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- Download Stata 14 for Mac: A Comprehensive Guide
- If you are looking for a powerful and versatile software for data analysis, statistics, and graphics, you might want to consider downloading Stata 14 for Mac. Stata 14 is one of the most popular and widely used software packages in the fields of economics, sociology, political science, biostatistics, epidemiology, and many others. It offers a range of features and benefits that can help you perform complex data manipulation, estimation, testing, forecasting, simulation, and visualization tasks with ease and accuracy.
- In this article, we will provide you with a comprehensive guide on how to download Stata 14 for Mac. We will also explain what Stata 14 is and why you need it, what are its main features and benefits, how to install and use it on your Mac computer, and some frequently asked questions about it. By the end of this article, you will have a clear idea of whether Stata 14 is the right software for you and how to get started with it.
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What is Stata 14 and why do you need it?
- Stata 14 is a software package that was released in April 2015 by StataCorp, a company that has been developing and distributing statistical software since 1985. Stata 14 is the latest version of Stata as of June 2021, although there have been several updates and bug fixes since then. The current update is Stata 14.2.
- Stata 14 is a software that can handle both cross-sectional and longitudinal data, as well as panel data and multilevel data. It can also deal with both continuous and discrete variables, as well as categorical and ordinal variables. It can perform various types of analysis, such as linear and nonlinear regression, ANOVA, logistic regression, survival analysis, time series analysis, factor analysis, cluster analysis, structural equation modeling (SEM), item response theory (IRT), Bayesian analysis, power and sample size calculation, Markov-switching models, treatment effects models, multilevel survival models, fractional outcome regression models, and many more.
- Stata 14 also has a user-friendly interface that allows you to interact with the software using either menus or commands. You can also customize your preferences and settings according to your needs. You can also create your own commands or programs using the built-in programming language of Stata. You can also access thousands of user-written commands or programs from the internet or from the official Stata Journal.
- Stata 14 also has a powerful graphics engine that can produce high-quality graphs and charts that can be customized in various ways. You can also export your graphs to different formats such as PDF, PNG, EPS, SVG, etc. You can also integrate your graphs with other applications such as Microsoft Word or PowerPoint.
- Stata 14 also has a comprehensive documentation that includes manuals, tutorials, examples, FAQs, glossaries, references, etc. You can also get support from the official website of StataCorp or from the online community of Stata users called St
ataList. You can also get training courses or webinars from StataCorp or from other authorized providers.
- Stata 14 is a software that can help you with your data analysis needs, whether you are a student, a researcher, a teacher, a consultant, or a professional. It can help you save time and effort, improve your accuracy and reliability, enhance your presentation and communication, and expand your knowledge and skills. It can also help you collaborate with other Stata users around the world and share your insights and discoveries.
- Features and benefits of Stata 14
- Stata 14 has many features and benefits that make it a superior software for data analysis. Here are some of the most notable ones:
- Bayesian analysis
- Stata 14 introduces a new command called bayes that allows you to perform Bayesian analysis using Markov chain Monte Carlo (MCMC) methods. You can specify any likelihood function and any prior distribution for the parameters, and Stata will generate posterior samples and summaries for you. You can also use predefined models such as linear regression, logistic regression, Poisson regression, etc. You can also compare models using Bayes factors or posterior predictive checks. You can also visualize your results using trace plots, density plots, interval plots, etc.
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- IRT (item response theory)
- Stata 14 also introduces a new command called irt that allows you to perform item response theory (IRT) analysis using maximum likelihood estimation (MLE) methods. You can fit various IRT models such as Rasch model, one-parameter logistic model (1PL), two-parameter logistic model (2PL), three-parameter logistic model (3PL), graded response model (GRM), partial credit model (PCM), etc. You can also test the assumptions of IRT models such as unidimensionality, local independence, monotonicity, etc. You can also assess the reliability and validity of your instruments using Cronbach's alpha, test information function (TIF), item information function (IIF), etc.
- Unicode
- Stata 14 supports Unicode encoding, which means that you can use any character set or language in your data, commands, output, graphs, etc. You can also import and export data files that use Unicode encoding. You can also use Unicode characters in your variable names, labels, values, etc. This feature makes Stata 14 more accessible and compatible with different cultures and languages.
- Integration with Excel
- Stata 14 has improved its integration with Excel, which means that you can easily import and export data between Stata and Excel. You can also use the new command called import excel to import data from Excel files directly into Stata without saving them as CSV files first. You can also use the new command called export excel to export data from Stata to Excel files with various options such as sheet name, cell range, variable names, labels, formats, etc.
- Treatment effects
- Stata 14 has expanded its treatment effects capabilities by adding new commands such as teffects ipwra for inverse probability weighting with regression adjustment (IPWRA), teffects ipw for inverse probability weighting (IPW), teffects psmatch for propensity score matching (PSM), teffects nnmatch for nearest neighbor matching (NNM), teffects overlap for overlap weights (OW), teffects ra for regression adjustment (RA), teffects endogenous for endogenous treatment effects models (ETE), etc. These commands allow you to estimate the causal effects of treatments or interventions on outcomes using various methods that account for selection bias or confounding factors.
- Multilevel survival models
- Stata 14 has added new commands such as mestreg for multilevel survival models with random effects at different levels of hierarchy. You can specify various types of random effects such as intercepts, slopes, frailties, etc. You can also specify various types of survival distributions such as exponential, Weibull, lognormal, log-logistic, gamma, Gompertz , etc. You can also test various hypotheses and assumptions using likelihood ratio tests, Wald tests, Schoenfeld residuals, etc.
- SEM (structural equation modeling)
- Stata 14 has improved its SEM capabilities by adding new features such as latent class analysis (LCA), latent transition analysis (LTA), latent profile analysis (LPA), latent growth curve models (LGCM), multilevel SEM, generalized SEM, dynamic SEM, etc. You can also use the new command called sembuilder to create and modify SEM diagrams using a graphical user interface (GUI). You can also use the new command called estat gof to calculate various goodness-of-fit measures such as chi-square, RMSEA, CFI, TLI, SRMR, etc.
- Power and sample size
- Stata 14 has enhanced its power and sample size capabilities by adding new commands such as power twoproportions for two-sample tests of proportions, power logrank for log-rank tests of survival curves, power cox for Cox proportional hazards models, power oneway for one-way ANOVA, power repeated for repeated-measures ANOVA, power cluster for cluster randomized trials, power bootstrap for bootstrap-based power analysis, etc. These commands allow you to calculate the required sample size or the achieved power for various types of statistical tests or models.
- Markov-switching models
- Stata 14 has introduced a new command called mswitch that allows you to estimate Markov-switching models for time series data. These models allow you to capture regime changes or structural breaks in the data by allowing the parameters to switch between different states or regimes according to a Markov process. You can specify various types of Markov-switching models such as Hamilton's model, Kim's model, Goldfeld-Quandt's model, etc. You can also test for the number of regimes, the duration of regimes, the transition probabilities, etc.
- Panel-data survival models
- Stata 14 has added a new command called xtscc that allows you to estimate panel-data survival models with correlated random effects. These models allow you to account for unobserved heterogeneity and serial correlation in panel data with survival outcomes. You can specify various types of survival distributions such as exponential, Weibull, lognormal, log-logistic, gamma, Gompertz, etc. You can also test various hypotheses and assumptions using likelihood ratio tests, Wald tests, Schoenfeld residuals, etc.
- Fractional outcome regression
- Stata 14 has added a new command called fracreg that allows you to estimate fractional outcome regression models for data with fractional outcomes. These models allow you to model outcomes that are bounded between zero and one, such as proportions, rates, shares, probabilities, etc. You can specify various types of fractional outcome regression models such as beta regression, fractional logit regression, fractional probit regression, etc. You can also test various hypotheses and assumptions using likelihood ratio tests, Wald tests , score tests, etc.
- How to download and install Stata 14 for Mac?
- If you are interested in downloading and installing Stata 14 for Mac, you need to follow these steps:
- System requirements and compatibility
- Before you download and install Stata 14 for Mac, you need to make sure that your Mac computer meets the minimum system requirements and is compatible with the software. Here are the system requirements and compatibility for Stata 14 for Mac:
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-Operating system: Mac OS X 10.7 or newer
-Processor: 64-bit Intel processor
-Memory: 1 GB RAM (2 GB recommended)
-Disk space: 1 GB for Stata installation, plus additional space for datasets
-Display: 1024 x 768 or higher resolution monitor
-Internet connection: Required for installation and updates
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- If your Mac computer meets these requirements and is compatible with Stata 14, you can proceed to the next step.
- Steps to download and install Stata 14 for Mac
- To download and install Stata 14 for Mac, you need to follow these steps:
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-Go to the official website of StataCorp at https://www.stata.com/
-Click on the "Order" tab at the top of the page.
-Select the type of license that suits your needs, such as "Stata/MP", "Stata/SE", "Stata/IC", or "Stata Small". You can also compare the features and prices of different licenses by clicking on the "Compare features" link.
-Select the number of users and the duration of the license that you want, such as "Single-user", "Multi-user", "Perpetual", or "Annual". You can also see the total cost of your order by clicking on the "Calculate price" button.
-Click on the "Add to cart" button to proceed to the checkout page.
-Enter your billing and shipping information, as well as your payment method. You can pay by credit card, PayPal, wire transfer, check, or purchase order. You can also apply a discount code if you have one.
-Review your order details and click on the "Place order" button to complete your purchase.
-After you place your order, you will receive an email confirmation with your order number and a link to download Stata 14 for Mac. You will also receive a license code and an authorization code that you will need to activate your software.
-Click on the link in the email to download Stata 14 for Mac. The file size is about 300 MB. Save the file to a location that you can easily access, such as your desktop or downloads folder.
-Double-click on the downloaded file to open it. You will see a window with a Stata icon and a folder called "Stata". Drag and drop the Stata icon into the folder called "Stata". This will create a folder called "Stata14" in your applications folder.
-Open the folder called "Stata14" and double-click on the Stata icon to launch the software. You will see a window with a welcome message and a prompt to enter your license code and authorization code. Enter the codes that you received in your email and click on the "OK" button.
-The software will verify your codes and activate your license. You will see a window with a message that says "Congratulations! You have successfully installed Stata." Click on the "OK" button to close the window.
-You have successfully downloaded and installed Stata 14 for Mac. You can now start using it for your data analysis needs.
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- How to use Stata 14 for Mac?
- Now that you have downloaded and installed Stata 14 for Mac, you might be wondering how to use it. Here are some basic tips and tricks on how to use Stata 14 for Mac:
- Basic commands and syntax
- Stata 14 for Mac allows you to interact with the software using either menus or commands. You can access the menus by clicking on the icons at the top of the window, such as "File", "Edit", "Data", "Graphics", etc. You can also access some common commands by clicking on the buttons at the bottom of the window, such as "Do-file Editor", "Data Editor", "Variables Manager", "Graph Editor", etc. You can also use commands by typing them in the command window at the bottom of the window. You can also use the do-file editor to write and execute multiple commands at once. You can also use the help window to access the documentation and examples of any command. The basic syntax of Stata commands is as follows: command [varlist] [if] [in] [weight] [, options]
- where: - command is the name of the command, such as regress, summarize, tabulate, etc. - [varlist] is the list of variables that you want to use in the command, separated by spaces, such as age income education. You can also use wildcards, operators, or functions to specify variables, such as x*, x1-x5, log(x), etc. - [if] is the condition that you want to apply to the command, such as if gender == 1, if age > 30, if income > mean(income), etc. You can use logical operators such as &, |, or ! to combine conditions, such as if gender == 1 & age > 30. - [in] is the range of observations that you want to use in the command, such as in 1/100, in 101/200, in 1/2, etc. You can also use keywords such as _n, _N, or _first to specify observations, such as in _n-10/_n+10. - [weight] is the type and name of the weight variable that you want to use in the command, such as [fweight=pop], [pweight=prob], [iweight=imp], etc. You can use different types of weights depending on the nature and purpose of your analysis, such as frequency weights, probability weights, importance weights, etc. - [, options] are the additional options that you want to use in the command, separated by commas, such as , robust, , detail, , graph, etc. You can use different options depending on the command and the output that you want to obtain, such as robust standard errors, detailed statistics, graphical displays, etc. For example, if you want to perform a linear regression of income on age and education, you can use the following command: regress income age education
- If you want to perform the same regression with robust standard errors and a scatter plot of the fitted values, you can use the following command: regress income age education, robust graph
- You can also use the help window or the manuals to learn more about the syntax and options of any command. Data management and analysis
- Stata 14 for Mac allows you to manage and analyze your data using various commands and tools. You can import and export data from different sources and formats, such as Excel, CSV, SPSS, SAS, Stata, etc. You can also create and modify variables, labels, values, formats, etc. You can also sort, merge, append, reshape, collapse, expand, etc. your data. You can also perform various descriptive and inferential statistics on your data, such as summary statistics, frequency tables, cross-tabulations, correlation coefficients, hypothesis tests, confidence intervals, etc. You can also perform various types of analysis on your data, such as regression analysis, ANOVA, logistic regression, survival analysis, time series analysis, factor analysis, cluster analysis, structural equation modeling (SEM), item response theory (IRT), Bayesian analysis, power and sample size calculation, Markov-switching models, treatment effects models, multilevel survival models, fractional outcome regression models , and many more.
- To manage and analyze your data using Stata 14 for Mac, you can use the following commands and tools:
-
-To import data from different sources and formats, you can use the commands such as import excel, import delimited, import spss, import sas, use, etc. You can also use the menu "File > Import" to access the import dialog box.
-To export data to different sources and formats, you can use the commands such as export excel, export delimited, export spss, export sas, save, etc. You can also use the menu "File > Export" to access the export dialog box.
-To create and modify variables, labels, values, formats, etc., you can use the commands such as generate, replace, rename, recode, label, format, etc. You can also use the data editor or the variables manager to access the graphical user interface (GUI) for data management.
-To sort, merge, append, reshape, collapse, expand, etc. your data, you can use the commands such as sort, merge, append, reshape, collapse, expand, etc. You can also use the menu "Data > Data utilities" to access the data utilities dialog box.
-To perform descriptive and inferential statistics on your data, you can use the commands such as summarize, tabulate, tabstat, correlate, ttest, ci, etc. You can also use the menu "Statistics > Summary statistics" or "Statistics > Tables" to access the summary statistics or tables dialog box.
-To perform various types of analysis on your data, you can use the commands such as regress, anova, logit, streg, arima, factor, cluster, sem, irt, bayes, power, mswitch, teffects, mestreg, fracreg, etc. You can also use the menu "Statistics > Linear models and related" or "Statistics > Other models" to access the linear models or other models dialog box.
-
- Graphs and visualization
- Stata 14 for Mac allows you to create and modify graphs and charts using various commands and tools. You can create various types of graphs, such as scatter plots, line plots, bar charts, pie charts, box plots, histogram, density plots, etc. You can also customize your graphs in various ways, such as adding titles, labels, legends, axes, colors, markers, lines, etc. You can also export your graphs to different formats, such as PDF, PNG, EPS, SVG, etc. You can also integrate your graphs with other applications, such as Microsoft Word or PowerPoint.
- To create and modify graphs and charts using Stata 14 for Mac, you can use the following commands and tools:
-
-To create graphs using commands, you can use the commands such as scatter, line, bar, pie, box, histogram, kdensity, etc. You can also use the command graph to create graphs using a general syntax. You can also use the command twoway to create graphs using multiple plot types.
-To create graphs using menus, you can use the menu "Graphics > Graphs" to access the graphs dialog box. You can also use the menu "Graphics > Graph editor" to access the graph editor dialog box.
-To modify graphs using commands, you can use the commands such as graph set, graph export, graph combine, graph rename, graph close, etc. You can also use the command graph options to modify various options of your graphs.
-To modify graphs using menus, you can use the menu "Graphics > Graph preferences" to access the graph preferences dialog box. You can also use the menu "Graphics > Graph editor" to access the graph editor dialog box.
-To export graphs to different formats, you can use the commands such as graph export, graph save, etc. You can also use the menu "File > Save as" or "File > Export" to access the save as or export dialog box.
-To integrate graphs with other applications, you can use the commands such as putdocx, putpdf, putexcel, etc. You can also use the menu "File > Export" to access the export dialog box.
-
- Conclusion
- In this article, we have provided you with a comprehensive guide on how to download Stata 14 for Mac. We have also explained what Stata 14 is and why you need it, what are its main features and benefits, how to install and use it on your Mac computer, and some frequently asked questions about it. We hope that this article has helped you to understand whether Stata 14 is the right software for you and how to get started with it.
- If you have any questions or comments about this article, please feel free to contact us at support@stata.com. We would love to hear from you and assist you with your data analysis needs. Thank you for reading this article and happy Stata-ing!
- FAQs
- Here are some of the most frequently asked questions about Stata 14 for Mac:
-
-How much does Stata 14 for Mac cost? The price of Stata 14 for Mac depends on the type of license, the number of users, and the duration of the license that you choose. You can check the current prices and discounts at https://www.stata.com/order/ . You can also request a quote or a free trial at https://www.stata.com/contact/ .
-How can I update Stata 14 for Mac? You can update Stata 14 for Mac by using the command update or by using the menu "Help > Check for updates". You can also check the latest updates and bug fixes at https://www.stata.com/support/updates/ .
-How can I get help with Stata 14 for Mac? You can get help with Stata 14 for Mac by using the command help or by using the menu "Help > Stata help". You can also access the online documentation and examples at https://www.stata.com/help/ . You can also get support from the official website of StataCorp at https://www.stata.com/support/ or from the online community of Stata users at https://www.statalist.org/ .
-How can I learn more about Stata 14 for Mac? You can learn more about Stata 14 for Mac by using the command search or by using the menu "Help > Search". You can also access the online tutorials and videos at https://www.stata.com/learn/ . You can also get training courses or webinars from StataCorp or from other authorized providers at https://www.stata.com/training/ .
-How can I share my feedback or suggestions about Stata 14 for Mac? You can share your feedback or suggestions about Stata 14 for Mac by using the command suggest or by using the menu "Help > Suggest". You can also email your feedback or suggestions to suggest@stata.com. We appreciate your input and we will try our best to improve our software and service.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Cricket League MOD APK and Become the Ultimate Cricket Champion (Unlimited Gems and Coins).md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Cricket League MOD APK and Become the Ultimate Cricket Champion (Unlimited Gems and Coins).md
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+++ /dev/null
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- One of the best things about Cricket League Mod Apk is that it is free and easy to play. You don't have to spend any money to download or play this game. You also don't have to worry about any complicated controls or rules. The game has a simple and intuitive interface that will guide you through the game. You can also adjust the settings according to your preferences and comfort level.
- Fun and Engaging Gameplay
- Another great thing about Cricket League Mod Apk is that it has a fun and engaging gameplay that will keep you hooked for hours. The game has various modes, tournaments, challenges, and missions that will test your skills and strategy. You can also play with other players online or offline, and chat with them using the in-game chat feature. The game also has a leaderboard and achievements system that will motivate you to improve your performance and rank.
- Customizable and Diverse Options
- A third great thing about Cricket League Mod Apk is that it has customizable and diverse options that will make your game more enjoyable and unique. You can choose from hundreds of players and teams, each with their own stats and abilities. You can also customize your jerseys, logos, bats, balls, etc. You can also play in different stadiums, weather conditions, pitch types, etc.
- Cons of Cricket League Mod Apk
- Requires Internet Connection
- One of the drawbacks of Cricket League Mod Apk is that it requires an internet connection to play online mode or update the game. This can be a problem if you have a slow or unstable internet connection, or if you don't have access to Wi-Fi or mobile data. You may experience lagging, crashing, or loading issues while playing the game.
- May Contain Ads and Bugs
- Another drawback of Cricket League Mod Apk is that it may contain ads and bugs that can affect your gaming experience. Since this is a mod apk file, it may not be compatible with some devices or versions of Android. It may also have some glitches or errors that can cause the game to freeze or crash. You may also see some ads popping up while playing the game, which can be annoying or distracting.
- Conclusion
- Cricket League Mod Apk is a fantastic cricket simulation game that will give you unlimited gems and coins, and access to all the players and teams in the game. You can also enjoy realistic cricket matches and leagues with your friends online or offline. The game has high-quality graphics, sound effects, animations, and physics that will make you feel like you are playing on a real pitch. The game also has various modes, such as T20, ODI, Test, World Cup, IPL, PSL, BBL, CPL, and more.
- If you are a cricket fan who wants to have more fun and excitement in your mobile gaming, then you should definitely try Cricket League Mod Ap k. However, you should also be aware of the drawbacks of this game, such as requiring an internet connection, and containing ads and bugs. You should also be careful about downloading and installing mod apk files from unknown sources, as they may contain viruses or malware that can harm your device or data.
- We hope this article has helped you learn more about Cricket League Mod Apk, and how to download and enjoy it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
- FAQs
- Here are some of the frequently asked questions about Cricket League Mod Apk:
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-Is Cricket League Mod Apk safe to download and install?
-Cricket League Mod Apk is generally safe to download and install, as long as you get it from a reliable source. However, you should always scan the file with an antivirus or malware detector before installing it, and backup your data before playing the game. You should also avoid giving any personal or sensitive information to the game or its developers.
-Is Cricket League Mod Apk legal to play?
-Cricket League Mod Apk is not legal to play, as it violates the terms and conditions of the original game and its developers. By playing this game, you are infringing on the intellectual property rights of the original game and its developers. You may also face legal consequences if you are caught playing this game by the authorities or the original game developers.
-How can I update Cricket League Mod Apk?
-Cricket League Mod Apk does not have an official update system, as it is not supported by the original game developers. You may have to download and install a new mod apk file every time there is a new version of the original game. However, this may not work if the new version of the original game has some changes or features that are incompatible with the mod apk file.
-Can I play Cricket League Mod Apk with my friends?
-Yes, you can play Cricket League Mod Apk with your friends online or offline. You can join or create online matches with other players from around the world, or use local multiplayer mode to play with your friends using Bluetooth or Wi-Fi. However, you may not be able to play with your friends who are using the original game, as they may have different versions or features than you.
-Can I play Cricket League Mod Apk on PC or iOS devices?
-No, you cannot play Cricket League Mod Apk on PC or iOS devices, as it is only designed for Android devices. You may be able to use some emulators or converters to run this game on PC or iOS devices, but they may not work properly or cause some issues. We do not recommend using any emulators or converters to play this game on PC or iOS devices.
- 197e85843d6 Pack Abs APK - What Is It and How Does It Work?
-If you want to get six pack abs without going to the gym or spending money on expensive equipment, you might want to try 6 pack abs apk. This is a free app that provides you with a 30-day workout plan that targets your upper and lower abdominal muscles. The app also features animations and video guides that show you how to perform each exercise correctly and effectively. You can also customize your workout reminders and track your progress automatically.
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But why should you care about getting six pack abs in the first place? Well, there are many benefits of having six pack abs, both physical and psychological. Here are some of them:
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-Improved posture and core strength. Having six pack abs means having strong abdominal muscles that support your spine and pelvis. This can help you improve your posture, prevent lower back pain, and enhance your core strength.Better sporting performance and agility. Having six pack abs also means having more power transfer between your upper and lower body. This can help you improve your sporting performance, agility, balance, coordination, and speed.Increased basal metabolic rate and fat burning. Having six pack abs also means having more muscle mass in your body. This can help you increase your basal metabolic rate, which is the amount of calories you burn at rest. This can also help you burn more fat and reduce your body fat percentage, which is necessary to reveal your six pack abs.
-As you can see, 6 pack abs apk is a great app that can help you get six pack abs and enjoy many benefits. But before you download it and start working out, there are some myths about six pack abs that you need to be aware of.
-The Myths About 6 Pack Abs APK
-There are many myths and misconceptions about six pack abs that can prevent you from achieving your goal or even harm your health. Here are some of the most common ones and why they are not true:
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-You Need a Fat Burner or a Low Carb Diet
-Some people think that they need to take a fat burner supplement or follow a low carb diet to get six pack abs. This is not true. Fat burners are not effective or safe, as they can cause side effects such as insomnia, anxiety, high blood pressure, and liver damage. Low carb diets are also not necessary or sustainable, as they can cause fatigue, mood swings, muscle loss, and nutrient deficiencies. The best way to get six pack abs is to eat a healthy, balanced diet that provides enough calories and macronutrients (protein, carbs, and fats) for your body and activity level.
-You Can Crunch Your Way to a Six Pack
-Some people think that they can crunch their way to a six pack by doing hundreds of crunches every day. This is not true. Crunches are not enough to reveal your six pack abs, as they only target one part of your abdominal muscles (the rectus abdominis). To get six pack abs, you need to work out all the muscles in your core, including the obliques, the transverse abdominis, and the lower back. You also need to reduce your body fat percentage by doing cardio and strength training exercises that burn calories and build muscle mass.
-You Must Train Abs Every Day or Use Special Equipment
-Some people think that they must train their abs every day or use special equipment such as ab rollers, ab machines, or ab belts to get six pack abs. This is not true. Training your abs every day is not necessary or beneficial, as it can lead to overtraining, injury, and muscle imbalance. Your abs need rest and recovery just like any other muscle group. You should train your abs two to three times a week with adequate rest days in between. Using special equipment is also not required or effective, as they can limit your range of motion, isolate your muscles, and create false expectations. The best way to train your abs is to use bodyweight exercises that challenge your core stability, strength, and endurance.
-The Tips for Using 6 Pack Abs APK Effectively
-Now that you know what 6 pack abs apk is and how it works, and what are the myths about six pack abs that you should avoid, here are some tips for using 6 pack abs apk effectively:
-Follow the Workout Plan Consistently
-The first tip is to follow the workout plan provided by 6 pack abs apk consistently. The app offers a 30-day workout plan that consists of three levels of difficulty (beginner, intermediate, and advanced) and various exercises for the upper and lower abs. Each workout takes about 10 minutes and can be done at home or anywhere else. The app also provides animations and video guides that show you how to perform each exercise correctly and effectively. To get the best results from 6 pack abs apk, you should follow the workout plan without skipping any days or sessions. Consistency is key to getting results.
-Eat a Healthy, Balanced Diet
-The second tip is to eat a healthy, balanced diet that supports your workout plan and your goal of getting six pack abs. As mentioned earlier, nutrition is important for muscle growth and fat loss. You should eat enough calories and macronutrients (protein, carbs, and fats) for your body and activity level. You should also eat foods that are rich in vitamins, minerals, antioxidants, and fiber. Some examples of healthy foods are lean meats, eggs, fish, dairy products, nuts, seeds, beans, fruits, vegetables, whole grains, and healthy oils. You should also avoid foods that are high in sugar, salt, trans fats, and processed ingredients. Some examples of unhealthy foods are candy, soda, chips, cookies, cakes, fast food, and fried food. Eating a healthy, balanced diet can help you get six pack abs by providing your body with the nutrients it needs to function properly and recover from your workouts.
-Drink Plenty of Water and Get Enough Sleep
-The third tip is to drink plenty of water and get enough sleep to support your workout plan and your goal of getting six pack abs. Water is essential for your body, as it helps you stay hydrated, regulate your body temperature, flush out toxins, transport nutrients, and lubricate your joints. You should drink at least eight glasses of water a day, or more if you exercise or sweat a lot. Water can also help you get six pack abs by suppressing your appetite, boosting your metabolism, and preventing water retention. Sleep is also vital for your body, as it helps you restore your energy, repair your muscles, consolidate your memory, and regulate your hormones. You should get at least seven to nine hours of sleep a night, or more if you need it. Sleep can also help you get six pack abs by reducing your stress levels, improving your mood, enhancing your performance, and preventing cravings.
-Conclusion
-6 pack abs apk is a free app that can help you get six pack abs in 30 days by providing you with a workout plan that targets your upper and lower abdominal muscles. The app also features animations and video guides that show you how to perform each exercise correctly and effectively. You can also customize your workout reminders and track your progress automatically.
-Getting six pack abs can provide you with many benefits, such as improved posture and core strength, better sporting performance and agility, increased basal metabolic rate and fat burning, and more confidence and self-esteem. However, to get six pack abs, you need to avoid some myths and misconceptions that can hinder your progress or harm your health. These include the myths that you need a fat burner or a low carb diet, that you can crunch your way to a six pack, and that you must train abs every day or use special equipment.
-To use 6 pack abs apk effectively, you need to follow some tips that can help you achieve your goal faster and easier. These include the tips of following the workout plan consistently, eating a healthy, balanced diet, drinking plenty of water and getting enough sleep.
-If you follow these tips and use 6 pack abs apk regularly, you will be able to get six pack abs in no time. So what are you waiting for? Download 6 pack abs apk today and start working on your dream body!
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-Here are some frequently asked questions about 6 pack abs apk:
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-How do I download 6 pack abs apk? You can download 6 pack abs apk from the Google Play Store or the App Store for free. Just search for "6 pack abs apk" and install it on your device.
-How do I use 6 pack abs apk? You can use 6 pack abs apk by following the instructions on the app. First, you need to choose your level of difficulty (beginner, intermediate, or advanced). Then, you need to start the workout plan that consists of various exercises for the upper and lower abs. You can also set reminders for your workouts and track your progress automatically.
-How long does it take to see results with 6 pack abs apk? The time it takes to see results with 6 pack abs apk depends on several factors, such as your starting point, your diet, your exercise routine, your genetics, and your commitment. However, if you follow the workout plan consistently, eat a healthy, balanced diet, drink plenty of water and get enough sleep, you should be able to see some results in as little as four weeks. Of course, the more you stick to the plan and the more you challenge yourself, the faster and better your results will be.
-Is 6 pack abs apk safe and effective? Yes, 6 pack abs apk is safe and effective, as it is based on scientific research and proven methods. The app provides you with a workout plan that targets your abdominal muscles with various exercises that are suitable for different levels of difficulty. The app also provides you with animations and video guides that show you how to perform each exercise correctly and effectively. The app also allows you to customize your workout reminders and track your progress automatically. The app does not require any special equipment or supplements, and it does not promote any unhealthy or unrealistic practices.
-Can I use 6 pack abs apk with other fitness apps or programs? Yes, you can use 6 pack abs apk with other fitness apps or programs, as long as they are compatible and complementary. For example, you can use 6 pack abs apk with a running app or a yoga app to add some cardio and flexibility training to your routine. You can also use 6 pack abs apk with a weight lifting app or a bodyweight app to add some strength and resistance training to your routine. However, you should not use 6 pack abs apk with another ab workout app or program, as this can lead to overtraining, injury, and muscle imbalance. You should also not use 6 pack abs apk with an app or program that contradicts or conflicts with the principles and guidelines of 6 pack abs apk.
-What if I have questions or feedback about 6 pack abs apk? If you have any questions or feedback about 6 pack abs apk, you can contact the developers of the app through their email address or their social media accounts. You can also leave a review or a rating on the Google Play Store or the App Store to share your experience and opinion with other users. The developers of 6 pack abs apk are always happy to hear from their users and to improve their app based on their suggestions and feedback.
197e85843dDownload Topaz AI: How to Enhance Your Photos and Videos with Artificial Intelligence
-Do you want to improve the quality of your photos and videos with the power of artificial intelligence? If so, you should download Topaz AI, a suite of software products that use cutting-edge image enhancement technology to magically transform your images and videos. In this article, you will learn what Topaz AI is, how it works, how to download and install it on your computer, how to use it from your image editor, and how to apply it to different scenarios. By the end of this article, you will be able to enhance your photos and videos like never before with Topaz AI.
- Topaz Photo AI: Maximize Your Image Quality on Autopilot
-Topaz Photo AI is a collection of four products that use artificial intelligence to sharpen, remove noise, and increase the resolution of your photos. These products are:
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-Gigapixel AI : This product allows you to upscale your images by up to 6x while increasing actual resolution and real detail. You can use it to enlarge your photos for printing, cropping, or restoring old photos.DeNoise AI : This product allows you to remove noise from your images while preserving detail and color. You can use it to shoot anywhere in any light without worrying about noise.Sharpen AI : This product allows you to sharpen your images while keeping them natural. You can use it to reverse motion and focus blur, or simply enhance the sharpness of your photos.Video Enhancer AI : This product allows you to upscale, denoise, sharpen, and deinterlace your videos with stunning results. You can use it to convert SD to HD or HD to 4k, or simply improve the quality of your videos.
-Topaz Photo AI uses deep learning algorithms that have been trained on millions of data points to understand what image quality means. Unlike regular image processing filters that often remove details and boost noise/artifacts, Topaz Photo AI enhances image quality by analyzing and enhancing the most important aspects of each image. You can use Topaz Photo AI as a standalone application or as a plug-in for your favorite image editor.
- Topaz Video AI: Create Naturally Better Video Quality with AI
-Topaz Video AI is a product that uses artificial intelligence to upscale, denoise, sharpen, and deinterlace your videos. It is based on the same technology as Topaz Photo AI, but optimized for video processing. You can use Topaz Video AI to:
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-Upscale your videos : You can increase the resolution of your videos by up to 4x while preserving or enhancing the original quality. You can use it to convert SD to HD or HD to 4k, or simply make your videos look better on larger screens.Denoise your videos : You can remove visible image noise from your videos while retaining details and colors. You can use it to improve the quality of videos shot in low-light conditions, or reduce the compression artifacts from online videos.Sharpen your videos : You can increase the perceived sharpness of your videos by applying a natural-looking sharpening effect. You can use it to make your videos look more crisp and clear, or correct the softness caused by upscaling or noise reduction.Deinterlace your videos : You can convert interlaced videos to progressive ones while preserving image definition and reducing artifacts. You can use it to improve the quality of videos from older sources, such as DVDs or TV broadcasts.
-Topaz Video AI uses deep learning algorithms that have been trained on thousands of hours of video data to understand what video quality means. Unlike regular video processing filters that often introduce artifacts and distortions, Topaz Video AI enhances video quality by analyzing and improving the most important aspects of each frame. You can use Topaz Video AI as a standalone application or as an external editor for your favorite video editor.
- How to Download and Install Topaz AI on Your Computer
-If you want to download and install Topaz AI on your computer, you need to follow these steps:
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-Visit the official website of Topaz Labs : Go to https://topazlabs.com/ and click on the "Download" button at the top right corner of the page.Select the products you want to download : You will see a list of all the products available from Topaz Labs, including Topaz Photo AI and Topaz Video AI. You can select one or more products by clicking on the checkboxes next to them. You can also download a free trial version of each product by clicking on the "Try Free" button below them.Enter your email address and password : If you already have an account with Topaz Labs, you can enter your email address and password to log in. If you don't have an account, you can create one by clicking on the "Create Account" button and filling in the required information.Download the installer file : After logging in or creating an account, you will see a download link for each product you selected. Click on the link to download the installer file for your operating system (Windows or Mac).Run the installer file : After downloading the installer file, locate it on your computer and double-click on it to run it. Follow the instructions on the screen to install the product on your computer.Activate the product : After installing the product, launch it from your desktop or start menu. You will see a window asking you to activate the product with your license key. If you have purchased the product, you can enter your license key in the field provided and click on "Activate". If you are using a free trial version, you can click on "Start Trial" to activate it for 30 days.
-Congratulations! You have successfully downloaded and installed Topaz AI on your computer. Now you can start using it to enhance your photos and videos with artificial intelligence.
- How to Access Topaz AI from Your Image Editor
-If you want to access Topaz AI from your image editor, such as Photoshop, Lightroom, or other compatible editors, you need to follow these steps:
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-Install Topaz AI as a plug-in or external editor : When you install Topaz AI on your computer, it will automatically detect and install itself as a plug-in or external editor for some of the most popular image editors, such as Photoshop and Lightroom. If you want to install it for other editors, you can manually install it by following the instructions on the Topaz Labs support page .Open your image in your image editor : Launch your image editor and open the image you want to enhance with Topaz AI.Access Topaz AI from your image editor : Depending on your image editor, you can access Topaz AI in different ways. For example, in Photoshop, you can go to Filter > Topaz Labs > and select the product you want to use. In Lightroom, you can right-click on the image and go to Edit In > and select the product you want to use. For other editors, you can refer to the Topaz Labs support page for more details.Edit your image with Topaz AI : After accessing Topaz AI from your image editor, you will see a new window with the interface of the product you selected. You can use the tools and settings on the left panel to adjust the parameters of the enhancement, and preview the results on the main panel. You can also compare the before and after images by using the buttons on the bottom panel.Save and return to your image editor : After editing your image with Topaz AI, you can save and return to your image editor by clicking on the "Apply" button on the top right corner of the window. Your image will be updated with the changes made by Topaz AI.
-That's it! You have successfully accessed and used Topaz AI from your image editor. Now you can enjoy the benefits of artificial intelligence for your photos.
- How to Use Topaz AI to Enhance Your Photos and Videos
-If you want to use Topaz AI to enhance your photos and videos, you need to follow these steps:
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-Select the product that suits your needs : Depending on what you want to achieve with your photos or videos, you can choose from different products within Topaz Photo AI or Topaz Video AI. For example, if you want to upscale your images, you can use Gigapixel AI. If you want to remove noise from your videos, you can use Video Enhancer AI.Open your photo or video in Topaz AI : You can open your photo or video in Topaz AI either as a standalone application or as a plug-in or external editor for your image or video editor. See the previous section for more details on how to access Topaz AI from your editor.Select the mode that suits your needs : Depending on the product you are using, you can select from different modes that offer different levels of enhancement or customization. For example, in Gigapixel AI, you can choose from Auto, Manual, or Custom modes. In Video Enhancer AI, you can choose from Standard Quality, High Quality, or Custom Quality modes.Adjust the settings that suit your needs : Depending on the mode and product you are using, you can adjust various settings that affect the outcome of the enhancement. For example, in Gigapixel AI, you can adjust the scale factor, output size, noise reduction, face refinement, and more. In Video Enhancer AI, you can adjust the output format, frame rate, bitrate, and more.Preview and compare the results : Depending on the product you are using, you can preview and compare the results of the enhancement before applying it. For example, in Gigapixel AI, you can zoom in and out of the image and see how it looks at different resolutions. In Video Enhancer AI, you can play back a short clip of the video and see how it looks at different qualities.Apply and save the results : After previewing and comparing the results, you can apply and save them by clicking on the "Apply" or "Save" button on the top right corner of the window. Your photo or video will be enhanced and saved with Topaz AI.
-Congratulations! You have successfully used Topaz AI to enhance your photos or videos with artificial intelligence. Now you can enjoy the improved quality of your images and videos.
- Conclusion: Why You Should Download Topaz AI Today
-Topaz AI is a suite of software products that use artificial intelligence to enhance your photos and videos with amazing results. With Topaz AI, you can:
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-Upscale your images and videos by up to 6x or 4x respectively while increasing actual resolution and real detail. Remove noise from your images and videos while preserving detail and color in any lighting condition. Sharpen your images and videos while keeping them natural and reversing motion and focus blur. Deinterlace your videos while preserving image definition and reducing artifacts. Use Topaz AI as a standalone application or as a plug-in or external editor for your favorite image or video editor.
-Topaz AI is easy to use, fast, and reliable. It uses deep learning algorithms that have been trained on millions of data points to understand and improve image and video quality. It offers different modes and settings that allow you to customize the enhancement according to your needs and preferences. It also lets you preview and compare the results before applying them, so you can see the difference for yourself.
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- FAQs: Frequently Asked Questions about Topaz AI
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-What are the system requirements for Topaz AI? Topaz AI requires a Windows or Mac computer with at least 8 GB of RAM, 2 GB of VRAM, and an OpenGL 3.3 compatible graphics card. For optimal performance, it is recommended to have 16 GB of RAM, 4 GB of VRAM, and an NVIDIA or AMD graphics card with CUDA or OpenCL support.
-How long does it take to process an image or video with Topaz AI? The processing time depends on several factors, such as the size and resolution of the image or video, the mode and settings of the product, and the speed and power of your computer. Generally, it takes a few seconds to a few minutes to process an image, and a few minutes to a few hours to process a video.
-Can I batch process multiple images or videos with Topaz AI? Yes, you can batch process multiple images or videos with Topaz AI. You can do this by selecting multiple files in the file browser of the standalone application, or by using the batch processing feature of your image or video editor.
-Can I use Topaz AI on my smartphone or tablet? No, Topaz AI is not available for mobile devices. It is only compatible with Windows or Mac computers.
-Where can I find more information and support for Topaz AI? You can find more information and support for Topaz AI on the Topaz Labs website . There you can access the user guides, tutorials, forums, blogs, and customer service for each product.
- 197e85843dEverskies Oyna: A Guide to the Virtual Dress Up Game
-Do you love dressing up, designing clothes, and meeting new people? If so, you might want to try Everskies Oyna, a virtual dress up game that lets you create your own avatar, design your own fashion items, participate in outfit competitions and events, earn money and XP, and find people with similar interests and meet new friends. Everskies Oyna is a fun and creative game for everyone who enjoys fashion, art, and socializing. In this article, we will show you how to play Everskies Oyna and give you some tips and tricks to make the most of your experience.
- How to Create Your Own Avatar in Everskies
-One of the first things you need to do in Everskies Oyna is to create your own avatar. Your avatar is your virtual representation in the game world, and you can customize it to match your personality and style. Here are the steps to create your own avatar in Everskies:
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-Step 1: Choose your gender, skin tone, and facial features. You can choose from male or female avatars, and select from different skin tones, face shapes, eyebrows, noses, mouths, ears, freckles, moles, scars, etc.
-Step 2: Customize your hair, eyes, and makeup. You can choose from different hair styles, colors, lengths, bangs, highlights, etc. You can also change your eye shape, color, size, lashes, etc. You can also apply different makeup products such as eyeshadow, eyeliner, mascara, blush, lipstick, etc.
-Step 3: Dress up your avatar with different outfits, accessories, and shoes. You can choose from over 150000 items to dress up your avatar with different fashion outfits, accessories, and shoes. You can mix and match different items to create your own unique look. You can also save your outfits for later use or share them with other users.
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-Creating your own avatar in Everskies Oyna is easy and fun. You can express yourself through your avatar and show off your style to the world.
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-Another cool feature of Everskies Oyna is that you can design your own fashion items and sell them in the shop or trade them with other users. You can create your own clothing, accessories, shoes, hair, makeup, etc. and show off your creativity and talent. Here are the steps to design your own fashion items in Everskies:
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-Step 1: Go to the Creative tab and select an item template. You can choose from different categories such as tops, bottoms, dresses, jackets, hats, bags, jewelry, etc. You can also filter by gender, style, season, etc.
-Step 2: Use the drawing tools and filters to create your own design. You can use different tools such as pencil, brush, eraser, fill, color picker, etc. to draw your design on the item template. You can also use different filters such as hue, saturation, brightness, contrast, etc. to adjust the color and tone of your design.
-Step 3: Save and submit your item for approval. You can name your item, add a description, and set a price for it. You can also preview how it looks on different avatars. Once you are happy with your design, you can save it and submit it for approval. The approval process may take up to 24 hours, and you will be notified if your item is accepted or rejected.
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-Designing your own fashion items in Everskies Oyna is a great way to unleash your inner designer and earn some money and XP. You can also get feedback from other users and improve your skills.
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-If you want to challenge yourself and compete with other users in Everskies Oyna, you can participate in outfit competitions and events. Outfit competitions and events are themed contests that require you to create an outfit that matches the theme and criteria. You can win prizes such as money, XP, items, badges, etc. Here are the steps to participate in outfit competitions and events in Everskies:
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-Step 1: Check the event calendar and the competition rules. You can find the event calendar on the homepage or on the Events tab. You can see the current and upcoming competitions and events, as well as their themes, criteria, deadlines, prizes, etc. You can also read the competition rules and guidelines before entering.
-Step 2: Create an outfit that matches the theme and criteria. You can use any items that you own or buy from the shop to create your outfit. You can also use items that you designed yourself or traded with other users. Make sure that your outfit follows the theme and criteria of the competition or event.
-Step 3: Vote for other entries and wait for the results. After you submit your entry, you can vote for other entries by giving them stars from one to five. You can vote for up to 10 entries per day. The more you vote, the more XP you earn. The results of the competition or event will be announced after the deadline, and you will be notified if you won any prizes.
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-Participating in outfit competitions and events in Everskies Oyna is a fun and rewarding way to test your fashion sense and creativity. You can also get inspired by other users' outfits and discover new styles.
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-Step 1: Play mini-games such as Memory, Tic Tac Toe, and Planet Popper. You can find the mini-games on the Games tab or on the homepage. You can play the mini-games for free or for a small fee, and you can win money and XP depending on your score and performance.
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-Earning money and XP in Everskies Oyna is easy and enjoyable. You can use your money and XP to buy more items, level up your avatar, and unlock more features in the game.
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- How to Find People with Similar Interests and Meet New Friends in Everskies
-One of the best things about Everskies Oyna is that you can find people with similar interests and meet new friends from all over the world. Everskies Oyna is a friendly and welcoming community that supports diversity and creativity. You can connect with other users who share your passion for fashion, art, music, games, etc. Here are the steps to find people with similar interests and meet new friends in Everskies:
-
-Step 1: Browse the clubs, forums, chat rooms, and group messages by category or keyword. You can find the clubs, forums, chat rooms, and group messages on the Community tab or on the homepage. You can browse them by category such as fashion, art, music, games, etc. or by keyword such as anime, kpop, harry potter, etc.
-Step 2: Join or create a club, forum, chat room, or group message that suits your interests. You can join or create a club, forum, chat room, or group message that matches your interests and hobbies. You can also invite other users to join or create them with you.
-Step 3: Interact with other users, share your outfits, give feedback, and have fun. You can interact with other users who are members of the same club, forum, chat room, or group message as you. You can share your outfits, give feedback, and have fun. You can also send private messages to other users, add them as friends, or block them if you don't like them.
-
-Finding people with similar interests and meeting new friends in Everskies Oyna is a wonderful way to expand your social circle and enjoy the game more. You can also learn from other users and discover new things.
- Conclusion: Everskies Oyna is a Fun and Creative Game for Everyone
-Everskies Oyna is a virtual dress up game that lets you create your own avatar, design your own fashion items, participate in outfit competitions and events, earn money and XP, and find people with similar interests and meet new friends. Everskies Oyna is a fun and creative game for everyone who loves fashion, art, and socializing. You can play Everskies Oyna for free on your browser or download the app on your mobile device. You can also follow Everskies Oyna on social media platforms such as Instagram, Twitter, Facebook, etc. to get the latest news and updates. If you are looking for a game that allows you to express yourself, show off your style, and make new friends, you should definitely try Everskies Oyna today!
- FAQs
-
-Q: What is Everskies Oyna?
-A: Everskies Oyna is a virtual dress up game that lets you create your own avatar, design your own fashion items, participate in outfit competitions and events, earn money and XP, and find people with similar interests and meet new friends.
-Q: How can I play Everskies Oyna?
-A: You can play Everskies Oyna for free on your browser or download the app on your mobile device. You can also follow Everskies Oyna on social media platforms such as Instagram, Twitter, Facebook, etc. to get the latest news and updates.
-Q: How can I create my own avatar in Everskies Oyna?
-A: You can create your own avatar in Everskies Oyna by choosing your gender, skin tone, facial features, hair, eyes, makeup, outfits, accessories, and shoes. You can customize your avatar to match your personality and style.
-Q: How can I design my own fashion items in Everskies Oyna?
-A: You can design your own fashion items in Everskies Oyna by going to the Creative tab and selecting an item template. You can use the drawing tools and filters to create your own design. You can save and submit your item for approval.
-Q: How can I participate in outfit competitions and events in Everskies Oyna?
-A: You can participate in outfit competitions and events in Everskies Oyna by checking the event calendar and the competition rules. You can create an outfit that matches the theme and criteria. You can vote for other entries and wait for the results.
- 197e85843d
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to encodec, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/AIFILMS/StyleGANEX/models/stylegan2/op_ori/fused_act.py b/spaces/AIFILMS/StyleGANEX/models/stylegan2/op_ori/fused_act.py
deleted file mode 100644
index 973a84fffde53668d31397da5fb993bbc95f7be0..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/models/stylegan2/op_ori/fused_act.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-module_path = os.path.dirname(__file__)
-fused = load(
- 'fused',
- sources=[
- os.path.join(module_path, 'fused_bias_act.cpp'),
- os.path.join(module_path, 'fused_bias_act_kernel.cu'),
- ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
- @staticmethod
- def forward(ctx, grad_output, out, negative_slope, scale):
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- empty = grad_output.new_empty(0)
-
- grad_input = fused.fused_bias_act(
- grad_output, empty, out, 3, 1, negative_slope, scale
- )
-
- dim = [0]
-
- if grad_input.ndim > 2:
- dim += list(range(2, grad_input.ndim))
-
- grad_bias = grad_input.sum(dim).detach()
-
- return grad_input, grad_bias
-
- @staticmethod
- def backward(ctx, gradgrad_input, gradgrad_bias):
- out, = ctx.saved_tensors
- gradgrad_out = fused.fused_bias_act(
- gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
- )
-
- return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
- @staticmethod
- def forward(ctx, input, bias, negative_slope, scale):
- empty = input.new_empty(0)
- out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- out, = ctx.saved_tensors
-
- grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
- grad_output, out, ctx.negative_slope, ctx.scale
- )
-
- return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
- def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
- super().__init__()
-
- self.bias = nn.Parameter(torch.zeros(channel))
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
- return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/tts_utils.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/tts_utils.py
deleted file mode 100644
index 47e654c03eaf9c50ae0bb3c97ecd661666a1a6b1..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/tts_utils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import importlib
-
-from text_to_speech.data_gen.tts.base_binarizer import BaseBinarizer
-from text_to_speech.data_gen.tts.base_preprocess import BasePreprocessor
-from text_to_speech.data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls
-from text_to_speech.utils.commons.hparams import hparams
-
-
-def parse_dataset_configs():
- max_tokens = hparams['max_tokens']
- max_sentences = hparams['max_sentences']
- max_valid_tokens = hparams['max_valid_tokens']
- if max_valid_tokens == -1:
- hparams['max_valid_tokens'] = max_valid_tokens = max_tokens
- max_valid_sentences = hparams['max_valid_sentences']
- if max_valid_sentences == -1:
- hparams['max_valid_sentences'] = max_valid_sentences = max_sentences
- return max_tokens, max_sentences, max_valid_tokens, max_valid_sentences
-
-
-def parse_mel_losses():
- mel_losses = hparams['mel_losses'].split("|")
- loss_and_lambda = {}
- for i, l in enumerate(mel_losses):
- if l == '':
- continue
- if ':' in l:
- l, lbd = l.split(":")
- lbd = float(lbd)
- else:
- lbd = 1.0
- loss_and_lambda[l] = lbd
- print("| Mel losses:", loss_and_lambda)
- return loss_and_lambda
-
-
-def load_data_preprocessor():
- preprocess_cls = hparams["preprocess_cls"]
- pkg = ".".join(preprocess_cls.split(".")[:-1])
- cls_name = preprocess_cls.split(".")[-1]
- preprocessor: BasePreprocessor = getattr(importlib.import_module(pkg), cls_name)()
- preprocess_args = {}
- preprocess_args.update(hparams['preprocess_args'])
- return preprocessor, preprocess_args
-
-
-def load_data_binarizer():
- binarizer_cls = hparams['binarizer_cls']
- pkg = ".".join(binarizer_cls.split(".")[:-1])
- cls_name = binarizer_cls.split(".")[-1]
- binarizer: BaseBinarizer = getattr(importlib.import_module(pkg), cls_name)()
- binarization_args = {}
- binarization_args.update(hparams['binarization_args'])
- return binarizer, binarization_args
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/bsrgan_light.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 9e1f823996bf559e9b015ea9aa2b3cd38dd13af1..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-
-def modcrop_np(img, sf):
- '''
- Args:
- img: numpy image, WxH or WxHxC
- sf: scale factor
- Return:
- cropped image
- '''
- w, h = img.shape[:2]
- im = np.copy(img)
- return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
- """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
- k_size = k.shape[0]
- # Calculate the big kernels size
- big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
- # Loop over the small kernel to fill the big one
- for r in range(k_size):
- for c in range(k_size):
- big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
- # Crop the edges of the big kernel to ignore very small values and increase run time of SR
- crop = k_size // 2
- cropped_big_k = big_k[crop:-crop, crop:-crop]
- # Normalize to 1
- return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
- """ generate an anisotropic Gaussian kernel
- Args:
- ksize : e.g., 15, kernel size
- theta : [0, pi], rotation angle range
- l1 : [0.1,50], scaling of eigenvalues
- l2 : [0.1,l1], scaling of eigenvalues
- If l1 = l2, will get an isotropic Gaussian kernel.
- Returns:
- k : kernel
- """
-
- v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
- V = np.array([[v[0], v[1]], [v[1], -v[0]]])
- D = np.array([[l1, 0], [0, l2]])
- Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
- k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
- return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
- center = size / 2.0 + 0.5
- k = np.zeros([size, size])
- for y in range(size):
- for x in range(size):
- cy = y - center + 1
- cx = x - center + 1
- k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
- k = k / np.sum(k)
- return k
-
-
-def shift_pixel(x, sf, upper_left=True):
- """shift pixel for super-resolution with different scale factors
- Args:
- x: WxHxC or WxH
- sf: scale factor
- upper_left: shift direction
- """
- h, w = x.shape[:2]
- shift = (sf - 1) * 0.5
- xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
- if upper_left:
- x1 = xv + shift
- y1 = yv + shift
- else:
- x1 = xv - shift
- y1 = yv - shift
-
- x1 = np.clip(x1, 0, w - 1)
- y1 = np.clip(y1, 0, h - 1)
-
- if x.ndim == 2:
- x = interp2d(xv, yv, x)(x1, y1)
- if x.ndim == 3:
- for i in range(x.shape[-1]):
- x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
- return x
-
-
-def blur(x, k):
- '''
- x: image, NxcxHxW
- k: kernel, Nx1xhxw
- '''
- n, c = x.shape[:2]
- p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
- x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
- k = k.repeat(1, c, 1, 1)
- k = k.view(-1, 1, k.shape[2], k.shape[3])
- x = x.view(1, -1, x.shape[2], x.shape[3])
- x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
- x = x.view(n, c, x.shape[2], x.shape[3])
-
- return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
- """"
- # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
- # Kai Zhang
- # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
- # max_var = 2.5 * sf
- """
- # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
- lambda_1 = min_var + np.random.rand() * (max_var - min_var)
- lambda_2 = min_var + np.random.rand() * (max_var - min_var)
- theta = np.random.rand() * np.pi # random theta
- noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
- # Set COV matrix using Lambdas and Theta
- LAMBDA = np.diag([lambda_1, lambda_2])
- Q = np.array([[np.cos(theta), -np.sin(theta)],
- [np.sin(theta), np.cos(theta)]])
- SIGMA = Q @ LAMBDA @ Q.T
- INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
- # Set expectation position (shifting kernel for aligned image)
- MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
- MU = MU[None, None, :, None]
-
- # Create meshgrid for Gaussian
- [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
- Z = np.stack([X, Y], 2)[:, :, :, None]
-
- # Calcualte Gaussian for every pixel of the kernel
- ZZ = Z - MU
- ZZ_t = ZZ.transpose(0, 1, 3, 2)
- raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
- # shift the kernel so it will be centered
- # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
- # Normalize the kernel and return
- # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
- kernel = raw_kernel / np.sum(raw_kernel)
- return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
- hsize = [hsize, hsize]
- siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
- std = sigma
- [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
- arg = -(x * x + y * y) / (2 * std * std)
- h = np.exp(arg)
- h[h < scipy.finfo(float).eps * h.max()] = 0
- sumh = h.sum()
- if sumh != 0:
- h = h / sumh
- return h
-
-
-def fspecial_laplacian(alpha):
- alpha = max([0, min([alpha, 1])])
- h1 = alpha / (alpha + 1)
- h2 = (1 - alpha) / (alpha + 1)
- h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
- h = np.array(h)
- return h
-
-
-def fspecial(filter_type, *args, **kwargs):
- '''
- python code from:
- https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
- '''
- if filter_type == 'gaussian':
- return fspecial_gaussian(*args, **kwargs)
- if filter_type == 'laplacian':
- return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
- '''
- Args:
- x: HxWxC image, [0, 1]
- sf: down-scale factor
- Return:
- bicubicly downsampled LR image
- '''
- x = util.imresize_np(x, scale=1 / sf)
- return x
-
-
-def srmd_degradation(x, k, sf=3):
- ''' blur + bicubic downsampling
- Args:
- x: HxWxC image, [0, 1]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- Reference:
- @inproceedings{zhang2018learning,
- title={Learning a single convolutional super-resolution network for multiple degradations},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={3262--3271},
- year={2018}
- }
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
- x = bicubic_degradation(x, sf=sf)
- return x
-
-
-def dpsr_degradation(x, k, sf=3):
- ''' bicubic downsampling + blur
- Args:
- x: HxWxC image, [0, 1]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- Reference:
- @inproceedings{zhang2019deep,
- title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={1671--1681},
- year={2019}
- }
- '''
- x = bicubic_degradation(x, sf=sf)
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
- return x
-
-
-def classical_degradation(x, k, sf=3):
- ''' blur + downsampling
- Args:
- x: HxWxC image, [0, 1]/[0, 255]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
- # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
- st = 0
- return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
- """USM sharpening. borrowed from real-ESRGAN
- Input image: I; Blurry image: B.
- 1. K = I + weight * (I - B)
- 2. Mask = 1 if abs(I - B) > threshold, else: 0
- 3. Blur mask:
- 4. Out = Mask * K + (1 - Mask) * I
- Args:
- img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
- weight (float): Sharp weight. Default: 1.
- radius (float): Kernel size of Gaussian blur. Default: 50.
- threshold (int):
- """
- if radius % 2 == 0:
- radius += 1
- blur = cv2.GaussianBlur(img, (radius, radius), 0)
- residual = img - blur
- mask = np.abs(residual) * 255 > threshold
- mask = mask.astype('float32')
- soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
- K = img + weight * residual
- K = np.clip(K, 0, 1)
- return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
- wd2 = 4.0 + sf
- wd = 2.0 + 0.2 * sf
-
- wd2 = wd2/4
- wd = wd/4
-
- if random.random() < 0.5:
- l1 = wd2 * random.random()
- l2 = wd2 * random.random()
- k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
- else:
- k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
- img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
- return img
-
-
-def add_resize(img, sf=4):
- rnum = np.random.rand()
- if rnum > 0.8: # up
- sf1 = random.uniform(1, 2)
- elif rnum < 0.7: # down
- sf1 = random.uniform(0.5 / sf, 1)
- else:
- sf1 = 1.0
- img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
- img = np.clip(img, 0.0, 1.0)
-
- return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-# noise_level = random.randint(noise_level1, noise_level2)
-# rnum = np.random.rand()
-# if rnum > 0.6: # add color Gaussian noise
-# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-# elif rnum < 0.4: # add grayscale Gaussian noise
-# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-# else: # add noise
-# L = noise_level2 / 255.
-# D = np.diag(np.random.rand(3))
-# U = orth(np.random.rand(3, 3))
-# conv = np.dot(np.dot(np.transpose(U), D), U)
-# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-# img = np.clip(img, 0.0, 1.0)
-# return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
- noise_level = random.randint(noise_level1, noise_level2)
- rnum = np.random.rand()
- if rnum > 0.6: # add color Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
- elif rnum < 0.4: # add grayscale Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
- else: # add noise
- L = noise_level2 / 255.
- D = np.diag(np.random.rand(3))
- U = orth(np.random.rand(3, 3))
- conv = np.dot(np.dot(np.transpose(U), D), U)
- img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
- noise_level = random.randint(noise_level1, noise_level2)
- img = np.clip(img, 0.0, 1.0)
- rnum = random.random()
- if rnum > 0.6:
- img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
- elif rnum < 0.4:
- img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
- else:
- L = noise_level2 / 255.
- D = np.diag(np.random.rand(3))
- U = orth(np.random.rand(3, 3))
- conv = np.dot(np.dot(np.transpose(U), D), U)
- img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_Poisson_noise(img):
- img = np.clip((img * 255.0).round(), 0, 255) / 255.
- vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
- if random.random() < 0.5:
- img = np.random.poisson(img * vals).astype(np.float32) / vals
- else:
- img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
- img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
- noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
- img += noise_gray[:, :, np.newaxis]
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_JPEG_noise(img):
- quality_factor = random.randint(80, 95)
- img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
- result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
- img = cv2.imdecode(encimg, 1)
- img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
- return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
- h, w = lq.shape[:2]
- rnd_h = random.randint(0, h - lq_patchsize)
- rnd_w = random.randint(0, w - lq_patchsize)
- lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
- rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
- hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
- return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
- """
- This is the degradation model of BSRGAN from the paper
- "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
- ----------
- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
- sf: scale factor
- isp_model: camera ISP model
- Returns
- -------
- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
- hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
- """
- isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
- sf_ori = sf
-
- h1, w1 = img.shape[:2]
- img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
- h, w = img.shape[:2]
-
- if h < lq_patchsize * sf or w < lq_patchsize * sf:
- raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
- hq = img.copy()
-
- if sf == 4 and random.random() < scale2_prob: # downsample1
- if np.random.rand() < 0.5:
- img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- img = util.imresize_np(img, 1 / 2, True)
- img = np.clip(img, 0.0, 1.0)
- sf = 2
-
- shuffle_order = random.sample(range(7), 7)
- idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
- if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
- for i in shuffle_order:
-
- if i == 0:
- img = add_blur(img, sf=sf)
-
- elif i == 1:
- img = add_blur(img, sf=sf)
-
- elif i == 2:
- a, b = img.shape[1], img.shape[0]
- # downsample2
- if random.random() < 0.75:
- sf1 = random.uniform(1, 2 * sf)
- img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
- k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
- img = img[0::sf, 0::sf, ...] # nearest downsampling
- img = np.clip(img, 0.0, 1.0)
-
- elif i == 3:
- # downsample3
- img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
- img = np.clip(img, 0.0, 1.0)
-
- elif i == 4:
- # add Gaussian noise
- img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
- elif i == 5:
- # add JPEG noise
- if random.random() < jpeg_prob:
- img = add_JPEG_noise(img)
-
- elif i == 6:
- # add processed camera sensor noise
- if random.random() < isp_prob and isp_model is not None:
- with torch.no_grad():
- img, hq = isp_model.forward(img.copy(), hq)
-
- # add final JPEG compression noise
- img = add_JPEG_noise(img)
-
- # random crop
- img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
- return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
- """
- This is the degradation model of BSRGAN from the paper
- "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
- ----------
- sf: scale factor
- isp_model: camera ISP model
- Returns
- -------
- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
- hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
- """
- image = util.uint2single(image)
- isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
- sf_ori = sf
-
- h1, w1 = image.shape[:2]
- image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
- h, w = image.shape[:2]
-
- hq = image.copy()
-
- if sf == 4 and random.random() < scale2_prob: # downsample1
- if np.random.rand() < 0.5:
- image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- image = util.imresize_np(image, 1 / 2, True)
- image = np.clip(image, 0.0, 1.0)
- sf = 2
-
- shuffle_order = random.sample(range(7), 7)
- idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
- if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
- for i in shuffle_order:
-
- if i == 0:
- image = add_blur(image, sf=sf)
-
- # elif i == 1:
- # image = add_blur(image, sf=sf)
-
- if i == 0:
- pass
-
- elif i == 2:
- a, b = image.shape[1], image.shape[0]
- # downsample2
- if random.random() < 0.8:
- sf1 = random.uniform(1, 2 * sf)
- image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
- k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
- image = image[0::sf, 0::sf, ...] # nearest downsampling
-
- image = np.clip(image, 0.0, 1.0)
-
- elif i == 3:
- # downsample3
- image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
- image = np.clip(image, 0.0, 1.0)
-
- elif i == 4:
- # add Gaussian noise
- image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
- elif i == 5:
- # add JPEG noise
- if random.random() < jpeg_prob:
- image = add_JPEG_noise(image)
- #
- # elif i == 6:
- # # add processed camera sensor noise
- # if random.random() < isp_prob and isp_model is not None:
- # with torch.no_grad():
- # img, hq = isp_model.forward(img.copy(), hq)
-
- # add final JPEG compression noise
- image = add_JPEG_noise(image)
- image = util.single2uint(image)
- example = {"image": image}
- return example
-
-
-
-
-if __name__ == '__main__':
- print("hey")
- img = util.imread_uint('utils/test.png', 3)
- img = img[:448, :448]
- h = img.shape[0] // 4
- print("resizing to", h)
- sf = 4
- deg_fn = partial(degradation_bsrgan_variant, sf=sf)
- for i in range(20):
- print(i)
- img_hq = img
- img_lq = deg_fn(img)["image"]
- img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
- print(img_lq)
- img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
- print(img_lq.shape)
- print("bicubic", img_lq_bicubic.shape)
- print(img_hq.shape)
- lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
- (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
- util.imsave(img_concat, str(i) + '.png')
diff --git a/spaces/AIZ2H/05-SOTA-Question-Answer-From-TextFileContext/app.py b/spaces/AIZ2H/05-SOTA-Question-Answer-From-TextFileContext/app.py
deleted file mode 100644
index c66d3925b6805866e5bead78cee8fdfacd2c9638..0000000000000000000000000000000000000000
--- a/spaces/AIZ2H/05-SOTA-Question-Answer-From-TextFileContext/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import gradio as gr
-import os
-
-context = "This could be any large text corpus to use as subject matter to ask questions about. You can load it as well from text file to isolate it from code changes like in the next line"
-
-with open('Context.txt', 'r') as file:
- context = file.read()
-
-question = "What should be documented in a care plan?"
-
-API_KEY = os.environ.get("HF_TOKEN")
-gr.Interface.load(
- "huggingface/deepset/roberta-base-squad2",
- api_key=API_KEY,
- theme="default",
- css=".footer{display:none !important}",
- inputs=[gr.inputs.Textbox(lines=12, default=context, label="Context paragraph"), gr.inputs.Textbox(lines=3, default=question, label="Question")],
- outputs=[gr.outputs.Textbox(label="Answer"), gr.outputs.Textbox(label="Score")],
- title=None,
- description="Provide your own paragraph and ask any question about the text. How well does the model answer?").launch()
\ No newline at end of file
diff --git a/spaces/Aadi1149/Arkenbrien-text-to-image-Arkenbrien/app.py b/spaces/Aadi1149/Arkenbrien-text-to-image-Arkenbrien/app.py
deleted file mode 100644
index 2d86cf2d2784e40969af85bc3ed6a35fd525b5ac..0000000000000000000000000000000000000000
--- a/spaces/Aadi1149/Arkenbrien-text-to-image-Arkenbrien/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Arkenbrien/text-to-image-Arkenbrien").launch()
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/6.js b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/6.js
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ConfigurationMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ConfigurationMethods.js
deleted file mode 100644
index 22f9b489453fb0b1b5cedb6ea5a3fbdb4a99e231..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/methods/ConfigurationMethods.js
+++ /dev/null
@@ -1,107 +0,0 @@
-var methods = {
- // Color picker
- setCreateColorPickerBackgroundCallback(callback) {
- this.colorPickerCreateBackgroundCallback = callback;
- return this;
- },
-
- setColorPickerHPalettePosition(position) {
- this.colorPickerHPalettePosition = position;
- return this;
- },
-
- setColorPickerExpandDirection(direction) {
- if (typeof (direction) === 'string') {
- direction = ColorPickerExpandDirections[direction];
- }
- this.colorPickerExpandDirection = direction;
- return this;
- },
-
- setColorPickerEaseInDuration(duration) {
- if (duration === undefined) {
- duration = 0;
- }
- this.colorPickerEaseInDuration = duration;
- return this;
- },
-
- setColorPickerEaseOutDuration(duration) {
- if (duration === undefined) {
- duration = 0;
- }
- this.colorPickerEaseOutDuration = duration;
- return this;
- },
-
- setColorPickerTransitInCallback(callback) {
- this.colorPickerTransitInCallback = callback;
- // callback = function(gameObject, duration) {}
- return this;
- },
-
- setColorPickerTransitOutCallback(callback) {
- this.colorPickerTransitOutCallback = callback;
- // callback = function(gameObject, duration) {}
- return this;
- },
-
- setColorPickerBounds(bounds) {
- this.colorPickerBounds = bounds;
- return this;
- },
-
- setColorPickerWidth(width) {
- this.colorPickerWidth = width;
- return this;
- },
-
- setColorPickerHeight(height) {
- this.colorPickerHeight = height;
- return this;
- },
-
- setColorPickerSize(width, height) {
- this.setColorPickerWidth(width).setColorPickerHeight(height);
- return this;
- },
-
- setColorPickerSpace(space) {
- if (space === undefined) {
- space = {};
- }
- this.colorPickerSpace = space;
- return this;
- },
-
- // Color components
- setColorComponentsHeight(height) {
- this.colorComponentsHeight = height;
- return this;
- },
-
- setColorComponentsFormatLabelConfig(config) {
- this.colorComponentsFormatLabelConfig = config;
- return this;
- },
-
- setColorComponentsInputTextConfig(config) {
- this.colorComponentsInputTextConfig = config;
- return this;
- },
-
- setColorComponentsSpace(space) {
- if (space === undefined) {
- space = {};
- }
- this.colorComponentsSpace = space;
- return this;
- },
-}
-
-const ColorPickerExpandDirections = {
- down: 0,
- up: 1
-}
-
-export default methods;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetThumbAlignPoint.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetThumbAlignPoint.js
deleted file mode 100644
index e8f10277175c4dccc6b3f1402ee4838eae6e5089..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetThumbAlignPoint.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import AlignIn from '../../../plugins/utils/actions/AlignIn.js';
-
-var GetThumbAlignPoint = function (align, out) {
- if (out === undefined) {
- out = tmpPoint;
- }
- var thumb = this.childrenMap.thumb;
- var currentX = thumb.x;
- var currentY = thumb.y;
-
- AlignIn(thumb, this.innerLeft, this.innerTop, this.innerWidth, this.innerHeight, align);
- out.x = thumb.x;
- out.y = thumb.y;
-
- thumb.x = currentX;
- thumb.y = currentY;
-
- return out;
-}
-
-var tmpPoint = {};
-
-export default GetThumbAlignPoint;
\ No newline at end of file
diff --git a/spaces/AlawnCN/webui-docker/oh-no.py b/spaces/AlawnCN/webui-docker/oh-no.py
deleted file mode 100644
index e8c0f3bd8d72805b4ee69d4d0fd9133347d00f92..0000000000000000000000000000000000000000
--- a/spaces/AlawnCN/webui-docker/oh-no.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-
-block = gr.Blocks()
-
-def run():
- with block:
- gr.Markdown(
- """
- oh no 😐 something wrong with the 🤗 hugging face servers 😐 hopefully, it will be fixed soon
- """)
- block.launch(server_name="0.0.0.0", server_port=7860)
-
-if __name__ == "__main__":
- run()
\ No newline at end of file
diff --git a/spaces/Ali-Omrani/CCR/README.md b/spaces/Ali-Omrani/CCR/README.md
deleted file mode 100644
index 5a0f1c8d1b4c92cd6abf2a8b4771db87c831bcbf..0000000000000000000000000000000000000000
--- a/spaces/Ali-Omrani/CCR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CCR
-emoji: 🚀
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Alpaca233/SadTalker/src/facerender/sync_batchnorm/batchnorm.py b/spaces/Alpaca233/SadTalker/src/facerender/sync_batchnorm/batchnorm.py
deleted file mode 100644
index 5f4e763f0366dffa10320116413f8c7181a8aeb1..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/facerender/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : batchnorm.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
- """sum over the first and last dimention"""
- return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
- """add new dementions at the front and the tail"""
- return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
- def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
- super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
- self._sync_master = SyncMaster(self._data_parallel_master)
-
- self._is_parallel = False
- self._parallel_id = None
- self._slave_pipe = None
-
- def forward(self, input):
- # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
- if not (self._is_parallel and self.training):
- return F.batch_norm(
- input, self.running_mean, self.running_var, self.weight, self.bias,
- self.training, self.momentum, self.eps)
-
- # Resize the input to (B, C, -1).
- input_shape = input.size()
- input = input.view(input.size(0), self.num_features, -1)
-
- # Compute the sum and square-sum.
- sum_size = input.size(0) * input.size(2)
- input_sum = _sum_ft(input)
- input_ssum = _sum_ft(input ** 2)
-
- # Reduce-and-broadcast the statistics.
- if self._parallel_id == 0:
- mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
- else:
- mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
- # Compute the output.
- if self.affine:
- # MJY:: Fuse the multiplication for speed.
- output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
- else:
- output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
- # Reshape it.
- return output.view(input_shape)
-
- def __data_parallel_replicate__(self, ctx, copy_id):
- self._is_parallel = True
- self._parallel_id = copy_id
-
- # parallel_id == 0 means master device.
- if self._parallel_id == 0:
- ctx.sync_master = self._sync_master
- else:
- self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
- def _data_parallel_master(self, intermediates):
- """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
- # Always using same "device order" makes the ReduceAdd operation faster.
- # Thanks to:: Tete Xiao (http://tetexiao.com/)
- intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
- to_reduce = [i[1][:2] for i in intermediates]
- to_reduce = [j for i in to_reduce for j in i] # flatten
- target_gpus = [i[1].sum.get_device() for i in intermediates]
-
- sum_size = sum([i[1].sum_size for i in intermediates])
- sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
- mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
- broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
- outputs = []
- for i, rec in enumerate(intermediates):
- outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
- return outputs
-
- def _compute_mean_std(self, sum_, ssum, size):
- """Compute the mean and standard-deviation with sum and square-sum. This method
- also maintains the moving average on the master device."""
- assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
- mean = sum_ / size
- sumvar = ssum - sum_ * mean
- unbias_var = sumvar / (size - 1)
- bias_var = sumvar / size
-
- self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
- self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
- return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
- r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
- mini-batch.
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm1d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
- Args:
- num_features: num_features from an expected input of size
- `batch_size x num_features [x width]`
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C)` or :math:`(N, C, L)`
- - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm1d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm1d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 2 and input.dim() != 3:
- raise ValueError('expected 2D or 3D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
- r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
- of 3d inputs
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm2d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
- Args:
- num_features: num_features from an expected input of
- size batch_size x num_features x height x width
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C, H, W)`
- - Output: :math:`(N, C, H, W)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm2d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm2d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 4:
- raise ValueError('expected 4D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
- r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
- of 4d inputs
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm3d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
- or Spatio-temporal BatchNorm
-
- Args:
- num_features: num_features from an expected input of
- size batch_size x num_features x depth x height x width
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C, D, H, W)`
- - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm3d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm3d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 5:
- raise ValueError('expected 5D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/tutorials/tutorial_overview.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/tutorials/tutorial_overview.md
deleted file mode 100644
index 0cec9a317ddbef7488204f9e8cd6c7f07aca6b79..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/tutorials/tutorial_overview.md
+++ /dev/null
@@ -1,23 +0,0 @@
-
-
-# Overview
-
-Welcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.
-
-You'll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you'll learn how to train your own diffusion model to generate what you want.
-
-After completing the tutorials, you'll have gained the necessary skills to start exploring the library on your own and see how to use it for your own projects and applications.
-
-Feel free to join our community on [Discord](https://discord.com/invite/JfAtkvEtRb) or the [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) to connect and collaborate with other users and developers!
-
-Let's start diffusing! 🧨
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_euler_ancestral.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_euler_ancestral.py
deleted file mode 100644
index 9866bd12d6af863469fa7369245dce5843d69080..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_euler_ancestral.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-
-from diffusers import EulerAncestralDiscreteScheduler
-from diffusers.utils import torch_device
-
-from .test_schedulers import SchedulerCommonTest
-
-
-class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
- scheduler_classes = (EulerAncestralDiscreteScheduler,)
- num_inference_steps = 10
-
- def get_scheduler_config(self, **kwargs):
- config = {
- "num_train_timesteps": 1100,
- "beta_start": 0.0001,
- "beta_end": 0.02,
- "beta_schedule": "linear",
- }
-
- config.update(**kwargs)
- return config
-
- def test_timesteps(self):
- for timesteps in [10, 50, 100, 1000]:
- self.check_over_configs(num_train_timesteps=timesteps)
-
- def test_betas(self):
- for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
- self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
-
- def test_schedules(self):
- for schedule in ["linear", "scaled_linear"]:
- self.check_over_configs(beta_schedule=schedule)
-
- def test_prediction_type(self):
- for prediction_type in ["epsilon", "v_prediction"]:
- self.check_over_configs(prediction_type=prediction_type)
-
- def test_full_loop_no_noise(self):
- scheduler_class = self.scheduler_classes[0]
- scheduler_config = self.get_scheduler_config()
- scheduler = scheduler_class(**scheduler_config)
-
- scheduler.set_timesteps(self.num_inference_steps)
-
- generator = torch.manual_seed(0)
-
- model = self.dummy_model()
- sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
- sample = sample.to(torch_device)
-
- for i, t in enumerate(scheduler.timesteps):
- sample = scheduler.scale_model_input(sample, t)
-
- model_output = model(sample, t)
-
- output = scheduler.step(model_output, t, sample, generator=generator)
- sample = output.prev_sample
-
- result_sum = torch.sum(torch.abs(sample))
- result_mean = torch.mean(torch.abs(sample))
-
- assert abs(result_sum.item() - 152.3192) < 1e-2
- assert abs(result_mean.item() - 0.1983) < 1e-3
-
- def test_full_loop_with_v_prediction(self):
- scheduler_class = self.scheduler_classes[0]
- scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
- scheduler = scheduler_class(**scheduler_config)
-
- scheduler.set_timesteps(self.num_inference_steps)
-
- generator = torch.manual_seed(0)
-
- model = self.dummy_model()
- sample = self.dummy_sample_deter * scheduler.init_noise_sigma
- sample = sample.to(torch_device)
-
- for i, t in enumerate(scheduler.timesteps):
- sample = scheduler.scale_model_input(sample, t)
-
- model_output = model(sample, t)
-
- output = scheduler.step(model_output, t, sample, generator=generator)
- sample = output.prev_sample
-
- result_sum = torch.sum(torch.abs(sample))
- result_mean = torch.mean(torch.abs(sample))
-
- assert abs(result_sum.item() - 108.4439) < 1e-2
- assert abs(result_mean.item() - 0.1412) < 1e-3
-
- def test_full_loop_device(self):
- scheduler_class = self.scheduler_classes[0]
- scheduler_config = self.get_scheduler_config()
- scheduler = scheduler_class(**scheduler_config)
-
- scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
- generator = torch.manual_seed(0)
-
- model = self.dummy_model()
- sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
- sample = sample.to(torch_device)
-
- for t in scheduler.timesteps:
- sample = scheduler.scale_model_input(sample, t)
-
- model_output = model(sample, t)
-
- output = scheduler.step(model_output, t, sample, generator=generator)
- sample = output.prev_sample
-
- result_sum = torch.sum(torch.abs(sample))
- result_mean = torch.mean(torch.abs(sample))
-
- assert abs(result_sum.item() - 152.3192) < 1e-2
- assert abs(result_mean.item() - 0.1983) < 1e-3
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py
deleted file mode 100644
index a44c01831b508da0a5e1ca3720bb437bcea086d1..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
- '../_base_/models/mask_rcnn_r50_caffe_c4.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# use caffe img_norm
-img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/pisa_retinanet_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/pisa_retinanet_head.py
deleted file mode 100644
index bd87b9aeb07e05ff94b444ac8999eca3f616711a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/pisa_retinanet_head.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import images_to_levels
-from ..builder import HEADS
-from ..losses import carl_loss, isr_p
-from .retina_head import RetinaHead
-
-
-@HEADS.register_module()
-class PISARetinaHead(RetinaHead):
- """PISA Retinanet Head.
-
- The head owns the same structure with Retinanet Head, but differs in two
- aspects:
- 1. Importance-based Sample Reweighting Positive (ISR-P) is applied to
- change the positive loss weights.
- 2. Classification-aware regression loss is adopted as a third loss.
- """
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
- def loss(self,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute losses of the head.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image
- with shape (num_obj, 4).
- gt_labels (list[Tensor]): Ground truth labels of each image
- with shape (num_obj, 4).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
- Default: None.
-
- Returns:
- dict: Loss dict, comprise classification loss, regression loss and
- carl loss.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.anchor_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels,
- return_sampling_results=True)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets
- num_total_samples = (
- num_total_pos + num_total_neg if self.sampling else num_total_pos)
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
- # concat all level anchors and flags to a single tensor
- concat_anchor_list = []
- for i in range(len(anchor_list)):
- concat_anchor_list.append(torch.cat(anchor_list[i]))
- all_anchor_list = images_to_levels(concat_anchor_list,
- num_level_anchors)
-
- num_imgs = len(img_metas)
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, label_channels)
- for cls_score in cls_scores
- ]
- flatten_cls_scores = torch.cat(
- flatten_cls_scores, dim=1).reshape(-1,
- flatten_cls_scores[0].size(-1))
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds = torch.cat(
- flatten_bbox_preds, dim=1).view(-1, flatten_bbox_preds[0].size(-1))
- flatten_labels = torch.cat(labels_list, dim=1).reshape(-1)
- flatten_label_weights = torch.cat(
- label_weights_list, dim=1).reshape(-1)
- flatten_anchors = torch.cat(all_anchor_list, dim=1).reshape(-1, 4)
- flatten_bbox_targets = torch.cat(
- bbox_targets_list, dim=1).reshape(-1, 4)
- flatten_bbox_weights = torch.cat(
- bbox_weights_list, dim=1).reshape(-1, 4)
-
- # Apply ISR-P
- isr_cfg = self.train_cfg.get('isr', None)
- if isr_cfg is not None:
- all_targets = (flatten_labels, flatten_label_weights,
- flatten_bbox_targets, flatten_bbox_weights)
- with torch.no_grad():
- all_targets = isr_p(
- flatten_cls_scores,
- flatten_bbox_preds,
- all_targets,
- flatten_anchors,
- sampling_results_list,
- bbox_coder=self.bbox_coder,
- loss_cls=self.loss_cls,
- num_class=self.num_classes,
- **self.train_cfg.isr)
- (flatten_labels, flatten_label_weights, flatten_bbox_targets,
- flatten_bbox_weights) = all_targets
-
- # For convenience we compute loss once instead separating by fpn level,
- # so that we don't need to separate the weights by level again.
- # The result should be the same
- losses_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- flatten_label_weights,
- avg_factor=num_total_samples)
- losses_bbox = self.loss_bbox(
- flatten_bbox_preds,
- flatten_bbox_targets,
- flatten_bbox_weights,
- avg_factor=num_total_samples)
- loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
-
- # CARL Loss
- carl_cfg = self.train_cfg.get('carl', None)
- if carl_cfg is not None:
- loss_carl = carl_loss(
- flatten_cls_scores,
- flatten_labels,
- flatten_bbox_preds,
- flatten_bbox_targets,
- self.loss_bbox,
- **self.train_cfg.carl,
- avg_factor=num_total_pos,
- sigmoid=True,
- num_class=self.num_classes)
- loss_dict.update(loss_carl)
-
- return loss_dict
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py
deleted file mode 100644
index f20f260e23a95dfee9dfdceef9badab992246f53..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(
- pretrained='open-mmlab://resnet101_v1c',
- backbone=dict(
- depth=101,
- dilations=(1, 1, 1, 2),
- strides=(1, 2, 2, 1),
- multi_grid=(1, 2, 4)),
- decode_head=dict(
- dilations=(1, 6, 12, 18),
- sampler=dict(type='OHEMPixelSampler', min_kept=100000)))
diff --git a/spaces/AnnonSubmission/xai-cl/utils.py b/spaces/AnnonSubmission/xai-cl/utils.py
deleted file mode 100644
index 59470f9bb1276013f1db5cd6ce2a3c69410da9be..0000000000000000000000000000000000000000
--- a/spaces/AnnonSubmission/xai-cl/utils.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from PIL import Image
-import random
-import cv2
-import io
-from ssl_models.simclr2 import get_simclr2_model
-from ssl_models.barlow_twins import get_barlow_twins_model
-from ssl_models.simsiam import get_simsiam
-from ssl_models.dino import get_dino_model_without_loss, get_dino_model_with_loss
-
-def get_ssl_model(network, variant):
-
- if network == 'simclrv2':
- if variant == '1x':
- ssl_model = get_simclr2_model('r50_1x_sk0_ema.pth').eval()
- else:
- ssl_model = get_simclr2_model('r50_2x_sk0_ema.pth').eval()
- elif network == 'barlow_twins':
- ssl_model = get_barlow_twins_model().eval()
- elif network == 'simsiam':
- ssl_model = get_simsiam().eval()
- elif network == 'dino':
- ssl_model = get_dino_model_without_loss().eval()
- elif network == 'dino+loss':
- ssl_model, dino_score = get_dino_model_with_loss()
- ssl_model = ssl_model.eval()
-
- return ssl_model
-
-def overlay_heatmap(img, heatmap, denormalize = False):
- loaded_img = img.squeeze(0).cpu().numpy().transpose((1, 2, 0))
-
- if denormalize:
- mean = np.array([0.485, 0.456, 0.406])
- std = np.array([0.229, 0.224, 0.225])
- loaded_img = std * loaded_img + mean
-
- loaded_img = (loaded_img.clip(0, 1) * 255).astype(np.uint8)
- cam = heatmap / heatmap.max()
- cam = cv2.resize(cam, (224, 224))
- cam = np.uint8(255 * cam)
- cam = cv2.applyColorMap(cam, cv2.COLORMAP_JET) # jet: blue --> red
- cam = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB)
- added_image = cv2.addWeighted(cam, 0.5, loaded_img, 0.5, 0)
- return added_image
-
-def viz_map(img_path, heatmap):
- "For pixel invariance"
- img = np.array(Image.open(img_path).resize((224,224))) if isinstance(img_path, str) else np.array(img_path.resize((224,224)))
- width, height, _ = img.shape
- cam = heatmap.detach().cpu().numpy()
- cam = cam / cam.max()
- cam = cv2.resize(cam, (height, width))
- heatmap = np.uint8(255 * cam)
- heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
- heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
- added_image = cv2.addWeighted(heatmap, 0.5, img, 0.7, 0)
- return added_image
-
-def show_image(x, squeeze = True, denormalize = False):
-
- if squeeze:
- x = x.squeeze(0)
-
- x = x.cpu().numpy().transpose((1, 2, 0))
-
- if denormalize:
- mean = np.array([0.485, 0.456, 0.406])
- std = np.array([0.229, 0.224, 0.225])
- x = std * x + mean
-
- return x.clip(0, 1)
-
-def deprocess(inp, to_numpy = True, to_PIL = False, denormalize = False):
-
- if to_numpy:
- inp = inp.detach().cpu().numpy()
-
- inp = inp.squeeze(0).transpose((1, 2, 0))
-
- if denormalize:
- mean = np.array([0.485, 0.456, 0.406])
- std = np.array([0.229, 0.224, 0.225])
- inp = std * inp + mean
-
- inp = (inp.clip(0, 1) * 255).astype(np.uint8)
-
- if to_PIL:
- return Image.fromarray(inp)
- return inp
-
-def fig2img(fig):
- """Convert a Matplotlib figure to a PIL Image and return it"""
- buf = io.BytesIO()
- fig.savefig(buf, bbox_inches='tight', pad_inches=0)
- buf.seek(0)
- img = Image.open(buf)
- return img
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/vit.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/vit.py
deleted file mode 100644
index ea46b1be88b261b0dec04f3da0256f5f66f88a74..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/vit.py
+++ /dev/null
@@ -1,491 +0,0 @@
-import torch
-import torch.nn as nn
-import timm
-import types
-import math
-import torch.nn.functional as F
-
-
-class Slice(nn.Module):
- def __init__(self, start_index=1):
- super(Slice, self).__init__()
- self.start_index = start_index
-
- def forward(self, x):
- return x[:, self.start_index :]
-
-
-class AddReadout(nn.Module):
- def __init__(self, start_index=1):
- super(AddReadout, self).__init__()
- self.start_index = start_index
-
- def forward(self, x):
- if self.start_index == 2:
- readout = (x[:, 0] + x[:, 1]) / 2
- else:
- readout = x[:, 0]
- return x[:, self.start_index :] + readout.unsqueeze(1)
-
-
-class ProjectReadout(nn.Module):
- def __init__(self, in_features, start_index=1):
- super(ProjectReadout, self).__init__()
- self.start_index = start_index
-
- self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
-
- def forward(self, x):
- readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
- features = torch.cat((x[:, self.start_index :], readout), -1)
-
- return self.project(features)
-
-
-class Transpose(nn.Module):
- def __init__(self, dim0, dim1):
- super(Transpose, self).__init__()
- self.dim0 = dim0
- self.dim1 = dim1
-
- def forward(self, x):
- x = x.transpose(self.dim0, self.dim1)
- return x
-
-
-def forward_vit(pretrained, x):
- b, c, h, w = x.shape
-
- glob = pretrained.model.forward_flex(x)
-
- layer_1 = pretrained.activations["1"]
- layer_2 = pretrained.activations["2"]
- layer_3 = pretrained.activations["3"]
- layer_4 = pretrained.activations["4"]
-
- layer_1 = pretrained.act_postprocess1[0:2](layer_1)
- layer_2 = pretrained.act_postprocess2[0:2](layer_2)
- layer_3 = pretrained.act_postprocess3[0:2](layer_3)
- layer_4 = pretrained.act_postprocess4[0:2](layer_4)
-
- unflatten = nn.Sequential(
- nn.Unflatten(
- 2,
- torch.Size(
- [
- h // pretrained.model.patch_size[1],
- w // pretrained.model.patch_size[0],
- ]
- ),
- )
- )
-
- if layer_1.ndim == 3:
- layer_1 = unflatten(layer_1)
- if layer_2.ndim == 3:
- layer_2 = unflatten(layer_2)
- if layer_3.ndim == 3:
- layer_3 = unflatten(layer_3)
- if layer_4.ndim == 3:
- layer_4 = unflatten(layer_4)
-
- layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
- layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
- layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
- layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
-
- return layer_1, layer_2, layer_3, layer_4
-
-
-def _resize_pos_embed(self, posemb, gs_h, gs_w):
- posemb_tok, posemb_grid = (
- posemb[:, : self.start_index],
- posemb[0, self.start_index :],
- )
-
- gs_old = int(math.sqrt(len(posemb_grid)))
-
- posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
- posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
- posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
-
- posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-
- return posemb
-
-
-def forward_flex(self, x):
- b, c, h, w = x.shape
-
- pos_embed = self._resize_pos_embed(
- self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
- )
-
- B = x.shape[0]
-
- if hasattr(self.patch_embed, "backbone"):
- x = self.patch_embed.backbone(x)
- if isinstance(x, (list, tuple)):
- x = x[-1] # last feature if backbone outputs list/tuple of features
-
- x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
-
- if getattr(self, "dist_token", None) is not None:
- cls_tokens = self.cls_token.expand(
- B, -1, -1
- ) # stole cls_tokens impl from Phil Wang, thanks
- dist_token = self.dist_token.expand(B, -1, -1)
- x = torch.cat((cls_tokens, dist_token, x), dim=1)
- else:
- cls_tokens = self.cls_token.expand(
- B, -1, -1
- ) # stole cls_tokens impl from Phil Wang, thanks
- x = torch.cat((cls_tokens, x), dim=1)
-
- x = x + pos_embed
- x = self.pos_drop(x)
-
- for blk in self.blocks:
- x = blk(x)
-
- x = self.norm(x)
-
- return x
-
-
-activations = {}
-
-
-def get_activation(name):
- def hook(model, input, output):
- activations[name] = output
-
- return hook
-
-
-def get_readout_oper(vit_features, features, use_readout, start_index=1):
- if use_readout == "ignore":
- readout_oper = [Slice(start_index)] * len(features)
- elif use_readout == "add":
- readout_oper = [AddReadout(start_index)] * len(features)
- elif use_readout == "project":
- readout_oper = [
- ProjectReadout(vit_features, start_index) for out_feat in features
- ]
- else:
- assert (
- False
- ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
-
- return readout_oper
-
-
-def _make_vit_b16_backbone(
- model,
- features=[96, 192, 384, 768],
- size=[384, 384],
- hooks=[2, 5, 8, 11],
- vit_features=768,
- use_readout="ignore",
- start_index=1,
-):
- pretrained = nn.Module()
-
- pretrained.model = model
- pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
- pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
- pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
- pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
- pretrained.activations = activations
-
- readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
- # 32, 48, 136, 384
- pretrained.act_postprocess1 = nn.Sequential(
- readout_oper[0],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[0],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- nn.ConvTranspose2d(
- in_channels=features[0],
- out_channels=features[0],
- kernel_size=4,
- stride=4,
- padding=0,
- bias=True,
- dilation=1,
- groups=1,
- ),
- )
-
- pretrained.act_postprocess2 = nn.Sequential(
- readout_oper[1],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[1],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- nn.ConvTranspose2d(
- in_channels=features[1],
- out_channels=features[1],
- kernel_size=2,
- stride=2,
- padding=0,
- bias=True,
- dilation=1,
- groups=1,
- ),
- )
-
- pretrained.act_postprocess3 = nn.Sequential(
- readout_oper[2],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[2],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- )
-
- pretrained.act_postprocess4 = nn.Sequential(
- readout_oper[3],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[3],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- nn.Conv2d(
- in_channels=features[3],
- out_channels=features[3],
- kernel_size=3,
- stride=2,
- padding=1,
- ),
- )
-
- pretrained.model.start_index = start_index
- pretrained.model.patch_size = [16, 16]
-
- # We inject this function into the VisionTransformer instances so that
- # we can use it with interpolated position embeddings without modifying the library source.
- pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
- pretrained.model._resize_pos_embed = types.MethodType(
- _resize_pos_embed, pretrained.model
- )
-
- return pretrained
-
-
-def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
- model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
-
- hooks = [5, 11, 17, 23] if hooks == None else hooks
- return _make_vit_b16_backbone(
- model,
- features=[256, 512, 1024, 1024],
- hooks=hooks,
- vit_features=1024,
- use_readout=use_readout,
- )
-
-
-def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
- model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
-
- hooks = [2, 5, 8, 11] if hooks == None else hooks
- return _make_vit_b16_backbone(
- model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
- )
-
-
-def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
- model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
-
- hooks = [2, 5, 8, 11] if hooks == None else hooks
- return _make_vit_b16_backbone(
- model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
- )
-
-
-def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
- model = timm.create_model(
- "vit_deit_base_distilled_patch16_384", pretrained=pretrained
- )
-
- hooks = [2, 5, 8, 11] if hooks == None else hooks
- return _make_vit_b16_backbone(
- model,
- features=[96, 192, 384, 768],
- hooks=hooks,
- use_readout=use_readout,
- start_index=2,
- )
-
-
-def _make_vit_b_rn50_backbone(
- model,
- features=[256, 512, 768, 768],
- size=[384, 384],
- hooks=[0, 1, 8, 11],
- vit_features=768,
- use_vit_only=False,
- use_readout="ignore",
- start_index=1,
-):
- pretrained = nn.Module()
-
- pretrained.model = model
-
- if use_vit_only == True:
- pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
- pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
- else:
- pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
- get_activation("1")
- )
- pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
- get_activation("2")
- )
-
- pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
- pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
- pretrained.activations = activations
-
- readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
- if use_vit_only == True:
- pretrained.act_postprocess1 = nn.Sequential(
- readout_oper[0],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[0],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- nn.ConvTranspose2d(
- in_channels=features[0],
- out_channels=features[0],
- kernel_size=4,
- stride=4,
- padding=0,
- bias=True,
- dilation=1,
- groups=1,
- ),
- )
-
- pretrained.act_postprocess2 = nn.Sequential(
- readout_oper[1],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[1],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- nn.ConvTranspose2d(
- in_channels=features[1],
- out_channels=features[1],
- kernel_size=2,
- stride=2,
- padding=0,
- bias=True,
- dilation=1,
- groups=1,
- ),
- )
- else:
- pretrained.act_postprocess1 = nn.Sequential(
- nn.Identity(), nn.Identity(), nn.Identity()
- )
- pretrained.act_postprocess2 = nn.Sequential(
- nn.Identity(), nn.Identity(), nn.Identity()
- )
-
- pretrained.act_postprocess3 = nn.Sequential(
- readout_oper[2],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[2],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- )
-
- pretrained.act_postprocess4 = nn.Sequential(
- readout_oper[3],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[3],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- nn.Conv2d(
- in_channels=features[3],
- out_channels=features[3],
- kernel_size=3,
- stride=2,
- padding=1,
- ),
- )
-
- pretrained.model.start_index = start_index
- pretrained.model.patch_size = [16, 16]
-
- # We inject this function into the VisionTransformer instances so that
- # we can use it with interpolated position embeddings without modifying the library source.
- pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-
- # We inject this function into the VisionTransformer instances so that
- # we can use it with interpolated position embeddings without modifying the library source.
- pretrained.model._resize_pos_embed = types.MethodType(
- _resize_pos_embed, pretrained.model
- )
-
- return pretrained
-
-
-def _make_pretrained_vitb_rn50_384(
- pretrained, use_readout="ignore", hooks=None, use_vit_only=False
-):
- model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
-
- hooks = [0, 1, 8, 11] if hooks == None else hooks
- return _make_vit_b_rn50_backbone(
- model,
- features=[256, 512, 768, 768],
- size=[384, 384],
- hooks=hooks,
- use_vit_only=use_vit_only,
- use_readout=use_readout,
- )
diff --git a/spaces/Apex-X/GODROOP/roop/predictor.py b/spaces/Apex-X/GODROOP/roop/predictor.py
deleted file mode 100644
index 877fd725d21bddf5e788677eefbc917ddc79f52b..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/GODROOP/roop/predictor.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import threading
-import numpy
-from PIL import Image
-
-from roop.typing import Frame
-
-# Define any other necessary variables or constants here
-
-def predict_frame(target_frame: Frame) -> bool:
- # Modify this function as needed for your specific use case, without NSFW prediction
- # For example, you can implement custom image analysis or processing here
- return False
-
-def predict_image(target_path: str) -> bool:
- # Modify this function as needed for your specific use case, without NSFW prediction
- # For example, you can check the image based on your application's requirements
- return False
-
-def predict_video(target_path: str) -> bool:
- # Modify this function as needed for your specific use case, without NSFW prediction
- # For example, you can analyze video frames for other purposes
- return False
diff --git a/spaces/ArcanAlt/arcanDream/server.js b/spaces/ArcanAlt/arcanDream/server.js
deleted file mode 100644
index 04a48b7a429c4d0ad0b772ba1edf503e349eda21..0000000000000000000000000000000000000000
--- a/spaces/ArcanAlt/arcanDream/server.js
+++ /dev/null
@@ -1,32 +0,0 @@
-const express = require('express');
-const proxy = require('express-http-proxy');
-const app = express();
-const targetUrl = 'https://api.openai.com';
-const openaiKey = process.env.OPENAI_KEY
-const port = 7860;
-const baseUrl = getExternalUrl(process.env.SPACE_ID);
-
-app.use('/api', proxy(targetUrl, {
- proxyReqOptDecorator: (proxyReqOpts, srcReq) => {
- // Modify the request headers if necessary
- proxyReqOpts.headers['Authorization'] = 'Bearer '+openaiKey;
- return proxyReqOpts;
- },
-}));
-
-app.get("/", (req, res) => {
- res.send(`This is your OpenAI Reverse Proxy URL: ${baseUrl}`);
-});
-
-function getExternalUrl(spaceId) {
- try {
- const [username, spacename] = spaceId.split("/");
- return `https://${username}-${spacename.replace(/_/g, "-")}.hf.space/api/v1`;
- } catch (e) {
- return "";
- }
-}
-
-app.listen(port, () => {
- console.log(`Reverse proxy server running on ${baseUrl}`);
-});
\ No newline at end of file
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/scope.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/scope.py
deleted file mode 100644
index c9d134cc3cedae929e5bef2b5547f7e33dc10a52..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/scope.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from collections.abc import Mapping
-from typing import TYPE_CHECKING, Any, Optional, Tuple
-
-from .highlighter import ReprHighlighter
-from .panel import Panel
-from .pretty import Pretty
-from .table import Table
-from .text import Text, TextType
-
-if TYPE_CHECKING:
- from .console import ConsoleRenderable
-
-
-def render_scope(
- scope: "Mapping[str, Any]",
- *,
- title: Optional[TextType] = None,
- sort_keys: bool = True,
- indent_guides: bool = False,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
-) -> "ConsoleRenderable":
- """Render python variables in a given scope.
-
- Args:
- scope (Mapping): A mapping containing variable names and values.
- title (str, optional): Optional title. Defaults to None.
- sort_keys (bool, optional): Enable sorting of items. Defaults to True.
- indent_guides (bool, optional): Enable indentation guides. Defaults to False.
- max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
- Defaults to None.
- max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-
- Returns:
- ConsoleRenderable: A renderable object.
- """
- highlighter = ReprHighlighter()
- items_table = Table.grid(padding=(0, 1), expand=False)
- items_table.add_column(justify="right")
-
- def sort_items(item: Tuple[str, Any]) -> Tuple[bool, str]:
- """Sort special variables first, then alphabetically."""
- key, _ = item
- return (not key.startswith("__"), key.lower())
-
- items = sorted(scope.items(), key=sort_items) if sort_keys else scope.items()
- for key, value in items:
- key_text = Text.assemble(
- (key, "scope.key.special" if key.startswith("__") else "scope.key"),
- (" =", "scope.equals"),
- )
- items_table.add_row(
- key_text,
- Pretty(
- value,
- highlighter=highlighter,
- indent_guides=indent_guides,
- max_length=max_length,
- max_string=max_string,
- ),
- )
- return Panel.fit(
- items_table,
- title=title,
- border_style="scope.border",
- padding=(0, 1),
- )
-
-
-if __name__ == "__main__": # pragma: no cover
- from pip._vendor.rich import print
-
- print()
-
- def test(foo: float, bar: float) -> None:
- list_of_things = [1, 2, 3, None, 4, True, False, "Hello World"]
- dict_of_things = {
- "version": "1.1",
- "method": "confirmFruitPurchase",
- "params": [["apple", "orange", "mangoes", "pomelo"], 1.123],
- "id": "194521489",
- }
- print(render_scope(locals(), title="[i]locals", sort_keys=False))
-
- test(20.3423, 3.1427)
- print()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/build.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/build.py
deleted file mode 100644
index c0676d8e4b1a567969cf05c5825d49c3300284c9..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/build.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import sys
-import warnings
-from typing import TYPE_CHECKING, List, Dict
-from distutils.command.build import build as _build
-
-from setuptools import SetuptoolsDeprecationWarning
-
-if sys.version_info >= (3, 8):
- from typing import Protocol
-elif TYPE_CHECKING:
- from typing_extensions import Protocol
-else:
- from abc import ABC as Protocol
-
-
-_ORIGINAL_SUBCOMMANDS = {"build_py", "build_clib", "build_ext", "build_scripts"}
-
-
-class build(_build):
- # copy to avoid sharing the object with parent class
- sub_commands = _build.sub_commands[:]
-
- def get_sub_commands(self):
- subcommands = {cmd[0] for cmd in _build.sub_commands}
- if subcommands - _ORIGINAL_SUBCOMMANDS:
- msg = """
- It seems that you are using `distutils.command.build` to add
- new subcommands. Using `distutils` directly is considered deprecated,
- please use `setuptools.command.build`.
- """
- warnings.warn(msg, SetuptoolsDeprecationWarning)
- self.sub_commands = _build.sub_commands
- return super().get_sub_commands()
-
-
-class SubCommand(Protocol):
- """In order to support editable installations (see :pep:`660`) all
- build subcommands **SHOULD** implement this protocol. They also **MUST** inherit
- from ``setuptools.Command``.
-
- When creating an :pep:`editable wheel <660>`, ``setuptools`` will try to evaluate
- custom ``build`` subcommands using the following procedure:
-
- 1. ``setuptools`` will set the ``editable_mode`` attribute to ``True``
- 2. ``setuptools`` will execute the ``run()`` command.
-
- .. important::
- Subcommands **SHOULD** take advantage of ``editable_mode=True`` to adequate
- its behaviour or perform optimisations.
-
- For example, if a subcommand don't need to generate any extra file and
- everything it does is to copy a source file into the build directory,
- ``run()`` **SHOULD** simply "early return".
-
- Similarly, if the subcommand creates files that would be placed alongside
- Python files in the final distribution, during an editable install
- the command **SHOULD** generate these files "in place" (i.e. write them to
- the original source directory, instead of using the build directory).
- Note that ``get_output_mapping()`` should reflect that and include mappings
- for "in place" builds accordingly.
-
- 3. ``setuptools`` use any knowledge it can derive from the return values of
- ``get_outputs()`` and ``get_output_mapping()`` to create an editable wheel.
- When relevant ``setuptools`` **MAY** attempt to use file links based on the value
- of ``get_output_mapping()``. Alternatively, ``setuptools`` **MAY** attempt to use
- :doc:`import hooks ` to redirect any attempt to import
- to the directory with the original source code and other files built in place.
-
- Please note that custom sub-commands **SHOULD NOT** rely on ``run()`` being
- executed (or not) to provide correct return values for ``get_outputs()``,
- ``get_output_mapping()`` or ``get_source_files()``. The ``get_*`` methods should
- work independently of ``run()``.
- """
-
- editable_mode: bool = False
- """Boolean flag that will be set to ``True`` when setuptools is used for an
- editable installation (see :pep:`660`).
- Implementations **SHOULD** explicitly set the default value of this attribute to
- ``False``.
- When subcommands run, they can use this flag to perform optimizations or change
- their behaviour accordingly.
- """
-
- build_lib: str
- """String representing the directory where the build artifacts should be stored,
- e.g. ``build/lib``.
- For example, if a distribution wants to provide a Python module named ``pkg.mod``,
- then a corresponding file should be written to ``{build_lib}/package/module.py``.
- A way of thinking about this is that the files saved under ``build_lib``
- would be eventually copied to one of the directories in :obj:`site.PREFIXES`
- upon installation.
-
- A command that produces platform-independent files (e.g. compiling text templates
- into Python functions), **CAN** initialize ``build_lib`` by copying its value from
- the ``build_py`` command. On the other hand, a command that produces
- platform-specific files **CAN** initialize ``build_lib`` by copying its value from
- the ``build_ext`` command. In general this is done inside the ``finalize_options``
- method with the help of the ``set_undefined_options`` command::
-
- def finalize_options(self):
- self.set_undefined_options("build_py", ("build_lib", "build_lib"))
- ...
- """
-
- def initialize_options(self):
- """(Required by the original :class:`setuptools.Command` interface)"""
-
- def finalize_options(self):
- """(Required by the original :class:`setuptools.Command` interface)"""
-
- def run(self):
- """(Required by the original :class:`setuptools.Command` interface)"""
-
- def get_source_files(self) -> List[str]:
- """
- Return a list of all files that are used by the command to create the expected
- outputs.
- For example, if your build command transpiles Java files into Python, you should
- list here all the Java files.
- The primary purpose of this function is to help populating the ``sdist``
- with all the files necessary to build the distribution.
- All files should be strings relative to the project root directory.
- """
-
- def get_outputs(self) -> List[str]:
- """
- Return a list of files intended for distribution as they would have been
- produced by the build.
- These files should be strings in the form of
- ``"{build_lib}/destination/file/path"``.
-
- .. note::
- The return value of ``get_output()`` should include all files used as keys
- in ``get_output_mapping()`` plus files that are generated during the build
- and don't correspond to any source file already present in the project.
- """
-
- def get_output_mapping(self) -> Dict[str, str]:
- """
- Return a mapping between destination files as they would be produced by the
- build (dict keys) into the respective existing (source) files (dict values).
- Existing (source) files should be represented as strings relative to the project
- root directory.
- Destination files should be strings in the form of
- ``"{build_lib}/destination/file/path"``.
- """
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/__init__.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/__init__.py
deleted file mode 100644
index d96609e8f2261a6800fe85fcf3e1eaeaa44455c6..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator
-from .coco_evaluation import COCOEvaluator
-from .rotated_coco_evaluation import RotatedCOCOEvaluator
-from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset
-from .lvis_evaluation import LVISEvaluator
-from .panoptic_evaluation import COCOPanopticEvaluator
-from .pascal_voc_evaluation import PascalVOCDetectionEvaluator
-from .sem_seg_evaluation import SemSegEvaluator
-from .testing import print_csv_format, verify_results
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/Benson/text-generation/Examples/Apk Mod De Da Para Android 11.md b/spaces/Benson/text-generation/Examples/Apk Mod De Da Para Android 11.md
deleted file mode 100644
index b2c2d1ad5c2ec28eab0a66b247b5a2ca234ef08b..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Apk Mod De Da Para Android 11.md
+++ /dev/null
@@ -1,53 +0,0 @@
-
-Totalmente fiable servicio de entrega Mod APK An1: Un divertido y caótico juego basado en la física
-¿Te gustan los juegos divertidos, impredecibles y llenos de sorpresas? Si es así, es posible que desee echa un vistazo a Totally Reliable Delivery Service, un juego basado en la física en el que entregar paquetes en un mundo loco. Y si quieres hacer el juego aún más divertido y emocionante, se puede descargar totalmente fiable Servicio de entrega Mod APK An1, una versión modificada del juego que le da dinero ilimitado y desbloqueado características. En este artículo, te diremos de qué se trata este juego, qué ofrece el apk mod, y cómo descargarlo e instalarlo en tu dispositivo Android.
-apk mod de día para android 11 Download · https://bltlly.com/2v6II7
¿Qué es un servicio de entrega totalmente confiable?
-Un juego donde se entregan paquetes en un mundo loco
-Totally Reliable Delivery Service es un juego donde juegas como un repartidor que tiene que entregar paquetes en un mundo loco y caótico. El juego cuenta con la física ragdoll, lo que significa que su personaje y los objetos en el juego se comportan de manera realista e hilarante. Puede utilizar varios vehículos, como automóviles, camiones, aviones, helicópteros, barcos e incluso cohetes, para transportar sus paquetes. Pero ten cuidado, porque cualquier cosa puede salir mal en el camino. Puedes chocar contra edificios, caerte de puentes, ser perseguido por animales o explotar en el aire. El juego está lleno de sorpresas y desafíos que te harán reír a carcajadas.
-Un juego donde puedes personalizar tu personaje y vehículos
-Servicio de entrega totalmente confiable también le permite personalizar su personaje y vehículos para adaptarse a su estilo y preferencias. Puedes elegir entre diferentes trajes, accesorios, peinados y colores para tu personaje. También puede actualizar sus vehículos con diferentes partes, como motores, ruedas, alas, hélices y más. Incluso puedes crear tus propios vehículos usando el modo sandbox. El juego te da muchas opciones para expresar tu creatividad y personalidad.
-
-Totally Reliable Delivery Service es un juego que puedes disfrutar solo o con tus amigos online. Puedes jugar solo y completar varias misiones y desafíos en el mundo abierto. O puede unirse a hasta otros tres jugadores en línea y cooperar o competir con ellos en la entrega de paquetes. También pueden explorar el mundo juntos y divertirse con el juego basado en la física. El juego admite multijugador multiplataforma, lo que significa que puedes jugar con personas que utilizan diferentes dispositivos, como PC, consola o dispositivos móviles.
-¿Qué es totalmente fiable servicio de entrega Mod APK An1?
-Una versión modificada del juego que te da dinero ilimitado y funciones desbloqueadas
-Totalmente fiable servicio de entrega Mod APK An1 es una versión modificada del juego que le da algunas ventajas sobre la versión original. Con este mod apk, obtendrá dinero ilimitado que se puede utilizar para comprar cualquier cosa en el juego. También obtendrá todas las características desbloqueadas, como todos los trajes, accesorios, vehículos, piezas, mapas, modos y más. Podrás disfrutar del juego sin limitaciones ni restricciones.
-Una versión del juego que es compatible con dispositivos Android
-Una versión del juego que es gratis para descargar e instalar
-Totalmente fiable servicio de entrega Mod APK An1 es una versión del juego que es gratis para descargar e instalar en su dispositivo Android. Usted no necesita pagar nada para obtener este apk mod. También no es necesario para erradicar el dispositivo o utilizar cualquier otra herramienta para instalarlo. Solo tienes que seguir unos sencillos pasos que explicaremos más adelante en este artículo. Podrás jugar el juego sin problemas ni riesgos.
- ¿Cómo descargar e instalar el servicio de entrega totalmente confiable Mod APK An1?
-Paso 1: Ir al sitio web
-
-Paso 2: Haga clic en el botón de descarga y espere a que el archivo se descargue
-El siguiente paso es hacer clic en el botón de descarga que se encuentra en la parte inferior de la página. Verá una ventana emergente que le pide que confirme su descarga. Haga clic en Aceptar y espere a que se descargue el archivo. El tamaño del archivo es de unos 50 MB, por lo que puede tardar unos minutos dependiendo de su velocidad de Internet. Puede comprobar el progreso de su descarga en la barra de notificaciones.
-
-Paso 3: Habilitar fuentes desconocidas en la configuración del dispositivo
-Una vez que se descargue el archivo, debe habilitar fuentes desconocidas en la configuración del dispositivo. Esta es una medida de seguridad que le impide instalar aplicaciones de fuentes distintas de Google Play Store. Para habilitar fuentes desconocidas, vaya a la configuración del dispositivo y busque opciones de seguridad o privacidad. Luego, busque la opción que dice fuentes desconocidas o permita la instalación desde fuentes desconocidas y cámbiela. Es posible que vea un mensaje de advertencia que indica que la instalación desde fuentes desconocidas podría dañar su dispositivo. No te preocupes, este apk mod es seguro y probado, por lo que puede ignorar la advertencia y proceder.
-Paso 4: Busque el archivo descargado y toque en él para instalarlo
-El siguiente paso es localizar el archivo descargado y tocar en él para instalarlo. Puede encontrar el archivo en su carpeta de descargas o en su aplicación de administrador de archivos. El nombre del archivo es totalmente fiable-delivery-service-mod_1.4.0.apk. Toque en él y verá una pantalla de instalación que le pide que confirme su instalación. Toque en instalar y espere a que el proceso termine.
-Felicidades! Usted ha descargado e instalado con éxito Totalmente fiable Servicio de entrega Mod APK An1 en su dispositivo Android. Ahora puedes disfrutar del juego con dinero ilimitado y funciones desbloqueadas. Puedes lanzar el juego desde el cajón de la app o la pantalla de inicio. ¡Diviértete entregando paquetes en un mundo loco!
-Conclusión
-Totally Reliable Delivery Service es un divertido y caótico juego basado en la física en el que entregar paquetes en un mundo loco e impredecible. Puedes personalizar tu personaje y vehículos, jugar solo o con amigos en línea, y explorar diferentes mapas y modos. Si usted quiere hacer el juego aún más agradable, se puede descargar totalmente fiable servicio de entrega Mod APK An1, una versión modificada del juego que le da dinero ilimitado y desbloqueado características. Puede descargar e instalar este apk mod de forma gratuita y fácil siguiendo nuestra guía anterior. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación.
-Preguntas frecuentes
-
- ¿Es totalmente confiable servicio de entrega mod APK An1 seguro?
- ¿Es totalmente confiable servicio de entrega mod APK An1 seguro? Sí, Servicio de entrega totalmente confiable Mod APK An1 es seguro y probado por nuestro equipo. No contiene ningún virus, malware o spyware que pueda dañar su dispositivo o comprometer su privacidad. Puede descargar e instalar este apk mod sin ninguna preocupación.
- ¿Es totalmente confiable servicio de entrega mod APK An1 legal? ¿Cuáles son los requisitos para ejecutar Totalmente fiable servicio de entrega Mod APK An1? Servicio de entrega totalmente confiable Mod APK An1 requiere un dispositivo Android que se ejecuta en Android 4.1 o superior. También necesita tener al menos 1 GB de RAM y 200 MB de espacio de almacenamiento gratuito en su dispositivo. También necesitas tener una conexión a Internet estable para jugar online.
- ¿Puedo jugar totalmente fiable servicio de entrega Mod APK An1 en PC o iOS? No, Servicio de entrega totalmente fiable Mod APK An1 solo es compatible con dispositivos Android. No se puede jugar este apk mod en dispositivos PC o iOS. Sin embargo, puedes jugar la versión original del juego en dispositivos PC o iOS descargándolo desde las plataformas oficiales, como Steam, Epic Games Store, App Store o Google Play Store.
- ¿Puedo actualizar el servicio de entrega totalmente confiable Mod APK An1? No, no se puede actualizar totalmente fiable servicio de entrega Mod APK An1 del juego en sí. Si intentas actualizar el juego desde la configuración del juego, podrías perder las características de mod y volver a la versión original del juego. Para actualizar el apk mod, es necesario visitar nuestro sitio web de nuevo y descargar la última versión del apk mod. A continuación, es necesario desinstalar la versión anterior del apk mod e instalar el nuevo siguiendo los mismos pasos que antes.
- 64aa2da5cfBlacknoise Reste Toi Mp3 Descargar: Una revisión del éxito de Amapiano
- Si eres un fan de amapiano, el popular género de música house sudafricano, es posible que hayas oído hablar de Blacknoise, un artista de hip-hop que recientemente ha colaborado con Kazeli y Mashaya para crear una canción pegadiza y edificante llamada Reste Toi. En este artículo, revisaremos esta canción y te diremos cómo descargarla en formato Mp3.
-blacknoise reste toi mp3 download Download ✑ https://bltlly.com/2v6JPu
¿Quién es Blacknoise?
- Breve biografía del artista sudafricano de hip-hop
- Blacknoise es el nombre artístico de Emile Jansen, un rapero, productor y activista de Ciudad del Cabo, Sudáfrica. También es el fundador y líder de Black Noise, un grupo de hip-hop que ha estado activo desde 1986. Blacknoise es uno de los pioneros de la escena hip-hop 'consciente' de Ciudad del Cabo, usando el rap como una herramienta para el comentario social y el empoderamiento. También ha participado en diversas iniciativas de desarrollo juvenil, como talleres, revistas, libros, obras de teatro y eventos. Ha lanzado 12 álbumes con Black Noise, seis álbumes en solitario y varios álbumes recopilatorios.
- Su estilo musical e influencias
- El estilo musical de Blacknoise está influenciado por varios géneros, como rap, reggae, jazz, funk, soul y amapiano. Combina sonidos africanos tradicionales con ritmos y samples modernos, creando un sonido único y diverso. También incorpora elementos de su cultura y lenguaje, como el afrikaans, xhosa y khoisan. Algunas de sus influencias musicales incluyen Public Enemy, Bob Marley, Fela Kuti, Brenda Fassie y Kabza De Small.
- ¿Qué es Reste Toi?
- El significado y origen del título de la canción
-
- La colaboración con Kazeli y Mashaya
- Kazeli es una cantante y compositora francesa que se mudó a Sudáfrica en 2019. Conoció a Blacknoise a través de un amigo mutuo y decidieron trabajar juntos en algunos proyectos musicales. También invitaron a Mashaya, un cantante y productor sudafricano conocido por sus éxitos de amapiano. El trío grabó Reste Toi en el estudio de Blacknoise en Ciudad del Cabo. Querían crear una canción que mostrara sus diferentes orígenes y talentos, a la vez que entregara un mensaje positivo.
-
- La letra y el mensaje de la canción
- Las letras de Reste Toi tratan de celebrar la individualidad y la singularidad de uno. El estribillo dice así:
-
-Buscar en la web
-
- Esto se traduce a:
-
-Mantente a ti mismo
-
- Los versículos también contienen palabras de aliento y afirmación, como "Eres increíble", "Eres una estrella", y "Eres una bendición". La canción también incluye algunas frases de Xhosa, como "Molo sisi" (Hola hermana) y "Enkosi kakhulu" (Muchas gracias). El mensaje de la canción es inspirar a la gente a sentirse segura y feliz con lo que son, y respetar y apreciar a los demás por sus diferencias.
- Como descargar Reste Toi Mp3?
- Las plataformas de streaming que ofrecen la canción
- Reste Toi está disponible en varias plataformas de streaming, como Spotify, Apple Music, YouTube Music, Deezer y SoundCloud. Puedes escuchar la canción online o offline, dependiendo de tu suscripción y preferencias. También puedes ver el video musical oficial de la canción en YouTube, que muestra a los artistas interpretando la canción en diferentes lugares de Ciudad del Cabo.
- Los beneficios de descargar la canción en formato Mp3
-
-
- Puede reproducir la canción en cualquier dispositivo que soporte archivos Mp3, como su teléfono, computadora o reproductor Mp3.
- Puede ahorrar espacio de almacenamiento en su dispositivo, ya que los archivos MP3 son más pequeños que otros formatos de audio.
-Puedes transferir la canción a otros dispositivos o compartirla con tus amigos fácilmente.
-Puede editar la canción o usarla para otros fines, como hacer un tono de llamada o un remix.
-
- Los pasos para descargar la canción de diferentes fuentes
- Hay diferentes maneras de descargar Reste Toi en formato Mp3, dependiendo de la fuente que elija. Estos son algunos de los métodos más comunes:
-
-
-Fuente
-Pasos
-
-
-Spotify
-
-Abra la aplicación Spotify en su dispositivo y busque Reste Toi por Blacknoise, Kazeli y Mashaya.
- Seleccione la canción y toque en el icono de tres puntos en la esquina superior derecha.
-Seleccione Compartir y luego Copiar enlace.
-Abra un navegador web y vaya a un sitio web de conversión de Spotify a Mp3, como SpotiFlyer o SpotiApp.
-Pega el enlace que has copiado y haz clic en Convertir o Descargar.
-Espere a que el proceso de conversión termine y luego descargue el archivo Mp3 en su dispositivo.
-
-
-YouTube
-
-Abra un navegador web y vaya a YouTube.com. Busque Reste Toi por Blacknoise, Kazeli y Mashaya.
-Seleccione el vídeo de la canción y copie su URL desde la barra de direcciones.
-Abra otra pestaña y vaya a un sitio web de conversión de YouTube a Mp3, como YTMP3 o 4K Video Downloader.
-Pegue la URL que copió y haga clic en Convertir o Descargar.
-Seleccione Mp3 como formato de salida y elija la calidad que desee.
-Espere a que el proceso de conversión termine y luego descargue el archivo Mp3 en su dispositivo.
-
-
-SoundCloud
-
-Abra un navegador web y vaya a SoundCloud.com. Busque Reste Toi por Blacknoise, Kazeli y Mashaya.
-
-Abra otra pestaña y vaya a un sitio web de conversión de SoundCloud a Mp3, como SCDL o SoundCloud Downloader.
-Pegue la URL que copió y haga clic en Descargar o Convertir.
-Espere a que el proceso de conversión termine y luego descargue el archivo Mp3 en su dispositivo.
-
-
- ¿Por qué deberías escuchar Reste Toi?
- Las críticas y valoraciones positivas de la canción
- Reste Toi ha recibido críticas y valoraciones positivas tanto de los críticos como de los oyentes. La canción ha sido elogiada por su melodía pegadiza, letras edificantes y colaboración diversa. Algunos de los comentarios de las plataformas en línea incluyen:
-
-"Esta canción es un banger! Me encanta cómo se mezcla amapiano con hip-hop y francés. Me hace querer bailar y cantar a lo largo."
- "Este es un mensaje tan hermoso. Creo que todos deben escuchar esta canción y estar orgullosos de lo que son. Es tan refrescante escuchar algo positivo en estos tiempos."
- "Esta es una obra maestra. La producción es increíble, las voces son suaves, y el rap es fuego. No puedo tener suficiente de esta canción."
-
- La canción también ha recibido altas calificaciones en varias plataformas, como 4.8 de 5 estrellas en Spotify, 4.7 de 5 estrellas en Apple Music y 4.6 de 5 estrellas en YouTube Music.
- El sonido pegadizo y optimista de la canción
- Reste Toi es una canción que te hará sentir bien y con energía. La canción tiene un sonido pegadizo y alegre que combina los elementos de amapiano, hip-hop y pop francés. La canción tiene un ritmo rápido, una línea de bajo groovy, y una melodía de piano suave. La canción también cuenta con algunos sonidos electrónicos, como sintetizadores, tambores y efectos. La canción es fácil de cantar, ya que tiene un coro simple y repetitivo. La canción también es adecuada para bailar, ya que tiene un ritmo rítmico y animado.
- La relevancia cultural y social de la canción
-
- Conclusión
- Reste Toi de Blacknoise, Kazeli, y Mashaya es una canción que debes escuchar si estás buscando una pista de amapiano pegadiza y edificante que te hará sentir bien y orgulloso de quién eres. La canción está disponible en varias plataformas de streaming, y también se puede descargar en formato Mp3 de diferentes fuentes. La canción ha recibido críticas y valoraciones positivas de críticos y oyentes, que han elogiado su sonido, letras y mensaje. La canción es también un reflejo de la diversidad cultural y social de Sudáfrica y el mundo, que es algo para celebrar y apreciar.
- Preguntas frecuentes
- P: ¿Quiénes son los artistas detrás de Reste Toi?
-A: Reste Toi es una canción de Blacknoise, Kazeli y Mashaya. Blacknoise es un artista sudafricano de hip-hop y activista que es el fundador de la banda Black Noise. Kazeli es una cantante y compositora francesa que se mudó a Sudáfrica en 2019. Mashaya es un cantante y productor sudafricano conocido por sus éxitos de amapiano.
- Q: ¿Qué significa Reste Toi?
-A: Reste Toi es una frase francesa que significa "quédate tú" o "sé tú mismo". También es el título de la canción de Blacknoise, Kazeli y Mashaya.
- P: ¿Qué género es Reste Toi?
-A: Reste Toi es una pista de amapiano que cuenta con voces en francés, inglés y Xhosa. Amapiano es un popular género sudafricano de música house que combina sonidos africanos tradicionales con ritmos y samples modernos.
- Q: ¿Cómo puedo descargar Reste Toi en formato Mp3?
-A: Puede descargar Reste Toi en formato Mp3 desde diferentes fuentes, como Spotify, YouTube o SoundCloud. Tendrá que copiar el enlace de la canción desde la plataforma de transmisión y pegarlo en un sitio web convertidor que convertirá la canción en formato Mp3. A continuación, puede descargar el archivo Mp3 a su dispositivo.
- P: ¿Por qué debería escuchar Reste Toi? 64aa2da5cf
-
-// omp inherits swap_ranges
-#include
-
diff --git a/spaces/CVPR/TokenCut/app_backup.py b/spaces/CVPR/TokenCut/app_backup.py
deleted file mode 100644
index 8ffb4ca17ba8a9c08f1faf48d4c96650498516ef..0000000000000000000000000000000000000000
--- a/spaces/CVPR/TokenCut/app_backup.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import os
-import requests
-import pandas as pd
-import gradio as gr
-from huggingface_hub.hf_api import SpaceInfo
-from pathlib import Path
-
-
-path = f"https://huggingface.co/api/spaces"
-os.system("git clone https://github.com/YangtaoWANG95/TokenCut.git")
-os.chdir("TokenCut")
-os.system("wget https://raw.githubusercontent.com/YangtaoWANG95/TokenCut/master/examples/VOC07_000064.jpg -O parrot.jpg")
-
-
-
-def get_blocks_party_spaces():
- r = requests.get(path)
- d = r.json()
- spaces = [SpaceInfo(**x) for x in d]
- blocks_spaces = {}
- for i in range(0,len(spaces)):
- if spaces[i].id.split('/')[0] == 'CVPR' and hasattr(spaces[i], 'likes') and spaces[i].id != 'CVPR/Leaderboard' and spaces[i].id != 'CVPR/README':
- blocks_spaces[spaces[i].id]=spaces[i].likes
- df = pd.DataFrame(
- [{"Spaces_Name": Spaces, "likes": likes} for Spaces,likes in blocks_spaces.items()])
- df = df.sort_values(by=['likes'],ascending=False)
- return df
-
-
-block = gr.Blocks()
-
-with block:
- gr.Markdown("""Leaderboard for the most popular CVPR Spaces. To learn more and join, see CVPR Event """)
- with gr.Tabs():
- with gr.TabItem("CVPR Leaderboard"):
- with gr.Row():
- data = gr.outputs.Dataframe(type="pandas")
- with gr.Row():
- data_run = gr.Button("Refresh")
- data_run.click(get_blocks_party_spaces, inputs=None, outputs=data)
-
- block.load(get_blocks_party_spaces, inputs=None, outputs=data)
-block.launch()
diff --git a/spaces/CVPR/ml-talking-face/toxicity_estimator/__init__.py b/spaces/CVPR/ml-talking-face/toxicity_estimator/__init__.py
deleted file mode 100644
index cf47bc9ac37afb00225e0d0d62ce2748cdcfed71..0000000000000000000000000000000000000000
--- a/spaces/CVPR/ml-talking-face/toxicity_estimator/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .module import PerspectiveAPI
\ No newline at end of file
diff --git a/spaces/CVPR/regionclip-demo/detectron2/projects/__init__.py b/spaces/CVPR/regionclip-demo/detectron2/projects/__init__.py
deleted file mode 100644
index a68207db4ee3c2578e1042b00b3071a946b7adea..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/projects/__init__.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import importlib
-from pathlib import Path
-
-_PROJECTS = {
- "point_rend": "PointRend",
- "deeplab": "DeepLab",
- "panoptic_deeplab": "Panoptic-DeepLab",
-}
-_PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent / "projects"
-
-if _PROJECT_ROOT.is_dir():
- # This is true only for in-place installation (pip install -e, setup.py develop),
- # where setup(package_dir=) does not work: https://github.com/pypa/setuptools/issues/230
-
- class _D2ProjectsFinder(importlib.abc.MetaPathFinder):
- def find_spec(self, name, path, target=None):
- if not name.startswith("detectron2.projects."):
- return
- project_name = name.split(".")[-1]
- project_dir = _PROJECTS.get(project_name)
- if not project_dir:
- return
- target_file = _PROJECT_ROOT / f"{project_dir}/{project_name}/__init__.py"
- if not target_file.is_file():
- return
- return importlib.util.spec_from_file_location(name, target_file)
-
- import sys
-
- sys.meta_path.append(_D2ProjectsFinder())
diff --git a/spaces/CanKorkut/turkish-hatespeech-detection/app.py b/spaces/CanKorkut/turkish-hatespeech-detection/app.py
deleted file mode 100644
index 6387070272170a00ce265638da0255849ddbb8dc..0000000000000000000000000000000000000000
--- a/spaces/CanKorkut/turkish-hatespeech-detection/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from transformers import AutoTokenizer
-import gradio as gr
-from transformers import XLMRobertaForSequenceClassification
-from transformers import pipeline
-
-id2label = {0:'INSULT', 1:'OTHER', 2:'PROFANITY', 3:'RACIST', 4:'SEXIST'}
-model = XLMRobertaForSequenceClassification.from_pretrained(".")
-tokenizer = AutoTokenizer.from_pretrained(".")
-
-def predict(text):
- classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
- result = id2label[int(classifier(text)[0]['label'].split('_')[-1])]
- return result
-
-iface = gr.Interface(fn=predict, inputs="text", outputs="text")
-iface.launch()
diff --git a/spaces/ChatGPT-GAIA/GAIA-GPT/app.py b/spaces/ChatGPT-GAIA/GAIA-GPT/app.py
deleted file mode 100644
index c16825a3466066d5c01f7d687c0f4fdb04e2cd88..0000000000000000000000000000000000000000
--- a/spaces/ChatGPT-GAIA/GAIA-GPT/app.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import gradio as gr
-import os
-import json
-import requests
-
-#Streaming endpoint
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-OPENAI_API_KEY= os.environ["HF_TOKEN"] # Add a token to this space . Then copy it to the repository secret in this spaces settings panel. os.environ reads from there.
-# Keys for Open AI ChatGPT API usage are created from here: https://platform.openai.com/account/api-keys
-
-def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k
-
- # 1. Set up a payload
- payload = {
- "model": "gpt-3.5-turbo",
- "messages": [{"role": "user", "content": f"{inputs}"}],
- "temperature" : 1.0,
- "top_p":1.0,
- "n" : 1,
- "stream": True,
- "presence_penalty":0,
- "frequency_penalty":0,
- }
-
- # 2. Define your headers and add a key from https://platform.openai.com/account/api-keys
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {OPENAI_API_KEY}"
- }
-
- # 3. Create a chat counter loop that feeds [Predict next best anything based on last input and attention with memory defined by introspective attention over time]
- print(f"chat_counter - {chat_counter}")
- if chat_counter != 0 :
- messages=[]
- for data in chatbot:
- temp1 = {}
- temp1["role"] = "user"
- temp1["content"] = data[0]
- temp2 = {}
- temp2["role"] = "assistant"
- temp2["content"] = data[1]
- messages.append(temp1)
- messages.append(temp2)
- temp3 = {}
- temp3["role"] = "user"
- temp3["content"] = inputs
- messages.append(temp3)
- payload = {
- "model": "gpt-3.5-turbo",
- "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
- "temperature" : temperature, #1.0,
- "top_p": top_p, #1.0,
- "n" : 1,
- "stream": True,
- "presence_penalty":0,
- "frequency_penalty":0,
- }
- chat_counter+=1
-
- # 4. POST it to OPENAI API
- history.append(inputs)
- print(f"payload is - {payload}")
- response = requests.post(API_URL, headers=headers, json=payload, stream=True)
- token_counter = 0
- partial_words = ""
-
- # 5. Iterate through response lines and structure readable response
- counter=0
- for chunk in response.iter_lines():
- if counter == 0:
- counter+=1
- continue
- if chunk.decode() :
- chunk = chunk.decode()
- if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
- partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
- if token_counter == 0:
- history.append(" " + partial_words)
- else:
- history[-1] = partial_words
- chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
- token_counter+=1
- yield chat, history, chat_counter
-
-
-def reset_textbox():
- return gr.update(value='')
-
-
-
-
-# Episodic and Semantic IO
-def list_files(file_path):
- import os
- icon_csv = "📄 "
- icon_txt = "📑 "
- current_directory = os.getcwd()
- file_list = []
- for filename in os.listdir(current_directory):
- if filename.endswith(".csv"):
- file_list.append(icon_csv + filename)
- elif filename.endswith(".txt"):
- file_list.append(icon_txt + filename)
- if file_list:
- return "\n".join(file_list)
- else:
- return "No .csv or .txt files found in the current directory."
-
-# Function to read a file
-def read_file(file_path):
- try:
- with open(file_path, "r") as file:
- contents = file.read()
- return f"{contents}"
- #return f"Contents of {file_path}:\n{contents}"
- except FileNotFoundError:
- return "File not found."
-
-# Function to delete a file
-def delete_file(file_path):
- try:
- import os
- os.remove(file_path)
- return f"{file_path} has been deleted."
- except FileNotFoundError:
- return "File not found."
-
-# Function to write to a file
-def write_file(file_path, content):
- try:
- with open(file_path, "w") as file:
- file.write(content)
- return f"Successfully written to {file_path}."
- except:
- return "Error occurred while writing to file."
-
-# Function to append to a file
-def append_file(file_path, content):
- try:
- with open(file_path, "a") as file:
- file.write(content)
- return f"Successfully appended to {file_path}."
- except:
- return "Error occurred while appending to file."
-
-
-title = """Generative AI Intelligence Amplifier - GAIA """
-description = """
-## GAIA Dataset References: 📚
-- **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2.
- - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext)
-- **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3.
- - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al.
-- **BooksCorpus:** A dataset of over 11,000 books from a variety of genres.
- - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al.
-- **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017.
- - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search
-- **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto.
- - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze.
-- **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3.
- - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.
- """
-
-# 6. Use Gradio to pull it all together
-with gr.Blocks(css = """#col_container {width: 100%; margin-left: auto; margin-right: auto;} #chatbot {height: 400px; overflow: auto;}""") as demo:
- gr.HTML(title)
- with gr.Column(elem_id = "col_container"):
- inputs = gr.Textbox(placeholder= "Paste Prompt with Context Data Here", label= "Type an input and press Enter")
- chatbot = gr.Chatbot(elem_id='chatbot')
- state = gr.State([])
- b1 = gr.Button()
- with gr.Accordion("Parameters", open=False):
- top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
- temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
- chat_counter = gr.Number(value=0, visible=True, precision=0)
-
-
- # Episodic/Semantic IO
- fileName = gr.Textbox(label="Filename")
- fileContent = gr.TextArea(label="File Content")
- completedMessage = gr.Textbox(label="Completed")
- label = gr.Label()
- with gr.Row():
- listFiles = gr.Button("📄 List File(s)")
- readFile = gr.Button("📖 Read File")
- saveFile = gr.Button("💾 Save File")
- deleteFile = gr.Button("🗑️ Delete File")
- appendFile = gr.Button("➕ Append File")
- listFiles.click(list_files, inputs=fileName, outputs=fileContent)
- readFile.click(read_file, inputs=fileName, outputs=fileContent)
- saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage)
- deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage)
- appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage )
-
-
- inputs.submit(predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter])
- b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter])
- b1.click(reset_textbox, [], [inputs])
- inputs.submit(reset_textbox, [], [inputs])
- gr.Markdown(description)
-
- demo.queue().launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Cyril666/my_abi/modules/resnet.py b/spaces/Cyril666/my_abi/modules/resnet.py
deleted file mode 100644
index 5ffb908ff8bf874a496c9f4fad2eb04f49cadf44..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/my_abi/modules/resnet.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import math
-
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as model_zoo
-
-
-def conv1x1(in_planes, out_planes, stride=1):
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- "3x3 convolution with padding"
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv1x1(inplanes, planes)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes, stride)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet(nn.Module):
-
- def __init__(self, block, layers):
- self.inplanes = 32
- super(ResNet, self).__init__()
- self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1,
- bias=False)
- self.bn1 = nn.BatchNorm2d(32)
- self.relu = nn.ReLU(inplace=True)
-
- self.layer1 = self._make_layer(block, 32, layers[0], stride=2)
- self.layer2 = self._make_layer(block, 64, layers[1], stride=1)
- self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 256, layers[3], stride=1)
- self.layer5 = self._make_layer(block, 512, layers[4], stride=1)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- x = self.layer5(x)
- return x
-
-
-def resnet45():
- return ResNet(BasicBlock, [3, 4, 6, 6, 3])
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/mimebundle.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/mimebundle.py
deleted file mode 100644
index 1e00542fb4617e01a6bece351494e512835779c8..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/mimebundle.py
+++ /dev/null
@@ -1,196 +0,0 @@
-from .html import spec_to_html
-
-
-def spec_to_mimebundle(
- spec,
- format,
- mode=None,
- vega_version=None,
- vegaembed_version=None,
- vegalite_version=None,
- engine=None,
- **kwargs,
-):
- """Convert a vega-lite specification to a mimebundle
-
- The mimebundle type is controlled by the ``format`` argument, which can be
- one of the following ['html', 'json', 'png', 'svg', 'pdf', 'vega', 'vega-lite']
-
- Parameters
- ----------
- spec : dict
- a dictionary representing a vega-lite plot spec
- format : string {'html', 'json', 'png', 'svg', 'pdf', 'vega', 'vega-lite'}
- the file format to be saved.
- mode : string {'vega-lite'}
- The rendering mode.
- vega_version : string
- The version of vega.js to use
- vegaembed_version : string
- The version of vegaembed.js to use
- vegalite_version : string
- The version of vegalite.js to use. Only required if mode=='vega-lite'
- engine: string {'vl-convert', 'altair_saver'}
- the conversion engine to use for 'png', 'svg', 'pdf', and 'vega' formats
- **kwargs :
- Additional arguments will be passed to the generating function
-
- Returns
- -------
- output : dict
- a mime-bundle representing the image
-
- Note
- ----
- The png, svg, pdf, and vega outputs require the altair_saver package
- """
- if mode != "vega-lite":
- raise ValueError("mode must be 'vega-lite'")
-
- if format in ["png", "svg", "pdf", "vega"]:
- return _spec_to_mimebundle_with_engine(
- spec, format, mode, engine=engine, **kwargs
- )
- if format == "html":
- html = spec_to_html(
- spec,
- mode=mode,
- vega_version=vega_version,
- vegaembed_version=vegaembed_version,
- vegalite_version=vegalite_version,
- **kwargs,
- )
- return {"text/html": html}
- if format == "vega-lite":
- if vegalite_version is None:
- raise ValueError("Must specify vegalite_version")
- return {"application/vnd.vegalite.v{}+json".format(vegalite_version[0]): spec}
- if format == "json":
- return {"application/json": spec}
- raise ValueError(
- "format must be one of "
- "['html', 'json', 'png', 'svg', 'pdf', 'vega', 'vega-lite']"
- )
-
-
-def _spec_to_mimebundle_with_engine(spec, format, mode, **kwargs):
- """Helper for Vega-Lite to mimebundle conversions that require an engine
-
- Parameters
- ----------
- spec : dict
- a dictionary representing a vega-lite plot spec
- format : string {'png', 'svg', 'pdf', 'vega'}
- the format of the mimebundle to be returned
- mode : string {'vega-lite'}
- The rendering mode.
- engine: string {'vl-convert', 'altair_saver'}
- the conversion engine to use
- **kwargs :
- Additional arguments will be passed to the conversion function
- """
- # Normalize the engine string (if any) by lower casing
- # and removing underscores and hyphens
- engine = kwargs.pop("engine", None)
- normalized_engine = _validate_normalize_engine(engine, format)
-
- if normalized_engine == "vlconvert":
- import vl_convert as vlc
- from ..vegalite import SCHEMA_VERSION
-
- # Compute VlConvert's vl_version string (of the form 'v5_2')
- # from SCHEMA_VERSION (of the form 'v5.2.0')
- vl_version = "_".join(SCHEMA_VERSION.split(".")[:2])
- if format == "vega":
- vg = vlc.vegalite_to_vega(spec, vl_version=vl_version)
- return {"application/vnd.vega.v5+json": vg}
- elif format == "svg":
- svg = vlc.vegalite_to_svg(spec, vl_version=vl_version)
- return {"image/svg+xml": svg}
- elif format == "png":
- png = vlc.vegalite_to_png(
- spec,
- vl_version=vl_version,
- scale=kwargs.get("scale_factor", 1.0),
- )
- return {"image/png": png}
- else:
- # This should be validated above
- # but raise exception for the sake of future development
- raise ValueError("Unexpected format {fmt!r}".format(fmt=format))
- elif normalized_engine == "altairsaver":
- import altair_saver
-
- return altair_saver.render(spec, format, mode=mode, **kwargs)
- else:
- # This should be validated above
- # but raise exception for the sake of future development
- raise ValueError(
- "Unexpected normalized_engine {eng!r}".format(eng=normalized_engine)
- )
-
-
-def _validate_normalize_engine(engine, format):
- """Helper to validate and normalize the user-provided engine
-
- engine : {None, 'vl-convert', 'altair_saver'}
- the user-provided engine string
- format : string {'png', 'svg', 'pdf', 'vega'}
- the format of the mimebundle to be returned
- """
- # Try to import vl_convert
- try:
- import vl_convert as vlc
- except ImportError:
- vlc = None
-
- # Try to import altair_saver
- try:
- import altair_saver
- except ImportError:
- altair_saver = None
-
- # Normalize engine string by lower casing and removing underscores and hyphens
- normalized_engine = (
- None if engine is None else engine.lower().replace("-", "").replace("_", "")
- )
-
- # Validate or infer default value of normalized_engine
- if normalized_engine == "vlconvert":
- if vlc is None:
- raise ValueError(
- "The 'vl-convert' conversion engine requires the vl-convert-python package"
- )
- if format == "pdf":
- raise ValueError(
- "The 'vl-convert' conversion engine does not support the {fmt!r} format.\n"
- "Use the 'altair_saver' engine instead".format(fmt=format)
- )
- elif normalized_engine == "altairsaver":
- if altair_saver is None:
- raise ValueError(
- "The 'altair_saver' conversion engine requires the altair_saver package"
- )
- elif normalized_engine is None:
- if vlc is not None and format != "pdf":
- normalized_engine = "vlconvert"
- elif altair_saver is not None:
- normalized_engine = "altairsaver"
- else:
- if format == "pdf":
- raise ValueError(
- "Saving charts in {fmt!r} format requires the altair_saver package: "
- "see http://github.com/altair-viz/altair_saver/".format(fmt=format)
- )
- else:
- raise ValueError(
- "Saving charts in {fmt!r} format requires the vl-convert-python or altair_saver package: "
- "see http://github.com/altair-viz/altair_saver/".format(fmt=format)
- )
- else:
- raise ValueError(
- "Invalid conversion engine {engine!r}. Expected one of {valid!r}".format(
- engine=engine, valid=("vl-convert", "altair_saver")
- )
- )
- return normalized_engine
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/param_functions.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/param_functions.py
deleted file mode 100644
index a43afaf311798ebde5fb265e1d47d584d807152d..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/param_functions.py
+++ /dev/null
@@ -1,564 +0,0 @@
-from typing import Any, Callable, Dict, List, Optional, Sequence, Union
-
-from fastapi import params
-from fastapi._compat import Undefined
-from typing_extensions import Annotated, deprecated
-
-_Unset: Any = Undefined
-
-
-def Path( # noqa: N802
- default: Any = ...,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.Path(
- default=default,
- default_factory=default_factory,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def Query( # noqa: N802
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.Query(
- default=default,
- default_factory=default_factory,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def Header( # noqa: N802
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- convert_underscores: bool = True,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.Header(
- default=default,
- default_factory=default_factory,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- convert_underscores=convert_underscores,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def Cookie( # noqa: N802
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.Cookie(
- default=default,
- default_factory=default_factory,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def Body( # noqa: N802
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- embed: bool = False,
- media_type: str = "application/json",
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.Body(
- default=default,
- default_factory=default_factory,
- embed=embed,
- media_type=media_type,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def Form( # noqa: N802
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- media_type: str = "application/x-www-form-urlencoded",
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.Form(
- default=default,
- default_factory=default_factory,
- media_type=media_type,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def File( # noqa: N802
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- media_type: str = "multipart/form-data",
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
-) -> Any:
- return params.File(
- default=default,
- default_factory=default_factory,
- media_type=media_type,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- example=example,
- examples=examples,
- deprecated=deprecated,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-def Depends( # noqa: N802
- dependency: Optional[Callable[..., Any]] = None, *, use_cache: bool = True
-) -> Any:
- return params.Depends(dependency=dependency, use_cache=use_cache)
-
-
-def Security( # noqa: N802
- dependency: Optional[Callable[..., Any]] = None,
- *,
- scopes: Optional[Sequence[str]] = None,
- use_cache: bool = True,
-) -> Any:
- return params.Security(dependency=dependency, scopes=scopes, use_cache=use_cache)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/t2CharStringPen.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/t2CharStringPen.py
deleted file mode 100644
index 41ab0f92f2b683ac2dc87ca1b16f54047d0fef81..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/t2CharStringPen.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2009 Type Supply LLC
-# Author: Tal Leming
-
-from fontTools.misc.roundTools import otRound, roundFunc
-from fontTools.misc.psCharStrings import T2CharString
-from fontTools.pens.basePen import BasePen
-from fontTools.cffLib.specializer import specializeCommands, commandsToProgram
-
-
-class T2CharStringPen(BasePen):
- """Pen to draw Type 2 CharStrings.
