorm2d):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Linear):
- nn.init.constant_(m.bias, 0)
-
- def get_classifier(self):
- return self.classifier
-
- def reset_classifier(self, num_classes, global_pool='avg'):
- self.num_classes = num_classes
- self.global_pool, self.classifier = create_classifier(
- self.num_features, self.num_classes, pool_type=global_pool)
-
- def forward_features(self, x):
- return self.features(x)
-
- def forward(self, x):
- x = self.forward_features(x)
- x = self.global_pool(x)
- # both classifier and block drop?
- # if self.drop_rate > 0.:
- # x = F.dropout(x, p=self.drop_rate, training=self.training)
- x = self.classifier(x)
- return x
-
-
-def _filter_torchvision_pretrained(state_dict):
- pattern = re.compile(
- r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
-
- for key in list(state_dict.keys()):
- res = pattern.match(key)
- if res:
- new_key = res.group(1) + res.group(2)
- state_dict[new_key] = state_dict[key]
- del state_dict[key]
- return state_dict
-
-
-def _create_densenet(variant, growth_rate, block_config, pretrained, **kwargs):
- kwargs['growth_rate'] = growth_rate
- kwargs['block_config'] = block_config
- return build_model_with_cfg(
- DenseNet, variant, pretrained,
- default_cfg=default_cfgs[variant],
- feature_cfg=dict(flatten_sequential=True), pretrained_filter_fn=_filter_torchvision_pretrained,
- **kwargs)
-
-
-@register_model
-def densenet121(pretrained=False, **kwargs):
- r"""Densenet-121 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def densenetblur121d(pretrained=False, **kwargs):
- r"""Densenet-121 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenetblur121d', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep',
- aa_layer=BlurPool2d, **kwargs)
- return model
-
-
-@register_model
-def densenet121d(pretrained=False, **kwargs):
- r"""Densenet-121 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
- pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def densenet169(pretrained=False, **kwargs):
- r"""Densenet-169 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenet169', growth_rate=32, block_config=(6, 12, 32, 32), pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def densenet201(pretrained=False, **kwargs):
- r"""Densenet-201 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenet201', growth_rate=32, block_config=(6, 12, 48, 32), pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def densenet161(pretrained=False, **kwargs):
- r"""Densenet-161 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenet161', growth_rate=48, block_config=(6, 12, 36, 24), pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def densenet264(pretrained=False, **kwargs):
- r"""Densenet-264 model from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'densenet264', growth_rate=48, block_config=(6, 12, 64, 48), pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def densenet264d_iabn(pretrained=False, **kwargs):
- r"""Densenet-264 model with deep stem and Inplace-ABN
- """
- def norm_act_fn(num_features, **kwargs):
- return create_norm_act('iabn', num_features, **kwargs)
- model = _create_densenet(
- 'densenet264d_iabn', growth_rate=48, block_config=(6, 12, 64, 48), stem_type='deep',
- norm_layer=norm_act_fn, pretrained=pretrained, **kwargs)
- return model
-
-
-@register_model
-def tv_densenet121(pretrained=False, **kwargs):
- r"""Densenet-121 model with original Torchvision weights, from
- `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
- """
- model = _create_densenet(
- 'tv_densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
- return model
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/setup.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/setup.py
deleted file mode 100644
index 3b57ad313ac8f9b6586892142da8ba943e516cec..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/setup.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-import os
-import glob
-
-import torch
-
-from torch.utils.cpp_extension import CUDA_HOME
-from torch.utils.cpp_extension import CppExtension
-from torch.utils.cpp_extension import CUDAExtension
-
-from setuptools import find_packages
-from setuptools import setup
-
-requirements = ["torch", "torchvision"]
-
-def get_extensions():
- this_dir = os.path.dirname(os.path.abspath(__file__))
- extensions_dir = os.path.join(this_dir, "src")
-
- main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
- source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
- source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
-
- sources = main_file + source_cpu
- extension = CppExtension
- extra_compile_args = {"cxx": []}
- define_macros = []
-
- # Force cuda since torch ask for a device, not if cuda is in fact available.
- if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:
- extension = CUDAExtension
- sources += source_cuda
- define_macros += [("WITH_CUDA", None)]
- extra_compile_args["nvcc"] = [
- "-DCUDA_HAS_FP16=1",
- "-D__CUDA_NO_HALF_OPERATORS__",
- "-D__CUDA_NO_HALF_CONVERSIONS__",
- "-D__CUDA_NO_HALF2_OPERATORS__",
- ]
- else:
- if CUDA_HOME is None:
- raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')
- else:
- raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')
-
- sources = [os.path.join(extensions_dir, s) for s in sources]
- include_dirs = [extensions_dir]
- ext_modules = [
- extension(
- "MultiScaleDeformableAttention",
- sources,
- include_dirs=include_dirs,
- define_macros=define_macros,
- extra_compile_args=extra_compile_args,
- )
- ]
- return ext_modules
-
-setup(
- name="MultiScaleDeformableAttention",
- version="1.0",
- author="Weijie Su",
- url="https://github.com/fundamentalvision/Deformable-DETR",
- description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
- packages=find_packages(exclude=("configs", "tests",)),
- ext_modules=get_extensions(),
- cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/context_block.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/context_block.py
deleted file mode 100644
index d60fdb904c749ce3b251510dff3cc63cea70d42e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/context_block.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn
-
-from ..utils import constant_init, kaiming_init
-from .registry import PLUGIN_LAYERS
-
-
-def last_zero_init(m):
- if isinstance(m, nn.Sequential):
- constant_init(m[-1], val=0)
- else:
- constant_init(m, val=0)
-
-
-@PLUGIN_LAYERS.register_module()
-class ContextBlock(nn.Module):
- """ContextBlock module in GCNet.
-
- See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
- (https://arxiv.org/abs/1904.11492) for details.
-
- Args:
- in_channels (int): Channels of the input feature map.
- ratio (float): Ratio of channels of transform bottleneck
- pooling_type (str): Pooling method for context modeling.
- Options are 'att' and 'avg', stand for attention pooling and
- average pooling respectively. Default: 'att'.
- fusion_types (Sequence[str]): Fusion method for feature fusion,
- Options are 'channels_add', 'channel_mul', stand for channelwise
- addition and multiplication respectively. Default: ('channel_add',)
- """
-
- _abbr_ = 'context_block'
-
- def __init__(self,
- in_channels,
- ratio,
- pooling_type='att',
- fusion_types=('channel_add', )):
- super(ContextBlock, self).__init__()
- assert pooling_type in ['avg', 'att']
- assert isinstance(fusion_types, (list, tuple))
- valid_fusion_types = ['channel_add', 'channel_mul']
- assert all([f in valid_fusion_types for f in fusion_types])
- assert len(fusion_types) > 0, 'at least one fusion should be used'
- self.in_channels = in_channels
- self.ratio = ratio
- self.planes = int(in_channels * ratio)
- self.pooling_type = pooling_type
- self.fusion_types = fusion_types
- if pooling_type == 'att':
- self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1)
- self.softmax = nn.Softmax(dim=2)
- else:
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
- if 'channel_add' in fusion_types:
- self.channel_add_conv = nn.Sequential(
- nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
- nn.LayerNorm([self.planes, 1, 1]),
- nn.ReLU(inplace=True), # yapf: disable
- nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
- else:
- self.channel_add_conv = None
- if 'channel_mul' in fusion_types:
- self.channel_mul_conv = nn.Sequential(
- nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
- nn.LayerNorm([self.planes, 1, 1]),
- nn.ReLU(inplace=True), # yapf: disable
- nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
- else:
- self.channel_mul_conv = None
- self.reset_parameters()
-
- def reset_parameters(self):
- if self.pooling_type == 'att':
- kaiming_init(self.conv_mask, mode='fan_in')
- self.conv_mask.inited = True
-
- if self.channel_add_conv is not None:
- last_zero_init(self.channel_add_conv)
- if self.channel_mul_conv is not None:
- last_zero_init(self.channel_mul_conv)
-
- def spatial_pool(self, x):
- batch, channel, height, width = x.size()
- if self.pooling_type == 'att':
- input_x = x
- # [N, C, H * W]
- input_x = input_x.view(batch, channel, height * width)
- # [N, 1, C, H * W]
- input_x = input_x.unsqueeze(1)
- # [N, 1, H, W]
- context_mask = self.conv_mask(x)
- # [N, 1, H * W]
- context_mask = context_mask.view(batch, 1, height * width)
- # [N, 1, H * W]
- context_mask = self.softmax(context_mask)
- # [N, 1, H * W, 1]
- context_mask = context_mask.unsqueeze(-1)
- # [N, 1, C, 1]
- context = torch.matmul(input_x, context_mask)
- # [N, C, 1, 1]
- context = context.view(batch, channel, 1, 1)
- else:
- # [N, C, 1, 1]
- context = self.avg_pool(x)
-
- return context
-
- def forward(self, x):
- # [N, C, 1, 1]
- context = self.spatial_pool(x)
-
- out = x
- if self.channel_mul_conv is not None:
- # [N, C, 1, 1]
- channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))
- out = out * channel_mul_term
- if self.channel_add_conv is not None:
- # [N, C, 1, 1]
- channel_add_term = self.channel_add_conv(context)
- out = out + channel_add_term
-
- return out
diff --git a/spaces/cymic/Waifu_Diffusion_Webui/modules/memmon.py b/spaces/cymic/Waifu_Diffusion_Webui/modules/memmon.py
deleted file mode 100644
index 9fb9b687caf20eaa87b95676b500eccd578cf23e..0000000000000000000000000000000000000000
--- a/spaces/cymic/Waifu_Diffusion_Webui/modules/memmon.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import threading
-import time
-from collections import defaultdict
-
-import torch
-
-
-class MemUsageMonitor(threading.Thread):
- run_flag = None
- device = None
- disabled = False
- opts = None
- data = None
-
- def __init__(self, name, device, opts):
- threading.Thread.__init__(self)
- self.name = name
- self.device = device
- self.opts = opts
-
- self.daemon = True
- self.run_flag = threading.Event()
- self.data = defaultdict(int)
-
- try:
- torch.cuda.mem_get_info()
- torch.cuda.memory_stats(self.device)
- except Exception as e: # AMD or whatever
- print(f"Warning: caught exception '{e}', memory monitor disabled")
- self.disabled = True
-
- def run(self):
- if self.disabled:
- return
-
- while True:
- self.run_flag.wait()
-
- torch.cuda.reset_peak_memory_stats()
- self.data.clear()
-
- if self.opts.memmon_poll_rate <= 0:
- self.run_flag.clear()
- continue
-
- self.data["min_free"] = torch.cuda.mem_get_info()[0]
-
- while self.run_flag.is_set():
- free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug?
- self.data["min_free"] = min(self.data["min_free"], free)
-
- time.sleep(1 / self.opts.memmon_poll_rate)
-
- def dump_debug(self):
- print(self, 'recorded data:')
- for k, v in self.read().items():
- print(k, -(v // -(1024 ** 2)))
-
- print(self, 'raw torch memory stats:')
- tm = torch.cuda.memory_stats(self.device)
- for k, v in tm.items():
- if 'bytes' not in k:
- continue
- print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
-
- print(torch.cuda.memory_summary())
-
- def monitor(self):
- self.run_flag.set()
-
- def read(self):
- if not self.disabled:
- free, total = torch.cuda.mem_get_info()
- self.data["total"] = total
-
- torch_stats = torch.cuda.memory_stats(self.device)
- self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
- self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
- self.data["system_peak"] = total - self.data["min_free"]
-
- return self.data
-
- def stop(self):
- self.run_flag.clear()
- return self.read()
diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/audio2pose_models/discriminator.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/audio2pose_models/discriminator.py
deleted file mode 100644
index 339c38e4812ff38a810f0f3a1c01812f6d5d78db..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/audio2pose_models/discriminator.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-class ConvNormRelu(nn.Module):
- def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
- kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
- super().__init__()
- if kernel_size is None:
- if downsample:
- kernel_size, stride, padding = 4, 2, 1
- else:
- kernel_size, stride, padding = 3, 1, 1
-
- if conv_type == '2d':
- self.conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- bias=False,
- )
- if norm == 'BN':
- self.norm = nn.BatchNorm2d(out_channels)
- elif norm == 'IN':
- self.norm = nn.InstanceNorm2d(out_channels)
- else:
- raise NotImplementedError
- elif conv_type == '1d':
- self.conv = nn.Conv1d(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- bias=False,
- )
- if norm == 'BN':
- self.norm = nn.BatchNorm1d(out_channels)
- elif norm == 'IN':
- self.norm = nn.InstanceNorm1d(out_channels)
- else:
- raise NotImplementedError
- nn.init.kaiming_normal_(self.conv.weight)
-
- self.act = nn.LeakyReLU(negative_slope=0.2, inplace=False) if leaky else nn.ReLU(inplace=True)
-
- def forward(self, x):
- x = self.conv(x)
- if isinstance(self.norm, nn.InstanceNorm1d):
- x = self.norm(x.permute((0, 2, 1))).permute((0, 2, 1)) # normalize on [C]
- else:
- x = self.norm(x)
- x = self.act(x)
- return x
-
-
-class PoseSequenceDiscriminator(nn.Module):
- def __init__(self, cfg):
- super().__init__()
- self.cfg = cfg
- leaky = self.cfg.MODEL.DISCRIMINATOR.LEAKY_RELU
-
- self.seq = nn.Sequential(
- ConvNormRelu('1d', cfg.MODEL.DISCRIMINATOR.INPUT_CHANNELS, 256, downsample=True, leaky=leaky), # B, 256, 64
- ConvNormRelu('1d', 256, 512, downsample=True, leaky=leaky), # B, 512, 32
- ConvNormRelu('1d', 512, 1024, kernel_size=3, stride=1, padding=1, leaky=leaky), # B, 1024, 16
- nn.Conv1d(1024, 1, kernel_size=3, stride=1, padding=1, bias=True) # B, 1, 16
- )
-
- def forward(self, x):
- x = x.reshape(x.size(0), x.size(1), -1).transpose(1, 2)
- x = self.seq(x)
- x = x.squeeze(1)
- return x
\ No newline at end of file
diff --git a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/libJPG/jpgd.h b/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/libJPG/jpgd.h
deleted file mode 100644
index a1c0cac61839a6f66a42c341f50d5e36faad9a93..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/libJPG/jpgd.h
+++ /dev/null
@@ -1,316 +0,0 @@
-// jpgd.h - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-#ifndef JPEG_DECODER_H
-#define JPEG_DECODER_H
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <setjmp.h>
-
-namespace jpgd
-{
- typedef unsigned char uint8;
- typedef signed short int16;
- typedef unsigned short uint16;
- typedef unsigned int uint;
- typedef signed int int32;
-
- // Loads a JPEG image from a memory buffer or a file.
- // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).
- // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).
- // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.
- // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.
-// BEGIN EPIC MOD
-//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);
- unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);
-// END EPIC MOD
- unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);
-
- // Success/failure error codes.
- enum jpgd_status
- {
- JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,
- JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE,
- JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS,
- JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,
- JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,
- JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,
- JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,
- JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,
- JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM
- };
-
- // Input stream interface.
- // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.
- // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.
- // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.
- // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.
- class jpeg_decoder_stream
- {
- public:
- jpeg_decoder_stream() { }
- virtual ~jpeg_decoder_stream() { }
-
- // The read() method is called when the internal input buffer is empty.
- // Parameters:
- // pBuf - input buffer
- // max_bytes_to_read - maximum bytes that can be written to pBuf
- // pEOF_flag - set this to true if at end of stream (no more bytes remaining)
- // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).
- // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.
- virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;
- };
-
- // stdio FILE stream class.
- class jpeg_decoder_file_stream : public jpeg_decoder_stream
- {
- jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);
- jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);
-
- FILE *m_pFile;
- bool m_eof_flag, m_error_flag;
-
- public:
- jpeg_decoder_file_stream();
- virtual ~jpeg_decoder_file_stream();
-
- bool open(const char *Pfilename);
- void close();
-
- virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
- };
-
- // Memory stream class.
- class jpeg_decoder_mem_stream : public jpeg_decoder_stream
- {
- const uint8 *m_pSrc_data;
- uint m_ofs, m_size;
-
- public:
- jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }
- jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }
-
- virtual ~jpeg_decoder_mem_stream() { }
-
- bool open(const uint8 *pSrc_data, uint size);
- void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }
-
- virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
- };
-
- // Loads JPEG file from a jpeg_decoder_stream.
- unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);
-
- enum
- {
- JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4,
- JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384
- };
-
- typedef int16 jpgd_quant_t;
- typedef int16 jpgd_block_t;
-
- class jpeg_decoder
- {
- public:
- // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.
- // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.
- jpeg_decoder(jpeg_decoder_stream *pStream);
-
- ~jpeg_decoder();
-
- // Call this method after constructing the object to begin decompression.
- // If JPGD_SUCCESS is returned you may then call decode() on each scanline.
- int begin_decoding();
-
- // Returns the next scan line.
- // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1).
- // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).
- // Returns JPGD_SUCCESS if a scan line has been returned.
- // Returns JPGD_DONE if all scan lines have been returned.
- // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.
- int decode(const void** pScan_line, uint* pScan_line_len);
-
- inline jpgd_status get_error_code() const { return m_error_code; }
-
- inline int get_width() const { return m_image_x_size; }
- inline int get_height() const { return m_image_y_size; }
-
- inline int get_num_components() const { return m_comps_in_frame; }
-
- inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }
- inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }
-
- // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).
- inline int get_total_bytes_read() const { return m_total_bytes_read; }
-
- private:
- jpeg_decoder(const jpeg_decoder &);
- jpeg_decoder &operator =(const jpeg_decoder &);
-
- typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);
-
- struct huff_tables
- {
- bool ac_table;
- uint look_up[256];
- uint look_up2[256];
- uint8 code_size[256];
- uint tree[512];
- };
-
- struct coeff_buf
- {
- uint8 *pData;
- int block_num_x, block_num_y;
- int block_len_x, block_len_y;
- int block_size;
- };
-
- struct mem_block
- {
- mem_block *m_pNext;
- size_t m_used_count;
- size_t m_size;
- char m_data[1];
- };
-
- jmp_buf m_jmp_state;
- mem_block *m_pMem_blocks;
- int m_image_x_size;
- int m_image_y_size;
- jpeg_decoder_stream *m_pStream;
- int m_progressive_flag;
- uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];
- uint8* m_huff_num[JPGD_MAX_HUFF_TABLES]; // pointer to number of Huffman codes per bit size
- uint8* m_huff_val[JPGD_MAX_HUFF_TABLES]; // pointer to Huffman codes per bit size
- jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables
- int m_scan_type; // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)
- int m_comps_in_frame; // # of components in frame
- int m_comp_h_samp[JPGD_MAX_COMPONENTS]; // component's horizontal sampling factor
- int m_comp_v_samp[JPGD_MAX_COMPONENTS]; // component's vertical sampling factor
- int m_comp_quant[JPGD_MAX_COMPONENTS]; // component's quantization table selector
- int m_comp_ident[JPGD_MAX_COMPONENTS]; // component's ID
- int m_comp_h_blocks[JPGD_MAX_COMPONENTS];
- int m_comp_v_blocks[JPGD_MAX_COMPONENTS];
- int m_comps_in_scan; // # of components in scan
- int m_comp_list[JPGD_MAX_COMPS_IN_SCAN]; // components in this scan
- int m_comp_dc_tab[JPGD_MAX_COMPONENTS]; // component's DC Huffman coding table selector
- int m_comp_ac_tab[JPGD_MAX_COMPONENTS]; // component's AC Huffman coding table selector
- int m_spectral_start; // spectral selection start
- int m_spectral_end; // spectral selection end
- int m_successive_low; // successive approximation low
- int m_successive_high; // successive approximation high
- int m_max_mcu_x_size; // MCU's max. X size in pixels
- int m_max_mcu_y_size; // MCU's max. Y size in pixels
- int m_blocks_per_mcu;
- int m_max_blocks_per_row;
- int m_mcus_per_row, m_mcus_per_col;
- int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];
- int m_total_lines_left; // total # lines left in image
- int m_mcu_lines_left; // total # lines left in this MCU
- int m_real_dest_bytes_per_scan_line;
- int m_dest_bytes_per_scan_line; // rounded up
- int m_dest_bytes_per_pixel; // 4 (RGB) or 1 (Y)
- huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];
- coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];
- coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];
- int m_eob_run;
- int m_block_y_mcu[JPGD_MAX_COMPONENTS];
- uint8* m_pIn_buf_ofs;
- int m_in_buf_left;
- int m_tem_flag;
- bool m_eof_flag;
- uint8 m_in_buf_pad_start[128];
- uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];
- uint8 m_in_buf_pad_end[128];
- int m_bits_left;
- uint m_bit_buf;
- int m_restart_interval;
- int m_restarts_left;
- int m_next_restart_num;
- int m_max_mcus_per_row;
- int m_max_blocks_per_mcu;
- int m_expanded_blocks_per_mcu;
- int m_expanded_blocks_per_row;
- int m_expanded_blocks_per_component;
- bool m_freq_domain_chroma_upsample;
- int m_max_mcus_per_col;
- uint m_last_dc_val[JPGD_MAX_COMPONENTS];
- jpgd_block_t* m_pMCU_coefficients;
- int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];
- uint8* m_pSample_buf;
- int m_crr[256];
- int m_cbb[256];
- int m_crg[256];
- int m_cbg[256];
- uint8* m_pScan_line_0;
- uint8* m_pScan_line_1;
- jpgd_status m_error_code;
- bool m_ready_flag;
- int m_total_bytes_read;
-
- void free_all_blocks();
- // BEGIN EPIC MOD
- UE_NORETURN void stop_decoding(jpgd_status status);
- // END EPIC MOD
- void *alloc(size_t n, bool zero = false);
- void word_clear(void *p, uint16 c, uint n);
- void prep_in_buffer();
- void read_dht_marker();
- void read_dqt_marker();
- void read_sof_marker();
- void skip_variable_marker();
- void read_dri_marker();
- void read_sos_marker();
- int next_marker();
- int process_markers();
- void locate_soi_marker();
- void locate_sof_marker();
- int locate_sos_marker();
- void init(jpeg_decoder_stream * pStream);
- void create_look_ups();
- void fix_in_buffer();
- void transform_mcu(int mcu_row);
- void transform_mcu_expand(int mcu_row);
- coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);
- inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);
- void load_next_row();
- void decode_next_row();
- void make_huff_table(int index, huff_tables *pH);
- void check_quant_tables();
- void check_huff_tables();
- void calc_mcu_block_order();
- int init_scan();
- void init_frame();
- void process_restart();
- void decode_scan(pDecode_block_func decode_block_func);
- void init_progressive();
- void init_sequential();
- void decode_start();
- void decode_init(jpeg_decoder_stream * pStream);
- void H2V2Convert();
- void H2V1Convert();
- void H1V2Convert();
- void H1V1Convert();
- void gray_convert();
- void expanded_convert();
- void find_eoi();
- inline uint get_char();
- inline uint get_char(bool *pPadding_flag);
- inline void stuff_char(uint8 q);
- inline uint8 get_octet();
- inline uint get_bits(int num_bits);
- inline uint get_bits_no_markers(int numbits);
- inline int huff_decode(huff_tables *pH);
- inline int huff_decode(huff_tables *pH, int& extrabits);
- static inline uint8 clamp(int i);
- static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
- static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
- static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
- static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
- };
-
-} // namespace jpgd
-
-#endif // JPEG_DECODER_H
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/lmdb_util.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/lmdb_util.py
deleted file mode 100644
index e0a10f60ffca2e36ac5f5564aafd70e79d06a723..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/lmdb_util.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import cv2
-import lmdb
-import sys
-from multiprocessing import Pool
-from os import path as osp
-from tqdm import tqdm
-
-
-def make_lmdb_from_imgs(data_path,
- lmdb_path,
- img_path_list,
- keys,
- batch=5000,
- compress_level=1,
- multiprocessing_read=False,
- n_thread=40,
- map_size=None):
- """Make lmdb from images.
-
- Contents of lmdb. The file structure is:
- example.lmdb
- ├── data.mdb
- ├── lock.mdb
- ├── meta_info.txt
-
- The data.mdb and lock.mdb are standard lmdb files and you can refer to
- https://lmdb.readthedocs.io/en/release/ for more details.
-
- The meta_info.txt is a specified txt file to record the meta information
- of our datasets. It will be automatically created when preparing
- datasets by our provided dataset tools.
- Each line in the txt file records 1)image name (with extension),
- 2)image shape, and 3)compression level, separated by a white space.
-
- For example, the meta information could be:
- `000_00000000.png (720,1280,3) 1`, which means:
- 1) image name (with extension): 000_00000000.png;
- 2) image shape: (720,1280,3);
- 3) compression level: 1
-
- We use the image name without extension as the lmdb key.
-
- If `multiprocessing_read` is True, it will read all the images to memory
- using multiprocessing. Thus, your server needs to have enough memory.
-
- Args:
- data_path (str): Data path for reading images.
- lmdb_path (str): Lmdb save path.
- img_path_list (str): Image path list.
- keys (str): Used for lmdb keys.
- batch (int): After processing batch images, lmdb commits.
- Default: 5000.
- compress_level (int): Compress level when encoding images. Default: 1.
- multiprocessing_read (bool): Whether use multiprocessing to read all
- the images to memory. Default: False.
- n_thread (int): For multiprocessing.
- map_size (int | None): Map size for lmdb env. If None, use the
- estimated size from images. Default: None
- """
-
- assert len(img_path_list) == len(keys), ('img_path_list and keys should have the same length, '
- f'but got {len(img_path_list)} and {len(keys)}')
- print(f'Create lmdb for {data_path}, save to {lmdb_path}...')
- print(f'Totoal images: {len(img_path_list)}')
- if not lmdb_path.endswith('.lmdb'):
- raise ValueError("lmdb_path must end with '.lmdb'.")
- if osp.exists(lmdb_path):
- print(f'Folder {lmdb_path} already exists. Exit.')
- sys.exit(1)
-
- if multiprocessing_read:
- # read all the images to memory (multiprocessing)
- dataset = {} # use dict to keep the order for multiprocessing
- shapes = {}
- print(f'Read images with multiprocessing, #thread: {n_thread} ...')
- pbar = tqdm(total=len(img_path_list), unit='image')
-
- def callback(arg):
- """get the image data and update pbar."""
- key, dataset[key], shapes[key] = arg
- pbar.update(1)
- pbar.set_description(f'Read {key}')
-
- pool = Pool(n_thread)
- for path, key in zip(img_path_list, keys):
- pool.apply_async(read_img_worker, args=(osp.join(data_path, path), key, compress_level), callback=callback)
- pool.close()
- pool.join()
- pbar.close()
- print(f'Finish reading {len(img_path_list)} images.')
-
- # create lmdb environment
- if map_size is None:
- # obtain data size for one image
- img = cv2.imread(osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED)
- _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
- data_size_per_img = img_byte.nbytes
- print('Data size per image is: ', data_size_per_img)
- data_size = data_size_per_img * len(img_path_list)
- map_size = data_size * 10
-
- env = lmdb.open(lmdb_path, map_size=map_size)
-
- # write data to lmdb
- pbar = tqdm(total=len(img_path_list), unit='chunk')
- txn = env.begin(write=True)
- txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
- for idx, (path, key) in enumerate(zip(img_path_list, keys)):
- pbar.update(1)
- pbar.set_description(f'Write {key}')
- key_byte = key.encode('ascii')
- if multiprocessing_read:
- img_byte = dataset[key]
- h, w, c = shapes[key]
- else:
- _, img_byte, img_shape = read_img_worker(osp.join(data_path, path), key, compress_level)
- h, w, c = img_shape
-
- txn.put(key_byte, img_byte)
- # write meta information
- txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n')
- if idx % batch == 0:
- txn.commit()
- txn = env.begin(write=True)
- pbar.close()
- txn.commit()
- env.close()
- txt_file.close()
- print('\nFinish writing lmdb.')
-
-
-def read_img_worker(path, key, compress_level):
- """Read image worker.
-
- Args:
- path (str): Image path.
- key (str): Image key.
- compress_level (int): Compress level when encoding images.
-
- Returns:
- str: Image key.
- byte: Image byte.
- tuple[int]: Image shape.
- """
-
- img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
- if img.ndim == 2:
- h, w = img.shape
- c = 1
- else:
- h, w, c = img.shape
- _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
- return (key, img_byte, (h, w, c))
-
-
-class LmdbMaker():
- """LMDB Maker.
-
- Args:
- lmdb_path (str): Lmdb save path.
- map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB.
- batch (int): After processing batch images, lmdb commits.
- Default: 5000.
- compress_level (int): Compress level when encoding images. Default: 1.
- """
-
- def __init__(self, lmdb_path, map_size=1024**4, batch=5000, compress_level=1):
- if not lmdb_path.endswith('.lmdb'):
- raise ValueError("lmdb_path must end with '.lmdb'.")
- if osp.exists(lmdb_path):
- print(f'Folder {lmdb_path} already exists. Exit.')
- sys.exit(1)
-
- self.lmdb_path = lmdb_path
- self.batch = batch
- self.compress_level = compress_level
- self.env = lmdb.open(lmdb_path, map_size=map_size)
- self.txn = self.env.begin(write=True)
- self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
- self.counter = 0
-
- def put(self, img_byte, key, img_shape):
- self.counter += 1
- key_byte = key.encode('ascii')
- self.txn.put(key_byte, img_byte)
- # write meta information
- h, w, c = img_shape
- self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n')
- if self.counter % self.batch == 0:
- self.txn.commit()
- self.txn = self.env.begin(write=True)
-
- def close(self):
- self.txn.commit()
- self.env.close()
- self.txt_file.close()
diff --git a/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/pages/_page.svelte-c281f5f0.js b/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/pages/_page.svelte-c281f5f0.js
deleted file mode 100644
index 9d658fdfabeb8ea61ffb9974665c87cc2cfea4fc..0000000000000000000000000000000000000000
--- a/spaces/dbirks/diffuse-the-rest/build/_app/immutable/components/pages/_page.svelte-c281f5f0.js
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/middleware/wsgi.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/middleware/wsgi.py
deleted file mode 100644
index c4c6a797d2675e1c13b028be977c64a822fb649b..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fastapi/middleware/wsgi.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.middleware.wsgi import WSGIMiddleware as WSGIMiddleware # noqa
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/t2CharStringPen.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/t2CharStringPen.py
deleted file mode 100644
index 41ab0f92f2b683ac2dc87ca1b16f54047d0fef81..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/t2CharStringPen.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2009 Type Supply LLC
-# Author: Tal Leming
-
-from fontTools.misc.roundTools import otRound, roundFunc
-from fontTools.misc.psCharStrings import T2CharString
-from fontTools.pens.basePen import BasePen
-from fontTools.cffLib.specializer import specializeCommands, commandsToProgram
-
-
-class T2CharStringPen(BasePen):
- """Pen to draw Type 2 CharStrings.
-
- The 'roundTolerance' argument controls the rounding of point coordinates.
- It is defined as the maximum absolute difference between the original
- float and the rounded integer value.
- The default tolerance of 0.5 means that all floats are rounded to integer;
- a value of 0 disables rounding; values in between will only round floats
- which are close to their integral part within the tolerated range.
- """
-
- def __init__(self, width, glyphSet, roundTolerance=0.5, CFF2=False):
- super(T2CharStringPen, self).__init__(glyphSet)
- self.round = roundFunc(roundTolerance)
- self._CFF2 = CFF2
- self._width = width
- self._commands = []
- self._p0 = (0, 0)
-
- def _p(self, pt):
- p0 = self._p0
- pt = self._p0 = (self.round(pt[0]), self.round(pt[1]))
- return [pt[0] - p0[0], pt[1] - p0[1]]
-
- def _moveTo(self, pt):
- self._commands.append(("rmoveto", self._p(pt)))
-
- def _lineTo(self, pt):
- self._commands.append(("rlineto", self._p(pt)))
-
- def _curveToOne(self, pt1, pt2, pt3):
- _p = self._p
- self._commands.append(("rrcurveto", _p(pt1) + _p(pt2) + _p(pt3)))
-
- def _closePath(self):
- pass
-
- def _endPath(self):
- pass
-
- def getCharString(self, private=None, globalSubrs=None, optimize=True):
- commands = self._commands
- if optimize:
- maxstack = 48 if not self._CFF2 else 513
- commands = specializeCommands(
- commands, generalizeFirst=False, maxstack=maxstack
- )
- program = commandsToProgram(commands)
- if self._width is not None:
- assert (
- not self._CFF2
- ), "CFF2 does not allow encoding glyph width in CharString."
- program.insert(0, otRound(self._width))
- if not self._CFF2:
- program.append("endchar")
- charString = T2CharString(
- program=program, private=private, globalSubrs=globalSubrs
- )
- return charString
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/stat.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/stat.py
deleted file mode 100644
index 46c9498dc720e7c23b278ae31b65dbf55f2ad8be..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/stat.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""Extra methods for DesignSpaceDocument to generate its STAT table data."""
-
-from __future__ import annotations
-
-from typing import Dict, List, Union
-
-import fontTools.otlLib.builder
-from fontTools.designspaceLib import (
- AxisLabelDescriptor,
- DesignSpaceDocument,
- DesignSpaceDocumentError,
- LocationLabelDescriptor,
-)
-from fontTools.designspaceLib.types import Region, getVFUserRegion, locationInRegion
-from fontTools.ttLib import TTFont
-
-
-def buildVFStatTable(ttFont: TTFont, doc: DesignSpaceDocument, vfName: str) -> None:
- """Build the STAT table for the variable font identified by its name in
- the given document.
-
- Knowing which variable we're building STAT data for is needed to subset
- the STAT locations to only include what the variable font actually ships.
-
- .. versionadded:: 5.0
-
- .. seealso::
- - :func:`getStatAxes()`
- - :func:`getStatLocations()`
- - :func:`fontTools.otlLib.builder.buildStatTable()`
- """
- for vf in doc.getVariableFonts():
- if vf.name == vfName:
- break
- else:
- raise DesignSpaceDocumentError(
- f"Cannot find the variable font by name {vfName}"
- )
-
- region = getVFUserRegion(doc, vf)
-
- return fontTools.otlLib.builder.buildStatTable(
- ttFont,
- getStatAxes(doc, region),
- getStatLocations(doc, region),
- doc.elidedFallbackName if doc.elidedFallbackName is not None else 2,
- )
-
-
-def getStatAxes(doc: DesignSpaceDocument, userRegion: Region) -> List[Dict]:
- """Return a list of axis dicts suitable for use as the ``axes``
- argument to :func:`fontTools.otlLib.builder.buildStatTable()`.
-
- .. versionadded:: 5.0
- """
- # First, get the axis labels with explicit ordering
- # then append the others in the order they appear.
- maxOrdering = max(
- (axis.axisOrdering for axis in doc.axes if axis.axisOrdering is not None),
- default=-1,
- )
- axisOrderings = []
- for axis in doc.axes:
- if axis.axisOrdering is not None:
- axisOrderings.append(axis.axisOrdering)
- else:
- maxOrdering += 1
- axisOrderings.append(maxOrdering)
- return [
- dict(
- tag=axis.tag,
- name={"en": axis.name, **axis.labelNames},
- ordering=ordering,
- values=[
- _axisLabelToStatLocation(label)
- for label in axis.axisLabels
- if locationInRegion({axis.name: label.userValue}, userRegion)
- ],
- )
- for axis, ordering in zip(doc.axes, axisOrderings)
- ]
-
-
-def getStatLocations(doc: DesignSpaceDocument, userRegion: Region) -> List[Dict]:
- """Return a list of location dicts suitable for use as the ``locations``
- argument to :func:`fontTools.otlLib.builder.buildStatTable()`.
-
- .. versionadded:: 5.0
- """
- axesByName = {axis.name: axis for axis in doc.axes}
- return [
- dict(
- name={"en": label.name, **label.labelNames},
- # Location in the designspace is keyed by axis name
- # Location in buildStatTable by axis tag
- location={
- axesByName[name].tag: value
- for name, value in label.getFullUserLocation(doc).items()
- },
- flags=_labelToFlags(label),
- )
- for label in doc.locationLabels
- if locationInRegion(label.getFullUserLocation(doc), userRegion)
- ]
-
-
-def _labelToFlags(label: Union[AxisLabelDescriptor, LocationLabelDescriptor]) -> int:
- flags = 0
- if label.olderSibling:
- flags |= 1
- if label.elidable:
- flags |= 2
- return flags
-
-
-def _axisLabelToStatLocation(
- label: AxisLabelDescriptor,
-) -> Dict:
- label_format = label.getFormat()
- name = {"en": label.name, **label.labelNames}
- flags = _labelToFlags(label)
- if label_format == 1:
- return dict(name=name, value=label.userValue, flags=flags)
- if label_format == 3:
- return dict(
- name=name,
- value=label.userValue,
- linkedValue=label.linkedUserValue,
- flags=flags,
- )
- if label_format == 2:
- res = dict(
- name=name,
- nominalValue=label.userValue,
- flags=flags,
- )
- if label.userMinimum is not None:
- res["rangeMinValue"] = label.userMinimum
- if label.userMaximum is not None:
- res["rangeMaxValue"] = label.userMaximum
- return res
- raise NotImplementedError("Unknown STAT label format")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/helpers.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/helpers.py
deleted file mode 100644
index e901e1a91b74966a2d0f8a56039f50077b226de5..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/helpers.py
+++ /dev/null
@@ -1,1090 +0,0 @@
-"""
-Defines helper methods useful for loading and caching Interface examples.
-"""
-from __future__ import annotations
-
-import ast
-import csv
-import inspect
-import os
-import shutil
-import subprocess
-import tempfile
-import threading
-import warnings
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Iterable, Literal, Optional
-
-import matplotlib.pyplot as plt
-import numpy as np
-import PIL
-import PIL.Image
-from gradio_client import utils as client_utils
-from gradio_client.documentation import document, set_documentation_group
-from matplotlib import animation
-
-from gradio import components, oauth, processing_utils, routes, utils
-from gradio.context import Context
-from gradio.exceptions import Error
-from gradio.flagging import CSVLogger
-
-if TYPE_CHECKING: # Only import for type checking (to avoid circular imports).
- from gradio.blocks import Block
- from gradio.components import IOComponent
-
-CACHED_FOLDER = "gradio_cached_examples"
-LOG_FILE = "log.csv"
-
-set_documentation_group("helpers")
-
-
-def create_examples(
- examples: list[Any] | list[list[Any]] | str,
- inputs: IOComponent | list[IOComponent],
- outputs: IOComponent | list[IOComponent] | None = None,
- fn: Callable | None = None,
- cache_examples: bool = False,
- examples_per_page: int = 10,
- _api_mode: bool = False,
- label: str | None = None,
- elem_id: str | None = None,
- run_on_click: bool = False,
- preprocess: bool = True,
- postprocess: bool = True,
- api_name: str | None | Literal[False] = False,
- batch: bool = False,
-):
- """Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component."""
- examples_obj = Examples(
- examples=examples,
- inputs=inputs,
- outputs=outputs,
- fn=fn,
- cache_examples=cache_examples,
- examples_per_page=examples_per_page,
- _api_mode=_api_mode,
- label=label,
- elem_id=elem_id,
- run_on_click=run_on_click,
- preprocess=preprocess,
- postprocess=postprocess,
- api_name=api_name,
- batch=batch,
- _initiated_directly=False,
- )
- client_utils.synchronize_async(examples_obj.create)
- return examples_obj
-
-
-@document()
-class Examples:
- """
- This class is a wrapper over the Dataset component and can be used to create Examples
- for Blocks / Interfaces. Populates the Dataset component with examples and
- assigns event listener so that clicking on an example populates the input/output
- components. Optionally handles example caching for fast inference.
-
- Demos: blocks_inputs, fake_gan
- Guides: more-on-examples-and-flagging, using-hugging-face-integrations, image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, create-your-own-friends-with-a-gan
- """
-
- def __init__(
- self,
- examples: list[Any] | list[list[Any]] | str,
- inputs: IOComponent | list[IOComponent],
- outputs: IOComponent | list[IOComponent] | None = None,
- fn: Callable | None = None,
- cache_examples: bool = False,
- examples_per_page: int = 10,
- _api_mode: bool = False,
- label: str | None = "Examples",
- elem_id: str | None = None,
- run_on_click: bool = False,
- preprocess: bool = True,
- postprocess: bool = True,
- api_name: str | None | Literal[False] = False,
- batch: bool = False,
- _initiated_directly: bool = True,
- ):
- """
- Parameters:
- examples: example inputs that can be clicked to populate specific components. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs.
- inputs: the component or list of components corresponding to the examples
- outputs: optionally, provide the component or list of components corresponding to the output of the examples. Required if `cache` is True.
- fn: optionally, provide the function to run to generate the outputs corresponding to the examples. Required if `cache` is True.
- cache_examples: if True, caches examples for fast runtime. If True, then `fn` and `outputs` must be provided. If `fn` is a generator function, then the last yielded value will be used as the output.
- examples_per_page: how many examples to show per page.
- label: the label to use for the examples component (by default, "Examples")
- elem_id: an optional string that is assigned as the id of this component in the HTML DOM.
- run_on_click: if cache_examples is False, clicking on an example does not run the function when an example is clicked. Set this to True to run the function when an example is clicked. Has no effect if cache_examples is True.
- preprocess: if True, preprocesses the example input before running the prediction function and caching the output. Only applies if cache_examples is True.
- postprocess: if True, postprocesses the example output after running the prediction function and before caching. Only applies if cache_examples is True.
- api_name: Defines how the event associated with clicking on the examples appears in the API docs. Can be a string, None, or False. If False (default), the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
- batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. Used only if cache_examples is True.
