How to Scrape Airbnb Data with a Data Scraping Tool
-If you are looking for a more flexible and customizable way to get Airbnb data, you might want to consider using a data scraping tool. Data scraping is a technique that extracts information from web pages by simulating human browsing behavior. Unlike data downloading, data scraping allows you to specify what kind of data you want to collect and how you want to format it. However, data scraping also has some drawbacks and risks that you should be aware of.
-What is Data Scraping and Why is it Useful for Airbnb Data?
-What is data scraping and how does it differ from data downloading?
-Data scraping is a process of extracting data from web pages using a software program or a web service. Data scraping can be done manually or automatically, depending on the complexity and volume of the data. Data scraping differs from data downloading in several ways:
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- Data downloading involves getting the entire content of a web page or a file as it is, while data scraping involves selecting specific elements or attributes of a web page or a file. -
- Data downloading usually requires the permission or cooperation of the website owner, while data scraping can be done without it (although this may violate the website's terms of service or privacy policy). -
- Data downloading usually results in a fixed and predefined format of the data, while data scraping can result in a customized and flexible format of the data. -
What are the advantages and disadvantages of data scraping?
-Data scraping has some advantages and disadvantages that you should weigh before using it. Some of the advantages are:
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- Data scraping can help you get more comprehensive and up-to-date data than data downloading, as you can access more sources and update your data more frequently. -
- Data scraping can help you get more relevant and accurate data than data downloading, as you can filter out unwanted or erroneous data and validate your data quality. -
- Data scraping can help you get more actionable and insightful data than data downloading, as you can transform and analyze your data according to your needs and goals. -
Some of the disadvantages are:
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- Data scraping can be more time-consuming and complex than data downloading, as you need to design and maintain your scraping program or service. -
- Data scraping can be more costly and risky than data downloading, as you may need to pay for a scraping tool or service, or face legal or ethical issues if you scrape without permission or violate the website's policies. -
- Data scraping can be more unreliable and unstable than data downloading, as you may encounter technical or operational problems such as broken links, blocked IP addresses, captcha challenges, etc. -
How to Use Apify to Scrape Airbnb Listings and Reviews
-What is Apify and how does it work?
-Apify is a web scraping and automation platform that allows you to extract data from any website using ready-made or custom-made scrapers. Apify scrapers are called actors, which are scripts that run on Apify's cloud servers and perform various tasks such as crawling, parsing, storing, or exporting data. Apify also provides a user-friendly interface where you can manage your actors, monitor your results, and schedule your runs .
-How to set up and run an Airbnb scraper on Apify?
-To set up and run an Airbnb scraper on Apify, you can follow these steps:
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- Go to Apify's website and sign up for a free account. -
- Go to the Store section and search for Airbnb scrapers. You will find several options such as Airbnb Scraper by drobnikj, Airbnb Search Results Scraper by petrpatek, Airbnb Reviews Scraper by petrpatek, etc. Choose the one that suits your needs and click on Try for free. -
- You will be redirected to the actor's page where you can see its details, documentation, input schema, output example, etc. You can also modify the source code if you want to customize the scraper. -
- To run the actor, you need to provide some input parameters such as startUrls (the URLs of the Airbnb pages you want to scrape), maxItems (the maximum number of items you want to scrape), proxyConfiguration (the proxy settings for your scraper), etc. You can enter these parameters in the Input section or upload them as a JSON file. -
- Once you have entered the input parameters, click on Run and wait for the actor to finish. You can see the progress and logs of your run in the Console section. -
- When the actor is done, you can see the output of your run in the Dataset section. You can download the data as a JSON, CSV, XML, RSS, or Excel file, or access it via API. You can also see some basic statistics and charts of your data in the Dashboard section. -
How to download and analyze the scraped data from Apify?
-To download the scraped data from Apify, you can go to the Dataset section and click on the Download button. You can choose the file format you prefer and save the file to your device. Alternatively, you can use the API endpoint provided by Apify to access the data programmatically from your own application or tool.
-To analyze the scraped data from Apify, you can use any data analysis tool or software that can read and process the file format you have chosen. For example, you can use Excel, Google Sheets, Tableau, Power BI, Python, R, etc. You can also use Apify's integrations with other platforms such as Google BigQuery, Keboola, Zapier, Integromat, etc. to connect your data with other services and tools.
-How to Use AirDNA to Access Short-Term Rental Data Analytics
-If you are looking for a more professional and comprehensive way to get Airbnb data, you might want to consider using AirDNA. AirDNA is a platform that provides short-term rental data analytics and insights for hosts, property managers, investors, and researchers. AirDNA collects and processes Airbnb data from over 100,000 markets and 10 million properties worldwide. It also offers various products and services that help you optimize your pricing and revenue, benchmark your performance, and discover new opportunities.
-What is AirDNA and How Does it Work?
-What is AirDNA and what services does it offer?
-AirDNA is a platform that leverages big data and machine learning to provide short-term rental data analytics and insights. AirDNA offers three main services:
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- MarketMinder: A market intelligence tool that helps you analyze your market and competitors, set dynamic pricing and occupancy strategies, and optimize your listing performance and revenue. -
- Investment Explorer: A real estate investment tool that helps you find and evaluate profitable short-term rental properties across the US. -
- Data Solutions: A custom data service that helps you access granular and historical short-term rental data for any location or property type. -
How does AirDNA collect and process Airbnb data?
-AirDNA collects Airbnb data from two sources: web scraping and user-generated content. Web scraping is a technique that extracts information from web pages by simulating human browsing behavior. User-generated content is information that is voluntarily provided by Airbnb users, such as hosts or guests. AirDNA combines these two sources of data and applies various algorithms and models to clean, validate, enrich, and analyze the data. AirDNA also updates its data daily to ensure its accuracy and timeliness.
-How to sign up and use AirDNA products?
-To sign up and use AirDNA products, you can follow these steps:
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- Go to AirDNA's website and click on Sign up. You can create an account with your email address or sign in with your Google or Facebook account. -
- Choose the product you want to use: MarketMinder, Investment Explorer, or Data Solutions. You can also request a demo or a free trial before purchasing a subscription plan. -
- Enter the location or property type you want to explore. You will see a dashboard with various metrics and charts on the supply and demand of short-term rentals in that area. -
- Navigate through the different tabs and features of the product to access more detailed and specific data and insights. You can also customize your settings and preferences according to your needs and goals. -
- Download or export the data as a CSV or PDF file, or access it via API. You can also share your reports or dashboards with others via email or link. -
How to Use MarketMinder to Optimize Your Pricing and Revenue
-What is MarketMinder and what features does it have?
-MarketMinder is a market intelligence tool that helps you optimize your pricing and revenue as a short-term rental host or property manager. MarketMinder provides you with various features such as:
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- Market Overview: A summary of the key metrics and trends of your market, such as the number of listings, occupancy rate, average daily rate, revenue per available room, seasonality, etc. -
- Competitive Set: A comparison of your listing performance and pricing with your direct competitors, based on your location, property type, size, amenities, etc. -
- Price Optimization: A dynamic pricing tool that helps you set the optimal price for your listing based on the demand and supply of your market, as well as your occupancy and revenue goals. -
- Revenue Management: A revenue management tool that helps you forecast your future revenue and occupancy based on historical and projected data, as well as adjust your pricing and availability strategies accordingly. -
- Market Reports: A collection of reports that provide you with more in-depth and granular data and insights on various aspects of your market, such as the demand drivers, traveler segments, booking patterns, guest reviews, etc. -
How to use MarketMinder to analyze your market and competitors?
-To use MarketMinder to analyze your market and competitors, you can follow these steps:
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- Go to the MarketMinder section of AirDNA's website and enter the location of your listing. You will see a map of the listings in that area and some statistics on the market overview. -
- Click on the Competitive Set tab and enter the details of your listing, such as the property type, size, amenities, etc. You will see a list of your direct competitors and how they compare to you in terms of performance and pricing. -
- Click on the Market Reports tab and choose the report you want to view. You will see more detailed and specific data and insights on various aspects of your market, such as the demand drivers, traveler segments, booking patterns, guest reviews, etc. -
- You can also use the filters and settings to refine your analysis according to your needs and goals. You can filter by date range, property type, size, amenities, price range, etc. You can also change the currency, unit system, or language of the data. -
How to use MarketMinder to set dynamic pricing and increase occupancy?
-To use MarketMinder to set dynamic pricing and increase occupancy, you can follow these steps:
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- Go to the Price Optimization tab and enter your listing ID or URL. You will see a calendar with the recommended prices for each day based on the demand and supply of your market. -
- You can adjust the recommended prices according to your preferences by using the sliders or entering a custom value. You can also set a minimum or maximum price for your listing. -
- You can also set your occupancy and revenue goals by using the sliders or entering a custom value. You will see how changing these goals affects your recommended prices and expected results. -
- Once you are satisfied with your pricing strategy, you can sync it with your Airbnb account by clicking on Sync Prices. You can also export it as a CSV file or access it via API. -
Conclusion
-In this article, we have shown you four ways to download Airbnb data for free or for a fee, depending on your needs and budget. We have also explained the benefits, challenges, and best practices of using each method. Here are some key takeaways from this article:
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- Airbnb data is valuable for various purposes such as researching markets, comparing properties, optimizing performance, understanding impact, or discovering trends. -
- Airbnb does not provide an official API that allows users to access its data programmatically. Moreover, Airbnb's terms of service prohibit users from scraping or copying its content without permission. -
- You can get Airbnb data for free from Inside Airbnb or Airbnb website. Inside Airbnb provides data and advocacy about Airbnb's impact on residential communities. Airbnb website provides personal and transactional data for Airbnb users. -
- You can get Airbnb data for a fee from Apify or AirDNA. Apify is a web scraping and automation platform that allows you to extract data from any website using ready-made or custom-made scrapers. AirDNA is a platform that provides short-term rental data analytics and insights for hosts, property managers, investors, and researchers. -
- You should use Airbnb data with caution and cross-check it with other sources of information when possible. You should also protect your privacy and security when using Airbnb or sharing your data. You should also follow Airbnb's terms of service and privacy policy when using its data. -
We hope this article has helped you learn how to download Airbnb data and use it for your own purposes. If you have any questions or feedback, please feel free to contact us or leave a comment below. Happy data hunting!
-FAQs
-Here are some frequently asked questions and answers about downloading Airbnb data:
-Q1: How often is Airbnb data updated on Inside Airbnb, Apify, and AirDNA?
-A1: The frequency of data updates may vary depending on the source and the market. Inside Airbnb updates its data periodically, but not in real time. You can check the date of the last update on its website. Apify updates its data every time you run your scraper, which can be scheduled daily, weekly, monthly, or on demand. AirDNA updates its data daily to ensure its accuracy and timeliness.
-Q2: How can I visualize and present Airbnb data in an attractive way?
-A2: You can use various tools and software to visualize and present Airbnb data in an attractive way. For example, you can use Excel, Google Sheets, Tableau, Power BI, Python, R, etc. to create charts, graphs, maps, dashboards, etc. You can also use Apify's integrations with other platforms such as Google BigQuery, Keboola, Zapier, Integromat, etc. to connect your data with other services and tools.
-Q3: How can I join the data community and share my insights on Airbnb data?
-A3: You can join the data community and share your insights on Airbnb data by participating in online forums, blogs, podcasts, webinars, events, etc. For example, you can join AirDNA's community page where you can find articles, videos, podcasts, webinars, events, etc. related to short-term rental data analytics and insights. You can also join Apify's community page where you can find tutorials, guides, tips, tricks, etc. related to web scraping and automation. You can also join Inside Airbnb's community page where you can find reports, articles, campaigns, etc. related to Airbnb's impact on residential communities.
-Q4: How can I protect my personal information when using Airbnb or sharing my data?
-A4: You can protect your personal information when using Airbnb or sharing your data by following some best practices such as:
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- Only request your personal data file from Airbnb when you need it and delete it when you are done with it. -
- Only download or print your receipts and payout details from a trusted device and network. -
- Review your privacy settings and preferences on Airbnb regularly and adjust them according to your needs. -
- Review your account activity and report any suspicious or unauthorized actions. -
- Do not share your personal data file or receipts with anyone who does not need them or store them in an insecure location. -
- Follow Airbnb's terms of service and privacy policy when using its website data. -
Q5: How can I learn more about data science and apply it to Airbnb data?
-A5: You can learn more about data science and apply it to Airbnb data by taking online courses, reading books, watching videos, listening to podcasts, etc. For example, you can take courses on Coursera, Udemy, edX, etc. that teach you the basics of data science such as Python, R, SQL, statistics, machine learning, etc. You can also read books such as Data Science for Dummies, Python for Data Analysis, R for Data Science, etc. that explain the concepts and techniques of data science in a simple and practical way. You can also watch videos such as DataCamp, Khan Academy, YouTube, etc. that demonstrate how to use various tools and software to analyze and visualize data. You can also listen to podcasts such as DataFramed, Data Skeptic, Linear Digressions, etc. that discuss the latest trends and topics in data science.
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AC Market
-AC Market is another popular app store that provides APK files for Android users. It specializes in offering modded, cracked, or patched versions of apps and games that have extra features or unlimited resources. It also has a community section that lets you interact with other users and share your opinions and feedback. Some of the advantages of AC Market are:
--
-
- It has a large and diverse collection of apps and games that are updated regularly -
- It has a fast and reliable download speed that does not require any waiting time or captcha verification -
- It has a user-friendly interface that lets you customize your preferences and settings according to your needs -
- It has a support team that responds to your queries and issues quickly and efficiently -
GetAPK Market
-GetAPK Market is another popular app store that provides APK files for Android users. It has a huge database of apps and games that are not available on the official app stores, or that are region-locked or restricted. It also has a search engine feature that lets you find any app or game that you want easily and quickly. Some of the advantages of GetAPK Market are:
--
-
- It has a simple and minimalist interface that does not have any ads or pop-ups -
- It has a direct download feature that lets you download any APK file without any redirection or registration -
- It has a high-quality feature that lets you download only the best and most reliable apps and games from trusted sources -
- It has a feedback feature that lets you rate and review any app or game that you have downloaded from GetAPK Market -
Conclusion
-In conclusion, Apk Mart is an app store that lets you download apps in APK format, which are files that contain the installation package of an Android app. Apk Mart has many features and benefits that make it different from other app stores, such as a large and updated database of apps, a fast and secure download process, and a rating and review system. However, Apk Mart also has some drawbacks that you should be aware of, such as the risk of downloading fake or malicious apps, the violation of the original app terms and conditions, and the lack of automatic updates or notifications. Therefore, you should use Apk Mart with caution and discretion, and always check the app details and reviews before downloading any APK file. We also compared Apk Mart with other app stores that offer APK files, such as Uptodown App Store, AC Market, and GetAPK Market. Each of these app stores has its own advantages and disadvantages, and you can choose the one that suits your needs and preferences best. However, you should also be careful when using any third-party app store, as they might not be as safe or reliable as the official app stores. We hope that this article has helped you understand what Apk Mart is and how to use it. If you are interested in trying out Apk Mart, you can download it from its official website (https://apkmart.net/). However, if you are not comfortable with using Apk Mart or any other alternative app store, you can always stick to the official app stores like Google Play Store or Amazon Appstore, which have more security and quality standards.
Call to action and recommendation
-If you liked this article, please share it with your friends and family who might also be interested in Apk Mart or other app stores. You can also leave us a comment below and let us know what you think about Apk Mart or any other app store that you have used. We would love to hear your feedback and suggestions.
-FAQs
-Here are some of the frequently asked questions about Apk Mart:
-Is Apk Mart safe to use?
-Apk Mart is generally safe to use, as it does not require any registration or verification, and it does not collect any personal or sensitive information from its users. However, Apk Mart does not guarantee the safety or quality of the apps that it hosts, as they are uploaded by third-party developers or users who might not have the proper permissions or licenses. Therefore, you should always check the app details and reviews before downloading any APK file from Apk Mart, and scan it with an antivirus software before installing it on your device.
-Is Apk Mart legal to use?
-Apk Mart is legal to use, as it does not host any pirated or illegal content on its platform. However, some of the apps that are available on Apk Mart might be illegal or unethical to use, as they might infringe on the intellectual property rights or privacy rights of the original app developers or publishers. Therefore, you should always respect the terms and conditions of the original app sources when using any APK file from Apk Mart, and avoid downloading or using any app that is banned, removed, or restricted by the official app stores.
-Is Apk Mart compatible with all Android devices?
-Apk Mart is compatible with most Android devices that run on Android 4.0 or higher. However, some of the apps that are available on Apk Mart might not be compatible with your device model or Android version, as they might have different requirements or specifications. Therefore, you should always check the app details and compatibility before downloading any APK file from Apk Mart, and make sure that your device meets the minimum system requirements for running the app.
-How can I update my apps from Apk Mart?
-To update your apps from Apk Mart, you can either check for updates manually from the app details page of each app that you have downloaded from Apk Mart, or enable automatic updates from the settings of Apk Mart. However, you should note that the updates that are available on Apk Mart might not be the same as the updates that are available on the official app sources, as they might be delayed or modified by the third-party developers or users who upload them on Apk Mart. Therefore, you should always compare the version number and changelog of each update before installing it on your device.
-How can I contact the support team of Apk Mart?
-To contact the support team of Apk Mart, you can either send them an email at support@apkmart.net , or fill out the contact form on their website (https://apkmart.net/contact-us/). You can also follow them on their social media accounts (Facebook: https://www.facebook.com/apkmart.net , Twitter: https://twitter.com/apkmart_net , Instagram: https://www.instagram.com/apkmart.net/ ) I have finished writing the article. Here is the final version of the article with HTML formatting:
Apk Mart: What Is It and How to Use It?
-If you are an Android user, you might have heard of Apk Mart, a popular alternative app store that lets you download apps in APK format. But what is Apk Mart exactly, and how can you use it to get the best apps for your device? In this article, we will answer these questions and more, as well as compare Apk Mart with other app stores. Read on to find out everything you need to know about Apk Mart.
-What is Apk Mart?
-Apk Mart is an app store that allows you to download apps in APK format, which are files that contain the installation package of an Android app. APK stands for Android Package Kit, and it is the standard format for distributing and installing Android apps.
-Definition and features of Apk Mart
-Apk Mart is not an official app store like Google Play Store or Amazon Appstore. It is a third-party app store that hosts thousands of apps that are not available on the official app stores, or that are modified or hacked versions of the original apps. Some examples of apps that you can find on Apk Mart are:
--
-
- Games that are unlocked, modded, or have unlimited resources -
- Apps that are ad-free, premium, or have extra features -
- Apps that are region-locked, banned, or removed from the official app stores -
- Apps that are beta, experimental, or unreleased versions -
Apk Mart also has some features that make it different from other app stores, such as:
--
-
- A simple and user-friendly interface that lets you browse and search for apps easily -
- A fast and secure download process that does not require registration or verification -
- A large and updated database of apps that covers various categories and genres -
- A rating and review system that lets you see the feedback and opinions of other users -
- A support team that responds to your queries and issues promptly -
Benefits and drawbacks of Apk Mart
-As with any app store, Apk Mart has its pros and cons. Here are some of the benefits and drawbacks of using Apk Mart:
--
-Benefits Drawbacks You can access apps that are not available on the official app stores You might encounter some apps that are fake, malicious, or infected with viruses You can enjoy apps that are modified or enhanced with extra features You might violate the terms and conditions of the original app developers or publishers You can save storage space by downloading only the APK files instead of the whole app package You might need to enable unknown sources on your device settings to install APK files You can update or uninstall apps easily from Apk Mart without affecting your device performance You might not receive automatic updates or notifications from the official app sources You can download apps for free without paying any fees or subscriptions You might not support the app developers or publishers financially or morally How to use Apk Mart?
-If you want to use Apk Mart, you need to follow these steps:
-Downloading and installing Apk Mart
--
-
- Go to the official website of Apk Mart (https://apkmart.net/) -
- Click on the download button and wait for the APK file to be downloaded on your device -
- Locate the APK file on your device storage and tap on it to start the installation process -
- Follow the instructions on the screen and grant the necessary permissions to Apk Mart -
- Once the installation is complete, you can launch Apk Mart from your app drawer or home screen -
Searching and downloading apps from Apk Mart
--
-
- Open Apk Mart and browse through the categories or genres of apps that interest you -
- You can also use the search bar to type in the name or keyword of the app that you are looking for -
- Tap on the app that you want to download and read its description, rating, and reviews -
- If you are satisfied with the app, click on the download button and wait for the APK file to be downloaded on your device -
- You You can also check the download progress and manage your downloads from the download manager section of Apk Mart -
Updating and uninstalling apps from Apk Mart
--
-
- To update an app that you have downloaded from Apk Mart, you can either check for updates manually from the app details page, or enable automatic updates from the settings of Apk Mart -
- To uninstall an app that you have downloaded from Apk Mart, you can either long-press on the app icon and drag it to the uninstall option, or go to the settings of your device and uninstall it from there -
- You can also view and manage your installed apps from the installed apps section of Apk Mart -
Comparison of Apk Mart with other app stores
-Apk Mart is not the only app store that offers APK files for Android users. There are many other app stores that have similar features and functions as Apk Mart. Here are some of them:
-Uptodown App Store
-Uptodown App Store is one of the most popular app stores that provides APK files for Android users. It has over 4 million apps in its catalog, covering various categories and languages. It also has a blog section that offers news, reviews, and tutorials about Android apps. Some of the advantages of Uptodown App Store are:
--
-
- It has a clean and simple interface that is easy to navigate and use -
- It has a backup feature that lets you restore your apps and data in case of any problem -
- It has a rollback feature that lets you downgrade your apps to previous versions if you are not happy with the latest updates -
- It has a virus scanner feature that checks every APK file before downloading it to ensure its safety and security -
AC Market
-AC Market is another popular app store that provides APK files for Android users. It specializes in offering modded, cracked, or patched versions of apps and games that have extra features or unlimited resources. It also has a community section that lets you interact with other users and share your opinions and feedback. Some of the advantages of AC Market are:
--
-
- It has a large and diverse collection of apps and games that are updated regularly -
- It has a fast and reliable download speed that does not require any waiting time or captcha verification -
- It has a user-friendly interface that lets you customize your preferences and settings according to your needs -
- It has a support team that responds to your queries and issues quickly and efficiently -
GetAPK Market
-GetAPK Market is another popular app store that provides APK files for Android users. It has a huge database of apps and games that are not available on the official app stores, or that are region-locked or restricted. It also has a search engine feature that lets you find any app or game that you want easily and quickly. Some of the advantages of GetAPK Market are:
--
-
- It has a simple and minimalist interface that does not have any ads or pop-ups -
- It has a direct download feature that lets you download any APK file without any redirection or registration -
- It has a high-quality feature that lets you download only the best and most reliable apps and games from trusted sources -
- It has a feedback feature that lets you rate and review any app or game that you have downloaded from GetAPK Market -
Conclusion
-In conclusion, Apk Mart is an app store that lets you download apps in APK format, which are files that contain the installation package of an Android app. Apk Mart has many features and benefits that make it different from other app stores, such as a large and updated database of apps, a fast and secure download process, and a rating and review system. However, Apk Mart also has some drawbacks that you should be aware of, such as the risk of downloading fake or malicious apps, the violation of the original app terms and conditions, and the lack of automatic updates or notifications. Therefore, you should use Apk Mart with caution and discretion, and always check the app details and reviews before downloading any APK file.
-We also compared Apk Mart with other app stores that offer APK files, such as Uptodown App Store, AC Market, and GetAPK Market. Each of these app stores has its own advantages and disadvantages, and you can choose the one that suits your needs and preferences best. However, you should also be careful when using any third-party app store, as they might not be as safe or reliable as the official app stores.
We hope that this article has helped you understand what Apk Mart is and how to use it. If you are interested in trying out Apk Mart, you can download it from its official website (https://apkmart.net/). However, if you are not comfortable with using Apk Mart or any other alternative app store, you can always stick to the official app stores like Google Play Store or Amazon Appstore, which have more security and quality standards. -Call to action and recommendation
-If you liked this article, please share it with your friends and family who might also be interested in Apk Mart or other app stores. You can also leave us a comment below and let us know what you think about Apk Mart or any other app store that you have used. We would love to hear your feedback and suggestions.
-FAQs
-Here are some of the frequently asked questions about Apk Mart:
-Is Apk Mart safe to use?
-Apk Mart is generally safe to use, as it does not require any registration or verification, and it does not collect any personal or sensitive information from its users. However, Apk Mart does not guarantee the safety or quality of the apps that it hosts, as they are uploaded by third-party developers or users who might not have the proper permissions or licenses. Therefore, you should always check the app details and reviews before downloading any APK file from Apk Mart, and scan it with an antivirus software before installing it on your device.