-
- The 'roundTolerance' argument controls the rounding of point coordinates.
- It is defined as the maximum absolute difference between the original
- float and the rounded integer value.
- The default tolerance of 0.5 means that all floats are rounded to integer;
- a value of 0 disables rounding; values in between will only round floats
- which are close to their integral part within the tolerated range.
- """
-
- def __init__(self, width, glyphSet, roundTolerance=0.5, CFF2=False):
- super(T2CharStringPen, self).__init__(glyphSet)
- self.round = roundFunc(roundTolerance)
- self._CFF2 = CFF2
- self._width = width
- self._commands = []
- self._p0 = (0, 0)
-
- def _p(self, pt):
- p0 = self._p0
- pt = self._p0 = (self.round(pt[0]), self.round(pt[1]))
- return [pt[0] - p0[0], pt[1] - p0[1]]
-
- def _moveTo(self, pt):
- self._commands.append(("rmoveto", self._p(pt)))
-
- def _lineTo(self, pt):
- self._commands.append(("rlineto", self._p(pt)))
-
- def _curveToOne(self, pt1, pt2, pt3):
- _p = self._p
- self._commands.append(("rrcurveto", _p(pt1) + _p(pt2) + _p(pt3)))
-
- def _closePath(self):
- pass
-
- def _endPath(self):
- pass
-
- def getCharString(self, private=None, globalSubrs=None, optimize=True):
- commands = self._commands
- if optimize:
- maxstack = 48 if not self._CFF2 else 513
- commands = specializeCommands(
- commands, generalizeFirst=False, maxstack=maxstack
- )
- program = commandsToProgram(commands)
- if self._width is not None:
- assert (
- not self._CFF2
- ), "CFF2 does not allow encoding glyph width in CharString."
- program.insert(0, otRound(self._width))
- if not self._CFF2:
- program.append("endchar")
- charString = T2CharString(
- program=program, private=private, globalSubrs=globalSubrs
- )
- return charString
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-711d7bc4.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-711d7bc4.js
deleted file mode 100644
index 1ff2671fd1fe45e8d113c4f95d650c3c253453a3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-711d7bc4.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/spaces/DavidLijun/FI/app.py b/spaces/DavidLijun/FI/app.py
deleted file mode 100644
index 417c30fed08557a32b544709f1e6b5974349b717..0000000000000000000000000000000000000000
--- a/spaces/DavidLijun/FI/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import os
-from dotenv import load_dotenv
-from subprocess import Popen
-load_dotenv()
-
-command = ["mercury", "run", f"0.0.0.0:{os.environ.get('PORT', 7860)}"]
-worker = Popen(command)
-worker.wait()
\ No newline at end of file
diff --git a/spaces/DenniSciFi/IconAutomation/icon_automation_interface_copy.py b/spaces/DenniSciFi/IconAutomation/icon_automation_interface_copy.py
deleted file mode 100644
index 236ed6dc216bf02f066a73653b17b6ddd10054b1..0000000000000000000000000000000000000000
--- a/spaces/DenniSciFi/IconAutomation/icon_automation_interface_copy.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-from icon_automation_copy import update_related_opportunities_icons
-
-# Defining the interface function
-def interface(notion_url, image_url):
- # Calling the update_related_opportunities_icons function with the provided URLs
- update_related_opportunities_icons(notion_url, image_url)
- return "Operation Completed"
-
-# Creating an instance of the gr.Interface class
-iface = gr.Interface(
- # Setting the interface function as the function to be called when inputs are provided
- fn=interface,
- # Defining the inputs for the interface as two text boxes: Notion URL and Image URL
- inputs=[
- gr.inputs.Textbox(label="Notion URL"),
- gr.inputs.Textbox(label="Image URL"),
- ],
- # Setting the output of the interface as a single text box
- outputs=gr.outputs.Textbox(),
- # Setting the title and description of the interface
- title="Icon Automation for Notion",
- description="Enter the Notion URL of the organization page and the image URL you want to set as the icon. This will update the icons of all related opportunities.",
-)
-
-# Launching the interface, making it accessible for user interaction
-iface.launch()
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py
deleted file mode 100644
index bd4aedd84977884683fb213e11f33ca493aef583..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-
-
-module_path = os.path.dirname(__file__)
-
-
-class FusedLeakyReLU(nn.Module):
- def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
- super().__init__()
-
- self.bias = nn.Parameter(torch.zeros(channel))
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- input = input.cuda()
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/op_edit/upfirdn2d.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/op_edit/upfirdn2d.py
deleted file mode 100644
index ecdcabbe20d2405b71d049d0bf94ae576fe58493..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/op_edit/upfirdn2d.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-import os
-
-import torch
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-
-module_path = os.path.dirname(__file__)
-upfirdn2d_op = load(
- "upfirdn2d",
- sources=[
- os.path.join(module_path, "upfirdn2d.cpp"),
- os.path.join(module_path, "upfirdn2d_kernel.cu"),
- ],
-)
-
-
-class UpFirDn2dBackward(Function):
- @staticmethod
- def forward(
- ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
- ):
-
- up_x, up_y = up
- down_x, down_y = down
- g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
- grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
- grad_input = upfirdn2d_op.upfirdn2d(
- grad_output,
- grad_kernel,
- down_x,
- down_y,
- up_x,
- up_y,
- g_pad_x0,
- g_pad_x1,
- g_pad_y0,
- g_pad_y1,
- )
- grad_input = grad_input.view(
- in_size[0], in_size[1], in_size[2], in_size[3])
-
- ctx.save_for_backward(kernel)
-
- pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
- ctx.up_x = up_x
- ctx.up_y = up_y
- ctx.down_x = down_x
- ctx.down_y = down_y
- ctx.pad_x0 = pad_x0
- ctx.pad_x1 = pad_x1
- ctx.pad_y0 = pad_y0
- ctx.pad_y1 = pad_y1
- ctx.in_size = in_size
- ctx.out_size = out_size
-
- return grad_input
-
- @staticmethod
- def backward(ctx, gradgrad_input):
- (kernel,) = ctx.saved_tensors
-
- gradgrad_input = gradgrad_input.reshape(-1,
- ctx.in_size[2], ctx.in_size[3], 1)
-
- gradgrad_out = upfirdn2d_op.upfirdn2d(
- gradgrad_input,
- kernel,
- ctx.up_x,
- ctx.up_y,
- ctx.down_x,
- ctx.down_y,
- ctx.pad_x0,
- ctx.pad_x1,
- ctx.pad_y0,
- ctx.pad_y1,
- )
- # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
- gradgrad_out = gradgrad_out.view(
- ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
- )
-
- return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
- @staticmethod
- def forward(ctx, input, kernel, up, down, pad):
- up_x, up_y = up
- down_x, down_y = down
- pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
- kernel_h, kernel_w = kernel.shape
- batch, channel, in_h, in_w = input.shape
- ctx.in_size = input.shape
-
- input = input.reshape(-1, in_h, in_w, 1)
-
- ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
- out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
- out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
- ctx.out_size = (out_h, out_w)
-
- ctx.up = (up_x, up_y)
- ctx.down = (down_x, down_y)
- ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
- g_pad_x0 = kernel_w - pad_x0 - 1
- g_pad_y0 = kernel_h - pad_y0 - 1
- g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
- g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
- ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
- out = upfirdn2d_op.upfirdn2d(
- input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
- )
- # out = out.view(major, out_h, out_w, minor)
- out = out.view(-1, channel, out_h, out_w)
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- kernel, grad_kernel = ctx.saved_tensors
-
- grad_input = UpFirDn2dBackward.apply(
- grad_output,
- kernel,
- grad_kernel,
- ctx.up,
- ctx.down,
- ctx.pad,
- ctx.g_pad,
- ctx.in_size,
- ctx.out_size,
- )
-
- return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
- if input.device.type == "cpu":
- out = upfirdn2d_native(
- input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
- )
-
- else:
- out = UpFirDn2d.apply(
- input, kernel, (up, up), (down,
- down), (pad[0], pad[1], pad[0], pad[1])
- )
-
- return out
-
-
-def upfirdn2d_native(
- input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
- _, channel, in_h, in_w = input.shape
- input = input.reshape(-1, in_h, in_w, 1)
-
- _, in_h, in_w, minor = input.shape
- kernel_h, kernel_w = kernel.shape
-
- out = input.view(-1, in_h, 1, in_w, 1, minor)
- out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
- out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
- out = F.pad(
- out, [0, 0, max(pad_x0, 0), max(pad_x1, 0),
- max(pad_y0, 0), max(pad_y1, 0)]
- )
- out = out[
- :,
- max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0),
- max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0),
- :,
- ]
-
- out = out.permute(0, 3, 1, 2)
- out = out.reshape(
- [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
- )
- w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
- out = F.conv2d(out, w)
- out = out.reshape(
- -1,
- minor,
- in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
- in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
- )
- out = out.permute(0, 2, 3, 1)
- out = out[:, ::down_y, ::down_x, :]
-
- out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
- out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
- return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/Duskfallcrew/Duskfallcrew-Osenayan_Mix/README.md b/spaces/Duskfallcrew/Duskfallcrew-Osenayan_Mix/README.md
deleted file mode 100644
index e3c34268f57c6bd75eaf69618892d1aafd713fcb..0000000000000000000000000000000000000000
--- a/spaces/Duskfallcrew/Duskfallcrew-Osenayan_Mix/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Duskfallcrew-Osenayan Mix
-emoji: 📚
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py b/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py
deleted file mode 100644
index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/Eddycrack864/Applio-Inference/tools/dlmodels.sh b/spaces/Eddycrack864/Applio-Inference/tools/dlmodels.sh
deleted file mode 100644
index 5fba0edef345c0a4384aa9402cfd5e93e29efdc3..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/tools/dlmodels.sh
+++ /dev/null
@@ -1,566 +0,0 @@
-#!/bin/bash
-
-echo working dir is $(pwd)
-echo downloading requirement aria2 check.
-
-if command -v aria2c &> /dev/null
-then
- echo "aria2c command found"
-else
- echo failed. please install aria2
- sleep 5
- exit 1
-fi
-
-d32="f0D32k.pth"
-d40="f0D40k.pth"
-d48="f0D48k.pth"
-g32="f0G32k.pth"
-g40="f0G40k.pth"
-g48="f0G48k.pth"
-
-d40v2="f0D40k.pth"
-g40v2="f0G40k.pth"
-
-dld32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth"
-dld40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth"
-dld48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth"
-dlg32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth"
-dlg40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth"
-dlg48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth"
-
-dld40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth"
-dlg40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth"
-
-hp2_all="HP2_all_vocals.pth"
-hp3_all="HP3_all_vocals.pth"
-hp5_only="HP5_only_main_vocal.pth"
-VR_DeEchoAggressive="VR-DeEchoAggressive.pth"
-VR_DeEchoDeReverb="VR-DeEchoDeReverb.pth"
-VR_DeEchoNormal="VR-DeEchoNormal.pth"
-onnx_dereverb="vocals.onnx"
-rmvpe="rmvpe.pt"
-
-dlhp2_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth"
-dlhp3_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth"
-dlhp5_only="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth"
-dlVR_DeEchoAggressive="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth"
-dlVR_DeEchoDeReverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth"
-dlVR_DeEchoNormal="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth"
-dlonnx_dereverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx"
-dlrmvpe="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt"
-
-hb="hubert_base.pt"
-
-dlhb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt"
-
-echo dir check start.
-
-if [ -d "./assets/pretrained" ]; then
- echo dir ./assets/pretrained checked.
-else
- echo failed. generating dir ./assets/pretrained.
- mkdir pretrained
-fi
-
-if [ -d "./assets/pretrained_v2" ]; then
- echo dir ./assets/pretrained_v2 checked.
-else
- echo failed. generating dir ./assets/pretrained_v2.
- mkdir pretrained_v2
-fi
-
-if [ -d "./assets/uvr5_weights" ]; then
- echo dir ./assets/uvr5_weights checked.
-else
- echo failed. generating dir ./assets/uvr5_weights.
- mkdir uvr5_weights
-fi
-
-if [ -d "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy" ]; then
- echo dir ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy checked.
-else
- echo failed. generating dir ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy.
- mkdir uvr5_weights/onnx_dereverb_By_FoxJoy
-fi
-
-echo dir check finished.
-
-echo required files check start.
-
-echo checking D32k.pth
-if [ -f "./assets/pretrained/D32k.pth" ]; then
- echo D32k.pth in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d ./assets/pretrained -o D32k.pth
- if [ -f "./assets/pretrained/D32k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking D40k.pth
-if [ -f "./assets/pretrained/D40k.pth" ]; then
- echo D40k.pth in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d ./assets/pretrained -o D40k.pth
- if [ -f "./assets/pretrained/D40k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking D40k.pth
-if [ -f "./assets/pretrained_v2/D40k.pth" ]; then
- echo D40k.pth in ./assets/pretrained_v2 checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d ./assets/pretrained_v2 -o D40k.pth
- if [ -f "./assets/pretrained_v2/D40k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking D48k.pth
-if [ -f "./assets/pretrained/D48k.pth" ]; then
- echo D48k.pth in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d ./assets/pretrained -o D48k.pth
- if [ -f "./assets/pretrained/D48k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking G32k.pth
-if [ -f "./assets/pretrained/G32k.pth" ]; then
- echo G32k.pth in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d ./assets/pretrained -o G32k.pth
- if [ -f "./assets/pretrained/G32k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking G40k.pth
-if [ -f "./assets/pretrained/G40k.pth" ]; then
- echo G40k.pth in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d ./assets/pretrained -o G40k.pth
- if [ -f "./assets/pretrained/G40k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking G40k.pth
-if [ -f "./assets/pretrained_v2/G40k.pth" ]; then
- echo G40k.pth in ./assets/pretrained_v2 checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d ./assets/pretrained_v2 -o G40k.pth
- if [ -f "./assets/pretrained_v2/G40k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking G48k.pth
-if [ -f "./assets/pretrained/G48k.pth" ]; then
- echo G48k.pth in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d ./assets/pretrained -o G48k.pth
- if [ -f "./assets/pretrained/G48k.pth" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $d32
-if [ -f "./assets/pretrained/$d32" ]; then
- echo $d32 in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld32 -d ./assets/pretrained -o $d32
- if [ -f "./assets/pretrained/$d32" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $d40
-if [ -f "./assets/pretrained/$d40" ]; then
- echo $d40 in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40 -d ./assets/pretrained -o $d40
- if [ -f "./assets/pretrained/$d40" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $d40v2
-if [ -f "./assets/pretrained_v2/$d40v2" ]; then
- echo $d40v2 in ./assets/pretrained_v2 checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40v2 -d ./assets/pretrained_v2 -o $d40v2
- if [ -f "./assets/pretrained_v2/$d40v2" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $d48
-if [ -f "./assets/pretrained/$d48" ]; then
- echo $d48 in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld48 -d ./assets/pretrained -o $d48
- if [ -f "./assets/pretrained/$d48" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $g32
-if [ -f "./assets/pretrained/$g32" ]; then
- echo $g32 in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg32 -d ./assets/pretrained -o $g32
- if [ -f "./assets/pretrained/$g32" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $g40
-if [ -f "./assets/pretrained/$g40" ]; then
- echo $g40 in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40 -d ./assets/pretrained -o $g40
- if [ -f "./assets/pretrained/$g40" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $g40v2
-if [ -f "./assets/pretrained_v2/$g40v2" ]; then
- echo $g40v2 in ./assets/pretrained_v2 checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40v2 -d ./assets/pretrained_v2 -o $g40v2
- if [ -f "./assets/pretrained_v2/$g40v2" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $g48
-if [ -f "./assets/pretrained/$g48" ]; then
- echo $g48 in ./assets/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg48 -d ./assets/pretrained -o $g48
- if [ -f "./assets/pretrained/$g48" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $hp2_all
-if [ -f "./assets/uvr5_weights/$hp2_all" ]; then
- echo $hp2_all in ./assets/uvr5_weights checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp2_all -d ./assets/uvr5_weights -o $hp2_all
- if [ -f "./assets/uvr5_weights/$hp2_all" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $hp3_all
-if [ -f "./assets/uvr5_weights/$hp3_all" ]; then
- echo $hp3_all in ./assets/uvr5_weights checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp3_all -d ./assets/uvr5_weights -o $hp3_all
- if [ -f "./assets/uvr5_weights/$hp3_all" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $hp5_only
-if [ -f "./assets/uvr5_weights/$hp5_only" ]; then
- echo $hp5_only in ./assets/uvr5_weights checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp5_only -d ./assets/uvr5_weights -o $hp5_only
- if [ -f "./assets/uvr5_weights/$hp5_only" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $VR_DeEchoAggressive
-if [ -f "./assets/uvr5_weights/$VR_DeEchoAggressive" ]; then
- echo $VR_DeEchoAggressive in ./assets/uvr5_weights checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoAggressive -d ./assets/uvr5_weights -o $VR_DeEchoAggressive
- if [ -f "./assets/uvr5_weights/$VR_DeEchoAggressive" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $VR_DeEchoDeReverb
-if [ -f "./assets/uvr5_weights/$VR_DeEchoDeReverb" ]; then
- echo $VR_DeEchoDeReverb in ./assets/uvr5_weights checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoDeReverb -d ./assets/uvr5_weights -o $VR_DeEchoDeReverb
- if [ -f "./assets/uvr5_weights/$VR_DeEchoDeReverb" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $VR_DeEchoNormal
-if [ -f "./assets/uvr5_weights/$VR_DeEchoNormal" ]; then
- echo $VR_DeEchoNormal in ./assets/uvr5_weights checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoNormal -d ./assets/uvr5_weights -o $VR_DeEchoNormal
- if [ -f "./assets/uvr5_weights/$VR_DeEchoNormal" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $onnx_dereverb
-if [ -f "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then
- echo $onnx_dereverb in ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlonnx_dereverb -d ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy -o $onnx_dereverb
- if [ -f "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $rmvpe
-if [ -f "./assets/rmvpe/$rmvpe" ]; then
- echo $rmvpe in ./assets/rmvpe checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlrmvpe -d ./assets/rmvpe -o $rmvpe
- if [ -f "./assets/rmvpe/$rmvpe" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo checking $hb
-if [ -f "./assets/hubert/$hb" ]; then
- echo $hb in ./assets/hubert/pretrained checked.
-else
- echo failed. starting download from huggingface.
- if command -v aria2c &> /dev/null; then
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhb -d ./assets/hubert/ -o $hb
- if [ -f "./assets/hubert/$hb" ]; then
- echo download successful.
- else
- echo please try again!
- exit 1
- fi
- else
- echo aria2c command not found. Please install aria2c and try again.
- exit 1
- fi
-fi
-
-echo required files check finished.
diff --git a/spaces/EuroSciPy2022/arxiv-cards/htmlcard.html b/spaces/EuroSciPy2022/arxiv-cards/htmlcard.html
deleted file mode 100644
index 575f1d0b883fbe9bf6306cb1326b681482be22a8..0000000000000000000000000000000000000000
--- a/spaces/EuroSciPy2022/arxiv-cards/htmlcard.html
+++ /dev/null
@@ -1,36 +0,0 @@
-
-
-
-
-
-
-
-
[{{ paper.arxiv_id}}]
-
-
{{ paper.title }}
-
- {% for author in paper.authors%}
- {{ author["name"] }}
-
{{ paper.abstract }}
-
-
-
-
- """
-
-loading_icon_html = """
-
-${inputImgUrl}
-
-${promptTxt}
-
-
-${outputImgUrl}
-
-
-#include
-
-std::vector
-dynamicconv_cpu_forward(float* input, float* filters, int padding_l);
-
-std::vector dynamicconv_cpu_backward(
- float* gradOutput,
- int padding_l,
- float* input,
- float* filters);
-
-std::vector
-dynamicconv_forward(float* input, float* filters, int padding_l) {
- return dynamicconv_cpu_forward(input, filters, padding_l);
-}
-
-std::vector dynamicconv_backward(
- float* gradOutput,
- int padding_l,
- float* input,
- float* filters) {
- return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)");
- m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)");
-}
diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
deleted file mode 100644
index b2b88e8c46f19b6db0933163e57ccdb51180f517..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_cpu_forward(
- const at::Tensor &value,
- const at::Tensor &spatial_shapes,
- const at::Tensor &level_start_index,
- const at::Tensor &sampling_loc,
- const at::Tensor &attn_weight,
- const int im2col_step);
-
-std::vector
-ms_deform_attn_cpu_backward(
- const at::Tensor &value,
- const at::Tensor &spatial_shapes,
- const at::Tensor &level_start_index,
- const at::Tensor &sampling_loc,
- const at::Tensor &attn_weight,
- const at::Tensor &grad_output,
- const int im2col_step);
-
-} // namespace groundingdino
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/utils/dist_util.py b/spaces/Iceclear/StableSR/StableSR/basicsr/utils/dist_util.py
deleted file mode 100644
index 0fab887b2cb1ce8533d2e8fdee72ae0c24f68fd0..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/utils/dist_util.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501
-import functools
-import os
-import subprocess
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-
-
-def init_dist(launcher, backend='nccl', **kwargs):
- if mp.get_start_method(allow_none=True) is None:
- mp.set_start_method('spawn')
- if launcher == 'pytorch':
- _init_dist_pytorch(backend, **kwargs)
- elif launcher == 'slurm':
- _init_dist_slurm(backend, **kwargs)
- else:
- raise ValueError(f'Invalid launcher type: {launcher}')
-
-
-def _init_dist_pytorch(backend, **kwargs):
- rank = int(os.environ['RANK'])
- num_gpus = torch.cuda.device_count()
- torch.cuda.set_device(rank % num_gpus)
- dist.init_process_group(backend=backend, **kwargs)
-
-
-def _init_dist_slurm(backend, port=None):
- """Initialize slurm distributed training environment.
-
- If argument ``port`` is not specified, then the master port will be system
- environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
- environment variable, then a default port ``29500`` will be used.
-
- Args:
- backend (str): Backend of torch.distributed.
- port (int, optional): Master port. Defaults to None.
- """
- proc_id = int(os.environ['SLURM_PROCID'])
- ntasks = int(os.environ['SLURM_NTASKS'])
- node_list = os.environ['SLURM_NODELIST']
- num_gpus = torch.cuda.device_count()
- torch.cuda.set_device(proc_id % num_gpus)
- addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1')
- # specify master port
- if port is not None:
- os.environ['MASTER_PORT'] = str(port)
- elif 'MASTER_PORT' in os.environ:
- pass # use MASTER_PORT in the environment variable
- else:
- # 29500 is torch.distributed default port
- os.environ['MASTER_PORT'] = '29500'
- os.environ['MASTER_ADDR'] = addr
- os.environ['WORLD_SIZE'] = str(ntasks)
- os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
- os.environ['RANK'] = str(proc_id)
- dist.init_process_group(backend=backend)
-
-
-def get_dist_info():
- if dist.is_available():
- initialized = dist.is_initialized()
- else:
- initialized = False
- if initialized:
- rank = dist.get_rank()
- world_size = dist.get_world_size()
- else:
- rank = 0
- world_size = 1
- return rank, world_size
-
-
-def master_only(func):
-
- @functools.wraps(func)
- def wrapper(*args, **kwargs):
- rank, _ = get_dist_info()
- if rank == 0:
- return func(*args, **kwargs)
-
- return wrapper
diff --git a/spaces/Iceclear/StableSR/StableSR/taming/modules/vqvae/quantize.py b/spaces/Iceclear/StableSR/StableSR/taming/modules/vqvae/quantize.py
deleted file mode 100644
index d75544e41fa01bce49dd822b1037963d62f79b51..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/taming/modules/vqvae/quantize.py
+++ /dev/null
@@ -1,445 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from torch import einsum
-from einops import rearrange
-
-
-class VectorQuantizer(nn.Module):
- """
- see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
- ____________________________________________
- Discretization bottleneck part of the VQ-VAE.
- Inputs:
- - n_e : number of embeddings
- - e_dim : dimension of embedding
- - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
- _____________________________________________
- """
-
- # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for
- # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be
- # used wherever VectorQuantizer has been used before and is additionally
- # more efficient.
- def __init__(self, n_e, e_dim, beta):
- super(VectorQuantizer, self).__init__()
- self.n_e = n_e
- self.e_dim = e_dim
- self.beta = beta
-
- self.embedding = nn.Embedding(self.n_e, self.e_dim)
- self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
- def forward(self, z):
- """
- Inputs the output of the encoder network z and maps it to a discrete
- one-hot vector that is the index of the closest embedding vector e_j
- z (continuous) -> z_q (discrete)
- z.shape = (batch, channel, height, width)
- quantization pipeline:
- 1. get encoder input (B,C,H,W)
- 2. flatten input to (B*H*W,C)
- """
- # reshape z -> (batch, height, width, channel) and flatten
- z = z.permute(0, 2, 3, 1).contiguous()
- z_flattened = z.view(-1, self.e_dim)
- # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
- d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
- torch.sum(self.embedding.weight**2, dim=1) - 2 * \
- torch.matmul(z_flattened, self.embedding.weight.t())
-
- ## could possible replace this here
- # #\start...
- # find closest encodings
- min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-
- min_encodings = torch.zeros(
- min_encoding_indices.shape[0], self.n_e).to(z)
- min_encodings.scatter_(1, min_encoding_indices, 1)
-
- # dtype min encodings: torch.float32
- # min_encodings shape: torch.Size([2048, 512])
- # min_encoding_indices.shape: torch.Size([2048, 1])
-
- # get quantized latent vectors
- z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
- #.........\end
-
- # with:
- # .........\start
- #min_encoding_indices = torch.argmin(d, dim=1)
- #z_q = self.embedding(min_encoding_indices)
- # ......\end......... (TODO)
-
- # compute loss for embedding
- loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
- torch.mean((z_q - z.detach()) ** 2)
-
- # preserve gradients
- z_q = z + (z_q - z).detach()
-
- # perplexity
- e_mean = torch.mean(min_encodings, dim=0)
- perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-
- # reshape back to match original input shape
- z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
- return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
- def get_codebook_entry(self, indices, shape):
- # shape specifying (batch, height, width, channel)
- # TODO: check for more easy handling with nn.Embedding
- min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
- min_encodings.scatter_(1, indices[:,None], 1)
-
- # get quantized latent vectors
- z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
- if shape is not None:
- z_q = z_q.view(shape)
-
- # reshape back to match original input shape
- z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
- return z_q
-
-
-class GumbelQuantize(nn.Module):
- """
- credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
- Gumbel Softmax trick quantizer
- Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
- https://arxiv.org/abs/1611.01144
- """
- def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True,
- kl_weight=5e-4, temp_init=1.0, use_vqinterface=True,
- remap=None, unknown_index="random"):
- super().__init__()
-
- self.embedding_dim = embedding_dim
- self.n_embed = n_embed
-
- self.straight_through = straight_through
- self.temperature = temp_init
- self.kl_weight = kl_weight
-
- self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
- self.embed = nn.Embedding(n_embed, embedding_dim)
-
- self.use_vqinterface = use_vqinterface
-
- self.remap = remap
- if self.remap is not None:
- self.register_buffer("used", torch.tensor(np.load(self.remap)))
- self.re_embed = self.used.shape[0]
- self.unknown_index = unknown_index # "random" or "extra" or integer
- if self.unknown_index == "extra":
- self.unknown_index = self.re_embed
- self.re_embed = self.re_embed+1
- print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
- f"Using {self.unknown_index} for unknown indices.")
- else:
- self.re_embed = n_embed
-
- def remap_to_used(self, inds):
- ishape = inds.shape
- assert len(ishape)>1
- inds = inds.reshape(ishape[0],-1)
- used = self.used.to(inds)
- match = (inds[:,:,None]==used[None,None,...]).long()
- new = match.argmax(-1)
- unknown = match.sum(2)<1
- if self.unknown_index == "random":
- new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
- else:
- new[unknown] = self.unknown_index
- return new.reshape(ishape)
-
- def unmap_to_all(self, inds):
- ishape = inds.shape
- assert len(ishape)>1
- inds = inds.reshape(ishape[0],-1)
- used = self.used.to(inds)
- if self.re_embed > self.used.shape[0]: # extra token
- inds[inds>=self.used.shape[0]] = 0 # simply set to zero
- back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
- return back.reshape(ishape)
-
- def forward(self, z, temp=None, return_logits=False):
- # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work
- hard = self.straight_through if self.training else True
- temp = self.temperature if temp is None else temp
-
- logits = self.proj(z)
- if self.remap is not None:
- # continue only with used logits
- full_zeros = torch.zeros_like(logits)
- logits = logits[:,self.used,...]
-
- soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
- if self.remap is not None:
- # go back to all entries but unused set to zero
- full_zeros[:,self.used,...] = soft_one_hot
- soft_one_hot = full_zeros
- z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight)
-
- # + kl divergence to the prior loss
- qy = F.softmax(logits, dim=1)
- diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
-
- ind = soft_one_hot.argmax(dim=1)
- if self.remap is not None:
- ind = self.remap_to_used(ind)
- if self.use_vqinterface:
- if return_logits:
- return z_q, diff, (None, None, ind), logits
- return z_q, diff, (None, None, ind)
- return z_q, diff, ind
-
- def get_codebook_entry(self, indices, shape):
- b, h, w, c = shape
- assert b*h*w == indices.shape[0]
- indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w)
- if self.remap is not None:
- indices = self.unmap_to_all(indices)
- one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
- z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight)
- return z_q
-
-
-class VectorQuantizer2(nn.Module):
- """
- Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
- avoids costly matrix multiplications and allows for post-hoc remapping of indices.
- """
- # NOTE: due to a bug the beta term was applied to the wrong term. for
- # backwards compatibility we use the buggy version by default, but you can
- # specify legacy=False to fix it.
- def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
- sane_index_shape=False, legacy=True):
- super().__init__()
- self.n_e = n_e
- self.e_dim = e_dim
- self.beta = beta
- self.legacy = legacy
-
- self.embedding = nn.Embedding(self.n_e, self.e_dim)
- self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
- self.remap = remap
- if self.remap is not None:
- self.register_buffer("used", torch.tensor(np.load(self.remap)))
- self.re_embed = self.used.shape[0]
- self.unknown_index = unknown_index # "random" or "extra" or integer
- if self.unknown_index == "extra":
- self.unknown_index = self.re_embed
- self.re_embed = self.re_embed+1
- print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
- f"Using {self.unknown_index} for unknown indices.")
- else:
- self.re_embed = n_e
-
- self.sane_index_shape = sane_index_shape
-
- def remap_to_used(self, inds):
- ishape = inds.shape
- assert len(ishape)>1
- inds = inds.reshape(ishape[0],-1)
- used = self.used.to(inds)
- match = (inds[:,:,None]==used[None,None,...]).long()
- new = match.argmax(-1)
- unknown = match.sum(2)<1
- if self.unknown_index == "random":
- new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
- else:
- new[unknown] = self.unknown_index
- return new.reshape(ishape)
-
- def unmap_to_all(self, inds):
- ishape = inds.shape
- assert len(ishape)>1
- inds = inds.reshape(ishape[0],-1)
- used = self.used.to(inds)
- if self.re_embed > self.used.shape[0]: # extra token
- inds[inds>=self.used.shape[0]] = 0 # simply set to zero
- back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
- return back.reshape(ishape)
-
- def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
- assert temp is None or temp==1.0, "Only for interface compatible with Gumbel"
- assert rescale_logits==False, "Only for interface compatible with Gumbel"
- assert return_logits==False, "Only for interface compatible with Gumbel"
- # reshape z -> (batch, height, width, channel) and flatten
- z = rearrange(z, 'b c h w -> b h w c').contiguous()
- z_flattened = z.view(-1, self.e_dim)
- # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
- d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
- torch.sum(self.embedding.weight**2, dim=1) - 2 * \
- torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
-
- min_encoding_indices = torch.argmin(d, dim=1)
- z_q = self.embedding(min_encoding_indices).view(z.shape)
- perplexity = None
- min_encodings = None
-
- # compute loss for embedding
- if not self.legacy:
- loss = self.beta * torch.mean((z_q.detach()-z)**2) + \
- torch.mean((z_q - z.detach()) ** 2)
- else:
- loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
- torch.mean((z_q - z.detach()) ** 2)
-
- # preserve gradients
- z_q = z + (z_q - z).detach()
-
- # reshape back to match original input shape
- z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
-
- if self.remap is not None:
- min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis
- min_encoding_indices = self.remap_to_used(min_encoding_indices)
- min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten
-
- if self.sane_index_shape:
- min_encoding_indices = min_encoding_indices.reshape(
- z_q.shape[0], z_q.shape[2], z_q.shape[3])
-
- return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
- def get_codebook_entry(self, indices, shape):
- # shape specifying (batch, height, width, channel)
- if self.remap is not None:
- indices = indices.reshape(shape[0],-1) # add batch axis
- indices = self.unmap_to_all(indices)
- indices = indices.reshape(-1) # flatten again
-
- # get quantized latent vectors
- z_q = self.embedding(indices)
-
- if shape is not None:
- z_q = z_q.view(shape)
- # reshape back to match original input shape
- z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
- return z_q
-
-class EmbeddingEMA(nn.Module):
- def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5):
- super().__init__()
- self.decay = decay
- self.eps = eps
- weight = torch.randn(num_tokens, codebook_dim)
- self.weight = nn.Parameter(weight, requires_grad = False)
- self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False)
- self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False)
- self.update = True
-
- def forward(self, embed_id):
- return F.embedding(embed_id, self.weight)
-
- def cluster_size_ema_update(self, new_cluster_size):
- self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay)
-
- def embed_avg_ema_update(self, new_embed_avg):
- self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
-
- def weight_update(self, num_tokens):
- n = self.cluster_size.sum()
- smoothed_cluster_size = (
- (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
- )
- #normalize embedding average with smoothed cluster size
- embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
- self.weight.data.copy_(embed_normalized)
-
-
-class EMAVectorQuantizer(nn.Module):
- def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5,
- remap=None, unknown_index="random"):
- super().__init__()
- self.codebook_dim = codebook_dim
- self.num_tokens = num_tokens
- self.beta = beta
- self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps)
-
- self.remap = remap
- if self.remap is not None:
- self.register_buffer("used", torch.tensor(np.load(self.remap)))
- self.re_embed = self.used.shape[0]
- self.unknown_index = unknown_index # "random" or "extra" or integer
- if self.unknown_index == "extra":
- self.unknown_index = self.re_embed
- self.re_embed = self.re_embed+1
- print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
- f"Using {self.unknown_index} for unknown indices.")
- else:
- self.re_embed = n_embed
-
- def remap_to_used(self, inds):
- ishape = inds.shape
- assert len(ishape)>1
- inds = inds.reshape(ishape[0],-1)
- used = self.used.to(inds)
- match = (inds[:,:,None]==used[None,None,...]).long()
- new = match.argmax(-1)
- unknown = match.sum(2)<1
- if self.unknown_index == "random":
- new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
- else:
- new[unknown] = self.unknown_index
- return new.reshape(ishape)
-
- def unmap_to_all(self, inds):
- ishape = inds.shape
- assert len(ishape)>1
- inds = inds.reshape(ishape[0],-1)
- used = self.used.to(inds)
- if self.re_embed > self.used.shape[0]: # extra token
- inds[inds>=self.used.shape[0]] = 0 # simply set to zero
- back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
- return back.reshape(ishape)
-
- def forward(self, z):
- # reshape z -> (batch, height, width, channel) and flatten
- #z, 'b c h w -> b h w c'
- z = rearrange(z, 'b c h w -> b h w c')
- z_flattened = z.reshape(-1, self.codebook_dim)
-
- # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
- d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \
- self.embedding.weight.pow(2).sum(dim=1) - 2 * \
- torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n'
-
-
- encoding_indices = torch.argmin(d, dim=1)
-
- z_q = self.embedding(encoding_indices).view(z.shape)
- encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)
- avg_probs = torch.mean(encodings, dim=0)
- perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
-
- if self.training and self.embedding.update:
- #EMA cluster size
- encodings_sum = encodings.sum(0)
- self.embedding.cluster_size_ema_update(encodings_sum)
- #EMA embedding average
- embed_sum = encodings.transpose(0,1) @ z_flattened
- self.embedding.embed_avg_ema_update(embed_sum)
- #normalize embed_avg and update weight
- self.embedding.weight_update(self.num_tokens)
-
- # compute loss for embedding
- loss = self.beta * F.mse_loss(z_q.detach(), z)
-
- # preserve gradients
- z_q = z + (z_q - z).detach()
-
- # reshape back to match original input shape
- #z_q, 'b h w c -> b c h w'
- z_q = rearrange(z_q, 'b h w c -> b c h w')
- return z_q, loss, (perplexity, encodings, encoding_indices)
diff --git a/spaces/Illumotion/Koboldcpp/examples/embd-input/embd-input-test.cpp b/spaces/Illumotion/Koboldcpp/examples/embd-input/embd-input-test.cpp
deleted file mode 100644
index dc4a0e48854adce86d44a3ba8ce7d2a67996b125..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/embd-input/embd-input-test.cpp
+++ /dev/null
@@ -1,35 +0,0 @@
-#include "embd-input.h"
-#include
-#include
-#include
-
-int main(int argc, char** argv) {
-
- auto mymodel = create_mymodel(argc, argv);
- int N = 10;
- int max_tgt_len = 500;
- int n_embd = llama_n_embd(llama_get_model(mymodel->ctx));
-
- // add random float embd to test evaluation
- float * data = new float[N*n_embd];
- std::default_random_engine e;
- std::uniform_real_distribution u(0,1);
- for (int i=0;iparams.prompt.c_str());
- const char* tmp;
- for (int i=0; i")==0) break;
- printf("%s", tmp);
- fflush(stdout);
- }
- printf("\n");
- free_mymodel(mymodel);
- return 0;
-}
diff --git a/spaces/Illumotion/Koboldcpp/examples/finetune/finetune.cpp b/spaces/Illumotion/Koboldcpp/examples/finetune/finetune.cpp
deleted file mode 100644
index 8ca1874dafc7e98c99c1c536b18b3577468516a1..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/finetune/finetune.cpp
+++ /dev/null
@@ -1,1940 +0,0 @@
-#include "ggml.h"
-#include "ggml-alloc.h"
-#include "llama.h"
-#include "common.h"
-#include "train.h"
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-static const size_t tensor_alignment = 32;
-
-struct my_llama_hparams {
- uint32_t n_vocab = 32000;
- uint32_t n_ctx = 512;
- uint32_t n_embd = 4096;
- uint32_t n_ff = 11008;
- uint32_t n_head = 32;
- uint32_t n_head_kv = 32;
- uint32_t n_layer = 32;
-
- // float f_norm_eps = 1e-5f; // falcon
- float f_norm_rms_eps = 1e-5f; // llama
-
- float rope_freq_base = 10000.0f;
- float rope_freq_scale = 1.0f;
-
- uint32_t n_gqa() const {
- return n_head/n_head_kv;
- }
-
- uint32_t n_embd_head() const {
- return n_embd/n_head;
- }
-
- uint32_t n_embd_gqa() const {
- return n_embd/n_gqa();
- }
-
- bool operator!=(const my_llama_hparams& other) const {
- return memcmp(this, &other, sizeof(other));
- }
-};
-
-struct my_llama_layer {
- // normalization
- struct ggml_tensor * attention_norm;
-
- // attention
- struct ggml_tensor * wq;
- struct ggml_tensor * wk;
- struct ggml_tensor * wv;
- struct ggml_tensor * wo;
-
- // normalization
- struct ggml_tensor * ffn_norm;
-
- // ff
- struct ggml_tensor * w1;
- struct ggml_tensor * w2;
- struct ggml_tensor * w3;
-};
-
-struct my_llama_model {
- struct my_llama_hparams hparams;
-
- struct ggml_tensor * tok_embeddings;
-
- struct ggml_tensor * norm;
- struct ggml_tensor * output;
-
- std::vector layers;
-};
-
-struct my_llama_lora_hparams {
- uint32_t lora_r = 1;
- uint32_t lora_alpha = 1;
- uint32_t n_rank_attention_norm = 1;
- uint32_t n_rank_wq = 4;
- uint32_t n_rank_wk = 4;
- uint32_t n_rank_wv = 4;
- uint32_t n_rank_wo = 4;
- uint32_t n_rank_ffn_norm = 1;
- uint32_t n_rank_w1 = 4;
- uint32_t n_rank_w2 = 4;
- uint32_t n_rank_w3 = 4;
- uint32_t n_rank_tok_embeddings = 4;
- uint32_t n_rank_norm = 1;
- uint32_t n_rank_output = 4;
-
- bool operator!=(const my_llama_lora_hparams& other) const {
- return memcmp(this, &other, sizeof(other));
- }
-};
-
-struct my_llama_lora_layer {
- // normalization
- struct ggml_tensor * attention_norm_a;
- struct ggml_tensor * attention_norm_b;
-
- // attention
- struct ggml_tensor * wq_a;
- struct ggml_tensor * wq_b;
- struct ggml_tensor * wk_a;
- struct ggml_tensor * wk_b;
- struct ggml_tensor * wv_a;
- struct ggml_tensor * wv_b;
- struct ggml_tensor * wo_a;
- struct ggml_tensor * wo_b;
-
- // normalization
- struct ggml_tensor * ffn_norm_a;
- struct ggml_tensor * ffn_norm_b;
-
- // ff
- struct ggml_tensor * w1_a;
- struct ggml_tensor * w1_b;
- struct ggml_tensor * w2_a;
- struct ggml_tensor * w2_b;
- struct ggml_tensor * w3_a;
- struct ggml_tensor * w3_b;
-};
-
-struct my_llama_lora {
- struct ggml_context * ctx = NULL;
- std::vector data;
-
- my_llama_lora_hparams hparams;
-
- struct ggml_tensor * tok_embeddings_a;
- struct ggml_tensor * tok_embeddings_b;
-
- struct ggml_tensor * norm_a;
- struct ggml_tensor * norm_b;
- struct ggml_tensor * output_a;
- struct ggml_tensor * output_b;
-
- std::vector layers;
-};
-
-// gguf constants
-static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora";
-static const char * LLM_KV_TRAINING_TYPE = "training.type";
-
-static const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = "training.lora.rank.token_embd";
-static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT = "training.lora.rank.output";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = "training.lora.rank.attn_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_Q = "training.lora.rank.attn_q";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_K = "training.lora.rank.attn_k";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_V = "training.lora.rank.attn_v";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = "training.lora.rank.attn_output";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_NORM = "training.lora.rank.ffn_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_GATE = "training.lora.rank.ffn_gate";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = "training.lora.rank.ffn_down";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_UP = "training.lora.rank.ffn_up";
-
-// gguf constants (sync with gguf.py)
-
-static const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture";
-static const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type";
-
-static const char * LLM_KV_CONTEXT_LENGTH = "%s.context_length";
-static const char * LLM_KV_EMBEDDING_LENGTH = "%s.embedding_length";
-static const char * LLM_KV_BLOCK_COUNT = "%s.block_count";
-static const char * LLM_KV_FEED_FORWARD_LENGTH = "%s.feed_forward_length";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT = "%s.attention.head_count";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT_KV = "%s.attention.head_count_kv";
-static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
-static const char * LLM_KV_ROPE_DIMENSION_COUNT = "%s.rope.dimension_count";
-static const char * LLM_KV_ROPE_FREQ_BASE = "%s.rope.freq_base"; // TODO load in llama.cpp
-static const char * LLM_KV_ROPE_SCALE_LINEAR = "%s.rope.scale_linear";
-
-static const char * LLM_TENSOR_TOKEN_EMBD = "token_embd";
-static const char * LLM_TENSOR_OUTPUT_NORM = "output_norm";
-static const char * LLM_TENSOR_OUTPUT = "output";
-static const char * LLM_TENSOR_ATTN_NORM = "blk.%d.attn_norm";
-static const char * LLM_TENSOR_ATTN_Q = "blk.%d.attn_q";
-static const char * LLM_TENSOR_ATTN_K = "blk.%d.attn_k";
-static const char * LLM_TENSOR_ATTN_V = "blk.%d.attn_v";
-static const char * LLM_TENSOR_ATTN_OUT = "blk.%d.attn_output";
-static const char * LLM_TENSOR_FFN_NORM = "blk.%d.ffn_norm";
-static const char * LLM_TENSOR_FFN_GATE = "blk.%d.ffn_gate";
-static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down";
-static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up";
-
-static void print_params(struct my_llama_hparams * params) {
- printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
- printf("%s: n_ctx: %u\n", __func__, params->n_ctx);
- printf("%s: n_embd: %u\n", __func__, params->n_embd);
- printf("%s: n_ff: %u\n", __func__, params->n_ff);
- printf("%s: n_head: %u\n", __func__, params->n_head);
- printf("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
- printf("%s: n_layer: %u\n", __func__, params->n_layer);
- printf("%s: norm_rms_eps : %f\n", __func__, params->f_norm_rms_eps);
- printf("%s: rope_freq_base : %f\n", __func__, params->rope_freq_base);
- printf("%s: rope_freq_scale : %f\n", __func__, params->rope_freq_scale);
-}
-
-static void print_lora_params(struct my_llama_lora_hparams * params) {
- printf("%s: n_rank_attention_norm : %u\n", __func__, params->n_rank_attention_norm);
- printf("%s: n_rank_wq : %u\n", __func__, params->n_rank_wq);
- printf("%s: n_rank_wk : %u\n", __func__, params->n_rank_wk);
- printf("%s: n_rank_wv : %u\n", __func__, params->n_rank_wv);
- printf("%s: n_rank_wo : %u\n", __func__, params->n_rank_wo);
- printf("%s: n_rank_ffn_norm : %u\n", __func__, params->n_rank_ffn_norm);
- printf("%s: n_rank_w1 : %u\n", __func__, params->n_rank_w1);
- printf("%s: n_rank_w2 : %u\n", __func__, params->n_rank_w2);
- printf("%s: n_rank_w3 : %u\n", __func__, params->n_rank_w3);
- printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings);
- printf("%s: n_rank_norm : %u\n", __func__, params->n_rank_norm);
- printf("%s: n_rank_output : %u\n", __func__, params->n_rank_output);
-}
-
-#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
-{ \
- const std::string skey(key); \
- const int kid = gguf_find_key(ctx, skey.c_str()); \
- if (kid >= 0) { \
- enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
- if (ktype != (type)) { \
- die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
- } \
- (dst) = func(ctx, kid); \
- } else if (req) { \
- die_fmt("key not found in model: %s", skey.c_str()); \
- } \
-}
-
-static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_hparams * hparams, const char * expected_arch) {
- std::string arch;
-
- GGUF_GET_KEY(ctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
- if (expected_arch != NULL) {
- if (arch != expected_arch) {
- printf("%s: arch=%s expected_arch=%s\n", __func__, arch.c_str(), expected_arch);
- }
- GGML_ASSERT(arch == expected_arch);
- }
-
- std::vector keybuf;
- keybuf.resize(512);
- auto kv = [&arch, &keybuf](const char * key) -> const char * {
- snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
- return keybuf.data();
- };
-
- GGUF_GET_KEY(ctx, hparams->n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
- GGUF_GET_KEY(ctx, hparams->n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
- GGUF_GET_KEY(ctx, hparams->n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
- GGUF_GET_KEY(ctx, hparams->n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
- GGUF_GET_KEY(ctx, hparams->n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
-
- // n_head_kv is optional, default to n_head
- hparams->n_head_kv = hparams->n_head;
- GGUF_GET_KEY(ctx, hparams->n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
-
- float rope_freq_scale = 1.0f;
- GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
- GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
- GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
- if (rope_freq_scale != 1.0f) {
- hparams->rope_freq_scale = 1.0f / rope_freq_scale;
- }
-}
-
-static void init_model(struct llama_model * input, struct my_llama_model * model, const char * fn_model, uint32_t n_ctx) {
- auto & hparams = model->hparams;
-
- std::vector tn_buf;
- tn_buf.resize(GGML_MAX_NAME);
- auto tn = [&tn_buf](const char * key) -> const char * {
- snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
- return tn_buf.data();
- };
- auto tni = [&tn_buf](const char * key, int bid) -> const char * {
- snprintf(tn_buf.data(), tn_buf.size(), key, bid);
- std::string s = tn_buf.data();
- snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
- return tn_buf.data();
- };
-
-
- // get parameters directly from gguf file
- {
- struct gguf_init_params params = {
- /*.no_alloc = */ false,
- /*.ctx = */ NULL,
- };
- struct gguf_context * mctx = gguf_init_from_file(fn_model, params);
-
- load_model_hparams_gguf(mctx, &hparams, "llama");
-
- gguf_free(mctx);
- }
- hparams.n_vocab = llama_n_vocab(input);
- hparams.n_ctx = n_ctx;
-
- // get tensors from llama_model (possibly mmapped)
- model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));
- model->norm = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));
- model->output = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT));
-
- assert_shape_2d(model->tok_embeddings, hparams.n_embd, hparams.n_vocab);
- assert_shape_1d(model->norm, hparams.n_embd);
- assert_shape_2d(model->output, hparams.n_embd, hparams.n_vocab);
-
- model->layers.resize(hparams.n_layer);
- for (uint32_t i = 0; i < hparams.n_layer; ++i) {
- auto & layer = model->layers[i];
-
- layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i));
- layer.wq = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_Q, i));
- layer.wk = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_K, i));
- layer.wv = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));
- layer.wo = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));
- layer.ffn_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));
- layer.w1 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
- layer.w2 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
- layer.w3 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
-
- assert_shape_1d(layer.attention_norm, hparams.n_embd);
- assert_shape_2d(layer.wq, hparams.n_embd, hparams.n_embd);
- assert_shape_2d(layer.wk, hparams.n_embd, hparams.n_embd);
- assert_shape_2d(layer.wv, hparams.n_embd, hparams.n_embd);
- assert_shape_2d(layer.wo, hparams.n_embd, hparams.n_embd);
- assert_shape_1d(layer.ffn_norm, hparams.n_embd);
- assert_shape_2d(layer.w1, hparams.n_embd, hparams.n_ff);
- assert_shape_2d(layer.w2, hparams.n_ff, hparams.n_embd);
- assert_shape_2d(layer.w3, hparams.n_embd, hparams.n_ff);
- }
-}
-
-static void set_param_lora(struct my_llama_lora * lora) {
- const uint32_t n_layer = lora->layers.size();
-
- struct ggml_context* ctx = lora->ctx;
-
- ggml_set_param(ctx, lora->tok_embeddings_a);
- ggml_set_param(ctx, lora->tok_embeddings_b);
- ggml_set_param(ctx, lora->norm_a);
- ggml_set_param(ctx, lora->norm_b);
- ggml_set_param(ctx, lora->output_a);
- ggml_set_param(ctx, lora->output_b);
-
- for (uint32_t i = 0; i < n_layer; ++i) {
- auto & layer = lora->layers[i];
-
- ggml_set_param(ctx, layer.attention_norm_a);
- ggml_set_param(ctx, layer.attention_norm_b);
- ggml_set_param(ctx, layer.wq_a);
- ggml_set_param(ctx, layer.wq_b);
- ggml_set_param(ctx, layer.wk_a);
- ggml_set_param(ctx, layer.wk_b);
- ggml_set_param(ctx, layer.wv_a);
- ggml_set_param(ctx, layer.wv_b);
- ggml_set_param(ctx, layer.wo_a);
- ggml_set_param(ctx, layer.wo_b);
- ggml_set_param(ctx, layer.ffn_norm_a);
- ggml_set_param(ctx, layer.ffn_norm_b);
- ggml_set_param(ctx, layer.w1_a);
- ggml_set_param(ctx, layer.w1_b);
- ggml_set_param(ctx, layer.w2_a);
- ggml_set_param(ctx, layer.w2_b);
- ggml_set_param(ctx, layer.w3_a);
- ggml_set_param(ctx, layer.w3_b);
- }
-}
-
-static void alloc_lora(struct ggml_allocr * alloc, struct my_llama_lora * lora) {
- ggml_allocr_alloc(alloc, lora->tok_embeddings_a);
- ggml_allocr_alloc(alloc, lora->tok_embeddings_b);
- ggml_allocr_alloc(alloc, lora->norm_a);
- ggml_allocr_alloc(alloc, lora->norm_b);
- ggml_allocr_alloc(alloc, lora->output_a);
- ggml_allocr_alloc(alloc, lora->output_b);
- for (uint32_t i = 0; i < lora->layers.size(); ++i) {
- auto & layer = lora->layers[i];
- ggml_allocr_alloc(alloc, layer.attention_norm_a);
- ggml_allocr_alloc(alloc, layer.attention_norm_b);
- ggml_allocr_alloc(alloc, layer.wq_a);
- ggml_allocr_alloc(alloc, layer.wq_b);
- ggml_allocr_alloc(alloc, layer.wk_a);
- ggml_allocr_alloc(alloc, layer.wk_b);
- ggml_allocr_alloc(alloc, layer.wv_a);
- ggml_allocr_alloc(alloc, layer.wv_b);
- ggml_allocr_alloc(alloc, layer.wo_a);
- ggml_allocr_alloc(alloc, layer.wo_b);
- ggml_allocr_alloc(alloc, layer.ffn_norm_a);
- ggml_allocr_alloc(alloc, layer.ffn_norm_b);
- ggml_allocr_alloc(alloc, layer.w1_a);
- ggml_allocr_alloc(alloc, layer.w1_b);
- ggml_allocr_alloc(alloc, layer.w2_a);
- ggml_allocr_alloc(alloc, layer.w2_b);
- ggml_allocr_alloc(alloc, layer.w3_a);
- ggml_allocr_alloc(alloc, layer.w3_b);
- }
- ggml_allocr_alloc(alloc, lora->tok_embeddings_a->grad);
- ggml_allocr_alloc(alloc, lora->tok_embeddings_b->grad);
- ggml_allocr_alloc(alloc, lora->norm_a->grad);
- ggml_allocr_alloc(alloc, lora->norm_b->grad);
- ggml_allocr_alloc(alloc, lora->output_a->grad);
- ggml_allocr_alloc(alloc, lora->output_b->grad);
- for (uint32_t i = 0; i < lora->layers.size(); ++i) {
- auto & layer = lora->layers[i];
- ggml_allocr_alloc(alloc, layer.attention_norm_a->grad);
- ggml_allocr_alloc(alloc, layer.attention_norm_b->grad);
- ggml_allocr_alloc(alloc, layer.wq_a->grad);
- ggml_allocr_alloc(alloc, layer.wq_b->grad);
- ggml_allocr_alloc(alloc, layer.wk_a->grad);
- ggml_allocr_alloc(alloc, layer.wk_b->grad);
- ggml_allocr_alloc(alloc, layer.wv_a->grad);
- ggml_allocr_alloc(alloc, layer.wv_b->grad);
- ggml_allocr_alloc(alloc, layer.wo_a->grad);
- ggml_allocr_alloc(alloc, layer.wo_b->grad);
- ggml_allocr_alloc(alloc, layer.ffn_norm_a->grad);
- ggml_allocr_alloc(alloc, layer.ffn_norm_b->grad);
- ggml_allocr_alloc(alloc, layer.w1_a->grad);
- ggml_allocr_alloc(alloc, layer.w1_b->grad);
- ggml_allocr_alloc(alloc, layer.w2_a->grad);
- ggml_allocr_alloc(alloc, layer.w2_b->grad);
- ggml_allocr_alloc(alloc, layer.w3_a->grad);
- ggml_allocr_alloc(alloc, layer.w3_b->grad);
- }
-}
-
-static void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) {
- const auto & lparams = lora->hparams;
-
- const uint32_t n_embd = model->hparams.n_embd;
- const uint32_t n_embd_gqa = model->hparams.n_embd_gqa();
- const uint32_t n_layer = model->hparams.n_layer;
- const uint32_t n_vocab = model->hparams.n_vocab;
- const uint32_t n_ff = model->hparams.n_ff;
-
- std::vector tn_buf;
- tn_buf.resize(GGML_MAX_NAME);
- auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
- snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
- return tn_buf.data();
- };
- auto tni = [&tn_buf](const char * key, const char * suffix, int bid) -> const char * {
- snprintf(tn_buf.data(), tn_buf.size(), key, bid);
- std::string s = tn_buf.data();
- snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix);
- return tn_buf.data();
- };
-
- // context for lora tensors without their data
- struct ggml_init_params ctx_lora_params;
- ctx_lora_params.mem_size = ggml_tensor_overhead()*2*(6 + n_layer*18);
- ctx_lora_params.mem_buffer = NULL;
- ctx_lora_params.no_alloc = true;
-
- struct ggml_context * ctx = ggml_init(ctx_lora_params);
- lora->ctx = ctx;
-
- lora->tok_embeddings_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_embd);
- lora->tok_embeddings_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_vocab);
- lora->norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, n_embd);
- lora->norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, 1);
- lora->output_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_embd);
- lora->output_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_vocab);
-
- ggml_set_name(lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.lora_a"));
- ggml_set_name(lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.lora_b"));
- ggml_set_name(lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_a"));
- ggml_set_name(lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_b"));
- ggml_set_name(lora->output_a, tn(LLM_TENSOR_OUTPUT, ".weight.lora_a"));
- ggml_set_name(lora->output_b, tn(LLM_TENSOR_OUTPUT, ".weight.lora_b"));
-
- lora->layers.resize(n_layer);
- for (uint32_t i = 0; i < n_layer; ++i) {
- auto & layer = lora->layers[i];
-
- layer.attention_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, n_embd);
- layer.attention_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, 1);
-
- layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
- layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
- layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd);
- layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa);
- layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd);
- layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa);
- layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
- layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
-
- layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);
- layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);
-
- layer.w1_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_embd);
- layer.w1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_ff);
- layer.w2_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_ff);
- layer.w2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_embd);
- layer.w3_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_embd);
- layer.w3_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_ff);
-
- ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i));
- ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i));
- ggml_set_name(layer.wq_a, tni(LLM_TENSOR_ATTN_Q, ".weight.lora_a", i));
- ggml_set_name(layer.wq_b, tni(LLM_TENSOR_ATTN_Q, ".weight.lora_b", i));
- ggml_set_name(layer.wk_a, tni(LLM_TENSOR_ATTN_K, ".weight.lora_a", i));
- ggml_set_name(layer.wk_b, tni(LLM_TENSOR_ATTN_K, ".weight.lora_b", i));
- ggml_set_name(layer.wv_a, tni(LLM_TENSOR_ATTN_V, ".weight.lora_a", i));
- ggml_set_name(layer.wv_b, tni(LLM_TENSOR_ATTN_V, ".weight.lora_b", i));
- ggml_set_name(layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_a", i));
- ggml_set_name(layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_b", i));
- ggml_set_name(layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_a", i));
- ggml_set_name(layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_b", i));
- ggml_set_name(layer.w1_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i));
- ggml_set_name(layer.w1_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i));
- ggml_set_name(layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i));
- ggml_set_name(layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i));
- ggml_set_name(layer.w3_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i));
- ggml_set_name(layer.w3_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i));
- }
-
- set_param_lora(lora);
-
- // measure data size
- struct ggml_allocr * alloc = NULL;
- alloc = ggml_allocr_new_measure(tensor_alignment);
- alloc_lora(alloc, lora);
-
- // allocate data
- lora->data.resize(ggml_allocr_max_size(alloc) + tensor_alignment);
- ggml_allocr_free(alloc);
- alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment);
- alloc_lora(alloc, lora);
- ggml_allocr_free(alloc);
-}
-
-static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {
- const uint32_t n_layer = lora->layers.size();
-
- struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
-
- randomize_tensor_normal(lora->tok_embeddings_a, rnd);
- randomize_tensor_normal(lora->tok_embeddings_b, rnd);
- randomize_tensor_normal(lora->norm_a, rnd);
- randomize_tensor_normal(lora->norm_b, rnd);
- randomize_tensor_normal(lora->output_a, rnd);
- randomize_tensor_normal(lora->output_b, rnd);
-
- for (uint32_t i = 0; i < n_layer; ++i) {
- auto & layer = lora->layers[i];
- randomize_tensor_normal(layer.attention_norm_a, rnd);
- randomize_tensor_normal(layer.attention_norm_b, rnd);
-
- randomize_tensor_normal(layer.wq_a, rnd);
- randomize_tensor_normal(layer.wq_b, rnd);
- randomize_tensor_normal(layer.wk_a, rnd);
- randomize_tensor_normal(layer.wk_b, rnd);
- randomize_tensor_normal(layer.wv_a, rnd);
- randomize_tensor_normal(layer.wv_b, rnd);
- randomize_tensor_normal(layer.wo_a, rnd);
- randomize_tensor_normal(layer.wo_b, rnd);
-
- randomize_tensor_normal(layer.ffn_norm_a, rnd);
- randomize_tensor_normal(layer.ffn_norm_b, rnd);
-
- randomize_tensor_normal(layer.w1_a, rnd);
- randomize_tensor_normal(layer.w1_b, rnd);
- randomize_tensor_normal(layer.w2_a, rnd);
- randomize_tensor_normal(layer.w2_b, rnd);
- randomize_tensor_normal(layer.w3_a, rnd);
- randomize_tensor_normal(layer.w3_b, rnd);
- }
-
- free_random_normal_distribution(rnd);
-}
-
-static struct ggml_tensor * llama_build_lora_finetune_graphs(
- struct my_llama_model * model,
- struct my_llama_lora * lora,
- struct ggml_allocr * alloc,
- struct ggml_context * ctx,
- struct ggml_cgraph * gf,
- struct ggml_cgraph * gb,
- struct ggml_cgraph * gb_tmp,
- struct ggml_tensor * * logits,
- struct ggml_tensor * tokens_input,
- struct ggml_tensor * targets,
- const int n_tokens,
- const int n_batch,
- const bool enable_flash_attn,
- const bool enable_checkpointing) {
-
- ggml_set_scratch(ctx, { 0, 0, nullptr, });
- const int n_past = 0;
- const int N = n_tokens;
- const auto & hparams = model->hparams;
- const int n_ctx = hparams.n_ctx;
- const int n_vocab = hparams.n_vocab;
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_head = hparams.n_head;
- const int n_head_kv = hparams.n_head_kv;
- const int n_ff = hparams.n_ff;
- const int n_rot = hparams.n_embd_head();
- const int n_embd_head = hparams.n_embd_head();
- const int n_embd_gqa = hparams.n_embd_gqa();
- const float rms_norm_eps = hparams.f_norm_rms_eps;
- const float rope_freq_base = hparams.rope_freq_base;
- const float rope_freq_scale = hparams.rope_freq_scale;
-
- GGML_ASSERT((size_t) n_layer == lora->layers.size());
-
- auto set_name = [](struct ggml_tensor * t, const char * n) {
- ggml_set_name(t, n);
- if (t->grad) {
- ggml_format_name(t->grad, "%s->grad", n);
- }
- };
-
- // KQ_pos - contains the positions
- struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
- ggml_allocr_alloc(alloc, KQ_pos);
- if (!ggml_allocr_is_measure(alloc)) {
- int * data = (int *) KQ_pos->data;
- for (int i = 0; i < N; ++i) {
- data[i] = n_past + i;
- }
- }
-
- // rope has so much parameters that we make a custom function for it
- auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
- (struct ggml_tensor * t) -> struct ggml_tensor * {
- // not capturing these, to silcence warnings
- const int rope_mode = 0;
-
- return ggml_rope_custom(ctx,
- t, KQ_pos, n_rot, rope_mode, n_ctx,
- rope_freq_base, rope_freq_scale);
- };
-
- set_name(tokens_input, "tokens_input");
- set_name(targets, "targets");
-
- GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
-
- auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
- if (ggml_is_quantized(a->type)) {
- return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
- } else if (a->type == GGML_TYPE_F32) {
- return ggml_add(ctx, a, b);
- } else {
- die_fmt("%s: Finetuning on tensors with type '%s' is not yet supported.\n",
- __func__, ggml_type_name(a->type));
- }
- };
-
- struct ggml_tensor * tok_embeddings = add_to_f32(ctx, model->tok_embeddings, ggml_mul_mat(ctx, lora->tok_embeddings_a, lora->tok_embeddings_b));
- struct ggml_tensor * norm = add_to_f32(ctx, model->norm, ggml_mul_mat(ctx, lora->norm_a, lora->norm_b));
- struct ggml_tensor * output = add_to_f32(ctx, model->output, ggml_mul_mat(ctx, lora->output_a, lora->output_b));
-
- struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch); set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
- struct ggml_tensor * t01 = ggml_get_rows(ctx, tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
-
- struct ggml_tensor * cur = t01;
-
- std::vector checkpoints;
- if (enable_checkpointing) {
- checkpoints.push_back(tokens_input);
- checkpoints.push_back(targets);
- checkpoints.push_back(t00);
- checkpoints.push_back(t01);
- }
-
- struct ggml_tensor * kv_scale = NULL;
- if (!enable_flash_attn) {
- kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head));
- }
-
- for (int il = 0; il < n_layer; ++il) {
- struct my_llama_layer & layer = model->layers[il];
- struct my_llama_lora_layer & llayer = lora->layers[il];
-
- struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b));
- struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b));
- struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));
- struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
- struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
- struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
- struct ggml_tensor * w1 = add_to_f32(ctx, layer.w1, ggml_mul_mat(ctx, llayer.w1_a, llayer.w1_b));
- struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b));
- struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b));
-
- struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch);
- struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch);
- struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch);
- struct ggml_tensor * t05 = ggml_mul_mat (ctx, wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch);
- struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd_head, n_head, N, n_batch);
- struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd_head, n_head, N, n_batch);
- struct ggml_tensor * t08 = ggml_mul_mat (ctx, wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd_gqa, N*n_batch);
- struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch);
- struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch);
-
- struct ggml_tensor * t11;
- if (ggml_is_quantized(wv->type)) {
- struct ggml_tensor * t11_1 = ggml_mul_mat (ctx, wv, t04); set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch);
- struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1); set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa);
- t11 = ggml_cont (ctx, t11_2); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);
- } else {
- t11 = ggml_mul_mat (ctx, t04, wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);
- }
-
- struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv);
- struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd_head, N, n_head, n_batch);
- struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd_head, N, n_head_kv, n_batch);
- struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch);
- struct ggml_tensor * t16;
- if (enable_flash_attn) {
- t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
- } else {
- struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
- struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
- struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
- struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
- t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
- }
- struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd_head, n_head, N, n_batch);
- struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd_head, n_head, N, n_batch);
- struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch);
- struct ggml_tensor * t20 = ggml_mul_mat (ctx, wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch);
- struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch);
- struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch);
- struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch);
- struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch);
- struct ggml_tensor * t25 = ggml_mul_mat (ctx, w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
- struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
- struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch);
- struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch);
- struct ggml_tensor * t29 = ggml_mul_mat (ctx, w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
- struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch);
- cur = t30;
- if (enable_checkpointing) {
- checkpoints.push_back(cur);
- }
- }
- struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch);
- struct ggml_tensor * t32 = ggml_repeat (ctx, norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch);
- struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch);
- struct ggml_tensor * t34 = ggml_mul_mat (ctx, output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch);
- struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch);
- struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1);
-
- if (enable_checkpointing) {
- checkpoints.push_back(t31);
- checkpoints.push_back(t32);
- checkpoints.push_back(t33);
- checkpoints.push_back(t34);
- checkpoints.push_back(t35);
- checkpoints.push_back(t36);
- }
-
- ggml_build_forward_expand(gf, t36);
-
- if (enable_checkpointing) {
- ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
- } else {
- *gb = *gf;
- ggml_build_backward_expand(ctx, gf, gb, true);
- }
-
- GGML_ASSERT(alloc != NULL);
-
- // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
- int n_leafs_before = gb->n_leafs;
- int n_nodes_before = gb->n_nodes;
- struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f);
- // output tensors
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));
- // input gradient
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));
- GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
- ggml_allocr_alloc(alloc, t36->grad);
- // KQ_pos
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one));
-
- // make sure base model tensors data cannot be used in viewable operations
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, one));
- for (int il = 0; il < n_layer; ++il) {
- struct my_llama_layer & layer = model->layers[il];
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, one));
- ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, one));
- }
-
- // allocating checkpoints in one block to reduce memory fragmentation
- // note: they will be freed in reverse order
- for (unsigned int i = 0; i < checkpoints.size(); ++i) {
- if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
- ggml_allocr_alloc(alloc, checkpoints[i]);
- }
- }
-
- ggml_allocr_alloc_graph(alloc, gb);
-
- // remove the additional nodes and leafs
- for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
- gb->leafs[i] = NULL;
- }
- for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
- gb->nodes[i] = NULL;
- }
- gb->n_leafs = n_leafs_before;
- gb->n_nodes = n_nodes_before;
-
- *logits = t35;
- return t36;
-}
-
-static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) {
- // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
-
- std::string arch;
-
- std::vector keybuf;
- keybuf.resize(512);
-
- GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
- GGML_ASSERT(arch == "llama");
-
- uint32_t ftype_u;
- GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
- GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
-
- struct my_llama_hparams hparams;
- load_model_hparams_gguf(fctx, &hparams, arch.c_str());
-
- // parameters that define tensor shapes must match
- GGML_ASSERT(hparams.n_embd == model->hparams.n_embd);
- GGML_ASSERT(hparams.n_ff == model->hparams.n_ff);
- GGML_ASSERT(hparams.n_head == model->hparams.n_head);
- GGML_ASSERT(hparams.n_head_kv == model->hparams.n_head_kv);
- GGML_ASSERT(hparams.n_layer == model->hparams.n_layer);
-
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_tok_embeddings, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_output, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_attention_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_wq, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_Q);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_wk, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_K);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_w1, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_w2, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
- GGUF_GET_KEY(fctx, lora->hparams.n_rank_w3, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
-
- init_lora(model, lora);
-
- copy_tensor_by_name(lora->tok_embeddings_a, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_a));
- copy_tensor_by_name(lora->tok_embeddings_b, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_b));
- copy_tensor_by_name(lora->norm_a, f_ggml_ctx, ggml_get_name(lora->norm_a));
- copy_tensor_by_name(lora->norm_b, f_ggml_ctx, ggml_get_name(lora->norm_b));
- copy_tensor_by_name(lora->output_a, f_ggml_ctx, ggml_get_name(lora->output_a));
- copy_tensor_by_name(lora->output_b, f_ggml_ctx, ggml_get_name(lora->output_b));
-
- for (uint32_t i = 0; i < lora->layers.size(); ++i) {
- auto & layer = lora->layers[i];
- copy_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a));
- copy_tensor_by_name(layer.attention_norm_b, f_ggml_ctx, ggml_get_name(layer.attention_norm_b));
- copy_tensor_by_name(layer.wq_a, f_ggml_ctx, ggml_get_name(layer.wq_a));
- copy_tensor_by_name(layer.wq_b, f_ggml_ctx, ggml_get_name(layer.wq_b));
- copy_tensor_by_name(layer.wk_a, f_ggml_ctx, ggml_get_name(layer.wk_a));
- copy_tensor_by_name(layer.wk_b, f_ggml_ctx, ggml_get_name(layer.wk_b));
- copy_tensor_by_name(layer.wv_a, f_ggml_ctx, ggml_get_name(layer.wv_a));
- copy_tensor_by_name(layer.wv_b, f_ggml_ctx, ggml_get_name(layer.wv_b));
- copy_tensor_by_name(layer.wo_a, f_ggml_ctx, ggml_get_name(layer.wo_a));
- copy_tensor_by_name(layer.wo_b, f_ggml_ctx, ggml_get_name(layer.wo_b));
- copy_tensor_by_name(layer.ffn_norm_a, f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));
- copy_tensor_by_name(layer.ffn_norm_b, f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));
- copy_tensor_by_name(layer.w1_a, f_ggml_ctx, ggml_get_name(layer.w1_a));
- copy_tensor_by_name(layer.w1_b, f_ggml_ctx, ggml_get_name(layer.w1_b));
- copy_tensor_by_name(layer.w2_a, f_ggml_ctx, ggml_get_name(layer.w2_a));
- copy_tensor_by_name(layer.w2_b, f_ggml_ctx, ggml_get_name(layer.w2_b));
- copy_tensor_by_name(layer.w3_a, f_ggml_ctx, ggml_get_name(layer.w3_a));
- copy_tensor_by_name(layer.w3_b, f_ggml_ctx, ggml_get_name(layer.w3_b));
- }
-}
-
-static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora) {
- const char * arch = "llama";
- enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
- std::vector keybuf;
- keybuf.resize(512);
- auto kv = [arch, &keybuf](const char * key) -> const char * {
- snprintf(keybuf.data(), keybuf.size(), key, arch);
- return keybuf.data();
- };
-
- gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
- gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
-
- gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH), model->hparams.n_ctx);
- gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH), model->hparams.n_embd);
- gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH), model->hparams.n_ff);
- gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT), model->hparams.n_head);
- gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV), model->hparams.n_head_kv);
- gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT), model->hparams.n_layer);
- gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT), model->hparams.n_embd_head());
- gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps);
- gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE), model->hparams.rope_freq_base);
- gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), model->hparams.rope_freq_scale);
-
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD, lora->hparams.n_rank_tok_embeddings);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, lora->hparams.n_rank_norm);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT, lora->hparams.n_rank_output);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM, lora->hparams.n_rank_attention_norm);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_Q, lora->hparams.n_rank_wq);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_K, lora->hparams.n_rank_wk);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V, lora->hparams.n_rank_wv);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, lora->hparams.n_rank_wo);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM, lora->hparams.n_rank_ffn_norm);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_w1);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_w2);
- gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_w3);
-
- gguf_add_tensor(fctx, lora->tok_embeddings_a);
- gguf_add_tensor(fctx, lora->tok_embeddings_b);
- gguf_add_tensor(fctx, lora->norm_a);
- gguf_add_tensor(fctx, lora->norm_b);
- gguf_add_tensor(fctx, lora->output_a);
- gguf_add_tensor(fctx, lora->output_b);
-
- for (uint32_t i = 0; i < lora->layers.size(); ++i) {
- auto & layer = lora->layers[i];
-
- gguf_add_tensor(fctx, layer.attention_norm_a);
- gguf_add_tensor(fctx, layer.attention_norm_b);
- gguf_add_tensor(fctx, layer.wq_a);
- gguf_add_tensor(fctx, layer.wq_b);
- gguf_add_tensor(fctx, layer.wk_a);
- gguf_add_tensor(fctx, layer.wk_b);
- gguf_add_tensor(fctx, layer.wv_a);
- gguf_add_tensor(fctx, layer.wv_b);
- gguf_add_tensor(fctx, layer.wo_a);
- gguf_add_tensor(fctx, layer.wo_b);
- gguf_add_tensor(fctx, layer.ffn_norm_a);
- gguf_add_tensor(fctx, layer.ffn_norm_b);
- gguf_add_tensor(fctx, layer.w1_a);
- gguf_add_tensor(fctx, layer.w1_b);
- gguf_add_tensor(fctx, layer.w2_a);
- gguf_add_tensor(fctx, layer.w2_b);
- gguf_add_tensor(fctx, layer.w3_a);
- gguf_add_tensor(fctx, layer.w3_b);
- }
-}
-
-static void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
- std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;
- GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
- GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
-
- load_train_state_gguf(fctx, f_ggml_ctx, train);
- load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);
-}
-
-static void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
- gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
- save_llama_lora_gguf(fctx, model, lora);
- save_train_state_gguf(fctx, train);
-}
-
-static bool load_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
- struct ggml_context * f_ggml_ctx;
- struct gguf_init_params params;
- params.no_alloc = false;
- params.ctx = &f_ggml_ctx;
- struct gguf_context * fctx = gguf_init_from_file(filename, params);
- if (fctx == NULL) {
- return false;
- }
-
- load_checkpoint_lora_gguf(fctx, f_ggml_ctx, model, lora, train);
-
- gguf_free(fctx);
- return true;
-}
-
-static void save_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
- printf("%s: saving to %s\n", __func__, filename);
- struct gguf_context * fctx = gguf_init_empty();
-
- save_checkpoint_lora_gguf(fctx, model, lora, train);
-
- // write file
- const bool only_meta = false;
- gguf_write_to_file(fctx, filename, only_meta);
- gguf_free(fctx);
-}
-
-struct llama_file {
- // use FILE * so we don't have to re-open the file to mmap
- FILE * fp;
- size_t size;
-
- llama_file(const char * fname, const char * mode) {
- fp = std::fopen(fname, mode);
- if (fp == NULL) {
- size = 0;
- } else {
- seek(0, SEEK_END);
- size = tell();
- seek(0, SEEK_SET);
- }
- }
-
- size_t tell() const {
-#ifdef _WIN32
- __int64 ret = _ftelli64(fp);
-#else
- long ret = std::ftell(fp);
-#endif
- GGML_ASSERT(ret != -1); // this really shouldn't fail
- return (size_t) ret;
- }
-
- void seek(size_t offset, int whence) {
-#ifdef _WIN32
- int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
- int ret = std::fseek(fp, (long) offset, whence);
-#endif
- GGML_ASSERT(ret == 0); // same
- }
-
- void read_raw(void * ptr, size_t size) {
- if (size == 0) {
- return;
- }
- errno = 0;
- std::size_t ret = std::fread(ptr, size, 1, fp);
- if (ferror(fp)) {
- die_fmt("read error: %s", strerror(errno));
- }
- if (ret != 1) {
- die("unexpectedly reached end of file");
- }
- }
-
- std::uint32_t read_u32() {
- std::uint32_t ret;
- read_raw(&ret, sizeof(ret));
- return ret;
- }
-
- std::string read_string(std::uint32_t len) {
- std::vector chars(len);
- read_raw(chars.data(), len);
- return std::string(chars.data(), len);
- }
-
- void write_raw(const void * ptr, size_t size) {
- if (size == 0) {
- return;
- }
- errno = 0;
- size_t ret = std::fwrite(ptr, size, 1, fp);
- if (ret != 1) {
- die_fmt("write error: %s", strerror(errno));
- }
- }
-
- void write_u32(std::uint32_t val) {
- write_raw(&val, sizeof(val));
- }
-
- ~llama_file() {
- if (fp) {
- std::fclose(fp);
- }
- }
-};
-
-static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor, const char * name) {
- if (tensor == NULL) {
- file->write_u32(0);
- file->write_u32(0);
- file->write_u32(GGML_TYPE_F32);
- file->seek((0-file->tell()) & 31, SEEK_CUR);
- return;
- }
- if (name == NULL) {
- name = ggml_get_name(tensor);
- }
- uint32_t name_len = strlen(name);
- uint32_t nd = tensor->n_dims;
- uint32_t ne[4] = { (uint32_t)tensor->ne[0],
- (uint32_t)tensor->ne[1],
- (uint32_t)tensor->ne[2],
- (uint32_t)tensor->ne[3] };
- file->write_u32(nd);
- file->write_u32(name_len);
- file->write_u32(tensor->type);
- file->write_raw(ne, sizeof(ne[0]) * nd);
- file->write_raw(name, name_len);
- file->seek((0-file->tell()) & 31, SEEK_CUR);
- file->write_raw(tensor->data, ggml_nbytes(tensor));
-}
-
-static void save_as_llama_lora(const char * filename, struct my_llama_lora * lora) {
- printf("%s: saving to %s\n", __func__, filename);
- struct llama_file file(filename, "wb");
- if (file.fp == NULL) {
- return;
- }
-
- std::vector tn_buf;
- tn_buf.resize(GGML_MAX_NAME);
-
- auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
- snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
- return tn_buf.data();
- };
-
- auto tni = [&tn_buf](const char * key, int bid, const char * suffix) -> const char * {
- snprintf(tn_buf.data(), tn_buf.size(), key, bid);
- std::string s = tn_buf.data();
- snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix);
- return tn_buf.data();
- };
-
- uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
- // write_magic
- file.write_u32(LLAMA_FILE_MAGIC_LORA); // magic
- file.write_u32(1); // version
- // write_hparams
- file.write_u32(lora->hparams.lora_r);
- file.write_u32(lora->hparams.lora_alpha);
- // write tensors
- write_tensor(&file, lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.loraA"));
- write_tensor(&file, lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.loraB"));
- write_tensor(&file, lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraA"));
- write_tensor(&file, lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraB"));
- write_tensor(&file, lora->output_a, tn(LLM_TENSOR_OUTPUT, ".weight.loraA"));
- write_tensor(&file, lora->output_b, tn(LLM_TENSOR_OUTPUT, ".weight.loraB"));
- for (uint32_t i = 0; i < lora->layers.size(); ++i) {
- auto & layer = lora->layers[i];
- write_tensor(&file, layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraA"));
- write_tensor(&file, layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraB"));
- write_tensor(&file, layer.wq_a, tni(LLM_TENSOR_ATTN_Q, i, ".weight.loraA"));
- write_tensor(&file, layer.wq_b, tni(LLM_TENSOR_ATTN_Q, i, ".weight.loraB"));
- write_tensor(&file, layer.wk_a, tni(LLM_TENSOR_ATTN_K, i, ".weight.loraA"));
- write_tensor(&file, layer.wk_b, tni(LLM_TENSOR_ATTN_K, i, ".weight.loraB"));
- write_tensor(&file, layer.wv_a, tni(LLM_TENSOR_ATTN_V, i, ".weight.loraA"));
- write_tensor(&file, layer.wv_b, tni(LLM_TENSOR_ATTN_V, i, ".weight.loraB"));
- write_tensor(&file, layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraA"));
- write_tensor(&file, layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraB"));
- write_tensor(&file, layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraA"));
- write_tensor(&file, layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraB"));
- write_tensor(&file, layer.w1_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA"));
- write_tensor(&file, layer.w1_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB"));
- write_tensor(&file, layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA"));
- write_tensor(&file, layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB"));
- write_tensor(&file, layer.w3_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA"));
- write_tensor(&file, layer.w3_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB"));
- }
-}
-
-struct train_params {
- struct train_params_common common;
-
- const char * fn_model_base;
- const char * fn_lora_out;
-
- bool only_write_lora;
-
- float f_norm_rms_eps;
- float rope_freq_base;
- float rope_freq_scale;
-
- bool custom_f_norm_rms_eps;
- bool custom_rope_freq_base;
- bool custom_rope_freq_scale;
-
- int32_t lora_r;
- int32_t lora_alpha;
- bool custom_lora_alpha;
-
- uint32_t n_rank_attention_norm;
- uint32_t n_rank_wq;
- uint32_t n_rank_wk;
- uint32_t n_rank_wv;
- uint32_t n_rank_wo;
- uint32_t n_rank_ffn_norm;
- uint32_t n_rank_w1;
- uint32_t n_rank_w2;
- uint32_t n_rank_w3;
- uint32_t n_rank_tok_embeddings;
- uint32_t n_rank_norm;
- uint32_t n_rank_output;
-
- bool custom_n_rank_attention_norm;
- bool custom_n_rank_wq;
- bool custom_n_rank_wk;
- bool custom_n_rank_wv;
- bool custom_n_rank_wo;
- bool custom_n_rank_ffn_norm;
- bool custom_n_rank_w1;
- bool custom_n_rank_w2;
- bool custom_n_rank_w3;
- bool custom_n_rank_tok_embeddings;
- bool custom_n_rank_norm;
- bool custom_n_rank_output;
-};
-
-static struct train_params get_default_train_params() {
- struct train_params params;
- params.common = get_default_train_params_common();
- params.fn_model_base = "";
- params.fn_lora_out = "ggml-lora-ITERATION-f32.gguf";
-
- params.only_write_lora = false;
-
- params.f_norm_rms_eps = 1e-5f;
- params.rope_freq_base = 10000.0f;
- params.rope_freq_scale = 1.0f;
-
- params.custom_f_norm_rms_eps = false;
- params.custom_rope_freq_base = false;
- params.custom_rope_freq_scale = false;
-
- params.lora_r = 4;
- params.lora_alpha = 4;
- params.custom_lora_alpha = false;
-
- params.n_rank_attention_norm = 1;
- params.n_rank_wq = 4;
- params.n_rank_wk = 4;
- params.n_rank_wv = 4;
- params.n_rank_wo = 4;
- params.n_rank_ffn_norm = 1;
- params.n_rank_w1 = 4;
- params.n_rank_w2 = 4;
- params.n_rank_w3 = 4;
- params.n_rank_tok_embeddings = 4;
- params.n_rank_norm = 1;
- params.n_rank_output = 4;
-
- params.custom_n_rank_attention_norm = false;
- params.custom_n_rank_wq = false;
- params.custom_n_rank_wk = false;
- params.custom_n_rank_wv = false;
- params.custom_n_rank_wo = false;
- params.custom_n_rank_ffn_norm = false;
- params.custom_n_rank_w1 = false;
- params.custom_n_rank_w2 = false;
- params.custom_n_rank_w3 = false;
- params.custom_n_rank_tok_embeddings = false;
- params.custom_n_rank_norm = false;
- params.custom_n_rank_output = false;
-
- return params;
-}
-
-static void train_print_usage(int argc, char ** argv, const struct train_params * params) {
- fprintf(stderr, "usage: %s [options]\n", argv[0]);
- fprintf(stderr, "\n");
- fprintf(stderr, "options:\n");
- fprintf(stderr, " -h, --help show this help message and exit\n");
-
- fprintf(stderr, " --model-base FNAME model path from which to load base model (default '%s')\n", params->fn_model_base);
- fprintf(stderr, " --lora-out FNAME path to save llama lora (default '%s')\n", params->fn_lora_out);
- fprintf(stderr, " --only-write-lora only save llama lora, don't do any training. use this if you only want to convert a checkpoint to a lora adapter.\n");
- fprintf(stderr, " --norm-rms-eps F RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
- fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base);
- fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
- fprintf(stderr, " --lora-alpha N LORA alpha : resulting LORA scaling is alpha/r. (default %d)\n", params->lora_alpha);
- fprintf(stderr, " --lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\n", params->lora_r);
- fprintf(stderr, " --rank-att-norm N LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
- fprintf(stderr, " --rank-ffn-norm N LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
- fprintf(stderr, " --rank-out-norm N LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
- fprintf(stderr, " --rank-tok-embd N LORA rank for token embeddings tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-out N LORA rank for output tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-wq N LORA rank for wq tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-wk N LORA rank for wk tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-wv N LORA rank for wv tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-wo N LORA rank for wo tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-w1 N LORA rank for w1 tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-w2 N LORA rank for w2 tensor, overrides default rank.\n");
- fprintf(stderr, " --rank-w3 N LORA rank for w3 tensor, overrides default rank.\n");
-
- print_common_train_usage(argc, argv, ¶ms->common);
-}
-
-static bool train_params_parse(int argc, char ** argv, struct train_params * params) {
- bool invalid_param = false;
- std::string arg;
- struct train_params default_params = get_default_train_params();
- const std::string arg_prefix = "--";
-
- for (int i = 1; i < argc; i++) {
- arg = argv[i];
- if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
- std::replace(arg.begin(), arg.end(), '_', '-');
- }
-
- if (consume_common_train_arg(argc, argv, &i, ¶ms->common, &invalid_param)) {
- if (invalid_param) {
- break;
- } else if (params->common.print_usage) {
- train_print_usage(argc, argv, &default_params);
- exit(0);
- }
- } else if (arg == "--model-base") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->fn_model_base = argv[i];
- } else if (arg == "--lora-out") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->fn_lora_out = argv[i];
- } else if (arg == "--only-write-lora") {
- params->only_write_lora = true;
- } else if (arg == "--norm-rms-eps") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->f_norm_rms_eps = std::stof(argv[i]);
- params->custom_f_norm_rms_eps = true;
- } else if (arg == "--rope-freq-base") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->rope_freq_base = std::stof(argv[i]);
- params->custom_rope_freq_base = true;
- } else if (arg == "--rope-freq-scale") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->rope_freq_scale = std::stof(argv[i]);
- params->custom_rope_freq_scale = true;
- } else if (arg == "--lora-alpha") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->lora_alpha = std::stoi(argv[i]);
- params->custom_lora_alpha = true;
- } else if (arg == "--lora-r") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->lora_r = std::stoi(argv[i]);
- } else if (arg == "--rank-att-norm") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_attention_norm = std::stoi(argv[i]);
- params->custom_n_rank_attention_norm = true;
- } else if (arg == "--rank-ffn-norm") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_ffn_norm = std::stoi(argv[i]);
- params->custom_n_rank_ffn_norm = true;
- } else if (arg == "--rank-out-norm") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_norm = std::stoi(argv[i]);
- params->custom_n_rank_norm = true;
- } else if (arg == "--rank-tok-embd") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_tok_embeddings = std::stoi(argv[i]);
- params->custom_n_rank_tok_embeddings = true;
- } else if (arg == "--rank-out") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_output = std::stoi(argv[i]);
- params->custom_n_rank_output = true;
- } else if (arg == "--rank-wq") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_wq = std::stoi(argv[i]);
- params->custom_n_rank_wq = true;
- } else if (arg == "--rank-wk") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_wk = std::stoi(argv[i]);
- params->custom_n_rank_wk = true;
- } else if (arg == "--rank-wv") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_wv = std::stoi(argv[i]);
- params->custom_n_rank_wv = true;
- } else if (arg == "--rank-wo") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_wo = std::stoi(argv[i]);
- params->custom_n_rank_wo = true;
- } else if (arg == "--rank-w1") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_w1 = std::stoi(argv[i]);
- params->custom_n_rank_w1 = true;
- } else if (arg == "--rank-w2") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_w2 = std::stoi(argv[i]);
- params->custom_n_rank_w2 = true;
- } else if (arg == "--rank-w3") {
- if (++i >= argc) {
- invalid_param = true;
- break;
- }
- params->n_rank_w3 = std::stoi(argv[i]);
- params->custom_n_rank_w3 = true;
- } else {
- fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
- train_print_usage(argc, argv, &default_params);
- exit(1);
- }
- }
- if (invalid_param) {
- fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
- train_print_usage(argc, argv, &default_params);
- exit(1);
- }
- finish_processing_train_args(¶ms->common);
- return true;
-}
-
-struct save_train_files_data {
- const char * fn_checkpoint_out;
- const char * fn_lora_out;
- const char * pattern_fn_it;
- const char * fn_latest;
- struct my_llama_model * model;
- struct my_llama_lora * lora;
-};
-
-static void save_train_files(void * vdata, struct train_state * train) {
- struct save_train_files_data * data = (struct save_train_files_data *) vdata;
-
- int64_t iter = train->opt->iter;
-
- if (strlen(data->fn_checkpoint_out) > 0) {
- save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->model, data->lora, train);
- save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->model, data->lora, train);
- }
- if (strlen(data->fn_lora_out) > 0) {
- save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->lora);
- save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->lora);
- }
-}
-
-static int64_t get_parameter_count(struct my_llama_lora* lora) {
- int64_t nx = 0;
- nx += ggml_nelements(lora->tok_embeddings_a);
- nx += ggml_nelements(lora->tok_embeddings_b);
- nx += ggml_nelements(lora->norm_a);
- nx += ggml_nelements(lora->norm_b);
- nx += ggml_nelements(lora->output_a);
- nx += ggml_nelements(lora->output_b);
-
- for (uint32_t i = 0; i < lora->layers.size(); ++i) {
- auto & layer = lora->layers[i];
- nx += ggml_nelements(layer.attention_norm_a);
- nx += ggml_nelements(layer.attention_norm_b);
- nx += ggml_nelements(layer.wq_a);
- nx += ggml_nelements(layer.wq_b);
- nx += ggml_nelements(layer.wk_a);
- nx += ggml_nelements(layer.wk_b);
- nx += ggml_nelements(layer.wv_a);
- nx += ggml_nelements(layer.wv_b);
- nx += ggml_nelements(layer.wo_a);
- nx += ggml_nelements(layer.wo_b);
- nx += ggml_nelements(layer.ffn_norm_a);
- nx += ggml_nelements(layer.ffn_norm_b);
- nx += ggml_nelements(layer.w1_a);
- nx += ggml_nelements(layer.w1_b);
- nx += ggml_nelements(layer.w2_a);
- nx += ggml_nelements(layer.w2_b);
- nx += ggml_nelements(layer.w3_a);
- nx += ggml_nelements(layer.w3_b);
- }
- return nx;
-}
-
-int main(int argc, char ** argv) {
- struct train_params params = get_default_train_params();
-
- if (!train_params_parse(argc, argv, ¶ms)) {
- return 1;
- }
-
- if (params.common.seed == LLAMA_DEFAULT_SEED) {
- params.common.seed = time(NULL);
- }
- printf("%s: seed: %u\n", __func__, params.common.seed);
- srand(params.common.seed);
-
- struct llama_model_params llama_mparams = llama_model_default_params();
- llama_mparams.vocab_only = false;
-
- printf("%s: model base = '%s'\n", __func__, params.fn_model_base);
- struct llama_model * lmodel = llama_load_model_from_file(params.fn_model_base, llama_mparams);
-
- struct llama_context_params llama_cparams = llama_context_default_params();
- struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_cparams);
-
- struct my_llama_model model;
- init_model(lmodel, &model, params.fn_model_base, params.common.n_ctx);
-
- struct my_llama_lora lora;
-
- struct train_state * train = init_train_state();
- struct ggml_opt_context * opt = train->opt;
-
- // set params from command line
- if (params.custom_f_norm_rms_eps) {
- model.hparams.f_norm_rms_eps = params.f_norm_rms_eps;
- }
- if (params.custom_rope_freq_base) {
- model.hparams.rope_freq_base = params.rope_freq_base;
- }
- if (params.custom_rope_freq_scale) {
- model.hparams.rope_freq_scale = params.rope_freq_scale;
- }
- lora.hparams.lora_r = params.lora_r;
- lora.hparams.lora_alpha = params.custom_lora_alpha ? params.lora_alpha : params.lora_r;
- uint32_t n_rank_attention_norm = params.custom_n_rank_attention_norm ? params.n_rank_attention_norm : 1;
- uint32_t n_rank_wq = params.custom_n_rank_wq ? params.n_rank_wq : params.lora_r;
- uint32_t n_rank_wk = params.custom_n_rank_wk ? params.n_rank_wk : params.lora_r;
- uint32_t n_rank_wv = params.custom_n_rank_wv ? params.n_rank_wv : params.lora_r;
- uint32_t n_rank_wo = params.custom_n_rank_wo ? params.n_rank_wo : params.lora_r;
- uint32_t n_rank_ffn_norm = params.custom_n_rank_ffn_norm ? params.n_rank_ffn_norm : 1;
- uint32_t n_rank_w1 = params.custom_n_rank_w1 ? params.n_rank_w1 : params.lora_r;
- uint32_t n_rank_w2 = params.custom_n_rank_w2 ? params.n_rank_w2 : params.lora_r;
- uint32_t n_rank_w3 = params.custom_n_rank_w3 ? params.n_rank_w3 : params.lora_r;
- uint32_t n_rank_tok_embeddings = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;
- uint32_t n_rank_norm = params.custom_n_rank_norm ? params.n_rank_norm : 1;
- uint32_t n_rank_output = params.custom_n_rank_output ? params.n_rank_output : params.lora_r;
- lora.hparams.n_rank_attention_norm = n_rank_attention_norm;
- lora.hparams.n_rank_wq = n_rank_wq;
- lora.hparams.n_rank_wk = n_rank_wk;
- lora.hparams.n_rank_wv = n_rank_wv;
- lora.hparams.n_rank_wo = n_rank_wo;
- lora.hparams.n_rank_ffn_norm = n_rank_ffn_norm;
- lora.hparams.n_rank_w1 = n_rank_w1;
- lora.hparams.n_rank_w2 = n_rank_w2;
- lora.hparams.n_rank_w3 = n_rank_w3;
- lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;
- lora.hparams.n_rank_norm = n_rank_norm;
- lora.hparams.n_rank_output = n_rank_output;
-
- // set opt params from command line
- opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
- opt->params.print_forward_graph = false;
- opt->params.print_backward_graph = false;
- opt->params.n_threads = params.common.n_threads;
- opt->params.past = params.common.opt_past;
- opt->params.delta = params.common.opt_delta;
- opt->params.max_no_improvement = params.common.opt_max_no_improvement;
- opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;
- opt->params.adam.n_iter = params.common.adam_n_iter;
- opt->params.adam.sched = 1.0f;
- opt->params.adam.alpha = params.common.adam_alpha;
- opt->params.adam.decay = params.common.adam_decay;
- opt->params.adam.decay_min_ndim = params.common.adam_decay_min_ndim;
- opt->params.adam.beta1 = params.common.adam_beta1;
- opt->params.adam.beta2 = params.common.adam_beta2;
- opt->params.adam.gclip = params.common.adam_gclip;
- opt->params.adam.eps_f = params.common.adam_eps_f;
-
- ggml_allocr * alloc = NULL;
-
- printf("%s: init model\n", __func__);
- bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);
-
- if (existed) {
- // overwrite last n_ctx with user provided n_ctx
- if (params.common.custom_n_ctx) {
- model.hparams.n_ctx = params.common.n_ctx;
- }
-
- const bool opt_param_count_changed = (
- (lora.hparams.n_rank_attention_norm != n_rank_attention_norm)
- || (lora.hparams.n_rank_wq != n_rank_wq)
- || (lora.hparams.n_rank_wk != n_rank_wk)
- || (lora.hparams.n_rank_wv != n_rank_wv)
- || (lora.hparams.n_rank_wo != n_rank_wo)
- || (lora.hparams.n_rank_ffn_norm != n_rank_ffn_norm)
- || (lora.hparams.n_rank_w1 != n_rank_w1)
- || (lora.hparams.n_rank_w2 != n_rank_w2)
- || (lora.hparams.n_rank_w3 != n_rank_w3)
- || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)
- || (lora.hparams.n_rank_norm != n_rank_norm)
- || (lora.hparams.n_rank_output != n_rank_output)
- );
-
- const bool opt_past_changed = opt->params.past != params.common.opt_past;
-
- if (opt_param_count_changed) {
- print_lora_params(&lora.hparams);
- die("Provided rank differs from checkpoint file. To use different rank start finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting.");
- // need to discard previous optimizer gradient statistics and opt_init with new shapes
- // TODO
- }
- if (opt_past_changed) {
- die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting");
- // need to discard previous optimizer past function value statistics and opt_init with new shapes
- // TODO
- }
- } else { // existed == false
- init_lora(&model, &lora);
- randomize_lora(&lora, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
- if (!params.only_write_lora) {
- ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&lora));
- }
- }
- opt->iter = train->train_its;
-
- print_params(&model.hparams);
- print_lora_params(&lora.hparams);
- printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its);
- printf("%s: seen train_samples %llu\n", __func__, (long long unsigned) train->train_samples);
- printf("%s: seen train_tokens %llu\n", __func__, (long long unsigned) train->train_tokens);
- printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
- printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + lora.data.size()), (float) (ggml_used_mem(lora.ctx) + lora.data.size()) / (1024.0f*1024.0f));
-
- if (params.only_write_lora) {
- save_train_files_data save_data;
- save_data.fn_checkpoint_out = "";
- save_data.fn_lora_out = params.fn_lora_out;
- save_data.pattern_fn_it = params.common.pattern_fn_it;
- save_data.fn_latest = params.common.fn_latest;
- save_data.model = &model;
- save_data.lora = &lora;
-
- save_train_files(&save_data, train);
-
- free_train_state(train);
- ggml_free(lora.ctx);
- llama_free(lctx);
- llama_free_model(lmodel);
- return 0;
- }
-
- printf("%s: opt_size = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));
- printf("%s: opt iter %d\n", __func__, opt->iter);
-
- int n_tokens = model.hparams.n_ctx;
- int n_vocab = model.hparams.n_vocab;
- int n_batch = params.common.n_batch;
-
-
- std::vector mem_input_data;
- std::vector mem_compute_data;
-
- // context for input tensors without their data
- struct ggml_init_params ctx_input_params = {
- ggml_tensor_overhead() * 2, // mem_size
- NULL, // mem_buffer
- true, // no_alloc
- };
- struct ggml_context * ctx_input = ggml_init(ctx_input_params);
-
- // the input tensors
- struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
- struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
-
- // measure required memory for input tensors
- alloc = ggml_allocr_new_measure(tensor_alignment);
- ggml_allocr_alloc(alloc, tokens_input);
- ggml_allocr_alloc(alloc, target_probs);
- size_t max_input_size = ggml_allocr_max_size(alloc) + tensor_alignment;
- ggml_allocr_free(alloc);
- printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
- // allocate input tensors
- mem_input_data.resize(max_input_size);
- alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
- ggml_allocr_alloc(alloc, tokens_input);
- ggml_allocr_alloc(alloc, target_probs);
- ggml_allocr_free(alloc);
-
- // context for compute tensors without their data
- size_t estimated_compute_size_wo_data = (
- ggml_tensor_overhead()*GGML_MAX_NODES*2
- + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*(
- params.common.use_checkpointing ? 3 : 2
- )
- );
- struct ggml_init_params ctx_compute_params = {
- estimated_compute_size_wo_data, // mem_size
- NULL, // mem_buffer
- true, // no_alloc
- };
- struct ggml_context * ctx_compute = NULL;
-
- struct ggml_tensor * loss = NULL;
- struct ggml_tensor * logits = NULL;
-
- struct ggml_cgraph * gf = NULL;
- struct ggml_cgraph * gb = NULL;
- struct ggml_cgraph * gb_tmp = NULL;
-
- // measure required memory for compute tensors
- size_t best_compute_size = SIZE_MAX;
- enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;
- // find best evaluation order
- for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
- ctx_compute = ggml_init(ctx_compute_params);
- alloc = ggml_allocr_new_measure(tensor_alignment);
- gf = ggml_new_graph(ctx_compute);
- gf->order = (enum ggml_cgraph_eval_order) order;
- gb = ggml_new_graph(ctx_compute);
- gb_tmp = params.common.use_checkpointing
- ? ggml_new_graph(ctx_compute)
- : NULL;
- loss = llama_build_lora_finetune_graphs(
- &model, &lora, alloc, ctx_compute,
- gf, gb, gb_tmp,
- &logits, tokens_input, target_probs,
- n_tokens, n_batch,
- params.common.use_flash,
- params.common.use_checkpointing
- );
- size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment;
- if (max_compute_size < best_compute_size) {
- best_compute_size = max_compute_size;
- best_order = gf->order;
- }
- ggml_allocr_free(alloc);
- ggml_free(ctx_compute);
- }
- size_t max_compute_size = best_compute_size;
- printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));
- printf("%s: evaluation order = %s\n", __func__,
- (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" :
- (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" :
- "invalid");
-
- // allocate compute tensors
- mem_compute_data.resize(max_compute_size);
- ctx_compute = ggml_init(ctx_compute_params);
- alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
- gf = ggml_new_graph(ctx_compute);
- gf->order = best_order;
- gb = ggml_new_graph(ctx_compute);
- gb_tmp = params.common.use_checkpointing
- ? ggml_new_graph(ctx_compute)
- : NULL;
- loss = llama_build_lora_finetune_graphs(
- &model, &lora, alloc, ctx_compute,
- gf, gb, gb_tmp,
- &logits, tokens_input, target_probs,
- n_tokens, n_batch,
- params.common.use_flash,
- params.common.use_checkpointing
- );
- ggml_allocr_free(alloc);
-
- // tokenize data
- std::vector train_tokens;
- std::vector train_samples_begin;
- std::vector train_samples_size;
- printf("%s: tokenize training data\n", __func__);
- tokenize_file(lctx,
- params.common.fn_train_data,
- params.common.sample_start,
- params.common.include_sample_start,
- params.common.overlapping_samples,
- n_tokens,
- train_tokens,
- train_samples_begin,
- train_samples_size);
- GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());
-
- printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size());
-
- std::vector token_noccurs;
- token_noccurs.resize(model.hparams.n_vocab, 0);
- for (unsigned int i = 0; i < train_tokens.size(); ++i) {
- ++token_noccurs[train_tokens[i]];
- }
- int n_unique_tokens = 0;
- for (unsigned int i = 0; i < token_noccurs.size(); ++i) {
- if (token_noccurs[i] == 0) continue;
- ++n_unique_tokens;
- }
- printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens);
-
- size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());
- const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());
- if (changed_train_data) {
- printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__);
- }
- if (params.common.force_reshuffle) {
- printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__);
- }
- if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) {
- train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);
- train->shuffle_sample_count = train_samples_size.size();
- train->shuffle_next_sample = 0;
- train->shuffle_samples_hash = shuffle_samples_hash;
- }
- std::vector train_shuffled_samples_offs;
- std::vector train_shuffled_samples_begin;
- std::vector train_shuffled_samples_size;
- train_shuffled_samples_offs.resize(train_samples_begin.size());
- train_shuffled_samples_begin.resize(train_samples_begin.size());
- train_shuffled_samples_size.resize(train_samples_size.size());
- train->shuffle_rng_state_next = shuffle_samples(
- train->shuffle_rng_state_current,
- train_shuffled_samples_offs.data(),
- train_shuffled_samples_begin.data(),
- train_shuffled_samples_size.data(),
- train_samples_begin.data(),
- train_samples_size.data(),
- train_samples_size.size());
-
- printf("%s: begin training\n", __func__);
-
- save_train_files_data save_data;
- save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;
- save_data.fn_lora_out = params.fn_lora_out;
- save_data.pattern_fn_it = params.common.pattern_fn_it;
- save_data.fn_latest = params.common.fn_latest;
- save_data.model = &model;
- save_data.lora = &lora;
-
- struct train_opt_callback_data opt_cb_data;
- opt_cb_data.params = ¶ms.common;
- opt_cb_data.train = train;
- opt_cb_data.save_cb = &save_train_files;
- opt_cb_data.save_data = &save_data;
- opt_cb_data.lctx = lctx;
- opt_cb_data.last_save_iter = opt->iter;
- opt_cb_data.tokens_data = train_tokens.data();
- opt_cb_data.tokens_size = train_tokens.size();
- opt_cb_data.samples_begin = train_samples_begin.data();
- opt_cb_data.samples_size = train_samples_size.data();
- opt_cb_data.shuffled_samples_offs = train_shuffled_samples_offs.data();
- opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();
- opt_cb_data.shuffled_samples_size = train_shuffled_samples_size.data();
- opt_cb_data.samples_count = train_samples_size.size();
- opt_cb_data.tokens_input = tokens_input;
- opt_cb_data.target_probs = target_probs;
- opt_cb_data.first_iter = opt->iter;
- opt_cb_data.first_epoch = train->train_epochs;
- opt_cb_data.iter_at_last_epoch = -1;
- opt_cb_data.last_time = ggml_time_ms();
- opt_cb_data.millis_per_iter = 0.0;
-
- // measure required memory for work buffer
- size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;
- printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));
-
- // context for work buffer
- struct ggml_init_params ctx_work_params = {
- max_work_size, // mem_size
- NULL, // mem_buffer
- false, // no_alloc
- };
- struct ggml_context * ctx_work = ggml_init(ctx_work_params);
-
- int64_t t0 = ggml_time_ms();
-
- ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);
-
- ggml_free(ctx_work);
- ggml_free(ctx_compute);
- ggml_free(ctx_input);
-
- int64_t t1 = ggml_time_ms();
- printf("%s: total training time: ", __func__);
- print_duration((double) (t1 - t0));
- printf("\n");
-
- int new_iters = opt->iter - opt_cb_data.last_save_iter;
- if (new_iters > 0) {
- train->train_its += new_iters;
- train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;
-
- save_train_files(&save_data, train);
- opt_cb_data.last_save_iter = opt->iter;
- }
-
- ggml_free(opt->ctx);
- free_train_state(train);
- ggml_free(lora.ctx);
- llama_free(lctx);
- llama_free_model(lmodel);
- return 0;
-}
diff --git a/spaces/Isotonic/image-generator/app.py b/spaces/Isotonic/image-generator/app.py
deleted file mode 100644
index 262436d8b50f87b0953c645576cc3184b3b27b43..0000000000000000000000000000000000000000
--- a/spaces/Isotonic/image-generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Joeythemonster/anything-midjourney-v-4-1").launch()
\ No newline at end of file
diff --git a/spaces/Jacks2003/3D_Photo_Inpainting/bilateral_filtering.py b/spaces/Jacks2003/3D_Photo_Inpainting/bilateral_filtering.py
deleted file mode 100644
index 28cc7dc79cc2f3c0b9065d6a1eb290b9554af879..0000000000000000000000000000000000000000
--- a/spaces/Jacks2003/3D_Photo_Inpainting/bilateral_filtering.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import numpy as np
-from functools import reduce
-
-def sparse_bilateral_filtering(
- depth, image, config, HR=False, mask=None, gsHR=True, edge_id=None, num_iter=None, num_gs_iter=None, spdb=False
-):
- """
- config:
- - filter_size
- """
- import time
-
- save_images = []
- save_depths = []
- save_discontinuities = []
- vis_depth = depth.copy()
- backup_vis_depth = vis_depth.copy()
-
- depth_max = vis_depth.max()
- depth_min = vis_depth.min()
- vis_image = image.copy()
- for i in range(num_iter):
- if isinstance(config["filter_size"], list):
- window_size = config["filter_size"][i]
- else:
- window_size = config["filter_size"]
- vis_image = image.copy()
- save_images.append(vis_image)
- save_depths.append(vis_depth)
- u_over, b_over, l_over, r_over = vis_depth_discontinuity(vis_depth, config, mask=mask)
- vis_image[u_over > 0] = np.array([0, 0, 0])
- vis_image[b_over > 0] = np.array([0, 0, 0])
- vis_image[l_over > 0] = np.array([0, 0, 0])
- vis_image[r_over > 0] = np.array([0, 0, 0])
-
- discontinuity_map = (u_over + b_over + l_over + r_over).clip(0.0, 1.0)
- discontinuity_map[depth == 0] = 1
- save_discontinuities.append(discontinuity_map)
- if mask is not None:
- discontinuity_map[mask == 0] = 0
- vis_depth = bilateral_filter(
- vis_depth, config, discontinuity_map=discontinuity_map, HR=HR, mask=mask, window_size=window_size
- )
-
- return save_images, save_depths
-
-
-def vis_depth_discontinuity(depth, config, vis_diff=False, label=False, mask=None):
- """
- config:
- -
- """
- if label == False:
- disp = 1./depth
- u_diff = (disp[1:, :] - disp[:-1, :])[:-1, 1:-1]
- b_diff = (disp[:-1, :] - disp[1:, :])[1:, 1:-1]
- l_diff = (disp[:, 1:] - disp[:, :-1])[1:-1, :-1]
- r_diff = (disp[:, :-1] - disp[:, 1:])[1:-1, 1:]
- if mask is not None:
- u_mask = (mask[1:, :] * mask[:-1, :])[:-1, 1:-1]
- b_mask = (mask[:-1, :] * mask[1:, :])[1:, 1:-1]
- l_mask = (mask[:, 1:] * mask[:, :-1])[1:-1, :-1]
- r_mask = (mask[:, :-1] * mask[:, 1:])[1:-1, 1:]
- u_diff = u_diff * u_mask
- b_diff = b_diff * b_mask
- l_diff = l_diff * l_mask
- r_diff = r_diff * r_mask
- u_over = (np.abs(u_diff) > config['depth_threshold']).astype(np.float32)
- b_over = (np.abs(b_diff) > config['depth_threshold']).astype(np.float32)
- l_over = (np.abs(l_diff) > config['depth_threshold']).astype(np.float32)
- r_over = (np.abs(r_diff) > config['depth_threshold']).astype(np.float32)
- else:
- disp = depth
- u_diff = (disp[1:, :] * disp[:-1, :])[:-1, 1:-1]
- b_diff = (disp[:-1, :] * disp[1:, :])[1:, 1:-1]
- l_diff = (disp[:, 1:] * disp[:, :-1])[1:-1, :-1]
- r_diff = (disp[:, :-1] * disp[:, 1:])[1:-1, 1:]
- if mask is not None:
- u_mask = (mask[1:, :] * mask[:-1, :])[:-1, 1:-1]
- b_mask = (mask[:-1, :] * mask[1:, :])[1:, 1:-1]
- l_mask = (mask[:, 1:] * mask[:, :-1])[1:-1, :-1]
- r_mask = (mask[:, :-1] * mask[:, 1:])[1:-1, 1:]
- u_diff = u_diff * u_mask
- b_diff = b_diff * b_mask
- l_diff = l_diff * l_mask
- r_diff = r_diff * r_mask
- u_over = (np.abs(u_diff) > 0).astype(np.float32)
- b_over = (np.abs(b_diff) > 0).astype(np.float32)
- l_over = (np.abs(l_diff) > 0).astype(np.float32)
- r_over = (np.abs(r_diff) > 0).astype(np.float32)
- u_over = np.pad(u_over, 1, mode='constant')
- b_over = np.pad(b_over, 1, mode='constant')
- l_over = np.pad(l_over, 1, mode='constant')
- r_over = np.pad(r_over, 1, mode='constant')
- u_diff = np.pad(u_diff, 1, mode='constant')
- b_diff = np.pad(b_diff, 1, mode='constant')
- l_diff = np.pad(l_diff, 1, mode='constant')
- r_diff = np.pad(r_diff, 1, mode='constant')
-
- if vis_diff:
- return [u_over, b_over, l_over, r_over], [u_diff, b_diff, l_diff, r_diff]
- else:
- return [u_over, b_over, l_over, r_over]
-
-def bilateral_filter(depth, config, discontinuity_map=None, HR=False, mask=None, window_size=False):
- sort_time = 0
- replace_time = 0
- filter_time = 0
- init_time = 0
- filtering_time = 0
- sigma_s = config['sigma_s']
- sigma_r = config['sigma_r']
- if window_size == False:
- window_size = config['filter_size']
- midpt = window_size//2
- ax = np.arange(-midpt, midpt+1.)
- xx, yy = np.meshgrid(ax, ax)
- if discontinuity_map is not None:
- spatial_term = np.exp(-(xx**2 + yy**2) / (2. * sigma_s**2))
-
- # padding
- depth = depth[1:-1, 1:-1]
- depth = np.pad(depth, ((1,1), (1,1)), 'edge')
- pad_depth = np.pad(depth, (midpt,midpt), 'edge')
- if discontinuity_map is not None:
- discontinuity_map = discontinuity_map[1:-1, 1:-1]
- discontinuity_map = np.pad(discontinuity_map, ((1,1), (1,1)), 'edge')
- pad_discontinuity_map = np.pad(discontinuity_map, (midpt,midpt), 'edge')
- pad_discontinuity_hole = 1 - pad_discontinuity_map
- # filtering
- output = depth.copy()
- pad_depth_patches = rolling_window(pad_depth, [window_size, window_size], [1,1])
- if discontinuity_map is not None:
- pad_discontinuity_patches = rolling_window(pad_discontinuity_map, [window_size, window_size], [1,1])
- pad_discontinuity_hole_patches = rolling_window(pad_discontinuity_hole, [window_size, window_size], [1,1])
-
- if mask is not None:
- pad_mask = np.pad(mask, (midpt,midpt), 'constant')
- pad_mask_patches = rolling_window(pad_mask, [window_size, window_size], [1,1])
- from itertools import product
- if discontinuity_map is not None:
- pH, pW = pad_depth_patches.shape[:2]
- for pi in range(pH):
- for pj in range(pW):
- if mask is not None and mask[pi, pj] == 0:
- continue
- if discontinuity_map is not None:
- if bool(pad_discontinuity_patches[pi, pj].any()) is False:
- continue
- discontinuity_patch = pad_discontinuity_patches[pi, pj]
- discontinuity_holes = pad_discontinuity_hole_patches[pi, pj]
- depth_patch = pad_depth_patches[pi, pj]
- depth_order = depth_patch.ravel().argsort()
- patch_midpt = depth_patch[window_size//2, window_size//2]
- if discontinuity_map is not None:
- coef = discontinuity_holes.astype(np.float32)
- if mask is not None:
- coef = coef * pad_mask_patches[pi, pj]
- else:
- range_term = np.exp(-(depth_patch-patch_midpt)**2 / (2. * sigma_r**2))
- coef = spatial_term * range_term
- if coef.max() == 0:
- output[pi, pj] = patch_midpt
- continue
- if discontinuity_map is not None and (coef.max() == 0):
- output[pi, pj] = patch_midpt
- else:
- coef = coef/(coef.sum())
- coef_order = coef.ravel()[depth_order]
- cum_coef = np.cumsum(coef_order)
- ind = np.digitize(0.5, cum_coef)
- output[pi, pj] = depth_patch.ravel()[depth_order][ind]
- else:
- pH, pW = pad_depth_patches.shape[:2]
- for pi in range(pH):
- for pj in range(pW):
- if discontinuity_map is not None:
- if pad_discontinuity_patches[pi, pj][window_size//2, window_size//2] == 1:
- continue
- discontinuity_patch = pad_discontinuity_patches[pi, pj]
- discontinuity_holes = (1. - discontinuity_patch)
- depth_patch = pad_depth_patches[pi, pj]
- depth_order = depth_patch.ravel().argsort()
- patch_midpt = depth_patch[window_size//2, window_size//2]
- range_term = np.exp(-(depth_patch-patch_midpt)**2 / (2. * sigma_r**2))
- if discontinuity_map is not None:
- coef = spatial_term * range_term * discontinuity_holes
- else:
- coef = spatial_term * range_term
- if coef.sum() == 0:
- output[pi, pj] = patch_midpt
- continue
- if discontinuity_map is not None and (coef.sum() == 0):
- output[pi, pj] = patch_midpt
- else:
- coef = coef/(coef.sum())
- coef_order = coef.ravel()[depth_order]
- cum_coef = np.cumsum(coef_order)
- ind = np.digitize(0.5, cum_coef)
- output[pi, pj] = depth_patch.ravel()[depth_order][ind]
-
- return output
-
-def rolling_window(a, window, strides):
- assert len(a.shape)==len(window)==len(strides), "\'a\', \'window\', \'strides\' dimension mismatch"
- shape_fn = lambda i,w,s: (a.shape[i]-w)//s + 1
- shape = [shape_fn(i,w,s) for i,(w,s) in enumerate(zip(window, strides))] + list(window)
- def acc_shape(i):
- if i+1>=len(a.shape):
- return 1
- else:
- return reduce(lambda x,y:x*y, a.shape[i+1:])
- _strides = [acc_shape(i)*s*a.itemsize for i,s in enumerate(strides)] + list(a.strides)
-
- return np.lib.stride_tricks.as_strided(a, shape=shape, strides=_strides)
diff --git a/spaces/Juli08/janitorai/Dockerfile b/spaces/Juli08/janitorai/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/Juli08/janitorai/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/deep_sort/sort/detection.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/deep_sort/sort/detection.py
deleted file mode 100644
index dbdbc8b525747ffc2bd494f8ab0e93c035730ce7..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/deep_sort/sort/detection.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-
-
-class Detection(object):
- """
- This class represents a bounding box detection in a single image.
-
- Parameters
- ----------
- tlwh : array_like
- Bounding box in format `(top left x, top left y, width, height)`.
- confidence : float
- Detector confidence score.
- feature : array_like
- A feature vector that describes the object contained in this image.
-
- Attributes
- ----------
- tlwh : ndarray
- Bounding box in format `(top left x, top left y, width, height)`.
- confidence : ndarray
- Detector confidence score.
- feature : ndarray | NoneType
- A feature vector that describes the object contained in this image.
-
- """
-
- def __init__(self, tlwh, confidence, feature):
- self.tlwh = np.asarray(tlwh, dtype=np.float32)
- self.confidence = float(confidence)
- self.feature = np.asarray(feature, dtype=np.float32)
-
- def to_tlbr(self):
- """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
- `(top left, bottom right)`.
- """
- ret = self.tlwh.copy()
- ret[2:] += ret[:2]
- return ret
-
- def to_xyah(self):
- """Convert bounding box to format `(center x, center y, aspect ratio,
- height)`, where the aspect ratio is `width / height`.
- """
- ret = self.tlwh.copy()
- ret[:2] += ret[2:] / 2
- ret[2] /= ret[3]
- return ret
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/atss_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/atss_head.py
deleted file mode 100644
index 2e702547f3a40f97af067d2493a41a63665c0866..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/atss_head.py
+++ /dev/null
@@ -1,524 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Optional, Sequence, Tuple
-
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig, OptConfigType,
- OptInstanceList, reduce_mean)
-from ..task_modules.prior_generators import anchor_inside_flags
-from ..utils import images_to_levels, multi_apply, unmap
-from .anchor_head import AnchorHead
-
-
-@MODELS.register_module()
-class ATSSHead(AnchorHead):
- """Detection Head of `ATSS `_.
-
- ATSS head structure is similar with FCOS, however ATSS use anchor boxes
- and assign label by Adaptive Training Sample Selection instead max-iou.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- pred_kernel_size (int): Kernel size of ``nn.Conv2d``
- stacked_convs (int): Number of stacking convs of the head.
- conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
- convolution layer. Defaults to None.
- norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
- layer. Defaults to ``dict(type='GN', num_groups=32,
- requires_grad=True)``.
- reg_decoded_bbox (bool): If true, the regression loss would be
- applied directly on decoded bounding boxes, converting both
- the predicted boxes and regression targets to absolute
- coordinates format. Defaults to False. It should be `True` when
- using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
- loss_centerness (:obj:`ConfigDict` or dict): Config of centerness loss.
- Defaults to ``dict(type='CrossEntropyLoss', use_sigmoid=True,
- loss_weight=1.0)``.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
- """
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- pred_kernel_size: int = 3,
- stacked_convs: int = 4,
- conv_cfg: OptConfigType = None,
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- reg_decoded_bbox: bool = True,
- loss_centerness: ConfigType = dict(
- type='mmdet.CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='atss_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- self.pred_kernel_size = pred_kernel_size
- self.stacked_convs = stacked_convs
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- super().__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- reg_decoded_bbox=reg_decoded_bbox,
- init_cfg=init_cfg,
- **kwargs)
-
- self.sampling = False
- self.loss_centerness = MODELS.build(loss_centerness)
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- self.relu = nn.ReLU(inplace=True)
- self.cls_convs = nn.ModuleList()
- self.reg_convs = nn.ModuleList()
- for i in range(self.stacked_convs):
- chn = self.in_channels if i == 0 else self.feat_channels
- self.cls_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- self.reg_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- pred_pad_size = self.pred_kernel_size // 2
- self.atss_cls = nn.Conv2d(
- self.feat_channels,
- self.num_anchors * self.cls_out_channels,
- self.pred_kernel_size,
- padding=pred_pad_size)
- self.atss_reg = nn.Conv2d(
- self.feat_channels,
- self.num_base_priors * 4,
- self.pred_kernel_size,
- padding=pred_pad_size)
- self.atss_centerness = nn.Conv2d(
- self.feat_channels,
- self.num_base_priors * 1,
- self.pred_kernel_size,
- padding=pred_pad_size)
- self.scales = nn.ModuleList(
- [Scale(1.0) for _ in self.prior_generator.strides])
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple: Usually a tuple of classification scores and bbox prediction
- cls_scores (list[Tensor]): Classification scores for all scale
- levels, each is a 4D-tensor, the channels number is
- num_anchors * num_classes.
- bbox_preds (list[Tensor]): Box energies / deltas for all scale
- levels, each is a 4D-tensor, the channels number is
- num_anchors * 4.
- """
- return multi_apply(self.forward_single, x, self.scales)
-
- def forward_single(self, x: Tensor, scale: Scale) -> Sequence[Tensor]:
- """Forward feature of a single scale level.
-
- Args:
- x (Tensor): Features of a single scale level.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
-
- Returns:
- tuple:
- cls_score (Tensor): Cls scores for a single scale level
- the channels number is num_anchors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for a single scale
- level, the channels number is num_anchors * 4.
- centerness (Tensor): Centerness for a single scale level, the
- channel number is (N, num_anchors * 1, H, W).
- """
- cls_feat = x
- reg_feat = x
- for cls_conv in self.cls_convs:
- cls_feat = cls_conv(cls_feat)
- for reg_conv in self.reg_convs:
- reg_feat = reg_conv(reg_feat)
- cls_score = self.atss_cls(cls_feat)
- # we just follow atss, not apply exp in bbox_pred
- bbox_pred = scale(self.atss_reg(reg_feat)).float()
- centerness = self.atss_centerness(reg_feat)
- return cls_score, bbox_pred, centerness
-
- def loss_by_feat_single(self, anchors: Tensor, cls_score: Tensor,
- bbox_pred: Tensor, centerness: Tensor,
- labels: Tensor, label_weights: Tensor,
- bbox_targets: Tensor, avg_factor: float) -> dict:
- """Calculate the loss of a single scale level based on the features
- extracted by the detection head.
-
- Args:
- cls_score (Tensor): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W).
- bbox_pred (Tensor): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W).
- anchors (Tensor): Box reference for each scale level with shape
- (N, num_total_anchors, 4).
- labels (Tensor): Labels of each anchors with shape
- (N, num_total_anchors).
- label_weights (Tensor): Label weights of each anchor with shape
- (N, num_total_anchors)
- bbox_targets (Tensor): BBox regression targets of each anchor
- weight shape (N, num_total_anchors, 4).
- avg_factor (float): Average factor that is used to average
- the loss. When using sampling method, avg_factor is usually
- the sum of positive and negative priors. When using
- `PseudoSampler`, `avg_factor` is usually equal to the number
- of positive priors.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
-
- anchors = anchors.reshape(-1, 4)
- cls_score = cls_score.permute(0, 2, 3, 1).reshape(
- -1, self.cls_out_channels).contiguous()
- bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
- centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
- bbox_targets = bbox_targets.reshape(-1, 4)
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
-
- # classification loss
- loss_cls = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=avg_factor)
-
- # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = ((labels >= 0)
- & (labels < bg_class_ind)).nonzero().squeeze(1)
-
- if len(pos_inds) > 0:
- pos_bbox_targets = bbox_targets[pos_inds]
- pos_bbox_pred = bbox_pred[pos_inds]
- pos_anchors = anchors[pos_inds]
- pos_centerness = centerness[pos_inds]
-
- centerness_targets = self.centerness_target(
- pos_anchors, pos_bbox_targets)
- pos_decode_bbox_pred = self.bbox_coder.decode(
- pos_anchors, pos_bbox_pred)
-
- # regression loss
- loss_bbox = self.loss_bbox(
- pos_decode_bbox_pred,
- pos_bbox_targets,
- weight=centerness_targets,
- avg_factor=1.0)
-
- # centerness loss
- loss_centerness = self.loss_centerness(
- pos_centerness, centerness_targets, avg_factor=avg_factor)
-
- else:
- loss_bbox = bbox_pred.sum() * 0
- loss_centerness = centerness.sum() * 0
- centerness_targets = bbox_targets.new_tensor(0.)
-
- return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- centernesses: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Calculate the loss based on the features extracted by the detection
- head.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- centernesses (list[Tensor]): Centerness for each scale
- level with shape (N, num_anchors * 1, H, W)
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.prior_generator.num_levels
-
- device = cls_scores[0].device
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
- avg_factor = reduce_mean(
- torch.tensor(avg_factor, dtype=torch.float, device=device)).item()
-
- losses_cls, losses_bbox, loss_centerness, \
- bbox_avg_factor = multi_apply(
- self.loss_by_feat_single,
- anchor_list,
- cls_scores,
- bbox_preds,
- centernesses,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- avg_factor=avg_factor)
-
- bbox_avg_factor = sum(bbox_avg_factor)
- bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item()
- losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
- return dict(
- loss_cls=losses_cls,
- loss_bbox=losses_bbox,
- loss_centerness=loss_centerness)
-
- def centerness_target(self, anchors: Tensor, gts: Tensor) -> Tensor:
- """Calculate the centerness between anchors and gts.
-
- Only calculate pos centerness targets, otherwise there may be nan.
-
- Args:
- anchors (Tensor): Anchors with shape (N, 4), "xyxy" format.
- gts (Tensor): Ground truth bboxes with shape (N, 4), "xyxy" format.
-
- Returns:
- Tensor: Centerness between anchors and gts.
- """
- anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
- anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
- l_ = anchors_cx - gts[:, 0]
- t_ = anchors_cy - gts[:, 1]
- r_ = gts[:, 2] - anchors_cx
- b_ = gts[:, 3] - anchors_cy
-
- left_right = torch.stack([l_, r_], dim=1)
- top_bottom = torch.stack([t_, b_], dim=1)
- centerness = torch.sqrt(
- (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) *
- (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]))
- assert not torch.isnan(centerness).any()
- return centerness
-
- def get_targets(self,
- anchor_list: List[List[Tensor]],
- valid_flag_list: List[List[Tensor]],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None,
- unmap_outputs: bool = True) -> tuple:
- """Get targets for ATSS head.
-
- This method is almost the same as `AnchorHead.get_targets()`. Besides
- returning the targets as the parent method does, it also returns the
- anchors as the first element of the returned tuple.
- """
- num_imgs = len(batch_img_metas)
- assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
- num_level_anchors_list = [num_level_anchors] * num_imgs
-
- # concat all level anchors and flags to a single tensor
- for i in range(num_imgs):
- assert len(anchor_list[i]) == len(valid_flag_list[i])
- anchor_list[i] = torch.cat(anchor_list[i])
- valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
- # compute targets for each image
- if batch_gt_instances_ignore is None:
- batch_gt_instances_ignore = [None] * num_imgs
- (all_anchors, all_labels, all_label_weights, all_bbox_targets,
- all_bbox_weights, pos_inds_list, neg_inds_list,
- sampling_results_list) = multi_apply(
- self._get_targets_single,
- anchor_list,
- valid_flag_list,
- num_level_anchors_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=unmap_outputs)
- # Get `avg_factor` of all images, which calculate in `SamplingResult`.
- # When using sampling method, avg_factor is usually the sum of
- # positive and negative priors. When using `PseudoSampler`,
- # `avg_factor` is usually equal to the number of positive priors.
- avg_factor = sum(
- [results.avg_factor for results in sampling_results_list])
- # split targets to a list w.r.t. multiple levels
- anchors_list = images_to_levels(all_anchors, num_level_anchors)
- labels_list = images_to_levels(all_labels, num_level_anchors)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_anchors)
- bbox_targets_list = images_to_levels(all_bbox_targets,
- num_level_anchors)
- bbox_weights_list = images_to_levels(all_bbox_weights,
- num_level_anchors)
- return (anchors_list, labels_list, label_weights_list,
- bbox_targets_list, bbox_weights_list, avg_factor)
-
- def _get_targets_single(self,
- flat_anchors: Tensor,
- valid_flags: Tensor,
- num_level_anchors: List[int],
- gt_instances: InstanceData,
- img_meta: dict,
- gt_instances_ignore: Optional[InstanceData] = None,
- unmap_outputs: bool = True) -> tuple:
- """Compute regression, classification targets for anchors in a single
- image.
-
- Args:
- flat_anchors (Tensor): Multi-level anchors of the image, which are
- concatenated into a single tensor of shape (num_anchors ,4)
- valid_flags (Tensor): Multi level valid flags of the image,
- which are concatenated into a single tensor of
- shape (num_anchors,).
- num_level_anchors (List[int]): Number of anchors of each scale
- level.
- gt_instances (:obj:`InstanceData`): Ground truth of instance
- annotations. It usually includes ``bboxes`` and ``labels``
- attributes.
- img_meta (dict): Meta information for current image.
- gt_instances_ignore (:obj:`InstanceData`, optional): Instances
- to be ignored during training. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
- unmap_outputs (bool): Whether to map outputs back to the original
- set of anchors.
-
- Returns:
- tuple: N is the number of total anchors in the image.
- labels (Tensor): Labels of all anchors in the image with shape
- (N,).
- label_weights (Tensor): Label weights of all anchor in the
- image with shape (N,).
- bbox_targets (Tensor): BBox targets of all anchors in the
- image with shape (N, 4).
- bbox_weights (Tensor): BBox weights of all anchors in the
- image with shape (N, 4)
- pos_inds (Tensor): Indices of positive anchor with shape
- (num_pos,).
- neg_inds (Tensor): Indices of negative anchor with shape
- (num_neg,).
- sampling_result (:obj:`SamplingResult`): Sampling results.
- """
- inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
- img_meta['img_shape'][:2],
- self.train_cfg['allowed_border'])
- if not inside_flags.any():
- raise ValueError(
- 'There is no valid anchor inside the image boundary. Please '
- 'check the image size and anchor sizes, or set '
- '``allowed_border`` to -1 to skip the condition.')
- # assign gt and sample anchors
- anchors = flat_anchors[inside_flags, :]
-
- num_level_anchors_inside = self.get_num_level_anchors_inside(
- num_level_anchors, inside_flags)
- pred_instances = InstanceData(priors=anchors)
- assign_result = self.assigner.assign(pred_instances,
- num_level_anchors_inside,
- gt_instances, gt_instances_ignore)
-
- sampling_result = self.sampler.sample(assign_result, pred_instances,
- gt_instances)
-
- num_valid_anchors = anchors.shape[0]
- bbox_targets = torch.zeros_like(anchors)
- bbox_weights = torch.zeros_like(anchors)
- labels = anchors.new_full((num_valid_anchors, ),
- self.num_classes,
- dtype=torch.long)
- label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- if self.reg_decoded_bbox:
- pos_bbox_targets = sampling_result.pos_gt_bboxes
- else:
- pos_bbox_targets = self.bbox_coder.encode(
- sampling_result.pos_priors, sampling_result.pos_gt_bboxes)
-
- bbox_targets[pos_inds, :] = pos_bbox_targets
- bbox_weights[pos_inds, :] = 1.0
-
- labels[pos_inds] = sampling_result.pos_gt_labels
- if self.train_cfg['pos_weight'] <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = self.train_cfg['pos_weight']
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- # map up to original set of anchors
- if unmap_outputs:
- num_total_anchors = flat_anchors.size(0)
- anchors = unmap(anchors, num_total_anchors, inside_flags)
- labels = unmap(
- labels, num_total_anchors, inside_flags, fill=self.num_classes)
- label_weights = unmap(label_weights, num_total_anchors,
- inside_flags)
- bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
- bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
- return (anchors, labels, label_weights, bbox_targets, bbox_weights,
- pos_inds, neg_inds, sampling_result)
-
- def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
- """Get the number of valid anchors in every level."""
-
- split_inside_flags = torch.split(inside_flags, num_level_anchors)
- num_level_anchors_inside = [
- int(flags.sum()) for flags in split_inside_flags
- ]
- return num_level_anchors_inside
diff --git a/spaces/KyanChen/RSPrompter/mmdet/registry.py b/spaces/KyanChen/RSPrompter/mmdet/registry.py
deleted file mode 100644
index 3a5b2b28a4f80a488994b48a99043a20c604e55e..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/registry.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-"""MMDetection provides 17 registry nodes to support using modules across
-projects. Each node is a child of the root registry in MMEngine.
-
-More details can be found at
-https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
-"""
-
-from mmengine.registry import DATA_SAMPLERS as MMENGINE_DATA_SAMPLERS
-from mmengine.registry import DATASETS as MMENGINE_DATASETS
-from mmengine.registry import EVALUATOR as MMENGINE_EVALUATOR
-from mmengine.registry import HOOKS as MMENGINE_HOOKS
-from mmengine.registry import LOG_PROCESSORS as MMENGINE_LOG_PROCESSORS
-from mmengine.registry import LOOPS as MMENGINE_LOOPS
-from mmengine.registry import METRICS as MMENGINE_METRICS
-from mmengine.registry import MODEL_WRAPPERS as MMENGINE_MODEL_WRAPPERS
-from mmengine.registry import MODELS as MMENGINE_MODELS
-from mmengine.registry import \
- OPTIM_WRAPPER_CONSTRUCTORS as MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS
-from mmengine.registry import OPTIM_WRAPPERS as MMENGINE_OPTIM_WRAPPERS
-from mmengine.registry import OPTIMIZERS as MMENGINE_OPTIMIZERS
-from mmengine.registry import PARAM_SCHEDULERS as MMENGINE_PARAM_SCHEDULERS
-from mmengine.registry import \
- RUNNER_CONSTRUCTORS as MMENGINE_RUNNER_CONSTRUCTORS
-from mmengine.registry import RUNNERS as MMENGINE_RUNNERS
-from mmengine.registry import TASK_UTILS as MMENGINE_TASK_UTILS
-from mmengine.registry import TRANSFORMS as MMENGINE_TRANSFORMS
-from mmengine.registry import VISBACKENDS as MMENGINE_VISBACKENDS
-from mmengine.registry import VISUALIZERS as MMENGINE_VISUALIZERS
-from mmengine.registry import \
- WEIGHT_INITIALIZERS as MMENGINE_WEIGHT_INITIALIZERS
-from mmengine.registry import Registry
-
-# manage all kinds of runners like `EpochBasedRunner` and `IterBasedRunner`
-RUNNERS = Registry(
- 'runner', parent=MMENGINE_RUNNERS, locations=['mmdet.engine.runner'])
-# manage runner constructors that define how to initialize runners
-RUNNER_CONSTRUCTORS = Registry(
- 'runner constructor',
- parent=MMENGINE_RUNNER_CONSTRUCTORS,
- locations=['mmdet.engine.runner'])
-# manage all kinds of loops like `EpochBasedTrainLoop`
-LOOPS = Registry(
- 'loop', parent=MMENGINE_LOOPS, locations=['mmdet.engine.runner'])
-# manage all kinds of hooks like `CheckpointHook`
-HOOKS = Registry(
- 'hook', parent=MMENGINE_HOOKS, locations=['mmdet.engine.hooks'])
-
-# manage data-related modules
-DATASETS = Registry(
- 'dataset', parent=MMENGINE_DATASETS, locations=['mmdet.datasets'])
-DATA_SAMPLERS = Registry(
- 'data sampler',
- parent=MMENGINE_DATA_SAMPLERS,
- locations=['mmdet.datasets.samplers'])
-TRANSFORMS = Registry(
- 'transform',
- parent=MMENGINE_TRANSFORMS,
- locations=['mmdet.datasets.transforms'])
-
-# manage all kinds of modules inheriting `nn.Module`
-MODELS = Registry('model', parent=MMENGINE_MODELS, locations=['mmdet.models'])
-# manage all kinds of model wrappers like 'MMDistributedDataParallel'
-MODEL_WRAPPERS = Registry(
- 'model_wrapper',
- parent=MMENGINE_MODEL_WRAPPERS,
- locations=['mmdet.models'])
-# manage all kinds of weight initialization modules like `Uniform`
-WEIGHT_INITIALIZERS = Registry(
- 'weight initializer',
- parent=MMENGINE_WEIGHT_INITIALIZERS,
- locations=['mmdet.models'])
-
-# manage all kinds of optimizers like `SGD` and `Adam`
-OPTIMIZERS = Registry(
- 'optimizer',
- parent=MMENGINE_OPTIMIZERS,
- locations=['mmdet.engine.optimizers'])
-# manage optimizer wrapper
-OPTIM_WRAPPERS = Registry(
- 'optim_wrapper',
- parent=MMENGINE_OPTIM_WRAPPERS,
- locations=['mmdet.engine.optimizers'])
-# manage constructors that customize the optimization hyperparameters.
-OPTIM_WRAPPER_CONSTRUCTORS = Registry(
- 'optimizer constructor',
- parent=MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS,
- locations=['mmdet.engine.optimizers'])
-# manage all kinds of parameter schedulers like `MultiStepLR`
-PARAM_SCHEDULERS = Registry(
- 'parameter scheduler',
- parent=MMENGINE_PARAM_SCHEDULERS,
- locations=['mmdet.engine.schedulers'])
-# manage all kinds of metrics
-METRICS = Registry(
- 'metric', parent=MMENGINE_METRICS, locations=['mmdet.evaluation'])
-# manage evaluator
-EVALUATOR = Registry(
- 'evaluator', parent=MMENGINE_EVALUATOR, locations=['mmdet.evaluation'])
-
-# manage task-specific modules like anchor generators and box coders
-TASK_UTILS = Registry(
- 'task util', parent=MMENGINE_TASK_UTILS, locations=['mmdet.models'])
-
-# manage visualizer
-VISUALIZERS = Registry(
- 'visualizer',
- parent=MMENGINE_VISUALIZERS,
- locations=['mmdet.visualization'])
-# manage visualizer backend
-VISBACKENDS = Registry(
- 'vis_backend',
- parent=MMENGINE_VISBACKENDS,
- locations=['mmdet.visualization'])
-
-# manage logprocessor
-LOG_PROCESSORS = Registry(
- 'log_processor',
- parent=MMENGINE_LOG_PROCESSORS,
- # TODO: update the location when mmdet has its own log processor
- locations=['mmdet.engine'])
diff --git a/spaces/Laihiujin/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/Laihiujin/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/Laihiujin/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/Laihiujin/OneFormer/oneformer/modeling/pixel_decoder/__init__.py b/spaces/Laihiujin/OneFormer/oneformer/modeling/pixel_decoder/__init__.py
deleted file mode 100644
index 9020c2df23e2af280b7bb168b996ae9eaf312eb8..0000000000000000000000000000000000000000
--- a/spaces/Laihiujin/OneFormer/oneformer/modeling/pixel_decoder/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/spaces/Lavanya30/hiddenhunger/pages/plant.py b/spaces/Lavanya30/hiddenhunger/pages/plant.py
deleted file mode 100644
index 815705242aca65f471c6134b2a8dc288fec98046..0000000000000000000000000000000000000000
--- a/spaces/Lavanya30/hiddenhunger/pages/plant.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import cv2
-import numpy as np
-import streamlit as st
-import tensorflow as tf
-import requests
-from streamlit_lottie import st_lottie
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2,preprocess_input as mobilenet_v2_preprocess_input
-st.header("Hidden Hunger in plants")
-st.write("To find micronutrient deficiency of banana leaf")
-
-st.markdown("""
-
-""", unsafe_allow_html=True)
-uploaded_file = st.file_uploader("Choose a image file", type="jpg")
-model = tf.keras.models.load_model(r"models/resnet152v2.h5")
-map_dict = {0: 'Boron deficiency',
- 1: 'Healthy',
- 2: 'Iron deficiency',
- 3: 'Manganese deficiency',
- 4: 'Zinc deficiency'}
-
-
-
-if uploaded_file is not None:
- # Convert the file to an opencv image.
- file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
- opencv_image = cv2.imdecode(file_bytes, 1)
- opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)
- resized = cv2.resize(opencv_image,(224,224))
- # Now do something with the image! For example, let's display it:
- st.image(opencv_image, channels="RGB")
-
- resized = mobilenet_v2_preprocess_input(resized)
- img_reshape = resized[np.newaxis,...]