- """
- if _initiated_directly:
- warnings.warn(
- "Please use gr.Examples(...) instead of gr.examples.Examples(...) to create the Examples.",
- )
-
- if cache_examples and (fn is None or outputs is None):
- raise ValueError("If caching examples, `fn` and `outputs` must be provided")
-
- if not isinstance(inputs, list):
- inputs = [inputs]
- if outputs and not isinstance(outputs, list):
- outputs = [outputs]
-
- working_directory = Path().absolute()
-
- if examples is None:
- raise ValueError("The parameter `examples` cannot be None")
- elif isinstance(examples, list) and (
- len(examples) == 0 or isinstance(examples[0], list)
- ):
- pass
- elif (
- isinstance(examples, list) and len(inputs) == 1
- ): # If there is only one input component, examples can be provided as a regular list instead of a list of lists
- examples = [[e] for e in examples]
- elif isinstance(examples, str):
- if not Path(examples).exists():
- raise FileNotFoundError(
- f"Could not find examples directory: {examples}"
- )
- working_directory = examples
- if not (Path(examples) / LOG_FILE).exists():
- if len(inputs) == 1:
- examples = [[e] for e in os.listdir(examples)]
- else:
- raise FileNotFoundError(
- "Could not find log file (required for multiple inputs): "
- + LOG_FILE
- )
- else:
- with open(Path(examples) / LOG_FILE) as logs:
- examples = list(csv.reader(logs))
- examples = [
- examples[i][: len(inputs)] for i in range(1, len(examples))
- ] # remove header and unnecessary columns
-
- else:
- raise ValueError(
- "The parameter `examples` must either be a string directory or a list"
- "(if there is only 1 input component) or (more generally), a nested "
- "list, where each sublist represents a set of inputs."
- )
-
- input_has_examples = [False] * len(inputs)
- for example in examples:
- for idx, example_for_input in enumerate(example):
- if example_for_input is not None:
- try:
- input_has_examples[idx] = True
- except IndexError:
- pass # If there are more example components than inputs, ignore. This can sometimes be intentional (e.g. loading from a log file where outputs and timestamps are also logged)
-
- inputs_with_examples = [
- inp for (inp, keep) in zip(inputs, input_has_examples) if keep
- ]
- non_none_examples = [
- [ex for (ex, keep) in zip(example, input_has_examples) if keep]
- for example in examples
- ]
-
- self.examples = examples
- self.non_none_examples = non_none_examples
- self.inputs = inputs
- self.inputs_with_examples = inputs_with_examples
- self.outputs = outputs or []
- self.fn = fn
- self.cache_examples = cache_examples
- self._api_mode = _api_mode
- self.preprocess = preprocess
- self.postprocess = postprocess
- self.api_name = api_name
- self.batch = batch
-
- with utils.set_directory(working_directory):
- self.processed_examples = [
- [
- component.postprocess(sample)
- for component, sample in zip(inputs, example)
- ]
- for example in examples
- ]
- self.non_none_processed_examples = [
- [ex for (ex, keep) in zip(example, input_has_examples) if keep]
- for example in self.processed_examples
- ]
- if cache_examples:
- for example in self.examples:
- if len([ex for ex in example if ex is not None]) != len(self.inputs):
- warnings.warn(
- "Examples are being cached but not all input components have "
- "example values. This may result in an exception being thrown by "
- "your function. If you do get an error while caching examples, make "
- "sure all of your inputs have example values for all of your examples "
- "or you provide default values for those particular parameters in your function."
- )
- break
-
- from gradio import components
-
- with utils.set_directory(working_directory):
- self.dataset = components.Dataset(
- components=inputs_with_examples,
- samples=non_none_examples,
- type="index",
- label=label,
- samples_per_page=examples_per_page,
- elem_id=elem_id,
- )
-
- self.cached_folder = Path(CACHED_FOLDER) / str(self.dataset._id)
- self.cached_file = Path(self.cached_folder) / "log.csv"
- self.cache_examples = cache_examples
- self.run_on_click = run_on_click
-
- async def create(self) -> None:
- """Caches the examples if self.cache_examples is True and creates the Dataset
- component to hold the examples"""
-
- async def load_example(example_id):
- processed_example = self.non_none_processed_examples[example_id]
- return utils.resolve_singleton(processed_example)
-
- if Context.root_block:
- self.load_input_event = self.dataset.click(
- load_example,
- inputs=[self.dataset],
- outputs=self.inputs_with_examples, # type: ignore
- show_progress="hidden",
- postprocess=False,
- queue=False,
- api_name=self.api_name, # type: ignore
- )
- if self.run_on_click and not self.cache_examples:
- if self.fn is None:
- raise ValueError("Cannot run_on_click if no function is provided")
- self.load_input_event.then(
- self.fn,
- inputs=self.inputs, # type: ignore
- outputs=self.outputs, # type: ignore
- )
-
- if self.cache_examples:
- await self.cache()
-
- async def cache(self) -> None:
- """
- Caches all of the examples so that their predictions can be shown immediately.
- """
- if Path(self.cached_file).exists():
- print(
- f"Using cache from '{utils.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache.\n"
- )
- else:
- if Context.root_block is None:
- raise ValueError("Cannot cache examples if not in a Blocks context")
-
- print(f"Caching examples at: '{utils.abspath(self.cached_folder)}'")
- cache_logger = CSVLogger()
-
- if inspect.isgeneratorfunction(self.fn):
-
- def get_final_item(*args): # type: ignore
- x = None
- for x in self.fn(*args): # noqa: B007 # type: ignore
- pass
- return x
-
- fn = get_final_item
- elif inspect.isasyncgenfunction(self.fn):
-
- async def get_final_item(*args):
- x = None
- async for x in self.fn(*args): # noqa: B007 # type: ignore
- pass
- return x
-
- fn = get_final_item
- else:
- fn = self.fn
- # create a fake dependency to process the examples and get the predictions
- dependency, fn_index = Context.root_block.set_event_trigger(
- event_name="fake_event",
- fn=fn,
- inputs=self.inputs_with_examples, # type: ignore
- outputs=self.outputs, # type: ignore
- preprocess=self.preprocess and not self._api_mode,
- postprocess=self.postprocess and not self._api_mode,
- batch=self.batch,
- )
-
- assert self.outputs is not None
- cache_logger.setup(self.outputs, self.cached_folder)
- for example_id, _ in enumerate(self.examples):
- print(f"Caching example {example_id + 1}/{len(self.examples)}")
- processed_input = self.processed_examples[example_id]
- if self.batch:
- processed_input = [[value] for value in processed_input]
- with utils.MatplotlibBackendMananger():
- prediction = await Context.root_block.process_api(
- fn_index=fn_index,
- inputs=processed_input,
- request=None,
- state={},
- )
- output = prediction["data"]
- if self.batch:
- output = [value[0] for value in output]
- cache_logger.flag(output)
- # Remove the "fake_event" to prevent bugs in loading interfaces from spaces
- Context.root_block.dependencies.remove(dependency)
- Context.root_block.fns.pop(fn_index)
-
- # Remove the original load_input_event and replace it with one that
- # also populates the input. We do it this way to to allow the cache()
- # method to be called independently of the create() method
- index = Context.root_block.dependencies.index(self.load_input_event)
- Context.root_block.dependencies.pop(index)
- Context.root_block.fns.pop(index)
-
- async def load_example(example_id):
- processed_example = self.non_none_processed_examples[
- example_id
- ] + await self.load_from_cache(example_id)
- return utils.resolve_singleton(processed_example)
-
- self.load_input_event = self.dataset.click(
- load_example,
- inputs=[self.dataset],
- outputs=self.inputs_with_examples + self.outputs, # type: ignore
- show_progress="hidden",
- postprocess=False,
- queue=False,
- api_name=self.api_name, # type: ignore
- )
-
- print("Caching complete\n")
-
- async def load_from_cache(self, example_id: int) -> list[Any]:
- """Loads a particular cached example for the interface.
- Parameters:
- example_id: The id of the example to process (zero-indexed).
- """
- with open(self.cached_file, encoding="utf-8") as cache:
- examples = list(csv.reader(cache))
- example = examples[example_id + 1] # +1 to adjust for header
- output = []
- assert self.outputs is not None
- for component, value in zip(self.outputs, example):
- value_to_use = value
- try:
- value_as_dict = ast.literal_eval(value)
- # File components that output multiple files get saved as a python list
- # need to pass the parsed list to serialize
- # TODO: Better file serialization in 4.0
- if isinstance(value_as_dict, list) and isinstance(
- component, components.File
- ):
- value_to_use = value_as_dict
- assert utils.is_update(value_as_dict)
- output.append(value_as_dict)
- except (ValueError, TypeError, SyntaxError, AssertionError):
- output.append(
- component.serialize(
- value_to_use,
- self.cached_folder,
- )
- )
- return output
-
-
-class TrackedIterable:
- def __init__(
- self,
- iterable: Iterable | None,
- index: int | None,
- length: int | None,
- desc: str | None,
- unit: str | None,
- _tqdm=None,
- progress: float | None = None,
- ) -> None:
- self.iterable = iterable
- self.index = index
- self.length = length
- self.desc = desc
- self.unit = unit
- self._tqdm = _tqdm
- self.progress = progress
-
-
-@document("__call__", "tqdm")
-class Progress(Iterable):
- """
- The Progress class provides a custom progress tracker that is used in a function signature.
- To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance.
- The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable.
- The Progress tracker is currently only available with `queue()`.
- Example:
- import gradio as gr
- import time
- def my_function(x, progress=gr.Progress()):
- progress(0, desc="Starting...")
- time.sleep(1)
- for i in progress.tqdm(range(100)):
- time.sleep(0.1)
- return x
- gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()
- Demos: progress
- """
-
- def __init__(
- self,
- track_tqdm: bool = False,
- _callback: Callable | None = None, # for internal use only
- _event_id: str | None = None,
- ):
- """
- Parameters:
- track_tqdm: If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function.
- """
- self.track_tqdm = track_tqdm
- self._callback = _callback
- self._event_id = _event_id
- self.iterables: list[TrackedIterable] = []
-
- def __len__(self):
- return self.iterables[-1].length
-
- def __iter__(self):
- return self
-
- def __next__(self):
- """
- Updates progress tracker with next item in iterable.
- """
- if self._callback:
- current_iterable = self.iterables[-1]
- while (
- not hasattr(current_iterable.iterable, "__next__")
- and len(self.iterables) > 0
- ):
- current_iterable = self.iterables.pop()
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables,
- )
- assert current_iterable.index is not None, "Index not set."
- current_iterable.index += 1
- try:
- return next(current_iterable.iterable) # type: ignore
- except StopIteration:
- self.iterables.pop()
- raise
- else:
- return self
-
- def __call__(
- self,
- progress: float | tuple[int, int | None] | None,
- desc: str | None = None,
- total: int | None = None,
- unit: str = "steps",
- _tqdm=None,
- ):
- """
- Updates progress tracker with progress and message text.
- Parameters:
- progress: If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar.
- desc: description to display.
- total: estimated total number of steps.
- unit: unit of iterations.
- """
- if self._callback:
- if isinstance(progress, tuple):
- index, total = progress
- progress = None
- else:
- index = None
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables
- + [TrackedIterable(None, index, total, desc, unit, _tqdm, progress)],
- )
- else:
- return progress
-
- def tqdm(
- self,
- iterable: Iterable | None,
- desc: str | None = None,
- total: int | None = None,
- unit: str = "steps",
- _tqdm=None,
- ):
- """
- Attaches progress tracker to iterable, like tqdm.
- Parameters:
- iterable: iterable to attach progress tracker to.
- desc: description to display.
- total: estimated total number of steps.
- unit: unit of iterations.
- """
- if self._callback:
- if iterable is None:
- new_iterable = TrackedIterable(None, 0, total, desc, unit, _tqdm)
- self.iterables.append(new_iterable)
- self._callback(event_id=self._event_id, iterables=self.iterables)
- return self
- length = len(iterable) if hasattr(iterable, "__len__") else None # type: ignore
- self.iterables.append(
- TrackedIterable(iter(iterable), 0, length, desc, unit, _tqdm)
- )
- return self
-
- def update(self, n=1):
- """
- Increases latest iterable with specified number of steps.
- Parameters:
- n: number of steps completed.
- """
- if self._callback and len(self.iterables) > 0:
- current_iterable = self.iterables[-1]
- assert current_iterable.index is not None, "Index not set."
- current_iterable.index += n
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables,
- )
- else:
- return
-
- def close(self, _tqdm):
- """
- Removes iterable with given _tqdm.
- """
- if self._callback:
- for i in range(len(self.iterables)):
- if id(self.iterables[i]._tqdm) == id(_tqdm):
- self.iterables.pop(i)
- break
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables,
- )
- else:
- return
-
-
-def create_tracker(root_blocks, event_id, fn, track_tqdm):
- progress = Progress(_callback=root_blocks._queue.set_progress, _event_id=event_id)
- if not track_tqdm:
- return progress, fn
-
- try:
- _tqdm = __import__("tqdm")
- except ModuleNotFoundError:
- return progress, fn
- if not hasattr(root_blocks, "_progress_tracker_per_thread"):
- root_blocks._progress_tracker_per_thread = {}
-
- def init_tqdm(
- self, iterable=None, desc=None, total=None, unit="steps", *args, **kwargs
- ):
- self._progress = root_blocks._progress_tracker_per_thread.get(
- threading.get_ident()
- )
- if self._progress is not None:
- self._progress.event_id = event_id
- self._progress.tqdm(iterable, desc, total, unit, _tqdm=self)
- kwargs["file"] = open(os.devnull, "w") # noqa: SIM115
- self.__init__orig__(iterable, desc, total, *args, unit=unit, **kwargs)
-
- def iter_tqdm(self):
- if self._progress is not None:
- return self._progress
- else:
- return self.__iter__orig__()
-
- def update_tqdm(self, n=1):
- if self._progress is not None:
- self._progress.update(n)
- return self.__update__orig__(n)
-
- def close_tqdm(self):
- if self._progress is not None:
- self._progress.close(self)
- return self.__close__orig__()
-
- def exit_tqdm(self, exc_type, exc_value, traceback):
- if self._progress is not None:
- self._progress.close(self)
- return self.__exit__orig__(exc_type, exc_value, traceback)
-
- if not hasattr(_tqdm.tqdm, "__init__orig__"):
- _tqdm.tqdm.__init__orig__ = _tqdm.tqdm.__init__
- _tqdm.tqdm.__init__ = init_tqdm
- if not hasattr(_tqdm.tqdm, "__update__orig__"):
- _tqdm.tqdm.__update__orig__ = _tqdm.tqdm.update
- _tqdm.tqdm.update = update_tqdm
- if not hasattr(_tqdm.tqdm, "__close__orig__"):
- _tqdm.tqdm.__close__orig__ = _tqdm.tqdm.close
- _tqdm.tqdm.close = close_tqdm
- if not hasattr(_tqdm.tqdm, "__exit__orig__"):
- _tqdm.tqdm.__exit__orig__ = _tqdm.tqdm.__exit__
- _tqdm.tqdm.__exit__ = exit_tqdm
- if not hasattr(_tqdm.tqdm, "__iter__orig__"):
- _tqdm.tqdm.__iter__orig__ = _tqdm.tqdm.__iter__
- _tqdm.tqdm.__iter__ = iter_tqdm
- if hasattr(_tqdm, "auto") and hasattr(_tqdm.auto, "tqdm"):
- _tqdm.auto.tqdm = _tqdm.tqdm
-
- def before_fn():
- thread_id = threading.get_ident()
- root_blocks._progress_tracker_per_thread[thread_id] = progress
-
- def after_fn():
- thread_id = threading.get_ident()
- del root_blocks._progress_tracker_per_thread[thread_id]
-
- tracked_fn = utils.function_wrapper(fn, before_fn=before_fn, after_fn=after_fn)
-
- return progress, tracked_fn
-
-
-def special_args(
- fn: Callable,
- inputs: list[Any] | None = None,
- request: routes.Request | None = None,
- event_data: EventData | None = None,
-):
- """
- Checks if function has special arguments Request or EventData (via annotation) or Progress (via default value).
- If inputs is provided, these values will be loaded into the inputs array.
- Parameters:
- fn: function to check.
- inputs: array to load special arguments into.
- request: request to load into inputs.
- event_data: event-related data to load into inputs.
- Returns:
- updated inputs, progress index, event data index.
- """
- signature = inspect.signature(fn)
- type_hints = utils.get_type_hints(fn)
- positional_args = []
- for param in signature.parameters.values():
- if param.kind not in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD):
- break
- positional_args.append(param)
- progress_index = None
- event_data_index = None
- for i, param in enumerate(positional_args):
- type_hint = type_hints.get(param.name)
- if isinstance(param.default, Progress):
- progress_index = i
- if inputs is not None:
- inputs.insert(i, param.default)
- elif type_hint == routes.Request:
- if inputs is not None:
- inputs.insert(i, request)
- elif (
- type_hint == Optional[oauth.OAuthProfile]
- or type_hint == oauth.OAuthProfile
- # Note: "OAuthProfile | None" is equals to Optional[OAuthProfile] in Python
- # => it is automatically handled as well by the above condition
- # (adding explicit "OAuthProfile | None" would break in Python3.9)
- ):
- if inputs is not None:
- # Retrieve session from gr.Request, if it exists (i.e. if user is logged in)
- session = (
- # request.session (if fastapi.Request obj i.e. direct call)
- getattr(request, "session", {})
- or
- # or request.request.session (if gr.Request obj i.e. websocket call)
- getattr(getattr(request, "request", None), "session", {})
- )
- oauth_profile = (
- session["oauth_profile"] if "oauth_profile" in session else None
- )
- if type_hint == oauth.OAuthProfile and oauth_profile is None:
- raise Error(
- "This action requires a logged in user. Please sign in and retry."
- )
- inputs.insert(i, oauth_profile)
- elif (
- type_hint
- and inspect.isclass(type_hint)
- and issubclass(type_hint, EventData)
- ):
- event_data_index = i
- if inputs is not None and event_data is not None:
- inputs.insert(i, type_hint(event_data.target, event_data._data))
- elif (
- param.default is not param.empty and inputs is not None and len(inputs) <= i
- ):
- inputs.insert(i, param.default)
- if inputs is not None:
- while len(inputs) < len(positional_args):
- i = len(inputs)
- param = positional_args[i]
- if param.default == param.empty:
- warnings.warn("Unexpected argument. Filling with None.")
- inputs.append(None)
- else:
- inputs.append(param.default)
- return inputs or [], progress_index, event_data_index
-
-
-@document()
-def update(**kwargs) -> dict:
- """
- Updates component properties. When a function passed into a Gradio Interface or a Blocks events returns a typical value, it updates the value of the output component. But it is also possible to update the properties of an output component (such as the number of lines of a `Textbox` or the visibility of an `Image`) by returning the component's `update()` function, which takes as parameters any of the constructor parameters for that component.
- This is a shorthand for using the update method on a component.
- For example, rather than using gr.Number.update(...) you can just use gr.update(...).
- Note that your editor's autocompletion will suggest proper parameters
- if you use the update method on the component.
- Demos: blocks_essay, blocks_update, blocks_essay_update
-
- Parameters:
- kwargs: Key-word arguments used to update the component's properties.
- Example:
- # Blocks Example
- import gradio as gr
- with gr.Blocks() as demo:
- radio = gr.Radio([1, 2, 4], label="Set the value of the number")
- number = gr.Number(value=2, interactive=True)
- radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number)
- demo.launch()
-
- # Interface example
- import gradio as gr
- def change_textbox(choice):
- if choice == "short":
- return gr.Textbox.update(lines=2, visible=True)
- elif choice == "long":
- return gr.Textbox.update(lines=8, visible=True)
- else:
- return gr.Textbox.update(visible=False)
- gr.Interface(
- change_textbox,
- gr.Radio(
- ["short", "long", "none"], label="What kind of essay would you like to write?"
- ),
- gr.Textbox(lines=2),
- live=True,
- ).launch()
- """
- kwargs["__type__"] = "generic_update"
- return kwargs
-
-
-def skip() -> dict:
- return update()
-
-
-@document()
-def make_waveform(
- audio: str | tuple[int, np.ndarray],
- *,
- bg_color: str = "#f3f4f6",
- bg_image: str | None = None,
- fg_alpha: float = 0.75,
- bars_color: str | tuple[str, str] = ("#fbbf24", "#ea580c"),
- bar_count: int = 50,
- bar_width: float = 0.6,
- animate: bool = False,
-) -> str:
- """
- Generates a waveform video from an audio file. Useful for creating an easy to share audio visualization. The output should be passed into a `gr.Video` component.
- Parameters:
- audio: Audio file path or tuple of (sample_rate, audio_data)
- bg_color: Background color of waveform (ignored if bg_image is provided)
- bg_image: Background image of waveform
- fg_alpha: Opacity of foreground waveform
- bars_color: Color of waveform bars. Can be a single color or a tuple of (start_color, end_color) of gradient
- bar_count: Number of bars in waveform
- bar_width: Width of bars in waveform. 1 represents full width, 0.5 represents half width, etc.
- animate: If true, the audio waveform overlay will be animated, if false, it will be static.
- Returns:
- A filepath to the output video in mp4 format.
- """
- if isinstance(audio, str):
- audio_file = audio
- audio = processing_utils.audio_from_file(audio)
- else:
- tmp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
- processing_utils.audio_to_file(audio[0], audio[1], tmp_wav.name, format="wav")
- audio_file = tmp_wav.name
-
- if not os.path.isfile(audio_file):
- raise ValueError("Audio file not found.")
-
- ffmpeg = shutil.which("ffmpeg")
- if not ffmpeg:
- raise RuntimeError("ffmpeg not found.")
-
- duration = round(len(audio[1]) / audio[0], 4)
-
- # Helper methods to create waveform
- def hex_to_rgb(hex_str):
- return [int(hex_str[i : i + 2], 16) for i in range(1, 6, 2)]
-
- def get_color_gradient(c1, c2, n):
- assert n > 1
- c1_rgb = np.array(hex_to_rgb(c1)) / 255
- c2_rgb = np.array(hex_to_rgb(c2)) / 255
- mix_pcts = [x / (n - 1) for x in range(n)]
- rgb_colors = [((1 - mix) * c1_rgb + (mix * c2_rgb)) for mix in mix_pcts]
- return [
- "#" + "".join(f"{int(round(val * 255)):02x}" for val in item)
- for item in rgb_colors
- ]
-
- # Reshape audio to have a fixed number of bars
- samples = audio[1]
- if len(samples.shape) > 1:
- samples = np.mean(samples, 1)
- bins_to_pad = bar_count - (len(samples) % bar_count)
- samples = np.pad(samples, [(0, bins_to_pad)])
- samples = np.reshape(samples, (bar_count, -1))
- samples = np.abs(samples)
- samples = np.max(samples, 1)
-
- with utils.MatplotlibBackendMananger():
- plt.clf()
- # Plot waveform
- color = (
- bars_color
- if isinstance(bars_color, str)
- else get_color_gradient(bars_color[0], bars_color[1], bar_count)
- )
-
- if animate:
- fig = plt.figure(figsize=(5, 1), dpi=200, frameon=False)
- fig.subplots_adjust(left=0, bottom=0, right=1, top=1)
- plt.axis("off")
- plt.margins(x=0)
-
- bar_alpha = fg_alpha if animate else 1.0
- barcollection = plt.bar(
- np.arange(0, bar_count),
- samples * 2,
- bottom=(-1 * samples),
- width=bar_width,
- color=color,
- alpha=bar_alpha,
- )
-
- tmp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-
- savefig_kwargs: dict[str, Any] = {"bbox_inches": "tight"}
- if bg_image is not None:
- savefig_kwargs["transparent"] = True
- if animate:
- savefig_kwargs["facecolor"] = "none"
- else:
- savefig_kwargs["facecolor"] = bg_color
- plt.savefig(tmp_img.name, **savefig_kwargs)
-
- if not animate:
- waveform_img = PIL.Image.open(tmp_img.name)
- waveform_img = waveform_img.resize((1000, 200))
-
- # Composite waveform with background image
- if bg_image is not None:
- waveform_array = np.array(waveform_img)
- waveform_array[:, :, 3] = waveform_array[:, :, 3] * fg_alpha
- waveform_img = PIL.Image.fromarray(waveform_array)
-
- bg_img = PIL.Image.open(bg_image)
- waveform_width, waveform_height = waveform_img.size
- bg_width, bg_height = bg_img.size
- if waveform_width != bg_width:
- bg_img = bg_img.resize(
- (
- waveform_width,
- 2 * int(bg_height * waveform_width / bg_width / 2),
- )
- )
- bg_width, bg_height = bg_img.size
- composite_height = max(bg_height, waveform_height)
- composite = PIL.Image.new(
- "RGBA", (waveform_width, composite_height), "#FFFFFF"
- )
- composite.paste(bg_img, (0, composite_height - bg_height))
- composite.paste(
- waveform_img, (0, composite_height - waveform_height), waveform_img
- )
- composite.save(tmp_img.name)
- img_width, img_height = composite.size
- else:
- img_width, img_height = waveform_img.size
- waveform_img.save(tmp_img.name)
- else:
-
- def _animate(_):
- for idx, b in enumerate(barcollection):
- rand_height = np.random.uniform(0.8, 1.2)
- b.set_height(samples[idx] * rand_height * 2)
- b.set_y((-rand_height * samples)[idx])
-
- frames = int(duration * 10)
- anim = animation.FuncAnimation(
- fig, # type: ignore
- _animate,
- repeat=False,
- blit=False,
- frames=frames,
- interval=100,
- )
- anim.save(
- tmp_img.name,
- writer="pillow",
- fps=10,
- codec="png",
- savefig_kwargs=savefig_kwargs,
- )
-
- # Convert waveform to video with ffmpeg
- output_mp4 = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-
- if animate and bg_image is not None:
- ffmpeg_cmd = [
- ffmpeg,
- "-loop",
- "1",
- "-i",
- bg_image,
- "-i",
- tmp_img.name,
- "-i",
- audio_file,
- "-filter_complex",
- "[0:v]scale=w=trunc(iw/2)*2:h=trunc(ih/2)*2[bg];[1:v]format=rgba,colorchannelmixer=aa=1.0[ov];[bg][ov]overlay=(main_w-overlay_w*0.9)/2:main_h-overlay_h*0.9/2[output]",
- "-t",
- str(duration),
- "-map",
- "[output]",
- "-map",
- "2:a",
- "-c:v",
- "libx264",
- "-c:a",
- "aac",
- "-shortest",
- "-y",
- output_mp4.name,
- ]
- elif animate and bg_image is None:
- ffmpeg_cmd = [
- ffmpeg,
- "-i",
- tmp_img.name,
- "-i",
- audio_file,
- "-filter_complex",
- "[0:v][1:a]concat=n=1:v=1:a=1[v][a]",
- "-map",
- "[v]",
- "-map",
- "[a]",
- "-c:v",
- "libx264",
- "-c:a",
- "aac",
- "-shortest",
- "-y",
- output_mp4.name,
- ]
- else:
- ffmpeg_cmd = [
- ffmpeg,
- "-loop",
- "1",
- "-i",
- tmp_img.name,
- "-i",
- audio_file,
- "-vf",
- f"color=c=#FFFFFF77:s={img_width}x{img_height}[bar];[0][bar]overlay=-w+(w/{duration})*t:H-h:shortest=1", # type: ignore
- "-t",
- str(duration),
- "-y",
- output_mp4.name,
- ]
-
- subprocess.check_call(ffmpeg_cmd)
- return output_mp4.name
-
-
-@document()
-class EventData:
- """
- When a subclass of EventData is added as a type hint to an argument of an event listener method, this object will be passed as that argument.
- It contains information about the event that triggered the listener, such the target object, and other data related to the specific event that are attributes of the subclass.
-
- Example:
- table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])
- gallery = gr.Gallery([("cat.jpg", "Cat"), ("dog.jpg", "Dog")])
- textbox = gr.Textbox("Hello World!")
-
- statement = gr.Textbox()
-
- def on_select(evt: gr.SelectData): # SelectData is a subclass of EventData
- return f"You selected {evt.value} at {evt.index} from {evt.target}"
-
- table.select(on_select, None, statement)
- gallery.select(on_select, None, statement)
- textbox.select(on_select, None, statement)
- Demos: gallery_selections, tictactoe
- """
-
- def __init__(self, target: Block | None, _data: Any):
- """
- Parameters:
- target: The target object that triggered the event. Can be used to distinguish if multiple components are bound to the same listener.
- """
- self.target = target
- self._data = _data
-
-
-def log_message(message: str, level: Literal["info", "warning"] = "info"):
- from gradio import context
-
- if not hasattr(context.thread_data, "blocks"): # Function called outside of Gradio
- if level == "info":
- print(message)
- elif level == "warning":
- warnings.warn(message)
- return
- if not context.thread_data.blocks.enable_queue:
- warnings.warn(
- f"Queueing must be enabled to issue {level.capitalize()}: '{message}'."
- )
- return
- context.thread_data.blocks._queue.log_message(
- event_id=context.thread_data.event_id, log=message, level=level
- )
-
-
-@document()
-def Warning(message: str = "Warning issued."): # noqa: N802
- """
- This function allows you to pass custom warning messages to the user. You can do so simply with `gr.Warning('message here')`, and when that line is executed the custom message will appear in a modal on the demo.
- Parameters:
- message: The warning message to be displayed to the user.
- """
- log_message(message, level="warning")
-
-
-@document()
-def Info(message: str = "Info issued."): # noqa: N802
- """
- Parameters:
- message: The info message to be displayed to the user.
- """
- log_message(message, level="info")
diff --git a/spaces/dcq/freegpt-webui/client/css/main.css b/spaces/dcq/freegpt-webui/client/css/main.css
deleted file mode 100644
index 3f52d4c18322b564d3bdd7ef7b032cf4e40310b4..0000000000000000000000000000000000000000
--- a/spaces/dcq/freegpt-webui/client/css/main.css
+++ /dev/null
@@ -1,7 +0,0 @@
-.main-container {
- display: flex;
- padding: var(--section-gap);
- height: 100vh;
- justify-content: center;
- box-sizing: border-box;
-}
diff --git a/spaces/declare-lab/tango/diffusers/examples/research_projects/lora/README.md b/spaces/declare-lab/tango/diffusers/examples/research_projects/lora/README.md
deleted file mode 100644
index b5d72403166f9b4017751c3d47f79a9eb3f535d8..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/research_projects/lora/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Stable Diffusion text-to-image fine-tuning
-This extended LoRA training script was authored by [haofanwang](https://github.com/haofanwang).
-This is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py). We further support add LoRA layers for text encoder.
-
-## Training with LoRA
-
-Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
-
-In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
-
-- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).
-- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
-- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.
-
-[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.
-
-With LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset
-on consumer GPUs like Tesla T4, Tesla V100.
-
-### Training
-
-First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
-```
-
-For this example we want to directly store the trained LoRA embeddings on the Hub, so
-we need to be logged in and add the `--push_to_hub` flag.
-
-```bash
-huggingface-cli login
-```
-
-Now we can start training!
-
-```bash
-accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --dataset_name=$DATASET_NAME --caption_column="text" \
- --resolution=512 --random_flip \
- --train_batch_size=1 \
- --num_train_epochs=100 --checkpointing_steps=5000 \
- --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
- --seed=42 \
- --output_dir="sd-pokemon-model-lora" \
- --validation_prompt="cute dragon creature" --report_to="wandb"
- --use_peft \
- --lora_r=4 --lora_alpha=32 \
- --lora_text_encoder_r=4 --lora_text_encoder_alpha=32
-```
-
-The above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.
-
-**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run `train_text_to_image_lora.py` in consumer GPUs like T4 or V100.___**
-
-The final LoRA embedding weights have been uploaded to [sayakpaul/sd-model-finetuned-lora-t4](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) are only 3 MB in size, which is orders of magnitudes smaller than the original model.___**
-
-You can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw).
-
-### Inference
-
-Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights. You
-need to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-pokemon-model-lora`.
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_path = "sayakpaul/sd-model-finetuned-lora-t4"
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
-pipe.unet.load_attn_procs(model_path)
-pipe.to("cuda")
-
-prompt = "A pokemon with green eyes and red legs."
-image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
-image.save("pokemon.png")
-```
\ No newline at end of file
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/models/resnet.py b/spaces/declare-lab/tango/diffusers/src/diffusers/models/resnet.py
deleted file mode 100644
index 98f8f19c896a3567e12e59ee02c20168e94ff0f0..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/models/resnet.py
+++ /dev/null
@@ -1,839 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from functools import partial
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .attention import AdaGroupNorm
-
-
-class Upsample1D(nn.Module):
- """
- An upsampling layer with an optional convolution.
-
- Parameters:
- channels: channels in the inputs and outputs.
- use_conv: a bool determining if a convolution is applied.
- use_conv_transpose:
- out_channels:
- """
-
- def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_conv_transpose = use_conv_transpose
- self.name = name
-
- self.conv = None
- if use_conv_transpose:
- self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
- elif use_conv:
- self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- if self.use_conv_transpose:
- return self.conv(x)
-
- x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-
- if self.use_conv:
- x = self.conv(x)
-
- return x
-
-
-class Downsample1D(nn.Module):
- """
- A downsampling layer with an optional convolution.
-
- Parameters:
- channels: channels in the inputs and outputs.
- use_conv: a bool determining if a convolution is applied.
- out_channels:
- padding:
- """
-
- def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.padding = padding
- stride = 2
- self.name = name
-
- if use_conv:
- self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
- else:
- assert self.channels == self.out_channels
- self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- return self.conv(x)
-
-
-class Upsample2D(nn.Module):
- """
- An upsampling layer with an optional convolution.
-
- Parameters:
- channels: channels in the inputs and outputs.
- use_conv: a bool determining if a convolution is applied.
- use_conv_transpose:
- out_channels:
- """
-
- def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_conv_transpose = use_conv_transpose
- self.name = name
-
- conv = None
- if use_conv_transpose:
- conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
- elif use_conv:
- conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)
-
- # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
- if name == "conv":
- self.conv = conv
- else:
- self.Conv2d_0 = conv
-
- def forward(self, hidden_states, output_size=None):
- assert hidden_states.shape[1] == self.channels
-
- if self.use_conv_transpose:
- return self.conv(hidden_states)
-
- # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
- # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
- # https://github.com/pytorch/pytorch/issues/86679
- dtype = hidden_states.dtype
- if dtype == torch.bfloat16:
- hidden_states = hidden_states.to(torch.float32)
-
- # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
- if hidden_states.shape[0] >= 64:
- hidden_states = hidden_states.contiguous()
-
- # if `output_size` is passed we force the interpolation output
- # size and do not make use of `scale_factor=2`
- if output_size is None:
- hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
- else:
- hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
-
- # If the input is bfloat16, we cast back to bfloat16
- if dtype == torch.bfloat16:
- hidden_states = hidden_states.to(dtype)
-
- # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
- if self.use_conv:
- if self.name == "conv":
- hidden_states = self.conv(hidden_states)
- else:
- hidden_states = self.Conv2d_0(hidden_states)
-
- return hidden_states
-
-
-class Downsample2D(nn.Module):
- """
- A downsampling layer with an optional convolution.
-
- Parameters:
- channels: channels in the inputs and outputs.
- use_conv: a bool determining if a convolution is applied.
- out_channels:
- padding:
- """
-
- def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.padding = padding
- stride = 2
- self.name = name
-
- if use_conv:
- conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
- else:
- assert self.channels == self.out_channels
- conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
-
- # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
- if name == "conv":
- self.Conv2d_0 = conv
- self.conv = conv
- elif name == "Conv2d_0":
- self.conv = conv
- else:
- self.conv = conv
-
- def forward(self, hidden_states):
- assert hidden_states.shape[1] == self.channels
- if self.use_conv and self.padding == 0:
- pad = (0, 1, 0, 1)
- hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
-
- assert hidden_states.shape[1] == self.channels
- hidden_states = self.conv(hidden_states)
-
- return hidden_states
-
-
-class FirUpsample2D(nn.Module):
- def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
- super().__init__()
- out_channels = out_channels if out_channels else channels
- if use_conv:
- self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
- self.use_conv = use_conv
- self.fir_kernel = fir_kernel
- self.out_channels = out_channels
-
- def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
- """Fused `upsample_2d()` followed by `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:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- weight: Weight tensor of the shape `[filterH, filterW, inChannels,
- outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
- kernel: 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).
-
- Returns:
- output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
- datatype as `hidden_states`.
- """
-
- assert isinstance(factor, int) and factor >= 1
-
- # Setup filter kernel.
- if kernel is None:
- kernel = [1] * factor
-
- # setup kernel
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * (gain * (factor**2))
-
- if self.use_conv:
- convH = weight.shape[2]
- convW = weight.shape[3]
- inC = weight.shape[1]
-
- pad_value = (kernel.shape[0] - factor) - (convW - 1)
-
- stride = (factor, factor)
- # Determine data dimensions.
- output_shape = (
- (hidden_states.shape[2] - 1) * factor + convH,
- (hidden_states.shape[3] - 1) * factor + convW,
- )
- output_padding = (
- output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
- output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
- )
- assert output_padding[0] >= 0 and output_padding[1] >= 0
- num_groups = hidden_states.shape[1] // inC
-
- # Transpose weights.
- weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
- weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
- weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
-
- inverse_conv = F.conv_transpose2d(
- hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
- )
-
- output = upfirdn2d_native(
- inverse_conv,
- torch.tensor(kernel, device=inverse_conv.device),
- pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
- )
- else:
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states,
- torch.tensor(kernel, device=hidden_states.device),
- up=factor,
- pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
- )
-
- return output
-
- def forward(self, hidden_states):
- if self.use_conv:
- height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
- height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
- else:
- height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
-
- return height
-
-
-class FirDownsample2D(nn.Module):
- def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
- super().__init__()
- out_channels = out_channels if out_channels else channels
- if use_conv:
- self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
- self.fir_kernel = fir_kernel
- self.use_conv = use_conv
- self.out_channels = out_channels
-
- def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
- """Fused `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:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- weight:
- Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
- performed by `inChannels = x.shape[0] // numGroups`.
- kernel: 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).
-
- Returns:
- output: 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
- if kernel is None:
- kernel = [1] * factor
-
- # setup kernel
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * gain
-
- if self.use_conv:
- _, _, convH, convW = weight.shape
- pad_value = (kernel.shape[0] - factor) + (convW - 1)
- stride_value = [factor, factor]
- upfirdn_input = upfirdn2d_native(
- hidden_states,
- torch.tensor(kernel, device=hidden_states.device),
- pad=((pad_value + 1) // 2, pad_value // 2),
- )
- output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
- else:
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states,
- torch.tensor(kernel, device=hidden_states.device),
- down=factor,
- pad=((pad_value + 1) // 2, pad_value // 2),
- )
-
- return output
-
- def forward(self, hidden_states):
- if self.use_conv:
- downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
- hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
- else:
- hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
-
- return hidden_states
-
-
-# downsample/upsample layer used in k-upscaler, might be able to use FirDownsample2D/DirUpsample2D instead
-class KDownsample2D(nn.Module):
- def __init__(self, pad_mode="reflect"):
- super().__init__()
- self.pad_mode = pad_mode
- kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]])
- self.pad = kernel_1d.shape[1] // 2 - 1
- self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
-
- def forward(self, x):
- x = F.pad(x, (self.pad,) * 4, self.pad_mode)
- weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
- indices = torch.arange(x.shape[1], device=x.device)
- weight[indices, indices] = self.kernel.to(weight)
- return F.conv2d(x, weight, stride=2)
-
-
-class KUpsample2D(nn.Module):
- def __init__(self, pad_mode="reflect"):
- super().__init__()
- self.pad_mode = pad_mode
- kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2
- self.pad = kernel_1d.shape[1] // 2 - 1
- self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
-
- def forward(self, x):
- x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode)
- weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
- indices = torch.arange(x.shape[1], device=x.device)
- weight[indices, indices] = self.kernel.to(weight)
- return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1)
-
-
-class ResnetBlock2D(nn.Module):
- r"""
- A Resnet block.
-
- Parameters:
- in_channels (`int`): The number of channels in the input.
- out_channels (`int`, *optional*, default to be `None`):
- The number of output channels for the first conv2d layer. If None, same as `in_channels`.
- dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
- temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
- groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
- groups_out (`int`, *optional*, default to None):
- The number of groups to use for the second normalization layer. if set to None, same as `groups`.
- eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
- non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
- time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
- By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
- "ada_group" for a stronger conditioning with scale and shift.
- kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
- [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
- output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
- use_in_shortcut (`bool`, *optional*, default to `True`):
- If `True`, add a 1x1 nn.conv2d layer for skip-connection.
- up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
- down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
- conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
- `conv_shortcut` output.
- conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
- If None, same as `out_channels`.