-Is Apk Mart legal to use?
-Apk Mart is legal to use, as it does not host any pirated or illegal content on its platform. However, some of the apps that are available on Apk Mart might be illegal or unethical to use, as they might infringe on the intellectual property rights or privacy rights of the original app developers or publishers. Therefore, you should always respect the terms and conditions of the original app sources when using any APK file from Apk Mart, and avoid downloading or using any app that is banned, removed, or restricted by the official app stores.
-Is Apk Mart compatible with all Android devices?
-Apk Mart is compatible with most Android devices that run on Android 4.0 or higher. However, some of the apps that are available on Apk Mart might not be compatible with your device model or Android version, as they might have different requirements or specifications. Therefore, you should always check the app details and compatibility before downloading any APK file from Apk Mart, and make sure that your device meets the minimum system requirements for running the app.
-How can I update my apps from Apk Mart?
-To update your apps from Apk Mart, you can either check for updates manually from the app details page of each app that you have downloaded from Apk Mart, or enable automatic updates from the settings of Apk Mart. However, you should note that the updates that are available on Apk Mart might not be the same as the updates that are available on the official app sources, as they might be delayed or modified by the third-party developers or users who upload them on Apk Mart. Therefore, you should always compare the version number and changelog of each update before installing it on your device.
-How can I contact the support team of Apk Mart?
-To contact the support team of Apk Mart, you can either send them an email at support@apkmart.net , or fill out the contact form on their website (https://apkmart.net/contact-us/). You can also follow them on their social media accounts (Facebook: https://www.facebook.com/apkmart.net , Twitter: https://twitter.com/apkmart_net , Instagram: https://www.instagram.com/apkmart.net/ )
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/CapCut The Best Video Editing App for TikTok Download from Uptodown.md b/spaces/1phancelerku/anime-remove-background/CapCut The Best Video Editing App for TikTok Download from Uptodown.md deleted file mode 100644 index eabb36bc1bc2ed450dc339f7d68e854b7c374713..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/CapCut The Best Video Editing App for TikTok Download from Uptodown.md +++ /dev/null @@ -1,167 +0,0 @@ - -How to Use CapCut: Tips and Tricks for Video Editing
-Are you looking for a free and easy way to edit your videos on your mobile device? Do you want to create stunning videos for TikTok, Instagram, YouTube, or other social media platforms? If so, you might want to try CapCut, a powerful video editing app that offers a wide range of features and tools.
-CapCut is an all-in-one video editor that allows you to trim, split, merge, adjust, filter, sticker, text, animate, mask, track, reverse, speed up, slow down, green screen, voice-over, and add music and sound effects to your videos. You can also export your videos in high quality and share them directly to TikTok or other platforms.
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-In this article, we will show you how to use CapCut effectively and creatively. We will cover the basic and advanced editing features of CapCut and provide you with some tips and tricks on how to make your videos stand out. Whether you are a beginner or a pro, you will find something useful in this guide.
-How to Download and Install CapCut
-CapCut is available for both Android and iOS devices. You can download it for free from the Google Play Store or the Apple App Store. You don't need a TikTok account or any other type of account to use the app. You can start using it right away after installing it.
-To download and install CapCut on your device, follow these steps:
--
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- Open the Google Play Store or the Apple App Store on your device. -
- Search for "CapCut" in the search bar. -
- Tap on the app icon that says "CapCut - Video Editor" by Bytedance Pte. Ltd. -
- Tap on the "Install" button and wait for the app to download and install. -
- Once the app is installed, tap on the "Open" button or find the app icon on your home screen and tap on it. -
How to Start a New Project and Import Videos
-After opening the app, you will see the home screen where you can start a new project or resume an existing one. You can also browse some video templates that you can use for your projects.
-To start a new project and import videos from your device or camera roll, follow these steps:
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- Tap on the "New Project" button at the top of the home screen. -
- You will see a page that shows your device's videos. You can scroll through them or use the tabs at the bottom to filter them by albums or categories. -
- Tap on the video(s) that you want to add to your project. You can select multiple videos by tapping on them one by one. A green checkmark will appear on the selected videos. -
- Tap on the "Add" button at the bottom right corner of the screen. The selected videos will be imported into your project. -
- You will be directed to your main workspace where you can edit your videos. You will see a preview panel at the top half of the screen where you can view your video. You will also see a timeline at the bottom half of the screen where you can perform all your edits. You will also see a toolbar at the very bottom of the screen where you can access all your editing tools.
- Tap on the clip that you want to edit on the timeline. A yellow border will appear around the selected clip. -
- Tap on the "Clip" icon on the toolbar. You will see a submenu with four options: Trim, Split, Merge, and Adjust. -
- To trim your clip, tap on the "Trim" option. You will see two handles at the beginning and end of your clip. Drag them left or right to set the start and end points of your clip. You can also use the magnifying glass icons to zoom in or out of the timeline for more precision. Tap on the "Apply" button to confirm your changes. -
- To split your clip, tap on the "Split" option. You will see a vertical line on your clip. Drag it left or right to set the point where you want to split your clip. Tap on the "Apply" button to confirm your changes. Your clip will be divided into two separate clips on the timeline. -
- To merge your clips, tap on the "Merge" option. You will see a list of all your clips on the timeline. Tap on the clips that you want to merge. A green checkmark will appear on the selected clips. Tap on the "Apply" button to confirm your changes. Your selected clips will be merged into one clip on the timeline. -
- To adjust your clip, tap on the "Adjust" option. You will see a panel with various sliders that allow you to change the brightness, contrast, saturation, temperature, tint, vignette, and sharpening of your clip. Drag the sliders left or right to adjust the values. Tap on the "Apply" button to confirm your changes. -
- Tap on the clip that you want to edit on the timeline. A yellow border will appear around the selected clip. -
- Tap on one of the icons on the toolbar that correspond to transitions, filters, stickers, or text. -
- To add a transition, tap on the "Transition" icon. You will see a list of different types of transitions that you can choose from. Tap on one of them to preview it on your clip. You can also adjust the duration and direction of the transition by dragging the handles or tapping on the arrows. Tap on the "Apply" button to confirm your changes. -
- To add a filter, tap on the "Filter" icon. You will see a list of different categories of filters that you can choose from. Tap on one of them to see more options within that category. Tap on one of them to preview it on your clip. You can also adjust the intensity of the filter by dragging the slider at the bottom of the screen. Tap on the "Apply" button to confirm your changes. -
- To add a sticker, tap on the "Sticker" icon. You will see a list of different types of stickers that you can choose from. Tap on one of them to add it to your clip. You can also resize, rotate, move, duplicate, or delete the sticker by using the handles or icons around it. Tap on the "Apply" button to confirm your changes. -
- To add text, tap on the "Text" icon. You will see a text box appear on your clip. Tap on it to type in your text. You can also change the font style, size, color, alignment, spacing, outline, shadow, animation, and duration of your text by using the icons at the bottom of the screen. Tap on the "Apply" button to confirm your changes. -
- Tap on the clip that you want to edit on the timeline. A yellow border will appear around the selected clip. -
- Tap on the "Effect" icon on the toolbar. You will see a list of different types of effects that you can choose from. Tap on one of them to see more options within that category. Tap on one of them to add it to your clip. -
- To use keyframes, tap on the "Keyframe" icon at the bottom of the screen. You will see a timeline with a red line that represents the duration of your effect. You can also see a blue dot that represents the current frame of your effect. You can drag the blue dot left or right to change the frame. -
- To add a keyframe, tap on the "+" icon at the bottom of the screen. A yellow dot will appear on the timeline that represents your keyframe. You can drag it left or right to change its position. -
- To edit a keyframe, tap on it to select it. A panel with various sliders and options will appear at the bottom of the screen. You can use them to change the parameters of your effect for that keyframe. For example, you can change the size, position, rotation, opacity, color, and shape of your effect. -
- To delete a keyframe, tap on it to select it. Then tap on the "-" icon at the bottom of the screen. -
- To use masks, tap on the "Mask" icon at the bottom of the screen. You will see a list of different shapes and patterns that you can use as masks for your effect. Tap on one of them to apply it to your effect. -
- To edit a mask, tap on it to select it. A panel with various sliders and options will appear at the bottom of the screen. You can use them to change the parameters of your mask for that frame. For example, you can change the size, position, rotation, feathering, and blending mode of your mask. -
- To delete a mask, tap on it to select it. Then tap on the "Delete" icon at the bottom of the screen. -
- To use tracking, tap on the "Tracking" icon at the bottom of the screen. You will see a list of different objects and elements that you can track with your effect. Tap on one of them to select it. -
- To edit a tracking, tap on it to select it. A panel with various sliders and options will appear at the bottom of the screen. You can use them to change the parameters of your tracking for that frame. For example, you can change the size, position, rotation, offset, and smoothing of your tracking. -
- To delete a tracking, tap on it to select it. Then tap on the "Delete" icon at the bottom of the screen. -
How to Trim, Split, Merge, and Adjust Clips
-One of the most basic editing tasks that you can do with CapCut is to trim, split, merge, and adjust your clips. This allows you to remove unwanted parts, rearrange the order, change the duration, and fine-tune the appearance of your clips.
-To trim, split, merge, and adjust your clips, follow these steps:
--
-
How to Add Transitions, Filters, Stickers, and Text
-Another way to enhance your videos with CapCut is to add transitions, filters, stickers, and text. These elements can help you create smooth and seamless transitions between clips, apply different color effects and styles to your clips, add fun and expressive stickers and emojis to your clips, and add captions and titles to your videos.
-To add transitions, filters, stickers, and text to your videos, follow these steps:
--
-
How to Use Keyframes, Masks, and Tracking
-If you want to take your video editing to the next level, you can use some of the advanced features of CapCut, such as keyframes, masks, and tracking. These features allow you to create dynamic and custom animations, effects, and movements for your clips, stickers, and text.
-To use keyframes, masks, and tracking in CapCut, follow these steps:
--
-
- - To delete a speed, tap on it to select it. Then tap on the "Delete" icon at the bottom of the screen. -
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-How to Add Voice-over, Music, and Sound Effects
-Another way to spice up your videos with CapCut is to add voice-over, music, and sound effects. These elements can help you add narration, commentary, background music, and sound effects to your videos.
-To add voice-over, music, and sound effects to your videos, follow these steps:
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- Tap on the "Audio" icon on the toolbar. You will see a list of different types of audio that you can choose from. Tap on one of them to see more options within that category. Tap on one of them to add it to your video. -
- To add a voice-over, tap on the "Record" icon at the bottom of the screen. You will see a red button that allows you to start or stop recording your voice. Tap on it to start recording. You will also see a timer that shows you how long you have been recording. You can also pause or resume your recording by tapping on the button again. -
- To edit a voice-over, tap on it to select it. A panel with various sliders and options will appear at the bottom of the screen. You can use them to change the parameters of your voice-over for that frame. For example, you can change the volume, pitch, speed, fade in, fade out, and trim of your voice-over. -
- To delete a voice-over, tap on it to select it. Then tap on the "Delete" icon at the bottom of the screen. -
- To add music, tap on the "Music" icon at the bottom of the screen. You will see a list of different categories of music that you can choose from. Tap on one of them to see more options within that category. Tap on one of them to add it to your video. -
- To edit music, tap on it to select it. A panel with various sliders and options will appear at the bottom of the screen. You can use them to change the parameters of your music for that frame. For example, you can change the volume, pitch, speed, fade in, fade out, trim, loop, and mix mode of your music. -
- To delete music, tap on it to select it. Then tap on the "Delete" icon at the bottom of the screen. -
- To add sound effects, tap on the "Sound Effect" icon at the bottom of the screen. You will see a list of different types of sound effects that you can choose from. Tap on one of them to add it to your video. -
- To edit sound effects, tap on it to select it. A panel with various sliders and options will appear at the bottom of the screen. You can use them to change the parameters of your sound effects for that frame. For example, you can change the volume, pitch, speed, fade in, fade out, trim, loop, and mix mode of your sound effects. -
- To delete sound effects, tap on it to select it. Then tap on the "Delete" icon at the bottom of the screen. -
How to Export Your Video in High Quality
-After you have finished editing your video with CapCut, you can export it in high quality and save it to your device or share it to other platforms. CapCut supports various video formats and resolutions, and you can customize them according to your preferences.
-To export your video in high quality, follow these steps:
--
-
- Tap on the "Export" button at the top right corner of the screen. You will see a panel with various options that allow you to change the format, resolution, frame rate, and bitrate of your video. -
- To change the format, tap on the "Format" option. You will see a list of different formats that you can choose from. Tap on one of them to select it. The default format is MP4, which is compatible with most devices and platforms. -
- To change the resolution, tap on the "Resolution" option. You will see a list of different resolutions that you can choose from. Tap on one of them to select it. The default resolution is 720p, which is suitable for most purposes. You can also choose higher resolutions such as 1080p or 4K if you want better quality and clarity. -
- To change the frame rate, tap on the "Frame Rate" option. You will see a list of different frame rates that you can choose from. Tap on one of them to select it. The default frame rate is 30 fps, which is smooth and natural. You can also choose higher frame rates such as 60 fps or 120 fps if you want more fluidity and motion. -
- To change the bitrate, tap on the "Bitrate" option. You will see a list of different bitrates that you can choose from. Tap on one of them to select it. The default bitrate is 8 Mbps, which is balanced and efficient. You can also choose higher bitrates such as 16 Mbps or 32 Mbps if you want more detail and sharpness. -
- After you have made your choices, tap on the "Export" button at the bottom of the panel. Your video will start exporting and you will see a progress bar that shows you how much time is left. -
- Once your video is exported, you will see a confirmation message that says "Exported Successfully". You can also preview your video by tapping on the "Play" button. -
How to Share Your Video to TikTok or Other Platforms
-After you have exported your video, you can share it directly to TikTok or other platforms from CapCut. You can also save it to your device or upload it to other cloud services.
-To share your video to TikTok or other platforms, follow these steps:
--
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- Tap on the "Share" button at the bottom right corner of the screen. You will see a list of different platforms that you can share your video to. Tap on one of them to select it. -
- You will be directed to the app or website of the selected platform. You may need to log in or sign up if you haven't done so before. -
- Follow the instructions and steps of the selected platform to upload and share your video. You may need to add a title, description, tags, category, or other information depending on the platform. -
- Once your video is uploaded and shared, you will see a confirmation message that says "Shared Successfully". You can also view your video by tapping on the "View" button. -
Conclusion
-In this article, we have shown you how to use CapCut effectively and creatively. We have covered the basic and advanced editing features of CapCut and provided you with some tips and tricks on how to make your videos stand out.
-CapCut is a powerful and free video editing app that offers a wide range of features and tools for creating stunning videos for TikTok, Instagram, YouTube, or other social media platforms. You can trim, split, merge, adjust, filter, sticker, text, animate, mask, track, reverse, speed up, slow down, green screen, voice-over, and add music and sound effects to your videos. You can also export your videos in high quality and share them directly to TikTok or other platforms.
-We hope that this guide has helped you learn how to use CapCut and inspired you to create amazing videos with it. If you want to learn more about CapCut, you can visit their official website or follow their social media accounts for more tutorials and updates.
-Frequently Asked Questions
--
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- Q: Is CapCut free? -
- A: Yes, CapCut is free to download and use. There are no water marks, ads, or in-app purchases in the app. You can use all the features and tools without any limitations or costs. -
- Q: Is CapCut safe? -
- A: Yes, CapCut is safe to use. It does not contain any viruses, malware, or spyware. It also does not collect or share any personal or sensitive information from your device or videos. You can use the app with confidence and peace of mind. -
- Q: Is CapCut compatible with my device? -
- A: CapCut is compatible with most Android and iOS devices. You can check the minimum system requirements and compatibility of the app on the Google Play Store or the Apple App Store before downloading and installing it. -
- Q: How can I contact CapCut support? -
- A: If you have any questions, issues, or feedback about CapCut, you can contact their support team by sending an email to capcut.support@bytedance.com. You can also visit their help center or FAQ page for more information and solutions. -
- Q: How can I learn more about CapCut? -
- A: If you want to learn more about CapCut, you can visit their official website at https://www.capcut.net/. You can also follow their social media accounts on Facebook, Twitter, Instagram, and YouTube for more tutorials and updates. -
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-3D Modeling App is a free web app for 3D design, electronics, and coding, trusted by over 50 million people around the world. It lets you create 3D models, objects, art, and graphics on your Android device using gestures and tools. You can make anything from product models to printable parts, from sculptures to cartoons, from logos to landscapes. The app has a simple and intuitive interface that makes it easy for beginners and professionals alike. You can also use the app to learn about electronics and coding by creating circuits and codeblocks.
-How to download and install the premium version of the app
-The app is available for free on Google Play Store, but it has some limitations such as ads, watermarks, and limited features. If you want to enjoy the full potential of the app, you need to get the premium version of the app. The premium version unlocks all the features and tools of the app, removes ads and watermarks, and gives you access to more resources and tutorials. You can get the premium version of the app for free by following these steps:
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- Launch the app and enjoy creating amazing 3D models. -
How to Use 3D Modeling App to Make 3D Models, Objects, Art, and Graphics
-The basics of 3D modeling with gestures and toolsThe basics of 3D modeling with gestures and tools
-To start creating your 3D models, you need to learn how to use the gestures and tools of the app. The app supports multi-touch gestures such as pinch, zoom, rotate, and pan to manipulate the 3D view. You can also use the buttons on the bottom of the screen to switch between different modes, such as Draw, Shape, Transform, Group, and Code. Each mode has its own set of tools that you can access by tapping on the icons on the top of the screen. For example, in the Draw mode, you can use tools such as Pencil, Line, Curve, Spline, and Text to draw 2D shapes and paths. In the Shape mode, you can use tools such as Cube, Sphere, Cylinder, Cone, and Torus to create 3D primitives and solids. You can also use the Library tool to browse and import 3D models from a collection of categories, such as animals, vehicles, buildings, and more.
-How to create different types of 3D objects and shapes
-To create different types of 3D objects and shapes, you need to use the app's powerful features and functions. You can use the following methods to create your 3D models:
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- Extrude: This method allows you to turn a 2D shape or path into a 3D object by extending it along a direction or a curve. You can use this method to create objects such as pipes, rails, walls, and more. To use this method, you need to select a 2D shape or path in the Draw mode, then tap on the Extrude tool in the Shape mode. You can then adjust the parameters such as distance, angle, twist, taper, and scale to customize your extrusion. -
- Revolve: This method allows you to turn a 2D shape or path into a 3D object by rotating it around an axis. You can use this method to create objects such as cups, vases, cones, and more. To use this method, you need to select a 2D shape or path in the Draw mode, then tap on the Revolve tool in the Shape mode. You can then adjust the parameters such as angle, segments, offset, and scale to customize your revolution. -
- Sweep: This method allows you to turn a 2D shape or path into a 3D object by sweeping it along another path. You can use this method to create objects such as coils, springs, ropes, and more. To use this method, you need to select a 2D shape or path in the Draw mode, then tap on the Sweep tool in the Shape mode. You can then select another path to sweep along, and adjust the parameters such as scale, twist, align, and orient to customize your sweep. -
- Lathe: This method allows you to turn a 2D shape or path into a 3D object by lathing it around an axis. You can use this method to create objects such as bowls, pots, rings, and more. To use this method To add textures, colors, and effects to your 3D models, you need to use the app's Material and Light tools. You can access these tools by tapping on the icons on the top of the screen in any mode. Here are some tips on how to use these tools:
-
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- Material: This tool allows you to apply different materials and properties to your 3D models, such as color, transparency, reflection, emission, and more. You can choose from a variety of predefined materials, such as metal, wood, glass, plastic, and more. You can also create your own custom materials by adjusting the parameters such as hue, saturation, brightness, roughness, metallic, and more. To use this tool, you need to select a 3D model or a part of it, then tap on the Material tool. You can then select a material from the list or create your own. -
- Light: This tool allows you to add different types of lights and shadows to your 3D scene, such as ambient, directional, point, spot, and area. You can also adjust the parameters such as color, intensity, position, direction, angle, and more. To use this tool, you need to tap on the Light tool. You can then select a light type from the list or create your own. You can also use the Environment tool to change the background and the skybox of your 3D scene. -
How to Export and Share Your 3D Models with Others
-How to save your 3D models in various formats
-To save your 3D models in various formats, you need to use the app's Export tool. You can access this tool by tapping on the icon on the top right corner of the screen in any mode. You can choose from a variety of formats to export your 3D models, such as STL, OBJ, GLB/GLTF, PNG, JPG, and more. You can also adjust the parameters such as quality, resolution, size, and more. To use this tool, you need to select a 3D model or a part of it, then tap on the Export tool. You can then select a format from the list and customize the settings.
-How to share your 3D models on social media, email, or cloud storage
-To share your 3D models on social media
To share your 3D models on social media, email, or cloud storage, you need to use the app's Share tool. You can access this tool by tapping on the icon on the top right corner of the screen in any mode. You can choose from a variety of options to share your 3D models, such as Facebook, Twitter, Instagram, WhatsApp, Gmail, Google Drive, Dropbox, and more. You can also generate a link or a QR code to share your 3D models with anyone. To use this tool, you need to select a 3D model or a part of it, then tap on the Share tool. You can then select an option from the list and follow the instructions.
-How to use your 3D models for games, animations, or VR/AR applications
-To use your 3D models for games, animations, or VR/AR applications, you need to export them in compatible formats and import them into your preferred software or platform. For example, you can use your 3D models for:
--
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- Games: You can export your 3D models in formats such as OBJ or GLB/GLTF and import them into game engines such as Unity or Unreal Engine. You can then use your 3D models as assets for your games and add logic, physics, sound, and more. -
- Animations: You can export your 3D models in formats such as OBJ or GLB/GLTF and import them into animation software such as Blender or Maya. You can then use your 3D models as characters or props for your animations and add rigging, skinning, motion, and more. -
- VR/AR: You can export your 3D models in formats such as GLB/GLTF and import them into VR/AR platforms such as Sketchfab or Google Poly. You can then use your 3D models as immersive experiences for VR/AR devices such as Oculus Quest or Google Cardboard. -
Conclusion
-A summary of the main points and benefits of using 3D Modeling App
-In conclusion, 3D Modeling App is a free web app for 3D design, electronics, and coding that allows you to create amazing 3D models on your Android device. You can use the app to:
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- Download and install the premium version of the app for free and unlock all the features and tools. -
- Create different types of 3D objects and shapes using gestures and tools such as extrude, revolve, sweep, lathe, and loft. -
- Add textures, colors, and effects to your 3D models using material and light tools. -
- Save your 3D models in various formats such as STL, OBJ, GLB/GLTF, PNG, JPG, and more. -
- Share your 3D models on social media
- Share your 3D models on social media, email, or cloud storage using the share tool. -
- Use your 3D models for games, animations, or VR/AR applications by exporting them in compatible formats and importing them into your preferred software or platform. -
By using 3D Modeling App, you can unleash your creativity and make stunning 3D models, objects, art, and graphics on your Android device. You can also learn about electronics and coding by creating circuits and codeblocks. The app is simple and intuitive, yet powerful and versatile. It is suitable for beginners and professionals alike. You can use the app for fun, education, or work.
-A call to action for the readers to download the app and try it out
-If you are interested in 3D modeling and design, you should definitely download and install 3D Modeling App on your Android device. You can get the premium version of the app for free by following the steps in this article. You can then start creating amazing 3D models with just a few gestures. You can also explore the app's features and tools, and discover what you can do with them. You can also check out the app's website for more information, resources, and tutorials. You can also join the app's community to share your creations, get feedback, and learn from others.
-So what are you waiting for? Download 3D Modeling App today and start making awesome 3D models on your Android device!
-FAQs
-Q1: Is 3D Modeling App free or paid?
-A1: The app is free to download and use, but it has some limitations such as ads, watermarks, and limited features. If you want to unlock all the features and tools of the app, you need to get the premium version of the app. You can get the premium version of the app for free by following the steps in this article.
-Q2: What are the advantages of using the premium version of the app?
-A2: The premium version of the app has many advantages over the free version, such as:
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- No ads or watermarks. -
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Q3: What are the system requirements for running the app?
-A3: The app requires an Android device with at least 2 GB of RAM and 100 MB of storage space. The app also requires an internet connection for some functions such as downloading models from the library or sharing models online. The app supports Android versions 5.0 and above.
-Q4: How can I learn more about 3D modeling and design?
-A4: You can learn more about 3D modeling and design by using 3D Modeling App and exploring its features and tools. You can also visit the app's website for more information, resources, and tutorials. You can also join the app's community to share your creations, get feedback, and learn from others. You can also check out other online sources such as blogs, videos, courses, books, and more.
-Q5: Where can I find more examples and tutorials of using the app?