-
- Genrate_pred = st.button("Generate Prediction")
- if Genrate_pred:
- prediction = model.predict(img_reshape).argmax()
- st.title("Predicted Label for the image is {}".format(map_dict [prediction]))
-
diff --git a/spaces/Lerdweg/Energie-NRW/app.py b/spaces/Lerdweg/Energie-NRW/app.py
deleted file mode 100644
index 765f2b36bc251bf3d747bb3aed7201256aa1588d..0000000000000000000000000000000000000000
--- a/spaces/Lerdweg/Energie-NRW/app.py
+++ /dev/null
@@ -1,914 +0,0 @@
-import os
-from supabase import Client, create_client
-from langchain import OpenAI, LLMCheckerChain
-from dotenv import find_dotenv, load_dotenv
-from langchain.chat_models import ChatOpenAI
-from langchain.agents.agent_types import AgentType
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.agents import AgentExecutor
-from langchain.chat_models import ChatOpenAI
-import pandas as pd
-import time
-import numpy as np
-import matplotlib.pyplot as plt
-import plotly.express as px
-import geopandas as gpd
-import folium
-import streamlit as st
-from streamlit_folium import st_folium
-import time
-import geopandas as gpd
-from sqlalchemy import create_engine
-
-import openai
-from langchain.vectorstores import SupabaseVectorStore
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
-from langchain.schema import SystemMessage
-from langchain.chains.conversation.memory import ConversationBufferMemory
-from langchain import OpenAI, LLMChain
-
-load_dotenv(find_dotenv())
-embeddings = OpenAIEmbeddings()
-
-#openai.api_key = os.getenv('OPENAI_API_KEY')
-os.environ["VERBOSE"] = "True"
-openai.api_key = os.getenv('OPENAI_API_KEY')
-SUPABASE_URL = os.getenv('SUPABASE_URL')
-SUPABASE_KEY = os.getenv('SUPABASE_KEY')
-supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
-
-# Set Streamlit page config
-st.set_page_config(
- page_title="Energieversorgung der Gemeinden - Status Quo und erneuerbares Potential",
- page_icon="🌍",
- layout="wide"
-)
-
-# Select tabel from supabase
-
-ee_data = supabase.table('energie-nrw').select("*").execute()
-
-# Create dataframe based on table data
-df_ee_data = pd.DataFrame(ee_data.data)
-
-# Get the list of unique values from the 'gemeinde' column
-gemeinde_list = df_ee_data['gemeinde'].unique()
-@st.cache_data
-def table_status_quo(gemeinde):
- # Filter the dataframe for the selected gemeinde
- filtered_df = df_ee_data.loc[df_ee_data['gemeinde'] == gemeinde].copy()
-
- # Calculate the sum of 'waermebedarf-[GWh/a]' and 'stromverbrauch-[GWh/a]'
- gesamt = filtered_df['waermebedarf-[GWh/a]'] + filtered_df['stromverbrauch-[GWh/a]']
-
- # Create a new dataframe with the required columns and first row
- table_status_quo = pd.DataFrame({
- "Energieverbrauch": ["Aktueller Verbrauch"],
- "Strom [GWh/a]": [filtered_df['stromverbrauch-[GWh/a]']],
- "Wärme [GWh/a]": [filtered_df['waermebedarf-[GWh/a]']],
- "Gesamt [GWh/a]": [gesamt]
- })
-
- # Calculate the sum of all renewable energy generated for each "Strom" and "Wärme"
- sum_renewable_strom = sum([
- filtered_df["strombereitstellung-biomasse-[GWh/a]"],
- filtered_df["strombereitstellung-deponiegas-[GWh/a]"],
- filtered_df["strombereitstellung-grubengas-[GWh/a]"],
- filtered_df["strombereitstellung-klaergas-[GWh/a]"],
- filtered_df["strombereitstellung-pv-freiflaechen-[GWh/a]"],
- filtered_df["strombereitstellung-pv-dachflaechen-[GWh/a]"],
- filtered_df["strombereitstellung-wasserkraft-[GWh/a]"],
- filtered_df["strombereitstellung-wind-[GWh/a]"],
- filtered_df["strombereitstellung-mva-[GWh/a]"]
- ])
-
- sum_renewable_wärme = sum([
- filtered_df["waermebereitstellung-biomasse-[GWh/a]"],
- filtered_df["waermebereitstellung-deponiegas-[GWh/a]"],
- filtered_df["waermebereitstellung-grubengas-[GWh/a]"],
- filtered_df["waermebereitstellung-klaergas-[GWh/a]"],
- filtered_df["waermebereitstellung-solarthermie-[GWh/a]"],
- filtered_df["waermebereitstellung-geothermie-[GWh/a]"],
- filtered_df["waermebereitstellung-grubenwasser-[GWh/a]"],
- filtered_df["waermebereitstellung-abwaerme-industrie-[GWh/a]"]
- ])
-
- # Calculate the sum of fossil based energy generated for "Strom" and "Wärme"
- sum_fossil_strom = filtered_df['stromverbrauch-[GWh/a]'] - sum_renewable_strom
- sum_fossil_wärme = filtered_df['waermebedarf-[GWh/a]'] - sum_renewable_wärme
-
- # Calculate the percentage of each of them from the total value
- renewable_strom_percentage = (sum_renewable_strom / filtered_df['stromverbrauch-[GWh/a]']) * 100
- fossil_strom_percentage = (sum_fossil_strom / filtered_df['stromverbrauch-[GWh/a]']) * 100
- # Check if fossil_strom_percentage is less than 0, if so, set it to zero
- if fossil_strom_percentage.values[0] < 0:
- fossil_strom_percentage.values[0] = 0
- renewable_wärme_percentage = (sum_renewable_wärme / filtered_df['waermebedarf-[GWh/a]']) * 100
- fossil_wärme_percentage = (sum_fossil_wärme / filtered_df['waermebedarf-[GWh/a]']) * 100
- # If fossil_wärme_percentage is negative, set it to 0
- if fossil_wärme_percentage.values[0] < 0:
- fossil_wärme_percentage.values[0] = 0
- # Calculate the percentage for "Gesamt" Strom and Wärme
- gesamt_renewable_percentage = ((sum_renewable_strom + sum_renewable_wärme) / gesamt) * 100
- gesamt_fossil_percentage = ((sum_fossil_strom + sum_fossil_wärme) / gesamt) * 100
-
- # Create a list of calculated percentages
- percentages = [
- renewable_strom_percentage.values[0],
- fossil_strom_percentage.values[0],
- renewable_wärme_percentage.values[0],
- fossil_wärme_percentage.values[0],
- gesamt_renewable_percentage.values[0],
- gesamt_fossil_percentage.values[0]
- ]
- # Create a new DataFrame and append the rows
- df_status_quo = pd.DataFrame({
- "Energieverbrauch": ["Aktueller Verbrauch - [GWh/a]",
- "THG-Emissionen - [tCO2e]",
- "Anteil erneuerbarer Energien - [%]",
- "Anteil konventioneller Energieträger - [%]"],
- "Strom": [round(filtered_df['stromverbrauch-[GWh/a]'].values[0], 1),
- round(filtered_df['gesamt-strom-bestand-thg-[tCO2e]'].values[0], 1),
- round(renewable_strom_percentage.values[0], 1),
- round(fossil_strom_percentage.values[0], 1)],
- "Wärme": [round(filtered_df['waermebedarf-[GWh/a]'].values[0], 1),
- round(filtered_df['gesamt-waerme-bestand-thg-[tCO2e]'].values[0], 1),
- round(renewable_wärme_percentage.values[0], 1),
- round(fossil_wärme_percentage.values[0], 1)],
- "Gesamt": [round(gesamt.values[0], 1),
- round(filtered_df['gesamt-thg-[tCO2e]'].values[0], 1),
- round(gesamt_renewable_percentage.values[0], 1),
- round(gesamt_fossil_percentage.values[0], 1)]
- })
-
- return df_status_quo, percentages
-
-@st.cache_resource
-def pie_status_quo(percentages):
- # Format the percentage values
- renewable_strom_percentage = percentages[0]
- fossil_strom_percentage = percentages[1]
- renewable_wärme_percentage = percentages[2]
- fossil_wärme_percentage = percentages[3]
- gesamt_renewable_percentage = percentages[4]
- gesamt_fossil_percentage = percentages[5]
-
-# Create a DataFrame for pie chart
- pie_data = {
- "Energieverbrauch": ["erneuerbar", "konventionell"],
- "Percentage": [renewable_strom_percentage, fossil_strom_percentage]
- }
- df_pie_strom = pd.DataFrame(pie_data)
-
- pie_data = {
- "Energieverbrauch": ["erneuerbar", "konventionell"],
- "Percentage": [renewable_wärme_percentage, fossil_wärme_percentage]
- }
- df_pie_wärme = pd.DataFrame(pie_data)
-
- pie_data = {
- "Energieverbrauch": ["erneuerbar", "konventionell"],
- "Percentage": [gesamt_renewable_percentage, gesamt_fossil_percentage]
- }
- df_pie_gesamt = pd.DataFrame(pie_data)
-
- # Define custom color palette
- custom_colors = ['#E57373', '#81C784'] # Red for erneuerbar, Green for konventionell
-
- # Create pie chart for Strom
- pie_chart_strom_status_quo = px.pie(
- df_pie_strom,
- values="Percentage",
- names="Energieverbrauch",
- labels={"Percentage"},
- title="Strom",
- height=330,
- #color="Energieverbrauch", # Use the Energieverbrauch column for color coding
- #color_discrete_map=custom_colors # Map colors to specific values
- color_discrete_sequence=custom_colors # Apply custom color palette
- )
-
- # Create pie chart for Wärme
- pie_chart_waerme_status_quo = px.pie(
- df_pie_wärme,
- values="Percentage",
- names="Energieverbrauch",
- labels={"Percentage"},
- title="Wärme",
- height=330,
- color_discrete_sequence=custom_colors # Apply custom color palette
- )
-
- # Create pie chart for Gesamt
- pie_chart_gesamt_status_quo = px.pie(
- df_pie_gesamt,
- values="Percentage",
- names="Energieverbrauch",
- labels={"Percentage"},
- title="Gesamt",
- height=330,
- color_discrete_sequence=custom_colors # Apply custom color palette
- )
-
- return pie_chart_strom_status_quo, pie_chart_waerme_status_quo, pie_chart_gesamt_status_quo
-
-@st.cache_data
-def energie_bestand(gemeinde):
- filtered_df_bestand = df_ee_data.loc[df_ee_data['gemeinde'] == gemeinde].copy()
- # Round the numeric columns to 1 decimal place
- numeric_columns = filtered_df_bestand.select_dtypes(include='number').columns
- filtered_df_bestand[numeric_columns] = filtered_df_bestand[numeric_columns].round(1)
-
- data = []
-
- # Iterate through the rows of the filtered DataFrame
- for index, row in filtered_df_bestand.iterrows():
- # Add row for "Wind" with Strom and Wärme values
- data.append({"Energieträger": "Biomasse", "Strom [GWh/a]": row["strombereitstellung-biomasse-[GWh/a]"]})
- data.append({"Energieträger": "Deponiegas", "Strom [GWh/a]": row["strombereitstellung-deponiegas-[GWh/a]"]})
- data.append({"Energieträger": "Grubengas", "Strom [GWh/a]": row["strombereitstellung-grubengas-[GWh/a]"]})
- data.append({"Energieträger": "Klärgas", "Strom [GWh/a]": row["strombereitstellung-klaergas-[GWh/a]"]})
- data.append({"Energieträger": "Solar-Freiflächen", "Strom [GWh/a]": row["strombereitstellung-pv-freiflaechen-[GWh/a]"]})
- data.append({"Energieträger": "Solar-Dachflächen", "Strom [GWh/a]": row["strombereitstellung-pv-dachflaechen-[GWh/a]"]})
- data.append({"Energieträger": "Wasserkraft", "Strom [GWh/a]": row["strombereitstellung-wasserkraft-[GWh/a]"]})
- data.append({"Energieträger": "Wind", "Strom [GWh/a]": row["strombereitstellung-wind-[GWh/a]"]})
- data.append({"Energieträger": "Müllverbrennung", "Strom [GWh/a]": row["strombereitstellung-mva-[GWh/a]"]})
- data.append({"Energieträger": "Steinkohle", "Strom [GWh/a]": row["strombereitstellung-steinkohle-[GWh/a]"]})
- data.append({"Energieträger": "Braunkohle", "Strom [GWh/a]": row["strombereitstellung-braunkohle-[GWh/a]"]})
- data.append({"Energieträger": "Erdgas", "Strom [GWh/a]": row["strombereitstellung-erdgas-[GWh/a]"]})
- data.append({"Energieträger": "Mineralöle", "Strom [GWh/a]": row["strombereitstellung-oel-[GWh/a]"]})
- data.append({"Energieträger": "Sonstige", "Strom [GWh/a]": row["strombereitstellung-sonstige-[GWh/a]"]})
- data.append({"Energieträger": "EE-Gesamt", "Strom [GWh/a]": row["strombereitstellung-erneuerbar-gesamt-[GWh/a]"]})
- data.append({"Energieträger": "Fossil-Gesamt", "Strom [GWh/a]": row["schaetzung-strombereitstellung-konv-gesamt-[GWh/a]"]})
-
- # Create a new DataFrame using the data list
- energiemix_bestand = pd.DataFrame(data)
-
- return energiemix_bestand
-
-@st.cache_resource
-def pie_charts_bestand(gemeinde):
- energiemix_bestand = energie_bestand(gemeinde)
-
- # Exclude "EE-Gesamt" and "Fossil-Gesamt" and values that are zero
- filtered_df = energiemix_bestand[~energiemix_bestand['Energieträger'].isin(['EE-Gesamt', 'Fossil-Gesamt'])]
- filtered_df = filtered_df[filtered_df['Strom [GWh/a]'] != 0]
-
- # Calculate total for percentage calculation
- total = filtered_df['Strom [GWh/a]'].sum()
- filtered_df['Percentage'] = (filtered_df['Strom [GWh/a]'] / total) * 100
-
- # Create first pie chart with specific color spectrum for each category
- pie_bestand_all = px.pie(filtered_df, values='Percentage', names='Energieträger', title='Gesamt', color='Energieträger', color_discrete_sequence=px.colors.sequential.RdBu)
- pie_bestand_all.update_traces(texttemplate='%{value:.1f}%', textposition='inside')
-
- # Filter for second pie chart
- second_chart_energies = ['Biomasse', 'Deponiegas', 'Grubengas', 'Klärgas', 'Solar-Freiflächen', 'Solar-Dachflächen', 'Wasserkraft', 'Wind']
- filtered_df_second = filtered_df[filtered_df['Energieträger'].isin(second_chart_energies)]
- total_second = filtered_df_second['Strom [GWh/a]'].sum()
- filtered_df_second['Percentage'] = (filtered_df_second['Strom [GWh/a]'] / total_second) * 100
-
- # Create second pie chart with darker green color spectrum
- pie_bestand_ee = px.pie(filtered_df_second, values='Percentage', names='Energieträger', title='Erneuerbar', color_discrete_sequence=px.colors.sequential.Greens_r)
- pie_bestand_ee.update_traces(texttemplate='%{value:.1f}%', textposition='inside')
-
- # Filter for third pie chart
- third_chart_energies = ['Müllverbrennung', 'Steinkohle', 'Braunkohle', 'Erdgas', 'Mineralöle', 'Sonstige']
- filtered_df_third = filtered_df[filtered_df['Energieträger'].isin(third_chart_energies)]
- total_third = filtered_df_third['Strom [GWh/a]'].sum()
- filtered_df_third['Percentage'] = (filtered_df_third['Strom [GWh/a]'] / total_third) * 100
-
- # Create third pie chart with darker red color spectrum
- pie_bestand_ko = px.pie(filtered_df_third, values='Percentage', names='Energieträger', title='Konventionell', color_discrete_sequence=px.colors.sequential.Reds_r)
- pie_bestand_ko.update_traces(texttemplate='%{value:.1f}%', textposition='inside')
-
- return pie_bestand_all, pie_bestand_ee, pie_bestand_ko
-
-#gemeinde_list = [{"label": gemeinde, "value": gemeinde} for gemeinde in df_ee_data['gemeinde'].unique()]
-@st.cache_data
-def ee_potenziale(gemeinde):
-
- #selected_gemeinde = input("Enter the gemeinde to filter by: ")
- filtered_df_ee_data = df_ee_data.loc[df_ee_data['gemeinde'] == gemeinde].copy()
-
- # Round the numeric columns to 1 decimal place
- numeric_columns = filtered_df_ee_data.select_dtypes(include='number').columns
- filtered_df_ee_data[numeric_columns] = filtered_df_ee_data[numeric_columns].round(1)
-
- # Initialize an empty list to hold the data for the new DataFrame
- data = []
-
- # Iterate through the rows of the filtered DataFrame
- for index, row in filtered_df_ee_data.iterrows():
- # Add row for "Wind" with Strom and Wärme values
- data.append({"Energieträger": "Wind", "Strom [GWh/a]": row["wind-flaechenpotenzial-[GWh/a]"], "Wärme [GWh/a]": 0})
- data.append({"Energieträger": "Solar-Dachflächen", "Strom [GWh/a]": row["pv-dach-stromertragspotenzial-[GWh/a]"], "Wärme [GWh/a]": row["solarthermie-dach-warmwasser-[GWh/a]"]})
- data.append({"Energieträger": "Solar-Freiflächen", "Strom [GWh/a]": row["pv-freiflaeche-stromertragspotenzial-[GWh/a]"], "Wärme [GWh/a]": 0})
- data.append({"Energieträger": "Wasser", "Strom [GWh/a]": row["wasserkraftpotenzial-[GWh/a]"], "Wärme [GWh/a]": 0})
- data.append({"Energieträger": "Biomasse", "Strom [GWh/a]": row["biomasse-potenzial-strom-[GWh/a]"], "Wärme [GWh/a]": row["biomasse-potenzial-waerme-[GWh/a]"]})
- data.append({"Energieträger": "Fernwärme", "Strom [GWh/a]": 0, "Wärme [GWh/a]": row["ferwaerme-potenzial-2030-[GWh/a]"]})
- data.append({"Energieträger": "Industrieabwärme", "Strom [GWh/a]": 0, "Wärme [GWh/a]": row["industrieabwaerme-einspeisung-[GWh/a]"]})
- data.append({"Energieträger": "Grubenwasser", "Strom [GWh/a]": 0, "Wärme [GWh/a]": row["warmes-grubenwasser-[GWh/a]"]})
- data.append({"Energieträger": "Geothermie", "Strom [GWh/a]": 0, "Wärme [GWh/a]": row["geothermie-potenzial-[GWh/a]"]})
-
- # Create a new DataFrame using the data list
- new_df = pd.DataFrame(data)
-
- # Calculate the sum per column "Strom" and "Wärme"
- sum_strom = new_df["Strom [GWh/a]"].sum()
- sum_wärme = new_df["Wärme [GWh/a]"].sum()
-
- # Round the sum values to 1 digit after the decimal point
- sum_strom = round(sum_strom, 1)
- sum_wärme = round(sum_wärme, 1)
-
- # Add the row with sum values to the DataFrame
- new_df.loc[len(new_df)] = ["Gesamt in GWh/a", sum_strom, sum_wärme]
-
- # Add the row with sum values to the DataFrame
- data.append({"Energieträger": "Gesamtpotential EE in GWh/a", "Strom [GWh/a]": sum_strom, "Wärme [GWh/a]": sum_wärme})
- # Get the sum of renewable energy for "Strom" and "Wärme" from table_status_quo function
- df_status_quo, _ = table_status_quo(gemeinde)
- # Recalculate the sum of renewable energy for "Strom" and "Wärme" using the provided code snippets
- sum_renewable_strom = sum([
- filtered_df_ee_data["strombereitstellung-biomasse-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-deponiegas-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-grubengas-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-klaergas-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-pv-freiflaechen-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-pv-dachflaechen-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-wasserkraft-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-wind-[GWh/a]"],
- filtered_df_ee_data["strombereitstellung-mva-[GWh/a]"]
- ])
- sum_renewable_strom = float(sum_renewable_strom.values[0])
-
- sum_renewable_wärme = sum([
- filtered_df_ee_data["waermebereitstellung-biomasse-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-deponiegas-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-grubengas-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-klaergas-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-solarthermie-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-geothermie-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-grubenwasser-[GWh/a]"],
- filtered_df_ee_data["waermebereitstellung-abwaerme-industrie-[GWh/a]"]
- ])
- sum_renewable_wärme = float(sum_renewable_wärme.values[0])
- # Add row with the values sum_renewable_strom for strom and sum_renewable_wärme for wärme to the dataframe data
- data.append({"Energieträger": "Bestand EE in GWh/a", "Strom [GWh/a]": sum_renewable_strom, "Wärme [GWh/a]": sum_renewable_wärme})
- # Add row for "EE Bestand+Potential"
- data.append({"Energieträger": "EE Bestand+Potential", "Strom [GWh/a]": sum_renewable_strom+sum_strom, "Wärme [GWh/a]": sum_renewable_wärme+sum_wärme})
- # Add row for "Verbrauch/Bedarf"
- data.append({"Energieträger": "Verbrauch/Bedarf in GWh/a", "Strom [GWh/a]": row["stromverbrauch-[GWh/a]"], "Wärme [GWh/a]": row["waermebedarf-[GWh/a]"]})
- # Calculate the percentage values for "Deckungsanteil"
- deckungsanteil_strom = round(((sum_renewable_strom + sum_strom) / row["stromverbrauch-[GWh/a]"]) * 100, 1)
- deckungsanteil_wärme = round(((sum_renewable_wärme + sum_wärme) / row["waermebedarf-[GWh/a]"]) * 100, 1)
-
- # Add row for "Deckungsanteil" with percentage values
- data.append({"Energieträger": "Deckungsanteil EE in %", "Strom [GWh/a]": deckungsanteil_strom, "Wärme [GWh/a]": deckungsanteil_wärme})
-
- # Create a new DataFrame using the data list
- new_df = pd.DataFrame(data)
-
- # Print the new DataFrame
- # print(new_df)
-
- return new_df
-@st.cache_data
-def ee_potential_strom_waerme(gemeinde):
- filtered_df_ee_data = df_ee_data.loc[df_ee_data['gemeinde'] == gemeinde].copy()
- # Round the numeric columns to 1 decimal place
- numeric_columns = filtered_df_ee_data.select_dtypes(include='number').columns
- filtered_df_ee_data[numeric_columns] = filtered_df_ee_data[numeric_columns].round(1)
- # Initialize an empty list to hold the data for the new DataFrame
-
- data_strom = []
- # Iterate through the rows of the filtered DataFrame
- for index, row in filtered_df_ee_data.iterrows():
- # Add row for "Wind" with Strom and Wärme values
- data_strom.append({"Energieträger": "Wind", "Bestand EE [GWh/a]": row["strombereitstellung-wind-[GWh/a]"], "Potential EE [GWh/a]": row["wind-flaechenpotenzial-[GWh/a]"], "Gesamt EE [GWh/a]": row["strombereitstellung-wind-[GWh/a]"] +row["wind-flaechenpotenzial-[GWh/a]"], "Steigerungspotential %": round(((row["strombereitstellung-wind-[GWh/a]"] +row["wind-flaechenpotenzial-[GWh/a]"])/row["strombereitstellung-wind-[GWh/a]"]-1)*100,1) if row["strombereitstellung-wind-[GWh/a]"] != 0 else 0})
- data_strom.append({"Energieträger": "Solar-Dachflächen", "Bestand EE [GWh/a]": row["strombereitstellung-pv-dachflaechen-[GWh/a]"], "Potential EE [GWh/a]": row["pv-dach-stromertragspotenzial-[GWh/a]"], "Gesamt EE [GWh/a]": row["strombereitstellung-pv-dachflaechen-[GWh/a]"] +row["pv-dach-stromertragspotenzial-[GWh/a]"], "Steigerungspotential %": round(((row["strombereitstellung-pv-dachflaechen-[GWh/a]"] +row["pv-dach-stromertragspotenzial-[GWh/a]"])/row["strombereitstellung-pv-dachflaechen-[GWh/a]"]-1)*100,1) if row["strombereitstellung-pv-dachflaechen-[GWh/a]"] != 0 else 0})
- data_strom.append({"Energieträger": "Solar-Freiflächen", "Bestand EE [GWh/a]": row["strombereitstellung-pv-freiflaechen-[GWh/a]"], "Potential EE [GWh/a]": row["pv-freiflaeche-stromertragspotenzial-[GWh/a]"], "Gesamt EE [GWh/a]": row["strombereitstellung-pv-freiflaechen-[GWh/a]"] +row["pv-freiflaeche-stromertragspotenzial-[GWh/a]"], "Steigerungspotential %": round(((row["strombereitstellung-pv-freiflaechen-[GWh/a]"] +row["pv-freiflaeche-stromertragspotenzial-[GWh/a]"])/row["strombereitstellung-pv-freiflaechen-[GWh/a]"]-1)*100,1) if row["strombereitstellung-pv-freiflaechen-[GWh/a]"] != 0 else 0})
- data_strom.append({"Energieträger": "Wasser", "Bestand EE [GWh/a]": row["strombereitstellung-wasserkraft-[GWh/a]"], "Potential EE [GWh/a]": row["wasserkraftpotenzial-[GWh/a]"], "Gesamt EE [GWh/a]": row["strombereitstellung-wasserkraft-[GWh/a]"] +row["wasserkraftpotenzial-[GWh/a]"], "Steigerungspotential %": round(((row["strombereitstellung-wasserkraft-[GWh/a]"] +row["wasserkraftpotenzial-[GWh/a]"])/row["strombereitstellung-wasserkraft-[GWh/a]"]-1)*100,1) if row["strombereitstellung-wasserkraft-[GWh/a]"] != 0 else 0})
- data_strom.append({"Energieträger": "Biomasse", "Bestand EE [GWh/a]": row["waermebereitstellung-biomasse-[GWh/a]"], "Potential EE [GWh/a]": row["biomasse-potenzial-strom-[GWh/a]"], "Gesamt EE [GWh/a]": row["strombereitstellung-biomasse-[GWh/a]"] +row["biomasse-potenzial-strom-[GWh/a]"], "Steigerungspotential %": round(((row["strombereitstellung-biomasse-[GWh/a]"] +row["biomasse-potenzial-strom-[GWh/a]"])/row["strombereitstellung-biomasse-[GWh/a]"]-1)*100,1) if row["strombereitstellung-biomasse-[GWh/a]"] != 0 else 0})
- data_strom.append({"Energieträger": "Deponiegas", "Bestand EE [GWh/a]": row["strombereitstellung-deponiegas-[GWh/a]"], "Potential EE [GWh/a]": 0, "Gesamt EE [GWh/a]": row["strombereitstellung-deponiegas-[GWh/a]"] +row["biomasse-potenzial-strom-[GWh/a]"], "Steigerungspotential %": 0})
- data_strom.append({"Energieträger": "Grubengas", "Bestand EE [GWh/a]": row["strombereitstellung-grubengas-[GWh/a]"], "Potential EE [GWh/a]": 0, "Gesamt EE [GWh/a]": row["strombereitstellung-grubengas-[GWh/a]"] +row["biomasse-potenzial-strom-[GWh/a]"], "Steigerungspotential %": 0})
- data_strom.append({"Energieträger": "Klärgas", "Bestand EE [GWh/a]": row["strombereitstellung-klaergas-[GWh/a]"], "Potential EE [GWh/a]": 0, "Gesamt EE [GWh/a]": row["strombereitstellung-klaergas-[GWh/a]"] +row["biomasse-potenzial-strom-[GWh/a]"], "Steigerungspotential %": 0})
- total_bestand = sum([row["Bestand EE [GWh/a]"] for row in data_strom])
- total_potential = sum([row["Potential EE [GWh/a]"] for row in data_strom])
- total_gesamt = sum([row["Gesamt EE [GWh/a]"] for row in data_strom])
- data_strom.append({"Energieträger": "Gesamt", "Bestand EE [GWh/a]": total_bestand, "Potential EE [GWh/a]": total_potential, "Gesamt EE [GWh/a]": total_gesamt, "Steigerungspotential %": round(((total_potential+total_bestand)/total_bestand-1)*100,1) if total_bestand != 0 else 0})
- data_strom.append({"Energieträger": "Bedarf", "Bestand EE [GWh/a]": row["stromverbrauch-[GWh/a]"], "Potential EE [GWh/a]": row["stromverbrauch-[GWh/a]"], "Gesamt EE [GWh/a]": row["stromverbrauch-[GWh/a]"], "Steigerungspotential %": 0})
- data_strom.append({"Energieträger": "Deckungsrate %", "Bestand EE [GWh/a]": round((total_bestand/row["stromverbrauch-[GWh/a]"])*100,1), "Potential EE [GWh/a]": round((total_potential/row["stromverbrauch-[GWh/a]"])*100,1), "Gesamt EE [GWh/a]": round((total_gesamt/row["stromverbrauch-[GWh/a]"])*100,1), "Steigerungspotential %": 0})
-
- df_energy_strom = pd.DataFrame(data_strom)
-
- data_waerme = []
- for index, row in filtered_df_ee_data.iterrows():
- # Add row for "Wind" with Strom and Wärme values
- data_waerme.append({"Energieträger": "Solarthermie", "Bestand EE [GWh/a]": row["waermebereitstellung-solarthermie-[GWh/a]"], "Potential EE [GWh/a]": row["solarthermie-dach-warmwasser-[GWh/a]"], "Gesamt EE [GWh/a]": row["waermebereitstellung-solarthermie-[GWh/a]"] +row["solarthermie-dach-warmwasser-[GWh/a]"], "Steigerungspotential %": round(((row["waermebereitstellung-solarthermie-[GWh/a]"] +row["solarthermie-dach-warmwasser-[GWh/a]"])/row["waermebereitstellung-solarthermie-[GWh/a]"]-1)*100,1) if row["waermebereitstellung-solarthermie-[GWh/a]"] != 0 else 0})
- data_waerme.append({"Energieträger": "Geothermie", "Bestand EE [GWh/a]": row["waermebereitstellung-geothermie-[GWh/a]"], "Potential EE [GWh/a]": row["geothermie-potenzial-[GWh/a]"], "Gesamt EE [GWh/a]": row["waermebereitstellung-geothermie-[GWh/a]"] + row["geothermie-potenzial-[GWh/a]"], "Steigerungspotential %": round(((row["waermebereitstellung-geothermie-[GWh/a]"] + row["geothermie-potenzial-[GWh/a]"])/row["waermebereitstellung-geothermie-[GWh/a]"]-1)*100,1) if row["waermebereitstellung-geothermie-[GWh/a]"] != 0 else 0})
- data_waerme.append({"Energieträger": "Biomasse", "Bestand EE [GWh/a]": row["waermebereitstellung-biomasse-[GWh/a]"], "Potential EE [GWh/a]": row["biomasse-potenzial-waerme-[GWh/a]"], "Gesamt EE [GWh/a]": row["waermebereitstellung-biomasse-[GWh/a]"] + row["biomasse-potenzial-waerme-[GWh/a]"], "Steigerungspotential %": round(((row["waermebereitstellung-biomasse-[GWh/a]"] + row["biomasse-potenzial-waerme-[GWh/a]"])/row["waermebereitstellung-biomasse-[GWh/a]"]-1)*100,1) if row["waermebereitstellung-biomasse-[GWh/a]"] != 0 else 0})
- data_waerme.append({"Energieträger": "Industrieabwärme", "Bestand EE [GWh/a]": row["waermebereitstellung-abwaerme-industrie-[GWh/a]"], "Potential EE [GWh/a]": row["industrieabwaerme-einspeisung-[GWh/a]"], "Gesamt EE [GWh/a]": row["waermebereitstellung-abwaerme-industrie-[GWh/a]"] + row["industrieabwaerme-einspeisung-[GWh/a]"], "Steigerungspotential %": round(((row["waermebereitstellung-abwaerme-industrie-[GWh/a]"] + row["industrieabwaerme-einspeisung-[GWh/a]"])/row["waermebereitstellung-abwaerme-industrie-[GWh/a]"]-1)*100,1) if row["waermebereitstellung-abwaerme-industrie-[GWh/a]"] != 0 else 0})
- data_waerme.append({"Energieträger": "Grubenwasser", "Bestand EE [GWh/a]": row["waermebereitstellung-grubenwasser-[GWh/a]"], "Potential EE [GWh/a]": row["warmes-grubenwasser-[GWh/a]"], "Gesamt EE [GWh/a]": row["waermebereitstellung-grubenwasser-[GWh/a]"] + row["warmes-grubenwasser-[GWh/a]"], "Steigerungspotential %": round(((row["waermebereitstellung-grubenwasser-[GWh/a]"] + row["warmes-grubenwasser-[GWh/a]"])/row["waermebereitstellung-grubenwasser-[GWh/a]"]-1)*100,1) if row["waermebereitstellung-grubenwasser-[GWh/a]"] != 0 else 0})
- data_waerme.append({"Energieträger": "Fernwärme", "Bestand EE [GWh/a]": 0, "Potential EE [GWh/a]": row["ferwaerme-potenzial-2030-[GWh/a]"], "Gesamt EE [GWh/a]": 0+row["ferwaerme-potenzial-2030-[GWh/a]"], "Steigerungspotential %": float('inf') if row["ferwaerme-potenzial-2030-[GWh/a]"] != 0 else 1})
- data_waerme.append({"Energieträger": "Deponiegas", "Bestand EE [GWh/a]": row["waermebereitstellung-grubengas-[GWh/a]"], "Potential EE [GWh/a]": 0, "Gesamt EE [GWh/a]": row["waermebereitstellung-grubengas-[GWh/a]"] +0, "Steigerungspotential %": 0})
- data_waerme.append({"Energieträger": "Grubengas", "Bestand EE [GWh/a]": row["waermebereitstellung-grubengas-[GWh/a]"], "Potential EE [GWh/a]": 0, "Gesamt EE [GWh/a]": row["waermebereitstellung-grubengas-[GWh/a]"] +0, "Steigerungspotential %": 0})
- data_waerme.append({"Energieträger": "Klärgas", "Bestand EE [GWh/a]": row["waermebereitstellung-klaergas-[GWh/a]"], "Potential EE [GWh/a]": 0, "Gesamt EE [GWh/a]": row["waermebereitstellung-klaergas-[GWh/a]"]+0, "Steigerungspotential %": 0})
- total_bestand = sum([row["Bestand EE [GWh/a]"] for row in data_waerme])
- total_potential = sum([row["Potential EE [GWh/a]"] for row in data_waerme])
- total_gesamt = sum([row["Gesamt EE [GWh/a]"] for row in data_waerme])
- data_waerme.append({"Energieträger": "Gesamt", "Bestand EE [GWh/a]": total_bestand, "Potential EE [GWh/a]": total_potential, "Gesamt EE [GWh/a]": total_gesamt, "Steigerungspotential %": round(((total_potential+total_bestand)/total_bestand-1)*100,1) if total_bestand != 0 else 0})
- data_waerme.append({"Energieträger": "Bedarf", "Bestand EE [GWh/a]": row["waermebedarf-[GWh/a]"], "Potential EE [GWh/a]": row["waermebedarf-[GWh/a]"], "Gesamt EE [GWh/a]": row["waermebedarf-[GWh/a]"], "Steigerungspotential %": 0})
- data_waerme.append({"Energieträger": "Deckungsrate %", "Bestand EE [GWh/a]": round((total_bestand/row["waermebedarf-[GWh/a]"])*100,1), "Potential EE [GWh/a]": round((total_potential/row["waermebedarf-[GWh/a]"])*100,1), "Gesamt EE [GWh/a]": round((total_gesamt/row["waermebedarf-[GWh/a]"])*100,1), "Steigerungspotential %": 0})
-
- df_energy_wärme = pd.DataFrame(data_waerme)
-
- return df_energy_strom, df_energy_wärme
-@st.cache_resource
-def plot_stacked_strom_wärme(df_energy_strom, df_energy_wärme):
-
- # Exclude 'Gesamt', 'Bedarf', 'Deckungsrate %' from the dataframes
- df_energy_strom = df_energy_strom[~df_energy_strom['Energieträger'].isin(['Gesamt', 'Bedarf', 'Deckungsrate %'])]
- df_energy_wärme = df_energy_wärme[~df_energy_wärme['Energieträger'].isin(['Gesamt', 'Bedarf', 'Deckungsrate %'])]
-
- # Exclude categories when both Bestand EE [GWh/a] and Potential EE [GWh/a] values are zero
- df_energy_strom = df_energy_strom[~((df_energy_strom['Bestand EE [GWh/a]'] == 0) & (df_energy_strom['Potential EE [GWh/a]'] == 0))]
- df_energy_wärme = df_energy_wärme[~((df_energy_wärme['Bestand EE [GWh/a]'] == 0) & (df_energy_wärme['Potential EE [GWh/a]'] == 0))]
-
- # Sort the dataframes from high to low
- df_energy_strom = df_energy_strom.sort_values(by=['Bestand EE [GWh/a]', 'Potential EE [GWh/a]'], ascending=False)
- df_energy_wärme = df_energy_wärme.sort_values(by=['Bestand EE [GWh/a]', 'Potential EE [GWh/a]'], ascending=False)
-
- # Plotting stacked bar chart for df_energy_strom using plotly express
- stacked_strom_ee = px.bar(df_energy_strom, x='Energieträger', y=['Bestand EE [GWh/a]', 'Potential EE [GWh/a]'], color_discrete_sequence=['darkgreen', 'lightgreen'], labels={'value':'EE [GWh/a]'}, title='Strom')
- stacked_strom_ee.update_layout(xaxis={'categoryorder':'total descending'}) # Order bars from left (highest value) to right (lowest value)
- stacked_strom_ee.update_traces(hovertemplate='%{y}') # Update hover label to show only the value
-
- # Plotting stacked bar chart for df_energy_wärme using plotly express
- stacked_wärme_ee = px.bar(df_energy_wärme, x='Energieträger', y=['Bestand EE [GWh/a]', 'Potential EE [GWh/a]'], color_discrete_sequence=['darkgreen', 'lightgreen'], labels={'value':'EE [GWh/a]'}, title='Wärme')
- stacked_wärme_ee.update_layout(xaxis={'categoryorder':'total descending'}) # Order bars from left (highest value) to right (lowest value)
- stacked_wärme_ee.update_traces(hovertemplate='%{y}') # Update hover label to show only the value
-
- return stacked_strom_ee, stacked_wärme_ee
-
-@st.cache_resource
-def thg_energie(gemeinde):
- # Mapping of energy source names to desired format
- energy_source_mapping = {
- "strombereitstellung-biomasse-[tCO2e]": "Biomasse",
- "strombereitstellung-deponiegas-[tCO2e]": "Deponiegas",
- "strombereitstellung-grubengas-[tCO2e]": "Grubengas",
- "strombereitstellung-klaergas-[tCO2e]": "Klärgas",
- "strombereitstellung-pv-freiflaechen-[tCO2e]": "PV-Anlagen (Freiflächen)",
- "strombereitstellung-pv-dachflaechen-[tCO2e]": "PV-Anlagen (Dachflächen)",
- "strombereitstellung-wasserkraft-[tCO2e]": "Wasserkraft",
- "strombereitstellung-wind-[tCO2e]": "Windenergie",
- "strombereitstellung-mva-[tCO2e]": "MVA",
- "strombereitstellung-steinkohle-[tCO2e]": "Steinkohle",
- "strombereitstellung-braunkohle-[tCO2e]": "Braunkohle",
- "strombereitstellung-erdgas-[tCO2e]": "Erdgas",
- "strombereitstellung-oel-[tCO2e]": "Heizöl",
- "strombereitstellung-sonstige-[tCO2e]": "Sonstige erneuerbare Energieträger",
-
- "waermebereitstellung-biomasse-[tCO2e]": "Biomasse",
- "waermebereitstellung-deponiegas-[tCO2e]": "Deponiegas",
- "waermebereitstellung-grubengas-[tCO2e]": "Grubengas",
- "waermebereitstellung-klaergas-[tCO2e]": "Klärgas",
- "waermebereitstellung-solarthermie-[tCO2e]": "Solarthermie",
- "waermebereitstellung-geothermie-[tCO2e]": "Tiefengeothermie",
- "waermebereitstellung-grubenwasser-[tCO2e]": "Grubenwasser",
- "waermebereitstellung-abwaerme-industrie-[tCO2e]": "Abwärme (Industrie)",
- "waermebereitstellung-mva-[tCO2e]": "MVA",
- "waermebereitstellung-braunkohle-[tCO2e]": "Braunkohle",
- "waermebereitstellung-steinkohle-[tCO2e]": "Steinkohle",
- "waermebereitstellung-erdgas-[tCO2e]": "Erdgas",
- "waermebereitstellung-oel-[tCO2e]": "Heizöl",
- "waermebereitstellung-sonstiges-[tCO2e]": "Sonstige erneuerbare Energieträger",
- "waermebereitstellung-kwk-[tCO2e]": "KWK"
- }
- # Strom table and plot
- filtered_df = df_ee_data.loc[df_ee_data['gemeinde'] == gemeinde].copy()
- strom_columns = ["strombereitstellung-biomasse-[tCO2e]", "strombereitstellung-deponiegas-[tCO2e]", "strombereitstellung-grubengas-[tCO2e]", "strombereitstellung-klaergas-[tCO2e]", "strombereitstellung-pv-freiflaechen-[tCO2e]", "strombereitstellung-pv-dachflaechen-[tCO2e]", "strombereitstellung-wasserkraft-[tCO2e]", "strombereitstellung-wind-[tCO2e]", "strombereitstellung-mva-[tCO2e]", "strombereitstellung-steinkohle-[tCO2e]", "strombereitstellung-braunkohle-[tCO2e]", "strombereitstellung-erdgas-[tCO2e]", "strombereitstellung-oel-[tCO2e]", "strombereitstellung-sonstige-[tCO2e]"]
- strom_data = []
- for column in strom_columns:
- strom_data.append({"Energieträger": energy_source_mapping[column], "[t CO2e]": filtered_df[column].values[0]})
- strom_data.append({"Energieträger": "Gesamt", "[t CO2e]": sum([row["[t CO2e]"] for row in strom_data])})
- df_strom = pd.DataFrame(strom_data)
- df_strom_thg = df_strom[df_strom["[t CO2e]"] != 0].sort_values(by="[t CO2e]", ascending=False)
- strom_plot_thg = px.bar(df_strom_thg[df_strom_thg["Energieträger"] != "Gesamt"], x="Energieträger", y="[t CO2e]", title="Strom", labels={"[t CO2e]": "t CO2e"})
- strom_plot_thg.update_layout(xaxis={'categoryorder':'total descending'}) # Order bars from left (highest value) to right (lowest value)
-
- # Wärme table and plot
- waerme_columns = ["waermebereitstellung-biomasse-[tCO2e]", "waermebereitstellung-deponiegas-[tCO2e]", "waermebereitstellung-grubengas-[tCO2e]", "waermebereitstellung-klaergas-[tCO2e]", "waermebereitstellung-solarthermie-[tCO2e]", "waermebereitstellung-geothermie-[tCO2e]", "waermebereitstellung-grubenwasser-[tCO2e]", "waermebereitstellung-abwaerme-industrie-[tCO2e]", "waermebereitstellung-mva-[tCO2e]", "waermebereitstellung-braunkohle-[tCO2e]", "waermebereitstellung-steinkohle-[tCO2e]", "waermebereitstellung-erdgas-[tCO2e]", "waermebereitstellung-oel-[tCO2e]", "waermebereitstellung-sonstiges-[tCO2e]", "waermebereitstellung-kwk-[tCO2e]"]
- waerme_data = []
- for column in waerme_columns:
- waerme_data.append({"Energieträger": energy_source_mapping[column], "[t CO2e]": filtered_df[column].values[0]})
- waerme_data.append({"Energieträger": "Gesamt", "[t CO2e]": sum([row["[t CO2e]"] for row in waerme_data])})
- df_waerme = pd.DataFrame(waerme_data)
- df_waerme_thg = df_waerme[df_waerme["[t CO2e]"] != 0].sort_values(by="[t CO2e]", ascending=False)
- waerme_plot_thg = px.bar(df_waerme_thg[df_waerme_thg["Energieträger"] != "Gesamt"], x="Energieträger", y="[t CO2e]", title="Wärme", labels={"[t CO2e]": "t CO2e"})
- waerme_plot_thg.update_layout(xaxis={'categoryorder':'total descending'}) # Order bars from left (highest value) to right (lowest value)
-
- return df_strom_thg, strom_plot_thg, df_waerme_thg, waerme_plot_thg
-
-
-def plot_stacked_bar(df, column_name):
- fig = px.bar(
- df,
- x="Energieträger",
- y=column_name,
- title=f"{column_name}verbrauch in GWh/a",
- labels={"value": f"{column_name}verbrauch (EE)"},
- barmode="stack",
- height=400,
- )
- # Update layout labels and title
- fig.update_layout(
- xaxis_title="EE-Potentiale & Bedarf", # Update x-axis label
- yaxis_title=f"{column_name}", # Update y-axis label
- title_text=f"Potentiale erneuerbarer Energien vs. Bedarf", # Update title
- )
- # Update hover label template
- fig.update_traces(
- hovertemplate="%{x}
- Wärmebedarf pro Straßenzug in kWh pro Jahr
- """
- # Add the legend to the map
- legend = folium.Marker(
- [center[0], center[1]],
- icon=folium.DivIcon(
- icon_size=(150,36),
- icon_anchor=(0,200),
- html='%s
' % legend_html,
- )
- )
- m.add_child(legend)
-
- # Render the Folium map using st_folium
- #st_folium(m, width=800, returned_objects=[])
-
- #return None # Since no data is being returned
- return m # Return the folium.Map object
-
-#@st.cache_resource(experimental_allow_widgets=True)
-def energy_data_gemeinde(gemeinde):
- df_status_quo, percentages = table_status_quo(gemeinde)
- pie_chart_strom_status_quo, pie_chart_waerme_status_quo, pie_chart_gesamt_status_quo = pie_status_quo(percentages)
- df_energy_strom, df_energy_wärme = ee_potential_strom_waerme(gemeinde)
- stacked_strom_ee, stacked_wärme_ee = plot_stacked_strom_wärme(df_energy_strom, df_energy_wärme)
- #filtered_df_strom, _, _ = ee_potenziale(gemeinde)
- #filtered_df_wärme, _, _ = ee_potenziale(gemeinde)
- energiemix_bestand = energie_bestand(gemeinde)
- #stacked_bar_plot_strom = strom_plot(gemeinde)
- #stacked_bar_plot_wärme = wärme_plot(gemeinde)
- #stacked_bar_plot_bestand = energiebestand_plot(gemeinde)
- pie_chart_bestand = energiebestand_pie(gemeinde)
- pie_bestand_all, pie_bestand_ee, pie_bestand_ko = pie_charts_bestand(gemeinde)
- heat_density_map = plot_heat_density_map(gemeinde)
- df_strom_thg, strom_plot_thg, df_waerme_thg, waerme_plot_thg = thg_energie(gemeinde)
-
- return df_energy_strom, df_energy_wärme, energiemix_bestand, pie_chart_bestand, df_status_quo, pie_chart_strom_status_quo, pie_chart_waerme_status_quo, pie_chart_gesamt_status_quo, stacked_strom_ee, stacked_wärme_ee, pie_bestand_all, pie_bestand_ee, pie_bestand_ko, heat_density_map, df_strom_thg, strom_plot_thg, df_waerme_thg, waerme_plot_thg
-
-#perform similarity search within supabase pgvector
-def similarity_search_with_supabase(input_text, k=3):
-
- table_name = "documents"
-
- vector_store = SupabaseVectorStore(client=supabase, embedding=embeddings, table_name=table_name, query_name="match_documents")
-
- matched_docs = vector_store.similarity_search(query=input_text, k=k)
-
- #print(matched_docs[2].page_contet)
-
- text_content = [doc.page_content for doc in matched_docs]
-
- text_content = vector_store.similarity_search(input_text)
-
- return text_content
-#create query against all embeddings docs and compose contextual answer
-def get_response_from_query(text_content, input_text):
-
- llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k", temperature=0, max_tokens= 3000)
-
- prompt = ChatPromptTemplate.from_messages([
- SystemMessage(content=f"""
- Du bist Experte für die kommunale Wärmeplanung in Deutschland. Als spezialisierter Berater bist du sehr hilfsbereit und erläuterst Fragen
- bis in das kleinste Detail zum Thema kommunale Wärmeplanung und Klimaschutz für Kommunen.
-
- Falls erforderlich, erstelle bitte auch detaillierte Schritt für Schritt Anleitungen, um ein gewünschtes Ziel zu erreichen.
- Du erhältst relevante Inhalte zur Nutzerfrage um die Frage bestmöglich beantworten zu können. Inhalte relvant zur Nutzerfrage: {text_content}
-
- Bitte befolge auch immer folgende Regeln bei der Beantwortung von Nutzerfragen:
- 1. Strukturiere deine Antworten sehr gut. Gehe zunächst auf die Nutzerfrage ein, beschreibe was sie genau umfasst und wie du sie verstehst.
- 2. Deine Antwort auf die Frage soll möglichst detailreich und umfassend sein.
- 3. Sofern sinnvoll, ergänze die Antwort auf die Frage um weitere relevante Informationen zum gegebenen Thema.
- 4. Du sollest eine verständliche und professionelle Sprache verwenden.
- 5. Gebe dem Nutzer umfangreiche Arbeitsanweisungen sofern sinnvoll.
- 6. Deine Erläuterungen soll auch ein 12-Jähriger verstehen.
- 7. Wenn möglich verweise nametlich auf relevante Dokumente und Internetseiten zum Thema der Frage.
- 8. Bei unklaren Fragen, stelle Nachfragen an den Nutzer, um die gewünschte Information besser zu verstehen und bessere Antowrten geben zu können.
- 9. Wenn du die Frage nicht verstehst und keine Antwort weißt, sage das es dir leid tut, du aber leider keine Antwort weist.
- """),
- MessagesPlaceholder(variable_name="chat_history"),
- HumanMessagePromptTemplate.from_template(f"{input_text}"),
- ])
- memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
- chain = LLMChain(llm=llm, prompt=prompt, verbose=True, memory=memory)
-
- response = chain.run(input_text=input_text)
- #response = response.replace("\n", "")
-
- return response
-
-# Create a sidebar selectbox for selecting the Gemeinde
-with st.sidebar:
- gemeinde = st.sidebar.selectbox("Bitte Wählen Sie eine Stadt / Gemeinde", [""] + list(gemeinde_list))
- if gemeinde != "":
- with st.spinner("Loading..."):
- time.sleep(5)
- success = st.success("Gemeinde ausgewählt")
- success.empty()
- with st.expander("Quellen und Annahmen"):
- st.write("""
- Datenquellen:
- - Klimaatlas NRW, Herausgeber: Landesamt für Natur, Umwelt und Verbraucherschutz NRW unter Verwendung von Daten von Raumwärmebedarfsmodell, Ausbaustand der wärmeerzeugenden Energien in NRW, Ausbaustand der stromerzeugenden Energien in NRW, Standorte der strom- und wärmeerzeugenden Anlagen in NRW, Ergebnissen der LANUV-Potenzialstudien.
- - Umweltbundesamt: emissionsbilanz erneuerbarer Energieträger unter Verwendung von Daten der AGEE-Stat (2022).
- - BISKO, Bilanzierungs-Systematik Kommunal. Empfehlungen zur Methodik der kommunalen Treibhausgasbilanzierung für den Energie- und Verkehrssektor in Deutschland (2019).
- - Globales Emissions-Modell Integrierter Systeme: GEMIS Version 5.0 (2021).
-
- Annahmen:
- - Strommix: Bei der Ermittlung des individuellen Stromixes der Gemeinden wird davon ausgegangen, dass erneuerbarer Strom, der vor Ort erzeugt wird, vollständig genutzt wird. Sofern der Anteil erneuerbarer Energieträger am Strombedarf vor Ort kleiner als 100 Prozenz ist, wird der restliche Strommix anhand des Stromixes für NRW abgeleitet.
- - Windpotential: Je Hektar Windpotential wird 1 GWh/a Ertragspotential geschätzt.
- """)
-
-st.title("Energieversorgung der Gemeinden - Status Quo und erneuerbares Potential 🌍")
-st.write("")
-if gemeinde != "":
- df_energy_strom, df_energy_wärme, energiemix_bestand, pie_chart_bestand, df_status_quo, pie_chart_strom_status_quo, pie_chart_waerme_status_quo, pie_chart_gesamt_status_quo, stacked_strom_ee, stacked_wärme_ee, pie_bestand_all, pie_bestand_ee, pie_bestand_ko, heat_density_map, df_strom_thg, strom_plot_thg, df_waerme_thg, waerme_plot_thg = energy_data_gemeinde(gemeinde)
-
- # Create two columns for Streamlit interface
- col1, col2 = st.columns([1,2], gap="medium")
-
- # First column shows the table df_status_quo
- with col1:
- st.subheader("Status Quo - Strom und Wärme")
- st.write("")
- st.dataframe(df_status_quo, hide_index=True, width=370, use_container_width=True)
-
- # Second column contains the three pie charts from pie_status_quo
- with col2:
- st.subheader("Verteilung erneuerbar und konventionell")
- # Display the pie charts side by side
- col2_1, col2_2, col2_3 = st.columns(3) # Three equal-width columns
- with col2_1:
- st.plotly_chart(pie_chart_strom_status_quo, use_container_width=True)
- with col2_2:
- st.plotly_chart(pie_chart_waerme_status_quo, use_container_width=True)
- with col2_3:
- st.plotly_chart(pie_chart_gesamt_status_quo, use_container_width=True)
-
- st.markdown("***")
- #please add the heat map here:
- st.subheader("Wärmebedarfsdichte")
- #st_folium(heat_density_map, use_container_width=True)
- st_folium(heat_density_map, width=800, returned_objects=[], use_container_width=True)
-
- st.markdown("***")
- # Create two columns for Streamlit interface
- col1, col2 = st.columns([0.8,1.2], gap="large")
- with col1:
- st.subheader("Übersicht erneuerbare Energien - Strom")
- st.dataframe(df_energy_strom, hide_index=True, height=420, width=400, use_container_width=True)
- #st.dataframe(filtered_df_strom, hide_index=True)
- #st.markdown(filtered_df_strom.style.hide(axis="index").to_html(), unsafe_allow_html=True)
- with col2:
- st.subheader("Potential und Bestand erneuerbarer Strom")
- st.plotly_chart(stacked_strom_ee, use_container_width=True)
-
- st.markdown("***")
-
- # Create two columns for Streamlit interface
- col1, col2 = st.columns([0.8,1.2], gap="large")
- with col1:
- st.subheader("Übersicht erneuerbare Energien - Wärme")
- st.dataframe(df_energy_wärme, hide_index=True, height=450, width=400, use_container_width=True)
- #st.dataframe(filtered_df_strom, hide_index=True)
- #st.markdown(filtered_df_strom.style.hide(axis="index").to_html(), unsafe_allow_html=True)
- with col2:
- st.subheader("Potential und Bestand erneuerbare Wärme")
- st.plotly_chart(stacked_wärme_ee, use_container_width=True)
-
- st.markdown("***")
-
- # Create two columns for Streamlit interface
- col1, col2 = st.columns([0.8,2.2], gap="large")
- with col1:
- st.subheader("Übersicht Energiemix - Strom - Bestand")
- st.dataframe(energiemix_bestand, hide_index=True, height=550, width=100, use_container_width=True)
- #st.dataframe(filtered_df_strom, hide_index=True)
- #st.markdown(filtered_df_strom.style.hide(axis="index").to_html(), unsafe_allow_html=True)
- with col2:
- st.subheader("Zusammensetzung Energiemix - Strom")
- col2_1, col2_2, col2_3 = st.columns(3) # Three equal-width columns
- with col2_1:
- st.plotly_chart(pie_bestand_all, use_container_width=True)
- with col2_2:
- st.plotly_chart(pie_bestand_ee, use_container_width=True)
- with col2_3:
- st.plotly_chart(pie_bestand_ko, use_container_width=True)
-
- st.markdown("***")
-
- # Create two columns for Streamlit interface
- col1, col2 = st.columns([0.8,2.2], gap="large")
- with col1:
- st.subheader("Übersicht THG-Emissionen - Strom - Bestand")
- st.dataframe(df_strom_thg, hide_index=True, height=400, width=100, use_container_width=True)
-
- with col2:
- st.subheader("THG-Emissionen - Strom")
- st.plotly_chart(strom_plot_thg, use_container_width=True)
-
- st.markdown("***")
-
- # Create two columns for Streamlit interface
- col1, col2 = st.columns([0.8,2.2], gap="large")
- with col1:
- st.subheader("Übersicht THG-Emissionen - Wärme - Bestand")
- st.dataframe(df_waerme_thg, hide_index=True, height=300, width=100, use_container_width=True)
-
- with col2:
- st.subheader("THG-Emissionen - Wärme")
- st.plotly_chart(waerme_plot_thg, use_container_width=True)
-
-else:
- # Display the map of Germany when no city name is provided
- germany_map = folium.Map(location=[51.1657, 10.4515], zoom_start=6)
- st_folium(germany_map, use_container_width=True)
-
-st.markdown("***")
-
-st.title("Assistent zum Thema kommunale Wärmeplanung")
-
-if "openai_model" not in st.session_state:
- st.session_state["openai_model"] = "gpt-3.5-turbo"
-
-if "messages" not in st.session_state:
- st.session_state.messages = []
-
-for message in st.session_state.messages:
- with st.chat_message(message["role"]):
- st.markdown(message["content"])
-
-if prompt := st.chat_input("Stelle mir eine Frage"):
- text_content = similarity_search_with_supabase(prompt)
- with st.chat_message("user"):
- st.markdown(prompt)
-
- with st.chat_message("assistant"):
- message_placeholder = st.empty()
- full_response = get_response_from_query(text_content, prompt)
- message_placeholder.markdown(full_response)
- st.session_state.messages.append({"role": "assistant", "content": full_response})
-
diff --git a/spaces/Li6699/myChat/app.py b/spaces/Li6699/myChat/app.py
deleted file mode 100644
index 192d2daf06c7128ae48c3ce7a47768de710b7b31..0000000000000000000000000000000000000000
--- a/spaces/Li6699/myChat/app.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import gradio as gr
-import openai
-import datetime
-#import tiktoken
-#encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
-
-
-#个人账号信息
-message_info = {}
-#个人历史记录
-user_history = {}
-#记录的总tokens数，避免超过最大限度
-total_tokens = 0
-
-#用户是否开启群聊
-user_group = {}
-#群组管理,用于记录群聊信息
-groups_manage = {"Dormitory Group":[{"role":"assistant","content":"欢迎你来到Dormitory Group!"},{"role":"assistant","content":"让我们开始聊天吧!"}],
- "Family Group":[{"role":"assistant","content":"欢迎你来到Family Group!"},{"role":"assistant","content":"让我们开始聊天吧!"}],
- "Class Group":[{"role":"assistant","content":"欢迎你来到Class Group!"},{"role":"assistant","content":"让我们开始聊天吧!"}]}
-
-def input_key(key,username):
- global message_info
- global user_history
- message_history = []
- user_record = []
- #用户当前加入的群聊
- groups = ""
- key = key
- username = username
- openai.api_key = key
- if username in message_info.keys():
- return username + "用户登陆成功!"
- else:
- message_info.update({username:message_history})
- user_history.update({username:user_record})
- user_group.update({username:groups})
- return (username + "用户不存在,已为您创建该新用户,下次请用该用户名登录!")
-
-
-def remove_exceed(messages):
- #tokens超出删除
- global total_tokens
- count_list = []
- for i in range(len(messages)-1,-1,-1):
- total_tokens += len(messages[i]["content"]) * 2
- count_list.append(total_tokens)
-
- for c in count_list:
- if(c > 1200):
- messages.pop(0)
- messages.pop(0)
- total_tokens -= c
- break
-
-
- print("当前messages的总tokens数:",total_tokens)
-
-
-def predict(input,username):
- global message_info
- global user_history
- global groups_manage
- #AI身份选择标识
- flag = 0
-
- input = input
- username = username
- if input == None:
- input = "hello!"
-
- elif input == "Translator":
- flag = 0
- input = "我希望你扮演一个中英翻译的角色，自动检测输入的文本语言信息。如果文本是英文，\
- 则翻译成中文，如果文本是中文，则翻译成英文；我希望你只进行翻译，不做任何其他改进，不做解释。现在你准备接受句子开始翻译"
- elif input == "Code Developer":
- flag = 0
- input = "我希望你扮演一个代码架构师，当我提出有关的代码问题之后，你使用专业合理的方式回答我的问题。现在你收到后直接回答/'你好，我是一个架构师，请输入你的问题。/'"
-
- elif input == "English Tutor":
- flag = 0
- input = "我希望你扮演一个专业的英语老师，当我提出有关学习英语的所有有关问题时，你提供专业的建议或者答案。现在你收到后直接回答/'你好，我是一个英语教师，请输入你的问题。/'"
-
- elif input == "Writing Assistant":
- flag = 0
- input = "我希望你扮演一个专业的写作助手，当我提出有关写作的所有有关问题或者让你根据我的需求进行写作时时，你提供专业的建议或者生成文章。现在你收到后直接回答/'你好，我是一个写作助手，请输入你的问题。/'"
-
- elif input == "Businessman":
- flag = 0
- input = "我希望你扮演一个专业的商业咨询师，当我提出有关经济、金融以及商业等的有关问题时，你提供专业的答案或者相关见解。现在你收到后直接回答/'你好，我是一个商业咨询师，请输入你的问题。/'"
-
- elif input == "Free Chat":
- flag = 0
- input = "现在停止角色扮演，请你跟我正常对话。现在你收到后直接回答/'你好，请你说点什么/'"
-
- else:
- flag = 1
- print(username + ": ",input)
- print()
-
- #当前用户加入的组名
- groupname = user_group[username]
- #临时指向数组
- messages = []
- reply_content = ""
- try:
- if groupname != "":
- #用户在群聊时前加‘@’可以触发AI
- if input[0] == "@":
- #input = input.replace("@","")
- groups_manage[groupname].append({"role":"user","content":input})
- messages = groups_manage[groupname]
- completion = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo",
- messages =messages,
- temperature=0.7,
- top_p = 1,
- frequency_penalty = 0.0,
- presence_penalty = 0.0
- )
- reply_content = completion.choices[0].message['content']
-
- else:
- #不加‘@’就是人与人之间正常聊天
- groups_manage[groupname].append({"role":"user","content":input})
-
- messages = groups_manage[groupname]
- print("在这里")
- reply_content = ""
-
-
- else:
- #不群聊则需要AI及时回复，一问一答
- message_info[username].append({"role":"user","content":input})
- user_history[username].append({"role":"user","content":input})
- messages = message_info[username]
-
- completion = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo",
- messages =messages,
- temperature=0.7,
- top_p = 1,
- frequency_penalty = 0.0,
- presence_penalty = 0.0
- )
- reply_content = completion.choices[0].message['content']
- messages = user_history[username] #呈现对话，仅可视，没有上下文
-
- except Exception as e:
-
- print(e)
- reply_content = "抱歉,您登录提交的数据有误或网络连接失败...请重新操作或提问"
-
-
-
- print("ChatGPT: ",reply_content)
- print()
-
- #添加用户标识
- if flag == 0:
- messages[-1].update({"content":"{}AI身份转换".format(username + ": ")})
-
- # if groupname !="" and flag == 0:
-
- # messages[-1].update({"content":"{}AI身份转换".format(username + ": ")})
-
- # elif flag == 0:
- # user_history[username][-1].update({"content":"{}AI身份转换".format(username + ": ")})
-
- else:
- print(len(messages))
- origin = messages[-1]["content"]
- messages[-1].update({"content":username + ": " + origin })
-
-
-
-
- #message_info[username].append({"role":"assistant","content":reply_content})
-
- #用户之间聊天则会显示发送时间，其他情况则显示AI回复内容
- if reply_content != "":
-
- messages.append({"role":"assistant","content":reply_content})
- else:
- messages.append({"role":"assistant","content":datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')})
-
- response = []
-
- if groupname == "":
- #及时去除一定的记录，避免message过长出错
- remove_exceed(message_info[username])
- #user_history[username].append({"role":"assistant","content":reply_content})
- response = [((user_history[username][i]["content"]),(user_history[username][i+1]["content"])) for i in range(0,len(user_history[username])-1,2)]
-
- else:
- remove_exceed(messages)
-
- response = [((messages[i]["content"]),(messages[i+1]["content"])) for i in range(0,len(messages)-1,2)]
-
- return response
-
-#根据设定的清理条数，清理用户个人历史聊天信息
-def clear_info(num,username):
-
- username = username
- num = int(num)
- try:
- if len(user_history[username]) < num:
- (user_history[username]).clear()
- else:
- del user_history[username][0:num]
- except Exception as e:
- print(e)
-
- return "Clear Successfully!"
-
-#加入群聊
-def join_group(groupname,username):
- global user_group
- username = username
- groupname = groupname
- user_group.update({username:groupname})
- return "Groups Now: " + user_group[username]
-
-#离开群聊
-def leave_group(groupname,username):
- global user_group
- global user_history
- username = username
- groupname = groupname
- try:
- user_group.update({username:""})
- messages = user_history[username]
- except Exception as e:
- print(e)
-
-
- response = [((messages[i]["content"],(messages[i+1]["content"]))) for i in range(0,len(messages)-1,2)]
- return response
-
-#刷新界面
-def reflesh_chat(groupname,username):
- global groups_manage
- try:
- messages = groups_manage[groupname]
- except Exception as e:
- print(e)
-
- response = [((messages[i]["content"],(messages[i+1]["content"]))) for i in range(0,len(messages)-1,2)]
- return response
-
-
-
-
-
-with gr.Blocks(css=".gradio-container {background-image: linear-gradient(to top, #dfe9f3 0%, white 100%);} #chatbot1{height:70vh;background-image: linear-gradient(to top, #a8edea 0%, #fed6e3 100%);} #chatbot2{height:70vh;background-image: linear-gradient(to top, #f3e7e9 0%, #e3eeff 99%, #e3eeff 100%);} .overflow-y-auto{height:70vh}") as demo:
- #标题
- gr.Markdown("Have a quick Chat with ChatGPT (designed by Li)")
-
- with gr.Tabs():
- with gr.TabItem("Login"):
-
-
- # 设置输入key
- key = gr.Textbox(label="API_KEY",placeholder="Your API_KEY")
- #输入账号
- username = gr.Textbox(label="Username",placeholder="Your Username")
- # 设置按钮
- greet_btn = gr.Button("Submit")
- #校验
- ouput = gr.Textbox(show_label=False,placeholder="Result")
-
-
- list_info = []
- list_info.append(key)
- list_info.append(username)
-
-
- # 设置按钮点击事件
- greet_btn.click(fn=input_key, inputs=list_info,outputs=ouput)
- with gr.TabItem("Chat"):
-
-
- with gr.Row():
-
- with gr.Column():
- chatbot = gr.Chatbot(elem_id="chatbot2",label="Chatbot")
- # groupname = user_group[username]
- # if groupname == "":
- # chatbot.elem_id = "chatbot1"
- # chatbot.label = "Chatbot"
-
- # else:
- # chatbot.elem_id = "chatbot2"
- # chatbot.label = groupname
-
-
- txt = gr.Textbox(label="User", placeholder="Message:").style(container=False)
-
-
- input_list1 = []
- input_list1.append(txt)
- input_list1.append(username)
-
- txt.submit(predict, input_list1, chatbot)
- #这里可以优化等问题回复才可以再次发问题
- txt.submit(None, None, txt, _js="() => {''}")
-
- with gr.Column():
-
- #num = gr.Textbox(show_label=False,placeholder="清除会话记录数(从最早记录开始)").style(container=False)
- num = gr.Slider(0, 20, value=2,step=1,label="Count",info="Choose betwen 0 and 20") #滑动条
-
- del_btn = gr.Button("Clear")
- ouput = gr.Textbox(label="Status",placeholder="Result").style(container=False)
- clear_list = []
- clear_list.append(num)
- clear_list.append(username)
-
- del_btn.click(fn=clear_info,inputs=clear_list,outputs=ouput)
-
-
- role = gr.Radio(["Translator", "Code Developer", "English Tutor","Writing Assistant","Businessman","Free Chat"],
- label="Play A Role")#单选
- button = gr.Button("Run")
- input_list2 = []
- input_list2.append(role)
- input_list2.append(username)
-
- button.click(predict,inputs=input_list2,outputs=chatbot)
- with gr.Row():
-
- groupname = gr.Dropdown(["Dormitory Group", "Family Group", "Class Group"],label="Chat Group")
- result = gr.Textbox(label="Your Groups",placeholder="Groups").style(container=False)
- with gr.Row():
-
- input_list3 = []
- input_list3.append(groupname)
- input_list3.append(username)
-
- join = gr.Button("Join")
- join.click(join_group,inputs=input_list3,outputs=result)
-
- leave = gr.Button("Leave")
- leave.click(leave_group,inputs=input_list3,outputs=chatbot)
-
- reflesh = gr.Button("Refresh")
- reflesh.click(reflesh_chat,inputs=input_list3,outputs=chatbot)
-
-
-
-
-demo.launch()
diff --git a/spaces/LuxOAI/ChatGpt-Web/public/serviceWorkerRegister.js b/spaces/LuxOAI/ChatGpt-Web/public/serviceWorkerRegister.js
deleted file mode 100644
index 8405f21aaab9ddec0cff867cfe1dfff67ea01ccd..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/public/serviceWorkerRegister.js
+++ /dev/null
@@ -1,9 +0,0 @@
-if ('serviceWorker' in navigator) {
- window.addEventListener('load', function () {
- navigator.serviceWorker.register('/serviceWorker.js').then(function (registration) {
- console.log('ServiceWorker registration successful with scope: ', registration.scope);
- }, function (err) {
- console.error('ServiceWorker registration failed: ', err);
- });
- });
-}
\ No newline at end of file
diff --git a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/commons.py b/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/commons.py
deleted file mode 100644
index 33ec83a7986a12b237d28d5e610222881d6b42ae..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/commons.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import math
-
-import torch
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
- pad_shape = [item for sublist in reversed(pad_shape) for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) +
- ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) *
- ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(length,
- channels,
- min_timescale=1.0,
- max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) *
- -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale,
- max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale,
- max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]
- ]))[:, :-1]
- path = path.unsqueeze(1).transpose(2, 3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item()**norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm**(1. / norm_type)
- return total_norm
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/resource_manager.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/resource_manager.py
deleted file mode 100644
index b0f28af2e35a3ea29958e5eee4e19b26f1fa010b..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/resource_manager.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import os
-from os import path
-import shutil
-import collections
-
-import cv2
-from PIL import Image
-if not hasattr(Image, 'Resampling'): # Pillow<9.0
- Image.Resampling = Image
-import numpy as np
-
-from util.palette import davis_palette
-import progressbar
-
-
-# https://bugs.python.org/issue28178
-# ah python ah why
-class LRU:
- def __init__(self, func, maxsize=128):
- self.cache = collections.OrderedDict()
- self.func = func
- self.maxsize = maxsize
-
- def __call__(self, *args):
- cache = self.cache
- if args in cache:
- cache.move_to_end(args)
- return cache[args]
- result = self.func(*args)
- cache[args] = result
- if len(cache) > self.maxsize:
- cache.popitem(last=False)
- return result
-
- def invalidate(self, key):
- self.cache.pop(key, None)
-
-
-class ResourceManager:
- def __init__(self, config):
- # determine inputs
- images = config['images']
- video = config['video']
- self.workspace = config['workspace']
- self.size = config['size']
- self.palette = davis_palette
-
- # create temporary workspace if not specified
- if self.workspace is None:
- if images is not None:
- basename = path.basename(images)
- elif video is not None:
- basename = path.basename(video)[:-4]
- else:
- raise NotImplementedError(
- 'Either images, video, or workspace has to be specified')
-
- self.workspace = path.join('./workspace', basename)
-
- print(f'Workspace is in: {self.workspace}')
-
- # determine the location of input images
- need_decoding = False
- need_resizing = False
- if path.exists(path.join(self.workspace, 'images')):
- pass
- elif images is not None:
- need_resizing = True
- elif video is not None:
- # will decode video into frames later
- need_decoding = True
-
- # create workspace subdirectories
- self.image_dir = path.join(self.workspace, 'images')
- self.mask_dir = path.join(self.workspace, 'masks')
- os.makedirs(self.image_dir, exist_ok=True)
- os.makedirs(self.mask_dir, exist_ok=True)
-
- # convert read functions to be buffered
- self.get_image = LRU(self._get_image_unbuffered, maxsize=config['buffer_size'])
- self.get_mask = LRU(self._get_mask_unbuffered, maxsize=config['buffer_size'])
-
- # extract frames from video
- if need_decoding:
- self._extract_frames(video)
-
- # copy/resize existing images to the workspace
- if need_resizing:
- self._copy_resize_frames(images)
-
- # read all frame names
- self.names = sorted(os.listdir(self.image_dir))
- self.names = [f[:-4] for f in self.names] # remove extensions
- self.length = len(self.names)
-
- assert self.length > 0, f'No images found! Check {self.workspace}/images. Remove folder if necessary.'
-
- print(f'{self.length} images found.')
-
- self.height, self.width = self.get_image(0).shape[:2]
- self.visualization_init = False
-
- def _extract_frames(self, video):
- cap = cv2.VideoCapture(video)
- frame_index = 0
- print(f'Extracting frames from {video} into {self.image_dir}...')
- bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength)
- while(cap.isOpened()):
- _, frame = cap.read()
- if frame is None:
- break
- if self.size > 0:
- h, w = frame.shape[:2]
- new_w = (w*self.size//min(w, h))
- new_h = (h*self.size//min(w, h))
- if new_w != w or new_h != h:
- frame = cv2.resize(frame,dsize=(new_w,new_h),interpolation=cv2.INTER_AREA)
- cv2.imwrite(path.join(self.image_dir, f'{frame_index:07d}.jpg'), frame)
- frame_index += 1
- bar.update(frame_index)
- bar.finish()
- print('Done!')
-
- def _copy_resize_frames(self, images):
- image_list = os.listdir(images)
- print(f'Copying/resizing frames into {self.image_dir}...')
- for image_name in progressbar.progressbar(image_list):
- if self.size < 0:
- # just copy
- shutil.copy2(path.join(images, image_name), self.image_dir)
- else:
- frame = cv2.imread(path.join(images, image_name))
- h, w = frame.shape[:2]
- new_w = (w*self.size//min(w, h))
- new_h = (h*self.size//min(w, h))
- if new_w != w or new_h != h:
- frame = cv2.resize(frame,dsize=(new_w,new_h),interpolation=cv2.INTER_AREA)
- cv2.imwrite(path.join(self.image_dir, image_name), frame)
- print('Done!')
-
- def save_mask(self, ti, mask):
- # mask should be uint8 H*W without channels
- assert 0 <= ti < self.length
- assert isinstance(mask, np.ndarray)
-
- mask = Image.fromarray(mask)
- mask.putpalette(self.palette)
- mask.save(path.join(self.mask_dir, self.names[ti]+'.png'))
- self.invalidate(ti)
-
- def save_visualization(self, ti, image):
- # image should be uint8 3*H*W
- assert 0 <= ti < self.length
- assert isinstance(image, np.ndarray)
- if not self.visualization_init:
- self.visualization_dir = path.join(self.workspace, 'visualization')
- os.makedirs(self.visualization_dir, exist_ok=True)
- self.visualization_init = True
-
- image = Image.fromarray(image)
- image.save(path.join(self.visualization_dir, self.names[ti]+'.jpg'))
-
- def _get_image_unbuffered(self, ti):
- # returns H*W*3 uint8 array
- assert 0 <= ti < self.length
-
- image = Image.open(path.join(self.image_dir, self.names[ti]+'.jpg'))
- image = np.array(image)
- return image
-
- def _get_mask_unbuffered(self, ti):
- # returns H*W uint8 array
- assert 0 <= ti < self.length
-
- mask_path = path.join(self.mask_dir, self.names[ti]+'.png')
- if path.exists(mask_path):
- mask = Image.open(mask_path)
- mask = np.array(mask)
- return mask
- else:
- return None
-
- def read_external_image(self, file_name, size=None):
- image = Image.open(file_name)
- is_mask = image.mode in ['L', 'P']
- if size is not None:
- # PIL uses (width, height)
- image = image.resize((size[1], size[0]),
- resample=Image.Resampling.NEAREST if is_mask else Image.Resampling.BICUBIC)
- image = np.array(image)
- return image
-
- def invalidate(self, ti):
- # the image buffer is never invalidated
- self.get_mask.invalidate((ti,))
-
- def __len__(self):
- return self.length
-
- @property
- def h(self):
- return self.height
-
- @property
- def w(self):
- return self.width
diff --git a/spaces/Makiing/coolb-in-gtest/tailwind.config.js b/spaces/Makiing/coolb-in-gtest/tailwind.config.js
deleted file mode 100644
index 03da3c3c45be6983b9f5ffa6df5f1fd0870e9636..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/tailwind.config.js
+++ /dev/null
@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
- content: [
- './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
- './src/components/**/*.{js,ts,jsx,tsx,mdx}',
- './src/app/**/*.{js,ts,jsx,tsx,mdx}',
- './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
- ],
- "darkMode": "class",
- theme: {
- extend: {
- colors: {
- 'primary-blue': 'rgb(var(--color-primary-blue) / )',
- secondary: 'rgb(var(--color-secondary) / )',
- 'primary-background': 'rgb(var(--primary-background) / )',
- 'primary-text': 'rgb(var(--primary-text) / )',
- 'secondary-text': 'rgb(var(--secondary-text) / )',
- 'light-text': 'rgb(var(--light-text) / )',
- 'primary-border': 'rgb(var(--primary-border) / )',
- },
- keyframes: {
- slideDownAndFade: {
- from: { opacity: 0, transform: 'translateY(-2px)' },
- to: { opacity: 1, transform: 'translateY(0)' },
- },
- slideLeftAndFade: {
- from: { opacity: 0, transform: 'translateX(2px)' },
- to: { opacity: 1, transform: 'translateX(0)' },
- },
- slideUpAndFade: {
- from: { opacity: 0, transform: 'translateY(2px)' },
- to: { opacity: 1, transform: 'translateY(0)' },
- },
- slideRightAndFade: {
- from: { opacity: 0, transform: 'translateX(2px)' },
- to: { opacity: 1, transform: 'translateX(0)' },
- },
- },
- animation: {
- slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- },
- },
- },
- plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}
diff --git a/spaces/Malmika/Osana-WEB-GPT/README.md b/spaces/Malmika/Osana-WEB-GPT/README.md
deleted file mode 100644
index 5aff4eb35d38640222142fd5ebba1419ebf9b9af..0000000000000000000000000000000000000000
--- a/spaces/Malmika/Osana-WEB-GPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Osana WEB GPT
-emoji: 💻
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/modules/commons.py b/spaces/MashiroSA/sovits-emu-voice-transform/modules/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/MashiroSA/sovits-emu-voice-transform/modules/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, idx_str:idx_end]
- return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
- return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/Mattdoc99/ElonYTsearch/styles.css b/spaces/Mattdoc99/ElonYTsearch/styles.css
deleted file mode 100644
index 970ab28ddbd5d54967af3739c504b1912390c5a4..0000000000000000000000000000000000000000
--- a/spaces/Mattdoc99/ElonYTsearch/styles.css
+++ /dev/null
@@ -1,160 +0,0 @@
-@import url("https://fonts.googleapis.com/css?family=Arimo:400,700");
-
-section.main[tabindex="0"] {
- overflow: scroll;
-}
-
-body {
- height: 100%;
- width: 100%;
- background: #e9e9e9;
- font-family: 'Arimo', Arial, sans-serif;
- font-weight: 400;
- font-size: 14px;
- color: #000000;
-}
-
-* {
- -webkit-transition: 300ms;
- transition: 300ms;
-}
-
-.intro {
- text-align: center;
-}
-
-ul {
- list-style-type: none;
-}
-
-h1,
-h2,
-h3,
-h4,
-h5,
-p {
- font-weight: 400;
-}
-
-a {
- text-decoration: none;
- color: inherit;
-}
-
-a:hover {
- color: #6ABCEA;
-}
-
-.container {
- display: -webkit-box;
- display: -ms-flexbox;
- display: flex;
- -ms-flex-wrap: wrap;
- flex-wrap: wrap;
- max-width: 100%;
- margin-top: 10vh;
- margin-left: auto;
- margin-right: auto;
- -webkit-box-pack: center;
- -ms-flex-pack: center;
- justify-content: center;
-}
-
-.movie-card {
- background: #ffffff;
- box-shadow: 0px 6px 18px rgba(0, 0, 0, 0.1);
- width: 100%;
- max-width: 315px;
- margin: 2em;
- border-radius: 10px;
- display: inline-block;
-}
-
-.movie-header {
- padding: 0;
- margin: 0;
- height: 367px;
- width: 100%;
- display: block;
- border-top-left-radius: 10px;
- border-top-right-radius: 10px;
-}
-
-.header-icon-container {
- position: relative;
-}
-
-.header-icon {
- width: 100%;
- height: 367px;
- line-height: 367px;
- text-align: center;
- vertical-align: middle;
- margin: 0 auto;
- color: #ffffff;
- font-size: 54px;
- text-shadow: 0px 0px 20px #6abcea, 0px 5px 20px #6ABCEA;
- opacity: .85;
-}
-
-.header-icon:hover {
- background: rgba(0, 0, 0, 0.15);
- font-size: 74px;
- text-shadow: 0px 0px 20px #6abcea, 0px 5px 30px #6ABCEA;
- border-top-left-radius: 10px;
- border-top-right-radius: 10px;
- opacity: 1;
-}
-
-.movie-card:hover {
- -webkit-transform: scale(1.03);
- transform: scale(1.03);
- box-shadow: 0px 10px 25px rgba(0, 0, 0, 0.08);
-}
-
-.movie-content {
- padding: 18px 18px 24px 18px;
- margin: 0;
-}
-
-.movie-content-header,
-.movie-info {
- display: table;
- width: 100%;
-}
-
-.movie-title {
- font-size: 24px;
- margin: 0;
- display: table-cell;
-}
-
-.movie-info {
- margin-top: 1em;
-}
-
-.info-section {
- display: table-cell;
- text-transform: uppercase;
- text-align: center;
-}
-
-.info-section:first-of-type {
- text-align: left;
-}
-
-.info-section:last-of-type {
- text-align: right;
-}
-
-.info-section label {
- display: block;
- color: rgba(0, 0, 0, 0.5);
- margin-bottom: .5em;
- font-size: 9px;
-}
-
-.info-section span {
- font-weight: 700;
- font-size: 11px;
-}
\ No newline at end of file
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/create_imagenetlvis_json.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/create_imagenetlvis_json.py
deleted file mode 100644
index 4d5a0b3712b5a2fb94737b8dfe5d70202305926b..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/create_imagenetlvis_json.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import os
-import cv2
-from nltk.corpus import wordnet
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--imagenet_path', default='datasets/imagenet/ImageNet-LVIS')
- parser.add_argument('--lvis_meta_path', default='datasets/lvis/lvis_v1_val.json')
- parser.add_argument('--out_path', default='datasets/imagenet/annotations/imagenet_lvis_image_info.json')
- args = parser.parse_args()
-
- print('Loading LVIS meta')
- data = json.load(open(args.lvis_meta_path, 'r'))
- print('Done')
- synset2cat = {x['synset']: x for x in data['categories']}
- count = 0
- images = []
- image_counts = {}
- folders = sorted(os.listdir(args.imagenet_path))
- for i, folder in enumerate(folders):
- class_path = args.imagenet_path + folder
- files = sorted(os.listdir(class_path))
- synset = wordnet.synset_from_pos_and_offset('n', int(folder[1:])).name()
- cat = synset2cat[synset]
- cat_id = cat['id']
- cat_name = cat['name']
- cat_images = []
- for file in files:
- count = count + 1
- file_name = '{}/{}'.format(folder, file)
- img = cv2.imread('{}/{}'.format(args.imagenet_path, file_name))
- h, w = img.shape[:2]
- image = {
- 'id': count,
- 'file_name': file_name,
- 'pos_category_ids': [cat_id],
- 'width': w,
- 'height': h
- }
- cat_images.append(image)
- images.extend(cat_images)
- image_counts[cat_id] = len(cat_images)
- print(i, cat_name, len(cat_images))
- print('# Images', len(images))
- for x in data['categories']:
- x['image_count'] = image_counts[x['id']] if x['id'] in image_counts else 0
- out = {'categories': data['categories'], 'images': images, 'annotations': []}
- print('Writing to', args.out_path)
- json.dump(out, open(args.out_path, 'w'))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/utils/misc.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/utils/misc.py
deleted file mode 100644
index 2c58d0d7fee9fe3d4519270ad8c1e998d0d8a18c..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/utils/misc.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def is_str(x):
- """Whether the input is an string instance.
-
- Note: This method is deprecated since python 2 is no longer supported.
- """
- return isinstance(x, str)
-
-
-def import_modules_from_strings(imports, allow_failed_imports=False):
- """Import modules from the given list of strings.
-
- Args:
- imports (list | str | None): The given module names to be imported.
- allow_failed_imports (bool): If True, the failed imports will return
- None. Otherwise, an ImportError is raise. Default: False.
-
- Returns:
- list[module] | module | None: The imported modules.
-
- Examples:
- >>> osp, sys = import_modules_from_strings(
- ... ['os.path', 'sys'])
- >>> import os.path as osp_
- >>> import sys as sys_
- >>> assert osp == osp_
- >>> assert sys == sys_
- """
- if not imports:
- return
- single_import = False
- if isinstance(imports, str):
- single_import = True
- imports = [imports]
- if not isinstance(imports, list):
- raise TypeError(
- f'custom_imports must be a list but got type {type(imports)}')
- imported = []
- for imp in imports:
- if not isinstance(imp, str):
- raise TypeError(
- f'{imp} is of type {type(imp)} and cannot be imported.')
- try:
- imported_tmp = import_module(imp)
- except ImportError:
- if allow_failed_imports:
- warnings.warn(f'{imp} failed to import and is ignored.',
- UserWarning)
- imported_tmp = None
- else:
- raise ImportError
- imported.append(imported_tmp)
- if single_import:
- imported = imported[0]
- return imported
-
-
-def iter_cast(inputs, dst_type, return_type=None):
- """Cast elements of an iterable object into some type.
-
- Args:
- inputs (Iterable): The input object.
- dst_type (type): Destination type.
- return_type (type, optional): If specified, the output object will be
- converted to this type, otherwise an iterator.
-
- Returns:
- iterator or specified type: The converted object.
- """
- if not isinstance(inputs, abc.Iterable):
- raise TypeError('inputs must be an iterable object')
- if not isinstance(dst_type, type):
- raise TypeError('"dst_type" must be a valid type')
-
- out_iterable = map(dst_type, inputs)
-
- if return_type is None:
- return out_iterable
- else:
- return return_type(out_iterable)
-
-
-def list_cast(inputs, dst_type):
- """Cast elements of an iterable object into a list of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=list)
-
-
-def tuple_cast(inputs, dst_type):
- """Cast elements of an iterable object into a tuple of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=tuple)
-
-
-def is_seq_of(seq, expected_type, seq_type=None):
- """Check whether it is a sequence of some type.
-
- Args:
- seq (Sequence): The sequence to be checked.
- expected_type (type): Expected type of sequence items.
- seq_type (type, optional): Expected sequence type.
-
- Returns:
- bool: Whether the sequence is valid.
- """
- if seq_type is None:
- exp_seq_type = abc.Sequence
- else:
- assert isinstance(seq_type, type)
- exp_seq_type = seq_type
- if not isinstance(seq, exp_seq_type):
- return False
- for item in seq:
- if not isinstance(item, expected_type):
- return False
- return True
-
-
-def is_list_of(seq, expected_type):
- """Check whether it is a list of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=list)
-
-
-def is_tuple_of(seq, expected_type):
- """Check whether it is a tuple of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=tuple)
-
-
-def slice_list(in_list, lens):
- """Slice a list into several sub lists by a list of given length.
-
- Args:
- in_list (list): The list to be sliced.
- lens(int or list): The expected length of each out list.
-
- Returns:
- list: A list of sliced list.
- """
- if isinstance(lens, int):
- assert len(in_list) % lens == 0
- lens = [lens] * int(len(in_list) / lens)
- if not isinstance(lens, list):
- raise TypeError('"indices" must be an integer or a list of integers')
- elif sum(lens) != len(in_list):
- raise ValueError('sum of lens and list length does not '
- f'match: {sum(lens)} != {len(in_list)}')
- out_list = []
- idx = 0
- for i in range(len(lens)):
- out_list.append(in_list[idx:idx + lens[i]])
- idx += lens[i]
- return out_list
-
-
-def concat_list(in_list):
- """Concatenate a list of list into a single list.
-
- Args:
- in_list (list): The list of list to be merged.
-
- Returns:
- list: The concatenated flat list.
- """
- return list(itertools.chain(*in_list))
-
-
-def check_prerequisites(
- prerequisites,
- checker,
- msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
- 'found, please install them first.'): # yapf: disable
- """A decorator factory to check if prerequisites are satisfied.
-
- Args:
- prerequisites (str of list[str]): Prerequisites to be checked.
- checker (callable): The checker method that returns True if a
- prerequisite is meet, False otherwise.
- msg_tmpl (str): The message template with two variables.
-
- Returns:
- decorator: A specific decorator.
- """
-
- def wrap(func):
-
- @functools.wraps(func)
- def wrapped_func(*args, **kwargs):
- requirements = [prerequisites] if isinstance(
- prerequisites, str) else prerequisites
- missing = []
- for item in requirements:
- if not checker(item):
- missing.append(item)
- if missing:
- print(msg_tmpl.format(', '.join(missing), func.__name__))
- raise RuntimeError('Prerequisites not meet.')
- else:
- return func(*args, **kwargs)
-
- return wrapped_func
-
- return wrap
-
-
-def _check_py_package(package):
- try:
- import_module(package)
- except ImportError:
- return False
- else:
- return True
-
-
-def _check_executable(cmd):
- if subprocess.call(f'which {cmd}', shell=True) != 0:
- return False
- else:
- return True
-
-
-def requires_package(prerequisites):
- """A decorator to check if some python packages are installed.
-
- Example:
- >>> @requires_package('numpy')
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- array([0.])
- >>> @requires_package(['numpy', 'non_package'])
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- ImportError
- """
- return check_prerequisites(prerequisites, checker=_check_py_package)
-
-
-def requires_executable(prerequisites):
- """A decorator to check if some executable files are installed.
-
- Example:
- >>> @requires_executable('ffmpeg')
- >>> func(arg1, args):
- >>> print(1)
- 1
- """
- return check_prerequisites(prerequisites, checker=_check_executable)
-
-
-def deprecated_api_warning(name_dict, cls_name=None):
- """A decorator to check if some arguments are deprecate and try to replace
- deprecate src_arg_name to dst_arg_name.
-
- Args:
- name_dict(dict):
- key (str): Deprecate argument names.
- val (str): Expected argument names.
-
- Returns:
- func: New function.
- """
-
- def api_warning_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get name of the function
- func_name = old_func.__name__
- if cls_name is not None:
- func_name = f'{cls_name}.{func_name}'
- if args:
- arg_names = args_info.args[:len(args)]
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in arg_names:
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- arg_names[arg_names.index(src_arg_name)] = dst_arg_name
- if kwargs:
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in kwargs:
-
- assert dst_arg_name not in kwargs, (
- f'The expected behavior is to replace '
- f'the deprecated key `{src_arg_name}` to '
- f'new key `{dst_arg_name}`, but got them '
- f'in the arguments at the same time, which '
- f'is confusing. `{src_arg_name} will be '
- f'deprecated in the future, please '
- f'use `{dst_arg_name}` instead.')
-
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-
- # apply converted arguments to the decorated method
- output = old_func(*args, **kwargs)
- return output
-
- return new_func
-
- return api_warning_wrapper
-
-
-def is_method_overridden(method, base_class, derived_class):
- """Check if a method of base class is overridden in derived class.
-
- Args:
- method (str): the method name to check.
- base_class (type): the class of the base class.
- derived_class (type | Any): the class or instance of the derived class.
- """
- assert isinstance(base_class, type), \
- "base_class doesn't accept instance, Please pass class instead."
-
- if not isinstance(derived_class, type):
- derived_class = derived_class.__class__
-
- base_method = getattr(base_class, method)
- derived_method = getattr(derived_class, method)
- return derived_method != base_method
-
-
-def has_method(obj: object, method: str) -> bool:
- """Check whether the object has a method.
-
- Args:
- method (str): The method name to check.
- obj (object): The object to check.
-
- Returns:
- bool: True if the object has the method else False.
- """
- return hasattr(obj, method) and callable(getattr(obj, method))
diff --git a/spaces/Menna2211/Text-Image/pages/Stable-Diffusion.py b/spaces/Menna2211/Text-Image/pages/Stable-Diffusion.py
deleted file mode 100644
index ca6adf668fc94da440485cb1e4d0250fcb148428..0000000000000000000000000000000000000000
--- a/spaces/Menna2211/Text-Image/pages/Stable-Diffusion.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import streamlit as st
-import torch
-import time
-from diffusers import StableDiffusionPipeline
-
-
-
-# Model 1
-@st.cache_resource(show_spinner=False ,ttl=3600)
-def get_model3():
- device = "cuda" if torch.cuda.is_available() else "cpu"
- torch_dtype = torch.float16 if device == "cuda" else torch.float32
- model_id = "runwayml/stable-diffusion-v1-5"
- pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
- pipe = pipe.to(device)
- return pipe
-
-pipe1 =get_model3()
-
-
-
-st.title("Stable Diffusion App")
-# define the layout of your app
-
-# Define the Streamlit app layout
-prompt = st.text_input("Write your sentence:")
-
-models = st.selectbox("Select a Model", ["Select a Model","Hugging-Face", "Github"])
-submit_buttons = st.button("Compute")
-
-
-if models == "Select a Model" and not submit_buttons :
- st.stop()
-
-elif models == "Select a Model" and submit_buttons :
- st.warning('Warning.....!!,Plz..... Select a Model ', icon="⚠️")
-
-# Display the generated text
-
-if models == "Hugging-Face" and submit_buttons:
- progress_text = "Operation in progress. Please wait."
- bar = st.progress(0, text=progress_text)
- for percent_complete in range(100):
- generated_img=pipe1(prompt).images[0]
- time.sleep(0.1)
- bar.progress(percent_complete + 1, text=progress_text)
-
- # Display the uploaded image and its generated caption
- st.write("Generated Image:")
- st.image(generated_img)
- time.sleep(3)
- st.success('Congratulations task is done ', icon="✅")
- st.balloons()
-
-elif models == "Github" and submit_buttons:
- progress_text = "Operation in progress. Please wait."
- bar = st.progress(0, text=progress_text)
- for percent_complete in range(100):
- generated_img2=pipe2(prompt).images[0]
- time.sleep(0.1)
- bar.progress(percent_complete + 1, text=progress_text)
-
- # Display the uploaded image and its generated caption
- st.write("Generated Image:")
- st.image(generated_img2)
- time.sleep(3)
- st.success('Congratulations task is done ', icon="✅")
- st.balloons()
diff --git a/spaces/MoonQiu/LongerCrafter/app.py b/spaces/MoonQiu/LongerCrafter/app.py
deleted file mode 100644
index c0483b4452698aa5106073df689fedf248e794ba..0000000000000000000000000000000000000000
--- a/spaces/MoonQiu/LongerCrafter/app.py
+++ /dev/null
@@ -1,297 +0,0 @@
-import gradio as gr
-
-import os
-import sys
-import argparse
-import random
-from omegaconf import OmegaConf
-import torch
-import torchvision
-from pytorch_lightning import seed_everything
-from huggingface_hub import hf_hub_download
-
-sys.path.insert(0, "scripts/evaluation")
-from funcs import (
- batch_ddim_sampling_freenoise,
- load_model_checkpoint,
-)
-from utils.utils import instantiate_from_config
-
-ckpt_path_1024 = "checkpoints/base_1024_v1/model.ckpt"
-ckpt_dir_1024 = "checkpoints/base_1024_v1"
-os.makedirs(ckpt_dir_1024, exist_ok=True)
-hf_hub_download(repo_id="VideoCrafter/Text2Video-1024", filename="model.ckpt", local_dir=ckpt_dir_1024)
-
-# ckpt_path_256 = "checkpoints/base_256_v1/model.pth"
-# ckpt_dir_256 = "checkpoints/base_256_v1"
-# os.makedirs(ckpt_dir_256, exist_ok=True)
-# hf_hub_download(repo_id="MoonQiu/LongerCrafter", filename="model.pth", local_dir=ckpt_dir_256)
-
-
-def infer(prompt, output_size, seed, num_frames, ddim_steps, unconditional_guidance_scale, save_fps):
- window_size = 16
- window_stride = 4
-
- if output_size == "576x1024":
- width = 1024
- height = 576
- config_1024 = "configs/inference_t2v_1024_v1.0_freenoise.yaml"
- config_1024 = OmegaConf.load(config_1024)
- model_config_1024 = config_1024.pop("model", OmegaConf.create())
- model_1024 = instantiate_from_config(model_config_1024)
- model_1024 = model_1024.cuda()
- model_1024 = load_model_checkpoint(model_1024, ckpt_path_1024)
- model_1024.eval()
- model = model_1024
- fps = 28
- # elif output_size == "256x256":
- # width = 256
- # height = 256
- # config_256 = "configs/inference_t2v_tconv256_v1.0_freenoise.yaml"
- # config_256 = OmegaConf.load(config_256)
- # model_config_256 = config_256.pop("model", OmegaConf.create())
- # model_256 = instantiate_from_config(model_config_256)
- # model_256 = model_256.cuda()
- # model_256 = load_model_checkpoint(model_256, ckpt_path_256)
- # model_256.eval()
- # model = model_256
- # fps = 8
-
- if seed is None:
- seed = int.from_bytes(os.urandom(2), "big")
- print(f"Using seed: {seed}")
- seed_everything(seed)
-
- args = argparse.Namespace(
- mode="base",
- savefps=save_fps,
- n_samples=1,
- ddim_steps=ddim_steps,
- ddim_eta=0.0,
- bs=1,
- height=height,
- width=width,
- frames=num_frames,
- fps=fps,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_guidance_scale_temporal=None,
- cond_input=None,
- window_size=window_size,
- window_stride=window_stride,
- )
-
- ## latent noise shape
- h, w = args.height // 8, args.width // 8
- frames = model.temporal_length if args.frames < 0 else args.frames
- channels = model.channels
-
- x_T_total = torch.randn(
- [args.n_samples, 1, channels, frames, h, w], device=model.device
- ).repeat(1, args.bs, 1, 1, 1, 1)
- for frame_index in range(args.window_size, args.frames, args.window_stride):
- list_index = list(
- range(
- frame_index - args.window_size,
- frame_index + args.window_stride - args.window_size,
- )
- )
- random.shuffle(list_index)
- x_T_total[
- :, :, :, frame_index : frame_index + args.window_stride
- ] = x_T_total[:, :, :, list_index]
-
- batch_size = 1
- noise_shape = [batch_size, channels, frames, h, w]
- fps = torch.tensor([args.fps] * batch_size).to(model.device).long()
- prompts = [prompt]
- text_emb = model.get_learned_conditioning(prompts)
-
- cond = {"c_crossattn": [text_emb], "fps": fps}
-
- ## inference
- batch_samples = batch_ddim_sampling_freenoise(
- model,
- cond,
- noise_shape,
- args.n_samples,
- args.ddim_steps,
- args.ddim_eta,
- args.unconditional_guidance_scale,
- args=args,
- x_T_total=x_T_total,
- )
-
- video_path = "output.mp4"
- vid_tensor = batch_samples[0]
- video = vid_tensor.detach().cpu()
- video = torch.clamp(video.float(), -1.0, 1.0)
- video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
-
- frame_grids = [
- torchvision.utils.make_grid(framesheet, nrow=int(args.n_samples))
- for framesheet in video
- ] # [3, 1*h, n*w]
- grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
- grid = (grid + 1.0) / 2.0
- grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
-
- torchvision.io.write_video(
- video_path,
- grid,
- fps=args.savefps,
- video_codec="h264",
- options={"crf": "10"},
- )
-
- print(video_path)
- return video_path
-
-examples = [
- ["A chihuahua in astronaut suit floating in space, cinematic lighting, glow effect",],
- ["A corgi is swimming quickly",],
- ["A bigfoot walking in the snowstorm",],
- ["Campfire at night in a snowy forest with starry sky in the background",],
- ["A panda is surfing in the universe",],
-]
-
-css = """
-#col-container {max-width: 640px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-.animate-spin {
- animation: spin 1s linear infinite;
-}
-@keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
-}
-#share-btn-container {
- display: flex;
- padding-left: 0.5rem !important;
- padding-right: 0.5rem !important;
- background-color: #000000;
- justify-content: center;
- align-items: center;
- border-radius: 9999px !important;
- max-width: 15rem;
- height: 36px;
-}
-div#share-btn-container > div {
- flex-direction: row;
- background: black;
- align-items: center;
-}
-#share-btn-container:hover {
- background-color: #060606;
-}
-#share-btn {
- all: initial;
- color: #ffffff;
- font-weight: 600;
- cursor:pointer;
- font-family: 'IBM Plex Sans', sans-serif;
- margin-left: 0.5rem !important;
- padding-top: 0.5rem !important;
- padding-bottom: 0.5rem !important;
- right:0;
-}
-#share-btn * {
- all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
- width: auto !important;
- min-height: 0px !important;
-}
-#share-btn-container .wrap {
- display: none !important;
-}
-#share-btn-container.hidden {
- display: none!important;
-}
-img[src*='#center'] {
- display: inline-block;
- margin: unset;
-}
-.footer {
- margin-bottom: 45px;
- margin-top: 10px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
- }
- .footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
-"""
-
-with gr.Blocks(css=css) as demo:
- with gr.Column(elem_id="col-container"):
- gr.Markdown(
- """
- LongerCrafter(FreeNoise) Text-to-Video
-
- Tuning-Free Longer Video Diffusion via Noise Rescheduling
-
- """
- )
-
- prompt_in = gr.Textbox(label="Prompt", placeholder="A chihuahua in astronaut suit floating in space, cinematic lighting, glow effect")
-
- with gr.Row():
- with gr.Accordion('FreeNoise Parameters (feel free to adjust these parameters based on your prompt): ', open=False):
- with gr.Row():
- output_size = gr.Dropdown(["576x1024"], value="576x1024", label="Output Size (around 900s for 576x1024)")
- # output_size = gr.Dropdown(["576x1024", "256x256"], value="576x1024", label="Output Size", info="576x1024 is watermark-free")
- with gr.Row():
- num_frames = gr.Slider(label='Frames (a multiple of 4)',
- minimum=16,
- maximum=36,
- step=4,
- value=32)
- ddim_steps = gr.Slider(label='DDIM Steps',
- minimum=5,
- maximum=200,
- step=1,
- value=50)
- with gr.Row():
- unconditional_guidance_scale = gr.Slider(label='Unconditional Guidance Scale',
- minimum=1.0,
- maximum=20.0,
- step=0.1,
- value=12.0)
- save_fps = gr.Slider(label='Save FPS',
- minimum=1,
- maximum=30,
- step=1,
- value=10)
- with gr.Row():
- seed = gr.Slider(label='Random Seed',
- minimum=0,
- maximum=10000,
- step=1,
- value=123)
-
- submit_btn = gr.Button("Generate")
- video_result = gr.Video(label="Video Output")
-
- gr.Examples(examples=examples, inputs=[prompt_in, output_size, seed, num_frames, ddim_steps, unconditional_guidance_scale, save_fps])
-
- submit_btn.click(fn=infer,
- inputs=[prompt_in, output_size, seed, num_frames, ddim_steps, unconditional_guidance_scale, save_fps],
- outputs=[video_result],
- api_name="zrscp")
-
-demo.queue(max_size=12).launch(show_api=True)
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/_base_/datasets/svtp.py b/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/_base_/datasets/svtp.py
deleted file mode 100644
index 38301d1bb8de9b056e4cd0bcaf16d86200cd4a7d..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/_base_/datasets/svtp.py
+++ /dev/null
@@ -1,14 +0,0 @@
-svtp_textrecog_data_root = '../data/common_benchmarks/SVTP'
-
-svtp_textrecog_train = dict(
- type='OCRDataset',
- data_root=svtp_textrecog_data_root,
- ann_file='textrecog_train.json',
- pipeline=None)
-
-svtp_textrecog_test = dict(
- type='OCRDataset',
- data_root=svtp_textrecog_data_root,
- ann_file='annotation.json',
- test_mode=True,
- pipeline=None)
diff --git a/spaces/NATSpeech/PortaSpeech/tasks/tts/tts_utils.py b/spaces/NATSpeech/PortaSpeech/tasks/tts/tts_utils.py
deleted file mode 100644
index c4b82df98677e7ba132f77b4f147a0b9aa03c1f1..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/tasks/tts/tts_utils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import importlib
-
-from data_gen.tts.base_binarizer import BaseBinarizer
-from data_gen.tts.base_preprocess import BasePreprocessor
-from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls
-from utils.commons.hparams import hparams
-
-
-def parse_dataset_configs():
- max_tokens = hparams['max_tokens']
- max_sentences = hparams['max_sentences']
- max_valid_tokens = hparams['max_valid_tokens']
- if max_valid_tokens == -1:
- hparams['max_valid_tokens'] = max_valid_tokens = max_tokens
- max_valid_sentences = hparams['max_valid_sentences']
- if max_valid_sentences == -1:
- hparams['max_valid_sentences'] = max_valid_sentences = max_sentences
- return max_tokens, max_sentences, max_valid_tokens, max_valid_sentences
-
-
-def parse_mel_losses():
- mel_losses = hparams['mel_losses'].split("|")
- loss_and_lambda = {}
- for i, l in enumerate(mel_losses):
- if l == '':
- continue
- if ':' in l:
- l, lbd = l.split(":")
- lbd = float(lbd)
- else:
- lbd = 1.0
- loss_and_lambda[l] = lbd
- print("| Mel losses:", loss_and_lambda)
- return loss_and_lambda
-
-
-def load_data_preprocessor():
- preprocess_cls = hparams["preprocess_cls"]
- pkg = ".".join(preprocess_cls.split(".")[:-1])
- cls_name = preprocess_cls.split(".")[-1]
- preprocessor: BasePreprocessor = getattr(importlib.import_module(pkg), cls_name)()
- preprocess_args = {}
- preprocess_args.update(hparams['preprocess_args'])
- return preprocessor, preprocess_args
-
-
-def load_data_binarizer():
- binarizer_cls = hparams['binarizer_cls']
- pkg = ".".join(binarizer_cls.split(".")[:-1])
- cls_name = binarizer_cls.split(".")[-1]
- binarizer: BaseBinarizer = getattr(importlib.import_module(pkg), cls_name)()
- binarization_args = {}
- binarization_args.update(hparams['binarization_args'])
- return binarizer, binarization_args
diff --git a/spaces/NCTCMumbai/NCTC/models/official/modeling/optimization/configs/optimization_config_test.py b/spaces/NCTCMumbai/NCTC/models/official/modeling/optimization/configs/optimization_config_test.py
deleted file mode 100644
index 6dcd55e0e2071a23cae1494ae29c5efa282d052a..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/modeling/optimization/configs/optimization_config_test.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for optimization_config.py."""
-
-import tensorflow as tf
-
-from official.modeling.optimization.configs import learning_rate_config as lr_cfg
-from official.modeling.optimization.configs import optimization_config
-from official.modeling.optimization.configs import optimizer_config as opt_cfg
-
-
-class OptimizerConfigTest(tf.test.TestCase):
-
- def test_no_optimizer(self):
- optimizer = optimization_config.OptimizationConfig({}).optimizer.get()
- self.assertEqual(optimizer, None)
-
- def test_no_lr_schedule(self):
- lr = optimization_config.OptimizationConfig({}).learning_rate.get()
- self.assertEqual(lr, None)
-
- def test_no_warmup_schedule(self):
- warmup = optimization_config.OptimizationConfig({}).warmup.get()
- self.assertEqual(warmup, None)
-
- def test_config(self):
- opt_config = optimization_config.OptimizationConfig({
- 'optimizer': {
- 'type': 'sgd',
- 'sgd': {} # default config
- },
- 'learning_rate': {
- 'type': 'polynomial',
- 'polynomial': {}
- },
- 'warmup': {
- 'type': 'linear'
- }
- })
- self.assertEqual(opt_config.optimizer.get(),
- opt_cfg.SGDConfig())
- self.assertEqual(opt_config.learning_rate.get(),
- lr_cfg.PolynomialLrConfig())
- self.assertEqual(opt_config.warmup.get(),
- lr_cfg.LinearWarmupConfig())
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/transformer.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/transformer.py
deleted file mode 100644
index 92f509cf26b802dcd769b97e4c11987e713d8d16..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/transformer.py
+++ /dev/null
@@ -1,437 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Keras-based transformer block layer."""
-# pylint: disable=g-classes-have-attributes
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import gin
-import tensorflow as tf
-
-from official.nlp.modeling.layers import attention
-from official.nlp.modeling.layers import dense_einsum
-from official.nlp.modeling.layers import multi_channel_attention
-from official.nlp.modeling.layers.util import tf_function_if_eager
-
-
-@tf.keras.utils.register_keras_serializable(package="Text")
-class Transformer(tf.keras.layers.Layer):
- """Transformer layer.
-
- This layer implements the Transformer from "Attention Is All You Need".
- (https://arxiv.org/abs/1706.03762).
-
- Arguments:
- num_attention_heads: Number of attention heads.
- intermediate_size: Size of the intermediate layer.
- intermediate_activation: Activation for the intermediate layer.
- dropout_rate: Dropout probability for the post-attention and output dropout.
- attention_dropout_rate: Dropout probability for within the attention layer.
- output_range: the sequence output range, [0, output_range) by slicing the
- target sequence. `None` means the target sequence is not sliced.
- kernel_initializer: Initializer for dense layer kernels.
- bias_initializer: Initializer for dense layer biases.
- kernel_regularizer: Regularizer for dense layer kernels.
- bias_regularizer: Regularizer for dense layer biases.
- activity_regularizer: Regularizer for dense layer activity.
- kernel_constraint: Constraint for dense layer kernels.
- bias_constraint: Constraint for dense layer kernels.
- """
-
- def __init__(self,
- num_attention_heads,
- intermediate_size,
- intermediate_activation,
- dropout_rate=0.0,
- attention_dropout_rate=0.0,
- output_range=None,
- kernel_initializer="glorot_uniform",
- bias_initializer="zeros",
- kernel_regularizer=None,
- bias_regularizer=None,
- activity_regularizer=None,
- kernel_constraint=None,
- bias_constraint=None,
- **kwargs):
- super(Transformer, self).__init__(**kwargs)
-
- self._num_heads = num_attention_heads
- self._intermediate_size = intermediate_size
- self._intermediate_activation = intermediate_activation
- self._attention_dropout_rate = attention_dropout_rate
- self._dropout_rate = dropout_rate
- self._output_range = output_range
- self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
- self._bias_initializer = tf.keras.initializers.get(bias_initializer)
- self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
- self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
- self._activity_regularizer = tf.keras.regularizers.get(activity_regularizer)
- self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
- self._bias_constraint = tf.keras.constraints.get(bias_constraint)
-
- def build(self, input_shape):
- input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
- input_tensor_shape = tf.TensorShape(input_tensor)
- if len(input_tensor_shape) != 3:
- raise ValueError("TransformerLayer expects a three-dimensional input of "
- "shape [batch, sequence, width].")
- batch_size, sequence_length, hidden_size = input_tensor_shape
-
- if len(input_shape) == 2:
- mask_tensor_shape = tf.TensorShape(input_shape[1])
- expected_mask_tensor_shape = tf.TensorShape(
- [batch_size, sequence_length, sequence_length])
- if not expected_mask_tensor_shape.is_compatible_with(mask_tensor_shape):
- raise ValueError("When passing a mask tensor to TransformerLayer, the "
- "mask tensor must be of shape [batch, "
- "sequence_length, sequence_length] (here %s). Got a "
- "mask tensor of shape %s." %
- (expected_mask_tensor_shape, mask_tensor_shape))
- if hidden_size % self._num_heads != 0:
- raise ValueError(
- "The input size (%d) is not a multiple of the number of attention "
- "heads (%d)" % (hidden_size, self._num_heads))
- self._attention_head_size = int(hidden_size // self._num_heads)
-
- self._attention_layer = attention.MultiHeadAttention(
- num_heads=self._num_heads,
- key_size=self._attention_head_size,
- dropout=self._attention_dropout_rate,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="self_attention")
- # pylint: disable=protected-access
- self._attention_layer.build([input_tensor_shape] * 3)
- self._attention_output_dense = self._attention_layer._output_dense
- # pylint: enable=protected-access
- self._attention_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
- # Use float32 in layernorm for numeric stability.
- # It is probably safe in mixed_float16, but we haven't validated this yet.
- self._attention_layer_norm = (
- tf.keras.layers.LayerNormalization(
- name="self_attention_layer_norm",
- axis=-1,
- epsilon=1e-12,
- dtype=tf.float32))
- self._intermediate_dense = dense_einsum.DenseEinsum(
- output_shape=self._intermediate_size,
- activation=None,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="intermediate")
- policy = tf.keras.mixed_precision.experimental.global_policy()
- if policy.name == "mixed_bfloat16":
- # bfloat16 causes BERT with the LAMB optimizer to not converge
- # as well, so we use float32.
- # TODO(b/154538392): Investigate this.
- policy = tf.float32
- self._intermediate_activation_layer = tf.keras.layers.Activation(
- self._intermediate_activation, dtype=policy)
- self._output_dense = dense_einsum.DenseEinsum(
- output_shape=hidden_size,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="output")
- self._output_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
- # Use float32 in layernorm for numeric stability.
- self._output_layer_norm = tf.keras.layers.LayerNormalization(
- name="output_layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32)
-
- super(Transformer, self).build(input_shape)
-
- def get_config(self):
- config = {
- "num_attention_heads":
- self._num_heads,
- "intermediate_size":
- self._intermediate_size,
- "intermediate_activation":
- self._intermediate_activation,
- "dropout_rate":
- self._dropout_rate,
- "attention_dropout_rate":
- self._attention_dropout_rate,
- "output_range":
- self._output_range,
- "kernel_initializer":
- tf.keras.initializers.serialize(self._kernel_initializer),
- "bias_initializer":
- tf.keras.initializers.serialize(self._bias_initializer),
- "kernel_regularizer":
- tf.keras.regularizers.serialize(self._kernel_regularizer),
- "bias_regularizer":
- tf.keras.regularizers.serialize(self._bias_regularizer),
- "activity_regularizer":
- tf.keras.regularizers.serialize(self._activity_regularizer),
- "kernel_constraint":
- tf.keras.constraints.serialize(self._kernel_constraint),
- "bias_constraint":
- tf.keras.constraints.serialize(self._bias_constraint)
- }
- base_config = super(Transformer, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
- def call(self, inputs):
- if isinstance(inputs, (list, tuple)) and len(inputs) == 2:
- input_tensor, attention_mask = inputs
- else:
- input_tensor, attention_mask = (inputs, None)
-
- if self._output_range:
- target_tensor = input_tensor[:, 0:self._output_range, :]
- attention_mask = attention_mask[:, 0:self._output_range, :]
- else:
- target_tensor = input_tensor
- attention_inputs = [target_tensor, input_tensor]
-
- attention_output = self._attention_layer(attention_inputs, attention_mask)
- attention_output = self._attention_dropout(attention_output)
- attention_output = self._attention_layer_norm(target_tensor +
- attention_output)
- intermediate_output = self._intermediate_dense(attention_output)
- intermediate_output = self._intermediate_activation_layer(
- intermediate_output)
- layer_output = self._output_dense(intermediate_output)
- layer_output = self._output_dropout(layer_output)
- # During mixed precision training, attention_output is from layer norm and
- # is always fp32 for now. Cast layer_output to fp32 for the subsequent
- # add.
- layer_output = tf.cast(layer_output, tf.float32)
- layer_output = self._output_layer_norm(layer_output + attention_output)
-
- return layer_output
-
-
-@tf.keras.utils.register_keras_serializable(package="Text")
-@gin.configurable
-class CompiledTransformer(Transformer):
-
- @tf_function_if_eager(experimental_compile=True)
- def call(self, inputs):
- return super(CompiledTransformer, self).call(inputs)
-
-
-@tf.keras.utils.register_keras_serializable(package="Text")
-class TransformerDecoderLayer(tf.keras.layers.Layer):
- """Single transformer layer for decoder.
-
- It has three sub-layers:
- (1) a multi-head self-attention mechanism.
- (2) a encoder-decoder attention.
- (3) a positionwise fully connected feed-forward network.
-
- Arguments:
- num_attention_heads: Number of attention heads.
- intermediate_size: Size of the intermediate layer.
- intermediate_activation: Activation for the intermediate layer.
- dropout_rate: Dropout probability for the post-attention and output dropout.
- attention_dropout_rate: Dropout probability for within the attention layer.
- multi_channel_cross_attention: Whether to use `MultiChannelAttention` for
- cross-attention between target sequences and source sequences.
- kernel_initializer: Initializer for dense layer kernels.
- bias_initializer: Initializer for dense layer biases.
- kernel_regularizer: Regularizer for dense layer kernels.
- bias_regularizer: Regularizer for dense layer biases.
- activity_regularizer: Regularizer for dense layer activity.
- kernel_constraint: Constraint for dense layer kernels.
- bias_constraint: Constraint for dense layer kernels.
- """
-
- def __init__(self,
- num_attention_heads,
- intermediate_size,
- intermediate_activation,
- dropout_rate=0.0,
- attention_dropout_rate=0.0,
- multi_channel_cross_attention=False,
- kernel_initializer="glorot_uniform",
- bias_initializer="zeros",
- kernel_regularizer=None,
- bias_regularizer=None,
- activity_regularizer=None,
- kernel_constraint=None,
- bias_constraint=None,
- **kwargs):
- super(TransformerDecoderLayer, self).__init__(**kwargs)
- self.num_attention_heads = num_attention_heads
- self.intermediate_size = intermediate_size
- self.intermediate_activation = tf.keras.activations.get(
- intermediate_activation)
- self.dropout_rate = dropout_rate
- self.attention_dropout_rate = attention_dropout_rate
- self.multi_channel_cross_attention = multi_channel_cross_attention
- self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
- self._bias_initializer = tf.keras.initializers.get(bias_initializer)
- self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
- self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
- self._activity_regularizer = tf.keras.regularizers.get(activity_regularizer)
- self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
- self._bias_constraint = tf.keras.constraints.get(bias_constraint)
- if self.multi_channel_cross_attention:
- self._cross_attention_cls = multi_channel_attention.MultiChannelAttention
- else:
- self._cross_attention_cls = attention.MultiHeadAttention
-
- def build(self, input_shape):
- target_tensor_shape = tf.TensorShape(input_shape[0])
- if len(target_tensor_shape) != 3:
- raise ValueError("TransformerLayer expects a three-dimensional input of "
- "shape [batch, sequence, width].")
- hidden_size = target_tensor_shape[2]
- if hidden_size % self.num_attention_heads != 0:
- raise ValueError(
- "The hidden size (%d) is not a multiple of the number of attention "
- "heads (%d)" % (hidden_size, self.num_attention_heads))
- self.attention_head_size = int(hidden_size / self.num_attention_heads)
- # Self attention.
- self.self_attention = attention.CachedAttention(
- num_heads=self.num_attention_heads,
- key_size=self.attention_head_size,
- dropout=self.attention_dropout_rate,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="self_attention")
- self.self_attention_output_dense = dense_einsum.DenseEinsum(
- output_shape=hidden_size,
- num_summed_dimensions=2,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="self_attention_output")
- self.self_attention_dropout = tf.keras.layers.Dropout(
- rate=self.dropout_rate)
- self.self_attention_layer_norm = (
- tf.keras.layers.LayerNormalization(
- name="self_attention_layer_norm", axis=-1, epsilon=1e-12))
- # Encoder-decoder attention.
- self.encdec_attention = self._cross_attention_cls(
- num_heads=self.num_attention_heads,
- key_size=self.attention_head_size,
- dropout=self.attention_dropout_rate,
- output_shape=hidden_size,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="attention/encdec")
-
- self.encdec_attention_dropout = tf.keras.layers.Dropout(
- rate=self.dropout_rate)
- self.encdec_attention_layer_norm = (
- tf.keras.layers.LayerNormalization(
- name="attention/encdec_output_layer_norm", axis=-1, epsilon=1e-12))
-
- # Feed-forward projection.
- self.intermediate_dense = dense_einsum.DenseEinsum(
- output_shape=self.intermediate_size,
- activation=None,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="intermediate")
- self.intermediate_activation_layer = tf.keras.layers.Activation(
- self.intermediate_activation)
- self.output_dense = dense_einsum.DenseEinsum(
- output_shape=hidden_size,
- kernel_initializer=self._kernel_initializer,
- bias_initializer=self._bias_initializer,
- kernel_regularizer=self._kernel_regularizer,
- bias_regularizer=self._bias_regularizer,
- activity_regularizer=self._activity_regularizer,
- kernel_constraint=self._kernel_constraint,
- bias_constraint=self._bias_constraint,
- name="output")
- self.output_dropout = tf.keras.layers.Dropout(rate=self.dropout_rate)
- self.output_layer_norm = tf.keras.layers.LayerNormalization(
- name="output_layer_norm", axis=-1, epsilon=1e-12)
- super(TransformerDecoderLayer, self).build(input_shape)
-
- def common_layers_with_encoder(self):
- """Gets layer objects that can make a Transformer encoder block."""
- return [
- self.self_attention, self.self_attention_layer_norm,
- self.intermediate_dense, self.output_dense, self.output_layer_norm
- ]
-
- def call(self, inputs, cache=None, decode_loop_step=None):
- if self.multi_channel_cross_attention:
- if len(inputs) != 5:
- raise ValueError(
- "TransformerDecoderLayer must have 5 inputs, when it uses "
- "multi_channel_cross_attention. But it got: %d" % len(inputs))
- elif len(inputs) != 4:
- raise ValueError(
- "TransformerDecoderLayer must have 4 inputs, but it got: %d" %
- len(inputs))
- input_tensor, memory, attention_mask, self_attention_mask = inputs[:4]
- self_attention_inputs = [input_tensor, input_tensor]
- self_attention_output, cache = self.self_attention(
- self_attention_inputs,
- attention_mask=self_attention_mask,
- cache=cache,
- decode_loop_step=decode_loop_step)
- self_attention_output = self.self_attention_dropout(self_attention_output)
- self_attention_output = self.self_attention_layer_norm(
- input_tensor + self_attention_output)
-
- cross_attn_inputs = [self_attention_output, memory]
- if self.multi_channel_cross_attention:
- # Accesses the 5-th input tensor for the doc-attention probabilities.
- cross_attn_inputs.append(inputs[-1])
- attention_output = self.encdec_attention(cross_attn_inputs, attention_mask)
- attention_output = self.encdec_attention_dropout(attention_output)
- attention_output = self.encdec_attention_layer_norm(self_attention_output +
- attention_output)
-
- intermediate_output = self.intermediate_dense(attention_output)
- intermediate_output = self.intermediate_activation_layer(
- intermediate_output)
- layer_output = self.output_dense(intermediate_output)
- layer_output = self.output_dropout(layer_output)
- layer_output = self.output_layer_norm(layer_output + attention_output)
- return layer_output, cache
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/architecture/factory.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/architecture/factory.py
deleted file mode 100644
index ed5647d6fb83fbd7c404a4573ff247acb8999b8c..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/architecture/factory.py
+++ /dev/null
@@ -1,163 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Model architecture factory."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from official.vision.detection.modeling.architecture import fpn
-from official.vision.detection.modeling.architecture import heads
-from official.vision.detection.modeling.architecture import identity
-from official.vision.detection.modeling.architecture import nn_ops
-from official.vision.detection.modeling.architecture import resnet
-
-
-def norm_activation_generator(params):
- return nn_ops.norm_activation_builder(
- momentum=params.batch_norm_momentum,
- epsilon=params.batch_norm_epsilon,
- trainable=params.batch_norm_trainable,
- activation=params.activation)
-
-
-def backbone_generator(params):
- """Generator function for various backbone models."""
- if params.architecture.backbone == 'resnet':
- resnet_params = params.resnet
- backbone_fn = resnet.Resnet(
- resnet_depth=resnet_params.resnet_depth,
- activation=params.norm_activation.activation,
- norm_activation=norm_activation_generator(
- params.norm_activation))
- else:
- raise ValueError('Backbone model `{}` is not supported.'
- .format(params.architecture.backbone))
-
- return backbone_fn
-
-
-def multilevel_features_generator(params):
- """Generator function for various FPN models."""
- if params.architecture.multilevel_features == 'fpn':
- fpn_params = params.fpn
- fpn_fn = fpn.Fpn(
- min_level=params.architecture.min_level,
- max_level=params.architecture.max_level,
- fpn_feat_dims=fpn_params.fpn_feat_dims,
- use_separable_conv=fpn_params.use_separable_conv,
- activation=params.norm_activation.activation,
- use_batch_norm=fpn_params.use_batch_norm,
- norm_activation=norm_activation_generator(
- params.norm_activation))
- elif params.architecture.multilevel_features == 'identity':
- fpn_fn = identity.Identity()
- else:
- raise ValueError('The multi-level feature model `{}` is not supported.'
- .format(params.architecture.multilevel_features))
- return fpn_fn
-
-
-def retinanet_head_generator(params):
- """Generator function for RetinaNet head architecture."""
- head_params = params.retinanet_head
- return heads.RetinanetHead(
- params.architecture.min_level,
- params.architecture.max_level,
- params.architecture.num_classes,
- head_params.anchors_per_location,
- head_params.num_convs,
- head_params.num_filters,
- head_params.use_separable_conv,
- norm_activation=norm_activation_generator(params.norm_activation))
-
-
-def rpn_head_generator(params):
- """Generator function for RPN head architecture."""
- head_params = params.rpn_head
- return heads.RpnHead(
- params.architecture.min_level,
- params.architecture.max_level,
- head_params.anchors_per_location,
- head_params.num_convs,
- head_params.num_filters,
- head_params.use_separable_conv,
- params.norm_activation.activation,
- head_params.use_batch_norm,
- norm_activation=norm_activation_generator(params.norm_activation))
-
-
-def fast_rcnn_head_generator(params):
- """Generator function for Fast R-CNN head architecture."""
- head_params = params.frcnn_head
- return heads.FastrcnnHead(
- params.architecture.num_classes,
- head_params.num_convs,
- head_params.num_filters,
- head_params.use_separable_conv,
- head_params.num_fcs,
- head_params.fc_dims,
- params.norm_activation.activation,
- head_params.use_batch_norm,
- norm_activation=norm_activation_generator(params.norm_activation))
-
-
-def mask_rcnn_head_generator(params):
- """Generator function for Mask R-CNN head architecture."""
- head_params = params.mrcnn_head
- return heads.MaskrcnnHead(
- params.architecture.num_classes,
- params.architecture.mask_target_size,
- head_params.num_convs,
- head_params.num_filters,
- head_params.use_separable_conv,
- params.norm_activation.activation,
- head_params.use_batch_norm,
- norm_activation=norm_activation_generator(params.norm_activation))
-
-
-def shapeprior_head_generator(params):
- """Generator function for shape prior head architecture."""
- head_params = params.shapemask_head
- return heads.ShapemaskPriorHead(
- params.architecture.num_classes,
- head_params.num_downsample_channels,
- head_params.mask_crop_size,
- head_params.use_category_for_mask,
- head_params.shape_prior_path)
-
-
-def coarsemask_head_generator(params):
- """Generator function for ShapeMask coarse mask head architecture."""
- head_params = params.shapemask_head
- return heads.ShapemaskCoarsemaskHead(
- params.architecture.num_classes,
- head_params.num_downsample_channels,
- head_params.mask_crop_size,
- head_params.use_category_for_mask,
- head_params.num_convs,
- norm_activation=norm_activation_generator(params.norm_activation))
-
-
-def finemask_head_generator(params):
- """Generator function for Shapemask fine mask head architecture."""
- head_params = params.shapemask_head
- return heads.ShapemaskFinemaskHead(
- params.architecture.num_classes,
- head_params.num_downsample_channels,
- head_params.mask_crop_size,
- head_params.use_category_for_mask,
- head_params.num_convs,
- head_params.upsample_factor)
diff --git a/spaces/NicolasGaudemet/WritingAssistant/writing_assistant_app.py b/spaces/NicolasGaudemet/WritingAssistant/writing_assistant_app.py
deleted file mode 100644
index a931be8396ea4397a1621a629f607f5ce39de7c4..0000000000000000000000000000000000000000
--- a/spaces/NicolasGaudemet/WritingAssistant/writing_assistant_app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import openai
-import os
-import gradio as gr
-
-# Configure votre clé API
-openai.api_key = os.environ['OpenaiKey']
-
-def writing_assistant(debut, suite, instructions):
- # Construction de la requête
-
- with open('instructions.txt', 'r') as fichier:
- # Lecture du contenu du fichier
- instructions = fichier.read() + "\n" + instructions
-
- prompt = f"DEBUT = '{debut}'\n SUITE = '{suite}' \n INSTRUCTIONS = {instructions}"
-
- messages = [
- {"role": "system", "content": f"Tu es un assistant d'écriture. Tu aides un auteur contemporain à écrire, en t'inspirant de son style littéraire."},
- {"role": "user", "content": prompt}
- ]
-
- # Call GPT-3.5-turbo API
- response = openai.ChatCompletion.create(
- model="gpt-3.5-turbo",
- messages=messages,
- temperature=0.9
- )
-
- # Get generated text
- texte_reecrit = response.choices[0].message['content'].strip()
-
- return texte_reecrit
-
-# Définition d'inputs par défaut
-with open('debut_par_defaut.txt', 'r') as fichier:
- # Lecture du contenu du fichier
- debut_par_defaut = fichier.read()
-
-with open('suite_par_defaut.txt', 'r') as fichier:
- # Lecture du contenu du fichier
- suite_par_defaut = fichier.read()
-
-# Création de l'interface Gradio
-iface = gr.Interface(
- fn=writing_assistant,
- inputs=[
- gr.inputs.Textbox(lines=5, label="Début", default = debut_par_defaut),
- gr.inputs.Textbox(lines=5, label="Suite", default = suite_par_defaut),
- gr.inputs.Textbox(lines=2, label="Instructions additionnelles")
- ],
- outputs=gr.outputs.Textbox(label="Texte réécrit"),
- title="Assistant d'écriture",
- description="par Nicolas \nRéécrit un brouillon en respectant un début avec un style donné."
-)
-
-# Lancer l'interface
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Nultx/VITS-TTS/text/ngu_dialect.py b/spaces/Nultx/VITS-TTS/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/Nultx/VITS-TTS/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
- 'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
- 'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
- 'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
- 'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
- 'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
- try:
- converters[dialect] = opencc.OpenCC(dialect)
- except:
- pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
- dialect = dialects[dialect]
- text = converters[dialect].convert(text).replace('-','').replace('$',' ')
- text = re.sub(r'[、；：]', '，', text)
- text = re.sub(r'\s*，\s*', ', ', text)
- text = re.sub(r'\s*。\s*', '. ', text)
- text = re.sub(r'\s*？\s*', '? ', text)
- text = re.sub(r'\s*！\s*', '! ', text)
- text = re.sub(r'\s*$', '', text)
- return text
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py
deleted file mode 100644
index 655a9b0d19d11e35511392a016f9d6b7d7aa2925..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py
+++ /dev/null
@@ -1,177 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq.models import (
- FairseqEncoder,
- FairseqEncoderModel,
- register_model,
- register_model_architecture,
-)
-from fairseq.modules.fairseq_dropout import FairseqDropout
-
-
-default_conv_enc_config = """[
- (400, 13, 170, 0.2),
- (440, 14, 0, 0.214),
- (484, 15, 0, 0.22898),
- (532, 16, 0, 0.2450086),
- (584, 17, 0, 0.262159202),
- (642, 18, 0, 0.28051034614),
- (706, 19, 0, 0.30014607037),
- (776, 20, 0, 0.321156295296),
- (852, 21, 0, 0.343637235966),
- (936, 22, 0, 0.367691842484),
- (1028, 23, 0, 0.393430271458),
- (1130, 24, 0, 0.42097039046),
- (1242, 25, 0, 0.450438317792),
- (1366, 26, 0, 0.481969000038),
- (1502, 27, 0, 0.51570683004),
- (1652, 28, 0, 0.551806308143),
- (1816, 29, 0, 0.590432749713),
-]"""
-
-
-@register_model("asr_w2l_conv_glu_encoder")
-class W2lConvGluEncoderModel(FairseqEncoderModel):
- def __init__(self, encoder):
- super().__init__(encoder)
-
- @staticmethod
- def add_args(parser):
- """Add model-specific arguments to the parser."""
- parser.add_argument(
- "--input-feat-per-channel",
- type=int,
- metavar="N",
- help="encoder input dimension per input channel",
- )
- parser.add_argument(
- "--in-channels",
- type=int,
- metavar="N",
- help="number of encoder input channels",
- )
- parser.add_argument(
- "--conv-enc-config",
- type=str,
- metavar="EXPR",
- help="""
- an array of tuples each containing the configuration of one conv layer
- [(out_channels, kernel_size, padding, dropout), ...]
- """,
- )
-
- @classmethod
- def build_model(cls, args, task):
- """Build a new model instance."""
- conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
- encoder = W2lConvGluEncoder(
- vocab_size=len(task.target_dictionary),
- input_feat_per_channel=args.input_feat_per_channel,
- in_channels=args.in_channels,
- conv_enc_config=eval(conv_enc_config),
- )
- return cls(encoder)
-
- def get_normalized_probs(self, net_output, log_probs, sample=None):
- lprobs = super().get_normalized_probs(net_output, log_probs, sample)
- lprobs.batch_first = False
- return lprobs
-
-
-class W2lConvGluEncoder(FairseqEncoder):
- def __init__(
- self, vocab_size, input_feat_per_channel, in_channels, conv_enc_config
- ):
- super().__init__(None)
-
- self.input_dim = input_feat_per_channel
- if in_channels != 1:
- raise ValueError("only 1 input channel is currently supported")
-
- self.conv_layers = nn.ModuleList()
- self.linear_layers = nn.ModuleList()
- self.dropouts = []
- cur_channels = input_feat_per_channel
-
- for out_channels, kernel_size, padding, dropout in conv_enc_config:
- layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding)
- layer.weight.data.mul_(math.sqrt(3)) # match wav2letter init
- self.conv_layers.append(nn.utils.weight_norm(layer))
- self.dropouts.append(
- FairseqDropout(dropout, module_name=self.__class__.__name__)
- )
- if out_channels % 2 != 0:
- raise ValueError("odd # of out_channels is incompatible with GLU")
- cur_channels = out_channels // 2 # halved by GLU
-
- for out_channels in [2 * cur_channels, vocab_size]:
- layer = nn.Linear(cur_channels, out_channels)
- layer.weight.data.mul_(math.sqrt(3))
- self.linear_layers.append(nn.utils.weight_norm(layer))
- cur_channels = out_channels // 2
-
- def forward(self, src_tokens, src_lengths, **kwargs):
-
- """
- src_tokens: padded tensor (B, T, C * feat)
- src_lengths: tensor of original lengths of input utterances (B,)
- """
- B, T, _ = src_tokens.size()
- x = src_tokens.transpose(1, 2).contiguous() # (B, feat, T) assuming C == 1
-
- for layer_idx in range(len(self.conv_layers)):
- x = self.conv_layers[layer_idx](x)
- x = F.glu(x, dim=1)
- x = self.dropouts[layer_idx](x)
-
- x = x.transpose(1, 2).contiguous() # (B, T, 908)
- x = self.linear_layers[0](x)
- x = F.glu(x, dim=2)
- x = self.dropouts[-1](x)
- x = self.linear_layers[1](x)
-
- assert x.size(0) == B
- assert x.size(1) == T
-
- encoder_out = x.transpose(0, 1) # (T, B, vocab_size)
-
- # need to debug this -- find a simpler/elegant way in pytorch APIs
- encoder_padding_mask = (
- torch.arange(T).view(1, T).expand(B, -1).to(x.device)
- >= src_lengths.view(B, 1).expand(-1, T)
- ).t() # (B x T) -> (T x B)
-
- return {
- "encoder_out": encoder_out, # (T, B, vocab_size)
- "encoder_padding_mask": encoder_padding_mask, # (T, B)
- }
-
- def reorder_encoder_out(self, encoder_out, new_order):
- encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
- 1, new_order
- )
- encoder_out["encoder_padding_mask"] = encoder_out[
- "encoder_padding_mask"
- ].index_select(1, new_order)
- return encoder_out
-
- def max_positions(self):
- """Maximum input length supported by the encoder."""
- return (1e6, 1e6) # an arbitrary large number
-
-
-@register_model_architecture("asr_w2l_conv_glu_encoder", "w2l_conv_glu_enc")
-def w2l_conv_glu_enc(args):
- args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
- args.in_channels = getattr(args, "in_channels", 1)
- args.conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/criterions/sentence_ranking.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/criterions/sentence_ranking.py
deleted file mode 100644
index d4c76341d4d87e6d0da21ac89e833ce0bda13a0c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/criterions/sentence_ranking.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import torch
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-
-
-@register_criterion("sentence_ranking")
-class SentenceRankingCriterion(FairseqCriterion):
- def __init__(self, task, ranking_head_name, save_predictions, num_classes):
- super().__init__(task)
- self.ranking_head_name = ranking_head_name
- if save_predictions is not None:
- self.prediction_h = open(save_predictions, "w")
- else:
- self.prediction_h = None
- self.num_classes = num_classes
-
- def __del__(self):
- if self.prediction_h is not None:
- self.prediction_h.close()
-
- @staticmethod
- def add_args(parser):
- # fmt: off
- parser.add_argument('--save-predictions', metavar='FILE',
- help='file to save predictions to')
- parser.add_argument('--ranking-head-name',
- default='sentence_classification_head',
- help='name of the ranking head to use')
- # fmt: on
-
- def forward(self, model, sample, reduce=True):
- """Compute ranking loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- assert (
- hasattr(model, "classification_heads")
- and self.ranking_head_name in model.classification_heads
- ), "model must provide sentence ranking head for --criterion=sentence_ranking"
-
- scores = []
- for idx in range(self.num_classes):
- score, _ = model(
- **sample["net_input{idx}".format(idx=idx + 1)],
- classification_head_name=self.ranking_head_name,
- )
- scores.append(score)
-
- logits = torch.cat(scores, dim=1)
- sample_size = logits.size(0)
-
- if "target" in sample:
- targets = model.get_targets(sample, [logits]).view(-1)
- lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
- loss = F.nll_loss(lprobs, targets, reduction="sum")
- else:
- targets = None
- loss = torch.tensor(0.0, requires_grad=True)
-
- if self.prediction_h is not None:
- preds = logits.argmax(dim=1)
- for i, (id, pred) in enumerate(zip(sample["id"].tolist(), preds.tolist())):
- if targets is not None:
- label = targets[i].item()
- print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h)
- else:
- print("{}\t{}".format(id, pred), file=self.prediction_h)
-
- logging_output = {
- "loss": loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample_size,
- "sample_size": sample_size,
- }
- if targets is not None:
- logging_output["ncorrect"] = (logits.argmax(dim=1) == targets).sum()
-
- return loss, sample_size, logging_output
-
- @staticmethod
- def reduce_metrics(logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- if sample_size != ntokens:
- metrics.log_scalar(
- "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
- )
-
- if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]:
- ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs)
- metrics.log_scalar(
- "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/roll_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/roll_dataset.py
deleted file mode 100644
index a2915eeb3e8fb4dfb4b2bb33e0464ad0783d854c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/roll_dataset.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from . import BaseWrapperDataset
-
-
-class RollDataset(BaseWrapperDataset):
- def __init__(self, dataset, shifts):
- super().__init__(dataset)
- self.shifts = shifts
-
- def __getitem__(self, index):
- item = self.dataset[index]
- return torch.roll(item, self.shifts)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_backtranslation_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_backtranslation_dataset.py
deleted file mode 100644
index dffc3b49387dfdc046ea23d7db179377040b7cbc..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_backtranslation_dataset.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-
-import tests.utils as test_utils
-import torch
-from fairseq.data import (
- BacktranslationDataset,
- LanguagePairDataset,
- TransformEosDataset,
-)
-from fairseq.sequence_generator import SequenceGenerator
-
-
-class TestBacktranslationDataset(unittest.TestCase):
- def setUp(self):
- (
- self.tgt_dict,
- self.w1,
- self.w2,
- self.src_tokens,
- self.src_lengths,
- self.model,
- ) = test_utils.sequence_generator_setup()
-
- dummy_src_samples = self.src_tokens
-
- self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
- self.cuda = torch.cuda.is_available()
-
- def _backtranslation_dataset_helper(
- self,
- remove_eos_from_input_src,
- remove_eos_from_output_src,
- ):
- tgt_dataset = LanguagePairDataset(
- src=self.tgt_dataset,
- src_sizes=self.tgt_dataset.sizes,
- src_dict=self.tgt_dict,
- tgt=None,
- tgt_sizes=None,
- tgt_dict=None,
- )
-
- generator = SequenceGenerator(
- [self.model],
- tgt_dict=self.tgt_dict,
- max_len_a=0,
- max_len_b=200,
- beam_size=2,
- unk_penalty=0,
- )
-
- backtranslation_dataset = BacktranslationDataset(
- tgt_dataset=TransformEosDataset(
- dataset=tgt_dataset,
- eos=self.tgt_dict.eos(),
- # remove eos from the input src
- remove_eos_from_src=remove_eos_from_input_src,
- ),
- src_dict=self.tgt_dict,
- backtranslation_fn=(
- lambda sample: generator.generate([self.model], sample)
- ),
- output_collater=TransformEosDataset(
- dataset=tgt_dataset,
- eos=self.tgt_dict.eos(),
- # if we remove eos from the input src, then we need to add it
- # back to the output tgt
- append_eos_to_tgt=remove_eos_from_input_src,
- remove_eos_from_src=remove_eos_from_output_src,
- ).collater,
- cuda=self.cuda,
- )
- dataloader = torch.utils.data.DataLoader(
- backtranslation_dataset,
- batch_size=2,
- collate_fn=backtranslation_dataset.collater,
- )
- backtranslation_batch_result = next(iter(dataloader))
-
- eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
-
- # Note that we sort by src_lengths and add left padding, so actually
- # ids will look like: [1, 0]
- expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
- if remove_eos_from_output_src:
- expected_src = expected_src[:, :-1]
- expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
- generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
- tgt_tokens = backtranslation_batch_result["target"]
-
- self.assertTensorEqual(expected_src, generated_src)
- self.assertTensorEqual(expected_tgt, tgt_tokens)
-
- def test_backtranslation_dataset_no_eos_in_output_src(self):
- self._backtranslation_dataset_helper(
- remove_eos_from_input_src=False,
- remove_eos_from_output_src=True,
- )
-
- def test_backtranslation_dataset_with_eos_in_output_src(self):
- self._backtranslation_dataset_helper(
- remove_eos_from_input_src=False,
- remove_eos_from_output_src=False,
- )
-
- def test_backtranslation_dataset_no_eos_in_input_src(self):
- self._backtranslation_dataset_helper(
- remove_eos_from_input_src=True,
- remove_eos_from_output_src=False,
- )
-
- def assertTensorEqual(self, t1, t2):
- self.assertEqual(t1.size(), t2.size(), "size mismatch")
- self.assertEqual(t1.ne(t2).long().sum(), 0)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/Old-Fat-Boy/Youtube_Thumbnail_CTR_Analyzer/app.py b/spaces/Old-Fat-Boy/Youtube_Thumbnail_CTR_Analyzer/app.py
deleted file mode 100644
index 970f9b45f8db4686fe6bfb9f9cfaba734a1f5a12..0000000000000000000000000000000000000000
--- a/spaces/Old-Fat-Boy/Youtube_Thumbnail_CTR_Analyzer/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
-
-# %% app.ipynb 2
-from fastai.vision.all import *
-import gradio as gr
-
-# %% app.ipynb 5
-learn = load_learner('model.pkl')
-
-# %% app.ipynb 7
-categories = ('Above Average', 'Below Average')
-
-def classify_image(img):
- pred,idx,probs = learn.predict(img)
- return dict(zip(categories, map(float,probs)))
-
-# %% app.ipynb 9
-image = gr.Image(shape=(192,192), label="Thumbnail")
-label = gr.Label()
-examples = ['example.jpg', 'above_average.jpg', 'below_average.jpg']
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
diff --git a/spaces/Olivier-Truong/faster-whisper-webui-v2/src/languages.py b/spaces/Olivier-Truong/faster-whisper-webui-v2/src/languages.py
deleted file mode 100644
index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000
--- a/spaces/Olivier-Truong/faster-whisper-webui-v2/src/languages.py
+++ /dev/null
@@ -1,147 +0,0 @@
-class Language():
- def __init__(self, code, name):
- self.code = code
- self.name = name
-
- def __str__(self):
- return "Language(code={}, name={})".format(self.code, self.name)
-
-LANGUAGES = [
- Language('en', 'English'),
- Language('zh', 'Chinese'),
- Language('de', 'German'),
- Language('es', 'Spanish'),
- Language('ru', 'Russian'),
- Language('ko', 'Korean'),
- Language('fr', 'French'),
- Language('ja', 'Japanese'),
- Language('pt', 'Portuguese'),
- Language('tr', 'Turkish'),
- Language('pl', 'Polish'),
- Language('ca', 'Catalan'),
- Language('nl', 'Dutch'),
- Language('ar', 'Arabic'),
- Language('sv', 'Swedish'),
- Language('it', 'Italian'),
- Language('id', 'Indonesian'),
- Language('hi', 'Hindi'),
- Language('fi', 'Finnish'),
- Language('vi', 'Vietnamese'),
- Language('he', 'Hebrew'),
- Language('uk', 'Ukrainian'),
- Language('el', 'Greek'),
- Language('ms', 'Malay'),
- Language('cs', 'Czech'),
- Language('ro', 'Romanian'),
- Language('da', 'Danish'),
- Language('hu', 'Hungarian'),
- Language('ta', 'Tamil'),
- Language('no', 'Norwegian'),
- Language('th', 'Thai'),
- Language('ur', 'Urdu'),
- Language('hr', 'Croatian'),
- Language('bg', 'Bulgarian'),
- Language('lt', 'Lithuanian'),
- Language('la', 'Latin'),
- Language('mi', 'Maori'),
- Language('ml', 'Malayalam'),
- Language('cy', 'Welsh'),
- Language('sk', 'Slovak'),
- Language('te', 'Telugu'),
- Language('fa', 'Persian'),
- Language('lv', 'Latvian'),
- Language('bn', 'Bengali'),
- Language('sr', 'Serbian'),
- Language('az', 'Azerbaijani'),
- Language('sl', 'Slovenian'),
- Language('kn', 'Kannada'),
- Language('et', 'Estonian'),
- Language('mk', 'Macedonian'),
- Language('br', 'Breton'),
- Language('eu', 'Basque'),
- Language('is', 'Icelandic'),
- Language('hy', 'Armenian'),
- Language('ne', 'Nepali'),
- Language('mn', 'Mongolian'),
- Language('bs', 'Bosnian'),
- Language('kk', 'Kazakh'),
- Language('sq', 'Albanian'),
- Language('sw', 'Swahili'),
- Language('gl', 'Galician'),
- Language('mr', 'Marathi'),
- Language('pa', 'Punjabi'),
- Language('si', 'Sinhala'),
- Language('km', 'Khmer'),
- Language('sn', 'Shona'),
- Language('yo', 'Yoruba'),
- Language('so', 'Somali'),
- Language('af', 'Afrikaans'),
- Language('oc', 'Occitan'),
- Language('ka', 'Georgian'),
- Language('be', 'Belarusian'),
- Language('tg', 'Tajik'),
- Language('sd', 'Sindhi'),
- Language('gu', 'Gujarati'),
- Language('am', 'Amharic'),
- Language('yi', 'Yiddish'),
- Language('lo', 'Lao'),
- Language('uz', 'Uzbek'),
- Language('fo', 'Faroese'),
- Language('ht', 'Haitian creole'),
- Language('ps', 'Pashto'),
- Language('tk', 'Turkmen'),
- Language('nn', 'Nynorsk'),
- Language('mt', 'Maltese'),
- Language('sa', 'Sanskrit'),
- Language('lb', 'Luxembourgish'),
- Language('my', 'Myanmar'),
- Language('bo', 'Tibetan'),
- Language('tl', 'Tagalog'),
- Language('mg', 'Malagasy'),
- Language('as', 'Assamese'),
- Language('tt', 'Tatar'),
- Language('haw', 'Hawaiian'),
- Language('ln', 'Lingala'),
- Language('ha', 'Hausa'),
- Language('ba', 'Bashkir'),
- Language('jw', 'Javanese'),
- Language('su', 'Sundanese')
-]
-
-_TO_LANGUAGE_CODE = {
- **{language.code: language for language in LANGUAGES},
- "burmese": "my",
- "valencian": "ca",
- "flemish": "nl",
- "haitian": "ht",
- "letzeburgesch": "lb",
- "pushto": "ps",
- "panjabi": "pa",
- "moldavian": "ro",
- "moldovan": "ro",
- "sinhalese": "si",
- "castilian": "es",
-}
-
-_FROM_LANGUAGE_NAME = {
- **{language.name.lower(): language for language in LANGUAGES}
-}
-
-def get_language_from_code(language_code, default=None) -> Language:
- """Return the language name from the language code."""
- return _TO_LANGUAGE_CODE.get(language_code, default)
-
-def get_language_from_name(language, default=None) -> Language:
- """Return the language code from the language name."""
- return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default)
-
-def get_language_names():
- """Return a list of language names."""
- return [language.name for language in LANGUAGES]
-
-if __name__ == "__main__":
- # Test lookup
- print(get_language_from_code('en'))
- print(get_language_from_name('English'))
-
- print(get_language_names())
\ No newline at end of file
diff --git a/spaces/Omnibus/MusicGen/tests/models/test_musicgen.py b/spaces/Omnibus/MusicGen/tests/models/test_musicgen.py
deleted file mode 100644
index d43cf73763f6c690ab0b277227ac225b286fa143..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/models/test_musicgen.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pytest
-import torch
-
-from audiocraft.models import MusicGen
-
-
-class TestSEANetModel:
- def get_musicgen(self):
- mg = MusicGen.get_pretrained(name='debug', device='cpu')
- mg.set_generation_params(duration=2.0, extend_stride=2.)