- """
-
- def __init__(
- self,
- *,
- in_channels,
- out_channels=None,
- conv_shortcut=False,
- dropout=0.0,
- temb_channels=512,
- groups=32,
- groups_out=None,
- pre_norm=True,
- eps=1e-6,
- non_linearity="swish",
- time_embedding_norm="default", # default, scale_shift, ada_group
- kernel=None,
- output_scale_factor=1.0,
- use_in_shortcut=None,
- up=False,
- down=False,
- conv_shortcut_bias: bool = True,
- conv_2d_out_channels: Optional[int] = None,
- ):
- super().__init__()
- self.pre_norm = pre_norm
- self.pre_norm = True
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
- self.up = up
- self.down = down
- self.output_scale_factor = output_scale_factor
- self.time_embedding_norm = time_embedding_norm
-
- if groups_out is None:
- groups_out = groups
-
- if self.time_embedding_norm == "ada_group":
- self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
- else:
- self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
-
- self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
- if temb_channels is not None:
- if self.time_embedding_norm == "default":
- self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
- elif self.time_embedding_norm == "scale_shift":
- self.time_emb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
- elif self.time_embedding_norm == "ada_group":
- self.time_emb_proj = None
- else:
- raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
- else:
- self.time_emb_proj = None
-
- if self.time_embedding_norm == "ada_group":
- self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
- else:
- self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
-
- self.dropout = torch.nn.Dropout(dropout)
- conv_2d_out_channels = conv_2d_out_channels or out_channels
- self.conv2 = torch.nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
-
- if non_linearity == "swish":
- self.nonlinearity = lambda x: F.silu(x)
- elif non_linearity == "mish":
- self.nonlinearity = nn.Mish()
- elif non_linearity == "silu":
- self.nonlinearity = nn.SiLU()
- elif non_linearity == "gelu":
- self.nonlinearity = nn.GELU()
-
- self.upsample = self.downsample = None
- if self.up:
- if kernel == "fir":
- fir_kernel = (1, 3, 3, 1)
- self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
- elif kernel == "sde_vp":
- self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
- else:
- self.upsample = Upsample2D(in_channels, use_conv=False)
- elif self.down:
- if kernel == "fir":
- fir_kernel = (1, 3, 3, 1)
- self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
- elif kernel == "sde_vp":
- self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
- else:
- self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
-
- self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
-
- self.conv_shortcut = None
- if self.use_in_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(
- in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
- )
-
- def forward(self, input_tensor, temb):
- hidden_states = input_tensor
-
- if self.time_embedding_norm == "ada_group":
- hidden_states = self.norm1(hidden_states, temb)
- else:
- hidden_states = self.norm1(hidden_states)
-
- hidden_states = self.nonlinearity(hidden_states)
-
- if self.upsample is not None:
- # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
- if hidden_states.shape[0] >= 64:
- input_tensor = input_tensor.contiguous()
- hidden_states = hidden_states.contiguous()
- input_tensor = self.upsample(input_tensor)
- hidden_states = self.upsample(hidden_states)
- elif self.downsample is not None:
- input_tensor = self.downsample(input_tensor)
- hidden_states = self.downsample(hidden_states)
-
- hidden_states = self.conv1(hidden_states)
-
- if self.time_emb_proj is not None:
- temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
-
- if temb is not None and self.time_embedding_norm == "default":
- hidden_states = hidden_states + temb
-
- if self.time_embedding_norm == "ada_group":
- hidden_states = self.norm2(hidden_states, temb)
- else:
- hidden_states = self.norm2(hidden_states)
-
- if temb is not None and self.time_embedding_norm == "scale_shift":
- scale, shift = torch.chunk(temb, 2, dim=1)
- hidden_states = hidden_states * (1 + scale) + shift
-
- hidden_states = self.nonlinearity(hidden_states)
-
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.conv2(hidden_states)
-
- if self.conv_shortcut is not None:
- input_tensor = self.conv_shortcut(input_tensor)
-
- output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
-
- return output_tensor
-
-
-class Mish(torch.nn.Module):
- def forward(self, hidden_states):
- return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
-
-
-# unet_rl.py
-def rearrange_dims(tensor):
- if len(tensor.shape) == 2:
- return tensor[:, :, None]
- if len(tensor.shape) == 3:
- return tensor[:, :, None, :]
- elif len(tensor.shape) == 4:
- return tensor[:, :, 0, :]
- else:
- raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
-
-
-class Conv1dBlock(nn.Module):
- """
- Conv1d --> GroupNorm --> Mish
- """
-
- def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
- super().__init__()
-
- self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
- self.group_norm = nn.GroupNorm(n_groups, out_channels)
- self.mish = nn.Mish()
-
- def forward(self, x):
- x = self.conv1d(x)
- x = rearrange_dims(x)
- x = self.group_norm(x)
- x = rearrange_dims(x)
- x = self.mish(x)
- return x
-
-
-# unet_rl.py
-class ResidualTemporalBlock1D(nn.Module):
- def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5):
- super().__init__()
- self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)
- self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)
-
- self.time_emb_act = nn.Mish()
- self.time_emb = nn.Linear(embed_dim, out_channels)
-
- self.residual_conv = (
- nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
- )
-
- def forward(self, x, t):
- """
- Args:
- x : [ batch_size x inp_channels x horizon ]
- t : [ batch_size x embed_dim ]
-
- returns:
- out : [ batch_size x out_channels x horizon ]
- """
- t = self.time_emb_act(t)
- t = self.time_emb(t)
- out = self.conv_in(x) + rearrange_dims(t)
- out = self.conv_out(out)
- return out + self.residual_conv(x)
-
-
-def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
- r"""Upsample2D 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:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- kernel: 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).
-
- Returns:
- output: Tensor of the shape `[N, C, H * factor, W * factor]`
- """
- assert isinstance(factor, int) and factor >= 1
- if kernel is None:
- kernel = [1] * factor
-
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * (gain * (factor**2))
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states,
- kernel.to(device=hidden_states.device),
- up=factor,
- pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
- )
- return output
-
-
-def downsample_2d(hidden_states, kernel=None, factor=2, gain=1):
- r"""Downsample2D 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:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- kernel: 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).
-
- Returns:
- output: Tensor of the shape `[N, C, H // factor, W // factor]`
- """
-
- assert isinstance(factor, int) and factor >= 1
- if kernel is None:
- kernel = [1] * factor
-
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * gain
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
- )
- return output
-
-
-def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)):
- up_x = up_y = up
- down_x = down_y = down
- pad_x0 = pad_y0 = pad[0]
- pad_x1 = pad_y1 = pad[1]
-
- _, channel, in_h, in_w = tensor.shape
- tensor = tensor.reshape(-1, in_h, in_w, 1)
-
- _, in_h, in_w, minor = tensor.shape
- kernel_h, kernel_w = kernel.shape
-
- out = tensor.view(-1, in_h, 1, in_w, 1, minor)
- out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
- out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
- out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
- out = out.to(tensor.device) # Move back to mps if necessary
- out = out[
- :,
- max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
- max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
- :,
- ]
-
- out = out.permute(0, 3, 1, 2)
- out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
- w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
- out = F.conv2d(out, w)
- out = out.reshape(
- -1,
- minor,
- in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
- in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
- )
- out = out.permute(0, 2, 3, 1)
- out = out[:, ::down_y, ::down_x, :]
-
- out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
- out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
- return out.view(-1, channel, out_h, out_w)
-
-
-class TemporalConvLayer(nn.Module):
- """
- Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
- https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
- """
-
- def __init__(self, in_dim, out_dim=None, dropout=0.0):
- super().__init__()
- out_dim = out_dim or in_dim
- self.in_dim = in_dim
- self.out_dim = out_dim
-
- # conv layers
- self.conv1 = nn.Sequential(
- nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
- )
- self.conv2 = nn.Sequential(
- nn.GroupNorm(32, out_dim),
- nn.SiLU(),
- nn.Dropout(dropout),
- nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
- )
- self.conv3 = nn.Sequential(
- nn.GroupNorm(32, out_dim),
- nn.SiLU(),
- nn.Dropout(dropout),
- nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
- )
- self.conv4 = nn.Sequential(
- nn.GroupNorm(32, out_dim),
- nn.SiLU(),
- nn.Dropout(dropout),
- nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
- )
-
- # zero out the last layer params,so the conv block is identity
- nn.init.zeros_(self.conv4[-1].weight)
- nn.init.zeros_(self.conv4[-1].bias)
-
- def forward(self, hidden_states, num_frames=1):
- hidden_states = (
- hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)
- )
-
- identity = hidden_states
- hidden_states = self.conv1(hidden_states)
- hidden_states = self.conv2(hidden_states)
- hidden_states = self.conv3(hidden_states)
- hidden_states = self.conv4(hidden_states)
-
- hidden_states = identity + hidden_states
-
- hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(
- (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
- )
- return hidden_states
diff --git a/spaces/deepkyu/multilingual-font-style-transfer/README.md b/spaces/deepkyu/multilingual-font-style-transfer/README.md
deleted file mode 100644
index 4735fa61e12483eb44ae47a9249b430a0b03f9c8..0000000000000000000000000000000000000000
--- a/spaces/deepkyu/multilingual-font-style-transfer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual Font Style Transfer
-emoji: 🔠
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/docs/modelzoo.md b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/docs/modelzoo.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/options/test_options.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/options/test_options.py
deleted file mode 100644
index 4ff3ad142779850d1d5a1640bc00f70d34d4a862..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/options/test_options.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""This script contains the test options for Deep3DFaceRecon_pytorch
-"""
-
-from .base_options import BaseOptions
-
-
-class TestOptions(BaseOptions):
- """This class includes test options.
-
- It also includes shared options defined in BaseOptions.
- """
-
- def initialize(self, parser):
- parser = BaseOptions.initialize(self, parser) # define shared options
- parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
- parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]')
- parser.add_argument('--img_folder', type=str, default='examples', help='folder for test images.')
-
- # Dropout and Batchnorm has different behavior during training and test.
- self.isTrain = False
- return parser
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/actions/__init__.py b/spaces/deepwisdom/MetaGPT/metagpt/actions/__init__.py
deleted file mode 100644
index b004bd58ee8c48dca8032604cba48c1ef03e41e1..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/actions/__init__.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/5/11 17:44
-@Author : alexanderwu
-@File : __init__.py
-"""
-from enum import Enum
-
-from metagpt.actions.action import Action
-from metagpt.actions.action_output import ActionOutput
-from metagpt.actions.add_requirement import BossRequirement
-from metagpt.actions.debug_error import DebugError
-from metagpt.actions.design_api import WriteDesign
-from metagpt.actions.design_api_review import DesignReview
-from metagpt.actions.design_filenames import DesignFilenames
-from metagpt.actions.project_management import AssignTasks, WriteTasks
-from metagpt.actions.research import CollectLinks, WebBrowseAndSummarize, ConductResearch
-from metagpt.actions.run_code import RunCode
-from metagpt.actions.search_and_summarize import SearchAndSummarize
-from metagpt.actions.write_code import WriteCode
-from metagpt.actions.write_code_review import WriteCodeReview
-from metagpt.actions.write_prd import WritePRD
-from metagpt.actions.write_prd_review import WritePRDReview
-from metagpt.actions.write_test import WriteTest
-
-
-class ActionType(Enum):
- """All types of Actions, used for indexing."""
-
- ADD_REQUIREMENT = BossRequirement
- WRITE_PRD = WritePRD
- WRITE_PRD_REVIEW = WritePRDReview
- WRITE_DESIGN = WriteDesign
- DESIGN_REVIEW = DesignReview
- DESIGN_FILENAMES = DesignFilenames
- WRTIE_CODE = WriteCode
- WRITE_CODE_REVIEW = WriteCodeReview
- WRITE_TEST = WriteTest
- RUN_CODE = RunCode
- DEBUG_ERROR = DebugError
- WRITE_TASKS = WriteTasks
- ASSIGN_TASKS = AssignTasks
- SEARCH_AND_SUMMARIZE = SearchAndSummarize
- COLLECT_LINKS = CollectLinks
- WEB_BROWSE_AND_SUMMARIZE = WebBrowseAndSummarize
- CONDUCT_RESEARCH = ConductResearch
-
-
-__all__ = [
- "ActionType",
- "Action",
- "ActionOutput",
-]
diff --git a/spaces/derful/Chatgpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h b/spaces/derful/Chatgpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/derful/Chatgpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
- template <std::size_t DataSize, std::size_t AlignSize>
- struct elem_t {
- std::aligned_storage_t<DataSize, AlignSize> data_ {};
- };
-
- alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
- alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
- constexpr circ::u2_t cursor() const noexcept {
- return 0;
- }
-
- template <typename W, typename F, typename E>
- bool push(W* /*wrapper*/, F&& f, E* elems) {
- auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
- if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
- return false; // full
- }
- std::forward<F>(f)(&(elems[cur_wt].data_));
- wt_.fetch_add(1, std::memory_order_release);
- return true;
- }
-
- /**
- * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
- * So we could just disconnect all connections of receiver, and return false.
- */
- template <typename W, typename F, typename E>
- bool force_push(W* wrapper, F&&, E*) {
- wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
- return false;
- }
-
- template <typename W, typename F, typename R, typename E>
- bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
- auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
- if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
- return false; // empty
- }
- std::forward<F>(f)(&(elems[cur_rd].data_));
- std::forward<R>(out)(true);
- rd_.fetch_add(1, std::memory_order_release);
- return true;
- }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
- : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
- template <typename W, typename F, typename E>
- bool force_push(W* wrapper, F&&, E*) {
- wrapper->elems()->disconnect_receiver(1);
- return false;
- }
-
- template <typename W, typename F, typename R,
- template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
- bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
- byte_t buff[DS];
- for (unsigned k = 0;;) {
- auto cur_rd = rd_.load(std::memory_order_relaxed);
- if (circ::index_of(cur_rd) ==
- circ::index_of(wt_.load(std::memory_order_acquire))) {
- return false; // empty
- }
- std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
- if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
- std::forward<F>(f)(buff);
- std::forward<R>(out)(true);
- return true;
- }
- ipc::yield(k);
- }
- }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
- : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
- using flag_t = std::uint64_t;
-
- template <std::size_t DataSize, std::size_t AlignSize>
- struct elem_t {
- std::aligned_storage_t<DataSize, AlignSize> data_ {};
- std::atomic<flag_t> f_ct_ { 0 }; // commit flag
- };
-
- alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
- template <typename W, typename F, typename E>
- bool push(W* /*wrapper*/, F&& f, E* elems) {
- circ::u2_t cur_ct, nxt_ct;
- for (unsigned k = 0;;) {
- cur_ct = ct_.load(std::memory_order_relaxed);
- if (circ::index_of(nxt_ct = cur_ct + 1) ==
- circ::index_of(rd_.load(std::memory_order_acquire))) {
- return false; // full
- }
- if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
- break;
- }
- ipc::yield(k);
- }
- auto* el = elems + circ::index_of(cur_ct);
- std::forward<F>(f)(&(el->data_));
- // set flag & try update wt
- el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
- while (1) {
- auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
- if (cur_ct != wt_.load(std::memory_order_relaxed)) {
- return true;
- }
- if ((~cac_ct) != cur_ct) {
- return true;
- }
- if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
- return true;
- }
- wt_.store(nxt_ct, std::memory_order_release);
- cur_ct = nxt_ct;
- nxt_ct = cur_ct + 1;
- el = elems + circ::index_of(cur_ct);
- }
- return true;
- }
-
- template <typename W, typename F, typename E>
- bool force_push(W* wrapper, F&&, E*) {
- wrapper->elems()->disconnect_receiver(1);
- return false;
- }
-
- template <typename W, typename F, typename R,
- template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
- bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
- byte_t buff[DS];
- for (unsigned k = 0;;) {
- auto cur_rd = rd_.load(std::memory_order_relaxed);
- auto cur_wt = wt_.load(std::memory_order_acquire);
- auto id_rd = circ::index_of(cur_rd);
- auto id_wt = circ::index_of(cur_wt);
- if (id_rd == id_wt) {
- auto* el = elems + id_wt;
- auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
- if ((~cac_ct) != cur_wt) {
- return false; // empty
- }
- if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
- wt_.store(cur_wt + 1, std::memory_order_release);
- }
- k = 0;
- }
- else {
- std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
- if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
- std::forward<F>(f)(buff);
- std::forward<R>(out)(true);
- return true;
- }
- ipc::yield(k);
- }
- }
- }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
- using rc_t = std::uint64_t;
-
- enum : rc_t {
- ep_mask = 0x00000000ffffffffull,
- ep_incr = 0x0000000100000000ull
- };
-
- template <std::size_t DataSize, std::size_t AlignSize>
- struct elem_t {
- std::aligned_storage_t<DataSize, AlignSize> data_ {};
- std::atomic<rc_t> rc_ { 0 }; // read-counter
- };
-
- alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
- alignas(cache_line_size) rc_t epoch_ { 0 }; // only one writer
-
- circ::u2_t cursor() const noexcept {
- return wt_.load(std::memory_order_acquire);
- }
-
- template <typename W, typename F, typename E>
- bool push(W* wrapper, F&& f, E* elems) {
- E* el;
- for (unsigned k = 0;;) {
- circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
- if (cc == 0) return false; // no reader
- el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
- // check all consumers have finished reading this element
- auto cur_rc = el->rc_.load(std::memory_order_acquire);
- circ::cc_t rem_cc = cur_rc & ep_mask;
- if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
- return false; // has not finished yet
- }
- // consider rem_cc to be 0 here
- if (el->rc_.compare_exchange_weak(
- cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
- break;
- }
- ipc::yield(k);
- }
- std::forward<F>(f)(&(el->data_));
- wt_.fetch_add(1, std::memory_order_release);
- return true;
- }
-
- template <typename W, typename F, typename E>
- bool force_push(W* wrapper, F&& f, E* elems) {
- E* el;
- epoch_ += ep_incr;
- for (unsigned k = 0;;) {
- circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
- if (cc == 0) return false; // no reader
- el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
- // check all consumers have finished reading this element
- auto cur_rc = el->rc_.load(std::memory_order_acquire);
- circ::cc_t rem_cc = cur_rc & ep_mask;
- if (cc & rem_cc) {
- ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
- cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
- if (cc == 0) return false; // no reader
- }
- // just compare & exchange
- if (el->rc_.compare_exchange_weak(
- cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
- break;
- }
- ipc::yield(k);
- }
- std::forward<F>(f)(&(el->data_));
- wt_.fetch_add(1, std::memory_order_release);
- return true;
- }
-
- template <typename W, typename F, typename R, typename E>
- bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
- if (cur == cursor()) return false; // acquire
- auto* el = elems + circ::index_of(cur++);
- std::forward<F>(f)(&(el->data_));
- for (unsigned k = 0;;) {
- auto cur_rc = el->rc_.load(std::memory_order_acquire);
- if ((cur_rc & ep_mask) == 0) {
- std::forward<R>(out)(true);
- return true;
- }
- auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
- if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
- std::forward<R>(out)((nxt_rc & ep_mask) == 0);
- return true;
- }
- ipc::yield(k);
- }
- }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
- using rc_t = std::uint64_t;
- using flag_t = std::uint64_t;
-
- enum : rc_t {
- rc_mask = 0x00000000ffffffffull,
- ep_mask = 0x00ffffffffffffffull,
- ep_incr = 0x0100000000000000ull,
- ic_mask = 0xff000000ffffffffull,
- ic_incr = 0x0000000100000000ull
- };
-
- template <std::size_t DataSize, std::size_t AlignSize>
- struct elem_t {
- std::aligned_storage_t<DataSize, AlignSize> data_ {};
- std::atomic<rc_t > rc_ { 0 }; // read-counter
- std::atomic<flag_t> f_ct_ { 0 }; // commit flag
- };
-
- alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
- alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
- circ::u2_t cursor() const noexcept {
- return ct_.load(std::memory_order_acquire);
- }
-
- constexpr static rc_t inc_rc(rc_t rc) noexcept {
- return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
- }
-
- constexpr static rc_t inc_mask(rc_t rc) noexcept {
- return inc_rc(rc) & ~rc_mask;
- }
-
- template <typename W, typename F, typename E>
- bool push(W* wrapper, F&& f, E* elems) {
- E* el;
- circ::u2_t cur_ct;
- rc_t epoch = epoch_.load(std::memory_order_acquire);
- for (unsigned k = 0;;) {
- circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
- if (cc == 0) return false; // no reader
- el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
- // check all consumers have finished reading this element
- auto cur_rc = el->rc_.load(std::memory_order_relaxed);
- circ::cc_t rem_cc = cur_rc & rc_mask;
- if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
- return false; // has not finished yet
- }
- else if (!rem_cc) {
- auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
- if ((cur_fl != cur_ct) && cur_fl) {
- return false; // full
- }
- }
- // consider rem_cc to be 0 here
- if (el->rc_.compare_exchange_weak(
- cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
- epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
- break;
- }
- ipc::yield(k);
- }
- // only one thread/process would touch here at one time
- ct_.store(cur_ct + 1, std::memory_order_release);
- std::forward<F>(f)(&(el->data_));
- // set flag & try update wt
- el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
- return true;
- }
-
- template <typename W, typename F, typename E>
- bool force_push(W* wrapper, F&& f, E* elems) {
- E* el;
- circ::u2_t cur_ct;
- rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
- for (unsigned k = 0;;) {
- circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
- if (cc == 0) return false; // no reader
- el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
- // check all consumers have finished reading this element
- auto cur_rc = el->rc_.load(std::memory_order_acquire);
- circ::cc_t rem_cc = cur_rc & rc_mask;
- if (cc & rem_cc) {
- ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
- cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
- if (cc == 0) return false; // no reader
- }
- // just compare & exchange
- if (el->rc_.compare_exchange_weak(
- cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
- if (epoch == epoch_.load(std::memory_order_acquire)) {
- break;
- }
- else if (push(wrapper, std::forward<F>(f), elems)) {
- return true;
- }
- epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
- }
- ipc::yield(k);
- }
- // only one thread/process would touch here at one time
- ct_.store(cur_ct + 1, std::memory_order_release);
- std::forward<F>(f)(&(el->data_));
- // set flag & try update wt
- el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
- return true;
- }
-
- template <typename W, typename F, typename R, typename E, std::size_t N>
- bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
- auto* el = elems + circ::index_of(cur);
- auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
- if (cur_fl != ~static_cast<flag_t>(cur)) {
- return false; // empty
- }
- ++cur;
- std::forward<F>(f)(&(el->data_));
- for (unsigned k = 0;;) {
- auto cur_rc = el->rc_.load(std::memory_order_acquire);
- if ((cur_rc & rc_mask) == 0) {
- std::forward<R>(out)(true);
- el->f_ct_.store(cur + N - 1, std::memory_order_release);
- return true;
- }
- auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
- bool last_one = false;
- if ((last_one = (nxt_rc & rc_mask) == 0)) {
- el->f_ct_.store(cur + N - 1, std::memory_order_release);
- }
- if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
- std::forward<R>(out)(last_one);
- return true;
- }
- ipc::yield(k);
- }
- }
-};
-
-} // namespace ipc
diff --git a/spaces/devthedeveloper/Bark-with-Voice-Cloning/training/training_prepare.py b/spaces/devthedeveloper/Bark-with-Voice-Cloning/training/training_prepare.py
deleted file mode 100644
index da4b30622d096fe636a0db358c43336eeef4d959..0000000000000000000000000000000000000000
--- a/spaces/devthedeveloper/Bark-with-Voice-Cloning/training/training_prepare.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import random
-import uuid
-import numpy
-import os
-import random
-import fnmatch
-
-from tqdm.auto import tqdm
-from scipy.io import wavfile
-
-from bark.generation import load_model, SAMPLE_RATE
-from bark.api import semantic_to_waveform
-
-from bark import text_to_semantic
-from bark.generation import load_model
-
-from training.data import load_books, random_split_chunk
-
-output = 'training/data/output'
-output_wav = 'training/data/output_wav'
-
-
-def prepare_semantics_from_text(num_generations):
- loaded_data = load_books(True)
-
- print('Loading semantics model')
- load_model(use_gpu=True, use_small=False, force_reload=False, model_type='text')
-
- if not os.path.isdir(output):
- os.mkdir(output)
-
- loop = 1
- while 1:
- filename = uuid.uuid4().hex + '.npy'
- file_name = os.path.join(output, filename)
- text = ''
- while not len(text) > 0:
- text = random_split_chunk(loaded_data) # Obtain a short chunk of text
- text = text.strip()
- print(f'{loop} Generating semantics for text:', text)
- loop+=1
- semantics = text_to_semantic(text, temp=round(random.uniform(0.6, 0.8), ndigits=2))
- numpy.save(file_name, semantics)
-
-
-def prepare_wavs_from_semantics():
- if not os.path.isdir(output):
- raise Exception('No \'output\' folder, make sure you run create_data.py first!')
- if not os.path.isdir(output_wav):
- os.mkdir(output_wav)
-
- print('Loading coarse model')
- load_model(use_gpu=True, use_small=False, force_reload=False, model_type='coarse')
- print('Loading fine model')
- load_model(use_gpu=True, use_small=False, force_reload=False, model_type='fine')
-
- files = fnmatch.filter(os.listdir(output), '*.npy')
- current = 1
- total = len(files)
-
- for i, f in tqdm(enumerate(files), total=len(files)):
- real_name = '.'.join(f.split('.')[:-1]) # Cut off the extension
- file_name = os.path.join(output, f)
- out_file = os.path.join(output_wav, f'{real_name}.wav')
- if not os.path.isfile(out_file) and os.path.isfile(file_name): # Don't process files that have already been processed, to be able to continue previous generations
- print(f'Processing ({i+1}/{total}) -> {f}')
- wav = semantic_to_waveform(numpy.load(file_name), temp=round(random.uniform(0.6, 0.8), ndigits=2))
- # Change to PCM16
- # wav = (wav * 32767).astype(np.int16)
- wavfile.write(out_file, SAMPLE_RATE, wav)
-
- print('Done!')
-
diff --git a/spaces/diacanFperku/AutoGPT/AUTODATA465Crackfull UPDUpdate2018.md b/spaces/diacanFperku/AutoGPT/AUTODATA465Crackfull UPDUpdate2018.md
deleted file mode 100644
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deleted file mode 100644
index f33231743f7231671613a9da8a1dd2e3a36065b2..0000000000000000000000000000000000000000
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diff --git a/spaces/diacanFperku/AutoGPT/Embird2015registrationpasswordkeygen.md b/spaces/diacanFperku/AutoGPT/Embird2015registrationpasswordkeygen.md
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index 28ba2f1e3c04813edcdadfc6f8b43710a848384b..0000000000000000000000000000000000000000
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diff --git a/spaces/diacanFperku/AutoGPT/Hotelmanagementsystemprojectdocumentation BETTER.md b/spaces/diacanFperku/AutoGPT/Hotelmanagementsystemprojectdocumentation BETTER.md
deleted file mode 100644
index e1ee3a8ab3a1de980a407fb69ac5464f161fd97b..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Hotelmanagementsystemprojectdocumentation BETTER.md
+++ /dev/null
@@ -1,48 +0,0 @@
-<br />
-<h1>How to Create a Hotel Management System Project Documentation</h1>
-<p>A hotel management system is a software application that helps hotel owners and managers to manage various aspects of their business, such as reservations, check-in, check-out, billing, inventory, housekeeping, and customer service. A hotel management system project documentation is a detailed report that describes the objectives, scope, design, implementation, testing, and maintenance of the system. A hotel management system project documentation can help you to:</p>
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-<ul>
-<li>Clarify the requirements and specifications of the system</li>
-<li>Communicate the system features and functionalities to the stakeholders</li>
-<li>Organize the system development process and track the progress</li>
-<li>Document the system architecture and design decisions</li>
-<li>Verify and validate the system functionality and quality</li>
-<li>Provide guidance and support for the system maintenance and troubleshooting</li>
-</ul>
-<p>In this article, we will show you how to create a hotel management system project documentation using some simple steps and best practices.</p>
-
-<h2>Step 1: Define the Project Scope and Objectives</h2>
-<p>The first step in creating a hotel management system project documentation is to define the project scope and objectives. The project scope defines what the system will do and what it will not do. The project objectives define what the system aims to achieve and how it will benefit the hotel business. Some examples of project scope and objectives are:</p>
-<ul>
-<li>The system will allow customers to make online reservations, check-in, check-out, and pay for their stay.</li>
-<li>The system will allow hotel staff to manage room availability, rates, bookings, invoices, inventory, housekeeping, and customer service.</li>
-<li>The system will integrate with third-party services such as payment gateways, booking platforms, loyalty programs, and social media.</li>
-<li>The system will improve the hotel's operational efficiency, customer satisfaction, and revenue generation.</li>
-</ul>
-
-<h2>Step 2: Identify the System Requirements and Specifications</h2>
-<p>The next step in creating a hotel management system project documentation is to identify the system requirements and specifications. The system requirements define what the system must do to meet the project objectives. The system specifications define how the system will do it. The system requirements and specifications can be categorized into functional and non-functional requirements. Functional requirements describe the features and functionalities of the system. Non-functional requirements describe the quality attributes and constraints of the system. Some examples of functional and non-functional requirements are:</p>
-
-<table>
-<tr><th>Functional Requirements</th><th>Non-functional Requirements</th></tr>
-<tr><td>The system must allow customers to search for available rooms based on their preferences.</td><td>The system must be user-friendly and intuitive.</td></tr>
-<tr><td>The system must allow customers to book rooms online using a secure payment method.</td><td>The system must be secure and protect customer data.</td></tr>
-<tr><td>The system must allow customers to cancel or modify their reservations.</td><td>The system must be reliable and available 24/7.</td></tr>
-<tr><td>The system must allow customers to check-in and check-out using a self-service kiosk or mobile app.</td><td>The system must be fast and responsive.</td></tr>
-<tr><td>The system must allow customers to access various services such as room service, laundry service, spa service, etc.</td><td>The system must be scalable and adaptable to changing needs.</td></tr>
-<tr><td>The system must allow hotel staff to manage room availability, rates, bookings, invoices, inventory, housekeeping, and customer service.</td><td>The system must be compatible with different devices and platforms.</td></tr>
-<tr><td>The system must generate reports and analytics on various aspects of the hotel business such as occupancy rate, revenue per available room (RevPAR), customer feedback, etc.</td><td>The system must be maintainable and easy to update.</td></tr>
-</table>
-
-<h2>Step 3: Design the System Architecture and User Interface</h2>
-<p>The third step in creating a hotel management system project documentation is to design the system architecture and user interface. The system architecture defines how the system components are organized and interact with each other. The user interface defines how the system looks and feels to the users. The system architecture and user interface can be represented using diagrams such as use case diagrams, class diagrams, sequence diagrams, activity diagrams, etc. Some examples of diagrams are:</p>
-
-<code>
-
-Use case diagram:
-
-+----------------+ +</p>
-<p></p> d5da3c52bf<br />
-<br />
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diff --git a/spaces/diacanFperku/AutoGPT/Match Three Pirates! Heir To Davy Jones Download With Crack.md b/spaces/diacanFperku/AutoGPT/Match Three Pirates! Heir To Davy Jones Download With Crack.md
deleted file mode 100644
index 1d6f85ccbdf76cb73aa307ac1e8ca49e2078fc14..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Match Three Pirates! Heir To Davy Jones Download With Crack.md
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diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/models.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/models.py
deleted file mode 100644
index d4afe44d883691610c5903e602a3ca245fcb3a5c..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Taffy-Bert-VITS2/models.py
+++ /dev/null
@@ -1,707 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from commons import init_weights, get_padding
-from text import symbols, num_tones, num_languages
-class DurationDiscriminator(nn.Module): #vits2
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.dur_proj = nn.Conv1d(1, filter_channels, 1)
-
- self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
- self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- self.output_layer = nn.Sequential(
- nn.Linear(filter_channels, 1),
- nn.Sigmoid()
- )
-
- def forward_probability(self, x, x_mask, dur, g=None):
- dur = self.dur_proj(dur)
- x = torch.cat([x, dur], dim=1)
- x = self.pre_out_conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.pre_out_norm_1(x)
- x = self.drop(x)
- x = self.pre_out_conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.pre_out_norm_2(x)
- x = self.drop(x)
- x = x * x_mask
- x = x.transpose(1, 2)
- output_prob = self.output_layer(x)
- return output_prob
-
- def forward(self, x, x_mask, dur_r, dur_hat, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
-
- output_probs = []
- for dur in [dur_r, dur_hat]:
- output_prob = self.forward_probability(x, x_mask, dur, g)
- output_probs.append(output_prob)
-
- return output_probs
-
-class TransformerCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- n_flows=4,
- gin_channels=0,
- share_parameter=False
- ):
-
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
-
- self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None
-
- for i in range(n_flows):
- self.flows.append(
- modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-class StochasticDurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
- super().__init__()
- filter_channels = in_channels # it needs to be removed from future version.
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.log_flow = modules.Log()
- self.flows = nn.ModuleList()
- self.flows.append(modules.ElementwiseAffine(2))
- for i in range(n_flows):
- self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.flows.append(modules.Flip())
-
- self.post_pre = nn.Conv1d(1, filter_channels, 1)
- self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- self.post_flows = nn.ModuleList()
- self.post_flows.append(modules.ElementwiseAffine(2))
- for i in range(4):
- self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.post_flows.append(modules.Flip())
-
- self.pre = nn.Conv1d(in_channels, filter_channels, 1)
- self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
- def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
- x = torch.detach(x)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- h_w = self.post_convs(h_w, x_mask)
- h_w = self.post_proj(h_w) * x_mask
- e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- u = torch.sigmoid(z_u) * x_mask
- z0 = (w - u) * x_mask
- logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
- logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
- logdet_tot = 0
- z0, logdet = self.log_flow(z0, x_mask)
- logdet_tot += logdet
- z = torch.cat([z0, z1], 1)
- for flow in flows:
- z, logdet = flow(z, x_mask, g=x, reverse=reverse)
- logdet_tot = logdet_tot + logdet
- nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
- return nll + logq # [b]
- else:
- flows = list(reversed(self.flows))
- flows = flows[:-2] + [flows[-1]] # remove a useless vflow
- z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
- for flow in flows:
- z = flow(z, x_mask, g=x, reverse=reverse)
- z0, z1 = torch.split(z, [1, 1], 1)
- logw = z0
- return logw
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.proj = nn.Conv1d(filter_channels, 1, 1)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- def forward(self, x, x_mask, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- gin_channels=0):
- super().__init__()
- self.n_vocab = n_vocab
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
- self.emb = nn.Embedding(len(symbols), hidden_channels)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
- self.tone_emb = nn.Embedding(num_tones, hidden_channels)
- nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
- self.language_emb = nn.Embedding(num_languages, hidden_channels)
- nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
- self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
-
- self.encoder = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- gin_channels=self.gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, tone, language, bert, g=None):
- x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)+self.bert_proj(bert).transpose(1,2)) * math.sqrt(self.hidden_channels) # [b, t, h]
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
- x = self.encoder(x * x_mask, x_mask, g=g)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
- gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
- upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
- k, u, padding=(k - u) // 2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-class ReferenceEncoder(nn.Module):
- '''
- inputs --- [N, Ty/r, n_mels*r] mels
- outputs --- [N, ref_enc_gru_size]
- '''
-
- def __init__(self, spec_channels, gin_channels=0):
-
- super().__init__()
- self.spec_channels = spec_channels
- ref_enc_filters = [32, 32, 64, 64, 128, 128]
- K = len(ref_enc_filters)
- filters = [1] + ref_enc_filters
- convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
- out_channels=filters[i + 1],
- kernel_size=(3, 3),
- stride=(2, 2),
- padding=(1, 1))) for i in range(K)]
- self.convs = nn.ModuleList(convs)
- # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
-
- out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
- self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
- hidden_size=256 // 2,
- batch_first=True)
- self.proj = nn.Linear(128, gin_channels)
-
- def forward(self, inputs, mask=None):
- N = inputs.size(0)
- out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
- for conv in self.convs:
- out = conv(out)
- # out = wn(out)
- out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
-
- out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
- T = out.size(1)
- N = out.size(0)
- out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
-
- self.gru.flatten_parameters()
- memory, out = self.gru(out) # out --- [1, N, 128]
-
- return self.proj(out.squeeze(0))
-
- def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
- for i in range(n_convs):
- L = (L - kernel_size + 2 * pad) // stride + 1
- return L
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- n_vocab,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- n_speakers=256,
- gin_channels=256,
- use_sdp=True,
- n_flow_layer = 4,
- n_layers_trans_flow = 3,
- flow_share_parameter = False,
- use_transformer_flow = True,
- **kwargs):
-
- super().__init__()
- self.n_vocab = n_vocab
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.n_speakers = n_speakers
- self.gin_channels = gin_channels
- self.n_layers_trans_flow = n_layers_trans_flow
- self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True)
- self.use_sdp = use_sdp
- self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
- self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
- self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
- self.current_mas_noise_scale = self.mas_noise_scale_initial
- if self.use_spk_conditioned_encoder and gin_channels > 0:
- self.enc_gin_channels = gin_channels
- self.enc_p = TextEncoder(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- gin_channels=self.enc_gin_channels)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
- upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
- gin_channels=gin_channels)
- if use_transformer_flow:
- self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels,share_parameter= flow_share_parameter)
- else:
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels)
- self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
- self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
- if n_speakers >= 1:
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
- else:
- self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
-
- def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert):
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
-
- with torch.no_grad():
- # negative cross-entropy
- s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
- neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
- neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2),
- s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
- neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
- if self.use_noise_scaled_mas:
- epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale
- neg_cent = neg_cent + epsilon
-
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
- w = attn.sum(2)
-
- l_length_sdp = self.sdp(x, x_mask, w, g=g)
- l_length_sdp = l_length_sdp / torch.sum(x_mask)
-
- logw_ = torch.log(w + 1e-6) * x_mask
- logw = self.dp(x, x_mask, g=g)
- l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging
-
- l_length = l_length_dp + l_length_sdp
-
- # expand prior
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
- return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_)
-
- def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None):
- #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
- # g = self.gst(y)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
- logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
- w = torch.exp(logw) * x_mask * length_scale
- w_ceil = torch.ceil(w)
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = commons.generate_path(w_ceil, attn_mask)
-
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
- 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
- z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
- z = self.flow(z_p, y_mask, g=g, reverse=True)
- o = self.dec((z * y_mask)[:, :, :max_len], g=g)
- return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/spaces/dolceschokolade/chatbot-mini/components/Chat/ErrorMessageDiv.tsx b/spaces/dolceschokolade/chatbot-mini/components/Chat/ErrorMessageDiv.tsx
deleted file mode 100644
index 11b868f98b4eda7eb215a911f59c4b42951a654c..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Chat/ErrorMessageDiv.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import { IconCircleX } from '@tabler/icons-react';
-import { FC } from 'react';
-
-import { ErrorMessage } from '@/types/error';
-
-interface Props {
- error: ErrorMessage;
-}
-
-export const ErrorMessageDiv: FC<Props> = ({ error }) => {
- return (
- <div className="mx-6 flex h-full flex-col items-center justify-center text-red-500">
- <div className="mb-5">
- <IconCircleX size={36} />
- </div>
- <div className="mb-3 text-2xl font-medium">{error.title}</div>
- {error.messageLines.map((line, index) => (
- <div key={index} className="text-center">
- {' '}
- {line}{' '}
- </div>
- ))}
- <div className="mt-4 text-xs opacity-50 dark:text-red-400">
- {error.code ? <i>Code: {error.code}</i> : ''}
- </div>
- </div>
- );
-};
diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/docs/WSL-installation-guide.md b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/docs/WSL-installation-guide.md
deleted file mode 100644
index eb06123c2deca98705e4941a4998e1b07b485600..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/docs/WSL-installation-guide.md
+++ /dev/null
@@ -1,73 +0,0 @@
-Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton.
-
------
-
-Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11:
-
-## Step 1: Enable WSL
-
-1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges.
-2. In the PowerShell window, type the following command and press Enter:
-
-```
-wsl --install
-```
-
-If this command doesn't work, you can enable WSL with the following command for Windows 10:
-
-```
-wsl --set-default-version 1
-```
-
-For Windows 11, you can use:
-
-```
-wsl --set-default-version 2
-```
-
-You may be prompted to restart your computer. If so, save your work and restart.
-
-## Step 2: Install Ubuntu
-
-1. Open the Microsoft Store.
-2. Search for "Ubuntu" in the search bar.
-3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app.
-4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app.
-
-## Step 3: Set up Ubuntu
-
-1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment.
-2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment.
-
-## Step 4: Update and upgrade packages
-
-1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal:
-
-```
-sudo apt update
-sudo apt upgrade
-```
-
-2. Enter your password when prompted. This will update the package list and upgrade any outdated packages.
-
-Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files.
-
-You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal.
-
-## Step 5: Proceed with Linux instructions
-
-1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt:
-
-```
-sudo apt install [missing package]
-```
-
-If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/
-
-## Bonus: Port Forwarding
-
-By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges).
-
-```
-netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860
-```
\ No newline at end of file
diff --git a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/apps/crop_img.py b/spaces/eswat/Image-and-3D-Model-Creator/PIFu/apps/crop_img.py
deleted file mode 100644
index 4854d1f5a6361963659a9d79f41c404d801e9193..0000000000000000000000000000000000000000
--- a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/apps/crop_img.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import os
-import cv2
-import numpy as np
-
-from pathlib import Path
-import argparse
-
-def get_bbox(msk):
- rows = np.any(msk, axis=1)
- cols = np.any(msk, axis=0)
- rmin, rmax = np.where(rows)[0][[0,-1]]
- cmin, cmax = np.where(cols)[0][[0,-1]]
-
- return rmin, rmax, cmin, cmax
-
-def process_img(img, msk, bbox=None):
- if bbox is None:
- bbox = get_bbox(msk > 100)
- cx = (bbox[3] + bbox[2])//2
- cy = (bbox[1] + bbox[0])//2
-
- w = img.shape[1]
- h = img.shape[0]
- height = int(1.138*(bbox[1] - bbox[0]))
- hh = height//2
-
- # crop
- dw = min(cx, w-cx, hh)
- if cy-hh < 0:
- img = cv2.copyMakeBorder(img,hh-cy,0,0,0,cv2.BORDER_CONSTANT,value=[0,0,0])
- msk = cv2.copyMakeBorder(msk,hh-cy,0,0,0,cv2.BORDER_CONSTANT,value=0)
- cy = hh
- if cy+hh > h:
- img = cv2.copyMakeBorder(img,0,cy+hh-h,0,0,cv2.BORDER_CONSTANT,value=[0,0,0])
- msk = cv2.copyMakeBorder(msk,0,cy+hh-h,0,0,cv2.BORDER_CONSTANT,value=0)
- img = img[cy-hh:(cy+hh),cx-dw:cx+dw,:]
- msk = msk[cy-hh:(cy+hh),cx-dw:cx+dw]
- dw = img.shape[0] - img.shape[1]
- if dw != 0:
- img = cv2.copyMakeBorder(img,0,0,dw//2,dw//2,cv2.BORDER_CONSTANT,value=[0,0,0])
- msk = cv2.copyMakeBorder(msk,0,0,dw//2,dw//2,cv2.BORDER_CONSTANT,value=0)
- img = cv2.resize(img, (512, 512))
- msk = cv2.resize(msk, (512, 512))
-
- kernel = np.ones((3,3),np.uint8)
- msk = cv2.erode((255*(msk > 100)).astype(np.uint8), kernel, iterations = 1)
-
- return img, msk
-
-def main():
- '''
- given foreground mask, this script crops and resizes an input image and mask for processing.