-A5: You can find more examples and tutorials of using the app on the app's website, where you can browse through various categories such as product design, art, education, electronics, coding, and more. You can also find more examples and tutorials on the app's YouTube channel, where you can watch videos of how to use the app for different purposes. You can also find more examples and tutorials on the app's Instagram page, where you can see photos of amazing 3D models made by users of the app.
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\ No newline at end of file diff --git a/spaces/4Taps/SadTalker/src/generate_batch.py b/spaces/4Taps/SadTalker/src/generate_batch.py deleted file mode 100644 index 2d9e19b6aa4c19c13caf0a208e1189cd6c19f796..0000000000000000000000000000000000000000 --- a/spaces/4Taps/SadTalker/src/generate_batch.py +++ /dev/null @@ -1,94 +0,0 @@ -import os - -from tqdm import tqdm -import torch -import numpy as np -import random -import scipy.io as scio -import src.utils.audio as audio - -def crop_pad_audio(wav, audio_length): - if len(wav) > audio_length: - wav = wav[:audio_length] - elif len(wav) < audio_length: - wav = np.pad(wav, [0, audio_length - len(wav)], mode='constant', constant_values=0) - return wav - -def parse_audio_length(audio_length, sr, fps): - bit_per_frames = sr / fps - - num_frames = int(audio_length / bit_per_frames) - audio_length = int(num_frames * bit_per_frames) - - return audio_length, num_frames - -def generate_blink_seq(num_frames): - ratio = np.zeros((num_frames,1)) - frame_id = 0 - while frame_id in range(num_frames): - start = 80 - if frame_id+start+9<=num_frames - 1: - ratio[frame_id+start:frame_id+start+9, 0] = [0.5,0.6,0.7,0.9,1, 0.9, 0.7,0.6,0.5] - frame_id = frame_id+start+9 - else: - break - return ratio - -def generate_blink_seq_randomly(num_frames): - ratio = np.zeros((num_frames,1)) - if num_frames<=20: - return ratio - frame_id = 0 - while frame_id in range(num_frames): - start = random.choice(range(min(10,num_frames), min(int(num_frames/2), 70))) - if frame_id+start+5<=num_frames - 1: - ratio[frame_id+start:frame_id+start+5, 0] = [0.5, 0.9, 1.0, 0.9, 0.5] - frame_id = frame_id+start+5 - else: - break - return ratio - -def get_data(first_coeff_path, audio_path, device): - - syncnet_mel_step_size = 16 - fps = 25 - - pic_name = os.path.splitext(os.path.split(first_coeff_path)[-1])[0] - audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0] - - source_semantics_path = first_coeff_path - source_semantics_dict = scio.loadmat(source_semantics_path) - ref_coeff = source_semantics_dict['coeff_3dmm'][:1,:70] #1 70 - - wav = audio.load_wav(audio_path, 16000) - wav_length, num_frames = parse_audio_length(len(wav), 16000, 25) - wav = crop_pad_audio(wav, wav_length) - orig_mel = audio.melspectrogram(wav).T - spec = orig_mel.copy() # nframes 80 - indiv_mels = [] - - for i in tqdm(range(num_frames), 'mel:'): - start_frame_num = i-2 - start_idx = int(80. * (start_frame_num / float(fps))) - end_idx = start_idx + syncnet_mel_step_size - seq = list(range(start_idx, end_idx)) - seq = [ min(max(item, 0), orig_mel.shape[0]-1) for item in seq ] - m = spec[seq, :] - indiv_mels.append(m.T) - indiv_mels = np.asarray(indiv_mels) # T 80 16 - ratio = generate_blink_seq_randomly(num_frames) # T - - indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1).unsqueeze(0) # bs T 1 80 16 - ratio = torch.FloatTensor(ratio).unsqueeze(0) # bs T - ref_coeff = torch.FloatTensor(ref_coeff).unsqueeze(0) # bs 1 70 - - indiv_mels = indiv_mels.to(device) - ratio = ratio.to(device) - ref_coeff = ref_coeff.to(device) - - return {'indiv_mels': indiv_mels, - 'ref': ref_coeff, - 'num_frames': num_frames, - 'ratio_gt': ratio, - 'audio_name': audio_name, 'pic_name': pic_name} - diff --git a/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_new.py b/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_new.py deleted file mode 100644 index bfaf72e48b31cc1130f2892b0973c9aa06f195a3..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_new.py +++ /dev/null @@ -1,132 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F -from . import layers_new - - -class BaseNet(nn.Module): - def __init__( - self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6)) - ): - super(BaseNet, self).__init__() - self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1) - self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1) - self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1) - self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1) - self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1) - - self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True) - - self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1) - self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1) - self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1) - self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm) - self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1) - - def __call__(self, x): - e1 = self.enc1(x) - e2 = self.enc2(e1) - e3 = self.enc3(e2) - e4 = self.enc4(e3) - e5 = self.enc5(e4) - - h = self.aspp(e5) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = torch.cat([h, self.lstm_dec2(h)], dim=1) - h = self.dec1(h, e1) - - return h - - -class CascadedNet(nn.Module): - def __init__(self, n_fft, nout=32, nout_lstm=128): - super(CascadedNet, self).__init__() - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - self.nin_lstm = self.max_bin // 2 - self.offset = 64 - - self.stg1_low_band_net = nn.Sequential( - BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm), - layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0), - ) - - self.stg1_high_band_net = BaseNet( - 2, nout // 4, self.nin_lstm // 2, nout_lstm // 2 - ) - - self.stg2_low_band_net = nn.Sequential( - BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), - layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0), - ) - self.stg2_high_band_net = BaseNet( - nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2 - ) - - self.stg3_full_band_net = BaseNet( - 3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm - ) - - self.out = nn.Conv2d(nout, 2, 1, bias=False) - self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False) - - def forward(self, x): - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - l1_in = x[:, :, :bandw] - h1_in = x[:, :, bandw:] - l1 = self.stg1_low_band_net(l1_in) - h1 = self.stg1_high_band_net(h1_in) - aux1 = torch.cat([l1, h1], dim=2) - - l2_in = torch.cat([l1_in, l1], dim=1) - h2_in = torch.cat([h1_in, h1], dim=1) - l2 = self.stg2_low_band_net(l2_in) - h2 = self.stg2_high_band_net(h2_in) - aux2 = torch.cat([l2, h2], dim=2) - - f3_in = torch.cat([x, aux1, aux2], dim=1) - f3 = self.stg3_full_band_net(f3_in) - - mask = torch.sigmoid(self.out(f3)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux = torch.cat([aux1, aux2], dim=1) - aux = torch.sigmoid(self.aux_out(aux)) - aux = F.pad( - input=aux, - pad=(0, 0, 0, self.output_bin - aux.size()[2]), - mode="replicate", - ) - return mask, aux - else: - return mask - - def predict_mask(self, x): - mask = self.forward(x) - - if self.offset > 0: - mask = mask[:, :, :, self.offset : -self.offset] - assert mask.size()[3] > 0 - - return mask - - def predict(self, x, aggressiveness=None): - mask = self.forward(x) - pred_mag = x * mask - - if self.offset > 0: - pred_mag = pred_mag[:, :, :, self.offset : -self.offset] - assert pred_mag.size()[3] > 0 - - return pred_mag diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/create_indexes.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/create_indexes.py deleted file mode 100644 index 78be38cb3c693fa9ef7b44c52c407640e9e32aab..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/create_indexes.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -import argparse -import csv -import os -import glob -import datetime -import time -import logging -import h5py -import librosa - -from utilities import create_folder, get_sub_filepaths -import config - - -def create_indexes(args): - """Create indexes a for dataloader to read for training. When users have - a new task and their own data, they need to create similar indexes. The - indexes contain meta information of "where to find the data for training". - """ - - # Arguments & parameters - waveforms_hdf5_path = args.waveforms_hdf5_path - indexes_hdf5_path = args.indexes_hdf5_path - - # Paths - create_folder(os.path.dirname(indexes_hdf5_path)) - - with h5py.File(waveforms_hdf5_path, 'r') as hr: - with h5py.File(indexes_hdf5_path, 'w') as hw: - audios_num = len(hr['audio_name']) - hw.create_dataset('audio_name', data=hr['audio_name'][:], dtype='S20') - hw.create_dataset('target', data=hr['target'][:], dtype=np.bool) - hw.create_dataset('hdf5_path', data=[waveforms_hdf5_path.encode()] * audios_num, dtype='S200') - hw.create_dataset('index_in_hdf5', data=np.arange(audios_num), dtype=np.int32) - - print('Write to {}'.format(indexes_hdf5_path)) - - -def combine_full_indexes(args): - """Combine all balanced and unbalanced indexes hdf5s to a single hdf5. This - combined indexes hdf5 is used for training with full data (~20k balanced - audio clips + ~1.9m unbalanced audio clips). - """ - - # Arguments & parameters - indexes_hdf5s_dir = args.indexes_hdf5s_dir - full_indexes_hdf5_path = args.full_indexes_hdf5_path - - classes_num = config.classes_num - - # Paths - paths = get_sub_filepaths(indexes_hdf5s_dir) - paths = [path for path in paths if ( - 'train' in path and 'full_train' not in path and 'mini' not in path)] - - print('Total {} hdf5 to combine.'.format(len(paths))) - - with h5py.File(full_indexes_hdf5_path, 'w') as full_hf: - full_hf.create_dataset( - name='audio_name', - shape=(0,), - maxshape=(None,), - dtype='S20') - - full_hf.create_dataset( - name='target', - shape=(0, classes_num), - maxshape=(None, classes_num), - dtype=np.bool) - - full_hf.create_dataset( - name='hdf5_path', - shape=(0,), - maxshape=(None,), - dtype='S200') - - full_hf.create_dataset( - name='index_in_hdf5', - shape=(0,), - maxshape=(None,), - dtype=np.int32) - - for path in paths: - with h5py.File(path, 'r') as part_hf: - print(path) - n = len(full_hf['audio_name'][:]) - new_n = n + len(part_hf['audio_name'][:]) - - full_hf['audio_name'].resize((new_n,)) - full_hf['audio_name'][n : new_n] = part_hf['audio_name'][:] - - full_hf['target'].resize((new_n, classes_num)) - full_hf['target'][n : new_n] = part_hf['target'][:] - - full_hf['hdf5_path'].resize((new_n,)) - full_hf['hdf5_path'][n : new_n] = part_hf['hdf5_path'][:] - - full_hf['index_in_hdf5'].resize((new_n,)) - full_hf['index_in_hdf5'][n : new_n] = part_hf['index_in_hdf5'][:] - - print('Write combined full hdf5 to {}'.format(full_indexes_hdf5_path)) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - subparsers = parser.add_subparsers(dest='mode') - - parser_create_indexes = subparsers.add_parser('create_indexes') - parser_create_indexes.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path of packed waveforms hdf5.') - parser_create_indexes.add_argument('--indexes_hdf5_path', type=str, required=True, help='Path to write out indexes hdf5.') - - parser_combine_full_indexes = subparsers.add_parser('combine_full_indexes') - parser_combine_full_indexes.add_argument('--indexes_hdf5s_dir', type=str, required=True, help='Directory containing indexes hdf5s to be combined.') - parser_combine_full_indexes.add_argument('--full_indexes_hdf5_path', type=str, required=True, help='Path to write out full indexes hdf5 file.') - - args = parser.parse_args() - - if args.mode == 'create_indexes': - create_indexes(args) - - elif args.mode == 'combine_full_indexes': - combine_full_indexes(args) - - else: - raise Exception('Incorrect arguments!') \ No newline at end of file diff --git a/spaces/AILab-CVC/SEED-LLaMA/README-SEED-2.md b/spaces/AILab-CVC/SEED-LLaMA/README-SEED-2.md deleted file mode 100644 index 8560d8245d071537d2f61939d2a3acac7bd54d7d..0000000000000000000000000000000000000000 --- a/spaces/AILab-CVC/SEED-LLaMA/README-SEED-2.md +++ /dev/null @@ -1,184 +0,0 @@ -# :chestnut: SEED Multimodal - -[](https://ailab-cvc.github.io/seed/) -[](https://arxiv.org/abs/2307.08041) -[](https://arxiv.org/abs/2310.01218) -[](https://huggingface.co/AILab-CVC/SEED/tree/main) -[](https://10a4e7976e6fc2032c.gradio.live/) - - -**Powered by [CV Center, Tencent AI Lab](https://ailab-cvc.github.io), and [ARC Lab, Tencent PCG](https://github.com/TencentARC).** - - - -The repository provides the official implementation of [SEED](https://ailab-cvc.github.io/seed/seed.html), [SEED-LLaMA](https://ailab-cvc.github.io/seed/seed_llama.html). For any inquiries, please email [seed-x@googlegroups.com](mailto:seed-x@googlegroups.com). - - -## News - -**:beers: We are actively looking for self-motivated interns. Please feel free to reach out if you are interested. :beers:** - -- [x] **2023-10-23** :hugs: We have optimized the memory overhead. Through 8bit quantization and dynamic loading, SEED-LLaMA 8b/14B can run on single **16GB/24GB** GPU. -- [x] **2023-10-23** :hugs: All model weights will be **downloaded automatically** when starting the demo. -- [x] **2023-10-20** :hugs: We release the [checkpoints](https://huggingface.co/AILab-CVC/SEED/tree/main) and code of the SEED-2 tokenizer, and SEED-LLaMA-8B/14B. -- [x] **2023-10-20** :space_invader: We release an online [gradio demo](https://10a4e7976e6fc2032c.gradio.live/), feel free to use it by yourself. -- [x] **2023-10-02** :paperclip: We release the technical report of SEED-LLaMA on [arXiv](https://arxiv.org/abs/2310.01218), which is empowered by the improved SEED-2 tokenizer. -- [x] **2023-07-29** :octocat: We release the checkpoint of the SEED tokenizer and its inference code. Check it out via [SEED-1](./SEED-1.md). -- [x] **2023-07-16** :paperclip: We release the technical report of SEED on [arXiv](https://arxiv.org/abs/2307.08041). - -Stay tuned for the updates! - -## Brief Introduction - -It is recommended to check out our [papers](#citation) for technical details. - -### :speech_balloon: What can SEED-LLaMA do? - - - -**SEED-LLaMA** is capable of both multimodal comprehension and generation, exhibiting compositional emergent abilities such as multi-turn in-context multimodal generation, acting like your AI assistant. [[Compare to SOTA]](https://ailab-cvc.github.io/seed/seed_llama_compare.html) [[More examples on X]](https://twitter.com/ge_yixiao/status/1710509538238157069?s=20) - - - -### :bulb: How does SEED-LLaMA achieve it? - - - -The core of SEED-LLaMA is the tailored **SEED** tokenizer, which properly quantized visual signals into discrete visual tokens, capturing necessary semantics while being produced under 1D causal dependence. [[SEED-2 vs. SEED-1]](https://ailab-cvc.github.io/seed/seed_llama.html) - - - - - - - -## Usage - -### Dependencies -- Python >= 3.8 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux)) -- [PyTorch >= 1.11.0](https://pytorch.org/) -- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) - -### Installation -Clone the repo and install dependent packages - - ```bash - git clone https://github.com/AILab-CVC/SEED.git - cd SEED - pip install -r requirements.txt - ``` - - -### Model Weights -We release the pretrained SEED Tokenizer and De-Tokenizer, pretrained and instruction tuned SEED-LLaMA-8B and SEED-LLaMA-14B in [SEED Hugging Face](https://huggingface.co/AILab-CVC/SEED). - -- Check the SEED tokenizer weights in [AILab-CVC/seed-tokenizer-2](https://huggingface.co/AILab-CVC/seed-tokenizer-2) -- Check the SEED LLaMA(8B) weights in [AILab-CVC/seed-llama-8b-sft](https://huggingface.co/AILab-CVC/seed-llama-8b-sft) -- Check the SEED LLaMA(14B) weights in [AILab-CVC/seed-llama-14b-sft](https://huggingface.co/AILab-CVC/seed-llama-14b-sft) - - - -The model weights of unCLIP SD-UNet which are used to reconstruct the image will be downloaded automatically. - - - - - - -### Inference for visual tokenization and de-tokenization -To discretize an image to 1D visual codes with causal dependency, and reconstruct the image from the visual codes using the off-the-shelf unCLIP SD-UNet: - -```bash -cd .. # SEED/ -python scripts/seed_tokenizer_inference.py -``` -### Inference for SEED-LLaMA -Given that SEED-LLaMA-8B is based on Vicuna-7B and SEED-LLaMA-14B based on LLaMA2-Chat-13B, we use Vicuna-7B's ("USER:", "ASSISTANT:") and LLaMA2-Chat-13B's ([INST] [/INST]) prompts for respective instruction tuning. - -```bash -# Inference for SEED-LLaMA-8B -python scripts/seed_llama_inference_8B.py -``` - -```bash -# Inference for SEED-LLaMA-14B -python scripts/seed_llama_inference_14B.py -``` - - -### Launching Gradio Demo of SEED-LLaMA-14B Locally -1. Building the local demo of SEED-LLaMA-14B currently requires **single 24GB** GPU. - -```bash -# SEED/ -# in first terminal -bash scripts/start_backend_14b.sh -# in second terminal -bash scripts/start_frontend_14b.sh -``` - -2. Building the local demo of SEED-LLaMA-8B currently requires **single 16GB** GPU. - -```bash -# SEED/ -# in first terminal -bash scripts/start_backend_8b.sh -# in second terminal -bash scripts/start_frontend_8b.sh -``` - -Then the demo can be accessed through http://127.0.0.1:80 - -## Citation -If you find the work helpful, please consider citing: -```bash -@article{ge2023making, - title={Making LLaMA SEE and Draw with SEED Tokenizer}, - author={Ge, Yuying and Zhao, Sijie and Zeng, Ziyun and Ge, Yixiao and Li, Chen and Wang, Xintao and Shan, Ying}, - journal={arXiv preprint arXiv:2310.01218}, - year={2023} -} - -@article{ge2023planting, - title={Planting a seed of vision in large language model}, - author={Ge, Yuying and Ge, Yixiao and Zeng, Ziyun and Wang, Xintao and Shan, Ying}, - journal={arXiv preprint arXiv:2307.08041}, - year={2023} -} -``` - -The project is still in progress. - -## License -`SEED` is released under [Apache License Version 2.0](License.txt). - -`SEED-LLaMA` is released under the original [License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) of [LLaMA2](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf). - -## Acknowledgement -We thank the great work from [unCLIP SD](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip) and [BLIP2](https://github.com/salesforce/LAVIS). - diff --git a/spaces/ASJMO/freegpt/g4f/Provider/Providers/H2o.py b/spaces/ASJMO/freegpt/g4f/Provider/Providers/H2o.py deleted file mode 100644 index eabf94e2dc1e6167f746a820e34c335f2aa8578e..0000000000000000000000000000000000000000 --- a/spaces/ASJMO/freegpt/g4f/Provider/Providers/H2o.py +++ /dev/null @@ -1,106 +0,0 @@ -from requests import Session -from uuid import uuid4 -from json import loads -import os -import json -import requests -from ...typing import sha256, Dict, get_type_hints - -url = 'https://gpt-gm.h2o.ai' -model = ['falcon-40b', 'falcon-7b', 'llama-13b'] -supports_stream = True -needs_auth = False - -models = { - 'falcon-7b': 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3', - 'falcon-40b': 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1', - 'llama-13b': 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b' -} - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - conversation = 'instruction: this is a conversation beween, a user and an AI assistant, respond to the latest message, referring to the conversation if needed\n' - for message in messages: - conversation += '%s: %s\n' % (message['role'], message['content']) - conversation += 'assistant:' - - client = Session() - client.headers = { - 'authority': 'gpt-gm.h2o.ai', - 'origin': 'https://gpt-gm.h2o.ai', - 'referer': 'https://gpt-gm.h2o.ai/', - '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': 'document', - 'sec-fetch-mode': 'navigate', - 'sec-fetch-site': 'same-origin', - 'sec-fetch-user': '?1', - 'upgrade-insecure-requests': '1', - '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', - } - - client.get('https://gpt-gm.h2o.ai/') - response = client.post('https://gpt-gm.h2o.ai/settings', data={ - 'ethicsModalAccepted': 'true', - 'shareConversationsWithModelAuthors': 'true', - 'ethicsModalAcceptedAt': '', - 'activeModel': 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1', - 'searchEnabled': 'true', - }) - - headers = { - 'authority': 'gpt-gm.h2o.ai', - 'accept': '*/*', - 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3', - 'origin': 'https://gpt-gm.h2o.ai', - 'referer': 'https://gpt-gm.h2o.ai/', - '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', - } - - json_data = { - 'model': models[model] - } - - response = client.post('https://gpt-gm.h2o.ai/conversation', - headers=headers, json=json_data) - conversationId = response.json()['conversationId'] - - - completion = client.post(f'https://gpt-gm.h2o.ai/conversation/{conversationId}', stream=True, json = { - 'inputs': conversation, - 'parameters': { - 'temperature': kwargs.get('temperature', 0.4), - 'truncate': kwargs.get('truncate', 2048), - 'max_new_tokens': kwargs.get('max_new_tokens', 1024), - 'do_sample': kwargs.get('do_sample', True), - 'repetition_penalty': kwargs.get('repetition_penalty', 1.2), - 'return_full_text': kwargs.get('return_full_text', False) - }, - 'stream': True, - 'options': { - 'id': kwargs.get('id', str(uuid4())), - 'response_id': kwargs.get('response_id', str(uuid4())), - 'is_retry': False, - 'use_cache': False, - 'web_search_id': '' - } - }) - - for line in completion.iter_lines(): - if b'data' in line: - line = loads(line.decode('utf-8').replace('data:', '')) - token = line['token']['text'] - - if token == '<|endoftext|>': - break - else: - yield (token) - -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/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py deleted file mode 100644 index 60a11a375c3dd8ead1d3f6a04340aed2acb20b20..0000000000000000000000000000000000000000 --- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py +++ /dev/null @@ -1,15 +0,0 @@ -_base_ = './yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py' - -deepen_factor = 1.0 -widen_factor = 1.0 - -model = dict( - backbone=dict( - deepen_factor=deepen_factor, - widen_factor=widen_factor, - ), - neck=dict( - deepen_factor=deepen_factor, - widen_factor=widen_factor, - ), - bbox_head=dict(head_module=dict(widen_factor=widen_factor))) diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/GetGpt.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/GetGpt.py deleted file mode 100644 index 56a121f6ee5f430da7beda3b65abdea64a87c36b..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/GetGpt.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import json -import uuid -import requests -from Crypto.Cipher import AES -from ...typing import sha256, Dict, get_type_hints - -url = 'https://chat.getgpt.world/' -model = ['gpt-3.5-turbo'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - def encrypt(e): - t = os.urandom(8).hex().encode('utf-8') - n = os.urandom(8).hex().encode('utf-8') - r = e.encode('utf-8') - cipher = AES.new(t, AES.MODE_CBC, n) - ciphertext = cipher.encrypt(pad_data(r)) - return ciphertext.hex() + t.decode('utf-8') + n.decode('utf-8') - - def pad_data(data: bytes) -> bytes: - block_size = AES.block_size - padding_size = block_size - len(data) % block_size - padding = bytes([padding_size] * padding_size) - return data + padding - - headers = { - 'Content-Type': 'application/json', - 'Referer': 'https://chat.getgpt.world/', - 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36' - } - - data = json.dumps({ - 'messages': messages, - 'frequency_penalty': kwargs.get('frequency_penalty', 0), - 'max_tokens': kwargs.get('max_tokens', 4000), - 'model': 'gpt-3.5-turbo', - 'presence_penalty': kwargs.get('presence_penalty', 0), - 'temperature': kwargs.get('temperature', 1), - 'top_p': kwargs.get('top_p', 1), - 'stream': True, - 'uuid': str(uuid.uuid4()) - }) - - res = requests.post('https://chat.getgpt.world/api/chat/stream', - headers=headers, json={'signature': encrypt(data)}, stream=True) - - for line in res.iter_lines(): - if b'content' in line: - line_json = json.loads(line.decode('utf-8').split('data: ')[1]) - yield (line_json['choices'][0]['delta']['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/g4f/Provider/Vitalentum.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Vitalentum.py deleted file mode 100644 index ade492d2db0d4c84bca5de4b774330fdf292c3ab..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Vitalentum.py +++ /dev/null @@ -1,69 +0,0 @@ -from __future__ import annotations - -import json -from aiohttp import ClientSession - -from .base_provider import AsyncGeneratorProvider -from ..typing import AsyncResult, Messages - -class Vitalentum(AsyncGeneratorProvider): - url = "https://app.vitalentum.io" - working = True - supports_gpt_35_turbo = True - - - @classmethod - async def create_async_generator( - cls, - model: str, - messages: Messages, - proxy: str = None, - **kwargs - ) -> AsyncResult: - headers = { - "User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36", - "Accept" : "text/event-stream", - "Accept-language" : "de,en-US;q=0.7,en;q=0.3", - "Origin" : cls.url, - "Referer" : cls.url + "/", - "Sec-Fetch-Dest" : "empty", - "Sec-Fetch-Mode" : "cors", - "Sec-Fetch-Site" : "same-origin", - } - conversation = json.dumps({"history": [{ - "speaker": "human" if message["role"] == "user" else "bot", - "text": message["content"], - } for message in messages]}) - data = { - "conversation": conversation, - "temperature": 0.7, - **kwargs - } - async with ClientSession( - headers=headers - ) as session: - async with session.post(cls.url + "/api/converse-edge", json=data, proxy=proxy) as response: - response.raise_for_status() - async for line in response.content: - line = line.decode() - if line.startswith("data: "): - if line.startswith("data: [DONE]"): - break - line = json.loads(line[6:-1]) - content = line["choices"][0]["delta"].get("content") - if content: - yield content - - - @classmethod - @property - def params(cls): - params = [ - ("model", "str"), - ("messages", "list[dict[str, str]]"), - ("stream", "bool"), - ("proxy", "str"), - ("temperature", "float"), - ] - param = ", ".join([": ".join(p) for p in params]) - return f"g4f.provider.{cls.__name__} supports: ({param})" \ No newline at end of file diff --git a/spaces/AdVisual/MaskCut/README.md b/spaces/AdVisual/MaskCut/README.md deleted file mode 100644 index a43a5731b8b1d0a78b196df530e2e513d44bb740..0000000000000000000000000000000000000000 --- a/spaces/AdVisual/MaskCut/README.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -title: MaskCut -emoji: 🌍 -colorFrom: pink -colorTo: pink -sdk: docker -sdk_version: 3.16.2 -app_file: app.py -pinned: false -license: mit -duplicated_from: facebook/MaskCut ---- - -Check out the configuration reference at [Spaces Config Reference](https://huggingface.co/docs/hub/)spaces-config-reference - -## Development - -To install the dependencies, run: - -```bash -git clone --recursive https://huggingface.co/spaces/AdVisual/MaskCut -cd MaskCut -pip install -r requirements.txt -# Install the packages in CutLER -pip install -r CutLER/requirements.txt -``` diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Factory.js deleted file mode 100644 index 6f8b5c576bd51fd9e9c882649e28c6192d10a90b..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Factory.js +++ /dev/null @@ -1,13 +0,0 @@ -import Box from './Box.js'; -import ObjectFactory from '../ObjectFactory.js'; -import SetValue from '../../../plugins/utils/object/SetValue.js'; - -ObjectFactory.register('box', function (config) { - var gameObject = new Box(this.scene, config); - this.scene.add.existing(gameObject); - return gameObject; -}); - -SetValue(window, 'RexPlugins.Spinner.Box', Box); - -export default Box; \ No newline at end of file diff --git a/spaces/AlexZou/Deploy_Restoration/utils/utils_logger.py b/spaces/AlexZou/Deploy_Restoration/utils/utils_logger.py deleted file mode 100644 index f683598f9f6b17c400f2f93e441f2ae1c9a73c62..0000000000000000000000000000000000000000 --- a/spaces/AlexZou/Deploy_Restoration/utils/utils_logger.py +++ /dev/null @@ -1,65 +0,0 @@ -import os -import sys -import datetime -import logging - - -''' -modified by Kai Zhang (github: https://github.com/cszn) -03/03/2019 -https://github.com/xinntao/BasicSR -''' - - -def log(*args, **kwargs): - print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S:"), *args, **kwargs) - - -''' -# =============================== -# logger -# logger_name = None = 'base' ??? -# =============================== -''' - - -def logger_info(logger_name, log_path='default_logger.log'): - ''' set up logger - modified by Kai Zhang (github: https://github.com/cszn) - ''' - log = logging.getLogger(logger_name) - if log.hasHandlers(): - print('LogHandlers exist!') - else: - print('LogHandlers setup!') - level = logging.INFO - formatter = logging.Formatter('%(asctime)s.