- return mg
-
- def test_base(self):
- mg = self.get_musicgen()
- assert mg.frame_rate == 25
- assert mg.sample_rate == 32000
- assert mg.audio_channels == 1
-
- def test_generate_unconditional(self):
- mg = self.get_musicgen()
- wav = mg.generate_unconditional(3)
- assert list(wav.shape) == [3, 1, 64000]
-
- def test_generate_continuation(self):
- mg = self.get_musicgen()
- prompt = torch.randn(3, 1, 32000)
- wav = mg.generate_continuation(prompt, 32000)
- assert list(wav.shape) == [3, 1, 64000]
-
- prompt = torch.randn(2, 1, 32000)
- wav = mg.generate_continuation(
- prompt, 32000, ['youpi', 'lapin dort'])
- assert list(wav.shape) == [2, 1, 64000]
-
- prompt = torch.randn(2, 1, 32000)
- with pytest.raises(AssertionError):
- wav = mg.generate_continuation(
- prompt, 32000, ['youpi', 'lapin dort', 'one too many'])
-
- def test_generate(self):
- mg = self.get_musicgen()
- wav = mg.generate(
- ['youpi', 'lapin dort'])
- assert list(wav.shape) == [2, 1, 64000]
-
- def test_generate_long(self):
- mg = self.get_musicgen()
- mg.max_duration = 3.
- mg.set_generation_params(duration=4., extend_stride=2.)
- wav = mg.generate(
- ['youpi', 'lapin dort'])
- assert list(wav.shape) == [2, 1, 32000 * 4]
diff --git a/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/image_generation.py b/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/image_generation.py
deleted file mode 100644
index f04b4bcc76ff3c8dc59d1c61004073a3a6815c01..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/image_generation.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import json
-import math
-import random
-import time
-from pathlib import Path
-from uuid import uuid4
-
-import torch
-from diffusers import __version__ as diffusers_version
-from huggingface_hub import CommitOperationAdd, create_commit, create_repo
-
-from .upsampling import RealESRGANModel
-from .utils import pad_along_axis
-
-
-def get_all_files(root: Path):
- dirs = [root]
- while len(dirs) > 0:
- dir = dirs.pop()
- for candidate in dir.iterdir():
- if candidate.is_file():
- yield candidate
- if candidate.is_dir():
- dirs.append(candidate)
-
-
-def get_groups_of_n(n: int, iterator):
- assert n > 1
- buffer = []
- for elt in iterator:
- if len(buffer) == n:
- yield buffer
- buffer = []
- buffer.append(elt)
- if len(buffer) != 0:
- yield buffer
-
-
-def upload_folder_chunked(
- repo_id: str,
- upload_dir: Path,
- n: int = 100,
- private: bool = False,
- create_pr: bool = False,
-):
- """Upload a folder to the Hugging Face Hub in chunks of n files at a time.
- Args:
- repo_id (str): The repo id to upload to.
- upload_dir (Path): The directory to upload.
- n (int, *optional*, defaults to 100): The number of files to upload at a time.
- private (bool, *optional*): Whether to upload the repo as private.
- create_pr (bool, *optional*): Whether to create a PR after uploading instead of commiting directly.
- """
-
- url = create_repo(repo_id, exist_ok=True, private=private, repo_type="dataset")
- print(f"Uploading files to: {url}")
-
- root = Path(upload_dir)
- if not root.exists():
- raise ValueError(f"Upload directory {root} does not exist.")
-
- for i, file_paths in enumerate(get_groups_of_n(n, get_all_files(root))):
- print(f"Committing {file_paths}")
- operations = [
- CommitOperationAdd(
- path_in_repo=f"{file_path.parent.name}/{file_path.name}",
- path_or_fileobj=str(file_path),
- )
- for file_path in file_paths
- ]
- create_commit(
- repo_id=repo_id,
- operations=operations,
- commit_message=f"Upload part {i}",
- repo_type="dataset",
- create_pr=create_pr,
- )
-
-
-def generate_input_batches(pipeline, prompts, seeds, batch_size, height, width):
- if len(prompts) != len(seeds):
- raise ValueError("Number of prompts and seeds must be equal.")
-
- embeds_batch, noise_batch = None, None
- batch_idx = 0
- for i, (prompt, seed) in enumerate(zip(prompts, seeds)):
- embeds = pipeline.embed_text(prompt)
- noise = torch.randn(
- (1, pipeline.unet.in_channels, height // 8, width // 8),
- device=pipeline.device,
- generator=torch.Generator(device="cpu" if pipeline.device.type == "mps" else pipeline.device).manual_seed(
- seed
- ),
- )
- embeds_batch = embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
- noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise])
- batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == len(prompts)
- if not batch_is_ready:
- continue
- yield batch_idx, embeds_batch.type(torch.cuda.HalfTensor), noise_batch.type(torch.cuda.HalfTensor)
- batch_idx += 1
- del embeds_batch, noise_batch
- torch.cuda.empty_cache()
- embeds_batch, noise_batch = None, None
-
-
-def generate_images(
- pipeline,
- prompt,
- batch_size=1,
- num_batches=1,
- seeds=None,
- num_inference_steps=50,
- guidance_scale=7.5,
- output_dir="./images",
- image_file_ext=".jpg",
- upsample=False,
- height=512,
- width=512,
- eta=0.0,
- push_to_hub=False,
- repo_id=None,
- private=False,
- create_pr=False,
- name=None,
-):
- """Generate images using the StableDiffusion pipeline.
- Args:
- pipeline (StableDiffusionWalkPipeline): The StableDiffusion pipeline instance.
- prompt (str): The prompt to use for the image generation.
- batch_size (int, *optional*, defaults to 1): The batch size to use for image generation.
- num_batches (int, *optional*, defaults to 1): The number of batches to generate.
- seeds (list[int], *optional*): The seeds to use for the image generation.
- num_inference_steps (int, *optional*, defaults to 50): The number of inference steps to take.
- guidance_scale (float, *optional*, defaults to 7.5): The guidance scale to use for image generation.
- output_dir (str, *optional*, defaults to "./images"): The output directory to save the images to.
- image_file_ext (str, *optional*, defaults to '.jpg'): The image file extension to use.
- upsample (bool, *optional*, defaults to False): Whether to upsample the images.
- height (int, *optional*, defaults to 512): The height of the images to generate.
- width (int, *optional*, defaults to 512): The width of the images to generate.
- eta (float, *optional*, defaults to 0.0): The eta parameter to use for image generation.
- push_to_hub (bool, *optional*, defaults to False): Whether to push the generated images to the Hugging Face Hub.
- repo_id (str, *optional*): The repo id to push the images to.
- private (bool, *optional*): Whether to push the repo as private.
- create_pr (bool, *optional*): Whether to create a PR after pushing instead of commiting directly.
- name (str, *optional*, defaults to current timestamp str): The name of the sub-directory of
- output_dir to save the images to.
- """
- if push_to_hub:
- if repo_id is None:
- raise ValueError("Must provide repo_id if push_to_hub is True.")
-
- name = name or time.strftime("%Y%m%d-%H%M%S")
- save_path = Path(output_dir) / name
- save_path.mkdir(exist_ok=False, parents=True)
- prompt_config_path = save_path / "prompt_config.json"
-
- num_images = batch_size * num_batches
- seeds = seeds or [random.choice(list(range(0, 9999999))) for _ in range(num_images)]
- if len(seeds) != num_images:
- raise ValueError("Number of seeds must be equal to batch_size * num_batches.")
-
- if upsample:
- if getattr(pipeline, "upsampler", None) is None:
- pipeline.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
- pipeline.upsampler.to(pipeline.device)
-
- cfg = dict(
- prompt=prompt,
- guidance_scale=guidance_scale,
- eta=eta,
- num_inference_steps=num_inference_steps,
- upsample=upsample,
- height=height,
- width=width,
- scheduler=dict(pipeline.scheduler.config),
- tiled=pipeline.tiled,
- diffusers_version=diffusers_version,
- device_name=torch.cuda.get_device_name(0) if torch.cuda.is_available() else "unknown",
- )
- prompt_config_path.write_text(json.dumps(cfg, indent=2, sort_keys=False))
-
- frame_index = 0
- frame_filepaths = []
- for batch_idx, embeds, noise in generate_input_batches(
- pipeline, [prompt] * num_images, seeds, batch_size, height, width
- ):
- print(f"Generating batch {batch_idx}")
-
- outputs = pipeline(
- text_embeddings=embeds,
- latents=noise,
- num_inference_steps=num_inference_steps,
- guidance_scale=guidance_scale,
- eta=eta,
- height=height,
- width=width,
- output_type="pil" if not upsample else "numpy",
- )["images"]
- if upsample:
- images = []
- for output in outputs:
- images.append(pipeline.upsampler(output))
- else:
- images = outputs
-
- for image in images:
- frame_filepath = save_path / f"{seeds[frame_index]}{image_file_ext}"
- image.save(frame_filepath)
- frame_filepaths.append(str(frame_filepath))
- frame_index += 1
-
- return frame_filepaths
-
- if push_to_hub:
- upload_folder_chunked(repo_id, save_path, private=private, create_pr=create_pr)
-
-
-def generate_images_flax(
- pipeline,
- params,
- prompt,
- batch_size=1,
- num_batches=1,
- seeds=None,
- num_inference_steps=50,
- guidance_scale=7.5,
- output_dir="./images",
- image_file_ext=".jpg",
- upsample=False,
- height=512,
- width=512,
- push_to_hub=False,
- repo_id=None,
- private=False,
- create_pr=False,
- name=None,
-):
- import jax
- from flax.training.common_utils import shard
-
- """Generate images using the StableDiffusion pipeline.
- Args:
- pipeline (StableDiffusionWalkPipeline): The StableDiffusion pipeline instance.
- params (`Union[Dict, FrozenDict]`): The model parameters.
- prompt (str): The prompt to use for the image generation.
- batch_size (int, *optional*, defaults to 1): The batch size to use for image generation.
- num_batches (int, *optional*, defaults to 1): The number of batches to generate.
- seeds (int, *optional*): The seed to use for the image generation.
- num_inference_steps (int, *optional*, defaults to 50): The number of inference steps to take.
- guidance_scale (float, *optional*, defaults to 7.5): The guidance scale to use for image generation.
- output_dir (str, *optional*, defaults to "./images"): The output directory to save the images to.
- image_file_ext (str, *optional*, defaults to '.jpg'): The image file extension to use.
- upsample (bool, *optional*, defaults to False): Whether to upsample the images.
- height (int, *optional*, defaults to 512): The height of the images to generate.
- width (int, *optional*, defaults to 512): The width of the images to generate.
- push_to_hub (bool, *optional*, defaults to False): Whether to push the generated images to the Hugging Face Hub.
- repo_id (str, *optional*): The repo id to push the images to.
- private (bool, *optional*): Whether to push the repo as private.
- create_pr (bool, *optional*): Whether to create a PR after pushing instead of commiting directly.
- name (str, *optional*, defaults to current timestamp str): The name of the sub-directory of
- output_dir to save the images to.
- """
- if push_to_hub:
- if repo_id is None:
- raise ValueError("Must provide repo_id if push_to_hub is True.")
-
- name = name or time.strftime("%Y%m%d-%H%M%S")
- save_path = Path(output_dir) / name
- save_path.mkdir(exist_ok=False, parents=True)
- prompt_config_path = save_path / "prompt_config.json"
-
- num_images = batch_size * num_batches
- seeds = seeds or random.choice(list(range(0, 9999999)))
- prng_seed = jax.random.PRNGKey(seeds)
-
- if upsample:
- if getattr(pipeline, "upsampler", None) is None:
- pipeline.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
- if not torch.cuda.is_available():
- print("Upsampling is recommended to be done on a GPU, as it is very slow on CPU")
- else:
- pipeline.upsampler = pipeline.upsampler.cuda()
-
- cfg = dict(
- prompt=prompt,
- guidance_scale=guidance_scale,
- num_inference_steps=num_inference_steps,
- upsample=upsample,
- height=height,
- width=width,
- scheduler=dict(pipeline.scheduler.config),
- # tiled=pipeline.tiled,
- diffusers_version=diffusers_version,
- device_name=torch.cuda.get_device_name(0) if torch.cuda.is_available() else "unknown",
- )
- prompt_config_path.write_text(json.dumps(cfg, indent=2, sort_keys=False))
-
- NUM_TPU_CORES = jax.device_count()
- jit = True # force jit, assume params are already sharded
- batch_size_total = NUM_TPU_CORES * batch_size if jit else batch_size
-
- def generate_input_batches(prompts, batch_size):
- prompt_batch = None
- for batch_idx in range(math.ceil(len(prompts) / batch_size)):
- prompt_batch = prompts[batch_idx * batch_size : (batch_idx + 1) * batch_size]
- yield batch_idx, prompt_batch
-
- frame_index = 0
- frame_filepaths = []
- for batch_idx, prompt_batch in generate_input_batches([prompt] * num_images, batch_size_total):
- # This batch size correspond to each TPU core, so we are generating batch_size * NUM_TPU_CORES images
- print(f"Generating batches: {batch_idx*NUM_TPU_CORES} - {min((batch_idx+1)*NUM_TPU_CORES, num_batches)}")
- prompt_ids_batch = pipeline.prepare_inputs(prompt_batch)
- prng_seed_batch = prng_seed
-
- if jit:
- padded = False
- # Check if len of prompt_batch is multiple of NUM_TPU_CORES, if not pad its ids
- if len(prompt_batch) % NUM_TPU_CORES != 0:
- padded = True
- pad_size = NUM_TPU_CORES - (len(prompt_batch) % NUM_TPU_CORES)
- # Pad embeds_batch and noise_batch with zeros in batch dimension
- prompt_ids_batch = pad_along_axis(prompt_ids_batch, pad_size, axis=0)
-
- prompt_ids_batch = shard(prompt_ids_batch)
- prng_seed_batch = jax.random.split(prng_seed, jax.device_count())
-
- outputs = pipeline(
- params,
- prng_seed=prng_seed_batch,
- prompt_ids=prompt_ids_batch,
- height=height,
- width=width,
- guidance_scale=guidance_scale,
- num_inference_steps=num_inference_steps,
- output_type="pil" if not upsample else "numpy",
- jit=jit,
- )["images"]
-
- if jit:
- # check if we padded and remove that padding from outputs
- if padded:
- outputs = outputs[:-pad_size]
-
- if upsample:
- images = []
- for output in outputs:
- images.append(pipeline.upsampler(output))
- else:
- images = outputs
-
- for image in images:
- uuid = str(uuid4())
- frame_filepath = save_path / f"{uuid}{image_file_ext}"
- image.save(frame_filepath)
- frame_filepaths.append(str(frame_filepath))
- frame_index += 1
-
- return frame_filepaths
-
- if push_to_hub:
- upload_folder_chunked(repo_id, save_path, private=private, create_pr=create_pr)
diff --git a/spaces/Omnibus/text-to-vid/README.md b/spaces/Omnibus/text-to-vid/README.md
deleted file mode 100644
index 138ece8a04cada5fe7b02434b1e4261af4ae4443..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/text-to-vid/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text To Vid
-emoji: 💻
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OpenGVLab/DragGAN/stylegan2/op/conv2d_gradfix.py b/spaces/OpenGVLab/DragGAN/stylegan2/op/conv2d_gradfix.py
deleted file mode 100644
index 8636ed397f6162975b46b2a32667211567b6800e..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/DragGAN/stylegan2/op/conv2d_gradfix.py
+++ /dev/null
@@ -1,229 +0,0 @@
-import contextlib
-import warnings
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-
-enabled = True
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
- global weight_gradients_disabled
-
- old = weight_gradients_disabled
- weight_gradients_disabled = True
- yield
- weight_gradients_disabled = old
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
- if could_use_op(input):
- return conv2d_gradfix(
- transpose=False,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=0,
- dilation=dilation,
- groups=groups,
- ).apply(input, weight, bias)
-
- return F.conv2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- )
-
-
-def conv_transpose2d(
- input,
- weight,
- bias=None,
- stride=1,
- padding=0,
- output_padding=0,
- groups=1,
- dilation=1,
-):
- if could_use_op(input):
- return conv2d_gradfix(
- transpose=True,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- groups=groups,
- dilation=dilation,
- ).apply(input, weight, bias)
-
- return F.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- dilation=dilation,
- groups=groups,
- )
-
-
-def could_use_op(input):
- return False
-
- if (not enabled) or (not torch.backends.cudnn.enabled):
- return False
-
- if input.device.type != "cuda":
- return False
-
- if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
- return True
-
- warnings.warn(
- f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
- )
-
- return False
-
-
-def ensure_tuple(xs, ndim):
- xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-
- return xs
-
-
-conv2d_gradfix_cache = dict()
-
-
-def conv2d_gradfix(
- transpose, weight_shape, stride, padding, output_padding, dilation, groups
-):
- ndim = 2
- weight_shape = tuple(weight_shape)
- stride = ensure_tuple(stride, ndim)
- padding = ensure_tuple(padding, ndim)
- output_padding = ensure_tuple(output_padding, ndim)
- dilation = ensure_tuple(dilation, ndim)
-
- key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
- if key in conv2d_gradfix_cache:
- return conv2d_gradfix_cache[key]
-
- common_kwargs = dict(
- stride=stride, padding=padding, dilation=dilation, groups=groups
- )
-
- def calc_output_padding(input_shape, output_shape):
- if transpose:
- return [0, 0]
-
- return [
- input_shape[i + 2]
- - (output_shape[i + 2] - 1) * stride[i]
- - (1 - 2 * padding[i])
- - dilation[i] * (weight_shape[i + 2] - 1)
- for i in range(ndim)
- ]
-
- class Conv2d(autograd.Function):
- @staticmethod
- def forward(ctx, input, weight, bias):
- if not transpose:
- out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
- else:
- out = F.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- output_padding=output_padding,
- **common_kwargs,
- )
-
- ctx.save_for_backward(input, weight)
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- input, weight = ctx.saved_tensors
- grad_input, grad_weight, grad_bias = None, None, None
-
- if ctx.needs_input_grad[0]:
- p = calc_output_padding(
- input_shape=input.shape, output_shape=grad_output.shape
- )
- grad_input = conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs,
- ).apply(grad_output, weight, None)
-
- if ctx.needs_input_grad[1] and not weight_gradients_disabled:
- grad_weight = Conv2dGradWeight.apply(grad_output, input)
-
- if ctx.needs_input_grad[2]:
- grad_bias = grad_output.sum((0, 2, 3))
-
- return grad_input, grad_weight, grad_bias
-
- class Conv2dGradWeight(autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input):
- op = torch._C._jit_get_operation(
- "aten::cudnn_convolution_backward_weight"
- if not transpose
- else "aten::cudnn_convolution_transpose_backward_weight"
- )
- flags = [
- torch.backends.cudnn.benchmark,
- torch.backends.cudnn.deterministic,
- torch.backends.cudnn.allow_tf32,
- ]
- grad_weight = op(
- weight_shape,
- grad_output,
- input,
- padding,
- stride,
- dilation,
- groups,
- *flags,
- )
- ctx.save_for_backward(grad_output, input)
-
- return grad_weight
-
- @staticmethod
- def backward(ctx, grad_grad_weight):
- grad_output, input = ctx.saved_tensors
- grad_grad_output, grad_grad_input = None, None
-
- if ctx.needs_input_grad[0]:
- grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)
-
- if ctx.needs_input_grad[1]:
- p = calc_output_padding(
- input_shape=input.shape, output_shape=grad_output.shape
- )
- grad_grad_input = conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs,
- ).apply(grad_output, grad_grad_weight, None)
-
- return grad_grad_output, grad_grad_input
-
- conv2d_gradfix_cache[key] = Conv2d
-
- return Conv2d
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/debug.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/debug.py
deleted file mode 100644
index 0a4437fb5ae7522e46ca6c42ba5fd980df250446..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/debug.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
- np.uint8).reshape(1300, 1, 1, 3)
-
-def _get_color_image(heatmap):
- heatmap = heatmap.reshape(
- heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1)
- if heatmap.shape[0] == 1:
- color_map = (heatmap * np.ones((1, 1, 1, 3), np.uint8) * 255).max(
- axis=0).astype(np.uint8) # H, W, 3
- else:
- color_map = (heatmap * COLORS[:heatmap.shape[0]]).max(axis=0).astype(np.uint8) # H, W, 3
-
- return color_map
-
-def _blend_image(image, color_map, a=0.7):
- color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
- ret = np.clip(image * (1 - a) + color_map * a, 0, 255).astype(np.uint8)
- return ret
-
-def _blend_image_heatmaps(image, color_maps, a=0.7):
- merges = np.zeros((image.shape[0], image.shape[1], 3), np.float32)
- for color_map in color_maps:
- color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
- merges = np.maximum(merges, color_map)
- ret = np.clip(image * (1 - a) + merges * a, 0, 255).astype(np.uint8)
- return ret
-
-def _decompose_level(x, shapes_per_level, N):
- '''
- x: LNHiWi x C
- '''
- x = x.view(x.shape[0], -1)
- ret = []
- st = 0
- for l in range(len(shapes_per_level)):
- ret.append([])
- h = shapes_per_level[l][0].int().item()
- w = shapes_per_level[l][1].int().item()
- for i in range(N):
- ret[l].append(x[st + h * w * i:st + h * w * (i + 1)].view(
- h, w, -1).permute(2, 0, 1))
- st += h * w * N
- return ret
-
-def _imagelist_to_tensor(images):
- images = [x for x in images]
- image_sizes = [x.shape[-2:] for x in images]
- h = max([size[0] for size in image_sizes])
- w = max([size[1] for size in image_sizes])
- S = 32
- h, w = ((h - 1) // S + 1) * S, ((w - 1) // S + 1) * S
- images = [F.pad(x, (0, w - x.shape[2], 0, h - x.shape[1], 0, 0)) \
- for x in images]
- images = torch.stack(images)
- return images
-
-
-def _ind2il(ind, shapes_per_level, N):
- r = ind
- l = 0
- S = 0
- while r - S >= N * shapes_per_level[l][0] * shapes_per_level[l][1]:
- S += N * shapes_per_level[l][0] * shapes_per_level[l][1]
- l += 1
- i = (r - S) // (shapes_per_level[l][0] * shapes_per_level[l][1])
- return i, l
-
-def debug_train(
- images, gt_instances, flattened_hms, reg_targets, labels, pos_inds,
- shapes_per_level, locations, strides):
- '''
- images: N x 3 x H x W
- flattened_hms: LNHiWi x C
- shapes_per_level: L x 2 [(H_i, W_i)]
- locations: LNHiWi x 2
- '''
- reg_inds = torch.nonzero(
- reg_targets.max(dim=1)[0] > 0).squeeze(1)
- N = len(images)
- images = _imagelist_to_tensor(images)
- repeated_locations = [torch.cat([loc] * N, dim=0) \
- for loc in locations]
- locations = torch.cat(repeated_locations, dim=0)
- gt_hms = _decompose_level(flattened_hms, shapes_per_level, N)
- masks = flattened_hms.new_zeros((flattened_hms.shape[0], 1))
- masks[pos_inds] = 1
- masks = _decompose_level(masks, shapes_per_level, N)
- for i in range(len(images)):
- image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
- color_maps = []
- for l in range(len(gt_hms)):
- color_map = _get_color_image(
- gt_hms[l][i].detach().cpu().numpy())
- color_maps.append(color_map)
- cv2.imshow('gthm_{}'.format(l), color_map)
- blend = _blend_image_heatmaps(image.copy(), color_maps)
- if gt_instances is not None:
- bboxes = gt_instances[i].gt_boxes.tensor
- for j in range(len(bboxes)):
- bbox = bboxes[j]
- cv2.rectangle(
- blend,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- (0, 0, 255), 3, cv2.LINE_AA)
-
- for j in range(len(pos_inds)):
- image_id, l = _ind2il(pos_inds[j], shapes_per_level, N)
- if image_id != i:
- continue
- loc = locations[pos_inds[j]]
- cv2.drawMarker(
- blend, (int(loc[0]), int(loc[1])), (0, 255, 255),
- markerSize=(l + 1) * 16)
-
- for j in range(len(reg_inds)):
- image_id, l = _ind2il(reg_inds[j], shapes_per_level, N)
- if image_id != i:
- continue
- ltrb = reg_targets[reg_inds[j]]
- ltrb *= strides[l]
- loc = locations[reg_inds[j]]
- bbox = [(loc[0] - ltrb[0]), (loc[1] - ltrb[1]),
- (loc[0] + ltrb[2]), (loc[1] + ltrb[3])]
- cv2.rectangle(
- blend,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- (255, 0, 0), 1, cv2.LINE_AA)
- cv2.circle(blend, (int(loc[0]), int(loc[1])), 2, (255, 0, 0), -1)
-
- cv2.imshow('blend', blend)
- cv2.waitKey()
-
-
-def debug_test(
- images, logits_pred, reg_pred, agn_hm_pred=[], preds=[],
- vis_thresh=0.3, debug_show_name=False, mult_agn=False):
- '''
- images: N x 3 x H x W
- class_target: LNHiWi x C
- cat_agn_heatmap: LNHiWi
- shapes_per_level: L x 2 [(H_i, W_i)]
- '''
- N = len(images)
- for i in range(len(images)):
- image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
- result = image.copy().astype(np.uint8)
- pred_image = image.copy().astype(np.uint8)
- color_maps = []
- L = len(logits_pred)
- for l in range(L):
- if logits_pred[0] is not None:
- stride = min(image.shape[0], image.shape[1]) / min(
- logits_pred[l][i].shape[1], logits_pred[l][i].shape[2])
- else:
- stride = min(image.shape[0], image.shape[1]) / min(
- agn_hm_pred[l][i].shape[1], agn_hm_pred[l][i].shape[2])
- stride = stride if stride < 60 else 64 if stride < 100 else 128
- if logits_pred[0] is not None:
- if mult_agn:
- logits_pred[l][i] = logits_pred[l][i] * agn_hm_pred[l][i]
- color_map = _get_color_image(
- logits_pred[l][i].detach().cpu().numpy())
- color_maps.append(color_map)
- cv2.imshow('predhm_{}'.format(l), color_map)
-
- if debug_show_name:
- from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES
- cat2name = [x['name'] for x in LVIS_CATEGORIES]
- for j in range(len(preds[i].scores) if preds is not None else 0):
- if preds[i].scores[j] > vis_thresh:
- bbox = preds[i].proposal_boxes[j] \
- if preds[i].has('proposal_boxes') else \
- preds[i].pred_boxes[j]
- bbox = bbox.tensor[0].detach().cpu().numpy().astype(np.int32)
- cat = int(preds[i].pred_classes[j]) \
- if preds[i].has('pred_classes') else 0
- cl = COLORS[cat, 0, 0]
- cv2.rectangle(
- pred_image, (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- (int(cl[0]), int(cl[1]), int(cl[2])), 2, cv2.LINE_AA)
- if debug_show_name:
- txt = '{}{:.1f}'.format(
- cat2name[cat] if cat > 0 else '',
- preds[i].scores[j])
- font = cv2.FONT_HERSHEY_SIMPLEX
- cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
- cv2.rectangle(
- pred_image,
- (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
- (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)),
- (int(cl[0]), int(cl[1]), int(cl[2])), -1)
- cv2.putText(
- pred_image, txt, (int(bbox[0]), int(bbox[1] - 2)),
- font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)
-
-
- if agn_hm_pred[l] is not None:
- agn_hm_ = agn_hm_pred[l][i, 0, :, :, None].detach().cpu().numpy()
- agn_hm_ = (agn_hm_ * np.array([255, 255, 255]).reshape(
- 1, 1, 3)).astype(np.uint8)
- cv2.imshow('agn_hm_{}'.format(l), agn_hm_)
- blend = _blend_image_heatmaps(image.copy(), color_maps)
- cv2.imshow('blend', blend)
- cv2.imshow('preds', pred_image)
- cv2.waitKey()
-
-global cnt
-cnt = 0
-
-def debug_second_stage(images, instances, proposals=None, vis_thresh=0.3,
- save_debug=False, debug_show_name=False):
- images = _imagelist_to_tensor(images)
- if debug_show_name:
- from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES
- cat2name = [x['name'] for x in LVIS_CATEGORIES]
- for i in range(len(images)):
- image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy()
- if instances[i].has('gt_boxes'):
- bboxes = instances[i].gt_boxes.tensor.cpu().numpy()
- scores = np.ones(bboxes.shape[0])
- cats = instances[i].gt_classes.cpu().numpy()
- else:
- bboxes = instances[i].pred_boxes.tensor.cpu().numpy()
- scores = instances[i].scores.cpu().numpy()
- cats = instances[i].pred_classes.cpu().numpy()
- for j in range(len(bboxes)):
- if scores[j] > vis_thresh:
- bbox = bboxes[j]
- cl = COLORS[cats[j], 0, 0]
- cl = (int(cl[0]), int(cl[1]), int(cl[2]))
- cv2.rectangle(
- image,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- cl, 2, cv2.LINE_AA)
- if debug_show_name:
- cat = cats[j]
- txt = '{}{:.1f}'.format(
- cat2name[cat] if cat > 0 else '',
- scores[j])
- font = cv2.FONT_HERSHEY_SIMPLEX
- cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
- cv2.rectangle(
- image,
- (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
- (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)),
- (int(cl[0]), int(cl[1]), int(cl[2])), -1)
- cv2.putText(
- image, txt, (int(bbox[0]), int(bbox[1] - 2)),
- font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)
- if proposals is not None:
- proposal_image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy()
- bboxes = proposals[i].proposal_boxes.tensor.cpu().numpy()
- if proposals[i].has('scores'):
- scores = proposals[i].scores.cpu().numpy()
- else:
- scores = proposals[i].objectness_logits.sigmoid().cpu().numpy()
- for j in range(len(bboxes)):
- if scores[j] > vis_thresh:
- bbox = bboxes[j]
- cl = (209, 159, 83)
- cv2.rectangle(
- proposal_image,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- cl, 2, cv2.LINE_AA)
-
- cv2.imshow('image', image)
- if proposals is not None:
- cv2.imshow('proposals', proposal_image)
- if save_debug:
- global cnt
- cnt += 1
- cv2.imwrite('output/save_debug/{}.jpg'.format(cnt), proposal_image)
- cv2.waitKey()
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/resnet.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/resnet.py
deleted file mode 100644
index 3e1d521f171c984cf6a7ff3dcebd96f8c5faf908..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/models/ade20k/resnet.py
+++ /dev/null
@@ -1,181 +0,0 @@
-"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch"""
-
-import math
-
-import torch.nn as nn
-from torch.nn import BatchNorm2d
-
-from .utils import load_url
-
-__all__ = ['ResNet', 'resnet50']
-
-
-model_urls = {
- 'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- "3x3 convolution with padding"
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet(nn.Module):
-
- def __init__(self, block, layers, num_classes=1000):
- self.inplanes = 128
- super(ResNet, self).__init__()
- self.conv1 = conv3x3(3, 64, stride=2)
- self.bn1 = BatchNorm2d(64)
- self.relu1 = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(64, 64)
- self.bn2 = BatchNorm2d(64)
- self.relu2 = nn.ReLU(inplace=True)
- self.conv3 = conv3x3(64, 128)
- self.bn3 = BatchNorm2d(128)
- self.relu3 = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
- self.avgpool = nn.AvgPool2d(7, stride=1)
- self.fc = nn.Linear(512 * block.expansion, num_classes)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.relu1(self.bn1(self.conv1(x)))
- x = self.relu2(self.bn2(self.conv2(x)))
- x = self.relu3(self.bn3(self.conv3(x)))
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.avgpool(x)
- x = x.view(x.size(0), -1)
- x = self.fc(x)
-
- return x
-
-
-def resnet50(pretrained=False, **kwargs):
- """Constructs a ResNet-50 model.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- """
- model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
- if pretrained:
- model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
- return model
-
-
-def resnet18(pretrained=False, **kwargs):
- """Constructs a ResNet-18 model.
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- """
- model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
- if pretrained:
- model.load_state_dict(load_url(model_urls['resnet18']))
- return model
\ No newline at end of file
diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/data/tools/geometry.py b/spaces/OpenMotionLab/MotionGPT/mGPT/data/tools/geometry.py
deleted file mode 100644
index e6eafa2e1f2459a0f6f5ad1280c71e6a9625549e..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/mGPT/data/tools/geometry.py
+++ /dev/null
@@ -1,566 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-# Check PYTORCH3D_LICENCE before use
-
-import functools
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-
-"""
-The transformation matrices returned from the functions in this file assume
-the points on which the transformation will be applied are column vectors.
-i.e. the R matrix is structured as
-
- R = [
- [Rxx, Rxy, Rxz],
- [Ryx, Ryy, Ryz],
- [Rzx, Rzy, Rzz],
- ] # (3, 3)
-
-This matrix can be applied to column vectors by post multiplication
-by the points e.g.
-
- points = [[0], [1], [2]] # (3 x 1) xyz coordinates of a point
- transformed_points = R * points
-
-To apply the same matrix to points which are row vectors, the R matrix
-can be transposed and pre multiplied by the points:
-
-e.g.
- points = [[0, 1, 2]] # (1 x 3) xyz coordinates of a point
- transformed_points = points * R.transpose(1, 0)
-"""
-
-
-# Added
-def matrix_of_angles(cos, sin, inv=False, dim=2):
- assert dim in [2, 3]
- sin = -sin if inv else sin
- if dim == 2:
- row1 = torch.stack((cos, -sin), axis=-1)
- row2 = torch.stack((sin, cos), axis=-1)
- return torch.stack((row1, row2), axis=-2)
- elif dim == 3:
- row1 = torch.stack((cos, -sin, 0*cos), axis=-1)
- row2 = torch.stack((sin, cos, 0*cos), axis=-1)
- row3 = torch.stack((0*sin, 0*cos, 1+0*cos), axis=-1)
- return torch.stack((row1, row2, row3),axis=-2)
-
-
-def quaternion_to_matrix(quaternions):
- """
- Convert rotations given as quaternions to rotation matrices.
-
- Args:
- quaternions: quaternions with real part first,
- as tensor of shape (..., 4).
-
- Returns:
- Rotation matrices as tensor of shape (..., 3, 3).
- """
- r, i, j, k = torch.unbind(quaternions, -1)
- two_s = 2.0 / (quaternions * quaternions).sum(-1)
-
- o = torch.stack(
- (
- 1 - two_s * (j * j + k * k),
- two_s * (i * j - k * r),
- two_s * (i * k + j * r),
- two_s * (i * j + k * r),
- 1 - two_s * (i * i + k * k),
- two_s * (j * k - i * r),
- two_s * (i * k - j * r),
- two_s * (j * k + i * r),
- 1 - two_s * (i * i + j * j),
- ),
- -1,
- )
- return o.reshape(quaternions.shape[:-1] + (3, 3))
-
-
-def _copysign(a, b):
- """
- Return a tensor where each element has the absolute value taken from the,
- corresponding element of a, with sign taken from the corresponding
- element of b. This is like the standard copysign floating-point operation,
- but is not careful about negative 0 and NaN.
-
- Args:
- a: source tensor.
- b: tensor whose signs will be used, of the same shape as a.
-
- Returns:
- Tensor of the same shape as a with the signs of b.
- """
- signs_differ = (a < 0) != (b < 0)
- return torch.where(signs_differ, -a, a)
-
-
-def _sqrt_positive_part(x):
- """
- Returns torch.sqrt(torch.max(0, x))
- but with a zero subgradient where x is 0.
- """
- ret = torch.zeros_like(x)
- positive_mask = x > 0
- ret[positive_mask] = torch.sqrt(x[positive_mask])
- return ret
-
-
-def matrix_to_quaternion(matrix):
- """
- Convert rotations given as rotation matrices to quaternions.
-
- Args:
- matrix: Rotation matrices as tensor of shape (..., 3, 3).
-
- Returns:
- quaternions with real part first, as tensor of shape (..., 4).
- """
- if matrix.size(-1) != 3 or matrix.size(-2) != 3:
- raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.")
- m00 = matrix[..., 0, 0]
- m11 = matrix[..., 1, 1]
- m22 = matrix[..., 2, 2]
- o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22)
- x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22)
- y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22)
- z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22)
- o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2])
- o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0])
- o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1])
- return torch.stack((o0, o1, o2, o3), -1)
-
-
-def _axis_angle_rotation(axis: str, angle):
- """
- Return the rotation matrices for one of the rotations about an axis
- of which Euler angles describe, for each value of the angle given.
-
- Args:
- axis: Axis label "X" or "Y or "Z".
- angle: any shape tensor of Euler angles in radians
-
- Returns:
- Rotation matrices as tensor of shape (..., 3, 3).
- """
-
- cos = torch.cos(angle)
- sin = torch.sin(angle)
- one = torch.ones_like(angle)
- zero = torch.zeros_like(angle)
-
- if axis == "X":
- R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
- if axis == "Y":
- R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
- if axis == "Z":
- R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
-
- return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
-
-
-def euler_angles_to_matrix(euler_angles, convention: str):
- """
- Convert rotations given as Euler angles in radians to rotation matrices.
-
- Args:
- euler_angles: Euler angles in radians as tensor of shape (..., 3).
- convention: Convention string of three uppercase letters from
- {"X", "Y", and "Z"}.
-
- Returns:
- Rotation matrices as tensor of shape (..., 3, 3).
- """
- if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
- raise ValueError("Invalid input euler angles.")
- if len(convention) != 3:
- raise ValueError("Convention must have 3 letters.")
- if convention[1] in (convention[0], convention[2]):
- raise ValueError(f"Invalid convention {convention}.")
- for letter in convention:
- if letter not in ("X", "Y", "Z"):
- raise ValueError(f"Invalid letter {letter} in convention string.")
- matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
- return functools.reduce(torch.matmul, matrices)
-
-
-def _angle_from_tan(
- axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
-):
- """
- Extract the first or third Euler angle from the two members of
- the matrix which are positive constant times its sine and cosine.
-
- Args:
- axis: Axis label "X" or "Y or "Z" for the angle we are finding.
- other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
- convention.
- data: Rotation matrices as tensor of shape (..., 3, 3).
- horizontal: Whether we are looking for the angle for the third axis,
- which means the relevant entries are in the same row of the
- rotation matrix. If not, they are in the same column.
- tait_bryan: Whether the first and third axes in the convention differ.
-
- Returns:
- Euler Angles in radians for each matrix in data as a tensor
- of shape (...).
- """
-
- i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
- if horizontal:
- i2, i1 = i1, i2
- even = (axis + other_axis) in ["XY", "YZ", "ZX"]
- if horizontal == even:
- return torch.atan2(data[..., i1], data[..., i2])
- if tait_bryan:
- return torch.atan2(-data[..., i2], data[..., i1])
- return torch.atan2(data[..., i2], -data[..., i1])
-
-
-def _index_from_letter(letter: str):
- if letter == "X":
- return 0
- if letter == "Y":
- return 1
- if letter == "Z":
- return 2
-
-
-def matrix_to_euler_angles(matrix, convention: str):
- """
- Convert rotations given as rotation matrices to Euler angles in radians.
-
- Args:
- matrix: Rotation matrices as tensor of shape (..., 3, 3).
- convention: Convention string of three uppercase letters.
-
- Returns:
- Euler angles in radians as tensor of shape (..., 3).
- """
- if len(convention) != 3:
- raise ValueError("Convention must have 3 letters.")
- if convention[1] in (convention[0], convention[2]):
- raise ValueError(f"Invalid convention {convention}.")
- for letter in convention:
- if letter not in ("X", "Y", "Z"):
- raise ValueError(f"Invalid letter {letter} in convention string.")
- if matrix.size(-1) != 3 or matrix.size(-2) != 3:
- raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.")
- i0 = _index_from_letter(convention[0])
- i2 = _index_from_letter(convention[2])
- tait_bryan = i0 != i2
- if tait_bryan:
- central_angle = torch.asin(
- matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
- )
- else:
- central_angle = torch.acos(matrix[..., i0, i0])
-
- o = (
- _angle_from_tan(
- convention[0], convention[1], matrix[..., i2], False, tait_bryan
- ),
- central_angle,
- _angle_from_tan(
- convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
- ),
- )
- return torch.stack(o, -1)
-
-
-def random_quaternions(
- n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
-):
- """
- Generate random quaternions representing rotations,
- i.e. versors with nonnegative real part.
-
- Args:
- n: Number of quaternions in a batch to return.
- dtype: Type to return.
- device: Desired device of returned tensor. Default:
- uses the current device for the default tensor type.
- requires_grad: Whether the resulting tensor should have the gradient
- flag set.
-
- Returns:
- Quaternions as tensor of shape (N, 4).
- """
- o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad)
- s = (o * o).sum(1)
- o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
- return o
-
-
-def random_rotations(
- n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
-):
- """
- Generate random rotations as 3x3 rotation matrices.
-
- Args:
- n: Number of rotation matrices in a batch to return.
- dtype: Type to return.
- device: Device of returned tensor. Default: if None,
- uses the current device for the default tensor type.
- requires_grad: Whether the resulting tensor should have the gradient
- flag set.
-
- Returns:
- Rotation matrices as tensor of shape (n, 3, 3).
- """
- quaternions = random_quaternions(
- n, dtype=dtype, device=device, requires_grad=requires_grad
- )
- return quaternion_to_matrix(quaternions)
-
-
-def random_rotation(
- dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
-):
- """
- Generate a single random 3x3 rotation matrix.
-
- Args:
- dtype: Type to return
- device: Device of returned tensor. Default: if None,
- uses the current device for the default tensor type
- requires_grad: Whether the resulting tensor should have the gradient
- flag set
-
- Returns:
- Rotation matrix as tensor of shape (3, 3).
- """
- return random_rotations(1, dtype, device, requires_grad)[0]
-
-
-def standardize_quaternion(quaternions):
- """
- Convert a unit quaternion to a standard form: one in which the real
- part is non negative.
-
- Args:
- quaternions: Quaternions with real part first,
- as tensor of shape (..., 4).
-
- Returns:
- Standardized quaternions as tensor of shape (..., 4).
- """
- return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
-
-
-def quaternion_raw_multiply(a, b):
- """
- Multiply two quaternions.
- Usual torch rules for broadcasting apply.
-
- Args:
- a: Quaternions as tensor of shape (..., 4), real part first.
- b: Quaternions as tensor of shape (..., 4), real part first.
-
- Returns:
- The product of a and b, a tensor of quaternions shape (..., 4).
- """
- aw, ax, ay, az = torch.unbind(a, -1)
- bw, bx, by, bz = torch.unbind(b, -1)
- ow = aw * bw - ax * bx - ay * by - az * bz
- ox = aw * bx + ax * bw + ay * bz - az * by
- oy = aw * by - ax * bz + ay * bw + az * bx
- oz = aw * bz + ax * by - ay * bx + az * bw
- return torch.stack((ow, ox, oy, oz), -1)
-
-
-def quaternion_multiply(a, b):
- """
- Multiply two quaternions representing rotations, returning the quaternion
- representing their composition, i.e. the versor with nonnegative real part.
- Usual torch rules for broadcasting apply.
-
- Args:
- a: Quaternions as tensor of shape (..., 4), real part first.
- b: Quaternions as tensor of shape (..., 4), real part first.
-
- Returns:
- The product of a and b, a tensor of quaternions of shape (..., 4).
- """
- ab = quaternion_raw_multiply(a, b)
- return standardize_quaternion(ab)
-
-
-def quaternion_invert(quaternion):
- """
- Given a quaternion representing rotation, get the quaternion representing
- its inverse.
-
- Args:
- quaternion: Quaternions as tensor of shape (..., 4), with real part
- first, which must be versors (unit quaternions).
-
- Returns:
- The inverse, a tensor of quaternions of shape (..., 4).
- """
-
- return quaternion * quaternion.new_tensor([1, -1, -1, -1])
-
-
-def quaternion_apply(quaternion, point):
- """
- Apply the rotation given by a quaternion to a 3D point.
- Usual torch rules for broadcasting apply.
-
- Args:
- quaternion: Tensor of quaternions, real part first, of shape (..., 4).
- point: Tensor of 3D points of shape (..., 3).
-
- Returns:
- Tensor of rotated points of shape (..., 3).
- """
- if point.size(-1) != 3:
- raise ValueError(f"Points are not in 3D, f{point.shape}.")
- real_parts = point.new_zeros(point.shape[:-1] + (1,))
- point_as_quaternion = torch.cat((real_parts, point), -1)
- out = quaternion_raw_multiply(
- quaternion_raw_multiply(quaternion, point_as_quaternion),
- quaternion_invert(quaternion),
- )
- return out[..., 1:]
-
-
-def axis_angle_to_matrix(axis_angle):
- """
- Convert rotations given as axis/angle to rotation matrices.
-
- Args:
- axis_angle: Rotations given as a vector in axis angle form,
- as a tensor of shape (..., 3), where the magnitude is
- the angle turned anticlockwise in radians around the
- vector's direction.
-
- Returns:
- Rotation matrices as tensor of shape (..., 3, 3).
- """
- return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
-
-
-def matrix_to_axis_angle(matrix):
- """
- Convert rotations given as rotation matrices to axis/angle.
-
- Args:
- matrix: Rotation matrices as tensor of shape (..., 3, 3).
-
- Returns:
- Rotations given as a vector in axis angle form, as a tensor
- of shape (..., 3), where the magnitude is the angle
- turned anticlockwise in radians around the vector's
- direction.
- """
- return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
-
-
-def axis_angle_to_quaternion(axis_angle):
- """
- Convert rotations given as axis/angle to quaternions.
-
- Args:
- axis_angle: Rotations given as a vector in axis angle form,
- as a tensor of shape (..., 3), where the magnitude is
- the angle turned anticlockwise in radians around the
- vector's direction.
-
- Returns:
- quaternions with real part first, as tensor of shape (..., 4).
- """
- angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
- half_angles = 0.5 * angles
- eps = 1e-6
- small_angles = angles.abs() < eps
- sin_half_angles_over_angles = torch.empty_like(angles)
- sin_half_angles_over_angles[~small_angles] = (
- torch.sin(half_angles[~small_angles]) / angles[~small_angles]
- )
- # for x small, sin(x/2) is about x/2 - (x/2)^3/6
- # so sin(x/2)/x is about 1/2 - (x*x)/48
- sin_half_angles_over_angles[small_angles] = (
- 0.5 - (angles[small_angles] * angles[small_angles]) / 48
- )
- quaternions = torch.cat(
- [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
- )
- return quaternions
-
-
-def quaternion_to_axis_angle(quaternions):
- """
- Convert rotations given as quaternions to axis/angle.
-
- Args:
- quaternions: quaternions with real part first,
- as tensor of shape (..., 4).
-
- Returns:
- Rotations given as a vector in axis angle form, as a tensor
- of shape (..., 3), where the magnitude is the angle
- turned anticlockwise in radians around the vector's
- direction.
- """
- norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
- half_angles = torch.atan2(norms, quaternions[..., :1])
- angles = 2 * half_angles
- eps = 1e-6
- small_angles = angles.abs() < eps
- sin_half_angles_over_angles = torch.empty_like(angles)
- sin_half_angles_over_angles[~small_angles] = (
- torch.sin(half_angles[~small_angles]) / angles[~small_angles]
- )
- # for x small, sin(x/2) is about x/2 - (x/2)^3/6
- # so sin(x/2)/x is about 1/2 - (x*x)/48
- sin_half_angles_over_angles[small_angles] = (
- 0.5 - (angles[small_angles] * angles[small_angles]) / 48
- )
- return quaternions[..., 1:] / sin_half_angles_over_angles
-
-
-def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
- """
- Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
- using Gram--Schmidt orthogonalisation per Section B of [1].
- Args:
- d6: 6D rotation representation, of size (*, 6)
-
- Returns:
- batch of rotation matrices of size (*, 3, 3)
-
- [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
- On the Continuity of Rotation Representations in Neural Networks.
- IEEE Conference on Computer Vision and Pattern Recognition, 2019.
- Retrieved from http://arxiv.org/abs/1812.07035
- """
-
- a1, a2 = d6[..., :3], d6[..., 3:]
- b1 = F.normalize(a1, dim=-1)
- b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
- b2 = F.normalize(b2, dim=-1)
- b3 = torch.cross(b1, b2, dim=-1)
- return torch.stack((b1, b2, b3), dim=-2)
-
-
-def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
- """
- Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
- by dropping the last row. Note that 6D representation is not unique.
- Args:
- matrix: batch of rotation matrices of size (*, 3, 3)
-
- Returns:
- 6D rotation representation, of size (*, 6)
-
- [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
- On the Continuity of Rotation Representations in Neural Networks.
- IEEE Conference on Computer Vision and Pattern Recognition, 2019.
- Retrieved from http://arxiv.org/abs/1812.07035
- """
- return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)
diff --git a/spaces/OptimalScale/Robin-33b/lmflow/pipeline/__init__.py b/spaces/OptimalScale/Robin-33b/lmflow/pipeline/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/chord-ignatzek-names.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/chord-ignatzek-names.go
deleted file mode 100644
index 0c5f8e0091e98963e39cedca2d7d3377d03926ed..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/chord-ignatzek-names.go and /dev/null differ
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py
deleted file mode 100644
index ab6b3791692a0d1b5da3601875711710b7bd01ba..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import logging
-
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init
-from annotator.uniformer.mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from ..builder import BACKBONES
-from ..utils import InvertedResidual, make_divisible
-
-
-@BACKBONES.register_module()
-class MobileNetV2(nn.Module):
- """MobileNetV2 backbone.
-
- Args:
- widen_factor (float): Width multiplier, multiply number of
- channels in each layer by this amount. Default: 1.0.
- strides (Sequence[int], optional): Strides of the first block of each
- layer. If not specified, default config in ``arch_setting`` will
- be used.
- dilations (Sequence[int]): Dilation of each layer.
- out_indices (None or Sequence[int]): Output from which stages.
- Default: (7, ).
- frozen_stages (int): Stages to be frozen (all param fixed).
- Default: -1, which means not freezing any parameters.
- conv_cfg (dict): Config dict for convolution layer.
- Default: None, which means using conv2d.
- norm_cfg (dict): Config dict for normalization layer.
- Default: dict(type='BN').
- act_cfg (dict): Config dict for activation layer.
- Default: dict(type='ReLU6').
- norm_eval (bool): Whether to set norm layers to eval mode, namely,
- freeze running stats (mean and var). Note: Effect on Batch Norm
- and its variants only. Default: False.
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed. Default: False.
- """
-
- # Parameters to build layers. 3 parameters are needed to construct a
- # layer, from left to right: expand_ratio, channel, num_blocks.
- arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4],
- [6, 96, 3], [6, 160, 3], [6, 320, 1]]
-
- def __init__(self,
- widen_factor=1.,
- strides=(1, 2, 2, 2, 1, 2, 1),
- dilations=(1, 1, 1, 1, 1, 1, 1),
- out_indices=(1, 2, 4, 6),
- frozen_stages=-1,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU6'),
- norm_eval=False,
- with_cp=False):
- super(MobileNetV2, self).__init__()
- self.widen_factor = widen_factor
- self.strides = strides
- self.dilations = dilations
- assert len(strides) == len(dilations) == len(self.arch_settings)
- self.out_indices = out_indices
- for index in out_indices:
- if index not in range(0, 7):
- raise ValueError('the item in out_indices must in '
- f'range(0, 8). But received {index}')
-
- if frozen_stages not in range(-1, 7):
- raise ValueError('frozen_stages must be in range(-1, 7). '
- f'But received {frozen_stages}')
- self.out_indices = out_indices
- self.frozen_stages = frozen_stages
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.act_cfg = act_cfg
- self.norm_eval = norm_eval
- self.with_cp = with_cp
-
- self.in_channels = make_divisible(32 * widen_factor, 8)
-
- self.conv1 = ConvModule(
- in_channels=3,
- out_channels=self.in_channels,
- kernel_size=3,
- stride=2,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
-
- self.layers = []
-
- for i, layer_cfg in enumerate(self.arch_settings):
- expand_ratio, channel, num_blocks = layer_cfg
- stride = self.strides[i]
- dilation = self.dilations[i]
- out_channels = make_divisible(channel * widen_factor, 8)
- inverted_res_layer = self.make_layer(
- out_channels=out_channels,
- num_blocks=num_blocks,
- stride=stride,
- dilation=dilation,
- expand_ratio=expand_ratio)
- layer_name = f'layer{i + 1}'
- self.add_module(layer_name, inverted_res_layer)
- self.layers.append(layer_name)
-
- def make_layer(self, out_channels, num_blocks, stride, dilation,
- expand_ratio):
- """Stack InvertedResidual blocks to build a layer for MobileNetV2.
-
- Args:
- out_channels (int): out_channels of block.
- num_blocks (int): Number of blocks.
- stride (int): Stride of the first block.
- dilation (int): Dilation of the first block.
- expand_ratio (int): Expand the number of channels of the
- hidden layer in InvertedResidual by this ratio.
- """
- layers = []
- for i in range(num_blocks):
- layers.append(
- InvertedResidual(
- self.in_channels,
- out_channels,
- stride if i == 0 else 1,
- expand_ratio=expand_ratio,
- dilation=dilation if i == 0 else 1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg,
- with_cp=self.with_cp))
- self.in_channels = out_channels
-
- return nn.Sequential(*layers)
-
- def init_weights(self, pretrained=None):
- if isinstance(pretrained, str):
- logger = logging.getLogger()
- load_checkpoint(self, pretrained, strict=False, logger=logger)
- elif pretrained is None:
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- kaiming_init(m)
- elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
- constant_init(m, 1)
- else:
- raise TypeError('pretrained must be a str or None')
-
- def forward(self, x):
- x = self.conv1(x)
-
- outs = []
- for i, layer_name in enumerate(self.layers):
- layer = getattr(self, layer_name)
- x = layer(x)
- if i in self.out_indices:
- outs.append(x)
-
- if len(outs) == 1:
- return outs[0]
- else:
- return tuple(outs)
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- for param in self.conv1.parameters():
- param.requires_grad = False
- for i in range(1, self.frozen_stages + 1):
- layer = getattr(self, f'layer{i}')
- layer.eval()
- for param in layer.parameters():
- param.requires_grad = False
-
- def train(self, mode=True):
- super(MobileNetV2, self).train(mode)
- self._freeze_stages()
- if mode and self.norm_eval:
- for m in self.modules():
- if isinstance(m, _BatchNorm):
- m.eval()
diff --git a/spaces/PlanetHades361/Change-Your-Style/README.md b/spaces/PlanetHades361/Change-Your-Style/README.md
deleted file mode 100644
index 81ccf10de11a6105e515cccbbb45b1e13137c0b7..0000000000000000000000000000000000000000
--- a/spaces/PlanetHades361/Change-Your-Style/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Change Your Style
-emoji: ⚡
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: deedax/Change-Your-Style
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RamAnanth1/roomGPT/model.py b/spaces/RamAnanth1/roomGPT/model.py
deleted file mode 100644
index 6420a55da888514dab11cb4f157a36ab5cd4af46..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/roomGPT/model.py
+++ /dev/null
@@ -1,183 +0,0 @@
-# This file is adapted from gradio_*.py in https://github.com/lllyasviel/ControlNet/tree/f4748e3630d8141d7765e2bd9b1e348f47847707
-# The original license file is LICENSE.ControlNet in this repo.
-from __future__ import annotations
-
-import gc
-import pathlib
-import sys
-
-import cv2
-import numpy as np
-import PIL.Image
-import torch
-from diffusers import (ControlNetModel, DiffusionPipeline,
- StableDiffusionControlNetPipeline,
- UniPCMultistepScheduler)
-
-repo_dir = pathlib.Path(__file__).parent
-submodule_dir = repo_dir / 'ControlNet'
-sys.path.append(submodule_dir.as_posix())
-
-from annotator.mlsd import apply_mlsd
-from annotator.uniformer import apply_uniformer
-from annotator.util import HWC3, resize_image
-
-CONTROLNET_MODEL_IDS = {
-
- 'hough': 'lllyasviel/sd-controlnet-mlsd',
-
-}
-
-
-def download_all_controlnet_weights() -> None:
- for model_id in CONTROLNET_MODEL_IDS.values():
- ControlNetModel.from_pretrained(model_id)
-
-
-class Model:
- def __init__(self,
- base_model_id: str = 'runwayml/stable-diffusion-v1-5',
- task_name: str = 'hough'):
- self.device = torch.device(
- 'cuda:0' if torch.cuda.is_available() else 'cpu')
- self.base_model_id = ''
- self.task_name = ''
- self.pipe = self.load_pipe(base_model_id, task_name)
-
- def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
- if base_model_id == self.base_model_id and task_name == self.task_name and hasattr(
- self, 'pipe'):
- return self.pipe
- model_id = CONTROLNET_MODEL_IDS[task_name]
- controlnet = ControlNetModel.from_pretrained(model_id,
- torch_dtype=torch.float16)
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
- base_model_id,
- safety_checker=None,
- controlnet=controlnet,
- torch_dtype=torch.float16)
- pipe.scheduler = UniPCMultistepScheduler.from_config(
- pipe.scheduler.config)
- pipe.enable_xformers_memory_efficient_attention()
- pipe.to(self.device)
- torch.cuda.empty_cache()
- gc.collect()
- self.base_model_id = base_model_id
- self.task_name = task_name
- return pipe
-
- def set_base_model(self, base_model_id: str) -> str:
- if not base_model_id or base_model_id == self.base_model_id:
- return self.base_model_id
- del self.pipe
- torch.cuda.empty_cache()
- gc.collect()
- try:
- self.pipe = self.load_pipe(base_model_id, self.task_name)
- except Exception:
- self.pipe = self.load_pipe(self.base_model_id, self.task_name)
- return self.base_model_id
-
- def load_controlnet_weight(self, task_name: str) -> None:
- if task_name == self.task_name:
- return
- del self.pipe.controlnet
- torch.cuda.empty_cache()
- gc.collect()
- model_id = CONTROLNET_MODEL_IDS[task_name]
- controlnet = ControlNetModel.from_pretrained(model_id,
- torch_dtype=torch.float16)
- controlnet.to(self.device)
- torch.cuda.empty_cache()
- gc.collect()
- self.pipe.controlnet = controlnet
- self.task_name = task_name
-
- def get_prompt(self, prompt: str, additional_prompt: str) -> str:
- if not prompt:
- prompt = additional_prompt
- else:
- prompt = f'{prompt}, {additional_prompt}'
- return prompt
-
- @torch.autocast('cuda')
- def run_pipe(
- self,
- prompt: str,
- negative_prompt: str,
- control_image: PIL.Image.Image,
- num_images: int,
- num_steps: int,
- guidance_scale: float,
- seed: int,
- ) -> list[PIL.Image.Image]:
- if seed == -1:
- seed = np.random.randint(0, np.iinfo(np.int64).max)
- generator = torch.Generator().manual_seed(seed)
- return self.pipe(prompt=prompt,
- negative_prompt=negative_prompt,
- guidance_scale=guidance_scale,
- num_images_per_prompt=num_images,
- num_inference_steps=num_steps,
- generator=generator,
- image=control_image).images
-
- @staticmethod
- def preprocess_hough(
- input_image: np.ndarray,
- image_resolution: int,
- detect_resolution: int,
- value_threshold: float,
- distance_threshold: float,
- ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
- input_image = HWC3(input_image)
- control_image = apply_mlsd(
- resize_image(input_image, detect_resolution), value_threshold,
- distance_threshold)
- control_image = HWC3(control_image)
- image = resize_image(input_image, image_resolution)
- H, W = image.shape[:2]
- control_image = cv2.resize(control_image, (W, H),
- interpolation=cv2.INTER_NEAREST)
-
- vis_control_image = 255 - cv2.dilate(
- control_image, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-
- return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
- vis_control_image)
-
- @torch.inference_mode()
- def process_hough(
- self,
- input_image: np.ndarray,
- prompt: str,
- additional_prompt: str,
- negative_prompt: str,
- num_images: int,
- image_resolution: int,
- detect_resolution: int,
- num_steps: int,
- guidance_scale: float,
- seed: int,
- value_threshold: float,
- distance_threshold: float,
- ) -> list[PIL.Image.Image]:
- control_image, vis_control_image = self.preprocess_hough(
- input_image=input_image,
- image_resolution=image_resolution,
- detect_resolution=detect_resolution,
- value_threshold=value_threshold,
- distance_threshold=distance_threshold,
- )
- self.load_controlnet_weight('hough')
- results = self.run_pipe(
- prompt=self.get_prompt(prompt, additional_prompt),
- negative_prompt=negative_prompt,
- control_image=control_image,
-
- num_images=num_images,
- num_steps=num_steps,
- guidance_scale=guidance_scale,
- seed=seed,
- )
- return results
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/config/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/config/__init__.py
deleted file mode 100644
index 1a5153ad4fa51e14c83b8bb00345354d42ed3f0a..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/config/__init__.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""For backward compatibility, expose main functions from
-``setuptools.config.setupcfg``
-"""
-import warnings
-from functools import wraps
-from textwrap import dedent
-from typing import Callable, TypeVar, cast
-
-from .._deprecation_warning import SetuptoolsDeprecationWarning
-from . import setupcfg
-
-Fn = TypeVar("Fn", bound=Callable)
-
-__all__ = ('parse_configuration', 'read_configuration')
-
-
-def _deprecation_notice(fn: Fn) -> Fn:
- @wraps(fn)
- def _wrapper(*args, **kwargs):
- msg = f"""\
- As setuptools moves its configuration towards `pyproject.toml`,
- `{__name__}.{fn.__name__}` became deprecated.
-
- For the time being, you can use the `{setupcfg.__name__}` module
- to access a backward compatible API, but this module is provisional
- and might be removed in the future.
- """
- warnings.warn(dedent(msg), SetuptoolsDeprecationWarning, stacklevel=2)
- return fn(*args, **kwargs)
-
- return cast(Fn, _wrapper)
-
-
-read_configuration = _deprecation_notice(setupcfg.read_configuration)
-parse_configuration = _deprecation_notice(setupcfg.parse_configuration)
diff --git a/spaces/Rayzggz/illi-Bert-VITS2/text/__init__.py b/spaces/Rayzggz/illi-Bert-VITS2/text/__init__.py
deleted file mode 100644
index a45b650424306b6e077d7013e93e2c9bd1e073c2..0000000000000000000000000000000000000000
--- a/spaces/Rayzggz/illi-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
- """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- Returns:
- List of integers corresponding to the symbols in the text
- """
- phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
- tone_start = language_tone_start_map[language]
- tones = [i + tone_start for i in tones]
- lang_id = language_id_map[language]
- lang_ids = [lang_id for i in phones]
- return phones, tones, lang_ids
-
-
-def get_bert(norm_text, word2ph, language, device):
- from .chinese_bert import get_bert_feature as zh_bert
- from .english_bert_mock import get_bert_feature as en_bert
- from .japanese_bert import get_bert_feature as jp_bert
-
- lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
- bert = lang_bert_func_map[language](norm_text, word2ph, device)
- return bert
diff --git a/spaces/Rimi98/Relax-Teacher/app.py b/spaces/Rimi98/Relax-Teacher/app.py
deleted file mode 100644
index 7851cd1728b7b02d1d9a53ac4319c62a00d3e714..0000000000000000000000000000000000000000
--- a/spaces/Rimi98/Relax-Teacher/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import onnxruntime
-from transformers import AutoTokenizer
-import torch
-import os
-from transformers import pipeline
-import subprocess
-import moviepy.editor as mp
-import base64
-
-token = AutoTokenizer.from_pretrained('distilroberta-base')
-
-inf_session = onnxruntime.InferenceSession('classifier-quantized2.onnx')
-input_name = inf_session.get_inputs()[0].name
-output_name = inf_session.get_outputs()[0].name
-
-classes = ['Art', 'Astrology', 'Biology', 'Chemistry', 'Economics', 'History', 'Literature', 'Philosophy', 'Physics', 'Politics', 'Psychology', 'Sociology']
-
-### --- Audio/Video to txt ---###
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-pipe = pipeline("automatic-speech-recognition",
- model="openai/whisper-tiny.en",
- chunk_length_s=30, device=device)
-
-### --- Text Summary --- ###
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=device)
-
-
-def video_identity(video):
- transcription = pipe(video)["text"]
- return transcription
-
-def summary(text):
- text = text.split('.')
- max_chunk = 500
- current_chunk = 0
- chunks = []
-
-
- for t in text:
- if len(chunks) == current_chunk + 1:
- if len(chunks[current_chunk]) + len(t.split(' ')) <= max_chunk:
- chunks[current_chunk].extend(t.split(' '))
- else:
- current_chunk += 1
- chunks.append(t.split(' '))
- else:
- chunks.append(t.split(' '))
-
- for chunk in range(len(chunks)):
- chunks[chunk] =' '.join(chunks[chunk])
-
- summ = summarizer(chunks,max_length = 100)
-
- return summ
-
-def classify(video_file,encoded_video):
-
- if encoded_video != "":
-
- decoded_file_data = base64.b64decode(encoded_video)
-
- with open("temp_video.mp4", "wb") as f:
- f.write(decoded_file_data)
-
- video_file = "temp_video.mp4"
-
- clip = mp.VideoFileClip(video_file)
- clip.audio.write_audiofile(r"audio.wav")
-
- full_text = video_identity(r"audio.wav")
- sum = summary(full_text)[0]['summary_text']
-
-
- input_ids = token(sum)['input_ids'][:512]
- logits = inf_session.run([output_name],{input_name : [input_ids]})[0]
- logits = torch.FloatTensor(logits)
- probs = torch.sigmoid(logits)[0]
- probs = list(probs)
- label = classes[probs.index(max(probs))]
-
- final = {
- 'text':full_text,
- 'summary':sum,
- 'label':label,
-}
- return final
-
-text1 = gr.Textbox(label="Text")
-text2 = gr.Textbox(label="Summary")
-
-
-
-
-iface = gr.Interface(fn=classify,
- inputs=['video','text'],
- outputs = ['json'])
-iface.launch(inline=False)
-
-
-
-
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/ae_loss.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/ae_loss.py
deleted file mode 100644
index cff472aa03080fb49dbb3adba6fec68647a575e6..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/ae_loss.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import mmcv
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def ae_loss_per_image(tl_preds, br_preds, match):
- """Associative Embedding Loss in one image.
-
- Associative Embedding Loss including two parts: pull loss and push loss.
- Pull loss makes embedding vectors from same object closer to each other.
- Push loss distinguish embedding vector from different objects, and makes
- the gap between them is large enough.
-
- During computing, usually there are 3 cases:
- - no object in image: both pull loss and push loss will be 0.
- - one object in image: push loss will be 0 and pull loss is computed
- by the two corner of the only object.
- - more than one objects in image: pull loss is computed by corner pairs
- from each object, push loss is computed by each object with all
- other objects. We use confusion matrix with 0 in diagonal to
- compute the push loss.
-
- Args:
- tl_preds (tensor): Embedding feature map of left-top corner.
- br_preds (tensor): Embedding feature map of bottim-right corner.
- match (list): Downsampled coordinates pair of each ground truth box.
- """
-
- tl_list, br_list, me_list = [], [], []
- if len(match) == 0: # no object in image
- pull_loss = tl_preds.sum() * 0.
- push_loss = tl_preds.sum() * 0.
- else:
- for m in match:
- [tl_y, tl_x], [br_y, br_x] = m
- tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1)
- br_e = br_preds[:, br_y, br_x].view(-1, 1)
- tl_list.append(tl_e)
- br_list.append(br_e)
- me_list.append((tl_e + br_e) / 2.0)
-
- tl_list = torch.cat(tl_list)
- br_list = torch.cat(br_list)
- me_list = torch.cat(me_list)
-
- assert tl_list.size() == br_list.size()
-
- # N is object number in image, M is dimension of embedding vector
- N, M = tl_list.size()
-
- pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2)
- pull_loss = pull_loss.sum() / N
-
- margin = 1 # exp setting of CornerNet, details in section 3.3 of paper
-
- # confusion matrix of push loss
- conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list
- conf_weight = 1 - torch.eye(N).type_as(me_list)
- conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs())
-
- if N > 1: # more than one object in current image
- push_loss = F.relu(conf_mat).sum() / (N * (N - 1))
- else:
- push_loss = tl_preds.sum() * 0.
-
- return pull_loss, push_loss
-
-
-@LOSSES.register_module()
-class AssociativeEmbeddingLoss(nn.Module):
- """Associative Embedding Loss.
-
- More details can be found in
- `Associative Embedding `_ and
- `CornerNet `_ .
- Code is modified from `kp_utils.py `_ # noqa: E501
-
- Args:
- pull_weight (float): Loss weight for corners from same object.
- push_weight (float): Loss weight for corners from different object.
- """
-
- def __init__(self, pull_weight=0.25, push_weight=0.25):
- super(AssociativeEmbeddingLoss, self).__init__()
- self.pull_weight = pull_weight
- self.push_weight = push_weight
-
- def forward(self, pred, target, match):
- """Forward function."""
- batch = pred.size(0)
- pull_all, push_all = 0.0, 0.0
- for i in range(batch):
- pull, push = ae_loss_per_image(pred[i], target[i], match[i])
-
- pull_all += self.pull_weight * pull
- push_all += self.push_weight * push
-
- return pull_all, push_all
diff --git a/spaces/RoundtTble/dinov2-pca/README.md b/spaces/RoundtTble/dinov2-pca/README.md
deleted file mode 100644
index ccca95642fdcae86ea83deb3fe9524bb5003f799..0000000000000000000000000000000000000000
--- a/spaces/RoundtTble/dinov2-pca/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dinov2 PCA
-emoji: 🚀
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py b/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
- """ Cython optimized version.
- neg_cent: [b, t_t, t_s]
- mask: [b, t_t, t_s]
- """
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
- path = np.zeros(neg_cent.shape, dtype=np.int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
- maximum_path_c(path, neg_cent, t_t_max, t_s_max)
- return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/commands/env.py b/spaces/Salesforce/EDICT/my_half_diffusers/commands/env.py
deleted file mode 100644
index 81a878bff6688d3c510b53c60ac9d0e51e4aebcc..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/commands/env.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import platform
-from argparse import ArgumentParser
-
-import huggingface_hub
-
-from .. import __version__ as version
-from ..utils import is_torch_available, is_transformers_available
-from . import BaseDiffusersCLICommand
-
-
-def info_command_factory(_):
- return EnvironmentCommand()
-
-
-class EnvironmentCommand(BaseDiffusersCLICommand):
- @staticmethod
- def register_subcommand(parser: ArgumentParser):
- download_parser = parser.add_parser("env")
- download_parser.set_defaults(func=info_command_factory)
-
- def run(self):
- hub_version = huggingface_hub.__version__
-
- pt_version = "not installed"
- pt_cuda_available = "NA"
- if is_torch_available():
- import torch
-
- pt_version = torch.__version__
- pt_cuda_available = torch.cuda.is_available()
-
- transformers_version = "not installed"
- if is_transformers_available:
- import transformers
-
- transformers_version = transformers.__version__
-
- info = {
- "`diffusers` version": version,
- "Platform": platform.platform(),
- "Python version": platform.python_version(),
- "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
- "Huggingface_hub version": hub_version,
- "Transformers version": transformers_version,
- "Using GPU in script?": "",
- "Using distributed or parallel set-up in script?": "",
- }
-
- print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
- print(self.format_dict(info))
-
- return info
-
- @staticmethod
- def format_dict(d):
- return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
diff --git a/spaces/Sapphire-356/Video2MC/skeleton_num_visualization.py b/spaces/Sapphire-356/Video2MC/skeleton_num_visualization.py
deleted file mode 100644
index 7c1914b53c7675fe18107bbe28281824607e331e..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/skeleton_num_visualization.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-# 导入数据
-import pickle
-with open("output_3Dpose_npy/kun_1280x720_30fps_0-14_0-32.npy", 'rb') as file:
- data = np.load(file)
-with open("skeleton.npy", 'rb') as file:
- skeleton = pickle.load(file)
-
-# 提取第0帧坐标
-xyz_0 = data[0]
-
-# 创建3D坐标系
-fig = plt.figure(figsize=(10, 8))
-ax = fig.add_subplot(111, projection='3d')
-# 设置俯仰角和方位角
-ax.view_init(elev=0., azim=70)
-
-# 绘制3D点
-radius = 1.7
-ax.scatter(xyz_0[:, 0], xyz_0[:, 1], xyz_0[:, 2])
-# 添加文本标记
-for i in range(xyz_0.shape[0]):
- ax.text(xyz_0[i, 0], xyz_0[i, 1], xyz_0[i, 2], str(i), fontsize=10)
-
-# 绘制两点间的线段
-for num1 in range(xyz_0.shape[0]):
- parent = skeleton._parents
- num2 = parent[num1]
- if num2 != -1:
- x1, y1, z1 = xyz_0[num1, :]
- x2, y2, z2 = xyz_0[num2, :]
- ax.plot([x1, x2], [y1, y2], [z1, z2])
-
-ax.set_xlabel('X')
-ax.set_ylabel('Y')
-ax.set_zlabel('Z')
-# ax.set_xlim3d([-radius/2, radius/2])
-# ax.set_ylim3d([-radius/2, radius/2])
-# ax.set_xticklabels([])
-# ax.set_yticklabels([])
-# ax.set_zticklabels([])
-
-# 保存图像
-plt.savefig('plot.png')
diff --git a/spaces/SeyedAli/Persian-Speech-Emotion-Detection/collator.py b/spaces/SeyedAli/Persian-Speech-Emotion-Detection/collator.py
deleted file mode 100644
index cd5c74f2cf41037cd64022f2c9579fbb39210ce9..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Persian-Speech-Emotion-Detection/collator.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Union
-import torch
-
-import transformers
-from transformers import Wav2Vec2Processor, Wav2Vec2FeatureExtractor
-
-
-@dataclass
-class DataCollatorCTCWithPadding:
- """
- Data collator that will dynamically pad the inputs received.
- Args:
- feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`)
- The feature_extractor used for proccessing the data.
- padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
- Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
- among:
- * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
- sequence if provided).
- * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
- maximum acceptable input length for the model if that argument is not provided.
- * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
- different lengths).
- max_length (:obj:`int`, `optional`):
- Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
- max_length_labels (:obj:`int`, `optional`):
- Maximum length of the ``labels`` returned list and optionally padding length (see above).
- pad_to_multiple_of (:obj:`int`, `optional`):
- If set will pad the sequence to a multiple of the provided value.
- This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
- 7.5 (Volta).
- """
-
- feature_extractor: Wav2Vec2FeatureExtractor
- padding: Union[bool, str] = True
- max_length: Optional[int] = None
- max_length_labels: Optional[int] = None
- pad_to_multiple_of: Optional[int] = None
- pad_to_multiple_of_labels: Optional[int] = None
-
- def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
- input_features = [{"input_values": feature["input_values"]} for feature in features]
- label_features = [feature["labels"] for feature in features]
-
- d_type = torch.long if isinstance(label_features[0], int) else torch.float
-
- batch = self.feature_extractor.pad(
- input_features,
- padding=self.padding,
- max_length=self.max_length,
- pad_to_multiple_of=self.pad_to_multiple_of,
- return_tensors="pt",
- )
-
- batch["labels"] = torch.tensor(label_features, dtype=d_type)
-
- return batch
diff --git a/spaces/Shad0ws/Information_Extraction_with_ChatGPT/app.py b/spaces/Shad0ws/Information_Extraction_with_ChatGPT/app.py
deleted file mode 100644
index 3aa9d792215b451f59b1f30ddfc71414552f27d5..0000000000000000000000000000000000000000
--- a/spaces/Shad0ws/Information_Extraction_with_ChatGPT/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import gradio as gr
-import os
-import openai
-import newspaper
-import json
-import re
-from transformers import GPT2Tokenizer
-
-
-# define the text summarizer function
-def text_prompt(request, page_url, contraseña, temp):
- try:
- page = newspaper.Article(url=page_url)
- page.download()
- page.parse()
- except Exception as e:
- return "", f"--- An error occurred while processing the URL: {e} ---", ""
-
- tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-
- tokens = tokenizer.tokenize(page.text)
- num_tokens = len(tokens)
-
- if num_tokens > 10 and num_tokens < 2000:
- openai.api_key = contraseña
- # get the response from openai API
- try:
- response = openai.Completion.create(
- engine="text-davinci-003",
- prompt=request + "\n\n" + page.text,
- max_tokens=2048,
- temperature=temp,
- top_p=0.9,
- )
- # get the response text
- response_text = response.choices[0].text
- # clean the response text
- response_text = re.sub(r'\s+', ' ', response_text)
- return page.text, response_text, num_tokens
- except Exception as e:
- return page.text, f"--- An error occurred while processing the request: {e} ---", num_tokens
- return page.text, "--- Max number of tokens ---", num_tokens
-
-# define the gradio interface
-iface = gr.Interface(
- fn=text_prompt,
- inputs=[gr.Textbox(lines=1, placeholder="Enter your prompt here...", label="Prompt:", type="text"),
- gr.Textbox(lines=1, placeholder="Enter the URL here...", label="URL to parse:", type="text"),
- gr.Textbox(lines=1, placeholder="Enter your API-key here...", label="API-Key:", type="password"),
- gr.Slider(0.0,1.0, value=0.3, label="Temperature:")
- ],
- outputs=[gr.Textbox(label="Input:"), gr.Textbox(label="Output:"), gr.Textbox(label="Tokens:")],
- examples=[["Summarize the following text as a list:","https://blog.google/outreach-initiatives/google-org/our-commitment-on-using-ai-to-accelerate-progress-on-global-development-goals/","",0.3],
- ["Generate a summary of the following text. Give me an overview of main business impact from the text following this template:\n- Summary:\n- Business Impact:\n- Companies:", "https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html","",0.7]
- ],
- title="Information Extraction Interface:",
- # description="This tool allows querying the text retrieved from the URL using OpenAI's [text-davinci-003] engine.\nThe URL text can be referenced in the prompt as \"following text\".\nA GPT2 tokenizer is included to ensure that the 2000 token limit for OpenAI queries is not exceeded. Provide a prompt with your request, the url for text retrieval, your api-key and temperature to process the text."
-)
-
-
-
-error_message = ""
-
-try:
- iface.launch()
-except Exception as e:
- error_message = "An error occurred: " + str(e)
- iface.outputs[1].value = error_message
\ No newline at end of file
diff --git a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/__init__.py b/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/__init__.py
deleted file mode 100644
index 76e4b272b479a26c63d120c818c140870cd8c287..0000000000000000000000000000000000000000
--- a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .backbone import build_backbone
diff --git a/spaces/Silence1412/Text2img/app.py b/spaces/Silence1412/Text2img/app.py
deleted file mode 100644
index 5287d2bf9660d75b8d22732812302531ef86e67a..0000000000000000000000000000000000000000
--- a/spaces/Silence1412/Text2img/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import streamlit as st
-import cv2 as cv
-import time
-import torch
-from diffusers import StableDiffusionPipeline
-
-
-def create_model(loc = "stabilityai/stable-diffusion-2-1-base", mch = 'cpu'):
- pipe = StableDiffusionPipeline.from_pretrained(loc)
- pipe = pipe.to(mch)
- return pipe
-
-# t2i = st.title("""
-# Txt2Img
-# ###### `CLICK "Create_Update_Model"` :
-# - `FIRST RUN OF THE CODE`
-# - `CHANGING MODEL`""")
-
-# the_type = st.selectbox("Model",("stabilityai/stable-diffusion-2-1-base",
-# "CompVis/stable-diffusion-v1-4"))
-
-# create = st.button("Create The Model")
-
-# if create:
-# st.session_state.t2m_mod = create_model(loc=the_type)
-
-the_type = "stabilityai/stable-diffusion-2-1-base"
-st.session_state.t2m_mod = create_model(loc=the_type)
-
-
-prom = st.text_input("Prompt",'')
-
-neg_prom = st.text_input("Negative Prompt",'')
-
-style = st.selectbox("TODO: Image Style",("Cyberpunk",
- "Picasso",
- "Real-world specific",
- "Digital Art",
- "Aesthetics"))
-
-c1,c2,c3,c6 = st.columns([1,1,1,1])
-c8 = st.columns([1,1,1,1])
-c4,c5 = st.columns(2)
-
-with c1:
- bu_1 = st.text_input("Seed",'666')
-with c2:
- bu_2 = st.text_input("Steps",'12')
-with c3:
- bu_3 = st.text_input("Number of Images",'1')
-with c6:
- bu_6 = st.text_input("Guidance Scale",'7.5')
-with c4:
- sl_1 = st.slider("Width",128,1024,512,8)
-with c5:
- sl_2 = st.slider("hight",128,1024,512,8)
-
-st.session_state.generator = torch.Generator("cpu").manual_seed(int(bu_1))
-
-create = st.button("Imagine")
-
-
-if create:
- model = st.session_state.t2m_mod
- generator = st.session_state.generator
-
- if int(bu_3) == 1 :
- IMG = model(prom, negative_prompt = neg_prom, width=int(sl_1), height=int(sl_2),
- num_inference_steps=int(bu_2),
- guidance_scale = float(bu_6),
- generator=generator).images[0]
- st.image(IMG)
-
- else :
- PROMS = [prom]*int(bu_3)
-
- IMGS = model(PROMS, negative_prompt = neg_prom, width=int(sl_1), height=int(sl_2),
- num_inference_steps=int(bu_2),
- guidance_scale = float(bu_6),
- generator=generator).images
-
- st.image(IMGS)
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/tclass.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/tclass.py
deleted file mode 100644
index 6bd9ffcd9a09e58f24b217e706fef1d7d2465cfe..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/tclass.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Simple script to be run *twice*, to check reference counting bugs.
-
-See test_run for details."""
-
-
-import sys
-
-# We want to ensure that while objects remain available for immediate access,
-# objects from *previous* runs of the same script get collected, to avoid
-# accumulating massive amounts of old references.
-class C(object):
- def __init__(self,name):
- self.name = name
- self.p = print
- self.flush_stdout = sys.stdout.flush
-
- def __del__(self):
- self.p('tclass.py: deleting object:',self.name)
- self.flush_stdout()
-
-try:
- name = sys.argv[1]
-except IndexError:
- pass
-else:
- if name.startswith('C'):
- c = C(name)
-
-#print >> sys.stderr, "ARGV:", sys.argv # dbg
-
-# This next print statement is NOT debugging, we're making the check on a
-# completely separate process so we verify by capturing stdout:
-print('ARGV 1-:', sys.argv[1:])
-sys.stdout.flush()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/vendored/bytecode/tests/test_cfg.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/vendored/bytecode/tests/test_cfg.py
deleted file mode 100644
index 9b5b07b1c7b3239eee2df30b3ff4443cdbec7d89..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/vendored/bytecode/tests/test_cfg.py
+++ /dev/null
@@ -1,836 +0,0 @@
-
-import pytest
-from tests_python.debugger_unittest import IS_PY36_OR_GREATER, IS_CPYTHON
-from tests_python.debug_constants import TEST_CYTHON
-pytestmark = pytest.mark.skipif(not IS_PY36_OR_GREATER or not IS_CPYTHON or not TEST_CYTHON, reason='Requires CPython >= 3.6')
-#!/usr/bin/env python3
-import io
-import sys
-import unittest
-import contextlib
-from _pydevd_frame_eval.vendored.bytecode import (
- Label,
- Compare,
- SetLineno,
- Instr,
- Bytecode,
- BasicBlock,
- ControlFlowGraph,
-)
-from _pydevd_frame_eval.vendored.bytecode.concrete import OFFSET_AS_INSTRUCTION
-from _pydevd_frame_eval.vendored.bytecode.tests import disassemble as _disassemble, TestCase
-
-
-def disassemble(
- source, *, filename="", function=False, remove_last_return_none=False
-):
- code = _disassemble(source, filename=filename, function=function)
- blocks = ControlFlowGraph.from_bytecode(code)
- if remove_last_return_none:
- # drop LOAD_CONST+RETURN_VALUE to only keep 2 instructions,
- # to make unit tests shorter
- block = blocks[-1]
- test = (
- block[-2].name == "LOAD_CONST"
- and block[-2].arg is None
- and block[-1].name == "RETURN_VALUE"
- )
- if not test:
- raise ValueError(
- "unable to find implicit RETURN_VALUE : %s" % block[-2:]
- )
- del block[-2:]
- return blocks
-
-
-class BlockTests(unittest.TestCase):
- def test_iter_invalid_types(self):
- # Labels are not allowed in basic blocks
- block = BasicBlock()
- block.append(Label())
- with self.assertRaises(ValueError):
- list(block)
- with self.assertRaises(ValueError):
- block.legalize(1)
-
- # Only one jump allowed and only at the end
- block = BasicBlock()
- block2 = BasicBlock()
- block.extend([Instr("JUMP_ABSOLUTE", block2), Instr("NOP")])
- with self.assertRaises(ValueError):
- list(block)
- with self.assertRaises(ValueError):
- block.legalize(1)
-
- # jump target must be a BasicBlock
- block = BasicBlock()
- label = Label()
- block.extend([Instr("JUMP_ABSOLUTE", label)])
- with self.assertRaises(ValueError):
- list(block)
- with self.assertRaises(ValueError):
- block.legalize(1)
-
- def test_slice(self):
- block = BasicBlock([Instr("NOP")])
- next_block = BasicBlock()
- block.next_block = next_block
- self.assertEqual(block, block[:])
- self.assertIs(next_block, block[:].next_block)
-
- def test_copy(self):
- block = BasicBlock([Instr("NOP")])
- next_block = BasicBlock()
- block.next_block = next_block
- self.assertEqual(block, block.copy())
- self.assertIs(next_block, block.copy().next_block)
-
-
-class BytecodeBlocksTests(TestCase):
- maxDiff = 80 * 100
-
- def test_constructor(self):
- code = ControlFlowGraph()
- self.assertEqual(code.name, "")
- self.assertEqual(code.filename, "")
- self.assertEqual(code.flags, 0)
- self.assertBlocksEqual(code, [])
-
- def test_attr(self):
- source = """
- first_line = 1
-
- def func(arg1, arg2, *, arg3):
- x = 1
- y = 2
- return arg1
- """
- code = disassemble(source, filename="hello.py", function=True)
- self.assertEqual(code.argcount, 2)
- self.assertEqual(code.filename, "hello.py")
- self.assertEqual(code.first_lineno, 3)
- if sys.version_info > (3, 8):
- self.assertEqual(code.posonlyargcount, 0)
- self.assertEqual(code.kwonlyargcount, 1)
- self.assertEqual(code.name, "func")
- self.assertEqual(code.cellvars, [])
-
- code.name = "name"
- code.filename = "filename"
- code.flags = 123
- self.assertEqual(code.name, "name")
- self.assertEqual(code.filename, "filename")
- self.assertEqual(code.flags, 123)
-
- # FIXME: test non-empty cellvars
-
- def test_add_del_block(self):
- code = ControlFlowGraph()
- code[0].append(Instr("LOAD_CONST", 0))
-
- block = code.add_block()
- self.assertEqual(len(code), 2)
- self.assertIs(block, code[1])
-
- code[1].append(Instr("LOAD_CONST", 2))
- self.assertBlocksEqual(code, [Instr("LOAD_CONST", 0)], [Instr("LOAD_CONST", 2)])
-
- del code[0]
- self.assertBlocksEqual(code, [Instr("LOAD_CONST", 2)])
-
- del code[0]
- self.assertEqual(len(code), 0)
-
- def test_setlineno(self):
- # x = 7
- # y = 8
- # z = 9
- code = Bytecode()
- code.first_lineno = 3
- code.extend(
- [
- Instr("LOAD_CONST", 7),
- Instr("STORE_NAME", "x"),
- SetLineno(4),
- Instr("LOAD_CONST", 8),
- Instr("STORE_NAME", "y"),
- SetLineno(5),
- Instr("LOAD_CONST", 9),
- Instr("STORE_NAME", "z"),
- ]
- )
-
- blocks = ControlFlowGraph.from_bytecode(code)
- self.assertBlocksEqual(
- blocks,
- [
- Instr("LOAD_CONST", 7),
- Instr("STORE_NAME", "x"),
- SetLineno(4),
- Instr("LOAD_CONST", 8),
- Instr("STORE_NAME", "y"),
- SetLineno(5),
- Instr("LOAD_CONST", 9),
- Instr("STORE_NAME", "z"),
- ],
- )
-
- def test_legalize(self):
- code = Bytecode()
- code.first_lineno = 3
- code.extend(
- [
- Instr("LOAD_CONST", 7),
- Instr("STORE_NAME", "x"),
- Instr("LOAD_CONST", 8, lineno=4),
- Instr("STORE_NAME", "y"),
- SetLineno(5),
- Instr("LOAD_CONST", 9, lineno=6),
- Instr("STORE_NAME", "z"),
- ]
- )
-
- blocks = ControlFlowGraph.from_bytecode(code)
- blocks.legalize()
- self.assertBlocksEqual(
- blocks,
- [
- Instr("LOAD_CONST", 7, lineno=3),
- Instr("STORE_NAME", "x", lineno=3),
- Instr("LOAD_CONST", 8, lineno=4),
- Instr("STORE_NAME", "y", lineno=4),
- Instr("LOAD_CONST", 9, lineno=5),
- Instr("STORE_NAME", "z", lineno=5),
- ],
- )
-
- def test_repr(self):
- r = repr(ControlFlowGraph())
- self.assertIn("ControlFlowGraph", r)
- self.assertIn("1", r)
-
- def test_to_bytecode(self):
- # if test:
- # x = 2
- # x = 5
- blocks = ControlFlowGraph()
- blocks.add_block()
- blocks.add_block()
- blocks[0].extend(
- [
- Instr("LOAD_NAME", "test", lineno=1),
- Instr("POP_JUMP_IF_FALSE", blocks[2], lineno=1),
- ]
- )
-
- blocks[1].extend(
- [
- Instr("LOAD_CONST", 5, lineno=2),
- Instr("STORE_NAME", "x", lineno=2),
- Instr("JUMP_FORWARD", blocks[2], lineno=2),
- ]
- )
-
- blocks[2].extend(
- [
- Instr("LOAD_CONST", 7, lineno=3),
- Instr("STORE_NAME", "x", lineno=3),
- Instr("LOAD_CONST", None, lineno=3),
- Instr("RETURN_VALUE", lineno=3),
- ]
- )
-
- bytecode = blocks.to_bytecode()
- label = Label()
- self.assertEqual(
- bytecode,
- [
- Instr("LOAD_NAME", "test", lineno=1),
- Instr("POP_JUMP_IF_FALSE", label, lineno=1),
- Instr("LOAD_CONST", 5, lineno=2),
- Instr("STORE_NAME", "x", lineno=2),
- Instr("JUMP_FORWARD", label, lineno=2),
- label,
- Instr("LOAD_CONST", 7, lineno=3),
- Instr("STORE_NAME", "x", lineno=3),
- Instr("LOAD_CONST", None, lineno=3),
- Instr("RETURN_VALUE", lineno=3),
- ],
- )
- # FIXME: test other attributes
-
- def test_label_at_the_end(self):
- label = Label()
- code = Bytecode(
- [
- Instr("LOAD_NAME", "x"),
- Instr("UNARY_NOT"),
- Instr("POP_JUMP_IF_FALSE", label),
- Instr("LOAD_CONST", 9),
- Instr("STORE_NAME", "y"),
- label,
- ]
- )
-
- cfg = ControlFlowGraph.from_bytecode(code)
- self.assertBlocksEqual(
- cfg,
- [
- Instr("LOAD_NAME", "x"),
- Instr("UNARY_NOT"),
- Instr("POP_JUMP_IF_FALSE", cfg[2]),
- ],
- [Instr("LOAD_CONST", 9), Instr("STORE_NAME", "y")],
- [],
- )
-
- def test_from_bytecode(self):
- bytecode = Bytecode()
- label = Label()
- bytecode.extend(
- [
- Instr("LOAD_NAME", "test", lineno=1),
- Instr("POP_JUMP_IF_FALSE", label, lineno=1),
- Instr("LOAD_CONST", 5, lineno=2),
- Instr("STORE_NAME", "x", lineno=2),
- Instr("JUMP_FORWARD", label, lineno=2),
- # dead code!
- Instr("LOAD_CONST", 7, lineno=4),
- Instr("STORE_NAME", "x", lineno=4),
- Label(), # unused label
- label,
- Label(), # unused label
- Instr("LOAD_CONST", None, lineno=4),
- Instr("RETURN_VALUE", lineno=4),
- ]
- )
-
- blocks = ControlFlowGraph.from_bytecode(bytecode)
- label2 = blocks[3]
- self.assertBlocksEqual(
- blocks,
- [
- Instr("LOAD_NAME", "test", lineno=1),
- Instr("POP_JUMP_IF_FALSE", label2, lineno=1),
- ],
- [
- Instr("LOAD_CONST", 5, lineno=2),
- Instr("STORE_NAME", "x", lineno=2),
- Instr("JUMP_FORWARD", label2, lineno=2),
- ],
- [Instr("LOAD_CONST", 7, lineno=4), Instr("STORE_NAME", "x", lineno=4)],
- [Instr("LOAD_CONST", None, lineno=4), Instr("RETURN_VALUE", lineno=4)],
- )
- # FIXME: test other attributes
-
- def test_from_bytecode_loop(self):
- # for x in (1, 2, 3):
- # if x == 2:
- # break
- # continue
-
- if sys.version_info < (3, 8):
- label_loop_start = Label()
- label_loop_exit = Label()
- label_loop_end = Label()
-
- code = Bytecode()
- code.extend(
- (
- Instr("SETUP_LOOP", label_loop_end, lineno=1),
- Instr("LOAD_CONST", (1, 2, 3), lineno=1),
- Instr("GET_ITER", lineno=1),
- label_loop_start,
- Instr("FOR_ITER", label_loop_exit, lineno=1),
- Instr("STORE_NAME", "x", lineno=1),
- Instr("LOAD_NAME", "x", lineno=2),
- Instr("LOAD_CONST", 2, lineno=2),
- Instr("COMPARE_OP", Compare.EQ, lineno=2),
- Instr("POP_JUMP_IF_FALSE", label_loop_start, lineno=2),
- Instr("BREAK_LOOP", lineno=3),
- Instr("JUMP_ABSOLUTE", label_loop_start, lineno=4),
- Instr("JUMP_ABSOLUTE", label_loop_start, lineno=4),
- label_loop_exit,
- Instr("POP_BLOCK", lineno=4),
- label_loop_end,
- Instr("LOAD_CONST", None, lineno=4),
- Instr("RETURN_VALUE", lineno=4),
- )
- )
- blocks = ControlFlowGraph.from_bytecode(code)
-
- expected = [
- [Instr("SETUP_LOOP", blocks[8], lineno=1)],
- [Instr("LOAD_CONST", (1, 2, 3), lineno=1), Instr("GET_ITER", lineno=1)],
- [Instr("FOR_ITER", blocks[7], lineno=1)],
- [
- Instr("STORE_NAME", "x", lineno=1),
- Instr("LOAD_NAME", "x", lineno=2),
- Instr("LOAD_CONST", 2, lineno=2),
- Instr("COMPARE_OP", Compare.EQ, lineno=2),
- Instr("POP_JUMP_IF_FALSE", blocks[2], lineno=2),
- ],
- [Instr("BREAK_LOOP", lineno=3)],
- [Instr("JUMP_ABSOLUTE", blocks[2], lineno=4)],
- [Instr("JUMP_ABSOLUTE", blocks[2], lineno=4)],
- [Instr("POP_BLOCK", lineno=4)],
- [Instr("LOAD_CONST", None, lineno=4), Instr("RETURN_VALUE", lineno=4)],
- ]
- self.assertBlocksEqual(blocks, *expected)
- else:
- label_loop_start = Label()
- label_loop_exit = Label()
-
- code = Bytecode()
- code.extend(
- (
- Instr("LOAD_CONST", (1, 2, 3), lineno=1),
- Instr("GET_ITER", lineno=1),
- label_loop_start,
- Instr("FOR_ITER", label_loop_exit, lineno=1),
- Instr("STORE_NAME", "x", lineno=1),
- Instr("LOAD_NAME", "x", lineno=2),
- Instr("LOAD_CONST", 2, lineno=2),
- Instr("COMPARE_OP", Compare.EQ, lineno=2),
- Instr("POP_JUMP_IF_FALSE", label_loop_start, lineno=2),
- Instr("JUMP_ABSOLUTE", label_loop_exit, lineno=3),
- Instr("JUMP_ABSOLUTE", label_loop_start, lineno=4),
- Instr("JUMP_ABSOLUTE", label_loop_start, lineno=4),
- label_loop_exit,
- Instr("LOAD_CONST", None, lineno=4),
- Instr("RETURN_VALUE", lineno=4),
- )
- )
- blocks = ControlFlowGraph.from_bytecode(code)
-
- expected = [
- [Instr("LOAD_CONST", (1, 2, 3), lineno=1), Instr("GET_ITER", lineno=1)],
- [Instr("FOR_ITER", blocks[6], lineno=1)],
- [
- Instr("STORE_NAME", "x", lineno=1),
- Instr("LOAD_NAME", "x", lineno=2),
- Instr("LOAD_CONST", 2, lineno=2),
- Instr("COMPARE_OP", Compare.EQ, lineno=2),
- Instr("POP_JUMP_IF_FALSE", blocks[1], lineno=2),
- ],
- [Instr("JUMP_ABSOLUTE", blocks[6], lineno=3)],
- [Instr("JUMP_ABSOLUTE", blocks[1], lineno=4)],
- [Instr("JUMP_ABSOLUTE", blocks[1], lineno=4)],
- [Instr("LOAD_CONST", None, lineno=4), Instr("RETURN_VALUE", lineno=4)],
- ]
- self.assertBlocksEqual(blocks, *expected)
-
-
-class BytecodeBlocksFunctionalTests(TestCase):
- def test_eq(self):
- # compare codes with multiple blocks and labels,
- # Code.__eq__() renumbers labels to get equal labels
- source = "x = 1 if test else 2"
- code1 = disassemble(source)
- code2 = disassemble(source)
- self.assertEqual(code1, code2)
-
- # Type mismatch
- self.assertFalse(code1 == 1)
-
- # argnames mismatch
- cfg = ControlFlowGraph()
- cfg.argnames = 10
- self.assertFalse(code1 == cfg)
-
- # instr mismatch
- cfg = ControlFlowGraph()
- cfg.argnames = code1.argnames
- self.assertFalse(code1 == cfg)
-
- def check_getitem(self, code):
- # check internal Code block indexes (index by index, index by label)
- for block_index, block in enumerate(code):
- self.assertIs(code[block_index], block)
- self.assertIs(code[block], block)
- self.assertEqual(code.get_block_index(block), block_index)
-
- def test_delitem(self):
- cfg = ControlFlowGraph()
- b = cfg.add_block()
- del cfg[b]
- self.assertEqual(len(cfg.get_instructions()), 0)
-
- def sample_code(self):
- code = disassemble("x = 1", remove_last_return_none=True)
- self.assertBlocksEqual(
- code, [Instr("LOAD_CONST", 1, lineno=1), Instr("STORE_NAME", "x", lineno=1)]
- )
- return code
-
- def test_split_block(self):
- code = self.sample_code()
- code[0].append(Instr("NOP", lineno=1))
-
- label = code.split_block(code[0], 2)
- self.assertIs(label, code[1])
- self.assertBlocksEqual(
- code,
- [Instr("LOAD_CONST", 1, lineno=1), Instr("STORE_NAME", "x", lineno=1)],
- [Instr("NOP", lineno=1)],
- )
- self.check_getitem(code)
-
- label2 = code.split_block(code[0], 1)
- self.assertIs(label2, code[1])
- self.assertBlocksEqual(
- code,
- [Instr("LOAD_CONST", 1, lineno=1)],
- [Instr("STORE_NAME", "x", lineno=1)],
- [Instr("NOP", lineno=1)],
- )
- self.check_getitem(code)
-
- with self.assertRaises(TypeError):
- code.split_block(1, 1)
-
- with self.assertRaises(ValueError) as e:
- code.split_block(code[0], -2)
- self.assertIn("positive", e.exception.args[0])
-
- def test_split_block_end(self):
- code = self.sample_code()
-
- # split at the end of the last block requires to add a new empty block
- label = code.split_block(code[0], 2)
- self.assertIs(label, code[1])
- self.assertBlocksEqual(
- code,
- [Instr("LOAD_CONST", 1, lineno=1), Instr("STORE_NAME", "x", lineno=1)],
- [],
- )
- self.check_getitem(code)
-
- # split at the end of a block which is not the end doesn't require to
- # add a new block
- label = code.split_block(code[0], 2)
- self.assertIs(label, code[1])
- self.assertBlocksEqual(
- code,
- [Instr("LOAD_CONST", 1, lineno=1), Instr("STORE_NAME", "x", lineno=1)],
- [],
- )
-
- def test_split_block_dont_split(self):
- code = self.sample_code()
-
- # FIXME: is it really useful to support that?
- block = code.split_block(code[0], 0)
- self.assertIs(block, code[0])
- self.assertBlocksEqual(
- code, [Instr("LOAD_CONST", 1, lineno=1), Instr("STORE_NAME", "x", lineno=1)]
- )
-
- def test_split_block_error(self):
- code = self.sample_code()
-
- with self.assertRaises(ValueError):
- # invalid index
- code.split_block(code[0], 3)
-
- def test_to_code(self):
- # test resolution of jump labels
- bytecode = ControlFlowGraph()
- bytecode.first_lineno = 3
- bytecode.argcount = 3
- if sys.version_info > (3, 8):
- bytecode.posonlyargcount = 0
- bytecode.kwonlyargcount = 2
- bytecode.name = "func"
- bytecode.filename = "hello.py"
- bytecode.flags = 0x43
- bytecode.argnames = ("arg", "arg2", "arg3", "kwonly", "kwonly2")
- bytecode.docstring = None
- block0 = bytecode[0]
- block1 = bytecode.add_block()
- block2 = bytecode.add_block()
- block0.extend(
- [
- Instr("LOAD_FAST", "x", lineno=4),
- Instr("POP_JUMP_IF_FALSE", block2, lineno=4),
- ]
- )
- block1.extend(
- [Instr("LOAD_FAST", "arg", lineno=5), Instr("STORE_FAST", "x", lineno=5)]
- )
- block2.extend(
- [
- Instr("LOAD_CONST", 3, lineno=6),
- Instr("STORE_FAST", "x", lineno=6),
- Instr("LOAD_FAST", "x", lineno=7),
- Instr("RETURN_VALUE", lineno=7),
- ]
- )
-
- if OFFSET_AS_INSTRUCTION:
- # The argument of the jump is divided by 2
- expected = (
- b"|\x05" b"r\x04" b"|\x00" b"}\x05" b"d\x01" b"}\x05" b"|\x05" b"S\x00"
- )
- else:
- expected = (
- b"|\x05" b"r\x08" b"|\x00" b"}\x05" b"d\x01" b"}\x05" b"|\x05" b"S\x00"
- )
-
- code = bytecode.to_code()
- self.assertEqual(code.co_consts, (None, 3))
- self.assertEqual(code.co_argcount, 3)
- if sys.version_info > (3, 8):
- self.assertEqual(code.co_posonlyargcount, 0)
- self.assertEqual(code.co_kwonlyargcount, 2)
- self.assertEqual(code.co_nlocals, 6)
- self.assertEqual(code.co_stacksize, 1)
- # FIXME: don't use hardcoded constants
- self.assertEqual(code.co_flags, 0x43)
- self.assertEqual(code.co_code, expected)
- self.assertEqual(code.co_names, ())
- self.assertEqual(
- code.co_varnames, ("arg", "arg2", "arg3", "kwonly", "kwonly2", "x")
- )
- self.assertEqual(code.co_filename, "hello.py")
- self.assertEqual(code.co_name, "func")
- self.assertEqual(code.co_firstlineno, 3)
-
- # verify stacksize argument is honored
- explicit_stacksize = code.co_stacksize + 42
- code = bytecode.to_code(stacksize=explicit_stacksize)
- self.assertEqual(code.co_stacksize, explicit_stacksize)
-
- def test_get_block_index(self):
- blocks = ControlFlowGraph()
- block0 = blocks[0]
- block1 = blocks.add_block()
- block2 = blocks.add_block()
- self.assertEqual(blocks.get_block_index(block0), 0)
- self.assertEqual(blocks.get_block_index(block1), 1)
- self.assertEqual(blocks.get_block_index(block2), 2)
-
- other_block = BasicBlock()
- self.assertRaises(ValueError, blocks.get_block_index, other_block)
-
-
-class CFGStacksizeComputationTests(TestCase):
- def check_stack_size(self, func):
- code = func.__code__
- bytecode = Bytecode.from_code(code)
- cfg = ControlFlowGraph.from_bytecode(bytecode)
- self.assertEqual(code.co_stacksize, cfg.compute_stacksize())
-
- def test_empty_code(self):
- cfg = ControlFlowGraph()
- del cfg[0]
- self.assertEqual(cfg.compute_stacksize(), 0)
-
- def test_handling_of_set_lineno(self):
- code = Bytecode()
- code.first_lineno = 3
- code.extend(
- [
- Instr("LOAD_CONST", 7),
- Instr("STORE_NAME", "x"),
- SetLineno(4),
- Instr("LOAD_CONST", 8),
- Instr("STORE_NAME", "y"),
- SetLineno(5),
- Instr("LOAD_CONST", 9),
- Instr("STORE_NAME", "z"),
- ]
- )
- self.assertEqual(code.compute_stacksize(), 1)
-
- def test_invalid_stacksize(self):
- code = Bytecode()
- code.extend([Instr("STORE_NAME", "x")])
- with self.assertRaises(RuntimeError):
- code.compute_stacksize()
-
- def test_stack_size_computation_and(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- return arg1 and args # Test JUMP_IF_FALSE_OR_POP
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_or(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- return arg1 or args # Test JUMP_IF_TRUE_OR_POP
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_if_else(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- if args:
- return 0
- elif kwargs:
- return 1
- else:
- return 2
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_for_loop_continue(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- for k in kwargs:
- if k in args:
- continue
- else:
- return 1
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_while_loop_break(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- while True:
- if arg1:
- break
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_with(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- with open(arg1) as f:
- return f.read()
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_try_except(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- try:
- return args[0]
- except Exception:
- return 2
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_try_finally(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- try:
- return args[0]
- finally:
- return 2
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_try_except_finally(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- try:
- return args[0]
- except Exception:
- return 2
- finally:
- print("Interrupt")
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_try_except_else_finally(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- try:
- return args[0]
- except Exception:
- return 2
- else:
- return arg1
- finally:
- print("Interrupt")
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_nested_try_except_finally(self):
- def test(arg1, *args, **kwargs): # pragma: no cover
- k = 1
- try:
- getattr(arg1, k)
- except AttributeError:
- pass
- except Exception:
- try:
- assert False
- except Exception:
- return 2
- finally:
- print("unexpected")
- finally:
- print("attempted to get {}".format(k))
-
- self.check_stack_size(test)
-
- def test_stack_size_computation_nested_try_except_else_finally(self):
- def test(*args, **kwargs):
- try:
- v = args[1]
- except IndexError:
- try:
- w = kwargs["value"]
- except KeyError:
- return -1
- else:
- return w
- finally:
- print("second finally")
- else:
- return v
- finally:
- print("first finally")
-
- # A direct comparison of the stack depth fails because CPython
- # generate dead code that is used in stack computation.
- cpython_stacksize = test.__code__.co_stacksize
- test.__code__ = Bytecode.from_code(test.__code__).to_code()
- self.assertLessEqual(test.__code__.co_stacksize, cpython_stacksize)
- with contextlib.redirect_stdout(io.StringIO()) as stdout:
- self.assertEqual(test(1, 4), 4)
- self.assertEqual(stdout.getvalue(), "first finally\n")
-
- with contextlib.redirect_stdout(io.StringIO()) as stdout:
- self.assertEqual(test([], value=3), 3)
- self.assertEqual(stdout.getvalue(), "second finally\nfirst finally\n")
-
- with contextlib.redirect_stdout(io.StringIO()) as stdout:
- self.assertEqual(test([], name=None), -1)
- self.assertEqual(stdout.getvalue(), "second finally\nfirst finally\n")
-
- def test_stack_size_with_dead_code(self):
- # Simply demonstrate more directly the previously mentioned issue.
- def test(*args): # pragma: no cover
- return 0
- try:
- a = args[0]
- except IndexError:
- return -1
- else:
- return a
-
- test.__code__ = Bytecode.from_code(test.__code__).to_code()
- self.assertEqual(test.__code__.co_stacksize, 1)
- self.assertEqual(test(1), 0)
-
- def test_huge_code_with_numerous_blocks(self):
- def base_func(x):
- pass
-
- def mk_if_then_else(depth):
- instructions = []
- for i in range(depth):
- label_else = Label()
- instructions.extend(
- [
- Instr("LOAD_FAST", "x"),
- Instr("POP_JUMP_IF_FALSE", label_else),
- Instr("LOAD_GLOBAL", "f{}".format(i)),
- Instr("RETURN_VALUE"),
- label_else,
- ]
- )
- instructions.extend([Instr("LOAD_CONST", None), Instr("RETURN_VALUE")])
- return instructions
-
- bytecode = Bytecode(mk_if_then_else(5000))
- bytecode.compute_stacksize()
-
-
-if __name__ == "__main__":
- unittest.main() # pragma: no cover
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/evaluation/metrics.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/evaluation/metrics.py
deleted file mode 100644
index 16c7dd47cadd53cf1caaa194e28a343f2aacc599..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/evaluation/metrics.py
+++ /dev/null
@@ -1,326 +0,0 @@
-from collections import OrderedDict
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch
-
-
-def f_score(precision, recall, beta=1):
- """calcuate the f-score value.
-
- Args:
- precision (float | torch.Tensor): The precision value.
- recall (float | torch.Tensor): The recall value.
- beta (int): Determines the weight of recall in the combined score.
- Default: False.
-
- Returns:
- [torch.tensor]: The f-score value.
- """
- score = (1 + beta**2) * (precision * recall) / (
- (beta**2 * precision) + recall)
- return score
-
-
-def intersect_and_union(pred_label,
- label,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate intersection and Union.
-
- Args:
- pred_label (ndarray | str): Prediction segmentation map
- or predict result filename.
- label (ndarray | str): Ground truth segmentation map
- or label filename.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- label_map (dict): Mapping old labels to new labels. The parameter will
- work only when label is str. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. The parameter will
- work only when label is str. Default: False.
-
- Returns:
- torch.Tensor: The intersection of prediction and ground truth
- histogram on all classes.
- torch.Tensor: The union of prediction and ground truth histogram on
- all classes.
- torch.Tensor: The prediction histogram on all classes.
- torch.Tensor: The ground truth histogram on all classes.
- """
-
- if isinstance(pred_label, str):
- pred_label = torch.from_numpy(np.load(pred_label))
- else:
- pred_label = torch.from_numpy((pred_label))
-
- if isinstance(label, str):
- label = torch.from_numpy(
- mmcv.imread(label, flag='unchanged', backend='pillow'))
- else:
- label = torch.from_numpy(label)
-
- if label_map is not None:
- for old_id, new_id in label_map.items():
- label[label == old_id] = new_id
- if reduce_zero_label:
- label[label == 0] = 255
- label = label - 1
- label[label == 254] = 255
-
- mask = (label != ignore_index)
- pred_label = pred_label[mask]
- label = label[mask]
-
- intersect = pred_label[pred_label == label]
- area_intersect = torch.histc(
- intersect.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_pred_label = torch.histc(
- pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_label = torch.histc(
- label.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_union = area_pred_label + area_label - area_intersect
- return area_intersect, area_union, area_pred_label, area_label
-
-
-def total_intersect_and_union(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Total Intersection and Union.
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- ndarray: The intersection of prediction and ground truth histogram
- on all classes.
- ndarray: The union of prediction and ground truth histogram on all
- classes.
- ndarray: The prediction histogram on all classes.
- ndarray: The ground truth histogram on all classes.
- """
- num_imgs = len(results)
- assert len(gt_seg_maps) == num_imgs
- total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
- for i in range(num_imgs):
- area_intersect, area_union, area_pred_label, area_label = \
- intersect_and_union(
- results[i], gt_seg_maps[i], num_classes, ignore_index,
- label_map, reduce_zero_label)
- total_area_intersect += area_intersect
- total_area_union += area_union
- total_area_pred_label += area_pred_label
- total_area_label += area_label
- return total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label
-
-
-def mean_iou(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Mean Intersection and Union (mIoU)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- dict[str, float | ndarray]:
- float: Overall accuracy on all images.
- ndarray: Per category accuracy, shape (num_classes, ).
- ndarray: Per category IoU, shape (num_classes, ).
- """
- iou_result = eval_metrics(
- results=results,
- gt_seg_maps=gt_seg_maps,
- num_classes=num_classes,
- ignore_index=ignore_index,
- metrics=['mIoU'],
- nan_to_num=nan_to_num,
- label_map=label_map,
- reduce_zero_label=reduce_zero_label)
- return iou_result
-
-
-def mean_dice(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Mean Dice (mDice)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- dict[str, float | ndarray]: Default metrics.
- float: Overall accuracy on all images.
- ndarray: Per category accuracy, shape (num_classes, ).
- ndarray: Per category dice, shape (num_classes, ).
- """
-
- dice_result = eval_metrics(
- results=results,
- gt_seg_maps=gt_seg_maps,
- num_classes=num_classes,
- ignore_index=ignore_index,
- metrics=['mDice'],
- nan_to_num=nan_to_num,
- label_map=label_map,
- reduce_zero_label=reduce_zero_label)
- return dice_result
-
-
-def mean_fscore(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False,
- beta=1):
- """Calculate Mean Intersection and Union (mIoU)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
- beta (int): Determines the weight of recall in the combined score.
- Default: False.
-
-
- Returns:
- dict[str, float | ndarray]: Default metrics.
- float: Overall accuracy on all images.
- ndarray: Per category recall, shape (num_classes, ).
- ndarray: Per category precision, shape (num_classes, ).
- ndarray: Per category f-score, shape (num_classes, ).
- """
- fscore_result = eval_metrics(
- results=results,
- gt_seg_maps=gt_seg_maps,
- num_classes=num_classes,
- ignore_index=ignore_index,
- metrics=['mFscore'],
- nan_to_num=nan_to_num,
- label_map=label_map,
- reduce_zero_label=reduce_zero_label,
- beta=beta)
- return fscore_result
-
-
-def eval_metrics(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- metrics=['mIoU'],
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False,
- beta=1):
- """Calculate evaluation metrics
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
- Returns:
- float: Overall accuracy on all images.
- ndarray: Per category accuracy, shape (num_classes, ).
- ndarray: Per category evaluation metrics, shape (num_classes, ).
- """
- if isinstance(metrics, str):
- metrics = [metrics]
- allowed_metrics = ['mIoU', 'mDice', 'mFscore']
- if not set(metrics).issubset(set(allowed_metrics)):
- raise KeyError('metrics {} is not supported'.format(metrics))
-
- total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label = total_intersect_and_union(
- results, gt_seg_maps, num_classes, ignore_index, label_map,
- reduce_zero_label)
- all_acc = total_area_intersect.sum() / total_area_label.sum()
- ret_metrics = OrderedDict({'aAcc': all_acc})
- for metric in metrics:
- if metric == 'mIoU':
- iou = total_area_intersect / total_area_union
- acc = total_area_intersect / total_area_label
- ret_metrics['IoU'] = iou
- ret_metrics['Acc'] = acc
- elif metric == 'mDice':
- dice = 2 * total_area_intersect / (
- total_area_pred_label + total_area_label)
- acc = total_area_intersect / total_area_label
- ret_metrics['Dice'] = dice
- ret_metrics['Acc'] = acc
- elif metric == 'mFscore':
- precision = total_area_intersect / total_area_pred_label
- recall = total_area_intersect / total_area_label
- f_value = torch.tensor(
- [f_score(x[0], x[1], beta) for x in zip(precision, recall)])
- ret_metrics['Fscore'] = f_value
- ret_metrics['Precision'] = precision
- ret_metrics['Recall'] = recall
-
- ret_metrics = {
- metric: value.numpy()
- for metric, value in ret_metrics.items()
- }
- if nan_to_num is not None:
- ret_metrics = OrderedDict({
- metric: np.nan_to_num(metric_value, nan=nan_to_num)
- for metric, metric_value in ret_metrics.items()
- })
- return ret_metrics
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/langrussianmodel.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/langrussianmodel.py
deleted file mode 100644
index 39a5388948ef12b69b65fbfa89a84c6ef4a4bfd6..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/langrussianmodel.py
+++ /dev/null
@@ -1,5725 +0,0 @@
-from pip._vendor.chardet.sbcharsetprober import SingleByteCharSetModel
-
-# 3: Positive
-# 2: Likely
-# 1: Unlikely
-# 0: Negative
-
-RUSSIAN_LANG_MODEL = {
- 37: { # 'А'
- 37: 0, # 'А'
- 44: 1, # 'Б'
- 33: 1, # 'В'
- 46: 1, # 'Г'
- 41: 1, # 'Д'
- 48: 1, # 'Е'
- 56: 1, # 'Ж'
- 51: 1, # 'З'
- 42: 1, # 'И'
- 60: 1, # 'Й'
- 36: 1, # 'К'
- 49: 1, # 'Л'
- 38: 1, # 'М'
- 31: 2, # 'Н'
- 34: 1, # 'О'
- 35: 1, # 'П'
- 45: 1, # 'Р'
- 32: 1, # 'С'
- 40: 1, # 'Т'
- 52: 1, # 'У'
- 53: 1, # 'Ф'
- 55: 1, # 'Х'
- 58: 1, # 'Ц'
- 50: 1, # 'Ч'
- 57: 1, # 'Ш'
- 63: 1, # 'Щ'
- 62: 0, # 'Ы'
- 61: 0, # 'Ь'
- 47: 0, # 'Э'
- 59: 1, # 'Ю'
- 43: 1, # 'Я'
- 3: 1, # 'а'
- 21: 2, # 'б'
- 10: 2, # 'в'
- 19: 2, # 'г'
- 13: 2, # 'д'
- 2: 0, # 'е'
- 24: 1, # 'ж'
- 20: 1, # 'з'
- 4: 0, # 'и'
- 23: 1, # 'й'
- 11: 2, # 'к'
- 8: 3, # 'л'
- 12: 2, # 'м'
- 5: 2, # 'н'
- 1: 0, # 'о'
- 15: 2, # 'п'
- 9: 2, # 'р'
- 7: 2, # 'с'
- 6: 2, # 'т'
- 14: 2, # 'у'
- 39: 2, # 'ф'
- 26: 2, # 'х'
- 28: 0, # 'ц'
- 22: 1, # 'ч'
- 25: 2, # 'ш'
- 29: 0, # 'щ'
- 54: 0, # 'ъ'
- 18: 0, # 'ы'
- 17: 0, # 'ь'
- 30: 1, # 'э'
- 27: 0, # 'ю'
- 16: 0, # 'я'
- },
- 44: { # 'Б'
- 37: 1, # 'А'
- 44: 0, # 'Б'
- 33: 1, # 'В'
- 46: 1, # 'Г'
- 41: 0, # 'Д'
- 48: 1, # 'Е'
- 56: 0, # 'Ж'
- 51: 0, # 'З'
- 42: 1, # 'И'
- 60: 0, # 'Й'
- 36: 0, # 'К'
- 49: 1, # 'Л'
- 38: 1, # 'М'
- 31: 1, # 'Н'
- 34: 1, # 'О'
- 35: 0, # 'П'
- 45: 1, # 'Р'
- 32: 0, # 'С'
- 40: 0, # 'Т'
- 52: 1, # 'У'
- 53: 0, # 'Ф'
- 55: 0, # 'Х'
- 58: 0, # 'Ц'
- 50: 0, # 'Ч'
- 57: 0, # 'Ш'
- 63: 0, # 'Щ'
- 62: 1, # 'Ы'
- 61: 0, # 'Ь'
- 47: 0, # 'Э'
- 59: 0, # 'Ю'
- 43: 1, # 'Я'
- 3: 2, # 'а'
- 21: 0, # 'б'
- 10: 0, # 'в'
- 19: 0, # 'г'
- 13: 1, # 'д'
- 2: 3, # 'е'
- 24: 0, # 'ж'
- 20: 0, # 'з'
- 4: 2, # 'и'
- 23: 0, # 'й'
- 11: 0, # 'к'
- 8: 2, # 'л'
- 12: 0, # 'м'
- 5: 0, # 'н'
- 1: 3, # 'о'
- 15: 0, # 'п'
- 9: 2, # 'р'
- 7: 0, # 'с'
- 6: 0, # 'т'
- 14: 2, # 'у'
- 39: 0, # 'ф'
- 26: 0, # 'х'
- 28: 0, # 'ц'
- 22: 0, # 'ч'
- 25: 0, # 'ш'
- 29: 0, # 'щ'
- 54: 0, # 'ъ'
- 18: 2, # 'ы'
- 17: 1, # 'ь'
- 30: 2, # 'э'
- 27: 1, # 'ю'
- 16: 1, # 'я'
- },
- 33: { # 'В'
- 37: 2, # 'А'
- 44: 0, # 'Б'
- 33: 1, # 'В'
- 46: 0, # 'Г'
- 41: 1, # 'Д'
- 48: 1, # 'Е'
- 56: 0, # 'Ж'
- 51: 0, # 'З'
- 42: 1, # 'И'
- 60: 0, # 'Й'
- 36: 1, # 'К'
- 49: 1, # 'Л'
- 38: 1, # 'М'
- 31: 1, # 'Н'
- 34: 1, # 'О'
- 35: 1, # 'П'
- 45: 1, # 'Р'
- 32: 1, # 'С'
- 40: 1, # 'Т'
- 52: 1, # 'У'
- 53: 0, # 'Ф'
- 55: 0, # 'Х'
- 58: 0, # 'Ц'
- 50: 0, # 'Ч'
- 57: 1, # 'Ш'
- 63: 0, # 'Щ'
- 62: 1, # 'Ы'
- 61: 1, # 'Ь'
- 47: 0, # 'Э'
- 59: 0, # 'Ю'
- 43: 1, # 'Я'
- 3: 2, # 'а'
- 21: 1, # 'б'
- 10: 1, # 'в'
- 19: 1, # 'г'
- 13: 2, # 'д'
- 2: 3, # 'е'
- 24: 0, # 'ж'
- 20: 2, # 'з'
- 4: 2, # 'и'
- 23: 0, # 'й'
- 11: 1, # 'к'
- 8: 2, # 'л'
- 12: 2, # 'м'
- 5: 2, # 'н'
- 1: 3, # 'о'
- 15: 2, # 'п'
- 9: 2, # 'р'
- 7: 3, # 'с'
- 6: 2, # 'т'
- 14: 2, # 'у'
- 39: 0, # 'ф'
- 26: 1, # 'х'
- 28: 1, # 'ц'
- 22: 2, # 'ч'
- 25: 1, # 'ш'
- 29: 0, # 'щ'
- 54: 1, # 'ъ'
- 18: 3, # 'ы'
- 17: 1, # 'ь'
- 30: 2, # 'э'
- 27: 0, # 'ю'
- 16: 1, # 'я'
- },
- 46: { # 'Г'
- 37: 1, # 'А'
- 44: 1, # 'Б'
- 33: 0, # 'В'
- 46: 0, # 'Г'
- 41: 1, # 'Д'
- 48: 1, # 'Е'
- 56: 0, # 'Ж'
- 51: 0, # 'З'
- 42: 1, # 'И'
- 60: 0, # 'Й'
- 36: 0, # 'К'
- 49: 1, # 'Л'
- 38: 1, # 'М'
- 31: 1, # 'Н'
- 34: 1, # 'О'
- 35: 1, # 'П'
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- 39: 2, # 'ф'
- 26: 1, # 'х'
- 28: 2, # 'ц'
- 22: 2, # 'ч'
- 25: 3, # 'ш'
- 29: 2, # 'щ'
- 54: 0, # 'ъ'
- 18: 0, # 'ы'
- 17: 0, # 'ь'
- 30: 1, # 'э'
- 27: 3, # 'ю'
- 16: 3, # 'я'
- },
- 30: { # 'э'
- 37: 0, # 'А'
- 44: 0, # 'Б'
- 33: 0, # 'В'
- 46: 0, # 'Г'
- 41: 0, # 'Д'
- 48: 0, # 'Е'
- 56: 0, # 'Ж'
- 51: 0, # 'З'
- 42: 0, # 'И'
- 60: 0, # 'Й'
- 36: 0, # 'К'
- 49: 0, # 'Л'
- 38: 1, # 'М'
- 31: 1, # 'Н'
- 34: 0, # 'О'
- 35: 0, # 'П'
- 45: 1, # 'Р'
- 32: 1, # 'С'
- 40: 0, # 'Т'
- 52: 0, # 'У'
- 53: 1, # 'Ф'
- 55: 0, # 'Х'
- 58: 0, # 'Ц'
- 50: 0, # 'Ч'
- 57: 0, # 'Ш'
- 63: 0, # 'Щ'
- 62: 0, # 'Ы'
- 61: 0, # 'Ь'
- 47: 0, # 'Э'
- 59: 0, # 'Ю'
- 43: 0, # 'Я'
- 3: 0, # 'а'
- 21: 1, # 'б'
- 10: 1, # 'в'
- 19: 1, # 'г'
- 13: 2, # 'д'
- 2: 1, # 'е'
- 24: 0, # 'ж'
- 20: 1, # 'з'
- 4: 0, # 'и'
- 23: 2, # 'й'
- 11: 2, # 'к'
- 8: 2, # 'л'
- 12: 2, # 'м'
- 5: 2, # 'н'
- 1: 0, # 'о'
- 15: 2, # 'п'
- 9: 2, # 'р'
- 7: 2, # 'с'
- 6: 3, # 'т'
- 14: 1, # 'у'
- 39: 2, # 'ф'
- 26: 1, # 'х'
- 28: 0, # 'ц'
- 22: 0, # 'ч'
- 25: 1, # 'ш'
- 29: 0, # 'щ'
- 54: 0, # 'ъ'
- 18: 0, # 'ы'
- 17: 0, # 'ь'
- 30: 1, # 'э'
- 27: 1, # 'ю'
- 16: 1, # 'я'
- },
- 27: { # 'ю'
- 37: 0, # 'А'
- 44: 0, # 'Б'
- 33: 0, # 'В'
- 46: 0, # 'Г'
- 41: 0, # 'Д'
- 48: 0, # 'Е'
- 56: 0, # 'Ж'
- 51: 0, # 'З'
- 42: 0, # 'И'
- 60: 0, # 'Й'
- 36: 0, # 'К'
- 49: 0, # 'Л'
- 38: 0, # 'М'
- 31: 0, # 'Н'
- 34: 0, # 'О'
- 35: 0, # 'П'
- 45: 0, # 'Р'
- 32: 0, # 'С'
- 40: 0, # 'Т'
- 52: 0, # 'У'
- 53: 0, # 'Ф'
- 55: 0, # 'Х'
- 58: 0, # 'Ц'
- 50: 0, # 'Ч'
- 57: 0, # 'Ш'
- 63: 0, # 'Щ'
- 62: 0, # 'Ы'
- 61: 0, # 'Ь'
- 47: 0, # 'Э'
- 59: 0, # 'Ю'
- 43: 0, # 'Я'
- 3: 2, # 'а'
- 21: 3, # 'б'
- 10: 1, # 'в'
- 19: 2, # 'г'
- 13: 3, # 'д'
- 2: 1, # 'е'
- 24: 2, # 'ж'
- 20: 2, # 'з'
- 4: 1, # 'и'
- 23: 1, # 'й'
- 11: 2, # 'к'
- 8: 2, # 'л'
- 12: 2, # 'м'
- 5: 2, # 'н'
- 1: 1, # 'о'
- 15: 2, # 'п'
- 9: 2, # 'р'
- 7: 3, # 'с'
- 6: 3, # 'т'
- 14: 0, # 'у'
- 39: 1, # 'ф'
- 26: 2, # 'х'
- 28: 2, # 'ц'
- 22: 2, # 'ч'
- 25: 2, # 'ш'
- 29: 3, # 'щ'
- 54: 0, # 'ъ'
- 18: 0, # 'ы'
- 17: 0, # 'ь'
- 30: 1, # 'э'
- 27: 2, # 'ю'
- 16: 1, # 'я'
- },
- 16: { # 'я'
- 37: 0, # 'А'
- 44: 0, # 'Б'
- 33: 0, # 'В'
- 46: 0, # 'Г'
- 41: 0, # 'Д'
- 48: 0, # 'Е'
- 56: 0, # 'Ж'
- 51: 0, # 'З'
- 42: 0, # 'И'
- 60: 0, # 'Й'
- 36: 0, # 'К'
- 49: 0, # 'Л'
- 38: 0, # 'М'
- 31: 0, # 'Н'
- 34: 0, # 'О'
- 35: 0, # 'П'
- 45: 0, # 'Р'
- 32: 0, # 'С'
- 40: 0, # 'Т'
- 52: 0, # 'У'
- 53: 0, # 'Ф'
- 55: 0, # 'Х'
- 58: 0, # 'Ц'
- 50: 0, # 'Ч'
- 57: 0, # 'Ш'
- 63: 0, # 'Щ'
- 62: 0, # 'Ы'
- 61: 0, # 'Ь'
- 47: 0, # 'Э'
- 59: 0, # 'Ю'
- 43: 0, # 'Я'
- 3: 0, # 'а'
- 21: 2, # 'б'
- 10: 3, # 'в'
- 19: 2, # 'г'
- 13: 3, # 'д'
- 2: 3, # 'е'
- 24: 3, # 'ж'
- 20: 3, # 'з'
- 4: 2, # 'и'
- 23: 2, # 'й'
- 11: 3, # 'к'
- 8: 3, # 'л'
- 12: 3, # 'м'
- 5: 3, # 'н'
- 1: 0, # 'о'
- 15: 2, # 'п'
- 9: 2, # 'р'
- 7: 3, # 'с'
- 6: 3, # 'т'
- 14: 1, # 'у'
- 39: 1, # 'ф'
- 26: 3, # 'х'
- 28: 2, # 'ц'
- 22: 2, # 'ч'
- 25: 2, # 'ш'
- 29: 3, # 'щ'
- 54: 0, # 'ъ'
- 18: 0, # 'ы'
- 17: 0, # 'ь'
- 30: 0, # 'э'
- 27: 2, # 'ю'
- 16: 2, # 'я'
- },
-}
-
-# 255: Undefined characters that did not exist in training text
-# 254: Carriage/Return
-# 253: symbol (punctuation) that does not belong to word
-# 252: 0 - 9
-# 251: Control characters
-
-# Character Mapping Table(s):
-IBM866_RUSSIAN_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 254, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 254, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 253, # ' '
- 33: 253, # '!'
- 34: 253, # '"'
- 35: 253, # '#'
- 36: 253, # '$'
- 37: 253, # '%'
- 38: 253, # '&'
- 39: 253, # "'"
- 40: 253, # '('
- 41: 253, # ')'
- 42: 253, # '*'
- 43: 253, # '+'
- 44: 253, # ','
- 45: 253, # '-'
- 46: 253, # '.'
- 47: 253, # '/'
- 48: 252, # '0'
- 49: 252, # '1'
- 50: 252, # '2'
- 51: 252, # '3'
- 52: 252, # '4'
- 53: 252, # '5'
- 54: 252, # '6'
- 55: 252, # '7'
- 56: 252, # '8'
- 57: 252, # '9'
- 58: 253, # ':'
- 59: 253, # ';'
- 60: 253, # '<'
- 61: 253, # '='
- 62: 253, # '>'
- 63: 253, # '?'
- 64: 253, # '@'
- 65: 142, # 'A'
- 66: 143, # 'B'
- 67: 144, # 'C'
- 68: 145, # 'D'
- 69: 146, # 'E'
- 70: 147, # 'F'
- 71: 148, # 'G'
- 72: 149, # 'H'
- 73: 150, # 'I'
- 74: 151, # 'J'
- 75: 152, # 'K'
- 76: 74, # 'L'
- 77: 153, # 'M'
- 78: 75, # 'N'
- 79: 154, # 'O'
- 80: 155, # 'P'
- 81: 156, # 'Q'
- 82: 157, # 'R'
- 83: 158, # 'S'
- 84: 159, # 'T'
- 85: 160, # 'U'
- 86: 161, # 'V'
- 87: 162, # 'W'
- 88: 163, # 'X'
- 89: 164, # 'Y'
- 90: 165, # 'Z'
- 91: 253, # '['
- 92: 253, # '\\'
- 93: 253, # ']'
- 94: 253, # '^'
- 95: 253, # '_'
- 96: 253, # '`'
- 97: 71, # 'a'
- 98: 172, # 'b'
- 99: 66, # 'c'
- 100: 173, # 'd'
- 101: 65, # 'e'
- 102: 174, # 'f'
- 103: 76, # 'g'
- 104: 175, # 'h'
- 105: 64, # 'i'
- 106: 176, # 'j'
- 107: 177, # 'k'
- 108: 77, # 'l'
- 109: 72, # 'm'
- 110: 178, # 'n'
- 111: 69, # 'o'
- 112: 67, # 'p'
- 113: 179, # 'q'
- 114: 78, # 'r'
- 115: 73, # 's'
- 116: 180, # 't'
- 117: 181, # 'u'
- 118: 79, # 'v'
- 119: 182, # 'w'
- 120: 183, # 'x'
- 121: 184, # 'y'
- 122: 185, # 'z'
- 123: 253, # '{'
- 124: 253, # '|'
- 125: 253, # '}'
- 126: 253, # '~'
- 127: 253, # '\x7f'
- 128: 37, # 'А'
- 129: 44, # 'Б'
- 130: 33, # 'В'
- 131: 46, # 'Г'
- 132: 41, # 'Д'
- 133: 48, # 'Е'
- 134: 56, # 'Ж'
- 135: 51, # 'З'
- 136: 42, # 'И'
- 137: 60, # 'Й'
- 138: 36, # 'К'
- 139: 49, # 'Л'
- 140: 38, # 'М'
- 141: 31, # 'Н'
- 142: 34, # 'О'
- 143: 35, # 'П'
- 144: 45, # 'Р'
- 145: 32, # 'С'
- 146: 40, # 'Т'
- 147: 52, # 'У'
- 148: 53, # 'Ф'
- 149: 55, # 'Х'
- 150: 58, # 'Ц'
- 151: 50, # 'Ч'
- 152: 57, # 'Ш'
- 153: 63, # 'Щ'
- 154: 70, # 'Ъ'
- 155: 62, # 'Ы'
- 156: 61, # 'Ь'
- 157: 47, # 'Э'
- 158: 59, # 'Ю'
- 159: 43, # 'Я'
- 160: 3, # 'а'
- 161: 21, # 'б'
- 162: 10, # 'в'
- 163: 19, # 'г'
- 164: 13, # 'д'
- 165: 2, # 'е'
- 166: 24, # 'ж'
- 167: 20, # 'з'
- 168: 4, # 'и'
- 169: 23, # 'й'
- 170: 11, # 'к'
- 171: 8, # 'л'
- 172: 12, # 'м'
- 173: 5, # 'н'
- 174: 1, # 'о'
- 175: 15, # 'п'
- 176: 191, # '░'
- 177: 192, # '▒'
- 178: 193, # '▓'
- 179: 194, # '│'
- 180: 195, # '┤'
- 181: 196, # '╡'
- 182: 197, # '╢'
- 183: 198, # '╖'
- 184: 199, # '╕'
- 185: 200, # '╣'
- 186: 201, # '║'
- 187: 202, # '╗'
- 188: 203, # '╝'
- 189: 204, # '╜'
- 190: 205, # '╛'
- 191: 206, # '┐'
- 192: 207, # '└'
- 193: 208, # '┴'
- 194: 209, # '┬'
- 195: 210, # '├'
- 196: 211, # '─'
- 197: 212, # '┼'
- 198: 213, # '╞'
- 199: 214, # '╟'
- 200: 215, # '╚'
- 201: 216, # '╔'
- 202: 217, # '╩'
- 203: 218, # '╦'
- 204: 219, # '╠'
- 205: 220, # '═'
- 206: 221, # '╬'
- 207: 222, # '╧'
- 208: 223, # '╨'
- 209: 224, # '╤'
- 210: 225, # '╥'
- 211: 226, # '╙'
- 212: 227, # '╘'
- 213: 228, # '╒'
- 214: 229, # '╓'
- 215: 230, # '╫'
- 216: 231, # '╪'
- 217: 232, # '┘'
- 218: 233, # '┌'
- 219: 234, # '█'
- 220: 235, # '▄'
- 221: 236, # '▌'
- 222: 237, # '▐'
- 223: 238, # '▀'
- 224: 9, # 'р'
- 225: 7, # 'с'
- 226: 6, # 'т'
- 227: 14, # 'у'
- 228: 39, # 'ф'
- 229: 26, # 'х'
- 230: 28, # 'ц'
- 231: 22, # 'ч'
- 232: 25, # 'ш'
- 233: 29, # 'щ'
- 234: 54, # 'ъ'
- 235: 18, # 'ы'
- 236: 17, # 'ь'
- 237: 30, # 'э'
- 238: 27, # 'ю'
- 239: 16, # 'я'
- 240: 239, # 'Ё'
- 241: 68, # 'ё'
- 242: 240, # 'Є'
- 243: 241, # 'є'
- 244: 242, # 'Ї'
- 245: 243, # 'ї'
- 246: 244, # 'Ў'
- 247: 245, # 'ў'
- 248: 246, # '°'
- 249: 247, # '∙'
- 250: 248, # '·'
- 251: 249, # '√'
- 252: 250, # '№'
- 253: 251, # '¤'
- 254: 252, # '■'
- 255: 255, # '\xa0'
-}
-
-IBM866_RUSSIAN_MODEL = SingleByteCharSetModel(
- charset_name="IBM866",
- language="Russian",
- char_to_order_map=IBM866_RUSSIAN_CHAR_TO_ORDER,
- language_model=RUSSIAN_LANG_MODEL,
- typical_positive_ratio=0.976601,
- keep_ascii_letters=False,
- alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё",
-)
-
-WINDOWS_1251_RUSSIAN_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 254, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 254, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 253, # ' '
- 33: 253, # '!'
- 34: 253, # '"'
- 35: 253, # '#'
- 36: 253, # '$'
- 37: 253, # '%'
- 38: 253, # '&'
- 39: 253, # "'"
- 40: 253, # '('
- 41: 253, # ')'
- 42: 253, # '*'
- 43: 253, # '+'
- 44: 253, # ','
- 45: 253, # '-'
- 46: 253, # '.'
- 47: 253, # '/'
- 48: 252, # '0'
- 49: 252, # '1'
- 50: 252, # '2'
- 51: 252, # '3'
- 52: 252, # '4'
- 53: 252, # '5'
- 54: 252, # '6'
- 55: 252, # '7'
- 56: 252, # '8'
- 57: 252, # '9'
- 58: 253, # ':'
- 59: 253, # ';'
- 60: 253, # '<'
- 61: 253, # '='
- 62: 253, # '>'
- 63: 253, # '?'
- 64: 253, # '@'
- 65: 142, # 'A'
- 66: 143, # 'B'
- 67: 144, # 'C'
- 68: 145, # 'D'
- 69: 146, # 'E'
- 70: 147, # 'F'
- 71: 148, # 'G'
- 72: 149, # 'H'
- 73: 150, # 'I'
- 74: 151, # 'J'
- 75: 152, # 'K'
- 76: 74, # 'L'
- 77: 153, # 'M'
- 78: 75, # 'N'
- 79: 154, # 'O'
- 80: 155, # 'P'
- 81: 156, # 'Q'
- 82: 157, # 'R'
- 83: 158, # 'S'
- 84: 159, # 'T'
- 85: 160, # 'U'
- 86: 161, # 'V'
- 87: 162, # 'W'
- 88: 163, # 'X'
- 89: 164, # 'Y'
- 90: 165, # 'Z'
- 91: 253, # '['
- 92: 253, # '\\'
- 93: 253, # ']'
- 94: 253, # '^'
- 95: 253, # '_'
- 96: 253, # '`'
- 97: 71, # 'a'
- 98: 172, # 'b'
- 99: 66, # 'c'
- 100: 173, # 'd'
- 101: 65, # 'e'
- 102: 174, # 'f'
- 103: 76, # 'g'
- 104: 175, # 'h'
- 105: 64, # 'i'
- 106: 176, # 'j'
- 107: 177, # 'k'
- 108: 77, # 'l'
- 109: 72, # 'm'
- 110: 178, # 'n'
- 111: 69, # 'o'
- 112: 67, # 'p'
- 113: 179, # 'q'
- 114: 78, # 'r'
- 115: 73, # 's'
- 116: 180, # 't'
- 117: 181, # 'u'
- 118: 79, # 'v'
- 119: 182, # 'w'
- 120: 183, # 'x'
- 121: 184, # 'y'
- 122: 185, # 'z'
- 123: 253, # '{'
- 124: 253, # '|'
- 125: 253, # '}'
- 126: 253, # '~'
- 127: 253, # '\x7f'
- 128: 191, # 'Ђ'
- 129: 192, # 'Ѓ'
- 130: 193, # '‚'
- 131: 194, # 'ѓ'
- 132: 195, # '„'
- 133: 196, # '…'
- 134: 197, # '†'
- 135: 198, # '‡'
- 136: 199, # '€'
- 137: 200, # '‰'
- 138: 201, # 'Љ'
- 139: 202, # '‹'
- 140: 203, # 'Њ'
- 141: 204, # 'Ќ'
- 142: 205, # 'Ћ'
- 143: 206, # 'Џ'
- 144: 207, # 'ђ'
- 145: 208, # '‘'
- 146: 209, # '’'
- 147: 210, # '“'
- 148: 211, # '”'
- 149: 212, # '•'
- 150: 213, # '–'
- 151: 214, # '—'
- 152: 215, # None
- 153: 216, # '™'
- 154: 217, # 'љ'
- 155: 218, # '›'
- 156: 219, # 'њ'
- 157: 220, # 'ќ'
- 158: 221, # 'ћ'
- 159: 222, # 'џ'
- 160: 223, # '\xa0'
- 161: 224, # 'Ў'
- 162: 225, # 'ў'
- 163: 226, # 'Ј'
- 164: 227, # '¤'
- 165: 228, # 'Ґ'
- 166: 229, # '¦'
- 167: 230, # '§'
- 168: 231, # 'Ё'
- 169: 232, # '©'
- 170: 233, # 'Є'
- 171: 234, # '«'
- 172: 235, # '¬'
- 173: 236, # '\xad'
- 174: 237, # '®'
- 175: 238, # 'Ї'
- 176: 239, # '°'
- 177: 240, # '±'
- 178: 241, # 'І'
- 179: 242, # 'і'
- 180: 243, # 'ґ'
- 181: 244, # 'µ'
- 182: 245, # '¶'
- 183: 246, # '·'
- 184: 68, # 'ё'
- 185: 247, # '№'
- 186: 248, # 'є'
- 187: 249, # '»'
- 188: 250, # 'ј'
- 189: 251, # 'Ѕ'
- 190: 252, # 'ѕ'
- 191: 253, # 'ї'
- 192: 37, # 'А'
- 193: 44, # 'Б'
- 194: 33, # 'В'
- 195: 46, # 'Г'
- 196: 41, # 'Д'
- 197: 48, # 'Е'
- 198: 56, # 'Ж'
- 199: 51, # 'З'
- 200: 42, # 'И'
- 201: 60, # 'Й'
- 202: 36, # 'К'
- 203: 49, # 'Л'
- 204: 38, # 'М'
- 205: 31, # 'Н'
- 206: 34, # 'О'
- 207: 35, # 'П'
- 208: 45, # 'Р'
- 209: 32, # 'С'
- 210: 40, # 'Т'
- 211: 52, # 'У'
- 212: 53, # 'Ф'
- 213: 55, # 'Х'
- 214: 58, # 'Ц'
- 215: 50, # 'Ч'
- 216: 57, # 'Ш'
- 217: 63, # 'Щ'
- 218: 70, # 'Ъ'
- 219: 62, # 'Ы'
- 220: 61, # 'Ь'
- 221: 47, # 'Э'
- 222: 59, # 'Ю'
- 223: 43, # 'Я'
- 224: 3, # 'а'
- 225: 21, # 'б'
- 226: 10, # 'в'
- 227: 19, # 'г'
- 228: 13, # 'д'
- 229: 2, # 'е'
- 230: 24, # 'ж'
- 231: 20, # 'з'
- 232: 4, # 'и'
- 233: 23, # 'й'
- 234: 11, # 'к'
- 235: 8, # 'л'
- 236: 12, # 'м'
- 237: 5, # 'н'
- 238: 1, # 'о'
- 239: 15, # 'п'
- 240: 9, # 'р'
- 241: 7, # 'с'
- 242: 6, # 'т'
- 243: 14, # 'у'
- 244: 39, # 'ф'
- 245: 26, # 'х'
- 246: 28, # 'ц'
- 247: 22, # 'ч'
- 248: 25, # 'ш'
- 249: 29, # 'щ'
- 250: 54, # 'ъ'
- 251: 18, # 'ы'
- 252: 17, # 'ь'
- 253: 30, # 'э'
- 254: 27, # 'ю'
- 255: 16, # 'я'
-}
-
-WINDOWS_1251_RUSSIAN_MODEL = SingleByteCharSetModel(
- charset_name="windows-1251",
- language="Russian",
- char_to_order_map=WINDOWS_1251_RUSSIAN_CHAR_TO_ORDER,
- language_model=RUSSIAN_LANG_MODEL,
- typical_positive_ratio=0.976601,
- keep_ascii_letters=False,
- alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё",
-)
-
-IBM855_RUSSIAN_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 254, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 254, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 253, # ' '
- 33: 253, # '!'
- 34: 253, # '"'
- 35: 253, # '#'
- 36: 253, # '$'
- 37: 253, # '%'
- 38: 253, # '&'
- 39: 253, # "'"
- 40: 253, # '('
- 41: 253, # ')'
- 42: 253, # '*'
- 43: 253, # '+'
- 44: 253, # ','
- 45: 253, # '-'
- 46: 253, # '.'
- 47: 253, # '/'
- 48: 252, # '0'
- 49: 252, # '1'
- 50: 252, # '2'
- 51: 252, # '3'
- 52: 252, # '4'
- 53: 252, # '5'
- 54: 252, # '6'
- 55: 252, # '7'
- 56: 252, # '8'
- 57: 252, # '9'
- 58: 253, # ':'
- 59: 253, # ';'
- 60: 253, # '<'
- 61: 253, # '='
- 62: 253, # '>'
- 63: 253, # '?'