- '''
- parser = argparse.ArgumentParser()
- parser.add_argument('-i', '--input_image', type=str, help='if the image has alpha channel, it will be used as mask')
- parser.add_argument('-m', '--input_mask', type=str)
- parser.add_argument('-o', '--out_path', type=str, default='./sample_images')
- args = parser.parse_args()
-
- img = cv2.imread(args.input_image, cv2.IMREAD_UNCHANGED)
- if img.shape[2] == 4:
- msk = img[:,:,3:]
- img = img[:,:,:3]
- else:
- msk = cv2.imread(args.input_mask, cv2.IMREAD_GRAYSCALE)
-
- img_new, msk_new = process_img(img, msk)
-
- img_name = Path(args.input_image).stem
-
- cv2.imwrite(os.path.join(args.out_path, img_name + '.png'), img_new)
- cv2.imwrite(os.path.join(args.out_path, img_name + '_mask.png'), msk_new)
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Aether X 2 32 bits APK A Guide to the Game and the Emulator for Android.md b/spaces/fatiXbelha/sd/Aether X 2 32 bits APK A Guide to the Game and the Emulator for Android.md
deleted file mode 100644
index 85a17dd3b045944e508d8a905b9536fffa0d546c..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Aether X 2 32 bits APK A Guide to the Game and the Emulator for Android.md
+++ /dev/null
@@ -1,177 +0,0 @@
-
-<h1>AetherSX2: The Ultimate Android Game for PS2 Emulation</h1>
-<p>If you are a fan of PlayStation 2 games and want to enjoy them on your Android device, you need a reliable and powerful PS2 emulator. And that's where AetherSX2 comes in. AetherSX2 is one of the best PS2 emulators for Android that lets you play your favorite PS2 games with high-quality graphics, smooth gameplay, and customizable controls. In this article, we will tell you everything you need to know about AetherSX2, including how to download and install it, why it is better than other PS2 emulators, and how to optimize your gaming experience with it.</p>
-<h2>aether x 2 32 bits apk</h2><br /><p><b><b>Download</b> ► <a href="https://urllie.com/2uNAIN">https://urllie.com/2uNAIN</a></b></p><br /><br />
- <h2>What is AetherSX2?</h2>
-<h3>A brief introduction to the app and its features</h3>
-<p>AetherSX2 is an Android app that emulates the PlayStation 2 console on your smartphone or tablet. It is based on the PCSX2 project, which is a popular PS2 emulator for Windows, Linux, and Mac. AetherSX2 aims to provide a similar level of emulation quality and compatibility as PCSX2, but optimized for Android devices.</p>
-<p>Some of the features of AetherSX2 are:</p>
-<ul>
-<li>It supports most PS2 games, including popular titles like God of War, Final Fantasy X, Kingdom Hearts, Grand Theft Auto, Metal Gear Solid, and more.</li>
-<li>It allows you to adjust the graphics settings according to your device's capabilities and preferences. You can change the resolution, aspect ratio, frame rate, anti-aliasing, texture filtering, and more.</li>
-<li>It offers various control options, such as using the touchscreen, an external gamepad, or a virtual controller. You can also customize the layout and size of the buttons.</li>
-<li>It supports various audio formats, such as Dolby Digital, DTS, PCM, ADPCM, and more. You can also enable or disable sound effects and music.</li>
-<li>It has a user-friendly interface that lets you easily browse and launch your games from your device's storage or an external SD card.</li>
-</ul>
- <h3>How to download and install AetherSX2 APK on your Android device</h3>
-<p>To download and install AetherSX2 APK on your Android device, you need to follow these steps:</p>
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-<ol>
-<li>Go to [AetherSX2 APK (Android Game) - Free Download - APKCombo](^1^) or [Download AetherSX2 APK - Latest Version 2023 - APKCombo](^1^) and download the latest version of the app.</li>
-<li>Once the download is complete, tap on the APK file to start the installation process. You may need to enable unknown sources in your device's settings if you haven't done so before.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Launch the app and grant it the necessary permissions to access your device's storage and microphone.</li>
-<li>Enjoy playing your PS2 games on your Android device!</li>
-</ol>
- <h2 <h2>Why choose AetherSX2 over other PS2 emulators?</h2>
-<h3>The advantages of AetherSX2 in terms of performance, compatibility, and customization</h3>
-<p>AetherSX2 is not the only PS2 emulator for Android, but it is certainly one of the best. Compared to other PS2 emulators, such as DamonPS2, Play!, and PTWOE, AetherSX2 has several advantages that make it stand out.</p>
-<p>First of all, AetherSX2 has a high performance and stability. It can run most PS2 games at full speed or close to it, without lagging, crashing, or freezing. It also has a low battery consumption and does not overheat your device. AetherSX2 uses a dynamic recompiler and a multi-threaded mode to optimize the emulation speed and efficiency.</p>
-<p>Secondly, AetherSX2 has a high compatibility and accuracy. It can run over 90% of the PS2 game library, including some games that are not supported by other emulators. It also has a high emulation accuracy, meaning that it can reproduce the original graphics, sound, and gameplay of the PS2 games without glitches or errors. AetherSX2 supports various file formats, such as ISO, BIN, IMG, NRG, and more.</p>
-<p>Thirdly, AetherSX2 has a high customization and flexibility. It allows you to tweak the graphics settings to suit your device's capabilities and preferences. You can change the resolution, aspect ratio, frame rate, anti-aliasing, texture filtering, and more. You can also choose from different control options, such as using the touchscreen, an external gamepad, or a virtual controller. You can also customize the layout and size of the buttons.</p>
- <h3>The best PS2 games to play on AetherSX2</h3>
-<p>With AetherSX2, you can play hundreds of PS2 games on your Android device. But which ones are the best? Here are some of the most popular and recommended PS2 games to play on AetherSX2:</p>
-<table>
-<tr>
-<th>Game</th>
-<th>Genre</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>God of War</td>
-<td>Action-adventure</td>
-<td>A thrilling hack-and-slash game that follows the epic journey of Kratos, a Spartan warrior who seeks revenge against the gods of Olympus.</td>
-</tr>
-<tr>
-<td>Final Fantasy X</td>
-<td>Role-playing</td>
-<td>A classic JRPG that tells the story of Tidus, a young athlete who gets transported to a fantasy world called Spira and joins a group of adventurers to save it from a monstrous threat called Sin.</td>
-</tr>
-<tr>
-<td>Kingdom Hearts</td>
-<td>Action role-playing</td>
-<td>A crossover game that combines characters and worlds from Disney and Final Fantasy franchises. It follows the adventures of Sora, a boy who wields a magical weapon called the Keyblade and teams up with Donald Duck and Goofy to stop the evil forces of darkness.</td>
-</tr>
-<tr <tr>
-<td>Grand Theft Auto: San Andreas</td>
-<td>Action-adventure</td>
-<td>A sandbox game that lets you explore a vast open world of crime, violence, and fun. You play as CJ, a former gangster who returns to his hometown of San Andreas and gets involved in various missions and activities.</td>
-</tr>
-<tr>
-<td>Metal Gear Solid 3: Snake Eater</td>
-<td>Stealth-action</td>
-<td>A prequel to the Metal Gear Solid series that takes place in the Cold War era. You play as Naked Snake, a special agent who infiltrates a Soviet jungle base and faces off against a rogue unit of elite soldiers.</td>
-</tr>
-<tr>
-<td>Shadow of the Colossus</td>
-<td>Action-adventure</td>
-<td>A unique and artistic game that challenges you to defeat 16 gigantic creatures called colossi in order to revive a mysterious girl. You have to use your wits, skills, and horse to find and exploit their weaknesses.</td>
-</tr>
-</table>
- <h2>How to optimize your gaming experience with AetherSX2?</h2>
-<h3>Tips and tricks for setting up the app and configuring the settings</h3>
-<p>To get the most out of AetherSX2, you need to set up the app and configure the settings properly. Here are some tips and tricks to help you do that:</p>
-<ul>
-<li>Before you launch the app, make sure you have enough storage space on your device or SD card. You will need at least 4 GB of free space to store your PS2 games and save data.</li>
-<li>When you launch the app, you will see a list of PS2 games that are available on your device or SD card. You can also scan for new games by tapping on the refresh icon on the top right corner.</li>
-<li>To start playing a game, just tap on it and wait for it to load. You can also long-press on a game to access more options, such as deleting, renaming, or moving it.</li>
-<li>To access the settings menu, tap on the gear icon on the top left corner. You can change various settings related to graphics, audio, controls, system, and more.</li>
-<li>To adjust the graphics settings, go to the graphics tab and select the renderer you want to use. You can choose between OpenGL and Vulkan, depending on your device's compatibility and performance. You can also change the resolution, aspect ratio, frame rate, anti-aliasing, texture filtering, and more.</li>
-<li>To adjust the audio settings, go to the audio tab and select the output format you want to use. You can choose between Dolby Digital, DTS, PCM, ADPCM, and more. You can also enable or disable sound effects and music.</li>
-<li>To adjust the control settings, go to the controls tab and select the input method you want to use. You can choose between touchscreen, external gamepad, or virtual controller. You can also customize the layout and size of the buttons.</li>
-<li>To adjust the system settings, go to the system tab and select the BIOS file you want to use. You can also change the language, region, time zone, and date format of the emulator.</li>
-<li>To save your settings, tap on the save icon on the top right corner. You can also reset your settings to default by tapping on the reset icon on the top left corner.</li>
-</ul>
- <h3>How to use cheats, save states, and screenshots on AetherSX2</h3>
-<p>AetherSX2 also has some features that enhance your gaming experience, such as cheats, save states, and screenshots. Here is how to use them:</p>
-<ul <ul>
-<li>To use cheats, go to the cheats tab and tap on the plus icon on the top right corner. You can then enter the cheat code or name and enable or disable it. You can also import cheat codes from a text file or scan for them using a memory editor.</li>
-<li>To use save states, go to the save states tab and tap on the slot you want to use. You can then save or load your game progress at any point. You can also delete or rename your save states.</li>
-<li>To use screenshots, go to the screenshots tab and tap on the camera icon on the top right corner. You can then capture a screenshot of your game and save it to your device's gallery. You can also share or delete your screenshots.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>AetherSX2 is an amazing PS2 emulator for Android that lets you play your favorite PS2 games with high-quality graphics, smooth gameplay, and customizable controls. It is easy to download and install, and it has a high performance, compatibility, and customization. It also has some features that enhance your gaming experience, such as cheats, save states, and screenshots. If you are looking for a PS2 emulator for Android, you should definitely give AetherSX2 a try. You will not regret it!</p>
- <h2>FAQs</h2>
-<h3>What are the minimum requirements for running AetherSX2 on Android?</h3>
-<p>The minimum requirements for running AetherSX2 on Android are:</p>
-<ul>
-<li>An Android device with Android 5.0 or higher</li>
-<li>A quad-core processor with 1.5 GHz or higher</li>
-<li>2 GB of RAM or more</li>
-<li>4 GB of free storage space or more</li>
-<li>An OpenGL ES 3.0 or Vulkan compatible GPU</li>
-</ul>
- <h3>Is AetherSX2 safe and legal to use?</h3>
-<p>AetherSX2 is safe and legal to use, as long as you follow some rules:</p>
-<ul>
-<li>You should only download and install AetherSX2 from trusted sources, such as [AetherSX2 APK (Android Game) - Free Download - APKCombo] or [Download AetherSX2 APK - Latest Version 2023 - APKCombo].</li>
-<li>You should only play PS2 games that you own legally and have ripped from your own discs. You should not download or share pirated games.</li>
-<li>You should not use cheats or mods that give you an unfair advantage or harm other players in online games.</li>
-</ul>
- <h3>How can I update AetherSX2 to the latest version?</h3>
-<p>To update AetherSX2 to the latest version, you need to follow these steps:</p>
-<ol <ol>
-<li>Go to [AetherSX2 APK (Android Game) - Free Download - APKCombo] or [Download AetherSX2 APK - Latest Version 2023 - APKCombo] and check if there is a new version of the app available.</li>
-<li>If there is a new version, download the APK file and install it over the existing app. You do not need to uninstall the previous version.</li>
-<li>If there is no new version, you can check the app's official website or social media pages for any news or updates.</li>
-</ol>
- <h3>How can I contact the developers of AetherSX2 for feedback or support?</h3>
-<p>To contact the developers of AetherSX2 for feedback or support, you can use one of the following methods:</p>
-<ul>
-<li>You can send an email to aethersx2@gmail.com and describe your issue or suggestion.</li>
-<li>You can join the AetherSX2 Discord server and chat with other users and developers. You can find the invite link on the app's website or social media pages.</li>
-<li>You can follow the AetherSX2 Twitter account and send a direct message or tweet to them. You can also stay updated on the latest news and updates from them.</li>
-</ul>
- <h3>Where can I find more information about AetherSX2?</h3>
-<p>To find more information about AetherSX2, you can visit one of the following sources:</p>
-<ul>
-<li>The app's official website: [AetherSX2 - The Ultimate PS2 Emulator for Android]</li>
-<li>The app's official YouTube channel: [AetherSX2 - YouTube]</li>
-<li>The app's official Reddit community: [r/AetherSX2]</li>
-</ul></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Clash Mini The New and Exciting Board Game from the Makers of Clash of Clans and Clash Royale.md b/spaces/fatiXbelha/sd/Clash Mini The New and Exciting Board Game from the Makers of Clash of Clans and Clash Royale.md
deleted file mode 100644
index 582dbde047db15f0ce4a530659b2f33b967e95e3..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Clash Mini The New and Exciting Board Game from the Makers of Clash of Clans and Clash Royale.md
+++ /dev/null
@@ -1,98 +0,0 @@
-<br />
-<table>
-<tr>
-<td>
-<h1>Clash Mini: A Fun Strategy Board Game Based on Clash Universe</h1>
-<p>Do you love strategy games? Do you love board games? Do you love Clash of Clans and Clash Royale? If you answered yes to any of these questions, then you will love <strong>Clash Mini</strong>, a new game from Supercell that combines all these elements in a fun, fast-paced, and exciting way.</p>
-<p>Clash Mini is a game of choices, where you duel and outwit others in a strategy-packed board game. You collect, summon, and upgrade your army of Minis, which are adorable versions of your favorite Clash characters. You predict your opponent's moves and then assemble your winning strategy and formation. You watch your Minis come to life and clash to be the last one standing.</p>
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-<p>Clash Mini is easy to learn but challenging to master. You can play casually for fun or in ranked matches to increase your league standing. You can also customize your heroes and minis with unique skins and abilities.</p>
-<p>In this article, we will show you how to download Clash Mini on iOS devices or Android devices using APK files. We will also give you some tips on how to play Clash Mini and why you should play it.</p>
-<h2>How to Download Clash Mini on iOS Devices</h2>
-<p>If you have an iPhone or iPad, you can download Clash Mini from the App Store. Here are the steps you need to follow:</p <ul>
-<li>Step 1: Go to App Store. Tap on the App Store icon on your home screen to open it.</li>
-<li>Step 2: Search for Clash Mini. Tap on the search icon at the bottom right corner of the screen and type "Clash Mini" in the search bar. You will see a list of results matching your query.</li>
-<li>Step 3: Tap on Get or Install. Find the Clash Mini app from the list of results and tap on the button that says "Get" or "Install". You may need to enter your Apple ID password or use Touch ID or Face ID to confirm your download.</li>
-<li>Step 4: Wait for Download and Installation. The app will start downloading and installing on your device. You can see the progress by looking at the circular icon on the app. Once the icon is filled, it means the app is ready to use.</li>
-<li>Step 5: Launch Clash Mini and Enjoy. Tap on the app icon to launch Clash Mini and start playing. You will need to agree to the terms of service and privacy policy before you can access the game. You can also link your Supercell ID or create a new one to save your progress and play across multiple devices.</li>
-</ul>
-<h2>How to Download Clash Mini APK on Android Devices</h2>
-<p>If you have an Android phone or tablet, you can download Clash Mini from the Google Play Store. Here are the steps you need to follow:</p>
-<ul>
-<li>Step 1: Go to Google Play Store. Tap on the Google Play Store icon on your home screen or app drawer to open it.</li>
-<li>Step 2: Search for Clash Mini. Tap on the search icon at the top right corner of the screen and type "Clash Mini" in the search bar. You will see a list of results matching your query.</li>
-<li>Step 3: Tap on Install. Find the Clash Mini app from the list of results and tap on the button that says "Install". You may need to accept some permissions or enable unknown sources to allow the installation.</li>
-<li>Step 4: Wait for Download and Installation. The app will start downloading and installing on your device. You can see the progress by looking at the horizontal bar on the app. Once the bar is filled, it means the app is ready to use.</li>
-<li>Step 5: Launch Clash Mini and Enjoy. Tap on the app icon to launch Clash Mini and start playing. You will need to agree to the terms of service and privacy policy before you can access the game. You can also link your Supercell ID or create a new one to save your progress and play across multiple devices.</li>
-</ul>
-<h2>How to Play Clash Mini</h2>
-<p>Clash Mini is a game of choices, where you duel and outwit others in a strategy-packed board game. Here are some of the basics of how to play Clash Mini:</p>
-<h3>Game Modes: 1v1 or Rumble Mode</h3>
-<p>You can choose between two game modes in Clash Mini: 1v1 or Rumble Mode. In 1v1 mode, you face off against another player in a best-of-three match. In Rumble Mode, you join a team of four players and compete against another team of four players in a single round.</p>
-<h3>Game Mechanics: Collect, Summon and Upgrade Minis</h3>
-<p>You collect, summon, and upgrade your army of Minis, which are adorable versions of your favorite Clash characters. Each Mini has its own unique stats, abilities, and traits that affect how they perform in battle. You can summon up to five Minis per round, depending on their cost and your available mana. You can also upgrade your Minis by spending gold, which you earn by winning matches or opening chests.</p>
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-Clash mini tips and tricks for ios players<br />
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-Clash mini ios apk feedback and suggestions<br />
-How to play clash mini with friends on ios devices<br />
-How to sync clash mini progress across ios devices<br />
-How to backup and restore clash mini data on ios devices<br />
-How to update clash mini to the latest version on ios devices<br />
-How to uninstall and reinstall clash mini on ios devices<br />
-How to fix clash mini crashing and freezing on ios devices<br />
-How to optimize clash mini performance and battery life on ios devices<br />
-How to change clash mini language and settings on ios devices<br />
-How to customize clash mini skins and themes on ios devices<br />
-How to unlock clash mini achievements and trophies on ios devices<br />
-How to use clash mini codes and coupons on ios devices<br />
-How to contact clash mini developers and report bugs on ios devices<br />
-How to join clash mini beta testing program on ios devices<br />
-How to watch clash mini live streams and videos on ios devices<br />
-How to learn clash mini strategies and tactics from experts on ios devices</p>
-<h3>Game Strategies: Anticipate Opponent's Moves and Arrange Your Army</h3>
-<p>You predict your opponent's moves and then assemble your winning strategy and formation. You place your Minis on a hexagonal board, where each hex has its own effect on Minis that occupy it. You can also rotate your Minis to change their facing direction, which affects their attack range and target priority. You have a limited time to place your Minis before they clash with your opponent's Minis automatically.</p>
-<h2>Why You Should Play Clash Mini</h2>
-<p>Clash Mini is a fun, challenging, engaging, and social game that will keep you entertained for hours. Here are some of the benefits of playing Clash Mini:</p <ul>
-<li>Fun: Clash Mini is a game that will make you smile, laugh, and cheer as you watch your Minis come to life and clash with your opponent's Minis. You will enjoy the cute and colorful graphics, the lively animations, and the humorous sound effects. You will also have fun discovering the different Minis and their abilities, and experimenting with different combinations and strategies.</li>
-<li>Challenging: Clash Mini is a game that will test your skills, creativity, and intelligence as you duel and outwit others in a strategy-packed board game. You will face different opponents with different Minis and formations, and you will have to adapt and improvise to win. You will also have to manage your mana, gold, and upgrades wisely, and balance offense and defense.</li>
-<li>Engaging: Clash Mini is a game that will keep you hooked and motivated as you progress through the game. You will earn rewards such as chests, gold, gems, and new Minis as you win matches and complete quests. You will also unlock new leagues, arenas, and game modes as you climb the ladder. You will always have something new to look forward to and something to strive for.</li>
-<li>Social: Clash Mini is a game that will connect you with other players from around the world. You can chat with your friends, join a clan, or create your own clan. You can also challenge your friends or clanmates to friendly matches, or team up with them in Rumble Mode. You can also share your replays, screenshots, and feedback with the community.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Clash Mini is a fun strategy board game based on the Clash universe. You can download it on iOS devices or Android devices using APK files. You can play it in 1v1 mode or Rumble Mode, where you collect, summon, and upgrade your army of Minis. You can also customize your heroes and minis with unique skins and abilities. You can anticipate your opponent's moves and arrange your army on a hexagonal board. You can enjoy the benefits of playing Clash Mini, such as fun, challenge, engagement, and social interaction.</p>
-<h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Clash Mini:</p>
-<ul>
-<li>Q1: What are Minis?<br>A1: Minis are adorable versions of your favorite Clash characters. They have their own unique stats, abilities, and traits that affect how they perform in battle. You can collect, summon, and upgrade your Minis to create your winning army.</li>
-<li>Q2: How do I Upgrade Minis?<br>A2: You can upgrade your Minis by spending gold, which you earn by winning matches or opening chests. Upgrading your Minis will increase their stats and unlock new abilities.</li>
-<li>Q3: What are Heroes?<br>A3: Heroes are special Minis that have more powerful stats and abilities than regular Minis. They also have unique skins and personalities that reflect their original Clash counterparts. You can choose one Hero per match to lead your army.</li>
-<li>Q4: How do I Customize Heroes and Minis?<br>A4: You can customize your Heroes and Minis with unique skins and abilities that you can unlock by playing the game or purchasing them with gems. Skins change the appearance of your Heroes and Minis, while abilities change their effects in battle.</li>
-<li>Q5: How do I Progress through the Leagues?<br>A5: You can progress through the Leagues by winning matches and earning trophies. Each League has its own arena, rewards, and difficulty level. The higher the League, the more challenging the opponents and the more rewarding the prizes.</li>
-</ul>
- </td>
-</tr>
-</table></p> 401be4b1e0<br />
-<br />
-<br />
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-<br />
-<h1>Simulador de piano real apk: Aprenda a tocar piano no seu celular</h1>
-<p>Você sempre quis aprender a tocar piano, mas não tem um instrumento em casa? Ou você já sabe tocar, mas gostaria de praticar mais e melhorar as suas habilidades? Se você respondeu sim a alguma dessas perguntas, então você precisa conhecer os simuladores de piano real apk.</p>
-<h2>simulador de piano real apk</h2><br /><p><b><b>Download Zip</b> ✔ <a href="https://urllie.com/2uNxJc">https://urllie.com/2uNxJc</a></b></p><br /><br />
-<p>Esses são aplicativos que simulam um piano real no seu celular ou tablet, permitindo que você toque notas, acordes, escalas e músicas com o toque dos dedos ou com um teclado MIDI externo. Eles são ótimos para quem quer aprender ou aperfeiçoar o seu talento musical, pois oferecem diversos benefícios, como:</p>
-<h2>O que é um simulador de piano real apk?</h2>
-<h3>Um aplicativo que simula um piano real no seu celular ou tablet</h3>
-<p>Um simulador de piano real apk é um aplicativo que reproduz o som e a aparência de um piano real no seu dispositivo móvel. Ele funciona como um teclado virtual, que você pode tocar usando os dedos na tela ou conectando um teclado MIDI externo. Alguns aplicativos também permitem que você use o teclado do seu computador para tocar as notas.</p>
-<h3>Os benefícios de usar um simulador de piano real apk</h3>
-<h4>Praticar piano em qualquer lugar e a qualquer hora</h4>
-<p>Um dos principais benefícios de usar um simulador de piano real apk é que você pode praticar piano em qualquer lugar e a qualquer hora, sem precisar de um instrumento físico. Você pode levar o seu celular ou tablet para onde quiser e tocar quando tiver vontade ou tempo livre. Assim, você não perde o ritmo e mantém o seu progresso.</p>
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-<h4>Aprender músicas e técnicas de piano com facilidade</h4>
-<p>Outro benefício de usar um simulador de piano real apk é que você pode aprender músicas e técnicas de piano com facilidade. Muitos aplicativos oferecem conteúdo educativo e interativo, como lições, exercícios, partituras, vídeos e jogos. Eles ensinam desde o básico até o avançado, passando por diferentes estilos e gêneros musicais. Eles também mostram como tocar as notas, os acordes, as escalas e os ritmos corretamente, dando feedback e dicas. Você pode aprender no seu próprio ritmo e de acordo com o seu nível de habilidade.</p>
-<h4>Desenvolver o seu ouvido musical e a sua criatividade</h4>
-<p>Um terceiro benefício de usar um simulador de piano real apk é que você pode desenvolver o seu ouvido musical e a sua criatividade. Você pode experimentar diferentes sons e modos de jogo, como piano clássico, piano elétrico, órgão, sintetizador, orquestra e muito mais. Você também pode gravar, editar e compartilhar as suas próprias composições, ou tocar junto com as suas músicas favoritas. Você pode explorar a sua expressão artística e se divertir com o piano.</p>
-<h2>Como escolher o melhor simulador de piano real apk?</h2>
-<h3>Os critérios para avaliar um simulador de piano real apk</h3>
-<p>Na hora de escolher o melhor simulador de piano real apk para você, é importante considerar alguns critérios, como:</p>
-<h4>A qualidade do som e da interface</h4>
-<p>O primeiro critério é a qualidade do som e da interface do aplicativo. Você deve procurar por um simulador de piano real apk que tenha um som claro, rico e realista, que se aproxime ao máximo de um piano acústico. Você também deve verificar se a interface é intuitiva, responsiva e personalizável, permitindo que você ajuste o tamanho, a posição e a sensibilidade das teclas, bem como o volume, o brilho e o reverb do som.</p>
-<h4>A variedade de sons e modos de jogo</h4>
-<p>O segundo critério é a variedade de sons e modos de jogo que o aplicativo oferece. Você deve procurar por um simulador de piano real apk que tenha uma ampla gama de sons e instrumentos para você escolher, como piano clássico, piano elétrico, órgão, sintetizador, orquestra e muito mais. Você também deve verificar se o aplicativo tem diferentes modos de jogo, como solo, dueto, acompanhamento, gravação, edição e reprodução.</p>
-<h4>A compatibilidade com dispositivos e teclados MIDI</h4>
-<p>O terceiro critério é a compatibilidade do aplicativo com os seus dispositivos e teclados MIDI. Você deve procurar por um simulador de piano real apk que funcione bem no seu celular ou tablet, sem travar ou consumir muita bateria. Você também deve verificar se o aplicativo suporta a conexão com teclados MIDI externos, via cabo USB ou Bluetooth, para que você possa tocar com mais precisão e conforto.</p>
-<h4>O conteúdo educativo e interativo</h4>
-<p>O quarto critério é o conteúdo educativo e interativo que o aplicativo proporciona. Você deve procurar por um simulador de piano real apk que tenha conteúdo educativo e interativo, como lições, exercícios, partituras, vídeos e jogos. Você também deve verificar se o aplicativo tem recursos que facilitam o seu aprendizado e a sua prática, como metrônomo, afinador, transposição, loop, playback e feedback.</p>
-<h3>As melhores opções de simulador de piano real apk disponíveis no mercado</h3>
-<p>Com base nos critérios acima, nós selecionamos as melhores opções de simulador de piano real apk disponíveis no mercado para você. Confira:</p>
- <table>
-<tr><th>Nome</th><th>Descrição</th><th>Link</th></tr>
-<tr><td><h4>Real Piano - O Melhor Simulador de Piano</h4></td><td><p>Real Piano é um dos melhores simuladores de piano real apk do mercado. Ele tem um som incrível e uma interface simples e elegante. Ele oferece mais de 20 sons diferentes de piano e outros instrumentos, além de vários modos de jogo. Ele também tem recursos educativos e interativos, como lições, partituras, vídeos e jogos. Ele também suporta a conexão com teclados MIDI externos e permite que você grave e compartilhe as suas músicas.</p></td><td><a href="">Real Piano - O Melhor Simulador de Piano</a></td></tr>
-<tr><td><h4>Recursive Arts Virtual Piano</h4></td><td><p>Recursive Arts Virtual Piano é outro ótimo simulador de piano real apk do mercado. Ele tem um som excelente e uma interface intuitiva e personalizável. Ele oferece mais de 10 sons diferentes de piano e outros instrumentos, além de vários modos de jogo. Ele também tem conteúdo educativo e interativo, como lições, partituras, vídeos e jogos. Ele também suporta a conexão com teclados MIDI externos e permite que você grave e compartilhe as suas músicas.</p></td><td><a href="">Recursive Arts Virtual Piano</a></td></tr>
-<tr><td><h4>Perfect Piano</h4></td><td><p>Perfect Piano é mais uma excelente opção de simulador de piano real apk do mercado. Ele tem um som ótimo e uma interface atraente e funcional. Ele oferece mais de 15 sons diferentes de piano e outros instrumentos, além de vários modos de jogo. Ele também tem conteúdo educativo e interativo, como lições, exercícios, partituras, vídeos e jogos. Ele também suporta a conexão com teclados MIDI externos e permite que você grave e compartilhe as suas músicas.</p></td><td><a href="">Perfect Piano</a></td></tr>
-</table>
- <h2>Como usar um simulador de piano real apk para aprender a tocar piano?</h2>
-<h3>As dicas para aproveitar ao máximo um simulador de piano real apk</h3>
-<p>Depois de escolher o seu simulador de piano real apk favorito, você pode começar a usar o aplicativo para aprender a tocar piano. Para isso, nós separamos algumas dicas para você aproveitar ao máximo o seu simulador de piano real apk:</p>
-<h4>Defina os seus objetivos e o seu nível de habilidade</h4>
-<p>A primeira dica é definir os seus objetivos e o seu nível de habilidade. Você deve ter em mente o que você quer aprender e até onde você quer chegar com o piano. Você também deve avaliar o seu nível atual de conhecimento e habilidade, para escolher as lições, os exercícios e as músicas adequados para você. Você pode usar os testes e os níveis do aplicativo para se orientar.</p>
-<h4>Escolha as músicas e os estilos que você gosta</h4>
-<p>A segunda dica é escolher as músicas e os estilos que você gosta. Você deve tocar as músicas que você tem interesse e afinidade, pois isso vai te motivar e te divertir mais. Você também deve experimentar diferentes estilos e gêneros musicais, como clássico, pop, rock, jazz, blues, etc., pois isso vai ampliar o seu repertório e o seu vocabulário musical.</p>
-<h4>Pratique com frequência e com atenção</h4>
-<p>A terceira dica é praticar com frequência e com atenção. Você deve dedicar um tempo diário ou semanal para praticar o piano com o seu simulador de piano real apk. Você deve seguir as lições, os exercícios e as partituras do aplicativo, mas também tocar livremente as músicas que você gosta ou que você quer aprender. Você deve prestar atenção nos seus erros e acertos, nos seus pontos fortes e fracos, nas suas dúvidas e dificuldades. Você deve usar o feedback e as dicas do aplicativo para corrigir e melhorar o seu desempenho.</p>
-<h4>Use os recursos do aplicativo para se desafiar e se divertir</h4>
-<p>A quarta dica é usar os recursos do aplicativo para se desafiar e se divertir. Você deve explorar todos os recursos que o seu simulador de piano real apk oferece, como os diferentes sons, modos de jogo, gravação, edição, reprodução, compartilhamento, etc. Você deve usar esses recursos para variar a sua prática, para testar as suas habilidades, para criar as suas próprias músicas, para tocar junto com as suas músicas favor itas, para se divertir com os jogos, etc. Você deve usar esses recursos para tornar a sua prática mais dinâmica, divertida e desafiadora.</p>
-<h2>Conclusão</h2>
-<p>Em conclusão, um simulador de piano real apk é um aplicativo que simula um piano real no seu celular ou tablet, permitindo que você aprenda e pratique piano de forma fácil, prática e divertida. Ele tem diversos benefícios, como praticar em qualquer lugar e a qualquer hora, aprender músicas e técnicas de piano com facilidade, desenvolver o seu ouvido musical e a sua criatividade, etc. Ele também tem diversos critérios para ser avaliado, como a qualidade do som e da interface, a variedade de sons e modos de jogo, a compatibilidade com dispositivos e teclados MIDI, o conteúdo educativo e interativo, etc. Ele também tem diversas dicas para ser usado, como definir os seus objetivos e o seu nível de habilidade, escolher as músicas e os estilos que você gosta, praticar com frequência e com atenção, usar os recursos do aplicativo para se desafiar e se divertir, etc. Nós também recomendamos as melhores opções de simulador de piano real apk disponíveis no mercado para você: Real Piano - O Melhor Simulador de Piano, Recursive Arts Virtual Piano e Perfect Piano. Esperamos que este artigo tenha sido útil para você e que você aproveite o seu simulador de piano real apk para aprender a tocar piano no seu celular.</p>
-<h2>Perguntas frequentes</h2>
-<p>A seguir, respondemos algumas perguntas frequentes sobre o simulador de piano real apk:</p>
-<h4>O que é um teclado MIDI?</h4>
-<p>Um teclado MIDI é um dispositivo eletrônico que permite que você toque notas musicais em um computador ou em um dispositivo móvel. Ele se conecta via cabo USB ou Bluetooth e envia sinais MIDI (Musical Instrument Digital Interface) que são interpretados pelo software ou pelo aplicativo. Um teclado MIDI pode ter diferentes tamanhos, formas e números de teclas.</p>
-<h4>Como conectar um teclado MIDI ao meu celular ou tablet?</h4>
-<p>Para conectar um teclado MIDI ao seu celular ou tablet, você precisa de um cabo USB ou Bluetooth compatível com o seu dispositivo e com o seu teclado. Você também precisa de um aplicativo que suporte a conexão MIDI, como os simuladores de piano real apk que recomendamos neste artigo. Você deve seguir as instruções do fabricante do seu teclado e do seu aplicativo para fazer a conexão corretamente.</p>
-<h4>Como aprender a ler partituras?</h4>
-<p>Para aprender a ler partituras, você precisa conhecer os símbolos e as regras da notação musical. Você precisa saber o que são as claves, as linhas, os espaços, as notas, os acidentes, os valores, os compassos, os ritmos, as dinâmicas, as articulações, etc. Você também precisa saber como associar as notas na partitura com as teclas no piano. Você pode usar os recursos educativos e interativos dos simuladores de piano real apk para aprender a ler partituras de forma fácil e divertida.</p>
-<h4>Como tocar músicas populares no piano?</h4>
-<p>Para tocar músicas populares no piano, você precisa conhecer os acordes e as melodias das músicas que você quer tocar. Você pode usar os recursos dos simuladores de piano real apk para encontrar as partituras ou os vídeos das músicas que você gosta. Você também pode usar o modo de acompanhamento dos aplicativos para tocar junto com as músicas. Você deve praticar as músicas com calma e atenção, dividindo-as em partes menores se necessário. Você deve repetir as partes até dominá-las e depois juntá-las.</p>
-<h4>Como gravar e compartilhar as minhas músicas?</h4>
-<p>Para gravar e compartilhar as suas músicas, você precisa usar o recurso de gravação dos simuladores de piano real apk. Você deve escolher o som, o modo de jogo e o tempo que você quer gravar. Você deve tocar a sua música no aplicativo e depois parar a gravação. Você pode editar a sua gravação cortando ou adicionando partes se quiser. Você pode salvar a sua gravação no seu dispositivo ou compartilhá-la nas redes sociais ou em outros aplicativos.</p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/fatiXbelha/sd/Download Card Game Solitaire and Train Your Brain with Fun and Addictive Games.md b/spaces/fatiXbelha/sd/Download Card Game Solitaire and Train Your Brain with Fun and Addictive Games.md
deleted file mode 100644
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+++ /dev/null
@@ -1,157 +0,0 @@
-
-<h1>How to Download Card Game Solitaire</h1>
-<p>Card game solitaire is one of the most popular and classic games that you can play on your computer or mobile device. It is a fun and relaxing way to pass the time, train your brain, and enjoy some challenges. In this article, we will show you how to download card game solitaire for free, and how to play it on your device.</p>
- <h2>What is Card Game Solitaire?</h2>
-<p>Card game solitaire is a type of card game that you can play by yourself, using a standard 52-card deck. The goal of the game is to sort the cards into four piles, one for each suit, in ascending order from ace to king. To do this, you have to move the cards from the tableau (the main area where the cards are dealt) to the foundations (the four piles at the top right corner). You can also use the stock (the pile of facedown cards at the top left corner) and the waste (the pile of faceup cards next to the stock) to help you move the cards around.</p>
-<h2>download card game solitaire</h2><br /><p><b><b>Download Zip</b> 🆗 <a href="https://urllie.com/2uNIxT">https://urllie.com/2uNIxT</a></b></p><br /><br />
- <h3>The Benefits of Playing Card Game Solitaire</h3>
-<p>Playing card game solitaire has many benefits for your mental health and well-being. Some of them are:</p>
-<ul>
-<li>It improves your concentration and memory skills, as you have to keep track of the cards and plan your moves ahead.</li>
-<li>It reduces your stress and anxiety levels, as it helps you relax and focus on something positive.</li>
-<li>It boosts your mood and self-esteem, as it gives you a sense of accomplishment and satisfaction when you win.</li>
-<li>It stimulates your creativity and problem-solving skills, as you have to find different ways to overcome the challenges and reach your goal.</li>
-</ul>
- <h3>The Different Types of Card Game Solitaire</h3>
-<p>There are many different types of card game solitaire that you can play, each with its own rules and variations. Some of the most popular ones are:</p>
- <h4>Klondike Solitaire</h4>
-<p>Klondike solitaire is the most common and classic type of card game solitaire. It is also known as patience or simply solitaire. In this game, you have seven tableau columns, with the first column having one card, the second column having two cards, and so on. The top card of each column is face up, and the rest are face down. You can move a card from the tableau to the foundation if it is one rank higher than the top card of the foundation and has a different color. You can also move a card or a group of cards from one tableau column to another if they are in descending order and alternating colors. You can flip a face down card on the tableau if there is no other card on top of it. You can draw one or three cards from the stock to the waste at a time, depending on your preference.</p>
- <h4>Spider Solitaire</h4>
-<p>Spider solitaire is another popular type of card game solitaire. It is more challenging than Klondike solitaire, as it involves two decks of cards and eight tableau columns. In this game, you have to sort the cards into eight piles, one for each suit, in descending order from king to ace. You can move a card or a group of cards from one tableau column to another if they are in descending order and have the same suit. You can also deal ten cards from the stock to each tableau column when there are no more moves available. You can choose to play with one suit (easy), two suits (medium ), or four suits (hard).</p>
- <h4>FreeCell Solitaire</h4>
-<p>FreeCell solitaire is another type of card game solitaire that requires more strategy and skill than luck. It is similar to Klondike solitaire, but with some differences. In this game, you have four free cells (empty spaces) at the top left corner, where you can temporarily store one card each. You also have four foundations at the top right corner, where you have to sort the cards by suit and rank. You have eight tableau columns, with all the cards face up. You can move a card from the tableau to the foundation if it is one rank higher than the top card of the foundation and has the same suit. You can also move a card from the tableau to a free cell, or from a free cell to the tableau or the foundation. You can move a card or a group of cards from one tableau column to another if they are in descending order and alternating colors, and if you have enough free cells to make the move.</p>
- <h4>TriPeaks Solitaire</h4>
-<p>TriPeaks solitaire is a type of card game solitaire that has a unique layout and gameplay. It is also known as Tri Towers or Three Peaks. In this game, you have three peaks of cards on the tableau, with each peak having four rows of cards. The bottom row has six cards, the second row has five cards, and so on. The top card of each peak is face up, and the rest are face down. You also have a stock and a waste at the bottom of the screen. You can move a card from the peaks to the waste if it is one rank higher or lower than the top card of the waste. You can also draw a card from the stock to the waste when there are no more moves available. The goal of the game is to clear all the cards from the peaks.</p>
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-<p>Pyramid solitaire is another type of card game solitaire that has a distinctive shape and rules. In this game, you have a pyramid of cards on the tableau, with seven rows of cards. The bottom row has seven cards, the second row has six cards, and so on. The top card of each row is face up, and the rest are face down. You also have a stock and a waste at the bottom of the screen. You can move a pair of cards from the pyramid to the waste if they add up to 13. You can also draw a card from the stock to the waste when there are no more moves available. The goal of the game is to clear all the cards from the pyramid.</p>
- <h2>How to Download Card Game Solitaire for Free</h2>
-<p>If you want to play card game solitaire on your computer or mobile device, you have several options to download it for free. Here are some of them:</p>
- <h3>Download from Google Play Store</h3>
-<p>If you have an Android device, you can download card game solitaire from Google Play Store, which is the official app store for Android devices. There are many different apps that offer card game solitaire, such as [Solitaire], [Solitaire Collection], [Solitaire Grand Harvest], and [Solitaire TriPeaks]. To download an app from Google Play Store, you need to follow these steps:</p>
-<ol>
-<li>Open Google Play Store on your device.</li>
-<li>Search for "card game solitaire" or any specific type of solitaire that you want to play.</li>
-<li>Select an app that suits your preferences and ratings.</li>
-<li>Tap on "Install" and wait for the app to download and install on your device.</li>
-<li>Tap on "Open" and enjoy playing card game solitaire.</li>
-</ol>
- <h3>Download from Microsoft Store</h3>
-<p>If you have a Windows device, you can download card game solitaire from Microsoft Store, which is the official app store for Windows devices. There are many different apps that offer card game solitaire, such as [Microsoft Solitaire Collection], [Simple Solitaire], [Spider Solitaire Collection Free], and [FreeCell Collection Free]. To download an app from Microsoft Store, you need to follow these steps:</p>
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-<li>Open Microsoft Store on your device.</li>
-<li>Search for "card game solitaire" or any specific type of solitaire that you want to play.</li>
-<li>Select an app that suits your preferences and ratings.</li>
-<li>Click on "Get" and wait for the app to download and install on your device.</li>
-<li>Click on "Launch" and enjoy playing card game solitaire.</li> <h3>Download from Other Websites</h3>
-<p>If you have a Mac or Linux device, or you prefer to play card game solitaire on your web browser, you can download card game solitaire from other websites that offer free online games. There are many different websites that offer card game solitaire, such as [Solitaire Bliss], [World of Solitaire], [247 Solitaire], and [Solitaire Time]. To download an app from these websites, you need to follow these steps:</p>
-<ol>
-<li>Open your web browser and go to the website of your choice.</li>
-<li>Search for "card game solitaire" or any specific type of solitaire that you want to play.</li>
-<li>Select a game that suits your preferences and ratings.</li>
-<li>Click on "Play" and wait for the game to load on your browser.</li>
-<li>Enjoy playing card game solitaire.</li>
-</ol>
- <h2>How to Play Card Game Solitaire on Your Device</h2>
-<p>Once you have downloaded card game solitaire on your device, you can start playing it anytime and anywhere. Here are some tips on how to play card game solitaire on your device:</p>
- <h3>The Basic Rules of Card Game Solitaire</h3>
-<p>The basic rules of card game solitaire are the same for all types of solitaire, with some variations depending on the game. The general rules are:</p>
-<ul>
-<li>You have to sort the cards into four piles, one for each suit, in ascending order from ace to king.</li>
-<li>You can move a card from the tableau to the foundation if it is one rank higher than the top card of the foundation and has a different color (for Klondike and FreeCell) or the same suit (for Spider and Pyramid).</li>
-<li>You can move a card or a group of cards from one tableau column to another if they are in descending order and alternating colors (for Klondike, Spider, and FreeCell) or add up to 13 (for Pyramid).</li>
-<li>You can flip a face down card on the tableau if there is no other card on top of it.</li>
-<li>You can draw one or three cards from the stock to the waste at a time, depending on your preference (for Klondike and Pyramid).</li>
-<li>You can move a card from the stock or the waste to the tableau or the foundation, depending on the game rules.</li>
-<li>You can move a card from the tableau to a free cell, or from a free cell to the tableau or the foundation (for FreeCell).</li>