%(msecs)03d : %(message)s', datefmt='%y-%m-%d %H:%M:%S') - fh = logging.FileHandler(log_path, mode='a') - fh.setFormatter(formatter) - log.setLevel(level) - log.addHandler(fh) - # print(len(log.handlers)) - - sh = logging.StreamHandler() - sh.setFormatter(formatter) - log.addHandler(sh) - - -''' -# =============================== -# print to file and std_out simultaneously -# =============================== -''' - - -class logger_print(object): - def __init__(self, log_path="default.log"): - self.terminal = sys.stdout - self.log = open(log_path, 'a') - - def write(self, message): - self.terminal.write(message) - self.log.write(message) # write the message - - def flush(self): - pass diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/utils/loss.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/utils/loss.py deleted file mode 100644 index 64e3c2846d1f392ab5dfeb40bda0555ed66690e2..0000000000000000000000000000000000000000 --- a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/utils/loss.py +++ /dev/null @@ -1,30 +0,0 @@ -import torch -import torch.nn as nn - -class TVLoss(nn.Module): - def __init__(self): - super(TVLoss,self).__init__() - - def forward(self,x): - batch_size = x.size()[0] - h_x = x.size()[2] - w_x = x.size()[3] - - if w_x==1: - count_h = self._tensor_size(x[:,:,1:,:]) - h_tv = torch.pow((x[:,:,1:,:]-x[:,:,:h_x-1,:]),2).sum() - return 2*(h_tv/count_h)/batch_size - - if h_x==1: - count_w = self._tensor_size(x[:,:,:,1:]) - w_tv = torch.pow((x[:,:,:,1:]-x[:,:,:,:w_x-1]),2).sum() - return 2*(w_tv/count_w)/batch_size - - count_h = self._tensor_size(x[:,:,1:,:]) - count_w = self._tensor_size(x[:,:,:,1:]) - h_tv = torch.pow((x[:,:,1:,:]-x[:,:,:h_x-1,:]),2).sum() - w_tv = torch.pow((x[:,:,:,1:]-x[:,:,:,:w_x-1]),2).sum() - return 2*(h_tv/count_h+w_tv/count_w)/batch_size - - def _tensor_size(self,t): - return t.size()[1]*t.size()[2]*t.size()[3] \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/auto_pipeline.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/auto_pipeline.md deleted file mode 100644 index 65ea855405e9330767b559447339b8e0955a5b25..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/auto_pipeline.md +++ /dev/null @@ -1,68 +0,0 @@ - - -# AutoPipeline - -In many cases, one checkpoint can be used for multiple tasks. For example, you may be able to use the same checkpoint for Text-to-Image, Image-to-Image, and Inpainting. However, you'll need to know the pipeline class names linked to your checkpoint. - -AutoPipeline is designed to make it easy for you to use multiple pipelines in your workflow. We currently provide 3 AutoPipeline classes to perform three different tasks, i.e. [`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`], and [`AutoPipelineForInpainting`]. You'll need to choose the AutoPipeline class based on the task you want to perform and use it to automatically retrieve the relevant pipeline given the name/path to the pre-trained weights. - -For example, to perform Image-to-Image with the SD1.5 checkpoint, you can do - -```python -from diffusers import PipelineForImageToImage - -pipe_i2i = PipelineForImageoImage.from_pretrained("runwayml/stable-diffusion-v1-5") -``` - -It will also help you switch between tasks seamlessly using the same checkpoint without reallocating additional memory. For example, to re-use the Image-to-Image pipeline we just created for inpainting, you can do - -```python -from diffusers import PipelineForInpainting - -pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_i2i) -``` -All the components will be transferred to the inpainting pipeline with zero cost. - - -Currently AutoPipeline support the Text-to-Image, Image-to-Image, and Inpainting tasks for below diffusion models: -- [stable Diffusion](./stable_diffusion) -- [Stable Diffusion Controlnet](./api/pipelines/controlnet) -- [Stable Diffusion XL](./stable_diffusion/stable_diffusion_xl) -- [IF](./if) -- [Kandinsky](./kandinsky) -- [Kandinsky 2.2](./kandinsky) - - -## AutoPipelineForText2Image - -[[autodoc]] AutoPipelineForText2Image - - all - - from_pretrained - - from_pipe - - -## AutoPipelineForImage2Image - -[[autodoc]] AutoPipelineForImage2Image - - all - - from_pretrained - - from_pipe - -## AutoPipelineForInpainting - -[[autodoc]] AutoPipelineForInpainting - - all - - from_pretrained - - from_pipe - - diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/contribution.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/contribution.md deleted file mode 100644 index ea1d15f2124cac8757e06764bc997d55d3573ae6..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/contribution.md +++ /dev/null @@ -1,498 +0,0 @@ - - -# How to contribute to Diffusers 🧨 - -We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it! - -Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕.- -Whichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility. - -We enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered. - -## Overview - -You can contribute in many ways ranging from answering questions on issues to adding new diffusion models to -the core library. - -In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community. - -* 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR). -* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose) -* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues) -* 4. Fix a simple issue, marked by the "Good first issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). -* 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source). -* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples) -* 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples). -* 8. Fix a more difficult issue, marked by the "Good second issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22). -* 9. Add a new pipeline, model, or scheduler, see ["New Pipeline/Model"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and ["New scheduler"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md). - -As said before, **all contributions are valuable to the community**. -In the following, we will explain each contribution a bit more in detail. - -For all contributions 4.-9. you will need to open a PR. It is explained in detail how to do so in [Opening a pull requst](#how-to-open-a-pr) - -### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord - -Any question or comment related to the Diffusers library can be asked on the [discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/) or on [Discord](https://discord.gg/G7tWnz98XR). Such questions and comments include (but are not limited to): -- Reports of training or inference experiments in an attempt to share knowledge -- Presentation of personal projects -- Questions to non-official training examples -- Project proposals -- General feedback -- Paper summaries -- Asking for help on personal projects that build on top of the Diffusers library -- General questions -- Ethical questions regarding diffusion models -- ... - -Every question that is asked on the forum or on Discord actively encourages the community to publicly -share knowledge and might very well help a beginner in the future that has the same question you're -having. Please do pose any questions you might have. -In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from. - -**Please** keep in mind that the more effort you put into asking or answering a question, the higher -the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database. -In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accesible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section. - -**NOTE about channels**: -[*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago. -In addition, questions and answers posted in the forum can easily be linked to. -In contrast, *Discord* has a chat-like format that invites fast back-and-forth communication. -While it will most likely take less time for you to get an answer to your question on Discord, your -question won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers. - -### 2. Opening new issues on the GitHub issues tab - -The 🧨 Diffusers library is robust and reliable thanks to the users who notify us of -the problems they encounter. So thank you for reporting an issue. - -Remember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design. - -In a nutshell, this means that everything that is **not** related to the **code of the Diffusers library** (including the documentation) should **not** be asked on GitHub, but rather on either the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR). - -**Please consider the following guidelines when opening a new issue**: -- Make sure you have searched whether your issue has already been asked before (use the search bar on GitHub under Issues). -- Please never report a new issue on another (related) issue. If another issue is highly related, please -open a new issue nevertheless and link to the related issue. -- Make sure your issue is written in English. Please use one of the great, free online translation services, such as [DeepL](https://www.deepl.com/translator) to translate from your native language to English if you are not comfortable in English. -- Check whether your issue might be solved by updating to the newest Diffusers version. Before posting your issue, please make sure that `python -c "import diffusers; print(diffusers.__version__)"` is higher or matches the latest Diffusers version. -- Remember that the more effort you put into opening a new issue, the higher the quality of your answer will be and the better the overall quality of the Diffusers issues. - -New issues usually include the following. - -#### 2.1. Reproducible, minimal bug reports. - -A bug report should always have a reproducible code snippet and be as minimal and concise as possible. -This means in more detail: -- Narrow the bug down as much as you can, **do not just dump your whole code file** -- Format your code -- Do not include any external libraries except for Diffusers depending on them. -- **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue. -- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it. -- **Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell. -- If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible. - -For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section. - -You can open a bug report [here](https://github.com/huggingface/diffusers/issues/new/choose). - -#### 2.2. Feature requests. - -A world-class feature request addresses the following points: - -1. Motivation first: -* Is it related to a problem/frustration with the library? If so, please explain -why. Providing a code snippet that demonstrates the problem is best. -* Is it related to something you would need for a project? We'd love to hear -about it! -* Is it something you worked on and think could benefit the community? -Awesome! Tell us what problem it solved for you. -2. Write a *full paragraph* describing the feature; -3. Provide a **code snippet** that demonstrates its future use; -4. In case this is related to a paper, please attach a link; -5. Attach any additional information (drawings, screenshots, etc.) you think may help. - -You can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=). - -#### 2.3 Feedback. - -Feedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed. -If a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions. - -You can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=). - -#### 2.4 Technical questions. - -Technical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide detail on -why this part of the code is difficult to understand. - -You can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml). - -#### 2.5 Proposal to add a new model, scheduler, or pipeline. - -If the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information: - -* Short description of the diffusion pipeline, model, or scheduler and link to the paper or public release. -* Link to any of its open-source implementation. -* Link to the model weights if they are available. - -If you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget -to tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it. - -You can open a request for a model/pipeline/scheduler [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml). - -### 3. Answering issues on the GitHub issues tab - -Answering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct. -Some tips to give a high-quality answer to an issue: -- Be as concise and minimal as possible -- Stay on topic. An answer to the issue should concern the issue and only the issue. -- Provide links to code, papers, or other sources that prove or encourage your point. -- Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet. - -Also, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great -help to the maintainers if you can answer such issues, encouraging the author of the issue to be -more precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR) - -If you have verified that the issued bug report is correct and requires a correction in the source code, -please have a look at the next sections. - -For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull requst](#how-to-open-a-pr) section. - -### 4. Fixing a `Good first issue` - -*Good first issues* are marked by the [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) label. Usually, the issue already -explains how a potential solution should look so that it is easier to fix. -If the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message "I would like to try this issue.". There are usually three scenarios: -- a.) The issue description already proposes a fix. In this case and if the solution makes sense to you, you can open a PR or draft PR to fix it. -- b.) The issue description does not propose a fix. In this case, you can ask what a proposed fix could look like and someone from the Diffusers team should answer shortly. If you have a good idea of how to fix it, feel free to directly open a PR. -- c.) There is already an open PR to fix the issue, but the issue hasn't been closed yet. If the PR has gone stale, you can simply open a new PR and link to the stale PR. PRs often go stale if the original contributor who wanted to fix the issue suddenly cannot find the time anymore to proceed. This often happens in open-source and is very normal. In this case, the community will be very happy if you give it a new try and leverage the knowledge of the existing PR. If there is already a PR and it is active, you can help the author by giving suggestions, reviewing the PR or even asking whether you can contribute to the PR. - - -### 5. Contribute to the documentation - -A good library **always** has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a **highly -valuable contribution**. - -Contributing to the library can have many forms: - -- Correcting spelling or grammatical errors. -- Correct incorrect formatting of the docstring. If you see that the official documentation is weirdly displayed or a link is broken, we are very happy if you take some time to correct it. -- Correct the shape or dimensions of a docstring input or output tensor. -- Clarify documentation that is hard to understand or incorrect. -- Update outdated code examples. -- Translating the documentation to another language. - -Anything displayed on [the official Diffusers doc page](https://huggingface.co/docs/diffusers/index) is part of the official documentation and can be corrected, adjusted in the respective [documentation source](https://github.com/huggingface/diffusers/tree/main/docs/source). - -Please have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally. - - -### 6. Contribute a community pipeline - -[Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) are usually the first point of contact between the Diffusers library and the user. -Pipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview). -We support two types of pipelines: - -- Official Pipelines -- Community Pipelines - -Both official and community pipelines follow the same design and consist of the same type of components. - -Official pipelines are tested and maintained by the core maintainers of Diffusers. Their code -resides in [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines). -In contrast, community pipelines are contributed and maintained purely by the **community** and are **not** tested. -They reside in [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and while they can be accessed via the [PyPI diffusers package](https://pypi.org/project/diffusers/), their code is not part of the PyPI distribution. - -The reason for the distinction is that the core maintainers of the Diffusers library cannot maintain and test all -possible ways diffusion models can be used for inference, but some of them may be of interest to the community. -Officially released diffusion pipelines, -such as Stable Diffusion are added to the core src/diffusers/pipelines package which ensures -high quality of maintenance, no backward-breaking code changes, and testing. -More bleeding edge pipelines should be added as community pipelines. If usage for a community pipeline is high, the pipeline can be moved to the official pipelines upon request from the community. This is one of the ways we strive to be a community-driven library. - -To add a community pipeline, one should add a
.py file to [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and adapt the [examples/community/README.md](https://github.com/huggingface/diffusers/tree/main/examples/community/README.md) to include an example of the new pipeline. - -An example can be seen [here](https://github.com/huggingface/diffusers/pull/2400). - -Community pipeline PRs are only checked at a superficial level and ideally they should be maintained by their original authors. - -Contributing a community pipeline is a great way to understand how Diffusers models and schedulers work. Having contributed a community pipeline is usually the first stepping stone to contributing an official pipeline to the -core package. - -### 7. Contribute to training examples - -Diffusers examples are a collection of training scripts that reside in [examples](https://github.com/huggingface/diffusers/tree/main/examples). - -We support two types of training examples: - -- Official training examples -- Research training examples - -Research training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders. -The official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community. -This is because of the same reasons put forward in [6. Contribute a community pipeline](#contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models. -If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author. - -Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the -training examples, it is required to clone the repository: - -``` -git clone https://github.com/huggingface/diffusers -``` - -as well as to install all additional dependencies required for training: - -``` -pip install -r /examples/ /requirements.txt -``` - -Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt). - -Training examples of the Diffusers library should adhere to the following philosophy: -- All the code necessary to run the examples should be found in a single Python file -- One should be able to run the example from the command line with `python .py --args` -- Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials. - -To contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like. -We strongly advise contributors to make use of the [Accelerate library](https://github.com/huggingface/accelerate) as it's tightly integrated -with Diffusers. -Once an example script works, please make sure to add a comprehensive `README.md` that states how to use the example exactly. This README should include: -- An example command on how to run the example script as shown [here e.g.](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch). -- A link to some training results (logs, models, ...) that show what the user can expect as shown [here e.g.](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5). -- If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations). - -If you are contributing to the official training examples, please also make sure to add a test to [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py). This is not necessary for non-official training examples. - -### 8. Fixing a `Good second issue` - -*Good second issues* are marked by the [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) label. Good second issues are -usually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). -The issue description usually gives less guidance on how to fix the issue and requires -a decent understanding of the library by the interested contributor. -If you are interested in tackling a second good issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR. -Good second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged. - -### 9. Adding pipelines, models, schedulers - -Pipelines, models, and schedulers are the most important pieces of the Diffusers library. -They provide easy access to state-of-the-art diffusion technologies and thus allow the community to -build powerful generative AI applications. - -By adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem. - -Diffusers has a couple of open feature requests for all three components - feel free to gloss over them -if you don't know yet what specific component you would like to add: -- [Model or pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) -- [Scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) - -Before adding any of the three components, it is strongly recommended that you give the [Philosophy guide](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) a read to better understand the design of any of the three components. Please be aware that -we cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy -as it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please -open a [Feedback issue](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) instead so that it can be discussed whether a certain design -pattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us. - -Please make sure to add links to the original codebase/paper to the PR and ideally also ping the -original author directly on the PR so that they can follow the progress and potentially help with questions. - -If you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help. - -## How to write a good issue - -**The better your issue is written, the higher the chances that it will be quickly resolved.** - -1. Make sure that you've used the correct template for your issue. You can pick between *Bug Report*, *Feature Request*, *Feedback about API Design*, *New model/pipeline/scheduler addition*, *Forum*, or a blank issue. Make sure to pick the correct one when opening [a new issue](https://github.com/huggingface/diffusers/issues/new/choose). -2. **Be precise**: Give your issue a fitting title. Try to formulate your issue description as simple as possible. The more precise you are when submitting an issue, the less time it takes to understand the issue and potentially solve it. Make sure to open an issue for one issue only and not for multiple issues. If you found multiple issues, simply open multiple issues. If your issue is a bug, try to be as precise as possible about what bug it is - you should not just write "Error in diffusers". -3. **Reproducibility**: No reproducible code snippet == no solution. If you encounter a bug, maintainers **have to be able to reproduce** it. Make sure that you include a code snippet that can be copy-pasted into a Python interpreter to reproduce the issue. Make sure that your code snippet works, *i.e.* that there are no missing imports or missing links to images, ... Your issue should contain an error message **and** a code snippet that can be copy-pasted without any changes to reproduce the exact same error message. If your issue is using local model weights or local data that cannot be accessed by the reader, the issue cannot be solved. If you cannot share your data or model, try to make a dummy model or dummy data. -4. **Minimalistic**: Try to help the reader as much as you can to understand the issue as quickly as possible by staying as concise as possible. Remove all code / all information that is irrelevant to the issue. If you have found a bug, try to create the easiest code example you can to demonstrate your issue, do not just dump your whole workflow into the issue as soon as you have found a bug. E.g., if you train a model and get an error at some point during the training, you should first try to understand what part of the training code is responsible for the error and try to reproduce it with a couple of lines. Try to use dummy data instead of full datasets. -5. Add links. If you are referring to a certain naming, method, or model make sure to provide a link so that the reader can better understand what you mean. If you are referring to a specific PR or issue, make sure to link it to your issue. Do not assume that the reader knows what you are talking about. The more links you add to your issue the better. -6. Formatting. Make sure to nicely format your issue by formatting code into Python code syntax, and error messages into normal code syntax. See the [official GitHub formatting docs](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for more information. -7. Think of your issue not as a ticket to be solved, but rather as a beautiful entry to a well-written encyclopedia. Every added issue is a contribution to publicly available knowledge. By adding a nicely written issue you not only make it easier for maintainers to solve your issue, but you are helping the whole community to better understand a certain aspect of the library. - -## How to write a good PR - -1. Be a chameleon. Understand existing design patterns and syntax and make sure your code additions flow seamlessly into the existing code base. Pull requests that significantly diverge from existing design patterns or user interfaces will not be merged. -2. Be laser focused. A pull request should solve one problem and one problem only. Make sure to not fall into the trap of "also fixing another problem while we're adding it". It is much more difficult to review pull requests that solve multiple, unrelated problems at once. -3. If helpful, try to add a code snippet that displays an example of how your addition can be used. -4. The title of your pull request should be a summary of its contribution. -5. If your pull request addresses an issue, please mention the issue number in -the pull request description to make sure they are linked (and people -consulting the issue know you are working on it); -6. To indicate a work in progress please prefix the title with `[WIP]`. These -are useful to avoid duplicated work, and to differentiate it from PRs ready -to be merged; -7. Try to formulate and format your text as explained in [How to write a good issue](#how-to-write-a-good-issue). -8. Make sure existing tests pass; -9. Add high-coverage tests. No quality testing = no merge. -- If you are adding new `@slow` tests, make sure they pass using -`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. -CircleCI does not run the slow tests, but GitHub actions does every night! -10. All public methods must have informative docstrings that work nicely with markdown. See `[pipeline_latent_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)` for an example. -11. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like -[`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files. -If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images -to this dataset. - -## How to open a PR - -Before writing code, we strongly advise you to search through the existing PRs or -issues to make sure that nobody is already working on the same thing. If you are -unsure, it is always a good idea to open an issue to get some feedback. - -You will need basic `git` proficiency to be able to contribute to -🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest -manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro -Git](https://git-scm.com/book/en/v2) is a very good reference. - -Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)): - -1. Fork the [repository](https://github.com/huggingface/diffusers) by -clicking on the 'Fork' button on the repository's page. This creates a copy of the code -under your GitHub user account. - -2. Clone your fork to your local disk, and add the base repository as a remote: - - ```bash - $ git clone git@github.com: /diffusers.git - $ cd diffusers - $ git remote add upstream https://github.com/huggingface/diffusers.git - ``` - -3. Create a new branch to hold your development changes: - - ```bash - $ git checkout -b a-descriptive-name-for-my-changes - ``` - -**Do not** work on the `main` branch. - -4. Set up a development environment by running the following command in a virtual environment: - - ```bash - $ pip install -e ".