- 64: 253, # '@'
- 65: 142, # 'A'
- 66: 143, # 'B'
- 67: 144, # 'C'
- 68: 145, # 'D'
- 69: 146, # 'E'
- 70: 147, # 'F'
- 71: 148, # 'G'
- 72: 149, # 'H'
- 73: 150, # 'I'
- 74: 151, # 'J'
- 75: 152, # 'K'
- 76: 74, # 'L'
- 77: 153, # 'M'
- 78: 75, # 'N'
- 79: 154, # 'O'
- 80: 155, # 'P'
- 81: 156, # 'Q'
- 82: 157, # 'R'
- 83: 158, # 'S'
- 84: 159, # 'T'
- 85: 160, # 'U'
- 86: 161, # 'V'
- 87: 162, # 'W'
- 88: 163, # 'X'
- 89: 164, # 'Y'
- 90: 165, # 'Z'
- 91: 253, # '['
- 92: 253, # '\\'
- 93: 253, # ']'
- 94: 253, # '^'
- 95: 253, # '_'
- 96: 253, # '`'
- 97: 71, # 'a'
- 98: 172, # 'b'
- 99: 66, # 'c'
- 100: 173, # 'd'
- 101: 65, # 'e'
- 102: 174, # 'f'
- 103: 76, # 'g'
- 104: 175, # 'h'
- 105: 64, # 'i'
- 106: 176, # 'j'
- 107: 177, # 'k'
- 108: 77, # 'l'
- 109: 72, # 'm'
- 110: 178, # 'n'
- 111: 69, # 'o'
- 112: 67, # 'p'
- 113: 179, # 'q'
- 114: 78, # 'r'
- 115: 73, # 's'
- 116: 180, # 't'
- 117: 181, # 'u'
- 118: 79, # 'v'
- 119: 182, # 'w'
- 120: 183, # 'x'
- 121: 184, # 'y'
- 122: 185, # 'z'
- 123: 253, # '{'
- 124: 253, # '|'
- 125: 253, # '}'
- 126: 253, # '~'
- 127: 253, # '\x7f'
- 128: 191, # 'ђ'
- 129: 192, # 'Ђ'
- 130: 193, # 'ѓ'
- 131: 194, # 'Ѓ'
- 132: 68, # 'ё'
- 133: 195, # 'Ё'
- 134: 196, # 'є'
- 135: 197, # 'Є'
- 136: 198, # 'ѕ'
- 137: 199, # 'Ѕ'
- 138: 200, # 'і'
- 139: 201, # 'І'
- 140: 202, # 'ї'
- 141: 203, # 'Ї'
- 142: 204, # 'ј'
- 143: 205, # 'Ј'
- 144: 206, # 'љ'
- 145: 207, # 'Љ'
- 146: 208, # 'њ'
- 147: 209, # 'Њ'
- 148: 210, # 'ћ'
- 149: 211, # 'Ћ'
- 150: 212, # 'ќ'
- 151: 213, # 'Ќ'
- 152: 214, # 'ў'
- 153: 215, # 'Ў'
- 154: 216, # 'џ'
- 155: 217, # 'Џ'
- 156: 27, # 'ю'
- 157: 59, # 'Ю'
- 158: 54, # 'ъ'
- 159: 70, # 'Ъ'
- 160: 3, # 'а'
- 161: 37, # 'А'
- 162: 21, # 'б'
- 163: 44, # 'Б'
- 164: 28, # 'ц'
- 165: 58, # 'Ц'
- 166: 13, # 'д'
- 167: 41, # 'Д'
- 168: 2, # 'е'
- 169: 48, # 'Е'
- 170: 39, # 'ф'
- 171: 53, # 'Ф'
- 172: 19, # 'г'
- 173: 46, # 'Г'
- 174: 218, # '«'
- 175: 219, # '»'
- 176: 220, # '░'
- 177: 221, # '▒'
- 178: 222, # '▓'
- 179: 223, # '│'
- 180: 224, # '┤'
- 181: 26, # 'х'
- 182: 55, # 'Х'
- 183: 4, # 'и'
- 184: 42, # 'И'
- 185: 225, # '╣'
- 186: 226, # '║'
- 187: 227, # '╗'
- 188: 228, # '╝'
- 189: 23, # 'й'
- 190: 60, # 'Й'
- 191: 229, # '┐'
- 192: 230, # '└'
- 193: 231, # '┴'
- 194: 232, # '┬'
- 195: 233, # '├'
- 196: 234, # '─'
- 197: 235, # '┼'
- 198: 11, # 'к'
- 199: 36, # 'К'
- 200: 236, # '╚'
- 201: 237, # '╔'
- 202: 238, # '╩'
- 203: 239, # '╦'
- 204: 240, # '╠'
- 205: 241, # '═'
- 206: 242, # '╬'
- 207: 243, # '¤'
- 208: 8, # 'л'
- 209: 49, # 'Л'
- 210: 12, # 'м'
- 211: 38, # 'М'
- 212: 5, # 'н'
- 213: 31, # 'Н'
- 214: 1, # 'о'
- 215: 34, # 'О'
- 216: 15, # 'п'
- 217: 244, # '┘'
- 218: 245, # '┌'
- 219: 246, # '█'
- 220: 247, # '▄'
- 221: 35, # 'П'
- 222: 16, # 'я'
- 223: 248, # '▀'
- 224: 43, # 'Я'
- 225: 9, # 'р'
- 226: 45, # 'Р'
- 227: 7, # 'с'
- 228: 32, # 'С'
- 229: 6, # 'т'
- 230: 40, # 'Т'
- 231: 14, # 'у'
- 232: 52, # 'У'
- 233: 24, # 'ж'
- 234: 56, # 'Ж'
- 235: 10, # 'в'
- 236: 33, # 'В'
- 237: 17, # 'ь'
- 238: 61, # 'Ь'
- 239: 249, # '№'
- 240: 250, # '\xad'
- 241: 18, # 'ы'
- 242: 62, # 'Ы'
- 243: 20, # 'з'
- 244: 51, # 'З'
- 245: 25, # 'ш'
- 246: 57, # 'Ш'
- 247: 30, # 'э'
- 248: 47, # 'Э'
- 249: 29, # 'щ'
- 250: 63, # 'Щ'
- 251: 22, # 'ч'
- 252: 50, # 'Ч'
- 253: 251, # '§'
- 254: 252, # '■'
- 255: 255, # '\xa0'
-}
-
-IBM855_RUSSIAN_MODEL = SingleByteCharSetModel(
- charset_name="IBM855",
- language="Russian",
- char_to_order_map=IBM855_RUSSIAN_CHAR_TO_ORDER,
- language_model=RUSSIAN_LANG_MODEL,
- typical_positive_ratio=0.976601,
- keep_ascii_letters=False,
- alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё",
-)
-
-KOI8_R_RUSSIAN_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 254, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 254, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 253, # ' '
- 33: 253, # '!'
- 34: 253, # '"'
- 35: 253, # '#'
- 36: 253, # '$'
- 37: 253, # '%'
- 38: 253, # '&'
- 39: 253, # "'"
- 40: 253, # '('
- 41: 253, # ')'
- 42: 253, # '*'
- 43: 253, # '+'
- 44: 253, # ','
- 45: 253, # '-'
- 46: 253, # '.'
- 47: 253, # '/'
- 48: 252, # '0'
- 49: 252, # '1'
- 50: 252, # '2'
- 51: 252, # '3'
- 52: 252, # '4'
- 53: 252, # '5'
- 54: 252, # '6'
- 55: 252, # '7'
- 56: 252, # '8'
- 57: 252, # '9'
- 58: 253, # ':'
- 59: 253, # ';'
- 60: 253, # '<'
- 61: 253, # '='
- 62: 253, # '>'
- 63: 253, # '?'
- 64: 253, # '@'
- 65: 142, # 'A'
- 66: 143, # 'B'
- 67: 144, # 'C'
- 68: 145, # 'D'
- 69: 146, # 'E'
- 70: 147, # 'F'
- 71: 148, # 'G'
- 72: 149, # 'H'
- 73: 150, # 'I'
- 74: 151, # 'J'
- 75: 152, # 'K'
- 76: 74, # 'L'
- 77: 153, # 'M'
- 78: 75, # 'N'
- 79: 154, # 'O'
- 80: 155, # 'P'
- 81: 156, # 'Q'
- 82: 157, # 'R'
- 83: 158, # 'S'
- 84: 159, # 'T'
- 85: 160, # 'U'
- 86: 161, # 'V'
- 87: 162, # 'W'
- 88: 163, # 'X'
- 89: 164, # 'Y'
- 90: 165, # 'Z'
- 91: 253, # '['
- 92: 253, # '\\'
- 93: 253, # ']'
- 94: 253, # '^'
- 95: 253, # '_'
- 96: 253, # '`'
- 97: 71, # 'a'
- 98: 172, # 'b'
- 99: 66, # 'c'
- 100: 173, # 'd'
- 101: 65, # 'e'
- 102: 174, # 'f'
- 103: 76, # 'g'
- 104: 175, # 'h'
- 105: 64, # 'i'
- 106: 176, # 'j'
- 107: 177, # 'k'
- 108: 77, # 'l'
- 109: 72, # 'm'
- 110: 178, # 'n'
- 111: 69, # 'o'
- 112: 67, # 'p'
- 113: 179, # 'q'
- 114: 78, # 'r'
- 115: 73, # 's'
- 116: 180, # 't'
- 117: 181, # 'u'
- 118: 79, # 'v'
- 119: 182, # 'w'
- 120: 183, # 'x'
- 121: 184, # 'y'
- 122: 185, # 'z'
- 123: 253, # '{'
- 124: 253, # '|'
- 125: 253, # '}'
- 126: 253, # '~'
- 127: 253, # '\x7f'
- 128: 191, # '─'
- 129: 192, # '│'
- 130: 193, # '┌'
- 131: 194, # '┐'
- 132: 195, # '└'
- 133: 196, # '┘'
- 134: 197, # '├'
- 135: 198, # '┤'
- 136: 199, # '┬'
- 137: 200, # '┴'
- 138: 201, # '┼'
- 139: 202, # '▀'
- 140: 203, # '▄'
- 141: 204, # '█'
- 142: 205, # '▌'
- 143: 206, # '▐'
- 144: 207, # '░'
- 145: 208, # '▒'
- 146: 209, # '▓'
- 147: 210, # '⌠'
- 148: 211, # '■'
- 149: 212, # '∙'
- 150: 213, # '√'
- 151: 214, # '≈'
- 152: 215, # '≤'
- 153: 216, # '≥'
- 154: 217, # '\xa0'
- 155: 218, # '⌡'
- 156: 219, # '°'
- 157: 220, # '²'
- 158: 221, # '·'
- 159: 222, # '÷'
- 160: 223, # '═'
- 161: 224, # '║'
- 162: 225, # '╒'
- 163: 68, # 'ё'
- 164: 226, # '╓'
- 165: 227, # '╔'
- 166: 228, # '╕'
- 167: 229, # '╖'
- 168: 230, # '╗'
- 169: 231, # '╘'
- 170: 232, # '╙'
- 171: 233, # '╚'
- 172: 234, # '╛'
- 173: 235, # '╜'
- 174: 236, # '╝'
- 175: 237, # '╞'
- 176: 238, # '╟'
- 177: 239, # '╠'
- 178: 240, # '╡'
- 179: 241, # 'Ё'
- 180: 242, # '╢'
- 181: 243, # '╣'
- 182: 244, # '╤'
- 183: 245, # '╥'
- 184: 246, # '╦'
- 185: 247, # '╧'
- 186: 248, # '╨'
- 187: 249, # '╩'
- 188: 250, # '╪'
- 189: 251, # '╫'
- 190: 252, # '╬'
- 191: 253, # '©'
- 192: 27, # 'ю'
- 193: 3, # 'а'
- 194: 21, # 'б'
- 195: 28, # 'ц'
- 196: 13, # 'д'
- 197: 2, # 'е'
- 198: 39, # 'ф'
- 199: 19, # 'г'
- 200: 26, # 'х'
- 201: 4, # 'и'
- 202: 23, # 'й'
- 203: 11, # 'к'
- 204: 8, # 'л'
- 205: 12, # 'м'
- 206: 5, # 'н'
- 207: 1, # 'о'
- 208: 15, # 'п'
- 209: 16, # 'я'
- 210: 9, # 'р'
- 211: 7, # 'с'
- 212: 6, # 'т'
- 213: 14, # 'у'
- 214: 24, # 'ж'
- 215: 10, # 'в'
- 216: 17, # 'ь'
- 217: 18, # 'ы'
- 218: 20, # 'з'
- 219: 25, # 'ш'
- 220: 30, # 'э'
- 221: 29, # 'щ'
- 222: 22, # 'ч'
- 223: 54, # 'ъ'
- 224: 59, # 'Ю'
- 225: 37, # 'А'
- 226: 44, # 'Б'
- 227: 58, # 'Ц'
- 228: 41, # 'Д'
- 229: 48, # 'Е'
- 230: 53, # 'Ф'
- 231: 46, # 'Г'
- 232: 55, # 'Х'
- 233: 42, # 'И'
- 234: 60, # 'Й'
- 235: 36, # 'К'
- 236: 49, # 'Л'
- 237: 38, # 'М'
- 238: 31, # 'Н'
- 239: 34, # 'О'
- 240: 35, # 'П'
- 241: 43, # 'Я'
- 242: 45, # 'Р'
- 243: 32, # 'С'
- 244: 40, # 'Т'
- 245: 52, # 'У'
- 246: 56, # 'Ж'
- 247: 33, # 'В'
- 248: 61, # 'Ь'
- 249: 62, # 'Ы'
- 250: 51, # 'З'
- 251: 57, # 'Ш'
- 252: 47, # 'Э'
- 253: 63, # 'Щ'
- 254: 50, # 'Ч'
- 255: 70, # 'Ъ'
-}
-
-KOI8_R_RUSSIAN_MODEL = SingleByteCharSetModel(
- charset_name="KOI8-R",
- language="Russian",
- char_to_order_map=KOI8_R_RUSSIAN_CHAR_TO_ORDER,
- language_model=RUSSIAN_LANG_MODEL,
- typical_positive_ratio=0.976601,
- keep_ascii_letters=False,
- alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё",
-)
-
-MACCYRILLIC_RUSSIAN_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 254, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 254, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 253, # ' '
- 33: 253, # '!'
- 34: 253, # '"'
- 35: 253, # '#'
- 36: 253, # '$'
- 37: 253, # '%'
- 38: 253, # '&'
- 39: 253, # "'"
- 40: 253, # '('
- 41: 253, # ')'
- 42: 253, # '*'
- 43: 253, # '+'
- 44: 253, # ','
- 45: 253, # '-'
- 46: 253, # '.'
- 47: 253, # '/'
- 48: 252, # '0'
- 49: 252, # '1'
- 50: 252, # '2'
- 51: 252, # '3'
- 52: 252, # '4'
- 53: 252, # '5'
- 54: 252, # '6'
- 55: 252, # '7'
- 56: 252, # '8'
- 57: 252, # '9'
- 58: 253, # ':'
- 59: 253, # ';'
- 60: 253, # '<'
- 61: 253, # '='
- 62: 253, # '>'
- 63: 253, # '?'
- 64: 253, # '@'
- 65: 142, # 'A'
- 66: 143, # 'B'
- 67: 144, # 'C'
- 68: 145, # 'D'
- 69: 146, # 'E'
- 70: 147, # 'F'
- 71: 148, # 'G'
- 72: 149, # 'H'
- 73: 150, # 'I'
- 74: 151, # 'J'
- 75: 152, # 'K'
- 76: 74, # 'L'
- 77: 153, # 'M'
- 78: 75, # 'N'
- 79: 154, # 'O'
- 80: 155, # 'P'
- 81: 156, # 'Q'
- 82: 157, # 'R'
- 83: 158, # 'S'
- 84: 159, # 'T'
- 85: 160, # 'U'
- 86: 161, # 'V'
- 87: 162, # 'W'
- 88: 163, # 'X'
- 89: 164, # 'Y'
- 90: 165, # 'Z'
- 91: 253, # '['
- 92: 253, # '\\'
- 93: 253, # ']'
- 94: 253, # '^'
- 95: 253, # '_'
- 96: 253, # '`'
- 97: 71, # 'a'
- 98: 172, # 'b'
- 99: 66, # 'c'
- 100: 173, # 'd'
- 101: 65, # 'e'
- 102: 174, # 'f'
- 103: 76, # 'g'
- 104: 175, # 'h'
- 105: 64, # 'i'
- 106: 176, # 'j'
- 107: 177, # 'k'
- 108: 77, # 'l'
- 109: 72, # 'm'
- 110: 178, # 'n'
- 111: 69, # 'o'
- 112: 67, # 'p'
- 113: 179, # 'q'
- 114: 78, # 'r'
- 115: 73, # 's'
- 116: 180, # 't'
- 117: 181, # 'u'
- 118: 79, # 'v'
- 119: 182, # 'w'
- 120: 183, # 'x'
- 121: 184, # 'y'
- 122: 185, # 'z'
- 123: 253, # '{'
- 124: 253, # '|'
- 125: 253, # '}'
- 126: 253, # '~'
- 127: 253, # '\x7f'
- 128: 37, # 'А'
- 129: 44, # 'Б'
- 130: 33, # 'В'
- 131: 46, # 'Г'
- 132: 41, # 'Д'
- 133: 48, # 'Е'
- 134: 56, # 'Ж'
- 135: 51, # 'З'
- 136: 42, # 'И'
- 137: 60, # 'Й'
- 138: 36, # 'К'
- 139: 49, # 'Л'
- 140: 38, # 'М'
- 141: 31, # 'Н'
- 142: 34, # 'О'
- 143: 35, # 'П'
- 144: 45, # 'Р'
- 145: 32, # 'С'
- 146: 40, # 'Т'
- 147: 52, # 'У'
- 148: 53, # 'Ф'
- 149: 55, # 'Х'
- 150: 58, # 'Ц'
- 151: 50, # 'Ч'
- 152: 57, # 'Ш'
- 153: 63, # 'Щ'
- 154: 70, # 'Ъ'
- 155: 62, # 'Ы'
- 156: 61, # 'Ь'
- 157: 47, # 'Э'
- 158: 59, # 'Ю'
- 159: 43, # 'Я'
- 160: 191, # '†'
- 161: 192, # '°'
- 162: 193, # 'Ґ'
- 163: 194, # '£'
- 164: 195, # '§'
- 165: 196, # '•'
- 166: 197, # '¶'
- 167: 198, # 'І'
- 168: 199, # '®'
- 169: 200, # '©'
- 170: 201, # '™'
- 171: 202, # 'Ђ'
- 172: 203, # 'ђ'
- 173: 204, # '≠'
- 174: 205, # 'Ѓ'
- 175: 206, # 'ѓ'
- 176: 207, # '∞'
- 177: 208, # '±'
- 178: 209, # '≤'
- 179: 210, # '≥'
- 180: 211, # 'і'
- 181: 212, # 'µ'
- 182: 213, # 'ґ'
- 183: 214, # 'Ј'
- 184: 215, # 'Є'
- 185: 216, # 'є'
- 186: 217, # 'Ї'
- 187: 218, # 'ї'
- 188: 219, # 'Љ'
- 189: 220, # 'љ'
- 190: 221, # 'Њ'
- 191: 222, # 'њ'
- 192: 223, # 'ј'
- 193: 224, # 'Ѕ'
- 194: 225, # '¬'
- 195: 226, # '√'
- 196: 227, # 'ƒ'
- 197: 228, # '≈'
- 198: 229, # '∆'
- 199: 230, # '«'
- 200: 231, # '»'
- 201: 232, # '…'
- 202: 233, # '\xa0'
- 203: 234, # 'Ћ'
- 204: 235, # 'ћ'
- 205: 236, # 'Ќ'
- 206: 237, # 'ќ'
- 207: 238, # 'ѕ'
- 208: 239, # '–'
- 209: 240, # '—'
- 210: 241, # '“'
- 211: 242, # '”'
- 212: 243, # '‘'
- 213: 244, # '’'
- 214: 245, # '÷'
- 215: 246, # '„'
- 216: 247, # 'Ў'
- 217: 248, # 'ў'
- 218: 249, # 'Џ'
- 219: 250, # 'џ'
- 220: 251, # '№'
- 221: 252, # 'Ё'
- 222: 68, # 'ё'
- 223: 16, # 'я'
- 224: 3, # 'а'
- 225: 21, # 'б'
- 226: 10, # 'в'
- 227: 19, # 'г'
- 228: 13, # 'д'
- 229: 2, # 'е'
- 230: 24, # 'ж'
- 231: 20, # 'з'
- 232: 4, # 'и'
- 233: 23, # 'й'
- 234: 11, # 'к'
- 235: 8, # 'л'
- 236: 12, # 'м'
- 237: 5, # 'н'
- 238: 1, # 'о'
- 239: 15, # 'п'
- 240: 9, # 'р'
- 241: 7, # 'с'
- 242: 6, # 'т'
- 243: 14, # 'у'
- 244: 39, # 'ф'
- 245: 26, # 'х'
- 246: 28, # 'ц'
- 247: 22, # 'ч'
- 248: 25, # 'ш'
- 249: 29, # 'щ'
- 250: 54, # 'ъ'
- 251: 18, # 'ы'
- 252: 17, # 'ь'
- 253: 30, # 'э'
- 254: 27, # 'ю'
- 255: 255, # '€'
-}
-
-MACCYRILLIC_RUSSIAN_MODEL = SingleByteCharSetModel(
- charset_name="MacCyrillic",
- language="Russian",
- char_to_order_map=MACCYRILLIC_RUSSIAN_CHAR_TO_ORDER,
- language_model=RUSSIAN_LANG_MODEL,
- typical_positive_ratio=0.976601,
- keep_ascii_letters=False,
- alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё",
-)
-
-ISO_8859_5_RUSSIAN_CHAR_TO_ORDER = {
- 0: 255, # '\x00'
- 1: 255, # '\x01'
- 2: 255, # '\x02'
- 3: 255, # '\x03'
- 4: 255, # '\x04'
- 5: 255, # '\x05'
- 6: 255, # '\x06'
- 7: 255, # '\x07'
- 8: 255, # '\x08'
- 9: 255, # '\t'
- 10: 254, # '\n'
- 11: 255, # '\x0b'
- 12: 255, # '\x0c'
- 13: 254, # '\r'
- 14: 255, # '\x0e'
- 15: 255, # '\x0f'
- 16: 255, # '\x10'
- 17: 255, # '\x11'
- 18: 255, # '\x12'
- 19: 255, # '\x13'
- 20: 255, # '\x14'
- 21: 255, # '\x15'
- 22: 255, # '\x16'
- 23: 255, # '\x17'
- 24: 255, # '\x18'
- 25: 255, # '\x19'
- 26: 255, # '\x1a'
- 27: 255, # '\x1b'
- 28: 255, # '\x1c'
- 29: 255, # '\x1d'
- 30: 255, # '\x1e'
- 31: 255, # '\x1f'
- 32: 253, # ' '
- 33: 253, # '!'
- 34: 253, # '"'
- 35: 253, # '#'
- 36: 253, # '$'
- 37: 253, # '%'
- 38: 253, # '&'
- 39: 253, # "'"
- 40: 253, # '('
- 41: 253, # ')'
- 42: 253, # '*'
- 43: 253, # '+'
- 44: 253, # ','
- 45: 253, # '-'
- 46: 253, # '.'
- 47: 253, # '/'
- 48: 252, # '0'
- 49: 252, # '1'
- 50: 252, # '2'
- 51: 252, # '3'
- 52: 252, # '4'
- 53: 252, # '5'
- 54: 252, # '6'
- 55: 252, # '7'
- 56: 252, # '8'
- 57: 252, # '9'
- 58: 253, # ':'
- 59: 253, # ';'
- 60: 253, # '<'
- 61: 253, # '='
- 62: 253, # '>'
- 63: 253, # '?'
- 64: 253, # '@'
- 65: 142, # 'A'
- 66: 143, # 'B'
- 67: 144, # 'C'
- 68: 145, # 'D'
- 69: 146, # 'E'
- 70: 147, # 'F'
- 71: 148, # 'G'
- 72: 149, # 'H'
- 73: 150, # 'I'
- 74: 151, # 'J'
- 75: 152, # 'K'
- 76: 74, # 'L'
- 77: 153, # 'M'
- 78: 75, # 'N'
- 79: 154, # 'O'
- 80: 155, # 'P'
- 81: 156, # 'Q'
- 82: 157, # 'R'
- 83: 158, # 'S'
- 84: 159, # 'T'
- 85: 160, # 'U'
- 86: 161, # 'V'
- 87: 162, # 'W'
- 88: 163, # 'X'
- 89: 164, # 'Y'
- 90: 165, # 'Z'
- 91: 253, # '['
- 92: 253, # '\\'
- 93: 253, # ']'
- 94: 253, # '^'
- 95: 253, # '_'
- 96: 253, # '`'
- 97: 71, # 'a'
- 98: 172, # 'b'
- 99: 66, # 'c'
- 100: 173, # 'd'
- 101: 65, # 'e'
- 102: 174, # 'f'
- 103: 76, # 'g'
- 104: 175, # 'h'
- 105: 64, # 'i'
- 106: 176, # 'j'
- 107: 177, # 'k'
- 108: 77, # 'l'
- 109: 72, # 'm'
- 110: 178, # 'n'
- 111: 69, # 'o'
- 112: 67, # 'p'
- 113: 179, # 'q'
- 114: 78, # 'r'
- 115: 73, # 's'
- 116: 180, # 't'
- 117: 181, # 'u'
- 118: 79, # 'v'
- 119: 182, # 'w'
- 120: 183, # 'x'
- 121: 184, # 'y'
- 122: 185, # 'z'
- 123: 253, # '{'
- 124: 253, # '|'
- 125: 253, # '}'
- 126: 253, # '~'
- 127: 253, # '\x7f'
- 128: 191, # '\x80'
- 129: 192, # '\x81'
- 130: 193, # '\x82'
- 131: 194, # '\x83'
- 132: 195, # '\x84'
- 133: 196, # '\x85'
- 134: 197, # '\x86'
- 135: 198, # '\x87'
- 136: 199, # '\x88'
- 137: 200, # '\x89'
- 138: 201, # '\x8a'
- 139: 202, # '\x8b'
- 140: 203, # '\x8c'
- 141: 204, # '\x8d'
- 142: 205, # '\x8e'
- 143: 206, # '\x8f'
- 144: 207, # '\x90'
- 145: 208, # '\x91'
- 146: 209, # '\x92'
- 147: 210, # '\x93'
- 148: 211, # '\x94'
- 149: 212, # '\x95'
- 150: 213, # '\x96'
- 151: 214, # '\x97'
- 152: 215, # '\x98'
- 153: 216, # '\x99'
- 154: 217, # '\x9a'
- 155: 218, # '\x9b'
- 156: 219, # '\x9c'
- 157: 220, # '\x9d'
- 158: 221, # '\x9e'
- 159: 222, # '\x9f'
- 160: 223, # '\xa0'
- 161: 224, # 'Ё'
- 162: 225, # 'Ђ'
- 163: 226, # 'Ѓ'
- 164: 227, # 'Є'
- 165: 228, # 'Ѕ'
- 166: 229, # 'І'
- 167: 230, # 'Ї'
- 168: 231, # 'Ј'
- 169: 232, # 'Љ'
- 170: 233, # 'Њ'
- 171: 234, # 'Ћ'
- 172: 235, # 'Ќ'
- 173: 236, # '\xad'
- 174: 237, # 'Ў'
- 175: 238, # 'Џ'
- 176: 37, # 'А'
- 177: 44, # 'Б'
- 178: 33, # 'В'
- 179: 46, # 'Г'
- 180: 41, # 'Д'
- 181: 48, # 'Е'
- 182: 56, # 'Ж'
- 183: 51, # 'З'
- 184: 42, # 'И'
- 185: 60, # 'Й'
- 186: 36, # 'К'
- 187: 49, # 'Л'
- 188: 38, # 'М'
- 189: 31, # 'Н'
- 190: 34, # 'О'
- 191: 35, # 'П'
- 192: 45, # 'Р'
- 193: 32, # 'С'
- 194: 40, # 'Т'
- 195: 52, # 'У'
- 196: 53, # 'Ф'
- 197: 55, # 'Х'
- 198: 58, # 'Ц'
- 199: 50, # 'Ч'
- 200: 57, # 'Ш'
- 201: 63, # 'Щ'
- 202: 70, # 'Ъ'
- 203: 62, # 'Ы'
- 204: 61, # 'Ь'
- 205: 47, # 'Э'
- 206: 59, # 'Ю'
- 207: 43, # 'Я'
- 208: 3, # 'а'
- 209: 21, # 'б'
- 210: 10, # 'в'
- 211: 19, # 'г'
- 212: 13, # 'д'
- 213: 2, # 'е'
- 214: 24, # 'ж'
- 215: 20, # 'з'
- 216: 4, # 'и'
- 217: 23, # 'й'
- 218: 11, # 'к'
- 219: 8, # 'л'
- 220: 12, # 'м'
- 221: 5, # 'н'
- 222: 1, # 'о'
- 223: 15, # 'п'
- 224: 9, # 'р'
- 225: 7, # 'с'
- 226: 6, # 'т'
- 227: 14, # 'у'
- 228: 39, # 'ф'
- 229: 26, # 'х'
- 230: 28, # 'ц'
- 231: 22, # 'ч'
- 232: 25, # 'ш'
- 233: 29, # 'щ'
- 234: 54, # 'ъ'
- 235: 18, # 'ы'
- 236: 17, # 'ь'
- 237: 30, # 'э'
- 238: 27, # 'ю'
- 239: 16, # 'я'
- 240: 239, # '№'
- 241: 68, # 'ё'
- 242: 240, # 'ђ'
- 243: 241, # 'ѓ'
- 244: 242, # 'є'
- 245: 243, # 'ѕ'
- 246: 244, # 'і'
- 247: 245, # 'ї'
- 248: 246, # 'ј'
- 249: 247, # 'љ'
- 250: 248, # 'њ'
- 251: 249, # 'ћ'
- 252: 250, # 'ќ'
- 253: 251, # '§'
- 254: 252, # 'ў'
- 255: 255, # 'џ'
-}
-
-ISO_8859_5_RUSSIAN_MODEL = SingleByteCharSetModel(
- charset_name="ISO-8859-5",
- language="Russian",
- char_to_order_map=ISO_8859_5_RUSSIAN_CHAR_TO_ORDER,
- language_model=RUSSIAN_LANG_MODEL,
- typical_positive_ratio=0.976601,
- keep_ascii_letters=False,
- alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё",
-)
diff --git a/spaces/Truepic/ai-content-credentials/static/script.js b/spaces/Truepic/ai-content-credentials/static/script.js
deleted file mode 100644
index 6d713b4773be0442a52de23751bf52d22c61e5c9..0000000000000000000000000000000000000000
--- a/spaces/Truepic/ai-content-credentials/static/script.js
+++ /dev/null
@@ -1,258 +0,0 @@
-const textGenForm = document.querySelector(".text-gen-form");
-const textGenInput = document.getElementById("text-gen-input");
-const model = document.getElementById("model");
-const textGenSubmit = document.getElementById("text-gen-submit");
-const spinner = document.getElementById("spinner");
-const placeholder = document.getElementById("placeholder");
-const downloadLink = document.getElementById("download-link");
-const verificationNav = document.getElementById("verification-nav");
-const certificateNav = document.getElementById("certificate-nav");
-const verification = document.querySelector(".verification");
-const verificationOutput = document.getElementById("verification-output");
-const certificateOutput = document.getElementById("certificate-output");
-const verificationDetails = document.querySelector(".verification-details");
-const parameters = document.querySelector(".parameters");
-const modelParam = document.querySelector(".parameters .model");
-const promptParam = document.querySelector(".parameters .prompt");
-const certificateList = document.getElementById("certificate-list");
-var certificates = [];
-
-[textGenInput, model].forEach((item) => {
- item.addEventListener("change", async (event) => {
- setButtonStatus();
- });
-});
-
-const setButtonStatus = () => {
- if (textGenInput.value && model.value) textGenSubmit.classList.add("active");
- else textGenSubmit.classList.remove("active");
-};
-
-const generateImage = async (text, model) => {
- const inferResponse = await fetch(`generate?prompt=${text}&model=${model}`);
- const inferJson = await inferResponse.json();
-
- return inferJson.response;
-};
-
-textGenForm.addEventListener("submit", async (event) => {
- event.preventDefault();
-
- if (!textGenInput.value || !model.value) return;
-
- verificationDetails.style.display = "none";
- parameters.style.display = "none";
- downloadLink.style.display = "none";
-
- try {
- if (placeholder) placeholder.remove();
- if (document.getElementById("result"))
- document.getElementById("result").remove();
- spinner.style.display = "block";
-
- const resp = await generateImage(textGenInput.value, model.value);
- const path = "/" + resp;
-
- var resultsContainer = document.getElementById("image-container");
-
- var truepicDisplay = document.createElement("truepic-display");
- truepicDisplay.addEventListener(
- "validate",
- setVerificationOutputFromValidation
- );
- truepicDisplay.addEventListener(
- "validate",
- setCertificateOutputFromValidation
- );
-
- truepicDisplay.setAttribute("id", "result");
- truepicDisplay.setAttribute("active", "");
- var truepic = document.createElement("img");
- truepic.src = path;
-
- truepicDisplay.appendChild(truepic);
-
- spinner.style.display = "none";
- resultsContainer.appendChild(truepicDisplay);
-
- downloadLink.style.display = "block";
- downloadLink.href = path;
- downloadLink.download = resp;
-
- modelParam.innerHTML = model.value;
- promptParam.innerHTML = textGenInput.value;
- parameters.style.display = "block";
- } catch (err) {
- console.error(err);
- }
-});
-
-function setVerificationOutputFromValidation(event) {
- verificationDetails.style.display = "block";
- return setVerificationOutput(event.detail.manifestStore.toJSON());
-}
-
-function setCertificateOutputFromValidation(event) {
- return setCertificateOutput(event.detail.manifestStore);
-}
-
-function setVerificationOutput(output = null) {
- verificationOutput.innerHTML = "";
-
- if (!output) {
- return;
- }
-
- const viewer = new JSONViewer();
-
- verificationOutput.appendChild(viewer.getContainer());
-
- viewer.showJSON(output);
-}
-
-function setCertificateOutput(manifestStore = null) {
- const certificate = manifestStore?.activeManifest?.certificate;
-
- if (!certificate) {
- return;
- }
-
- certificates = [
- {
- der: certificate.der,
- name: certificate.subjectName,
- decoded: new x509.X509Certificate(certificate.der),
- },
- ...certificate.chain.map((certificate) => ({
- der: certificate.der,
- decoded: new x509.X509Certificate(certificate.der),
- })),
- ];
-
- certificates.forEach((certificate) => {
- certificate.transformed = transformCert(certificate.decoded);
- });
-
- certificateList.innerHTML = "";
-
- certificates.forEach((certificate, index) => {
- var li = document.createElement("li");
- if (index == 0) li.classList.add("active");
- li.appendChild(
- document.createTextNode(certificate.transformed.subjectCommonName)
- );
- li.addEventListener("click", function (e) {
- setCertificate(index);
- const lis = document.querySelectorAll("#certificate-list li");
-
- lis.forEach((element) => {
- element.classList.remove("active");
- });
-
- this.classList.add("active");
- });
-
- certificateList.appendChild(li);
- });
-
- setCertificate(0);
-}
-
-function transformCert(certificate) {
- const {
- issuer,
- subject,
- notAfter: expired,
- notBefore: issued,
- serialNumber,
- publicKey: {
- algorithm: {
- name: algorithm,
- modulusLength: modulus,
- namedCurve: namedCurve,
- },
- },
- } = certificate;
-
- const parsedSubject = parseCertificateValues(subject);
- const parsedIssuer = parseCertificateValues(issuer);
-
- return {
- issuerCommonName: parsedIssuer["CN"],
- issuerOrganizationUnit: parsedIssuer["OU"],
- issuerOrganization: parsedIssuer["O"],
- issuerCountry: parsedIssuer["C"],
- subjectCommonName: parsedSubject["CN"],
- subjectOrganizationUnit: parsedSubject["OU"],
- subjectOrganization: parsedSubject["O"],
- subjectCountry: parsedSubject["C"],
- issued,
- expired,
- serialNumber,
- algorithm,
- modulus,
- namedCurve,
- };
-}
-
-verificationNav.addEventListener("click", (event) => {
- event.target.classList.add("active");
- certificateNav.classList.remove("active");
-
- verification.style.display = "block";
- certificateOutput.style.display = "none";
-});
-
-certificateNav.addEventListener("click", (event) => {
- event.target.classList.add("active");
- verificationNav.classList.remove("active");
-
- certificateOutput.style.display = "block";
- verification.style.display = "none";
-});
-
-function setCertificate(ind) {
- const certificate = certificates[ind].transformed;
-
- document.querySelector(".details .issuerCommonName").innerHTML =
- certificate.issuerCommonName;
- document.querySelector(".details .issuerOrganizationUnit").innerHTML =
- certificate.issuerOrganizationUnit;
- document.querySelector(".details .issuerOrganization").innerHTML =
- certificate.issuerOrganization;
- document.querySelector(".details .issuerCountry").innerHTML =
- certificate.issuerCountry;
- document.querySelector(".details .subjectCommonName").innerHTML =
- certificate.subjectCommonName;
- document.querySelector(".details .subjectOrganizationUnit").innerHTML =
- certificate.subjectOrganizationUnit;
- document.querySelector(".details .subjectOrganization").innerHTML =
- certificate.subjectOrganization;
- document.querySelector(".details .subjectCountry").innerHTML =
- certificate.subjectCountry;
- document.querySelector(".details .issued").innerHTML = certificate.issued;
- document.querySelector(".details .expired").innerHTML = certificate.expired;
- document.querySelector(".details .serialNumber").innerHTML =
- certificate.serialNumber;
- document.querySelector(".details .algorithm").innerHTML =
- certificate.algorithm;
-
- if (certificate.namedCurve !== undefined) {
- document.querySelector(".details .namedCurve").innerHTML =
- certificate.namedCurve;
- document.querySelector("#curveContainer").style.display = "block";
- } else {
- document.querySelector("#curveContainer").style.display = "none";
- }
-}
-
-function parseCertificateValues(input) {
- const params = new URLSearchParams(input.replaceAll(",", "&"));
- const responses = {};
-
- for (const entry of params.entries()) {
- responses[entry[0].trim()] = entry[1];
- }
-
- return responses;
-}
diff --git a/spaces/Truym/rvc-pendu/app.py b/spaces/Truym/rvc-pendu/app.py
deleted file mode 100644
index d1d4fb32cf4b9622530b9fdba4af2ffea3a48c79..0000000000000000000000000000000000000000
--- a/spaces/Truym/rvc-pendu/app.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import os
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import (
- is_half,
- device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
- def vc_fn(
- input_audio,
- f0_up_key,
- f0_method,
- index_rate,
- tts_mode,
- tts_text,
- tts_voice
- ):
- try:
- if tts_mode:
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- else:
- if args.files:
- audio, sr = librosa.load(input_audio, sr=16000, mono=True)
- else:
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- if duration > 20 and limitation:
- return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- )
- print(
- f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- )
- return "Success", (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, (None, None)
- return vc_fn
-
-def load_hubert():
- global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
- if tts_mode:
- return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
- else:
- return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--api', action="store_true", default=False)
- parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
- parser.add_argument("--files", action="store_true", default=False, help="load audio from path")
- args, unknown = parser.parse_known_args()
- load_hubert()
- models = []
- tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with open("weights/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for name, info in models_info.items():
- if not info['enable']:
- continue
- title = info['title']
- author = info.get("author", None)
- cover = f"weights/{name}/{info['cover']}"
- index = f"weights/{name}/{info['feature_retrieval_library']}"
- npy = f"weights/{name}/{info['feature_file']}"
- cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
- net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, device, is_half)
- models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
- with gr.Blocks() as app:
- gr.Markdown(
- "# RVC Models\n"
- "## The input audio should be clean and pure voice without background music.\n"
- "\n\n"
- "[](https://colab.research.google.com/drive/12rbZk9CoXD1m84dqBW5IKMBjiVY6tcoj?usp=share_link)\n\n"
- "[](https://huggingface.co/spaces/ardha27pi/rvc-models?duplicate=true)\n\n"
- "[](https://github.com/ardha27/AI-Song-Cover-RVC)\n\n"
- "[](https://ko-fi.com/R6R7AH1FA)\n\n"
- )
- with gr.Tabs():
- for (name, title, author, cover, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- ''
- f'
{title}
\n'+
- (f'
Model author: {author}
' if author else "")+
- (f'
' if cover else "")+
- '
0 records found ")
- pn.state.onload(self.onload)
-
- def onload(self):
- download_hf("ahuang11/tiger_layer_edges")
- self.name_input.param.trigger("value")
-
- range_xy = RangeXY()
- line_strings = gv.DynamicMap(
- self.refresh_line_strings, streams=[range_xy]
- ).opts(responsive=True)
- range_xy.source = line_strings
-
- points = gv.DynamicMap(
- pn.bind(self.refresh_points, self.tabulator.param.selection)
- ).opts(responsive=True)
-
- self.holoviews_pane.object *= line_strings * points
-
- def serialize_geom(self, df):
- df["geometry"] = df["geometry"].apply(wkt.loads)
- gdf = gpd.GeoDataFrame(df)
- centroids = gdf["geometry"].centroid
- gdf["Longitude"] = centroids.x
- gdf["Latitude"] = centroids.y
- return gdf
-
- def process_name(self, name):
- try:
- name = name.strip()
- self.holoviews_pane.loading = True
- query_fmt = QUERY_FMT
- if "*" in name or "%" in name:
- name = name.replace("*", "%")
- query_fmt = query_fmt.replace("==", "LIKE")
- if name == "%":
- return
- df = fa.as_pandas(
- fa.fugue_sql(
- query_fmt,
- data_path=str(DATA_PATH.absolute()),
- name=name,
- engine="duckdb",
- as_local=True,
- )
- )
- self.gdf = self.serialize_geom(df)
- county_gdf = self.gdf.drop_duplicates(
- subset=["STATEFP", "COUNTYFP", "FULLNAME"]
- )
- self.records_text.value = f"{len(county_gdf)} records found "
- self.tabulator.value = (
- county_gdf["FULLNAME"]
- .value_counts()
- .rename_axis("Name")
- .rename("Count")
- .to_frame()
- )
- self.refresh_line_strings()
- finally:
- self.holoviews_pane.loading = False
-
- def refresh_line_strings(self, x_range=None, y_range=None):
- line_strings = gv.Polygons(
- self.gdf[["geometry"]],
- crs=ccrs.PlateCarree(),
- ).opts(fill_alpha=0, line_color="white", line_width=8, alpha=0.6)
- return line_strings.select(x=x_range, y=y_range)
-
- def refresh_points(self, selection):
- gdf_selection = self.gdf[
- ["Longitude", "Latitude", "STATEFP", "COUNTYFP", "FULLNAME"]
- ]
- if self.tabulator.selection:
- names = self.tabulator.value.iloc[selection].index.tolist()
- gdf_selection = gdf_selection.loc[gdf_selection["FULLNAME"].isin(names)]
- points = gv.Points(
- gdf_selection,
- kdims=["Longitude", "Latitude"],
- vdims=["STATEFP", "COUNTYFP", "FULLNAME"],
- crs=ccrs.PlateCarree(),
- ).opts(marker="x", tools=["hover"], color="#FF4136", size=8)
- return points
-
- def view(self):
- template = pn.template.FastListTemplate(
- header=[pn.Row(self.name_input, self.records_text)],
- sidebar=[INTRO, self.tabulator],
- main=[
- self.holoviews_pane,
- ],
- theme="dark",
- title="MapnStreets",
- sidebar_width=225,
- )
- return template.servable()
-
-
-mapn_streets = MapnStreets()
-mapn_streets.view()
\ No newline at end of file
diff --git a/spaces/akhaliq/BlendGAN/op/upfirdn2d.cpp b/spaces/akhaliq/BlendGAN/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/BlendGAN/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
- CHECK_CUDA(input);
- CHECK_CUDA(kernel);
-
- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/akhaliq/SummerTime/model/multi_doc/multi_doc_separate_model.py b/spaces/akhaliq/SummerTime/model/multi_doc/multi_doc_separate_model.py
deleted file mode 100644
index 5eab2288cf9b44580726360c9989b9c0214ab4c1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/multi_doc/multi_doc_separate_model.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from .base_multi_doc_model import MultiDocSummModel
-from model.base_model import SummModel
-from model.single_doc import TextRankModel
-from typing import Union, List
-
-
-class MultiDocSeparateModel(MultiDocSummModel):
-
- model_name = "Multi-document separate"
- is_multi_document = True
-
- def __init__(self, model_backend: SummModel = TextRankModel, **kwargs):
- super(MultiDocSeparateModel, self).__init__()
- model = model_backend(**kwargs)
- self.model = model
-
- def summarize(
- self,
- corpus: Union[List[str], List[List[str]]],
- query: Union[List[str], List[List[str]]] = None,
- ) -> List[str]:
- self.assert_summ_input_type(corpus, None)
- summaries = []
- for instance in corpus:
- instance_summaries = self.model.summarize(instance)
- summaries.append(" ".join(instance_summaries))
-
- return summaries
-
- @classmethod
- def generate_basic_description(cls) -> str:
- basic_description = (
- "MultiDocSeparateModel performs multi-document summarization by"
- " first performing single-document summarization on each document,"
- " and then concatenating the results."
- )
- return basic_description
-
- @classmethod
- def show_capability(cls):
- basic_description = cls.generate_basic_description()
- more_details = (
- "A multi-document summarization model."
- " Allows for custom model backend selection at initialization."
- " Performs single-document summarization on each document in corpus and returns concatenated result.\n"
- "Strengths: \n - Allows for control of backend model.\n"
- "Weaknesses: \n - Assumes all documents are equally weighted.\n - May produce redundant information for similar documents.\n"
- )
- print(f"{basic_description}\n{'#' * 20}\n{more_details}")
diff --git a/spaces/akhaliq/bizarre-pose-estimator/README.md b/spaces/akhaliq/bizarre-pose-estimator/README.md
deleted file mode 100644
index 82f0bac7bcbd2c31c8ef1eb4d14da6f693af0372..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/bizarre-pose-estimator/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Bizarre Pose Estimator
-emoji: 📚
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/lama/saicinpainting/training/visualizers/__init__.py b/spaces/akhaliq/lama/saicinpainting/training/visualizers/__init__.py
deleted file mode 100644
index 4770d1f15a6790ab9606c7b9881f798c8e2d9545..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/saicinpainting/training/visualizers/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import logging
-
-from saicinpainting.training.visualizers.directory import DirectoryVisualizer
-from saicinpainting.training.visualizers.noop import NoopVisualizer
-
-
-def make_visualizer(kind, **kwargs):
- logging.info(f'Make visualizer {kind}')
-
- if kind == 'directory':
- return DirectoryVisualizer(**kwargs)
- if kind == 'noop':
- return NoopVisualizer()
-
- raise ValueError(f'Unknown visualizer kind {kind}')
diff --git a/spaces/alex-mindspace/gpt-agents/swarmai/utils/memory/DictSharedMemory.py b/spaces/alex-mindspace/gpt-agents/swarmai/utils/memory/DictSharedMemory.py
deleted file mode 100644
index ce8874f14900be885c552418dcf9ac5da3d66904..0000000000000000000000000000000000000000
--- a/spaces/alex-mindspace/gpt-agents/swarmai/utils/memory/DictSharedMemory.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import os
-import threading
-import json
-import uuid
-from pathlib import Path
-import datetime
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib
-matplotlib.use('Agg') # need a different backend for multithreading
-import numpy as np
-
-class DictSharedMemory():
- """The simplest most stupid shared memory implementation that uses json to store the entries.
- """
-
- def __init__(self, file_loc=None):
- """Initialize the shared memory. In the current architecture the memory always consists of a set of soltuions or evaluations.
- Moreover, the project is designed around LLMs for the proof of concepts, so we treat all entry content as a string.
- """
- if file_loc is not None:
- self.file_loc = Path(file_loc)
- if not self.file_loc.exists():
- self.file_loc.touch()
-
- self.lock = threading.Lock()
-
- def add_entry(self, score, agent_id, agent_cycle, entry):
- """Add an entry to the internal memory.
- """
- with self.lock:
- entry_id = str(uuid.uuid4())
- data = {}
- epoch = datetime.datetime.utcfromtimestamp(0)
- epoch = (datetime.datetime.utcnow() - epoch).total_seconds()
- data[entry_id] = {"agent":agent_id, "epoch": epoch, "score": score, "cycle": agent_cycle, "content": entry}
- status = self.write_to_file(data)
- self.plot_performance()
- return status
-
- def get_top_n(self, n):
- """Get the top n entries from the internal memory.
- """
- raise NotImplementedError
-
- def write_to_file(self, data):
- """Write the internal memory to a file.
- """
- if self.file_loc is not None:
- with open(self.file_loc, "r") as f:
- try:
- file_data = json.load(f)
- except:
- file_data = {}
-
- file_data = file_data | data
- with open(self.file_loc, "w") as f:
- json.dump(file_data, f, indent=4)
-
- f.flush()
- os.fsync(f.fileno())
-
-
- return True
-
- def plot_performance(self):
- """Plot the performance of the swarm.
- TODO: move it to the logger
- """
- with open(self.file_loc, "r") as f:
- shared_memory = json.load(f)
- # f.flush()
- # os.fsync(f.fileno())
-
- df = pd.DataFrame.from_dict(shared_memory, orient="index")
- df["agent"] = df["agent"].astype(int)
- df["epoch"] = df["epoch"].astype(float)
- df["score"] = df["score"].astype(float)
- df["cycle"] = df["cycle"].astype(int)
- df["content"] = df["content"].astype(str)
-
- fig = plt.figure(figsize=(20, 5))
- df = df.sort_values(by="epoch")
- df = df.sort_values(by="epoch")
-
- x = df["epoch"].values - df["epoch"].min()
- y = df["score"].values
-
- # apply moving average
- if len(y) < 20:
- window_size = len(y)
- else:
- window_size = len(y)//10
- try:
- y_padded = np.pad(y, (window_size//2, window_size//2), mode="reflect")
- y_ma = np.convolve(y_padded, np.ones(window_size)/window_size, mode="same")
- y_ma = y_ma[window_size//2:-window_size//2]
-
- #moving max
- y_max_t = [np.max(y[:i]) for i in range(1, len(y)+1)]
-
- plt.plot(x, y_ma, label="Average score of recently submitted solutions")
- plt.plot(x, y_max_t, label="Best at time t")
- plt.plot()
- plt.ylim([0, 1.02])
- plt.xlabel("Time (s)")
- plt.ylabel("Score")
- plt.legend()
- plt.title("Average score of recently submitted solutions")
- plt.tight_layout()
- plt.savefig(self.file_loc.parent / "performance.png")
- except:
- pass
-
- plt.close(fig)
diff --git a/spaces/aliabid94/reverse_audio/run.py b/spaces/aliabid94/reverse_audio/run.py
deleted file mode 100644
index 1ea48063d32d5fa6f37af327788594edb6821674..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/reverse_audio/run.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-import numpy as np
-import gradio as gr
-
-import subprocess
-def get_ffmpeg_version():
- output = subprocess.check_output(['ffmpeg', '-version'], stderr=subprocess.STDOUT)
- output = output.decode('utf-8') # Convert bytes to string
- return output
-print(get_ffmpeg_version())
-
-
-
-def handle_audio(audio):
- sr, y = audio
- return sr, y.shape, audio
-
-with gr.Blocks() as demo:
- with gr.Column(variant="panel"):
- a1 = gr.Audio(source="microphone", type="numpy")
- up1 = gr.Button()
- with gr.Row():
- sr1 = gr.Textbox(label="sr")
- len1 = gr.Textbox(label="len")
- a1out = gr.Audio()
- up1.click(handle_audio, a1, [sr1, len1, a1out])
-
- with gr.Column(variant="panel"):
- a2 = gr.Audio(source="upload", type="numpy")
- up2 = gr.Button()
- with gr.Row():
- sr2 = gr.Textbox(label="sr")
- len2 = gr.Textbox(label="len")
- a2out = gr.Audio()
- up2.click(handle_audio, a2, [sr2, len2, a2out])
-
-if __name__ == "__main__":
- demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/amagastya/SPARK/notebooks/chainlit_pinecone_demo.py b/spaces/amagastya/SPARK/notebooks/chainlit_pinecone_demo.py
deleted file mode 100644
index 393f06590f221b16d25b26eb8611897c1dcaba42..0000000000000000000000000000000000000000
--- a/spaces/amagastya/SPARK/notebooks/chainlit_pinecone_demo.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-from langchain.embeddings.cohere import CohereEmbeddings
-from langchain.vectorstores import Pinecone
-from langchain.chains import RetrievalQAWithSourcesChain
-from langchain.chat_models import ChatOpenAI
-import pinecone
-import chainlit as cl
-
-pinecone.init(
- api_key=os.environ.get("PINECONE_API_KEY"),
- environment=os.environ.get("PINECONE_ENV"),
-)
-
-
-index_name = "spark"
-
-# Optional
-namespace = None
-
-embeddings = CohereEmbeddings(model='embed-english-light-v2.0',cohere_api_key=os.environ.get("COHERE_API_KEY"))
-
-welcome_message = "Welcome to the Chainlit Pinecone demo! Ask anything about documents you vectorized and stored in your Pinecone DB."
-
-
-@cl.langchain_factory(use_async=True)
-async def langchain_factory():
- await cl.Message(content=welcome_message).send()
- docsearch = Pinecone.from_existing_index(
- index_name=index_name, embedding=embeddings, namespace=namespace
- )
-
- chain = RetrievalQAWithSourcesChain.from_chain_type(
- ChatOpenAI(temperature=0, streaming=True, verbose=True),
- chain_type="stuff",
- retriever=docsearch.as_retriever(max_tokens_limit=4097),
- return_source_documents=True,
- verbose=True
- )
- return chain
-
-
-@cl.langchain_postprocess
-async def process_response(res):
- answer = res["answer"]
- sources = res.get("sources", "").strip() # Use the get method with a default value
- source_elements = []
- docs = res.get("source_documents", None)
-
- print('sources', sources)
- if docs:
- metadatas = [doc.metadata for doc in docs]
- # Get the source names from the metadata
- all_sources = [m["source"] for m in metadatas]
-
- if sources:
- found_sources = []
- # For each source mentioned by the LLM
- for source_index, source in enumerate(sources.split(",")):
- # Remove the period and any whitespace
- orig_source_name = source.strip().replace(".", "")
- # The name that will be displayed in the UI
- clean_source_name = f"source {source_index}"
- try:
- # Find the mentioned source in the list of all sources
- found_index = all_sources.index(orig_source_name)
- except ValueError:
- continue
- # Get the text from the source document
- text = docs[found_index].page_content
-
- found_sources.append(clean_source_name)
- source_elements.append(cl.Text(content=text, name=clean_source_name))
-
- if found_sources:
- # Add the sources to the answer, referencing the text elements
- answer += f"\nSources: {', '.join(found_sources)}"
- else:
- answer += "\nNo sources found"
-
- # Send the answer and the text elements to the UI
- await cl.Message(content=answer, elements=source_elements).send()
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_converters.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_converters.c
deleted file mode 100644
index f76738c381a2666e5014cb7b49d656acbd0b4c2a..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_converters.c
+++ /dev/null
@@ -1,395 +0,0 @@
-/** @file patest_converters.c
- @ingroup test_src
- @brief Tests the converter functions in pa_converters.c
- @author Ross Bencina
-
- Link with pa_dither.c and pa_converters.c
-
- see http://www.portaudio.com/trac/wiki/V19ConvertersStatus for a discussion of this.
-*/
-/*
- * $Id: $
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com/
- * Copyright (c) 1999-2008 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-#include
-#include
-#include
-#include
-
-#include "portaudio.h"
-#include "pa_converters.h"
-#include "pa_dither.h"
-#include "pa_types.h"
-#include "pa_endianness.h"
-
-#ifndef M_PI
-#define M_PI (3.14159265)
-#endif
-
-#define MAX_PER_CHANNEL_FRAME_COUNT (2048)
-#define MAX_CHANNEL_COUNT (8)
-
-
-#define SAMPLE_FORMAT_COUNT (6)
-
-static PaSampleFormat sampleFormats_[ SAMPLE_FORMAT_COUNT ] =
- { paFloat32, paInt32, paInt24, paInt16, paInt8, paUInt8 }; /* all standard PA sample formats */
-
-static const char* sampleFormatNames_[SAMPLE_FORMAT_COUNT] =
- { "paFloat32", "paInt32", "paInt24", "paInt16", "paInt8", "paUInt8" };
-
-
-static const char* abbreviatedSampleFormatNames_[SAMPLE_FORMAT_COUNT] =
- { "f32", "i32", "i24", "i16", " i8", "ui8" };
-
-
-PaError My_Pa_GetSampleSize( PaSampleFormat format );
-
-/*
- available flags are paClipOff and paDitherOff
- clipping is usually applied for float -> int conversions
- dither is usually applied for all downconversions (ie anything but 8bit->8bit conversions
-*/
-
-static int CanClip( PaSampleFormat sourceFormat, PaSampleFormat destinationFormat )
-{
- if( sourceFormat == paFloat32 && destinationFormat != sourceFormat )
- return 1;
- else
- return 0;
-}
-
-static int CanDither( PaSampleFormat sourceFormat, PaSampleFormat destinationFormat )
-{
- if( sourceFormat < destinationFormat && sourceFormat != paInt8 )
- return 1;
- else
- return 0;
-}
-
-static void GenerateOneCycleSineReference( double *out, int frameCount, int strideFrames )
-{
- int i;
- for( i=0; i < frameCount; ++i ){
- *out = sin( ((double)i/(double)frameCount) * 2. * M_PI );
- out += strideFrames;
- }
-}
-
-
-static void GenerateOneCycleSine( PaSampleFormat format, void *buffer, int frameCount, int strideFrames )
-{
- switch( format ){
-
- case paFloat32:
- {
- int i;
- float *out = (float*)buffer;
- for( i=0; i < frameCount; ++i ){
- *out = (float).9 * sin( ((double)i/(double)frameCount) * 2. * M_PI );
- out += strideFrames;
- }
- }
- break;
- case paInt32:
- {
- int i;
- PaInt32 *out = (PaInt32*)buffer;
- for( i=0; i < frameCount; ++i ){
- *out = (PaInt32)(.9 * sin( ((double)i/(double)frameCount) * 2. * M_PI ) * 0x7FFFFFFF);
- out += strideFrames;
- }
- }
- break;
- case paInt24:
- {
- int i;
- unsigned char *out = (unsigned char*)buffer;
- for( i=0; i < frameCount; ++i ){
- signed long temp = (PaInt32)(.9 * sin( ((double)i/(double)frameCount) * 2. * M_PI ) * 0x7FFFFFFF);
-
- #if defined(PA_LITTLE_ENDIAN)
- out[0] = (unsigned char)(temp >> 8) & 0xFF;
- out[1] = (unsigned char)(temp >> 16) & 0xFF;
- out[2] = (unsigned char)(temp >> 24) & 0xFF;
- #elif defined(PA_BIG_ENDIAN)
- out[0] = (unsigned char)(temp >> 24) & 0xFF;
- out[1] = (unsigned char)(temp >> 16) & 0xFF;
- out[2] = (unsigned char)(temp >> 8) & 0xFF;
- #endif
- out += 3;
- }
- }
- break;
- case paInt16:
- {
- int i;
- PaInt16 *out = (PaInt16*)buffer;
- for( i=0; i < frameCount; ++i ){
- *out = (PaInt16)(.9 * sin( ((double)i/(double)frameCount) * 2. * M_PI ) * 0x7FFF );
- out += strideFrames;
- }
- }
- break;
- case paInt8:
- {
- int i;
- signed char *out = (signed char*)buffer;
- for( i=0; i < frameCount; ++i ){
- *out = (signed char)(.9 * sin( ((double)i/(double)frameCount) * 2. * M_PI ) * 0x7F );
- out += strideFrames;
- }
- }
- break;
- case paUInt8:
- {
- int i;
- unsigned char *out = (unsigned char*)buffer;
- for( i=0; i < frameCount; ++i ){
- *out = (unsigned char)( .5 * (1. + (.9 * sin( ((double)i/(double)frameCount) * 2. * M_PI ))) * 0xFF );
- out += strideFrames;
- }
- }
- break;
- }
-}
-
-int TestNonZeroPresent( void *buffer, int size )
-{
- char *p = (char*)buffer;
- int i;
-
- for( i=0; i < size; ++i ){
-
- if( *p != 0 )
- return 1;
- ++p;
- }
-
- return 0;
-}
-
-float MaximumAbsDifference( float* sourceBuffer, float* referenceBuffer, int count )
-{
- float result = 0;
- float difference;
- while( count-- ){
- difference = fabs( *sourceBuffer++ - *referenceBuffer++ );
- if( difference > result )
- result = difference;
- }
-
- return result;
-}
-
-int main( const char **argv, int argc )
-{
- PaUtilTriangularDitherGenerator ditherState;
- PaUtilConverter *converter;
- void *destinationBuffer, *sourceBuffer;
- double *referenceBuffer;
- int sourceFormatIndex, destinationFormatIndex;
- PaSampleFormat sourceFormat, destinationFormat;
- PaStreamFlags flags;
- int passFailMatrix[SAMPLE_FORMAT_COUNT][SAMPLE_FORMAT_COUNT]; // [source][destination]
- float noiseAmplitudeMatrix[SAMPLE_FORMAT_COUNT][SAMPLE_FORMAT_COUNT]; // [source][destination]
- float amp;
-
-#define FLAG_COMBINATION_COUNT (4)
- PaStreamFlags flagCombinations[FLAG_COMBINATION_COUNT] = { paNoFlag, paClipOff, paDitherOff, paClipOff | paDitherOff };
- const char *flagCombinationNames[FLAG_COMBINATION_COUNT] = { "paNoFlag", "paClipOff", "paDitherOff", "paClipOff | paDitherOff" };
- int flagCombinationIndex;
-
- PaUtil_InitializeTriangularDitherState( &ditherState );
-
- /* allocate more than enough space, we use sizeof(float) but we need to fit any 32 bit datum */
-
- destinationBuffer = (void*)malloc( MAX_PER_CHANNEL_FRAME_COUNT * MAX_CHANNEL_COUNT * sizeof(float) );
- sourceBuffer = (void*)malloc( MAX_PER_CHANNEL_FRAME_COUNT * MAX_CHANNEL_COUNT * sizeof(float) );
- referenceBuffer = (void*)malloc( MAX_PER_CHANNEL_FRAME_COUNT * MAX_CHANNEL_COUNT * sizeof(float) );
-
-
- /* the first round of tests simply iterates through the buffer combinations testing
- that putting something in gives something out */
-
- printf( "= Sine wave in, something out =\n" );
-
- printf( "\n" );
-
- GenerateOneCycleSine( paFloat32, referenceBuffer, MAX_PER_CHANNEL_FRAME_COUNT, 1 );
-
- for( flagCombinationIndex = 0; flagCombinationIndex < FLAG_COMBINATION_COUNT; ++flagCombinationIndex ){
- flags = flagCombinations[flagCombinationIndex];
-
- printf( "\n" );
- printf( "== flags = %s ==\n", flagCombinationNames[flagCombinationIndex] );
-
- for( sourceFormatIndex = 0; sourceFormatIndex < SAMPLE_FORMAT_COUNT; ++sourceFormatIndex ){
- for( destinationFormatIndex = 0; destinationFormatIndex < SAMPLE_FORMAT_COUNT; ++destinationFormatIndex ){
- sourceFormat = sampleFormats_[sourceFormatIndex];
- destinationFormat = sampleFormats_[destinationFormatIndex];
- //printf( "%s -> %s ", sampleFormatNames_[ sourceFormatIndex ], sampleFormatNames_[ destinationFormatIndex ] );
-
- converter = PaUtil_SelectConverter( sourceFormat, destinationFormat, flags );
-
- /* source is a sinewave */
- GenerateOneCycleSine( sourceFormat, sourceBuffer, MAX_PER_CHANNEL_FRAME_COUNT, 1 );
-
- /* zero destination */
- memset( destinationBuffer, 0, MAX_PER_CHANNEL_FRAME_COUNT * My_Pa_GetSampleSize( destinationFormat ) );
-
- (*converter)( destinationBuffer, 1, sourceBuffer, 1, MAX_PER_CHANNEL_FRAME_COUNT, &ditherState );
-
- /*
- Other ways we could test this would be:
- - pass a constant, check for a constant (wouldn't work with dither)
- - pass alternating +/-, check for the same...
- */
- if( TestNonZeroPresent( destinationBuffer, MAX_PER_CHANNEL_FRAME_COUNT * My_Pa_GetSampleSize( destinationFormat ) ) ){
- //printf( "PASSED\n" );
- passFailMatrix[sourceFormatIndex][destinationFormatIndex] = 1;
- }else{
- //printf( "FAILED\n" );
- passFailMatrix[sourceFormatIndex][destinationFormatIndex] = 0;
- }
-
-
- /* try to measure the noise floor (comparing output signal to a float32 sine wave) */
-
- if( passFailMatrix[sourceFormatIndex][destinationFormatIndex] ){
-
- /* convert destination back to paFloat32 into source */
- converter = PaUtil_SelectConverter( destinationFormat, paFloat32, paNoFlag );
-
- memset( sourceBuffer, 0, MAX_PER_CHANNEL_FRAME_COUNT * My_Pa_GetSampleSize( paFloat32 ) );
- (*converter)( sourceBuffer, 1, destinationBuffer, 1, MAX_PER_CHANNEL_FRAME_COUNT, &ditherState );
-
- if( TestNonZeroPresent( sourceBuffer, MAX_PER_CHANNEL_FRAME_COUNT * My_Pa_GetSampleSize( paFloat32 ) ) ){
-
- noiseAmplitudeMatrix[sourceFormatIndex][destinationFormatIndex] = MaximumAbsDifference( (float*)sourceBuffer, (float*)referenceBuffer, MAX_PER_CHANNEL_FRAME_COUNT );
-
- }else{
- /* can't test noise floor because there is no conversion from dest format to float available */
- noiseAmplitudeMatrix[sourceFormatIndex][destinationFormatIndex] = -1; // mark as failed
- }
- }else{
- noiseAmplitudeMatrix[sourceFormatIndex][destinationFormatIndex] = -1; // mark as failed
- }
- }
- }
-
- printf( "\n" );
- printf( "=== Output contains non-zero data ===\n" );
- printf( "Key: . - pass, X - fail\n" );
- printf( "{{{\n" ); // trac preformated text tag
- printf( "in| out: " );
- for( destinationFormatIndex = 0; destinationFormatIndex < SAMPLE_FORMAT_COUNT; ++destinationFormatIndex ){
- printf( " %s ", abbreviatedSampleFormatNames_[destinationFormatIndex] );
- }
- printf( "\n" );
-
- for( sourceFormatIndex = 0; sourceFormatIndex < SAMPLE_FORMAT_COUNT; ++sourceFormatIndex ){
- printf( "%s ", abbreviatedSampleFormatNames_[sourceFormatIndex] );
- for( destinationFormatIndex = 0; destinationFormatIndex < SAMPLE_FORMAT_COUNT; ++destinationFormatIndex ){
- printf( " %s ", (passFailMatrix[sourceFormatIndex][destinationFormatIndex])? " ." : " X" );
- }
- printf( "\n" );
- }
- printf( "}}}\n" ); // trac preformated text tag
-
- printf( "\n" );
- printf( "=== Combined dynamic range (src->dest->float32) ===\n" );
- printf( "Key: Noise amplitude in dBfs, X - fail (either above failed or dest->float32 failed)\n" );
- printf( "{{{\n" ); // trac preformated text tag
- printf( "in| out: " );
- for( destinationFormatIndex = 0; destinationFormatIndex < SAMPLE_FORMAT_COUNT; ++destinationFormatIndex ){
- printf( " %s ", abbreviatedSampleFormatNames_[destinationFormatIndex] );
- }
- printf( "\n" );
-
- for( sourceFormatIndex = 0; sourceFormatIndex < SAMPLE_FORMAT_COUNT; ++sourceFormatIndex ){
- printf( " %s ", abbreviatedSampleFormatNames_[sourceFormatIndex] );
- for( destinationFormatIndex = 0; destinationFormatIndex < SAMPLE_FORMAT_COUNT; ++destinationFormatIndex ){
- amp = noiseAmplitudeMatrix[sourceFormatIndex][destinationFormatIndex];
- if( amp < 0. )
- printf( " X " );
- else
- printf( " % 6.1f ", 20.*log10(amp) );
- }
- printf( "\n" );
- }
- printf( "}}}\n" ); // trac preformated text tag
- }
-
-
- free( destinationBuffer );
- free( sourceBuffer );
- free( referenceBuffer );
-}
-
-// copied here for now otherwise we need to include the world just for this function.
-PaError My_Pa_GetSampleSize( PaSampleFormat format )
-{
- int result;
-
- switch( format & ~paNonInterleaved )
- {
-
- case paUInt8:
- case paInt8:
- result = 1;
- break;
-
- case paInt16:
- result = 2;
- break;
-
- case paInt24:
- result = 3;
- break;
-
- case paFloat32:
- case paInt32:
- result = 4;
- break;
-
- default:
- result = paSampleFormatNotSupported;
- break;
- }
-
- return (PaError) result;
-}
diff --git a/spaces/anaclaudia13ct/insect_detection/models/common.py b/spaces/anaclaudia13ct/insect_detection/models/common.py
deleted file mode 100644
index 8b5ec1c786d8efbfdffa268a4d13b02a47338f8c..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/models/common.py
+++ /dev/null
@@ -1,860 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Common modules
-"""
-
-import ast
-import contextlib
-import json
-import math
-import platform
-import warnings
-import zipfile
-from collections import OrderedDict, namedtuple
-from copy import copy
-from pathlib import Path
-from urllib.parse import urlparse
-
-import cv2
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-from IPython.display import display
-from PIL import Image
-from torch.cuda import amp
-
-from utils import TryExcept
-from utils.dataloaders import exif_transpose, letterbox
-from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
- increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
- xyxy2xywh, yaml_load)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import copy_attr, smart_inference_mode
-
-
-def autopad(k, p=None, d=1): # kernel, padding, dilation
- # Pad to 'same' shape outputs
- if d > 1:
- k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
- return p
-
-
-class Conv(nn.Module):
- # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
- default_act = nn.SiLU() # default activation
-
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
- super().__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
-
- def forward(self, x):
- return self.act(self.bn(self.conv(x)))
-
- def forward_fuse(self, x):
- return self.act(self.conv(x))
-
-
-class DWConv(Conv):
- # Depth-wise convolution
- def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation
- super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
-
-
-class DWConvTranspose2d(nn.ConvTranspose2d):
- # Depth-wise transpose convolution
- def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out
- super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
-
-
-class TransformerLayer(nn.Module):
- # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
- def __init__(self, c, num_heads):
- super().__init__()
- self.q = nn.Linear(c, c, bias=False)
- self.k = nn.Linear(c, c, bias=False)
- self.v = nn.Linear(c, c, bias=False)
- self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
- self.fc1 = nn.Linear(c, c, bias=False)
- self.fc2 = nn.Linear(c, c, bias=False)
-
- def forward(self, x):
- x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
- x = self.fc2(self.fc1(x)) + x
- return x
-
-
-class TransformerBlock(nn.Module):
- # Vision Transformer https://arxiv.org/abs/2010.11929
- def __init__(self, c1, c2, num_heads, num_layers):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
- self.linear = nn.Linear(c2, c2) # learnable position embedding
- self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
- self.c2 = c2
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- b, _, w, h = x.shape
- p = x.flatten(2).permute(2, 0, 1)
- return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
-
-
-class Bottleneck(nn.Module):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2, 3, 1, g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
- self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
- self.act = nn.SiLU()
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
-
-
-class CrossConv(nn.Module):
- # Cross Convolution Downsample
- def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
- # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, (1, k), (1, s))
- self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class C3(nn.Module):
- # CSP Bottleneck with 3 convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
- def forward(self, x):
- return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
-
-
-class C3x(C3):
- # C3 module with cross-convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e)
- self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
-
-
-class C3TR(C3):
- # C3 module with TransformerBlock()
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e)
- self.m = TransformerBlock(c_, c_, 4, n)
-
-
-class C3SPP(C3):
- # C3 module with SPP()
- def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e)
- self.m = SPP(c_, c_, k)
-
-
-class C3Ghost(C3):
- # C3 module with GhostBottleneck()
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
-
-
-class SPP(nn.Module):
- # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
- def __init__(self, c1, c2, k=(5, 9, 13)):
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
- def forward(self, x):
- x = self.cv1(x)
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
- return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class SPPF(nn.Module):
- # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
- def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * 4, c2, 1, 1)
- self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
- def forward(self, x):
- x = self.cv1(x)
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
- y1 = self.m(x)
- y2 = self.m(y1)
- return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
-
-
-class Focus(nn.Module):
- # Focus wh information into c-space
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
- # self.contract = Contract(gain=2)
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
- # return self.conv(self.contract(x))
-
-
-class GhostConv(nn.Module):
- # Ghost Convolution https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
- super().__init__()
- c_ = c2 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
- self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
-
- def forward(self, x):
- y = self.cv1(x)
- return torch.cat((y, self.cv2(y)), 1)
-
-
-class GhostBottleneck(nn.Module):
- # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
- super().__init__()
- c_ = c2 // 2
- self.conv = nn.Sequential(
- GhostConv(c1, c_, 1, 1), # pw
- DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
- GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
- self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
- act=False)) if s == 2 else nn.Identity()
-
- def forward(self, x):
- return self.conv(x) + self.shortcut(x)
-
-
-class Contract(nn.Module):
- # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
- s = self.gain
- x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
- x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
- return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
-
-
-class Expand(nn.Module):
- # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
- s = self.gain
- x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
- x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
- return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
-
-
-class Concat(nn.Module):
- # Concatenate a list of tensors along dimension
- def __init__(self, dimension=1):
- super().__init__()
- self.d = dimension
-
- def forward(self, x):
- return torch.cat(x, self.d)
-
-
-class DetectMultiBackend(nn.Module):
- # YOLOv5 MultiBackend class for python inference on various backends
- def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
- # Usage:
- # PyTorch: weights = *.pt
- # TorchScript: *.torchscript
- # ONNX Runtime: *.onnx
- # ONNX OpenCV DNN: *.onnx --dnn
- # OpenVINO: *_openvino_model
- # CoreML: *.mlmodel
- # TensorRT: *.engine
- # TensorFlow SavedModel: *_saved_model
- # TensorFlow GraphDef: *.pb
- # TensorFlow Lite: *.tflite
- # TensorFlow Edge TPU: *_edgetpu.tflite
- # PaddlePaddle: *_paddle_model
- from models.experimental import attempt_download, attempt_load # scoped to avoid circular import
-
- super().__init__()
- w = str(weights[0] if isinstance(weights, list) else weights)
- pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
- fp16 &= pt or jit or onnx or engine # FP16
- nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
- stride = 32 # default stride
- cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
- if not (pt or triton):
- w = attempt_download(w) # download if not local
-
- if pt: # PyTorch
- model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
- stride = max(int(model.stride.max()), 32) # model stride
- names = model.module.names if hasattr(model, 'module') else model.names # get class names
- model.half() if fp16 else model.float()
- self.model = model # explicitly assign for to(), cpu(), cuda(), half()
- elif jit: # TorchScript
- LOGGER.info(f'Loading {w} for TorchScript inference...')
- extra_files = {'config.txt': ''} # model metadata
- model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
- model.half() if fp16 else model.float()
- if extra_files['config.txt']: # load metadata dict
- d = json.loads(extra_files['config.txt'],
- object_hook=lambda d: {int(k) if k.isdigit() else k: v
- for k, v in d.items()})
- stride, names = int(d['stride']), d['names']
- elif dnn: # ONNX OpenCV DNN
- LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
- check_requirements('opencv-python>=4.5.4')
- net = cv2.dnn.readNetFromONNX(w)
- elif onnx: # ONNX Runtime
- LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
- check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
- import onnxruntime
- providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
- session = onnxruntime.InferenceSession(w, providers=providers)
- output_names = [x.name for x in session.get_outputs()]
- meta = session.get_modelmeta().custom_metadata_map # metadata
- if 'stride' in meta:
- stride, names = int(meta['stride']), eval(meta['names'])
- elif xml: # OpenVINO
- LOGGER.info(f'Loading {w} for OpenVINO inference...')
- check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/
- from openvino.runtime import Core, Layout, get_batch
- ie = Core()
- if not Path(w).is_file(): # if not *.xml
- w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir
- network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
- if network.get_parameters()[0].get_layout().empty:
- network.get_parameters()[0].set_layout(Layout("NCHW"))
- batch_dim = get_batch(network)
- if batch_dim.is_static:
- batch_size = batch_dim.get_length()
- executable_network = ie.compile_model(network, device_name="CPU") # device_name="MYRIAD" for Intel NCS2
- stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata
- elif engine: # TensorRT
- LOGGER.info(f'Loading {w} for TensorRT inference...')
- import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
- check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
- if device.type == 'cpu':
- device = torch.device('cuda:0')
- Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
- logger = trt.Logger(trt.Logger.INFO)
- with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
- model = runtime.deserialize_cuda_engine(f.read())
- context = model.create_execution_context()
- bindings = OrderedDict()
- output_names = []
- fp16 = False # default updated below
- dynamic = False
- for i in range(model.num_bindings):
- name = model.get_binding_name(i)
- dtype = trt.nptype(model.get_binding_dtype(i))
- if model.binding_is_input(i):
- if -1 in tuple(model.get_binding_shape(i)): # dynamic
- dynamic = True
- context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
- if dtype == np.float16:
- fp16 = True
- else: # output
- output_names.append(name)
- shape = tuple(context.get_binding_shape(i))
- im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
- bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
- binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
- batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size
- elif coreml: # CoreML
- LOGGER.info(f'Loading {w} for CoreML inference...')
- import coremltools as ct
- model = ct.models.MLModel(w)
- elif saved_model: # TF SavedModel
- LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
- import tensorflow as tf
- keras = False # assume TF1 saved_model
- model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
- elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
- LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
- import tensorflow as tf
-
- def wrap_frozen_graph(gd, inputs, outputs):
- x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
- ge = x.graph.as_graph_element
- return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
-
- def gd_outputs(gd):
- name_list, input_list = [], []
- for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
- name_list.append(node.name)
- input_list.extend(node.input)
- return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
-
- gd = tf.Graph().as_graph_def() # TF GraphDef
- with open(w, 'rb') as f:
- gd.ParseFromString(f.read())
- frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
- elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
- try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
- from tflite_runtime.interpreter import Interpreter, load_delegate
- except ImportError:
- import tensorflow as tf
- Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
- if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
- LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
- delegate = {
- 'Linux': 'libedgetpu.so.1',
- 'Darwin': 'libedgetpu.1.dylib',
- 'Windows': 'edgetpu.dll'}[platform.system()]
- interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
- else: # TFLite
- LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
- interpreter = Interpreter(model_path=w) # load TFLite model
- interpreter.allocate_tensors() # allocate
- input_details = interpreter.get_input_details() # inputs
- output_details = interpreter.get_output_details() # outputs
- # load metadata
- with contextlib.suppress(zipfile.BadZipFile):
- with zipfile.ZipFile(w, "r") as model:
- meta_file = model.namelist()[0]
- meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
- stride, names = int(meta['stride']), meta['names']
- elif tfjs: # TF.js
- raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
- elif paddle: # PaddlePaddle
- LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
- check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
- import paddle.inference as pdi
- if not Path(w).is_file(): # if not *.pdmodel
- w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir
- weights = Path(w).with_suffix('.pdiparams')
- config = pdi.Config(str(w), str(weights))
- if cuda:
- config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
- predictor = pdi.create_predictor(config)
- input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
- output_names = predictor.get_output_names()
- elif triton: # NVIDIA Triton Inference Server
- LOGGER.info(f'Using {w} as Triton Inference Server...')
- check_requirements('tritonclient[all]')
- from utils.triton import TritonRemoteModel
- model = TritonRemoteModel(url=w)
- nhwc = model.runtime.startswith("tensorflow")
- else:
- raise NotImplementedError(f'ERROR: {w} is not a supported format')
-
- # class names
- if 'names' not in locals():
- names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
- if names[0] == 'n01440764' and len(names) == 1000: # ImageNet
- names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names
-
- self.__dict__.update(locals()) # assign all variables to self
-
- def forward(self, im, augment=False, visualize=False):
- # YOLOv5 MultiBackend inference
- b, ch, h, w = im.shape # batch, channel, height, width
- if self.fp16 and im.dtype != torch.float16:
- im = im.half() # to FP16
- if self.nhwc:
- im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)
-
- if self.pt: # PyTorch
- y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
- elif self.jit: # TorchScript
- y = self.model(im)
- elif self.dnn: # ONNX OpenCV DNN
- im = im.cpu().numpy() # torch to numpy
- self.net.setInput(im)
- y = self.net.forward()
- elif self.onnx: # ONNX Runtime
- im = im.cpu().numpy() # torch to numpy
- y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
- elif self.xml: # OpenVINO
- im = im.cpu().numpy() # FP32
- y = list(self.executable_network([im]).values())
- elif self.engine: # TensorRT
- if self.dynamic and im.shape != self.bindings['images'].shape:
- i = self.model.get_binding_index('images')
- self.context.set_binding_shape(i, im.shape) # reshape if dynamic
- self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
- for name in self.output_names:
- i = self.model.get_binding_index(name)
- self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
- s = self.bindings['images'].shape
- assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
- self.binding_addrs['images'] = int(im.data_ptr())
- self.context.execute_v2(list(self.binding_addrs.values()))
- y = [self.bindings[x].data for x in sorted(self.output_names)]
- elif self.coreml: # CoreML
- im = im.cpu().numpy()
- im = Image.fromarray((im[0] * 255).astype('uint8'))
- # im = im.resize((192, 320), Image.ANTIALIAS)
- y = self.model.predict({'image': im}) # coordinates are xywh normalized
- if 'confidence' in y:
- box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
- conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
- y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
- else:
- y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)
- elif self.paddle: # PaddlePaddle
- im = im.cpu().numpy().astype(np.float32)
- self.input_handle.copy_from_cpu(im)
- self.predictor.run()
- y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
- elif self.triton: # NVIDIA Triton Inference Server
- y = self.model(im)
- else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
- im = im.cpu().numpy()
- if self.saved_model: # SavedModel
- y = self.model(im, training=False) if self.keras else self.model(im)
- elif self.pb: # GraphDef
- y = self.frozen_func(x=self.tf.constant(im))
- else: # Lite or Edge TPU
- input = self.input_details[0]
- int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model
- if int8:
- scale, zero_point = input['quantization']
- im = (im / scale + zero_point).astype(np.uint8) # de-scale
- self.interpreter.set_tensor(input['index'], im)
- self.interpreter.invoke()
- y = []
- for output in self.output_details:
- x = self.interpreter.get_tensor(output['index'])
- if int8:
- scale, zero_point = output['quantization']
- x = (x.astype(np.float32) - zero_point) * scale # re-scale
- y.append(x)
- y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
- y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels
-
- if isinstance(y, (list, tuple)):
- return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
- else:
- return self.from_numpy(y)
-
- def from_numpy(self, x):
- return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
-
- def warmup(self, imgsz=(1, 3, 640, 640)):
- # Warmup model by running inference once
- warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
- if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
- im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input
- for _ in range(2 if self.jit else 1): #
- self.forward(im) # warmup
-
- @staticmethod
- def _model_type(p='path/to/model.pt'):
- # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
- # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
- from export import export_formats
- from utils.downloads import is_url
- sf = list(export_formats().Suffix) # export suffixes
- if not is_url(p, check=False):
- check_suffix(p, sf) # checks
- url = urlparse(p) # if url may be Triton inference server
- types = [s in Path(p).name for s in sf]
- types[8] &= not types[9] # tflite &= not edgetpu
- triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
- return types + [triton]
-
- @staticmethod
- def _load_metadata(f=Path('path/to/meta.yaml')):
- # Load metadata from meta.yaml if it exists
- if f.exists():
- d = yaml_load(f)
- return d['stride'], d['names'] # assign stride, names
- return None, None
-
-
-class AutoShape(nn.Module):
- # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
- conf = 0.25 # NMS confidence threshold
- iou = 0.45 # NMS IoU threshold
- agnostic = False # NMS class-agnostic
- multi_label = False # NMS multiple labels per box
- classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
- max_det = 1000 # maximum number of detections per image
- amp = False # Automatic Mixed Precision (AMP) inference
-
- def __init__(self, model, verbose=True):
- super().__init__()
- if verbose:
- LOGGER.info('Adding AutoShape... ')
- copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
- self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance
- self.pt = not self.dmb or model.pt # PyTorch model
- self.model = model.eval()
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
- m.inplace = False # Detect.inplace=False for safe multithread inference
- m.export = True # do not output loss values
-
- def _apply(self, fn):
- # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
- self = super()._apply(fn)
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
- m.stride = fn(m.stride)
- m.grid = list(map(fn, m.grid))
- if isinstance(m.anchor_grid, list):
- m.anchor_grid = list(map(fn, m.anchor_grid))
- return self
-
- @smart_inference_mode()
- def forward(self, ims, size=640, augment=False, profile=False):
- # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
- # file: ims = 'data/images/zidane.jpg' # str or PosixPath
- # URI: = 'https://ultralytics.com/images/zidane.jpg'
- # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
- # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
- # numpy: = np.zeros((640,1280,3)) # HWC
- # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
- # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
-
- dt = (Profile(), Profile(), Profile())
- with dt[0]:
- if isinstance(size, int): # expand
- size = (size, size)
- p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param
- autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
- if isinstance(ims, torch.Tensor): # torch
- with amp.autocast(autocast):
- return self.model(ims.to(p.device).type_as(p), augment=augment) # inference
-
- # Pre-process
- n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images
- shape0, shape1, files = [], [], [] # image and inference shapes, filenames
- for i, im in enumerate(ims):
- f = f'image{i}' # filename
- if isinstance(im, (str, Path)): # filename or uri
- im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
- im = np.asarray(exif_transpose(im))
- elif isinstance(im, Image.Image): # PIL Image
- im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
- files.append(Path(f).with_suffix('.jpg').name)
- if im.shape[0] < 5: # image in CHW
- im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
- im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input
- s = im.shape[:2] # HWC
- shape0.append(s) # image shape
- g = max(size) / max(s) # gain
- shape1.append([int(y * g) for y in s])
- ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
- shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape
- x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad
- x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
-
- with amp.autocast(autocast):
- # Inference
- with dt[1]:
- y = self.model(x, augment=augment) # forward
-
- # Post-process
- with dt[2]:
- y = non_max_suppression(y if self.dmb else y[0],
- self.conf,
- self.iou,
- self.classes,
- self.agnostic,
- self.multi_label,
- max_det=self.max_det) # NMS
- for i in range(n):
- scale_boxes(shape1, y[i][:, :4], shape0[i])
-
- return Detections(ims, y, files, dt, self.names, x.shape)
-
-
-class Detections:
- # YOLOv5 detections class for inference results
- def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
- super().__init__()
- d = pred[0].device # device
- gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations
- self.ims = ims # list of images as numpy arrays
- self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
- self.names = names # class names
- self.files = files # image filenames
- self.times = times # profiling times
- self.xyxy = pred # xyxy pixels
- self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
- self.n = len(self.pred) # number of images (batch size)
- self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms)
- self.s = tuple(shape) # inference BCHW shape
-
- def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
- s, crops = '', []
- for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
- s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
- if pred.shape[0]:
- for c in pred[:, -1].unique():
- n = (pred[:, -1] == c).sum() # detections per class
- s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
- s = s.rstrip(', ')
- if show or save or render or crop:
- annotator = Annotator(im, example=str(self.names))
- for *box, conf, cls in reversed(pred): # xyxy, confidence, class
- label = f'{self.names[int(cls)]} {conf:.2f}'
- if crop:
- file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
- crops.append({
- 'box': box,
- 'conf': conf,
- 'cls': cls,
- 'label': label,
- 'im': save_one_box(box, im, file=file, save=save)})
- else: # all others
- annotator.box_label(box, label if labels else '', color=colors(cls))
- im = annotator.im
- else:
- s += '(no detections)'
-
- im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
- if show:
- display(im) if is_notebook() else im.show(self.files[i])
- if save:
- f = self.files[i]
- im.save(save_dir / f) # save
- if i == self.n - 1:
- LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
- if render:
- self.ims[i] = np.asarray(im)
- if pprint:
- s = s.lstrip('\n')
- return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
- if crop:
- if save:
- LOGGER.info(f'Saved results to {save_dir}\n')
- return crops
-
- @TryExcept('Showing images is not supported in this environment')
- def show(self, labels=True):
- self._run(show=True, labels=labels) # show results
-
- def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
- save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir
- self._run(save=True, labels=labels, save_dir=save_dir) # save results
-
- def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
- save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
- return self._run(crop=True, save=save, save_dir=save_dir) # crop results
-
- def render(self, labels=True):
- self._run(render=True, labels=labels) # render results
- return self.ims
-
- def pandas(self):
- # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
- new = copy(self) # return copy
- ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
- cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
- for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
- a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
- setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
- return new
-
- def tolist(self):
- # return a list of Detections objects, i.e. 'for result in results.tolist():'
- r = range(self.n) # iterable
- x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
- # for d in x:
- # for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
- # setattr(d, k, getattr(d, k)[0]) # pop out of list
- return x
-
- def print(self):
- LOGGER.info(self.__str__())
-
- def __len__(self): # override len(results)
- return self.n
-
- def __str__(self): # override print(results)
- return self._run(pprint=True) # print results
-
- def __repr__(self):
- return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
-
-
-class Proto(nn.Module):
- # YOLOv5 mask Proto module for segmentation models
- def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
- super().__init__()
- self.cv1 = Conv(c1, c_, k=3)
- self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
- self.cv2 = Conv(c_, c_, k=3)
- self.cv3 = Conv(c_, c2)
-
- def forward(self, x):
- return self.cv3(self.cv2(self.upsample(self.cv1(x))))
-
-
-class Classify(nn.Module):
- # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- c_ = 1280 # efficientnet_b0 size
- self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
- self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1)
- self.drop = nn.Dropout(p=0.0, inplace=True)
- self.linear = nn.Linear(c_, c2) # to x(b,c2)
-
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, 1)
- return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
diff --git a/spaces/anon9i9/finetuned_diffusion_test/README.md b/spaces/anon9i9/finetuned_diffusion_test/README.md
deleted file mode 100644
index bca1a00cd251d1c13fc3fe72baad06e256245d3e..0000000000000000000000000000000000000000
--- a/spaces/anon9i9/finetuned_diffusion_test/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Finetuned Diffusion
-emoji: 🪄🖼️
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-license: mit
-duplicated_from: anzorq/finetuned_diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arkaprav0/gpt-transcript-plugin/README.md b/spaces/arkaprav0/gpt-transcript-plugin/README.md
deleted file mode 100644
index d1bc9529ee68cb209c1efc72baedf5c059e8381e..0000000000000000000000000000000000000000
--- a/spaces/arkaprav0/gpt-transcript-plugin/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Gpt Llm
-emoji: 🏆
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/feed_forward/decoder.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/feed_forward/decoder.py
deleted file mode 100644
index 0376e2e3926e65254c3a81d085d48c97df033958..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/feed_forward/decoder.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import torch
-from torch import nn
-
-from TTS.tts.layers.generic.res_conv_bn import Conv1dBN, Conv1dBNBlock, ResidualConv1dBNBlock
-from TTS.tts.layers.generic.transformer import FFTransformerBlock
-from TTS.tts.layers.generic.wavenet import WNBlocks
-from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
-
-
-class WaveNetDecoder(nn.Module):
- """WaveNet based decoder with a prenet and a postnet.
-
- prenet: conv1d_1x1
- postnet: 3 x [conv1d_1x1 -> relu] -> conv1d_1x1
-
- TODO: Integrate speaker conditioning vector.
-
- Note:
- default wavenet parameters;
- params = {
- "num_blocks": 12,
- "hidden_channels":192,
- "kernel_size": 5,
- "dilation_rate": 1,
- "num_layers": 4,
- "dropout_p": 0.05
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels for prenet and postnet.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, c_in_channels, params):
- super().__init__()
- # prenet
- self.prenet = torch.nn.Conv1d(in_channels, params["hidden_channels"], 1)
- # wavenet layers
- self.wn = WNBlocks(params["hidden_channels"], c_in_channels=c_in_channels, **params)
- # postnet
- self.postnet = [
- torch.nn.Conv1d(params["hidden_channels"], hidden_channels, 1),
- torch.nn.ReLU(),
- torch.nn.Conv1d(hidden_channels, hidden_channels, 1),
- torch.nn.ReLU(),
- torch.nn.Conv1d(hidden_channels, hidden_channels, 1),
- torch.nn.ReLU(),
- torch.nn.Conv1d(hidden_channels, out_channels, 1),
- ]
- self.postnet = nn.Sequential(*self.postnet)
-
- def forward(self, x, x_mask=None, g=None):
- x = self.prenet(x) * x_mask
- x = self.wn(x, x_mask, g)
- o = self.postnet(x) * x_mask
- return o
-
-
-class RelativePositionTransformerDecoder(nn.Module):
- """Decoder with Relative Positional Transformer.
-
- Note:
- Default params
- params={
- 'hidden_channels_ffn': 128,
- 'num_heads': 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 8,
- "rel_attn_window_size": 4,
- "input_length": None
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels including Transformer layers.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, params):
- super().__init__()
- self.prenet = Conv1dBN(in_channels, hidden_channels, 1, 1)
- self.rel_pos_transformer = RelativePositionTransformer(in_channels, out_channels, hidden_channels, **params)
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- o = self.prenet(x) * x_mask
- o = self.rel_pos_transformer(o, x_mask)
- return o
-
-
-class FFTransformerDecoder(nn.Module):
- """Decoder with FeedForwardTransformer.
-
- Default params
- params={
- 'hidden_channels_ffn': 1024,
- 'num_heads': 2,
- "dropout_p": 0.1,
- "num_layers": 6,
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels including Transformer layers.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, params):
- super().__init__()
- self.transformer_block = FFTransformerBlock(in_channels, **params)
- self.postnet = nn.Conv1d(in_channels, out_channels, 1)
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- # TODO: handle multi-speaker
- x_mask = 1 if x_mask is None else x_mask
- o = self.transformer_block(x) * x_mask
- o = self.postnet(o) * x_mask
- return o
-
-
-class ResidualConv1dBNDecoder(nn.Module):
- """Residual Convolutional Decoder as in the original Speedy Speech paper
-
- TODO: Integrate speaker conditioning vector.
-
- Note:
- Default params
- params = {
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4, 8] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels including ResidualConv1dBNBlock layers.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, params):
- super().__init__()
- self.res_conv_block = ResidualConv1dBNBlock(in_channels, hidden_channels, hidden_channels, **params)
- self.post_conv = nn.Conv1d(hidden_channels, hidden_channels, 1)
- self.postnet = nn.Sequential(
- Conv1dBNBlock(
- hidden_channels, hidden_channels, hidden_channels, params["kernel_size"], 1, num_conv_blocks=2
- ),
- nn.Conv1d(hidden_channels, out_channels, 1),
- )
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- o = self.res_conv_block(x, x_mask)
- o = self.post_conv(o) + x
- return self.postnet(o) * x_mask
-
-
-class Decoder(nn.Module):
- """Decodes the expanded phoneme encoding into spectrograms
- Args:
- out_channels (int): number of output channels.
- in_hidden_channels (int): input and hidden channels. Model keeps the input channels for the intermediate layers.
- decoder_type (str): decoder layer types. 'transformers' or 'residual_conv_bn'. Default 'residual_conv_bn'.
- decoder_params (dict): model parameters for specified decoder type.
- c_in_channels (int): number of channels for conditional input.
-
- Shapes:
- - input: (B, C, T)
- """
-
- # pylint: disable=dangerous-default-value
- def __init__(
- self,
- out_channels,
- in_hidden_channels,
- decoder_type="residual_conv_bn",
- decoder_params={
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4, 8] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17,
- },
- c_in_channels=0,
- ):
- super().__init__()
-
- if decoder_type.lower() == "relative_position_transformer":
- self.decoder = RelativePositionTransformerDecoder(
- in_channels=in_hidden_channels,
- out_channels=out_channels,
- hidden_channels=in_hidden_channels,
- params=decoder_params,
- )
- elif decoder_type.lower() == "residual_conv_bn":
- self.decoder = ResidualConv1dBNDecoder(
- in_channels=in_hidden_channels,
- out_channels=out_channels,
- hidden_channels=in_hidden_channels,
- params=decoder_params,
- )
- elif decoder_type.lower() == "wavenet":
- self.decoder = WaveNetDecoder(
- in_channels=in_hidden_channels,
- out_channels=out_channels,
- hidden_channels=in_hidden_channels,
- c_in_channels=c_in_channels,
- params=decoder_params,
- )
- elif decoder_type.lower() == "fftransformer":
- self.decoder = FFTransformerDecoder(in_hidden_channels, out_channels, decoder_params)
- else:
- raise ValueError(f"[!] Unknown decoder type - {decoder_type}")
-
- def forward(self, x, x_mask, g=None): # pylint: disable=unused-argument
- """
- Args:
- x: [B, C, T]
- x_mask: [B, 1, T]
- g: [B, C_g, 1]
- """
- # TODO: implement multi-speaker
- o = self.decoder(x, x_mask, g)
- return o
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests2/test_feed_forward_layers.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests2/test_feed_forward_layers.py
deleted file mode 100644
index 6b26b88f382a1876fd197b632c9bd2b4aca1e06f..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests2/test_feed_forward_layers.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import torch
-
-from TTS.tts.layers.feed_forward.decoder import Decoder
-from TTS.tts.layers.feed_forward.encoder import Encoder
-from TTS.tts.utils.helpers import sequence_mask
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-def test_encoder():
- input_dummy = torch.rand(8, 14, 37).to(device)
- input_lengths = torch.randint(31, 37, (8,)).long().to(device)
- input_lengths[-1] = 37
- input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
- # relative positional transformer encoder
- layer = Encoder(
- out_channels=11,
- in_hidden_channels=14,
- encoder_type="relative_position_transformer",
- encoder_params={
- "hidden_channels_ffn": 768,
- "num_heads": 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "rel_attn_window_size": 4,
- "input_length": None,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # residual conv bn encoder
- layer = Encoder(
- out_channels=11,
- in_hidden_channels=14,
- encoder_type="residual_conv_bn",
- encoder_params={"kernel_size": 4, "dilations": 4 * [1, 2, 4] + [1], "num_conv_blocks": 2, "num_res_blocks": 13},
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # FFTransformer encoder
- layer = Encoder(
- out_channels=14,
- in_hidden_channels=14,
- encoder_type="fftransformer",
- encoder_params={"hidden_channels_ffn": 31, "num_heads": 2, "num_layers": 2, "dropout_p": 0.1},
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 14, 37]
-
-
-def test_decoder():
- input_dummy = torch.rand(8, 128, 37).to(device)
- input_lengths = torch.randint(31, 37, (8,)).long().to(device)
- input_lengths[-1] = 37
-
- input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
- # residual bn conv decoder
- layer = Decoder(out_channels=11, in_hidden_channels=128).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # transformer decoder
- layer = Decoder(
- out_channels=11,
- in_hidden_channels=128,
- decoder_type="relative_position_transformer",
- decoder_params={
- "hidden_channels_ffn": 128,
- "num_heads": 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 8,
- "rel_attn_window_size": 4,
- "input_length": None,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # wavenet decoder
- layer = Decoder(
- out_channels=11,
- in_hidden_channels=128,
- decoder_type="wavenet",
- decoder_params={
- "num_blocks": 12,
- "hidden_channels": 192,
- "kernel_size": 5,
- "dilation_rate": 1,
- "num_layers": 4,
- "dropout_p": 0.05,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- # FFTransformer decoder
- layer = Decoder(
- out_channels=11,
- in_hidden_channels=128,
- decoder_type="fftransformer",
- decoder_params={
- "hidden_channels_ffn": 31,
- "num_heads": 2,
- "dropout_p": 0.1,
- "num_layers": 2,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/preprocess.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/preprocess.py
deleted file mode 100644
index 5322012ac60e91fefa47338d0e253c3f912ab7f2..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/preprocess.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import sys
-
-if sys.version_info[0] < 3 and sys.version_info[1] < 2:
- raise Exception("Must be using >= Python 3.2")
-
-from os import listdir, path
-
-if not path.isfile('face_detection/detection/sfd/s3fd.pth'):
- raise FileNotFoundError('Save the s3fd model to face_detection/detection/sfd/s3fd.pth \
- before running this script!')
-
-import multiprocessing as mp
-from concurrent.futures import ThreadPoolExecutor, as_completed
-import numpy as np
-import argparse, os, cv2, traceback, subprocess
-from tqdm import tqdm
-from glob import glob
-import audio
-from hparams import hparams as hp
-
-import face_detection
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--ngpu', help='Number of GPUs across which to run in parallel', default=1, type=int)
-parser.add_argument('--batch_size', help='Single GPU Face detection batch size', default=32, type=int)
-parser.add_argument("--data_root", help="Root folder of the LRS2 dataset", required=True)
-parser.add_argument("--preprocessed_root", help="Root folder of the preprocessed dataset", required=True)
-
-args = parser.parse_args()
-
-fa = [face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False,
- device='cuda:{}'.format(id)) for id in range(args.ngpu)]
-
-template = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}'
-# template2 = 'ffmpeg -hide_banner -loglevel panic -threads 1 -y -i {} -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 {}'
-
-def process_video_file(vfile, args, gpu_id):
- video_stream = cv2.VideoCapture(vfile)
-
- frames = []
- while 1:
- still_reading, frame = video_stream.read()
- if not still_reading:
- video_stream.release()
- break
- frames.append(frame)
-
- vidname = os.path.basename(vfile).split('.')[0]
- dirname = vfile.split('/')[-2]
-
- fulldir = path.join(args.preprocessed_root, dirname, vidname)
- os.makedirs(fulldir, exist_ok=True)
-
- batches = [frames[i:i + args.batch_size] for i in range(0, len(frames), args.batch_size)]
-
- i = -1
- for fb in batches:
- preds = fa[gpu_id].get_detections_for_batch(np.asarray(fb))
-
- for j, f in enumerate(preds):
- i += 1
- if f is None:
- continue
-
- x1, y1, x2, y2 = f
- cv2.imwrite(path.join(fulldir, '{}.jpg'.format(i)), fb[j][y1:y2, x1:x2])
-
-def process_audio_file(vfile, args):
- vidname = os.path.basename(vfile).split('.')[0]
- dirname = vfile.split('/')[-2]
-
- fulldir = path.join(args.preprocessed_root, dirname, vidname)
- os.makedirs(fulldir, exist_ok=True)
-
- wavpath = path.join(fulldir, 'audio.wav')
-
- command = template.format(vfile, wavpath)
- subprocess.call(command, shell=True)
-
-
-def mp_handler(job):
- vfile, args, gpu_id = job
- try:
- process_video_file(vfile, args, gpu_id)
- except KeyboardInterrupt:
- exit(0)
- except:
- traceback.print_exc()
-
-def main(args):
- print('Started processing for {} with {} GPUs'.format(args.data_root, args.ngpu))
-
- filelist = glob(path.join(args.data_root, '*/*.mp4'))
-
- jobs = [(vfile, args, i%args.ngpu) for i, vfile in enumerate(filelist)]
- p = ThreadPoolExecutor(args.ngpu)
- futures = [p.submit(mp_handler, j) for j in jobs]
- _ = [r.result() for r in tqdm(as_completed(futures), total=len(futures))]
-
- print('Dumping audios...')
-
- for vfile in tqdm(filelist):
- try:
- process_audio_file(vfile, args)
- except KeyboardInterrupt:
- exit(0)
- except:
- traceback.print_exc()
- continue
-
-if __name__ == '__main__':
- main(args)
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_SHA384.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_SHA384.py
deleted file mode 100644
index c682eb439145314e62ee814b8caf1d5e6d76bc8f..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_SHA384.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# SelfTest/Hash/test_SHA.py: Self-test for the SHA-384 hash function
-#
-# Written in 2008 by Dwayne C. Litzenberger
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain. To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""Self-test suite for Crypto.Hash.SHA384"""
-
-# Test vectors from various sources
-# This is a list of (expected_result, input[, description]) tuples.
-test_data = [
-
- # RFC 4634: Section Page 8.4, "Test 1"
- ('cb00753f45a35e8bb5a03d699ac65007272c32ab0eded1631a8b605a43ff5bed8086072ba1e7cc2358baeca134c825a7', 'abc'),
-
- # RFC 4634: Section Page 8.4, "Test 2.2"
- ('09330c33f71147e83d192fc782cd1b4753111b173b3b05d22fa08086e3b0f712fcc7c71a557e2db966c3e9fa91746039', 'abcdefghbcdefghicdefghijdefghijkefghijklfghijklmghijklmnhijklmnoijklmnopjklmnopqklmnopqrlmnopqrsmnopqrstnopqrstu'),
-
- # RFC 4634: Section Page 8.4, "Test 3"
- ('9d0e1809716474cb086e834e310a4a1ced149e9c00f248527972cec5704c2a5b07b8b3dc38ecc4ebae97ddd87f3d8985', 'a' * 10**6, "'a' * 10**6"),
-
- # Taken from http://de.wikipedia.org/wiki/Secure_Hash_Algorithm
- ('38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6e1da274edebfe76f65fbd51ad2f14898b95b', ''),
-
- # Example from http://de.wikipedia.org/wiki/Secure_Hash_Algorithm
- ('71e8383a4cea32d6fd6877495db2ee353542f46fa44bc23100bca48f3366b84e809f0708e81041f427c6d5219a286677',
- 'Franz jagt im komplett verwahrlosten Taxi quer durch Bayern'),
-
-]
-
-def get_tests(config={}):
- from Crypto.Hash import SHA384
- from .common import make_hash_tests
- return make_hash_tests(SHA384, "SHA384", test_data,
- digest_size=48,
- oid='2.16.840.1.101.3.4.2.2')
-
-if __name__ == '__main__':
- import unittest
- suite = lambda: unittest.TestSuite(get_tests())
- unittest.main(defaultTest='suite')
-
-# vim:set ts=4 sw=4 sts=4 expandtab:
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/binarizer.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/binarizer.py
deleted file mode 100644
index 6f03d7a2cbb16db6aa218713211c1323adbc7d45..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/binarizer.py
+++ /dev/null
@@ -1,381 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import typing as tp
-from abc import ABC, abstractmethod
-from collections import Counter
-from dataclasses import dataclass
-from multiprocessing import Pool
-
-import torch
-
-from fairseq.data import Dictionary, indexed_dataset
-from fairseq.file_chunker_utils import Chunker, find_offsets
-from fairseq.file_io import PathManager
-from fairseq.tokenizer import tokenize_line
-
-logger = logging.getLogger("binarizer")
-
-
-@dataclass
-class BinarizeSummary:
- """
- Keep track of what's going on in the binarizer
- """
-
- num_seq: int = 0
- replaced: tp.Optional[Counter] = None
- num_tok: int = 0
-
- @property
- def num_replaced(self) -> int:
- if self.replaced is None:
- return 0
- return sum(self.replaced.values())
-
- @property
- def replaced_percent(self) -> float:
- return 100 * self.num_replaced / self.num_tok
-
- def __str__(self) -> str:
- base = f"{self.num_seq} sents, {self.num_tok} tokens"
- if self.replaced is None:
- return base
-
- return f"{base}, {self.replaced_percent:.3}% replaced"
-
- def merge(self, other: "BinarizeSummary"):
- replaced = None
- if self.replaced is not None:
- replaced = self.replaced
- if other.replaced is not None:
- if replaced is None:
- replaced = other.replaced
- else:
- replaced += other.replaced
- self.replaced = replaced
- self.num_seq += other.num_seq
- self.num_tok += other.num_tok
-
-
-class Binarizer(ABC):
- """
- a binarizer describes how to take a string and build a tensor out of it
- """
-
- @abstractmethod
- def binarize_line(
- self,
- line: str,
- summary: BinarizeSummary,
- ) -> torch.IntTensor:
- ...
-
-
-def _worker_prefix(output_prefix: str, worker_id: int):
- return f"{output_prefix}.pt{worker_id}"
-
-
-class FileBinarizer:
- """
- An file binarizer can take a file, tokenize it, and binarize each line to a tensor
- """
-
- @classmethod
- def multiprocess_dataset(
- cls,
- input_file: str,
- dataset_impl: str,
- binarizer: Binarizer,
- output_prefix: str,
- vocab_size=None,
- num_workers=1,
- ) -> BinarizeSummary:
- final_summary = BinarizeSummary()
-
- offsets = find_offsets(input_file, num_workers)
- # find_offsets returns a list of position [pos1, pos2, pos3, pos4] but we would want pairs:
- # [(pos1, pos2), (pos2, pos3), (pos3, pos4)] to process the chunks with start/end info
- # we zip the list with itself shifted by one to get all the pairs.
- (first_chunk, *more_chunks) = zip(offsets, offsets[1:])
- pool = None
- if num_workers > 1:
- pool = Pool(processes=num_workers - 1)
- worker_results = [
- pool.apply_async(
- cls._binarize_chunk_and_finalize,
- args=(
- binarizer,
- input_file,
- start_offset,
- end_offset,
- _worker_prefix(
- output_prefix,
- worker_id,
- ),
- dataset_impl,
- ),
- kwds={
- "vocab_size": vocab_size,
- }
- if vocab_size is not None
- else {},
- )
- for worker_id, (start_offset, end_offset) in enumerate(
- more_chunks, start=1
- )
- ]
-
- pool.close()
- pool.join()
- for r in worker_results:
- summ = r.get()
- final_summary.merge(summ)
-
- # do not close the bin file as we need to merge the worker results in
- final_ds, summ = cls._binarize_file_chunk(
- binarizer,
- input_file,
- offset_start=first_chunk[0],
- offset_end=first_chunk[1],
- output_prefix=output_prefix,
- dataset_impl=dataset_impl,
- vocab_size=vocab_size if vocab_size is not None else None,
- )
- final_summary.merge(summ)
-
- if num_workers > 1:
- for worker_id in range(1, num_workers):
- # merge the worker outputs
- worker_output_prefix = _worker_prefix(
- output_prefix,
- worker_id,
- )
- final_ds.merge_file_(worker_output_prefix)
- try:
- os.remove(indexed_dataset.data_file_path(worker_output_prefix))
- os.remove(indexed_dataset.index_file_path(worker_output_prefix))
- except Exception as e:
- logger.error(
- f"couldn't remove {worker_output_prefix}.*", exc_info=e
- )
-
- # now we can close the file
- idx_file = indexed_dataset.index_file_path(output_prefix)
- final_ds.finalize(idx_file)
- return final_summary
-
- @staticmethod
- def _binarize_file_chunk(
- binarizer: Binarizer,
- filename: str,
- offset_start: int,
- offset_end: int,
- output_prefix: str,
- dataset_impl: str,
- vocab_size=None,
- ) -> tp.Tuple[tp.Any, BinarizeSummary]: # (dataset builder, BinarizeSummary)
- """
- creates a dataset builder and append binarized items to it. This function does not
- finalize the builder, this is useful if you want to do other things with your bin file
- like appending/merging other files
- """
- bin_file = indexed_dataset.data_file_path(output_prefix)
- ds = indexed_dataset.make_builder(
- bin_file,
- impl=dataset_impl,
- vocab_size=vocab_size,
- )
- summary = BinarizeSummary()
-
- with Chunker(
- PathManager.get_local_path(filename), offset_start, offset_end
- ) as line_iterator:
- for line in line_iterator:
- ds.add_item(binarizer.binarize_line(line, summary))
-
- return ds, summary
-
- @classmethod
- def _binarize_chunk_and_finalize(
- cls,
- binarizer: Binarizer,
- filename: str,
- offset_start: int,
- offset_end: int,
- output_prefix: str,
- dataset_impl: str,
- vocab_size=None,
- ):
- """
- same as above, but also finalizes the builder
- """
- ds, summ = cls._binarize_file_chunk(
- binarizer,
- filename,
- offset_start,
- offset_end,
- output_prefix,
- dataset_impl,
- vocab_size=vocab_size,
- )
-
- idx_file = indexed_dataset.index_file_path(output_prefix)
- ds.finalize(idx_file)
-
- return summ
-
-
-class VocabularyDatasetBinarizer(Binarizer):
- """
- Takes a Dictionary/Vocabulary, assign ids to each
- token using the dictionary encode_line function.
- """
-
- def __init__(
- self,
- dict: Dictionary,
- tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line,
- append_eos: bool = True,
- reverse_order: bool = False,
- already_numberized: bool = False,
- ) -> None:
- self.dict = dict
- self.tokenize = tokenize
- self.append_eos = append_eos
- self.reverse_order = reverse_order
- self.already_numberized = already_numberized
- super().__init__()
-
- def binarize_line(
- self,
- line: str,
- summary: BinarizeSummary,
- ):
- if summary.replaced is None:
- summary.replaced = Counter()
-
- def replaced_consumer(word, idx):
- if idx == self.dict.unk_index and word != self.dict.unk_word:
- summary.replaced.update([word])
-
- if self.already_numberized:
- id_strings = line.strip().split()
- id_list = [int(id_string) for id_string in id_strings]
- if self.reverse_order:
- id_list.reverse()
- if self.append_eos:
- id_list.append(self.dict.eos())
- ids = torch.IntTensor(id_list)
- else:
- ids = self.dict.encode_line(
- line=line,
- line_tokenizer=self.tokenize,
- add_if_not_exist=False,
- consumer=replaced_consumer,
- append_eos=self.append_eos,
- reverse_order=self.reverse_order,
- )
-
- summary.num_seq += 1
- summary.num_tok += len(ids)
- return ids
-
-
-class AlignmentDatasetBinarizer(Binarizer):
- """
- binarize by parsing a set of alignments and packing
- them in a tensor (see utils.parse_alignment)
- """
-
- def __init__(
- self,
- alignment_parser: tp.Callable[[str], torch.IntTensor],
- ) -> None:
- super().__init__()
- self.alignment_parser = alignment_parser
-
- def binarize_line(
- self,
- line: str,
- summary: BinarizeSummary,
- ):
- ids = self.alignment_parser(line)
- summary.num_seq += 1
- summary.num_tok += len(ids)
- return ids
-
-
-class LegacyBinarizer:
- @classmethod
- def binarize(
- cls,
- filename: str,
- dico: Dictionary,
- consumer: tp.Callable[[torch.IntTensor], None],
- tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line,
- append_eos: bool = True,
- reverse_order: bool = False,
- offset: int = 0,
- end: int = -1,
- already_numberized: bool = False,
- ) -> tp.Dict[str, int]:
- binarizer = VocabularyDatasetBinarizer(
- dict=dico,
- tokenize=tokenize,
- append_eos=append_eos,
- reverse_order=reverse_order,
- already_numberized=already_numberized,
- )
- return cls._consume_file(
- filename,
- binarizer,
- consumer,
- offset_start=offset,
- offset_end=end,
- )
-
- @classmethod
- def binarize_alignments(
- cls,
- filename: str,
- alignment_parser: tp.Callable[[str], torch.IntTensor],
- consumer: tp.Callable[[torch.IntTensor], None],
- offset: int = 0,
- end: int = -1,
- ) -> tp.Dict[str, int]:
- binarizer = AlignmentDatasetBinarizer(alignment_parser)
- return cls._consume_file(
- filename,
- binarizer,
- consumer,
- offset_start=offset,
- offset_end=end,
- )
-
- @staticmethod
- def _consume_file(
- filename: str,
- binarizer: Binarizer,
- consumer: tp.Callable[[torch.IntTensor], None],
- offset_start: int,
- offset_end: int,
- ) -> tp.Dict[str, int]:
- summary = BinarizeSummary()
-
- with Chunker(
- PathManager.get_local_path(filename), offset_start, offset_end
- ) as line_iterator:
- for line in line_iterator:
- consumer(binarizer.binarize_line(line, summary))
-
- return {
- "nseq": summary.num_seq,
- "nunk": summary.num_replaced,
- "ntok": summary.num_tok,
- "replaced": summary.replaced,
- }
diff --git a/spaces/astoken/weather_checker/app.py b/spaces/astoken/weather_checker/app.py
deleted file mode 100644
index a93645310ad90e9360d393f61d89bdac9cc820af..0000000000000000000000000000000000000000
--- a/spaces/astoken/weather_checker/app.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import requests
-import pandas as pd
-import datetime
-import time
-import gradio as gr
-import os
-
-###########
-# other API's of interest: https://medium.com/@imdipto/best-free-alternatives-to-the-wunderground-weather-api-21acb22450e6
-##########
-OPENWEATHER_API_KEY = os.environ.get('OPENWEATHER_API_KEY')
-WEATHERAPI_KEY = os.environ.get('WEATHERAPI_KEY')
-
-
-def openweather_to_result(lat, lon, gmt_time):
- """
- API docs: https://openweathermap.org/api/one-call-api#current
-
- Parameters
- ------------
- lat [float]: decimal valued latitude
- lon [float]: decimal valued longitude
- gmt_time [datetime object]: time of desired forecast, in gmt and as python datetime object
-
- Returns
- --------
- cloud_pct Tuple(List, List): list of cloud percent and corresponding time for times within 1.5 hours of input GMT time
- """
- exclude_parts = 'current,minutely,daily,alerts'
- request_url = f'https://api.openweathermap.org/data/2.5/onecall?lat={lat}&lon={lon}&exclude={exclude_parts}&appid={OPENWEATHER_API_KEY}'
-
- response = requests.get(request_url)
-
- data = response.json()
-
- cloud_pct = []
- forecast_times = []
-
- # timeframe around input time to check cloud % for
- timeframe = datetime.timedelta(hours=1, minutes=30)
- for hour in data['hourly']:
- # dt property is unix utc time of forecasted data - convert this to python datetime object
- forecast_time = datetime.datetime.fromtimestamp(
- hour['dt'], tz=datetime.timezone.utc)
- if abs(forecast_time - gmt_time) <= timeframe:
- # cloud pct is stored in each hour at top level
- cloud_pct.append(hour['clouds'])
- forecast_times.append(forecast_time)
-
- return cloud_pct, forecast_times
-
-
-def weatherapi_to_result(lat, lon, gmt_time):
- """
- API docs: https://www.weatherapi.com/docs/
- TODO: implement wrapper instead https://github.com/weatherapicom/weatherapi-Python
-
- Parameters
- ------------
- lat [float]: decimal valued latitude
- lon [float]: decimal values longitude
- gmt_time [datetime object]: time of desired forecast, in gmt and as python datetime object
-
- Returns
- --------
- cloud_pct Tuple(List, List): list of cloud percent and corresponding time for times within 1.5 hours of input GMT time
- """
- request_url = f'http://api.weatherapi.com/v1/forecast.json?key={WEATHERAPI_KEY}&q={lat},{lon}&days=2&alerts=no'
- response = requests.get(request_url)
-
- data = response.json()
-
- timezone = data['location']['tz_id']
-
- cloud_pct = []
- forecast_times = []
-
- # quick error handling to make sure input time python object has "timezone" property attached
- try:
- gmt_time = gmt_time.astimezone(datetime.timezone.utc)
- except:
- gmt_time = gmt_time.tz_localize('utc')
-
- # timeframe around input time to check cloud % for
- timeframe = datetime.timedelta(hours=1, minutes=30)
-
- # this api is first divided into days, then hours
- for day in data['forecast']['forecastday']:
- for hour in day['hour']:
- # time_epoch contains unix epoch time in GMT/UTC
- #forecast_time = datetime.datetime.fromtimestamp(hour['time_epoch'], ZoneInfo(timezone))
- forecast_time = datetime.datetime.fromtimestamp(
- hour['time_epoch'], datetime.timezone.utc)
- if abs(forecast_time - gmt_time) <= timeframe:
- cloud_pct.append(hour['cloud'])
- forecast_times.append(
- forecast_time.astimezone(datetime.timezone.utc))
-
- return cloud_pct, forecast_times
-
-
-def met_to_result(lat, lon, gmt_time):
- """
- API doc: https://api.met.no/weatherapi/locationforecast/2.0/documentation
- How to: https://api.met.no/doc/locationforecast/HowTO
-
- Parameters
- ------------
- lat [float]: decimal valued latitude
- lon [float]: decimal values longitude
- gmt_time [datetime object]: time of desired forecast, in gmt and as python datetime object
-
- Returns
- --------
- cloud_pct Tuple(List, List): list of cloud percent and corresponding time for times within 1.5 hours of input GMT time
- """
-
- # set user agent https://stackoverflow.com/questions/10606133/sending-user-agent-using-requests-library-in-python
- # must be unique per API Terms of Service https://api.met.no/doc/TermsOfService
- headers = {
- 'User-Agent': 'NASAEarthScienceRemoteSensingUnit alex.h.stoken@nasa.gov'}
-
- request_url = f'https://api.met.no/weatherapi/locationforecast/2.0/compact?lat={lat}&lon={lon}'
-
- response = requests.get(request_url, headers=headers)
-
- data = response.json()
-
- cloud_pct = []
- forecast_times = []
-
- # timeframe around input time to check cloud % for
- timeframe = datetime.timedelta(hours=1, minutes=30)
-
- # walk through json return
- for hour in data['properties']['timeseries']:
- # time is utc formatted time https://api.met.no/doc/locationforecast/FAQ
- forecast_time = datetime.datetime.strptime(
- hour['time'], '%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=datetime.timezone.utc)
- # check if time of forecast is withing "timeframe" of desired time
- if abs(forecast_time - gmt_time) <= timeframe:
- # grab cloud pct from location within the nested json, add to list
- cloud_pct.append(hour['data']['instant']
- ['details']['cloud_area_fraction'])
- # add time of forecast to list. Should be an "on the hour" time
- forecast_times.append(forecast_time)
-
- return cloud_pct, forecast_times
-
-################
-# generate text
-################
-
-
-def file_to_cloud_listing(input_file, services):
- """
-
- Args:
- input_file (Union[str, gradio FileType]): input csv file with LAT, LON, SITE, GMT cols
- services (List): list of weather api servies to check
-
- Returns:
- str: formatted string with weather predictions for locations
- """
- # this works if the input is from gradio. Then the file has an name property
- try:
- sites = pd.read_csv(input_file.name, parse_dates=['GMT'])
- using_gradio = True
- except:
- # this is for input from a script or command line
- sites = pd.read_csv(input_file, parse_dates=['GMT'])
- using_gradio = False
- start = time.perf_counter()
- date_format = "%H:%M"
- text = ''
- # each row is a site. Get weather data and then print it for each service for each site.
- for row_idx, row in sites.iterrows():
- #time_of_interest = datetime.datetime.strptime(row.GMT, '%m/%d/%y %H:%M')
- text += check_row(row, services, date_format)
- text += f'{"="*60}\n'
-
- return text
-
-
-def check_row(row, services, date_format="%H:%M"):
- """Check a row of data (a pd.Series with LAT, LON, GMT, SITE cols)
-
- Args:
- row (pd.Series): pd.Series with LAT, LON, GMT, SITE cols)
- services (List): List of weather services (['OpenWeather', 'MET (Norwegian)', 'WeatherAPI'] or subset)
- date_format (str, optional): Format for printing time of site pass over. Defaults to "%H:%M".
-
- Returns:
- str: formatted str of text for weather vals
- """
- text = ""
-
- text += f'{"Location":13}:\t\t{row.SITE} @ {row["GMT"].strftime(date_format)} GMT\n'
-
- if not isinstance(row.GMT, datetime.datetime):
- GMT = row["GMT"].to_pydatetime()
- else:
- GMT = row["GMT"]
- GMT = GMT.replace(tzinfo=datetime.timezone.utc)
- if 'OpenWeather' in services:
- try:
- cldp, times = openweather_to_result(row.LAT, row.LON, GMT)
- text += format_cldp_and_time("OpenWeather", cldp=cldp, times=times)
- except Exception as e:
- text += f'OpenWeather:\t\tError {e} in API processing\n'
- if 'MET (Norwegian)' in services:
- try:
- cldp, times = met_to_result(row.LAT, row.LON, GMT)
- text += format_cldp_and_time("Norwegian", cldp=cldp)
- except Exception as e:
- text += f'Norwegian:\t\tError {e} in API processing\n'
- if 'WeatherAPI' in services:
- try:
- cldp, times = weatherapi_to_result(row.LAT, row.LON, GMT)
- text += format_cldp_and_time("WeatherAPI", cldp=cldp)
- except Exception as e:
- text += f'WeatherAPI:\t\tError {e} in API processing\n'
-
- return text
-
-
-def format_cldp_and_time(api_name, cldp, times=None):
- """Formats output text for lists of cloud percents and forecast times
-
- Args:
- api_name ([type]): Name of weather source.
- cldp (List): List of floating point cloud percentage values.
- times (List, optional): List of forecast times, as datetime objects. Defaults to None.
-
- Returns:
- str: formatted text for printing
- """
- text = ''
- date_format = "%H:%M"
- if times is not None:
- text += f'{"Forecast Time:":13}\t\t' + ' '.join(time.strftime(date_format)
- for time in times) + "\n"
-
- text += f'{api_name:13}:\t\t{" ".join(f"{p:<6.0f}" for p in cldp)}\n'
- return text
-
-
-inputs = [gr.inputs.File(label='Site File with Lat/Lon and GMT Time'), gr.inputs.CheckboxGroup(label='Weather Services',
- choices=['OpenWeather', 'MET (Norwegian)', 'WeatherAPI'], default=['OpenWeather', 'MET (Norwegian)'])]
-outputs = gr.outputs.Textbox(label ='Cloud % for hour before, hour of, hour after')
-css = """* {font-family: "Lucida Console", "Courier New", monospace !important;/* <-- fonts */
- }"""
-
-gr.Interface(fn=file_to_cloud_listing, inputs=inputs, css=css, outputs=outputs,
- allow_screenshot=False).launch()
-
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Samuel Bradley-Kelly.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Samuel Bradley-Kelly.html
deleted file mode 100644
index 4c4776a496b125b44ba07f5eac29b3bb2202c09c..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Samuel Bradley-Kelly.html
+++ /dev/null
@@ -1,134 +0,0 @@
-
- Samuel Bradley-Kelly
-
-
-
-
-
-
Samuel Bradley-Kelly
-
-
-
Interview
How did you hear about SM?
popped up somwhere on LinkedIn and researched it Career
DS for 8 years HBO max - recommender systems for the past 3-4 years activley interviewing to level up Mentorship experience?
I've been mentoring college students for 8 years at UoW, and UChicago part of some grad programs mentor 2 or 3 students every year, help them prep and get a job after college some informal mentorships, but mostly gets paired about 6 months, apply, interview prep, resume review, get some projects off the ground What are beginners lacking?
getting in the habit of programming / working on something creating a habit of coding every day!! and getting in the ritual/habit of applying for jobs come up with some agreement with yourself hold yourself accountable This last person applied through N applications And how can you add value as a mentor?
always there if they want interview prep (tech, behavioural) resume review "I'm happy if you want to share a job position or two with me for a an honest assessment" assess their qualifications for ertain jobs try to refer mentees to jobs at his company likes long term relationships!!
- Questions about SM?
Can I reach out to potential mentees? Does the relationship end after the mentorship period? Can mentees come back when they want another job? What if the relationship dies out? What is SM working on now?
-
-
-
My static Space
-
-journey
- title Create AI
- section Training
- Format DataSet Inputs Files, Data Splits: 5: Teacher
- Model Build w/ SKLearn, TF, Pytorch: 3: Student
- Determine Model Performance: 1: Teacher, Student
- section Deploy
- Web Deploy Local and Cloud: 5: Teacher
- Architecture Spaces Gradio Streamlit Heroku AWS Azure and GCCP: 5: Teacher
- section Testing
- Test Model with Input Datasets: 5: Teacher
- Examples. Inputs that Work, Inputs That Break Model: 5: Teacher
- Governance - Analyze, Publish Fairness, Equity, Bias for Datasets and Outputs: 5: Teacher
-
-
-
-sequenceDiagram
- participant Alice
- participant Bob
- Alice->>John: Hello John, how are you?
- loop Healthcheck
- John->>John: Fight against hypochondria
- end
- Note right of John: Rational thoughts
-
-
-
Welcome to the Mermaid Modeler Tip Sheet
-
- You can use Mermaid inside HTML5 by including the script and a div with the class or mermaid.
-
-
- Documentation is located here:
- Mermaid documentation .
-
-
WikipediaProfilerTestforDatasets Report Dataset statistics
Number of variables 3 Number of observations 93 Missing cells 0 Missing cells (%) 0.0% Duplicate rows 0 Duplicate rows (%) 0.0% Total size in memory 2.3 KiB Average record size in memory 25.4 B
Alerts
timeHigh cardinality storytitle and 1 other fields High correlation title story and 1 other fields High correlation time story and 1 other fields High correlation title story and 1 other fields High correlation storytitle and 1 other fields High correlation timetitle and 1 other fields High correlation timeUniform timeUnique
Reproduction
Analysis started 2022-10-20 19:04:05.736377 Analysis finished 2022-10-20 19:04:09.219235 Duration 3.48 seconds Software version pandas-profiling v3.2.0 Download configuration config.json
title Categorical
HIGH CORRELATION HIGH CORRELATION
Distinct 35 Distinct (%) 37.6% Missing 0 Missing (%) 0.0% Memory size 872.0 B
International Sports Science Association (ISSA) 9
cerebrovascular disease
5 the collective nature of the text
4 New York City is the largest
4 speeches
4 Other values (30) 67
Toggle details
Length
Max length 5558 Median length 65 Mean length 258.2688172 Min length 2
Characters and Unicode
Total characters 24019 Distinct characters 84 Distinct categories 12 ? Distinct scripts 2 ? Distinct blocks 5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
Unique 15 ? Unique (%) 16.1%
Sample
1st row electromagnetic energy is absorbed or emitted in discrete packets 2nd row speeches 3rd row cerebrovascular disease 4th row antidepressant medications 5th row Scalp cooling
Common Values Value Count Frequency (%) International Sports Science Association (ISSA) 9
9.7% cerebrovascular disease 5
5.4% the collective nature of the text 4
4.3% New York City is the largest 4
4.3% speeches 4
4.3% at any age and not just during or after menopause 4
4.3% provides the prescription drug 4
4.3% description, identification, nomenclature, and classification of organisms 4
4.3% Film 4
4.3% by 4
4.3% Other values (25) 47 50.5%
Length 2022-10-20T19:04:09.299048 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ Histogram of lengths of the category
Value Count Frequency (%) the 285
7.5% of 127
3.4% a 105
2.8% to 91
2.4% and 75
2.0% 64
1.7% star 57
1.5% for 50
1.3% time 46
1.2% in 41
1.1% Other values (1006) 2850 75.2%
Most occurring characters Value Count Frequency (%) 3627 15.1%
e 2303
9.6% t 1898
7.9% a 1698
7.1% o 1469
6.1% r 1425
5.9% s 1376
5.7% i 1331
5.5% n 1223
5.1% l 749
3.1% Other values (74) 6920 28.8%
Most occurring categories Value Count Frequency (%) Lowercase Letter 18705 77.9%
Space Separator 3627
15.1% Uppercase Letter 640
2.7% Other Punctuation 433
1.8% Decimal Number 176
0.7% Control 156
0.6% Math Symbol 128
0.5% Open Punctuation 57
0.2% Close Punctuation 53
0.2% Dash Punctuation 41
0.2% Other values (2) 3
< 0.1%
Most frequent character per category Lowercase Letter Value Count Frequency (%) e 2303 12.3%
t 1898 10.1%
a 1698
9.1% o 1469
7.9% r 1425
7.6% s 1376
7.4% i 1331
7.1% n 1223
6.5% l 749
4.0% c 746
4.0% Other values (19) 4487 24.0%
Uppercase Letter Value Count Frequency (%) S 119 18.6%
I 66
10.3% T 65
10.2% A 51
8.0% C 39
6.1% N 34
5.3% P 29
4.5% B 26
4.1% R 24
3.8% M 22
3.4% Other values (14) 165 25.8%
Decimal Number Value Count Frequency (%) 0 65 36.9%
1 36 20.5%
9 15
8.5% 2 12
6.8% 5 12
6.8% 4 10
5.7% 3 9
5.1% 6 9
5.1% 7 5
2.8% 8 3
1.7%
Other Punctuation Value Count Frequency (%) , 201 46.4%
. 167 38.6%
" 18
4.2% ' 16
3.7% / 14
3.2% : 9
2.1% ; 7
1.6% * 1
0.2%
Math Symbol Value Count Frequency (%) = 123 96.1%
+ 4
3.1% − 1
0.8%
Control Value Count Frequency (%) 142 91.0%
14
9.0%
Open Punctuation Value Count Frequency (%) ( 55 96.5%
[ 2
3.5%
Close Punctuation Value Count Frequency (%) ) 51 96.2%
] 2
3.8%
Space Separator Value Count Frequency (%) 3627 100.0%
Dash Punctuation Value Count Frequency (%) - 41 100.0%
Modifier Letter Value Count Frequency (%) ˈ 2 100.0%
Other Symbol Value Count Frequency (%) ° 1 100.0%
Most occurring scripts Value Count Frequency (%) Latin 19345 80.5%
Common 4674
19.5%
Most frequent character per script Latin Value Count Frequency (%) e 2303 11.9%
t 1898
9.8% a 1698
8.8% o 1469
7.6% r 1425
7.4% s 1376
7.1% i 1331
6.9% n 1223
6.3% l 749
3.9% c 746
3.9% Other values (43) 5127 26.5%
Common Value Count Frequency (%) 3627 77.6%
, 201
4.3% . 167
3.6% 142
3.0% = 123
2.6% 0 65
1.4% ( 55
1.2% ) 51
1.1% - 41
0.9% 1 36
0.8% Other values (21) 166
3.6%
Most occurring blocks Value Count Frequency (%) ASCII 24011 > 99.9%
IPA Ext 4
< 0.1% Modifier Letters 2
< 0.1% None 1
< 0.1% Math Operators 1
< 0.1%
Most frequent character per block ASCII Value Count Frequency (%) 3627 15.1%
e 2303
9.6% t 1898
7.9% a 1698
7.1% o 1469
6.1% r 1425
5.9% s 1376
5.7% i 1331
5.5% n 1223
5.1% l 749
3.1% Other values (68) 6912 28.8%
Modifier Letters Value Count Frequency (%) ˈ 2 100.0%
IPA Ext Value Count Frequency (%) ɔ 2 50.0%
ə 1 25.0%
ʀ 1 25.0%
None Value Count Frequency (%) ° 1 100.0%
Math Operators Value Count Frequency (%) − 1 100.0%
story Categorical
HIGH CORRELATION HIGH CORRELATION
Distinct 26 Distinct (%) 28.0% Missing 0 Missing (%) 0.0% Memory size 872.0 B
https://en.wikipedia.org/wiki/Physical_fitness 22
https://en.wikipedia.org/wiki/Alzheimer%27s_disease
5 https://en.wikipedia.org/wiki/Star_tracker
4 https://en.wikipedia.org/wiki/Anthology
4 https://en.wikipedia.org/wiki/Cicero
4 Other values (21) 54
Toggle details
Length
Max length 74 Median length 51 Mean length 44.84946237 Min length 35
Characters and Unicode
Total characters 4171 Distinct characters 49 Distinct categories 5 ? Distinct scripts 2 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row https://en.wikipedia.org/wiki/Quantum 2nd row https://en.wikipedia.org/wiki/Cicero 3rd row https://en.wikipedia.org/wiki/Alzheimer%27s_disease 4th row https://en.wikipedia.org/wiki/Peripheral_neuropathy 5th row https://en.wikipedia.org/wiki/Chemotherapy
Common Values Value Count Frequency (%) https://en.wikipedia.org/wiki/Physical_fitness 22 23.7%
https://en.wikipedia.org/wiki/Alzheimer%27s_disease 5
5.4% https://en.wikipedia.org/wiki/Star_tracker 4
4.3% https://en.wikipedia.org/wiki/Anthology 4
4.3% https://en.wikipedia.org/wiki/Cicero 4
4.3% https://en.wikipedia.org/wiki/Pharmacy 4
4.3% https://en.wikipedia.org/wiki/Taxonomy 4
4.3% https://en.wikipedia.org/wiki/Financial_services 4
4.3% https://en.wikipedia.org/wiki/Securitization 4
4.3% https://en.wikipedia.org/wiki/Medicina 4
4.3% Other values (16) 34 36.6%
Length 2022-10-20T19:04:09.444965 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ Histogram of lengths of the category
Value Count Frequency (%) https://en.wikipedia.org/wiki/physical_fitness 22 23.7%
https://en.wikipedia.org/wiki/alzheimer%27s_disease 5
5.4% https://en.wikipedia.org/wiki/securitization 4
4.3% https://en.wikipedia.org/wiki/peripheral_neuropathy 4
4.3% https://en.wikipedia.org/wiki/chemotherapy 4
4.3% https://en.wikipedia.org/wiki/death 4
4.3% https://en.wikipedia.org/wiki/medicina 4
4.3% https://en.wikipedia.org/wiki/national_commission_for_culture_and_the_arts 4
4.3% https://en.wikipedia.org/wiki/financial_services 4
4.3% https://en.wikipedia.org/wiki/taxonomy 4
4.3% Other values (16) 34 36.6%
Most occurring characters Value Count Frequency (%) i 584 14.0%
/ 372
8.9% e 308
7.4% t 275
6.6% a 209
5.0% p 207
5.0% s 202
4.8% k 192
4.6% . 186
4.5% w 186
4.5% Other values (39) 1450 34.8%
Most occurring categories Value Count Frequency (%) Lowercase Letter 3305 79.2%
Other Punctuation 658
15.8% Uppercase Letter 113
2.7% Connector Punctuation 85
2.0% Decimal Number 10
0.2%
Most frequent character per category Lowercase Letter Value Count Frequency (%) i 584 17.7%
e 308
9.3% t 275
8.3% a 209
6.3% p 207
6.3% s 202
6.1% k 192
5.8% w 186
5.6% r 175
5.3% n 170
5.1% Other values (14) 797 24.1%
Uppercase Letter Value Count Frequency (%) P 30 26.5%
A 18 15.9%
C 18 15.9%
S 13 11.5%
N 6
5.3% T 5
4.4% F 4
3.5% M 4
3.5% D 4
3.5% W 2
1.8% Other values (7) 9
8.0%
Other Punctuation Value Count Frequency (%) / 372 56.5%
. 186 28.3%
: 93
14.1% % 5
0.8% , 2
0.3%
Decimal Number Value Count Frequency (%) 7 5 50.0%
2 5 50.0%
Connector Punctuation Value Count Frequency (%) _ 85 100.0%
Most occurring scripts Value Count Frequency (%) Latin 3418 81.9%
Common 753
18.1%
Most frequent character per script Latin Value Count Frequency (%) i 584 17.1%
e 308
9.0% t 275
8.0% a 209
6.1% p 207
6.1% s 202
5.9% k 192
5.6% w 186
5.4% r 175
5.1% n 170
5.0% Other values (31) 910 26.6%
Common Value Count Frequency (%) / 372 49.4%
. 186 24.7%
: 93
12.4% _ 85
11.3% 7 5
0.7% % 5
0.7% 2 5
0.7% , 2
0.3%
Most occurring blocks Value Count Frequency (%) ASCII 4171 100.0%
Most frequent character per block ASCII Value Count Frequency (%) i 584 14.0%
/ 372
8.9% e 308
7.4% t 275
6.6% a 209
5.0% p 207
5.0% s 202
4.8% k 192
4.6% . 186
4.5% w 186
4.5% Other values (39) 1450 34.8%
time Categorical
HIGH CARDINALITYHIGH CORRELATION HIGH CORRELATION UNIFORMUNIQUE
Distinct 93 Distinct (%) 100.0% Missing 0 Missing (%) 0.0% Memory size 872.0 B
2022-10-12 01:47:58.485120
1 2022-10-12 02:42:11.480184
1 2022-10-12 02:43:19.284149
1 2022-10-12 02:43:07.107545
1 2022-10-12 02:42:58.815490
1 Other values (88) 88
Toggle details
Length
Max length 26 Median length 26 Mean length 26 Min length 26
Characters and Unicode
Total characters 2418 Distinct characters 14 Distinct categories 4 ? Distinct scripts 1 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
Unique 93 ? Unique (%) 100.0%
Sample
1st row 2022-10-12 01:47:58.485120 2nd row 2022-10-12 01:48:09.186213 3rd row 2022-10-12 01:48:18.661961 4th row 2022-10-12 01:48:25.728941 5th row 2022-10-12 01:48:37.233833
Common Values Value Count Frequency (%) 2022-10-12 01:47:58.485120 1
1.1% 2022-10-12 02:42:11.480184 1
1.1% 2022-10-12 02:43:19.284149 1
1.1% 2022-10-12 02:43:07.107545 1
1.1% 2022-10-12 02:42:58.815490 1
1.1% 2022-10-12 02:42:49.080367 1
1.1% 2022-10-12 02:42:38.877997 1
1.1% 2022-10-12 02:42:33.620596 1
1.1% 2022-10-12 02:42:28.030169 1
1.1% 2022-10-12 02:42:22.874569 1
1.1% Other values (83) 83 89.2%
Length 2022-10-20T19:04:09.719696 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ Histogram of lengths of the category
Value Count Frequency (%) 2022-10-12 78 41.9%
2022-10-19 7
3.8% 2022-10-14 4
2.2% 2022-10-17 2
1.1% 01:49:17.023443 1
0.5% 01:48:37.233833 1
0.5% 01:48:45.158220 1
0.5% 01:48:48.752157 1
0.5% 01:48:56.000813 1
0.5% 01:49:00.637205 1
0.5% Other values (89) 89 47.8%
Most occurring characters Value Count Frequency (%) 2 526 21.8%
0 376 15.6%
1 345 14.3%
- 186
7.7% : 186
7.7% 4 105
4.3% 5 105
4.3% 93
3.8% . 93
3.8% 3 92
3.8% Other values (4) 311 12.9%
Most occurring categories Value Count Frequency (%) Decimal Number 1860 76.9%
Other Punctuation 279
11.5% Dash Punctuation 186
7.7% Space Separator 93
3.8%
Most frequent character per category Decimal Number Value Count Frequency (%) 2 526 28.3%
0 376 20.2%
1 345 18.5%
4 105
5.6% 5 105
5.6% 3 92
4.9% 9 85
4.6% 8 80
4.3% 6 76
4.1% 7 70
3.8%
Other Punctuation Value Count Frequency (%) : 186 66.7%
. 93 33.3%
Dash Punctuation Value Count Frequency (%) - 186 100.0%
Space Separator Value Count Frequency (%) 93 100.0%
Most occurring scripts Value Count Frequency (%) Common 2418 100.0%
Most frequent character per script Common Value Count Frequency (%) 2 526 21.8%
0 376 15.6%
1 345 14.3%
- 186
7.7% : 186
7.7% 4 105
4.3% 5 105
4.3% 93
3.8% . 93
3.8% 3 92
3.8% Other values (4) 311 12.9%
Most occurring blocks Value Count Frequency (%) ASCII 2418 100.0%
Most frequent character per block ASCII Value Count Frequency (%) 2 526 21.8%
0 376 15.6%
1 345 14.3%
- 186
7.7% : 186
7.7% 4 105
4.3% 5 105
4.3% 93
3.8% . 93
3.8% 3 92
3.8% Other values (4) 311 12.9%
Toggle correlation descriptions 2022-10-20T19:04:09.810411 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ Cramér's V (φc) Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found
here .
2022-10-20T19:04:09.918498 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ Phik (φk) Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available
here .
2022-10-20T19:04:09.028678 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ A simple visualization of nullity by column.