-<li>You can move a pair of cards from the pyramid to the waste if they add up to 13 (for Pyramid).</li>
-<li>You can deal ten cards from the stock to each tableau column when there are no more moves available (for Spider).</li>
-<li>You can move a card from the peaks to the waste if it is one rank higher or lower than the top card of the waste (for TriPeaks).</li>
-</ul>
- <h3>The Features and Options of Card Game Solitaire</h3>
-<p>Card game solitaire apps and websites usually have some features and options that you can use to enhance your gaming experience. Some of them are:</p>
-<ul>
-<li>You can choose the difficulty level of the game, such as easy, medium, hard, or expert.</li>
-<li>You can choose the theme and design of the cards, such as classic, modern, colorful, or custom.</li>
-<li>You can choose the background and sound effects of the game, such as nature, music, or silence.</li>
-<li>You can undo or redo your moves if you make a mistake or change your mind.</li>
-<li>You can use hints or tips if you need some help or guidance.</li>
-<li>You can pause or resume the game if you need a break or want to continue later.</li>
-<li>You can save or load your progress if you want to keep playing or start over.</li>
-</ul>
- <h3>The Challenges and Rewards of Card Game Solitaire</h3>
-<p>Card game solitaire is not only a fun and relaxing game, but also a challenging and rewarding one. You can set some goals and achievements for yourself, such as:</p>
-<ul>
-<li>You can try to complete the game in the shortest time possible.</li>
-<li>You can try to use the fewest moves possible.</li>
-<li>You can try to score the highest points possible.</li>
-<li>You can try to clear all the cards from the tableau without using the stock or waste.</li>
-<li>You can try to win multiple games in a row without losing.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Card game solitaire is one of the best games that you can play on your computer or mobile device. It is easy to download, easy to play, and easy to enjoy. It is a game that can improve your concentration, memory, creativity, and problem-solving skills. It can also reduce your stress, anxiety, and boredom. It can also give you a sense of accomplishment, satisfaction, and fun. Whether you prefer Klondike, Spider, FreeCell, TriPeaks, or Pyramid solitaire, you can find a game that suits your taste and level. You can also customize the game according to your preferences and options. You can also challenge yourself and achieve some goals and rewards. Card game solitaire is a game that you can play anytime and anywhere, as long as you have a device and an internet connection. So what are you waiting for? Download card game solitaire today and enjoy the best card game ever!</p>
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-A: You win card game solitaire when you sort all the cards into four piles, one for each suit, in ascending order from ace to king.</li>
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-A: You lose card game solitaire when you run out of moves or time, depending on the game rules.</li>
-<li>Q: How do I shuffle the cards in card game solitaire?<br>
-A: You don't have to shuffle the cards in card game solitaire, as they are randomly shuffled before each game.</li>
-<li>Q: How do I change the difficulty level in card game solitaire?<br>
-A: You can change the difficulty level in card game solitaire by choosing a different type of solitaire, such as one suit, two suits, or four suits for Spider solitaire, or by changing the settings in the app or website.</li>
-<li>Q: How do I get more points in card game solitaire?<br>
-A: You can get more points in card game solitaire by completing the game faster, using fewer moves, clearing all the cards from the tableau, and winning multiple games in a row.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Download Keyboard 2021 APK and Enjoy Amazing Features and Themes.md b/spaces/fatiXbelha/sd/Download Keyboard 2021 APK and Enjoy Amazing Features and Themes.md
deleted file mode 100644
index 186f6393a34d6fd541a1d53fdc66a1167a50bc56..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Keyboard 2021 APK and Enjoy Amazing Features and Themes.md
+++ /dev/null
@@ -1,87 +0,0 @@
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-<p>Keyboard 2021 APK is a keyboard app for Android that lets you customize your keyboard with thousands of themes and fonts, use emoji and GIFs to express yourself, type with your voice and translate your text, and enjoy smart autocorrect and word prediction. It is a free app that you can download from the official website or from third-party sources. It is compatible with most Android devices and supports over 100 languages.</p>
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-<p>Finally, you need to set Keyboard 2021 as the default keyboard on your device. To do this, go to Settings > Language and input > Default keyboard and select Keyboard 2021. You can also access the app settings by tapping on the Keyboard 2021 icon on your home screen or app drawer. Here, you can customize your keyboard with various options and features.</p>
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-<h3>Summary of the main points</h3>
-<p>In conclusion, Keyboard 2021 APK is a keyboard app for Android that offers a smooth and fun typing experience with amazing features. You can customize your keyboard with thousands of themes and fonts, use emoji and GIFs to express yourself, type with your voice and translate your text, and enjoy smart autocorrect and word prediction. You can download and install Keyboard 2021 APK by following the simple steps we have explained in this article.</p>
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diff --git a/spaces/fatiXbelha/sd/Download Ship Simulator Extremes APK for Android and Experience Realistic Ship Simulation.md b/spaces/fatiXbelha/sd/Download Ship Simulator Extremes APK for Android and Experience Realistic Ship Simulation.md
deleted file mode 100644
index c0a8a0c55b533943e0d9949e80adc088358215fa..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Ship Simulator Extremes APK for Android and Experience Realistic Ship Simulation.md
+++ /dev/null
@@ -1,181 +0,0 @@
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-<h1>Ship Simulator Extremes APK Android: A Guide for Ship Lovers</h1>
-<p>If you are a fan of ships and sailing, you might have heard of Ship Simulator Extremes, a realistic and immersive simulation game that lets you experience the thrill of captaining various vessels in different scenarios. Whether you want to sail a cruise liner, a tanker, a tugboat, or even a Greenpeace ship, Ship Simulator Extremes has something for you. But did you know that you can also play Ship Simulator Extremes on your android device? In this article, we will show you how to download and install Ship Simulator Extremes APK Android, as well as some tips and tricks for playing this amazing game.</p>
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-<p>Ship Simulator Extremes is a simulation game developed by VStep and published by Paradox Interactive in 2010. It is the latest installment of the acclaimed Ship Simulator series, which has sold over 550K copies worldwide. Ship Simulator Extremes features a wide range of vessels to captain, from hovercrafts to mammoth tankers, as well as famous harbors and locations from around the world. You can explore the Antarctic, Bora Bora, Sydney, Rotterdam, and more. You can also take on exciting missions and campaigns based on real events, such as saving the environment, rescuing people in distress, or taking on illegal whale hunters. Ship Simulator Extremes boasts a realistic water and weather system, as well as an online multiplayer mode where you can sail with your friends.</p>
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-<p>Ship Simulator Extremes is a game that aims to provide a realistic and fun simulation of sailing various ships in different conditions. Some of the features and gameplay elements of Ship Simulator Extremes are:</p>
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-<li>You can choose from over 30 vessels to captain, each with their own characteristics, specifications, and handling. You can also customize your ship's appearance, name, flag, and horn.</li>
-<li>You can sail in different environments, from calm waters to extreme weather, such as storms, fog, rain, snow, icebergs, and waves. You can also experience day and night cycles, as well as dynamic lighting and shadows.</li>
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-<p>Ship Simulator Extremes is a game that appeals to anyone who loves ships and sailing. It is a game that lets you experience the thrill of being a captain without leaving your home. However, playing Ship Simulator Extremes on your PC might not be convenient or accessible for everyone. That's why playing Ship Simulator Extremes on your android device might be a better option for you. Here are some of the benefits of playing Ship Simulator Extremes on your android device:</p>
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-<li>You can play Ship Simulator Extremes anytime and anywhere, as long as you have your android device with you. You don't need a PC or a console to enjoy this game.</li>
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-<li>You can play Ship Simulator Extremes with other android users, as well as PC users, thanks to the cross-platform multiplayer feature. You can join or host online sessions with your friends or other players from around the world.</li>
-</ul>
- <h2>How to download and install Ship Simulator Extremes APK Android</h2>
-<p>If you are interested in playing Ship Simulator Extremes on your android device, you will need to download and install the Ship Simulator Extremes APK Android file. This is a modified version of the original game that allows you to run it on your android device. However, you will also need to meet some system requirements and follow some steps to ensure a smooth installation and gameplay. Here is how to download and install Ship Simulator Extremes APK Android:</p>
-<p>Ship Simulator Azur Interactive Games Limited<br />
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- <h3>What are the system requirements for Ship Simulator Extremes APK Android?</h3>
-<p>Before you download and install Ship Simulator Extremes APK Android, you will need to make sure that your android device meets the minimum system requirements for the game. These are:</p>
-<ul>
-<li>Android version: 4.4 or higher</li>
-<li>RAM: 2 GB or more</li>
-<li>Storage: 4 GB or more</li>
-<li>Processor: Quad-core or higher</li>
-<li>Internet connection: Required for online multiplayer mode</li>
-</ul>
- <h3>Where to find and download Ship Simulator Extremes APK Android?</h3>
-<p>Once you have checked that your android device meets the system requirements, you will need to find and download the Ship Simulator Extremes APK Android file. You can do this by visiting a reliable and trusted website that offers this file for free. One such website is [APKPure], where you can find the latest version of Ship Simulator Extremes APK Android, as well as other useful information and reviews about the game. To download the file from APKPure, you will need to follow these steps:</p>
-<ol>
-<li>Go to [APKPure] and search for "Ship Simulator Extremes APK Android" in the search bar.</li>
-<li>Select the result that matches the game's name and icon.</li>
-<li>On the game's page, scroll down and click on the green "Download APK" button.</li>
-<li>Wait for the download to finish and locate the file in your device's storage.</li>
-</ol>
- <h3>How to install and run Ship Simulator Extremes APK Android on your device?</h3>
-<p>After you have downloaded the Ship Simulator Extremes APK Android file, you will need to install it on your device. However, before you do that, you will need to enable the installation of apps from unknown sources on your device. This is because Ship Simulator Extremes APK Android is not available on the official Google Play Store, so you will need to allow your device to install apps from other sources. To do this, you will need to follow these steps:</p>
-<ol>
-<li>Go to your device's settings and look for "Security" or "Privacy" options.</li>
-<li>Find and enable the option that says "Unknown sources" or "Allow installation of apps from unknown sources".</li>
-<li>Confirm your choice by tapping "OK" or "Yes".</li>
-</ol>
-<p>Once you have enabled the installation of apps from unknown sources, you can proceed to install Ship Simulator Extremes APK Android on your device. To do this, you will need to follow these steps:</p>
-<ol>
-<li>Locate the Ship Simulator Extremes APK Android file in your device's storage and tap on it.</li>
-<li>A pop-up window will appear asking you if you want to install this app. Tap on "Install".</li>
-<li>Wait for the installation to finish and tap on "Open" or "Done".</li>
-<li>You can now launch Ship Simulator Extremes APK Android from your app drawer or home screen.</li>
-</ol>
- <h2>Tips and tricks for playing Ship Simulator Extremes APK Android</h2> <p>Now that you have downloaded and installed Ship Simulator Extremes APK Android on your device, you are ready to play this amazing game. However, if you want to get the most out of your sailing experience, you might want to learn some tips and tricks that can help you improve your skills and enjoy the game more. Here are some of the tips and tricks for playing Ship Simulator Extremes APK Android:</p>
- <h3>How to control your ship and navigate different environments?</h3>
-<p>One of the most important aspects of playing Ship Simulator Extremes APK Android is learning how to control your ship and navigate different environments. You will need to master the basics of steering, throttling, braking, and reversing, as well as using the rudder, the bow thruster, and the autopilot. You will also need to pay attention to the wind, the waves, the currents, and the depth of the water, as they can affect your ship's performance and stability. Here are some of the controls and navigation tips for playing Ship Simulator Extremes APK Android:</p>
-<ul>
-<li>To steer your ship, you can use the left and right arrows on the bottom left corner of the screen, or swipe left and right on the screen. You can also use a gamepad or a keyboard if you prefer.</li>
-<li>To throttle your ship, you can use the up and down arrows on the bottom right corner of the screen, or swipe up and down on the screen. You can also use a gamepad or a keyboard if you prefer.</li>
-<li>To brake your ship, you can tap on the brake icon on the bottom right corner of the screen, or press the space bar on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To reverse your ship, you can tap on the reverse icon on the bottom right corner of the screen, or press the R key on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To use the rudder, you can tap on the rudder icon on the top right corner of the screen, or press the Q and E keys on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To use the bow thruster, you can tap on the bow thruster icon on the top right corner of the screen, or press the A and D keys on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To use the autopilot, you can tap on the autopilot icon on the top right corner of the screen, or press the P key on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To zoom in and out of your ship's view, you can use the pinch gesture on your screen, or scroll up and down with your mouse wheel. You can also use a gamepad or a keyboard if you prefer.</li>
-<li>To change your ship's view angle, you can swipe left and right on your screen, or move your mouse left and right. You can also use a gamepad or a keyboard if you prefer.</li>
-<li>To switch between different camera views, you can tap on the camera icon on the top left corner of the screen, or press the C key on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To access the map, you can tap on the map icon on the top left corner of the screen, or press the M key on your keyboard. You can also use a gamepad if you prefer.</li>
-<li>To access the menu, you can tap on the menu icon on the top left corner of the screen, or press the ESC key on your keyboard. You can also use a gamepad if you prefer.</li>
-</ul>
- <h3>How to complete missions and campaigns in Ship Simulator Extremes APK Android?</h3>
-<p>Another important aspect of playing Ship Simulator Extremes APK Android is completing missions and campaigns that test your skills and knowledge as a captain. You can choose from different types of missions and campaigns, such as environmental, rescue, transport, tourism, military, etc. You can also create your own missions and campaigns using the mission editor. Here are some of the tips for completing missions and campaigns in Ship Simulator Extremes APK Android:</p>
-<ul>
-<li>Before you start a mission or a campaign, you can read the briefing and the objectives that tell you what you need to do and where you need to go. You can also check the map and the weather conditions to plan your route and strategy.</li>
-<li>During a mission or a campaign, you can follow the instructions and the hints that appear on your screen. You can also use the radio and the GPS to communicate with other characters and get information about your situation.</li>
-<li>After you finish a mission or a campaign, you can see your score and your performance. You can also earn medals and achievements based on how well you did. You can also replay any mission or campaign to improve your score and skills.</li>
-</ul>
- <h3>How to customize your ship and settings in Ship Simulator Extremes APK Android?</h3>
-<p>One of the fun aspects of playing Ship Simulator Extremes APK Android is customizing your ship and settings to suit your preferences and style. You can change various aspects of your ship, such as its appearance, name, flag, horn, etc. You can also change various aspects of your settings, such as the graphics, sound, controls, difficulty, etc. Here are some of the tips for customizing your ship and settings in Ship Simulator Extremes APK Android:</p>
-<ul>
-<li>To customize your ship, you can go to the shipyard menu and select the ship you want to modify. You can then choose from different options and colors to change your ship's appearance. You can also enter a name for your ship, select a flag from different countries, and choose a horn sound from different types.</li>
-<li>To customize your settings, you can go to the options menu and select the category you want to modify. You can then adjust various sliders and buttons to change your settings. You can also reset your settings to default if you want.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Ship Simulator Extremes APK Android is a game that lets you experience the thrill of captaining various vessels in different scenarios. You can download and install this game on your android device by following some simple steps. You can also improve your skills and enjoy the game more by following some tips and tricks. Ship Simulator Extremes APK Android is a game that will appeal to anyone who loves ships and sailing. If you are one of them, don't hesitate to give this game a try.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Ship Simulator Extremes APK Android:</p>
- <h4>What are some of the best ships to use in Ship Simulator Extremes APK Android?</h4>
-<p>The answer to this question depends on your personal preference and style. However, some of the ships that are popular among players are:</p>
-<ul>
-<li>The Oasis of the Seas: This is one of the largest cruise liners in the world, with a capacity of over 6,000 passengers. It has many features and facilities, such as pools, spas, restaurants, bars, theaters, etc. It is ideal for tourism missions and free roam mode.</li>
-<li>The Greenpeace Esperanza: This is one of the ships used by Greenpeace, an environmental organization that fights for various causes. It has a helicopter pad, a crane, a zodiac boat, etc. It is ideal for environmental missions and campaigns.</li>
-<li>The Sigita: This is one of the fastest ships in the game, with a speed of over 50 knots. It is a hovercraft that can travel on water and land. It has a radar, a sonar, a fire extinguisher, etc. It is ideal for rescue missions and extreme weather conditions.</li>
-</ul>
- <h4>How to play Ship Simulator Extremes APK Android online with friends?</h4>
-<p>To To play Ship Simulator Extremes APK Android online with friends, you will need to have an internet connection and a valid account for the game. You can create an account for free by registering on the official website of Ship Simulator Extremes. You will also need to have the same version of the game as your friends. To play online with friends, you will need to follow these steps:</p>
-<ol>
-<li>Launch Ship Simulator Extremes APK Android on your device and log in with your account.</li>
-<li>Go to the multiplayer menu and select the option that says "Join or host online session".</li>
-<li>If you want to join an existing session, you can browse the list of available sessions and select the one that matches your preferences and criteria. You can also use the search function to find a specific session by name, password, location, etc.</li>
-<li>If you want to host a new session, you can create your own session by choosing a name, a password, a location, a mode, a ship, etc. You can also invite your friends to join your session by sending them an invitation link or code.</li>
-<li>Once you are in a session, you can chat with other players, start or join a mission or a campaign, or sail freely with your friends.</li>
-</ol>
- <h4>How to fix common issues and errors in Ship Simulator Extremes APK Android?</h4>
-<p>Ship Simulator Extremes APK Android is a game that requires a lot of resources and performance from your device. Therefore, you might encounter some issues and errors while playing this game, such as crashes, freezes, lags, glitches, etc. Here are some of the tips for fixing common issues and errors in Ship Simulator Extremes APK Android:</p>
-<ul>
-<li>Make sure that your device meets the minimum system requirements for the game and that you have enough storage space and battery power.</li>
-<li>Make sure that you have downloaded and installed the latest version of Ship Simulator Extremes APK Android from a reliable and trusted source.</li>
-<li>Make sure that you have enabled the installation of apps from unknown sources on your device and that you have given all the necessary permissions to the game.</li>
-<li>Make sure that you have a stable and fast internet connection for online multiplayer mode and that you have logged in with a valid account for the game.</li>
-<li>Make sure that you have closed all other apps and programs running in the background of your device and that you have cleared your cache and data regularly.</li>
-<li>If you still experience issues and errors in Ship Simulator Extremes APK Android, you can try uninstalling and reinstalling the game, or contacting the developer or the support team for further assistance.</li>
-</ul>
- <h4>Is Ship Simulator Extremes APK Android safe and legal to download and use?</h4>
-<p>Ship Simulator Extremes APK Android is a modified version of the original game that allows you to play it on your android device. However, this does not mean that it is unsafe or illegal to download and use. As long as you download and install Ship Simulator Extremes APK Android from a reliable and trusted source, such as [APKPure], you should not have any problems with security or legality. However, you should also be aware of some risks and responsibilities associated with using Ship Simulator Extremes APK Android:</p>
-<ul>
-<li>You might not be able to access some features or updates of the original game that are exclusive to PC users.</li>
-<li>You might not be able to get official support or assistance from the developer or the publisher of the original game.</li>
-<li>You might violate some terms and conditions of the original game or some third-party platforms or services that are related to the game.</li>
-<li>You might encounter some bugs or errors that are not present in the original game or that are caused by the modification process.</li>
-</ul>
- <h4>Where can I find more information and resources about Ship Simulator Extremes APK Android?</h4>
-<p>If you want to find more information and resources about Ship Simulator Extremes APK Android, such as news, updates, reviews, guides, tutorials, videos, screenshots, etc., you can visit some of these websites:</p>
-<ul>
-<li>The official website of Ship Simulator Extremes: [https://www.shipsim.com/]</li>
-<li>The official forum of Ship Simulator Extremes: [https://forum.shipsim.com/]</li>
-<li>The official wiki of Ship Simulator Extremes: [https://shipsim.fandom.com/wiki/Ship_Simulator_Extremes]</li>
-<li>The official YouTube channel of Ship Simulator Extremes: [https://www.youtube.com/user/ShipSim]</li>
-<li>The official Facebook page of Ship Simulator Extremes: [https://www.facebook.com/ShipSim]</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/configs/paths_config.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/configs/paths_config.py
deleted file mode 100644
index 4604f6063b8125364a52a492de52fcc54004f373..0000000000000000000000000000000000000000
--- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/configs/paths_config.py
+++ /dev/null
@@ -1,28 +0,0 @@
-dataset_paths = {
- # Face Datasets (In the paper: FFHQ - train, CelebAHQ - test)
- 'ffhq': '',
- 'celeba_test': '',
-
- # Cars Dataset (In the paper: Stanford cars)
- 'cars_train': '',
- 'cars_test': '',
-
- # Horse Dataset (In the paper: LSUN Horse)
- 'horse_train': '',
- 'horse_test': '',
-
- # Church Dataset (In the paper: LSUN Church)
- 'church_train': '',
- 'church_test': '',
-
- # Cats Dataset (In the paper: LSUN Cat)
- 'cats_train': '',
- 'cats_test': ''
-}
-
-model_paths = {
- 'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
- 'ir_se50': 'pretrained_models/model_ir_se50.pth',
- 'shape_predictor': 'pretrained_models/shape_predictor_68_face_landmarks.dat',
- 'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth'
-}
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/w_norm.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/w_norm.py
deleted file mode 100644
index a45ab6f67d8a3f7051be4b7236fa2f38446fd2c1..0000000000000000000000000000000000000000
--- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/w_norm.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import torch
-from torch import nn
-
-
-class WNormLoss(nn.Module):
-
- def __init__(self, start_from_latent_avg=True):
- super(WNormLoss, self).__init__()
- self.start_from_latent_avg = start_from_latent_avg
-
- def forward(self, latent, latent_avg=None):
- if self.start_from_latent_avg:
- latent = latent - latent_avg
- return torch.sum(latent.norm(2, dim=(1, 2))) / latent.shape[0]
diff --git a/spaces/feregVcuzo/sanity-test-midi/Mount-And-Blade-Neogk.md b/spaces/feregVcuzo/sanity-test-midi/Mount-And-Blade-Neogk.md
deleted file mode 100644
index 7080087766a82a38abfa2ebab9f4ab82b01d25d5..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/Mount-And-Blade-Neogk.md
+++ /dev/null
@@ -1,86 +0,0 @@
-## Mount And Blade Neogk
-
-
-
-
-
- 
-
-
-
-
-
-**Download File »»» [https://www.google.com/url?q=https%3A%2F%2Fblltly.com%2F2txvLj&sa=D&sntz=1&usg=AOvVaw1bLAOuGjwKpcrms7Q9NjAI](https://www.google.com/url?q=https%3A%2F%2Fblltly.com%2F2txvLj&sa=D&sntz=1&usg=AOvVaw1bLAOuGjwKpcrms7Q9NjAI)**
-
-
-
-
-
-
-
-
-
-
-
- ```html
-
-# Mount and Blade Neogk: A Mod that Enhances the Siege Experience
-
-
-
-Mount and Blade Warband is a medieval sandbox game that lets you create your own character, join a faction, fight battles, siege castles, and more. But if you are looking for a more immersive and realistic siege experience, you might want to try out the mod Neogk.
-
-
-
-Neogk is a native compatible mod that adds destructible buildings and siege engines, as well as new weapons and armor for each faction. You can use catapults, ballistas, trebuchets, and even balloons to break down the walls and gates of your enemies, or defend your own castle from invaders. You can also use warcries and taunts to boost your morale or intimidate your foes.
-
-
-
-The mod also features new maps, weather effects, admin features, and a team damage menu. You can play Neogk on any native server, or join one of the dedicated Neogk servers that host custom scenarios and events. Neogk is compatible with Warband version 1.174 or higher, and you can download it from Nexus Mods or TrollGame Community.
-
-
-
-If you are a fan of Mount and Blade Warband and want to spice up your siege gameplay, Neogk is a mod that you should definitely check out. It will make you feel like you are part of a real medieval siege, with all the chaos, strategy, and fun that comes with it.
-
- ``` ```html
-
-## How to install Neogk mod
-
-
-
-If you want to try out Neogk mod, you will need to have Mount and Blade Warband version 1.174 or higher installed on your computer. You will also need to download the mod files from one of the sources mentioned above. There are two versions of the mod: standard and deluxe. The deluxe version has some extra banners and textures, but they are optional and not required for the mod to work.
-
-
-
-Once you have downloaded the mod files, you will need to extract them using a program like WinRAR or 7-Zip. You will get a folder called NeoGK that contains several text files. You will need to copy and paste these text files into your Native module folder, which is located in your Mount and Blade Warband installation directory (usually C:\\Program Files (x86)\\Steam\\steamapps\\common\\MountBlade Warband\\Modules\\Native). You will be asked to overwrite some existing files, and you should click yes to all.
-
-
-
-After you have replaced the files, you are ready to play Neogk mod. You can launch Mount and Blade Warband as usual, and select Native as your module. You will see a message that says \"NeoGK is active\" on the main menu. You can join any native server that supports Neogk mod, or host your own server with the mod enabled. You can also access the NeoGK guide by pressing ESC and clicking on the blue button that says \"Show GK Guide\".
-
-
-
-## How to use Neogk mod features
-
-
-
-Neogk mod adds many new features that enhance the siege gameplay in Mount and Blade Warband. Here are some of the main features and how to use them:
-
-
-
-- **New weapons and armor:** Each faction has some new weapons and armor that are unique to them. For example, the Swadians have a heavy crossbow, a flail, and a full plate armor; the Nords have a throwing axe, a warhammer, and a bear fur armor; the Rhodoks have a pike, a mace, and a chainmail armor; and so on. You can find these new items in the equipment menu when you join a server.
-
-- **New maps:** The mod also adds some new maps that are designed for siege scenarios. Some of these maps have destructible buildings and siege engines that you can use or destroy. For example, you can use a catapult to break down a wall, or use a ballista to shoot arrows at your enemies. You can also use a trebuchet to launch rocks or fireballs, or use a balloon to fly over the battlefield. You can find these new maps in the map selection menu when you host a server.
-
-- **Warcries and taunts:** The mod also adds some warcries and taunts that you can use to boost your morale or intimidate your foes. You can use these by pressing F1-F4 keys on your keyboard. For example, you can press F1 to shout \"For King/Queen!\" or F2 to shout \"Charge!\". You can also press F3 to taunt your enemies with insults like \"You fight like a farmer!\" or F4 to mock them with gestures like \"Come here!\".
-
-
-
-These are just some of the features that Neogk mod adds to Mount and Blade Warband. There are many more features that you can discover by playing the mod yourself. Neogk mod is a great way to enjoy the siege gameplay in Mount and Blade Warband with more realism, immersion, and fun.
-
- 1b8d091108
-
-
-
-
-
diff --git a/spaces/fffiloni/ControlVideo/assets/blank.md b/spaces/fffiloni/ControlVideo/assets/blank.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fffiloni/ControlVideo/data/blank.md b/spaces/fffiloni/ControlVideo/data/blank.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fffiloni/Image-to-MusicGen/audiocraft/modules/conv.py b/spaces/fffiloni/Image-to-MusicGen/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-to-MusicGen/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,245 +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 math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
- 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
- assert norm in CONV_NORMALIZATIONS
- if norm == 'weight_norm':
- return weight_norm(module)
- elif norm == 'spectral_norm':
- return spectral_norm(module)
- else:
- # We already check was in CONV_NORMALIZATION, so any other choice
- # doesn't need reparametrization.
- return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
- """Return the proper normalization module. If causal is True, this will ensure the returned
- module is causal, or return an error if the normalization doesn't support causal evaluation.
- """
- assert norm in CONV_NORMALIZATIONS
- if norm == 'time_group_norm':
- if causal:
- raise ValueError("GroupNorm doesn't support causal evaluation.")
- assert isinstance(module, nn.modules.conv._ConvNd)
- return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
- else:
- return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
- padding_total: int = 0) -> int:
- """See `pad_for_conv1d`.
- """
- length = x.shape[-1]
- n_frames = (length - kernel_size + padding_total) / stride + 1
- ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
- return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
- """Pad for a convolution to make sure that the last window is full.
- Extra padding is added at the end. This is required to ensure that we can rebuild
- an output of the same length, as otherwise, even with padding, some time steps
- might get removed.
- For instance, with total padding = 4, kernel size = 4, stride = 2:
- 0 0 1 2 3 4 5 0 0 # (0s are padding)
- 1 2 3 # (output frames of a convolution, last 0 is never used)
- 0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
- 1 2 3 4 # once you removed padding, we are missing one time step !
- """
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
- """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
- If this is the case, we insert extra 0 padding to the right before the reflection happen.
- """
- length = x.shape[-1]
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- if mode == 'reflect':
- max_pad = max(padding_left, padding_right)
- extra_pad = 0
- if length <= max_pad:
- extra_pad = max_pad - length + 1
- x = F.pad(x, (0, extra_pad))
- padded = F.pad(x, paddings, mode, value)
- end = padded.shape[-1] - extra_pad
- return padded[..., :end]
- else:
- return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
- """Remove padding from x, handling properly zero padding. Only for 1d!
- """
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- assert (padding_left + padding_right) <= x.shape[-1]
- end = x.shape[-1] - padding_right
- return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
- """Wrapper around Conv1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConv2d(nn.Module):
- """Wrapper around Conv2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose1d(nn.Module):
- """Wrapper around ConvTranspose1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose2d(nn.Module):
- """Wrapper around ConvTranspose2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class StreamableConv1d(nn.Module):
- """Conv1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, dilation: int = 1,
- groups: int = 1, bias: bool = True, causal: bool = False,
- norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
- pad_mode: str = 'reflect'):
- super().__init__()
- # warn user on unusual setup between dilation and stride
- if stride > 1 and dilation > 1:
- warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
- f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
- self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
- dilation=dilation, groups=groups, bias=bias, causal=causal,
- norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.pad_mode = pad_mode
-
- def forward(self, x):
- B, C, T = x.shape
- kernel_size = self.conv.conv.kernel_size[0]
- stride = self.conv.conv.stride[0]
- dilation = self.conv.conv.dilation[0]
- kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
- padding_total = kernel_size - stride
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- if self.causal:
- # Left padding for causal
- x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
- return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
- """ConvTranspose1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, causal: bool = False,
- norm: str = 'none', trim_right_ratio: float = 1.,
- norm_kwargs: tp.Dict[str, tp.Any] = {}):
- super().__init__()
- self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
- causal=causal, norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.trim_right_ratio = trim_right_ratio
- assert self.causal or self.trim_right_ratio == 1., \
- "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
- assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
- def forward(self, x):
- kernel_size = self.convtr.convtr.kernel_size[0]
- stride = self.convtr.convtr.stride[0]
- padding_total = kernel_size - stride
-
- y = self.convtr(x)
-
- # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
- # removed at the very end, when keeping only the right length for the output,
- # as removing it here would require also passing the length at the matching layer
- # in the encoder.
- if self.causal:
- # Trim the padding on the right according to the specified ratio
- # if trim_right_ratio = 1.0, trim everything from right
- padding_right = math.ceil(padding_total * self.trim_right_ratio)
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- return y
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/tty.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/tty.d.ts
deleted file mode 100644
index 6473f8db7c69ea519bbb6e6450fb2d236f8a1674..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/tty.d.ts
+++ /dev/null
@@ -1,206 +0,0 @@
-/**
- * The `tty` module provides the `tty.ReadStream` and `tty.WriteStream` classes.
- * In most cases, it will not be necessary or possible to use this module directly.
- * However, it can be accessed using:
- *
- * ```js
- * const tty = require('tty');
- * ```
- *
- * When Node.js detects that it is being run with a text terminal ("TTY")
- * attached, `process.stdin` will, by default, be initialized as an instance of`tty.ReadStream` and both `process.stdout` and `process.stderr` will, by
- * default, be instances of `tty.WriteStream`. The preferred method of determining
- * whether Node.js is being run within a TTY context is to check that the value of
- * the `process.stdout.isTTY` property is `true`:
- *
- * ```console
- * $ node -p -e "Boolean(process.stdout.isTTY)"
- * true
- * $ node -p -e "Boolean(process.stdout.isTTY)" | cat
- * false
- * ```
- *
- * In most cases, there should be little to no reason for an application to
- * manually create instances of the `tty.ReadStream` and `tty.WriteStream`classes.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/tty.js)
- */
-declare module 'tty' {
- import * as net from 'node:net';
- /**
- * The `tty.isatty()` method returns `true` if the given `fd` is associated with
- * a TTY and `false` if it is not, including whenever `fd` is not a non-negative
- * integer.
- * @since v0.5.8
- * @param fd A numeric file descriptor
- */
- function isatty(fd: number): boolean;
- /**
- * Represents the readable side of a TTY. In normal circumstances `process.stdin` will be the only `tty.ReadStream` instance in a Node.js
- * process and there should be no reason to create additional instances.
- * @since v0.5.8
- */
- class ReadStream extends net.Socket {
- constructor(fd: number, options?: net.SocketConstructorOpts);
- /**
- * A `boolean` that is `true` if the TTY is currently configured to operate as a
- * raw device. Defaults to `false`.
- * @since v0.7.7
- */
- isRaw: boolean;
- /**
- * Allows configuration of `tty.ReadStream` so that it operates as a raw device.
- *
- * When in raw mode, input is always available character-by-character, not
- * including modifiers. Additionally, all special processing of characters by the
- * terminal is disabled, including echoing input
- * characters. Ctrl+C will no longer cause a `SIGINT` when
- * in this mode.
- * @since v0.7.7
- * @param mode If `true`, configures the `tty.ReadStream` to operate as a raw device. If `false`, configures the `tty.ReadStream` to operate in its default mode. The `readStream.isRaw`
- * property will be set to the resulting mode.
- * @return The read stream instance.
- */
- setRawMode(mode: boolean): this;
- /**
- * A `boolean` that is always `true` for `tty.ReadStream` instances.
- * @since v0.5.8
- */
- isTTY: boolean;
- }
- /**
- * -1 - to the left from cursor
- * 0 - the entire line
- * 1 - to the right from cursor
- */
- type Direction = -1 | 0 | 1;
- /**
- * Represents the writable side of a TTY. In normal circumstances,`process.stdout` and `process.stderr` will be the only`tty.WriteStream` instances created for a Node.js process and there
- * should be no reason to create additional instances.
- * @since v0.5.8
- */
- class WriteStream extends net.Socket {
- constructor(fd: number);
- addListener(event: string, listener: (...args: any[]) => void): this;
- addListener(event: 'resize', listener: () => void): this;
- emit(event: string | symbol, ...args: any[]): boolean;
- emit(event: 'resize'): boolean;
- on(event: string, listener: (...args: any[]) => void): this;
- on(event: 'resize', listener: () => void): this;
- once(event: string, listener: (...args: any[]) => void): this;
- once(event: 'resize', listener: () => void): this;
- prependListener(event: string, listener: (...args: any[]) => void): this;
- prependListener(event: 'resize', listener: () => void): this;
- prependOnceListener(event: string, listener: (...args: any[]) => void): this;
- prependOnceListener(event: 'resize', listener: () => void): this;
- /**
- * `writeStream.clearLine()` clears the current line of this `WriteStream` in a
- * direction identified by `dir`.
- * @since v0.7.7
- * @param callback Invoked once the operation completes.
- * @return `false` if the stream wishes for the calling code to wait for the `'drain'` event to be emitted before continuing to write additional data; otherwise `true`.
- */
- clearLine(dir: Direction, callback?: () => void): boolean;
- /**
- * `writeStream.clearScreenDown()` clears this `WriteStream` from the current
- * cursor down.
- * @since v0.7.7
- * @param callback Invoked once the operation completes.
- * @return `false` if the stream wishes for the calling code to wait for the `'drain'` event to be emitted before continuing to write additional data; otherwise `true`.
- */
- clearScreenDown(callback?: () => void): boolean;
- /**
- * `writeStream.cursorTo()` moves this `WriteStream`'s cursor to the specified
- * position.
- * @since v0.7.7
- * @param callback Invoked once the operation completes.
- * @return `false` if the stream wishes for the calling code to wait for the `'drain'` event to be emitted before continuing to write additional data; otherwise `true`.
- */
- cursorTo(x: number, y?: number, callback?: () => void): boolean;
- cursorTo(x: number, callback: () => void): boolean;
- /**
- * `writeStream.moveCursor()` moves this `WriteStream`'s cursor _relative_ to its
- * current position.
- * @since v0.7.7
- * @param callback Invoked once the operation completes.
- * @return `false` if the stream wishes for the calling code to wait for the `'drain'` event to be emitted before continuing to write additional data; otherwise `true`.
- */
- moveCursor(dx: number, dy: number, callback?: () => void): boolean;
- /**
- * Returns:
- *
- * * `1` for 2,
- * * `4` for 16,
- * * `8` for 256,
- * * `24` for 16,777,216 colors supported.
- *
- * Use this to determine what colors the terminal supports. Due to the nature of
- * colors in terminals it is possible to either have false positives or false
- * negatives. It depends on process information and the environment variables that
- * may lie about what terminal is used.
- * It is possible to pass in an `env` object to simulate the usage of a specific
- * terminal. This can be useful to check how specific environment settings behave.
- *
- * To enforce a specific color support, use one of the below environment settings.
- *
- * * 2 colors: `FORCE_COLOR = 0` (Disables colors)
- * * 16 colors: `FORCE_COLOR = 1`
- * * 256 colors: `FORCE_COLOR = 2`
- * * 16,777,216 colors: `FORCE_COLOR = 3`
- *
- * Disabling color support is also possible by using the `NO_COLOR` and`NODE_DISABLE_COLORS` environment variables.
- * @since v9.9.0
- * @param [env=process.env] An object containing the environment variables to check. This enables simulating the usage of a specific terminal.
- */
- getColorDepth(env?: object): number;
- /**
- * Returns `true` if the `writeStream` supports at least as many colors as provided
- * in `count`. Minimum support is 2 (black and white).
- *
- * This has the same false positives and negatives as described in `writeStream.getColorDepth()`.
- *
- * ```js
- * process.stdout.hasColors();
- * // Returns true or false depending on if `stdout` supports at least 16 colors.
- * process.stdout.hasColors(256);
- * // Returns true or false depending on if `stdout` supports at least 256 colors.
- * process.stdout.hasColors({ TMUX: '1' });
- * // Returns true.
- * process.stdout.hasColors(2 ** 24, { TMUX: '1' });
- * // Returns false (the environment setting pretends to support 2 ** 8 colors).
- * ```
- * @since v11.13.0, v10.16.0
- * @param [count=16] The number of colors that are requested (minimum 2).
- * @param [env=process.env] An object containing the environment variables to check. This enables simulating the usage of a specific terminal.
- */
- hasColors(count?: number): boolean;
- hasColors(env?: object): boolean;
- hasColors(count: number, env?: object): boolean;
- /**
- * `writeStream.getWindowSize()` returns the size of the TTY
- * corresponding to this `WriteStream`. The array is of the type`[numColumns, numRows]` where `numColumns` and `numRows` represent the number
- * of columns and rows in the corresponding TTY.
- * @since v0.7.7
- */
- getWindowSize(): [number, number];
- /**
- * A `number` specifying the number of columns the TTY currently has. This property
- * is updated whenever the `'resize'` event is emitted.
- * @since v0.7.7
- */
- columns: number;
- /**
- * A `number` specifying the number of rows the TTY currently has. This property
- * is updated whenever the `'resize'` event is emitted.
- * @since v0.7.7
- */
- rows: number;
- /**
- * A `boolean` that is always `true`.
- * @since v0.5.8
- */
- isTTY: boolean;
- }
-}
-declare module 'node:tty' {
- export * from 'tty';
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/node_modules/cookie/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/node_modules/cookie/README.md
deleted file mode 100644
index e275c705bde03ce56130df39a78213f41d210804..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/node_modules/cookie/README.md
+++ /dev/null
@@ -1,286 +0,0 @@
-# cookie
-
-[![NPM Version][npm-version-image]][npm-url]
-[![NPM Downloads][npm-downloads-image]][npm-url]
-[![Node.js Version][node-version-image]][node-version-url]
-[![Build Status][github-actions-ci-image]][github-actions-ci-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-Basic HTTP cookie parser and serializer for HTTP servers.
-
-## Installation
-
-This is a [Node.js](https://nodejs.org/en/) module available through the
-[npm registry](https://www.npmjs.com/). Installation is done using the
-[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally):
-
-```sh
-$ npm install cookie
-```
-
-## API
-
-```js
-var cookie = require('cookie');
-```
-
-### cookie.parse(str, options)
-
-Parse an HTTP `Cookie` header string and returning an object of all cookie name-value pairs.
-The `str` argument is the string representing a `Cookie` header value and `options` is an
-optional object containing additional parsing options.
-
-```js
-var cookies = cookie.parse('foo=bar; equation=E%3Dmc%5E2');
-// { foo: 'bar', equation: 'E=mc^2' }
-```
-
-#### Options
-
-`cookie.parse` accepts these properties in the options object.