[dev]" - ``` - -If you have already cloned the repo, you might need to `git pull` to get the most recent changes in the -library. - -5. Develop the features on your branch. - -As you work on the features, you should make sure that the test suite -passes. You should run the tests impacted by your changes like this: - - ```bash - $ pytest tests/ .py - ``` - -You can also run the full suite with the following command, but it takes -a beefy machine to produce a result in a decent amount of time now that -Diffusers has grown a lot. Here is the command for it: - - ```bash - $ make test - ``` - -🧨 Diffusers relies on `black` and `isort` to format its source code -consistently. After you make changes, apply automatic style corrections and code verifications -that can't be automated in one go with: - - ```bash - $ make style - ``` - -🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality -control runs in CI, however, you can also run the same checks with: - - ```bash - $ make quality - ``` - -Once you're happy with your changes, add changed files using `git add` and -make a commit with `git commit` to record your changes locally: - - ```bash - $ git add modified_file.py - $ git commit - ``` - -It is a good idea to sync your copy of the code with the original -repository regularly. This way you can quickly account for changes: - - ```bash - $ git pull upstream main - ``` - -Push the changes to your account using: - - ```bash - $ git push -u origin a-descriptive-name-for-my-changes - ``` - -6. Once you are satisfied, go to the -webpage of your fork on GitHub. Click on 'Pull request' to send your changes -to the project maintainers for review. - -7. It's ok if maintainers ask you for changes. It happens to core contributors -too! So everyone can see the changes in the Pull request, work in your local -branch and push the changes to your fork. They will automatically appear in -the pull request. - -### Tests - -An extensive test suite is included to test the library behavior and several examples. Library tests can be found in -the [tests folder](https://github.com/huggingface/diffusers/tree/main/tests). - -We like `pytest` and `pytest-xdist` because it's faster. From the root of the -repository, here's how to run tests with `pytest` for the library: - -```bash -$ python -m pytest -n auto --dist=loadfile -s -v ./tests/ -``` - -In fact, that's how `make test` is implemented! - -You can specify a smaller set of tests in order to test only the feature -you're working on. - -By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to -`yes` to run them. This will download many gigabytes of models — make sure you -have enough disk space and a good Internet connection, or a lot of patience! - -```bash -$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/ -``` - -`unittest` is fully supported, here's how to run tests with it: - -```bash -$ python -m unittest discover -s tests -t . -v -$ python -m unittest discover -s examples -t examples -v -``` - -### Syncing forked main with upstream (HuggingFace) main - -To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs, -when syncing the main branch of a forked repository, please, follow these steps: -1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. -2. If a PR is absolutely necessary, use the following steps after checking out your branch: -``` -$ git checkout -b your-branch-for-syncing -$ git pull --squash --no-commit upstream main -$ git commit -m ' ' -$ git push --set-upstream origin your-branch-for-syncing -``` - -### Style guide - -For documentation strings, 🧨 Diffusers follows the [google style](https://google.github.io/styleguide/pyguide.html). diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_outputs.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_outputs.py deleted file mode 100644 index 50cbd1d54ee403f2b8e79c8ada629b6b97b1be66..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_outputs.py +++ /dev/null @@ -1,60 +0,0 @@ -import unittest -from dataclasses import dataclass -from typing import List, Union - -import numpy as np -import PIL.Image - -from diffusers.utils.outputs import BaseOutput - - -@dataclass -class CustomOutput(BaseOutput): - images: Union[List[PIL.Image.Image], np.ndarray] - - -class ConfigTester(unittest.TestCase): - def test_outputs_single_attribute(self): - outputs = CustomOutput(images=np.random.rand(1, 3, 4, 4)) - - # check every way of getting the attribute - assert isinstance(outputs.images, np.ndarray) - assert outputs.images.shape == (1, 3, 4, 4) - assert isinstance(outputs["images"], np.ndarray) - assert outputs["images"].shape == (1, 3, 4, 4) - assert isinstance(outputs[0], np.ndarray) - assert outputs[0].shape == (1, 3, 4, 4) - - # test with a non-tensor attribute - outputs = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))]) - - # check every way of getting the attribute - assert isinstance(outputs.images, list) - assert isinstance(outputs.images[0], PIL.Image.Image) - assert isinstance(outputs["images"], list) - assert isinstance(outputs["images"][0], PIL.Image.Image) - assert isinstance(outputs[0], list) - assert isinstance(outputs[0][0], PIL.Image.Image) - - def test_outputs_dict_init(self): - # test output reinitialization with a `dict` for compatibility with `accelerate` - outputs = CustomOutput({"images": np.random.rand(1, 3, 4, 4)}) - - # check every way of getting the attribute - assert isinstance(outputs.images, np.ndarray) - assert outputs.images.shape == (1, 3, 4, 4) - assert isinstance(outputs["images"], np.ndarray) - assert outputs["images"].shape == (1, 3, 4, 4) - assert isinstance(outputs[0], np.ndarray) - assert outputs[0].shape == (1, 3, 4, 4) - - # test with a non-tensor attribute - outputs = CustomOutput({"images": [PIL.Image.new("RGB", (4, 4))]}) - - # check every way of getting the attribute - assert isinstance(outputs.images, list) - assert isinstance(outputs.images[0], PIL.Image.Image) - assert isinstance(outputs["images"], list) - assert isinstance(outputs["images"][0], PIL.Image.Image) - assert isinstance(outputs[0], list) - assert isinstance(outputs[0][0], PIL.Image.Image) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py deleted file mode 100644 index bcdc0b459d23e4392e66c5ea615c6c3ad3147ace..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py +++ /dev/null @@ -1,10 +0,0 @@ -_base_ = [ - '../_base_/models/deeplabv3_r50-d8.py', - '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py', - '../_base_/schedules/schedule_80k.py' -] -model = dict( - decode_head=dict(num_classes=59), - auxiliary_head=dict(num_classes=59), - test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320))) -optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001) diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/fp16_utils.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/fp16_utils.py deleted file mode 100644 index 1981011d6859192e3e663e29d13500d56ba47f6c..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/fp16_utils.py +++ /dev/null @@ -1,410 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import functools -import warnings -from collections import abc -from inspect import getfullargspec - -import numpy as np -import torch -import torch.nn as nn - -from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version -from .dist_utils import allreduce_grads as _allreduce_grads - -try: - # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported - # and used; otherwise, auto fp16 will adopt mmcv's implementation. - # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16 - # manually, so the behavior may not be consistent with real amp. - from torch.cuda.amp import autocast -except ImportError: - pass - - -def cast_tensor_type(inputs, src_type, dst_type): - """Recursively convert Tensor in inputs from src_type to dst_type. - - Args: - inputs: Inputs that to be casted. - src_type (torch.dtype): Source type.. - dst_type (torch.dtype): Destination type. - - Returns: - The same type with inputs, but all contained Tensors have been cast. - """ - if isinstance(inputs, nn.Module): - return inputs - elif isinstance(inputs, torch.Tensor): - return inputs.to(dst_type) - elif isinstance(inputs, str): - return inputs - elif isinstance(inputs, np.ndarray): - return inputs - elif isinstance(inputs, abc.Mapping): - return type(inputs)({ - k: cast_tensor_type(v, src_type, dst_type) - for k, v in inputs.items() - }) - elif isinstance(inputs, abc.Iterable): - return type(inputs)( - cast_tensor_type(item, src_type, dst_type) for item in inputs) - else: - return inputs - - -def auto_fp16(apply_to=None, out_fp32=False): - """Decorator to enable fp16 training automatically. - - This decorator is useful when you write custom modules and want to support - mixed precision training. If inputs arguments are fp32 tensors, they will - be converted to fp16 automatically. Arguments other than fp32 tensors are - ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the - backend, otherwise, original mmcv implementation will be adopted. - - Args: - apply_to (Iterable, optional): The argument names to be converted. - `None` indicates all arguments. - out_fp32 (bool): Whether to convert the output back to fp32. - - Example: - - >>> import torch.nn as nn - >>> class MyModule1(nn.Module): - >>> - >>> # Convert x and y to fp16 - >>> @auto_fp16() - >>> def forward(self, x, y): - >>> pass - - >>> import torch.nn as nn - >>> class MyModule2(nn.Module): - >>> - >>> # convert pred to fp16 - >>> @auto_fp16(apply_to=('pred', )) - >>> def do_something(self, pred, others): - >>> pass - """ - - def auto_fp16_wrapper(old_func): - - @functools.wraps(old_func) - def new_func(*args, **kwargs): - # check if the module has set the attribute `fp16_enabled`, if not, - # just fallback to the original method. - if not isinstance(args[0], torch.nn.Module): - raise TypeError('@auto_fp16 can only be used to decorate the ' - 'method of nn.Module') - if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled): - return old_func(*args, **kwargs) - - # get the arg spec of the decorated method - args_info = getfullargspec(old_func) - # get the argument names to be casted - args_to_cast = args_info.args if apply_to is None else apply_to - # convert the args that need to be processed - new_args = [] - # NOTE: default args are not taken into consideration - if args: - arg_names = args_info.args[:len(args)] - for i, arg_name in enumerate(arg_names): - if arg_name in args_to_cast: - new_args.append( - cast_tensor_type(args[i], torch.float, torch.half)) - else: - new_args.append(args[i]) - # convert the kwargs that need to be processed - new_kwargs = {} - if kwargs: - for arg_name, arg_value in kwargs.items(): - if arg_name in args_to_cast: - new_kwargs[arg_name] = cast_tensor_type( - arg_value, torch.float, torch.half) - else: - new_kwargs[arg_name] = arg_value - # apply converted arguments to the decorated method - if (TORCH_VERSION != 'parrots' and - digit_version(TORCH_VERSION) >= digit_version('1.6.0')): - with autocast(enabled=True): - output = old_func(*new_args, **new_kwargs) - else: - output = old_func(*new_args, **new_kwargs) - # cast the results back to fp32 if necessary - if out_fp32: - output = cast_tensor_type(output, torch.half, torch.float) - return output - - return new_func - - return auto_fp16_wrapper - - -def force_fp32(apply_to=None, out_fp16=False): - """Decorator to convert input arguments to fp32 in force. - - This decorator is useful when you write custom modules and want to support - mixed precision training. If there are some inputs that must be processed - in fp32 mode, then this decorator can handle it. If inputs arguments are - fp16 tensors, they will be converted to fp32 automatically. Arguments other - than fp16 tensors are ignored. If you are using PyTorch >= 1.6, - torch.cuda.amp is used as the backend, otherwise, original mmcv - implementation will be adopted. - - Args: - apply_to (Iterable, optional): The argument names to be converted. - `None` indicates all arguments. - out_fp16 (bool): Whether to convert the output back to fp16. - - Example: - - >>> import torch.nn as nn - >>> class MyModule1(nn.Module): - >>> - >>> # Convert x and y to fp32 - >>> @force_fp32() - >>> def loss(self, x, y): - >>> pass - - >>> import torch.nn as nn - >>> class MyModule2(nn.Module): - >>> - >>> # convert pred to fp32 - >>> @force_fp32(apply_to=('pred', )) - >>> def post_process(self, pred, others): - >>> pass - """ - - def force_fp32_wrapper(old_func): - - @functools.wraps(old_func) - def new_func(*args, **kwargs): - # check if the module has set the attribute `fp16_enabled`, if not, - # just fallback to the original method. - if not isinstance(args[0], torch.nn.Module): - raise TypeError('@force_fp32 can only be used to decorate the ' - 'method of nn.Module') - if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled): - return old_func(*args, **kwargs) - # get the arg spec of the decorated method - args_info = getfullargspec(old_func) - # get the argument names to be casted - args_to_cast = args_info.args if apply_to is None else apply_to - # convert the args that need to be processed - new_args = [] - if args: - arg_names = args_info.args[:len(args)] - for i, arg_name in enumerate(arg_names): - if arg_name in args_to_cast: - new_args.append( - cast_tensor_type(args[i], torch.half, torch.float)) - else: - new_args.append(args[i]) - # convert the kwargs that need to be processed - new_kwargs = dict() - if kwargs: - for arg_name, arg_value in kwargs.items(): - if arg_name in args_to_cast: - new_kwargs[arg_name] = cast_tensor_type( - arg_value, torch.half, torch.float) - else: - new_kwargs[arg_name] = arg_value - # apply converted arguments to the decorated method - if (TORCH_VERSION != 'parrots' and - digit_version(TORCH_VERSION) >= digit_version('1.6.0')): - with autocast(enabled=False): - output = old_func(*new_args, **new_kwargs) - else: - output = old_func(*new_args, **new_kwargs) - # cast the results back to fp32 if necessary - if out_fp16: - output = cast_tensor_type(output, torch.float, torch.half) - return output - - return new_func - - return force_fp32_wrapper - - -def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): - warnings.warning( - '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be ' - 'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads') - _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb) - - -def wrap_fp16_model(model): - """Wrap the FP32 model to FP16. - - If you are using PyTorch >= 1.6, torch.cuda.amp is used as the - backend, otherwise, original mmcv implementation will be adopted. - - For PyTorch >= 1.6, this function will - 1. Set fp16 flag inside the model to True. - - Otherwise: - 1. Convert FP32 model to FP16. - 2. Remain some necessary layers to be FP32, e.g., normalization layers. - 3. Set `fp16_enabled` flag inside the model to True. - - Args: - model (nn.Module): Model in FP32. - """ - if (TORCH_VERSION == 'parrots' - or digit_version(TORCH_VERSION) < digit_version('1.6.0')): - # convert model to fp16 - model.half() - # patch the normalization layers to make it work in fp32 mode - patch_norm_fp32(model) - # set `fp16_enabled` flag - for m in model.modules(): - if hasattr(m, 'fp16_enabled'): - m.fp16_enabled = True - - -def patch_norm_fp32(module): - """Recursively convert normalization layers from FP16 to FP32. - - Args: - module (nn.Module): The modules to be converted in FP16. - - Returns: - nn.Module: The converted module, the normalization layers have been - converted to FP32. - """ - if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)): - module.float() - if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3': - module.forward = patch_forward_method(module.forward, torch.half, - torch.float) - for child in module.children(): - patch_norm_fp32(child) - return module - - -def patch_forward_method(func, src_type, dst_type, convert_output=True): - """Patch the forward method of a module. - - Args: - func (callable): The original forward method. - src_type (torch.dtype): Type of input arguments to be converted from. - dst_type (torch.dtype): Type of input arguments to be converted to. - convert_output (bool): Whether to convert the output back to src_type. - - Returns: - callable: The patched forward method. - """ - - def new_forward(*args, **kwargs): - output = func(*cast_tensor_type(args, src_type, dst_type), - **cast_tensor_type(kwargs, src_type, dst_type)) - if convert_output: - output = cast_tensor_type(output, dst_type, src_type) - return output - - return new_forward - - -class LossScaler: - """Class that manages loss scaling in mixed precision training which - supports both dynamic or static mode. - - The implementation refers to - https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py. - Indirectly, by supplying ``mode='dynamic'`` for dynamic loss scaling. - It's important to understand how :class:`LossScaler` operates. - Loss scaling is designed to combat the problem of underflowing - gradients encountered at long times when training fp16 networks. - Dynamic loss scaling begins by attempting a very high loss - scale. Ironically, this may result in OVERflowing gradients. - If overflowing gradients are encountered, :class:`FP16_Optimizer` then - skips the update step for this particular iteration/minibatch, - and :class:`LossScaler` adjusts the loss scale to a lower value. - If a certain number of iterations occur without overflowing gradients - detected,:class:`LossScaler` increases the loss scale once more. - In this way :class:`LossScaler` attempts to "ride the edge" of always - using the highest loss scale possible without incurring overflow. - - Args: - init_scale (float): Initial loss scale value, default: 2**32. - scale_factor (float): Factor used when adjusting the loss scale. - Default: 2. - mode (str): Loss scaling mode. 'dynamic' or 'static' - scale_window (int): Number of consecutive iterations without an - overflow to wait before increasing the loss scale. Default: 1000. - """ - - def __init__(self, - init_scale=2**32, - mode='dynamic', - scale_factor=2., - scale_window=1000): - self.cur_scale = init_scale - self.cur_iter = 0 - assert mode in ('dynamic', - 'static'), 'mode can only be dynamic or static' - self.mode = mode - self.last_overflow_iter = -1 - self.scale_factor = scale_factor - self.scale_window = scale_window - - def has_overflow(self, params): - """Check if params contain overflow.""" - if self.mode != 'dynamic': - return False - for p in params: - if p.grad is not None and LossScaler._has_inf_or_nan(p.grad.data): - return True - return False - - def _has_inf_or_nan(x): - """Check if params contain NaN.""" - try: - cpu_sum = float(x.float().sum()) - except RuntimeError as instance: - if 'value cannot be converted' not in instance.args[0]: - raise - return True - else: - if cpu_sum == float('inf') or cpu_sum == -float('inf') \ - or cpu_sum != cpu_sum: - return True - return False - - def update_scale(self, overflow): - """update the current loss scale value when overflow happens.""" - if self.mode != 'dynamic': - return - if overflow: - self.cur_scale = max(self.cur_scale / self.scale_factor, 1) - self.last_overflow_iter = self.cur_iter - else: - if (self.cur_iter - self.last_overflow_iter) % \ - self.scale_window == 0: - self.cur_scale *= self.scale_factor - self.cur_iter += 1 - - def state_dict(self): - """Returns the state of the scaler as a :class:`dict`.""" - return dict( - cur_scale=self.cur_scale, - cur_iter=self.cur_iter, - mode=self.mode, - last_overflow_iter=self.last_overflow_iter, - scale_factor=self.scale_factor, - scale_window=self.scale_window) - - def load_state_dict(self, state_dict): - """Loads the loss_scaler state dict. - - Args: - state_dict (dict): scaler state. - """ - self.cur_scale = state_dict['cur_scale'] - self.cur_iter = state_dict['cur_iter'] - self.mode = state_dict['mode'] - self.last_overflow_iter = state_dict['last_overflow_iter'] - self.scale_factor = state_dict['scale_factor'] - self.scale_window = state_dict['scale_window'] - - @property - def loss_scale(self): - return self.cur_scale diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/__main__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/__main__.py deleted file mode 100644 index 90cafd93426f6fb2e8ad57b140b7ae163a67a4a4..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/__main__.py +++ /dev/null @@ -1,17 +0,0 @@ -""" - pygments.__main__ - ~~~~~~~~~~~~~~~~~ - - Main entry point for ``python -m pygments``. - - :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import sys -from pip._vendor.pygments.cmdline import main - -try: - sys.exit(main(sys.argv)) -except KeyboardInterrupt: - sys.exit(1) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_compat.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_compat.py deleted file mode 100644 index cb9fc820cb352aa6e92705aab4f55cbc2eff96bc..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_compat.py +++ /dev/null @@ -1,98 +0,0 @@ -# flake8: noqa - -import abc -import sys -import pathlib -from contextlib import suppress - -if sys.version_info >= (3, 10): - from zipfile import Path as ZipPath # type: ignore -else: - from ..zipp import Path as ZipPath # type: ignore - - -try: - from typing import runtime_checkable # type: ignore -except ImportError: - - def runtime_checkable(cls): # type: ignore - return cls - - -try: - from typing import Protocol # type: ignore -except ImportError: - Protocol = abc.ABC # type: ignore - - -class TraversableResourcesLoader: - """ - Adapt loaders to provide TraversableResources and other - compatibility. - - Used primarily for Python 3.9 and earlier where the native - loaders do not yet implement TraversableResources. - """ - - def __init__(self, spec): - self.spec = spec - - @property - def path(self): - return self.spec.origin - - def get_resource_reader(self, name): - from . import readers, _adapters - - def _zip_reader(spec): - with suppress(AttributeError): - return readers.ZipReader(spec.loader, spec.name) - - def _namespace_reader(spec): - with suppress(AttributeError, ValueError): - return readers.NamespaceReader(spec.submodule_search_locations) - - def _available_reader(spec): - with suppress(AttributeError): - return spec.loader.get_resource_reader(spec.name) - - def _native_reader(spec): - reader = _available_reader(spec) - return reader if hasattr(reader, 'files') else None - - def _file_reader(spec): - try: - path = pathlib.Path(self.path) - except TypeError: - return None - if path.exists(): - return readers.FileReader(self) - - return ( - # native reader if it supplies 'files' - _native_reader(self.spec) - or - # local ZipReader if a zip module - _zip_reader(self.spec) - or - # local NamespaceReader if a namespace module - _namespace_reader(self.spec) - or - # local FileReader - _file_reader(self.spec) - # fallback - adapt the spec ResourceReader to TraversableReader - or _adapters.CompatibilityFiles(self.spec) - ) - - -def wrap_spec(package): - """ - Construct a package spec with traversable compatibility - on the spec/loader/reader. - - Supersedes _adapters.wrap_spec to use TraversableResourcesLoader - from above for older Python compatibility (<3.10). - """ - from . import _adapters - - return _adapters.SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader) diff --git a/spaces/Atualli/yoloxTeste/README.md b/spaces/Atualli/yoloxTeste/README.md deleted file mode 100644 index 6e0722c7a8e16db26884f25b6f3f1aefbcfaead2..0000000000000000000000000000000000000000 --- a/spaces/Atualli/yoloxTeste/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: YOLOX is a high-performance anchor-free YOLO. -emoji: 🌖 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: Atualli/yolox2 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Awiny/Image2Paragraph/models/gpt_model.py b/spaces/Awiny/Image2Paragraph/models/gpt_model.py deleted file mode 100644 index 3e0ce2cf6a161e25f0409e60be03a8ba3c5db40b..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/gpt_model.py +++ /dev/null @@ -1,44 +0,0 @@ -import openai - -class ImageToText: - def __init__(self, api_key, gpt_version="gpt-3.5-turbo"): - self.template = self.initialize_template() - openai.api_key = api_key - self.gpt_version = gpt_version - - def initialize_template(self): - prompt_prefix_1 = """Generate only an informative and nature paragraph based on the given information(a,b,c,d):\n""" - prompt_prefix_2 = """\n a. Image Resolution: """ - prompt_prefix_3 = """\n b. Image Caption: """ - prompt_prefix_4 = """\n c. Dense Caption: """ - prompt_prefix_5 = """\n d. Region Semantic: """ - prompt_suffix = """\n There are some rules: - Show object, color and position. - Use nouns rather than coordinates to show position information of each object. - No more than 7 sentences. - Only use one paragraph. - Describe position of each object. - Do not appear number. - """ - template = f"{prompt_prefix_1}{prompt_prefix_2}{{width}}X{{height}}{prompt_prefix_3}{{caption}}{prompt_prefix_4}{{dense_caption}}{prompt_prefix_5}{{region_semantic}}{prompt_suffix}" - return template - - def paragraph_summary_with_gpt(self, caption, dense_caption, region_semantic, width, height): - question = self.template.format(width=width, height=height, caption=caption, dense_caption=dense_caption, region_semantic=region_semantic) - print('\033[1;35m' + '*' * 100 + '\033[0m') - print('\nStep4, Paragraph Summary with GPT-3:') - print('\033[1;34m' + "Question:".ljust(10) + '\033[1;36m' + question + '\033[0m') - completion = openai.ChatCompletion.create( - model=self.gpt_version, - messages = [ - {"role": "user", "content" : question}] - ) - - print('\033[1;34m' + "ChatGPT Response:".ljust(18) + '\033[1;32m' + completion['choices'][0]['message']['content'] + '\033[0m') - print('\033[1;35m' + '*' * 100 + '\033[0m') - return completion['choices'][0]['message']['content'] - - def paragraph_summary_with_gpt_debug(self, caption, dense_caption, width, height): - question = self.template.format(width=width, height=height, caption=caption, dense_caption=dense_caption) - print("paragraph_summary_with_gpt_debug:") - return question diff --git a/spaces/BIASLab/sars-cov-2-classification-fcgr/src/preprocessing.py b/spaces/BIASLab/sars-cov-2-classification-fcgr/src/preprocessing.py deleted file mode 100644 index 259a4231db2aa2a2f625ca3f303028876083075d..0000000000000000000000000000000000000000 --- a/spaces/BIASLab/sars-cov-2-classification-fcgr/src/preprocessing.py +++ /dev/null @@ -1,15 +0,0 @@ -"""All functions that can be applied as preprocessing""" -from .pipeline import ( - register_in_pipeline, # decorator to make available a function to use with Pipeline class - Pipeline, -) - -@register_in_pipeline -def divide_by_max(npy,): - "The input npy divided by his maximum value" - return npy / npy.max() - -@register_in_pipeline -def divide_by_sum(npy,): - "The input npy divided by the sum of their values" - return npy / npy.sum() \ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk Guardar Tiktok.md b/spaces/Benson/text-generation/Examples/Descargar Apk Guardar Tiktok.md deleted file mode 100644 index 49f100e3246f90eb441371713d928ad7418968e5..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Apk Guardar Tiktok.md +++ /dev/null @@ -1,114 +0,0 @@ - - Cómo descargar APK Save TikTok - Una guía completa
-TikTok es una de las plataformas de redes sociales más populares del mundo, con más de mil millones de usuarios. Te permite crear y compartir videos cortos con música, filtros, pegatinas y más. Pero ¿qué pasa si desea guardar sus videos favoritos de TikTok y música sin conexión, o compartirlos con sus amigos en otras plataformas? En este artículo, le mostraremos cómo descargar apk save tiktok en dispositivos Android e iOS, utilizando diferentes métodos y aplicaciones. Podrás disfrutar de tu contenido TikTok en cualquier momento, en cualquier lugar, sin marca de agua o limitación.