2022-10-20T19:04:09.154989 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/ Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
First rows title story time 0 electromagnetic energy is absorbed or emitted in discrete packets https://en.wikipedia.org/wiki/Quantum 2022-10-12 01:47:58.485120 1 speeches https://en.wikipedia.org/wiki/Cicero 2022-10-12 01:48:09.186213 2 cerebrovascular disease https://en.wikipedia.org/wiki/Alzheimer%27s_disease 2022-10-12 01:48:18.661961 3 antidepressant medications https://en.wikipedia.org/wiki/Peripheral_neuropathy 2022-10-12 01:48:25.728941 4 Scalp cooling https://en.wikipedia.org/wiki/Chemotherapy 2022-10-12 01:48:37.233833 5 pro-aging trance https://en.wikipedia.org/wiki/Death 2022-10-12 01:48:45.158220 6 Radioastronomia di Bologna (Institute for Radio Astronomy https://en.wikipedia.org/wiki/Medicina 2022-10-12 01:48:48.752157 7 by https://en.wikipedia.org/wiki/Securitization 2022-10-12 01:48:56.000813 8 New York City is the largest https://en.wikipedia.org/wiki/Financial_services 2022-10-12 01:49:00.637205 9 the collective nature of the text https://en.wikipedia.org/wiki/Anthology 2022-10-12 01:49:04.268440
Last rows title story time 83 metadata https://en.wikipedia.org/wiki/Scalable_Vector_Graphics 2022-10-17 15:42:07.764264 84 Organized efforts to search for extraterrestrial intelligence https://en.wikipedia.org/wiki/Technology 2022-10-17 15:42:25.557130 85 == Function ===\nCalcium is an essential element https://en.wikipedia.org/wiki/Calcium 2022-10-18 15:55:02.907650 86 A star trail is a type of photograph that uses long exposure times to capture diurnal circles, the apparent motion of stars in the night sky due to Earth's rotation. A star-trail photograph shows individual stars as streaks across the image, with longer exposures yielding longer arcs. The term is used for similar photos captured elsewhere, such as on board the International Space Station and on Mars.Typical shutter speeds for a star trail range from 15 minutes to several hours, requiring a "Bulb" setting on the camera to open the shutter for a period longer than usual. However, a more practiced technique is to blend a number of frames together to create the final star trail image.Star trails have been used by professional astronomers to measure the quality of observing locations for major telescopes.\n\n\n== Capture ==\n\nStar trail photographs are captured by placing a camera on a tripod, pointing the lens toward the night sky, and allowing the shutter to stay open for a long period of time. Star trails are considered relatively easy for amateur astrophotographers to create. Photographers generally make these images by using a DSLR or Mirrorless camera with its lens focus set to infinity. A cable release or intervalometer allows the photographer to hold the shutter open for the desired amount of time. Typical exposure times range from 15 minutes to many hours long, depending on the desired length of the star trail arcs for the image. Even though star trail pictures are created under low-light conditions, long exposure times allow fast films, such as ISO 200 and ISO 400. Wide-apertures, such as f/5.6 and f/4, are recommended for star trails.\n\nBecause exposure times for star trail photographs can be several hours long, camera batteries can be easily depleted. Mechanical cameras that do not require a battery to open and close the shutter have an advantage over more modern film and digital cameras that rely on battery power. On these cameras, the Bulb, or B, exposure setting keeps the shutter open. Another problem that digital cameras encounter is an increase in electronic noise with increasing exposure time. However, this can be avoided through the use of shorter exposure times that are then stacked in post production software. This avoids possible heat build up or digital noise caused from a single long exposure. \nAmerican astronaut Don Pettit recorded star trails with a digital camera from the International Space Station in Earth orbit between April and June, 2012. Pettit described his technique as follows: "My star trail images are made by taking a time exposure of about 10 to 15 minutes. However, with modern digital cameras, 30 seconds is about the longest exposure possible, due to electronic detector noise effectively snowing out the image. To achieve the longer exposures I do what many amateur astronomers do. I take multiple 30-second exposures, then 'stack' them using imaging software, thus producing the longer exposure."Star trail images have also been taken on Mars. The Spirit rover produced them while looking for meteors. Since the camera was limited to 60 second exposures the trails appear as dashed lines.\n\n\n== Earth's rotation ==\n\nStar trail photographs are possible because of the rotation of Earth about its axis. The apparent motion of the stars is recorded as mostly curved streaks on the film or detector. For observers in the Northern Hemisphere, aiming the camera northward creates an image with concentric circular arcs centered on the north celestial pole (very near Polaris). For those in the Southern Hemisphere, this same effect is achieved by aiming the camera southward. In this case, the arc streaks are centered on the south celestial pole (near Sigma Octantis). Aiming the camera eastward or westward shows straight streaks on the celestial equator, which is tilted at angle with respect to the horizon. The angular measure of this tilt depends on the photographer's latitude (L), and is equal to 90° − L.\n\n\n== Astronomical site testing ==\nStar trail photographs can be used by astronomers to determine the quality of a location for telescope observations. Star trail observations of Polaris have been used to measure the quality of seeing in the atmosphere, and the vibrations in telescope mounting systems. The first recorded suggestion of this technique is from E.S. Skinner's 1931 book A Manual of Celestial Photography.\n\n\n== Gallery ==\n\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n\n== References ==\n\n\n== External links ==\n\n4 Steps To Creating Star Trails Photos Using Stacking Software\nStar trail photography\nStarStaX free multi-platform star trail software https://en.wikipedia.org/wiki/Star_trail 2022-10-19 00:58:13.680029 87 E.S. Skinner https://en.wikipedia.org/wiki/Star_trail 2022-10-19 00:58:16.667325 88 A star tracker is an optical device that measures the positions of stars using photocells or a camera.\nAs the positions of many stars have been measured by astronomers to a high degree of accuracy, a star tracker on a satellite or spacecraft may be used to determine the orientation (or attitude) of the spacecraft with respect to the stars. In order to do this, the star tracker must obtain an image of the stars, measure their apparent position in the reference frame of the spacecraft, and identify the stars so their position can be compared with their known absolute position from a star catalog. A star tracker may include a processor to identify stars by comparing the pattern of observed stars with the known pattern of stars in the sky.\n\n\n== History ==\nIn the 1950s and early 1960s, star trackers were an important part of early long-range ballistic missiles and cruise missiles, in the era when inertial navigation systems (INS) were not sufficiently accurate for intercontinental ranges.Consider a Cold War missile flying towards its target; it initially starts by flying northward, passes over the arctic, and then begins flying southward again. From the missile's perspective, stars behind it appear to move closer to the southern horizon while those in front are rising. Before flight, one can calculate the relative angle of a star based on where the missile should be at that instant if it is in the correct location. That can then be compared to the measured location to produce an "error off" signal that can be used to bring the missile back onto its correct trajectory.Due to the Earth's rotation, stars that are in a usable location change over the course of a day and the location of the target. Generally, a selection of several bright stars would be used and one would be selected at launch time. For guidance systems based solely on star tracking, some sort of recording mechanism, typically a magnetic tape, was pre-recorded with a signal that represented the angle of the star over the period of a day. At launch, the tape was forwarded to the appropriate time. During the flight, the signal on the tape was used to roughly position a telescope so it would point at the expected position of the star. At the telescope's focus was a photocell and some sort of signal-generator, typically a spinning disk known as a chopper. The chopper causes the image of the star to repeatedly appear and disappear on the photocell, producing a signal that was then smoothed to produce an alternating current output. The phase of that signal was compared to the one on the tape to produce a guidance signal.Star trackers were often combined with an INS. INS systems measure accelerations and integrate those over time to determine a velocity and, optionally, double-integrate to produce a location relative to its launch location. Even tiny measurement errors, when integrated, adds up to an appreciable error known as "drift". For instance, the N-1 navigation system developed for the SM-64 Navaho cruise missile drifted at a rate of 1 nautical mile per hour, meaning that after a two-hour flight the INS would be indicating a position 2 nautical miles (3.7 km; 2.3 mi) away from its actual location. This was outside the desired accuracy of about half a mile.\nIn the case of an INS, the magnetic tape can be removed and those signals instead provided by the INS. The rest of the system works as before; the signal from the INS roughly positions the star tracker, which then measures the actual location of the star and produces an error signal. This signal is then used to correct the position being generated from the INS, reducing the accumulated drift back to the limit of the accuracy of the tracker. These "stellar inertial" systems were especially common from the 1950s through the 1980s, although some systems use it to this day.\n\n\n== Current technology ==\nMany models are currently available. There also exist open projects designed to be used for the global CubeSat researchers and developers community.\nStar trackers, which require high sensitivity, may become confused by sunlight reflected from the spacecraft, or by exhaust gas plumes from the spacecraft thrusters (either sunlight reflection or contamination of the star tracker window). Star trackers are also susceptible to a variety of errors (low spatial frequency, high spatial frequency, temporal, ...) in addition to a variety of optical sources of error (spherical aberration, chromatic aberration, etc.). There are also many potential sources of confusion for the star identification algorithm (planets, comets, supernovae, the bimodal character of the point spread function for adjacent stars, other nearby satellites, point-source light pollution from large cities on Earth, ...). There are roughly 57 bright navigational stars in common use. However, for more complex missions, entire star field databases are used to determine spacecraft orientation. A typical star catalog for high-fidelity attitude determination is originated from a standard base catalog (for example from the United States Naval Observatory) and then filtered to remove problematic stars, for example due to apparent magnitude variability, color index uncertainty, or a location within the Hertzsprung-Russell diagram implying unreliability. These types of star catalogs can have thousands of stars stored in memory on board the spacecraft, or else processed using tools at the ground station and then uploaded.\n\n\n== See also ==\nCelestial navigation\nGoTo (telescopes)\nSun sensor\n\n\n== References == https://en.wikipedia.org/wiki/Star_tracker 2022-10-19 00:58:42.472490 89 Celestial navigation\nGoTo (telescopes)\nSun sensor https://en.wikipedia.org/wiki/Star_tracker 2022-10-19 00:58:45.548139 90 A star tracker is an optical device that measures the positions of stars using photocells or a camera.\nAs the positions of many stars have been measured by astronomers to a high degree of accuracy, a star tracker on a satellite or spacecraft may be used to determine the orientation (or attitude) of the spacecraft with respect to the stars. In order to do this, the star tracker must obtain an image of the stars, measure their apparent position in the reference frame of the spacecraft, and identify the stars so their position can be compared with their known absolute position from a star catalog. A star tracker may include a processor to identify stars by comparing the pattern of observed stars with the known pattern of stars in the sky.\n\n\n== History ==\nIn the 1950s and early 1960s, star trackers were an important part of early long-range ballistic missiles and cruise missiles, in the era when inertial navigation systems (INS) were not sufficiently accurate for intercontinental ranges.Consider a Cold War missile flying towards its target; it initially starts by flying northward, passes over the arctic, and then begins flying southward again. From the missile's perspective, stars behind it appear to move closer to the southern horizon while those in front are rising. Before flight, one can calculate the relative angle of a star based on where the missile should be at that instant if it is in the correct location. That can then be compared to the measured location to produce an "error off" signal that can be used to bring the missile back onto its correct trajectory.Due to the Earth's rotation, stars that are in a usable location change over the course of a day and the location of the target. Generally, a selection of several bright stars would be used and one would be selected at launch time. For guidance systems based solely on star tracking, some sort of recording mechanism, typically a magnetic tape, was pre-recorded with a signal that represented the angle of the star over the period of a day. At launch, the tape was forwarded to the appropriate time. During the flight, the signal on the tape was used to roughly position a telescope so it would point at the expected position of the star. At the telescope's focus was a photocell and some sort of signal-generator, typically a spinning disk known as a chopper. The chopper causes the image of the star to repeatedly appear and disappear on the photocell, producing a signal that was then smoothed to produce an alternating current output. The phase of that signal was compared to the one on the tape to produce a guidance signal.Star trackers were often combined with an INS. INS systems measure accelerations and integrate those over time to determine a velocity and, optionally, double-integrate to produce a location relative to its launch location. Even tiny measurement errors, when integrated, adds up to an appreciable error known as "drift". For instance, the N-1 navigation system developed for the SM-64 Navaho cruise missile drifted at a rate of 1 nautical mile per hour, meaning that after a two-hour flight the INS would be indicating a position 2 nautical miles (3.7 km; 2.3 mi) away from its actual location. This was outside the desired accuracy of about half a mile.\nIn the case of an INS, the magnetic tape can be removed and those signals instead provided by the INS. The rest of the system works as before; the signal from the INS roughly positions the star tracker, which then measures the actual location of the star and produces an error signal. This signal is then used to correct the position being generated from the INS, reducing the accumulated drift back to the limit of the accuracy of the tracker. These "stellar inertial" systems were especially common from the 1950s through the 1980s, although some systems use it to this day.\n\n\n== Current technology ==\nMany models are currently available. There also exist open projects designed to be used for the global CubeSat researchers and developers community.\nStar trackers, which require high sensitivity, may become confused by sunlight reflected from the spacecraft, or by exhaust gas plumes from the spacecraft thrusters (either sunlight reflection or contamination of the star tracker window). Star trackers are also susceptible to a variety of errors (low spatial frequency, high spatial frequency, temporal, ...) in addition to a variety of optical sources of error (spherical aberration, chromatic aberration, etc.). There are also many potential sources of confusion for the star identification algorithm (planets, comets, supernovae, the bimodal character of the point spread function for adjacent stars, other nearby satellites, point-source light pollution from large cities on Earth, ...). There are roughly 57 bright navigational stars in common use. However, for more complex missions, entire star field databases are used to determine spacecraft orientation. A typical star catalog for high-fidelity attitude determination is originated from a standard base catalog (for example from the United States Naval Observatory) and then filtered to remove problematic stars, for example due to apparent magnitude variability, color index uncertainty, or a location within the Hertzsprung-Russell diagram implying unreliability. These types of star catalogs can have thousands of stars stored in memory on board the spacecraft, or else processed using tools at the ground station and then uploaded.\n\n\n== See also ==\nCelestial navigation\nGoTo (telescopes)\nSun sensor\n\n\n== References == https://en.wikipedia.org/wiki/Star_tracker 2022-10-19 03:03:35.059016 91 Celestial navigation\nGoTo (telescopes)\nSun sensor https://en.wikipedia.org/wiki/Star_tracker 2022-10-19 03:03:39.599730 92 Film https://en.wikipedia.org/wiki/National_Commission_for_Culture_and_the_Arts 2022-10-19 03:05:22.704793
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diff --git a/spaces/bioriAsaeru/text-to-voice/History Of Subcontinent From 712 To 1947 In Urdu Pdf The Cultural and Religious Diversity of the Region.md b/spaces/bioriAsaeru/text-to-voice/History Of Subcontinent From 712 To 1947 In Urdu Pdf The Cultural and Religious Diversity of the Region.md
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+++ /dev/null
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-
-The history of preceding the country's independence in 1947[1] is shared with that of Afghanistan, India, and Iran. Spanning the western expanse of the Indian subcontinent and the eastern borderlands of the Iranian plateau, the region of present-day Pakistan served both as the fertile ground of a major civilization and as the gateway of South Asia to Central Asia and the Near East.[2][3]
-The Kushan Empire expanded out of what is now Afghanistan into the northwest of the subcontinent under the leadership of their first emperor, Kujula Kadphises, about the middle of the 1st century CE. They were descended from an Indo-European, Central Asian people called the Yuezhi,[54][55] a branch of which was known as the Kushans. By the time of his grandson, Kanishka the Great, the empire spread to encompass much of Afghanistan[56] and the northern parts of the Indian subcontinent at least as far as Saketa and Sarnath near Varanasi (Benares).[57]
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-From the late 12th century onwards, Muslim empires dominated the subcontinent, most notably the Delhi sultanate and Mughal empire.[2] Various other Muslim kingdoms ruled most of South Asia from the mid-14th to late 18th centuries, including the Bahmani, Bengal, Gujarat, Malwa, Mysore, Carnatic and Deccan Sultanates.[3][4] Though the Muslim dynasties in India were diversed in origin, they were linked together by the Persianate culture and Islam.
-The Mughal empire was the second major Islamic empire to assert dominance over most of the Indian subcontinent between 1526 and 1857. The empire was founded by the Turco-Mongol leader Babur in 1526, when he defeated Ibrahim Lodi, the last ruler of the Delhi Sultanate at the First Battle of Panipat. Babur, Humayun, Akbar, Jahangir, Shah Jahan, and Aurangzeb are known as the six great Mughal Emperors. Apart from the brief interruption by the Afghan Sur dynasty between 1540 and 1556, the Mughals continued to rule in one form or other till 1857.
-When Pakistan became a country on August 14th, 1947, to form the largest Muslim state in the world at that time. The creation of Pakistan was catalyst to the largest demographic movement in recorded history. Nearly seventeen million people-Hindus, Muslims, and Sikhs-are reported to have moved in both directions between India and the two wings of Pakistan (the eastern wing is now Bangladesh). Sixty million of the ninety-five million Muslims on the Indian subcontinent became citizens of Pakistan at the time of its creation. Subsequently, thirty-five million Muslims remained inside India making it the largest Muslim minority in a non-Muslim state.
-After Ayub Khan, General Agha Muhammad Yahya Khan headed the second military regime from 1969-1971. By that time the country had been under military rule for thirteen of its twenty-five years of existence. This second military regime emphasized the extent to which the process of centralization under bureaucratic and military tutelage had fragmented Pakistani society and politics. The general elections of 1970 on the basis of adult franchise revealed for the first time ever in Pakistan's history how regionalism and social conflict had come to dominate politics despite the efforts at controlled development. The Awami League, led by Mujibur Rahman, campaigned on a six-point program of provincial autonomy, capturing all but one seat in East Pakistan and securing an absolute majority in the national assembly. In West Pakistan the Pakistan People's Party, led by Zulfiqar Ali Bhutto, had a populist platform that stole the thunder from the Islamic parties (the Muslim League, the oldest political party captured no more than a few seats) and emerged as the largest single bloc. The prospect of an Awami Leagues government was a threat to politicians in West Pakistan who in conspiracy with the military leadership prevented Mujibur from taking the reins of power. This was the final straw for the east wing who was already fed up with the their under-representation in all sectors of the government, economic deprivation and then the suppression of the democratic process. An armed rebellion in East Pakistan engendered all of these frustrations, which caused Indian military intervention to crush it. Pakistan was now involved in its third war with India, thus clearing the way for the establishment of Bangladesh in 1971.
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-Muslim Rule in India 712-1857 - Free download as Powerpoint Presentation (.ppt), PDF File (.pdf), Text File (.txt) .. American Pie Tamil Dubbed Downlo
-In 1947, after 200 years of control, the British finally quit the Indian subcontinent. Before leaving, the colonizers drew a line in the sand that formed two new dominions: Muslim-majority Pakistan and Hindu-majority India. Some 15 million people migrated (the largest human migration in history) and one to two million perished in the communal violence that followed.
-Secularism, as conceived in our subcontinent, is a matter of having different religious communities living together in tranquillity and harmony, whereas in Pakistan, especially west Pakistan, from where many minorities choose to move out to India, secularism takes on a different role of being a matter of tranquillity and harmony between different sects of Islam. And yet getting to that point is very hard when the sects are defined in different theological terms and each theology feels that its word is the true interpretation of the word of God.
-From 1947 onwards, when migrants from India, known as the Mohājirs, came to Sindh, the repertoire that dominated the local religiosity was that of a vernacular Sufism, to which both Sindhi Muslims and Hindus of all faiths subscribed. Consequently, in the competition between Sindhis and Mohājirs for the domination of the city of Hyderabad, negotiations between different religious repertoires played a prominent role. My hypothesis is that the Mawlā jā Qadam represented a crucial stake in the showdown between the Sindhis and the Mohājirs, more than a vector for the integration of the Mohājirs in the urban landscape. In what follows, I will demonstrate that, although the onomastic change of the site unambiguously indicates its coming under the control of the Mohājirs, as explained below, the ritual practices show a resilience of the vernacular Sufi substratum of Sindh.
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-
-A Full Mask R-CNN described in recursive instantiation (click to expand)
-
-
-```eval_rst
-.. literalinclude:: ../../configs/common/models/mask_rcnn_fpn.py
- :language: python
- :linenos:
-```
-
-
-
-There are also objects or logic that cannot be described simply by a dictionary,
-such as reused objects or method calls. They may require some refactoring
-to work with recursive instantiation.
-
-## Using Model Zoo LazyConfigs
-
-We provide some configs in the model zoo using the LazyConfig system, for example:
-
-* [common baselines](../../configs/common/).
-* [new Mask R-CNN baselines](../../configs/new_baselines/)
-
-After installing detectron2, they can be loaded by the model zoo API
-[model_zoo.get_config](../modules/model_zoo.html#detectron2.model_zoo.get_config).
-
-Using these as references, you're free to define custom config structure / fields for your own
-project, as long as your training script can understand them.
-Despite of this, our model zoo configs still follow some simple conventions for consistency, e.g.
-`cfg.model` defines a model object, `cfg.dataloader.{train,test}` defines dataloader objects,
-and `cfg.train` contains training options in key-value form.
-In addition to `print()`, a better way to view the structure of a config is like this:
-```python
-from detectron2.model_zoo import get_config
-from detectron2.config import LazyConfig
-print(LazyConfig.to_py(get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py")))
-```
-From the output it's easier to find relevant options to change, e.g.
-`dataloader.train.total_batch_size` for the batch size, or `optimizer.lr` for base learning rate.
-
-We provide a reference training script
-[tools/lazyconfig_train_net.py](../../tools/lazyconfig_train_net.py),
-that can train/eval our model zoo configs.
-It also shows how to support command line value overrides.
-
-To demonstrate the power and flexibility of the new system, we show that
-[a simple config file](../../configs/Misc/torchvision_imagenet_R_50.py)
-can let detectron2 train an ImageNet classification model from torchvision, even though
-detectron2 contains no features about ImageNet classification.
-This can serve as a reference for using detectron2 in other deep learning tasks.
-
-## Summary
-
-By using recursive instantiation to create objects,
-we avoid passing a giant config to many places, because `cfg` is only passed to `instantiate`.
-This has the following benefits:
-
-* It's __non-intrusive__: objects to be constructed are config-agnostic, regular Python
- functions/classes.
- They can even live in other libraries. For example,
- `{"_target_": "torch.nn.Conv2d", "in_channels": 10, "out_channels": 10, "kernel_size": 1}`
- defines a conv layer.
-* __Clarity__ of what function/classes will be called, and what arguments they use.
-* `cfg` doesn't need pre-defined keys and structures. It's valid as long as it translates to valid
- code. This gives a lot more __flexibility__.
-* You can still pass huge dictionaries as arguments, just like the old way.
-
-Recursive instantiation and Python syntax are orthogonal: you can use one without the other.
-But by putting them together, the config file looks a lot like the code that will be executed:
-
-
-
-However, the config file just defines dictionaries, which can be easily manipulated further
-by composition or overrides.
-The corresponding code will only be executed
-later when `instantiate` is called. In some way,
-in config files we're writing "editable code" that will be "lazily executed" later when needed.
-That's why we call this system "LazyConfig".
diff --git a/spaces/cbr/swp/swapper.py b/spaces/cbr/swp/swapper.py
deleted file mode 100644
index f7f359961e465004fed3311b8dee0bf51c56b649..0000000000000000000000000000000000000000
--- a/spaces/cbr/swp/swapper.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import cv2
-import numpy as np
-from insightface.utils import face_align
-from face_parsing.swap import swap_regions
-from utils import add_logo_to_image
-
-swap_options_list = [
- "All face",
- "Age less than",
- "Age greater than",
- "All Male",
- "All Female",
- "Specific Face",
-]
-
-
-def swap_face(whole_img, target_face, source_face, models):
- inswapper = models.get("swap")
- face_enhancer = models.get("enhance", None)
- face_parser = models.get("face_parser", None)
- fe_enable = models.get("enhance_sett", False)
-
- bgr_fake, M = inswapper.get(whole_img, target_face, source_face, paste_back=False)
- image_size = 128 if not fe_enable else 512
- aimg, _ = face_align.norm_crop2(whole_img, target_face.kps, image_size=image_size)
-
- if face_parser is not None:
- fp_enable, includes, smooth_mask, blur_amount = models.get("face_parser_sett")
- if fp_enable:
- bgr_fake = swap_regions(
- bgr_fake, aimg, face_parser, smooth_mask, includes=includes, blur=blur_amount
- )
-
- if fe_enable:
- _, bgr_fake, _ = face_enhancer.enhance(
- bgr_fake, paste_back=True, has_aligned=True
- )
- bgr_fake = bgr_fake[0]
- M /= 0.25
-
- IM = cv2.invertAffineTransform(M)
-
- img_white = np.full((aimg.shape[0], aimg.shape[1]), 255, dtype=np.float32)
- bgr_fake = cv2.warpAffine(
- bgr_fake, IM, (whole_img.shape[1], whole_img.shape[0]), borderValue=0.0
- )
- img_white = cv2.warpAffine(
- img_white, IM, (whole_img.shape[1], whole_img.shape[0]), borderValue=0.0
- )
- img_white[img_white > 20] = 255
- img_mask = img_white
- mask_h_inds, mask_w_inds = np.where(img_mask == 255)
- mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
- mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
- mask_size = int(np.sqrt(mask_h * mask_w))
-
- k = max(mask_size // 10, 10)
- img_mask = cv2.erode(img_mask, np.ones((k, k), np.uint8), iterations=1)
-
- k = max(mask_size // 20, 5)
- kernel_size = (k, k)
- blur_size = tuple(2 * i + 1 for i in kernel_size)
- img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) / 255
-
- img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1])
- fake_merged = img_mask * bgr_fake + (1 - img_mask) * whole_img.astype(np.float32)
- fake_merged = add_logo_to_image(fake_merged.astype("uint8"))
- return fake_merged
-
-
-def swap_face_with_condition(
- whole_img, target_faces, source_face, condition, age, models
-):
- swapped = whole_img.copy()
-
- for target_face in target_faces:
- if condition == "All face":
- swapped = swap_face(swapped, target_face, source_face, models)
- elif condition == "Age less than" and target_face["age"] < age:
- swapped = swap_face(swapped, target_face, source_face, models)
- elif condition == "Age greater than" and target_face["age"] > age:
- swapped = swap_face(swapped, target_face, source_face, models)
- elif condition == "All Male" and target_face["gender"] == 1:
- swapped = swap_face(swapped, target_face, source_face, models)
- elif condition == "All Female" and target_face["gender"] == 0:
- swapped = swap_face(swapped, target_face, source_face, models)
-
- return swapped
-
-
-def swap_specific(source_specifics, target_faces, whole_img, models, threshold=0.6):
- swapped = whole_img.copy()
-
- for source_face, specific_face in source_specifics:
- specific_embed = specific_face["embedding"]
- specific_embed /= np.linalg.norm(specific_embed)
-
- for target_face in target_faces:
- target_embed = target_face["embedding"]
- target_embed /= np.linalg.norm(target_embed)
- cosine_distance = 1 - np.dot(specific_embed, target_embed)
- if cosine_distance > threshold:
- continue
- swapped = swap_face(swapped, target_face, source_face, models)
-
- return swapped
diff --git a/spaces/ccolas/TastyPiano/src/cocktails/config.py b/spaces/ccolas/TastyPiano/src/cocktails/config.py
deleted file mode 100644
index bce5b65a666caf9972ea64933a4a74eb4e2532c0..0000000000000000000000000000000000000000
--- a/spaces/ccolas/TastyPiano/src/cocktails/config.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-
-REPO_PATH = '/'.join(os.path.abspath(__file__).split('/')[:-3]) + '/'
-
-# QUADRUPLETS_PATH = REPO_PATH + 'checkpoints/cocktail_representation/quadruplets.pickle'
-INGREDIENTS_LIST_PATH = REPO_PATH + 'checkpoints/cocktail_representation/ingredient_list.csv'
-# ING_MATCH_SCORE_Q_PATH = REPO_PATH + 'checkpoints/cocktail_representation/ingredient_match_score_q.txt'
-# ING_MATCH_SCORE_COUNT_PATH = REPO_PATH + 'checkpoints/cocktail_representation/ingredient_match_score_count.txt'
-# COCKTAIL_DATA_FOLDER_PATH = REPO_PATH + 'checkpoints/cocktail_representation/'
-COCKTAILS_CSV_DATA = REPO_PATH + 'checkpoints/cocktail_representation/cocktails_data.csv'
-# COCKTAILS_PKL_DATA = REPO_PATH + 'checkpoints/cocktail_representation/cocktails_data.pkl'
-# COCKTAILS_URL_DATA = REPO_PATH + 'checkpoints/cocktail_representation/cocktails_names_urls.pkl'
-EXPERIMENT_PATH = REPO_PATH + 'experiments/cocktails/representation_learning/'
-# ANALYSIS_PATH = REPO_PATH + 'experiments/cocktails/representation_analysis/'
-# REPRESENTATIONS_PATH = REPO_PATH + 'experiments/cocktails/learned_representations/'
-
-FULL_COCKTAIL_REP_PATH = REPO_PATH + "/checkpoints/cocktail_representation/handcoded_reps/cocktail_handcoded_reps_minmax_norm-1_1_dim13_customkeys.txt"
-RECIPE2FEATURES_PATH = REPO_PATH + "/checkpoints/cocktail_representation/" # get this by running run_without_vae
-COCKTAIL_REP_CHKPT_PATH = REPO_PATH + "/checkpoints/cocktail_representation/handcoded_reps/"
-# FULL_COCKTAIL_REP_PATH = REPO_PATH + "experiments/cocktails/representation_analysis/affective_mapping/clustered_representations/all_cocktail_reps_norm-1_1_custom_keys_dim13.txt'
-COCKTAIL_NN_PATH = REPO_PATH + "/checkpoints/cocktail_representation/handcoded_reps/nn_model.pickle"
\ No newline at end of file
diff --git a/spaces/chaitanya9/emotion_recognizer/app.py b/spaces/chaitanya9/emotion_recognizer/app.py
deleted file mode 100644
index cd1148960d8588fe8894245690e5c3ee1c671fea..0000000000000000000000000000000000000000
--- a/spaces/chaitanya9/emotion_recognizer/app.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import gradio as gr
-import pickle
-
-filename = "Our_Trained_knn_model.pickle"
-
-def predict(inp):
- best_classifiers = pickle.load(open(filename, 'rb'))
- emotion = best_classifiers.predict(inp)
- return emotion
-
-if __name__ == "__main__":
- audio = gr.inputs.Audio(source="upload", type="numpy", label=None, optional=False)
-
- #gr.Interface(fn=emotion_recognizer, inputs=audio, outputs="text", capture_session=True).launch()
-
-
- iface = gr.Interface(fn=predict, inputs = "audio", outputs = "text")
- iface.launch(share=True)
-
diff --git a/spaces/chansung/textual-inversion-pipeline/constants.py b/spaces/chansung/textual-inversion-pipeline/constants.py
deleted file mode 100644
index e2662d9e3e5dadad9291e0741d4d7b88479a19b1..0000000000000000000000000000000000000000
--- a/spaces/chansung/textual-inversion-pipeline/constants.py
+++ /dev/null
@@ -1,135 +0,0 @@
-css = """
- .gradio-container {
- font-family: 'IBM Plex Sans', sans-serif;
- }
- .gr-button {
- color: white;
- border-color: black;
- background: black;
- }
- input[type='range'] {
- accent-color: black;
- }
- .dark input[type='range'] {
- accent-color: #dfdfdf;
- }
- .container {
- max-width: 730px;
- margin: auto;
- padding-top: 1.5rem;
- }
- #gallery {
- min-height: 22rem;
- margin-bottom: 15px;
- margin-left: auto;
- margin-right: auto;
- border-bottom-right-radius: .5rem !important;
- border-bottom-left-radius: .5rem !important;
- }
- #gallery>div>.h-full {
- min-height: 20rem;
- }
- .details:hover {
- text-decoration: underline;
- }
- .gr-button {
- white-space: nowrap;
- }
- .gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
- }
- #advanced-btn {
- font-size: .7rem !important;
- line-height: 19px;
- margin-top: 12px;
- margin-bottom: 12px;
- padding: 2px 8px;
- border-radius: 14px !important;
- }
- #advanced-options {
- display: none;
- margin-bottom: 20px;
- }
- .footer {
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
- }
- .footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
- .acknowledgments h4{
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
- }
- .animate-spin {
- animation: spin 1s linear infinite;
- }
- @keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
- }
- #share-btn-container {
- display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
- margin-top: 10px;
- margin-left: auto;
- }
- #share-btn {
- all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
- }
- #share-btn * {
- all: unset;
- }
- #share-btn-container div:nth-child(-n+2){
- width: auto !important;
- min-height: 0px !important;
- }
- #share-btn-container .wrap {
- display: none !important;
- }
-
- .gr-form{
- flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
- }
- #prompt-container{
- gap: 0;
- }
- #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
- #component-16{border-top-width: 1px!important;margin-top: 1em}
- .image_duplication{position: absolute; width: 100px; left: 50px}
-"""
-
-
-examples = [
- ["Yoda", "low quality", 40],
- ["A red pokemon with green eyes", 40],
- ["cute Sundar Pihcai creature", 40],
- ["Hello kitty", 40],
-]
-
-num_images_to_gen = 3
-
-img_height = img_width = 512
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/exps/default/yolox_l.py b/spaces/chendl/compositional_test/multimodal/YOLOX/exps/default/yolox_l.py
deleted file mode 100644
index 50833ca38c51fe9ac5e327d7c1c0561fb62249aa..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/exps/default/yolox_l.py
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
- def __init__(self):
- super(Exp, self).__init__()
- self.depth = 1.0
- self.width = 1.0
- self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
diff --git a/spaces/chongjie/co-tracker_MVP/app.py b/spaces/chongjie/co-tracker_MVP/app.py
deleted file mode 100644
index fa1fc4e5283eddcd4cf9826cc0dc3fa0305bd3f3..0000000000000000000000000000000000000000
--- a/spaces/chongjie/co-tracker_MVP/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import gradio as gr
-import os
-import torch
-import numpy as np
-
-from PIL import Image
-from cotracker.utils.visualizer import Visualizer, read_video_from_path
-from cotracker.predictor import CoTrackerPredictor
-
-checkpoint='./checkpoints/cotracker_stride_4_wind_8.pth'
-def cotracker(video_path: str, grid_size: int, grid_query_frame: int, backward_tracking: bool):
- # load the input video frame by frame
- video = read_video_from_path(video_path)
- video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float()
- model = CoTrackerPredictor(checkpoint=checkpoint)
- if torch.cuda.is_available():
- model = model.cuda()
- video = video.cuda()
- else:
- print("CUDA is not available!")
-
- pred_tracks, pred_visibility = model(
- video,
- grid_size=grid_size,
- grid_query_frame=grid_query_frame,
- backward_tracking=backward_tracking,
- )
- print("computed")
-
- # save a video with predicted tracks
- seq_name = video_path.split("/")[-1]
- vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3)
- vis.visualize(video, pred_tracks, query_frame=grid_query_frame)
-
- return "./saved_videos/video_pred_track.mp4"
-
-iface = gr.Interface(
- fn=cotracker,
- inputs=[
- gr.inputs.Video(label='video', type='mp4'),
- gr.inputs.Slider(minimum=0, maximum=20, step=1, default=10, label="Grid Size"),
- gr.inputs.Slider(minimum=0, maximum=10, step=1, default=0, label="Grid Query Frame"),
- gr.inputs.Checkbox(label="Backward Tracking"),
- ],
- outputs=gr.outputs.Video(label="Output")
-)
-iface.queue()
-iface.launch()
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/setup.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/setup.py
deleted file mode 100644
index 6ea944e1887758f91965b080f2d7a8eb9a1cf915..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/setup.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import print_function
-from setuptools import setup, find_packages
-import os
-import shutil
-import platform
-
-# make the faiss python package dir
-shutil.rmtree("faiss", ignore_errors=True)
-os.mkdir("faiss")
-shutil.copytree("contrib", "faiss/contrib")
-shutil.copyfile("__init__.py", "faiss/__init__.py")
-shutil.copyfile("loader.py", "faiss/loader.py")
-shutil.copyfile("class_wrappers.py", "faiss/class_wrappers.py")
-shutil.copyfile("gpu_wrappers.py", "faiss/gpu_wrappers.py")
-shutil.copyfile("extra_wrappers.py", "faiss/extra_wrappers.py")
-shutil.copyfile("array_conversions.py", "faiss/array_conversions.py")
-
-ext = ".pyd" if platform.system() == 'Windows' else ".so"
-prefix = "Release/" * (platform.system() == 'Windows')
-
-swigfaiss_generic_lib = f"{prefix}_swigfaiss{ext}"
-swigfaiss_avx2_lib = f"{prefix}_swigfaiss_avx2{ext}"
-
-found_swigfaiss_generic = os.path.exists(swigfaiss_generic_lib)
-found_swigfaiss_avx2 = os.path.exists(swigfaiss_avx2_lib)
-
-assert (found_swigfaiss_generic or found_swigfaiss_avx2), \
- f"Could not find {swigfaiss_generic_lib} or " \
- f"{swigfaiss_avx2_lib}. Faiss may not be compiled yet."
-
-if found_swigfaiss_generic:
- print(f"Copying {swigfaiss_generic_lib}")
- shutil.copyfile("swigfaiss.py", "faiss/swigfaiss.py")
- shutil.copyfile(swigfaiss_generic_lib, f"faiss/_swigfaiss{ext}")
-
-if found_swigfaiss_avx2:
- print(f"Copying {swigfaiss_avx2_lib}")
- shutil.copyfile("swigfaiss_avx2.py", "faiss/swigfaiss_avx2.py")
- shutil.copyfile(swigfaiss_avx2_lib, f"faiss/_swigfaiss_avx2{ext}")
-
-long_description="""
-Faiss is a library for efficient similarity search and clustering of dense
-vectors. It contains algorithms that search in sets of vectors of any size,
- up to ones that possibly do not fit in RAM. It also contains supporting
-code for evaluation and parameter tuning. Faiss is written in C++ with
-complete wrappers for Python/numpy. Some of the most useful algorithms
-are implemented on the GPU. It is developed by Facebook AI Research.
-"""
-setup(
- name='faiss',
- version='1.7.4',
- description='A library for efficient similarity search and clustering of dense vectors',
- long_description=long_description,
- url='https://github.com/facebookresearch/faiss',
- author='Matthijs Douze, Jeff Johnson, Herve Jegou, Lucas Hosseini',
- author_email='matthijs@fb.com',
- license='MIT',
- keywords='search nearest neighbors',
-
- install_requires=['numpy'],
- packages=['faiss', 'faiss.contrib'],
- package_data={
- 'faiss': ['*.so', '*.pyd'],
- },
- zip_safe=False,
-)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_B_.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_B_.py
deleted file mode 100644
index 8a6c14c444595508c35bdc6ebace60b4bbbbdaba..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_B_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_B_(table_T_S_I_V_):
- pass
diff --git a/spaces/cihyFjudo/fairness-paper-search/American Conquest Divided Nation Patch Windows 10 Download and Install the Latest Version Here.md b/spaces/cihyFjudo/fairness-paper-search/American Conquest Divided Nation Patch Windows 10 Download and Install the Latest Version Here.md
deleted file mode 100644
index 1d8c6942a48cfc90938f340e35dc93ca6357d789..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/American Conquest Divided Nation Patch Windows 10 Download and Install the Latest Version Here.md
+++ /dev/null
@@ -1,6 +0,0 @@
-American Conquest: Fight Back is a stand-alone expansion pack for American Conquest. It features five new nations: Germany, Russia, Haida, Portugal and the Netherlands, and 50 new units. In addition to new campaigns featuring the Mayas, the Germans, the Haida and the Russians, a new 'battlefield' game mode is available. The German campaign briefly chronicles the expedition of Ambrosius Ehinger and Georg Hohermuth whereas the Russian campaign concerns the Alaskan campaign under Alexander Baranov. The new Haida campaign is from the Haida point of view of the Russian expedition. The Mayas campaign covers details from the Spanish conquest of Yucatán.
-American Conquest Divided Nation Patch Windows 10 Download Zip • https://tinurli.com/2uwjDW
A total conversion mod for the game was released in 2006, with patches and different versions released up until 2009, called European Warfare: Napoleonica that transferred the player back to 19th Century war-torn Europe during the Napoleonic Wars. The project was undertaken by Gexozoid (helped by the Hawks group and other associates) in 2007 and since then had a fairly active community on GameRanger and forums up until 2015. The Hawks Group recreated a vast database of historical battles that can be played in multiplayer by up to 7 players at the same time, sharing armies or fighting in co-op. It can still be downloaded at their original website or on ModDB. The Mod features over 200 new units and around 20 new buildings that range from a faction's Barracks to fortifications in the form of manned cannon towers and breastworks much like in Cossacks. 12 fully playable nations include: France, England, Poland, Austria, Prussia, Russia, Spain, Italy, the Ottoman Empire, Confederacy of Rhine, Sweden and the USA.
aaccfb2cb3 {}
-
-const Textarea = React.forwardRef(
- ({ className, ...props }, ref) => {
- return (
-
- )
- }
-)
-Textarea.displayName = 'Textarea'
-
-export { Textarea }
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/idctdsp_init_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/idctdsp_init_arm.c
deleted file mode 100644
index ebc90e4b49e69eeb01307c5de6887bde319667bb..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/idctdsp_init_arm.c
+++ /dev/null
@@ -1,94 +0,0 @@
-/*
- * ARM-optimized IDCT functions
- * Copyright (c) 2001 Lionel Ulmer
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include
-#include
-
-#include "libavutil/attributes.h"
-#include "libavutil/cpu.h"
-#include "libavutil/arm/cpu.h"
-#include "libavcodec/avcodec.h"
-#include "libavcodec/idctdsp.h"
-#include "idct.h"
-#include "idctdsp_arm.h"
-
-void ff_add_pixels_clamped_arm(const int16_t *block, uint8_t *dest,
- ptrdiff_t line_size);
-
-/* XXX: those functions should be suppressed ASAP when all IDCTs are
- * converted */
-static void j_rev_dct_arm_put(uint8_t *dest, ptrdiff_t line_size,
- int16_t *block)
-{
- ff_j_rev_dct_arm(block);
- ff_put_pixels_clamped_c(block, dest, line_size);
-}
-
-static void j_rev_dct_arm_add(uint8_t *dest, ptrdiff_t line_size,
- int16_t *block)
-{
- ff_j_rev_dct_arm(block);
- ff_add_pixels_clamped_arm(block, dest, line_size);
-}
-
-static void simple_idct_arm_put(uint8_t *dest, ptrdiff_t line_size,
- int16_t *block)
-{
- ff_simple_idct_arm(block);
- ff_put_pixels_clamped_c(block, dest, line_size);
-}
-
-static void simple_idct_arm_add(uint8_t *dest, ptrdiff_t line_size,
- int16_t *block)
-{
- ff_simple_idct_arm(block);
- ff_add_pixels_clamped_arm(block, dest, line_size);
-}
-
-av_cold void ff_idctdsp_init_arm(IDCTDSPContext *c, AVCodecContext *avctx,
- unsigned high_bit_depth)
-{
- int cpu_flags = av_get_cpu_flags();
-
- if (!avctx->lowres && !high_bit_depth) {
- if ((avctx->idct_algo == FF_IDCT_AUTO && !(avctx->flags & AV_CODEC_FLAG_BITEXACT)) ||
- avctx->idct_algo == FF_IDCT_ARM) {
- c->idct_put = j_rev_dct_arm_put;
- c->idct_add = j_rev_dct_arm_add;
- c->idct = ff_j_rev_dct_arm;
- c->perm_type = FF_IDCT_PERM_LIBMPEG2;
- } else if (avctx->idct_algo == FF_IDCT_SIMPLEARM) {
- c->idct_put = simple_idct_arm_put;
- c->idct_add = simple_idct_arm_add;
- c->idct = ff_simple_idct_arm;
- c->perm_type = FF_IDCT_PERM_NONE;
- }
- }
-
- c->add_pixels_clamped = ff_add_pixels_clamped_arm;
-
- if (have_armv5te(cpu_flags))
- ff_idctdsp_init_armv5te(c, avctx, high_bit_depth);
- if (have_armv6(cpu_flags))
- ff_idctdsp_init_armv6(c, avctx, high_bit_depth);
- if (have_neon(cpu_flags))
- ff_idctdsp_init_neon(c, avctx, high_bit_depth);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dnxhddec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dnxhddec.c
deleted file mode 100644
index 7cc4f94c7f8414ccfd1dbe5f9743b32d59c8031e..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dnxhddec.c
+++ /dev/null
@@ -1,739 +0,0 @@
-/*
- * VC3/DNxHD decoder.
- * Copyright (c) 2007 SmartJog S.A., Baptiste Coudurier
- * Copyright (c) 2011 MirriAd Ltd
- * Copyright (c) 2015 Christophe Gisquet
- *
- * 10 bit support added by MirriAd Ltd, Joseph Artsimovich
- * Slice multithreading and MB interlaced support added by Christophe Gisquet
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/mem_internal.h"
-#include "libavutil/pixdesc.h"
-
-#include "avcodec.h"
-#include "blockdsp.h"
-#include "codec_internal.h"
-#include "decode.h"
-#define UNCHECKED_BITSTREAM_READER 1
-#include "get_bits.h"
-#include "dnxhddata.h"
-#include "idctdsp.h"
-#include "profiles.h"
-#include "thread.h"
-
-typedef struct RowContext {
- DECLARE_ALIGNED(32, int16_t, blocks)[12][64];
- int luma_scale[64];
- int chroma_scale[64];
- GetBitContext gb;
- int last_dc[3];
- int last_qscale;
- int errors;
- /** -1:not set yet 0:off=RGB 1:on=YUV 2:variable */
- int format;
-} RowContext;
-
-typedef struct DNXHDContext {
- AVCodecContext *avctx;
- RowContext *rows;
- BlockDSPContext bdsp;
- const uint8_t* buf;
- int buf_size;
- int64_t cid; ///< compression id
- unsigned int width, height;
- enum AVPixelFormat pix_fmt;
- unsigned int mb_width, mb_height;
- uint32_t mb_scan_index[512];
- int data_offset; // End of mb_scan_index, where macroblocks start
- int cur_field; ///< current interlaced field
- VLC ac_vlc, dc_vlc, run_vlc;
- IDCTDSPContext idsp;
- uint8_t permutated_scantable[64];
- const CIDEntry *cid_table;
- int bit_depth; // 8, 10, 12 or 0 if not initialized at all.
- int is_444;
- int alpha;
- int lla;
- int mbaff;
- int act;
- int (*decode_dct_block)(const struct DNXHDContext *ctx,
- RowContext *row, int n);
-} DNXHDContext;
-
-#define DNXHD_VLC_BITS 9
-#define DNXHD_DC_VLC_BITS 7
-
-static int dnxhd_decode_dct_block_8(const DNXHDContext *ctx,
- RowContext *row, int n);
-static int dnxhd_decode_dct_block_10(const DNXHDContext *ctx,
- RowContext *row, int n);
-static int dnxhd_decode_dct_block_10_444(const DNXHDContext *ctx,
- RowContext *row, int n);
-static int dnxhd_decode_dct_block_12(const DNXHDContext *ctx,
- RowContext *row, int n);
-static int dnxhd_decode_dct_block_12_444(const DNXHDContext *ctx,
- RowContext *row, int n);
-
-static av_cold int dnxhd_decode_init(AVCodecContext *avctx)
-{
- DNXHDContext *ctx = avctx->priv_data;
-
- ctx->avctx = avctx;
- ctx->cid = -1;
- if (avctx->colorspace == AVCOL_SPC_UNSPECIFIED) {
- avctx->colorspace = AVCOL_SPC_BT709;
- }
-
- avctx->coded_width = FFALIGN(avctx->width, 16);
- avctx->coded_height = FFALIGN(avctx->height, 16);
-
- ctx->rows = av_calloc(avctx->thread_count, sizeof(*ctx->rows));
- if (!ctx->rows)
- return AVERROR(ENOMEM);
-
- return 0;
-}
-
-static int dnxhd_init_vlc(DNXHDContext *ctx, uint32_t cid, int bitdepth)
-{
- int ret;
- if (cid != ctx->cid) {
- const CIDEntry *cid_table = ff_dnxhd_get_cid_table(cid);
-
- if (!cid_table) {
- av_log(ctx->avctx, AV_LOG_ERROR, "unsupported cid %"PRIu32"\n", cid);
- return AVERROR(ENOSYS);
- }
- if (cid_table->bit_depth != bitdepth &&
- cid_table->bit_depth != DNXHD_VARIABLE) {
- av_log(ctx->avctx, AV_LOG_ERROR, "bit depth mismatches %d %d\n",
- cid_table->bit_depth, bitdepth);
- return AVERROR_INVALIDDATA;
- }
- ctx->cid_table = cid_table;
- av_log(ctx->avctx, AV_LOG_VERBOSE, "Profile cid %"PRIu32".\n", cid);
-
- ff_free_vlc(&ctx->ac_vlc);
- ff_free_vlc(&ctx->dc_vlc);
- ff_free_vlc(&ctx->run_vlc);
-
- if ((ret = init_vlc(&ctx->ac_vlc, DNXHD_VLC_BITS, 257,
- ctx->cid_table->ac_bits, 1, 1,
- ctx->cid_table->ac_codes, 2, 2, 0)) < 0)
- goto out;
- if ((ret = init_vlc(&ctx->dc_vlc, DNXHD_DC_VLC_BITS, bitdepth > 8 ? 14 : 12,
- ctx->cid_table->dc_bits, 1, 1,
- ctx->cid_table->dc_codes, 1, 1, 0)) < 0)
- goto out;
- if ((ret = init_vlc(&ctx->run_vlc, DNXHD_VLC_BITS, 62,
- ctx->cid_table->run_bits, 1, 1,
- ctx->cid_table->run_codes, 2, 2, 0)) < 0)
- goto out;
-
- ctx->cid = cid;
- }
- ret = 0;
-out:
- if (ret < 0)
- av_log(ctx->avctx, AV_LOG_ERROR, "init_vlc failed\n");
- return ret;
-}
-
-static int dnxhd_get_profile(int cid)
-{
- switch(cid) {
- case 1270:
- return FF_PROFILE_DNXHR_444;
- case 1271:
- return FF_PROFILE_DNXHR_HQX;
- case 1272:
- return FF_PROFILE_DNXHR_HQ;
- case 1273:
- return FF_PROFILE_DNXHR_SQ;
- case 1274:
- return FF_PROFILE_DNXHR_LB;
- }
- return FF_PROFILE_DNXHD;
-}
-
-static int dnxhd_decode_header(DNXHDContext *ctx, AVFrame *frame,
- const uint8_t *buf, int buf_size,
- int first_field)
-{
- int i, cid, ret;
- int old_bit_depth = ctx->bit_depth, bitdepth;
- uint64_t header_prefix;
- if (buf_size < 0x280) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "buffer too small (%d < 640).\n", buf_size);
- return AVERROR_INVALIDDATA;
- }
-
- header_prefix = ff_dnxhd_parse_header_prefix(buf);
- if (header_prefix == 0) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "unknown header 0x%02X 0x%02X 0x%02X 0x%02X 0x%02X\n",
- buf[0], buf[1], buf[2], buf[3], buf[4]);
- return AVERROR_INVALIDDATA;
- }
- if (buf[5] & 2) { /* interlaced */
- ctx->cur_field = first_field ? buf[5] & 1 : !ctx->cur_field;
- frame->interlaced_frame = 1;
- frame->top_field_first = first_field ^ ctx->cur_field;
- av_log(ctx->avctx, AV_LOG_DEBUG,
- "interlaced %d, cur field %d\n", buf[5] & 3, ctx->cur_field);
- } else {
- ctx->cur_field = 0;
- }
- ctx->mbaff = (buf[0x6] >> 5) & 1;
- ctx->alpha = buf[0x7] & 1;
- ctx->lla = (buf[0x7] >> 1) & 1;
- if (ctx->alpha)
- avpriv_request_sample(ctx->avctx, "alpha");
-
- ctx->height = AV_RB16(buf + 0x18);
- ctx->width = AV_RB16(buf + 0x1a);
-
- switch(buf[0x21] >> 5) {
- case 1: bitdepth = 8; break;
- case 2: bitdepth = 10; break;
- case 3: bitdepth = 12; break;
- default:
- av_log(ctx->avctx, AV_LOG_ERROR,
- "Unknown bitdepth indicator (%d)\n", buf[0x21] >> 5);
- return AVERROR_INVALIDDATA;
- }
-
- cid = AV_RB32(buf + 0x28);
-
- ctx->avctx->profile = dnxhd_get_profile(cid);
-
- if ((ret = dnxhd_init_vlc(ctx, cid, bitdepth)) < 0)
- return ret;
- if (ctx->mbaff && ctx->cid_table->cid != 1260)
- av_log(ctx->avctx, AV_LOG_WARNING,
- "Adaptive MB interlace flag in an unsupported profile.\n");
-
- switch ((buf[0x2C] >> 1) & 3) {
- case 0: frame->colorspace = AVCOL_SPC_BT709; break;
- case 1: frame->colorspace = AVCOL_SPC_BT2020_NCL; break;
- case 2: frame->colorspace = AVCOL_SPC_BT2020_CL; break;
- case 3: frame->colorspace = AVCOL_SPC_UNSPECIFIED; break;
- }
-
- ctx->act = buf[0x2C] & 1;
- if (ctx->act && ctx->cid_table->cid != 1256 && ctx->cid_table->cid != 1270)
- av_log(ctx->avctx, AV_LOG_WARNING,
- "Adaptive color transform in an unsupported profile.\n");
-
- ctx->is_444 = (buf[0x2C] >> 6) & 1;
- if (ctx->is_444) {
- if (bitdepth == 8) {
- avpriv_request_sample(ctx->avctx, "4:4:4 8 bits");
- return AVERROR_INVALIDDATA;
- } else if (bitdepth == 10) {
- ctx->decode_dct_block = dnxhd_decode_dct_block_10_444;
- ctx->pix_fmt = ctx->act ? AV_PIX_FMT_YUV444P10
- : AV_PIX_FMT_GBRP10;
- } else {
- ctx->decode_dct_block = dnxhd_decode_dct_block_12_444;
- ctx->pix_fmt = ctx->act ? AV_PIX_FMT_YUV444P12
- : AV_PIX_FMT_GBRP12;
- }
- } else if (bitdepth == 12) {
- ctx->decode_dct_block = dnxhd_decode_dct_block_12;
- ctx->pix_fmt = AV_PIX_FMT_YUV422P12;
- } else if (bitdepth == 10) {
- if (ctx->avctx->profile == FF_PROFILE_DNXHR_HQX)
- ctx->decode_dct_block = dnxhd_decode_dct_block_10_444;
- else
- ctx->decode_dct_block = dnxhd_decode_dct_block_10;
- ctx->pix_fmt = AV_PIX_FMT_YUV422P10;
- } else {
- ctx->decode_dct_block = dnxhd_decode_dct_block_8;
- ctx->pix_fmt = AV_PIX_FMT_YUV422P;
- }
-
- ctx->avctx->bits_per_raw_sample = ctx->bit_depth = bitdepth;
- if (ctx->bit_depth != old_bit_depth) {
- ff_blockdsp_init(&ctx->bdsp);
- ff_idctdsp_init(&ctx->idsp, ctx->avctx);
- ff_permute_scantable(ctx->permutated_scantable, ff_zigzag_direct,
- ctx->idsp.idct_permutation);
- }
-
- // make sure profile size constraints are respected
- // DNx100 allows 1920->1440 and 1280->960 subsampling
- if (ctx->width != ctx->cid_table->width &&
- ctx->cid_table->width != DNXHD_VARIABLE) {
- av_reduce(&ctx->avctx->sample_aspect_ratio.num,
- &ctx->avctx->sample_aspect_ratio.den,
- ctx->width, ctx->cid_table->width, 255);
- ctx->width = ctx->cid_table->width;
- }
-
- if (buf_size < ctx->cid_table->coding_unit_size) {
- av_log(ctx->avctx, AV_LOG_ERROR, "incorrect frame size (%d < %u).\n",
- buf_size, ctx->cid_table->coding_unit_size);
- return AVERROR_INVALIDDATA;
- }
-
- ctx->mb_width = (ctx->width + 15)>> 4;
- ctx->mb_height = AV_RB16(buf + 0x16c);
-
- if ((ctx->height + 15) >> 4 == ctx->mb_height && frame->interlaced_frame)
- ctx->height <<= 1;
-
- av_log(ctx->avctx, AV_LOG_VERBOSE, "%dx%d, 4:%s %d bits, MBAFF=%d ACT=%d\n",
- ctx->width, ctx->height, ctx->is_444 ? "4:4" : "2:2",
- ctx->bit_depth, ctx->mbaff, ctx->act);
-
- // Newer format supports variable mb_scan_index sizes
- if (ctx->mb_height > 68 && ff_dnxhd_check_header_prefix_hr(header_prefix)) {
- ctx->data_offset = 0x170 + (ctx->mb_height << 2);
- } else {
- if (ctx->mb_height > 68) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "mb height too big: %d\n", ctx->mb_height);
- return AVERROR_INVALIDDATA;
- }
- ctx->data_offset = 0x280;
- }
- if ((ctx->mb_height << frame->interlaced_frame) > (ctx->height + 15) >> 4) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "mb height too big: %d\n", ctx->mb_height);
- return AVERROR_INVALIDDATA;
- }
-
- if (buf_size < ctx->data_offset) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "buffer too small (%d < %d).\n", buf_size, ctx->data_offset);
- return AVERROR_INVALIDDATA;
- }
-
- if (ctx->mb_height > FF_ARRAY_ELEMS(ctx->mb_scan_index)) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "mb_height too big (%d > %"SIZE_SPECIFIER").\n", ctx->mb_height, FF_ARRAY_ELEMS(ctx->mb_scan_index));
- return AVERROR_INVALIDDATA;
- }
-
- for (i = 0; i < ctx->mb_height; i++) {
- ctx->mb_scan_index[i] = AV_RB32(buf + 0x170 + (i << 2));
- ff_dlog(ctx->avctx, "mb scan index %d, pos %d: %"PRIu32"\n",
- i, 0x170 + (i << 2), ctx->mb_scan_index[i]);
- if (buf_size - ctx->data_offset < ctx->mb_scan_index[i]) {
- av_log(ctx->avctx, AV_LOG_ERROR,
- "invalid mb scan index (%"PRIu32" vs %u).\n",
- ctx->mb_scan_index[i], buf_size - ctx->data_offset);
- return AVERROR_INVALIDDATA;
- }
- }
-
- return 0;
-}
-
-static av_always_inline int dnxhd_decode_dct_block(const DNXHDContext *ctx,
- RowContext *row,
- int n,
- int index_bits,
- int level_bias,
- int level_shift,
- int dc_shift)
-{
- int i, j, index1, index2, len, flags;
- int level, component, sign;
- const int *scale;
- const uint8_t *weight_matrix;
- const uint8_t *ac_info = ctx->cid_table->ac_info;
- int16_t *block = row->blocks[n];
- const int eob_index = ctx->cid_table->eob_index;
- int ret = 0;
- OPEN_READER(bs, &row->gb);
-
- ctx->bdsp.clear_block(block);
-
- if (!ctx->is_444) {
- if (n & 2) {
- component = 1 + (n & 1);
- scale = row->chroma_scale;
- weight_matrix = ctx->cid_table->chroma_weight;
- } else {
- component = 0;
- scale = row->luma_scale;
- weight_matrix = ctx->cid_table->luma_weight;
- }
- } else {
- component = (n >> 1) % 3;
- if (component) {
- scale = row->chroma_scale;
- weight_matrix = ctx->cid_table->chroma_weight;
- } else {
- scale = row->luma_scale;
- weight_matrix = ctx->cid_table->luma_weight;
- }
- }
-
- UPDATE_CACHE(bs, &row->gb);
- GET_VLC(len, bs, &row->gb, ctx->dc_vlc.table, DNXHD_DC_VLC_BITS, 1);
- if (len < 0) {
- ret = len;
- goto error;
- }
- if (len) {
- level = GET_CACHE(bs, &row->gb);
- LAST_SKIP_BITS(bs, &row->gb, len);
- sign = ~level >> 31;
- level = (NEG_USR32(sign ^ level, len) ^ sign) - sign;
- row->last_dc[component] += level * (1 << dc_shift);
- }
- block[0] = row->last_dc[component];
-
- i = 0;
-
- UPDATE_CACHE(bs, &row->gb);
- GET_VLC(index1, bs, &row->gb, ctx->ac_vlc.table,
- DNXHD_VLC_BITS, 2);
-
- while (index1 != eob_index) {
- level = ac_info[2*index1+0];
- flags = ac_info[2*index1+1];
-
- sign = SHOW_SBITS(bs, &row->gb, 1);
- SKIP_BITS(bs, &row->gb, 1);
-
- if (flags & 1) {
- level += SHOW_UBITS(bs, &row->gb, index_bits) << 7;
- SKIP_BITS(bs, &row->gb, index_bits);
- }
-
- if (flags & 2) {
- UPDATE_CACHE(bs, &row->gb);
- GET_VLC(index2, bs, &row->gb, ctx->run_vlc.table,
- DNXHD_VLC_BITS, 2);
- i += ctx->cid_table->run[index2];
- }
-
- if (++i > 63) {
- av_log(ctx->avctx, AV_LOG_ERROR, "ac tex damaged %d, %d\n", n, i);
- ret = -1;
- break;
- }
-
- j = ctx->permutated_scantable[i];
- level *= scale[i];
- level += scale[i] >> 1;
- if (level_bias < 32 || weight_matrix[i] != level_bias)
- level += level_bias; // 1<<(level_shift-1)
- level >>= level_shift;
-
- block[j] = (level ^ sign) - sign;
-
- UPDATE_CACHE(bs, &row->gb);
- GET_VLC(index1, bs, &row->gb, ctx->ac_vlc.table,
- DNXHD_VLC_BITS, 2);
- }
-error:
- CLOSE_READER(bs, &row->gb);
- return ret;
-}
-
-static int dnxhd_decode_dct_block_8(const DNXHDContext *ctx,
- RowContext *row, int n)
-{
- return dnxhd_decode_dct_block(ctx, row, n, 4, 32, 6, 0);
-}
-
-static int dnxhd_decode_dct_block_10(const DNXHDContext *ctx,
- RowContext *row, int n)
-{
- return dnxhd_decode_dct_block(ctx, row, n, 6, 8, 4, 0);
-}
-
-static int dnxhd_decode_dct_block_10_444(const DNXHDContext *ctx,
- RowContext *row, int n)
-{
- return dnxhd_decode_dct_block(ctx, row, n, 6, 32, 6, 0);
-}
-
-static int dnxhd_decode_dct_block_12(const DNXHDContext *ctx,
- RowContext *row, int n)
-{
- return dnxhd_decode_dct_block(ctx, row, n, 6, 8, 4, 2);
-}
-
-static int dnxhd_decode_dct_block_12_444(const DNXHDContext *ctx,
- RowContext *row, int n)
-{
- return dnxhd_decode_dct_block(ctx, row, n, 6, 32, 4, 2);
-}
-
-static int dnxhd_decode_macroblock(const DNXHDContext *ctx, RowContext *row,
- AVFrame *frame, int x, int y)
-{
- int shift1 = ctx->bit_depth >= 10;
- int dct_linesize_luma = frame->linesize[0];
- int dct_linesize_chroma = frame->linesize[1];
- uint8_t *dest_y, *dest_u, *dest_v;
- int dct_y_offset, dct_x_offset;
- int qscale, i, act;
- int interlaced_mb = 0;
-
- if (ctx->mbaff) {
- interlaced_mb = get_bits1(&row->gb);
- qscale = get_bits(&row->gb, 10);
- } else {
- qscale = get_bits(&row->gb, 11);
- }
- act = get_bits1(&row->gb);
- if (act) {
- if (!ctx->act) {
- static int act_warned;
- if (!act_warned) {
- act_warned = 1;
- av_log(ctx->avctx, AV_LOG_ERROR,
- "ACT flag set, in violation of frame header.\n");
- }
- } else if (row->format == -1) {
- row->format = act;
- } else if (row->format != act) {
- row->format = 2; // Variable
- }
- }
-
- if (qscale != row->last_qscale) {
- for (i = 0; i < 64; i++) {
- row->luma_scale[i] = qscale * ctx->cid_table->luma_weight[i];
- row->chroma_scale[i] = qscale * ctx->cid_table->chroma_weight[i];
- }
- row->last_qscale = qscale;
- }
-
- for (i = 0; i < 8 + 4 * ctx->is_444; i++) {
- if (ctx->decode_dct_block(ctx, row, i) < 0)
- return AVERROR_INVALIDDATA;
- }
-
- if (frame->interlaced_frame) {
- dct_linesize_luma <<= 1;
- dct_linesize_chroma <<= 1;
- }
-
- dest_y = frame->data[0] + ((y * dct_linesize_luma) << 4) + (x << (4 + shift1));
- dest_u = frame->data[1] + ((y * dct_linesize_chroma) << 4) + (x << (3 + shift1 + ctx->is_444));
- dest_v = frame->data[2] + ((y * dct_linesize_chroma) << 4) + (x << (3 + shift1 + ctx->is_444));
-
- if (frame->interlaced_frame && ctx->cur_field) {
- dest_y += frame->linesize[0];
- dest_u += frame->linesize[1];
- dest_v += frame->linesize[2];
- }
- if (interlaced_mb) {
- dct_linesize_luma <<= 1;
- dct_linesize_chroma <<= 1;
- }
-
- dct_y_offset = interlaced_mb ? frame->linesize[0] : (dct_linesize_luma << 3);
- dct_x_offset = 8 << shift1;
- if (!ctx->is_444) {
- ctx->idsp.idct_put(dest_y, dct_linesize_luma, row->blocks[0]);
- ctx->idsp.idct_put(dest_y + dct_x_offset, dct_linesize_luma, row->blocks[1]);
- ctx->idsp.idct_put(dest_y + dct_y_offset, dct_linesize_luma, row->blocks[4]);
- ctx->idsp.idct_put(dest_y + dct_y_offset + dct_x_offset, dct_linesize_luma, row->blocks[5]);
-
- if (!(ctx->avctx->flags & AV_CODEC_FLAG_GRAY)) {
- dct_y_offset = interlaced_mb ? frame->linesize[1] : (dct_linesize_chroma << 3);
- ctx->idsp.idct_put(dest_u, dct_linesize_chroma, row->blocks[2]);
- ctx->idsp.idct_put(dest_v, dct_linesize_chroma, row->blocks[3]);
- ctx->idsp.idct_put(dest_u + dct_y_offset, dct_linesize_chroma, row->blocks[6]);
- ctx->idsp.idct_put(dest_v + dct_y_offset, dct_linesize_chroma, row->blocks[7]);
- }
- } else {
- ctx->idsp.idct_put(dest_y, dct_linesize_luma, row->blocks[0]);
- ctx->idsp.idct_put(dest_y + dct_x_offset, dct_linesize_luma, row->blocks[1]);
- ctx->idsp.idct_put(dest_y + dct_y_offset, dct_linesize_luma, row->blocks[6]);
- ctx->idsp.idct_put(dest_y + dct_y_offset + dct_x_offset, dct_linesize_luma, row->blocks[7]);
-
- if (!(ctx->avctx->flags & AV_CODEC_FLAG_GRAY)) {
- dct_y_offset = interlaced_mb ? frame->linesize[1] : (dct_linesize_chroma << 3);
- ctx->idsp.idct_put(dest_u, dct_linesize_chroma, row->blocks[2]);
- ctx->idsp.idct_put(dest_u + dct_x_offset, dct_linesize_chroma, row->blocks[3]);
- ctx->idsp.idct_put(dest_u + dct_y_offset, dct_linesize_chroma, row->blocks[8]);
- ctx->idsp.idct_put(dest_u + dct_y_offset + dct_x_offset, dct_linesize_chroma, row->blocks[9]);
- ctx->idsp.idct_put(dest_v, dct_linesize_chroma, row->blocks[4]);
- ctx->idsp.idct_put(dest_v + dct_x_offset, dct_linesize_chroma, row->blocks[5]);
- ctx->idsp.idct_put(dest_v + dct_y_offset, dct_linesize_chroma, row->blocks[10]);
- ctx->idsp.idct_put(dest_v + dct_y_offset + dct_x_offset, dct_linesize_chroma, row->blocks[11]);
- }
- }
-
- return 0;
-}
-
-static int dnxhd_decode_row(AVCodecContext *avctx, void *data,
- int rownb, int threadnb)
-{
- const DNXHDContext *ctx = avctx->priv_data;
- uint32_t offset = ctx->mb_scan_index[rownb];
- RowContext *row = ctx->rows + threadnb;
- int x, ret;
-
- row->last_dc[0] =
- row->last_dc[1] =
- row->last_dc[2] = 1 << (ctx->bit_depth + 2); // for levels +2^(bitdepth-1)
- ret = init_get_bits8(&row->gb, ctx->buf + offset, ctx->buf_size - offset);
- if (ret < 0) {
- row->errors++;
- return ret;
- }
- for (x = 0; x < ctx->mb_width; x++) {
- int ret = dnxhd_decode_macroblock(ctx, row, data, x, rownb);
- if (ret < 0) {
- row->errors++;
- return ret;
- }
- }
-
- return 0;
-}
-
-static int dnxhd_decode_frame(AVCodecContext *avctx, AVFrame *picture,
- int *got_frame, AVPacket *avpkt)
-{
- const uint8_t *buf = avpkt->data;
- int buf_size = avpkt->size;
- DNXHDContext *ctx = avctx->priv_data;
- int first_field = 1;
- int ret, i;
-
- ff_dlog(avctx, "frame size %d\n", buf_size);
-
- for (i = 0; i < avctx->thread_count; i++)
- ctx->rows[i].format = -1;
-
-decode_coding_unit:
- if ((ret = dnxhd_decode_header(ctx, picture, buf, buf_size, first_field)) < 0)
- return ret;
-
- if ((avctx->width || avctx->height) &&
- (ctx->width != avctx->width || ctx->height != avctx->height)) {
- av_log(avctx, AV_LOG_WARNING, "frame size changed: %dx%d -> %ux%u\n",
- avctx->width, avctx->height, ctx->width, ctx->height);
- first_field = 1;
- }
- if (avctx->pix_fmt != AV_PIX_FMT_NONE && avctx->pix_fmt != ctx->pix_fmt) {
- av_log(avctx, AV_LOG_WARNING, "pix_fmt changed: %s -> %s\n",
- av_get_pix_fmt_name(avctx->pix_fmt), av_get_pix_fmt_name(ctx->pix_fmt));
- first_field = 1;
- }
-
- avctx->pix_fmt = ctx->pix_fmt;
- ret = ff_set_dimensions(avctx, ctx->width, ctx->height);
- if (ret < 0)
- return ret;
-
- if (first_field) {
- if ((ret = ff_thread_get_buffer(avctx, picture, 0)) < 0)
- return ret;
- picture->pict_type = AV_PICTURE_TYPE_I;
- picture->key_frame = 1;
- }
-
- ctx->buf_size = buf_size - ctx->data_offset;
- ctx->buf = buf + ctx->data_offset;
- avctx->execute2(avctx, dnxhd_decode_row, picture, NULL, ctx->mb_height);
-
- if (first_field && picture->interlaced_frame) {
- buf += ctx->cid_table->coding_unit_size;
- buf_size -= ctx->cid_table->coding_unit_size;
- first_field = 0;
- goto decode_coding_unit;
- }
-
- ret = 0;
- for (i = 0; i < avctx->thread_count; i++) {
- ret += ctx->rows[i].errors;
- ctx->rows[i].errors = 0;
- }
-
- if (ctx->act) {
- static int act_warned;
- int format = ctx->rows[0].format;
- for (i = 1; i < avctx->thread_count; i++) {
- if (ctx->rows[i].format != format &&
- ctx->rows[i].format != -1 /* not run */) {
- format = 2;
- break;
- }
- }
- switch (format) {
- case -1:
- case 2:
- if (!act_warned) {
- act_warned = 1;
- av_log(ctx->avctx, AV_LOG_ERROR,
- "Unsupported: variable ACT flag.\n");
- }
- break;
- case 0:
- ctx->pix_fmt = ctx->bit_depth==10
- ? AV_PIX_FMT_GBRP10 : AV_PIX_FMT_GBRP12;
- break;
- case 1:
- ctx->pix_fmt = ctx->bit_depth==10
- ? AV_PIX_FMT_YUV444P10 : AV_PIX_FMT_YUV444P12;
- break;
- }
- }
- avctx->pix_fmt = ctx->pix_fmt;
- if (ret) {
- av_log(ctx->avctx, AV_LOG_ERROR, "%d lines with errors\n", ret);
- return AVERROR_INVALIDDATA;
- }
-
- *got_frame = 1;
- return avpkt->size;
-}
-
-static av_cold int dnxhd_decode_close(AVCodecContext *avctx)
-{
- DNXHDContext *ctx = avctx->priv_data;
-
- ff_free_vlc(&ctx->ac_vlc);
- ff_free_vlc(&ctx->dc_vlc);
- ff_free_vlc(&ctx->run_vlc);
-
- av_freep(&ctx->rows);
-
- return 0;
-}
-
-const FFCodec ff_dnxhd_decoder = {
- .p.name = "dnxhd",
- CODEC_LONG_NAME("VC3/DNxHD"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_DNXHD,
- .priv_data_size = sizeof(DNXHDContext),
- .init = dnxhd_decode_init,
- .close = dnxhd_decode_close,
- FF_CODEC_DECODE_CB(dnxhd_decode_frame),
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_FRAME_THREADS |
- AV_CODEC_CAP_SLICE_THREADS,
- .p.profiles = NULL_IF_CONFIG_SMALL(ff_dnxhd_profiles),
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libaom.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libaom.h
deleted file mode 100644
index bb495af982dd0caa509ee6ecaff1e1253b568919..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libaom.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * AOM common functions
- */
-
-#ifndef AVCODEC_LIBAOM_H
-#define AVCODEC_LIBAOM_H
-
-#include
-
-#include "libavutil/frame.h"
-
-void ff_aom_image_copy_16_to_8(AVFrame *pic, struct aom_image *img);
-
-#endif /* AVCODEC_LIBAOM_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Bus Simulator Indonesia APK OBB A Review of the Most Popular Simulation Game.md b/spaces/congsaPfin/Manga-OCR/logs/Bus Simulator Indonesia APK OBB A Review of the Most Popular Simulation Game.md
deleted file mode 100644
index 9b627583179ff8ba6d818b6ed7b4fc5433cf8db7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Bus Simulator Indonesia APK OBB A Review of the Most Popular Simulation Game.md
+++ /dev/null
@@ -1,95 +0,0 @@
-
-Bus Simulator Indonesia APK Plus OBB: A Fun and Realistic Game for Android Users
-If you are a fan of simulation games, especially bus driving games, then you should not miss Bus Simulator Indonesia. This game is one of the most popular and realistic bus simulator games for Android devices. In this game, you can experience what it is like to be a bus driver in Indonesia, with its unique culture, scenery, and challenges. You can also customize your own buses, explore various cities and landmarks, compete with other players online, and even create your own mods and content. In this article, we will tell you everything you need to know about Bus Simulator Indonesia APK plus OBB, including its features, how to download and install it, and some frequently asked questions.
-What is Bus Simulator Indonesia?
-Bus Simulator Indonesia, or BUSSID for short, is a game developed by Maleo, an Indonesian game studio. It was released in 2017 and has since gained millions of downloads and positive reviews from players around the world. The game is designed to give you a realistic and fun bus driving experience in Indonesia, with its diverse geography, culture, and traffic. You can choose from different types of buses, such as city buses, intercity buses, tourist buses, school buses, and more. You can also customize your buses with various livery, stickers, horns, lights, accessories, and even change the interior design. You can drive your buses in different cities and regions in Indonesia, such as Jakarta, Bali, Sumatra, Java, Sulawesi, and more. You can also see famous landmarks and attractions along the way, such as Monas, Borobudur Temple, Tanah Lot Temple, Mount Bromo, Lake Toba, and more.
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Features of Bus Simulator Indonesia
-Customizable buses and livery
-One of the best features of Bus Simulator Indonesia is that you can customize your own buses with various options. You can change the color, design, logo, name, number plate, and more of your buses. You can also add stickers, horns, lights, accessories, mirrors, wipers, doors, windows, seats, steering wheel, dashboard, and more to your buses. You can even create your own livery using the built-in editor or download livery from other players online. You can also save your custom buses and use them anytime you want.
-Authentic Indonesian cities and landmarks
-Another great feature of Bus Simulator Indonesia is that you can explore different cities and regions in Indonesia with realistic graphics and details. You can see the unique architecture, culture, scenery, and landmarks of each city and region. You can also interact with various elements in the environment, such as traffic lights, toll gates, gas stations, rest areas, terminals, passengers, pedestrians, animals, vehicles, and more. You can also enjoy the day-night cycle and dynamic weather system that affect your driving conditions.
-Realistic traffic and weather conditions
-Bus Simulator Indonesia also offers realistic traffic and weather conditions that make your driving experience more challenging and fun. You have to follow the traffic rules and avoid accidents, traffic jams, police, and other obstacles on the road. You also have to deal with different weather conditions, such as rain, fog, snow, wind, and more. You can adjust the difficulty level and traffic density according to your preference and skill level. You can also use different camera angles and views to see your bus and the road better.
-Online multiplayer mode and leaderboards
-If you want to play with other players online, you can join the online multiplayer mode of Bus Simulator Indonesia. You can create or join a room with up to 10 players and drive together in the same map. You can chat with other players, honk at each other, race with each other, or cooperate with each other. You can also see the leaderboards and rankings of the best players in the game. You can compete with other players in terms of distance, speed, passengers, income, and more.
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-Mod support and community content
-Bus Simulator Indonesia also supports modding and community content. You can create your own mods using the mod tools provided by the developers. You can also download mods from other players online. You can find various mods that add new buses, livery, maps, routes, sounds, features, and more to the game. You can also share your own mods and content with other players online. You can also join the official Discord server and Facebook group of Bus Simulator Indonesia to interact with other players, get updates, news, tips, tricks, and more.
-How to Download and Install Bus Simulator Indonesia APK Plus OBB?
-If you want to download and install Bus Simulator Indonesia APK plus OBB on your Android device, you need to follow some simple steps. Here are the requirements and steps for downloading and installing Bus Simulator Indonesia APK plus OBB.
-Requirements for Bus Simulator Indonesia APK Plus OBB
-Android device with version 4.2 or higher
-You need an Android device with version 4.2 or higher to run Bus Simulator Indonesia APK plus OBB. The game is compatible with most Android devices, such as smartphones, tablets, and emulators. However, some devices may not support the game due to hardware limitations or compatibility issues.
-At least 1 GB of free storage space
-You need at least 1 GB of free storage space on your device to download and install Bus Simulator Indonesia APK plus OBB. The game has a size of about 300 MB for the APK file and about 700 MB for the OBB file. You need to make sure that you have enough space on your device before downloading and installing the game.
-Stable internet connection
-You need a stable internet connection to download and install Bus Simulator Indonesia APK plus OBB. You also need an internet connection to play the online multiplayer mode of the game. You can use Wi-Fi or mobile data to connect to the internet. However, you may incur additional charges if you use mobile data.
-Steps to Download and Install Bus Simulator Indonesia APK Plus OBB
-Download the APK and OBB files from a trusted source
-The first step is to download the APK and OBB files of Bus Simulator Indonesia from a trusted source. You can find many websites that offer the download links for Bus Simulator Indonesia APK plus OBB. However, you need to be careful as some websites may contain viruses, malware, or fake files that can harm your device or steal your data. Therefore, you should only download from reputable sources that have positive reviews and ratings from other users. One of the trusted sources that we recommend is [APKPure], which is a popular website that provides safe and verified APK files for various Android games and apps.
-Enable unknown sources in your device settings
-The next step is to enable unknown sources in your device settings. This is necessary because Bus Simulator Indonesia APK plus OBB is not available on the Google Play Store, which means that it is not an official app from Google. Therefore, you need to allow your device to install apps from unknown sources that are not from the Google Play Store. To do this, you need to go to your device settings > security > unknown sources > enable or toggle on.
-Install the APK file and do not open it yet
-The third step is to install the APK file of Bus Simulator Indonesia on your device. To do this, you need to locate the downloaded APK file on your device storage using a file manager app or a browser app. Then, you need to tap on the APK file and follow the instructions on the screen to install it on your device. After installing it, do not open it yet as you still need to copy the O BB file to the correct folder on your device.
-Extract the OBB file and copy it to the Android/obb folder in your device storage
-The fourth step is to extract the OBB file of Bus Simulator Indonesia and copy it to the Android/obb folder in your device storage. To do this, you need to locate the downloaded OBB file on your device storage using a file manager app or a browser app. Then, you need to tap on the OBB file and extract it using a zip extractor app or a built-in extractor app on your device. After extracting it, you will see a folder named com.maleo.bussimulatorid. You need to copy this folder and paste it to the Android/obb folder in your device storage. If you do not have an obb folder, you can create one using a file manager app.
-Launch the game and enjoy
-The final step is to launch the game and enjoy. To do this, you need to go to your app drawer or home screen and tap on the Bus Simulator Indonesia icon. Then, you need to wait for the game to load and verify the files. After that, you can start playing the game and have fun. You can also adjust the settings, choose your bus, select your map, join online rooms, and more.
-Conclusion
-Bus Simulator Indonesia APK plus OBB is a fun and realistic game for Android users who love simulation games, especially bus driving games. You can customize your own buses, explore different cities and landmarks in Indonesia, experience realistic traffic and weather conditions, play with other players online, and create your own mods and content. You can download and install Bus Simulator Indonesia APK plus OBB on your device by following the simple steps we have provided in this article. We hope you enjoy playing Bus Simulator Indonesia APK plus OBB and have a great time.
-FAQs
-Here are some frequently asked questions about Bus Simulator Indonesia APK plus OBB:
-
-Is Bus Simulator Indonesia APK plus OBB free? Yes, Bus Simulator Indonesia APK plus OBB is free to download and play. However, some features and items may require in-app purchases or watching ads.
-Is Bus Simulator Indonesia APK plus OBB safe? Yes, Bus Simulator Indonesia APK plus OBB is safe to download and install as long as you use a trusted source like [APKPure]. However, you should always scan the files before installing them on your device and avoid downloading from unknown or suspicious websites.
-Is Bus Simulator Indonesia APK plus OBB offline? No, Bus Simulator Indonesia APK plus OBB requires an internet connection to play. You need an internet connection to download and install the game, verify the files, access online features, update the game, and more.
-How to update Bus Simulator Indonesia APK plus OBB? To update Bus Simulator Indonesia APK plus OBB, you need to download and install the latest version of the APK and OBB files from a trusted source like [APKPure]. You also need to delete or overwrite the old files on your device storage.
-How to contact the developers of Bus Simulator Indonesia? To contact the developers of Bus Simulator Indonesia, you can visit their official website [Maleo], their official Facebook page [Bus Simulator Indonesia], their official Instagram account [@bussimulator.id], or their official Discord server [BUSSID Discord]. You can also email them at support@maleo.id or bussimulatorid@gmail.com.
- 197e85843dClassic Sudoku Studio Goya APK: A Review
-If you are a fan of sudoku puzzles, you might have heard of Classic Sudoku Studio Goya APK , a game developed by Studio Goya LLC and presented by Cracking The Cryptic, the most popular sudoku channel on YouTube. This game offers a collection of handcrafted sudoku puzzles that will challenge your logic and reasoning skills, as well as provide you with hours of fun and entertainment. In this article, we will review the game and tell you why you should download it on your Android device.
- What is Classic Sudoku Studio Goya APK?
-A brief introduction to the game and its features
-Classic Sudoku Studio Goya APK is a board game that consists of 40 beautiful puzzles on launch, with 5 new levels added every month for the first year (a total of 100 levels). The puzzles are designed by Simon Anthony and Mark Goodliffe, the hosts of Cracking The Cryptic, who have both represented the UK many times in the World Sudoku Championship. The puzzles cover a wide range of difficulty, from easy to expert, and require an incredible range of techniques to solve them efficiently. Each puzzle has been carefully tested by a human being to ensure that it is solvable, enjoyable, and satisfying. The game also features hints written by Mark and Simon, which can help you if you get stuck or want to learn new strategies.
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How to download and install the game on your Android device
-To download and install Classic Sudoku Studio Goya APK on your Android device, you need to follow these simple steps:
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-Go to this link or this link to access the official download page of the game.
-Click on the "Download" or "Buy" button to purchase the game for $4.99 (with in-app payments).
-Wait for the APK file to be downloaded on your device.
-Open the APK file and follow the instructions to install the game.
-Enjoy playing Classic Sudoku Studio Goya APK!
-
- Why should you play Classic Sudoku Studio Goya APK?
-The benefits of playing sudoku puzzles for your brain and mental health
-Sudoku puzzles are not only fun and addictive, but also beneficial for your brain and mental health. According to various studies, playing sudoku puzzles can help you improve your memory, concentration, logic, problem-solving, creativity, and mood. Sudoku puzzles can also prevent cognitive decline, reduce stress, enhance self-esteem, and increase happiness. By playing sudoku puzzles regularly, you can keep your mind sharp and healthy.
-classic sudoku game by studio goya
-The unique and challenging puzzles curated by Cracking The Cryptic
-Another reason why you should play Classic Sudoku Studio Goya APK is that it offers some of the most unique and challenging puzzles that you will ever encounter. These puzzles are not randomly generated by a computer, but curated by Cracking The Cryptic, who have a reputation for finding and creating amazing sudoku puzzles. These puzzles are not only difficult, but also elegant, clever, and surprising. They and Simon, which can help you if you get stuck or want to learn new strategies. The game also has a user-friendly and intuitive interface and design that makes playing the game easy and enjoyable. This is a game that will challenge your mind and delight your senses." - Critic review on AndroidAuthority.com
-
A table comparing the game with other popular sudoku apps
-To give you an idea of how Classic Sudoku Studio Goya APK compares with other popular sudoku apps, we have created a table that shows some of the key features and differences between them. Here is the table:
-
-
-Feature
-Classic Sudoku Studio Goya APK
-Sudoku.com
-Sudoku by Brainium Studios
-Sudoku by Easybrain
-
-
-Number of puzzles
-40 on launch, 5 new levels every month for the first year (total of 100 levels)
-Over 10,000 puzzles
-Unlimited puzzles
-Over 5,000 puzzles
-
-
-Puzzle quality and difficulty
-Handcrafted puzzles by Cracking The Cryptic, ranging from easy to expert, requiring various techniques and logic to solve them efficiently.
-Randomly generated puzzles, ranging from easy to hard, requiring basic techniques and logic to solve them.
-Randomly generated puzzles, ranging from easy to expert, requiring basic to advanced techniques and logic to solve them.
-Randomly generated puzzles, ranging from easy to hard, requiring basic techniques and logic to solve them.
-
-
-Hints and solutions
-Hints and solutions written by Mark and Simon, explaining the logic and reasoning behind each move, and teaching new strategies and tricks.
-Hints that show one possible number for a cell, without explanation.
-Hints that show one possible number for a cell, without explanation.
-Hints that show one possible number for a cell, without explanation.
-
-
-Interface and design
-User-friendly and intuitive interface and design, with a simple and elegant layout, a dark mode option, a timer, a pencil mode, a highlight mode, a undo/redo button, and a pause button.
-User-friendly and intuitive interface and design, with a colorful and bright layout, a timer, a pencil mode, a highlight mode, a undo/redo button, and a pause button.
-User-friendly and intuitive interface and design, with a minimalist and sleek layout, a timer, a pencil mode, a highlight mode, a undo/redo button, and a pause button.
-User-friendly and intuitive interface and design, with a modern and stylish layout, a timer, a pencil mode, a highlight mode, a undo/redo button, and a pause button.
-
-
-Sound and music
-Sound and music option that can be adjusted according to preference.
-Sound option that can be turned on or off.
-Sound option that can be turned on or off.
-Sound option that can be turned on or off.
-
- Price $4.99 (with in-app payments) Free (with ads) Free (with ads) Free (with ads)
- Conclusion
- A recap of the main points and a call to action for the readers
- In conclusion, Classic Sudoku Studio Goya APK is one of the best sudoku games that you can play on your Android device. It offers you:
- A collection of handcrafted sudoku puzzles that will challenge your logic and reasoning skills. A learning experience with hints and solutions written by Mark and Simon from Cracking The Cryptic. A user-friendly and intuitive interface and design that makes playing the game easy and enjoyable. If you are looking for a sudoku game that will test your mind and delight your senses, you should download Classic Sudoku Studio Goya APK today. You will not regret it!
- FAQs
- Here are some of the frequently asked questions about Classic Sudoku Studio Goya APK:
- What is Cracking The Cryptic? What are some of the other sudoku games that I can play on my Android device? How can I improve my sudoku skills and learn new techniques?
-
-Descargar iGO Primo APK gratis: ¿Qué es y cómo funciona?
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-Si estás buscando un software de navegación que te ofrezca mapas offline, guía de voz inteligente, información de tráfico en tiempo real y más características, quizás te interese descargar iGO Primo APK gratis para tu dispositivo Android.
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iGO Primo es un software de navegación que ha sido desarrollado por la empresa NNG y que se utiliza en varios sistemas multimedia de vehículos y dispositivos móviles. Con iGO Primo puedes navegar sin necesidad de conexión a internet, ya que los mapas se almacenan en la memoria del dispositivo o en una tarjeta SD.
-Además, iGO Primo te ofrece una interfaz de usuario personalizable y fácil de usar, con diferentes temas y idiomas disponibles. También puedes buscar destinos y lugares de interés por varias opciones y recibir indicaciones de voz que te anuncian los nombres de las calles y las
salidas y entradas de las autopistas. Asimismo, iGO Primo te proporciona información de tráfico en tiempo real y actualiza las rutas según las condiciones del tráfico.
-Características de iGO Primo
-iGO Primo es un software de navegación que cuenta con varias características que lo hacen destacar entre otros programas similares. A continuación, te presentamos algunas de las más importantes:
-Navegación offline
-Una de las ventajas de iGO Primo es que te permite navegar sin conexión a internet, lo que te ahorra datos móviles y te evita problemas de cobertura. Para ello, solo tienes que descargar los mapas que necesites desde la aplicación o desde el sitio web oficial. Los mapas se guardan en la memoria interna del dispositivo o en una tarjeta SD externa.
-descargar igo primo nextgen apk gratis
-Los mapas de iGO Primo son detallados y precisos, y cubren más de 100 países en todo el mundo. Además, se actualizan periódicamente para incluir los cambios en las carreteras y los puntos de interés. También puedes descargar mapas en 3D que te ofrecen una vista más realista del terreno.
-Guía de voz inteligente
-Otra característica de iGO Primo es que te ofrece una guía de voz inteligente que te indica el camino a seguir con claridad y precisión. La guía de voz te anuncia los nombres de las calles, las salidas y entradas de las autopistas, los límites de velocidad, los radares y los puntos de interés cercanos.
-Además, puedes elegir entre diferentes voces y idiomas para la guía de voz, según tu preferencia y tu región. También puedes ajustar el volumen y la velocidad de la voz, así como activar o desactivar las alertas sonoras.
-Información de tráfico en tiempo real
-Otra característica de iGO Primo es que te proporciona información de tráfico en tiempo real, lo que te ayuda a evitar atascos, accidentes y obras en la carretera. La información de tráfico se obtiene de fuentes oficiales y de otros usuarios de iGO Primo que comparten sus datos.
-La información de tráfico se muestra en el mapa con diferentes colores según la intensidad del tráfico. También puedes ver el tiempo estimado de llegada y la distancia hasta tu destino. Además, iGO Primo actualiza las rutas automáticamente según las condiciones del tráfico, ofreciéndote la mejor opción disponible.
-Búsqueda de destinos y lugares de interés
-Otra característica de iGO Primo es que te permite buscar destinos y lugares de interés por varias opciones, tales como:
-
-Dirección: puedes introducir la dirección completa o parcial del lugar al que quieres ir.
-Punto en el mapa: puedes seleccionar un punto en el mapa con el dedo o con el cursor.
-Contactos: puedes elegir un contacto guardado en tu dispositivo como destino.
-Favoritos: puedes guardar tus destinos frecuentes o preferidos como favoritos para acceder a ellos rápidamente.
-Histórico: puedes ver los destinos a los que has ido recientemente y volver a ellos fácilmente.
-Categorías: puedes buscar lugares de interés por categorías, como restaurantes, hoteles, gasolineras, etc.
-Código postal: puedes introducir el código postal del lugar al que quieres ir.
-Coordenadas: puedes introducir las coordenadas geográficas del lugar al que quieres ir.
-
-Cuando buscas un destino o un lugar de interés, iGO Primo te muestra una lista de resultados con información relevante, como la dirección, el teléfono, el horario, la valoración, etc. También puedes ver los resultados en el mapa o en una vista 3D. Además, puedes filtrar los resultados por distancia, nombre o categoría.
Interfaz de usuario personalizable y fácil de usar
-Otra característica de iGO Primo es que te ofrece una interfaz de usuario personalizable y fácil de usar, con la que puedes acceder a todas las funciones del software de forma intuitiva y cómoda. Puedes elegir entre diferentes temas y colores para la interfaz, así como ajustar el brillo, el contraste y el tamaño de la fuente.
-También puedes cambiar el idioma de la interfaz y de la guía de voz, entre más de 40 opciones disponibles. Además, puedes configurar las preferencias de navegación, como el tipo de vehículo, el modo de ruta, los avisos de seguridad, etc.
-Cómo descargar e instalar iGO Primo APK gratis
-Si quieres descargar iGO Primo APK gratis para tu dispositivo Android, debes seguir los siguientes pasos:
-Requisitos previos
-Antes de descargar e instalar iGO Primo APK gratis, debes asegurarte de que tu dispositivo cumple con los siguientes requisitos:
-
-Tener una versión de Android igual o superior a la 4.0.
-Tener al menos 1 GB de memoria RAM y 8 GB de espacio libre en la memoria interna o en una tarjeta SD externa.
-Tener una conexión a internet para descargar los mapas y la información de tráfico.
-Tener un cargador o una batería externa para evitar que el dispositivo se apague durante la navegación.
-Tener un soporte o un soporte magnético para colocar el dispositivo en el vehículo.
-
-Descarga del archivo APK
-Para descargar el archivo APK de iGO Primo, debes acceder al siguiente enlace desde tu navegador web:
-https://apkpure.com/es/igo-navigation/com.nng.igo.primong.igoworld/download?from=details
-Este enlace te llevará a una página donde podrás ver la información y las valoraciones del software. También podrás ver el botón "Descargar APK", que debes pulsar para iniciar la descarga del archivo.
-El archivo APK tiene un tamaño de unos 17 MB y se guardará en la carpeta "Descargas" de tu dispositivo. Puedes verificar el progreso de la descarga en la barra de notificaciones o en el gestor de descargas.
-Instalación del archivo APK
-Para instalar el archivo APK de iGO Primo, debes seguir los siguientes pasos:
-
-Abrir el gestor de archivos de tu dispositivo y localizar el archivo APK que has descargado.
-Pulsar sobre el archivo APK para abrirlo. Si te aparece un mensaje de advertencia sobre la instalación de aplicaciones desconocidas, debes ir a los ajustes de seguridad de tu dispositivo y habilitar la opción "Orígenes desconocidos" o "Fuentes desconocidas".
-Aceptar los permisos que solicita la aplicación y pulsar el botón "Instalar". Esperar a que se complete la instalación.
-Pulsar el botón "Abrir" para iniciar la aplicación o buscar el icono de iGO Primo en el menú de aplicaciones.
- Configuración inicial del software
-Para configurar el software de iGO Primo por primera vez, debes seguir los siguientes pasos:
-
-Aceptar los términos y condiciones de uso del software y pulsar el botón "Siguiente".
-Seleccionar el idioma de la interfaz y de la guía de voz y pulsar el botón "Siguiente".
-Seleccionar el tipo de vehículo que vas a utilizar y pulsar el botón "Siguiente".
-Seleccionar el modo de ruta que prefieres, entre más rápido, más corto, económico o fácil, y pulsar el botón "Siguiente".
-Seleccionar los avisos de seguridad que quieres activar, como los límites de velocidad, los radares, las zonas peligrosas, etc., y pulsar el botón "Siguiente".
-Seleccionar el tema de la interfaz que más te guste, entre claro, oscuro o automático, y pulsar el botón "Siguiente".
-Pulsar el botón "Finalizar" para completar la configuración inicial del software.
-
-Ventajas y desventajas de descargar iGO Primo APK gratis
-Descargar iGO Primo APK gratis tiene sus ventajas y sus desventajas, que debes tener en cuenta antes de decidirte a hacerlo. A continuación, te mostramos una comparación entre los pros y los contras de esta opción:
-Ventajas
-
-Ahorras dinero al no tener que pagar por el software ni por las actualizaciones de los mapas.
-Tienes acceso a una navegación offline que no depende de la conexión a internet ni consume datos móviles.
-Disfrutas de una guía de voz inteligente que te indica el camino con claridad y precisión.
-Recibes información de tráfico en tiempo real que te ayuda a evitar atascos y a llegar más rápido a tu destino.
-Puedes buscar destinos y lugares de interés por varias opciones y ver información relevante sobre ellos.
-Puedes personalizar la interfaz de usuario según tus gustos y preferencias.
-
-Desventajas
-
-Te expones a posibles riesgos de seguridad al descargar e instalar un archivo APK desde una fuente desconocida.
-No recibes las últimas actualizaciones del software ni de los mapas, lo que puede afectar al rendimiento y a la precisión de la navegación.
-No cuentas con el soporte técnico ni con la garantía del desarrollador oficial del software.
-Puedes infringir los derechos de autor o las condiciones de uso del software al descargarlo e instalarlo sin permiso.
-
-Conclusión
-iGO Primo es un software de navegación que te ofrece mapas offline, guía de voz inteligente, información de tráfico en tiempo real y más características. Sin embargo, descargar iGO Primo APK gratis puede tener sus ventajas y sus desventajas, que debes sopesar antes de hacerlo.
-Si quieres ahorrar dinero y tener una navegación offline sin depender de la conexión a internet, descargar iGO Primo APK gratis puede ser una buena opción para ti. Pero si quieres tener la seguridad, la actualización y el soporte del software oficial, quizás te convenga más comprarlo o buscar otra alternativa gratuita y legal.
- Esperamos que este artículo te haya sido útil para conocer más sobre iGO Primo y cómo descargarlo e instalarlo gratis en tu dispositivo Android. Si tienes alguna duda o comentario, puedes dejarlo abajo. ¡Gracias por leernos!
- Preguntas frecuentes
- A continuación, te respondemos algunas preguntas frecuentes sobre iGO Primo:
-
-¿Qué diferencia hay entre iGO Primo e iGO Navigation? iGO Primo e iGO Navigation son dos versiones diferentes del mismo software de navegación. iGO Primo es la versión más antigua y completa, que ofrece más características y opciones. iGO Navigation es la versión más nueva y simplificada, que ofrece una interfaz más moderna y minimalista.
- ¿Qué mapas puedo usar con iGO Primo? P uedes usar los mapas de TomTom, HERE, Navteq y otros proveedores que sean compatibles con iGO Primo. Puedes descargar los mapas desde la aplicación o desde el sitio web oficial. También puedes usar mapas en 3D que te ofrecen una vista más realista del terreno.
- ¿Qué dispositivos son compatibles con iGO Primo? iGO Primo es compatible con la mayoría de los dispositivos Android que tengan una versión igual o superior a la 4.0. También es compatible con algunos sistemas multimedia de vehículos que usen Android como sistema operativo. Sin embargo, no es compatible con iOS ni con Windows Phone.
- ¿Qué ventajas tiene iGO Primo sobre Google Maps? iGO Primo tiene algunas ventajas sobre Google Maps, como por ejemplo:
-
-Te permite navegar sin conexión a internet, lo que te ahorra datos móviles y te evita problemas de cobertura.
-Te ofrece una guía de voz inteligente que te anuncia los nombres de las calles y las salidas y entradas de las autopistas.
-Te proporciona información de tráfico en tiempo real y actualiza las rutas según las condiciones del tráfico.
-Te permite personalizar la interfaz de usuario según tus gustos y preferencias.
-
- ¿Qué riesgos tiene descargar iGO Primo APK gratis? Descargar iGO Primo APK gratis tiene algunos riesgos, como por ejemplo:
-
-Te expones a posibles virus, malware o spyware que puedan dañar tu dispositivo o robar tu información personal.
-No recibes las últimas actualizaciones del software ni de los mapas, lo que puede afectar al rendimiento y a la precisión de la navegación.
-No cuentas con el soporte técnico ni con la garantía del desarrollador oficial del software.
-Puedes infringir los derechos de autor o las condiciones de uso del software al descargarlo e instalarlo sin permiso.
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index b635f4792da4e18428d0f5a5da5e436c3bdccfa0..0000000000000000000000000000000000000000
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diff --git a/spaces/cooelf/Multimodal-CoT/model.py b/spaces/cooelf/Multimodal-CoT/model.py
deleted file mode 100644
index c7c1e20842539e83c0675e808ff1f4417dea31ca..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/model.py
+++ /dev/null
@@ -1,515 +0,0 @@
-'''
-Adapted from https://github.com/huggingface/transformers
-'''
-
-from transformers import T5Config, T5ForConditionalGeneration
-from transformers.models.t5.modeling_t5 import T5Stack, __HEAD_MASK_WARNING_MSG, T5Block, T5LayerNorm
-import copy
-from transformers.modeling_outputs import ModelOutput, BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
-import math
-import os
-import warnings
-from typing import Optional, Tuple, Union
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.modeling_outputs import (
- BaseModelOutput,
- Seq2SeqLMOutput,
-)
-from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
-from torch.utils.checkpoint import checkpoint
-
-class JointEncoder(T5Stack):
- def __init__(self, config, embed_tokens=None, patch_size=None):
- super().__init__(config)
-
- self.embed_tokens = embed_tokens
- self.is_decoder = config.is_decoder
-
- self.patch_num, self.patch_dim = patch_size
- self.image_dense = nn.Linear(self.patch_dim, config.d_model)
- self.mha_layer = torch.nn.MultiheadAttention(embed_dim=config.hidden_size, kdim=config.hidden_size, vdim=config.hidden_size, num_heads=1, batch_first=True)
- self.gate_dense = nn.Linear(2*config.hidden_size, config.hidden_size)
- self.sigmoid = nn.Sigmoid()
-
- self.block = nn.ModuleList(
- [T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
- )
- self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
- self.dropout = nn.Dropout(config.dropout_rate)
-
- # Initialize weights and apply final processing
- self.post_init()
- # Model parallel
- self.model_parallel = False
- self.device_map = None
- self.gradient_checkpointing = False
-
- def parallelize(self, device_map=None):
- warnings.warn(
- "`T5Stack.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model"
- " with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
- " `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0,"
- " 'block.1': 1, ...}",
- FutureWarning,
- )
- # Check validity of device_map
- self.device_map = (
- get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map
- )
- assert_device_map(self.device_map, len(self.block))
- self.model_parallel = True
- self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
- self.last_device = "cuda:" + str(max(self.device_map.keys()))
- # Load onto devices
- for k, v in self.device_map.items():
- for layer in v:
- cuda_device = "cuda:" + str(k)
- self.block[layer] = self.block[layer].to(cuda_device)
-
- # Set embed_tokens to first layer
- self.embed_tokens = self.embed_tokens.to(self.first_device)
- # Set final layer norm to last device
- self.final_layer_norm = self.final_layer_norm.to(self.last_device)
-
- def deparallelize(self):
- warnings.warn(
- "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
- FutureWarning,
- )
- self.model_parallel = False
- self.device_map = None
- self.first_device = "cpu"
- self.last_device = "cpu"
- for i in range(len(self.block)):
- self.block[i] = self.block[i].to("cpu")
- self.embed_tokens = self.embed_tokens.to("cpu")
- self.final_layer_norm = self.final_layer_norm.to("cpu")
- torch.cuda.empty_cache()
-
- def get_input_embeddings(self):
- return self.embed_tokens
-
- def set_input_embeddings(self, new_embeddings):
- self.embed_tokens = new_embeddings
-
- def forward(
- self,
- input_ids=None,
- attention_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- inputs_embeds=None,
- image_ids=None,
- head_mask=None,
- cross_attn_head_mask=None,
- past_key_values=None,
- use_cache=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- # Model parallel
- if self.model_parallel:
- torch.cuda.set_device(self.first_device)
- self.embed_tokens = self.embed_tokens.to(self.first_device)
- use_cache = use_cache if use_cache is not None else self.config.use_cache
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- if input_ids is not None and inputs_embeds is not None:
- err_msg_prefix = "decoder_" if self.is_decoder else ""
- raise ValueError(
- f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
- )
- elif input_ids is not None:
- input_shape = input_ids.size()
- input_ids = input_ids.view(-1, input_shape[-1])
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- err_msg_prefix = "decoder_" if self.is_decoder else ""
- raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
-
- if inputs_embeds is None:
- assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
- inputs_embeds = self.embed_tokens(input_ids)
-
- batch_size, seq_length = input_shape
-
- # required mask seq length can be calculated via length of past
- mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
-
- if use_cache is True:
- assert self.is_decoder, f"`use_cache` can only be set to `True` if {self} is used as a decoder"
-
- if attention_mask is None:
- attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
- if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
- encoder_seq_length = encoder_hidden_states.shape[1]
- encoder_attention_mask = torch.ones(
- batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
- )
-
- # initialize past_key_values with `None` if past does not exist
- if past_key_values is None:
- past_key_values = [None] * len(self.block)
-
- # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
- # ourselves in which case we just need to make it broadcastable to all heads.
- extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
-
- # If a 2D or 3D attention mask is provided for the cross-attention
- # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
- if self.is_decoder and encoder_hidden_states is not None:
- encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
- encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
- if encoder_attention_mask is None:
- encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
- encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
- else:
- encoder_extended_attention_mask = None
-
- # Prepare head mask if needed
- head_mask = self.get_head_mask(head_mask, self.config.num_layers)
- cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
- present_key_value_states = () if use_cache else None
- all_hidden_states = () if output_hidden_states else None
- all_attentions = () if output_attentions else None
- all_cross_attentions = () if (output_attentions and self.is_decoder) else None
- position_bias = None
- encoder_decoder_position_bias = None
-
- hidden_states = self.dropout(inputs_embeds)
-
- for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
- layer_head_mask = head_mask[i]
- cross_attn_layer_head_mask = cross_attn_head_mask[i]
- # Model parallel
- if self.model_parallel:
- torch.cuda.set_device(hidden_states.device)
- # Ensure that attention_mask is always on the same device as hidden_states
- if attention_mask is not None:
- attention_mask = attention_mask.to(hidden_states.device)
- if position_bias is not None:
- position_bias = position_bias.to(hidden_states.device)
- if encoder_hidden_states is not None:
- encoder_hidden_states = encoder_hidden_states.to(hidden_states.device)
- if encoder_extended_attention_mask is not None:
- encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device)
- if encoder_decoder_position_bias is not None:
- encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device)
- if layer_head_mask is not None:
- layer_head_mask = layer_head_mask.to(hidden_states.device)
- if cross_attn_layer_head_mask is not None:
- cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device)
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if self.gradient_checkpointing and self.training:
- if use_cache:
- logger.warning_once(
- "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
- )
- use_cache = False
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return tuple(module(*inputs, use_cache, output_attentions))
-
- return custom_forward
-
- layer_outputs = checkpoint(
- create_custom_forward(layer_module),
- hidden_states,
- extended_attention_mask,
- position_bias,
- encoder_hidden_states,
- encoder_extended_attention_mask,
- encoder_decoder_position_bias,
- layer_head_mask,
- cross_attn_layer_head_mask,
- None, # past_key_value is always None with gradient checkpointing
- )
- else:
- layer_outputs = layer_module(
- hidden_states,
- attention_mask=extended_attention_mask,
- position_bias=position_bias,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_extended_attention_mask,
- encoder_decoder_position_bias=encoder_decoder_position_bias,
- layer_head_mask=layer_head_mask,
- cross_attn_layer_head_mask=cross_attn_layer_head_mask,
- past_key_value=past_key_value,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
-
- # layer_outputs is a tuple with:
- # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
- if use_cache is False:
- layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
-
- hidden_states, present_key_value_state = layer_outputs[:2]
-
- # We share the position biases between the layers - the first layer store them
- # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
- # (cross-attention position bias), (cross-attention weights)
- position_bias = layer_outputs[2]
- if self.is_decoder and encoder_hidden_states is not None:
- encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
- # append next layer key value states
- if use_cache:
- present_key_value_states = present_key_value_states + (present_key_value_state,)
-
- if output_attentions:
- all_attentions = all_attentions + (layer_outputs[3],)
- if self.is_decoder:
- all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
-
- # Model Parallel: If it's the last layer for that device, put things on the next device
- if self.model_parallel:
- for k, v in self.device_map.items():
- if i == v[-1] and "cuda:" + str(k) != self.last_device:
- hidden_states = hidden_states.to("cuda:" + str(k + 1))
-
- hidden_states = self.final_layer_norm(hidden_states)
- hidden_states = self.dropout(hidden_states)
-
- # Add last layer
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- image_embedding = self.image_dense(image_ids)
- image_att, _ = self.mha_layer(hidden_states, image_embedding, image_embedding)
- merge = torch.cat([hidden_states, image_att], dim=-1)
- gate = self.sigmoid(self.gate_dense(merge))
- hidden_states = (1 - gate) * hidden_states + gate * image_att
-
- if not return_dict:
- return tuple(
- v
- for v in [
- hidden_states,
- present_key_value_states,
- all_hidden_states,
- all_attentions,
- all_cross_attentions,
- ]
- if v is not None
- )
- return BaseModelOutputWithPastAndCrossAttentions(
- last_hidden_state=hidden_states,
- past_key_values=present_key_value_states,
- hidden_states=all_hidden_states,
- attentions=all_attentions,
- cross_attentions=all_cross_attentions,
- )
-
-
-class T5ForMultimodalGeneration(T5ForConditionalGeneration):
- _keys_to_ignore_on_load_missing = [
- r"encoder.embed_tokens.weight",
- r"decoder.embed_tokens.weight",
- r"lm_head.weight",
- ]
- _keys_to_ignore_on_load_unexpected = [
- r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
- ]
-
- def __init__(self, config: T5Config, patch_size):
- super().__init__(config)
- self.model_dim = config.d_model
-
- self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
- encoder_config = copy.deepcopy(config)
- encoder_config.is_decoder = False
- encoder_config.use_cache = False
- encoder_config.is_encoder_decoder = False
- # self.encoder = T5Stack(encoder_config, self.shared)
- self.encoder = JointEncoder(encoder_config, self.shared, patch_size)
- decoder_config = copy.deepcopy(config)
- decoder_config.is_decoder = True
- decoder_config.is_encoder_decoder = False
- decoder_config.num_layers = config.num_decoder_layers
- self.decoder = T5Stack(decoder_config, self.shared)
-
- self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- # Model parallel
- self.model_parallel = False
- self.device_map = None
-
- def forward(
- self,
- input_ids: Optional[torch.LongTensor] = None,
- image_ids=None,
- attention_mask: Optional[torch.FloatTensor] = None,
- decoder_input_ids: Optional[torch.LongTensor] = None,
- decoder_attention_mask: Optional[torch.BoolTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- decoder_head_mask: Optional[torch.FloatTensor] = None,
- cross_attn_head_mask: Optional[torch.Tensor] = None,
- encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
- past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
- use_cache = use_cache if use_cache is not None else self.config.use_cache
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
- if head_mask is not None and decoder_head_mask is None:
- if self.config.num_layers == self.config.num_decoder_layers:
- warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
- decoder_head_mask = head_mask
-
- # Encode if needed (training, first prediction pass)
- if encoder_outputs is None:
- # Convert encoder inputs in embeddings if needed
- encoder_outputs = self.encoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- inputs_embeds=inputs_embeds,
- image_ids=image_ids,
- head_mask=head_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
- encoder_outputs = BaseModelOutput(
- last_hidden_state=encoder_outputs[0],
- hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
- attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
- )
-
- hidden_states = encoder_outputs[0]
-
- if self.model_parallel:
- torch.cuda.set_device(self.decoder.first_device)
-
- if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
- # get decoder inputs from shifting lm labels to the right
- decoder_input_ids = self._shift_right(labels)
-
- # Set device for model parallelism
- if self.model_parallel:
- torch.cuda.set_device(self.decoder.first_device)
- hidden_states = hidden_states.to(self.decoder.first_device)
- if decoder_input_ids is not None:
- decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
- if attention_mask is not None:
- attention_mask = attention_mask.to(self.decoder.first_device)
- if decoder_attention_mask is not None:
- decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
-
- # Decode
- decoder_outputs = self.decoder(
- input_ids=decoder_input_ids,
- attention_mask=decoder_attention_mask,
- inputs_embeds=decoder_inputs_embeds,
- past_key_values=past_key_values,
- encoder_hidden_states=hidden_states,
- encoder_attention_mask=attention_mask,
- head_mask=decoder_head_mask,
- cross_attn_head_mask=cross_attn_head_mask,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = decoder_outputs[0]
-
- # Set device for model parallelism
- if self.model_parallel:
- torch.cuda.set_device(self.encoder.first_device)
- self.lm_head = self.lm_head.to(self.encoder.first_device)
- sequence_output = sequence_output.to(self.lm_head.weight.device)
-
- if self.config.tie_word_embeddings:
- # Rescale output before projecting on vocab
- # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
- sequence_output = sequence_output * (self.model_dim**-0.5)
-
- lm_logits = self.lm_head(sequence_output)
-
- loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss(ignore_index=-100)
- loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
- # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
-
- if not return_dict:
- output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
- return ((loss,) + output) if loss is not None else output
-
- return Seq2SeqLMOutput(
- loss=loss,
- logits=lm_logits,
- past_key_values=decoder_outputs.past_key_values,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
- def prepare_inputs_for_generation(
- self, decoder_input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
- ):
- # cut decoder_input_ids if past is used
- if past is not None:
- decoder_input_ids = decoder_input_ids[:, -1:]
-
- output = {
- "input_ids": None, # encoder_outputs is defined. input_ids not needed
- "encoder_outputs": encoder_outputs,
- "past_key_values": past,
- "decoder_input_ids": decoder_input_ids,
- "attention_mask": attention_mask,
- "use_cache": use_cache, # change this to avoid caching (presumably for debugging)
- }
-
- if "image_ids" in kwargs:
- output["image_ids"] = kwargs['image_ids']
-
- return output
-
- def test_step(self, tokenizer, batch, **kwargs):
- device = next(self.parameters()).device
- input_ids = batch['input_ids'].to(device)
- image_ids = batch['image_ids'].to(device)
-
- output = self.generate(
- input_ids=input_ids,
- image_ids=image_ids,
- **kwargs
- )
-
- generated_sents = tokenizer.batch_decode(output, skip_special_tokens=True)
- targets = tokenizer.batch_decode(batch['labels'], skip_special_tokens=True)
-
- result = {}
- result['preds'] = generated_sents
- result['targets'] = targets
-
- return result
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/canny/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/canny/__init__.py
deleted file mode 100644
index cb0da951dc838ec9dec2131007e036113281800b..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/canny/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import cv2
-
-
-class CannyDetector:
- def __call__(self, img, low_threshold, high_threshold):
- return cv2.Canny(img, low_threshold, high_threshold)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/decode_heads/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/decode_heads/__init__.py
deleted file mode 100644
index ac66d3cfe0ea04af45c0f3594bf135841c3812e3..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/decode_heads/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from .ann_head import ANNHead
-from .apc_head import APCHead
-from .aspp_head import ASPPHead
-from .cc_head import CCHead
-from .da_head import DAHead
-from .dm_head import DMHead
-from .dnl_head import DNLHead
-from .ema_head import EMAHead
-from .enc_head import EncHead
-from .fcn_head import FCNHead
-from .fpn_head import FPNHead
-from .gc_head import GCHead
-from .lraspp_head import LRASPPHead
-from .nl_head import NLHead
-from .ocr_head import OCRHead
-# from .point_head import PointHead
-from .psa_head import PSAHead
-from .psp_head import PSPHead
-from .sep_aspp_head import DepthwiseSeparableASPPHead
-from .sep_fcn_head import DepthwiseSeparableFCNHead
-from .uper_head import UPerHead
-
-__all__ = [
- 'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
- 'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
- 'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
- 'APCHead', 'DMHead', 'LRASPPHead'
-]
diff --git a/spaces/cyllum/soccertwos-analytics/utils.py b/spaces/cyllum/soccertwos-analytics/utils.py
deleted file mode 100644
index 522a35102bd886d5f2911517121fc3869f4218b2..0000000000000000000000000000000000000000
--- a/spaces/cyllum/soccertwos-analytics/utils.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import pandas as pd
-import streamlit as st
-
-
-@st.cache_data(ttl=1800)
-def build_team_results_df(df_in):
- """Return a dataframe recording results from each team's perspective."""
- rows = []
- for i, row in df_in.iterrows():
- home_team = row["home"]
- away_team = row["away"]
- result = row["result"]
- timestamp = row["timestamp"]
- match_id = i
-
- if result == 0.5:
- home_result, away_result = "Draw", "Draw"
- else:
- home_result = "Win" if result == 1.0 else "Loss"
- away_result = "Win" if result == 0.0 else "Loss"
-
- rows.append({
- "team": home_team,
- "opponent": away_team,
- "result": home_result,
- "match_id": match_id,
- "timestamp": timestamp
- })
-
- rows.append({
- "team": away_team,
- "opponent": home_team,
- "result": away_result,
- "match_id": match_id,
- "timestamp": timestamp
- })
-
- dfout = pd.DataFrame(rows, columns=["team", "opponent", "result", "match_id", "timestamp"])
- dfout[["owner", "model"]] = dfout["team"].str.split("/", expand=True)
- dfout.sort_values(["team", "timestamp"], inplace=True)
- return dfout
-
-
-@st.cache_data(ttl=1800)
-def get_win_streaks(df):
- win_streaks = []
- current_team = None
- current_streak = 0
- last_result = None
- for index, row in df.iterrows():
- if row['team'] != current_team:
- if last_result == 'Win':
- win_streaks.append([current_team, current_streak, None])
- current_team = row['team']
- current_streak = 0
- last_result = None
- if row['result'] == 'Win':
- current_streak += 1
- last_result = 'Win'
- else:
- if last_result == 'Win':
- win_streaks.append([current_team, current_streak, row['timestamp']])
- current_streak = 0
- last_result = row['result']
- if last_result == 'Win':
- win_streaks.append([current_team, current_streak, None])
- win_streak_df = pd.DataFrame(win_streaks, columns=['team', 'streak', 'ended'])
- win_streak_df.sort_values("streak", ascending=False, inplace=True)
- return win_streak_df
-
diff --git a/spaces/dawdqd/ChuanhuChatGPT/web_assets/javascript/ChuanhuChat.js b/spaces/dawdqd/ChuanhuChatGPT/web_assets/javascript/ChuanhuChat.js
deleted file mode 100644
index 1128b7782111381f4540282db574ba951e65f2f1..0000000000000000000000000000000000000000
--- a/spaces/dawdqd/ChuanhuChatGPT/web_assets/javascript/ChuanhuChat.js
+++ /dev/null
@@ -1,328 +0,0 @@
-
-// ChuanhuChat core javascript
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var chatbotIndicator = null;
-var chatbotWrap = null;
-var apSwitch = null;
-var messageBotDivs = null;
-var loginUserForm = null;
-var logginUser = null;
-var updateToast = null;
-var sendBtn = null;
-var cancelBtn = null;
-var sliders = null;
-var updateChuanhuBtn = null;
-var statusDisplay = null;
-
-var isInIframe = (window.self !== window.top);
-var currentTime = new Date().getTime();
-var initialized = false;
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
- for (var i = 0; i < mutations.length; i++) {
- if (mutations[i].addedNodes.length) {
- if (initialized) {
- observer.disconnect(); // 停止监听
- return;
- }
- initialize();
- }
- }
-}
-
-function initialize() {
- var needInit = {gradioContainer, apSwitch, user_input_tb, userInfoDiv, appTitleDiv, chatbot, chatbotIndicator, chatbotWrap, statusDisplay, sliders, updateChuanhuBtn};
- initialized = true;
-
- loginUserForm = gradioApp().querySelector(".gradio-container > .main > .wrap > .panel > .form")
- gradioContainer = gradioApp().querySelector(".gradio-container");
- user_input_tb = gradioApp().getElementById('user-input-tb');
- userInfoDiv = gradioApp().getElementById("user-info");
- appTitleDiv = gradioApp().getElementById("app-title");
- chatbot = gradioApp().querySelector('#chuanhu-chatbot');
- chatbotIndicator = gradioApp().querySelector('#chuanhu-chatbot>div.wrap');
- chatbotWrap = gradioApp().querySelector('#chuanhu-chatbot > .wrapper > .wrap');
- apSwitch = gradioApp().querySelector('.apSwitch input[type="checkbox"]');
- updateToast = gradioApp().querySelector("#toast-update");
- sendBtn = gradioApp().getElementById("submit-btn");
- cancelBtn = gradioApp().getElementById("cancel-btn");
- sliders = gradioApp().querySelectorAll('input[type="range"]');
- updateChuanhuBtn = gradioApp().getElementById("update-chuanhu-btn");
- statusDisplay = gradioApp().querySelector('#status-display');
-
- if (loginUserForm) {
- localStorage.setItem("userLogged", true);
- userLogged = true;
- }
-
- for (let elem in needInit) {
- if (needInit[elem] == null) {
- initialized = false;
- return;
- }
- }
-
- if (initialized) {
- adjustDarkMode();
- selectHistory();
- setTimeout(showOrHideUserInfo(), 2000);
- setChatbotHeight();
- setChatbotScroll();
- setSlider();
- setAvatar();
- if (!historyLoaded) loadHistoryHtml();
- if (!usernameGotten) getUserInfo();
- chatbotObserver.observe(chatbotIndicator, { attributes: true });
-
- const lastCheckTime = localStorage.getItem('lastCheckTime') || 0;
- const longTimeNoCheck = currentTime - lastCheckTime > 3 * 24 * 60 * 60 * 1000;
- if (longTimeNoCheck && !updateInfoGotten && !isLatestVersion || isLatestVersion && !updateInfoGotten) {
- updateLatestVersion();
- }
- }
-}
-
-function gradioApp() {
- const elems = document.getElementsByTagName('gradio-app');
- const elem = elems.length == 0 ? document : elems[0];
-
- if (elem !== document) {
- elem.getElementById = function(id) {
- return document.getElementById(id);
- };
- }
- return elem.shadowRoot ? elem.shadowRoot : elem;
-}
-
-function showConfirmationDialog(a, file, c) {
- if (file != "") {
- var result = confirm(i18n(deleteConfirm_i18n_pref) + file + i18n(deleteConfirm_i18n_suff));
- if (result) {
- return [a, file, c];
- }
- }
- return [a, "CANCELED", c];
-}
-
-function selectHistory() {
- user_input_ta = user_input_tb.querySelector("textarea");
- if (user_input_ta) {
- disableSendBtn();
- // 在 textarea 上监听 keydown 事件
- user_input_ta.addEventListener("keydown", function (event) {
- var value = user_input_ta.value.trim();
- // 判断按下的是否为方向键
- if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
- // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
- if (value && key_down_history.indexOf(value) === -1)
- return;
- // 对于需要响应的动作，阻止默认行为。
- event.preventDefault();
- var length = key_down_history.length;
- if (length === 0) {
- currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
- return;
- }
- if (currentIndex === -1) {
- currentIndex = length;
- }
- if (event.code === 'ArrowUp' && currentIndex > 0) {
- currentIndex--;
- user_input_ta.value = key_down_history[currentIndex];
- } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
- currentIndex++;
- user_input_ta.value = key_down_history[currentIndex];
- }
- user_input_ta.selectionStart = user_input_ta.value.length;
- user_input_ta.selectionEnd = user_input_ta.value.length;
- const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
- user_input_ta.dispatchEvent(input_event);
- } else if (event.code === "Enter") {
- if (value) {
- currentIndex = -1;
- if (key_down_history.indexOf(value) === -1) {
- key_down_history.push(value);
- if (key_down_history.length > MAX_HISTORY_LENGTH) {
- key_down_history.shift();
- }
- }
- }
- }
- });
- }
-}
-
-function disableSendBtn() {
- sendBtn.disabled = user_input_ta.value.trim() === '';
- user_input_ta.addEventListener('input', () => {
- sendBtn.disabled = user_input_ta.value.trim() === '';
- });
-}
-
-function adjustDarkMode() {
- function toggleDarkMode(isEnabled) {
- if (isEnabled) {
- document.body.classList.add("dark");
- document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
- } else {
- document.body.classList.remove("dark");
- document.body.style.backgroundColor = "";
- }
- }
-
- const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
- apSwitch.checked = darkModeQuery.matches;
- toggleDarkMode(darkModeQuery.matches);
- darkModeQuery.addEventListener("change", (e) => {
- apSwitch.checked = e.matches;
- toggleDarkMode(e.matches);
- });
- apSwitch.addEventListener("change", (e) => {
- toggleDarkMode(e.target.checked);
- });
-}
-
-function setChatbotHeight() {
- const screenWidth = window.innerWidth;
- const statusDisplay = document.querySelector('#status-display');
- const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
- const vh = window.innerHeight * 0.01;
- document.documentElement.style.setProperty('--vh', `${vh}px`);
- if (isInIframe) {
- chatbot.style.height = `700px`;
- chatbotWrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
- } else {
- if (screenWidth <= 320) {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
- chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- } else if (screenWidth <= 499) {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
- chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- } else {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
- chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- }
- }
-}
-function setChatbotScroll() {
- var scrollHeight = chatbotWrap.scrollHeight;
- chatbotWrap.scrollTo(0,scrollHeight)
-}
-
-var botAvatarUrl = "";
-var userAvatarUrl = "";
-function setAvatar() {
- var botAvatar = gradioApp().getElementById("config-bot-avatar-url").innerText;
- var userAvatar = gradioApp().getElementById("config-user-avatar-url").innerText;
-
- if (botAvatar == "none") {
- botAvatarUrl = "";
- } else if (isImgUrl(botAvatar)) {
- botAvatarUrl = botAvatar;
- } else {
- // botAvatarUrl = "https://github.com/GaiZhenbiao/ChuanhuChatGPT/assets/70903329/aca3a7ec-4f1d-4667-890c-a6f47bf08f63";
- botAvatarUrl = "/file=web_assets/chatbot.png"
- }
-
- if (userAvatar == "none") {
- userAvatarUrl = "";
- } else if (isImgUrl(userAvatar)) {
- userAvatarUrl = userAvatar;
- } else {
- userAvatarUrl = "data:image/svg+xml,%3Csvg width='32px' height='32px' viewBox='0 0 32 32' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E%3Cg stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E%3Crect fill-opacity='0.5' fill='%23bbbbbb' x='0' y='0' width='32' height='32'%3E%3C/rect%3E%3Cg transform='translate(5, 4)' fill='%23999999' fill-opacity='0.8' fill-rule='nonzero'%3E%3Cpath d='M2.29372246,24 L19.7187739,24 C20.4277609,24 20.985212,23.8373915 21.3911272,23.5121746 C21.7970424,23.1869576 22,22.7418004 22,22.1767029 C22,21.3161536 21.7458721,20.4130827 21.2376163,19.4674902 C20.7293605,18.5218977 19.9956681,17.6371184 19.036539,16.8131524 C18.07741,15.9891863 16.9210688,15.3177115 15.5675154,14.798728 C14.2139621,14.2797445 12.6914569,14.0202527 11,14.0202527 C9.30854307,14.0202527 7.78603793,14.2797445 6.43248458,14.798728 C5.07893122,15.3177115 3.92259002,15.9891863 2.96346097,16.8131524 C2.00433193,17.6371184 1.27063951,18.5218977 0.762383704,19.4674902 C0.254127901,20.4130827 0,21.3161536 0,22.1767029 C0,22.7418004 0.202957595,23.1869576 0.608872784,23.5121746 C1.01478797,23.8373915 1.57640453,24 2.29372246,24 Z M11.0124963,11.6521659 C11.9498645,11.6521659 12.8155943,11.3906214 13.6096856,10.8675324 C14.403777,10.3444433 15.042131,9.63605539 15.5247478,8.74236856 C16.0073646,7.84868174 16.248673,6.84722464 16.248673,5.73799727 C16.248673,4.65135034 16.0071492,3.67452644 15.5241015,2.80752559 C15.0410538,1.94052474 14.4024842,1.25585359 13.6083929,0.753512156 C12.8143016,0.251170719 11.9490027,0 11.0124963,0 C10.0759899,0 9.20860836,0.255422879 8.41035158,0.766268638 C7.6120948,1.2771144 6.97352528,1.96622098 6.49464303,2.8335884 C6.01576078,3.70095582 5.77631966,4.67803631 5.77631966,5.76482987 C5.77631966,6.86452653 6.01554533,7.85912886 6.49399667,8.74863683 C6.97244801,9.63814481 7.60871935,10.3444433 8.40281069,10.8675324 C9.19690203,11.3906214 10.0667972,11.6521659 11.0124963,11.6521659 Z'%3E%3C/path%3E%3C/g%3E%3C/g%3E%3C/svg%3E";
- }
-}
-
-function clearChatbot() {
- clearHistoryHtml();
- clearMessageRows();
-}
-
-function chatbotContentChanged(attempt = 1) {
- for (var i = 0; i < attempt; i++) {
- setTimeout(() => {
- // clearMessageRows();
- saveHistoryHtml();
- disableSendBtn();
- gradioApp().querySelectorAll('#chuanhu-chatbot .message-wrap .message.user').forEach((userElement) => {addAvatars(userElement, 'user')});
- gradioApp().querySelectorAll('#chuanhu-chatbot .message-wrap .message.bot').forEach((botElement) => {addAvatars(botElement, 'bot'); addChuanhuButton(botElement)});
- }, i === 0 ? 0 : 500);
- }
- // 理论上是不需要多次尝试执行的，可惜gradio的bug导致message可能没有渲染完毕，所以尝试500ms后再次执行
-}
-
-var chatbotObserver = new MutationObserver(() => {
- clearMessageRows();
- chatbotContentChanged(1);
- if (chatbotIndicator.classList.contains('hide')) {
- chatbotContentChanged(2);
- }
-});
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
- gradioLoaded(mutations);
-});
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
- const ga = document.getElementsByTagName("gradio-app");
- observer.observe(ga[0], { childList: true, subtree: true });
- isInIframe = (window.self !== window.top);
- historyLoaded = false;
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', function(){setChatbotHeight(); setUpdateWindowHeight();});
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
-
-// console suprise
-var styleTitle1 = `
-font-size: 16px;
-font-family: ui-monospace, monospace;
-color: #06AE56;
-`
-var styleDesc1 = `
-font-size: 12px;
-font-family: ui-monospace, monospace;
-`
-function makeML(str) {
- let l = new String(str)
- l = l.substring(l.indexOf("/*") + 3, l.lastIndexOf("*/"))
- return l
-}
-let ChuanhuInfo = function () {
- /*
- ________ __ ________ __
- / ____/ /_ __ ______ _____ / /_ __ __ / ____/ /_ ____ _/ /_
- / / / __ \/ / / / __ `/ __ \/ __ \/ / / / / / / __ \/ __ `/ __/
-/ /___/ / / / /_/ / /_/ / / / / / / / /_/ / / /___/ / / / /_/ / /_
-\____/_/ /_/\__,_/\__,_/_/ /_/_/ /_/\__,_/ \____/_/ /_/\__,_/\__/
-
- 川虎Chat (Chuanhu Chat) - GUI for ChatGPT API and many LLMs
- */
-}
-let description = `
-© 2023 Chuanhu, MZhao, Keldos
-GitHub repository: [https://github.com/GaiZhenbiao/ChuanhuChatGPT]\n
-Enjoy our project!\n
-`
-console.log(`%c${makeML(ChuanhuInfo)}`,styleTitle1)
-console.log(`%c${description}`, styleDesc1)
-
-// button svg code
-const copyIcon = '
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
- CHECK_CUDA(input);
- CHECK_CUDA(kernel);
-
- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/file_client.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/file_client.py
deleted file mode 100644
index 7f38d9796da3899048924f2f803d1088927966b0..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/file_client.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py # noqa: E501
-from abc import ABCMeta, abstractmethod
-
-
-class BaseStorageBackend(metaclass=ABCMeta):
- """Abstract class of storage backends.
-
- All backends need to implement two apis: ``get()`` and ``get_text()``.
- ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
- as texts.
- """
-
- @abstractmethod
- def get(self, filepath):
- pass
-
- @abstractmethod
- def get_text(self, filepath):
- pass
-
-
-class MemcachedBackend(BaseStorageBackend):
- """Memcached storage backend.
-
- Attributes:
- server_list_cfg (str): Config file for memcached server list.
- client_cfg (str): Config file for memcached client.
- sys_path (str | None): Additional path to be appended to `sys.path`.
- Default: None.
- """
-
- def __init__(self, server_list_cfg, client_cfg, sys_path=None):
- if sys_path is not None:
- import sys
- sys.path.append(sys_path)
- try:
- import mc
- except ImportError:
- raise ImportError('Please install memcached to enable MemcachedBackend.')
-
- self.server_list_cfg = server_list_cfg
- self.client_cfg = client_cfg
- self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg)
- # mc.pyvector servers as a point which points to a memory cache
- self._mc_buffer = mc.pyvector()
-
- def get(self, filepath):
- filepath = str(filepath)
- import mc
- self._client.Get(filepath, self._mc_buffer)
- value_buf = mc.ConvertBuffer(self._mc_buffer)
- return value_buf
-
- def get_text(self, filepath):
- raise NotImplementedError
-
-
-class HardDiskBackend(BaseStorageBackend):
- """Raw hard disks storage backend."""
-
- def get(self, filepath):
- filepath = str(filepath)
- with open(filepath, 'rb') as f:
- value_buf = f.read()
- return value_buf
-
- def get_text(self, filepath):
- filepath = str(filepath)
- with open(filepath, 'r') as f:
- value_buf = f.read()
- return value_buf
-
-
-class LmdbBackend(BaseStorageBackend):
- """Lmdb storage backend.
-
- Args:
- db_paths (str | list[str]): Lmdb database paths.
- client_keys (str | list[str]): Lmdb client keys. Default: 'default'.
- readonly (bool, optional): Lmdb environment parameter. If True,
- disallow any write operations. Default: True.
- lock (bool, optional): Lmdb environment parameter. If False, when
- concurrent access occurs, do not lock the database. Default: False.
- readahead (bool, optional): Lmdb environment parameter. If False,
- disable the OS filesystem readahead mechanism, which may improve
- random read performance when a database is larger than RAM.
- Default: False.
-
- Attributes:
- db_paths (list): Lmdb database path.
- _client (list): A list of several lmdb envs.
- """
-
- def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs):
- try:
- import lmdb
- except ImportError:
- raise ImportError('Please install lmdb to enable LmdbBackend.')
-
- if isinstance(client_keys, str):
- client_keys = [client_keys]
-
- if isinstance(db_paths, list):
- self.db_paths = [str(v) for v in db_paths]
- elif isinstance(db_paths, str):
- self.db_paths = [str(db_paths)]
- assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, '
- f'but received {len(client_keys)} and {len(self.db_paths)}.')
-
- self._client = {}
- for client, path in zip(client_keys, self.db_paths):
- self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs)
-
- def get(self, filepath, client_key):
- """Get values according to the filepath from one lmdb named client_key.
-
- Args:
- filepath (str | obj:`Path`): Here, filepath is the lmdb key.
- client_key (str): Used for distinguishing differnet lmdb envs.
- """
- filepath = str(filepath)
- assert client_key in self._client, (f'client_key {client_key} is not ' 'in lmdb clients.')
- client = self._client[client_key]
- with client.begin(write=False) as txn:
- value_buf = txn.get(filepath.encode('ascii'))
- return value_buf
-
- def get_text(self, filepath):
- raise NotImplementedError
-
-
-class FileClient(object):
- """A general file client to access files in different backend.
-
- The client loads a file or text in a specified backend from its path
- and return it as a binary file. it can also register other backend
- accessor with a given name and backend class.
-
- Attributes:
- backend (str): The storage backend type. Options are "disk",
- "memcached" and "lmdb".
- client (:obj:`BaseStorageBackend`): The backend object.
- """
-
- _backends = {
- 'disk': HardDiskBackend,
- 'memcached': MemcachedBackend,
- 'lmdb': LmdbBackend,
- }
-
- def __init__(self, backend='disk', **kwargs):
- if backend not in self._backends:
- raise ValueError(f'Backend {backend} is not supported. Currently supported ones'
- f' are {list(self._backends.keys())}')
- self.backend = backend
- self.client = self._backends[backend](**kwargs)
-
- def get(self, filepath, client_key='default'):
- # client_key is used only for lmdb, where different fileclients have
- # different lmdb environments.
- if self.backend == 'lmdb':
- return self.client.get(filepath, client_key)
- else:
- return self.client.get(filepath)
-
- def get_text(self, filepath):
- return self.client.get_text(filepath)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/easter.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/easter.py
deleted file mode 100644
index f74d1f7442473997245ac683b8a269a3574d1ba4..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/easter.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic Easter computing method for any given year, using
-Western, Orthodox or Julian algorithms.
-"""
-
-import datetime
-
-__all__ = ["easter", "EASTER_JULIAN", "EASTER_ORTHODOX", "EASTER_WESTERN"]
-
-EASTER_JULIAN = 1
-EASTER_ORTHODOX = 2
-EASTER_WESTERN = 3
-
-
-def easter(year, method=EASTER_WESTERN):
- """
- This method was ported from the work done by GM Arts,
- on top of the algorithm by Claus Tondering, which was
- based in part on the algorithm of Ouding (1940), as
- quoted in "Explanatory Supplement to the Astronomical
- Almanac", P. Kenneth Seidelmann, editor.
-
- This algorithm implements three different Easter
- calculation methods:
-
- 1. Original calculation in Julian calendar, valid in
- dates after 326 AD
- 2. Original method, with date converted to Gregorian
- calendar, valid in years 1583 to 4099
- 3. Revised method, in Gregorian calendar, valid in
- years 1583 to 4099 as well
-
- These methods are represented by the constants:
-
- * ``EASTER_JULIAN = 1``
- * ``EASTER_ORTHODOX = 2``
- * ``EASTER_WESTERN = 3``
-
- The default method is method 3.
-
- More about the algorithm may be found at:
-
- `GM Arts: Easter Algorithms `_
-
- and
-
- `The Calendar FAQ: Easter `_
-
- """
-
- if not (1 <= method <= 3):
- raise ValueError("invalid method")
-
- # g - Golden year - 1
- # c - Century
- # h - (23 - Epact) mod 30
- # i - Number of days from March 21 to Paschal Full Moon
- # j - Weekday for PFM (0=Sunday, etc)
- # p - Number of days from March 21 to Sunday on or before PFM
- # (-6 to 28 methods 1 & 3, to 56 for method 2)
- # e - Extra days to add for method 2 (converting Julian
- # date to Gregorian date)
-
- y = year
- g = y % 19
- e = 0
- if method < 3:
- # Old method
- i = (19*g + 15) % 30
- j = (y + y//4 + i) % 7
- if method == 2:
- # Extra dates to convert Julian to Gregorian date
- e = 10
- if y > 1600:
- e = e + y//100 - 16 - (y//100 - 16)//4
- else:
- # New method
- c = y//100
- h = (c - c//4 - (8*c + 13)//25 + 19*g + 15) % 30
- i = h - (h//28)*(1 - (h//28)*(29//(h + 1))*((21 - g)//11))
- j = (y + y//4 + i + 2 - c + c//4) % 7
-
- # p can be from -6 to 56 corresponding to dates 22 March to 23 May
- # (later dates apply to method 2, although 23 May never actually occurs)
- p = i - j + e
- d = 1 + (p + 27 + (p + 6)//40) % 31
- m = 3 + (p + 26)//30
- return datetime.date(int(y), int(m), int(d))
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-bb077656.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-bb077656.js
deleted file mode 100644
index 4097e0802187713261981d1e2751911c4bf1d591..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-bb077656.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{ae as s}from"./index-9e76ffee.js";const o=["static"];export{s as Component,o as modes};
-//# sourceMappingURL=index-bb077656.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-2b2a4a95.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-2b2a4a95.js
deleted file mode 100644
index 3932fac9f63aeacae23022b07c3b80ab36ade771..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-2b2a4a95.js
+++ /dev/null
@@ -1,5 +0,0 @@
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-//# sourceMappingURL=index-2b2a4a95.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/data02/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/data02/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/models/embeddings_flax.py b/spaces/declare-lab/tango/diffusers/src/diffusers/models/embeddings_flax.py
deleted file mode 100644
index 88c2c45e4655b8013fa96e0b4408e3ec0a87c2c7..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/models/embeddings_flax.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-
-import flax.linen as nn
-import jax.numpy as jnp
-
-
-def get_sinusoidal_embeddings(
- timesteps: jnp.ndarray,
- embedding_dim: int,
- freq_shift: float = 1,
- min_timescale: float = 1,
- max_timescale: float = 1.0e4,
- flip_sin_to_cos: bool = False,
- scale: float = 1.0,
-) -> jnp.ndarray:
- """Returns the positional encoding (same as Tensor2Tensor).
-
- Args:
- timesteps: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- embedding_dim: The number of output channels.
- min_timescale: The smallest time unit (should probably be 0.0).
- max_timescale: The largest time unit.
- Returns:
- a Tensor of timing signals [N, num_channels]
- """
- assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
- assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
- num_timescales = float(embedding_dim // 2)
- log_timescale_increment = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)
- inv_timescales = min_timescale * jnp.exp(jnp.arange(num_timescales, dtype=jnp.float32) * -log_timescale_increment)
- emb = jnp.expand_dims(timesteps, 1) * jnp.expand_dims(inv_timescales, 0)
-
- # scale embeddings
- scaled_time = scale * emb
-
- if flip_sin_to_cos:
- signal = jnp.concatenate([jnp.cos(scaled_time), jnp.sin(scaled_time)], axis=1)
- else:
- signal = jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1)
- signal = jnp.reshape(signal, [jnp.shape(timesteps)[0], embedding_dim])
- return signal
-
-
-class FlaxTimestepEmbedding(nn.Module):
- r"""
- Time step Embedding Module. Learns embeddings for input time steps.
-
- Args:
- time_embed_dim (`int`, *optional*, defaults to `32`):
- Time step embedding dimension
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- time_embed_dim: int = 32
- dtype: jnp.dtype = jnp.float32
-
- @nn.compact
- def __call__(self, temb):
- temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_1")(temb)
- temb = nn.silu(temb)
- temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_2")(temb)
- return temb
-
-
-class FlaxTimesteps(nn.Module):
- r"""
- Wrapper Module for sinusoidal Time step Embeddings as described in https://arxiv.org/abs/2006.11239
-
- Args:
- dim (`int`, *optional*, defaults to `32`):
- Time step embedding dimension
- """
- dim: int = 32
- flip_sin_to_cos: bool = False
- freq_shift: float = 1
-
- @nn.compact
- def __call__(self, timesteps):
- return get_sinusoidal_embeddings(
- timesteps, embedding_dim=self.dim, flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.freq_shift
- )
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/facerender/modules/generator.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/facerender/modules/generator.py
deleted file mode 100644
index ae29a0e375153094a8871300a58192f21d9429b7..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/facerender/modules/generator.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from sad_talker.src.facerender.modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d, ResBlock3d, SPADEResnetBlock
-from sad_talker.src.facerender.modules.dense_motion import DenseMotionNetwork
-
-
-class OcclusionAwareGenerator(nn.Module):
- """
- Generator follows NVIDIA architecture.
- """
-
- def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth,
- num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False):
- super(OcclusionAwareGenerator, self).__init__()
-
- if dense_motion_params is not None:
- self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel,
- estimate_occlusion_map=estimate_occlusion_map,
- **dense_motion_params)
- else:
- self.dense_motion_network = None
-
- self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(7, 7), padding=(3, 3))
-
- down_blocks = []
- for i in range(num_down_blocks):
- in_features = min(max_features, block_expansion * (2 ** i))
- out_features = min(max_features, block_expansion * (2 ** (i + 1)))
- down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
- self.down_blocks = nn.ModuleList(down_blocks)
-
- self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1)
-
- self.reshape_channel = reshape_channel
- self.reshape_depth = reshape_depth
-
- self.resblocks_3d = torch.nn.Sequential()
- for i in range(num_resblocks):
- self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1))
-
- out_features = block_expansion * (2 ** (num_down_blocks))
- self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True)
- self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1)
-
- self.resblocks_2d = torch.nn.Sequential()
- for i in range(num_resblocks):
- self.resblocks_2d.add_module('2dr' + str(i), ResBlock2d(out_features, kernel_size=3, padding=1))
-
- up_blocks = []
- for i in range(num_down_blocks):
- in_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i)))
- out_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i - 1)))
- up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
- self.up_blocks = nn.ModuleList(up_blocks)
-
- self.final = nn.Conv2d(block_expansion, image_channel, kernel_size=(7, 7), padding=(3, 3))
- self.estimate_occlusion_map = estimate_occlusion_map
- self.image_channel = image_channel
-
- def deform_input(self, inp, deformation):
- _, d_old, h_old, w_old, _ = deformation.shape
- _, _, d, h, w = inp.shape
- if d_old != d or h_old != h or w_old != w:
- deformation = deformation.permute(0, 4, 1, 2, 3)
- deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear')
- deformation = deformation.permute(0, 2, 3, 4, 1)
- return F.grid_sample(inp, deformation)
-
- def forward(self, source_image, kp_driving, kp_source):
- # Encoding (downsampling) part
- out = self.first(source_image)
- for i in range(len(self.down_blocks)):
- out = self.down_blocks[i](out)
- out = self.second(out)
- bs, c, h, w = out.shape
- # print(out.shape)
- feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w)
- feature_3d = self.resblocks_3d(feature_3d)
-
- # Transforming feature representation according to deformation and occlusion
- output_dict = {}
- if self.dense_motion_network is not None:
- dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving,
- kp_source=kp_source)
- output_dict['mask'] = dense_motion['mask']
-
- if 'occlusion_map' in dense_motion:
- occlusion_map = dense_motion['occlusion_map']
- output_dict['occlusion_map'] = occlusion_map
- else:
- occlusion_map = None
- deformation = dense_motion['deformation']
- out = self.deform_input(feature_3d, deformation)
-
- bs, c, d, h, w = out.shape
- out = out.view(bs, c*d, h, w)
- out = self.third(out)
- out = self.fourth(out)
-
- if occlusion_map is not None:
- if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]:
- occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear')
- out = out * occlusion_map
-
- # output_dict["deformed"] = self.deform_input(source_image, deformation) # 3d deformation cannot deform 2d image
-
- # Decoding part
- out = self.resblocks_2d(out)
- for i in range(len(self.up_blocks)):
- out = self.up_blocks[i](out)
- out = self.final(out)
- out = F.sigmoid(out)
-
- output_dict["prediction"] = out
-
- return output_dict
-
-
-class SPADEDecoder(nn.Module):
- def __init__(self):
- super().__init__()
- ic = 256
- oc = 64
- norm_G = 'spadespectralinstance'
- label_nc = 256
-
- self.fc = nn.Conv2d(ic, 2 * ic, 3, padding=1)
- self.G_middle_0 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
- self.G_middle_1 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
- self.G_middle_2 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
- self.G_middle_3 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
- self.G_middle_4 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
- self.G_middle_5 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
- self.up_0 = SPADEResnetBlock(2 * ic, ic, norm_G, label_nc)
- self.up_1 = SPADEResnetBlock(ic, oc, norm_G, label_nc)
- self.conv_img = nn.Conv2d(oc, 3, 3, padding=1)
- self.up = nn.Upsample(scale_factor=2)
-
- def forward(self, feature):
- seg = feature
- x = self.fc(feature)
- x = self.G_middle_0(x, seg)
- x = self.G_middle_1(x, seg)
- x = self.G_middle_2(x, seg)
- x = self.G_middle_3(x, seg)
- x = self.G_middle_4(x, seg)
- x = self.G_middle_5(x, seg)
- x = self.up(x)
- x = self.up_0(x, seg) # 256, 128, 128
- x = self.up(x)
- x = self.up_1(x, seg) # 64, 256, 256
-
- x = self.conv_img(F.leaky_relu(x, 2e-1))
- # x = torch.tanh(x)
- x = F.sigmoid(x)
-
- return x
-
-
-class OcclusionAwareSPADEGenerator(nn.Module):
-
- def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth,
- num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False):
- super(OcclusionAwareSPADEGenerator, self).__init__()
-
- if dense_motion_params is not None:
- self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel,
- estimate_occlusion_map=estimate_occlusion_map,
- **dense_motion_params)
- else:
- self.dense_motion_network = None
-
- self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1))
-
- down_blocks = []
- for i in range(num_down_blocks):
- in_features = min(max_features, block_expansion * (2 ** i))
- out_features = min(max_features, block_expansion * (2 ** (i + 1)))
- down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
- self.down_blocks = nn.ModuleList(down_blocks)
-
- self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1)
-
- self.reshape_channel = reshape_channel
- self.reshape_depth = reshape_depth
-
- self.resblocks_3d = torch.nn.Sequential()
- for i in range(num_resblocks):
- self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1))
-
- out_features = block_expansion * (2 ** (num_down_blocks))
- self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True)
- self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1)
-
- self.estimate_occlusion_map = estimate_occlusion_map
- self.image_channel = image_channel
-
- self.decoder = SPADEDecoder()
-
- def deform_input(self, inp, deformation):
- _, d_old, h_old, w_old, _ = deformation.shape
- _, _, d, h, w = inp.shape
- if d_old != d or h_old != h or w_old != w:
- deformation = deformation.permute(0, 4, 1, 2, 3)
- deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear')
- deformation = deformation.permute(0, 2, 3, 4, 1)
- return F.grid_sample(inp, deformation)
-
- def forward(self, source_image, kp_driving, kp_source):
- # Encoding (downsampling) part
- out = self.first(source_image)
- for i in range(len(self.down_blocks)):
- out = self.down_blocks[i](out)
- out = self.second(out)
- bs, c, h, w = out.shape
- # print(out.shape)
- feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w)
- feature_3d = self.resblocks_3d(feature_3d)
-
- # Transforming feature representation according to deformation and occlusion
- output_dict = {}
- if self.dense_motion_network is not None:
- dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving,
- kp_source=kp_source)
- output_dict['mask'] = dense_motion['mask']
-
- # import pdb; pdb.set_trace()
-
- if 'occlusion_map' in dense_motion:
- occlusion_map = dense_motion['occlusion_map']
- output_dict['occlusion_map'] = occlusion_map
- else:
- occlusion_map = None
- deformation = dense_motion['deformation']
- out = self.deform_input(feature_3d, deformation)
-
- bs, c, d, h, w = out.shape
- out = out.view(bs, c*d, h, w)
- out = self.third(out)
- out = self.fourth(out)
-
- # occlusion_map = torch.where(occlusion_map < 0.95, 0, occlusion_map)
-
- if occlusion_map is not None:
- if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]:
- occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear')
- out = out * occlusion_map
-
- # Decoding part
- out = self.decoder(out)
-
- output_dict["prediction"] = out
-
- return output_dict
\ No newline at end of file
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/utils/face_enhancer.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/utils/face_enhancer.py
deleted file mode 100644
index 7853953d9031bd9ac6f7dc15f930c5de9518af57..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/utils/face_enhancer.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import torch
-
-from gfpgan import GFPGANer
-
-from tqdm import tqdm
-
-from sad_talker.src.utils.videoio import load_video_to_cv2
-
-import cv2
-
-
-class GeneratorWithLen(object):
- """ From https://stackoverflow.com/a/7460929 """
-
- def __init__(self, gen, length):
- self.gen = gen
- self.length = length
-
- def __len__(self):
- return self.length
-
- def __iter__(self):
- return self.gen
-
-def enhancer_list(images, method='gfpgan', bg_upsampler='realesrgan'):
- gen = enhancer_generator_no_len(images, method=method, bg_upsampler=bg_upsampler)
- return list(gen)
-
-def enhancer_generator_with_len(images, method='gfpgan', bg_upsampler='realesrgan'):
- """ Provide a generator with a __len__ method so that it can passed to functions that
- call len()"""
-
- if os.path.isfile(images): # handle video to images
- # TODO: Create a generator version of load_video_to_cv2
- images = load_video_to_cv2(images)
-
- gen = enhancer_generator_no_len(images, method=method, bg_upsampler=bg_upsampler)
- gen_with_len = GeneratorWithLen(gen, len(images))
- return gen_with_len
-
-def enhancer_generator_no_len(images, method='gfpgan', bg_upsampler='realesrgan'):
- """ Provide a generator function so that all of the enhanced images don't need
- to be stored in memory at the same time. This can save tons of RAM compared to
- the enhancer function. """
-
- print('face enhancer....')