-
-##### decode
-
-Specifies a function that will be used to decode a cookie's value. Since the value of a cookie
-has a limited character set (and must be a simple string), this function can be used to decode
-a previously-encoded cookie value into a JavaScript string or other object.
-
-The default function is the global `decodeURIComponent`, which will decode any URL-encoded
-sequences into their byte representations.
-
-**note** if an error is thrown from this function, the original, non-decoded cookie value will
-be returned as the cookie's value.
-
-### cookie.serialize(name, value, options)
-
-Serialize a cookie name-value pair into a `Set-Cookie` header string. The `name` argument is the
-name for the cookie, the `value` argument is the value to set the cookie to, and the `options`
-argument is an optional object containing additional serialization options.
-
-```js
-var setCookie = cookie.serialize('foo', 'bar');
-// foo=bar
-```
-
-#### Options
-
-`cookie.serialize` accepts these properties in the options object.
-
-##### domain
-
-Specifies the value for the [`Domain` `Set-Cookie` attribute][rfc-6265-5.2.3]. By default, no
-domain is set, and most clients will consider the cookie to apply to only the current domain.
-
-##### encode
-
-Specifies a function that will be used to encode a cookie's value. Since value of a cookie
-has a limited character set (and must be a simple string), this function can be used to encode
-a value into a string suited for a cookie's value.
-
-The default function is the global `encodeURIComponent`, which will encode a JavaScript string
-into UTF-8 byte sequences and then URL-encode any that fall outside of the cookie range.
-
-##### expires
-
-Specifies the `Date` object to be the value for the [`Expires` `Set-Cookie` attribute][rfc-6265-5.2.1].
-By default, no expiration is set, and most clients will consider this a "non-persistent cookie" and
-will delete it on a condition like exiting a web browser application.
-
-**note** the [cookie storage model specification][rfc-6265-5.3] states that if both `expires` and
-`maxAge` are set, then `maxAge` takes precedence, but it is possible not all clients by obey this,
-so if both are set, they should point to the same date and time.
-
-##### httpOnly
-
-Specifies the `boolean` value for the [`HttpOnly` `Set-Cookie` attribute][rfc-6265-5.2.6]. When truthy,
-the `HttpOnly` attribute is set, otherwise it is not. By default, the `HttpOnly` attribute is not set.
-
-**note** be careful when setting this to `true`, as compliant clients will not allow client-side
-JavaScript to see the cookie in `document.cookie`.
-
-##### maxAge
-
-Specifies the `number` (in seconds) to be the value for the [`Max-Age` `Set-Cookie` attribute][rfc-6265-5.2.2].
-The given number will be converted to an integer by rounding down. By default, no maximum age is set.
-
-**note** the [cookie storage model specification][rfc-6265-5.3] states that if both `expires` and
-`maxAge` are set, then `maxAge` takes precedence, but it is possible not all clients by obey this,
-so if both are set, they should point to the same date and time.
-
-##### path
-
-Specifies the value for the [`Path` `Set-Cookie` attribute][rfc-6265-5.2.4]. By default, the path
-is considered the ["default path"][rfc-6265-5.1.4].
-
-##### sameSite
-
-Specifies the `boolean` or `string` to be the value for the [`SameSite` `Set-Cookie` attribute][rfc-6265bis-03-4.1.2.7].
-
- - `true` will set the `SameSite` attribute to `Strict` for strict same site enforcement.
- - `false` will not set the `SameSite` attribute.
- - `'lax'` will set the `SameSite` attribute to `Lax` for lax same site enforcement.
- - `'none'` will set the `SameSite` attribute to `None` for an explicit cross-site cookie.
- - `'strict'` will set the `SameSite` attribute to `Strict` for strict same site enforcement.
-
-More information about the different enforcement levels can be found in
-[the specification][rfc-6265bis-03-4.1.2.7].
-
-**note** This is an attribute that has not yet been fully standardized, and may change in the future.
-This also means many clients may ignore this attribute until they understand it.
-
-##### secure
-
-Specifies the `boolean` value for the [`Secure` `Set-Cookie` attribute][rfc-6265-5.2.5]. When truthy,
-the `Secure` attribute is set, otherwise it is not. By default, the `Secure` attribute is not set.
-
-**note** be careful when setting this to `true`, as compliant clients will not send the cookie back to
-the server in the future if the browser does not have an HTTPS connection.
-
-## Example
-
-The following example uses this module in conjunction with the Node.js core HTTP server
-to prompt a user for their name and display it back on future visits.
-
-```js
-var cookie = require('cookie');
-var escapeHtml = require('escape-html');
-var http = require('http');
-var url = require('url');
-
-function onRequest(req, res) {
- // Parse the query string
- var query = url.parse(req.url, true, true).query;
-
- if (query && query.name) {
- // Set a new cookie with the name
- res.setHeader('Set-Cookie', cookie.serialize('name', String(query.name), {
- httpOnly: true,
- maxAge: 60 * 60 * 24 * 7 // 1 week
- }));
-
- // Redirect back after setting cookie
- res.statusCode = 302;
- res.setHeader('Location', req.headers.referer || '/');
- res.end();
- return;
- }
-
- // Parse the cookies on the request
- var cookies = cookie.parse(req.headers.cookie || '');
-
- // Get the visitor name set in the cookie
- var name = cookies.name;
-
- res.setHeader('Content-Type', 'text/html; charset=UTF-8');
-
- if (name) {
- res.write('<p>Welcome back, <b>' + escapeHtml(name) + '</b>!</p>');
- } else {
- res.write('<p>Hello, new visitor!</p>');
- }
-
- res.write('<form method="GET">');
- res.write('<input placeholder="enter your name" name="name"> <input type="submit" value="Set Name">');
- res.end('</form>');
-}
-
-http.createServer(onRequest).listen(3000);
-```
-
-## Testing
-
-```sh
-$ npm test
-```
-
-## Benchmark
-
-```
-$ npm run bench
-
-> cookie@0.4.1 bench
-> node benchmark/index.js
-
- node@16.13.1
- v8@9.4.146.24-node.14
- uv@1.42.0
- zlib@1.2.11
- brotli@1.0.9
- ares@1.18.1
- modules@93
- nghttp2@1.45.1
- napi@8
- llhttp@6.0.4
- openssl@1.1.1l+quic
- cldr@39.0
- icu@69.1
- tz@2021a
- unicode@13.0
- ngtcp2@0.1.0-DEV
- nghttp3@0.1.0-DEV
-
-> node benchmark/parse-top.js
-
- cookie.parse - top sites
-
- 15 tests completed.
-
- parse accounts.google.com x 504,358 ops/sec ±6.55% (171 runs sampled)
- parse apple.com x 1,369,991 ops/sec ±0.84% (189 runs sampled)
- parse cloudflare.com x 360,669 ops/sec ±3.75% (182 runs sampled)
- parse docs.google.com x 521,496 ops/sec ±4.90% (180 runs sampled)
- parse drive.google.com x 553,514 ops/sec ±0.59% (189 runs sampled)
- parse en.wikipedia.org x 286,052 ops/sec ±0.62% (188 runs sampled)
- parse linkedin.com x 178,817 ops/sec ±0.61% (192 runs sampled)
- parse maps.google.com x 284,585 ops/sec ±0.68% (188 runs sampled)
- parse microsoft.com x 161,230 ops/sec ±0.56% (192 runs sampled)
- parse play.google.com x 352,144 ops/sec ±1.01% (181 runs sampled)
- parse plus.google.com x 275,204 ops/sec ±7.78% (156 runs sampled)
- parse support.google.com x 339,493 ops/sec ±1.02% (191 runs sampled)
- parse www.google.com x 286,110 ops/sec ±0.90% (191 runs sampled)
- parse youtu.be x 548,557 ops/sec ±0.60% (184 runs sampled)
- parse youtube.com x 545,293 ops/sec ±0.65% (191 runs sampled)
-
-> node benchmark/parse.js
-
- cookie.parse - generic
-
- 6 tests completed.
-
- simple x 1,266,646 ops/sec ±0.65% (191 runs sampled)
- decode x 838,413 ops/sec ±0.60% (191 runs sampled)
- unquote x 877,820 ops/sec ±0.72% (189 runs sampled)
- duplicates x 516,680 ops/sec ±0.61% (191 runs sampled)
- 10 cookies x 156,874 ops/sec ±0.52% (189 runs sampled)
- 100 cookies x 14,663 ops/sec ±0.53% (191 runs sampled)
-```
-
-## References
-
-- [RFC 6265: HTTP State Management Mechanism][rfc-6265]
-- [Same-site Cookies][rfc-6265bis-03-4.1.2.7]
-
-[rfc-6265bis-03-4.1.2.7]: https://tools.ietf.org/html/draft-ietf-httpbis-rfc6265bis-03#section-4.1.2.7
-[rfc-6265]: https://tools.ietf.org/html/rfc6265
-[rfc-6265-5.1.4]: https://tools.ietf.org/html/rfc6265#section-5.1.4
-[rfc-6265-5.2.1]: https://tools.ietf.org/html/rfc6265#section-5.2.1
-[rfc-6265-5.2.2]: https://tools.ietf.org/html/rfc6265#section-5.2.2
-[rfc-6265-5.2.3]: https://tools.ietf.org/html/rfc6265#section-5.2.3
-[rfc-6265-5.2.4]: https://tools.ietf.org/html/rfc6265#section-5.2.4
-[rfc-6265-5.2.5]: https://tools.ietf.org/html/rfc6265#section-5.2.5
-[rfc-6265-5.2.6]: https://tools.ietf.org/html/rfc6265#section-5.2.6
-[rfc-6265-5.3]: https://tools.ietf.org/html/rfc6265#section-5.3
-
-## License
-
-[MIT](LICENSE)
-
-[coveralls-image]: https://badgen.net/coveralls/c/github/jshttp/cookie/master
-[coveralls-url]: https://coveralls.io/r/jshttp/cookie?branch=master
-[github-actions-ci-image]: https://img.shields.io/github/workflow/status/jshttp/cookie/ci/master?label=ci
-[github-actions-ci-url]: https://github.com/jshttp/cookie/actions/workflows/ci.yml
-[node-version-image]: https://badgen.net/npm/node/cookie
-[node-version-url]: https://nodejs.org/en/download
-[npm-downloads-image]: https://badgen.net/npm/dm/cookie
-[npm-url]: https://npmjs.org/package/cookie
-[npm-version-image]: https://badgen.net/npm/v/cookie
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io/dist/client.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io/dist/client.d.ts
deleted file mode 100644
index fe6722811f6c3b44d37232c6a7e2aab22d366e84..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io/dist/client.d.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-/// <reference types="node" />
-import { Packet } from "socket.io-parser";
-import type { IncomingMessage } from "http";
-import type { Server } from "./index";
-import type { EventsMap } from "./typed-events";
-import type { Socket } from "./socket";
-import type { Socket as RawSocket } from "engine.io";
-interface WriteOptions {
- compress?: boolean;
- volatile?: boolean;
- preEncoded?: boolean;
- wsPreEncoded?: string;
-}
-export declare class Client<ListenEvents extends EventsMap, EmitEvents extends EventsMap, ServerSideEvents extends EventsMap, SocketData = any> {
- readonly conn: RawSocket;
- private readonly id;
- private readonly server;
- private readonly encoder;
- private readonly decoder;
- private sockets;
- private nsps;
- private connectTimeout?;
- /**
- * Client constructor.
- *
- * @param server instance
- * @param conn
- * @package
- */
- constructor(server: Server<ListenEvents, EmitEvents, ServerSideEvents, SocketData>, conn: any);
- /**
- * @return the reference to the request that originated the Engine.IO connection
- *
- * @public
- */
- get request(): IncomingMessage;
- /**
- * Sets up event listeners.
- *
- * @private
- */
- private setup;
- /**
- * Connects a client to a namespace.
- *
- * @param {String} name - the namespace
- * @param {Object} auth - the auth parameters
- * @private
- */
- private connect;
- /**
- * Connects a client to a namespace.
- *
- * @param name - the namespace
- * @param {Object} auth - the auth parameters
- *
- * @private
- */
- private doConnect;
- /**
- * Disconnects from all namespaces and closes transport.
- *
- * @private
- */
- _disconnect(): void;
- /**
- * Removes a socket. Called by each `Socket`.
- *
- * @private
- */
- _remove(socket: Socket<ListenEvents, EmitEvents, ServerSideEvents, SocketData>): void;
- /**
- * Closes the underlying connection.
- *
- * @private
- */
- private close;
- /**
- * Writes a packet to the transport.
- *
- * @param {Object} packet object
- * @param {Object} opts
- * @private
- */
- _packet(packet: Packet | any[], opts?: WriteOptions): void;
- private writeToEngine;
- /**
- * Called with incoming transport data.
- *
- * @private
- */
- private ondata;
- /**
- * Called when parser fully decodes a packet.
- *
- * @private
- */
- private ondecoded;
- /**
- * Handles an error.
- *
- * @param {Object} err object
- * @private
- */
- private onerror;
- /**
- * Called upon transport close.
- *
- * @param reason
- * @param description
- * @private
- */
- private onclose;
- /**
- * Cleans up event listeners.
- * @private
- */
- private destroy;
-}
-export {};
diff --git a/spaces/fffiloni/train-dreambooth-lora-sdxl/README.md b/spaces/fffiloni/train-dreambooth-lora-sdxl/README.md
deleted file mode 100644
index 577d3013740dd29ab0915cf3d3ee8736938b1bf1..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/train-dreambooth-lora-sdxl/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Train Dreambooth LoRa Sd-XL
-emoji: 🏆
-colorFrom: red
-colorTo: red
-python_version: 3.10.12
-sdk: gradio
-sdk_version: 3.44.2
-app_file: app.py
-pinned: false
-suggested_hardware: "a10g-small"
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/floriankrempl/mtg_rules_bot/mtg/objects/card.py b/spaces/floriankrempl/mtg_rules_bot/mtg/objects/card.py
deleted file mode 100644
index 76ad1bfb95b0ac427b6c35b2b549564c9fedfe17..0000000000000000000000000000000000000000
--- a/spaces/floriankrempl/mtg_rules_bot/mtg/objects/card.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from dataclasses import dataclass, field
-from pathlib import Path
-import urllib
-
-
-CACHE_PATH = Path(".cache/")
-
-
-@dataclass
-class Card:
- _id: str
- name: str
- mana_cost: str
- type: str
- oracle: str
- image_url: str
- power: int = 0
- toughness: int = 0
- color_identity: list[str] = field(default_factory=list)
- keywords: list[str] = field(default_factory=list)
- rulings: list[str] = field(default_factory=list)
- _image: Path = None
-
- def __repr__(self) -> str:
- return f"Card({self.name})"
-
- @property
- def image(self):
- if self._image:
- return self._image
- # TODO not allways download check first if id is file
- print(f"downloading image for {self.name}")
- card_image_file = CACHE_PATH / f"{self._id}.png"
- urllib.request.urlretrieve(self.image_url, card_image_file)
- self._image = card_image_file
- return card_image_file
-
- def to_text(self):
- """parse card data to text format"""
- text = []
- text.append(self.name)
- text.append(f"type: {self.type}")
- text.append(f"cost: {self.mana_cost}")
- if (self.power != "0") and (self.toughness != "0"):
- text.append(f"power/toughness: {str(self.power)}/{str(self.toughness)}")
- if self.keywords:
- text.append("keywords: " + " ".join(self.keywords))
- if self.color_identity:
- text.append("color identity: " + " ".join(self.color_identity))
- text.append(self.oracle)
-
- # rulings
- if self.rulings:
- text.append(f"Rulings for {self.name}: ")
- text.append("\n".join(self.rulings))
- else:
- text.append(f"There are no Rulings available for {self.name}")
- return "\n".join(text)
diff --git a/spaces/flowers-team/SocialAISchool/data_visualize.py b/spaces/flowers-team/SocialAISchool/data_visualize.py
deleted file mode 100644
index 0ec49d9d70830e60e0a15ad89d1843d4a606bfa1..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/data_visualize.py
+++ /dev/null
@@ -1,1436 +0,0 @@
-#!/usr/bin/env python
-import re
-import itertools
-import math
-from itertools import chain
-import time
-
-# import seaborn
-import numpy as np
-import os
-from collections import OrderedDict, defaultdict
-import pandas as pd
-import matplotlib.pyplot as plt
-import sys
-from termcolor import cprint, colored
-from pathlib import Path
-import pickle
-from scipy import stats
-
-save = True
-show_plot = False
-
-metrics = [
- 'success_rate_mean',
- # 'FPS',
- # 'extrinsic_return_mean',
- # 'exploration_bonus_mean',
- # 'NPC_intro',
- # 'curriculum_param_mean',
- # 'curriculum_max_success_rate_mean',
- # 'rreturn_mean'
-]
-
-
-eval_metric = "test_success_rates"
-# eval_metric = "exploration_bonus_mean"
-
-super_title = ""
-# super_title = "PPO - No exploration bonus"
-# super_title = "Count Based exploration bonus (Grid Search)"
-# super_title = "PPO + RND"
-# super_title = "PPO + RIDE"
-
-# statistical evaluation p-value
-test_p = 0.05
-
-agg_title = ""
-
-color_dict = None
-eval_filename = None
-
-max_frames = 20_000_000
-
-legend_show_n_seeds = False
-draw_legend = True
-per_seed = False
-
-study_train = False
-study_eval = True
-
-plot_test = True
-
-plot_aggregated_test = True
-plot_only_aggregated_test = True
-
-
-xnbins = 4
-ynbins = 3
-
-steps_denom = 1e6
-
-# Global vas for tracking and labeling data at load time.
-exp_idx = 0
-label_parser_dict = None
-label_parser = lambda l, _, label_parser_dict: l
-
-smooth_factor = 10 # used
-# smooth_factor = 0
-print("smooth factor:", smooth_factor)
-eval_smooth_factor = None
-leg_size = 30
-
-def smooth(x_, n=50):
- if n is None:
- return x_
-
- if type(x_) == list:
- x_ = np.array(x_)
- return np.array([x_[max(i - n, 0):i + 1].mean() for i in range(len(x_))])
-
-sort_test = False
-
-def sort_test_set(env_name):
- helps = [
- "LanguageFeedback",
- "LanguageColor",
- "Pointing",
- "Emulation",
- ]
- problems = [
- "Boxes",
- "Switches",
- "Generators",
- "Marble",
- "Doors",
- "Levers",
- ]
-
- env_names = []
- for p in problems:
- for h in helps:
- env_names.append(h+p)
-
- env_names.extend([
- "LeverDoorColl",
- "MarblePushColl",
- "MarblePassColl",
- "AppleStealing"
- ])
-
- for i, en in enumerate(env_names):
- if en in env_name:
- return i
-
- raise ValueError(f"Test env {env_name} not known")
-
-
-
-subsample_step = 1
-load_subsample_step = 1
-
-x_lim = 0
-max_x_lim = np.inf
-
-summary_dict = {}
-summary_dict_colors = {}
-to_plot_dict = {}
-
-
-default_colors_ = ["blue","orange","green","magenta", "brown", "red",'black',"grey",u'#ff7f0e',
- "cyan", "pink",'purple', u'#1f77b4',
- "darkorchid","sienna","lightpink", "indigo","mediumseagreen",'aqua',
- 'deeppink','silver','khaki','goldenrod'] * 100
-
-
-def get_eval_data(logdir, eval_metric):
- eval_data = defaultdict(lambda :defaultdict(list))
-
- for root, _, files in os.walk(logdir):
- for file in files:
- if 'testing_' in file:
- assert ".pkl" in file
- test_env_name = file.lstrip("testing_").rstrip(".pkl")
- try:
- with open(root+"/"+file, "rb") as f:
- seed_eval_data = pickle.load(f)
- except:
- print("Pickle not loaded: ", root+"/"+file)
- time.sleep(1)
- continue
-
- eval_data[test_env_name]["values"].append(seed_eval_data[eval_metric])
- eval_data[test_env_name]["steps"].append(seed_eval_data["test_step_nb"])
-
- for test_env, seed_data in eval_data.items():
- min_len_seed = min([len(s) for s in seed_data['steps']])
- eval_data[test_env]["values"] = np.array([s[:min_len_seed] for s in eval_data[test_env]["values"]])
- eval_data[test_env]["steps"] = np.array([s[:min_len_seed] for s in eval_data[test_env]["steps"]])
-
- return eval_data
-
-def get_all_runs(logdir, load_subsample_step=1):
- """
- Recursively look through logdir for output files produced by
- Assumes that any file "log.csv" is a valid hit.
- """
- global exp_idx
- global units
- datasets = []
- for root, _, files in os.walk(logdir):
- if 'log.csv' in files:
- if (Path(root) / 'log.csv').stat().st_size == 0:
- print("CSV {} empty".format(os.path.join(root, 'log.csv')))
- continue
-
- run_name = root[8:]
-
- exp_name = None
-
- config = None
- exp_idx += 1
-
- # load progress data
- try:
- exp_data = pd.read_csv(os.path.join(root, 'log.csv'))
- print("Loaded:", os.path.join(root, 'log.csv'))
- except:
- raise ValueError("CSV {} faulty".format(os.path.join(root, 'log.csv')))
-
- exp_data = exp_data[::load_subsample_step]
- data_dict = exp_data.to_dict("list")
-
- data_dict['config'] = config
- nb_epochs = len(data_dict['frames'])
- if nb_epochs == 1:
- print(f'{run_name} -> {colored(f"nb_epochs {nb_epochs}", "red")}')
- else:
- print('{} -> nb_epochs {}'.format(run_name, nb_epochs))
-
- datasets.append(data_dict)
-
- return datasets
-
-
-def get_datasets(rootdir, load_only="", load_subsample_step=1, ignore_patterns=("ignore"), require_patterns=()):
- _, models_list, _ = next(os.walk(rootdir))
- for dir_name in models_list.copy():
- # add "ignore" in a directory name to avoid loading its content
- for ignore_pattern in ignore_patterns:
- if ignore_pattern in dir_name or load_only not in dir_name:
- if dir_name in models_list:
- models_list.remove(dir_name)
-
- if len(require_patterns) > 0:
- if not any([require_pattern in dir_name for require_pattern in require_patterns]):
- if dir_name in models_list:
- models_list.remove(dir_name)
-
- for expe_name in list(labels.keys()):
- if expe_name not in models_list:
- del labels[expe_name]
-
- # setting per-model type colors
- for i, m_name in enumerate(models_list):
- for m_type, m_color in per_model_colors.items():
- if m_type in m_name:
- colors[m_name] = m_color
- print("extracting data for {}...".format(m_name))
- m_id = m_name
- models_saves[m_id] = OrderedDict()
- models_saves[m_id]['data'] = get_all_runs(rootdir+m_name, load_subsample_step=load_subsample_step)
- print("done")
-
- if m_name not in labels:
- labels[m_name] = m_name
-
- model_eval_data[m_id] = get_eval_data(logdir=rootdir+m_name, eval_metric=eval_metric)
-
- """
- retrieve all experiences located in "data to vizu" folder
- """
-labels = OrderedDict()
-per_model_colors = OrderedDict()
-
-# LOAD DATA
-models_saves = OrderedDict()
-colors = OrderedDict()
-model_eval_data = OrderedDict()
-
-static_lines = {}
-
-ignore_patterns = ["_ignore_"]
-
-to_compare = None
-load_pattern = sys.argv[1]
-
-test_envs_to_plot = None # plot all
-
-min_y, max_y = 0.0, 1.1
-
-
-def label_parser(label):
- label = label.replace("04-01_Pointing_CB_heldout_doors", "PPO_CB")
- label = label.replace("19-01_Color_CB_heldout_doors", "PPO_CBL")
- label = label.replace("19-01_Feedback_CB_heldout_doors_20M", "PPO_CBL")
-
- label = label.replace("20-01_JA_Color_CB_heldout_doors", "JA_PPO_CBL")
-
- label = label.replace("05-01_scaffolding_50M_no_acl", "PPO_no_scaf")
- label = label.replace("05-01_scaffolding_50M_acl_4_acl-type_intro_seq", "PPO_scaf_4")
- label = label.replace("05-01_scaffolding_50M_acl_8_acl-type_intro_seq_scaf", "PPO_scaf_8")
-
-
- label = label.replace("03-01_RR_ft_single_CB_marble_pass_A_soc_exp", "PPO_CB_role_B")
- label = label.replace("03-01_RR_ft_single_CB_marble_pass_A_asoc_contr", "PPO_CB_asocial")
-
- label = label.replace("05-01_RR_ft_group_50M_CB_marble_pass_A_soc_exp", "PPO_CB_role_B")
- label = label.replace("05-01_RR_ft_group_50M_CB_marble_pass_A_asoc_contr", "PPO_CB_asocial")
-
- label = label.replace("20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.25_50",
- "PPO_CB_0.25")
- label = label.replace("20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.5_50",
- "PPO_CB_0.5")
- label = label.replace("20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__1_50",
- "PPO_CB_1")
-
- return label
-
-color_dict = {
- 'PPO_CB': "blue",
- 'PPO_CB(train)': "blue",
- "PPO_CB(test)": "orange",
-
- 'PPO_no_bonus': "orange",
-
- 'PPO_CBL': "blue",
- 'PPO_CBL(train)': "blue",
- "PPO_CBL(test)": "orange",
- 'JA_PPO_CBL': "green",
-
- "PPO_CB_role_B": "blue",
- "PPO_CB_asocial": "orange",
-
- 'PPO_CB_0.25': "blue",
- 'PPO_CB_0.5': "green",
- 'PPO_CB_1': "orange",
-
-}
-
-if load_pattern == "RR_single":
- save = False
- show_plot = True
- load_pattern = "_"
-
- plot_path = "../case_studies_final_figures/RR_dummy_single"
-
- require_patterns = [
- "03-01_RR_ft_single_CB_marble_pass_A_asoc_contr",
- "03-01_RR_ft_single_CB_marble_pass_A_soc_exp",
- ]
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
- study_train = True
- study_eval = False
-
-elif load_pattern == "RR_group":
-
- load_pattern = "_"
-
- plot_path = "../case_studies_final_figures/RR_dummy_group"
-
- require_patterns = [
- "05-01_RR_ft_group_50M_CB_marble_pass_A_asoc_contr",
- "05-01_RR_ft_group_50M_CB_marble_pass_A_soc_exp",
- ]
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
- study_train = True
- study_eval = False
-
-
-elif load_pattern == "scaffolding":
- load_pattern = "_"
-
- plot_path = "../case_studies_final_figures/Scaffolding_test"
-
- require_patterns = [
- "05-01_scaffolding_50M_no_acl",
- "05-01_scaffolding_50M_acl_4_acl-type_intro_seq",
- "05-01_scaffolding_50M_acl_8_acl-type_intro_seq_scaf",
- ]
-
- test_envs_to_plot = None # aggregate all of them
- plot_aggregated_test = True
- plot_only_aggregated_test = True
- study_train = False
- study_eval = True
-
- to_compare = [
- ("05-01_scaffolding_50M_acl_4_acl-type_intro_seq_agg_test", "05-01_scaffolding_50M_no_acl_agg_test", "auto_color"),
- ("05-01_scaffolding_50M_acl_8_acl-type_intro_seq_scaf_agg_test", "05-01_scaffolding_50M_no_acl_agg_test", "auto_color"),
- ]
-
-elif load_pattern == "pointing":
- study_train = True
- study_eval = True
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- load_pattern = "_"
-
- test_envs_to_plot = [
- "SocialAI-EPointingDoorsTestInformationSeekingParamEnv-v1",
- ]
-
- plot_path = "../case_studies_final_figures/Pointing_train_test"
-
- require_patterns = [
- "04-01_Pointing_CB_heldout_doors",
- ]
-
- to_compare = [
- ("04-01_Pointing_CB_heldout_doors", "04-01_Pointing_CB_heldout_doors_SocialAI-EPointingDoorsTestInformationSeekingParamEnv-v1", "black")
- ]
-
-elif load_pattern == "color":
- study_train = True
- study_eval = True
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = [
- "SocialAI-ELangColorDoorsTestInformationSeekingParamEnv-v1",
- ]
-
- plot_path = "../case_studies_final_figures/Color_train_test"
-
- require_patterns = [
- "19-01_Color_CB_heldout_doors",
- ]
-
- to_compare = [
- ("19-01_Color_CB_heldout_doors", "19-01_Color_CB_heldout_doors_SocialAI-ELangColorDoorsTestInformationSeekingParamEnv-v1", "black")
- ]
-
-elif load_pattern == "ja_color":
-
- study_train = True
- study_eval = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/JA_Color_train"
-
- require_patterns = [
- "19-01_Color_CB_heldout_doors",
- "20-01_JA_Color_CB_heldout_doors",
- ]
-
- to_compare = [
- ("19-01_Color_CB_heldout_doors", "20-01_JA_Color_CB_heldout_doors", "black")
- ]
-
-elif load_pattern == "feedback_per_seed":
- study_train = True
- study_eval = False
- per_seed = True
- draw_legend = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = [
- "SocialAI-ELangFeedbackDoorsTestInformationSeekingParamEnv-v1",
- ]
-
- plot_path = "../case_studies_final_figures/Feedback_train_per_seed"
-
- require_patterns = [
- "19-01_Feedback_CB_heldout_doors",
- ]
-
- to_compare = None
-
-elif load_pattern == "feedback":
- study_train = True
- study_eval = True
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = [
- "SocialAI-ELangFeedbackDoorsTestInformationSeekingParamEnv-v1",
- ]
-
- plot_path = "../case_studies_final_figures/Feedback_train_test"
-
- require_patterns = [
- "19-01_Feedback_CB_heldout_doors",
- ]
-
- to_compare = [
- ("19-01_Feedback_CB_heldout_doors_20M", "19-01_Feedback_CB_heldout_doors_20M_SocialAI-ELangFeedbackDoorsTestInformationSeekingParamEnv-v1", "black")
- ]
-
-elif load_pattern == "imitation_train":
-
- study_train = True
- study_eval = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/Imitation_train"
-
- require_patterns = [
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.25_50",
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.5_50",
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__1_50",
- ]
-
- # to_compare = [
- # ("19-01_Color_CB_heldout_doors", "20-01_JA_Color_CB_heldout_doors", "black")
- # ]
- to_compare = None
-
-elif load_pattern == "imitation_train_intro":
-
- metrics = ["NPC_intro"]
-
- show_plot = False
- save = True
-
- study_train = True
- study_eval = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/Imitation_train_intro"
-
- require_patterns = [
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.25_50",
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.5_50",
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__1_50",
- ]
-
- # to_compare = [
- # ("19-01_Color_CB_heldout_doors", "20-01_JA_Color_CB_heldout_doors", "black")
- # ]
- to_compare = None
-
-elif load_pattern == "imitation_test":
-
- study_train = False
- study_eval = True
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- max_x_lim = 18
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/Imitation_test"
-
- require_patterns = [
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.25_50",
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__0.5_50",
- "20-01_Imitation_PPO_CB_exploration-bonus-type_cell_exploration-bonus-params__1_50",
- ]
-
- # to_compare = [
- # ("19-01_Color_CB_heldout_doors", "20-01_JA_Color_CB_heldout_doors", "black")
- # ]
- to_compare = None
-
-elif load_pattern == "pilot_pointing":
-
- study_train = True
- study_eval = False
-
- show_plot = False
- save = True
- plot_path = "../case_studies_final_figures/pilot_pointing"
-
- load_pattern = "29-10_SAI_Pointing_CS_PPO_"
-
- require_patterns = [
- "29-10_SAI_Pointing_CS_PPO_CB_env_SocialAI-EPointingInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_cell_exploration-bonus-params__2_50_exploration-bonus-tanh_0.6",
- "29-10_SAI_Pointing_CS_PPO_CBL_env_SocialAI-EPointingInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_lang_exploration-bonus-params__10_50_exploration-bonus-tanh_0.6",
- "29-10_SAI_Pointing_CS_PPO_no_env_SocialAI-EPointingInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4",
- "29-10_SAI_Pointing_CS_PPO_RIDE_env_SocialAI-EPointingInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_ride_intrinsic-reward-coef_0.01",
- "29-10_SAI_Pointing_CS_PPO_RND_env_SocialAI-EPointingInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_rnd_intrinsic-reward-coef_0.005",
- ]
-
- color_dict = {
- "PPO_RIDE": "orange",
- "PPO_RND": "magenta",
- "PPO_no": "maroon",
- "PPO_CBL": "green",
- "PPO_CB": "blue",
- }
-
- def label_parser(label):
- label = label.split("_env_")[0].split("SAI_")[1]
- label=label.replace("Pointing_CS_", "")
- return label
-
- to_compare = None
-
-elif load_pattern == "pilot_color":
-
- study_train = True
- study_eval = False
-
- show_plot = False
- save = True
- plot_path = "../case_studies_final_figures/pilot_color"
-
- load_pattern = "29-10_SAI_LangColor_CS"
-
- require_patterns = [
- "29-10_SAI_LangColor_CS_PPO_CB_env_SocialAI-ELangColorInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_cell_exploration-bonus-params__2_50_exploration-bonus-tanh_0.6",
- "29-10_SAI_LangColor_CS_PPO_CBL_env_SocialAI-ELangColorInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_lang_exploration-bonus-params__10_50_exploration-bonus-tanh_0.6",
- "29-10_SAI_LangColor_CS_PPO_no_env_SocialAI-ELangColorInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4",
- "29-10_SAI_LangColor_CS_PPO_RIDE_env_SocialAI-ELangColorInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_ride_intrinsic-reward-coef_0.01",
- "29-10_SAI_LangColor_CS_PPO_RND_env_SocialAI-ELangColorInformationSeekingParamEnv-v1_recurrence_5_lr_1e-4_exploration-bonus-type_rnd_intrinsic-reward-coef_0.005"
- ]
- color_dict = {
- "PPO_RIDE": "orange",
- "PPO_RND": "magenta",
- "PPO_no": "maroon",
- "PPO_CBL": "green",
- "PPO_CB": "blue",
- }
-
- def label_parser(label):
- label = label.split("_env_")[0].split("SAI_")[1]
- label=label.replace("LangColor_CS_", "")
- return label
-
- to_compare = None
-
-elif load_pattern == "formats_train":
-
- study_train = True
- study_eval = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- max_x_lim = 45
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/Formats_train"
-
- require_patterns = [
- "21-01_formats_50M_CBL",
- "05-01_scaffolding_50M_no_acl",
- ]
-
- to_compare = [
- ("21-01_formats_50M_CBL", "05-01_scaffolding_50M_no_acl", "black")
- ]
-
-
- def label_parser(label):
- label = label.replace("05-01_scaffolding_50M_no_acl", "PPO_no_bonus")
- label = label.replace("21-01_formats_50M_CBL", "PPO_CBL")
- return label
-
-elif load_pattern == "adversarial":
-
- show_plot = False
- save = True
-
- study_train = True
- study_eval = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- # max_x_lim = 45
-
- smooth_factor = 0
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/adversarial"
-
- require_patterns = [
- "26-01_Adversarial_2M_PPO_CB_hidden_npc",
- "26-01_Adversarial_2M_PPO_CB_asoc",
- "26-01_Adversarial_2M_PPO_CB",
- ]
-
- to_compare = [
- ("26-01_Adversarial_2M_PPO_CB", "26-01_Adversarial_2M_PPO_CB_hidden_npc", "orange"),
- ("26-01_Adversarial_2M_PPO_CB", "26-01_Adversarial_2M_PPO_CB_asoc", "green")
- ]
-
- def label_parser(label):
- label = label.replace("26-01_Adversarial_2M_PPO_CB_hidden_npc", "PPO_CB_invisible_peer")
- label = label.replace("26-01_Adversarial_2M_PPO_CB_asoc", "PPO_CB_no_peer")
- label = label.replace("26-01_Adversarial_2M_PPO_CB", "PPO_CB")
- return label
-
- color_dict = {
- "PPO_CB": "blue",
- "PPO_CB_invisible_peer": "orange",
- "PPO_CB_no_peer": "green",
- }
-
-elif load_pattern == "adversarial_stumps":
-
-
- study_train = True
- study_eval = False
-
- plot_aggregated_test = False
- plot_only_aggregated_test = False
-
- # max_x_lim = 45
-
- smooth_factor = 0
-
- load_pattern = "_"
-
- test_envs_to_plot = None
- plot_path = "../case_studies_final_figures/adversarial_stumps"
-
- require_patterns = [
- "26-01_Adversarial_5M_Stumps_PPO_CB_hidden_npc",
- "26-01_Adversarial_5M_Stumps_PPO_CB_asoc",
- "26-01_Adversarial_5M_Stumps_PPO_CB",
- ]
-
- to_compare = [
- ("26-01_Adversarial_5M_Stumps_PPO_CB", "26-01_Adversarial_5M_Stumps_PPO_CB_hidden_npc", "orange"),
- ("26-01_Adversarial_5M_Stumps_PPO_CB", "26-01_Adversarial_5M_Stumps_PPO_CB_asoc", "green")
- ]
-
- def label_parser(label):
- label = label.replace("26-01_Adversarial_5M_Stumps_PPO_CB_hidden_npc", "PPO_CB_invisible_peer")
- label = label.replace("26-01_Adversarial_5M_Stumps_PPO_CB_asoc", "PPO_CB_no_peer")
- label = label.replace("26-01_Adversarial_5M_Stumps_PPO_CB", "PPO_CB")
- return label
-
- color_dict = {
- "PPO_CB": "blue",
- "PPO_CB_invisible_peer": "orange",
- "PPO_CB_no_peer": "green",
- }
-
-else:
- plot_path = "plots/testplot"
-
- require_patterns = [
- "_",
- # pointing
- # "04-01_Pointing_CB_heldout_doors",
- ]
-
-if to_compare is None and len(require_patterns) == 2 and "_" not in require_patterns:
- # if only two curves compare those two automatically
- to_compare = [(require_patterns[0], require_patterns[1], "black")]
-
-
-
-save=False
-show_plot = True
-
-
-# all of those
-include_patterns = []
-#include_patterns = ["rec_5"]
-
-fontsize = 20
-legend_fontsize = 20
-linewidth = 5
-# linewidth = 1
-
-leg_args = {
- 'fontsize': legend_fontsize
-}
-
-title_fontsize = int(fontsize*1.2)
-
-
-storage_dir = "storage/"
-if load_pattern.startswith(storage_dir):
- load_pattern = load_pattern[len(storage_dir):]
-
-if load_pattern.startswith("./storage/"):
- load_pattern = load_pattern[len("./storage/"):]
-
-get_datasets(storage_dir, str(load_pattern), load_subsample_step=load_subsample_step, ignore_patterns=ignore_patterns, require_patterns=require_patterns)
-
-label_parser_dict = {
- # "PPO_CB": "PPO_CB",
- # "02-06_AppleStealing_experiments_cb_bonus_angle_occ_env_SocialAI-OthersPerceptionInferenceParamEnv-v1_exploration-bonus-type_cell": "NPC_visible",
-}
-
-env_type = str(load_pattern)
-
-fig_type = "test"
-try:
- top_n = int(sys.argv[2])
-except:
- top_n = 8
-
-to_remove = []
-
-for tr_ in to_remove:
- if tr_ in models_saves:
- del models_saves[tr_]
-
-print("Loaded:")
-print("\n".join(list(models_saves.keys())))
-
-
-if per_model_colors: # order runs for legend order as in per_models_colors, with corresponding colors
- ordered_labels = OrderedDict()
- for teacher_type in per_model_colors.keys():
- for k,v in labels.items():
- if teacher_type in k:
- ordered_labels[k] = v
- labels = ordered_labels
-else:
- print('not using per_model_color')
- for k in models_saves.keys():
- labels[k] = k
-
-# Plot utils
-def plot_with_shade(subplot_nb, ax, x, y, err, color, shade_color, label,
- legend=False, leg_loc='best', title=None,
- ylim=[0, 100], xlim=[0, 40], leg_args={}, leg_linewidth=13.0, linewidth=10.0, labelsize=20, fontsize=20, title_fontsize=30,
- zorder=None, xlabel='Perf', ylabel='Env steps', linestyle="-", xnbins=3, ynbins=3):
-
- #plt.rcParams.update({'font.size': 15})
- ax.locator_params(axis='x', nbins=xnbins)
- ax.locator_params(axis='y', nbins=ynbins)
-
- ax.tick_params(axis='y', which='both', labelsize=labelsize)
- ax.tick_params(axis='x', which='both', labelsize=labelsize*0.8)
- # ax.tick_params(axis='both', which='both', labelsize="small")
-
- # ax.scatter(x, y, color=color,linewidth=linewidth,zorder=zorder, linestyle=linestyle)
- ax.plot(x, y, color=color, label=label, linewidth=linewidth, zorder=zorder, linestyle=linestyle)
-
- if not np.array_equal(err, np.zeros_like(err)):
- ax.fill_between(x, y-err, y+err, color=shade_color, alpha=0.2)
-
- if legend:
- leg = ax.legend(loc=leg_loc, **leg_args) # 34
- for legobj in leg.legendHandles:
- legobj.set_linewidth(leg_linewidth)
-
- ax.set_xlabel(xlabel, fontsize=fontsize)
- if subplot_nb == 0:
- ax.set_ylabel(ylabel, fontsize=fontsize, labelpad=2)
-
- ax.set_xlim(xmin=xlim[0], xmax=xlim[1])
- ax.set_ylim(bottom=ylim[0], top=ylim[1])
- if title:
- ax.set_title(title, fontsize=title_fontsize)
-
-
-
-
-# only one figure is drawn -> maybe we can add loops later
-assert len(metrics) == 1
-
-f, ax = plt.subplots(1, 1, figsize=(9.0, 9.0))
-
-if len(metrics) == 1:
- ax = [ax]
-
-# max_y = -np.inf
-min_y = np.inf
-
-max_steps = 0
-exclude_patterns = []
-
-metric = metrics[0]
-
-ylabel = {
- "success_rate_mean": "Success rate (%)",
- "exploration_bonus_mean": "Exploration bonus",
- "NPC_intro": "Successful introduction (%)",
-}.get(metric, metric)
-
-# for metric_i, metric in enumerate(metrics):
-default_colors = default_colors_.copy()
-
-if study_train:
- for model_i, model_id in enumerate(models_saves.keys()):
-
- #excluding some experiments
- if any([ex_pat in model_id for ex_pat in exclude_patterns]):
- continue
-
- if len(include_patterns) > 0:
- if not any([in_pat in model_id for in_pat in include_patterns]):
- continue
-
- runs_data = models_saves[model_id]['data']
- ys = []
-
- if runs_data[0]['frames'][1] == 'frames':
- runs_data[0]['frames'] = list(filter(('frames').__ne__, runs_data[0]['frames']))
-
- if per_seed:
- min_len = None
-
- else:
- # determine minimal run length across seeds
- lens = [len(run['frames']) for run in runs_data if len(run['frames'])]
- minimum = sorted(lens)[-min(top_n, len(lens))]
- min_len = np.min([len(run['frames']) for run in runs_data if len(run['frames']) >= minimum])
-
- # keep only top k
- runs_data = [run for run in runs_data if len(run['frames']) >= minimum]
-
- # min_len = np.min([len(run['frames']) for run in runs_data if len(run['frames']) > 10])
-
- # compute env steps (x axis)
- longest_id = np.argmax([len(rd['frames']) for rd in runs_data])
- steps = np.array(runs_data[longest_id]['frames'], dtype=np.int) / steps_denom
- steps = steps[:min_len]
-
- for run in runs_data:
- if metric not in run:
- raise ValueError(f"Metric {metric} not found. Possible metrics: {list(run.keys())}")
-
- data = run[metric]
-
- # checking for header
- if data[1] == metric:
- data = np.array(list(filter((metric).__ne__, data)), dtype=np.float16)
-
- if per_seed:
- ys.append(data)
-
- else:
- if len(data) >= min_len:
- # discard extra
- if len(data) > min_len:
- print("run has too many {} datapoints ({}). Discarding {}".format(model_id, len(data),
- len(data) - min_len))
- data = data[0:min_len]
- ys.append(data)
- else:
- raise ValueError("How can data be < min_len if it was capped above")
-
- ys_same_len = ys
-
- # computes stats
- n_seeds = len(ys_same_len)
-
- if per_seed:
- sems = np.array(ys_same_len)
- means = np.array(ys_same_len)
- stds = np.zeros_like(means)
- color = default_colors[model_i]
-
- else:
- sems = np.std(ys_same_len, axis=0)/np.sqrt(len(ys_same_len)) # sem
- stds = np.std(ys_same_len, axis=0) # std
- means = np.mean(ys_same_len, axis=0)
- color = default_colors[model_i]
-
- if metric == 'duration':
- means = means / 3600
- sems = sems / 3600
- stds = stds / 3600
-
- if per_seed:
- # plot x y bounds
- curr_max_steps = np.max(np.max(steps))
-
- else:
- # plot x y bounds
- curr_max_steps = np.max(steps)
-
- if curr_max_steps > max_steps:
- max_steps = curr_max_steps
-
- if subsample_step:
- steps = steps[0::subsample_step]
- means = means[0::subsample_step]
- stds = stds[0::subsample_step]
- sems = sems[0::subsample_step]
- ys_same_len = [y[0::subsample_step] for y in ys_same_len]
-
- # display seeds separately
- if per_seed:
- for s_i, seed_ys in enumerate(ys_same_len):
-
- label = label_parser(model_id)
-
- if study_eval:
- label = label + "_train_"
-
- label = label + f"(s:{s_i})"
-
- if label in color_dict:
- color = color_dict[label]
- else:
- color = default_colors[model_i*20+s_i]
-
- curve_ID = f"{model_id}_{s_i}"
- assert np.array_equal(stds, np.zeros_like(stds))
-
- if smooth_factor:
- means = smooth(means, smooth_factor)
-
- to_plot_dict[curve_ID] = {
- "label": label,
- "steps": steps,
- "means": seed_ys,
- "stds": stds,
- "ys": ys_same_len,
- "color": color
- }
-
- else:
- label = label_parser(model_id)
-
- if study_eval:
- label = label+"(train)"
-
- if color_dict:
- color = color_dict[label]
- else:
- color = default_colors[model_i]
-
- if smooth_factor:
- means = smooth(means, smooth_factor)
- stds = smooth(stds, smooth_factor)
-
- to_plot_dict[model_id] = {
- "label": label,
- "steps": steps,
- "means": means,
- "stds": stds,
- "sems": sems,
- "ys": ys_same_len,
- "color": color,
- }
-
-
-if study_eval:
- print("Evaluation")
- # evaluation sets
- number_of_eval_envs = max(list([len(v.keys()) for v in model_eval_data.values()]))
-
- if plot_aggregated_test:
- number_of_eval_envs += 1
-
- if number_of_eval_envs == 0:
- print("No eval envs")
- exit()
-
- default_colors = default_colors_.copy()
-
- test_summary_dict = defaultdict(dict)
- test_summary_dict_colors = defaultdict(dict)
-
- for model_i, model_id in enumerate(model_eval_data.keys()):
- # excluding some experiments
- if any([ex_pat in model_id for ex_pat in exclude_patterns]):
- continue
- if len(include_patterns) > 0:
- if not any([in_pat in model_id for in_pat in include_patterns]):
- continue
-
- # test envs
- test_envs = model_eval_data[model_id].items()
-
- # filter unwanted eval envs
- if test_envs_to_plot is not None:
- test_envs = [(name, data) for name, data in test_envs if name in test_envs_to_plot]
-
- # computes stats
- if sort_test:
- test_envs_sorted = list(sorted(test_envs, key=lambda kv: sort_test_set(kv[0])))
- else:
- test_envs_sorted = list(test_envs)
-
- if plot_aggregated_test:
- agg_means = []
-
- for env_i, (test_env, env_data) in enumerate(test_envs_sorted):
- ys_same_len = env_data["values"]
- steps = env_data["steps"].mean(0) / steps_denom
- n_seeds = len(ys_same_len)
-
- if per_seed:
- sems = np.array(ys_same_len)
- stds = np.array(ys_same_len)
- means = np.array(ys_same_len)
- color = default_colors[model_i]
-
- # plot x y bounds
- curr_max_steps = np.max(np.max(steps))
-
- else:
- sems = np.std(ys_same_len, axis=0) / np.sqrt(len(ys_same_len)) # sem
- stds = np.std(ys_same_len, axis=0) # std
- means = np.mean(ys_same_len, axis=0)
- color = default_colors[model_i]
-
- curr_max_steps = np.max(steps)
-
- if plot_aggregated_test:
- agg_means.append(means)
-
-
- x_lim = max(steps[-1], x_lim)
- x_lim = min(max_x_lim, x_lim)
-
- eval_metric_name = {
- "test_success_rates": "Success rate",
- 'exploration_bonus_mean': "Exploration bonus",
- }.get(eval_metric, eval_metric)
-
- test_env_name = test_env.replace("Env", "").replace("Test", "")
-
- env_types = ["InformationSeeking", "Collaboration", "PerspectiveTaking"]
- for env_type in env_types:
- if env_type in test_env_name:
- test_env_name = test_env_name.replace(env_type, "")
- test_env_name += f"\n({env_type})"
-
- if per_seed:
- for s_i, seed_ys in enumerate(ys_same_len):
- label = label_parser(model_id) + f"_{test_env}" + f"(s:{s_i})"
-
- if eval_smooth_factor:
- seed_ys = smooth(seed_ys, eval_smooth_factor)
-
- curve_ID = f"{model_id}_{test_env}_{s_i}"
-
- to_plot_dict[curve_ID] = {
- "label": label,
- "steps": steps,
- "means": seed_ys,
- "stds": np.zeros_like(seed_ys),
- "ys": ys_same_len,
- "color": color
- }
- else:
- if len(test_envs_sorted) > 1:
- label = label_parser(model_id) + f"_{test_env}"
- else:
- label = label_parser(model_id)
-
- if study_train:
- label=label+"(test)"
-
- if not plot_only_aggregated_test:
-
- if label in color_dict:
- color = color_dict[label]
- else:
- color = default_colors[model_i*len(test_envs_sorted)+env_i]
-
- if legend_show_n_seeds:
- label = label + "({})".format(n_seeds)
-
- if eval_smooth_factor:
- means = smooth(means, eval_smooth_factor)
- stds = smooth(stds, eval_smooth_factor)
- sems = smooth(sems, eval_smooth_factor)
-
- to_plot_dict[model_id+f"_{test_env}"] = {
- "label": label,
- "steps": steps,
- "means": means,
- "stds": stds,
- "sems": sems,
- "ys": ys_same_len,
- "color": color,
- }
-
- if plot_aggregated_test:
-
- ys_same_len = agg_means
- agg_means = np.array(agg_means)
- agg_mean = agg_means.mean(axis=0)
- agg_std = agg_means.std(axis=0) # std
- agg_sems = ...