-descargar apk guardar tiktok
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-Qué es TikTok y por qué necesitas guardarlo
-TikTok - Una plataforma popular de medios sociales
-TikTok es una aplicación para compartir vídeos lanzada en 2016 por ByteDance, una empresa china. Originalmente se llamaba Douyin en China, pero más tarde se renombró como TikTok para el mercado internacional. TikTok permite a los usuarios crear videos cortos de hasta 60 segundos, con varias herramientas de edición, efectos, filtros, pegatinas, música y sonidos. Los usuarios también pueden navegar y ver videos de otros creadores, seguir sus cuentas favoritas, comentar y compartir. TikTok tiene una variedad de categorías y géneros, como comedia, danza, belleza, moda, deportes, educación, juegos y más.
-Beneficios de guardar vídeos y música de TikTok
-Hay muchas razones por las que es posible que desee descargar apk save tiktok en su dispositivo. Estos son algunos de ellos:
--
-
- Puedes ver tus videos favoritos de TikTok sin conexión a Internet ni uso de datos. -
- Puedes compartir tus videos y música descargados de TikTok con tus amigos en otras plataformas, como WhatsApp, Instagram, Facebook, YouTube, etc. -
- Puede editar sus videos y música descargados de TikTok con otras aplicaciones, como InShot, VivaVideo, Kinemaster, etc. -
- Puede realizar copias de seguridad de sus vídeos y música descargados de TikTok en su almacenamiento en la nube o en el ordenador. - -
Cómo descargar APK Save TikTok en Android
-Método 1: Uso de la aplicación sssTikTok.io
-Características de la aplicación sssTikTok.io
-sssTikTok.io es una aplicación gratuita que te permite descargar vídeos tiktok sin marca de agua apk para Android. Tiene las siguientes características:
- --
-
- Soporta la descarga de archivos MP4 y MP3 desde TikTok. -
- Tiene un reproductor de vídeo y audio incorporado. -
- Tiene un organizador fácil de usar para encontrar y administrar sus archivos. -
- Tiene una función de compartir para enviar sus archivos con correo electrónico o mensajero. -
Pasos para descargar vídeos de TikTok y música con la aplicación sssTikTok.io
-Para utilizar esta aplicación, siga estos pasos:
--
-
- Instalar la aplicación de Google Play Store o haga clic en para descargarla desde el sitio web oficial. -
- Abra la aplicación TikTok y encuentre el video o la música que desea descargar. -
- Copie el enlace del video o la música tocando el icono de compartir y luego en la opción de enlace de copia. -
- Abra la aplicación sssTikTok.io y pegue el enlace en el cuadro de búsqueda. -
- Toque en el botón de descarga y elija el formato y la calidad que desee. -
- Espere a que la descarga termine y disfrute de su archivo. -
Método 2: Usando la aplicación Uptodown
-Características de la aplicación Uptodown
-Uptodown es una tienda de aplicaciones que le permite descargar apk save tiktok para Android. Tiene las siguientes características:
--
-
- Ofrece un enorme catálogo de aplicaciones y juegos, incluyendo TikTok. -
- Proporciona descargas seguras y verificadas, sin virus ni malware. -
- Actualiza las aplicaciones automáticamente, sin restricciones o limitaciones. -
- Tiene una interfaz fácil de usar y una velocidad de descarga rápida. -
Pasos para descargar TikTok APK con Uptodown App
-Para utilizar esta aplicación, siga estos pasos:
--
-
-
- Abra la aplicación y busque TikTok en el cuadro de búsqueda. -
- Seleccione la aplicación TikTok de los resultados y toque en el botón de descarga. -
- Espere a que la descarga termine e instale el apk TikTok en su dispositivo. -
- Abra la aplicación TikTok y disfrute de sus vídeos y música. -
Cómo descargar APK Save TikTok en iOS
-Método 1: Uso de documentos por Readdle App
-Características de los documentos por Readdle App
-Documents by Readdle es una aplicación de gestión de archivos que te permite descargar vídeos tiktok sin marca de agua ios. Tiene las siguientes características:
--
-
- Soporta la descarga de vídeos, música, imágenes, documentos y más desde cualquier sitio web. -
- Tiene un navegador incorporado, reproductor de vídeo y audio, lector de PDF y extractor de zip. -
- Tiene una integración en la nube con Dropbox, iCloud, Google Drive, y más. -
- Tiene una función de transferencia de archivos para enviar sus archivos con Wi-Fi o Bluetooth. -
Pasos para descargar vídeos de TikTok y música con documentos por Readdle App
-Para utilizar esta aplicación, siga estos pasos:
--
-
- Instale la aplicación desde App Store o haga clic en here para descargarla desde el sitio web oficial. -
- Abra la aplicación y toque en el icono del navegador en la esquina inferior derecha. -
- Escriba https://ssstik.io/ en la barra de direcciones y vaya al sitio web. -
- Abra la aplicación TikTok y encuentre el video o la música que desea descargar. -
- Copie el enlace del video o la música tocando el icono de compartir y luego en la opción de enlace de copia. -
- Pegue el enlace en el sitio web ssstik.io y toque en descargar. -
- Seleccione el formato y la calidad que desea y toque en descargar de nuevo. -
- Cambia el nombre del archivo si quieres y toca en hecho. -
- Su archivo se guardará en la carpeta Descargas de la aplicación Documentos. Puede reproducirlo, compartirlo o moverlo como desee. -
Atajos es una aplicación que te permite crear y ejecutar acciones personalizadas en tu dispositivo iOS. Tiene las siguientes características:
--
-
- Ofrece una galería de accesos directos ya preparados para diversas tareas, como la descarga de vídeos, la fabricación de GIF, el escaneo de códigos QR, etc. -
- Le permite crear sus propios accesos directos con arrastrar y soltar pasos. -
- Se integra con Siri, widgets y hoja de acciones. -
- Sincroniza los accesos directos a través de sus dispositivos con iCloud. -
Pasos para descargar vídeos de TikTok y música con la aplicación de accesos directos
-Para utilizar esta aplicación, siga estos pasos:
--
-
- Instale la aplicación desde App Store o haga clic en here para descargarla desde el sitio web oficial. -
- Abra la aplicación y toque en el icono de la galería en la esquina inferior derecha. -
- Buscar TikTok Downloader en el cuadro de búsqueda y pulse sobre él. -
- Toque en Obtener acceso directo y agregarlo a su biblioteca. -
- Abra la aplicación TikTok y encuentre el video o la música que desea descargar. -
- Copie el enlace del video o la música tocando el icono de compartir y luego en la opción de enlace de copia. -
- Abra la aplicación de accesos directos y ejecute el acceso directo TikTok Downloader desde su biblioteca. -
- Seleccione el formato y la calidad que desea y toque en descargar. -
- Su archivo se guardará en la aplicación Fotos. Puede reproducirlo, compartirlo o editarlo como desee. -
Conclusión
-En este artículo, le hemos mostrado cómo descargar apk save tiktok en dispositivos Android e iOS, utilizando diferentes métodos y aplicaciones. Puede elegir el que se adapte a sus necesidades y preferencias, y disfrutar de sus vídeos TikTok y música sin conexión, sin ninguna marca de agua o limitación. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Feliz TikToking!
-Preguntas frecuentes
-P: ¿Es legal descargar videos y música de TikTok?
- -Q: ¿Cómo puedo descargar videos y música TikTok sin ninguna aplicación?
-A: Puede utilizar algunas herramientas en línea o sitios web que le permiten descargar videos y música TikTok sin ninguna aplicación. Por ejemplo, puede usar https://snaptik.app/, https://ttdownloader.com/, https://tiktokdownloader.net/, etc. Todo lo que necesita hacer es copiar y pegar el enlace del video o música que desea descargar, elegir el formato y la calidad que desea y hacer clic en descargar. Sin embargo, estas herramientas o sitios web en línea pueden no ser tan fiables, seguros o rápidos como las aplicaciones que mencionamos anteriormente.
-Q: ¿Cómo puedo quitar la marca de agua de los vídeos de TikTok?
-A: La forma más fácil de eliminar la marca de agua de vídeos TikTok es utilizar una aplicación que descarga vídeos TikTok sin marca de agua apk para Android o iOS. Por ejemplo, puede usar la aplicación sssTikTok.io o Documents by Readdle app, como explicamos anteriormente. Alternativamente, puede utilizar algunas aplicaciones de edición de vídeo que le permiten recortar o difuminar la marca de agua de los vídeos TikTok. Por ejemplo, puedes usar InShot, VivaVideo, Kinemaster, etc.
-Q: ¿Cómo puedo descargar vídeos de TikTok con sonido?
-A: Para descargar vídeos TikTok con sonido, debe elegir el formato MP4 al descargar. El formato MP4 incluye tanto pistas de vídeo como de audio en un archivo. Si elige el formato MP3, solo obtendrá la pista de audio del vídeo TikTok. Algunas aplicaciones también pueden permitirle elegir entre sonido original o sonido musical al descargar videos TikTok con sonido.
-Q: ¿Cómo puedo descargar videos en vivo de TikTok?
-A: Para descargar vídeos en vivo de TikTok, debe usar una aplicación que admita la grabación en vivo. Por ejemplo, puede usar la aplicación Screen Recorder para Android o iOS. Esta aplicación te permite grabar tu pantalla mientras ves videos TikTok en vivo y guardarlos como archivos de video en tu dispositivo. Sin embargo, este método puede no ser tan eficiente, conveniente o de alta calidad como los otros métodos que mencionamos anteriormente.
64aa2da5cf
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\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Gran Gangster Hroe De La Cuerda Ciudad Mod Apk.md b/spaces/Benson/text-generation/Examples/Descargar Gran Gangster Hroe De La Cuerda Ciudad Mod Apk.md deleted file mode 100644 index e3da98e032baa15150c829312a3da98c0c23d96f..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Gran Gangster Hroe De La Cuerda Ciudad Mod Apk.md +++ /dev/null @@ -1,48 +0,0 @@ - -Descargar Gran Gangster Cuerda Héroe Ciudad Mod APK
-Si estás buscando un juego lleno de acción que te permita explorar un vasto mundo abierto, luchar contra los enemigos y usar armas y vehículos increíbles, entonces deberías probar Grand Gangster Rope Hero City. Este juego es una combinación de superhéroe, simulador de crimen y géneros de juegos de disparos que te mantendrán entretenido durante horas. Y si quieres hacer el juego aún más divertido y emocionante, usted debe descargar Grand Gangster Rope Hero City Mod APK, que le da dinero ilimitado, sin anuncios, y el acceso a todas las armas y vehículos en el juego. En este artículo, te contaremos más sobre este juego y cómo descargar e instalar la versión apk mod en tu dispositivo Android.
-descargar gran gangster héroe de la cuerda ciudad mod apk
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-Introducción
-¿Qué es Grand Gangster Rope Hero City?
-Grand Gangster Rope Hero City es un juego desarrollado por Game Zee que fue lanzado en 2020. Tiene más de 10 millones de descargas en Google Play Store y tiene una calificación de 4.1 de 5 estrellas. El juego se desarrolla en una ciudad ficticia llamada Las Vegas, donde juegas como un héroe de cuerda que tiene superpoderes y puede usar una cuerda para girar alrededor de la ciudad. También puedes usar varias armas y vehículos para luchar contra pandillas, policías y otros enemigos. El juego tiene muchas misiones y actividades para completar, como robar bancos, robar coches, rescatar rehenes y más. También puedes personalizar la apariencia y las habilidades de tu personaje según tu preferencia.
-¿Por qué descargar Grand Gangster Rope Hero City Mod APK?
- -Características de Grand Gangster Rope Hero City Mod APK
-Dinero ilimitado
-Con Grand Gangster Rope Hero City Mod APK, obtendrá dinero ilimitado en el juego. Puedes usar este dinero para comprar cualquier arma, vehículo, atuendo o artículo que quieras en el juego. También puede mejorar sus habilidades y habilidades con este dinero. Ya no tienes que preocuparte por quedarte sin dinero o completar misiones para ganar dinero.
-No hay anuncios
-Otra característica de Grand Gangster Rope Hero City Mod APK es que elimina todos los anuncios del juego. No tienes que ver ningún anuncio de video o banner para obtener algunas recompensas o bonos en el juego. Puedes jugar sin distracciones ni interrupciones.
- -Desbloquear todas las armas y vehículos
-Gran Gangster Cuerda Héroe Ciudad Mod APK también desbloquea todas las armas y vehículos en el juego. Puedes usar cualquier arma o vehículo que quieras en el juego sin tener que comprarlos o desbloquearlos completando misiones. Puede elegir entre una variedad de armas, como pistolas, rifles, escopetas, granadas, lanzacohetes y más. También puede elegir entre una variedad de vehículos, como coches, bicicletas, helicópteros, tanques y más. También puedes usar tu cuerda para girar por la ciudad y realizar acrobacias increíbles.
-Gráficos y efectos de sonido de alta calidad
-Gran Gangster Cuerda Héroe Ciudad Mod APK también mejora los gráficos y efectos de sonido del juego. El juego tiene gráficos 3D de alta calidad que hacen que la ciudad se vea realista y detallada. El juego también tiene efectos de sonido realistas que hacen que el juego sea más inmersivo y emocionante. Puede escuchar los sonidos de disparos, explosiones, motores de automóviles, sirenas y más.
-¿Cómo descargar e instalar Grand Gangster Rope Hero City Mod APK?
-Si desea descargar e instalar Grand Gangster Rope Hero City Mod APK en su dispositivo Android, es necesario seguir estos sencillos pasos:
-Paso 1: Habilitar fuentes desconocidas
- -Paso 2: Descargar el archivo apk mod
-Siguiente, es necesario descargar el archivo apk mod de una fuente confiable. Puede utilizar el enlace a continuación para descargar la última versión de Grand Gangster Rope Hero City Mod APK. El tamaño del archivo es de unos 100 MB, así que asegúrate de tener suficiente espacio de almacenamiento en tu dispositivo.
-Descargar Grand Gangster Rope Hero City Mod APK
-Paso 3: Instalar el archivo apk mod
-Después de haber descargado el archivo apk mod, necesita instalarlo en su dispositivo. Para hacer esto, busque el archivo en su administrador de archivos y toque en él. Luego, siga las instrucciones en la pantalla para completar el proceso de instalación.
-Paso 4: Disfruta del juego
-Una vez que haya instalado el archivo apk mod, puede disfrutar del juego con todas las características desbloqueadas. Puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y empezar a jugar.
-Conclusión
-Grand Gangster Rope Hero City es un juego divertido y emocionante que te permite experimentar la vida de un héroe de cuerda en una ciudad llena de crímenes. Puedes usar tus superpoderes, armas y vehículos para luchar contra enemigos y completar misiones. También puedes explorar la ciudad y personalizar tu personaje como quieras. Y si quieres hacer el juego aún más agradable, usted debe descargar Grand Gangster Rope Hero City Mod APK, que le da dinero ilimitado, sin anuncios, y el acceso a todas las armas y vehículos en el juego. Entonces, ¿qué estás esperando? Descargar Grand Gangster Rope Hero City Mod APK ahora y divertirse!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Grand Gangster Rope Hero City Mod APK:
--
-
- ¿Es Grand Gangster Rope Hero City Mod APK seguro de usar? -
- ¿Necesito rootear mi dispositivo para usar Grand Gangster Rope Hero City Mod APK? - -
- Gran gangster cuerda héroe ciudad Mod APK trabajar en mi dispositivo? -
- ¿Puedo jugar Grand Gangster Rope Hero City en línea con otros jugadores? -
- ¿Puedo actualizar Grand Gangster Rope Hero City Mod APK? -
Sí, Grand Gangster Rope Hero City Mod APK es seguro de usar, siempre y cuando se descarga de una fuente de confianza. El archivo apk mod no contiene ningún virus o malware que pueda dañar su dispositivo o datos.
-Grand Gangster Rope Hero City Mod APK funciona en la mayoría de los dispositivos Android que tienen Android 4.4 o versiones superiores. Sin embargo, algunos dispositivos pueden no ser compatibles con el archivo apk mod debido a diferentes especificaciones o configuraciones.
-No, Grand Gangster Rope Hero City es un juego fuera de línea que no requiere una conexión a Internet para jugar. Puedes jugar el juego solo o con aliados y enemigos controlados por la IA.
-No, no puede actualizar Grand Gangster Rope Hero City Mod APK desde Google Play Store o cualquier otra fuente. Si desea obtener la última versión del juego, es necesario descargar e instalar el nuevo archivo apk mod manualmente.
-
-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/spinners.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/spinners.py deleted file mode 100644 index cf2b976f377c2656afb3d84add8d30b0fc280c03..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/spinners.py +++ /dev/null @@ -1,159 +0,0 @@ -import contextlib -import itertools -import logging -import sys -import time -from typing import IO, Generator, Optional - -from pip._internal.utils.compat import WINDOWS -from pip._internal.utils.logging import get_indentation - -logger = logging.getLogger(__name__) - - -class SpinnerInterface: - def spin(self) -> None: - raise NotImplementedError() - - def finish(self, final_status: str) -> None: - raise NotImplementedError() - - -class InteractiveSpinner(SpinnerInterface): - def __init__( - self, - message: str, - file: Optional[IO[str]] = None, - spin_chars: str = "-\\|/", - # Empirically, 8 updates/second looks nice - min_update_interval_seconds: float = 0.125, - ): - self._message = message - if file is None: - file = sys.stdout - self._file = file - self._rate_limiter = RateLimiter(min_update_interval_seconds) - self._finished = False - - self._spin_cycle = itertools.cycle(spin_chars) - - self._file.write(" " * get_indentation() + self._message + " ... ") - self._width = 0 - - def _write(self, status: str) -> None: - assert not self._finished - # Erase what we wrote before by backspacing to the beginning, writing - # spaces to overwrite the old text, and then backspacing again - backup = "\b" * self._width - self._file.write(backup + " " * self._width + backup) - # Now we have a blank slate to add our status - self._file.write(status) - self._width = len(status) - self._file.flush() - self._rate_limiter.reset() - - def spin(self) -> None: - if self._finished: - return - if not self._rate_limiter.ready(): - return - self._write(next(self._spin_cycle)) - - def finish(self, final_status: str) -> None: - if self._finished: - return - self._write(final_status) - self._file.write("\n") - self._file.flush() - self._finished = True - - -# Used for dumb terminals, non-interactive installs (no tty), etc. -# We still print updates occasionally (once every 60 seconds by default) to -# act as a keep-alive for systems like Travis-CI that take lack-of-output as -# an indication that a task has frozen. -class NonInteractiveSpinner(SpinnerInterface): - def __init__(self, message: str, min_update_interval_seconds: float = 60.0) -> None: - self._message = message - self._finished = False - self._rate_limiter = RateLimiter(min_update_interval_seconds) - self._update("started") - - def _update(self, status: str) -> None: - assert not self._finished - self._rate_limiter.reset() - logger.info("%s: %s", self._message, status) - - def spin(self) -> None: - if self._finished: - return - if not self._rate_limiter.ready(): - return - self._update("still running...") - - def finish(self, final_status: str) -> None: - if self._finished: - return - self._update(f"finished with status '{final_status}'") - self._finished = True - - -class RateLimiter: - def __init__(self, min_update_interval_seconds: float) -> None: - self._min_update_interval_seconds = min_update_interval_seconds - self._last_update: float = 0 - - def ready(self) -> bool: - now = time.time() - delta = now - self._last_update - return delta >= self._min_update_interval_seconds - - def reset(self) -> None: - self._last_update = time.time() - - -@contextlib.contextmanager -def open_spinner(message: str) -> Generator[SpinnerInterface, None, None]: - # Interactive spinner goes directly to sys.stdout rather than being routed - # through the logging system, but it acts like it has level INFO, - # i.e. it's only displayed if we're at level INFO or better. - # Non-interactive spinner goes through the logging system, so it is always - # in sync with logging configuration. - if sys.stdout.isatty() and logger.getEffectiveLevel() <= logging.INFO: - spinner: SpinnerInterface = InteractiveSpinner(message) - else: - spinner = NonInteractiveSpinner(message) - try: - with hidden_cursor(sys.stdout): - yield spinner - except KeyboardInterrupt: - spinner.finish("canceled") - raise - except Exception: - spinner.finish("error") - raise - else: - spinner.finish("done") - - -HIDE_CURSOR = "\x1b[?25l" -SHOW_CURSOR = "\x1b[?25h" - - -@contextlib.contextmanager -def hidden_cursor(file: IO[str]) -> Generator[None, None, None]: - # The Windows terminal does not support the hide/show cursor ANSI codes, - # even via colorama. So don't even try. - if WINDOWS: - yield - # We don't want to clutter the output with control characters if we're - # writing to a file, or if the user is running with --quiet. - # See https://github.com/pypa/pip/issues/3418 - elif not file.isatty() or logger.getEffectiveLevel() > logging.INFO: - yield - else: - file.write(HIDE_CURSOR) - try: - yield - finally: - file.write(SHOW_CURSOR) diff --git a/spaces/Bilalst/Gradio_Youtube_Transcript_v2/README.md b/spaces/Bilalst/Gradio_Youtube_Transcript_v2/README.md deleted file mode 100644 index ce2dca53a22b30eda780edfb2f90b4db133ce083..0000000000000000000000000000000000000000 --- a/spaces/Bilalst/Gradio_Youtube_Transcript_v2/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Gradio Youtube Transcript -emoji: 🦀 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 3.35.2 -app_file: app.py -pinned: false -duplicated_from: Bilalst/Gradio_Youtube_Transcript ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/BramVanroy/llama-2-13b-chat-dutch-space/app.py b/spaces/BramVanroy/llama-2-13b-chat-dutch-space/app.py deleted file mode 100644 index e5c1ba0e11ca7cd1b6f1a71b6bc6443cb68999aa..0000000000000000000000000000000000000000 --- a/spaces/BramVanroy/llama-2-13b-chat-dutch-space/app.py +++ /dev/null @@ -1,284 +0,0 @@ -from typing import Iterator - -import gradio as gr - -from model import get_input_token_length, run - -DEFAULT_SYSTEM_PROMPT = """\ -Je bent een behulpzame, respectvolle en eerlijke assistent. Antwoord altijd zo behulpzaam mogelijk. Je antwoorden mogen geen schadelijke, onethische, racistische, seksistische, gevaarlijke of illegale inhoud bevatten. Zorg ervoor dat je antwoorden sociaal onbevooroordeeld en positief van aard zijn.\n\nAls een vraag nergens op slaat of feitelijk niet coherent is, leg dan uit waarom in plaats van iets niet correct te antwoorden. Als je het antwoord op een vraag niet weet, deel dan geen onjuiste informatie.\ -""" -MAX_MAX_NEW_TOKENS = 2048 -DEFAULT_MAX_NEW_TOKENS = 1024 -MAX_INPUT_TOKEN_LENGTH = 4000 - -DESCRIPTION = """ -# Llama-2 13B Chat Dutch - -This Space demonstrates the [quantized version](https://huggingface.co/TheBloke/Llama-2-13B-Chat-Dutch-GPTQ) of model [BramVanroy/Llama-2-13b-chat-dutch](https://huggingface.co/BramVanroy/Llama-2-13b-chat-dutch), a Llama 2 model with 13B parameters fine-tuned for Dutch chat instructions. -**Do not expect ChatGPT-like performance!