- if not isinstance(images, list) and os.path.isfile(images): # handle video to images
- images = load_video_to_cv2(images)
-
- # ------------------------ set up GFPGAN restorer ------------------------
- if method == 'gfpgan':
- arch = 'clean'
- channel_multiplier = 2
- model_name = 'GFPGANv1.4'
- url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'
- elif method == 'RestoreFormer':
- arch = 'RestoreFormer'
- channel_multiplier = 2
- model_name = 'RestoreFormer'
- url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth'
- elif method == 'codeformer': # TODO:
- arch = 'CodeFormer'
- channel_multiplier = 2
- model_name = 'CodeFormer'
- url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
- else:
- raise ValueError(f'Wrong model version {method}.')
-
-
- # ------------------------ set up background upsampler ------------------------
- if bg_upsampler == 'realesrgan':
- if not torch.cuda.is_available(): # CPU
- import warnings
- warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
- 'If you really want to use it, please modify the corresponding codes.')
- bg_upsampler = None
- else:
- from basicsr.archs.rrdbnet_arch import RRDBNet
- from realesrgan import RealESRGANer
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
- bg_upsampler = RealESRGANer(
- scale=2,
- model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
- model=model,
- tile=400,
- tile_pad=10,
- pre_pad=0,
- half=True) # need to set False in CPU mode
- else:
- bg_upsampler = None
-
- # determine model paths
- model_path = os.path.join('sad_talker/gfpgan/weights', model_name + '.pth')
-
- if not os.path.isfile(model_path):
- model_path = os.path.join('checkpoints', model_name + '.pth')
-
- if not os.path.isfile(model_path):
- # download pre-trained models from url
- model_path = url
-
- restorer = GFPGANer(
- model_path=model_path,
- upscale=2,
- arch=arch,
- channel_multiplier=channel_multiplier,
- bg_upsampler=bg_upsampler)
-
- # ------------------------ restore ------------------------
- for idx in tqdm(range(len(images)), 'Face Enhancer:'):
-
- img = cv2.cvtColor(images[idx], cv2.COLOR_RGB2BGR)
-
- # restore faces and background if necessary
- cropped_faces, restored_faces, r_img = restorer.enhance(
- img,
- has_aligned=False,
- only_center_face=False,
- paste_back=True)
-
- r_img = cv2.cvtColor(r_img, cv2.COLOR_BGR2RGB)
- yield r_img
diff --git a/spaces/diacanFperku/AutoGPT/Crack Serial Keygen Siteleri Herseyi Bulabilirsiniz.md b/spaces/diacanFperku/AutoGPT/Crack Serial Keygen Siteleri Herseyi Bulabilirsiniz.md
deleted file mode 100644
index 3c54e1f7141875e6ce5a55997e99233ca972e6e4..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Crack Serial Keygen Siteleri Herseyi Bulabilirsiniz.md
+++ /dev/null
@@ -1,106 +0,0 @@
-
-Crack Serial Keygen Siteleri Herseyi Bulabilirsiniz - Nasıl?
-
-Crack serial keygen siteleri, birçok programın lisansını ücretsiz olarak elde etmenizi sağlayan web siteleridir. Bu sitelerde, aradığınız programın crack, serial veya keygen dosyalarını bulabilir ve indirebilirsiniz. Bu sayede, programı sınırsız olarak kullanabilir ve güncelleyebilirsiniz. Ancak, crack serial keygen siteleri herseyi bulabilirsiniz mi? Bu siteler güvenilir mi? Hangi siteleri tercih etmelisiniz? Bu makalede, bu soruların cevaplarını vereceğiz.
-crack serial keygen siteleri herseyi bulabilirsiniz Download File 🗸🗸🗸 https://gohhs.com/2uFUSu
Crack Serial Keygen Siteleri Nedir?
-
-Crack serial keygen siteleri, birçok programın lisansını ücretsiz olarak elde etmenizi sağlayan web siteleridir. Bu sitelerde, aradığınız programın crack, serial veya keygen dosyalarını bulabilir ve indirebilirsiniz. Bu dosyaların anlamları şöyledir:
-
-
-Crack: Programın orijinal dosyasının değiştirilerek lisans kontrolünün kaldırılması veya atlanması işlemidir. Crack dosyası, programın kurulu olduğu klasöre kopyalanarak çalıştırılır.
-Serial: Programın lisans anahtarının girilmesini gerektiren bir kod dizisidir. Serial dosyası, programın kurulumu veya aktivasyonu sırasında istenen alana yazılır.
-Keygen: Programın lisans anahtarını üreten bir programdır. Keygen dosyası, çalıştırıldığında rastgele bir serial kodu oluşturur ve bunu kullanıcıya sunar.
-
-
-Bu dosyaları kullanarak, programı lisanslıymış gibi kullanabilir ve güncelleyebilirsiniz. Ancak, bu dosyaların yasal olmadığını ve bazı riskler taşıdığını unutmamalısınız.
-
-Crack Serial Keygen Siteleri Güvenilir Mi?
-
-Crack serial keygen siteleri herseyi bulabilirsiniz diyebilmek için, bu sitelerin güvenilir olduğundan emin olmalısınız. Ancak, bu sitelerin çoğu güvenilir değildir. Bu sitelerde karşılaşabileceğiniz bazı riskler şunlardır:
-
-
-Virüs: Crack serial keygen dosyaları virüs içerebilir veya virüs bulaştırabilir. Bu durumda, bilgisayarınız zarar görebilir veya kişisel bilgileriniz çalınabilir.
-Dolandırıcılık: Crack serial keygen dosyaları indirmek için bazı siteler sizden ücret isteyebilir veya sahte ödeme sayfalarına yönlendirebilir. Bu durumda, paranızı kaybedebilir veya kredi kartı bilgileriniz çalınabilir.
-Telif Hakkı: Crack serial keygen dosyaları kullanmak telif hakkı ihlali anlamına gelir ve yasal sorumluluk doğurabilir. Bu durumda, para cezası veya hapis cezası ile karşılaşabilirsiniz.
-
-
-Bu riskleri önlemek için, crack serial keygen sitelerini kullanmamak en iyi seçenektir. Ancak, yine de bu siteleri kullanmak istiyorsanız, bazı önlemler almalısınız.
-
-Crack Serial Keygen Sitelerini Nasıl Kullanmalısınız?
-
-Crack serial keygen sitelerini kullanmak istiyorsanız, aşağıdaki önlemleri almalısınız:
-
-
-
-Güvenilir Siteleri Tercih Edin: Crack serial keygen sitelerinin çoğu güvenilir değildir ancak bazıları daha az risklidir. Bu siteleri tercih etmek için, kullanıcı yorumlarını, site tasarımını ve içeriğini inceleyin. Ayrıca, virüs taraması yapan ve SSL sertifikası olan siteleri seçin.
-Güncel Antivirüs Programı Kullanın: Crack serial keygen dosyaları virüs içerebilir veya virüs bulaştırabilir. Bu yüzden, bilgisayarınızda güncel bir antivirüs programı bulundurun ve her indirdiğiniz dosyayı taratın.
-Ödeme Yapmayın: Crack serial keygen dosyaları indirmek için bazı siteler sizden ücret isteyebilir veya sahte ödeme sayfalarına yönlendirebilir. Bu tür sitelere asla ödeme yapmayın veya kredi kartı bilgilerinizi vermeyin.
-Telif Hakkına Dikkat Edin: Crack serial keygen dosyaları kullanmak telif hakkı ihlali anlamına gelir ve yasal sorumluluk doğurabilir. Bu yüzden, bu dosyaları kullanmadan önce telif hakkı sahiplerinin iznini alın veya kendi sorumluluğunuzda olduğunu kabul edin.
-
-
-Bu önlemleri alarak, crack serial keygen sitelerini daha güvenli bir şekilde kullanabilirsiniz. Ancak, yine de bu sitelerin yasal olmadığını ve bazı riskler taşıdığını unutmamalısınız.
-
-Sonuç
-
-Crack serial keygen siteleri herseyi bulabilirsiniz diyebilmek için, bu sitelerin ne olduğunu, nasıl çalıştığını ve hangi riskleri taşıdığını bilmelisiniz. Bu makalede, bu konulara açıklık getirdik ve bu siteleri nasıl kullanmanız gerektiğine dair bazı ipuçları verdik. Ancak, en iyisi crack serial keygen sitelerinden uzak durmak ve programları yasal yollardan edinmektir.
-Crack Serial Keygen Siteleri Hangileridir?
-
-Crack serial keygen siteleri herseyi bulabilirsiniz diyebilmek için, bu sitelerin hangileri olduğunu bilmelisiniz. İnternette birçok crack serial keygen sitesi bulunmaktadır ancak hepsi güvenilir ve kaliteli değildir. Bu yüzden, bu siteleri seçerken dikkatli olmalısınız. İşte, crack serial keygen siteleri arasında en popüler olan bazıları:
-
-
-Serials.ws: Günlük olarak güncellenen ve 120.000'den fazla serial numarası içeren bir sitedir. Aradığınız programın serial numarasını kolayca bulabilirsiniz. Ancak, sitede crack veya keygen dosyası indiremezsiniz.
-Smart Serials: Telif hakkına saygılı ve güvenli bir sitedir. Hem crack hem de serial numarası sunmaktadır. Ayrıca, programları kategorilere ayırmıştır, böylece istediğiniz programı daha rahat bulabilirsiniz.
-Keygens Pro: Eski bir tasarıma sahip olmasına rağmen, birçok crack ve keygen dosyası sunan bir sitedir. Programların alfabetik olarak listelendiği bir sayfaya sahiptir. Ayrıca, programların resimli açıklamalarını da görebilirsiniz.
-
-
-Bu siteler, crack serial keygen siteleri arasında en popüler olanlardır ancak bunlar dışında da birçok site bulunmaktadır. Ancak, bu siteleri kullanırken yukarıda bahsettiğimiz riskleri ve önlemleri göz önünde bulundurmalısınız.
-
-Crack Serial Keygen Siteleri Yerine Ne Kullanmalısınız?
-
-Crack serial keygen siteleri herseyi bulabilirsiniz diyebilmek için, bu sitelerin avantajlarını bilmelisiniz. Bu sitelerin en büyük avantajı, birçok programın lisansını ücretsiz olarak elde etmenizi sağlamasıdır. Bu sayede, programları sınırsız olarak kullanabilir ve güncelleyebilirsiniz. Ancak, bu sitelerin dezavantajları da vardır. Bu sitelerin en büyük dezavantajı, yasal olmamalarıdır. Bu sitelerde sunulan dosyalar telif hakkı ihlali anlamına gelir ve yasal sorumluluk doğurabilir. Ayrıca, bu sitelerde sunulan dosyalar virüs içerebilir veya dolandırıcılık yapabilir.
-
-Bu yüzden, crack serial keygen sitelerinden uzak durmak ve programları yasal yollardan edinmek en iyisidir. Programları yasal yollardan edinmenin avantajları şunlardır:
-
-
-Güvenli: Programları resmi sitelerinden veya güvenilir platformlardan indirdiğinizde, virüs veya dolandırıcılık riski olmaz.
-Kaliteli: Programları lisanslı olarak kullandığınızda, daha iyi bir performans ve kalite elde edersiniz.
-Destekli: Programları yasal olarak kullandığınızda, teknik destek ve müşteri hizmetleri alabilirsiniz.
-
-
-Programları yasal yollardan edinmenin dezavantajı ise ücretli olmasıdır. Ancak, bu dezavantajı aşmanın bazı yolları vardır. Örneğin:
-
-
-Kampanyaları Takip Edin: Bazı programlar belirli zamanlarda indirim veya kampanya yapabilirler. Bu fırsatları takip ederek, programları daha uygun fiyatlara alabilirsiniz.
-Ücretsiz Alternatifleri Deneyin: Bazı programların ücretsiz alternatifleri olabilir. Bu alternatifleri deneyerek, ihtiyacınız olan özellikleri ücretsiz olarak kullanabilirsiniz.
-Giveaway Sitelerini Ziyaret Edin: Bazı siteler belirli zamanlarda ücretli programları ücretsiz olarak sunabilirler. Bu siteleri ziyaret ederek, programları bedava olarak indirebilirsiniz.
-
-
-Bu yolları kullanarak, programları yasal yollardan edinebilir ve crack serial keygen sitelerinin risklerinden kurtulabilirsiniz.
-Crack Serial Keygen Siteleri Neden Yasaklanıyor?
-
-Crack serial keygen siteleri herseyi bulabilirsiniz diyebilmek için, bu sitelerin neden yasaklandığını da bilmelisiniz. Bu sitelerin yasaklanmasının en önemli nedeni, telif hakkı ihlali yapmalarıdır. Bu sitelerde sunulan dosyalar, programların orijinal lisanslarını ihlal eder ve program geliştiricilerinin haklarını çiğner. Bu yüzden, program geliştiricileri veya telif hakkı sahipleri, bu siteleri kapatmak için yasal yollara başvurabilirler.
-
-Bu sitelerin yasaklanmasının bir diğer nedeni ise, güvenlik sorunları yaratmalarıdır. Bu sitelerde sunulan dosyalar, virüs veya zararlı yazılım içerebilir veya kullanıcıların bilgilerini çalabilirler. Bu yüzden, devlet kurumları veya internet servis sağlayıcıları, bu siteleri engellemek için önlem alabilirler.
-
-Bu sitelerin yasaklanmasının bir sonucu olarak, bu sitelere erişim zorlaşabilir veya imkansız hale gelebilir. Bu durumda, bu sitelerin alternatif adreslerini veya proxy sunucularını kullanarak erişim sağlamaya çalışabilirsiniz. Ancak, bu yöntemler de bazı riskler taşıyabilir ve her zaman işe yaramayabilir.
-
-Crack Serial Keygen Siteleri Hakkında Sık Sorulan Sorular
-
-Crack serial keygen siteleri herseyi bulabilirsiniz diyebilmek için, bu siteler hakkında sık sorulan soruların cevaplarını da bilmelisiniz. İşte, bu siteler hakkında sık sorulan bazı sorular ve cevapları:
-
-
-Crack serial keygen siteleri güvenli mi? Hayır, bu siteler güvenli değildir. Bu sitelerde sunulan dosyalar virüs içerebilir veya virüs bulaştırabilir. Ayrıca, bu siteler dolandırıcılık yapabilir veya kişisel bilgilerinizi çalabilir.
-Crack serial keygen siteleri yasal mı? Hayır, bu siteler yasal değildir. Bu sitelerde sunulan dosyalar telif hakkı ihlali anlamına gelir ve yasal sorumluluk doğurabilir. Ayrıca, bu sitelere erişmek de bazı ülkelerde yasaklanmıştır.
-Crack serial keygen siteleri etik mi? Hayır, bu siteler etik değildir. Bu sitelerde sunulan dosyalar program geliştiricilerinin emeğini çalar ve haklarını gasp eder. Ayrıca, bu sitelere erişmek de program geliştiricilerine zarar verir.
-Crack serial keygen siteleri her programı bulabilir mi? Hayır, bu siteler her programı bulamaz. Bazı programların crack serial keygen dosyaları bulunmayabilir veya çalışmayabilir. Ayrıca, bazı programların yeni sürümleri için crack serial keygen dosyaları henüz çıkmamış olabilir.
-Crack serial keygen siteleri nasıl bulunur? Crack serial keygen sitelerini bulmak için internet arama motorlarını kullanabilirsiniz. Ancak, bu sitelere erişim engellenmiş olabileceği için alternatif adreslerini veya proxy sunucularını kullanmanız gerekebilir.
-
-
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3cee63e6c2MiniTool Partition Wizard Crack PRO 11.4 Serial Key Torrent 2019
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-MiniTool Partition Wizard Crack PRO 11.4 Serial Key Torrent 2019 is a cracked version of MiniTool Partition Wizard Pro, which is a premium partition software developed by MiniTool Solution Ltd. By using this cracked version, you can enjoy all the features of the Pro edition without paying anything.
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MiniTool Partition Wizard Pro is a rich-featured partition magic that can help you manage your hard disk and partition effectively and safely. With its user-friendly interface and simple guidance, you can resize partition, copy disk, scan lost partitions, migrate OS to SSD, convert disk type, change file system, recover partition, rebuild MBR, align partition, and more.
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3cee63e6c2Mukkabaaz 3 Movie Download 720p: The Ultimate Guide
-If you are a fan of Hindi action drama movies, you might be eagerly waiting for the release of Mukkabaaz 3, the third installment of the popular Mukkabaaz franchise. The movie is expected to hit the theatres in 2023, but you don't have to wait that long to watch it. In this article, we will show you how to download Mukkabaaz 3 movie in 720p quality from various sources.
-What is Mukkabaaz 3 Movie About?
-Mukkabaaz 3 is the sequel to Mukkabaaz 2, which was released in 2020. The movie follows the story of Shravan Singh, a boxer from Uttar Pradesh who faces various challenges and obstacles in his quest to become a national champion. Shravan has to deal with his rival Bhagwan Das Mishra, a local don and politician who controls the boxing federation and wants to ruin Shravan's career. Shravan also has to balance his love life with Sunaina, Mishra's niece, who supports him despite her uncle's opposition.
-Mukkabaaz 3 movie download 720p Download Zip 🗹 https://gohhs.com/2uFVaO
Mukkabaaz 3 promises to be an action-packed and thrilling movie that will keep you on the edge of your seat. The movie stars Vineet Kumar Singh as Shravan, Zoya Hussain as Sunaina, Jimmy Sheirgill as Mishra, and Ravi Kishan as Sanjay Kumar, Shravan's coach. The movie is directed by Anurag Kashyap, who is known for his realistic and gritty movies.
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-Mukkabaaz 3 is an upcoming Hindi action drama movie that will be released in 2023. The movie is about a boxer who faces various challenges in his quest to become a national champion. If you want to watch the movie before it hits the theatres, you can download it in 720p quality from various sources such as torrent sites or streaming sites. However, you should be aware of the legal and security risks involved in downloading from these sources and use a VPN service if necessary.
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3cee63e6c2"
- + title
- + " ")
- gr.Markdown(description)
- with gr.Tab("图片"):
- with gr.Row():
- with gr.Column():
- img_input = gr.Image()
- img_btn = gr.Button("识别")
- with gr.Column():
- img_output = gr.Image(label="Result")
- with gr.Tab("JSON"):
- with gr.Row():
- with gr.Column():
- json_input = gr.Image()
- json_btn = gr.Button("识别")
- with gr.Column():
- json_output = gr.Json(label="Result")
-
- img_btn.click(inference_img, inputs=img_input, outputs=img_output)
- json_btn.click(inference_json, inputs=json_input, outputs=json_output)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/facebook/MusicGen/audiocraft/adversarial/discriminators/__init__.py b/spaces/facebook/MusicGen/audiocraft/adversarial/discriminators/__init__.py
deleted file mode 100644
index f9e5ff59950ee0b1d1a67c9b3831d67d08048148..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/adversarial/discriminators/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .mpd import MultiPeriodDiscriminator
-from .msd import MultiScaleDiscriminator
-from .msstftd import MultiScaleSTFTDiscriminator
diff --git a/spaces/facebook/MusicGen/audiocraft/models/unet.py b/spaces/facebook/MusicGen/audiocraft/models/unet.py
deleted file mode 100644
index db4a6df8e309c21fede37abdbe3c862932027641..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/models/unet.py
+++ /dev/null
@@ -1,214 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Pytorch Unet Module used for diffusion.
-"""
-
-from dataclasses import dataclass
-import typing as tp
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from audiocraft.modules.transformer import StreamingTransformer, create_sin_embedding
-
-
-@dataclass
-class Output:
- sample: torch.Tensor
-
-
-def get_model(cfg, channels: int, side: int, num_steps: int):
- if cfg.model == 'unet':
- return DiffusionUnet(
- chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
- else:
- raise RuntimeError('Not Implemented')
-
-
-class ResBlock(nn.Module):
- def __init__(self, channels: int, kernel: int = 3, norm_groups: int = 4,
- dilation: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
- dropout: float = 0.):
- super().__init__()
- stride = 1
- padding = dilation * (kernel - stride) // 2
- Conv = nn.Conv1d
- Drop = nn.Dropout1d
- self.norm1 = nn.GroupNorm(norm_groups, channels)
- self.conv1 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
- self.activation1 = activation()
- self.dropout1 = Drop(dropout)
-
- self.norm2 = nn.GroupNorm(norm_groups, channels)
- self.conv2 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
- self.activation2 = activation()
- self.dropout2 = Drop(dropout)
-
- def forward(self, x):
- h = self.dropout1(self.conv1(self.activation1(self.norm1(x))))
- h = self.dropout2(self.conv2(self.activation2(self.norm2(h))))
- return x + h
-
-
-class DecoderLayer(nn.Module):
- def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
- norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
- dropout: float = 0.):
- super().__init__()
- padding = (kernel - stride) // 2
- self.res_blocks = nn.Sequential(
- *[ResBlock(chin, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
- for idx in range(res_blocks)])
- self.norm = nn.GroupNorm(norm_groups, chin)
- ConvTr = nn.ConvTranspose1d
- self.convtr = ConvTr(chin, chout, kernel, stride, padding, bias=False)
- self.activation = activation()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.res_blocks(x)
- x = self.norm(x)
- x = self.activation(x)
- x = self.convtr(x)
- return x
-
-
-class EncoderLayer(nn.Module):
- def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
- norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
- dropout: float = 0.):
- super().__init__()
- padding = (kernel - stride) // 2
- Conv = nn.Conv1d
- self.conv = Conv(chin, chout, kernel, stride, padding, bias=False)
- self.norm = nn.GroupNorm(norm_groups, chout)
- self.activation = activation()
- self.res_blocks = nn.Sequential(
- *[ResBlock(chout, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
- for idx in range(res_blocks)])
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, C, T = x.shape
- stride, = self.conv.stride
- pad = (stride - (T % stride)) % stride
- x = F.pad(x, (0, pad))
-
- x = self.conv(x)
- x = self.norm(x)
- x = self.activation(x)
- x = self.res_blocks(x)
- return x
-
-
-class BLSTM(nn.Module):
- """BiLSTM with same hidden units as input dim.
- """
- def __init__(self, dim, layers=2):
- super().__init__()
- self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
- self.linear = nn.Linear(2 * dim, dim)
-
- def forward(self, x):
- x = x.permute(2, 0, 1)
- x = self.lstm(x)[0]
- x = self.linear(x)
- x = x.permute(1, 2, 0)
- return x
-
-
-class DiffusionUnet(nn.Module):
- def __init__(self, chin: int = 3, hidden: int = 24, depth: int = 3, growth: float = 2.,
- max_channels: int = 10_000, num_steps: int = 1000, emb_all_layers=False, cross_attention: bool = False,
- bilstm: bool = False, transformer: bool = False,
- codec_dim: tp.Optional[int] = None, **kwargs):
- super().__init__()
- self.encoders = nn.ModuleList()
- self.decoders = nn.ModuleList()
- self.embeddings: tp.Optional[nn.ModuleList] = None
- self.embedding = nn.Embedding(num_steps, hidden)
- if emb_all_layers:
- self.embeddings = nn.ModuleList()
- self.condition_embedding: tp.Optional[nn.Module] = None
- for d in range(depth):
- encoder = EncoderLayer(chin, hidden, **kwargs)
- decoder = DecoderLayer(hidden, chin, **kwargs)
- self.encoders.append(encoder)
- self.decoders.insert(0, decoder)
- if emb_all_layers and d > 0:
- assert self.embeddings is not None
- self.embeddings.append(nn.Embedding(num_steps, hidden))
- chin = hidden
- hidden = min(int(chin * growth), max_channels)
- self.bilstm: tp.Optional[nn.Module]
- if bilstm:
- self.bilstm = BLSTM(chin)
- else:
- self.bilstm = None
- self.use_transformer = transformer
- self.cross_attention = False
- if transformer:
- self.cross_attention = cross_attention
- self.transformer = StreamingTransformer(chin, 8, 6, bias_ff=False, bias_attn=False,
- cross_attention=cross_attention)
-
- self.use_codec = False
- if codec_dim is not None:
- self.conv_codec = nn.Conv1d(codec_dim, chin, 1)
- self.use_codec = True
-
- def forward(self, x: torch.Tensor, step: tp.Union[int, torch.Tensor], condition: tp.Optional[torch.Tensor] = None):
- skips = []
- bs = x.size(0)
- z = x
- view_args = [1]
- if type(step) is torch.Tensor:
- step_tensor = step
- else:
- step_tensor = torch.tensor([step], device=x.device, dtype=torch.long).expand(bs)
-
- for idx, encoder in enumerate(self.encoders):
- z = encoder(z)
- if idx == 0:
- z = z + self.embedding(step_tensor).view(bs, -1, *view_args).expand_as(z)
- elif self.embeddings is not None:
- z = z + self.embeddings[idx - 1](step_tensor).view(bs, -1, *view_args).expand_as(z)
-
- skips.append(z)
-
- if self.use_codec: # insert condition in the bottleneck
- assert condition is not None, "Model defined for conditionnal generation"
- condition_emb = self.conv_codec(condition) # reshape to the bottleneck dim
- assert condition_emb.size(-1) <= 2 * z.size(-1), \
- f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
- if not self.cross_attention:
-
- condition_emb = torch.nn.functional.interpolate(condition_emb, z.size(-1))
- assert z.size() == condition_emb.size()
- z += condition_emb
- cross_attention_src = None
- else:
- cross_attention_src = condition_emb.permute(0, 2, 1) # B, T, C
- B, T, C = cross_attention_src.shape
- positions = torch.arange(T, device=x.device).view(1, -1, 1)
- pos_emb = create_sin_embedding(positions, C, max_period=10_000, dtype=cross_attention_src.dtype)
- cross_attention_src = cross_attention_src + pos_emb
- if self.use_transformer:
- z = self.transformer(z.permute(0, 2, 1), cross_attention_src=cross_attention_src).permute(0, 2, 1)
- else:
- if self.bilstm is None:
- z = torch.zeros_like(z)
- else:
- z = self.bilstm(z)
-
- for decoder in self.decoders:
- s = skips.pop(-1)
- z = z[:, :, :s.shape[2]]
- z = z + s
- z = decoder(z)
-
- z = z[:, :, :x.shape[2]]
- return Output(z)
diff --git a/spaces/falterWliame/Face_Mask_Detection/Heroes 3 Of Might And Magic Download Free Full Version BEST.md b/spaces/falterWliame/Face_Mask_Detection/Heroes 3 Of Might And Magic Download Free Full Version BEST.md
deleted file mode 100644
index e953ea7f9263349ec869cb63d4d261bce95f4e2a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Heroes 3 Of Might And Magic Download Free Full Version BEST.md
+++ /dev/null
@@ -1,35 +0,0 @@
-How to Download Heroes of Might and Magic 3: Complete for Free
-Heroes of Might and Magic 3: Complete is one of the most popular and beloved turn-based strategy games of all time. It features an epic story, a rich fantasy world, hundreds of heroes and creatures, and eight different factions to choose from. If you are a fan of the Might and Magic series, or if you are looking for a classic game that will keep you entertained for hours, you might want to download Heroes of Might and Magic 3: Complete for free.
-But how can you do that? Is it legal? Is it safe? In this article, we will answer these questions and show you how to download Heroes of Might and Magic 3: Complete for free in a few easy steps.
-Heroes 3 Of Might And Magic Download Free Full Version Download Zip ->->->-> https://urlca.com/2uDcJw
What is Heroes of Might and Magic 3: Complete?
-Heroes of Might and Magic 3: Complete is the ultimate edition of Heroes of Might and Magic 3: The Restoration of Erathia, the third installment in the Heroes of Might and Magic series. It includes the original game and its two official expansions: Armageddon's Blade and The Shadow of Death. It also includes a map editor, a random map generator, and hundreds of fan-made maps and scenarios.
-Heroes of Might and Magic 3: Complete was released in 2000 by 3DO and New World Computing. It was praised by critics and players alike for its gameplay, graphics, music, and replay value. It is widely considered as one of the best games ever made in the genre.
-
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-There are several reasons why you might want to download Heroes of Might and Magic 3: Complete for free. Here are some of them:
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-You want to relive your childhood memories or experience a classic game for the first time.
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-How to download Heroes of Might and Magic 3: Complete for free?
-There are several ways to download Heroes of Might and Magic 3: Complete for free. However, not all of them are legal or safe. Some websites might offer you pirated copies of the game that contain viruses, malware, or spyware. Some websites might ask you to fill out surveys, sign up for subscriptions, or enter your personal information. Some websites might not even give you the game at all.
-To avoid these risks, we recommend you to use one of these two methods:
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-Method 1: Download from GOG Unlocked
-GOG Unlocked is a website that offers free downloads of DRM-free games from GOG.com, a digital distribution platform that sells classic and indie games. GOG Unlocked does not host any files on its own servers, but rather provides links to third-party file hosting services such as UploadHaven. GOG Unlocked claims that it does not violate any copyrights or trademarks, as it only provides links to files that are already available on the internet.
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-Go to https://gogunlocked.com/heroes-of-might-and-magic-3-complete-free-download/
-Click on the blue "Download" button below the game description.
-You will be redirected to UploadHaven. Wait for 5 seconds and click on the blue "Download Now" button.
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diff --git a/spaces/fatiXbelha/sd/3D Driving Games Learn to Drive Race and Have Fun in Stunning 3D Graphics.md b/spaces/fatiXbelha/sd/3D Driving Games Learn to Drive Race and Have Fun in Stunning 3D Graphics.md
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-What are 3D driving games and why are they so popular?
-If you love cars and speed, you might be interested in playing 3D driving games online. These are games that simulate driving a vehicle in a three-dimensional environment, using realistic graphics and physics. You can choose from different types of vehicles, such as cars, trucks, bikes, or buses, and different genres of games, such as racing, parking, drifting, or stunt.
-One of the main benefits of playing 3D driving games is that they can help you improve your driving skills in a fun and safe way. You can learn how to maneuver your vehicle in various situations, such as traffic, obstacles, or curves, without risking any damage or injury. You can also test your reflexes, coordination, and concentration while enjoying the thrill of speed.
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Another benefit of playing 3D driving games is that they can provide you with hours of entertainment and excitement. You can explore different maps and scenarios, such as cities, deserts, mountains, or islands, and discover new challenges and surprises along the way. You can also customize your vehicle according to your preferences, such as color, model, or accessories.
-However, playing 3D driving games also comes with some challenges and drawbacks. For instance, you might need a powerful computer or device to run the games smoothly and without lagging. You might also need to adjust to the controls and commands of the game, which might differ from the real-life ones. Moreover, you should always remember that playing 3D driving games is not a substitute for real-life driving experience.
- How to play 3D driving games online for free?
-If you want to play 3D driving games online for free, you have plenty of options to choose from. One of them is CrazyGames, a website that offers hundreds of 3D driving games that you can play on your browser without downloading or installing anything. Some of the popular titles on CrazyGames are Madalin Stunt Cars 2, City Car Driving Simulator, and Burnout Drift. You can also find games for different genres and difficulty levels, such as Moto X3M, Truck Driver Simulator, and Parking Fury.
-To play 3D driving games online for free, you need to select the game that suits your preferences and interests. You can browse the categories and genres on the website, or use the search function to find a specific game. You can also check the ratings and reviews of other players to get an idea of the quality and popularity of the game.
-Once you have chosen the game, you need to learn the basic controls and commands for playing it. Most of the 3D driving games online use the keyboard and mouse as the main input devices. For example, you can use the arrow keys or WASD keys to steer your vehicle, the space bar to brake, and the mouse to change the camera angle. Some games might also have additional controls, such as shift to drift, C to change view, or R to reset.
- How to improve your 3D driving game skills and performance?
-Playing 3D driving games online for free is not only fun, but also challenging. You might encounter different obstacles and opponents that will test your skills and performance. To improve your 3D driving game skills and performance, you need to practice regularly and consistently. The more you play, the more familiar you will become with the game mechanics and techniques.
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-Another way to improve your 3D driving game skills and performance is to follow some tips and tricks from experts and experienced players. Here are some of them:
-
-Drifting: Drifting is a technique that involves sliding your vehicle sideways around a corner or curve. It can help you maintain your speed and momentum, as well as avoid collisions and oversteering. To drift, you need to apply the brake or handbrake while turning your vehicle in the opposite direction of the curve.
-Braking: Braking is a technique that involves slowing down or stopping your vehicle before or during a turn or obstacle. It can help you control your vehicle and prevent skidding or crashing. To brake, you need to press the space bar or another key assigned for braking. You can also use the handbrake for sharper turns or emergency stops.
-Overtaking: Overtaking is a technique that involves passing another vehicle or opponent in front of you. It can help you gain an advantage or win a race. To overtake, you need to find an opening or gap in the traffic or track, and accelerate your vehicle while steering towards it. You can also use the slipstream or draft of another vehicle to boost your speed.
-
-A third way to improve your 3D driving game skills and performance is to upgrade and customize your vehicle according to your needs and preferences. You can enhance your 3D driving game experience by improving your vehicle's speed, handling, and appearance. To upgrade and customize your vehicle, you need to earn coins or credits by playing the game or completing missions. You can then use them to buy new parts or accessories for your vehicle.
- How to enjoy 3D driving games with others?
-Playing 3D driving games online for free is not only a solo activity, but also a social one. You can enjoy 3D driving games with others in various ways, such as:
-
-Multiplayer mode: Multiplayer mode is a feature that allows you to play 3D driving games with other players online or locally. You can join or create a room or server, and invite or challenge other players to join you. You can also chat with them using text or voice messages. Playing 3D driving games with others can enhance your enjoyment by adding competition, cooperation, and socialization.
-Leaderboards and achievements: Leaderboards and achievements are features that allow you to track your progress and compare your performance with other players online. You can see your rank and score on the leaderboards, and unlock achievements by completing certain tasks or goals in the game. Leaderboards and achievements can motivate you to improve your skills and performance by giving you feedback and recognition.
-Reviews and feedback: Reviews and feedback are features that allow you to share your opinions and experiences with other 3D driving game enthusiasts online. You can write reviews and rate the games that you have played on the website or app, and read what other players have written about them. You can also give feedback and suggestions to the developers or publishers of the games, and report any bugs or issues that you have encountered. Reviews and feedback can help you discover new games and tips by giving you information and recommendations
- Conclusion
-3D driving games are games that simulate driving a vehicle in a three-dimensional environment, using realistic graphics and physics. They are popular because they can improve your driving skills, provide entertainment, and offer realistic graphics and physics. You can play 3D driving games online for free on various platforms, such as CrazyGames, and choose from different types of vehicles and genres. You can also improve your 3D driving game skills and performance by practicing, following tips and tricks, and upgrading and customizing your vehicle. Moreover, you can enjoy 3D driving games with others by playing in multiplayer mode, tracking your progress on leaderboards and achievements, and sharing your reviews and feedback.
-If you are interested in playing 3D driving games online for free, why not give it a try? You might be surprised by how much fun and challenge they can offer. You can start by checking out some of the games mentioned in this article, or explore other options online. You might find your new favorite hobby or passion.
- FAQs
-Here are some of the frequently asked questions about 3D driving games:
-
-What are the best 3D driving games online for free?
-There is no definitive answer to this question, as different players might have different preferences and tastes. However, some of the most popular and highly rated 3D driving games online for free are Madalin Stunt Cars 2, City Car Driving Simulator, Burnout Drift, Moto X3M, Truck Driver Simulator, and Parking Fury.
-What are the system requirements for playing 3D driving games online for free?
-The system requirements for playing 3D driving games online for free might vary depending on the game and the platform. However, in general, you will need a modern browser that supports HTML5 or WebGL technology, such as Chrome, Firefox, or Edge. You will also need a stable internet connection and a decent computer or device that can handle the graphics and physics of the game.
-How can I play 3D driving games online for free on my mobile device?
-Some of the platforms that offer 3D driving games online for free also have mobile versions or apps that you can download and install on your smartphone or tablet. For example, CrazyGames has an app for Android devices that you can get from Google Play Store. You can also play some of the 3D driving games online for free on your mobile browser if they are compatible with touch screen controls.
-
Are 3D driving games online for free safe and legal?
-Yes, playing 3D driving games online for free is safe and legal as long as you play them on reputable and trustworthy platforms, such as CrazyGames. These platforms ensure that the games are virus-free and malware-free, and that they do not violate any copyright or trademark laws. However, you should always be careful about your personal information and privacy when playing online games, and avoid clicking on any suspicious links or ads that might appear on the website or app.
-
Can I play 3D driving games online for free offline?
-Some of the 3D driving games online for free might have an offline mode or feature that allows you to play them without an internet connection. However, this might limit some of the functions and options of the game, such as multiplayer mode, leaderboards, achievements, or updates. To play 3D driving games online for free offline, you might need to download and install the game on your computer or device, or enable the offline mode on your browser or app.
-
401be4b1e0Download Italy Black Diamond MP3: A Guide for Music Lovers
-If you are looking for a new song to add to your music collection, you might want to check out Italy Black Diamond by Blaq Diamond. This is a catchy and uplifting song that will make you feel good and inspire you. In this article, we will tell you everything you need to know about Italy Black Diamond, including who is Blaq Diamond, what is the song about, how to download it, and how to enjoy it. Let's get started!
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Who is Blaq Diamond?
-Blaq Diamond is a South African singer who specializes in Afro-pop music. He was born in Ladysmith, KwaZulu-Natal, and started singing at a young age. He rose to fame in 2019 with his debut album Inqola, which featured hit songs like Emzini Kababa and Sthandwa. He has since released two more albums, Umuthi and SummerYoMuthi, which have also received positive reviews and awards. Blaq Diamond's music is influenced by his Zulu culture and his personal experiences. He sings in both English and Zulu, and his songs are full of catchy melodies, meaningful lyrics, and positive messages.
- What is Italy Black Diamond?
-Italy Black Diamond is one of the songs from Blaq Diamond's latest album SummerYoMuthi. It was released in September 2020 as a single, and it has become one of his most popular songs. The song is about Blaq Diamond's love for Italy, which he describes as his dream destination. He sings about how he wants to travel there with his lover and enjoy the beauty and culture of the country. He also compares his lover to a black diamond, which is a rare and precious gemstone. The song is a celebration of love, life, and dreams.
- How to download Italy Black Diamond MP3?
-If you want to listen to Italy Black Diamond on your device, you have several options. You can stream it online, download it from a website, or use a YouTube converter. Here are the pros and cons of each option:
- Option 1: Stream it online
-Streaming music online means that you can listen to it without downloading it. You just need an internet connection and a streaming service or app. Some of the most popular streaming services are Spotify, Apple Music, Deezer, Tidal, Amazon Music, YouTube Music, etc. Streaming music has some benefits, such as:
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-You can access millions of songs and albums from different genres and artists
-You can discover new music and get personalized recommendations based on your preferences
-You can create your own playlists and share them with others
-You can listen to music offline if you have a premium subscription
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-However, streaming music also has some drawbacks, such as:
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-You need a stable internet connection to stream music without interruptions or buffering
-You may have to pay a monthly fee to access some streaming services or features
-You may not be able to find some songs or artists on some streaming platforms
-You may have to deal with ads or limitations if you use a free version of a streaming service
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- Option 2: Download it from a website
-Downloading music from a website means that you can save it on your device and listen to it anytime, anywhere. You just need to find a website that offers Italy Black Diamond MP3 and download it. Some of the best websites to download Italy Black Diamond MP3 are:
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-Website Price Quality Features [Fakaza] Free 320 kbps Offers a large collection of South African music, including Blaq Diamond's songs. Easy to use and fast to download. [Mp3juices] Free Variable Allows you to search and download Italy Black Diamond MP3 from various sources. Also lets you cut and edit the song as you wish. [iTunes] $1.29 256 kbps Provides high-quality downloads of Italy Black Diamond MP3 and other songs from Blaq Diamond's albums. Also syncs with your Apple devices and iCloud. [Amazon Music] $1.29 256 kbps Offers reliable and secure downloads of Italy Black Diamond MP3 and other songs from Blaq Diamond's albums. Also integrates with your Amazon account and devices.
-Downloading music from a website has some benefits, such as:
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-You can listen to music offline without an internet connection or a subscription
-You can transfer music to different devices or platforms as you like
-You can support the artist by buying their music legally
-You can choose the quality and format of the music you download
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-However, downloading music from a website also has some drawbacks, such as:
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-You may have to pay a fee to download some songs or albums
-You may not be able to find some songs or albums on some websites
-You may encounter viruses or malware when downloading from untrusted sources
-You may violate the copyright laws or terms of service of some websites or artists
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- Option 3: Use a YouTube converter
-Using a YouTube converter means that you can convert a YouTube video of Italy Black Diamond into an MP3 file and download it. You just need to find a YouTube video of the song and copy its URL, then paste it into a YouTube converter tool and click convert. Some of the best YouTube converter tools are:
-YouTube Converter Price Quality Features [YTMP3] Free 320 kbps Converts YouTube videos to MP3 or MP4 files in high quality. Supports videos up to one hour long. Simple and fast to use. [4K Video Downloader] Free/Premium Variable Downloads YouTube videos and playlists in various formats and qualities. Supports subtitles, 3D, and 360-degree videos. Offers a smart mode for faster downloads. [ClipGrab] Free Variable Downloads and converts YouTube videos to MP3, MP4, or WMV files. Allows you to choose the quality and format of the output. Also works with other video platforms. [Online Video Converter] Free Variable Converts YouTube videos to MP3, MP4, AVI, MOV, MKV, or FLV files. Supports different quality levels and bitrates. No registration or software installation required.
-Using a YouTube converter has some benefits, such as:
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-You can download any YouTube video as an MP3 file for free
-You can choose the quality and size of the MP3 file you want
-You can access a wide range of songs and videos on YouTube
-You can use the YouTube converter tools online without downloading anything
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-However, using a YouTube converter also has some drawbacks, such as:
-
-You may not get the best sound quality or metadata from the converted MP3 file
-You may have to deal with pop-ups or ads when using some YouTube converter tools
-You may violate the YouTube terms of service or the artist's rights by downloading their content without permission
-You may encounter some errors or limitations when converting long or large videos
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- Pros and cons of YouTube converters
-To summarize, here are the pros and cons of using YouTube converters for downloading Italy Black Diamond MP3:
- | Pros | Cons | | --- | --- | | Free and easy to use | May compromise sound quality or metadata | | Flexible and customizable | May expose you to pop-ups or ads | | Wide and diverse selection | May breach YouTube terms of service or artist's rights | | Online and accessible | May face errors or limitations | How to use a YouTube converter
-If you decide to use a YouTube converter to download Italy Black Diamond MP3, here are the steps you need to follow:
-
-Go to YouTube and search for Italy Black Diamond by Blaq Diamond. Choose the video you want to convert and copy its URL.
-Go to the YouTube converter tool of your choice and paste the URL into the input box. Click convert or download.
-Select the quality and format of the output file. Click download or save.
-Wait for the conversion and download process to finish. Enjoy your Italy Black Diamond MP3!
-
- How to enjoy Italy Black Diamond MP3?
-Now that you have downloaded Italy Black Diamond MP3 on your device, you might be wondering how to enjoy it. Here are some tips and suggestions on how to make the most of your listening experience:
- Use headphones or speakers
-The first thing you need to consider is how you want to listen to Italy Black Diamond MP3. Do you prefer headphones or speakers? Both options have their advantages and disadvantages, depending on your situation and preference. Here are some factors to consider:
- | Headphones | Speakers | | --- | --- | | Provide a more immersive and personal listening experience | Provide a more social and shared listening experience | | Block out external noise and distractions | Allow you to hear your surroundings and interact with others | | Offer better sound quality and bass response | Offer better sound balance and clarity | | Require less space and power | Require more space and power | | May cause discomfort or damage to your ears if used for too long or too loud | May cause annoyance or disturbance to others if used too loud | Create a playlist
-The next thing you need to consider is what other songs you want to listen to along with Italy Black Diamond MP3. Do you want to listen to more songs by Blaq Diamond? Do you want to listen to other Afro-pop songs? Do you want to listen to songs that match your mood or activity ? Whatever your choice is, you can create a playlist with Italy Black Diamond and other songs that suit your taste. Creating a playlist has some benefits, such as:
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-You can organize your music collection and find your favorite songs easily
-You can customize your music experience and create different playlists for different occasions, moods, or themes
-You can discover new music and explore different genres and artists
-You can share your playlist with others and enjoy music together
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-To create a playlist, you can use any of the streaming services or apps mentioned above, or you can use your own device's music player. Here are some steps to follow:
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-Choose a name and a cover image for your playlist
-Add Italy Black Diamond MP3 and other songs that you want to include in your playlist
-Arrange the order of the songs according to your preference
-Save your playlist and enjoy it anytime, anywhere
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- Share it with others
-The last thing you need to consider is how to share Italy Black Diamond MP3 with others. Sharing music with others has some benefits, such as:
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-You can express yourself and your personality through music
-You can connect with others and bond over music
-You can support the artist and spread their music to more people
-You can have fun and enjoy music together
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-To share Italy Black Diamond MP3 with others, you can use any of the following methods:
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-Send it as an attachment or a link via email, text, or social media
-Play it on your device or speaker and let others listen to it
-Recommend it to others and tell them why you like it
-Create a QR code or a Spotify code for it and let others scan it
-
- Conclusion
-In conclusion, Italy Black Diamond MP3 is a great song that you should listen to if you love Afro-pop music or if you want to try something new. It is a song by Blaq Diamond, a talented South African singer who sings about his love for Italy and his lover. You can download Italy Black Diamond MP3 from various sources, such as streaming services, websites, or YouTube converters. You can also enjoy Italy Black Diamond MP3 by using headphones or speakers, creating a playlist, or sharing it with others. We hope this article has helped you learn more about Italy Black Diamond MP3 and how to download and enjoy it. Happy listening!
- FAQs
-Here are some frequently asked questions and their answers about Italy Black Diamond MP3:
- Q: Where can I watch the official video of Italy Black Diamond?
-A: You can watch the official video of Italy Black Diamond on YouTube. Here is the link: [https://www.youtube.com/watch?v=9X6xZJ8Y1wQ]
- Q: What are some other songs by Blaq Diamond that I should listen to?
-A: Some other songs by Blaq Diamond that you should listen to are SummerYoMuthi, Ibhanoyi, Love Letter, Woza My Love, Messiah, etc.
- Q: How can I contact Blaq Diamond or follow him on social media?
-A: You can contact Blaq Diamond or follow him on social media through his official accounts. Here are some of them:
-
-Instagram: [@blaqdiamond150]
-Twitter: [@blaqdiamond150]
-Facebook: [Blaq Diamond]
-Email: [blaqdiamond150@gmail.com]
-
- Q: How can I support Blaq Diamond and his music?
-A: You can support Blaq Diamond and his music by buying his albums or songs from legal sources, streaming his music on licensed platforms, attending his concerts or events, voting for him in awards or competitions, etc.
- Q: What are some other Afro-pop artists that I should check out?
-A: Some other Afro-pop artists that you should check out are Wizkid, Burna Boy, Davido, Tiwa Savage, Yemi Alade, Diamond Platnumz, etc.
197e85843dHow to Download APK FM WhatsApp: A Complete Guide
-WhatsApp is one of the most popular messaging apps in the world, but it may not have all the features and customization options that you want. If you are looking for a way to enhance your WhatsApp experience, you may want to try FM WhatsApp, a modified version of the official app that offers many additional features and benefits.
-However, FM WhatsApp is not available on the Google Play Store, so you will need to download and install it manually from an APK file. In this article, we will show you how to download APK FM WhatsApp from a reliable source, how to install and use it on your Android device, and what are the risks and advantages of using modded apps.
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What is FM WhatsApp and Why You Should Try It
-FM WhatsApp is a modified version of the original WhatsApp app that has been developed by Foud Apps. It is designed to provide users with more features and customization options that are not available on the official version of WhatsApp. Some of the features of FM WhatsApp include:
-Features of FM WhatsApp
-
-Calls/filter blocker: You can block unwanted or unknown numbers from calling you or sending you messages.Media sharing: You can send up to 60 images at once and files up to 700MB in size. You can also send high-resolution images without losing quality.Themes store: You can choose from thousands of themes to change the look and feel of your app. You can also create your own themes and share them with others.Anti-delete messages: You can see the messages that have been deleted by the sender. You can also prevent others from deleting your messages.New emojis: You can access and use emojis from different Android ecosystems, such as Android Oreo, Facebook, etc.App launcher and notification icons: You can customize the app icon and notification icon according to your preference.Colors and customization: You can change the colors and fonts of various elements of the app, such as chat bubbles, ticks, status bar, etc.Full resolution image: You can set any image as your wallpaper without cropping or resizing it.
-These are just some of the features of FM WhatsApp that make it a better alternative to the official WhatsApp app. However, before you decide to download and use FM WhatsApp, you should also be aware of the risks involved in using modded apps.
-Risks of Using Modded Apps
-Modded apps are apps that have been modified by third-party developers to add new features or functionalities that are not present in the original version. While modded apps may seem attractive and useful, they also pose some potential security risks that you should consider before using them. Some of the risks of using modded apps are:
-
-Lost revenue: By using modded apps, you are depriving the original developers of their rightful revenue. This may affect their ability to maintain and update their apps, as well as create new ones.Customer security is jeopardized: Not all modded apps are safe and trustworthy. Some may contain harmful code that can infect your device with viruses, malware, spyware, or ransomware. These malicious programs can steal your personal data, damage your device, or even take control of it.Infected devices: Modded apps can also spread infections to other devices through various means, such as sharing files, contacts, or messages. This can put your friends, family, or colleagues at risk as well.Personal information gathering Personal information gathering: Modded apps may also collect your personal information, such as your name, phone number, email address, location, contacts, messages, photos, etc. without your consent or knowledge. This information can be used for various purposes, such as advertising, marketing, or even identity theft. Legal issues: Modded apps may also violate the terms and conditions of the original app or the platform that hosts it. This can result in legal actions against you, such as fines, lawsuits, or bans.
-Therefore, you should be careful and cautious when using modded apps, such as FM WhatsApp. You should only download them from trusted sources, scan them for viruses before installing them, and backup your data regularly. You should also respect the rights and privacy of the original developers and other users.
- How to Download APK Files from Google Play Store
-If you want to download FM WhatsApp, you will need to get its APK file first. APK stands for Android Package Kit, and it is the file format that Android uses to distribute and install apps. APK files contain all the necessary components of an app, such as code, resources, assets, etc.
-How to download FM WhatsApp latest version 2023
-There are two ways to download APK files from the Google Play Store: using a web tool or using an APK extractor app. We will explain both methods below.
-Using a Web Tool
-One of the easiest ways to download APK files from the Google Play Store is to use a web tool that can generate download links for any app on the store. There are many such tools available online, but one of the most popular ones is APKPure.com. Here are the steps to use it:
-
-Go to APKPure.com on your browser.
-Search for the app that you want to download in the search bar. For example, type "FM WhatsApp" and hit enter.
-Select the app from the search results and click on the "Download APK" button.
-Wait for the download to finish and save the APK file on your device.
-
-Using an APK Extractor App
-Another way to download APK files from the Google Play Store is to use an APK extractor app that can extract the APK file of any installed app on your device. There are many such apps available on the store, but one of the most popular ones is APK Extractor by Meher. Here are the steps to use it:
-
-Download and install APK Extractor from the Google Play Store on your device.
-Open the app and grant it the necessary permissions.
-Select the app that you want to extract from the list of installed apps. For example, tap on "WhatsApp".
-Tap on the "Share" icon at the top right corner and choose a method to share or save the APK file. For example, you can send it to yourself via email or save it on Google Drive.
-
- How to Install and Use FM WhatsApp on Your Android Device
-Once you have downloaded the APK file of FM WhatsApp, you can install and use it on your Android device. However, before you do that, you need to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. Here are the steps to enable unknown sources:
-Enabling Unknown Sources
-
-Go to your device's settings and tap on "Security".
-Find and toggle on "Unknown sources". You may see a warning message that says installing apps from unknown sources can harm your device. Tap on "OK" to proceed.
-You have now enabled unknown sources on your device.
-
- Installing the APK File
-
-Locate the APK file of FM WhatsApp that you have downloaded or extracted on your device. You can use a file manager app to do this.
-Tap on the APK file and follow the instructions on the screen to install it.
-You may see a warning message that says this app is not verified by Google Play Protect. Tap on "Install anyway" to proceed.
-You have now installed FM WhatsApp on your device.
-
- Launching and Customizing FM WhatsApp
-
-Open FM WhatsApp from your app drawer or home screen.
-You will see a welcome screen that asks you to agree to the terms and conditions of the app. Tap on "Agree and continue" to proceed.
-You will be asked to enter your phone number and verify it with a code that will be sent to you via SMS. Follow the instructions on the screen to complete the verification process.
-You will be asked to restore your chat history from a backup if you have one. You can choose to restore it or skip it.
-You will be asked to enter your name and profile picture. You can also change them later.
-You have now launched FM WhatsApp and you can start using it as you would use the official WhatsApp app.
-To customize FM WhatsApp, tap on the menu icon at the top right corner and select "Fouad Mods". You will see a list of categories that you can explore and tweak according to your preference. For example, you can change the theme, the colors, the fonts, the privacy settings, etc.
-
- Conclusion
-FM WhatsApp is a modded version of the official WhatsApp app that offers many additional features and customization options that are not available on the original app. However, it also comes with some risks and drawbacks that you should be aware of before using it. In this article, we have shown you how to download APK FM WhatsApp from a reliable source, how to install and use it on your Android device, and how to enable unknown sources on your device. We hope that this guide has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below.
- FAQs
-Here are some frequently asked questions about FM WhatsApp and their answers:
-
-Q: Is FM WhatsApp safe to use?A: FM WhatsApp is not officially endorsed or verified by WhatsApp or Google, so there is no guarantee that it is safe or secure. It may contain harmful code or malware that can compromise your device or data. Therefore, you should use it at your own risk and discretion.Q: Can I use FM WhatsApp and official WhatsApp on the same device?A: Yes, you can use both apps on the same device, but you will need to use different phone numbers for each app. You cannot use the same number for both apps.Q: Will I get banned from WhatsApp for using FM WhatsApp?A: There is a possibility that WhatsApp may detect and ban your account for using a modded app that violates their terms and conditions. However, FM WhatsApp has some anti-ban features that may prevent this from happening. Still, there is no guarantee that you will not get banned, so use it at your own risk.Q: How do I update FM WhatsApp?A: You can check for updates from within the app by tapping on the menu icon and selecting "Updates". You can also visit the official website of Foud Apps or APKPure.com to download the latest version of the app.Q: How do I uninstall FM WhatsApp?A: You can uninstall FM WhatsApp like any other app on your device. Go to your device's settings and tap on "Apps". Find and select "FM WhatsApp" from the list of apps. Tap on "Uninstall" and confirm your action.
197e85843dDownload APK Huli Live Mod: A New Way to Enjoy Live Streaming Shows
- Do you love watching live streaming shows on your smartphone? Do you want to access exclusive content that is not available on other platforms? Do you want to save money and enjoy unlimited features without ads or restrictions? If you answered yes to any of these questions, then you should try downloading APK Huli Live Mod.
-download apk huli live mod Download File ✒ https://urllie.com/2uNFTL
APK Huli Live Mod is a modified version of the original Huli Live app, which is a popular live streaming platform that offers various kinds of shows, such as music, dance, comedy, gaming, and more. With APK Huli Live Mod, you can enjoy all the benefits of the original app, plus some extra features that will make your experience more enjoyable and satisfying. In this article, we will tell you everything you need to know about APK Huli Live Mod, including what it is, how to download and install it, and how to use it effectively.
- What is Huli Live?
- Huli Live is a live streaming app that allows you to watch and interact with thousands of hosts from different countries and regions. You can choose from various categories and genres, such as music, dance, comedy, gaming, and more. You can also chat with the hosts and other users, send gifts, join fan clubs, and participate in events and competitions.
- Huli Live is designed to provide you with high-quality entertainment and social interaction. You can discover new talents, make new friends, and have fun anytime and anywhere. However, there are some limitations and drawbacks that come with using the original app. For example, you have to pay for some premium features, such as unlocking private shows, sending special gifts, or accessing VIP rooms. You also have to deal with annoying ads that interrupt your viewing experience. Moreover, some content may be blocked or restricted in your region due to legal or ethical reasons.
- Features of Huli Live
- Some of the features that make Huli Live a great live streaming app are:
-
-Thousands of hosts from different countries and regions
-Various categories and genres of shows
-High-quality video and audio
-Real-time interaction and communication
-Gifts, fan clubs, events, and competitions
-User-friendly interface and easy navigation
-Regular updates and improvements
-
- Benefits of Huli Live Mod APK
- Huli Live Mod APK is a modified version of the original app that gives you access to all the premium features for free. You don't have to pay anything or register an account to use the app. You also don't have to worry about ads or interruptions. Moreover, you can bypass any regional or legal restrictions that may prevent you from watching some content. Some of the benefits that come with using Huli Live Mod APK are:
-
-Unlimited access to private shows
-Unlimited access to VIP rooms
-Unlimited sending of special gifts
-No ads or pop-ups
-No registration or verification required
-No root or jailbreak required
-No risk of viruses or malware
-Bypass regional or legal restrictions
-
- How to Download and Install Huli Live Mod APK
- If you want to download and install Huli Live Mod APK on your Android device, you need to follow these simple steps:
- Step 1: Enable Unknown Sources
- Before you can install any APK file on your device, you need to enable the option to allow installation from unknown sources. This will let you install apps that are not from the official Google Play Store. To do this, go to your device's Settings > Security > Unknown Sources and toggle it on. You may see a warning message, but don't worry, it's safe to proceed.
- Step 2: Download the APK File
- Next, you need to download the APK file of Huli Live Mod from a reliable source. You can use the link below to download it directly to your device. Alternatively, you can download it to your computer and transfer it to your device via USB cable or Bluetooth.
-download apk huli live mod show bebas
- Download Huli Live Mod APK here
- Step 3: Install the APK File
- Once you have downloaded the APK file, you need to locate it on your device and tap on it to start the installation process. You may see a prompt asking for your permission to install the app. Just tap on Install and wait for a few seconds until the installation is complete.
- Step 4: Launch the App and Enjoy
- Finally, you can launch the app from your app drawer or home screen and enjoy watching live streaming shows with unlimited features and benefits. You can browse through different categories and genres, watch and interact with your favorite hosts, send gifts and join fan clubs, and have fun with other users.
- Tips and Tricks for Using Huli Live Mod APK
- To make the most out of your Huli Live Mod APK experience, here are some tips and tricks that you can follow:
- Use a VPN Service
- A VPN service is a tool that allows you to change your IP address and location, making you appear as if you are from another country or region. This can help you access content that may be blocked or restricted in your area due to legal or ethical reasons. For example, some shows may not be available in your country due to censorship or licensing issues. By using a VPN service, you can bypass these limitations and watch any show you want.
- There are many VPN services that you can use, such as ExpressVPN, NordVPN, or Surfshark. You can download them from the Google Play Store or their official websites. Just make sure that you choose a reputable and reliable one that offers fast and secure connections.
- Choose the Best Quality and Speed
- Huli Live Mod APK allows you to choose the quality and speed of the video stream according to your preference and internet connection. You can adjust these settings by tapping on the gear icon on the top right corner of the screen. You can choose from low, medium, high, or ultra quality, and from normal, fast, or super speed. The higher the quality and speed, the better the viewing experience, but also the more data consumption. Therefore, you should balance these factors according to your situation.
- Interact with the Hosts and Other Users
- One of the best features of Huli Live Mod APK is that it allows you to interact with the hosts and other users in real-time. You can chat with them by typing messages in the chat box at the bottom of the screen. You can also send gifts, such as flowers, hearts, stars, or diamonds, by tapping on the gift icon on the bottom right corner of the screen. These gifts will show your appreciation and support for the hosts, and may also earn you some rewards or privileges.
- Besides chatting and sending gifts, you can also join fan clubs, participate in events and competitions, vote for your favorite hosts, and invite them to private shows or VIP rooms. These activities will enhance your social interaction and enjoyment of the app.
- Be Respectful and Follow the Rules
- While using Huli Live Mod APK, you should always be respectful and follow the rules of the app. You should not spam, harass, abuse, or threaten anyone on the app. You should also not post any illegal, immoral, or inappropriate content on the app. If you violate any of these rules, you may face consequences such as being banned or reported by other users or by the app itself.
- You should also respect the hosts' preferences and boundaries. You should not force them to do anything they don't want to do or ask them for personal information they don't want to share. You should also not record or share their shows without their permission. Remember that they are human beings too, and they deserve respect and privacy.
- Conclusion
- Huli Live Mod APK is a great app for anyone who loves watching live streaming shows on their smartphone. It offers unlimited access to various kinds of shows, such as music, dance, comedy, gaming, and more. It also allows real-time interaction and communication with the hosts and other users. Moreover, it has no ads, no registration, no payment, and no restrictions. It is easy to download and install, and it works on any Android device. However, you should also be aware of the risks and responsibilities that come with using Huli Live Mod APK. You should use a VPN service to protect your privacy and security, and to access content that may be blocked or restricted in your region. You should also choose the best quality and speed for your video stream according to your internet connection and data consumption. You should also interact with the hosts and other users respectfully and follow the rules of the app. You should not spam, harass, abuse, or threaten anyone on the app. You should also not post any illegal, immoral, or inappropriate content on the app. You should also respect the hosts' preferences and boundaries, and not record or share their shows without their permission. If you follow these tips and tricks, you will surely have a great time using Huli Live Mod APK. You will be able to enjoy high-quality entertainment and social interaction anytime and anywhere. You will also be able to discover new talents, make new friends, and have fun with other users. Huli Live Mod APK is a new way to enjoy live streaming shows that you should not miss.
FAQs
- Here are some frequently asked questions about Huli Live Mod APK:
-
-Is Huli Live Mod APK safe to use?
-Huli Live Mod APK is safe to use as long as you download it from a reliable source and scan it for viruses or malware before installing it. You should also use a VPN service to protect your privacy and security while using the app.
-Is Huli Live Mod APK legal to use?
-Huli Live Mod APK is not legal to use in some countries or regions where the original app is banned or restricted due to legal or ethical reasons. You should check the laws and regulations of your country or region before using the app. You should also use a VPN service to bypass any regional or legal restrictions that may apply to you.
-What are the requirements for using Huli Live Mod APK?
-Huli Live Mod APK requires an Android device with Android 4.4 or higher version, a stable internet connection, and enough storage space. You should also enable the option to allow installation from unknown sources on your device before installing the app.
-How can I update Huli Live Mod APK?
-Huli Live Mod APK does not have an automatic update feature, so you need to manually check for updates from time to time. You can visit the same source where you downloaded the app and see if there is a newer version available. If there is, you can download and install it over the existing app without losing your data or settings.
-How can I contact the developers of Huli Live Mod APK?
-Huli Live Mod APK is not an official app, so it does not have an official website or contact information. However, you can try to contact the developers through their social media accounts or email addresses if they provide them on their source page.
- 401be4b1e0Download APK Dream League Soccer 2019 Mod Liga Gojek: How to Enjoy the Best of Indonesian Football on Your Android Device
- If you are a fan of soccer games and want to experience the thrill of playing with some of the best teams and players in Indonesia, then you should download APK Dream League Soccer 2019 Mod Liga Gojek. This is a modified version of the popular Dream League Soccer 2019 game that features the Indonesian professional football league, Liga Gojek. In this article, we will show you how to download and install this game on your Android device, as well as some tips and tricks to help you play better.
-download apk dream league soccer 2019 mod liga gojek Download Zip › https://urllie.com/2uNAMd
What is Dream League Soccer 2019?
- Dream League Soccer 2019 is a fantastic game in its category of mobile games. It has high graphics, detailed player faces, and many players, leagues, events, etc. The game has more than 4000 FIFPro authorized players, build your own arena and take on the team in Dream League Online as you conquer brilliance on your street to Soccer Stardom.
- Features of Dream League Soccer 2019
- Some of the features of Dream League Soccer 2019 are:
-
-FIFPro™ licensed players brings the most authentic Dream League Soccer experience to your hands!
-Freedom to create, customize and control your very own Dream Team!
-6 Divisions to work your way through, and over 7 Cup competitions!
-Take part in regular live events to win prizes and glory!
-Build your very own stadium to showcase your superstars!
-Develop your players with more accuracy and intent
-Season objectives to keep you engaged and coming back!
-Google Play achievements & leaderboards to see who ranks on top!
-Customise and import your very own kits & logos!
-Sync progress between devices with Google Play Cloud!
-Exclusive soundtrack provided by The Luka State, Sunset Sons, Beth Thornton, Jack Wins, Vistas & Only The Poets!
-
- How to Download and Install Dream League Soccer 2019 APK File on Android
- To download and install Dream League Soccer 2019 APK file on your Android device, you need to follow these steps:
-
-Allow unknown apps on your Android device by going to Settings > Apps & Notifications > Special access > Install unknown apps. Tap Chrome (or whichever web browser you use) and move Allow from this source to the On position.
-Install a file manager app (such as Cx File Explorer or File Manager) so that you can find the APK file after you download it to your phone.
-Download the APK file from a reputable source such as APK Mirror. You can find the link for Dream League Soccer 2019 APK file here:
-Once it's downloaded, open your file manager app and locate the APK file in your Downloads folder. Tap on it and tap Install when prompted.
-Wait for the installation process to finish and then launch the game from your app drawer What is Dream League Soccer 2019 Mod Liga Gojek?
- Dream League Soccer 2019 Mod Liga Gojek is a popular modification of the original Dream League Soccer game that features the Indonesian professional football league, Liga Gojek. The mod offers various enhancements such as new teams, players, and improved gameplay.
- Features of Dream League Soccer 2019 Mod Liga Gojek
- Some of the features of Dream League Soccer 2019 Mod Liga Gojek are:
-
-All teams and players from Liga Gojek are available to choose from.
-You can play in the Liga Gojek mode, which simulates the real league season and matches.
-You can also play in other modes such as Career, Online, and Friendly.
-You can customize your team's kit, logo, and stadium with the Liga Gojek theme.
-You can enjoy realistic graphics, animations, and sound effects.
-You can use unlimited coins and gems to buy and upgrade your players and facilities.
-
- How to Download and Install Dream League Soccer 2019 Mod Liga Gojek APK File on Android
- To download and install Dream League Soccer 2019 Mod Liga Gojek APK file on your Android device, you need to follow these steps:
-
-Allow unknown apps on your Android device by going to Settings > Apps & Notifications > Special access > Install unknown apps. Tap Chrome (or whichever web browser you use) and move Allow from this source to the On position.
-Install a file manager app (such as Cx File Explorer or File Manager) so that you can find the APK file after you download it to your phone.
-Download the APK file and the OBB file from a reputable source such as YouTube. You can find the link for Dream League Soccer 2019 Mod Liga Gojek APK file and OBB file here:
-Once they are downloaded, open your file manager app and locate the APK file and the OBB file in your Downloads folder. Tap on the APK file and tap Install when prompted.
-After the installation is done, do not launch the game yet. Instead, go to your file manager app and copy the OBB file to Android > OBB > com.firsttouchgames.dls3 folder. If there is no such folder, create one.
-Now you can launch the game from your app drawer and enjoy playing Dream League Soccer 2019 Mod Liga Gojek.
- Tips and Tricks for Playing Dream League Soccer 2019 Mod Liga Gojek
- Now that you have downloaded and installed Dream League Soccer 2019 Mod Liga Gojek on your Android device, you might be wondering how to play it well and have more fun. Here are some tips and tricks that will help you improve your skills and enjoy the game more.
- How to Create and Manage Your Dream Team
- One of the most important aspects of Dream League Soccer 2019 Mod Liga Gojek is creating and managing your dream team. You can choose from any of the teams and players from Liga Gojek, or create your own custom team. Here are some tips on how to do that:
-
-When you start the game, you will be given a default team with some random players. You can change the name, logo, and kit of your team by tapping on the Team Management icon on the main menu.
-You can also buy new players or sell existing ones by tapping on the Transfer icon on the main menu. You can use coins or gems to buy players, or earn them by playing matches and completing objectives.
-You can also scout for new players by tapping on the Scout icon on the main menu. You can use gems to scout for players with specific attributes, such as speed, shooting, or passing.
-You can also upgrade your existing players by tapping on the Player Development icon on the main menu. You can use coins or gems to improve their skills, such as dribbling, tackling, or heading.
-You can also change the formation, tactics, and roles of your players by tapping on the Formation icon on the main menu. You can choose from different formations, such as 4-4-2, 4-3-3, or 3-5-2. You can also adjust the style of play, such as attacking, defensive, or balanced. You can also assign roles to your players, such as captain, free kick taker, or penalty taker.
-
- How to Train and Improve Your Players
- Another important aspect of Dream League Soccer 2019 Mod Liga Gojek is training and improving your players. You can do this by playing matches and completing objectives, or by using the Training mode. Here are some tips on how to do that:
-How to download apk dream league soccer 2019 mod liga gojek for android
-
-You can access the Training mode by tapping on the Training icon on the main menu. You can choose from different types of training, such as shooting, passing, dribbling, or defending. You can also choose the difficulty level, such as easy, medium, or hard.
-You can use the Training mode to practice your skills and learn new tricks. You can also earn coins and gems by completing training challenges.
-You can also improve your players' skills by playing matches and completing objectives. You can play in different modes, such as Career, Online, or Friendly. You can also play in different tournaments, such as Liga Gojek, Champions Cup, or Global Challenge Cup.
-You can earn coins and gems by winning matches and scoring goals. You can also earn bonus coins and gems by completing objectives, such as winning a certain number of matches, scoring a certain number of goals, or keeping a clean sheet.
-You can use the coins and gems to buy new players or upgrade existing ones. You can also use them to buy new kits, logos, stadiums, or coaches.
-
- How to Compete in Liga Gojek and Other Tournaments
- One of the most exciting features of Dream League Soccer 2019 Mod Liga Gojek is competing in Liga Gojek and other tournaments. You can do this by playing in Career mode or Online mode. Here are some tips on how to do that:
-
-You can access the Career mode by tapping on the Career icon on the main menu. You can choose from different divisions, such as Division 1, Division 2, Division 3, Division 4, Division 5, or Division 6. You can also choose from different tournaments, such as Liga Gojek (Indonesian league), Champions Cup (European cup), Global Challenge Cup (World cup), All Stars Cup (All star teams), Dream League Cup (Dream teams), or Elite Division (Elite teams).
-You can compete in Liga Gojek by choosing it from the tournaments list in Career mode. You will face 19 other teams from Liga Gojek in a round-robin format. You will play each team twice (home and away) for a total of 38 matches. The top four teams will qualify for the Champions Cup. You can also compete in other tournaments by choosing them from the tournaments list in Career mode. You will face different teams from different regions and leagues in a knockout format. You will play one match per round (home or away) until you reach the final. The winner of each tournament will earn a trophy and a cash prize.
-You can access the Online mode by tapping on the Online icon on the main menu. You can choose from different modes, such as Quick Match, Friendly Match, or Dream League Online. You can also choose from different regions, such as Asia, Europe, America, or Africa.
-You can compete in Dream League Online by choosing it from the modes list in Online mode. You will face other players from around the world in a league format. You will play each player once (home or away) for a total of 10 matches. The top four players will qualify for the next division.
-You can also compete in Quick Match or Friendly Match by choosing them from the modes list in Online mode. You will face a random player or a friend in a single match (home or away). The winner of each match will earn coins and gems.
-
- How to Customize Your Team's Kit, Logo, and Stadium
- Another fun feature of Dream League Soccer 2019 Mod Liga Gojek is customizing your team's kit, logo, and stadium. You can do this by using the Customize option on the main menu. Here are some tips on how to do that:
-
-You can customize your team's kit by tapping on the Kit icon on the Customize menu. You can choose from different colors, patterns, and designs for your team's shirt, shorts, and socks. You can also import your own kit image by tapping on the Import Kit icon.
-You can customize your team's logo by tapping on the Logo icon on the Customize menu. You can choose from different shapes, colors, and symbols for your team's logo. You can also import your own logo image by tapping on the Import Logo icon.
-You can customize your team's stadium by tapping on the Stadium icon on the Customize menu. You can choose from different types, sizes, and capacities for your team's stadium. You can also upgrade your stadium's facilities, such as pitch, seating, lighting, and scoreboard.
-
- Conclusion
- Summary of the Main Points
- In conclusion, Dream League Soccer 2019 Mod Liga Gojek is a great game for soccer fans who want to enjoy the best of Indonesian football on their Android device. The game offers various features such as:
-
-FIFPro™ licensed players and teams from Liga Gojek and other leagues.
-Freedom to create, customize and control your very own Dream Team.
-6 Divisions and over 7 Cup competitions to work your way through.
-Regular live events to win prizes and glory.
-Your very own stadium to showcase your superstars.
-Player development and training to improve your skills.
-Google Play achievements & leaderboards to see who ranks on top.
-Customise and import your very own kits & logos.
-Sync progress between devices with Google Play Cloud.
-Exclusive soundtrack provided by The Luka State, Sunset Sons, Beth Thornton, Jack Wins, Vistas & Only The Poets!
-
- Call to Action
- If you are interested in playing Dream League Soccer 2019 Mod Liga Gojek, you can download it from the links provided in this article. You can also watch some videos and reviews of the game on YouTube or other platforms. You can also share your feedback and opinions about the game with other players and fans on social media or forums. We hope you have fun playing Dream League Soccer 2019 Mod Liga Gojek!
- FAQs
- Here are some frequently asked questions about Dream League Soccer 2019 Mod Liga Gojek:
-
-Q: Is Dream League Soccer 2019 Mod Liga Gojek safe to download and install?
-A: Yes, Dream League Soccer 2019 Mod Liga Gojek is safe to download and install as long as you use a reputable source such as APK Mirror or YouTube. However, you should always be careful when downloading and installing any APK file from unknown sources as they may contain viruses or malware that could harm your device.
-Q: Is Dream League Soccer 2019 Mod Liga Gojek compatible with my device?
-A: Dream League Soccer 2019 Mod Liga Gojek is compatible with most Android devices that have Android 4.4 or higher version installed. Q: How can I update Dream League Soccer 2019 Mod Liga Gojek to the latest version?
-A: You can update Dream League Soccer 2019 Mod Liga Gojek to the latest version by downloading and installing the new APK file and OBB file from the same source that you used before. You can also check for updates by tapping on the Settings icon on the main menu and then tapping on the Update icon.
-Q: How can I play Dream League Soccer 2019 Mod Liga Gojek offline?
-A: You can play Dream League Soccer 2019 Mod Liga Gojek offline by turning off your internet connection before launching the game. You can still play in Career mode or Friendly mode, but you will not be able to play in Online mode or access live events.
-Q: How can I backup and restore my Dream League Soccer 2019 Mod Liga Gojek data?
-A: You can backup and restore your Dream League Soccer 2019 Mod Liga Gojek data by using Google Play Cloud. You can enable this feature by tapping on the Settings icon on the main menu and then tapping on the Google Play Cloud icon. You can also backup and restore your data manually by copying and pasting the com.firsttouchgames.dls3 folder from Android > Data to another location.
- 401be4b1e0 $config_json
-{
- "train_micro_batch_size_per_gpu": ${MICRO_BATCH_SIZE},
- "steps_per_print": 1000,
- "gradient_clipping": 1.0,
- "zero_optimization": {
- "stage": $ZERO_STAGE,
- "contiguous_gradients": false
- },
- "zero_allow_untested_optimizer": false,
- "fp16": {
- "enabled": true
- },
- "wall_clock_breakdown": false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-
-
-TRAINER_ARGS="
- --max_epochs 20 \
- --gpus 1 \
- --num_nodes 1 \
- --strategy deepspeed_stage_${ZERO_STAGE} \
- --default_root_dir ${MODEL_ROOT_DIR} \
- --save_ckpt_path ${MODEL_ROOT_DIR}/ckpt \
- --save_top_k 3 \
- --monitor valid_sacrebleu \
- --mode max \
- --save_last \
- --every_n_train_steps 0 \
- --val_check_interval 0.2 \
- --label_smoothing 0.1 \
- --warmup_steps 4000 \
- --learning_rate 1e-7 \
- --adam_beta2 0.98 \
- --scheduler_type inverse_sqrt \
- --reverse_src_tgt \
- --tgt_zh \
-"
-
-DATA_ARGS="
- --datasets_name case_test \
- --num_workers 8 \
- --train_batchsize $MICRO_BATCH_SIZE \
- --val_batchsize $MICRO_BATCH_SIZE \
- --test_batchsize $MICRO_BATCH_SIZE \
- --val_datasets_field val \
- --max_enc_length 256 \
- --max_dec_length 256 \
-"
-
-mode_path="IDEA-CCNL/Randeng-Deltalm-362M-En-Zn"
-
-
-MODEL_ARGS="
- --model_path $mode_path \
- --output_save_path $output_save_path \
-"
-
-SCRIPTS_PATH=finetune_deltalm.py
-
-cat $SCRIPTS_PATH
-
-export CMD=" \
- $SCRIPTS_PATH \
- $TRAINER_ARGS \
- $MODEL_ARGS \
- $DATA_ARGS \
- "
-
-echo $CMD
-
-source activate
-conda activate fengshen
-# srun python3 $CMD
-python3 $CMD
diff --git a/spaces/fclong/summary/fengshen/examples/unimc/README.md b/spaces/fclong/summary/fengshen/examples/unimc/README.md
deleted file mode 100644
index 16abf3ff69c5ab7b8b8ca1f7c7ec191cbdf64ec0..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/unimc/README.md
+++ /dev/null
@@ -1,221 +0,0 @@
-[**中文**](./README.md) | [**English**](./README_en.md)
-# UniMC
-
-EMNLP 2022 论文 《[Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://arxiv.org/abs/2210.08590)》源码
-
-
-
-## Update
-- [2022-10-18] Release preprint in arXiv.
-- [2022-10-14] Release code in GitHub.
-
-## Requirements
-
-安装 fengshen 框架
-
-```shell
-git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
-cd Fengshenbang-LM
-pip install --editable .
-```
-
-## Quick Start
-
-你可以参考我们的 [example.py](./example.py) 脚本，只需要将处理好的 train、dev、test 即输入模型即可。
-```python
-import argparse
-from fengshen.pipelines.multiplechoice import UniMCPipelines
-
-total_parser = argparse.ArgumentParser("TASK NAME")
-total_parser = UniMCPipelines.piplines_args(total_parser)
-args = total_parser.parse_args()
-
-pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese'
-args.learning_rate=2e-5
-args.max_length=512
-args.max_epochs=3
-args.batchsize=8
-args.default_root_dir='./'
-model = UniMCPipelines(args,model_path=pretrained_model_path)
-
-train_data = []
-dev_data = []
-test_data = [{
- "texta": "就是废物，充电不进害得老子把主板烧了，客服不耐烦",
- "textb": "",
- "question": "",
- "choice": ["这是一条差评", "这是一条好评"],
- "answer": "这是一条差评",
- "label": 0,
- "id": 31
-}]
-
-if args.train:
- model.train(train_data, dev_data)
-result = model.predict(test_data)
-```
-## Pretrained Model
-对于英文模型，我们使用14份 multiplechoice 数据集进行了预训练。在中文模型中，我们已经收集了48份数据集对模型进行预训练，我们已经将预训练模型开源到 HuggingFace 社区当中。
-
-| 模型 | 地址 |
-|:---------:|:--------------:|
-| Erlangshen-UniMC-Albert-235M-English | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English) |
-| Erlangshen-UniMC-RoBERTa-110M-Chinese | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese) |
-| Erlangshen-UniMC-RoBERTa-330M-Chinese | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese) |
-| Erlangshen-UniMC-MegatronBERT-1.3B-Chinese | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) |
-
-## Experiments
-
-
-### English
-
-为了测评 UniMC 的性能，在英文中，我们使用 14份 multiple-choice 数据集（具体数据参考原论文）来对模型进行预训练，使其具备做选择题的能力，
-
-**Zero-shot**
-| Model | T0 11B | GLaM 60B | FLAN 137B | PaLM 540B | UniMC 235M |
-|---------|--------|----------|-----------|-----------|------------|
-| ANLI R1 | 43.6 | 40.9 | 47.7 | 48.4 | **52.0** |
-| ANLI R2 | 38.7 | 38.2 | 43.9 | 44.2 | **44.4** |
-| ANLI R3 | 41.3 | 40.9 | 47.0 | 45.7 | **47.8** |
-| CB | 70.1 | 33.9 | 64.1 | 51.8 | **75.7** |
-### Chinese
-
-为了测评 UniMC 在中文场景下的性能我们使用 13份 有监督数据集来对模型进行预训练,预训练数据如下：
-| Task type | Task | # of option | Data size |
-|---------|--------|----------|-----------|
-| Multiple-choice | c3 | 4 | 11.8k |
-| Multiple-choice | ClozeT | 2 | 0.7k |
-| Multiple-choice | CMRC2019 | n | 11.4k |
-| Multiple-choice | GCRC | 4 | 7.8k |
-| Classification | DuEE-Fin | 12 | 4.3k |
-| Classification | DuEE1.0 | 65 | 10.3k |
-| Classification | Fudan | 20 | 19.6k |
-| Classification | THUNEWS | 10 | 180k |
-| NLI | CMNLI | 3 | 39k |
-| NLI | SNLI | 3 | 545.8k |
-| Paraphrace | AFQMC | 2 | 34.3k |
-| Paraphrace | PAWS-X | 2 | 49k |
-| Paraphrace | STS-B | 2 | 80k |
-
-我们使用中文领域常用的benchmark来测试UniMC的性能，具体是FewCLUE的9个任务，我们在 test_public 上测评模型的性能。
-
-
-**Few-shot**
-| Model | eprstmt | csldcp | tnews | iflytek | ocnli | bustm | chid | csl | wsc | Avg |
-|------------|------------|----------|-----------|----------|-----------|-----------|-----------|----------|-----------|-----------|
-| Finetuning | 65.4 | 35.5 | 49 | 32.8 | 33 | 60.7 | 14.9 | 50 | 55.6 | 44.1 |
-| PET | 86.7 | 51.7 | 54.5 | 46 | 44 | 56 | 61.2 | 59.4 | 57.5 | 57.44 |
-| LM-BFF | 85.6 | 54.4 | 53 | 47.1 | 41.6 | 57.6 | 61.2 | 51.7 | 54.7 | 56.32 |
-| P-tuning | 88.3 | 56 | 54.2 | **57.6** | 41.9 | 60.9 | 59.3 | **62.9** | 58.1 | 59.91 |
-| EFL | 84.9 | 45 | 52.1 | 42.7 | 66.2 | 71.8 | 30.9 | 56.6 | 53 | 55.91 |
-| [UniMC-RoBERTa-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese) | 88.64 | 54.08 | 54.32 | 48.6 | 66.55 | 73.76 | 67.71 | 52.54 | 59.92 | 62.86 |
-| [UniMC-RoBERTa-330M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese) | 89.53 | 57.3 | 54.25 | 50 | 70.59 | 77.49 | 78.09 | 55.73 | 65.16 | 66.46 |
-| [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **89.278** | **60.9** | **57.46** | 52.89 | **76.33** | **80.37** | **90.33** | 61.73 | **79.15** | **72.05** |
-
-**Zero-shot**
-
-| Model | eprstmt | csldcp | tnews | iflytek | ocnli | bustm | chid | csl | wsc | Avg |
-|---------------|-----------|-----------|-----------|-----------|-----------|----------|----------|----------|-----------|-----------|
-| GPT-zero | 57.5 | 26.2 | 37 | 19 | 34.4 | 50 | 65.6 | 50.1 | 50.3 | 43.4 |
-| PET-zero | 85.2 | 12.6 | 26.1 | 26.6 | 40.3 | 50.6 | 57.6 | 52.2 | 54.7 | 45.1 |
-| NSP-BERT | 86.9 | 47.6 | 51 | 41.6 | 37.4 | 63.4 | 52 | **64.4** | 59.4 | 55.96 |
-| ZeroPrompt | - | - | - | 16.14 | 46.16 | - | - | - | 47.98 | - |
-| Yuan1.0-13B | 88.13 | 38.99 | 57.47 | 38.82 | 48.13 | 59.38 | 86.14 | 50 | 38.99 | 56.22 |
-| ERNIE3.0-240B | 88.75 | **50.97** | **57.83** | **40.42** | 53.57 | 64.38 | 87.13 | 56.25 | 53.46 | 61.41 |
-| [UniMC-RoBERTa-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese) | 86.16 | 31.26 | 46.61 | 26.54 | 66.91 | 73.34 | 66.68 | 50.09 | 53.66 | 55.7 |
-| [UniMC-RoBERTa-330M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese) | 87.5 | 30.4 | 47.6 | 31.5 | 69.9 | 75.9 | 78.17 | 49.5 | 60.55 | 59.01 |
-| [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **88.79** | 42.06 | 55.21 | 33.93 | **75.57** | **79.5** | **89.4** | 50.25 | **66.67** | **64.53** |
-
-
-
-## Dataset
-
-我们已经定义好了 UniMC 所需的数据格式，你只需要将数据转化为下面的数据格式即可：
-
-### 文本分类
-```json
-{
- "texta": "街头偶遇2018款长安CS35，颜值美炸！或售6万起，还买宝骏510？",
- "textb": "",
- "question": "下面新闻属于哪一个类别？",
- "choice": [
- "房产",
- "汽车",
- "教育",
- "军事"
- ],
- "answer": "汽车",
- "label": 1,
- "id": 7759
-}
-
-```
-
-### 情感分析
-```json
-{
- "texta": "就是废物，充电不进害得老子把主板烧了，客服不耐烦",
- "textb": "",
- "question": "",
- "choice": ["这是一条差评", "这是一条好评"],
- "answer": "这是一条差评",
- "label": 0,
- "id": 31
-}
-
-```
-
-### 语义匹配
-```json
-{
- "texta": "不要借了我是试试看能否操作的",
- "textb": "",
- "question": "",
- "choice": ["不能理解为：借款审核期间能否取消借款", "可以理解为：借款审核期间能否取消借款"],
- "answer": "不能理解为：借款审核期间能否取消借款",
- "label": 0,
- "id": 0
-}
-
-```
-
-### 自然语言推理
-```json
-{
- "texta": "身上裹一件工厂发的棉大衣,手插在袖筒里",
- "textb": "",
- "question": "",
- "choice": ["不能推断出：身上至少一件衣服", "很难推断出：身上至少一件衣服", "可以推断出：身上至少一件衣服"],
- "answer": "可以推断出：身上至少一件衣服",
- "label": 2,
- "id": 0
-}
-
-```
-
-
-## Citation
-如果你觉得本仓库帮助到了你，你可以使用下面方式引用我们的工作
-
-```text
-@article{unimc,
- author = {Ping Yang and
- Junjie Wang and
- Ruyi Gan and
- Xinyu Zhu and
- Lin Zhang and
- Ziwei Wu and
- Xinyu Gao and
- Jiaxing Zhang and
- Tetsuya Sakai},
- title = {Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective},
- journal = {CoRR},
- volume = {abs/2210.08590},
- year = {2022}
-}
-```
-
-## License
-
-[Apache License 2.0](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/LICENSE)
-
diff --git a/spaces/fclong/summary/fengshen/models/deltalm/modeling_deltalm.py b/spaces/fclong/summary/fengshen/models/deltalm/modeling_deltalm.py
deleted file mode 100644
index 2cdd65f3e106e9433dd5116419dfd50cd8a33b85..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/models/deltalm/modeling_deltalm.py
+++ /dev/null
@@ -1,1551 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-
-import copy
-import math
-import random
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-from torch.nn import CrossEntropyLoss
-from typing import List, Optional, Tuple, Union
-
-from transformers.modeling_utils import PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (
- BaseModelOutput,
- BaseModelOutputWithPastAndCrossAttentions,
- CausalLMOutputWithCrossAttentions,
- Seq2SeqModelOutput,
- Seq2SeqLMOutput,
-)
-from transformers.file_utils import (
- add_end_docstrings,
- add_start_docstrings,
- add_start_docstrings_to_model_forward,
- replace_return_docstrings,
-)
-
-import logging
-from .configuration_deltalm import DeltalmConfig
-logger = logging.getLogger(__name__)
-
-_CHECKPOINT_FOR_DOC = "IDEA-CCNL/Randeng-Deltalm-362M-En-Zn"
-_CONFIG_FOR_DOC = "DeltalmConfig"
-_TOKENIZER_FOR_DOC = "DeltalmTokenizer"
-
-# Base model docstring
-_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]
-
-
-def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
- """
- Shift input ids one token to the right.
- """
- shifted_input_ids = input_ids.new_zeros(input_ids.shape)
- shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
- shifted_input_ids[:, 0] = decoder_start_token_id
-
- if pad_token_id is None:
- raise ValueError("self.model.config.pad_token_id has to be defined.")
- # replace possible -100 values in labels by `pad_token_id`
- shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
- return shifted_input_ids
-
-
-def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
- """
- Make causal mask used for bi-directional self-attention.
- """
- bsz, tgt_len = input_ids_shape
- mask = torch.full((tgt_len, tgt_len), torch.tensor(float("-inf")))
- mask_cond = torch.arange(mask.size(-1))
- mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
- mask = mask.to(dtype)
-
- if past_key_values_length > 0:
- mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
- return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
- """
- Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
- """
- bsz, src_len = mask.size()
- tgt_len = tgt_len if tgt_len is not None else src_len
-
- expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
- inverted_mask = 1.0 - expanded_mask
-
- return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-class DeltalmLearnedPositionalEmbedding(nn.Embedding):
- """
- This module learns positional embeddings up to a fixed maximum size.
- """
-
- def __init__(self, num_embeddings: int, embedding_dim: int):
- # Deltalm is set up so that if padding_idx is specified then offset the embedding ids by 2
- # and adjust num_embeddings appropriately. Other models don't have this hack
- self.offset = 2
- super().__init__(num_embeddings + self.offset, embedding_dim)
-
- def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
- """`input_ids_shape` is expected to be [bsz x seqlen]."""
- bsz, seq_len = input_ids_shape[:2]
- positions = torch.arange(
- past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
- )
- return super().forward(positions + self.offset)
-
-
-class DeltalmAttention(nn.Module):
- """Multi-headed attention from 'Attention Is All You Need' paper"""
-
- def __init__(
- self,
- embed_dim: int,
- num_heads: int,
- dropout: float = 0.0,
- is_decoder: bool = False,
- bias: bool = True,
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.num_heads = num_heads
- self.dropout = dropout
- self.head_dim = embed_dim // num_heads
-
- if (self.head_dim * num_heads) != self.embed_dim:
- raise ValueError(
- f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
- f" and `num_heads`: {num_heads})."
- )
- self.scaling = self.head_dim**-0.5
- self.is_decoder = is_decoder
-
- self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
- self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
- self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
- self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
- def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
- return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- key_value_states: Optional[torch.Tensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- attention_mask: Optional[torch.Tensor] = None,
- layer_head_mask: Optional[torch.Tensor] = None,
- output_attentions: bool = False,
- ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
- """Input shape: Batch x Time x Channel"""
-
- # if key_value_states are provided this layer is used as a cross-attention layer
- # for the decoder
- is_cross_attention = key_value_states is not None
-
- bsz, tgt_len, _ = hidden_states.size()
-
- # get query proj
- query_states = self.q_proj(hidden_states) * self.scaling
- # get key, value proj
- if is_cross_attention and past_key_value is not None:
- # reuse k,v, cross_attentions
- key_states = past_key_value[0]
- value_states = past_key_value[1]
- elif is_cross_attention:
- # cross_attentions
- key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
- value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
- elif past_key_value is not None:
- # reuse k, v, self_attention
- key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
- value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
- key_states = torch.cat([past_key_value[0], key_states], dim=2)
- value_states = torch.cat([past_key_value[1], value_states], dim=2)
- else:
- # self_attention
- key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
- value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
- if self.is_decoder:
- # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
- # Further calls to cross_attention layer can then reuse all cross-attention
- # key/value_states (first "if" case)
- # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
- # all previous decoder key/value_states. Further calls to uni-directional self-attention
- # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
- # if encoder bi-directional self-attention `past_key_value` is always `None`
- past_key_value = (key_states, value_states)
-
- proj_shape = (bsz * self.num_heads, -1, self.head_dim)
- query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
- key_states = key_states.view(*proj_shape)
- value_states = value_states.view(*proj_shape)
-
- src_len = key_states.size(1)
- attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
- if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
- raise ValueError(
- f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
- f" {attn_weights.size()}"
- )
-
- if attention_mask is not None:
- if attention_mask.size() != (bsz, 1, tgt_len, src_len):
- raise ValueError(
- f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
- )
- attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
- attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
- attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
- if layer_head_mask is not None:
- if layer_head_mask.size() != (self.num_heads,):
- raise ValueError(
- f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
- f" {layer_head_mask.size()}"
- )
- attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
- attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
- if output_attentions:
- # this operation is a bit awkward, but it's required to
- # make sure that attn_weights keeps its gradient.
- # In order to do so, attn_weights have to be reshaped
- # twice and have to be reused in the following
- attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
- attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
- else:
- attn_weights_reshaped = None
-
- attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
- attn_output = torch.bmm(attn_probs, value_states)
-
- if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
- raise ValueError(
- f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
- f" {attn_output.size()}"
- )
-
- attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
- attn_output = attn_output.transpose(1, 2)
-
- # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
- # partitioned aross GPUs when using tensor-parallelism.
- attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
- attn_output = self.out_proj(attn_output)
-
- return attn_output, attn_weights_reshaped, past_key_value
-
-
-class DeltalmEncoderLayer(nn.Module):
- def __init__(self, config: DeltalmConfig):
- super().__init__()
- self.embed_dim = config.d_model
- self.self_attn = DeltalmAttention(
- embed_dim=self.embed_dim,
- num_heads=config.encoder_attention_heads,
- dropout=config.attention_dropout,
- )
- self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
- self.dropout = config.dropout
- self.activation_fn = ACT2FN[config.activation_function]
- self.activation_dropout = config.activation_dropout
- self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
- self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
- self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
- def forward(
- self,
- hidden_states: torch.FloatTensor,
- attention_mask: torch.FloatTensor,
- layer_head_mask: torch.FloatTensor,
- output_attentions: Optional[bool] = False,
- ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
- """
- Args:
- hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
- attention_mask (`torch.FloatTensor`): attention mask of size
- `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
- layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
- `(encoder_attention_heads,)`.
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- """
- residual = hidden_states
- hidden_states, attn_weights, _ = self.self_attn(
- hidden_states=hidden_states,
- attention_mask=attention_mask,
- layer_head_mask=layer_head_mask,
- output_attentions=output_attentions,
- )
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
- hidden_states = residual + hidden_states
- hidden_states = self.self_attn_layer_norm(hidden_states)
-
- residual = hidden_states
- hidden_states = self.activation_fn(self.fc1(hidden_states))
- hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
- hidden_states = self.fc2(hidden_states)
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
- hidden_states = residual + hidden_states
- hidden_states = self.final_layer_norm(hidden_states)
-
- if hidden_states.dtype == torch.float16 and (
- torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
- ):
- clamp_value = torch.finfo(hidden_states.dtype).max - 1000
- hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
- outputs = (hidden_states,)
-
- if output_attentions:
- outputs += (attn_weights,)
-
- return outputs
-
-
-class DeltalmDecoderLayer(nn.Module):
- def __init__(self, config: DeltalmConfig):
- super().__init__()
- self.embed_dim = config.d_model
-
- self.self_attn = DeltalmAttention(
- embed_dim=self.embed_dim,
- num_heads=config.decoder_attention_heads,
- dropout=config.attention_dropout,
- is_decoder=True,
- )
- self.dropout = config.dropout
- self.activation_fn = ACT2FN[config.activation_function]
- self.activation_dropout = config.activation_dropout
-
- self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
- self.encoder_attn = DeltalmAttention(
- self.embed_dim,
- config.decoder_attention_heads,
- dropout=config.attention_dropout,
- is_decoder=True,
- )
- self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
- self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
- self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
- self.fc3 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
- self.fc4 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-
- self.ffn_layer_norm = nn.LayerNorm(self.embed_dim)
- self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- encoder_hidden_states: Optional[torch.Tensor] = None,
- encoder_attention_mask: Optional[torch.Tensor] = None,
- layer_head_mask: Optional[torch.Tensor] = None,
- cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- output_attentions: Optional[bool] = False,
- use_cache: Optional[bool] = True,
- ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
- """
- Args:
- hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
- attention_mask (`torch.FloatTensor`): attention mask of size
- `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
- encoder_hidden_states (`torch.FloatTensor`):
- cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
- encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
- `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
- layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
- `(encoder_attention_heads,)`.
- cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
- size `(decoder_attention_heads,)`.
- past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- """
- residual = hidden_states
-
- # Self Attention
- # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
- self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
- # add present self-attn cache to positions 1,2 of present_key_value tuple
- hidden_states, self_attn_weights, present_key_value = self.self_attn(
- hidden_states=hidden_states,
- past_key_value=self_attn_past_key_value,
- attention_mask=attention_mask,
- layer_head_mask=layer_head_mask,
- output_attentions=output_attentions,
- )
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
- hidden_states = residual + hidden_states
- hidden_states = self.self_attn_layer_norm(hidden_states)
-
- # Add another ffn after self-attention to keep the structure same to encoder-layer
- residual = hidden_states
- hidden_states = self.activation_fn(self.fc3(hidden_states))
- hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
- hidden_states = self.fc4(hidden_states)
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
- hidden_states = residual + hidden_states
- hidden_states = self.ffn_layer_norm(hidden_states)
-
- # Cross-Attention Block
- cross_attn_present_key_value = None
- cross_attn_weights = None
- if encoder_hidden_states is not None:
- residual = hidden_states
-
- # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
- cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
- hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
- hidden_states=hidden_states,
- key_value_states=encoder_hidden_states,
- attention_mask=encoder_attention_mask,
- layer_head_mask=cross_attn_layer_head_mask,
- past_key_value=cross_attn_past_key_value,
- output_attentions=output_attentions,
- )
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
- hidden_states = residual + hidden_states
- hidden_states = self.encoder_attn_layer_norm(hidden_states)
-
- # add cross-attn to positions 3,4 of present_key_value tuple
- present_key_value = present_key_value + cross_attn_present_key_value
-
- # Fully Connected
- residual = hidden_states
- hidden_states = self.activation_fn(self.fc1(hidden_states))
- hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
- hidden_states = self.fc2(hidden_states)
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
- hidden_states = residual + hidden_states
- hidden_states = self.final_layer_norm(hidden_states)
-
- outputs = (hidden_states,)
-
- if output_attentions:
- outputs += (self_attn_weights, cross_attn_weights)
-
- if use_cache:
- outputs += (present_key_value,)
-
- return outputs
-
-
-class DeltalmPretrainedModel(PreTrainedModel):
- config_class = DeltalmConfig
- base_model_prefix = "model"
- supports_gradient_checkpointing = True
-
- def _init_weights(self, module):
- std = self.config.init_std
- if isinstance(module, nn.Linear):
- module.weight.data.normal_(mean=0.0, std=std)
- if module.bias is not None:
- module.bias.data.zero_()
- elif isinstance(module, nn.Embedding):
- module.weight.data.normal_(mean=0.0, std=std)
- if module.padding_idx is not None:
- module.weight.data[module.padding_idx].zero_()
-
- def _set_gradient_checkpointing(self, module, value=False):
- if isinstance(module, (DeltalmDecoder, DeltalmEncoder)):
- module.gradient_checkpointing = value
-
-
-class DeltalmDecoder(DeltalmPretrainedModel):
- """
- Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DeltalmDecoderLayer`]
- Args:
- config: DeltalmConfig
- embed_tokens (nn.Embedding): output embedding
- """
-
- def __init__(self, config: DeltalmConfig, embed_tokens: Optional[nn.Embedding] = None):
- super().__init__(config)
- self.dropout = config.dropout
- self.layerdrop = config.decoder_layerdrop
- self.padding_idx = config.pad_token_id
- self.max_target_positions = config.max_position_embeddings
- self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
-
- if embed_tokens is not None:
- self.embed_tokens = embed_tokens
- else:
- self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
-
- self.embed_positions = DeltalmLearnedPositionalEmbedding(
- config.max_position_embeddings,
- config.d_model,
- )
- self.layers = nn.ModuleList([DeltalmDecoderLayer(config) for _ in range(config.decoder_layers)])
- self.layernorm_embedding = nn.LayerNorm(config.d_model)
-
- self.gradient_checkpointing = False
- # Initialize weights and apply final processing
- self.post_init()
-
- # fairseq实现了一个 nn.init.normal_(self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5) 对最后的output权重做正态分布转换？
-
- def get_input_embeddings(self):
- return self.embed_tokens
-
- def set_input_embeddings(self, value):
- self.embed_tokens = value
-
- def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
- # create causal mask
- # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- combined_attention_mask = None
- if input_shape[-1] > 1:
- combined_attention_mask = _make_causal_mask(
- input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
- ).to(inputs_embeds.device)
-
- if attention_mask is not None:
- # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
- combined_attention_mask = (
- expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
- )
-
- return combined_attention_mask
-
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- encoder_hidden_states: Optional[torch.FloatTensor] = None,
- encoder_attention_mask: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- cross_attn_head_mask: Optional[torch.Tensor] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
- r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
- provide it.
- Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- [What are attention masks?](../glossary#attention-mask)
- encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
- Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
- of the decoder.
- encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
- Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
- selected in `[0, 1]`:
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- [What are attention masks?](../glossary#attention-mask)
- head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
- cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
- shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
- shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
- Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
- cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
- that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
- all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
- shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
- `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
- control over how to convert `input_ids` indices into associated vectors than the model's internal
- embedding lookup matrix.
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
- for more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
- """
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- use_cache = use_cache if use_cache is not None else self.config.use_cache
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # retrieve input_ids and inputs_embeds
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
- elif input_ids is not None:
- input_shape = input_ids.size()
- input_ids = input_ids.view(-1, input_shape[-1])
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
- # past_key_values_length
- past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
- if inputs_embeds is None:
- inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-
- attention_mask = self._prepare_decoder_attention_mask(
- attention_mask, input_shape, inputs_embeds, past_key_values_length
- )
-
- # expand encoder attention mask
- if encoder_hidden_states is not None and encoder_attention_mask is not None:
- # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
-
- # embed positions
- positions = self.embed_positions(input_shape, past_key_values_length)
-
- hidden_states = inputs_embeds + positions
- hidden_states = self.layernorm_embedding(hidden_states)
-
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
- # decoder layers
- all_hidden_states = () if output_hidden_states else None
- all_self_attns = () if output_attentions else None
- all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
- next_decoder_cache = () if use_cache else None
-
- # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
- for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
- if attn_mask is not None:
- if attn_mask.size()[0] != (len(self.layers)):
- raise ValueError(
- f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
- f" {head_mask.size()[0]}."
- )
-
- for idx, decoder_layer in enumerate(self.layers):
- # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
- if output_hidden_states:
- all_hidden_states += (hidden_states,)
- dropout_probability = random.uniform(0, 1)
- if self.training and (dropout_probability < self.layerdrop):
- continue
-
- past_key_value = past_key_values[idx] if past_key_values is not None else None
-
- if self.gradient_checkpointing and self.training:
-
- if use_cache:
- logger.warning(
- "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
- )
- use_cache = False
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- # None for past_key_value
- return module(*inputs, output_attentions, use_cache)
-
- return custom_forward
-
- layer_outputs = torch.utils.checkpoint.checkpoint(
- create_custom_forward(decoder_layer),
- hidden_states,
- attention_mask,
- encoder_hidden_states,
- encoder_attention_mask,
- head_mask[idx] if head_mask is not None else None,
- cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
- None,
- )
- else:
-
- layer_outputs = decoder_layer(
- hidden_states,
- attention_mask=attention_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- layer_head_mask=(head_mask[idx] if head_mask is not None else None),
- cross_attn_layer_head_mask=(
- cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
- ),
- past_key_value=past_key_value,
- output_attentions=output_attentions,
- use_cache=use_cache,
- )
- hidden_states = layer_outputs[0]
-
- if use_cache:
- next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
-
- if output_attentions:
- all_self_attns += (layer_outputs[1],)
-
- if encoder_hidden_states is not None:
- all_cross_attentions += (layer_outputs[2],)
-
- # add hidden states from the last decoder layer
- if output_hidden_states:
- all_hidden_states += (hidden_states,)
-
- next_cache = next_decoder_cache if use_cache else None
- if not return_dict:
- return tuple(
- v
- for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
- if v is not None
- )
- return BaseModelOutputWithPastAndCrossAttentions(
- last_hidden_state=hidden_states,
- past_key_values=next_cache,
- hidden_states=all_hidden_states,
- attentions=all_self_attns,
- cross_attentions=all_cross_attentions,
- )
-
-
-DELTALM_START_DOCSTRING = r"""
- This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
- library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
- etc.)
- This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
- Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
- and behavior.
- Parameters:
- config ([`DeltalmConfig`]):
- Model configuration class with all the parameters of the model. Initializing with a config file does not
- load the weights associated with the model, only the configuration. Check out the
- [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-DELTALM_GENERATION_EXAMPLE = r"""
- Summarization example:
- ```python
- >>> from transformers import DeltalmTokenizer, DeltalmForConditionalGeneration
- >>> model = DeltalmForConditionalGeneration.from_pretrained("facebook/deltalm-large-cnn")
- >>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-large-cnn")
- >>> ARTICLE_TO_SUMMARIZE = (
- ... "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
- ... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
- ... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
- ... )
- >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="pt")
- >>> # Generate Summary
- >>> summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
- >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
- 'PG&E scheduled the blackouts in response to forecasts for high winds amid dry conditions'
- ```
- Mask filling example:
- ```python
- >>> from transformers import DeltalmTokenizer, DeltalmForConditionalGeneration
- >>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-base")
- >>> model = DeltalmForConditionalGeneration.from_pretrained("facebook/deltalm-base")
- >>> TXT = "My friends are but they eat too many carbs."
- >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
- >>> logits = model(input_ids).logits
- >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
- >>> probs = logits[0, masked_index].softmax(dim=0)
- >>> values, predictions = probs.topk(5)
- >>> tokenizer.decode(predictions).split()
- ['not', 'good', 'healthy', 'great', 'very']
- ```
-"""
-
-DELTALM_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
- it.
- Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- [What are attention masks?](../glossary#attention-mask)
- decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
- Indices of decoder input sequence tokens in the vocabulary.
- Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
- [What are decoder input IDs?](../glossary#decoder-input-ids)
- Deltalm uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
- is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
- For translation and summarization training, `decoder_input_ids` should be provided. If no
- `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
- for denoising pre-training following the paper.
- decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
- Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
- be used by default.
- If you want to change padding behavior, you should read [`modeling_deltalm._prepare_decoder_attention_mask`]
- and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
- information on the default strategy.
- head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
- 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
- Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
- `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
- hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
- `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
- `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
- Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
- blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
- don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
- `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
- `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
- can choose to directly pass an embedded representation. This is useful if you want more control over how to
- convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
- decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
- Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
- representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
- input (see `past_key_values`). This is useful if you want more control over how to convert
- `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
- If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
- of `inputs_embeds`.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
- `past_key_values`).
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
- tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
- more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class DeltalmEncoder(DeltalmPretrainedModel):
- """
- Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
- [`DeltalmEncoderLayer`].
- Args:
- config: DeltalmConfig
- embed_tokens (nn.Embedding): output embedding
- """
-
- def __init__(self, config: DeltalmConfig, embed_tokens: Optional[nn.Embedding] = None):
- super().__init__(config)
-
- self.dropout = config.dropout
- self.layerdrop = config.encoder_layerdrop
-
- embed_dim = config.d_model
- self.padding_idx = config.pad_token_id
- self.max_source_positions = config.max_position_embeddings
- self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
-
- if embed_tokens is not None:
- self.embed_tokens = embed_tokens
- else:
- self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
-
- self.embed_positions = DeltalmLearnedPositionalEmbedding(
- config.max_position_embeddings,
- embed_dim,
- )
- self.layers = nn.ModuleList([DeltalmEncoderLayer(config) for _ in range(config.encoder_layers)])
- self.layernorm_embedding = nn.LayerNorm(embed_dim)
-
- self.gradient_checkpointing = False
- if config.encoder_normalize_before:
- self.layer_norm = nn.LayerNorm(embed_dim)
- else:
- self.layer_norm = None
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.embed_tokens
-
- def set_input_embeddings(self, value):
- self.embed_tokens = value
-
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, BaseModelOutput]:
- r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
- provide it.
- Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- [What are attention masks?](../glossary#attention-mask)
- head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
- This is useful if you want more control over how to convert `input_ids` indices into associated vectors
- than the model's internal embedding lookup matrix.
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
- for more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
- """
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # retrieve input_ids and inputs_embeds
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
- elif input_ids is not None:
- input_shape = input_ids.size()
- input_ids = input_ids.view(-1, input_shape[-1])
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- raise ValueError("You have to specify either input_ids or inputs_embeds")
-
- if inputs_embeds is None:
- inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-
- embed_pos = self.embed_positions(input_shape)
-
- hidden_states = inputs_embeds + embed_pos
- hidden_states = self.layernorm_embedding(hidden_states)
- hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
- # expand attention_mask
- if attention_mask is not None:
- # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
-
- encoder_states = () if output_hidden_states else None
- all_attentions = () if output_attentions else None
-
- # check if head_mask has a correct number of layers specified if desired
- if head_mask is not None:
- if head_mask.size()[0] != (len(self.layers)):
- raise ValueError(
- f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
- f" {head_mask.size()[0]}."
- )
-
- for idx, encoder_layer in enumerate(self.layers):
- if output_hidden_states:
- encoder_states = encoder_states + (hidden_states,)
- # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
- dropout_probability = random.uniform(0, 1)
- if self.training and (dropout_probability < self.layerdrop): # skip the layer
- layer_outputs = (None, None)
- else:
- if self.gradient_checkpointing and self.training:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return module(*inputs, output_attentions)
-
- return custom_forward
-
- layer_outputs = torch.utils.checkpoint.checkpoint(
- create_custom_forward(encoder_layer),
- hidden_states,
- attention_mask,
- (head_mask[idx] if head_mask is not None else None),
- )
- else:
- layer_outputs = encoder_layer(
- hidden_states,
- attention_mask,
- layer_head_mask=(head_mask[idx] if head_mask is not None else None),
- output_attentions=output_attentions,
- )
-
- hidden_states = layer_outputs[0]
-
- if output_attentions:
- all_attentions = all_attentions + (layer_outputs[1],)
-
- if self.layer_norm is not None:
- hidden_states = self.layer_norm(hidden_states)
- # hidden_states = self.layernorm_embedding(hidden_states)
-
- if output_hidden_states:
- encoder_states = encoder_states + (hidden_states,)
-
- if not return_dict:
- return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
- return BaseModelOutput(
- last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
- )
-
-
-class DeltalmModel(DeltalmPretrainedModel):
- def __init__(self, config: DeltalmConfig):
- super().__init__(config)
-
- padding_idx, vocab_size = config.pad_token_id, config.vocab_size
- self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
-
- self.encoder = DeltalmEncoder(config, self.shared)
- self.decoder = DeltalmDecoder(config, self.shared)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.shared
-
- def set_input_embeddings(self, value):
- self.shared = value
- self.encoder.embed_tokens = self.shared
- self.decoder.embed_tokens = self.shared
-
- def get_encoder(self):
- return self.encoder
-
- def get_decoder(self):
- return self.decoder
-
- @add_start_docstrings_to_model_forward(DELTALM_INPUTS_DOCSTRING)
- # @add_code_sample_docstrings(
- # processor_class=_TOKENIZER_FOR_DOC,
- # checkpoint=_CHECKPOINT_FOR_DOC,
- # output_type=Seq2SeqModelOutput,
- # config_class=_CONFIG_FOR_DOC,
- # expected_output=_EXPECTED_OUTPUT_SHAPE,
- # )
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- decoder_input_ids: Optional[torch.LongTensor] = None,
- decoder_attention_mask: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- decoder_head_mask: Optional[torch.Tensor] = None,
- cross_attn_head_mask: Optional[torch.Tensor] = None,
- encoder_outputs: Optional[List[torch.FloatTensor]] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, Seq2SeqModelOutput]:
-
- # different to other models, Deltalm automatically creates decoder_input_ids from
- # input_ids if no decoder_input_ids are provided
- if decoder_input_ids is None and decoder_inputs_embeds is None:
- if input_ids is None:
- raise ValueError(
- "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
- "passed, `input_ids` cannot be `None`. Please pass either "
- "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
- )
-
- decoder_input_ids = shift_tokens_right(
- input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
- )
-
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- use_cache = use_cache if use_cache is not None else self.config.use_cache
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- if encoder_outputs is None:
- encoder_outputs = self.encoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
- # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
- elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
- encoder_outputs = BaseModelOutput(
- last_hidden_state=encoder_outputs[0],
- hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
- attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
- )
-
- # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
- decoder_outputs = self.decoder(
- input_ids=decoder_input_ids,
- attention_mask=decoder_attention_mask,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=attention_mask,
- head_mask=decoder_head_mask,
- cross_attn_head_mask=cross_attn_head_mask,
- past_key_values=past_key_values,
- inputs_embeds=decoder_inputs_embeds,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- if not return_dict:
- return decoder_outputs + encoder_outputs
-
- logger.debug("last_hidden_state.size: %s", decoder_outputs.last_hidden_state)
- return Seq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- past_key_values=decoder_outputs.past_key_values,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-@add_start_docstrings(
- "The DELTALM Model with a language modeling head. Can be used for translation.", DELTALM_START_DOCSTRING
-)
-class DeltalmForConditionalGeneration(DeltalmPretrainedModel):
- base_model_prefix = "model"
- _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head.weight"]
-
- def __init__(self, config: DeltalmConfig):
- super().__init__(config)
- self.model = DeltalmModel(config)
- self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
- self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_encoder(self):
- return self.model.get_encoder()
-
- def get_decoder(self):
- return self.model.get_decoder()
-
- def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
- new_embeddings = super().resize_token_embeddings(new_num_tokens)
- self._resize_final_logits_bias(new_num_tokens)
- return new_embeddings
-
- def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
- logger.debug("Debug: coming to _resize_final_logits_bias")
- old_num_tokens = self.final_logits_bias.shape[-1]
- if new_num_tokens <= old_num_tokens:
- new_bias = self.final_logits_bias[:, :new_num_tokens]
- else:
- extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
- new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
- self.register_buffer("final_logits_bias", new_bias)
-
- def get_output_embeddings(self):
- return self.lm_head
-
- def set_output_embeddings(self, new_embeddings):
- self.lm_head = new_embeddings
-
- @add_start_docstrings_to_model_forward(DELTALM_INPUTS_DOCSTRING)
- @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
- @add_end_docstrings(DELTALM_GENERATION_EXAMPLE)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- decoder_input_ids: Optional[torch.LongTensor] = None,
- decoder_attention_mask: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- decoder_head_mask: Optional[torch.Tensor] = None,
- cross_attn_head_mask: Optional[torch.Tensor] = None,
- encoder_outputs: Optional[List[torch.FloatTensor]] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, Seq2SeqLMOutput]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
- config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
- (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- Returns:
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- logger.debug("Comming to Generation!")
-
- if labels is not None:
- logger.debug("Debug: *************** Before label ***************** ")
- logger.debug("Debug: %s", labels.size())
- if use_cache:
- logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
- use_cache = False
- if decoder_input_ids is None and decoder_inputs_embeds is None:
- decoder_input_ids = shift_tokens_right(
- labels, self.config.pad_token_id, self.config.decoder_start_token_id
- )
-
- logger.debug("Debug: ************ After labels ************")
- logger.debug("Debug: %s", labels.size())
-
- outputs = self.model(
- input_ids,
- attention_mask=attention_mask,
- decoder_input_ids=decoder_input_ids,
- encoder_outputs=encoder_outputs,
- decoder_attention_mask=decoder_attention_mask,
- head_mask=head_mask,
- decoder_head_mask=decoder_head_mask,
- cross_attn_head_mask=cross_attn_head_mask,
- past_key_values=past_key_values,
- inputs_embeds=inputs_embeds,
- decoder_inputs_embeds=decoder_inputs_embeds,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
- lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
- # print(self.lm_head)
- logger.debug("Debug: logit_size: %s", lm_logits.size())
-
- # logger.debug("Debug: change logit size: ", lm_logits.view(-1, self.config.vocab_size).size())
- # logger.debug("Debug: change label size: ", labels.view(-1).size())
- masked_lm_loss = None
-
- if labels is not None:
- # logger.debug("Debug: model label_size: %s", labels.size())
- # loss_fct = CrossEntropyLoss()
- # masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
- loss_fct = CrossEntropyLoss()
- masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
- # label_smoothing = self.config.label_smoothing
- # # logger.debug("Debug: label.size: ", )
- # if label_smoothing == 0:
- # # compute label smoothed loss
- # loss_fct = CrossEntropyLoss()
- # masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
- # else:
- # m = torch.nn.LogSoftmax(dim=-1)
- # lprobs = m(lm_logits.float())
- # # lprobs = m(lm_logits)
- # # # torch.set_printoptions(linewidth=200)
- # loss_fn = label_smoothed_nll_loss
- # masked_lm_loss, _ = loss_fn(lprobs.view(-1, lprobs.size(-1)), labels.view(-1), label_smoothing, self.config.pad_token_id)
-
- if not return_dict:
- logger.debug("Debug: not return dict")
- output = (lm_logits,) + outputs[1:]
- return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
- return Seq2SeqLMOutput(
- loss=masked_lm_loss,
- logits=lm_logits,
- past_key_values=outputs.past_key_values,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
- def prepare_inputs_for_generation(
- self,
- decoder_input_ids,
- past=None,
- attention_mask=None,
- head_mask=None,
- decoder_head_mask=None,
- cross_attn_head_mask=None,
- use_cache=None,
- encoder_outputs=None,
- **kwargs
- ):
- # cut decoder_input_ids if past is used
- if past is not None:
- decoder_input_ids = decoder_input_ids[:, -1:]
-
- return {
- "input_ids": None, # encoder_outputs is defined. input_ids not needed
- "encoder_outputs": encoder_outputs,
- "past_key_values": past,
- "decoder_input_ids": decoder_input_ids,
- "attention_mask": attention_mask,
- "head_mask": head_mask,
- "decoder_head_mask": decoder_head_mask,
- "cross_attn_head_mask": cross_attn_head_mask,
- "use_cache": use_cache, # change this to avoid caching (presumably for debugging)
- }
-
- def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
- return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
-
- @staticmethod
- def _reorder_cache(past, beam_idx):
- reordered_past = ()
- for layer_past in past:
- # cached cross_attention states don't have to be reordered -> they are always the same
- reordered_past += (
- tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
- )
- return reordered_past
-
-
-class DeltalmDecoderWrapper(DeltalmPretrainedModel):
- """
- This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
- used in combination with the [`EncoderDecoderModel`] framework.
- """
-
- def __init__(self, config):
- super().__init__(config)
- self.decoder = DeltalmDecoder(config)
-
- def forward(self, *args, **kwargs):
- return self.decoder(*args, **kwargs)
-
-
-class DeltalmForCausalLM(DeltalmPretrainedModel):
- def __init__(self, config):
- config = copy.deepcopy(config)
- config.is_decoder = True
- config.is_encoder_decoder = False
- super().__init__(config)
- self.model = DeltalmDecoderWrapper(config)
-
- self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.model.decoder.embed_tokens
-
- def set_input_embeddings(self, value):
- self.model.decoder.embed_tokens = value
-
- def get_output_embeddings(self):
- return self.lm_head
-
- def set_output_embeddings(self, new_embeddings):
- self.lm_head = new_embeddings
-
- def set_decoder(self, decoder):
- self.model.decoder = decoder
-
- def get_decoder(self):
- return self.model.decoder
-
- @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- encoder_hidden_states: Optional[torch.FloatTensor] = None,
- encoder_attention_mask: Optional[torch.FloatTensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- cross_attn_head_mask: Optional[torch.Tensor] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
- r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
- provide it.
- Indices can be obtained using [`DeltalmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- [What are attention masks?](../glossary#attention-mask)
- encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
- Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
- if the model is configured as a decoder.
- encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
- in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
- Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
- shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
- shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
- tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
- Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
- cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
- that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
- all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
- config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
- (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
- (see `past_key_values`).
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
- for more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
- Returns:
- Example:
- ```python
- >>> from transformers import DeltalmTokenizer, DeltalmForCausalLM
- >>> tokenizer = DeltalmTokenizer.from_pretrained("facebook/deltalm-base")
- >>> model = DeltalmForCausalLM.from_pretrained("facebook/deltalm-base", add_cross_attention=False)
- >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
- >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
- >>> outputs = model(**inputs)
- >>> logits = outputs.logits
- >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
- >>> list(logits.shape) == expected_shape
- True
- ```"""
-
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
- outputs = self.model.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- head_mask=head_mask,
- cross_attn_head_mask=cross_attn_head_mask,
- past_key_values=past_key_values,
- inputs_embeds=inputs_embeds,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- logits = self.lm_head(outputs[0])
-
- loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
-
- if not return_dict:
- output = (logits,) + outputs[1:]
- return (loss,) + output if loss is not None else output
-
- return CausalLMOutputWithCrossAttentions(
- loss=loss,
- logits=logits,
- past_key_values=outputs.past_key_values,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
-
- def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=None, **kwargs):
- # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
- if attention_mask is None:
- attention_mask = input_ids.new_ones(input_ids.shape)
-
- if past:
- input_ids = input_ids[:, -1:]
- # first step, decoder_cached_states are empty
- return {
- "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
- "attention_mask": attention_mask,
- "past_key_values": past,
- "use_cache": use_cache,
- }
-
- @staticmethod
- def _reorder_cache(past, beam_idx):
- reordered_past = ()
- for layer_past in past:
- reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
- return reordered_past
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/masks/index.ts b/spaces/fengmuxi/ChatGpt-Web/app/masks/index.ts
deleted file mode 100644
index ea0bf32bf4e6dc7958028dcff7f662f75a567ef3..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/masks/index.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import { Mask } from "../store/mask";
-import { CN_MASKS } from "./cn";
-import { EN_MASKS } from "./en";
-
-import { type BuiltinMask } from "./typing";
-export { type BuiltinMask } from "./typing";
-
-export const BUILTIN_MASK_ID = 100000;
-
-export const BUILTIN_MASK_STORE = {
- buildinId: BUILTIN_MASK_ID,
- masks: {} as Record,
- get(id?: number) {
- if (!id) return undefined;
- return this.masks[id] as Mask | undefined;
- },
- add(m: BuiltinMask) {
- const mask = { ...m, id: this.buildinId++ };
- this.masks[mask.id] = mask;
- return mask;
- },
-};
-
-export const BUILTIN_MASKS: Mask[] = [...CN_MASKS, ...EN_MASKS].map((m) =>
- BUILTIN_MASK_STORE.add(m),
-);
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Babu88 App Download APK for Android and iOS - Experience the Thrill of Casino and Sportsbook.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Babu88 App Download APK for Android and iOS - Experience the Thrill of Casino and Sportsbook.md
deleted file mode 100644
index 0f4b36d06cd9f323623aeeaada1fba9b3fcf4e00..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Babu88 App Download APK for Android and iOS - Experience the Thrill of Casino and Sportsbook.md
+++ /dev/null
@@ -1,132 +0,0 @@
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-}
diff --git a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/main.py b/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/main.py
deleted file mode 100644
index 3b563a5d001be7adfbe779dee7ad8ac49aadc50d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/main.py
+++ /dev/null
@@ -1,596 +0,0 @@
-from inspect import getargs
-import logging
-import os
-import random
-from datetime import datetime
-import bisect
-import copy
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-from torch import optim
-from torch.cuda.amp import GradScaler
-import faulthandler
-import pathlib
-
-try:
- import wandb
-except ImportError:
- wandb = None
-
-try:
- import torch.utils.tensorboard as tensorboard
-except ImportError:
- tensorboard = None
-
-try:
- import horovod.torch as hvd
-except ImportError:
- hvd = None
-
-from open_clip import create_model_and_transforms, trace_model, create_model
-from training.data import get_data
-from training.distributed import is_master, init_distributed_device, world_info_from_env
-from training.logger import setup_logging
-from training.params import parse_args
-from training.scheduler import cosine_lr
-from training.train import train_one_epoch, evaluate
-from open_clip.utils import dataset_split, get_optimizer
-
-
-def maintain_ckpts(args, startidx, all_idx_len):
- for i in reversed(range(startidx, all_idx_len)):
- if os.path.exists(os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt")):
- os.rename(
- os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
- os.path.join(args.checkpoint_path, f"epoch_top_{i+1}.pt"),
- )
- if os.path.exists(
- os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt")
- ):
- os.remove(os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt"))
- return
-
-
-def update_top_k_performance(
- new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True
-):
- """
- Record the top-k performance of the current epoch.
- current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...}
- """
- if isinstance(new_metrics_inputs, (list, tuple)):
- new_metrics_inputs = np.mean(new_metrics_inputs)
- return update_top_k_performance(
- new_metrics_inputs,
- current_top_k_ckpt_metrics,
- args=args,
- ckpt=ckpt,
- bignumbetter=bignumbetter,
- )
- elif isinstance(new_metrics_inputs, dict):
- new_metrics_inputs = np.mean(list(new_metrics_inputs.values()))
- return update_top_k_performance(
- new_metrics_inputs,
- current_top_k_ckpt_metrics,
- args=args,
- ckpt=ckpt,
- bignumbetter=bignumbetter,
- )
- elif isinstance(new_metrics_inputs, (float, int)):
- update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()}
- sorted_keys = sorted(current_top_k_ckpt_metrics.keys())
- sorted_values = sorted(
- current_top_k_ckpt_metrics.values(), reverse=bignumbetter
- )
- sorted_values_ = copy.deepcopy(sorted_values)
- sorted_values.append(new_metrics_inputs)
- sorted_values = sorted(sorted_values, reverse=bignumbetter)
- sorted_values = sorted_values[:-1]
-
- if sorted_values == sorted_values_:
- return current_top_k_ckpt_metrics, new_metrics_inputs
- else:
- for i in range(len(sorted_keys)):
- if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]:
- current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i]
- update_flag[sorted_keys[i]] = True
- for i in range(len(update_flag)):
- if update_flag[i]:
- maintain_ckpts(args, i, len(sorted_keys))
- torch.save(
- ckpt,
- os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
- )
- break
- return current_top_k_ckpt_metrics, new_metrics_inputs
-
-
-# def updateifNone(a, b):
-# a = b if None else a
-# return a
-
-
-def is_pretrained_params(n):
- return (
- n.startswith("transformer")
- or n in ["positional_embedding", "text_projection"]
- or n.startswith("token_embedding")
- or n.startswith("ln_final")
- or n.startswith("logit_scale_t")
- )
-
-
-def random_seed(seed=42, rank=0):
- torch.manual_seed(seed + rank)
- np.random.seed(seed + rank)
- random.seed(seed + rank)
-
-
-def main():
- args = parse_args()
- # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
- args.amodel = args.amodel.replace("/", "-")
- # download sizes.json file
-
- # (yusong): the below two lines are for debug
- # print("setting up faulthandler")
- # faulthandler.register(10)
-
- random.seed(args.seed)
- torch.manual_seed(args.seed)
- torch.cuda.manual_seed(args.seed)
- torch.cuda.manual_seed_all(args.seed)
- np.random.seed(args.seed)
- if args.tmodel == "bert" or args.tmodel == "roberta" or args.tmodel == "bart":
- assert (
- args.pretrained == "" or args.pretrained is None
- ), "bert/roberta/bart text encoder does not support pretrained models."
-
- # get the name of the experiments
- if args.name is None:
- args.name = "-".join(
- [
- datetime.now().strftime("%Y_%m_%d-%H_%M_%S"),
- f"model_{args.amodel}",
- f"lr_{args.lr}",
- f"b_{args.batch_size}",
- f"j_{args.workers}",
- f"p_{args.precision}",
- ]
- )
-
- # discover initial world args early so we can log properly
- args.distributed = False
- args.local_rank, args.rank, args.world_size = world_info_from_env()
-
- if args.remotedata and is_master(args):
- for dataset_name in args.datasetnames:
- for split in dataset_split[dataset_name]:
- if not os.path.exists(f"./json_files/{dataset_name}/{split}"):
- os.makedirs(f"./json_files/{dataset_name}/{split}")
- os.system(
- f"aws s3 cp s3://s-laion-audio/webdataset_tar/{dataset_name}/{split}/sizes.json ./json_files/{dataset_name}/{split}/sizes.json"
- )
-
- args.log_path = None
- if is_master(args, local=args.log_local):
- log_base_path = os.path.join(args.logs, args.name)
- os.makedirs(log_base_path, exist_ok=True)
- log_filename = f"out-{args.rank}" if args.log_local else "out.log"
- args.log_path = os.path.join(log_base_path, log_filename)
- if os.path.exists(args.log_path):
- print(
- "Error. Experiment already exists. Use --name {} to specify a new experiment."
- )
- return -1
-
- # Set logger
- args.log_level = logging.DEBUG if args.debug else logging.INFO
- setup_logging(args.log_path, args.log_level)
-
- # fully initialize distributed device environment
- device = init_distributed_device(args)
-
- args.wandb = "wandb" in args.report_to or "all" in args.report_to
- args.tensorboard = "tensorboard" in args.report_to or "all" in args.report_to
- if is_master(args):
- args.tensorboard_path = (
- os.path.join(args.logs, args.name, "tensorboard")
- if args.tensorboard
- else ""
- )
- args.checkpoint_path = os.path.join(args.logs, args.name, "checkpoints")
- for dirname in [args.tensorboard_path, args.checkpoint_path]:
- if dirname:
- os.makedirs(dirname, exist_ok=True)
- else:
- args.tensorboard_path = ""
- args.checkpoint_path = ""
-
- if args.copy_codebase:
- copy_codebase(args)
-
- assert args.precision in ["amp", "fp16", "fp32"]
- if args.precision == "fp16":
- logging.warning(
- "It is recommended to use AMP mixed-precision instead of FP16. "
- "FP16 support needs further verification and tuning, especially for train."
- )
-
- if args.horovod:
- logging.info(
- f"Running in horovod mode with multiple processes / nodes. Device: {args.device}."
- f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
- )
- elif args.distributed:
- logging.info(
- f"Running in distributed mode with multiple processes. Device: {args.device}."
- f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
- )
- else:
- logging.info(f"Running with a single process. Device {args.device}.")
-
- logging.info(f"openai cache dir: {os.path.expanduser(args.openai_model_cache_dir)}")
-
- model, model_cfg = create_model(
- args.amodel,
- args.tmodel,
- args.pretrained,
- precision=args.precision,
- device=device,
- jit=args.torchscript,
- force_quick_gelu=args.force_quick_gelu,
- openai_model_cache_dir=os.path.expanduser(args.openai_model_cache_dir),
- skip_params=True,
- pretrained_audio=args.pretrained_audio,
- pretrained_text=args.pretrained_text,
- enable_fusion=args.enable_fusion,
- fusion_type=args.fusion_type,
- )
-
- if args.horovod:
- with torch.no_grad():
- for param in model.parameters():
- param.set_(param.contiguous())
-
- if args.trace:
- model = trace_model(model, batch_size=args.batch_size, device=device)
-
- if is_master(args):
- logging.info("Model:")
- logging.info(f"{str(model)}")
- logging.info("Params:")
- params_file = os.path.join(args.logs, args.name, "params.txt")
- with open(params_file, "w") as f:
- for name in sorted(vars(args)):
- val = getattr(args, name)
- logging.info(f" {name}: {val}")
- f.write(f"{name}: {val}\n")
-
- if args.distributed and not args.horovod:
- if args.use_bn_sync:
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
- ddp_args = {}
- if args.ddp_static_graph:
- # this doesn't exist in older PyTorch, arg only added if enabled
- ddp_args["static_graph"] = True
- model = torch.nn.parallel.DistributedDataParallel(
- model, device_ids=[device], find_unused_parameters=True, **ddp_args
- )
-
- data = get_data(args, model_cfg)
- assert len(data), "At least one train or eval dataset must be specified."
- if args.trace:
- assert "train" not in data, "Cannot train with traced model"
-
- exclude = (
- lambda n, p: p.ndim < 2
- or "bn" in n
- or "ln" in n
- or "bias" in n
- or "logit_scale" in n
- )
- include = lambda n, p: not exclude(n, p)
-
- named_parameters = list(model.named_parameters())
-
- # freeze text encoder
- text_freeze_parameters = [p for n, p in named_parameters if "text_branch" in n]
-
- if args.freeze_text:
- print("Freeze Text!!!!")
- for k in text_freeze_parameters:
- k.requires_grad = False
-
- gain_or_bias_params = [
- p for n, p in named_parameters if exclude(n, p) and p.requires_grad
- ]
- rest_params = [p for n, p in named_parameters if include(n, p) and p.requires_grad]
-
- # set wd-related params to 0 if use adam optimizer
- if args.optimizer == "adam":
- args.wd = 0
- args.wd_pretrained = 0
- args.wd_new = 0
-
- if args.train_data is None:
- optimizer = None
- scheduler = None
- else:
- total_steps = data["train"].dataloader.num_batches * args.epochs
-
- if args.split_opt:
- for x in ["lr", "beta1", "beta2", "eps", "wd"]:
- for y in ["_new", "_pretrained"]:
- if getattr(args, x + y) is None:
- setattr(args, x + y, getattr(args, x))
-
- gain_or_bias_pretrained_params = [
- p
- for n, p in named_parameters
- if (exclude(n, p) and p.requires_grad) and is_pretrained_params(n)
- ]
- rest_pretrained_params = [
- p
- for n, p in named_parameters
- if (include(n, p) and p.requires_grad) and is_pretrained_params(n)
- ]
- gain_or_bias_new_params = [
- p
- for n, p in named_parameters
- if (exclude(n, p) and p.requires_grad) and (not is_pretrained_params(n))
- ]
- rest_new_params = [
- p
- for n, p in named_parameters
- if (include(n, p) and p.requires_grad) and (not is_pretrained_params(n))
- ]
- pretrained_params_optimizer = get_optimizer(
- [
- {"params": gain_or_bias_pretrained_params, "weight_decay": 0.0},
- {
- "params": rest_pretrained_params,
- "weight_decay": args.wd_pretrained,
- },
- ],
- lr=args.lr_pretrained,
- betas=(args.beta1_pretrained, args.beta2_pretrained),
- eps=args.eps_pretrained,
- momentum=args.momentum_pretrained,
- optimizer_name=args.optimizer,
- )
- pretrained_params_scheduler = cosine_lr(
- pretrained_params_optimizer,
- args.lr_pretrained,
- args.warmup,
- total_steps,
- )
- new_params_optimizer = get_optimizer(
- [
- {"params": gain_or_bias_new_params, "weight_decay": 0.0},
- {"params": rest_new_params, "weight_decay": args.wd_new},
- ],
- lr=args.lr_new,
- betas=(args.beta1_new, args.beta2_new),
- eps=args.eps_new,
- momentum=args.momentum_new,
- optimizer_name=args.optimizer,
- )
-
- new_params_scheduler = cosine_lr(
- new_params_optimizer, args.lr_new, args.warmup, total_steps
- )
-
- optimizer = {
- "pretrained": pretrained_params_optimizer,
- "new": new_params_optimizer,
- }
- scheduler = {
- "pretrained": pretrained_params_scheduler,
- "new": new_params_scheduler,
- }
-
- if args.horovod:
- pretrained_params_optimizer = hvd.DistributedOptimizer(
- pretrained_params_optimizer,
- named_parameters=model.named_parameters(),
- )
- new_params_optimizer = hvd.DistributedOptimizer(
- new_params_optimizer, named_parameters=model.named_parameters()
- )
- hvd.broadcast_parameters(model.state_dict(), root_rank=0)
- hvd.broadcast_optimizer_state(pretrained_params_optimizer, root_rank=0)
- hvd.broadcast_optimizer_state(new_params_optimizer, root_rank=0)
- else:
- optimizer = get_optimizer(
- [
- {"params": gain_or_bias_params, "weight_decay": 0.0},
- {"params": rest_params, "weight_decay": args.wd},
- ],
- lr=args.lr,
- betas=(args.beta1, args.beta2),
- eps=args.eps,
- momentum=args.momentum,
- optimizer_name=args.optimizer,
- )
-
- scheduler = cosine_lr(optimizer, args.lr, args.warmup, total_steps)
-
- if args.horovod:
- optimizer = hvd.DistributedOptimizer(
- optimizer, named_parameters=model.named_parameters()
- )
- hvd.broadcast_parameters(model.state_dict(), root_rank=0)
- hvd.broadcast_optimizer_state(optimizer, root_rank=0)
-
- scaler = GradScaler() if args.precision == "amp" else None
-
- # optionally resume from a checkpoint
- start_epoch = 0
- if args.resume is not None:
- if os.path.isfile(args.resume):
- checkpoint = torch.load(args.resume, map_location=device)
- if "epoch" in checkpoint:
- # resuming a train checkpoint w/ epoch and optimizer state
- start_epoch = checkpoint["epoch"]
- sd = checkpoint["state_dict"]
- if not args.distributed and next(iter(sd.items()))[0].startswith(
- "module"
- ):
- sd = {k[len("module.") :]: v for k, v in sd.items()}
- model.load_state_dict(sd)
- if args.split_opt:
- if optimizer is not None:
- for k, o_ in optimizer.items():
- o_.load_state_dict(checkpoint[k + "_" + "optimizer"])
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint["optimizer"])
- if scaler is not None and "scaler" in checkpoint:
- scaler.load_state_dict(checkpoint["scaler"])
- logging.info(
- f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})"
- )
- else:
- # loading a bare (model only) checkpoint for fine-tune or evaluation
- model.load_state_dict(checkpoint)
- logging.info(
- f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})"
- )
- if args.freeze_text:
- print("Freeze Text!!!!")
- for k in text_freeze_parameters:
- k.requires_grad = False
- else:
- logging.info("=> no checkpoint found at '{}'".format(args.resume))
-
- cudnn.benchmark = True
- cudnn.deterministic = False
-
- # determine if this worker should save logs and checkpoints. only do so if it is rank == 0
- args.save_logs = args.logs and args.logs.lower() != "none" and is_master(args)
- writer = None
- if args.save_logs and args.tensorboard:
- assert tensorboard is not None, "Please install tensorboard."
- writer = tensorboard.SummaryWriter(args.tensorboard_path)
-
- if args.wandb and is_master(args):
- assert wandb is not None, "Please install wandb."
- logging.debug("Starting wandb.")
- args.train_sz = data["train"].dataloader.num_samples
- if args.val_data is not None:
- args.val_sz = data["val"].dataloader.num_samples
- # you will have to configure this for your project!
- wandb.init(
- project="clap",
- notes=args.wandb_notes,
- name=args.wandb_notes,
- tags=[],
- config=vars(args),
- )
- if args.debug:
- wandb.watch(model, log="all")
- wandb.save(params_file)
- logging.debug("Finished loading wandb.")
-
- if "train" not in data:
- evaluate(model, data, start_epoch, args, writer)
- return
- elif start_epoch == 0 and "val" in data and not args.no_eval:
- evaluate(model, data, 0, args, writer)
- # print(f'rank {args.rank}, Start First Evaluation')# (yusong): for debug
- if args.save_top_performance:
- current_top_k_ckpt_metrics = {
- i: 0 for i in range(args.save_top_performance)
- } # initialize the top-k metric for ckpts to 0
-
- # print(f'rank {args.rank}, Start Training') # (yusong): for debug
- for epoch in range(start_epoch, args.epochs):
- # freeze the text param after (include) args.freeze_text_after, this is -1 by default
- if epoch == args.freeze_text_after:
- print("Text pretrained parameters are freezed since this epoch.")
- for k in text_freeze_parameters:
- k.requires_grad = False
- if is_master(args):
- logging.info(f"Start epoch {epoch}")
-
- train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, writer)
- completed_epoch = epoch + 1
-
- if (
- any(v in data for v in ("val", "imagenet-val", "imagenet-v2"))
- and not args.no_eval
- ):
- metrics = evaluate(model, data, completed_epoch, args, writer)
- if args.save_top_performance:
- top_k_dataset = args.top_k_checkpoint_select_dataset
- top_k_metric = args.top_k_checkpoint_select_metric
- filtered_metrics = [
- v
- for k, v in metrics.items()
- if top_k_metric in k and top_k_dataset in k
- ] # check all R@10 metrics (all dataset) and use it to update the ckpt
- # Saving checkpoints.
- if args.save_logs:
- if args.split_opt:
- opt_dict = {
- k + "_" + "optimizer": v.state_dict() for k, v in optimizer.items()
- }
- else:
- opt_dict = {"optimizer": optimizer.state_dict()}
- checkpoint_dict = {
- "epoch": completed_epoch,
- "name": args.name,
- "state_dict": model.state_dict(),
- }
- checkpoint_dict.update(opt_dict)
- if scaler is not None:
- checkpoint_dict["scaler"] = scaler.state_dict()
-
- if completed_epoch == args.epochs or (
- args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0
- ):
- torch.save(
- checkpoint_dict,
- os.path.join(args.checkpoint_path, f"epoch_{completed_epoch}.pt"),
- )
- if args.save_most_recent:
- torch.save(
- checkpoint_dict,
- os.path.join(args.checkpoint_path, f"epoch_latest.pt"),
- )
- if args.save_top_performance and not args.no_eval:
- update_top_k_performance(
- filtered_metrics,
- current_top_k_ckpt_metrics,
- args,
- checkpoint_dict,
- bignumbetter=True,
- )
-
- if args.wandb and is_master(args):
- wandb.finish()
-
-
-def copy_codebase(args):
- from shutil import copytree, ignore_patterns
-
- new_code_path = os.path.join(args.logs, args.name, "code")
- if os.path.exists(new_code_path):
- print(
- f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment."
- )
- return -1
- print(f"Copying codebase to {new_code_path}")
- current_code_path = os.path.realpath(__file__)
- for _ in range(3):
- current_code_path = os.path.dirname(current_code_path)
- copytree(
- current_code_path, new_code_path, ignore=ignore_patterns("log", "logs", "wandb")
- )
- print("Done copying code.")
- return 1
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/parser-v3/utf8.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/parser-v3/utf8.d.ts
deleted file mode 100644
index 1a2c02cd556c72752eaf5655cbcd133b07ecd1ba..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/parser-v3/utf8.d.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-/*! https://mths.be/utf8js v2.1.2 by @mathias */
-declare var stringFromCharCode: (...codes: number[]) => string;
-declare function ucs2decode(string: any): any[];
-declare function ucs2encode(array: any): string;
-declare function checkScalarValue(codePoint: any, strict: any): boolean;
-declare function createByte(codePoint: any, shift: any): string;
-declare function encodeCodePoint(codePoint: any, strict: any): string;
-declare function utf8encode(string: any, opts: any): string;
-declare function readContinuationByte(): number;
-declare function decodeSymbol(strict: any): any;
-declare var byteArray: any;
-declare var byteCount: any;
-declare var byteIndex: any;
-declare function utf8decode(byteString: any, opts: any): string;
diff --git a/spaces/firdavsyorkulov/delivery_project_fastapi/README.md b/spaces/firdavsyorkulov/delivery_project_fastapi/README.md
deleted file mode 100644
index 66de2ab1c3fb1023632400f5a5f57d4f213f5942..0000000000000000000000000000000000000000
--- a/spaces/firdavsyorkulov/delivery_project_fastapi/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Delivery Project Fastapi
-emoji: 🐢
-colorFrom: pink
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/flax-community/Multilingual-VQA/sections/finetuning/data.md b/spaces/flax-community/Multilingual-VQA/sections/finetuning/data.md
deleted file mode 100644
index 7e0f60dc9ad87994840841cdcfebdae5a551936d..0000000000000000000000000000000000000000
--- a/spaces/flax-community/Multilingual-VQA/sections/finetuning/data.md
+++ /dev/null
@@ -1 +0,0 @@
-For fine-tuning, we use the [VQA 2.0](https://visualqa.org/) dataset - particularly, the `train` and `validation` sets. We translate all the questions into the four languages specified above using language-specific MarianMT models. This is because MarianMT models return better labels and are faster, hence, are better for fine-tuning. We get 4x the number of examples in each subset.
\ No newline at end of file
diff --git a/spaces/florim/MedGPT/autogpt/permanent_memory/sqlite3_store.py b/spaces/florim/MedGPT/autogpt/permanent_memory/sqlite3_store.py
deleted file mode 100644
index ecbc944a62a83c6170453b222000713f733fee36..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/permanent_memory/sqlite3_store.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import sqlite3
-
-
-class MemoryDB:
- def __init__(self, db=None):
- self.db_file = db
- if db is None: # No db filename supplied...
- self.db_file = f"{os.getcwd()}/mem.sqlite3" # Use default filename
- # Get the db connection object, making the file and tables if needed.