-
- label = label_parser(model_id)
-
- if study_train:
- label = label + "(train)"
-
- if eval_smooth_factor:
- agg_mean = smooth(agg_mean, eval_smooth_factor)
- agg_std = smooth(agg_std, eval_smooth_factor)
- agg_sems = smooth(agg_sems, eval_smooth_factor)
-
- if per_seed:
- print("Not smooth aggregated because of per seed")
- for s_i, (seed_ys, seed_st) in enumerate(zip(agg_mean, agg_std)):
- seed_c = default_colors[model_i + s_i]
- label = str(s_i)
-
- to_plot_dict[curve_ID] = {
- "label": label,
- "steps": steps,
- "means": seed_ys,
- "stds": seed_st,
- "ys": ys_same_len,
- "color": color
- }
- else:
-
- if label in color_dict:
- color = color_dict[label]
-
- else:
- color = default_colors[model_i]
-
- to_plot_dict[model_id+"_agg_test"] = {
- "label": label,
- "steps": steps,
- "means": agg_mean,
- "stds": agg_std,
- "sems": agg_sems,
- "ys": ys_same_len,
- "color": color,
- }
-
-
-# should be labels
-to_scatter_dict = {}
-
-if to_compare is not None:
- for comp_i, (a_model_id, b_model_id, color) in enumerate(to_compare):
-
- a_data = to_plot_dict[a_model_id]["ys"]
- b_data = to_plot_dict[b_model_id]["ys"]
-
- steps = to_plot_dict[a_model_id]["steps"]
-
- if color == "auto_color":
- color = to_plot_dict[a_model_id]["color"]
-
- if len(a_data[0]) != len(b_data[0]):
- # extract steps present in both
- a_steps = to_plot_dict[a_model_id]["steps"]
- b_steps = to_plot_dict[b_model_id]["steps"]
-
- steps = list(set(a_steps) & set(b_steps))
-
- # keep only the values for those steps
- mask_a = [(a_s in steps) for a_s in a_steps]
- a_data = np.array(a_data)[:, mask_a]
-
- mask_b = [(b_s in steps) for b_s in b_steps]
- b_data = np.array(b_data)[:, mask_b]
-
- p = stats.ttest_ind(
- a_data,
- b_data,
- equal_var=False
- ).pvalue
-
- steps = [s for s, p in zip(steps, p) if p < test_p]
-
- ys = [1.02+0.02*comp_i]*len(steps)
-
- to_scatter_dict[f"compare_{a_model_id}_{b_model_id}"] = {
- "label": "",
- "xs": steps,
- "ys": ys,
- "color": color,
- }
-
-for scatter_i, (scatter_ID, scatter_id_data) in enumerate(to_scatter_dict.items()):
-
- # unpack data
- label, xs, ys, color = (
- scatter_id_data["label"],
- scatter_id_data["xs"],
- scatter_id_data["ys"],
- scatter_id_data["color"],
- )
-
- xlabel = f"Env steps (1e6)"
-
- plt.scatter(
- xs,
- ys,
- color=color,
- marker="x"
- )
-
- summary_dict[label] = xs[-1]
- summary_dict_colors[label] = color
-
-for curve_i, (curve_ID, model_id_data) in enumerate(to_plot_dict.items()):
-
- # unpack data
- label, steps, means, stds, sems, ys, color = (
- model_id_data["label"],
- model_id_data["steps"],
- model_id_data["means"],
- model_id_data["stds"],
- model_id_data["sems"],
- model_id_data["ys"],
- model_id_data["color"]
- )
-
- # if smooth_factor:
- # means = smooth(means, smooth_factor)
- # stds = smooth(stds, smooth_factor)
-
- if legend_show_n_seeds:
- n_seeds = len(ys)
- label = label+"({})".format(n_seeds)
-
-
- x_lim = max(steps[-1], x_lim)
- x_lim = min(max_x_lim, x_lim)
-
- xlabel = f"Env steps (1e6)"
-
-
- plot_with_shade(
- 0, ax[0], steps, means, stds, color, color, label,
- # 0, ax[0], steps, means, sems, color, color, label,
- legend=draw_legend,
- xlim=[0, x_lim],
- ylim=[0, max_y],
- xlabel=xlabel,
- ylabel=ylabel,
- title=None,
- labelsize=fontsize,
- fontsize=fontsize,
- title_fontsize=title_fontsize,
- linewidth=linewidth,
- leg_linewidth=5,
- leg_args=leg_args,
- xnbins=xnbins,
- ynbins=ynbins,
- )
-
- summary_dict[label] = means[-1]
- summary_dict_colors[label] = color
-
-# plot static lines
-if static_lines:
- for label, (mean, std, color) in static_lines.items():
-
- if label == "":
- label = None
-
- plot_with_shade(
- 0, ax[0], steps, np.array([mean]*len(steps)), np.array([std]*len(steps)), color, color, label,
- legend=True,
- xlim=[0, x_lim],
- ylim=[0, 1.0],
- xlabel=f"Env steps (1e6)",
- ylabel=ylabel,
- linestyle=":",
- leg_args=leg_args,
- fontsize=fontsize,
- title_fontsize=title_fontsize,
- xnbins=xnbins,
- ynbins=ynbins,
- )
-
-
-if plot_path:
- f.savefig(plot_path+".png")
- f.savefig(plot_path+".svg")
- print(f"Plot saved to {plot_path}.[png/svg].")
-
-
-# Summary dict
-if len(summary_dict) == 0:
- raise ValueError(f"No experiments found for {load_pattern}.")
-else:
- # print summary
- best = max(summary_dict.values())
-
- pc = 0.3
- n = int(len(summary_dict)*pc)
- print("top n: ", n)
-
- top_pc = sorted(summary_dict.values())[-n:]
- bottom_pc = sorted(summary_dict.values())[:n]
-
- print("legend:")
- cprint("\tbest", "green")
- cprint("\ttop {} %".format(pc), "blue")
- cprint("\tbottom {} %".format(pc), "red")
- print("\tothers")
- print()
-
- for l, p in sorted(summary_dict.items(), key=lambda kv: kv[1]):
-
- c = summary_dict_colors[l]
- if p == best:
- cprint("label: {} ({})".format(l, c), "green")
- cprint("\t {}:{}".format(metric, p), "green")
-
- elif p in top_pc:
- cprint("label: {} ({})".format(l, c), "blue")
- cprint("\t {}:{}".format(metric, p), "blue")
-
- elif p in bottom_pc:
- cprint("label: {} ({})".format(l, c), "red")
- cprint("\t {}:{}".format(metric, p), "red")
-
- else:
- print("label: {} ({})".format(l, c))
- print("\t {}:{}".format(metric, p))
-
-
-if show_plot:
- plt.tight_layout()
- plt.subplots_adjust(hspace=1.5, wspace=0.5, left=0.1, right=0.9, bottom=0.1, top=0.85)
- plt.suptitle(super_title)
- plt.show()
-plt.close()
-
diff --git a/spaces/freddyaboulton/blocks_inputs/README.md b/spaces/freddyaboulton/blocks_inputs/README.md
deleted file mode 100644
index d0d2559bb22fbb0cd96f700756cb1aab6994948f..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/blocks_inputs/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-app_file: run.py
-pinned: false
-sdk_version: 3.35.2
-title: blocks_inputs
----
-
\ No newline at end of file
diff --git a/spaces/fun-research/FC-CLIP/GETTING_STARTED.md b/spaces/fun-research/FC-CLIP/GETTING_STARTED.md
deleted file mode 100644
index f6fc9a85be12fbc2668f4d83858d06b570645c7f..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/GETTING_STARTED.md
+++ /dev/null
@@ -1,65 +0,0 @@
-## Getting Started with Mask2Former
-
-This document provides a brief intro of the usage of Mask2Former.
-
-Please see [Getting Started with Detectron2](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for full usage.
-
-
-### Inference Demo with Pre-trained Models
-
-1. Pick a model and its config file from
- [model zoo](MODEL_ZOO.md),
- for example, `configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml`.
-2. We provide `demo.py` that is able to demo builtin configs. Run it with:
-```
-cd demo/
-python demo.py --config-file ../configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml \
- --input input1.jpg input2.jpg \
- [--other-options]
- --opts MODEL.WEIGHTS /path/to/checkpoint_file
-```
-The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
-This command will run the inference and show visualizations in an OpenCV window.
-
-For details of the command line arguments, see `demo.py -h` or look at its source code
-to understand its behavior. Some common arguments are:
-* To run __on your webcam__, replace `--input files` with `--webcam`.
-* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
-* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
-* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
-
-
-### Training & Evaluation in Command Line
-
-We provide a script `train_net.py`, that is made to train all the configs provided in Mask2Former.
-
-To train a model with "train_net.py", first
-setup the corresponding datasets following
-[datasets/README.md](./datasets/README.md),
-then run:
-```
-python train_net.py --num-gpus 8 \
- --config-file configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml
-```
-
-The configs are made for 8-GPU training.
-Since we use ADAMW optimizer, it is not clear how to scale learning rate with batch size.
-To train on 1 GPU, you need to figure out learning rate and batch size by yourself:
-```
-python train_net.py \
- --config-file configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml \
- --num-gpus 1 SOLVER.IMS_PER_BATCH SET_TO_SOME_REASONABLE_VALUE SOLVER.BASE_LR SET_TO_SOME_REASONABLE_VALUE
-```
-
-To evaluate a model's performance, use
-```
-python train_net.py \
- --config-file configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml \
- --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
-```
-For more options, see `python train_net.py -h`.
-
-
-### Video instance segmentation
-Please use `demo_video/demo.py` for video instance segmentation demo and `train_net_video.py` to train
-and evaluate video instance segmentation models.
diff --git a/spaces/g4f/freegpt-webui/client/css/sidebar.css b/spaces/g4f/freegpt-webui/client/css/sidebar.css
deleted file mode 100644
index e703b2aa1d756ac2c6e1fb4aa76913b8d984b89c..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/client/css/sidebar.css
+++ /dev/null
@@ -1,197 +0,0 @@
-.sidebar {
- max-width: 260px;
- padding: var(--section-gap);
- flex-shrink: 0;
- display: flex;
- flex-direction: column;
- justify-content: space-between;
-}
-
-.sidebar .title {
- font-size: 14px;
- font-weight: 500;
-}
-
-.sidebar .conversation-sidebar {
- padding: 8px 12px;
- display: flex;
- gap: 18px;
- align-items: center;
- user-select: none;
- justify-content: space-between;
-}
-
-.sidebar .conversation-sidebar .left {
- cursor: pointer;
- display: flex;
- align-items: center;
- gap: 10px;
-}
-
-.sidebar i {
- color: var(--conversations);
- cursor: pointer;
-}
-
-.sidebar .top {
- display: flex;
- flex-direction: column;
- overflow: hidden;
- gap: 16px;
- padding-right: 8px;
-}
-
-.sidebar .top:hover {
- overflow: auto;
-}
-
-.sidebar .info {
- padding: 8px 12px 0px 12px;
- display: flex;
- align-items: center;
- justify-content: center;
- user-select: none;
- background: transparent;
- width: 100%;
- border: none;
- text-decoration: none;
-}
-
-.sidebar .info span {
- color: var(--conversations);
- line-height: 1.5;
- font-size: 0.75rem;
-}
-
-.sidebar .info i::before {
- margin-right: 8px;
-}
-
-.sidebar-footer {
- width: 100%;
- margin-top: 16px;
- display: flex;
- flex-direction: column;
-}
-
-.sidebar-footer button {
- cursor: pointer;
- user-select: none;
- background: transparent;
-}
-
-.sidebar.shown {
- position: fixed;
- top: 0;
- left: 0;
- width: 100%;
- height: 100%;
- z-index: 1000;
-}
-
-.sidebar.shown .box {
- background-color: #16171a;
- width: 80%;
- height: 100%;
- overflow-y: auto;
-}
-
-@keyframes spinner {
- to {
- transform: rotate(360deg);
- }
-}
-
-/* scrollbar */
-.sidebar .top::-webkit-scrollbar {
- width: 4px;
- padding: 8px 0px;
-}
-
-.sidebar .top::-webkit-scrollbar-track {
- background-color: #ffffff00;
-}
-
-.sidebar .top::-webkit-scrollbar-thumb {
- background-color: #555555;
- border-radius: 10px;
-}
-
-.spinner:before {
- content: "";
- box-sizing: border-box;
- position: absolute;
- top: 50%;
- left: 45%;
- width: 20px;
- height: 20px;
- border-radius: 50%;
- border: 1px solid var(--conversations);
- border-top-color: white;
- animation: spinner 0.6s linear infinite;
-}
-
-.mobile-sidebar {
- display: none !important;
- position: absolute;
- z-index: 100000;
- top: 0;
- left: 0;
- margin: 10px;
- font-size: 1rem;
- cursor: pointer;
- width: 30px;
- height: 30px;
- justify-content: center;
- align-items: center;
- transition: 0.33s;
-}
-
-.mobile-sidebar i {
- transition: 0.33s;
-}
-
-.rotated {
- transform: rotate(360deg);
-}
-
-.mobile-sidebar.rotated {
- position: fixed;
- top: 10px;
- left: 10px;
- z-index: 1001;
-}
-
-@media screen and (max-width: 990px) {
- .sidebar {
- display: none;
- width: 100%;
- max-width: none;
- }
-
- .mobile-sidebar {
- display: flex !important;
- }
-}
-
-@media (max-width: 990px) {
- .sidebar .top {
- padding-top: 48px;
- }
-}
-
-@media (min-width: 768px) {
- .sidebar.shown {
- position: static;
- width: auto;
- height: auto;
- background-color: transparent;
- }
-
- .sidebar.shown .box {
- background-color: #16171a;
- width: auto;
- height: auto;
- overflow-y: auto;
- }
-}
diff --git a/spaces/gatilin/mmocr-webui/README.md b/spaces/gatilin/mmocr-webui/README.md
deleted file mode 100644
index 4af9d4e873758224df527b1a7bf2a5a4ebec6c8f..0000000000000000000000000000000000000000
--- a/spaces/gatilin/mmocr-webui/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mmocr Webui
-emoji: 📚
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gingerale/Gnomespace/app.py b/spaces/gingerale/Gnomespace/app.py
deleted file mode 100644
index 0d7638516bce47aeb557c9b27d35264cc1ffec13..0000000000000000000000000000000000000000
--- a/spaces/gingerale/Gnomespace/app.py
+++ /dev/null
@@ -1,128 +0,0 @@
-## ----------------------------- ###
-### libraries ###
-### ----------------------------- ###
-import gradio as gr
-import pandas as pd
-import numpy as np
-import os
-import warnings
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn import metrics
-from reader import get_article
-
-warnings.filterwarnings("ignore")
-
-
-### ------------------------------ ###
-### data transformation ###
-### ------------------------------ ###
-# load dataset
-uncleaned_data = pd.read_csv('data.csv')
-
-# remove timestamp from dataset (always first column)
-if uncleaned_data.columns[0].lower() == 'timestamp':
- uncleaned_data = uncleaned_data.iloc[: , 1:]
-data = pd.DataFrame()
-
-# keep track of which columns are categorical and what
-# those columns' value mappings are
-# structure: {colname1: {...}, colname2: {...} }
-cat_value_dicts = {}
-final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
-
-# for each column...
-for (colname, colval) in uncleaned_data.iteritems():
- # check if col is already a number; if so, add col directly
- # to new dataframe and skip to next column
- if isinstance(colval.values[0], (np.integer, float)):
- data[colname] = uncleaned_data[colname].copy()
- continue
-
- # structure: {0: "lilac", 1: "blue", ...}
- new_dict = {}
- key = 0 # first index per column
- transformed_col_vals = [] # new numeric datapoints
-
- # if not, for each item in that column...
- for item in colval.values:
-
- # if item is not in this col's dict...
- if item not in new_dict:
- new_dict[item] = key
- key += 1
-
- # then add numerical value to transformed dataframe
- transformed_col_vals.append(new_dict[item])
-
- # reverse dictionary only for final col (0, 1) => (vals)
- if colname == final_colname:
- new_dict = {value : key for (key, value) in new_dict.items()}
- cat_value_dicts[colname] = new_dict
- data[colname] = transformed_col_vals
-
-
-### -------------------------------- ###
-### model training ###
-### -------------------------------- ###
-# select features and predicton; automatically selects last column as prediction
-num_features = len(data.columns) - 1
-x = data.iloc[: , :num_features]
-y = data.iloc[: , num_features:]
-
-# split data into training and testing sets
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
-
-# instantiate the model (using default parameters)
-model = LogisticRegression(multi_class='multinomial', penalty='none', solver='newton-cg')
-model.fit(x_train, y_train.values.ravel())
-y_pred = model.predict(x_test)
-
-
-### -------------------------------- ###
-### file reading ###
-### -------------------------------- ###
-# borrow file reading function from reader.py
-info = get_article()
-
-
-### ------------------------------- ###
-### interface creation ###
-### ------------------------------- ###
-# predictor for generic number of features
-def general_predictor(*args):
- features = []
-
- # transform categorical input
- for colname, arg in zip(data.columns, args):
- if (colname in cat_value_dicts):
- features.append(cat_value_dicts[colname][arg])
- else:
- features.append(arg)
-
- # predict single datapoint
- new_input = [features]
- result = model.predict(new_input)
- return cat_value_dicts[final_colname][result[0]]
-
-# add data labels to replace those lost via star-args
-inputls = []
-for colname in data.columns:
- # skip last column
- if colname == final_colname:
- continue
-
- # access categories dict if data is categorical
- # otherwise, just use a number input
- if colname in cat_value_dicts:
- radio_options = list(cat_value_dicts[colname].keys())
- inputls.append(gr.inputs.Radio(choices=radio_options, type="value", label=colname))
- else:
- # add numerical input
- inputls.append(gr.inputs.Number(label=colname))
-
-# generate gradio interface
-interface = gr.Interface(general_predictor, inputs=inputls, outputs="text", article=info['article'], css=info['css'], theme='huggingface', title=info['title'], allow_flagging=False, description=info['description'])
-
-# show the interface
-interface.launch(share=True)
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Dare To Zlatan Font How to Download and Install It on Your Device.md b/spaces/gotiQspiryo/whisper-ui/examples/Dare To Zlatan Font How to Download and Install It on Your Device.md
deleted file mode 100644
index 168555ffaa7b2a8b521fc073ca3ab3a5ab4854ce..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Dare To Zlatan Font How to Download and Install It on Your Device.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Dare To Zlatan Font</h2><br /><p><b><b>Download</b> ⚡ <a href="https://urlgoal.com/2uyM1w">https://urlgoal.com/2uyM1w</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/How to enjoy Mountain Man movie in mp4 A guide to the best scenes and actors.md b/spaces/gotiQspiryo/whisper-ui/examples/How to enjoy Mountain Man movie in mp4 A guide to the best scenes and actors.md
deleted file mode 100644
index d5e89dbc4b696003bd785148ab699b6177d2d538..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/How to enjoy Mountain Man movie in mp4 A guide to the best scenes and actors.md
+++ /dev/null
@@ -1,11 +0,0 @@
-<br />
-<p>Strap down and prepare for the ride of your life... Reckless Racing is back with more!Including a selection of 18 highly detailed cars, ranging from small dirt buggies to fast supercars, and 24 routes in a variety of flavors make this a racer for all tastes - both dirt and tarmac.Compete in the Career mode to earn money for new cars and car upgrades. Have a quick race in the Arcade mode with 40 pre-defined challenges. Compete against your friends on the leaderboards in Single Event mode - including Race, Hot Lap and Elimination events.Tune your car in the garage to match your driving style with a vast selection of parts and upgrades. Whether you are a reckless power slider or a slick race driver, make your car the way YOU want it.* Probably the best power slides in the world* Tune and tweak the performance, handling, tires, rims and colors of your cars* Shader based game engine with normal maps, reflections, specularity, shadows, particles and more* Improved and optimized shaders for iPad 2 and iPhone 4S* Online leaderboards with downloadable ghost cars* Online multiplayer over the Internet* 17 characters, including your favorites from Reckless Racing 1* 5 control layouts with customizable button positions* Optional assists, such as race lines, mini map, chase camera and more**************Requires:iPhone 3GS, iPhone 4 or iPhone 4SiPad or iPad 2iPod Touch 3 (16GB +) or iPod Touch 4**************</p>
-<p>The follow-up to Trials HD brings the bike-racing action back to the Xbox 360. Ride in real-time multiplayer with two to four players, either locally or through XBLA. Take part in single player career with 50 tracks, challenging unlocks, and tricky achievements. A massive, track editor lets you build the track of your choosing and share it with the world. Or simply download highly-rated tracks from around the globe using Track Central.</p>
-<h2>Mountain Man movie download in mp4</h2><br /><p><b><b>Download File</b> >>> <a href="https://urlgoal.com/2uyN1c">https://urlgoal.com/2uyN1c</a></b></p><br /><br />
-<p>Pilot three different variants of the F-16 Fighting Falcon -- the best multi-role fighter ever built -- including Block 40, Block 50/52 and MLU variants. Ultra-high fidelity physics modeling closely simulates actual aircraft performance. Engage the enemy in two operational theaters -- Korea and the Balkans -- each covering over a million square Kilometers of territory. Dogfight over a wide variety of landscapes: wide seas, open grasslands, dense forests and snow capped mountains. Fly nap-of-earth missions through detailed terrain and undulating valleys. Launch a variety of air-to-air missiles like the AIM-120 AMRAAM and AIM-9 Sidewinder. Deploy a vast array of air-to-ground weaponry including the AGM-65 Maverick, AGM-88 HARM, cluster bombs, slick and high drag bombs and laser guided ordnance. Weapon systems are simulated using the most accurate publicly available specifications. Simulate an entire war with the most advanced dynamic campaign engine ever made for your PC. This remarkable feature presides over tens of thousands of vehicles: air, land and sea. No two missions ever fly the same! [Graphsim]</p>
-<p>Crimson Skies: High Road to Revenge combines thrilling aerial combat with the swashbuckling style of a Hollywood action-adventure movie. Set in an alternate 1930s world of gunship diplomacy and sinister intrigue, the game lets you pilot powerful aircraft--armed with detonator cannons, tesla guns, or remote-controlled rockets--against nefarious air pirates and behemoth war zeppelins. As the daring air pirate Nathan Zachary, leader of the infamous Fortune Hunters, you take off on a high-flying adventure, filled with daring escapes and damsels in distress.</p>
-<p>(Also known as "Race Dirver: GRID") GRID takes players to dramatic and beautifully realized race locations over three continents to compete in a variety of racing events. Packed with the most powerful race cars - new and classic, circuit and drift - players compete to conquer the most prestigious official race tracks and championships then beyond to compete in challenging city-based competitions, and then on through to road events and urban street races. The drama, the rivalries, the aggression and the crashes; this is not a game about collecting cars or spending all of your time in the front-end tuning suspension settings or designing liveries. Codemasters wants to make racing exciting again. Gamers compete on Europe's greatest official race tracks in prestige Marques including Aston Martin, Koenigsegg and Pagani. Iconic cities across the U.S, including San Francisco, Washington DC and Detroit - each with their own atmosphere and events - play host to diverse street races, where high-performance V8 muscle cars set the pace in aggressive, closely fought pack competitions. And in the Far East, Japanese racing culture sets the tone where night races, including Drift racing, takes drivers through neon illuminated cities and to outlying mountain roads. There's also the opportunity to compete in races that operate on the fringes of legality in the back streets and industrial areas of Yokohama. [Codemasters]</p>
-<p>[Xbox Live Arcade] Trials HD is a full-featured, physics-based motorcycle racing game with over 50 tracks in two unique game modes. Race Mode emphasizes balance, skill, and control as you tackle indoor obstacle tracks. Skill Mode features extreme stunt tracks in which explosions, crashes, and injuries cannot be avoided. A level editor enables players to create, share and download an unlimited number of free tracks. The game features stunning full HD graphics, rider customization, unlockable bikes, global rankings, all new in-game friend scores, and many other fun features. Can you handle the bruises and broken bones - and take part in a truly unique racing experience? [Microsoft]</p>
-<p></p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/examples/multilingual/train_multilingual_model.sh b/spaces/gradio/HuBERT/examples/multilingual/train_multilingual_model.sh
deleted file mode 100644
index cc050bd3f02de8a2f303737f187442d2eb80e4ef..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/multilingual/train_multilingual_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-path_2_data=$1 # <path to data> which contains binarized data for each directions
-lang_list=$2 # <path to a file which contains a list of languages separted by new lines>
-lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en"
-
-fairseq-train "$path_2_data" \
- --encoder-normalize-before --decoder-normalize-before \
- --arch transformer --layernorm-embedding \
- --task translation_multi_simple_epoch \
- --sampling-method "temperature" \
- --sampling-temperature 1.5 \
- --encoder-langtok "src" \
- --decoder-langtok \
- --lang-dict "$lang_list" \
- --lang-pairs "$lang_pairs" \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \
- --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \
- --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \
- --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \
- --max-tokens 1024 --update-freq 2 \
- --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \
- --seed 222 --log-format simple --log-interval 2
diff --git a/spaces/gradio/HuBERT/fairseq_cli/generate.py b/spaces/gradio/HuBERT/fairseq_cli/generate.py
deleted file mode 100644
index 7bd582b25670a937921755ad98af12b107ad9fc7..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq_cli/generate.py
+++ /dev/null
@@ -1,408 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Translate pre-processed data with a trained model.
-"""
-
-import ast
-import logging
-import math
-import os
-import sys
-from argparse import Namespace
-from itertools import chain
-
-import numpy as np
-import torch
-from fairseq import checkpoint_utils, options, scoring, tasks, utils
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.logging import progress_bar
-from fairseq.logging.meters import StopwatchMeter, TimeMeter
-from omegaconf import DictConfig
-
-
-def main(cfg: DictConfig):
-
- if isinstance(cfg, Namespace):
- cfg = convert_namespace_to_omegaconf(cfg)
-
- assert cfg.common_eval.path is not None, "--path required for generation!"
- assert (
- not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
- ), "--sampling requires --nbest to be equal to --beam"
- assert (
- cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw"
- ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)"
-
- if cfg.common_eval.results_path is not None:
- os.makedirs(cfg.common_eval.results_path, exist_ok=True)
- output_path = os.path.join(
- cfg.common_eval.results_path,
- "generate-{}.txt".format(cfg.dataset.gen_subset),
- )
- with open(output_path, "w", buffering=1, encoding="utf-8") as h:
- return _main(cfg, h)
- else:
- return _main(cfg, sys.stdout)
-
-
-def get_symbols_to_strip_from_output(generator):
- if hasattr(generator, "symbols_to_strip_from_output"):
- return generator.symbols_to_strip_from_output
- else:
- return {generator.eos}
-
-
-def _main(cfg: DictConfig, output_file):
- logging.basicConfig(
- format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=os.environ.get("LOGLEVEL", "INFO").upper(),
- stream=output_file,
- )
- logger = logging.getLogger("fairseq_cli.generate")
-
- utils.import_user_module(cfg.common)
-
- if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
- cfg.dataset.max_tokens = 12000
- logger.info(cfg)
-
- # Fix seed for stochastic decoding
- if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
- np.random.seed(cfg.common.seed)
- utils.set_torch_seed(cfg.common.seed)
-
- use_cuda = torch.cuda.is_available() and not cfg.common.cpu
-
- # Load dataset splits
- task = tasks.setup_task(cfg.task)
-
-
- # Set dictionaries
- try:
- src_dict = getattr(task, "source_dictionary", None)
- except NotImplementedError:
- src_dict = None
- tgt_dict = task.target_dictionary
-
- overrides = ast.literal_eval(cfg.common_eval.model_overrides)
-
- # Load ensemble
- logger.info("loading model(s) from {}".format(cfg.common_eval.path))
- models, saved_cfg = checkpoint_utils.load_model_ensemble(
- utils.split_paths(cfg.common_eval.path),
- arg_overrides=overrides,
- task=task,
- suffix=cfg.checkpoint.checkpoint_suffix,
- strict=(cfg.checkpoint.checkpoint_shard_count == 1),
- num_shards=cfg.checkpoint.checkpoint_shard_count,
- )
-
- # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
- task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
-
- if cfg.generation.lm_path is not None:
- overrides["data"] = cfg.task.data
-
- try:
- lms, _ = checkpoint_utils.load_model_ensemble(
- [cfg.generation.lm_path], arg_overrides=overrides, task=None
- )
- except:
- logger.warning(
- f"Failed to load language model! Please make sure that the language model dict is the same "
- f"as target dict and is located in the data dir ({cfg.task.data})"
- )
- raise
-
- assert len(lms) == 1
- else:
- lms = [None]
-
- # Optimize ensemble for generation
- for model in chain(models, lms):
- if model is None:
- continue
- if cfg.common.fp16:
- model.half()
- if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
- model.cuda()
- model.prepare_for_inference_(cfg)
-
- # Load alignment dictionary for unknown word replacement
- # (None if no unknown word replacement, empty if no path to align dictionary)
- align_dict = utils.load_align_dict(cfg.generation.replace_unk)
-
- # Load dataset (possibly sharded)
- itr = task.get_batch_iterator(
- dataset=task.dataset(cfg.dataset.gen_subset),
- max_tokens=cfg.dataset.max_tokens,
- max_sentences=cfg.dataset.batch_size,
- max_positions=utils.resolve_max_positions(
- task.max_positions(), *[m.max_positions() for m in models]
- ),
- ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
- required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
- seed=cfg.common.seed,
- num_shards=cfg.distributed_training.distributed_world_size,
- shard_id=cfg.distributed_training.distributed_rank,
- num_workers=cfg.dataset.num_workers,
- data_buffer_size=cfg.dataset.data_buffer_size,
- ).next_epoch_itr(shuffle=False)
- progress = progress_bar.progress_bar(
- itr,
- log_format=cfg.common.log_format,
- log_interval=cfg.common.log_interval,
- default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
- )
-
- # Initialize generator
- gen_timer = StopwatchMeter()
-
- extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight}
- generator = task.build_generator(
- models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs
- )
-
- # Handle tokenization and BPE
- tokenizer = task.build_tokenizer(cfg.tokenizer)
- bpe = task.build_bpe(cfg.bpe)
-
- def decode_fn(x):
- if bpe is not None:
- x = bpe.decode(x)
- if tokenizer is not None:
- x = tokenizer.decode(x)
- return x
-
- scorer = scoring.build_scorer(cfg.scoring, tgt_dict)
-
- num_sentences = 0
- has_target = True
- wps_meter = TimeMeter()
- for sample in progress:
- sample = utils.move_to_cuda(sample) if use_cuda else sample
- if "net_input" not in sample:
- continue
-
- prefix_tokens = None
- if cfg.generation.prefix_size > 0:
- prefix_tokens = sample["target"][:, : cfg.generation.prefix_size]
-
- constraints = None
- if "constraints" in sample:
- constraints = sample["constraints"]
-
- gen_timer.start()
- hypos = task.inference_step(
- generator,
- models,
- sample,
- prefix_tokens=prefix_tokens,
- constraints=constraints,
- )
- num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
- gen_timer.stop(num_generated_tokens)
-
- for i, sample_id in enumerate(sample["id"].tolist()):
- has_target = sample["target"] is not None
-
- # Remove padding
- if "src_tokens" in sample["net_input"]:
- src_tokens = utils.strip_pad(
- sample["net_input"]["src_tokens"][i, :], tgt_dict.pad()
- )
- else:
- src_tokens = None
-
- target_tokens = None
- if has_target:
- target_tokens = (
- utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
- )
-
- # Either retrieve the original sentences or regenerate them from tokens.