** I found that because of its modest size and the scarce portion of Dutch that the original model was trained on, the model may still produce ungrammatical Dutch or misunderstand you. Consider this model and the accompanying space as a research project that is fun to tinker with. I recommend not using it as an actual assistant or in critical workflows! - - -Feel free to play with it, or duplicate to run generations without a queue! -""" - -LICENSE = """ - - ---- -Because the `BramVanroy/Llama-2-13b-chat-dutch` model was created with synthetic data generated with OpenAI services, you can not use this service to create competing, commercial products. - -Also see the original Llama v2 [license](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-hf/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-hf/blob/main/USE_POLICY.md). - -This demo uses a modifed version of the [Llama 2 13B space](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat). -""" - - -def clear_and_save_textbox(message: str) -> tuple[str, str]: - return "", message - - -def display_input(message: str, history: list[tuple[str, str]]) -> list[tuple[str, str]]: - history.append((message, "")) - return history - - -def delete_prev_fn(history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]: - try: - message, _ = history.pop() - except IndexError: - message = "" - return history, message or "" - - -def generate( - message: str, - history_with_input: list[tuple[str, str]], - system_prompt: str, - max_new_tokens: int, - temperature: float, - top_p: float, - top_k: int, - no_repeat_ngram_size: int, -) -> Iterator[list[tuple[str, str]]]: - if max_new_tokens > MAX_MAX_NEW_TOKENS: - raise ValueError - - history = history_with_input[:-1] - generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k, no_repeat_ngram_size) - try: - first_response = next(generator) - yield history + [(message, first_response)] - except StopIteration: - yield history + [(message, "")] - for response in generator: - yield history + [(message, response)] - - -def process_example(message: str) -> tuple[str, list[tuple[str, str]]]: - generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 0.7, 0.4, 50, 6) - for x in generator: - pass - return "", x - - -def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None: - input_token_length = get_input_token_length(message, chat_history, system_prompt) - if input_token_length > MAX_INPUT_TOKEN_LENGTH: - raise gr.Error( - f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again." - ) - - -with gr.Blocks(css="style.css") as demo: - gr.Markdown(DESCRIPTION) - with gr.Group(): - chatbot = gr.Chatbot(label="Chatbot") - with gr.Row(): - textbox = gr.Textbox( - container=False, - show_label=False, - placeholder="Type a message...", - scale=10, - ) - submit_button = gr.Button("Submit", variant="primary", scale=1, min_width=0) - with gr.Row(): - retry_button = gr.Button("🔄 Retry", variant="secondary") - undo_button = gr.Button("↩️ Undo", variant="secondary") - clear_button = gr.Button("🗑️ Clear", variant="secondary") - - saved_input = gr.State() - - with gr.Accordion(label="Advanced options", open=False): - system_prompt = gr.Textbox(label="System prompt", value=DEFAULT_SYSTEM_PROMPT, lines=6) - max_new_tokens = gr.Slider( - label="Max new tokens", - minimum=1, - maximum=MAX_MAX_NEW_TOKENS, - step=1, - value=DEFAULT_MAX_NEW_TOKENS, - ) - temperature = gr.Slider( - label="Temperature", - minimum=0.1, - maximum=1.0, - step=0.1, - value=0.7, - ) - top_p = gr.Slider( - label="Top-p (nucleus sampling)", - minimum=0.05, - maximum=1.0, - step=0.05, - value=0.4, - ) - top_k = gr.Slider( - label="Top-k", - minimum=1, - maximum=1000, - step=1, - value=50, - ) - no_repeat_ngram_size = gr.Slider( - label="No repeat n-gram", - minimum=0, - maximum=20, - step=1, - value=6, - ) - - gr.Examples( - examples=[ - "Wat zijn de buurlanden van België?", - 'Waarover gaat het boek "Lord of the Rings" en door wie werd het geschreven?', - "Hoe kan ik in Python over twee lijsten tegelijkertijd itereren? Bv. `a = [0, 1, 2]; b = [3, 4, 5]`", - "Los dit raadsel op: Ik ben altijd in beweging, maar ik kom nooit vooruit. Mijn handen draaien rondjes, keer op keer, luid. Wat ben ik?", - ], - inputs=textbox, - outputs=[textbox, chatbot], - fn=process_example, - cache_examples=True, - ) - - gr.Markdown(LICENSE) - - textbox.submit( - fn=clear_and_save_textbox, - inputs=textbox, - outputs=[textbox, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=check_input_token_length, - inputs=[saved_input, chatbot, system_prompt], - api_name=False, - queue=False, - ).success( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - no_repeat_ngram_size, - ], - outputs=chatbot, - api_name=False, - ) - - button_event_preprocess = ( - submit_button.click( - fn=clear_and_save_textbox, - inputs=textbox, - outputs=[textbox, saved_input], - api_name=False, - queue=False, - ) - .then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ) - .then( - fn=check_input_token_length, - inputs=[saved_input, chatbot, system_prompt], - api_name=False, - queue=False, - ) - .success( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - no_repeat_ngram_size, - ], - outputs=chatbot, - api_name=False, - ) - ) - - retry_button.click( - fn=delete_prev_fn, - inputs=chatbot, - outputs=[chatbot, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - no_repeat_ngram_size, - ], - outputs=chatbot, - api_name=False, - ) - - undo_button.click( - fn=delete_prev_fn, - inputs=chatbot, - outputs=[chatbot, saved_input], - api_name=False, - queue=False, - ).then( - fn=lambda x: x, - inputs=[saved_input], - outputs=textbox, - api_name=False, - queue=False, - ) - - clear_button.click( - fn=lambda: ([], ""), - outputs=[chatbot, saved_input], - queue=False, - api_name=False, - ) - -demo.queue(max_size=20).launch() diff --git a/spaces/BraydenMoore/a-random-unsecured-camera/README.md b/spaces/BraydenMoore/a-random-unsecured-camera/README.md deleted file mode 100644 index a54bf646f61138302a2d91d9a54d076c958be8b1..0000000000000000000000000000000000000000 --- a/spaces/BraydenMoore/a-random-unsecured-camera/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: A Random Unsecured Camera -emoji: 👁 -colorFrom: gray -colorTo: yellow -sdk: docker -pinned: true -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/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/export/api.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/export/api.py deleted file mode 100644 index 796425e7b3e6e5822de19cbb100149c7e37b929f..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/export/api.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -import copy -import logging -import os -from caffe2.proto import caffe2_pb2 -from torch import nn - -from detectron2.config import CfgNode as CN - -from .caffe2_export import export_caffe2_detection_model -from .caffe2_export import export_onnx_model as export_onnx_model_impl -from .caffe2_export import run_and_save_graph -from .caffe2_inference import ProtobufDetectionModel -from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format -from .shared import get_pb_arg_vali, get_pb_arg_vals, save_graph - -__all__ = ["add_export_config", "export_caffe2_model", "Caffe2Model", "export_onnx_model"] - - -def add_export_config(cfg): - """ - Args: - cfg (CfgNode): a detectron2 config - - Returns: - CfgNode: an updated config with new options that :func:`export_caffe2_model` will need. - """ - is_frozen = cfg.is_frozen() - cfg.defrost() - cfg.EXPORT_CAFFE2 = CN() - cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT = False - if is_frozen: - cfg.freeze() - return cfg - - -def export_caffe2_model(cfg, model, inputs): - """ - Export a detectron2 model to caffe2 format. - - Args: - cfg (CfgNode): a detectron2 config, with extra export-related options - added by :func:`add_export_config`. - model (nn.Module): a model built by - :func:`detectron2.modeling.build_model`. - It will be modified by this function. - inputs: sample inputs that the given model takes for inference. - Will be used to trace the model. - - Returns: - Caffe2Model - """ - assert isinstance(cfg, CN), cfg - C2MetaArch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[cfg.MODEL.META_ARCHITECTURE] - c2_compatible_model = C2MetaArch(cfg, model) - c2_format_input = c2_compatible_model.get_caffe2_inputs(inputs) - predict_net, init_net = export_caffe2_detection_model(c2_compatible_model, c2_format_input) - return Caffe2Model(predict_net, init_net) - - -def export_onnx_model(cfg, model, inputs): - """ - Export a detectron2 model to ONNX format. - Note that the exported model contains custom ops only available in caffe2, therefore it - cannot be directly executed by other runtime. Post-processing or transformation passes - may be applied on the model to accommodate different runtimes. - - Args: - cfg (CfgNode): a detectron2 config, with extra export-related options - added by :func:`add_export_config`. - model (nn.Module): a model built by - :func:`detectron2.modeling.build_model`. - It will be modified by this function. - inputs: sample inputs that the given model takes for inference. - Will be used to trace the model. - - Returns: - onnx.ModelProto: an onnx model. - """ - model = copy.deepcopy(model) - assert isinstance(cfg, CN), cfg - C2MetaArch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[cfg.MODEL.META_ARCHITECTURE] - c2_compatible_model = C2MetaArch(cfg, model) - c2_format_input = c2_compatible_model.get_caffe2_inputs(inputs) - return export_onnx_model_impl(c2_compatible_model, (c2_format_input,)) - - -class Caffe2Model(nn.Module): - def __init__(self, predict_net, init_net): - super().__init__() - self.eval() # always in eval mode - self._predict_net = predict_net - self._init_net = init_net - self._predictor = None - - @property - def predict_net(self): - """ - Returns: - core.Net: the underlying caffe2 predict net - """ - return self._predict_net - - @property - def init_net(self): - """ - Returns: - core.Net: the underlying caffe2 init net - """ - return self._init_net - - __init__.__HIDE_SPHINX_DOC__ = True - - def save_protobuf(self, output_dir): - """ - Save the model as caffe2's protobuf format. - - Args: - output_dir (str): the output directory to save protobuf files. - """ - logger = logging.getLogger(__name__) - logger.info("Saving model to {} ...".format(output_dir)) - os.makedirs(output_dir, exist_ok=True) - - with open(os.path.join(output_dir, "model.pb"), "wb") as f: - f.write(self._predict_net.SerializeToString()) - with open(os.path.join(output_dir, "model.pbtxt"), "w") as f: - f.write(str(self._predict_net)) - with open(os.path.join(output_dir, "model_init.pb"), "wb") as f: - f.write(self._init_net.SerializeToString()) - - def save_graph(self, output_file, inputs=None): - """ - Save the graph as SVG format. - - Args: - output_file (str): a SVG file - inputs: optional inputs given to the model. - If given, the inputs will be used to run the graph to record - shape of every tensor. The shape information will be - saved together with the graph. - """ - if inputs is None: - save_graph(self._predict_net, output_file, op_only=False) - else: - size_divisibility = get_pb_arg_vali(self._predict_net, "size_divisibility", 0) - device = get_pb_arg_vals(self._predict_net, "device", b"cpu").decode("ascii") - inputs = convert_batched_inputs_to_c2_format(inputs, size_divisibility, device) - inputs = [x.numpy() for x in inputs] - run_and_save_graph(self._predict_net, self._init_net, inputs, output_file) - - @staticmethod - def load_protobuf(dir): - """ - Args: - dir (str): a directory used to save Caffe2Model with - :meth:`save_protobuf`. - The files "model.pb" and "model_init.pb" are needed. - - Returns: - Caffe2Model: the caffe2 model loaded from this directory. - """ - predict_net = caffe2_pb2.NetDef() - with open(os.path.join(dir, "model.pb"), "rb") as f: - predict_net.ParseFromString(f.read()) - - init_net = caffe2_pb2.NetDef() - with open(os.path.join(dir, "model_init.pb"), "rb") as f: - init_net.ParseFromString(f.read()) - - return Caffe2Model(predict_net, init_net) - - def __call__(self, inputs): - """ - An interface that wraps around a caffe2 model and mimics detectron2's models' - input & output format. This is used to compare the caffe2 model - with its original torch model. - """ - if self._predictor is None: - self._predictor = ProtobufDetectionModel(self._predict_net, self._init_net) - return self._predictor(inputs) diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/README.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/README.md deleted file mode 100644 index 4b7a90102d008a498e93dff595a09206be5269e7..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/README.md +++ /dev/null @@ -1,60 +0,0 @@ - -# TridentNet in Detectron2 -**Scale-Aware Trident Networks for Object Detection** - -Yanghao Li\*, Yuntao Chen\*, Naiyan Wang, Zhaoxiang Zhang - -[[`TridentNet`](https://github.com/TuSimple/simpledet/tree/master/models/tridentnet)] [[`arXiv`](https://arxiv.org/abs/1901.01892)] [[`BibTeX`](#CitingTridentNet)] - --- -In this repository, we implement TridentNet-Fast in Detectron2. -Trident Network (TridentNet) aims to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. TridentNet-Fast is a fast approximation version of TridentNet that could achieve significant improvements without any additional parameters and computational cost. - -## Training - -To train a model, run -```bash -python /path/to/detectron2/projects/TridentNet/train_net.py --config-file-
-``` - -For example, to launch end-to-end TridentNet training with ResNet-50 backbone on 8 GPUs, -one should execute: -```bash -python /path/to/detectron2/projects/TridentNet/train_net.py --config-file configs/tridentnet_fast_R_50_C4_1x.yaml --num-gpus 8 -``` - -## Evaluation - -Model evaluation can be done similarly: -```bash -python /path/to/detectron2/projects/TridentNet/train_net.py --config-file configs/tridentnet_fast_R_50_C4_1x.yaml --eval-only MODEL.WEIGHTS model.pth -``` - -## Results on MS-COCO in Detectron2 - -|Model|Backbone|Head|lr sched|AP|AP50|AP75|APs|APm|APl|download| -|-----|--------|----|--------|--|----|----|---|---|---|--------| -|Faster|R50-C4|C5-512ROI|1X|35.7|56.1|38.0|19.2|40.9|48.7|model \| metrics| -|TridentFast|R50-C4|C5-128ROI|1X|38.0|58.1|40.8|19.5|42.2|54.6|model \| metrics| -|Faster|R50-C4|C5-512ROI|3X|38.4|58.7|41.3|20.7|42.7|53.1|model \| metrics| -|TridentFast|R50-C4|C5-128ROI|3X|40.6|60.8|43.6|23.4|44.7|57.1|model \| metrics| -|Faster|R101-C4|C5-512ROI|3X|41.1|61.4|44.0|22.2|45.5|55.9|model \| metrics| -|TridentFast|R101-C4|C5-128ROI|3X|43.6|63.4|47.0|24.3|47.8|60.0|model \| metrics| - - -## Citing TridentNet - -If you use TridentNet, please use the following BibTeX entry. - -``` -@InProceedings{li2019scale, - title={Scale-Aware Trident Networks for Object Detection}, - author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang}, - journal={The International Conference on Computer Vision (ICCV)}, - year={2019} -} -``` - diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/tridentnet/trident_rpn.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/tridentnet/trident_rpn.py deleted file mode 100644 index c30137f312232ccccd86182108949fbe34b97231..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/tridentnet/trident_rpn.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import torch - -from detectron2.modeling import PROPOSAL_GENERATOR_REGISTRY -from detectron2.modeling.proposal_generator.rpn import RPN -from detectron2.structures import ImageList - - -@PROPOSAL_GENERATOR_REGISTRY.register() -class TridentRPN(RPN): - """ - Trident RPN subnetwork. - """ - - def __init__(self, cfg, input_shape): - super(TridentRPN, self).__init__(cfg, input_shape) - - self.num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH - self.trident_fast = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX != -1 - - def forward(self, images, features, gt_instances=None): - """ - See :class:`RPN.forward`. - """ - num_branch = self.num_branch if self.training or not self.trident_fast else 1 - # Duplicate images and gt_instances for all branches in TridentNet. - all_images = ImageList( - torch.cat([images.tensor] * num_branch), images.image_sizes * num_branch - ) - all_gt_instances = gt_instances * num_branch if gt_instances is not None else None - - return super(TridentRPN, self).forward(all_images, features, all_gt_instances) diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_numpy_array.py b/spaces/CVPR/LIVE/pybind11/tests/test_numpy_array.py deleted file mode 100644 index ad3ca58c1af53e2b65ffed341a0512ebb5c20815..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/tests/test_numpy_array.py +++ /dev/null @@ -1,446 +0,0 @@ -# -*- coding: utf-8 -*- -import pytest - -import env # noqa: F401 - -from pybind11_tests import numpy_array as m - -np = pytest.importorskip("numpy") - - -def test_dtypes(): - # See issue #1328. - # - Platform-dependent sizes. - for size_check in m.get_platform_dtype_size_checks(): - print(size_check) - assert size_check.size_cpp == size_check.size_numpy, size_check - # - Concrete sizes. - for check in m.get_concrete_dtype_checks(): - print(check) - assert check.numpy == check.pybind11, check - if check.numpy.num != check.pybind11.num: - print("NOTE: typenum mismatch for {}: {} != {}".format( - check, check.numpy.num, check.pybind11.num)) - - -@pytest.fixture(scope='function') -def arr(): - return np.array([[1, 2, 3], [4, 5, 6]], '=u2') - - -def test_array_attributes(): - a = np.array(0, 'f8') - assert m.ndim(a) == 0 - assert all(m.shape(a) == []) - assert all(m.strides(a) == []) - with pytest.raises(IndexError) as excinfo: - m.shape(a, 0) - assert str(excinfo.value) == 'invalid axis: 0 (ndim = 0)' - with pytest.raises(IndexError) as excinfo: - m.strides(a, 0) - assert str(excinfo.value) == 'invalid axis: 0 (ndim = 0)' - assert m.writeable(a) - assert m.size(a) == 1 - assert m.itemsize(a) == 8 - assert m.nbytes(a) == 8 - assert m.owndata(a) - - a = np.array([[1, 2, 3], [4, 5, 6]], 'u2').view() - a.flags.writeable = False - assert m.ndim(a) == 2 - assert all(m.shape(a) == [2, 3]) - assert m.shape(a, 0) == 2 - assert m.shape(a, 1) == 3 - assert all(m.strides(a) == [6, 2]) - assert m.strides(a, 0) == 6 - assert m.strides(a, 1) == 2 - with pytest.raises(IndexError) as excinfo: - m.shape(a, 2) - assert str(excinfo.value) == 'invalid axis: 2 (ndim = 2)' - with pytest.raises(IndexError) as excinfo: - m.strides(a, 2) - assert str(excinfo.value) == 'invalid axis: 2 (ndim = 2)' - assert not m.writeable(a) - assert m.size(a) == 6 - assert m.itemsize(a) == 2 - assert m.nbytes(a) == 12 - assert not m.owndata(a) - - -@pytest.mark.parametrize('args, ret', [([], 0), ([0], 0), ([1], 3), ([0, 1], 1), ([1, 2], 5)]) -def test_index_offset(arr, args, ret): - assert m.index_at(arr, *args) == ret - assert m.index_at_t(arr, *args) == ret - assert m.offset_at(arr, *args) == ret * arr.dtype.itemsize - assert m.offset_at_t(arr, *args) == ret * arr.dtype.itemsize - - -def test_dim_check_fail(arr): - for func in (m.index_at, m.index_at_t, m.offset_at, m.offset_at_t, m.data, m.data_t, - m.mutate_data, m.mutate_data_t): - with pytest.raises(IndexError) as excinfo: - func(arr, 1, 2, 3) - assert str(excinfo.value) == 'too many indices for an array: 3 (ndim = 2)' - - -@pytest.mark.parametrize('args, ret', - [([], [1, 2, 3, 4, 5, 6]), - ([1], [4, 5, 6]), - ([0, 1], [2, 3, 4, 5, 6]), - ([1, 2], [6])]) -def test_data(arr, args, ret): - from sys import byteorder - assert all(m.data_t(arr, *args) == ret) - assert all(m.data(arr, *args)[(0 if byteorder == 'little' else 1)::2] == ret) - assert all(m.data(arr, *args)[(1 if byteorder == 'little' else 0)::2] == 0) - - -@pytest.mark.parametrize('dim', [0, 1, 3]) -def test_at_fail(arr, dim): - for func in m.at_t, m.mutate_at_t: - with pytest.raises(IndexError) as excinfo: - func(arr, *([0] * dim)) - assert str(excinfo.value) == 'index dimension mismatch: {} (ndim = 2)'.format(dim) - - -def test_at(arr): - assert m.at_t(arr, 0, 2) == 3 - assert m.at_t(arr, 1, 0) == 4 - - assert all(m.mutate_at_t(arr, 0, 2).ravel() == [1, 2, 4, 4, 5, 6]) - assert all(m.mutate_at_t(arr, 1, 0).ravel() == [1, 2, 4, 5, 5, 6]) - - -def test_mutate_readonly(arr): - arr.flags.writeable = False - for func, args in (m.mutate_data, ()), (m.mutate_data_t, ()), (m.mutate_at_t, (0, 0)): - with pytest.raises(ValueError) as excinfo: - func(arr, *args) - assert str(excinfo.value) == 'array is not writeable' - - -def test_mutate_data(arr): - assert all(m.mutate_data(arr).ravel() == [2, 4, 6, 8, 10, 12]) - assert all(m.mutate_data(arr).ravel() == [4, 8, 12, 16, 20, 24]) - assert all(m.mutate_data(arr, 1).ravel() == [4, 8, 12, 32, 40, 48]) - assert all(m.mutate_data(arr, 0, 1).ravel() == [4, 16, 24, 64, 80, 96]) - assert all(m.mutate_data(arr, 1, 2).ravel() == [4, 16, 24, 64, 80, 192]) - - assert all(m.mutate_data_t(arr).ravel() == [5, 17, 25, 65, 81, 193]) - assert all(m.mutate_data_t(arr).ravel() == [6, 18, 26, 66, 82, 194]) - assert all(m.mutate_data_t(arr, 1).ravel() == [6, 18, 26, 67, 83, 195]) - assert all(m.mutate_data_t(arr, 0, 1).ravel() == [6, 19, 27, 68, 84, 196]) - assert all(m.mutate_data_t(arr, 1, 2).ravel() == [6, 19, 27, 68, 84, 197]) - - -def test_bounds_check(arr): - for func in (m.index_at, m.index_at_t, m.data, m.data_t, - m.mutate_data, m.mutate_data_t, m.at_t, m.mutate_at_t): - with pytest.raises(IndexError) as excinfo: - func(arr, 2, 0) - assert str(excinfo.value) == 'index 2 is out of bounds for axis 0 with size 2' - with pytest.raises(IndexError) as excinfo: - func(arr, 0, 4) - assert str(excinfo.value) == 'index 4 is out of bounds for axis 1 with size 3' - - -def test_make_c_f_array(): - assert m.make_c_array().flags.c_contiguous - assert not m.make_c_array().flags.f_contiguous - assert m.make_f_array().flags.f_contiguous - assert not m.make_f_array().flags.c_contiguous - - -def test_make_empty_shaped_array(): - m.make_empty_shaped_array() - - # empty shape means numpy scalar, PEP 3118 - assert m.scalar_int().ndim == 0 - assert m.scalar_int().shape == () - assert m.scalar_int() == 42 - - -def test_wrap(): - def assert_references(a, b, base=None): - from distutils.version import LooseVersion - if base is None: - base = a - assert a is not b - assert a.__array_interface__['data'][0] == b.__array_interface__['data'][0] - assert a.shape == b.shape - assert a.strides == b.strides - assert a.flags.c_contiguous == b.flags.c_contiguous - assert a.flags.f_contiguous == b.flags.f_contiguous - assert a.flags.writeable == b.flags.writeable - assert a.flags.aligned == b.flags.aligned - if LooseVersion(np.__version__) >= LooseVersion("1.14.0"): - assert a.flags.writebackifcopy == b.flags.writebackifcopy - else: - assert a.flags.updateifcopy == b.flags.updateifcopy - assert np.all(a == b) - assert not b.flags.owndata - assert b.base is base - if a.flags.writeable and a.ndim == 2: - a[0, 0] = 1234 - assert b[0, 0] == 1234 - - a1 = np.array([1, 2], dtype=np.int16) - assert a1.flags.owndata and a1.base is None - a2 = m.wrap(a1) - assert_references(a1, a2) - - a1 = np.array([[1, 2], [3, 4]], dtype=np.float32, order='F') - assert a1.flags.owndata and a1.base is None - a2 = m.wrap(a1) - assert_references(a1, a2) - - a1 = np.array([[1, 2], [3, 4]], dtype=np.float32, order='C') - a1.flags.writeable = False - a2 = m.wrap(a1) - assert_references(a1, a2) - - a1 = np.random.random((4, 4, 4)) - a2 = m.wrap(a1) - assert_references(a1, a2) - - a1t = a1.transpose() - a2 = m.wrap(a1t) - assert_references(a1t, a2, a1) - - a1d = a1.diagonal() - a2 = m.wrap(a1d) - assert_references(a1d, a2, a1) - - a1m = a1[::-1, ::-1, ::-1] - a2 = m.wrap(a1m) - assert_references(a1m, a2, a1) - - -def test_numpy_view(capture): - with capture: - ac = m.ArrayClass() - ac_view_1 = ac.numpy_view() - ac_view_2 = ac.numpy_view() - assert np.all(ac_view_1 == np.array([1, 2], dtype=np.int32)) - del ac - pytest.gc_collect() - assert capture == """ - ArrayClass() - ArrayClass::numpy_view() - ArrayClass::numpy_view() - """ - ac_view_1[0] = 4 - ac_view_1[1] = 3 - assert ac_view_2[0] == 4 - assert ac_view_2[1] == 3 - with capture: - del ac_view_1 - del ac_view_2 - pytest.gc_collect() - pytest.gc_collect() - assert capture == """ - ~ArrayClass() - """ - - -def test_cast_numpy_int64_to_uint64(): - m.function_taking_uint64(123) - m.function_taking_uint64(np.uint64(123)) - - -def test_isinstance(): - assert m.isinstance_untyped(np.array([1, 2, 3]), "not an array") - assert m.isinstance_typed(np.array([1.0, 2.0, 3.0])) - - -def test_constructors(): - defaults = m.default_constructors() - for a in defaults.values(): - assert a.size == 0 - assert defaults["array"].dtype == np.array([]).dtype - assert defaults["array_t "].dtype == np.int32 - assert defaults["array_t "].dtype == np.float64 - - results = m.converting_constructors([1, 2, 3]) - for a in results.values(): - np.testing.assert_array_equal(a, [1, 2, 3]) - assert results["array"].dtype == np.int_ - assert results["array_t "].dtype == np.int32 - assert results["array_t "].dtype == np.float64 - - -def test_overload_resolution(msg): - # Exact overload matches: - assert m.overloaded(np.array([1], dtype='float64')) == 'double' - assert m.overloaded(np.array([1], dtype='float32')) == 'float' - assert m.overloaded(np.array([1], dtype='ushort')) == 'unsigned short' - assert m.overloaded(np.array([1], dtype='intc')) == 'int' - assert m.overloaded(np.array([1], dtype='longlong')) == 'long long' - assert m.overloaded(np.array([1], dtype='complex')) == 'double complex' - assert m.overloaded(np.array([1], dtype='csingle')) == 'float complex' - - # No exact match, should call first convertible version: - assert m.overloaded(np.array([1], dtype='uint8')) == 'double' - - with pytest.raises(TypeError) as excinfo: - m.overloaded("not an array") - assert msg(excinfo.value) == """ - overloaded(): incompatible function arguments. The following argument types are supported: - 1. (arg0: numpy.ndarray[numpy.float64]) -> str - 2. (arg0: numpy.ndarray[numpy.float32]) -> str - 3. (arg0: numpy.ndarray[numpy.int32]) -> str - 4. (arg0: numpy.ndarray[numpy.uint16]) -> str - 5. (arg0: numpy.ndarray[numpy.int64]) -> str - 6. (arg0: numpy.ndarray[numpy.complex128]) -> str - 7. (arg0: numpy.ndarray[numpy.complex64]) -> str - - Invoked with: 'not an array' - """ - - assert m.overloaded2(np.array([1], dtype='float64')) == 'double' - assert m.overloaded2(np.array([1], dtype='float32')) == 'float' - assert m.overloaded2(np.array([1], dtype='complex64')) == 'float complex' - assert m.overloaded2(np.array([1], dtype='complex128')) == 'double complex' - assert m.overloaded2(np.array([1], dtype='float32')) == 'float' - - assert m.overloaded3(np.array([1], dtype='float64')) == 'double' - assert m.overloaded3(np.array([1], dtype='intc')) == 'int' - expected_exc = """ - overloaded3(): incompatible function arguments. The following argument types are supported: - 1. (arg0: numpy.ndarray[numpy.int32]) -> str - 2. (arg0: numpy.ndarray[numpy.float64]) -> str - - Invoked with: """ - - with pytest.raises(TypeError) as excinfo: - m.overloaded3(np.array([1], dtype='uintc')) - assert msg(excinfo.value) == expected_exc + repr(np.array([1], dtype='uint32')) - with pytest.raises(TypeError) as excinfo: - m.overloaded3(np.array([1], dtype='float32')) - assert msg(excinfo.value) == expected_exc + repr(np.array([1.], dtype='float32')) - with pytest.raises(TypeError) as excinfo: - m.overloaded3(np.array([1], dtype='complex')) - assert msg(excinfo.value) == expected_exc + repr(np.array([1. + 0.j])) - - # Exact matches: - assert m.overloaded4(np.array([1], dtype='double')) == 'double' - assert m.overloaded4(np.array([1], dtype='longlong')) == 'long long' - # Non-exact matches requiring conversion. Since float to integer isn't a - # save conversion, it should go to the double overload, but short can go to - # either (and so should end up on the first-registered, the long long). - assert m.overloaded4(np.array([1], dtype='float32')) == 'double' - assert m.overloaded4(np.array([1], dtype='short')) == 'long long' - - assert m.overloaded5(np.array([1], dtype='double')) == 'double' - assert m.overloaded5(np.array([1], dtype='uintc')) == 'unsigned int' - assert m.overloaded5(np.array([1], dtype='float32')) == 'unsigned int' - - -def test_greedy_string_overload(): - """Tests fix for #685 - ndarray shouldn't go to std::string overload""" - - assert m.issue685("abc") == "string" - assert m.issue685(np.array([97, 98, 99], dtype='b')) == "array" - assert m.issue685(123) == "other" - - -def test_array_unchecked_fixed_dims(msg): - z1 = np.array([[1, 2], [3, 4]], dtype='float64') - m.proxy_add2(z1, 10) - assert np.all(z1 == [[11, 12], [13, 14]]) - - with pytest.raises(ValueError) as excinfo: - m.proxy_add2(np.array([1., 2, 3]), 5.0) - assert msg(excinfo.value) == "array has incorrect number of dimensions: 1; expected 2" - - expect_c = np.ndarray(shape=(3, 3, 3), buffer=np.array(range(3, 30)), dtype='int') - assert np.all(m.proxy_init3(3.0) == expect_c) - expect_f = np.transpose(expect_c) - assert np.all(m.proxy_init3F(3.0) == expect_f) - - assert m.proxy_squared_L2_norm(np.array(range(6))) == 55 - assert m.proxy_squared_L2_norm(np.array(range(6), dtype="float64")) == 55 - - assert m.proxy_auxiliaries2(z1) == [11, 11, True, 2, 8, 2, 2, 4, 32] - assert m.proxy_auxiliaries2(z1) == m.array_auxiliaries2(z1) - - -def test_array_unchecked_dyn_dims(msg): - z1 = np.array([[1, 2], [3, 4]], dtype='float64') - m.proxy_add2_dyn(z1, 10) - assert np.all(z1 == [[11, 12], [13, 14]]) - - expect_c = np.ndarray(shape=(3, 3, 3), buffer=np.array(range(3, 30)), dtype='int') - assert np.all(m.proxy_init3_dyn(3.0) == expect_c) - - assert m.proxy_auxiliaries2_dyn(z1) == [11, 11, True, 2, 8, 2, 2, 4, 32] - assert m.proxy_auxiliaries2_dyn(z1) == m.array_auxiliaries2(z1) - - -def test_array_failure(): - with pytest.raises(ValueError) as excinfo: - m.array_fail_test() - assert str(excinfo.value) == 'cannot create a pybind11::array from a nullptr' - - with pytest.raises(ValueError) as excinfo: - m.array_t_fail_test() - assert str(excinfo.value) == 'cannot create a pybind11::array_t from a nullptr' - - with pytest.raises(ValueError) as excinfo: - m.array_fail_test_negative_size() - assert str(excinfo.value) == 'negative dimensions are not allowed' - - -def test_initializer_list(): - assert m.array_initializer_list1().shape == (1,) - assert m.array_initializer_list2().shape == (1, 2) - assert m.array_initializer_list3().shape == (1, 2, 3) - assert m.array_initializer_list4().shape == (1, 2, 3, 4) - - -def test_array_resize(msg): - a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='float64') - m.array_reshape2(a) - assert(a.size == 9) - assert(np.all(a == [[1, 2, 3], [4, 5, 6], [7, 8, 9]])) - - # total size change should succced with refcheck off - m.array_resize3(a, 4, False) - assert(a.size == 64) - # ... and fail with refcheck on - try: - m.array_resize3(a, 3, True) - except ValueError as e: - assert(str(e).startswith("cannot resize an array")) - # transposed array doesn't own data - b = a.transpose() - try: - m.array_resize3(b, 3, False) - except ValueError as e: - assert(str(e).startswith("cannot resize this array: it does not own its data")) - # ... but reshape should be fine - m.array_reshape2(b) - assert(b.shape == (8, 8)) - - -@pytest.mark.xfail("env.PYPY") -def test_array_create_and_resize(msg): - a = m.create_and_resize(2) - assert(a.size == 4) - assert(np.all(a == 42.)) - - -def test_index_using_ellipsis(): - a = m.index_using_ellipsis(np.zeros((5, 6, 7))) - assert a.shape == (6,) - - -@pytest.mark.xfail("env.PYPY") -def test_dtype_refcount_leak(): - from sys import getrefcount - dtype = np.dtype(np.float_) - a = np.array([1], dtype=dtype) - before = getrefcount(dtype) - m.ndim(a) - after = getrefcount(dtype) - assert after == before diff --git a/spaces/CVPR/LIVE/pybind11/tools/libsize.py b/spaces/CVPR/LIVE/pybind11/tools/libsize.py deleted file mode 100644 index 50f88bdb3dc57ac6df11a249d789ac5018a7fc86..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/tools/libsize.py +++ /dev/null @@ -1,38 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import print_function, division -import os -import sys - -# Internal build script for generating debugging test .so size. -# Usage: -# python libsize.py file.so save.txt -- displays the size of file.so and, if save.txt exists, compares it to the -# size in it, then overwrites save.txt with the new size for future runs. - -if len(sys.argv) != 3: - sys.exit("Invalid arguments: usage: python libsize.py file.so save.txt") - -lib = sys.argv[1] -save = sys.argv[2] - -if not os.path.exists(lib): - sys.exit("Error: requested file ({}) does not exist".format(lib)) - -libsize = os.path.getsize(lib) - -print("------", os.path.basename(lib), "file size:", libsize, end='') - -if os.path.exists(save): - with open(save) as sf: - oldsize = int(sf.readline()) - - if oldsize > 0: - change = libsize - oldsize - if change == 0: - print(" (no change)") - else: - print(" (change of {:+} bytes = {:+.2%})".format(change, change / oldsize)) -else: - print() - -with open(save, 'w') as sf: - sf.write(str(libsize)) diff --git a/spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/histogram/histogram_smem_atomics.h b/spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/histogram/histogram_smem_atomics.h deleted file mode 100644 index 2c70702e2e267ac070ae2d2d04c73bfb6e6b2e88..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/histogram/histogram_smem_atomics.h +++ /dev/null @@ -1,195 +0,0 @@ -/****************************************************************************** - * Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of the NVIDIA CORPORATION nor the - * names of its contributors may be used to endorse or promote products - * derived from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY - * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND - * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - ******************************************************************************/ - -#include - -namespace histogram_smem_atomics -{ - // Decode float4 pixel into bins - template - __device__ __forceinline__ void DecodePixel(float4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS]) - { - float* samples = reinterpret_cast - __device__ __forceinline__ void DecodePixel(uchar4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS]) - { - unsigned char* samples = reinterpret_cast (&pixel); - - #pragma unroll - for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL) - bins[CHANNEL] = (unsigned int) (samples[CHANNEL]); - } - - // Decode uchar1 pixel into bins - template - __device__ __forceinline__ void DecodePixel(uchar1 pixel, unsigned int (&bins)[ACTIVE_CHANNELS]) - { - bins[0] = (unsigned int) pixel.x; - } - - // First-pass histogram kernel (binning into privatized counters) - template < - int NUM_PARTS, - int ACTIVE_CHANNELS, - int NUM_BINS, - typename PixelType> - __global__ void histogram_smem_atomics( - const PixelType *in, - int width, - int height, - unsigned int *out) - { - // global position and size - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int nx = blockDim.x * gridDim.x; - int ny = blockDim.y * gridDim.y; - - // threads in workgroup - int t = threadIdx.x + threadIdx.y * blockDim.x; // thread index in workgroup, linear in 0..nt-1 - int nt = blockDim.x * blockDim.y; // total threads in workgroup - - // group index in 0..ngroups-1 - int g = blockIdx.x + blockIdx.y * gridDim.x; - - // initialize smem - __shared__ unsigned int smem[ACTIVE_CHANNELS * NUM_BINS + 3]; - for (int i = t; i < ACTIVE_CHANNELS * NUM_BINS + 3; i += nt) - smem[i] = 0; - __syncthreads(); - - // process pixels - // updates our group's partial histogram in smem - for (int col = x; col < width; col += nx) - { - for (int row = y; row < height; row += ny) - { - PixelType pixel = in[row * width + col]; - - unsigned int bins[ACTIVE_CHANNELS]; - DecodePixel (pixel, bins); - - #pragma unroll - for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL) - atomicAdd(&smem[(NUM_BINS * CHANNEL) + bins[CHANNEL] + CHANNEL], 1); - } - } - - __syncthreads(); - - // move to our workgroup's slice of output - out += g * NUM_PARTS; - - // store local output to global - for (int i = t; i < NUM_BINS; i += nt) - { - #pragma unroll - for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL) - out[i + NUM_BINS * CHANNEL] = smem[i + NUM_BINS * CHANNEL + CHANNEL]; - } - } - - // Second pass histogram kernel (accumulation) - template < - int NUM_PARTS, - int ACTIVE_CHANNELS, - int NUM_BINS> - __global__ void histogram_smem_accum( - const unsigned int *in, - int n, - unsigned int *out) - { - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i > ACTIVE_CHANNELS * NUM_BINS) return; // out of range - unsigned int total = 0; - for (int j = 0; j < n; j++) - total += in[i + NUM_PARTS * j]; - out[i] = total; - } - -} // namespace histogram_smem_atomics - - -template < - int ACTIVE_CHANNELS, - int NUM_BINS, - typename PixelType> -double run_smem_atomics( - PixelType *d_image, - int width, - int height, - unsigned int *d_hist, - bool warmup) -{ - enum - { - NUM_PARTS = 1024 - }; - - cudaDeviceProp props; - cudaGetDeviceProperties(&props, 0); - - dim3 block(32, 4); - dim3 grid(16, 16); - int total_blocks = grid.x * grid.y; - - // allocate partial histogram - unsigned int *d_part_hist; - cudaMalloc(&d_part_hist, total_blocks * NUM_PARTS * sizeof(unsigned int)); - - dim3 block2(128); - dim3 grid2((ACTIVE_CHANNELS * NUM_BINS + block.x - 1) / block.x); - - GpuTimer gpu_timer; - gpu_timer.Start(); - - histogram_smem_atomics::histogram_smem_atomics - -#if THRUST_CPP_DIALECT >= 2011 -# include -#endif - -namespace thrust -{ - -// forward declaration of device_reference -template class device_reference; - -namespace detail -{ - /// helper classes [4.3]. - template - struct integral_constant - { - THRUST_INLINE_INTEGRAL_MEMBER_CONSTANT T value = v; - - typedef T value_type; - typedef integral_constant struct is_integral : public std::tr1::is_integral {}; -template struct is_integral : public false_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; -template<> struct is_integral : public true_type {}; - -template struct is_floating_point : public false_type {}; -template<> struct is_floating_point : public true_type {}; -template<> struct is_floating_point : public true_type {}; -template<> struct is_floating_point : public true_type {}; - -template struct is_arithmetic : public is_integral {}; -template<> struct is_arithmetic : public true_type {}; -template<> struct is_arithmetic : public true_type {}; -template<> struct is_arithmetic : public true_type {}; -template<> struct is_arithmetic : public true_type {}; - -template struct is_pointer : public false_type {}; -template struct is_pointer : public true_type {}; - -template struct is_device_ptr : public false_type {}; - -template struct is_void : public false_type {}; -template<> struct is_void : public true_type {}; -template<> struct is_void : public true_type {}; - -template struct is_non_bool_integral : public is_integral {}; -template<> struct is_non_bool_integral : public false_type {}; - -template struct is_non_bool_arithmetic : public is_arithmetic {}; -template<> struct is_non_bool_arithmetic : public false_type {}; - -template struct is_pod - : public integral_constant< - bool, - is_void ::value || is_pointer ::value || is_arithmetic ::value -#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC || \ - THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG -// use intrinsic type traits - || __is_pod(T) -#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC -// only use the intrinsic for >= 4.3 -#if (__GNUC__ >= 4) && (__GNUC_MINOR__ >= 3) - || __is_pod(T) -#endif // GCC VERSION -#endif // THRUST_HOST_COMPILER - > - {}; - - -template struct has_trivial_constructor - : public integral_constant< - bool, - is_pod ::value -#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC || \ - THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG - || __has_trivial_constructor(T) -#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC -// only use the intrinsic for >= 4.3 -#if (__GNUC__ >= 4) && (__GNUC_MINOR__ >= 3) - || __has_trivial_constructor(T) -#endif // GCC VERSION -#endif // THRUST_HOST_COMPILER - > -{}; - -template struct has_trivial_copy_constructor - : public integral_constant< - bool, - is_pod ::value -#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC || \ - THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG - || __has_trivial_copy(T) -#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC -// only use the intrinsic for >= 4.3 -#if (__GNUC__ >= 4) && (__GNUC_MINOR__ >= 3) - || __has_trivial_copy(T) -#endif // GCC VERSION -#endif // THRUST_HOST_COMPILER - > -{}; - -template struct has_trivial_destructor : public is_pod {}; - -template struct is_const : public false_type {}; -template struct is_const : public true_type {}; - -template struct is_volatile : public false_type {}; -template struct is_volatile : public true_type {}; - -template - struct add_const -{ - typedef T const type; -}; // end add_const - -template - struct remove_const -{ - typedef T type; -}; // end remove_const - -template - struct remove_const -{ - typedef T type; -}; // end remove_const - -template - struct add_volatile -{ - typedef volatile T type; -}; // end add_volatile - -template - struct remove_volatile -{ - typedef T type; -}; // end remove_volatile - -template - struct remove_volatile -{ - typedef T type; -}; // end remove_volatile - -template - struct add_cv -{ - typedef const volatile T type; -}; // end add_cv - -template - struct remove_cv -{ - typedef typename remove_const ::type>::type type; -}; // end remove_cv - - -template struct is_reference : public false_type {}; -template struct is_reference struct is_proxy_reference : public false_type {}; - -template struct is_device_reference : public false_type {}; -template struct is_device_reference< thrust::device_reference > : public true_type {}; - - -// NB: Careful with reference to void. -template ::value || is_reference<_Tp>::value)> - struct __add_reference_helper - { typedef _Tp& type; }; - -template - struct __add_reference_helper<_Tp, true> - { typedef _Tp type; }; - -template - struct add_reference - : public __add_reference_helper<_Tp>{}; - -template - struct remove_reference -{ - typedef T type; -}; // end remove_reference - -template - struct remove_reference - struct is_same - : public false_type -{ -}; // end is_same - -template - struct is_same - struct lazy_is_same - : is_same -{ -}; // end lazy_is_same - -template - struct is_different - : public true_type -{ -}; // end is_different - -template - struct is_different - struct lazy_is_different - : is_different -{ -}; // end lazy_is_different - -#if THRUST_CPP_DIALECT >= 2011 - -using std::is_convertible; - -#else - -namespace tt_detail -{ - -template - struct is_int_or_cref -{ - typedef typename remove_reference ::type type_sans_ref; - static const bool value = (is_integral ::value - || (is_integral ::value - && is_const ::value - && !is_volatile ::value)); -}; // end is_int_or_cref - - -THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_BEGIN -THRUST_DISABLE_MSVC_FORCING_VALUE_TO_BOOL_WARNING_BEGIN - -template - struct is_convertible_sfinae -{ - private: - typedef char yes; - typedef struct { char two_chars[2]; } no; - - static inline yes test(To) { return yes(); } - static inline no test(...) { return no(); } - static inline typename remove_reference ::type& from() { typename remove_reference ::type* ptr = 0; return *ptr; } - - public: - static const bool value = sizeof(test(from())) == sizeof(yes); -}; // end is_convertible_sfinae - - -THRUST_DISABLE_MSVC_FORCING_VALUE_TO_BOOL_WARNING_END -THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_END - - -template - struct is_convertible_needs_simple_test -{ - static const bool from_is_void = is_void ::value; - static const bool to_is_void = is_void ::value; - static const bool from_is_float = is_floating_point ::type>::value; - static const bool to_is_int_or_cref = is_int_or_cref ::value; - - static const bool value = (from_is_void || to_is_void || (from_is_float && to_is_int_or_cref)); -}; // end is_convertible_needs_simple_test - - -template ::value> - struct is_convertible -{ - static const bool value = (is_void ::value - || (is_int_or_cref ::value - && !is_void ::value)); -}; // end is_convertible - - -template - struct is_convertible ::type, To>::value); -}; // end is_convertible - - -} // end tt_detail - -template - struct is_convertible - : public integral_constant - struct is_one_convertible_to_the_other - : public integral_constant< - bool, - is_convertible - struct or_ - : public integral_constant< - bool, - Condition1::value || Condition2::value || Condition3::value || Condition4::value || Condition5::value || Condition6::value || Condition7::value || Condition8::value || Condition9::value || Condition10::value - > -{ -}; // end or_ - -template - struct and_ - : public integral_constant - struct not_ - : public integral_constant -struct conditional { typedef T type; }; - -template -struct conditional - struct eval_if - struct eval_if -// struct identity -// XXX WAR nvcc's confusion with thrust::identity - struct identity_ -{ - typedef T type; -}; // end identity - -template struct enable_if struct lazy_enable_if struct disable_if : enable_if {}; -template struct lazy_disable_if : lazy_enable_if {}; - - -template - struct enable_if_convertible - : enable_if< is_convertible - struct disable_if_convertible - : disable_if< is_convertible - struct enable_if_different - : enable_if - struct is_numeric - : and_< - is_convertible struct is_reference_to_const : false_type {}; -template struct is_reference_to_const : true_type {}; - - -// make_unsigned follows - -namespace tt_detail -{ - -template struct make_unsigned_simple; - -template<> struct make_unsigned_simple { typedef unsigned char type; }; -template<> struct make_unsigned_simple { typedef unsigned char type; }; -template<> struct make_unsigned_simple { typedef unsigned char type; }; -template<> struct make_unsigned_simple { typedef unsigned short type; }; -template<> struct make_unsigned_simple { typedef unsigned short type; }; -template<> struct make_unsigned_simple { typedef unsigned int type; }; -template<> struct make_unsigned_simple { typedef unsigned int type; }; -template<> struct make_unsigned_simple { typedef unsigned long int type; }; -template<> struct make_unsigned_simple { typedef unsigned long int type; }; -template<> struct make_unsigned_simple { typedef unsigned long long int type; }; -template<> struct make_unsigned_simple { typedef unsigned long long int type; }; - -template - struct make_unsigned_base -{ - // remove cv - typedef typename remove_cv ::type remove_cv_t; - - // get the simple unsigned type - typedef typename make_unsigned_simple ::type unsigned_remove_cv_t; - - // add back const, volatile, both, or neither to the simple result - typedef typename eval_if< - is_const ::value && is_volatile ::value, - // add cv back - add_cv , - // check const & volatile individually - eval_if< - is_const ::value, - // add c back - add_const , - eval_if< - is_volatile ::value, - // add v back - add_volatile , - // original type was neither cv, return the simple unsigned result - identity_ - > - > - >::type type; -}; - -} // end tt_detail - -template - struct make_unsigned - : tt_detail::make_unsigned_base -{}; - -struct largest_available_float -{ -#if defined(__CUDA_ARCH__) -# if (__CUDA_ARCH__ < 130) - typedef float type; -# else - typedef double type; -# endif -#else - typedef double type; -#endif -}; - -// T1 wins if they are both the same size -template - struct larger_type - : thrust::detail::eval_if< - (sizeof(T2) > sizeof(T1)), - thrust::detail::identity_ , - thrust::detail::identity_ - > -{}; - -#if THRUST_CPP_DIALECT >= 2011 - -using std::is_base_of; - -#else - -namespace is_base_of_ns -{ - -typedef char yes; -typedef struct { char two_chars[2]; } no; - -template - struct host -{ - operator Base*() const; - operator Derived*(); -}; // end host - -template - struct impl -{ - template static yes check(Derived *, T); - static no check(Base*, int); - - static const bool value = sizeof(check(host - struct is_base_of - : integral_constant< - bool, - is_base_of_ns::impl - struct enable_if_base_of - : enable_if< - is_base_of - class is_assignable -{ - typedef char yes_type; - typedef struct { char array[2]; } no_type; - - template static typename add_reference ::type declval(); - - template struct helper { typedef void * type; }; - - template static yes_type test(typename helper ())>::type); - - template - struct is_assignable - : integral_constant< - bool, - is_assignable_ns::is_assignable - struct is_copy_assignable - : is_assignable< - typename add_reference ::type, - typename add_reference ::type>::type - > -{}; - - -template struct promoted_numerical_type; - -template - struct promoted_numerical_type ::type> - ::value>::type> - { - typedef typename larger_type - struct promoted_numerical_type ::type> - ::value>::type> - { - typedef T2 type; - }; - -template - struct promoted_numerical_type ::type> - ::value>::type> - { - typedef T1 type; - }; - -template - struct is_empty_helper : public T - { - }; - -struct is_empty_helper_base -{ -}; - -template