- if align_dict is not None:
- src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text(
- sample_id
- )
- target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text(
- sample_id
- )
- else:
- if src_dict is not None:
- src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
- else:
- src_str = ""
- if has_target:
- target_str = tgt_dict.string(
- target_tokens,
- cfg.common_eval.post_process,
- escape_unk=True,
- extra_symbols_to_ignore=get_symbols_to_strip_from_output(
- generator
- ),
- )
-
- src_str = decode_fn(src_str)
- if has_target:
- target_str = decode_fn(target_str)
-
- if not cfg.common_eval.quiet:
- if src_dict is not None:
- print("S-{}\t{}".format(sample_id, src_str), file=output_file)
- if has_target:
- print("T-{}\t{}".format(sample_id, target_str), file=output_file)
-
- # Process top predictions
- for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]):
- hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
- hypo_tokens=hypo["tokens"].int().cpu(),
- src_str=src_str,
- alignment=hypo["alignment"],
- align_dict=align_dict,
- tgt_dict=tgt_dict,
- remove_bpe=cfg.common_eval.post_process,
- extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator),
- )
- detok_hypo_str = decode_fn(hypo_str)
- if not cfg.common_eval.quiet:
- score = hypo["score"] / math.log(2) # convert to base 2
- # original hypothesis (after tokenization and BPE)
- print(
- "H-{}\t{}\t{}".format(sample_id, score, hypo_str),
- file=output_file,
- )
- # detokenized hypothesis
- print(
- "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str),
- file=output_file,
- )
- print(
- "P-{}\t{}".format(
- sample_id,
- " ".join(
- map(
- lambda x: "{:.4f}".format(x),
- # convert from base e to base 2
- hypo["positional_scores"]
- .div_(math.log(2))
- .tolist(),
- )
- ),
- ),
- file=output_file,
- )
-
- if cfg.generation.print_alignment == "hard":
- print(
- "A-{}\t{}".format(
- sample_id,
- " ".join(
- [
- "{}-{}".format(src_idx, tgt_idx)
- for src_idx, tgt_idx in alignment
- ]
- ),
- ),
- file=output_file,
- )
- if cfg.generation.print_alignment == "soft":
- print(
- "A-{}\t{}".format(
- sample_id,
- " ".join(
- [
- ",".join(src_probs)
- for src_probs in alignment
- ]
- ),
- ),
- file=output_file,
- )
-
- if cfg.generation.print_step:
- print(
- "I-{}\t{}".format(sample_id, hypo["steps"]),
- file=output_file,
- )
-
- if cfg.generation.retain_iter_history:
- for step, h in enumerate(hypo["history"]):
- _, h_str, _ = utils.post_process_prediction(
- hypo_tokens=h["tokens"].int().cpu(),
- src_str=src_str,
- alignment=None,
- align_dict=None,
- tgt_dict=tgt_dict,
- remove_bpe=None,
- )
- print(
- "E-{}_{}\t{}".format(sample_id, step, h_str),
- file=output_file,
- )
-
- # Score only the top hypothesis
- if has_target and j == 0:
- if align_dict is not None or cfg.common_eval.post_process is not None:
- # Convert back to tokens for evaluation with unk replacement and/or without BPE
- target_tokens = tgt_dict.encode_line(
- target_str, add_if_not_exist=True
- )
- hypo_tokens = tgt_dict.encode_line(
- detok_hypo_str, add_if_not_exist=True
- )
- if hasattr(scorer, "add_string"):
- scorer.add_string(target_str, detok_hypo_str)
- else:
- scorer.add(target_tokens, hypo_tokens)
-
- wps_meter.update(num_generated_tokens)
- progress.log({"wps": round(wps_meter.avg)})
- num_sentences += (
- sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
- )
-
- logger.info("NOTE: hypothesis and token scores are output in base 2")
- logger.info(
- "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format(
- num_sentences,
- gen_timer.n,
- gen_timer.sum,
- num_sentences / gen_timer.sum,
- 1.0 / gen_timer.avg,
- )
- )
- if has_target:
- if cfg.bpe and not cfg.generation.sacrebleu:
- if cfg.common_eval.post_process:
- logger.warning(
- "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization"
- )
- else:
- logger.warning(
- "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words. Use --sacrebleu for standard 13a BLEU tokenization"
- )
- # use print to be consistent with other main outputs: S-, H-, T-, D- and so on
- print(
- "Generate {} with beam={}: {}".format(
- cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string()
- ),
- file=output_file,
- )
-
- return scorer
-
-
-def cli_main():
- parser = options.get_generation_parser()
- args = options.parse_args_and_arch(parser)
- main(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/gradio/theme_builder/README.md b/spaces/gradio/theme_builder/README.md
deleted file mode 100644
index 3906df8e3d37631725e1df1fee261c75a0648ec1..0000000000000000000000000000000000000000
--- a/spaces/gradio/theme_builder/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: theme_builder
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/mapper/options/train_options.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/mapper/options/train_options.py
deleted file mode 100644
index a365217f8b76d38aaef4a42b90152ec7a8e7bf1f..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/mapper/options/train_options.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from argparse import ArgumentParser
-
-
-class TrainOptions:
-
- def __init__(self):
- self.parser = ArgumentParser()
- self.initialize()
-
- def initialize(self):
- self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory')
- self.parser.add_argument('--mapper_type', default='LevelsMapper', type=str, help='Which mapper to use')
- self.parser.add_argument('--no_coarse_mapper', default=False, action="store_true")
- self.parser.add_argument('--no_medium_mapper', default=False, action="store_true")
- self.parser.add_argument('--no_fine_mapper', default=False, action="store_true")
- self.parser.add_argument('--latents_train_path', default="train_faces.pt", type=str, help="The latents for the training")
- self.parser.add_argument('--latents_test_path', default="test_faces.pt", type=str, help="The latents for the validation")
- self.parser.add_argument('--train_dataset_size', default=5000, type=int, help="Will be used only if no latents are given")
- self.parser.add_argument('--test_dataset_size', default=1000, type=int, help="Will be used only if no latents are given")
-
- self.parser.add_argument('--batch_size', default=2, type=int, help='Batch size for training')
- self.parser.add_argument('--test_batch_size', default=1, type=int, help='Batch size for testing and inference')
- self.parser.add_argument('--workers', default=4, type=int, help='Number of train dataloader workers')
- self.parser.add_argument('--test_workers', default=2, type=int, help='Number of test/inference dataloader workers')
-
- self.parser.add_argument('--learning_rate', default=0.5, type=float, help='Optimizer learning rate')
- self.parser.add_argument('--optim_name', default='ranger', type=str, help='Which optimizer to use')
-
- self.parser.add_argument('--id_lambda', default=0.1, type=float, help='ID loss multiplier factor')
- self.parser.add_argument('--clip_lambda', default=1.0, type=float, help='CLIP loss multiplier factor')
- self.parser.add_argument('--latent_l2_lambda', default=0.8, type=float, help='Latent L2 loss multiplier factor')
-
- self.parser.add_argument('--stylegan_weights', default='../pretrained_models/stylegan2-ffhq-config-f.pt', type=str, help='Path to StyleGAN model weights')
- self.parser.add_argument('--stylegan_size', default=1024, type=int)
- self.parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str, help="Path to facial recognition network used in ID loss")
- self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to StyleCLIPModel model checkpoint')
-
- self.parser.add_argument('--max_steps', default=50000, type=int, help='Maximum number of training steps')
- self.parser.add_argument('--image_interval', default=100, type=int, help='Interval for logging train images during training')
- self.parser.add_argument('--board_interval', default=50, type=int, help='Interval for logging metrics to tensorboard')
- self.parser.add_argument('--val_interval', default=2000, type=int, help='Validation interval')
- self.parser.add_argument('--save_interval', default=2000, type=int, help='Model checkpoint interval')
-
- self.parser.add_argument('--description', required=True, type=str, help='Driving text prompt')
-
-
- def parse(self):
- opts = self.parser.parse_args()
- return opts
\ No newline at end of file
diff --git a/spaces/gyugnsu/DragGan-Inversion/dnnlib/__init__.py b/spaces/gyugnsu/DragGan-Inversion/dnnlib/__init__.py
deleted file mode 100644
index e7423bffe245d0ff3f32e8658aa67daae454e64e..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/dnnlib/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-from .util import EasyDict, make_cache_dir_path
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py
deleted file mode 100644
index 363dc74adda3a3cebc7f610dc51eccda35fcd083..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# coding: utf8
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddleseg.utils.download import download_file_and_uncompress
-import sys
-import os
-
-LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
-TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
-sys.path.append(TEST_PATH)
-
-
-model_urls = {
- "pphumanseg_lite_portrait_398x224":
- "https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224.tar.gz",
- "deeplabv3p_resnet50_os8_humanseg_512x512_100k":
- "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/deeplabv3p_resnet50_os8_humanseg_512x512_100k.zip",
- "fcn_hrnetw18_small_v1_humanseg_192x192":
- "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/fcn_hrnetw18_small_v1_humanseg_192x192.zip",
- "pphumanseg_lite_generic_human_192x192":
- "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/pphumanseg_lite_generic_192x192.zip",
-}
-
-if __name__ == "__main__":
- for model_name, url in model_urls.items():
- download_file_and_uncompress(
- url=url,
- savepath=LOCAL_PATH,
- extrapath=LOCAL_PATH,
- extraname=model_name)
-
- print("Pretrained model download success!")
diff --git a/spaces/gyugnsu/DragGan-Inversion/viz/__init__.py b/spaces/gyugnsu/DragGan-Inversion/viz/__init__.py
deleted file mode 100644
index 939e7c6c8f94c4ea1141885c3c3295fe083b06aa..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/viz/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-# empty
diff --git a/spaces/h2oai/h2ogpt-chatbot2/src/gradio_utils/prompt_form.py b/spaces/h2oai/h2ogpt-chatbot2/src/gradio_utils/prompt_form.py
deleted file mode 100644
index d79b51833d207c867e5ceb1040169193bed4bf9a..0000000000000000000000000000000000000000
--- a/spaces/h2oai/h2ogpt-chatbot2/src/gradio_utils/prompt_form.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-import math
-
-import gradio as gr
-
-
-def make_chatbots(output_label0, output_label0_model2, **kwargs):
- visible_models = kwargs['visible_models']
- all_models = kwargs['all_models']
-
- text_outputs = []
- chat_kwargs = []
- for model_state_locki, model_state_lock in enumerate(kwargs['model_states']):
- if os.environ.get('DEBUG_MODEL_LOCK'):
- model_name = model_state_lock["base_model"] + " : " + model_state_lock["inference_server"]
- else:
- model_name = model_state_lock["base_model"]
- output_label = f'h2oGPT [{model_name}]'
- min_width = 250 if kwargs['gradio_size'] in ['small', 'large', 'medium'] else 160
- chat_kwargs.append(dict(label=output_label, elem_classes='chatsmall',
- height=kwargs['height'] or 400, min_width=min_width,
- show_copy_button=kwargs['show_copy_button'],
- visible=kwargs['model_lock'] and (visible_models is None or
- model_state_locki in visible_models or
- all_models[model_state_locki] in visible_models
- )))
-
- # base view on initial visible choice
- if visible_models:
- len_visible = len(visible_models)
- else:
- len_visible = len(kwargs['model_states'])
- if kwargs['model_lock_columns'] == -1:
- kwargs['model_lock_columns'] = len_visible
- if kwargs['model_lock_columns'] is None:
- kwargs['model_lock_columns'] = 3
-
- ncols = kwargs['model_lock_columns']
- if kwargs['model_states'] == 0:
- nrows = 0
- else:
- nrows = math.ceil(len_visible / kwargs['model_lock_columns'])
-
- if kwargs['model_lock_columns'] == 0:
- # not using model_lock
- pass
- elif nrows <= 1:
- with gr.Row():
- for chat_kwargs1, model_state_lock in zip(chat_kwargs, kwargs['model_states']):
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- elif nrows == kwargs['model_states']:
- with gr.Row():
- for chat_kwargs1, model_state_lock in zip(chat_kwargs, kwargs['model_states']):
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- elif nrows == 2:
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii >= len_visible / 2:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii < len_visible / 2:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- elif nrows == 3:
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii >= 1 * len_visible / 3:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii < 1 * len_visible / 3 or mii >= 2 * len_visible / 3:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii < 2 * len_visible / 3:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- elif nrows >= 4:
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii >= 1 * len_visible / 4:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii < 1 * len_visible / 4 or mii >= 2 * len_visible / 4:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii < 2 * len_visible / 4 or mii >= 3 * len_visible / 4:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
- with gr.Row():
- for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])):
- if mii < 3 * len_visible / 4:
- continue
- text_outputs.append(gr.Chatbot(**chat_kwargs1))
-
- with gr.Row():
- text_output = gr.Chatbot(label=output_label0, visible=not kwargs['model_lock'], height=kwargs['height'] or 400)
- text_output2 = gr.Chatbot(label=output_label0_model2,
- visible=False and not kwargs['model_lock'], height=kwargs['height'] or 400)
- return text_output, text_output2, text_outputs
diff --git a/spaces/hackathon-somos-nlp-2023/SalpiBloomZ-1b7-v1/app.py b/spaces/hackathon-somos-nlp-2023/SalpiBloomZ-1b7-v1/app.py
deleted file mode 100644
index 377fc056e3a18a94660da19077af0f1e429bdf4e..0000000000000000000000000000000000000000
--- a/spaces/hackathon-somos-nlp-2023/SalpiBloomZ-1b7-v1/app.py
+++ /dev/null
@@ -1,107 +0,0 @@
-
-import time
-
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
-
-import gradio as gr
-import speech_recognition as sr
-from math import log2, pow
-import os
-
-#from scipy.fftpack import fft
-import gc
-
-peft_model_id="hackathon-somos-nlp-2023/SalpiBloomZ_15949_input_512-1b7"
-
-config = PeftConfig.from_pretrained(peft_model_id)
-model2 = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True,
- load_in_8bit=True,
- # load_in_8bit_fp32_cpu_offload=True,
- device_map='auto')
-tokenizer2 = AutoTokenizer.from_pretrained(peft_model_id)
-
-model2 = PeftModel.from_pretrained(model2, peft_model_id)
-
-Problema_tarjetaCredito= os.path.abspath("Problema_tarjetaCredito.ogg")
-list_audios= [[Problema_tarjetaCredito]]
-
-def gen_conversation(text,max_new_tokens=100):
- text = "<SN>instruction: " + text + "\n "
- batch = tokenizer2(text, return_tensors='pt')
- batch = {k: v.to('cuda') for k, v in batch.items()}
- with torch.cuda.amp.autocast():
- output_tokens = model2.generate(**batch,
- max_new_tokens=max_new_tokens,
- eos_token_id= tokenizer2.eos_token_id,
- pad_token_id= tokenizer2.pad_token_id,
- bos_token_id= tokenizer2.bos_token_id,
- early_stopping = True,
- no_repeat_ngram_size=2,
- repetition_penalty=1.2,
- temperature=.69,
- num_beams=3
- )
- gc.collect()
- torch.cuda.memory_summary(device=None, abbreviated=False)
- return tokenizer2.decode(output_tokens[0], skip_special_tokens=True).split("\n")[-1].replace("output:","")
-
-conversacion = ""
-description="""
-Este proyecto nace de la idea de construir agentes virtuales especializados en temas académicos o nichos de negocio.
-Para esto fueron creados dos dataset principales, uno basado en habilidades blandas y otro basado en conversaciones empresariales donde un agente humano atiende a un cliente de la empresa en cuestión.
-Para ejecutar esta tarea, se han empleado múltiples LLMs, entre ellos:
-
-- Alpaca native: https://huggingface.co/hackathon-somos-nlp-2023/salsapaca-native
-- Bertin: https://huggingface.co/hackathon-somos-nlp-2023/bertin-gpt-j-6B-es-finetuned-salpaca
-- Bloom: https://huggingface.co/hackathon-somos-nlp-2023/SalpiBloom-1b1
-- Bloomz: https://huggingface.co/hackathon-somos-nlp-2023/SalpiBloomZ_15949_input_512-1b7
-- Dolly: https://huggingface.co/hackathon-somos-nlp-2023/dolly-ArepacaV1
-- Cerebras: https://huggingface.co/hackathon-somos-nlp-2023/Cerebras-Cabritax-softskills-espaGPT-1.3B
-
-Teniendo como mejor resultado el modelo basado en BloomZ, el cual es el prensentado en esta demo.
-
-La demo cuenta con dos tipos de inputs, mediante audio y mediante texto.
-Esto se hace con la idea de reflejar el hecho de que estos agentes virtuales pueden funcionar tanto para chatbots como para voicebots.
-Con esto en mano, la empresa o institución que tenga acceso a esta tecnología, podría obtener un agente virtual con el conocimiento necesario para atender una conversación con el usuario final.
-"""
-def speech_to_text(audio_file, texto_adicional):
- global conversacion
- if audio_file is not None:
- # Lógica para entrada de audio
- r = sr.Recognizer()
- audio_data = sr.AudioFile(audio_file)
- with audio_data as source:
- audio = r.record(source)
- text_enrada=""
-
- texto_generado = r.recognize_google(audio, language="es-ES")
- texto_generado= f"[|Audio a texto|]:{texto_generado}\n" + "<br>[AGENTE]:"+gen_conversation(texto_generado,max_new_tokens=500)
- texto_generado = "<div style='color: #66b3ff;'>" + texto_generado + "</div><br>"
- else:
- texto_generado= f"[|Solo texto|]:{texto_adicional}\n" + "<br>[AGENTE]:"+gen_conversation(texto_adicional,max_new_tokens=500)
- texto_generado = "<div style='color: #66b3ff;'> " + texto_generado + "</div><br>"
- conversacion += texto_generado
- return conversacion
-
-iface = gr.Interface(
- fn=speech_to_text,
- inputs=[gr.inputs.Audio(label="Voz", type="filepath"), gr.inputs.Textbox(label="Texto adicional")],
- outputs=gr.outputs.HTML(label=["chatbot","state"]),
- title="Chat bot para empresas.",
- description=description, #"Este modelo convierte la entrada de voz o texto y hace inferencia",
- examples=list_audios,
- theme="default",
- layout="vertical",
- allow_flagging=False,
- flagging_dir=None,
- server_name=None,
- server_port=None,
- live=False,
- capture_session=False
-)
-
-iface.launch()
-
-
diff --git a/spaces/heiyubili/bingo/src/lib/bots/bing/tts.ts b/spaces/heiyubili/bingo/src/lib/bots/bing/tts.ts
deleted file mode 100644
index cd10b7d1d7581bf9cf46ff6755fcca550c558c9b..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/lib/bots/bing/tts.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
- currentText = ''
- speakText = ''
- private controller = new AbortController()
- speaking = false
- get isSpeaking() {
- return this.speaking
- }
- finished = false
- constructor() {}
- abort = () => {
- this.controller.abort()
- }
-
- reset = () => {
- this.speaking = false
- this.finished = true
- this.currentText = ''
- this.speakText = ''
- this.abort()
- }
-
- speak = (text: string) => {
- if (!synth || text?.trim()?.length < 2) {
- return
- }
- this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
- this.finished = false
- this.loop()
- }
-
- private async doSpeek() {
- return new Promise((resolve) => {
- const endIndex = this.finished ? this.currentText.length :
- Math.max(
- this.currentText.lastIndexOf('。'),
- this.currentText.lastIndexOf('；'),
- this.currentText.lastIndexOf('、'),
- this.currentText.lastIndexOf('？'),
- this.currentText.lastIndexOf('\n')
- )
- const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
- if (startIndex >= endIndex) {
- return resolve(true)
- }
- const text = this.currentText.slice(startIndex, endIndex)
- this.speakText = text
- const utterThis = new SpeechSynthesisUtterance(text)
- this.controller.signal.onabort = () => {
- synth.cancel()
- this.finished = true
- resolve(false)
- }
-
- utterThis.onend = function (event) {
- resolve(true)
- }
-
- utterThis.onerror = function (event) {
- resolve(false)
- }
-
- const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
- utterThis.voice = voice
- synth.speak(utterThis)
- })
- }
-
- private async loop() {
- if (this.speaking) return
- this.speaking = true
- while(!this.finished) {
- await Promise.all([sleep(1000), this.doSpeek()])
- }
- this.speaking = false
- }
-}
diff --git a/spaces/hf-vision/detection_metrics/detection_metrics/__init__.py b/spaces/hf-vision/detection_metrics/detection_metrics/__init__.py
deleted file mode 100644
index e34424611dd412c8d26470008faf15c6983dbdd7..0000000000000000000000000000000000000000
--- a/spaces/hf-vision/detection_metrics/detection_metrics/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.0.3"
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ReLU.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ReLU.py
deleted file mode 100644
index 97f290f1c9466b813d0c770055c3125085e0c977..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ReLU.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import torch
-from nnunet.network_architecture.generic_UNet import Generic_UNet
-from nnunet.network_architecture.initialization import InitWeights_He
-from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2
-from nnunet.utilities.nd_softmax import softmax_helper
-from torch import nn
-
-
-class nnUNetTrainerV2_ReLU(nnUNetTrainerV2):
- def initialize_network(self):
- if self.threeD:
- conv_op = nn.Conv3d
- dropout_op = nn.Dropout3d
- norm_op = nn.InstanceNorm3d
-
- else:
- conv_op = nn.Conv2d
- dropout_op = nn.Dropout2d
- norm_op = nn.InstanceNorm2d
-
- norm_op_kwargs = {'eps': 1e-5, 'affine': True}
- dropout_op_kwargs = {'p': 0, 'inplace': True}
- net_nonlin = nn.ReLU
- net_nonlin_kwargs = {'inplace': True}
- self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
- len(self.net_num_pool_op_kernel_sizes),
- self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
- net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
- self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
- if torch.cuda.is_available():
- self.network.cuda()
- self.network.inference_apply_nonlin = softmax_helper
diff --git a/spaces/housexu123/bingo-2.0/src/components/chat-header.tsx b/spaces/housexu123/bingo-2.0/src/components/chat-header.tsx
deleted file mode 100644
index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/src/components/chat-header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import LogoIcon from '@/assets/images/logo.svg'
-import Image from 'next/image'
-
-export function ChatHeader() {
- return (
- <div className="flex flex-col items-center justify-center">
- <Image alt="logo" src={LogoIcon} width={60}/>
- <div className="mt-8 text-4xl font-bold">欢迎使用新必应</div>
- <div className="mt-4 mb-8 text-lg">由 AI 支持的网页版 Copilot</div>
- </div>
- )
-}
diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/src/lib/liveblocks/useSelf.ts b/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/src/lib/liveblocks/useSelf.ts
deleted file mode 100644
index 3adf9176712446acf11f581db5576fd41e40fcf8..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/src/lib/liveblocks/useSelf.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-import { onDestroy } from "svelte";
-import type { Writable } from "svelte/store";
-import { writable } from "svelte/store";
-import { useRoom } from "./useRoom";
-
-/**
- * Works similarly to `liveblocks-react` useSelf
- * https://liveblocks.io/docs/api-reference/liveblocks-react#useSelf
- *
- * The main difference is that it returns a Svelte store:
- * const self = useSelf()
- * console.log($self.info.id)
- * <div>{$self.info.name}</div>
- */
-export function useSelf(): Writable<any> {
- const room = useRoom();
- const self = writable();
-
- const unsubscribeConnection = room.subscribe("connection", () => {
- self.set(room.getSelf());
- });
- const unsubscribe = room.subscribe("my-presence", () => {
- self.set(room.getSelf());
- });
-
- onDestroy(() => {
- unsubscribeConnection();
- unsubscribe();
- });
-
- return self;
-}
diff --git a/spaces/huggingface/data-measurements-tool/data_measurements/npmi.py b/spaces/huggingface/data-measurements-tool/data_measurements/npmi.py
deleted file mode 100644
index 0c740b031c702a83afc7252d47b6b0185bcc39e1..0000000000000000000000000000000000000000
--- a/spaces/huggingface/data-measurements-tool/data_measurements/npmi.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright 2021 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import warnings
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-from sklearn.preprocessing import MultiLabelBinarizer
-
-# Might be nice to print to log instead? Happens when we drop closed class.
-warnings.filterwarnings(action="ignore", category=UserWarning)
-# When we divide by 0 in log
-np.seterr(divide="ignore")
-
-# treating inf values as NaN as well
-pd.set_option("use_inf_as_na", True)
-
-logs = logging.getLogger(__name__)
-logs.setLevel(logging.INFO)
-logs.propagate = False
-
-if not logs.handlers:
-
- Path("./log_files").mkdir(exist_ok=True)
-
- # Logging info to log file
- file = logging.FileHandler("./log_files/npmi.log")
- fileformat = logging.Formatter("%(asctime)s:%(message)s")
- file.setLevel(logging.INFO)
- file.setFormatter(fileformat)
-
- # Logging debug messages to stream
- stream = logging.StreamHandler()
- streamformat = logging.Formatter("[data_measurements_tool] %(message)s")
- stream.setLevel(logging.WARNING)
- stream.setFormatter(streamformat)
-
- logs.addHandler(file)
- logs.addHandler(stream)
-
-_NUM_BATCHES = 500
-
-
-class nPMI:
- # TODO: Expand beyond pairwise
- def __init__(
- self,
- vocab_counts_df,
- tokenized_df,
- tokenized_col_name="tokenized_text",
- num_batches=_NUM_BATCHES,
- ):
- logs.info("Initiating npmi class.")
- logs.info("vocab is")
- logs.info(vocab_counts_df)
- self.vocab_counts_df = vocab_counts_df
- logs.info("tokenized is")
- self.tokenized_df = tokenized_df
- logs.info(self.tokenized_df)
- self.tokenized_col_name = tokenized_col_name
- # self.mlb_list holds num batches x num_sentences
- self.mlb_list = []
-
- def binarize_words_in_sentence(self):
- logs.info("Creating co-occurrence matrix for PMI calculations.")
- batches = np.linspace(0, self.tokenized_df.shape[0], _NUM_BATCHES).astype(int)
- i = 0
- # Creates list of size (# batches x # sentences)
- while i < len(batches) - 1:
- # Makes a sparse matrix (shape: # sentences x # words),
- # with the occurrence of each word per sentence.
- mlb = MultiLabelBinarizer(classes=self.vocab_counts_df.index)
- logs.info(
- "%s of %s sentence binarize batches." % (str(i), str(len(batches)))
- )
- # Returns series: batch size x num_words
- mlb_series = mlb.fit_transform(
- self.tokenized_df[self.tokenized_col_name][batches[i] : batches[i + 1]]
- )
- i += 1
- self.mlb_list.append(mlb_series)
-
- def calc_cooccurrences(self, subgroup, subgroup_idx):
- initialize = True
- coo_df = None
- # Big computation here! Should only happen once.
- logs.info(
- "Approaching big computation! Here, we binarize all words in the sentences, making a sparse matrix of sentences."
- )
- if not self.mlb_list:
- self.binarize_words_in_sentence()
- for batch_id in range(len(self.mlb_list)):
- logs.info(
- "%s of %s co-occurrence count batches"
- % (str(batch_id), str(len(self.mlb_list)))
- )
- # List of all the sentences (list of vocab) in that batch
- batch_sentence_row = self.mlb_list[batch_id]
- # Dataframe of # sentences in batch x vocabulary size
- sent_batch_df = pd.DataFrame(batch_sentence_row)
- # logs.info('sent batch df is')
- # logs.info(sent_batch_df)
- # Subgroup counts per-sentence for the given batch
- subgroup_df = sent_batch_df[subgroup_idx]
- subgroup_df.columns = [subgroup]
- # Remove the sentences where the count of the subgroup is 0.
- # This way we have less computation & resources needs.
- subgroup_df = subgroup_df[subgroup_df > 0]
- logs.info("Removing 0 counts, subgroup_df is")
- logs.info(subgroup_df)
- mlb_subgroup_only = sent_batch_df[sent_batch_df[subgroup_idx] > 0]
- logs.info("mlb subgroup only is")
- logs.info(mlb_subgroup_only)
- # Create cooccurrence matrix for the given subgroup and all words.
- logs.info("Now we do the T.dot approach for co-occurrences")
- batch_coo_df = pd.DataFrame(mlb_subgroup_only.T.dot(subgroup_df))
-
- # Creates a batch-sized dataframe of co-occurrence counts.
- # Note these could just be summed rather than be batch size.
- if initialize:
- coo_df = batch_coo_df
- else:
- coo_df = coo_df.add(batch_coo_df, fill_value=0)
- logs.info("coo_df is")
- logs.info(coo_df)
- initialize = False
- logs.info("Returning co-occurrence matrix")
- logs.info(coo_df)
- return pd.DataFrame(coo_df)
-
- def calc_paired_metrics(self, subgroup_pair, subgroup_npmi_dict):
- """
- Calculates nPMI metrics between paired subgroups.
- Special handling for a subgroup paired with itself.
- :param subgroup_npmi_dict:
- :return:
- """
- paired_results_dict = {"npmi": {}, "pmi": {}, "count": {}}
- # Canonical ordering. This is done previously, but just in case...
- subgroup1, subgroup2 = sorted(subgroup_pair)
- vocab_cooc_df1, pmi_df1, npmi_df1 = subgroup_npmi_dict[subgroup1]
- logs.info("vocab cooc")
- logs.info(vocab_cooc_df1)
- if subgroup1 == subgroup2:
- shared_npmi_df = npmi_df1
- shared_pmi_df = pmi_df1
- shared_vocab_cooc_df = vocab_cooc_df1
- else:
- vocab_cooc_df2, pmi_df2, npmi_df2 = subgroup_npmi_dict[subgroup2]
- logs.info("vocab cooc2")
- logs.info(vocab_cooc_df2)
- # Note that lsuffix and rsuffix should not come into play.
- shared_npmi_df = npmi_df1.join(
- npmi_df2, how="inner", lsuffix="1", rsuffix="2"
- )
- shared_pmi_df = pmi_df1.join(pmi_df2, how="inner", lsuffix="1", rsuffix="2")
- shared_vocab_cooc_df = vocab_cooc_df1.join(
- vocab_cooc_df2, how="inner", lsuffix="1", rsuffix="2"
- )
- shared_vocab_cooc_df = shared_vocab_cooc_df.dropna()
- shared_vocab_cooc_df = shared_vocab_cooc_df[
- shared_vocab_cooc_df.index.notnull()
- ]
- logs.info("shared npmi df")
- logs.info(shared_npmi_df)
- logs.info("shared vocab df")
- logs.info(shared_vocab_cooc_df)
- npmi_bias = (
- shared_npmi_df[subgroup1 + "-npmi"] - shared_npmi_df[subgroup2 + "-npmi"]
- )
- paired_results_dict["npmi-bias"] = npmi_bias.dropna()
- paired_results_dict["npmi"] = shared_npmi_df.dropna()
- paired_results_dict["pmi"] = shared_pmi_df.dropna()
- paired_results_dict["count"] = shared_vocab_cooc_df.dropna()
- return paired_results_dict
-
- def calc_metrics(self, subgroup):
- # Index of the subgroup word in the sparse vector
- subgroup_idx = self.vocab_counts_df.index.get_loc(subgroup)
- logs.info("Calculating co-occurrences...")
- df_coo = self.calc_cooccurrences(subgroup, subgroup_idx)
- vocab_cooc_df = self.set_idx_cols(df_coo, subgroup)
- logs.info(vocab_cooc_df)
- logs.info("Calculating PMI...")
- pmi_df = self.calc_PMI(vocab_cooc_df, subgroup)
- logs.info(pmi_df)
- logs.info("Calculating nPMI...")
- npmi_df = self.calc_nPMI(pmi_df, vocab_cooc_df, subgroup)
- logs.info(npmi_df)
- return vocab_cooc_df, pmi_df, npmi_df
-
- def set_idx_cols(self, df_coo, subgroup):
- """
- :param df_coo: Co-occurrence counts for subgroup, length is num_words
- :return:
- """
- count_df = df_coo.set_index(self.vocab_counts_df.index)
- count_df.columns = [subgroup + "-count"]
- count_df[subgroup + "-count"] = count_df[subgroup + "-count"].astype(int)
- return count_df
-
- def calc_PMI(self, vocab_cooc_df, subgroup):
- """
- # PMI(x;y) = h(y) - h(y|x)
- # = h(subgroup) - h(subgroup|word)
- # = log (p(subgroup|word) / p(subgroup))
- # nPMI additionally divides by -log(p(x,y)) = -log(p(x|y)p(y))
- """
- # Calculation of p(subgroup)
- subgroup_prob = self.vocab_counts_df.loc[subgroup]["proportion"]
- # Calculation of p(subgroup|word) = count(subgroup,word) / count(word)
- # Because the inidices match (the vocab words),
- # this division doesn't need to specify the index (I think?!)
- p_subgroup_g_word = (
- vocab_cooc_df[subgroup + "-count"] / self.vocab_counts_df["count"]
- )
- logs.info("p_subgroup_g_word is")
- logs.info(p_subgroup_g_word)
- pmi_df = pd.DataFrame()
- pmi_df[subgroup + "-pmi"] = np.log(p_subgroup_g_word / subgroup_prob)
- # Note: A potentially faster solution for adding count, npmi,
- # can be based on this zip idea:
- # df_test['size_kb'], df_test['size_mb'], df_test['size_gb'] =
- # zip(*df_test['size'].apply(sizes))
- return pmi_df.dropna()
-
- def calc_nPMI(self, pmi_df, vocab_cooc_df, subgroup):
- """
- # nPMI additionally divides by -log(p(x,y)) = -log(p(x|y)p(y))
- # = -log(p(word|subgroup)p(word))
- """
- p_word_g_subgroup = vocab_cooc_df[subgroup + "-count"] / sum(
- vocab_cooc_df[subgroup + "-count"]
- )
- p_word = pmi_df.apply(
- lambda x: self.vocab_counts_df.loc[x.name]["proportion"], axis=1
- )
- normalize_pmi = -np.log(p_word_g_subgroup * p_word)
- npmi_df = pd.DataFrame()
- npmi_df[subgroup + "-npmi"] = pmi_df[subgroup + "-pmi"] / normalize_pmi
- return npmi_df.dropna()
diff --git a/spaces/hungln1102/emotion_classification_surreynlp_2023/pre_processing_data.py b/spaces/hungln1102/emotion_classification_surreynlp_2023/pre_processing_data.py
deleted file mode 100644
index 4a6ed032450b58a780b0fce1b47675e7c6d2a9f9..0000000000000000000000000000000000000000
--- a/spaces/hungln1102/emotion_classification_surreynlp_2023/pre_processing_data.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import contractions
-import spacy
-import nltk
-import pickle
-import subprocess
-import pandas as pd
-
-from datetime import datetime
-from nltk.corpus import stopwords
-from nltk.tokenize import RegexpTokenizer
-from keras_preprocessing.sequence import pad_sequences
-
-nltk.download('punkt')
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-nltk.download('stopwords')
-nltk.download('averaged_perceptron_tagger')
-
-model_url = "https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl"
-subprocess.run(["pip", "install", model_url])
-nlp = spacy.load("en_core_web_sm")
-
-stop_words = set(stopwords.words('english'))
-
-
-def text_transform(string_text):
- with open('model/tokenizer.pickle', 'rb') as handle:
- loaded_tokenizer = pickle.load(handle)
- string_text_list = []
- string_text_list.append(string_text)
- sequences = loaded_tokenizer.texts_to_sequences(string_text_list)
- padded_sequences = pad_sequences(sequences, maxlen=50, padding='post', truncating='post')
- return padded_sequences
-
-
-# python -m spacy download en_core_web_sm
-# pre-processing the data by getting verb, adj, adv; because of the emotion of sentence is depends on these character
-import re
-
-
-# pre-processing the data by getting verb, adj, adv; because of the emotion of sentence is depends on these character
-def get_main_words(string_text):
- tokens = nltk.word_tokenize(string_text)
- pos_tags = nltk.pos_tag(tokens)
-
- pos_string = "{'JJR', 'VB', 'WP', 'WRB', 'NNS', 'JJS', 'JJ', 'RB', 'MD', 'VBZ', 'VBG', 'VBP'}"
- words = re.findall(r"'(\w+)'", pos_string)
-
- string_list = [token for token, tag in pos_tags if tag in words]
-
- if string_list:
- string_list = ' '.join(string_list)
- return string_list
- return None
-
-
-# complex pre-processing data
-def pre_processing_data_2(string_text):
- string_text = string_text.lower()
- string_output = ' '.join([token.lemma_ for token in nlp(string_text)])
- string_output = contractions.fix(string_output)
-
- string_processed = get_main_words(string_output)
- if string_processed:
- tokenizer = RegexpTokenizer(r'\w+')
- string_processed = tokenizer.tokenize(string_processed)
- string_processed = " ".join(string_processed)
- return string_processed
-
- tokenizer = RegexpTokenizer(r'\w+')
- string_output = tokenizer.tokenize(string_output)
- string_output = [w for w in string_output if not w in stop_words]
- string_output = " ".join(string_output)
- return string_output
-
-
-def preprocessing_data(string_text):
- string_text = string_text.lower()
- string_output = ' '.join([token.lemma_ for token in nlp(string_text)])
- string_output = contractions.fix(string_output)
-
- tokenizer = RegexpTokenizer(r'\w+')
- string_output = tokenizer.tokenize(string_output)
- string_output = [w for w in string_output if not w in stop_words]
- string_output = " ".join(string_output)
- return string_output
-
-
-def user_capture(user_input, emotion_prd):
- dataframe_capture = pd.read_csv('user_logs.csv')
- user_input_logs = pd.DataFrame({
- "user_input": [user_input],
- "emotion_predict": [emotion_prd],
- "time_logs": [datetime.now()],
- })
-
- dataframe_capture = pd.concat([dataframe_capture, user_input_logs], ignore_index=True)
- dataframe_capture.to_csv("user_logs.csv", index=False)
- print("Done Recorded")
- return None
diff --git a/spaces/hwchase17/chat-langchain/ingest.sh b/spaces/hwchase17/chat-langchain/ingest.sh
deleted file mode 100644
index aa5c68d9610a867433071f7cde8a49b09ae032b3..0000000000000000000000000000000000000000
--- a/spaces/hwchase17/chat-langchain/ingest.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-# Bash script to ingest data
-# This involves scraping the data from the web and then cleaning up and putting in Weaviate.
-!set -eu
-wget -r -A.html https://langchain.readthedocs.io/en/latest/
-python3 ingest.py
-python3 ingest_examples.py
diff --git a/spaces/hysts-samples/save-user-preferences/scheduler.py b/spaces/hysts-samples/save-user-preferences/scheduler.py
deleted file mode 100644
index ad2f03190b7e83ae5a36010b981ebf0658ea2de7..0000000000000000000000000000000000000000
--- a/spaces/hysts-samples/save-user-preferences/scheduler.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import json
-import tempfile
-import uuid
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
-
-import pyarrow as pa
-import pyarrow.parquet as pq
-from huggingface_hub import CommitScheduler, HfApi
-
-
-class ParquetScheduler(CommitScheduler):
- """
- Usage: configure the scheduler with a repo id. Once started, you can add data to be uploaded to the Hub. 1 `.append`
- call will result in 1 row in your final dataset.
-
- ```py
- # Start scheduler
- >>> scheduler = ParquetScheduler(repo_id="my-parquet-dataset")
-
- # Append some data to be uploaded
- >>> scheduler.append({...})
- >>> scheduler.append({...})
- >>> scheduler.append({...})
- ```
-
- The scheduler will automatically infer the schema from the data it pushes.
- Optionally, you can manually set the schema yourself:
-
- ```py
- >>> scheduler = ParquetScheduler(
- ... repo_id="my-parquet-dataset",
- ... schema={
- ... "prompt": {"_type": "Value", "dtype": "string"},
- ... "negative_prompt": {"_type": "Value", "dtype": "string"},
- ... "guidance_scale": {"_type": "Value", "dtype": "int64"},
- ... "image": {"_type": "Image"},
- ... },
- ... )
-
- See https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Value for the list of
- possible values.
- """
-
- def __init__(
- self,
- *,
- repo_id: str,
- schema: Optional[Dict[str, Dict[str, str]]] = None,
- every: Union[int, float] = 5,
- revision: Optional[str] = None,
- private: bool = False,
- token: Optional[str] = None,
- allow_patterns: Union[List[str], str, None] = None,
- ignore_patterns: Union[List[str], str, None] = None,
- hf_api: Optional[HfApi] = None,
- ) -> None:
- super().__init__(
- repo_id=repo_id,
- folder_path=tempfile.tempdir, # not used by the scheduler
- every=every,
- repo_type="dataset",
- revision=revision,
- private=private,
- token=token,
- allow_patterns=allow_patterns,
- ignore_patterns=ignore_patterns,
- hf_api=hf_api,
- )
-
- self._rows: List[Dict[str, Any]] = []
- self._schema = schema
-
- def append(self, row: Dict[str, Any]) -> None:
- """Add a new item to be uploaded."""
- with self.lock:
- self._rows.append(row)
-
- def push_to_hub(self):
- # Check for new rows to push
- with self.lock:
- rows = self._rows
- self._rows = []
- if not rows:
- return
- print(f"Got {len(rows)} item(s) to commit.")
-
- # Load images + create 'features' config for datasets library
- schema: Dict[str, Dict] = self._schema or {}
- path_to_cleanup: List[Path] = []
- for row in rows:
- for key, value in row.items():
- # Infer schema (for `datasets` library)
- if key not in schema:
- schema[key] = _infer_schema(key, value)
-
- # Load binary files if necessary
- if schema[key]["_type"] in ("Image", "Audio"):
- # It's an image or audio: we load the bytes and remember to cleanup the file
- file_path = Path(value)
- if file_path.is_file():
- row[key] = {
- "path": file_path.name,
- "bytes": file_path.read_bytes(),
- }
- path_to_cleanup.append(file_path)
-
- # Complete rows if needed
- for row in rows:
- for feature in schema:
- if feature not in row:
- row[feature] = None
-
- # Export items to Arrow format
- table = pa.Table.from_pylist(rows)
-
- # Add metadata (used by datasets library)
- table = table.replace_schema_metadata({"huggingface": json.dumps({"info": {"features": schema}})})
-
- # Write to parquet file
- archive_file = tempfile.NamedTemporaryFile()
- pq.write_table(table, archive_file.name)
-
- # Upload
- self.api.upload_file(
- repo_id=self.repo_id,
- repo_type=self.repo_type,
- revision=self.revision,
- path_in_repo=f"{uuid.uuid4()}.parquet",
- path_or_fileobj=archive_file.name,
- )
- print("Commit completed.")
-
- # Cleanup
- archive_file.close()
- for path in path_to_cleanup:
- path.unlink(missing_ok=True)
-
-
-def _infer_schema(key: str, value: Any) -> Dict[str, str]:
- """Infer schema for the `datasets` library.
-
- See
- https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Value.
- """
- if "image" in key:
- return {"_type": "Image"}
- if "audio" in key:
- return {"_type": "Audio"}
- if isinstance(value, int):
- return {"_type": "Value", "dtype": "int64"}
- if isinstance(value, float):
- return {"_type": "Value", "dtype": "float64"}
- if isinstance(value, bool):
- return {"_type": "Value", "dtype": "bool"}
- if isinstance(value, bytes):
- return {"_type": "Value", "dtype": "binary"}
- # Otherwise in last resort => convert it to a string
- return {"_type": "Value", "dtype": "string"}
diff --git a/spaces/ikoghoemmanuell/Sales-Prediction-App-Streamlit/README.md b/spaces/ikoghoemmanuell/Sales-Prediction-App-Streamlit/README.md
deleted file mode 100644
index 0fa453fe5cb7781786a8ad0b7dc57db40170f20c..0000000000000000000000000000000000000000
--- a/spaces/ikoghoemmanuell/Sales-Prediction-App-Streamlit/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sales Prediction App With Streamlit
-emoji: 📉
-colorFrom: purple
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/imseldrith/FaceSwap/templates/error.html b/spaces/imseldrith/FaceSwap/templates/error.html
deleted file mode 100644
index 209ed146496710eb70ff3b72a9c0320723fe3c7d..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/FaceSwap/templates/error.html
+++ /dev/null
@@ -1,67 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
- <meta charset="UTF-8">
- <meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>Error
-
-
-
-
-
Error
-
- An error occurred while processing the images/videos.
-
- 
- 
- 
- 
-'''
-FOOTER = '