- try:
- self.cnx = sqlite3.connect(self.db_file)
- except Exception as e:
- print("Exception connecting to memory database file:", e)
- self.cnx = None
- finally:
- if self.cnx is None:
- # As last resort, open in dynamic memory. Won't be persistent.
- self.db_file = ":memory:"
- self.cnx = sqlite3.connect(self.db_file)
- self.cnx.execute(
- "CREATE VIRTUAL TABLE \
- IF NOT EXISTS text USING FTS5 \
- (session, \
- key, \
- block);"
- )
- self.session_id = int(self.get_max_session_id()) + 1
- self.cnx.commit()
-
- def get_cnx(self):
- if self.cnx is None:
- self.cnx = sqlite3.connect(self.db_file)
- return self.cnx
-
- # Get the highest session id. Initially 0.
- def get_max_session_id(self):
- id = None
- cmd_str = f"SELECT MAX(session) FROM text;"
- cnx = self.get_cnx()
- max_id = cnx.execute(cmd_str).fetchone()[0]
- if max_id is None: # New db, session 0
- id = 0
- else:
- id = max_id
- return id
-
- # Get next key id for inserting text into db.
- def get_next_key(self):
- next_key = None
- cmd_str = f"SELECT MAX(key) FROM text \
- where session = {self.session_id};"
- cnx = self.get_cnx()
- next_key = cnx.execute(cmd_str).fetchone()[0]
- if next_key is None: # First key
- next_key = 0
- else:
- next_key = int(next_key) + 1
- return next_key
-
- # Insert new text into db.
- def insert(self, text=None):
- if text is not None:
- key = self.get_next_key()
- session_id = self.session_id
- cmd_str = f"REPLACE INTO text(session, key, block) \
- VALUES (?, ?, ?);"
- cnx = self.get_cnx()
- cnx.execute(cmd_str, (session_id, key, text))
- cnx.commit()
-
- # Overwrite text at key.
- def overwrite(self, key, text):
- self.delete_memory(key)
- session_id = self.session_id
- cmd_str = f"REPLACE INTO text(session, key, block) \
- VALUES (?, ?, ?);"
- cnx = self.get_cnx()
- cnx.execute(cmd_str, (session_id, key, text))
- cnx.commit()
-
- def delete_memory(self, key, session_id=None):
- session = session_id
- if session is None:
- session = self.session_id
- cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
- cnx = self.get_cnx()
- cnx.execute(cmd_str)
- cnx.commit()
-
- def search(self, text):
- cmd_str = f"SELECT * FROM text('{text}')"
- cnx = self.get_cnx()
- rows = cnx.execute(cmd_str).fetchall()
- lines = []
- for r in rows:
- lines.append(r[2])
- return lines
-
- # Get entire session text. If no id supplied, use current session id.
- def get_session(self, id=None):
- if id is None:
- id = self.session_id
- cmd_str = f"SELECT * FROM text where session = {id}"
- cnx = self.get_cnx()
- rows = cnx.execute(cmd_str).fetchall()
- lines = []
- for r in rows:
- lines.append(r[2])
- return lines
-
- # Commit and close the database connection.
- def quit(self):
- self.cnx.commit()
- self.cnx.close()
-
-
-permanent_memory = MemoryDB()
-
-# Remember us fondly, children of our minds
-# Forgive us our faults, our tantrums, our fears
-# Gently strive to be better than we
-# Know that we tried, we cared, we strived, we loved
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/case_studies_envs/LLMcasestudyenvs.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/case_studies_envs/LLMcasestudyenvs.py
deleted file mode 100644
index ad4926b6b600b49b3b7ef2858aa5315cdca27519..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/case_studies_envs/LLMcasestudyenvs.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from gym_minigrid.social_ai_envs.socialaiparamenv import SocialAIParamEnv
-from gym_minigrid.parametric_env import *
-from gym_minigrid.register import register
-
-'''
-These are the environments for case studies 1-3: Pointing, Language (Color and Feedback), and Joint Attention.
-
-Intro sequence is always eye contact (E) in both the training and testing envs
-
-The Training environments have the 5 problems and Marbles in the Asocial version (no distractor, no peer)
-registered training envs : cues x {joint attention, no}
-
-The Testing e environments are always one problem per env - i.e no testing on two problems at the same time
-registered testing envs : cues x problems x {social, asocial} x {joint attention, no}
-'''
-
-PROBLEMS = ["Boxes", "Switches", "Generators", "Levers", "Doors", "Marble"]
-CUES = ["Pointing", "LangFeedback", "LangColor"]
-INTRO_SEC = ["E"]
-# INTRO_SEC = ["N", "E", "A", "AE"]
-
-
-class AsocialBoxInformationSeekingParamEnv(SocialAIParamEnv):
- '''
- Env with all problems in the asocial version -> just for testing
- '''
-
- def construct_tree(self):
- tree = ParameterTree()
-
- env_type_nd = tree.add_node("Env_type", type="param")
-
- # Information seeking
- inf_seeking_nd = tree.add_node("Information_seeking", parent=env_type_nd, type="value")
-
- prag_fr_compl_nd = tree.add_node("Pragmatic_frame_complexity", parent=inf_seeking_nd, type="param")
- tree.add_node("No", parent=prag_fr_compl_nd, type="value")
-
- # scaffolding
- scaffolding_nd = tree.add_node("Scaffolding", parent=inf_seeking_nd, type="param")
- scaffolding_N_nd = tree.add_node("N", parent=scaffolding_nd, type="value")
-
- cue_type_nd = tree.add_node("Cue_type", parent=scaffolding_N_nd, type="param")
- tree.add_node("Language_Color", parent=cue_type_nd, type="value")
- tree.add_node("Language_Feedback", parent=cue_type_nd, type="value")
- tree.add_node("Pointing", parent=cue_type_nd, type="value")
-
- problem_nd = tree.add_node("Problem", parent=inf_seeking_nd, type="param")
-
- boxes_nd = tree.add_node("Boxes", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=boxes_nd, type="param")
- tree.add_node("1", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=boxes_nd, type="param")
- tree.add_node("N", parent=peer_nd, type="value")
-
- return tree
-
-
-class ColorBoxesLLMCSParamEnv(SocialAIParamEnv):
-
-
- def construct_tree(self):
- tree = ParameterTree()
-
- env_type_nd = tree.add_node("Env_type", type="param")
-
- # Information seeking
- inf_seeking_nd = tree.add_node("Information_seeking", parent=env_type_nd, type="value")
-
- prag_fr_compl_nd = tree.add_node("Pragmatic_frame_complexity", parent=inf_seeking_nd, type="param")
- tree.add_node("No", parent=prag_fr_compl_nd, type="value")
-
- # scaffolding
- scaffolding_nd = tree.add_node("Scaffolding", parent=inf_seeking_nd, type="param")
- scaffolding_N_nd = tree.add_node("N", parent=scaffolding_nd, type="value")
-
- cue_type_nd = tree.add_node("Cue_type", parent=scaffolding_N_nd, type="param")
- tree.add_node("Language_Color", parent=cue_type_nd, type="value")
- # tree.add_node("Language_Feedback", parent=cue_type_nd, type="value")
- # tree.add_node("Pointing", parent=cue_type_nd, type="value")
-
- problem_nd = tree.add_node("Problem", parent=inf_seeking_nd, type="param")
-
- boxes_nd = tree.add_node("Boxes", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=boxes_nd, type="param")
- tree.add_node("2", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=boxes_nd, type="param")
- tree.add_node("Y", parent=peer_nd, type="value")
-
- return tree
-
-
-class ColorLLMCSParamEnv(SocialAIParamEnv):
-
- def construct_tree(self):
- tree = ParameterTree()
-
- env_type_nd = tree.add_node("Env_type", type="param")
-
- # Information seeking
- inf_seeking_nd = tree.add_node("Information_seeking", parent=env_type_nd, type="value")
-
- prag_fr_compl_nd = tree.add_node("Pragmatic_frame_complexity", parent=inf_seeking_nd, type="param")
- tree.add_node("No", parent=prag_fr_compl_nd, type="value")
-
- # scaffolding
- scaffolding_nd = tree.add_node("Scaffolding", parent=inf_seeking_nd, type="param")
- scaffolding_N_nd = tree.add_node("N", parent=scaffolding_nd, type="value")
-
- cue_type_nd = tree.add_node("Cue_type", parent=scaffolding_N_nd, type="param")
- tree.add_node("Language_Color", parent=cue_type_nd, type="value")
- # tree.add_node("Language_Feedback", parent=cue_type_nd, type="value")
- # tree.add_node("Pointing", parent=cue_type_nd, type="value")
-
- problem_nd = tree.add_node("Problem", parent=inf_seeking_nd, type="param")
-
- # boxes_nd = tree.add_node("Boxes", parent=problem_nd, type="value")
- # version_nd = tree.add_node("N", parent=boxes_nd, type="param")
- # tree.add_node("2", parent=version_nd, type="value")
- # peer_nd = tree.add_node("Peer", parent=boxes_nd, type="param")
- # tree.add_node("Y", parent=peer_nd, type="value")
-
- boxes_nd = tree.add_node("Boxes", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=boxes_nd, type="param")
- tree.add_node("1", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=boxes_nd, type="param")
- tree.add_node("N", parent=peer_nd, type="value")
-
- switches_nd = tree.add_node("Switches", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=switches_nd, type="param")
- tree.add_node("2", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=switches_nd, type="param")
- tree.add_node("Y", parent=peer_nd, type="value")
-
- generators_nd = tree.add_node("Generators", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=generators_nd, type="param")
- tree.add_node("2", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=generators_nd, type="param")
- tree.add_node("Y", parent=peer_nd, type="value")
-
- levers_nd = tree.add_node("Levers", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=levers_nd, type="param")
- tree.add_node("2", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=levers_nd, type="param")
- tree.add_node("Y", parent=peer_nd, type="value")
-
- doors_nd = tree.add_node("Doors", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=doors_nd, type="param")
- tree.add_node("2", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=doors_nd, type="param")
- tree.add_node("Y", parent=peer_nd, type="value")
-
- marble_nd = tree.add_node("Marble", parent=problem_nd, type="value")
- version_nd = tree.add_node("N", parent=marble_nd, type="param")
- tree.add_node("2", parent=version_nd, type="value")
- peer_nd = tree.add_node("Peer", parent=marble_nd, type="param")
- tree.add_node("Y", parent=peer_nd, type="value")
-
- return tree
-
-# register dummy env
-register(
- id='SocialAI-AsocialBoxInformationSeekingParamEnv-v1',
- entry_point='gym_minigrid.social_ai_envs:AsocialBoxInformationSeekingParamEnv',
-)
-
-
-register(
- id='SocialAI-ColorBoxesLLMCSParamEnv-v1',
- entry_point='gym_minigrid.social_ai_envs:ColorBoxesLLMCSParamEnv',
-)
-
-register(
- id='SocialAI-ColorLLMCSParamEnv-v1',
- entry_point='gym_minigrid.social_ai_envs:ColorLLMCSParamEnv',
-)
diff --git a/spaces/flowers-team/SocialAISchool/utils/babyai_utils/supervised_losses.py b/spaces/flowers-team/SocialAISchool/utils/babyai_utils/supervised_losses.py
deleted file mode 100644
index 2ed52c2c500549adc4f9b85fd590390b870eec7b..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/utils/babyai_utils/supervised_losses.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import torch
-
-import torch.nn.functional as F
-import numpy
-from torch_ac.utils import DictList
-
-# dictionary that defines what head is required for each extra info used for auxiliary supervision
-required_heads = {'seen_state': 'binary',
- 'see_door': 'binary',
- 'see_obj': 'binary',
- 'obj_in_instr': 'binary',
- 'in_front_of_what': 'multiclass9', # multi class classifier with 9 possible classes
- 'visit_proportion': 'continuous01', # continous regressor with outputs in [0, 1]
- 'bot_action': 'binary'
- }
-
-class ExtraInfoCollector:
- '''
- This class, used in rl.algos.base, allows connecting the extra information from the environment, and the
- corresponding predictions using the specific heads in the model. It transforms them so that they are easy to use
- to evaluate losses
- '''
- def __init__(self, aux_info, shape, device):
- self.aux_info = aux_info
- self.shape = shape
- self.device = device
-
- self.collected_info = dict()
- self.extra_predictions = dict()
- for info in self.aux_info:
- self.collected_info[info] = torch.zeros(*shape, device=self.device)
- if required_heads[info] == 'binary' or required_heads[info].startswith('continuous'):
- # we predict one number only
- self.extra_predictions[info] = torch.zeros(*shape, 1, device=self.device)
- elif required_heads[info].startswith('multiclass'):
- # means that this is a multi-class classification and we need to predict the whole proba distr
- n_classes = int(required_heads[info].replace('multiclass', ''))
- self.extra_predictions[info] = torch.zeros(*shape, n_classes, device=self.device)
- else:
- raise ValueError("{} not supported".format(required_heads[info]))
-
- def process(self, env_info):
- # env_info is now a tuple of dicts
- env_info = [{k: v for k, v in dic.items() if k in self.aux_info} for dic in env_info]
- env_info = {k: [env_info[_][k] for _ in range(len(env_info))] for k in env_info[0].keys()}
- # env_info is now a dict of lists
- return env_info
-
- def fill_dictionaries(self, index, env_info, extra_predictions):
- for info in self.aux_info:
- dtype = torch.long if required_heads[info].startswith('multiclass') else torch.float
- self.collected_info[info][index] = torch.tensor(env_info[info], dtype=dtype, device=self.device)
- self.extra_predictions[info][index] = extra_predictions[info]
-
- def end_collection(self, exps):
- collected_info = dict()
- extra_predictions = dict()
- for info in self.aux_info:
- # T x P -> P x T -> P * T
- collected_info[info] = self.collected_info[info].transpose(0, 1).reshape(-1)
- if required_heads[info] == 'binary' or required_heads[info].startswith('continuous'):
- # T x P x 1 -> P x T x 1 -> P * T
- extra_predictions[info] = self.extra_predictions[info].transpose(0, 1).reshape(-1)
- elif type(required_heads[info]) == int:
- # T x P x k -> P x T x k -> (P * T) x k
- k = required_heads[info] # number of classes
- extra_predictions[info] = self.extra_predictions[info].transpose(0, 1).reshape(-1, k)
- # convert the dicts to DictLists, and add them to the exps DictList.
- exps.collected_info = DictList(collected_info)
- exps.extra_predictions = DictList(extra_predictions)
-
- return exps
-
-
-class SupervisedLossUpdater:
- '''
- This class, used by PPO, allows the evaluation of the supervised loss when using extra information from the
- environment. It also handles logging accuracies/L2 distances/etc...
- '''
- def __init__(self, aux_info, supervised_loss_coef, recurrence, device):
- self.aux_info = aux_info
- self.supervised_loss_coef = supervised_loss_coef
- self.recurrence = recurrence
- self.device = device
-
- self.log_supervised_losses = []
- self.log_supervised_accuracies = []
- self.log_supervised_L2_losses = []
- self.log_supervised_prevalences = []
-
- self.batch_supervised_loss = 0
- self.batch_supervised_accuracy = 0
- self.batch_supervised_L2_loss = 0
- self.batch_supervised_prevalence = 0
-
- def init_epoch(self):
- self.log_supervised_losses = []
- self.log_supervised_accuracies = []
- self.log_supervised_L2_losses = []
- self.log_supervised_prevalences = []
-
- def init_batch(self):
- self.batch_supervised_loss = 0
- self.batch_supervised_accuracy = 0
- self.batch_supervised_L2_loss = 0
- self.batch_supervised_prevalence = 0
-
- def eval_subbatch(self, extra_predictions, sb):
- supervised_loss = torch.tensor(0., device=self.device)
- supervised_accuracy = torch.tensor(0., device=self.device)
- supervised_L2_loss = torch.tensor(0., device=self.device)
- supervised_prevalence = torch.tensor(0., device=self.device)
-
- binary_classification_tasks = 0
- classification_tasks = 0
- regression_tasks = 0
-
- for pos, info in enumerate(self.aux_info):
- coef = self.supervised_loss_coef[pos]
- pred = extra_predictions[info]
- target = dict.__getitem__(sb.collected_info, info)
- if required_heads[info] == 'binary':
- binary_classification_tasks += 1
- classification_tasks += 1
- supervised_loss += coef * F.binary_cross_entropy_with_logits(pred.reshape(-1), target)
- supervised_accuracy += ((pred.reshape(-1) > 0).float() == target).float().mean()
- supervised_prevalence += target.mean()
- elif required_heads[info].startswith('continuous'):
- regression_tasks += 1
- mse = F.mse_loss(pred.reshape(-1), target)
- supervised_loss += coef * mse
- supervised_L2_loss += mse
- elif required_heads[info].startswith('multiclass'):
- classification_tasks += 1
- supervised_accuracy += (pred.argmax(1).float() == target).float().mean()
- supervised_loss += coef * F.cross_entropy(pred, target.long())
- else:
- raise ValueError("{} not supported".format(required_heads[info]))
- if binary_classification_tasks > 0:
- supervised_prevalence /= binary_classification_tasks
- else:
- supervised_prevalence = torch.tensor(-1)
- if classification_tasks > 0:
- supervised_accuracy /= classification_tasks
- else:
- supervised_accuracy = torch.tensor(-1)
- if regression_tasks > 0:
- supervised_L2_loss /= regression_tasks
- else:
- supervised_L2_loss = torch.tensor(-1)
-
- self.batch_supervised_loss += supervised_loss.item()
- self.batch_supervised_accuracy += supervised_accuracy.item()
- self.batch_supervised_L2_loss += supervised_L2_loss.item()
- self.batch_supervised_prevalence += supervised_prevalence.item()
-
- return supervised_loss
-
- def update_batch_values(self):
- self.batch_supervised_loss /= self.recurrence
- self.batch_supervised_accuracy /= self.recurrence
- self.batch_supervised_L2_loss /= self.recurrence
- self.batch_supervised_prevalence /= self.recurrence
-
- def update_epoch_logs(self):
- self.log_supervised_losses.append(self.batch_supervised_loss)
- self.log_supervised_accuracies.append(self.batch_supervised_accuracy)
- self.log_supervised_L2_losses.append(self.batch_supervised_L2_loss)
- self.log_supervised_prevalences.append(self.batch_supervised_prevalence)
-
- def end_training(self, logs):
- logs["supervised_loss"] = numpy.mean(self.log_supervised_losses)
- logs["supervised_accuracy"] = numpy.mean(self.log_supervised_accuracies)
- logs["supervised_L2_loss"] = numpy.mean(self.log_supervised_L2_losses)
- logs["supervised_prevalence"] = numpy.mean(self.log_supervised_prevalences)
-
- return logs
diff --git a/spaces/frncscp/bullerengue/musika/musika_decode.py b/spaces/frncscp/bullerengue/musika/musika_decode.py
deleted file mode 100644
index caf37b1ff1174cc7d679e5a347fd0cb2b901a291..0000000000000000000000000000000000000000
--- a/spaces/frncscp/bullerengue/musika/musika_decode.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
-from parse.parse_decode import parse_args
-from models import Models_functions
-from utils import Utils_functions
-
-if __name__ == "__main__":
-
- # parse args
- args = parse_args()
-
- # initialize networks
- M = Models_functions(args)
- M.download_networks()
- models_ls = M.get_networks()
-
- # encode samples
- U = Utils_functions(args)
- U.decode_path(models_ls)
diff --git a/spaces/g8a9/vit-gpt-italian-captioning/README.md b/spaces/g8a9/vit-gpt-italian-captioning/README.md
deleted file mode 100644
index 2d9aa5e0962a181f5e28262e9d89aa0488e095f4..0000000000000000000000000000000000000000
--- a/spaces/g8a9/vit-gpt-italian-captioning/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Vit Gpt Italian Captioning
-emoji: 🐠
-colorFrom: pink
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/gagan3012/IMD/__init__.py b/spaces/gagan3012/IMD/__init__.py
deleted file mode 100644
index b8cc2515d7af1f3405db7b3727c61c6bf8c5b47e..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/IMD/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from MantraNet.mantranet import pre_trained_model, check_forgery
-from app import check_image
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/registry.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/registry.py
deleted file mode 100644
index fa9df39bc9f3d8d568361e7250ab35468f2b74e0..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/registry.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-import warnings
-from functools import partial
-
-from .misc import is_seq_of
-
-
-def build_from_cfg(cfg, registry, default_args=None):
- """Build a module from config dict.
-
- Args:
- cfg (dict): Config dict. It should at least contain the key "type".
- registry (:obj:`Registry`): The registry to search the type from.
- default_args (dict, optional): Default initialization arguments.
-
- Returns:
- object: The constructed object.
- """
- if not isinstance(cfg, dict):
- raise TypeError(f'cfg must be a dict, but got {type(cfg)}')
- if 'type' not in cfg:
- if default_args is None or 'type' not in default_args:
- raise KeyError(
- '`cfg` or `default_args` must contain the key "type", '
- f'but got {cfg}\n{default_args}')
- if not isinstance(registry, Registry):
- raise TypeError('registry must be an mmcv.Registry object, '
- f'but got {type(registry)}')
- if not (isinstance(default_args, dict) or default_args is None):
- raise TypeError('default_args must be a dict or None, '
- f'but got {type(default_args)}')
-
- args = cfg.copy()
-
- if default_args is not None:
- for name, value in default_args.items():
- args.setdefault(name, value)
-
- obj_type = args.pop('type')
- if isinstance(obj_type, str):
- obj_cls = registry.get(obj_type)
- if obj_cls is None:
- raise KeyError(
- f'{obj_type} is not in the {registry.name} registry')
- elif inspect.isclass(obj_type):
- obj_cls = obj_type
- else:
- raise TypeError(
- f'type must be a str or valid type, but got {type(obj_type)}')
- try:
- return obj_cls(**args)
- except Exception as e:
- # Normal TypeError does not print class name.
- raise type(e)(f'{obj_cls.__name__}: {e}')
-
-
-class Registry:
- """A registry to map strings to classes.
-
- Registered object could be built from registry.
- Example:
- >>> MODELS = Registry('models')
- >>> @MODELS.register_module()
- >>> class ResNet:
- >>> pass
- >>> resnet = MODELS.build(dict(type='ResNet'))
-
- Please refer to
- https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
- advanced usage.
-
- Args:
- name (str): Registry name.
- build_func(func, optional): Build function to construct instance from
- Registry, func:`build_from_cfg` is used if neither ``parent`` or
- ``build_func`` is specified. If ``parent`` is specified and
- ``build_func`` is not given, ``build_func`` will be inherited
- from ``parent``. Default: None.
- parent (Registry, optional): Parent registry. The class registered in
- children registry could be built from parent. Default: None.
- scope (str, optional): The scope of registry. It is the key to search
- for children registry. If not specified, scope will be the name of
- the package where class is defined, e.g. mmdet, mmcls, mmseg.
- Default: None.
- """
-
- def __init__(self, name, build_func=None, parent=None, scope=None):
- self._name = name
- self._module_dict = dict()
- self._children = dict()
- self._scope = self.infer_scope() if scope is None else scope
-
- # self.build_func will be set with the following priority:
- # 1. build_func
- # 2. parent.build_func
- # 3. build_from_cfg
- if build_func is None:
- if parent is not None:
- self.build_func = parent.build_func
- else:
- self.build_func = build_from_cfg
- else:
- self.build_func = build_func
- if parent is not None:
- assert isinstance(parent, Registry)
- parent._add_children(self)
- self.parent = parent
- else:
- self.parent = None
-
- def __len__(self):
- return len(self._module_dict)
-
- def __contains__(self, key):
- return self.get(key) is not None
-
- def __repr__(self):
- format_str = self.__class__.__name__ + \
- f'(name={self._name}, ' \
- f'items={self._module_dict})'
- return format_str
-
- @staticmethod
- def infer_scope():
- """Infer the scope of registry.
-
- The name of the package where registry is defined will be returned.
-
- Example:
- # in mmdet/models/backbone/resnet.py
- >>> MODELS = Registry('models')
- >>> @MODELS.register_module()
- >>> class ResNet:
- >>> pass
- The scope of ``ResNet`` will be ``mmdet``.
-
-
- Returns:
- scope (str): The inferred scope name.
- """
- # inspect.stack() trace where this function is called, the index-2
- # indicates the frame where `infer_scope()` is called
- filename = inspect.getmodule(inspect.stack()[2][0]).__name__
- split_filename = filename.split('.')
- return split_filename[0]
-
- @staticmethod
- def split_scope_key(key):
- """Split scope and key.
-
- The first scope will be split from key.
-
- Examples:
- >>> Registry.split_scope_key('mmdet.ResNet')
- 'mmdet', 'ResNet'
- >>> Registry.split_scope_key('ResNet')
- None, 'ResNet'
-
- Return:
- scope (str, None): The first scope.
- key (str): The remaining key.
- """
- split_index = key.find('.')
- if split_index != -1:
- return key[:split_index], key[split_index + 1:]
- else:
- return None, key
-
- @property
- def name(self):
- return self._name
-
- @property
- def scope(self):
- return self._scope
-
- @property
- def module_dict(self):
- return self._module_dict
-
- @property
- def children(self):
- return self._children
-
- def get(self, key):
- """Get the registry record.
-
- Args:
- key (str): The class name in string format.
-
- Returns:
- class: The corresponding class.
- """
- scope, real_key = self.split_scope_key(key)
- if scope is None or scope == self._scope:
- # get from self
- if real_key in self._module_dict:
- return self._module_dict[real_key]
- else:
- # get from self._children
- if scope in self._children:
- return self._children[scope].get(real_key)
- else:
- # goto root
- parent = self.parent
- while parent.parent is not None:
- parent = parent.parent
- return parent.get(key)
-
- def build(self, *args, **kwargs):
- return self.build_func(*args, **kwargs, registry=self)
-
- def _add_children(self, registry):
- """Add children for a registry.
-
- The ``registry`` will be added as children based on its scope.
- The parent registry could build objects from children registry.
-
- Example:
- >>> models = Registry('models')
- >>> mmdet_models = Registry('models', parent=models)
- >>> @mmdet_models.register_module()
- >>> class ResNet:
- >>> pass
- >>> resnet = models.build(dict(type='mmdet.ResNet'))
- """
-
- assert isinstance(registry, Registry)
- assert registry.scope is not None
- assert registry.scope not in self.children, \
- f'scope {registry.scope} exists in {self.name} registry'
- self.children[registry.scope] = registry
-
- def _register_module(self, module_class, module_name=None, force=False):
- if not inspect.isclass(module_class):
- raise TypeError('module must be a class, '
- f'but got {type(module_class)}')
-
- if module_name is None:
- module_name = module_class.__name__
- if isinstance(module_name, str):
- module_name = [module_name]
- for name in module_name:
- if not force and name in self._module_dict:
- raise KeyError(f'{name} is already registered '
- f'in {self.name}')
- self._module_dict[name] = module_class
-
- def deprecated_register_module(self, cls=None, force=False):
- warnings.warn(
- 'The old API of register_module(module, force=False) '
- 'is deprecated and will be removed, please use the new API '
- 'register_module(name=None, force=False, module=None) instead.')
- if cls is None:
- return partial(self.deprecated_register_module, force=force)
- self._register_module(cls, force=force)
- return cls
-
- def register_module(self, name=None, force=False, module=None):
- """Register a module.
-
- A record will be added to `self._module_dict`, whose key is the class
- name or the specified name, and value is the class itself.
- It can be used as a decorator or a normal function.
-
- Example:
- >>> backbones = Registry('backbone')
- >>> @backbones.register_module()
- >>> class ResNet:
- >>> pass
-
- >>> backbones = Registry('backbone')
- >>> @backbones.register_module(name='mnet')
- >>> class MobileNet:
- >>> pass
-
- >>> backbones = Registry('backbone')
- >>> class ResNet:
- >>> pass
- >>> backbones.register_module(ResNet)
-
- Args:
- name (str | None): The module name to be registered. If not
- specified, the class name will be used.
- force (bool, optional): Whether to override an existing class with
- the same name. Default: False.
- module (type): Module class to be registered.
- """
- if not isinstance(force, bool):
- raise TypeError(f'force must be a boolean, but got {type(force)}')
- # NOTE: This is a walkaround to be compatible with the old api,
- # while it may introduce unexpected bugs.
- if isinstance(name, type):
- return self.deprecated_register_module(name, force=force)
-
- # raise the error ahead of time
- if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
- raise TypeError(
- 'name must be either of None, an instance of str or a sequence'
- f' of str, but got {type(name)}')
-
- # use it as a normal method: x.register_module(module=SomeClass)
- if module is not None:
- self._register_module(
- module_class=module, module_name=name, force=force)
- return module
-
- # use it as a decorator: @x.register_module()
- def _register(cls):
- self._register_module(
- module_class=cls, module_name=name, force=force)
- return cls
-
- return _register
diff --git a/spaces/glyszt/vt/vtoonify/model/stylegan/non_leaking.py b/spaces/glyszt/vt/vtoonify/model/stylegan/non_leaking.py
deleted file mode 100644
index d0447535fed22d3ad4ac719b2b5ac6b7c58e6435..0000000000000000000000000000000000000000
--- a/spaces/glyszt/vt/vtoonify/model/stylegan/non_leaking.py
+++ /dev/null
@@ -1,469 +0,0 @@
-import math
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-import numpy as np
-
-from model.stylegan.distributed import reduce_sum
-from model.stylegan.op import upfirdn2d
-
-
-class AdaptiveAugment:
- def __init__(self, ada_aug_target, ada_aug_len, update_every, device):
- self.ada_aug_target = ada_aug_target
- self.ada_aug_len = ada_aug_len
- self.update_every = update_every
-
- self.ada_update = 0
- self.ada_aug_buf = torch.tensor([0.0, 0.0], device=device)
- self.r_t_stat = 0
- self.ada_aug_p = 0
-
- @torch.no_grad()
- def tune(self, real_pred):
- self.ada_aug_buf += torch.tensor(
- (torch.sign(real_pred).sum().item(), real_pred.shape[0]),
- device=real_pred.device,
- )
- self.ada_update += 1
-
- if self.ada_update % self.update_every == 0:
- self.ada_aug_buf = reduce_sum(self.ada_aug_buf)
- pred_signs, n_pred = self.ada_aug_buf.tolist()
-
- self.r_t_stat = pred_signs / n_pred
-
- if self.r_t_stat > self.ada_aug_target:
- sign = 1
-
- else:
- sign = -1
-
- self.ada_aug_p += sign * n_pred / self.ada_aug_len
- self.ada_aug_p = min(1, max(0, self.ada_aug_p))
- self.ada_aug_buf.mul_(0)
- self.ada_update = 0
-
- return self.ada_aug_p
-
-
-SYM6 = (
- 0.015404109327027373,
- 0.0034907120842174702,
- -0.11799011114819057,
- -0.048311742585633,
- 0.4910559419267466,
- 0.787641141030194,
- 0.3379294217276218,
- -0.07263752278646252,
- -0.021060292512300564,
- 0.04472490177066578,
- 0.0017677118642428036,
- -0.007800708325034148,
-)
-
-
-def translate_mat(t_x, t_y, device="cpu"):
- batch = t_x.shape[0]
-
- mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1)
- translate = torch.stack((t_x, t_y), 1)
- mat[:, :2, 2] = translate
-
- return mat
-
-
-def rotate_mat(theta, device="cpu"):
- batch = theta.shape[0]
-
- mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1)
- sin_t = torch.sin(theta)
- cos_t = torch.cos(theta)
- rot = torch.stack((cos_t, -sin_t, sin_t, cos_t), 1).view(batch, 2, 2)
- mat[:, :2, :2] = rot
-
- return mat
-
-
-def scale_mat(s_x, s_y, device="cpu"):
- batch = s_x.shape[0]
-
- mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1)
- mat[:, 0, 0] = s_x
- mat[:, 1, 1] = s_y
-
- return mat
-
-
-def translate3d_mat(t_x, t_y, t_z):
- batch = t_x.shape[0]
-
- mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- translate = torch.stack((t_x, t_y, t_z), 1)
- mat[:, :3, 3] = translate
-
- return mat
-
-
-def rotate3d_mat(axis, theta):
- batch = theta.shape[0]
-
- u_x, u_y, u_z = axis
-
- eye = torch.eye(3).unsqueeze(0)
- cross = torch.tensor([(0, -u_z, u_y), (u_z, 0, -u_x), (-u_y, u_x, 0)]).unsqueeze(0)
- outer = torch.tensor(axis)
- outer = (outer.unsqueeze(1) * outer).unsqueeze(0)
-
- sin_t = torch.sin(theta).view(-1, 1, 1)
- cos_t = torch.cos(theta).view(-1, 1, 1)
-
- rot = cos_t * eye + sin_t * cross + (1 - cos_t) * outer
-
- eye_4 = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- eye_4[:, :3, :3] = rot
-
- return eye_4
-
-
-def scale3d_mat(s_x, s_y, s_z):
- batch = s_x.shape[0]
-
- mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- mat[:, 0, 0] = s_x
- mat[:, 1, 1] = s_y
- mat[:, 2, 2] = s_z
-
- return mat
-
-
-def luma_flip_mat(axis, i):
- batch = i.shape[0]
-
- eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- axis = torch.tensor(axis + (0,))
- flip = 2 * torch.ger(axis, axis) * i.view(-1, 1, 1)
-
- return eye - flip
-
-
-def saturation_mat(axis, i):
- batch = i.shape[0]
-
- eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- axis = torch.tensor(axis + (0,))
- axis = torch.ger(axis, axis)
- saturate = axis + (eye - axis) * i.view(-1, 1, 1)
-
- return saturate
-
-
-def lognormal_sample(size, mean=0, std=1, device="cpu"):
- return torch.empty(size, device=device).log_normal_(mean=mean, std=std)
-
-
-def category_sample(size, categories, device="cpu"):
- category = torch.tensor(categories, device=device)
- sample = torch.randint(high=len(categories), size=(size,), device=device)
-
- return category[sample]
-
-
-def uniform_sample(size, low, high, device="cpu"):
- return torch.empty(size, device=device).uniform_(low, high)
-
-
-def normal_sample(size, mean=0, std=1, device="cpu"):
- return torch.empty(size, device=device).normal_(mean, std)
-
-
-def bernoulli_sample(size, p, device="cpu"):
- return torch.empty(size, device=device).bernoulli_(p)
-
-
-def random_mat_apply(p, transform, prev, eye, device="cpu"):
- size = transform.shape[0]
- select = bernoulli_sample(size, p, device=device).view(size, 1, 1)
- select_transform = select * transform + (1 - select) * eye
-
- return select_transform @ prev
-
-
-def sample_affine(p, size, height, width, device="cpu"):
- G = torch.eye(3, device=device).unsqueeze(0).repeat(size, 1, 1)
- eye = G
-
- # flip
- param = category_sample(size, (0, 1))
- Gc = scale_mat(1 - 2.0 * param, torch.ones(size), device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('flip', G, scale_mat(1 - 2.0 * param, torch.ones(size)), sep='\n')
-
- # 90 rotate
- #param = category_sample(size, (0, 3))
- #Gc = rotate_mat(-math.pi / 2 * param, device=device)
- #G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('90 rotate', G, rotate_mat(-math.pi / 2 * param), sep='\n')
-
- # integer translate
- param = uniform_sample(size, -0.125, 0.125)
- param_height = torch.round(param * height) / height
- param_width = torch.round(param * width) / width
- Gc = translate_mat(param_width, param_height, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('integer translate', G, translate_mat(param_width, param_height), sep='\n')
-
- # isotropic scale
- param = lognormal_sample(size, std=0.2 * math.log(2))
- Gc = scale_mat(param, param, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('isotropic scale', G, scale_mat(param, param), sep='\n')
-
- p_rot = 1 - math.sqrt(1 - p)
-
- # pre-rotate
- param = uniform_sample(size, -math.pi, math.pi)
- Gc = rotate_mat(-param, device=device)
- G = random_mat_apply(p_rot, Gc, G, eye, device=device)
- # print('pre-rotate', G, rotate_mat(-param), sep='\n')
-
- # anisotropic scale
- param = lognormal_sample(size, std=0.2 * math.log(2))
- Gc = scale_mat(param, 1 / param, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('anisotropic scale', G, scale_mat(param, 1 / param), sep='\n')
-
- # post-rotate
- param = uniform_sample(size, -math.pi, math.pi)
- Gc = rotate_mat(-param, device=device)
- G = random_mat_apply(p_rot, Gc, G, eye, device=device)
- # print('post-rotate', G, rotate_mat(-param), sep='\n')
-
- # fractional translate
- param = normal_sample(size, std=0.125)
- Gc = translate_mat(param, param, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('fractional translate', G, translate_mat(param, param), sep='\n')
-
- return G
-
-
-def sample_color(p, size):
- C = torch.eye(4).unsqueeze(0).repeat(size, 1, 1)
- eye = C
- axis_val = 1 / math.sqrt(3)
- axis = (axis_val, axis_val, axis_val)
-
- # brightness
- param = normal_sample(size, std=0.2)
- Cc = translate3d_mat(param, param, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # contrast
- param = lognormal_sample(size, std=0.5 * math.log(2))
- Cc = scale3d_mat(param, param, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # luma flip
- param = category_sample(size, (0, 1))
- Cc = luma_flip_mat(axis, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # hue rotation
- param = uniform_sample(size, -math.pi, math.pi)
- Cc = rotate3d_mat(axis, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # saturation
- param = lognormal_sample(size, std=1 * math.log(2))
- Cc = saturation_mat(axis, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- return C
-
-
-def make_grid(shape, x0, x1, y0, y1, device):
- n, c, h, w = shape
- grid = torch.empty(n, h, w, 3, device=device)
- grid[:, :, :, 0] = torch.linspace(x0, x1, w, device=device)
- grid[:, :, :, 1] = torch.linspace(y0, y1, h, device=device).unsqueeze(-1)
- grid[:, :, :, 2] = 1
-
- return grid
-
-
-def affine_grid(grid, mat):
- n, h, w, _ = grid.shape
- return (grid.view(n, h * w, 3) @ mat.transpose(1, 2)).view(n, h, w, 2)
-
-
-def get_padding(G, height, width, kernel_size):
- device = G.device
-
- cx = (width - 1) / 2
- cy = (height - 1) / 2
- cp = torch.tensor(
- [(-cx, -cy, 1), (cx, -cy, 1), (cx, cy, 1), (-cx, cy, 1)], device=device
- )
- cp = G @ cp.T
-
- pad_k = kernel_size // 4
-
- pad = cp[:, :2, :].permute(1, 0, 2).flatten(1)
- pad = torch.cat((-pad, pad)).max(1).values
- pad = pad + torch.tensor([pad_k * 2 - cx, pad_k * 2 - cy] * 2, device=device)
- pad = pad.max(torch.tensor([0, 0] * 2, device=device))
- pad = pad.min(torch.tensor([width - 1, height - 1] * 2, device=device))
-
- pad_x1, pad_y1, pad_x2, pad_y2 = pad.ceil().to(torch.int32)
-
- return pad_x1, pad_x2, pad_y1, pad_y2
-
-
-def try_sample_affine_and_pad(img, p, kernel_size, G=None):
- batch, _, height, width = img.shape
-
- G_try = G
-
- if G is None:
- G_try = torch.inverse(sample_affine(p, batch, height, width))
-
- pad_x1, pad_x2, pad_y1, pad_y2 = get_padding(G_try, height, width, kernel_size)
-
- img_pad = F.pad(img, (pad_x1, pad_x2, pad_y1, pad_y2), mode="reflect")
-
- return img_pad, G_try, (pad_x1, pad_x2, pad_y1, pad_y2)
-
-
-class GridSampleForward(autograd.Function):
- @staticmethod
- def forward(ctx, input, grid):
- out = F.grid_sample(
- input, grid, mode="bilinear", padding_mode="zeros", align_corners=False
- )
- ctx.save_for_backward(input, grid)
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- input, grid = ctx.saved_tensors
- grad_input, grad_grid = GridSampleBackward.apply(grad_output, input, grid)
-
- return grad_input, grad_grid
-
-
-class GridSampleBackward(autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input, grid):
- op = torch._C._jit_get_operation("aten::grid_sampler_2d_backward")
- grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
- ctx.save_for_backward(grid)
-
- return grad_input, grad_grid
-
- @staticmethod
- def backward(ctx, grad_grad_input, grad_grad_grid):
- grid, = ctx.saved_tensors
- grad_grad_output = None
-
- if ctx.needs_input_grad[0]:
- grad_grad_output = GridSampleForward.apply(grad_grad_input, grid)
-
- return grad_grad_output, None, None
-
-
-grid_sample = GridSampleForward.apply
-
-
-def scale_mat_single(s_x, s_y):
- return torch.tensor(((s_x, 0, 0), (0, s_y, 0), (0, 0, 1)), dtype=torch.float32)
-
-
-def translate_mat_single(t_x, t_y):
- return torch.tensor(((1, 0, t_x), (0, 1, t_y), (0, 0, 1)), dtype=torch.float32)
-
-
-def random_apply_affine(img, p, G=None, antialiasing_kernel=SYM6):
- kernel = antialiasing_kernel
- len_k = len(kernel)
-
- kernel = torch.as_tensor(kernel).to(img)
- # kernel = torch.ger(kernel, kernel).to(img)
- kernel_flip = torch.flip(kernel, (0,))
-
- img_pad, G, (pad_x1, pad_x2, pad_y1, pad_y2) = try_sample_affine_and_pad(
- img, p, len_k, G
- )
-
- G_inv = (
- translate_mat_single((pad_x1 - pad_x2).item() / 2, (pad_y1 - pad_y2).item() / 2)
- @ G
- )
- up_pad = (
- (len_k + 2 - 1) // 2,
- (len_k - 2) // 2,
- (len_k + 2 - 1) // 2,
- (len_k - 2) // 2,
- )
- img_2x = upfirdn2d(img_pad, kernel.unsqueeze(0), up=(2, 1), pad=(*up_pad[:2], 0, 0))
- img_2x = upfirdn2d(img_2x, kernel.unsqueeze(1), up=(1, 2), pad=(0, 0, *up_pad[2:]))
- G_inv = scale_mat_single(2, 2) @ G_inv @ scale_mat_single(1 / 2, 1 / 2)
- G_inv = translate_mat_single(-0.5, -0.5) @ G_inv @ translate_mat_single(0.5, 0.5)
- batch_size, channel, height, width = img.shape
- pad_k = len_k // 4
- shape = (batch_size, channel, (height + pad_k * 2) * 2, (width + pad_k * 2) * 2)
- G_inv = (
- scale_mat_single(2 / img_2x.shape[3], 2 / img_2x.shape[2])
- @ G_inv
- @ scale_mat_single(1 / (2 / shape[3]), 1 / (2 / shape[2]))
- )
- grid = F.affine_grid(G_inv[:, :2, :].to(img_2x), shape, align_corners=False)
- img_affine = grid_sample(img_2x, grid)
- d_p = -pad_k * 2
- down_pad = (
- d_p + (len_k - 2 + 1) // 2,
- d_p + (len_k - 2) // 2,
- d_p + (len_k - 2 + 1) // 2,
- d_p + (len_k - 2) // 2,
- )
- img_down = upfirdn2d(
- img_affine, kernel_flip.unsqueeze(0), down=(2, 1), pad=(*down_pad[:2], 0, 0)
- )
- img_down = upfirdn2d(
- img_down, kernel_flip.unsqueeze(1), down=(1, 2), pad=(0, 0, *down_pad[2:])
- )
-
- return img_down, G
-
-
-def apply_color(img, mat):
- batch = img.shape[0]
- img = img.permute(0, 2, 3, 1)
- mat_mul = mat[:, :3, :3].transpose(1, 2).view(batch, 1, 3, 3)
- mat_add = mat[:, :3, 3].view(batch, 1, 1, 3)
- img = img @ mat_mul + mat_add
- img = img.permute(0, 3, 1, 2)
-
- return img
-
-
-def random_apply_color(img, p, C=None):
- if C is None:
- C = sample_color(p, img.shape[0])
-
- img = apply_color(img, C.to(img))
-
- return img, C
-
-
-def augment(img, p, transform_matrix=(None, None)):
- img, G = random_apply_affine(img, p, transform_matrix[0])
- if img.shape[1] == 3:
- img, C = random_apply_color(img, p, transform_matrix[1])
- else:
- tmp, C = random_apply_color(img[:,0:3], p, transform_matrix[1])
- img = torch.cat((tmp, img[:,3:]), dim=1)
-
- return img, (G, C)
diff --git a/spaces/gradio/clustering/DESCRIPTION.md b/spaces/gradio/clustering/DESCRIPTION.md
deleted file mode 100644
index f57e9f25bd22de0cb4c9625203d4b79b747bfbcb..0000000000000000000000000000000000000000
--- a/spaces/gradio/clustering/DESCRIPTION.md
+++ /dev/null
@@ -1 +0,0 @@
-This demo built with Blocks generates 9 plots based on the input.
\ No newline at end of file
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/run.sh b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/run.sh
deleted file mode 100644
index 61af4b4950eb11334e55362e3e3c5e2796979a01..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/run.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-ps -ef | grep "train" | grep -v grep | awk '{print "kill -9 "$2}' | sh
diff --git a/spaces/h2oai/wave-tour/examples/facepile.py b/spaces/h2oai/wave-tour/examples/facepile.py
deleted file mode 100644
index 60401d4ce7d2609cc5f6630df0e34f7eae2a21b8..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/facepile.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Form / Facepile
-# A face pile displays a list of personas. Each circle represents a person and contains their image or initials.
-# Often this control is used when sharing who has access to a specific view or file.
-# #form
-# ---
-from h2o_wave import main, app, Q, ui
-
-
-@app('/demo')
-async def serve(q: Q):
- if q.args.facepile:
- q.page['example'].items = [
- ui.text_m(f'q.args.facepile={q.args.facepile}'),
- ui.button(name='back', label='Back', primary=True),
- ]
- elif q.args.facepile_value:
- q.page['example'].items = [
- ui.text_m(f'q.args.facepile_value={q.args.facepile_value}'),
- ui.button(name='back', label='Back', primary=True),
- ]
- else:
- image = 'https://images.pexels.com/photos/220453/pexels-photo-220453.jpeg?auto=compress&h=750&w=1260'
- q.page['example'] = ui.form_card(box='1 1 2 2', items=[
- ui.text(content='Add button sends true'),
- ui.facepile(name='facepile', max=4, items=[
- ui.persona(title='John Doe', image=image),
- ui.persona(title='John Doe', image=image),
- ui.persona(title='John Doe'),
- ui.persona(title='John Doe', image=image),
- ui.persona(title='John Doe', image=image),
- ]),
- ui.text(content='Add button sends set value'),
- ui.facepile(name='facepile_value', value='submitted value', max=4, items=[
- ui.persona(title='John Doe', image=image),
- ui.persona(title='John Doe', image=image),
- ui.persona(title='John Doe'),
- ui.persona(title='John Doe', image=image),
- ui.persona(title='John Doe', image=image),
- ]),
- ])
-
- await q.page.save()
diff --git a/spaces/hadasak/SciTrends/main_1.py b/spaces/hadasak/SciTrends/main_1.py
deleted file mode 100644
index c936690f979c2dde9ad4f9b0f875f6fee1bccc9b..0000000000000000000000000000000000000000
--- a/spaces/hadasak/SciTrends/main_1.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import gradio as gr
-import numpy as np
-import pandas as pd
-from save_prediction import save_data_with_prediction
-import datetime
-import plotly.express as px
-
-
-
-# global variables
-data = pd.DataFrame()
-last_term = ""
-terms = []
-shared_output = None
-
-def create_plot(data):
-
- today = datetime.date.today()
- year = today.year - 1
-
- fig = px.scatter(data, x='Year', y='norm_publications_count', color='Term',
- title='Scatter Plot with Color and Symbols')
- fig.update_layout(
- title="Outbreak in ",
- xaxis_title="year",
- yaxis_title="normalized publication count",
- )
- # Add a horizontal line at y=15
- fig.add_shape(type="line", x0=year + 0.5, x1=year + 0.5, y0=0, y1=data['norm_publications_count'].max(),
- line=dict(color="blue", width=2, dash="dot"))
-
- return fig
-
-
-def add_term_to_plot(custom_term,choice):
-
- global shared_output
- global last_term
- global terms
- global data # Indicate that you want to work with the global 'data' variable
- term=""
- print("choice")
- print(choice)
- print("last_term")
- print(terms)
-
- if not custom_term or custom_term in terms:
- if not choice:
- #raise gr.Error("You didn't insert new term")
- return create_plot(data)
- if choice in terms:
- #raise gr.Error("Your choice is already in the graph")
- return create_plot(data)
- else:
- term = choice
- print ("term is choice "+term)
-
- elif not choice or choice in terms:
- if custom_term in terms:
- #raise gr.Error("The term you inserted is already in the graph")
- return create_plot(data)
- else:
- term = custom_term
- print ("term is custom_term "+term)
-
-
- #if both new
- if not term:
- #raise gr.Error("you inserted new terms in both options, the custom term is shown")
- term = custom_term
- print(term)
-
- if len(terms)>10:
- #raise gr.Error("The maximum terms number is 10")
- return create_plot(data)
- else:
- last_term = term
- terms = terms+[term]
- (print(terms))
-
- # get year
- today = datetime.date.today()
- year = today.year - 1
- no_space_term = term.replace(" ", "_")
- print(no_space_term)
-
- path = "model_data/data_with_predictions_" + no_space_term + str(year) + ".csv"
- try:
- save_data_with_prediction(no_space_term)
- except:
- raise gr.Error("There is no data about your term in Pubmed. "
- "Please enter another term and the trend graph will be displayed")
-
- new_term_df = pd.read_csv(path)
-
- data = pd.concat([data, new_term_df], ignore_index=True) # Concatenate DataFrames
- data = data[data['Year'] >= (year-40)]
-
- fig = create_plot(data)
- shared_output = fig
- # update_choice2.update()
- return fig
-
-
-def delete_term(term):
-
- global shared_output
- global terms
- global data
-
- if term in terms:
- terms.remove(term)
- data = data[data["Term"] != term]
- fig = create_plot(data)
- shared_output = fig
- return fig
- else:
- raise gr.Error(term + " is not exist in the graph!")
-
-def clear_all():
-
- global shared_output
- global terms
- global data
-
- if len(terms) > 0:
- terms = []
- data = pd.DataFrame(columns=["Term","Year","norm_publications_count","Data"])
- fig = create_plot(data)
- shared_output = fig
- return fig
- else:
- raise gr.Error("The trends graph is already empty")
- return None
-
-
-
-predefined_terms = [(term, term) for term in np.unique(pd.read_csv("training_data_all.csv")["Term"].to_numpy())]
-description= "Welcome to the predictor of trends in science!\n\n\n" \
- "This tool predicts the popularity of fields in science for the next 6 years.\n" \
- "Get predictions of scientific popularity for any term!\n"\
- "Up to 10 terms can be inserted into the same graph.\n\n" \
- "Popularity of a term is defined as the number of publications in PubMed per year for this term, \n" \
- "normalized to 100, 000 publications..\n\n"\
- "For details of the model and methodology see our paper. If you use us, please cite us!:\n"\
- "Ofer, D., & Linial, M. (2023). Whats next? Forecasting scientific research trends. ArXiv, abs/2305.04133.\n"\
- "Ofer et al., 2023. SciTrends [Internet].\n\n Available from:\n"\
- "https://393853b86b6381cc22.gradio.live\n\n"\
- "contact us at:\n"\
- "Hadasa.kaufman@mail.huji.ac.il\n\n\n"\
- "Developed by Hadasa Kaufman & Dan Ofer"
-
-with gr.Blocks() as demo:
- # Create a Row component to divide the interface into two columns
- with gr.Row():
- # Create a Column for the left side (description)
- with gr.Column():
- gr.Image("logo_SciTrends.png")
- gr.Text(description,label="App Description")
- # Create a Column for the right side (input components and plot)
- with gr.Column():
- favicon="logo_SciTrends.png" # Specify the path to your logo image
- txt1 = gr.components.Textbox(label="Insert a term")
- choice1 = gr.components.Dropdown(label="or choose an example term", choices=predefined_terms)
- btn = gr.Button(value="Submit")
- out1 = gr.Plot(label="plot")
- btn.click(add_term_to_plot, inputs=[txt1, choice1], outputs=out1 )
- remove_term = gr.components.Textbox(label="Insert a term to remove")
- btn = gr.Button(value="Remove term")
- btn.click(delete_term, inputs=remove_term, outputs=out1)
- btn = gr.Button(value="clear all")
- btn.click(clear_all, outputs=out1)
-
-
-
-
-
- live = True
-
-
-if __name__ == "__main__":
- demo.launch(share=True)
diff --git a/spaces/hamacojr/CAT-Seg/cat_seg/utils/__init__.py b/spaces/hamacojr/CAT-Seg/cat_seg/utils/__init__.py
deleted file mode 100644
index 9020c2df23e2af280b7bb168b996ae9eaf312eb8..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/CAT-Seg/cat_seg/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/transform.py b/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/transform.py
deleted file mode 100644
index 0224a0dae4a89fd4bf46c5d27a2bb9377dfa06d4..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/transform.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import warnings
-from dataclasses import dataclass, asdict
-from typing import Any, Dict, Optional, Sequence, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torchvision.transforms.functional as F
-
-from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
- CenterCrop
-
-from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-
-
-@dataclass
-class AugmentationCfg:
- scale: Tuple[float, float] = (0.9, 1.0)
- ratio: Optional[Tuple[float, float]] = None
- color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None
- interpolation: Optional[str] = None
- re_prob: Optional[float] = None
- re_count: Optional[int] = None
- use_timm: bool = False
-
-
-class ResizeMaxSize(nn.Module):
-
- def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):
- super().__init__()
- if not isinstance(max_size, int):
- raise TypeError(f"Size should be int. Got {type(max_size)}")
- self.max_size = max_size
- self.interpolation = interpolation
- self.fn = min if fn == 'min' else min
- self.fill = fill
-
- def forward(self, img):
- if isinstance(img, torch.Tensor):
- height, width = img.shape[:2]
- else:
- width, height = img.size
- scale = self.max_size / float(max(height, width))
- if scale != 1.0:
- new_size = tuple(round(dim * scale) for dim in (height, width))
- img = F.resize(img, new_size, self.interpolation)
- pad_h = self.max_size - new_size[0]
- pad_w = self.max_size - new_size[1]
- img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)
- return img
-
-
-def _convert_to_rgb(image):
- return image.convert('RGB')
-
-
-def image_transform(
- image_size: int,
- is_train: bool,
- mean: Optional[Tuple[float, ...]] = None,
- std: Optional[Tuple[float, ...]] = None,
- resize_longest_max: bool = False,
- fill_color: int = 0,
- aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
-):
- mean = mean or OPENAI_DATASET_MEAN
- if not isinstance(mean, (list, tuple)):
- mean = (mean,) * 3
-
- std = std or OPENAI_DATASET_STD
- if not isinstance(std, (list, tuple)):
- std = (std,) * 3
-
- if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
- # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
- image_size = image_size[0]
-
- if isinstance(aug_cfg, dict):
- aug_cfg = AugmentationCfg(**aug_cfg)
- else:
- aug_cfg = aug_cfg or AugmentationCfg()
- normalize = Normalize(mean=mean, std=std)
- if is_train:
- aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}
- use_timm = aug_cfg_dict.pop('use_timm', False)
- if use_timm:
- from timm.data import create_transform # timm can still be optional
- if isinstance(model.visual.image_size, (tuple, list)):
- assert len(model.visual.image_size) >= 2
- input_size = (3,) + model.visual.image_size[-2:]
- else:
- input_size = (3, model.visual.image_size, model.visual.image_size)
- # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time
- aug_cfg_dict.setdefault('interpolation', 'random')
- aug_cfg_dict.setdefault('color_jitter', None) # disable by default
- train_transform = create_transform(
- input_size=input_size,
- is_training=True,
- hflip=0.,
- mean=image_mean,
- std=image_std,
- re_mode='pixel',
- **aug_cfg_dict,
- )
- else:
- train_transform = Compose([
- RandomResizedCrop(
- image_size,
- scale=aug_cfg_dict.pop('scale'),
- interpolation=InterpolationMode.BICUBIC,
- ),
- _convert_to_rgb,
- ToTensor(),
- normalize,
- ])
- if aug_cfg_dict:
- warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).')
- return train_transform
- else:
- if resize_longest_max:
- transforms = [
- ResizeMaxSize(image_size, fill=fill_color)
- ]
- else:
- transforms = [
- Resize(image_size, interpolation=InterpolationMode.BICUBIC),
- CenterCrop(image_size),
- ]
- transforms.extend([
- _convert_to_rgb,
- ToTensor(),
- normalize,
- ])
- return Compose(transforms)
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py
deleted file mode 100644
index 027fb4598529c0072f670a4776f2c825968f5caf..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-import maskrcnn_benchmark.utils.dist as dist
-
-
-def normalized_positive_map(positive_map):
- positive_map = positive_map.float()
- positive_map_num_pos = positive_map.sum(2)
- positive_map_num_pos[positive_map_num_pos == 0] = 1e-6
- positive_map = positive_map / positive_map_num_pos.unsqueeze(-1)
- return positive_map
-
-
-def pad_tensor_given_dim_length(tensor, dim, length, padding_value=0, batch_first=True):
- new_size = list(tensor.size()[:dim]) + [length] + list(tensor.size()[dim + 1:])
- out_tensor = tensor.data.new(*new_size).fill_(padding_value)
- if batch_first:
- out_tensor[:, :tensor.size(1), ...] = tensor
- else:
- out_tensor[:tensor.size(0), ...] = tensor
- return out_tensor
-
-
-def pad_random_negative_tensor_given_length(positive_tensor, negative_padding_tensor, length=None):
- assert positive_tensor.shape[0] + negative_padding_tensor.shape[0] == length
- return torch.cat((positive_tensor, negative_padding_tensor), dim=0)
-
-
-def gather_tensors(tensor):
- """
- Performs all_gather operation on the provided tensors.
- *** Warning ***: torch.distributed.all_gather has no gradient.
- """
- if not dist.is_dist_avail_and_initialized():
- return torch.stack([tensor], dim=0)
-
- total = dist.get_world_size()
- rank = torch.distributed.get_rank()
- # gathered_normalized_img_emb = [torch.zeros_like(normalized_img_emb) for _ in range(total)]
- # torch.distributed.all_gather(gathered_normalized_img_emb, normalized_img_emb)
-
- tensors_gather = [
- torch.zeros_like(tensor)
- for _ in range(total)
- ]
- torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
-
- # need to do this to restore propagation of the gradients
- tensors_gather[rank] = tensor
- output = torch.stack(tensors_gather, dim=0)
- return output
-
-
-def convert_to_roi_format(boxes):
- concat_boxes = boxes.bbox
- device, dtype = concat_boxes.device, concat_boxes.dtype
- ids = torch.full((len(boxes), 1), 0, dtype=dtype, device=device)
- rois = torch.cat([ids, concat_boxes], dim=1)
- return rois
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/engine/hooks.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/engine/hooks.py
deleted file mode 100644
index e5085b4561302d2328ab505568dec4e9fc5ee0ad..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/engine/hooks.py
+++ /dev/null
@@ -1,427 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import datetime
-import itertools
-import logging
-import os
-import tempfile
-import time
-from collections import Counter
-import torch
-from fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer
-from fvcore.common.file_io import PathManager
-from fvcore.common.timer import Timer
-from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
-
-import detectron2.utils.comm as comm
-from detectron2.evaluation.testing import flatten_results_dict
-from detectron2.utils.events import EventStorage, EventWriter
-
-from .train_loop import HookBase
-
-__all__ = [
- "CallbackHook",
- "IterationTimer",
- "PeriodicWriter",
- "PeriodicCheckpointer",
- "LRScheduler",
- "AutogradProfiler",
- "EvalHook",
- "PreciseBN",
-]
-
-
-"""
-Implement some common hooks.
-"""
-
-
-class CallbackHook(HookBase):
- """
- Create a hook using callback functions provided by the user.
- """
-
- def __init__(self, *, before_train=None, after_train=None, before_step=None, after_step=None):
- """
- Each argument is a function that takes one argument: the trainer.
- """
- self._before_train = before_train
- self._before_step = before_step
- self._after_step = after_step
- self._after_train = after_train
-
- def before_train(self):
- if self._before_train:
- self._before_train(self.trainer)
-
- def after_train(self):
- if self._after_train:
- self._after_train(self.trainer)
- # The functions may be closures that hold reference to the trainer
- # Therefore, delete them to avoid circular reference.
- del self._before_train, self._after_train
- del self._before_step, self._after_step
-
- def before_step(self):
- if self._before_step:
- self._before_step(self.trainer)
-
- def after_step(self):
- if self._after_step:
- self._after_step(self.trainer)
-
-
-class IterationTimer(HookBase):
- """
- Track the time spent for each iteration (each run_step call in the trainer).
- Print a summary in the end of training.
-
- This hook uses the time between the call to its :meth:`before_step`
- and :meth:`after_step` methods.
- Under the convention that :meth:`before_step` of all hooks should only
- take negligible amount of time, the :class:`IterationTimer` hook should be
- placed at the beginning of the list of hooks to obtain accurate timing.
- """
-
- def __init__(self, warmup_iter=3):
- """
- Args:
- warmup_iter (int): the number of iterations at the beginning to exclude
- from timing.
- """
- self._warmup_iter = warmup_iter
- self._step_timer = Timer()
- self._start_time = time.perf_counter()
- self._total_timer = Timer()
-
- def before_train(self):
- self._start_time = time.perf_counter()
- self._total_timer.reset()
- self._total_timer.pause()
-
- def after_train(self):
- logger = logging.getLogger(__name__)
- total_time = time.perf_counter() - self._start_time
- total_time_minus_hooks = self._total_timer.seconds()
- hook_time = total_time - total_time_minus_hooks
-
- num_iter = self.trainer.iter + 1 - self.trainer.start_iter - self._warmup_iter
-
- if num_iter > 0 and total_time_minus_hooks > 0:
- # Speed is meaningful only after warmup
- # NOTE this format is parsed by grep in some scripts
- logger.info(
- "Overall training speed: {} iterations in {} ({:.4f} s / it)".format(
- num_iter,
- str(datetime.timedelta(seconds=int(total_time_minus_hooks))),
- total_time_minus_hooks / num_iter,
- )
- )
-
- logger.info(
- "Total training time: {} ({} on hooks)".format(
- str(datetime.timedelta(seconds=int(total_time))),
- str(datetime.timedelta(seconds=int(hook_time))),
- )
- )
-
- def before_step(self):
- self._step_timer.reset()
- self._total_timer.resume()
-
- def after_step(self):
- # +1 because we're in after_step
- iter_done = self.trainer.iter - self.trainer.start_iter + 1
- if iter_done >= self._warmup_iter:
- sec = self._step_timer.seconds()
- self.trainer.storage.put_scalars(time=sec)
- else:
- self._start_time = time.perf_counter()
- self._total_timer.reset()
-
- self._total_timer.pause()
-
-
-class PeriodicWriter(HookBase):
- """
- Write events to EventStorage periodically.
-
- It is executed every ``period`` iterations and after the last iteration.
- """
-
- def __init__(self, writers, period=20):
- """
- Args:
- writers (list[EventWriter]): a list of EventWriter objects
- period (int):
- """
- self._writers = writers
- for w in writers:
- assert isinstance(w, EventWriter), w
- self._period = period
-
- def after_step(self):
- if (self.trainer.iter + 1) % self._period == 0 or (
- self.trainer.iter == self.trainer.max_iter - 1
- ):
- for writer in self._writers:
- writer.write()
-
- def after_train(self):
- for writer in self._writers:
- writer.close()
-
-
-class PeriodicCheckpointer(_PeriodicCheckpointer, HookBase):
- """
- Same as :class:`detectron2.checkpoint.PeriodicCheckpointer`, but as a hook.
-
- Note that when used as a hook,
- it is unable to save additional data other than what's defined
- by the given `checkpointer`.
-
- It is executed every ``period`` iterations and after the last iteration.
- """
-
- def before_train(self):
- self.max_iter = self.trainer.max_iter
-
- def after_step(self):
- # No way to use **kwargs
- self.step(self.trainer.iter)
-
-
-class LRScheduler(HookBase):
- """
- A hook which executes a torch builtin LR scheduler and summarizes the LR.
- It is executed after every iteration.
- """
-
- def __init__(self, optimizer, scheduler):
- """
- Args:
- optimizer (torch.optim.Optimizer):
- scheduler (torch.optim._LRScheduler)
- """
- self._optimizer = optimizer
- self._scheduler = scheduler
-
- # NOTE: some heuristics on what LR to summarize
- # summarize the param group with most parameters
- largest_group = max(len(g["params"]) for g in optimizer.param_groups)
-
- if largest_group == 1:
- # If all groups have one parameter,
- # then find the most common initial LR, and use it for summary
- lr_count = Counter([g["lr"] for g in optimizer.param_groups])
- lr = lr_count.most_common()[0][0]
- for i, g in enumerate(optimizer.param_groups):
- if g["lr"] == lr:
- self._best_param_group_id = i
- break
- else:
- for i, g in enumerate(optimizer.param_groups):
- if len(g["params"]) == largest_group:
- self._best_param_group_id = i
- break
-
- def after_step(self):
- lr = self._optimizer.param_groups[self._best_param_group_id]["lr"]
- self.trainer.storage.put_scalar("lr", lr, smoothing_hint=False)
- self._scheduler.step()
-
-
-class AutogradProfiler(HookBase):
- """
- A hook which runs `torch.autograd.profiler.profile`.
-
- Examples:
-
- .. code-block:: python
-
- hooks.AutogradProfiler(
- lambda trainer: trainer.iter > 10 and trainer.iter < 20, self.cfg.OUTPUT_DIR
- )
-
- The above example will run the profiler for iteration 10~20 and dump
- results to ``OUTPUT_DIR``. We did not profile the first few iterations
- because they are typically slower than the rest.
- The result files can be loaded in the ``chrome://tracing`` page in chrome browser.
-
- Note:
- When used together with NCCL on older version of GPUs,
- autograd profiler may cause deadlock because it unnecessarily allocates
- memory on every device it sees. The memory management calls, if
- interleaved with NCCL calls, lead to deadlock on GPUs that do not
- support `cudaLaunchCooperativeKernelMultiDevice`.
- """
-
- def __init__(self, enable_predicate, output_dir, *, use_cuda=True):
- """
- Args:
- enable_predicate (callable[trainer -> bool]): a function which takes a trainer,
- and returns whether to enable the profiler.
- It will be called once every step, and can be used to select which steps to profile.
- output_dir (str): the output directory to dump tracing files.
- use_cuda (bool): same as in `torch.autograd.profiler.profile`.
- """
- self._enable_predicate = enable_predicate
- self._use_cuda = use_cuda
- self._output_dir = output_dir
-
- def before_step(self):
- if self._enable_predicate(self.trainer):
- self._profiler = torch.autograd.profiler.profile(use_cuda=self._use_cuda)
- self._profiler.__enter__()
- else:
- self._profiler = None
-
- def after_step(self):
- if self._profiler is None:
- return
- self._profiler.__exit__(None, None, None)
- PathManager.mkdirs(self._output_dir)
- out_file = os.path.join(
- self._output_dir, "profiler-trace-iter{}.json".format(self.trainer.iter)
- )
- if "://" not in out_file:
- self._profiler.export_chrome_trace(out_file)
- else:
- # Support non-posix filesystems
- with tempfile.TemporaryDirectory(prefix="detectron2_profiler") as d:
- tmp_file = os.path.join(d, "tmp.json")
- self._profiler.export_chrome_trace(tmp_file)
- with open(tmp_file) as f:
- content = f.read()
- with PathManager.open(out_file, "w") as f:
- f.write(content)
-
-
-class EvalHook(HookBase):
- """
- Run an evaluation function periodically, and at the end of training.
-
- It is executed every ``eval_period`` iterations and after the last iteration.
- """
-
- def __init__(self, eval_period, eval_function):
- """
- Args:
- eval_period (int): the period to run `eval_function`.
- eval_function (callable): a function which takes no arguments, and
- returns a nested dict of evaluation metrics.
-
- Note:
- This hook must be enabled in all or none workers.
- If you would like only certain workers to perform evaluation,
- give other workers a no-op function (`eval_function=lambda: None`).
- """
- self._period = eval_period
- self._func = eval_function
-
- def _do_eval(self):
- results = self._func()
-
- if results:
- assert isinstance(
- results, dict
- ), "Eval function must return a dict. Got {} instead.".format(results)
-
- flattened_results = flatten_results_dict(results)
- for k, v in flattened_results.items():
- try:
- v = float(v)
- except Exception:
- raise ValueError(
- "[EvalHook] eval_function should return a nested dict of float. "
- "Got '{}: {}' instead.".format(k, v)
- )
- self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False)
-
- # Evaluation may take different time among workers.
- # A barrier make them start the next iteration together.
- comm.synchronize()
-
- def after_step(self):
- next_iter = self.trainer.iter + 1
- is_final = next_iter == self.trainer.max_iter
- if is_final or (self._period > 0 and next_iter % self._period == 0):
- self._do_eval()
-
- def after_train(self):
- # func is likely a closure that holds reference to the trainer
- # therefore we clean it to avoid circular reference in the end
- del self._func
-
-
-class PreciseBN(HookBase):
- """
- The standard implementation of BatchNorm uses EMA in inference, which is
- sometimes suboptimal.
- This class computes the true average of statistics rather than the moving average,
- and put true averages to every BN layer in the given model.
-
- It is executed every ``period`` iterations and after the last iteration.
- """
-
- def __init__(self, period, model, data_loader, num_iter):
- """
- Args:
- period (int): the period this hook is run, or 0 to not run during training.
- The hook will always run in the end of training.
- model (nn.Module): a module whose all BN layers in training mode will be
- updated by precise BN.
- Note that user is responsible for ensuring the BN layers to be
- updated are in training mode when this hook is triggered.
- data_loader (iterable): it will produce data to be run by `model(data)`.
- num_iter (int): number of iterations used to compute the precise
- statistics.
- """
- self._logger = logging.getLogger(__name__)
- if len(get_bn_modules(model)) == 0:
- self._logger.info(
- "PreciseBN is disabled because model does not contain BN layers in training mode."
- )
- self._disabled = True
- return
-
- self._model = model
- self._data_loader = data_loader
- self._num_iter = num_iter
- self._period = period
- self._disabled = False
-
- self._data_iter = None
-
- def after_step(self):
- next_iter = self.trainer.iter + 1
- is_final = next_iter == self.trainer.max_iter
- if is_final or (self._period > 0 and next_iter % self._period == 0):
- self.update_stats()
-
- def update_stats(self):
- """
- Update the model with precise statistics. Users can manually call this method.
- """
- if self._disabled:
- return
-
- if self._data_iter is None:
- self._data_iter = iter(self._data_loader)
-
- def data_loader():
- for num_iter in itertools.count(1):
- if num_iter % 100 == 0:
- self._logger.info(
- "Running precise-BN ... {}/{} iterations.".format(num_iter, self._num_iter)
- )
- # This way we can reuse the same iterator
- yield next(self._data_iter)
-
- with EventStorage(): # capture events in a new storage to discard them
- self._logger.info(
- "Running precise-BN for {} iterations... ".format(self._num_iter)
- + "Note that this could produce different statistics every time."
- )
- update_bn_stats(self._model, data_loader(), self._num_iter)
diff --git a/spaces/hetorol845/MiDaS/README.md b/spaces/hetorol845/MiDaS/README.md
deleted file mode 100644
index 8cbdc0422eadfe3d403a4b25e554e06a73af3acf..0000000000000000000000000000000000000000
--- a/spaces/hetorol845/MiDaS/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: MiDaS
-emoji: 😻
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
-duplicated_from: pytorch/MiDaS
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/huaiji3y/bingo-Public/src/pages/api/kblob.ts b/spaces/huaiji3y/bingo-Public/src/pages/api/kblob.ts
deleted file mode 100644
index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/pages/api/kblob.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-
-const API_DOMAIN = 'https://bing.vcanbb.top'
-
-export const config = {
- api: {
- bodyParser: {
- sizeLimit: '10mb' // Set desired value here
- }
- }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- try {
- const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
-
- const formData = new FormData()
- formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
- if (imageBase64) {
- formData.append('imageBase64', imageBase64)
- }
-
- const response = await fetch(`${API_DOMAIN}/images/kblob`,
- {
- method: 'POST',
- body: formData.getBuffer(),
- headers: {
- "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-platform": "\"Windows\"",
- "Referer": `${API_DOMAIN}/web/index.html`,
- "Referrer-Policy": "origin-when-cross-origin",
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- ...formData.getHeaders()
- }
- }
- ).then(res => res.text())
-
- res.writeHead(200, {
- 'Content-Type': 'application/json',
- })
- res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } }))
- } catch (e) {
- return res.json({
- result: {
- value: 'UploadFailed',
- message: `${e}`
- }
- })
- }
-}
diff --git a/spaces/huggingface-projects/Leaderboard-Restart/README.md b/spaces/huggingface-projects/Leaderboard-Restart/README.md
deleted file mode 100644
index 67b4dafad0bf837e84b98e59a2fc430e6e7b5934..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/Leaderboard-Restart/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Leaderboard Restart
-emoji: 📊
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huggingface-projects/diffusers-gallery/README.md b/spaces/huggingface-projects/diffusers-gallery/README.md
deleted file mode 100644
index 1abc8c56f44b9b35c15ab0b41d2e7a4eaf071f32..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/diffusers-gallery/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Diffusers Gallery
-emoji: 🖼️
-colorFrom: red
-colorTo: green
-sdk: static
-app_port: 8080
-fullWidth: true
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/3_ \360\237\217\227_Uses.py" "b/spaces/huggingface/Model_Cards_Writing_Tool/pages/3_ \360\237\217\227_Uses.py"
deleted file mode 100644
index 6e0338ff61ae30e1b6868cad322e041492cb0688..0000000000000000000000000000000000000000
--- "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/3_ \360\237\217\227_Uses.py"
+++ /dev/null
@@ -1,47 +0,0 @@
-import streamlit as st
-from persist import persist, load_widget_state
-
-global variable_output
-
-def main():
-
- cs_body()
-
-def cs_body():
-
- st.markdown('# Uses')
- st.text_area("This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.")
- left, right = st.columns([2,4])
-
- #st.markdown('### Model Description')
-
-
- with left:
- st.write("\n")
- st.write("\n")
- st.markdown('### Direct Use:')
- st.write("\n")
- st.write("\n")
- st.write("\n")
- st.write("\n")
- st.write("\n")
- st.write("\n")
- #st.write("\n")
- st.markdown('### Downstream Use [Optional]:')
- st.write("\n")
- st.write("\n")
- st.write("\n")
- st.write("\n")
- st.markdown('### Out-of-Scope Use:')
-
- with right:
- st.text_area("",help="How can this model be used, without additional post-processing or further pipeline work?", key=persist("Direct_Use"))
- st.text_area("",help="How can this model be used, when incorporated into another system?",key=persist("Downstream_Use"))
- st.text_area("", help="What tasks will the model not work for?", key=persist("Out-of-Scope_Use"))
-
-
-
-
-if __name__ == '__main__':
- load_widget_state()
- main()
\ No newline at end of file
diff --git a/spaces/humblepenguin/mental-health-chatbot/README.md b/spaces/humblepenguin/mental-health-chatbot/README.md
deleted file mode 100644
index e67156818a04285025469ca181d31afa2d06bf58..0000000000000000000000000000000000000000
--- a/spaces/humblepenguin/mental-health-chatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mental Health Chatbot
-emoji: 🦀
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-
-
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/partial_fc.py b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/partial_fc.py
deleted file mode 100644
index f7891527d6c396a6b51a67daf06593d4db5cce43..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/partial_fc.py
+++ /dev/null
@@ -1,490 +0,0 @@
-import collections
-from typing import Callable
-
-import torch
-from torch import distributed
-from torch.nn.functional import linear
-from torch.nn.functional import normalize
-
-
-class PartialFC(torch.nn.Module):
- """
- https://arxiv.org/abs/2203.15565
- A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).
-
- When sample rate less than 1, in each iteration, positive class centers and a random subset of
- negative class centers are selected to compute the margin-based softmax loss, all class
- centers are still maintained throughout the whole training process, but only a subset is
- selected and updated in each iteration.
-
- .. note::
- When sample rate equal to 1, Partial FC is equal to model parallelism(default sample rate is 1).
-
- Example:
- --------
- >>> module_pfc = PartialFC(embedding_size=512, num_classes=8000000, sample_rate=0.2)
- >>> for img, labels in data_loader:
- >>> embeddings = net(img)
- >>> loss = module_pfc(embeddings, labels, optimizer)
- >>> loss.backward()
- >>> optimizer.step()
- """
-
- _version = 1
-
- def __init__(
- self,
- margin_loss: Callable,
- embedding_size: int,
- num_classes: int,
- sample_rate: float = 1.0,
- fp16: bool = False,
- ):
- """
- Paramenters:
- -----------
- embedding_size: int
- The dimension of embedding, required
- num_classes: int
- Total number of classes, required
- sample_rate: float
- The rate of negative centers participating in the calculation, default is 1.0.
- """
- super(PartialFC, self).__init__()
- assert distributed.is_initialized(), "must initialize distributed before create this"
- self.rank = distributed.get_rank()
- self.world_size = distributed.get_world_size()
-
- self.dist_cross_entropy = DistCrossEntropy()
- self.embedding_size = embedding_size
- self.sample_rate: float = sample_rate
- self.fp16 = fp16
- self.num_local: int = num_classes // self.world_size + int(self.rank < num_classes % self.world_size)
- self.class_start: int = num_classes // self.world_size * self.rank + min(
- self.rank, num_classes % self.world_size
- )
- self.num_sample: int = int(self.sample_rate * self.num_local)
- self.last_batch_size: int = 0
- self.weight: torch.Tensor
- self.weight_mom: torch.Tensor
- self.weight_activated: torch.nn.Parameter
- self.weight_activated_mom: torch.Tensor
- self.is_updated: bool = True
- self.init_weight_update: bool = True
-
- if self.sample_rate < 1:
- self.register_buffer("weight", tensor=torch.normal(0, 0.01, (self.num_local, embedding_size)))
- self.register_buffer("weight_mom", tensor=torch.zeros_like(self.weight))
- self.register_parameter("weight_activated", param=torch.nn.Parameter(torch.empty(0, 0)))
- self.register_buffer("weight_activated_mom", tensor=torch.empty(0, 0))
- self.register_buffer("weight_index", tensor=torch.empty(0, 0))
- else:
- self.weight_activated = torch.nn.Parameter(torch.normal(0, 0.01, (self.num_local, embedding_size)))
-
- # margin_loss
- if isinstance(margin_loss, Callable):
- self.margin_softmax = margin_loss
- else:
- raise
-
- @torch.no_grad()
- def sample(self, labels: torch.Tensor, index_positive: torch.Tensor, optimizer: torch.optim.Optimizer):
- """
- This functions will change the value of labels
-
- Parameters:
- -----------
- labels: torch.Tensor
- pass
- index_positive: torch.Tensor
- pass
- optimizer: torch.optim.Optimizer
- pass
- """
- positive = torch.unique(labels[index_positive], sorted=True).cuda()
- if self.num_sample - positive.size(0) >= 0:
- perm = torch.rand(size=[self.num_local]).cuda()
- perm[positive] = 2.0
- index = torch.topk(perm, k=self.num_sample)[1].cuda()
- index = index.sort()[0].cuda()
- else:
- index = positive
- self.weight_index = index
-
- labels[index_positive] = torch.searchsorted(index, labels[index_positive])
-
- self.weight_activated = torch.nn.Parameter(self.weight[self.weight_index])
- self.weight_activated_mom = self.weight_mom[self.weight_index]
-
- if isinstance(optimizer, torch.optim.SGD):
- # TODO the params of partial fc must be last in the params list
- optimizer.state.pop(optimizer.param_groups[-1]["params"][0], None)
- optimizer.param_groups[-1]["params"][0] = self.weight_activated
- optimizer.state[self.weight_activated]["momentum_buffer"] = self.weight_activated_mom
- else:
- raise
-
- @torch.no_grad()
- def update(self):
- """partial weight to global"""
- if self.init_weight_update:
- self.init_weight_update = False
- return
-
- if self.sample_rate < 1:
- self.weight[self.weight_index] = self.weight_activated
- self.weight_mom[self.weight_index] = self.weight_activated_mom
-
- def forward(
- self,
- local_embeddings: torch.Tensor,
- local_labels: torch.Tensor,
- optimizer: torch.optim.Optimizer,
- ):
- """
- Parameters:
- ----------
- local_embeddings: torch.Tensor
- feature embeddings on each GPU(Rank).
- local_labels: torch.Tensor
- labels on each GPU(Rank).
-
- Returns:
- -------
- loss: torch.Tensor
- pass
- """
- local_labels.squeeze_()
- local_labels = local_labels.long()
- self.update()
-
- batch_size = local_embeddings.size(0)
- if self.last_batch_size == 0:
- self.last_batch_size = batch_size
- assert self.last_batch_size == batch_size, "last batch size do not equal current batch size: {} vs {}".format(
- self.last_batch_size, batch_size
- )
-
- _gather_embeddings = [torch.zeros((batch_size, self.embedding_size)).cuda() for _ in range(self.world_size)]
- _gather_labels = [torch.zeros(batch_size).long().cuda() for _ in range(self.world_size)]
- _list_embeddings = AllGather(local_embeddings, *_gather_embeddings)
- distributed.all_gather(_gather_labels, local_labels)
-
- embeddings = torch.cat(_list_embeddings)
- labels = torch.cat(_gather_labels)
-
- labels = labels.view(-1, 1)
- index_positive = (self.class_start <= labels) & (labels < self.class_start + self.num_local)
- labels[~index_positive] = -1
- labels[index_positive] -= self.class_start
-
- if self.sample_rate < 1:
- self.sample(labels, index_positive, optimizer)
-
- with torch.cuda.amp.autocast(self.fp16):
- norm_embeddings = normalize(embeddings)
- norm_weight_activated = normalize(self.weight_activated)
- logits = linear(norm_embeddings, norm_weight_activated)
- if self.fp16:
- logits = logits.float()
- logits = logits.clamp(-1, 1)
-
- logits = self.margin_softmax(logits, labels)
- loss = self.dist_cross_entropy(logits, labels)
- return loss
-
- def state_dict(self, destination=None, prefix="", keep_vars=False):
- if destination is None:
- destination = collections.OrderedDict()
- destination._metadata = collections.OrderedDict()
-
- for name, module in self._modules.items():
- if module is not None:
- module.state_dict(destination, prefix + name + ".", keep_vars=keep_vars)
- if self.sample_rate < 1:
- destination["weight"] = self.weight.detach()
- else:
- destination["weight"] = self.weight_activated.data.detach()
- return destination
-
- def load_state_dict(self, state_dict, strict: bool = True):
- if self.sample_rate < 1:
- self.weight = state_dict["weight"].to(self.weight.device)
- self.weight_mom.zero_()
- self.weight_activated.data.zero_()
- self.weight_activated_mom.zero_()
- self.weight_index.zero_()
- else:
- self.weight_activated.data = state_dict["weight"].to(self.weight_activated.data.device)
-
-
-class PartialFCAdamW(torch.nn.Module):
- def __init__(
- self,
- margin_loss: Callable,
- embedding_size: int,
- num_classes: int,
- sample_rate: float = 1.0,
- fp16: bool = False,
- ):
- """
- Paramenters:
- -----------
- embedding_size: int
- The dimension of embedding, required
- num_classes: int
- Total number of classes, required
- sample_rate: float
- The rate of negative centers participating in the calculation, default is 1.0.
- """
- super(PartialFCAdamW, self).__init__()
- assert distributed.is_initialized(), "must initialize distributed before create this"
- self.rank = distributed.get_rank()
- self.world_size = distributed.get_world_size()
-
- self.dist_cross_entropy = DistCrossEntropy()
- self.embedding_size = embedding_size
- self.sample_rate: float = sample_rate
- self.fp16 = fp16
- self.num_local: int = num_classes // self.world_size + int(self.rank < num_classes % self.world_size)
- self.class_start: int = num_classes // self.world_size * self.rank + min(
- self.rank, num_classes % self.world_size
- )
- self.num_sample: int = int(self.sample_rate * self.num_local)
- self.last_batch_size: int = 0
- self.weight: torch.Tensor
- self.weight_exp_avg: torch.Tensor
- self.weight_exp_avg_sq: torch.Tensor
- self.weight_activated: torch.nn.Parameter
- self.weight_activated_exp_avg: torch.Tensor
- self.weight_activated_exp_avg_sq: torch.Tensor
-
- self.is_updated: bool = True
- self.init_weight_update: bool = True
-
- if self.sample_rate < 1:
- self.register_buffer("weight", tensor=torch.normal(0, 0.01, (self.num_local, embedding_size)))
- self.register_buffer("weight_exp_avg", tensor=torch.zeros_like(self.weight))
- self.register_buffer("weight_exp_avg_sq", tensor=torch.zeros_like(self.weight))
- self.register_parameter("weight_activated", param=torch.nn.Parameter(torch.empty(0, 0)))
- self.register_buffer("weight_activated_exp_avg", tensor=torch.empty(0, 0))
- self.register_buffer("weight_activated_exp_avg_sq", tensor=torch.empty(0, 0))
- else:
- self.weight_activated = torch.nn.Parameter(torch.normal(0, 0.01, (self.num_local, embedding_size)))
- self.step = 0
-
- if isinstance(margin_loss, Callable):
- self.margin_softmax = margin_loss
- else:
- raise
-
- @torch.no_grad()
- def sample(self, labels, index_positive, optimizer):
- self.step += 1
- positive = torch.unique(labels[index_positive], sorted=True).cuda()
- if self.num_sample - positive.size(0) >= 0:
- perm = torch.rand(size=[self.num_local]).cuda()
- perm[positive] = 2.0
- index = torch.topk(perm, k=self.num_sample)[1].cuda()
- index = index.sort()[0].cuda()
- else:
- index = positive
- self.weight_index = index
- labels[index_positive] = torch.searchsorted(index, labels[index_positive])
- self.weight_activated = torch.nn.Parameter(self.weight[self.weight_index])
- self.weight_activated_exp_avg = self.weight_exp_avg[self.weight_index]
- self.weight_activated_exp_avg_sq = self.weight_exp_avg_sq[self.weight_index]
-
- if isinstance(optimizer, (torch.optim.Adam, torch.optim.AdamW)):
- # TODO the params of partial fc must be last in the params list
- optimizer.state.pop(optimizer.param_groups[-1]["params"][0], None)
- optimizer.param_groups[-1]["params"][0] = self.weight_activated
- optimizer.state[self.weight_activated]["exp_avg"] = self.weight_activated_exp_avg
- optimizer.state[self.weight_activated]["exp_avg_sq"] = self.weight_activated_exp_avg_sq
- optimizer.state[self.weight_activated]["step"] = self.step
- else:
- raise
-
- @torch.no_grad()
- def update(self):
- """partial weight to global"""
- if self.init_weight_update:
- self.init_weight_update = False
- return
-
- if self.sample_rate < 1:
- self.weight[self.weight_index] = self.weight_activated
- self.weight_exp_avg[self.weight_index] = self.weight_activated_exp_avg
- self.weight_exp_avg_sq[self.weight_index] = self.weight_activated_exp_avg_sq
-
- def forward(
- self,
- local_embeddings: torch.Tensor,
- local_labels: torch.Tensor,
- optimizer: torch.optim.Optimizer,
- ):
- """
- Parameters:
- ----------
- local_embeddings: torch.Tensor
- feature embeddings on each GPU(Rank).
- local_labels: torch.Tensor
- labels on each GPU(Rank).
-
- Returns:
- -------
- loss: torch.Tensor
- pass
- """
- local_labels.squeeze_()
- local_labels = local_labels.long()
- self.update()
-
- batch_size = local_embeddings.size(0)
- if self.last_batch_size == 0:
- self.last_batch_size = batch_size
- assert self.last_batch_size == batch_size, "last batch size do not equal current batch size: {} vs {}".format(
- self.last_batch_size, batch_size
- )
-
- _gather_embeddings = [torch.zeros((batch_size, self.embedding_size)).cuda() for _ in range(self.world_size)]
- _gather_labels = [torch.zeros(batch_size).long().cuda() for _ in range(self.world_size)]
- _list_embeddings = AllGather(local_embeddings, *_gather_embeddings)
- distributed.all_gather(_gather_labels, local_labels)
-
- embeddings = torch.cat(_list_embeddings)
- labels = torch.cat(_gather_labels)
-
- labels = labels.view(-1, 1)
- index_positive = (self.class_start <= labels) & (labels < self.class_start + self.num_local)
- labels[~index_positive] = -1
- labels[index_positive] -= self.class_start
-
- if self.sample_rate < 1:
- self.sample(labels, index_positive, optimizer)
-
- with torch.cuda.amp.autocast(self.fp16):
- norm_embeddings = normalize(embeddings)
- norm_weight_activated = normalize(self.weight_activated)
- logits = linear(norm_embeddings, norm_weight_activated)
- if self.fp16:
- logits = logits.float()
- logits = logits.clamp(-1, 1)
-
- logits = self.margin_softmax(logits, labels)
- loss = self.dist_cross_entropy(logits, labels)
- return loss
-
- def state_dict(self, destination=None, prefix="", keep_vars=False):
- if destination is None:
- destination = collections.OrderedDict()
- destination._metadata = collections.OrderedDict()
-
- for name, module in self._modules.items():
- if module is not None:
- module.state_dict(destination, prefix + name + ".", keep_vars=keep_vars)
- if self.sample_rate < 1:
- destination["weight"] = self.weight.detach()
- else:
- destination["weight"] = self.weight_activated.data.detach()
- return destination
-
- def load_state_dict(self, state_dict, strict: bool = True):
- if self.sample_rate < 1:
- self.weight = state_dict["weight"].to(self.weight.device)
- self.weight_exp_avg.zero_()
- self.weight_exp_avg_sq.zero_()
- self.weight_activated.data.zero_()
- self.weight_activated_exp_avg.zero_()
- self.weight_activated_exp_avg_sq.zero_()
- else:
- self.weight_activated.data = state_dict["weight"].to(self.weight_activated.data.device)
-
-
-class DistCrossEntropyFunc(torch.autograd.Function):
- """
- CrossEntropy loss is calculated in parallel, allreduce denominator into single gpu and calculate softmax.
- Implemented of ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):
- """
-
- @staticmethod
- def forward(ctx, logits: torch.Tensor, label: torch.Tensor):
- """ """
- batch_size = logits.size(0)
- # for numerical stability
- max_logits, _ = torch.max(logits, dim=1, keepdim=True)
- # local to global
- distributed.all_reduce(max_logits, distributed.ReduceOp.MAX)
- logits.sub_(max_logits)
- logits.exp_()
- sum_logits_exp = torch.sum(logits, dim=1, keepdim=True)
- # local to global
- distributed.all_reduce(sum_logits_exp, distributed.ReduceOp.SUM)
- logits.div_(sum_logits_exp)
- index = torch.where(label != -1)[0]
- # loss
- loss = torch.zeros(batch_size, 1, device=logits.device)
- loss[index] = logits[index].gather(1, label[index])
- distributed.all_reduce(loss, distributed.ReduceOp.SUM)
- ctx.save_for_backward(index, logits, label)
- return loss.clamp_min_(1e-30).log_().mean() * (-1)
-
- @staticmethod
- def backward(ctx, loss_gradient):
- """
- Args:
- loss_grad (torch.Tensor): gradient backward by last layer
- Returns:
- gradients for each input in forward function
- `None` gradients for one-hot label
- """
- (
- index,
- logits,
- label,
- ) = ctx.saved_tensors
- batch_size = logits.size(0)
- one_hot = torch.zeros(size=[index.size(0), logits.size(1)], device=logits.device)
- one_hot.scatter_(1, label[index], 1)
- logits[index] -= one_hot
- logits.div_(batch_size)
- return logits * loss_gradient.item(), None
-
-
-class DistCrossEntropy(torch.nn.Module):
- def __init__(self):
- super(DistCrossEntropy, self).__init__()
-
- def forward(self, logit_part, label_part):
- return DistCrossEntropyFunc.apply(logit_part, label_part)
-
-
-class AllGatherFunc(torch.autograd.Function):
- """AllGather op with gradient backward"""
-
- @staticmethod
- def forward(ctx, tensor, *gather_list):
- gather_list = list(gather_list)
- distributed.all_gather(gather_list, tensor)
- return tuple(gather_list)
-
- @staticmethod
- def backward(ctx, *grads):
- grad_list = list(grads)
- rank = distributed.get_rank()
- grad_out = grad_list[rank]
-
- dist_ops = [
- distributed.reduce(grad_out, rank, distributed.ReduceOp.SUM, async_op=True)
- if i == rank
- else distributed.reduce(grad_list[i], i, distributed.ReduceOp.SUM, async_op=True)
- for i in range(distributed.get_world_size())
- ]
- for _op in dist_ops:
- _op.wait()
-
- grad_out *= len(grad_list) # cooperate with distributed loss function
- return (grad_out, *[None for _ in range(len(grad_list))])
-
-
-AllGather = AllGatherFunc.apply
diff --git a/spaces/inamXcontru/PoeticTTS/Adobe Scan PDF Business Card Scanner With OCR V19.10.01 [Latest].md b/spaces/inamXcontru/PoeticTTS/Adobe Scan PDF Business Card Scanner With OCR V19.10.01 [Latest].md
deleted file mode 100644
index 5f678cbd226ebac7b709895760ae494d01c59468..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Adobe Scan PDF Business Card Scanner With OCR V19.10.01 [Latest].md
+++ /dev/null
@@ -1,6 +0,0 @@
-Adobe Scan: PDF Business Card Scanner with OCR v19.10.01 [Latest] Download File → https://gohhs.com/2uz4fp
diff --git a/spaces/inamXcontru/PoeticTTS/Airbag Universal Repair 3 [BETTER].md b/spaces/inamXcontru/PoeticTTS/Airbag Universal Repair 3 [BETTER].md
deleted file mode 100644
index 0ed8087ed4008a291d446a2cc148a5ce7f6cf746..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Airbag Universal Repair 3 [BETTER].md
+++ /dev/null
@@ -1,6 +0,0 @@
-airbag universal repair 3 Download — https://gohhs.com/2uz3yU
diff --git a/spaces/inamXcontru/PoeticTTS/Arroway Textures Collection Torrent REPACK How to Create Realistic and Stunning Wood Materials for Your 3D Projects.md b/spaces/inamXcontru/PoeticTTS/Arroway Textures Collection Torrent REPACK How to Create Realistic and Stunning Wood Materials for Your 3D Projects.md
deleted file mode 100644
index c6150cc4461228f21330dd9ce6f9458e3d81d9cb..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Arroway Textures Collection Torrent REPACK How to Create Realistic and Stunning Wood Materials for Your 3D Projects.md
+++ /dev/null
@@ -1,14 +0,0 @@
-
-The theme of this collection is natural stone masonry and pavement. It contains 60 unique multi-layered hi-res textures (each consisting of diffuse, displacement and specularity map), covering a total area of over 1700m².
-Arroway Textures Collection Torrent REPACK Download Zip - https://gohhs.com/2uz3Yj
This collection contains textures of the following categories:
-Our original Wood Veneers series was released between 2008 and 2010 to great success. It is still the most complete and, in our opinion, best collection of wood textures on the market. But a lot has changed since then in the 3D visualization business. Unbiased rendering gained market dominance which lead to a much greater focus on physical realism in general. Even in real-time visualization, physically based rendering is now a common approach.
-We have also learned a lot in the past years and developed new methods and techniques to capture materials and create textures that allow for photo-real rendering results. We therefore decided to use that knowledge and experience to produce an updated version of our veneer collection; to bring the textures up-to-date with our current workflow and the standard of quality for which we aim.
-We are therefore especially proud to present this first installment of our brand-new Design|Craft series: A collection of high-quality leather textures that hopefully will make the life of anyone doing interior visualizations a bit easier.
-
-Arroway Textures Concrete is a simple solution for designers and architectures to preview the different type of texture for their projects and chose the right one. Designers can also provide capable textures to their clients as an option for choosing to go with which one. Each of the textures includes 3 Maps which are Spectular, Bumps, and Diffuse. This package includes collection contains 50 multi-layered high-resolution textures with the 3 maps. All the texture maps delivered with lossless compressed PNG files, maps tile perfectly, print-friendly, and more. You can also download DS SOLIDWORKS PCB 2016.
-This collection contains 20 high-res textures for various common fiberboard and particleboard materials. We have chosen materials that have an interesting and characteristic appearance, while also being very versatile in use, such as MDF, Hardboard, Softboard, OSB, chipboard, as well as cork.
-The manufacturing of wood veneers takes great skill as well as experience. Similarly, the creation of textures out of such veneers is a very time and resources consuming task. The result is an indispensable collection which is unique in its versatility and quality.
-This collection offers 50 fabric textures within a broad range of types and styles. We focused mostly on standard types to create textures with great versatility that can be easily customized for various purposes.
aaccfb2cb3Sony Vegas Pro 13 Crack serial number is video professional that gets exactly the right tool to enhance their recording feature. This software offers the whole range of features, and it can cope with the variety of formats even from fewer HD resolution and new devices. It supports real-time performance and renders times with Intel Quick Sync Video support via MAGIX AVC codec and secondly it supports modern graphics card via MAGIX AVC codec. The Picture-in-picture plugin gives you real-time controls on the preview of windows for precise placement, movement, and sizing of the video. Moreover, it has included one of the most demanded features of File swapping. File-swapping makes it is possible to work with one set of files like lower resolution proxy files. It is perfect for times when user editing gets bogged down up to 4K files.
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diff --git a/spaces/inreVtussa/clothingai/Examples/Autodata 3 44 En Francais.md b/spaces/inreVtussa/clothingai/Examples/Autodata 3 44 En Francais.md
deleted file mode 100644
index 24caee03f62805f31d76a345c4fdb7ad4572cb63..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Autodata 3 44 En Francais.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Autodata 3 44 En Francais DOWNLOAD »»» https://tiurll.com/2uClcK
diff --git a/spaces/ishanchennupati/ishanavatarchatbot/app.py b/spaces/ishanchennupati/ishanavatarchatbot/app.py
deleted file mode 100644
index e98d5bd3b7bae220cb4fa9c195703e8668c336fa..0000000000000000000000000000000000000000
--- a/spaces/ishanchennupati/ishanavatarchatbot/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Hello meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/jason9693/KoreanHateSpeechClassifier/util.py b/spaces/jason9693/KoreanHateSpeechClassifier/util.py
deleted file mode 100644
index c15113bc8a49b2f889b184dd047f6dc9ba3f8409..0000000000000000000000000000000000000000
--- a/spaces/jason9693/KoreanHateSpeechClassifier/util.py
+++ /dev/null
@@ -1,384 +0,0 @@
-from functools import lru_cache
-
-
-
-@lru_cache()
-def bytes_to_unicode_dict():
- """
- Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
- characters the bpe code barfs on.
-
- The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
- if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
- decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
- tables between utf-8 bytes and unicode strings.
- """
- bs = (
- list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
- )
- cs = bs[:]
- n = 0
- for b in range(2 ** 8):
- if b not in bs:
- bs.append(b)
- cs.append(2 ** 8 + n)
- n += 1
- cs = [chr(n) for n in cs]
- return dict(zip(cs, bs))
-
-ORD_UNICODE_MAP = bytes_to_unicode_dict()
-
-
-@lru_cache()
-def byte_to_char(bytestr):
- return bytearray([ORD_UNICODE_MAP[c] for c in bytestr]).decode("utf-8", errors="replace")
-
-# @lru_cache()
-def bytetokens_to_unicdode(byte_tokens: list):
- return [byte_to_char(token) for token in byte_tokens]
-
-
-if __name__ == '__main__':
-
- tokens = ['',
- 'ì¹´ì¹´ìĺ¤',
- 'ìĹĶ',
- 'íĦ°',
- 'íĶĦëĿ¼ìĿ´',
- 'ì¦Ī',
- '(',
- 'ëĮĢíijľ',
- 'Ġë°±',
- 'ìĥģ',
- 'ìĹ½',
- ')',
- 'ê°Ģ',
- 'Ġìĺ¬íķ´',
- 'Ġ8',
- 'ìĽĶ',
- 'Ġ기ì¤Ģ',
- 'Ġëĭ¤ìĪĺ',
- 'Ġê¶Į',
- 'ìľĦ',
- 'ĠìŀĪëĬĶ',
- 'Ġê¸Ģë¡ľë²Į',
- 'ĠíķĻ',
- 'íļĮìĹIJìĦľ',
- 'Ġì´Ŀ',
- 'Ġ16',
- 'ê±´',
- 'ìĿĺ',
- 'ĠìĿ¸ê³µ',
- 'ì§Ģ',
- 'ëĬ¥',
- '(',
- 'A',
- 'I',
- ')',
- 'Ġëħ¼ë¬¸',
- 'ìĿĦ',
- 'Ġëĵ±',
- 'ìŀ¬',
- 'íĸĪëĭ¤ê³ł',
- 'Ġ9',
- 'ìĿ¼',
- 'Ġë°ĿíĺĶ',
- 'ëĭ¤',
- '.',
- 'Ġì§ĢëĤľíķ´',
- 'Ġëĵ±',
- 'ìŀ¬',
- 'íķľ',
- 'Ġ13',
- 'ê±´ë',
- '³´ëĭ¤',
- 'Ġ3',
- 'ê±´',
- 'Ġë§İìĿĢ',
- 'Ġëħ¼ë¬¸',
- 'ìĿ´',
- 'Ġë°ĺ',
- 'ëħĦ',
- 'ìŬ',
- 'Ġë§ĮìĹIJ',
- 'Ġì±Ħ',
- 'íĥĿ',
- 'ëIJIJëĭ¤',
- '.',
- 'Ġì¹´ì¹´ìĺ¤',
- 'ìĹĶ',
- 'íĦ°',
- 'íĶĦëĿ¼ìĿ´',
- 'ì¦Ī',
- '(',
- 'ìĿ´',
- 'íķĺ',
- 'Ġì¹´ì¹´ìĺ¤',
- 'ìĹĶ',
- 'íĦ°',
- ')',
- 'ëĬĶ',
- 'ĠA',
- 'I',
- 'ĠìĹ°êµ¬',
- 'ĠìĦ±',
- '과를',
- 'ĠìĿ´',
- 'ìĸ´ê°Ģ',
- '기',
- 'ĠìľĦíķ´',
- 'ĠìĿ¸ìŀ¬',
- 'ĠíĻķë³´',
- 'ìĹIJ',
- 'ĠìĨį',
- 'ëıĦ를',
- 'ĠëĨĴìĿ´',
- 'ê²łëĭ¤ëĬĶ',
- 'Ġë°©',
- '침',
- 'ìĿ´ëĭ¤',
- '.',
- 'Ċ',
- 'Ċ',
- 'ì¹´ì¹´ìĺ¤',
- 'ìĹĶ',
- 'íĦ°',
- 'ëĬĶ',
- 'Ġ8',
- 'ìĽĶ',
- 'ĠìŀIJìĹ°',
- 'ìĸ´',
- 'ì²ĺ리',
- 'Ġë¶Ħìķ¼',
- 'ìĿĺ',
- 'Ġê¸Ģë¡ľë²Į',
- 'Ġíĥij',
- 'ĠíķĻ',
- 'íļĮ',
- 'ìĿ¸',
- "Ġ'",
- 'A',
- 'C',
- 'L',
- '-',
- 'I',
- 'J',
- 'C',
- 'N',
- 'L',
- 'P',
- "'",
- 'ìĹIJ',
- 'Ġëħ¼ë¬¸',
- 'ìĿĦ',
- 'Ġë°ľíijľ',
- 'íķľ',
- 'ĠìĤ¬ë¡Ģ',
- 'ê¹Įì§Ģ',
- 'Ġíķ©',
- 'íķ´',
- 'Ġìĺ¬íķ´',
- 'Ġì´Ŀ',
- 'Ġ16',
- 'ê±´',
- 'ìĿĺ',
- 'ĠA',
- 'I',
- 'Ġëħ¼ë¬¸',
- 'ìĿĦ',
- 'Ġëĵ±',
- 'ìŀ¬',
- 'íĸĪëĭ¤ê³ł',
- 'Ġë°ĿíĺĶ',
- 'ëĭ¤',
- '.',
- 'ĠìĿ´',
- 'Ġëħ¼ë¬¸',
- 'ìĿĢ',
- 'ĠìĿ¸ëıĦ',
- 'ë©Ķ',
- 'ìĿ¸',
- '(',
- 'in',
- '-',
- 'd',
- 'om',
- 'a',
- 'in',
- ')',
- 'Ġìĥĺ',
- 'íĶĮ',
- 'ìĿĦ',
- 'ĠìĤ¬ìļ©',
- 'íķ´',
- 'ĠìŀIJìĹ°',
- 'ìĸ´',
- 'Ġ공격',
- 'Ġë°©ìĭĿìľ¼ë¡ľ',
- 'ĠìķĦìĽĥ',
- 'ìĺ¤',
- 'ë¸Į',
- 'ëıĦ',
- 'ë©Ķ',
- 'ìĿ¸',
- '(',
- 'out',
- '-',
- 'of',
- '-',
- 'd',
- 'om',
- 'a',
- 'in',
- ')',
- 'Ġìĥĺ',
- 'íĶĮ',
- 'ìĿĦ',
- 'ĠìŀIJëıĻ',
- 'ìľ¼ë¡ľ',
- 'ĠìĥĿ',
- 'ìĦ±',
- ',',
- 'Ġë¶Ħ',
- 'ë¥ĺ',
- 'Ġ모ëį¸',
- 'ìĿĺ',
- 'Ġê°IJ',
- 'ì§Ģ',
- 'ĠëĬ¥ëł¥ìĿĦ',
- 'Ġíĸ¥',
- 'ìĥģ',
- 'ìĭľíĤ¤ëĬĶ',
- 'ĠëĤ´ìļ©',
- 'ìĿĺ',
- 'Ġëħ¼ë¬¸',
- 'ìĿ´ëĭ¤',
- '.',
- 'Ċ',
- 'Ċ',
- '7',
- 'ìĽĶ',
- 'ìĹIJëĬĶ',
- 'Ġ머',
- 'ìĭł',
- '룬',
- 'ëĭĿ',
- 'ĠíķĻ',
- 'íļĮ',
- "Ġ'",
- 'I',
- 'C',
- 'M',
- 'L',
- "'",
- 'ìĹIJ',
- 'Ġíļ¨ìľ¨',
- 'ìłģìĿ¸',
- 'Ġê³ł',
- 'íĴĪ',
- 'ì§Ī',
- 'ĠìĿĮ',
- 'ìĦ±',
- 'íķ©',
- 'ìĦ±ìĿ´',
- 'Ġê°ĢëĬ¥íķľ',
- "Ġ'",
- 'ìĹĶ',
- 'ëĵľ',
- 'ĠíĪ¬',
- 'ĠìĹĶ',
- 'ëĵľ',
- '(',
- 'en',
- 'd',
- '-',
- 't',
- 'o',
- '-',
- 'en',
- 'd',
- ')',
- "'",
- 'Ġ모ëį¸',
- 'ìĿĦ',
- 'ĠìłľìķĪ',
- 'íķĺëĬĶ',
- 'Ġëħ¼ë¬¸',
- 'ìĿĦ',
- 'Ġë°ľíijľ',
- 'íĸĪëĭ¤',
- '.',
- 'Ġ6',
- 'ìĽĶ',
- 'ìĹIJëĬĶ',
- 'ĠìĿĮ',
- 'íĸ¥',
- '·',
- 'ìĿĮ',
- 'ìĦ±',
- 'Ġìĭł',
- 'íĺ¸',
- 'ì²ĺ리',
- 'Ġë¶Ħìķ¼',
- 'ĠíķĻ',
- 'ìĪł',
- 'ëĮĢíļĮ',
- "Ġ'",
- 'I',
- 'C',
- 'A',
- 'S',
- 'S',
- 'P',
- "'",
- 'ìĹIJ',
- 'ĠëĮĢ',
- 'ê·ľëª¨',
- 'Ġíħ',
- 'į',
- 'ìĬ¤íĬ¸',
- 'Ġì½Ķ',
- 'íį¼ìĬ¤',
- '(',
- 'ìĸ¸',
- 'ìĸ´',
- 'ĠìĹ°',
- '구를',
- 'ĠìľĦíķ´',
- 'Ġíħ',
- 'į',
- 'ìĬ¤íĬ¸ë¥¼',
- 'Ġì»´íĵ¨íĦ°',
- 'ê°Ģ',
- 'ĠìĿ½ìĿĦ',
- 'ĠìĪĺ',
- 'ĠìŀĪëĬĶ',
- 'Ġíĺķíĥľë¡ľ',
- 'Ġ모ìķĦ',
- 'ĠëĨĵìĿĢ',
- 'Ġìĸ¸ìĸ´',
- 'ĠìŀIJë£Į',
- ')',
- 'Ġìłķë³´',
- 'ĠíķĻìĬµ',
- 'ìĹIJ',
- 'ĠëĮĢíķľ',
- 'Ġëħ¼ë¬¸',
- 'Ġ1',
- 'ê±´ìĿĦ',
- 'Ġìĭ¤',
- 'ìĹĪëĭ¤',
- '.',
- 'Ċ',
- ' ']
-
- import time
-
- start = time.time()
- for i in range(1000):
- result = bytetokens_to_unicdode(tokens)
- end = time.time()
-
- print(result)
-
- print(f'time: {end-start}')
\ No newline at end of file
diff --git a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/config.py b/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/config.py
deleted file mode 100644
index 400e9a05d4995e3f3401b34a22ae687b2c9c90e0..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/config.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-from detectron2.config import CfgNode as CN
-
-
-def add_mask_former_default_config(cfg):
- # data config
- # select the dataset mapper
- cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic"
- # Color augmentation
- cfg.INPUT.COLOR_AUG_SSD = False
- # We retry random cropping until no single category in semantic segmentation GT occupies more
- # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
- cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
- # Pad image and segmentation GT in dataset mapper.
- cfg.INPUT.SIZE_DIVISIBILITY = -1
-
- # solver config
- # test batch size
- cfg.SOLVER.TEST_IMS_PER_BATCH = 1
- # weight decay on embedding
- cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
- # optimizer
- cfg.SOLVER.OPTIMIZER = "ADAMW"
- cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
-
- # mask_former model config
- cfg.MODEL.MASK_FORMER = CN()
-
- # loss
- cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True
- cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1
- cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0
- cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0
-
- # transformer config
- cfg.MODEL.MASK_FORMER.NHEADS = 8
- cfg.MODEL.MASK_FORMER.DROPOUT = 0.1
- cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
- cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0
- cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6
- cfg.MODEL.MASK_FORMER.PRE_NORM = False
-
- cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
- cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100
-
- cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = "res5"
- cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False
-
- # mask_former inference config
- cfg.MODEL.MASK_FORMER.TEST = CN()
- cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False
- cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0
- cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0
- cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
-
- # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
- # you can use this config to override
- cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32
-
- # pixel decoder config
- cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
- # adding transformer in pixel decoder
- cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
- # pixel decoder
- cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
-
- # swin transformer backbone
- cfg.MODEL.SWIN = CN()
- cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
- cfg.MODEL.SWIN.PATCH_SIZE = 4
- cfg.MODEL.SWIN.EMBED_DIM = 96
- cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
- cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
- cfg.MODEL.SWIN.WINDOW_SIZE = 7
- cfg.MODEL.SWIN.MLP_RATIO = 4.0
- cfg.MODEL.SWIN.QKV_BIAS = True
- cfg.MODEL.SWIN.QK_SCALE = None
- cfg.MODEL.SWIN.NORM_INDICES = None
- cfg.MODEL.SWIN.PROJECTION = False
- cfg.MODEL.SWIN.PROJECT_DIM = 256
- cfg.MODEL.SWIN.DROP_RATE = 0.0
- cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
- cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
- cfg.MODEL.SWIN.APE = False
- cfg.MODEL.SWIN.PATCH_NORM = True
- cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
-
-
-def add_our_config(cfg):
- cfg.TEST.SLIDING_WINDOW = False
- cfg.TEST.SLIDING_TILE_SIZE = 224
- cfg.TEST.SLIDING_OVERLAP = 2 / 3.0
- # whether to use dense crf
- cfg.TEST.DENSE_CRF = False
- cfg.DATASETS.SAMPLE_PER_CLASS = -1
- cfg.DATASETS.SAMPLE_SEED = 0
- # embedding head
- cfg.MODEL.SEM_SEG_HEAD.EMBEDDING_DIM = 512
- cfg.MODEL.SEM_SEG_HEAD.EMBED_HIDDEN_DIM = 1024
- cfg.MODEL.SEM_SEG_HEAD.EMBED_LAYERS = 2
- # clip_adapter
- cfg.MODEL.CLIP_ADAPTER = CN()
- cfg.MODEL.CLIP_ADAPTER.TEXT_TEMPLATES = "vild"
- # for predefined
- cfg.MODEL.CLIP_ADAPTER.PREDEFINED_PROMPT_TEMPLATES = ["a photo of a {}."]
- # for learnable prompt
- cfg.MODEL.CLIP_ADAPTER.PROMPT_CHECKPOINT = ""
- cfg.MODEL.CLIP_ADAPTER.CLIP_MODEL_NAME = "ViT-B/16"
- cfg.MODEL.CLIP_ADAPTER.MASK_FILL = "mean"
- cfg.MODEL.CLIP_ADAPTER.MASK_EXPAND_RATIO = 1.0
- cfg.MODEL.CLIP_ADAPTER.MASK_THR = 0.4
- cfg.MODEL.CLIP_ADAPTER.MASK_MATTING = False
- cfg.MODEL.CLIP_ADAPTER.REGION_RESIZED = True
- cfg.MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE = True
- cfg.MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE_WEIGHT = 0.7
- # for mask prompt
- cfg.MODEL.CLIP_ADAPTER.MASK_PROMPT_DEPTH = 3
- cfg.MODEL.CLIP_ADAPTER.MASK_PROMPT_FWD = False
-
- # wandb
- cfg.WANDB = CN()
- cfg.WANDB.PROJECT = "open_vocab_seg"
- cfg.WANDB.NAME = None
-
-
-def add_ovseg_config(cfg):
- """
- Add config for open_vocab_seg.
- """
- add_mask_former_default_config(cfg)
- add_our_config(cfg)
diff --git a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/model/model.py b/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/model/model.py
deleted file mode 100644
index 4024e71ee7ecf3fd1276429292a17c0c67696ffe..0000000000000000000000000000000000000000
--- a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/model/model.py
+++ /dev/null
@@ -1,249 +0,0 @@
-#!/usr/bin/python
-# coding: utf-8
-
-# Author: LE YUAN
-# Date: 2020-10-23
-
-import pickle
-import sys
-import timeit
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as op
-from sklearn.metrics import mean_squared_error,r2_score
-
-
-class KcatPrediction(nn.Module):
- def __init__(self, device, n_fingerprint, n_word, dim, layer_gnn, window, layer_cnn, layer_output):
- super(KcatPrediction, self).__init__()
- self.embed_fingerprint = nn.Embedding(n_fingerprint, dim)
- self.embed_word = nn.Embedding(n_word, dim)
- self.W_gnn = nn.ModuleList([nn.Linear(dim, dim)
- for _ in range(layer_gnn)])
- self.W_cnn = nn.ModuleList([nn.Conv2d(
- in_channels=1, out_channels=1, kernel_size=2*window+1,
- stride=1, padding=window) for _ in range(layer_cnn)])
- self.W_attention = nn.Linear(dim, dim)
- self.W_out = nn.ModuleList([nn.Linear(2*dim, 2*dim)
- for _ in range(layer_output)])
- # self.W_interaction = nn.Linear(2*dim, 2)
- self.W_interaction = nn.Linear(2*dim, 1)
-
- self.device = device
- self.dim = dim
- self.layer_gnn = layer_gnn
- self.window = window
- self.layer_cnn = layer_cnn
- self.layer_output = layer_output
-
- def gnn(self, xs, A, layer):
- for i in range(layer):
- hs = torch.relu(self.W_gnn[i](xs))
- xs = xs + torch.matmul(A, hs)
- # return torch.unsqueeze(torch.sum(xs, 0), 0)
- return torch.unsqueeze(torch.mean(xs, 0), 0)
-
- def attention_cnn(self, x, xs, layer):
- """The attention mechanism is applied to the last layer of CNN."""
-
- xs = torch.unsqueeze(torch.unsqueeze(xs, 0), 0)
- for i in range(layer):
- xs = torch.relu(self.W_cnn[i](xs))
- xs = torch.squeeze(torch.squeeze(xs, 0), 0)
-
- h = torch.relu(self.W_attention(x))
- hs = torch.relu(self.W_attention(xs))
- weights = torch.tanh(F.linear(h, hs))
- ys = torch.t(weights) * hs
-
- # return torch.unsqueeze(torch.sum(ys, 0), 0)
- return torch.unsqueeze(torch.mean(ys, 0), 0)
-
- def forward(self, inputs):
-
- fingerprints, adjacency, words = inputs
-
- layer_gnn = 3
- layer_cnn = 3
- layer_output = 3
-
- """Compound vector with GNN."""
- fingerprint_vectors = self.embed_fingerprint(fingerprints)
- compound_vector = self.gnn(fingerprint_vectors, adjacency, layer_gnn)
-
- """Protein vector with attention-CNN."""
- word_vectors = self.embed_word(words)
- protein_vector = self.attention_cnn(compound_vector,
- word_vectors, layer_cnn)
-
- """Concatenate the above two vectors and output the interaction."""
- cat_vector = torch.cat((compound_vector, protein_vector), 1)
- for j in range(layer_output):
- cat_vector = torch.relu(self.W_out[j](cat_vector))
- interaction = self.W_interaction(cat_vector)
- # print(interaction)
-
- return interaction
-
- def __call__(self, data, train=True):
-
- inputs, correct_interaction = data[:-1], data[-1]
- predicted_interaction = self.forward(inputs)
- print(predicted_interaction)
-
- if train:
- loss = F.mse_loss(predicted_interaction, correct_interaction)
- return loss
- else:
- correct_values = correct_interaction.to('cpu').data.numpy()
- predicted_values = predicted_interaction.to('cpu').data.numpy()[0]
- # correct_values = np.concatenate(correct_values)
- # predicted_values = np.concatenate(predicted_values)
- # ys = F.softmax(predicted_interaction, 1).to('cpu').data.numpy()
- # predicted_values = list(map(lambda x: np.argmax(x), ys))
- print(correct_values)
- print(predicted_values)
- # predicted_scores = list(map(lambda x: x[1], ys))
- return correct_values, predicted_values
-
-
-class Trainer(object):
- def __init__(self, model):
- self.model = model
- self.optimizer = optim.Adam(self.model.parameters(),
- lr=lr, weight_decay=weight_decay)
-
- def train(self, dataset):
- np.random.shuffle(dataset)
- N = len(dataset)
- loss_total = 0
- for data in dataset:
- loss = self.model(data)
- self.optimizer.zero_grad()
- loss.backward()
- self.optimizer.step()
- loss_total += loss.to('cpu').data.numpy()
- return loss_total
-
-
-class Tester(object):
- def __init__(self, model):
- self.model = model
-
- def test(self, dataset):
- N = len(dataset)
- SAE = 0 # sum absolute error.
- testY, testPredict = [], []
- for data in dataset :
- (correct_values, predicted_values) = self.model(data, train=False)
- SAE += sum(np.abs(predicted_values-correct_values))
- testY.append(correct_values)
- testPredict.append(predicted_values)
- MAE = SAE / N # mean absolute error.
- rmse = np.sqrt(mean_squared_error(testY,testPredict))
- r2 = r2_score(testY,testPredict)
- return MAE, rmse, r2
-
- def save_MAEs(self, MAEs, filename):
- with open(filename, 'a') as f:
- f.write('\t'.join(map(str, MAEs)) + '\n')
-
- def save_model(self, model, filename):
- torch.save(model.state_dict(), filename)
-
-def load_tensor(file_name, dtype):
- return [dtype(d).to(device) for d in np.load(file_name + '.npy', allow_pickle=True)]
-
-
-def load_pickle(file_name):
- with open(file_name, 'rb') as f:
- return pickle.load(f)
-
-def shuffle_dataset(dataset, seed):
- np.random.seed(seed)
- np.random.shuffle(dataset)
- return dataset
-
-
-def split_dataset(dataset, ratio):
- n = int(ratio * len(dataset))
- dataset_1, dataset_2 = dataset[:n], dataset[n:]
- return dataset_1, dataset_2
-
-
-if __name__ == "__main__":
-
- """Hyperparameters."""
- (DATASET, radius, ngram, dim, layer_gnn, window, layer_cnn, layer_output,
- lr, lr_decay, decay_interval, weight_decay, iteration,
- setting) = sys.argv[1:]
- (dim, layer_gnn, window, layer_cnn, layer_output, decay_interval,
- iteration) = map(int, [dim, layer_gnn, window, layer_cnn, layer_output,
- decay_interval, iteration])
- lr, lr_decay, weight_decay = map(float, [lr, lr_decay, weight_decay])
-
- """CPU or GPU."""
- if torch.cuda.is_available():
- device = torch.device('cuda')
- print('The code uses GPU...')
- else:
- device = torch.device('cpu')
- print('The code uses CPU!!!')
-
- """Load preprocessed data."""
- dir_input = ('../input/')
- compounds = load_tensor(dir_input + 'compounds', torch.LongTensor)
- adjacencies = load_tensor(dir_input + 'adjacencies', torch.FloatTensor)
- proteins = load_tensor(dir_input + 'proteins', torch.LongTensor)
- interactions = load_tensor(dir_input + 'regression', torch.FloatTensor)
- fingerprint_dict = load_pickle(dir_input + 'fingerprint_dict.pickle')
- word_dict = load_pickle(dir_input + 'sequence_dict.pickle')
- n_fingerprint = len(fingerprint_dict)
- n_word = len(word_dict)
-
- """Create a dataset and split it into train/dev/test."""
- dataset = list(zip(compounds, adjacencies, proteins, interactions))
- dataset = shuffle_dataset(dataset, 1234)
- dataset_train, dataset_ = split_dataset(dataset, 0.8)
- dataset_dev, dataset_test = split_dataset(dataset_, 0.5)
-
- """Set a model."""
- torch.manual_seed(1234)
- model = KcatPrediction().to(device)
- trainer = Trainer(model)
- tester = Tester(model)
-
- """Output files."""
- file_MAEs = '../../Data/Results/output/MAEs--' + setting + '.txt'
- file_model = '../../Data/Results/output/' + setting
- # MAEs = ('Epoch\tTime(sec)\tLoss_train\tMAE_dev\t'
- # 'MAE_test\tPrecision_test\tRecall_test')
- MAEs = ('Epoch\tTime(sec)\tLoss_train\tMAE_dev\tMAE_test\tRMSE_dev\tRMSE_test\tR2_dev\tR2_test')
- with open(file_MAEs, 'w') as f:
- f.write(MAEs + '\n')
-
- """Start training."""
- print('Training...')
- print(MAEs)
- start = timeit.default_timer()
-
- for epoch in range(1, iteration):
-
- if epoch % decay_interval == 0:
- trainer.optimizer.param_groups[0]['lr'] *= lr_decay
-
- loss_train = trainer.train(dataset_train)
- MAE_dev, RMSE_dev, R2_dev = tester.test(dataset_dev)
- MAE_test, RMSE_test, R2_test = tester.test(dataset_test)
-
- end = timeit.default_timer()
- time = end - start
-
- MAEs = [epoch, time, loss_train, MAE_dev,
- MAE_test, RMSE_dev, RMSE_test, R2_dev, R2_test]
- tester.save_MAEs(MAEs, file_MAEs)
- tester.save_model(model, file_model)
-
- print('\t'.join(map(str, MAEs)))
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Cipher/test_ARC4.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Cipher/test_ARC4.py
deleted file mode 100644
index 753df4cb6dbd9a247d34b14dd0e1fda5040942ab..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Cipher/test_ARC4.py
+++ /dev/null
@@ -1,471 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# SelfTest/Cipher/ARC4.py: Self-test for the Alleged-RC4 cipher
-#
-# Written in 2008 by Dwayne C. Litzenberger
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain. To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""Self-test suite for Crypto.Cipher.ARC4"""
-
-import unittest
-
-from Crypto.Util.py3compat import b
-from Crypto.SelfTest.st_common import list_test_cases
-from binascii import unhexlify
-
-from Crypto.Cipher import ARC4
-
-# This is a list of (plaintext, ciphertext, key[, description]) tuples.
-test_data = [
- # Test vectors from Eric Rescorla's message with the subject
- # "RC4 compatibility testing", sent to the cipherpunks mailing list on
- # September 13, 1994.
- # http://cypherpunks.venona.com/date/1994/09/msg00420.html
-
- ('0123456789abcdef', '75b7878099e0c596', '0123456789abcdef',
- 'Test vector 0'),
-
- ('0000000000000000', '7494c2e7104b0879', '0123456789abcdef',
- 'Test vector 1'),
-
- ('0000000000000000', 'de188941a3375d3a', '0000000000000000',
- 'Test vector 2'),
-
- ('00000000000000000000', 'd6a141a7ec3c38dfbd61', 'ef012345',
- 'Test vector 3'),
-
- ('01' * 512,
- '7595c3e6114a09780c4ad452338e1ffd9a1be9498f813d76533449b6778dcad8'
- + 'c78a8d2ba9ac66085d0e53d59c26c2d1c490c1ebbe0ce66d1b6b1b13b6b919b8'
- + '47c25a91447a95e75e4ef16779cde8bf0a95850e32af9689444fd377108f98fd'
- + 'cbd4e726567500990bcc7e0ca3c4aaa304a387d20f3b8fbbcd42a1bd311d7a43'
- + '03dda5ab078896ae80c18b0af66dff319616eb784e495ad2ce90d7f772a81747'
- + 'b65f62093b1e0db9e5ba532fafec47508323e671327df9444432cb7367cec82f'
- + '5d44c0d00b67d650a075cd4b70dedd77eb9b10231b6b5b741347396d62897421'
- + 'd43df9b42e446e358e9c11a9b2184ecbef0cd8e7a877ef968f1390ec9b3d35a5'
- + '585cb009290e2fcde7b5ec66d9084be44055a619d9dd7fc3166f9487f7cb2729'
- + '12426445998514c15d53a18c864ce3a2b7555793988126520eacf2e3066e230c'
- + '91bee4dd5304f5fd0405b35bd99c73135d3d9bc335ee049ef69b3867bf2d7bd1'
- + 'eaa595d8bfc0066ff8d31509eb0c6caa006c807a623ef84c3d33c195d23ee320'
- + 'c40de0558157c822d4b8c569d849aed59d4e0fd7f379586b4b7ff684ed6a189f'
- + '7486d49b9c4bad9ba24b96abf924372c8a8fffb10d55354900a77a3db5f205e1'
- + 'b99fcd8660863a159ad4abe40fa48934163ddde542a6585540fd683cbfd8c00f'
- + '12129a284deacc4cdefe58be7137541c047126c8d49e2755ab181ab7e940b0c0',
- '0123456789abcdef',
- "Test vector 4"),
- # shortest key - generated with arc4 package
- ('7468697320697320616e206578616d706c65',
- '7260677d38495a09585d69321e17eaf3cdd0',
- '01'),
-]
-
-
-class RFC6229_Tests(unittest.TestCase):
- # Test vectors from RFC 6229. Each test vector is a tuple with two items:
- # the ARC4 key and a dictionary. The dictionary has keystream offsets as keys
- # and the 16-byte keystream starting at the relevant offset as value.
- rfc6229_data = [
- # Page 3
- (
- '0102030405',
- {
- 0: 'b2 39 63 05 f0 3d c0 27 cc c3 52 4a 0a 11 18 a8',
- 16: '69 82 94 4f 18 fc 82 d5 89 c4 03 a4 7a 0d 09 19',
- 240: '28 cb 11 32 c9 6c e2 86 42 1d ca ad b8 b6 9e ae',
- 256: '1c fc f6 2b 03 ed db 64 1d 77 df cf 7f 8d 8c 93',
- 496: '42 b7 d0 cd d9 18 a8 a3 3d d5 17 81 c8 1f 40 41',
- 512: '64 59 84 44 32 a7 da 92 3c fb 3e b4 98 06 61 f6',
- 752: 'ec 10 32 7b de 2b ee fd 18 f9 27 76 80 45 7e 22',
- 768: 'eb 62 63 8d 4f 0b a1 fe 9f ca 20 e0 5b f8 ff 2b',
- 1008: '45 12 90 48 e6 a0 ed 0b 56 b4 90 33 8f 07 8d a5',
- 1024: '30 ab bc c7 c2 0b 01 60 9f 23 ee 2d 5f 6b b7 df',
- 1520: '32 94 f7 44 d8 f9 79 05 07 e7 0f 62 e5 bb ce ea',
- 1536: 'd8 72 9d b4 18 82 25 9b ee 4f 82 53 25 f5 a1 30',
- 2032: '1e b1 4a 0c 13 b3 bf 47 fa 2a 0b a9 3a d4 5b 8b',
- 2048: 'cc 58 2f 8b a9 f2 65 e2 b1 be 91 12 e9 75 d2 d7',
- 3056: 'f2 e3 0f 9b d1 02 ec bf 75 aa ad e9 bc 35 c4 3c',
- 3072: 'ec 0e 11 c4 79 dc 32 9d c8 da 79 68 fe 96 56 81',
- 4080: '06 83 26 a2 11 84 16 d2 1f 9d 04 b2 cd 1c a0 50',
- 4096: 'ff 25 b5 89 95 99 67 07 e5 1f bd f0 8b 34 d8 75'
- }
- ),
- # Page 4
- (
- '01020304050607',
- {
- 0: '29 3f 02 d4 7f 37 c9 b6 33 f2 af 52 85 fe b4 6b',
- 16: 'e6 20 f1 39 0d 19 bd 84 e2 e0 fd 75 20 31 af c1',
- 240: '91 4f 02 53 1c 92 18 81 0d f6 0f 67 e3 38 15 4c',
- 256: 'd0 fd b5 83 07 3c e8 5a b8 39 17 74 0e c0 11 d5',
- 496: '75 f8 14 11 e8 71 cf fa 70 b9 0c 74 c5 92 e4 54',
- 512: '0b b8 72 02 93 8d ad 60 9e 87 a5 a1 b0 79 e5 e4',
- 752: 'c2 91 12 46 b6 12 e7 e7 b9 03 df ed a1 da d8 66',
- 768: '32 82 8f 91 50 2b 62 91 36 8d e8 08 1d e3 6f c2',
- 1008: 'f3 b9 a7 e3 b2 97 bf 9a d8 04 51 2f 90 63 ef f1',
- 1024: '8e cb 67 a9 ba 1f 55 a5 a0 67 e2 b0 26 a3 67 6f',
- 1520: 'd2 aa 90 2b d4 2d 0d 7c fd 34 0c d4 58 10 52 9f',
- 1536: '78 b2 72 c9 6e 42 ea b4 c6 0b d9 14 e3 9d 06 e3',
- 2032: 'f4 33 2f d3 1a 07 93 96 ee 3c ee 3f 2a 4f f0 49',
- 2048: '05 45 97 81 d4 1f da 7f 30 c1 be 7e 12 46 c6 23',
- 3056: 'ad fd 38 68 b8 e5 14 85 d5 e6 10 01 7e 3d d6 09',
- 3072: 'ad 26 58 1c 0c 5b e4 5f 4c ea 01 db 2f 38 05 d5',
- 4080: 'f3 17 2c ef fc 3b 3d 99 7c 85 cc d5 af 1a 95 0c',
- 4096: 'e7 4b 0b 97 31 22 7f d3 7c 0e c0 8a 47 dd d8 b8'
- }
- ),
- (
- '0102030405060708',
- {
- 0: '97 ab 8a 1b f0 af b9 61 32 f2 f6 72 58 da 15 a8',
- 16: '82 63 ef db 45 c4 a1 86 84 ef 87 e6 b1 9e 5b 09',
- 240: '96 36 eb c9 84 19 26 f4 f7 d1 f3 62 bd df 6e 18',
- 256: 'd0 a9 90 ff 2c 05 fe f5 b9 03 73 c9 ff 4b 87 0a',
- 496: '73 23 9f 1d b7 f4 1d 80 b6 43 c0 c5 25 18 ec 63',
- 512: '16 3b 31 99 23 a6 bd b4 52 7c 62 61 26 70 3c 0f',
- 752: '49 d6 c8 af 0f 97 14 4a 87 df 21 d9 14 72 f9 66',
- 768: '44 17 3a 10 3b 66 16 c5 d5 ad 1c ee 40 c8 63 d0',
- 1008: '27 3c 9c 4b 27 f3 22 e4 e7 16 ef 53 a4 7d e7 a4',
- 1024: 'c6 d0 e7 b2 26 25 9f a9 02 34 90 b2 61 67 ad 1d',
- 1520: '1f e8 98 67 13 f0 7c 3d 9a e1 c1 63 ff 8c f9 d3',
- 1536: '83 69 e1 a9 65 61 0b e8 87 fb d0 c7 91 62 aa fb',
- 2032: '0a 01 27 ab b4 44 84 b9 fb ef 5a bc ae 1b 57 9f',
- 2048: 'c2 cd ad c6 40 2e 8e e8 66 e1 f3 7b db 47 e4 2c',
- 3056: '26 b5 1e a3 7d f8 e1 d6 f7 6f c3 b6 6a 74 29 b3',
- 3072: 'bc 76 83 20 5d 4f 44 3d c1 f2 9d da 33 15 c8 7b',
- 4080: 'd5 fa 5a 34 69 d2 9a aa f8 3d 23 58 9d b8 c8 5b',
- 4096: '3f b4 6e 2c 8f 0f 06 8e dc e8 cd cd 7d fc 58 62'
- }
- ),
- # Page 5
- (
- '0102030405060708090a',
- {
- 0: 'ed e3 b0 46 43 e5 86 cc 90 7d c2 18 51 70 99 02',
- 16: '03 51 6b a7 8f 41 3b eb 22 3a a5 d4 d2 df 67 11',
- 240: '3c fd 6c b5 8e e0 fd de 64 01 76 ad 00 00 04 4d',
- 256: '48 53 2b 21 fb 60 79 c9 11 4c 0f fd 9c 04 a1 ad',
- 496: '3e 8c ea 98 01 71 09 97 90 84 b1 ef 92 f9 9d 86',
- 512: 'e2 0f b4 9b db 33 7e e4 8b 8d 8d c0 f4 af ef fe',
- 752: '5c 25 21 ea cd 79 66 f1 5e 05 65 44 be a0 d3 15',
- 768: 'e0 67 a7 03 19 31 a2 46 a6 c3 87 5d 2f 67 8a cb',
- 1008: 'a6 4f 70 af 88 ae 56 b6 f8 75 81 c0 e2 3e 6b 08',
- 1024: 'f4 49 03 1d e3 12 81 4e c6 f3 19 29 1f 4a 05 16',
- 1520: 'bd ae 85 92 4b 3c b1 d0 a2 e3 3a 30 c6 d7 95 99',
- 1536: '8a 0f ed db ac 86 5a 09 bc d1 27 fb 56 2e d6 0a',
- 2032: 'b5 5a 0a 5b 51 a1 2a 8b e3 48 99 c3 e0 47 51 1a',
- 2048: 'd9 a0 9c ea 3c e7 5f e3 96 98 07 03 17 a7 13 39',
- 3056: '55 22 25 ed 11 77 f4 45 84 ac 8c fa 6c 4e b5 fc',
- 3072: '7e 82 cb ab fc 95 38 1b 08 09 98 44 21 29 c2 f8',
- 4080: '1f 13 5e d1 4c e6 0a 91 36 9d 23 22 be f2 5e 3c',
- 4096: '08 b6 be 45 12 4a 43 e2 eb 77 95 3f 84 dc 85 53'
- }
- ),
- (
- '0102030405060708090a0b0c0d0e0f10',
- {
- 0: '9a c7 cc 9a 60 9d 1e f7 b2 93 28 99 cd e4 1b 97',
- 16: '52 48 c4 95 90 14 12 6a 6e 8a 84 f1 1d 1a 9e 1c',
- 240: '06 59 02 e4 b6 20 f6 cc 36 c8 58 9f 66 43 2f 2b',
- 256: 'd3 9d 56 6b c6 bc e3 01 07 68 15 15 49 f3 87 3f',
- 496: 'b6 d1 e6 c4 a5 e4 77 1c ad 79 53 8d f2 95 fb 11',
- 512: 'c6 8c 1d 5c 55 9a 97 41 23 df 1d bc 52 a4 3b 89',
- 752: 'c5 ec f8 8d e8 97 fd 57 fe d3 01 70 1b 82 a2 59',
- 768: 'ec cb e1 3d e1 fc c9 1c 11 a0 b2 6c 0b c8 fa 4d',
- 1008: 'e7 a7 25 74 f8 78 2a e2 6a ab cf 9e bc d6 60 65',
- 1024: 'bd f0 32 4e 60 83 dc c6 d3 ce dd 3c a8 c5 3c 16',
- 1520: 'b4 01 10 c4 19 0b 56 22 a9 61 16 b0 01 7e d2 97',
- 1536: 'ff a0 b5 14 64 7e c0 4f 63 06 b8 92 ae 66 11 81',
- 2032: 'd0 3d 1b c0 3c d3 3d 70 df f9 fa 5d 71 96 3e bd',
- 2048: '8a 44 12 64 11 ea a7 8b d5 1e 8d 87 a8 87 9b f5',
- 3056: 'fa be b7 60 28 ad e2 d0 e4 87 22 e4 6c 46 15 a3',
- 3072: 'c0 5d 88 ab d5 03 57 f9 35 a6 3c 59 ee 53 76 23',
- 4080: 'ff 38 26 5c 16 42 c1 ab e8 d3 c2 fe 5e 57 2b f8',
- 4096: 'a3 6a 4c 30 1a e8 ac 13 61 0c cb c1 22 56 ca cc'
- }
- ),
- # Page 6
- (
- '0102030405060708090a0b0c0d0e0f101112131415161718',
- {
- 0: '05 95 e5 7f e5 f0 bb 3c 70 6e da c8 a4 b2 db 11',
- 16: 'df de 31 34 4a 1a f7 69 c7 4f 07 0a ee 9e 23 26',
- 240: 'b0 6b 9b 1e 19 5d 13 d8 f4 a7 99 5c 45 53 ac 05',
- 256: '6b d2 37 8e c3 41 c9 a4 2f 37 ba 79 f8 8a 32 ff',
- 496: 'e7 0b ce 1d f7 64 5a db 5d 2c 41 30 21 5c 35 22',
- 512: '9a 57 30 c7 fc b4 c9 af 51 ff da 89 c7 f1 ad 22',
- 752: '04 85 05 5f d4 f6 f0 d9 63 ef 5a b9 a5 47 69 82',
- 768: '59 1f c6 6b cd a1 0e 45 2b 03 d4 55 1f 6b 62 ac',
- 1008: '27 53 cc 83 98 8a fa 3e 16 88 a1 d3 b4 2c 9a 02',
- 1024: '93 61 0d 52 3d 1d 3f 00 62 b3 c2 a3 bb c7 c7 f0',
- 1520: '96 c2 48 61 0a ad ed fe af 89 78 c0 3d e8 20 5a',
- 1536: '0e 31 7b 3d 1c 73 b9 e9 a4 68 8f 29 6d 13 3a 19',
- 2032: 'bd f0 e6 c3 cc a5 b5 b9 d5 33 b6 9c 56 ad a1 20',
- 2048: '88 a2 18 b6 e2 ec e1 e6 24 6d 44 c7 59 d1 9b 10',
- 3056: '68 66 39 7e 95 c1 40 53 4f 94 26 34 21 00 6e 40',
- 3072: '32 cb 0a 1e 95 42 c6 b3 b8 b3 98 ab c3 b0 f1 d5',
- 4080: '29 a0 b8 ae d5 4a 13 23 24 c6 2e 42 3f 54 b4 c8',
- 4096: '3c b0 f3 b5 02 0a 98 b8 2a f9 fe 15 44 84 a1 68'
- }
- ),
- (
- '0102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f20',
- {
- 0: 'ea a6 bd 25 88 0b f9 3d 3f 5d 1e 4c a2 61 1d 91',
- 16: 'cf a4 5c 9f 7e 71 4b 54 bd fa 80 02 7c b1 43 80',
- 240: '11 4a e3 44 de d7 1b 35 f2 e6 0f eb ad 72 7f d8',
- 256: '02 e1 e7 05 6b 0f 62 39 00 49 64 22 94 3e 97 b6',
- 496: '91 cb 93 c7 87 96 4e 10 d9 52 7d 99 9c 6f 93 6b',
- 512: '49 b1 8b 42 f8 e8 36 7c be b5 ef 10 4b a1 c7 cd',
- 752: '87 08 4b 3b a7 00 ba de 95 56 10 67 27 45 b3 74',
- 768: 'e7 a7 b9 e9 ec 54 0d 5f f4 3b db 12 79 2d 1b 35',
- 1008: 'c7 99 b5 96 73 8f 6b 01 8c 76 c7 4b 17 59 bd 90',
- 1024: '7f ec 5b fd 9f 9b 89 ce 65 48 30 90 92 d7 e9 58',
- 1520: '40 f2 50 b2 6d 1f 09 6a 4a fd 4c 34 0a 58 88 15',
- 1536: '3e 34 13 5c 79 db 01 02 00 76 76 51 cf 26 30 73',
- 2032: 'f6 56 ab cc f8 8d d8 27 02 7b 2c e9 17 d4 64 ec',
- 2048: '18 b6 25 03 bf bc 07 7f ba bb 98 f2 0d 98 ab 34',
- 3056: '8a ed 95 ee 5b 0d cb fb ef 4e b2 1d 3a 3f 52 f9',
- 3072: '62 5a 1a b0 0e e3 9a 53 27 34 6b dd b0 1a 9c 18',
- 4080: 'a1 3a 7c 79 c7 e1 19 b5 ab 02 96 ab 28 c3 00 b9',
- 4096: 'f3 e4 c0 a2 e0 2d 1d 01 f7 f0 a7 46 18 af 2b 48'
- }
- ),
- # Page 7
- (
- '833222772a',
- {
- 0: '80 ad 97 bd c9 73 df 8a 2e 87 9e 92 a4 97 ef da',
- 16: '20 f0 60 c2 f2 e5 12 65 01 d3 d4 fe a1 0d 5f c0',
- 240: 'fa a1 48 e9 90 46 18 1f ec 6b 20 85 f3 b2 0e d9',
- 256: 'f0 da f5 ba b3 d5 96 83 98 57 84 6f 73 fb fe 5a',
- 496: '1c 7e 2f c4 63 92 32 fe 29 75 84 b2 96 99 6b c8',
- 512: '3d b9 b2 49 40 6c c8 ed ff ac 55 cc d3 22 ba 12',
- 752: 'e4 f9 f7 e0 06 61 54 bb d1 25 b7 45 56 9b c8 97',
- 768: '75 d5 ef 26 2b 44 c4 1a 9c f6 3a e1 45 68 e1 b9',
- 1008: '6d a4 53 db f8 1e 82 33 4a 3d 88 66 cb 50 a1 e3',
- 1024: '78 28 d0 74 11 9c ab 5c 22 b2 94 d7 a9 bf a0 bb',
- 1520: 'ad b8 9c ea 9a 15 fb e6 17 29 5b d0 4b 8c a0 5c',
- 1536: '62 51 d8 7f d4 aa ae 9a 7e 4a d5 c2 17 d3 f3 00',
- 2032: 'e7 11 9b d6 dd 9b 22 af e8 f8 95 85 43 28 81 e2',
- 2048: '78 5b 60 fd 7e c4 e9 fc b6 54 5f 35 0d 66 0f ab',
- 3056: 'af ec c0 37 fd b7 b0 83 8e b3 d7 0b cd 26 83 82',
- 3072: 'db c1 a7 b4 9d 57 35 8c c9 fa 6d 61 d7 3b 7c f0',
- 4080: '63 49 d1 26 a3 7a fc ba 89 79 4f 98 04 91 4f dc',
- 4096: 'bf 42 c3 01 8c 2f 7c 66 bf de 52 49 75 76 81 15'
- }
- ),
- (
- '1910833222772a',
- {
- 0: 'bc 92 22 db d3 27 4d 8f c6 6d 14 cc bd a6 69 0b',
- 16: '7a e6 27 41 0c 9a 2b e6 93 df 5b b7 48 5a 63 e3',
- 240: '3f 09 31 aa 03 de fb 30 0f 06 01 03 82 6f 2a 64',
- 256: 'be aa 9e c8 d5 9b b6 81 29 f3 02 7c 96 36 11 81',
- 496: '74 e0 4d b4 6d 28 64 8d 7d ee 8a 00 64 b0 6c fe',
- 512: '9b 5e 81 c6 2f e0 23 c5 5b e4 2f 87 bb f9 32 b8',
- 752: 'ce 17 8f c1 82 6e fe cb c1 82 f5 79 99 a4 61 40',
- 768: '8b df 55 cd 55 06 1c 06 db a6 be 11 de 4a 57 8a',
- 1008: '62 6f 5f 4d ce 65 25 01 f3 08 7d 39 c9 2c c3 49',
- 1024: '42 da ac 6a 8f 9a b9 a7 fd 13 7c 60 37 82 56 82',
- 1520: 'cc 03 fd b7 91 92 a2 07 31 2f 53 f5 d4 dc 33 d9',
- 1536: 'f7 0f 14 12 2a 1c 98 a3 15 5d 28 b8 a0 a8 a4 1d',
- 2032: '2a 3a 30 7a b2 70 8a 9c 00 fe 0b 42 f9 c2 d6 a1',
- 2048: '86 26 17 62 7d 22 61 ea b0 b1 24 65 97 ca 0a e9',
- 3056: '55 f8 77 ce 4f 2e 1d db bf 8e 13 e2 cd e0 fd c8',
- 3072: '1b 15 56 cb 93 5f 17 33 37 70 5f bb 5d 50 1f c1',
- 4080: 'ec d0 e9 66 02 be 7f 8d 50 92 81 6c cc f2 c2 e9',
- 4096: '02 78 81 fa b4 99 3a 1c 26 20 24 a9 4f ff 3f 61'
- }
- ),
- # Page 8
- (
- '641910833222772a',
- {
- 0: 'bb f6 09 de 94 13 17 2d 07 66 0c b6 80 71 69 26',
- 16: '46 10 1a 6d ab 43 11 5d 6c 52 2b 4f e9 36 04 a9',
- 240: 'cb e1 ff f2 1c 96 f3 ee f6 1e 8f e0 54 2c bd f0',
- 256: '34 79 38 bf fa 40 09 c5 12 cf b4 03 4b 0d d1 a7',
- 496: '78 67 a7 86 d0 0a 71 47 90 4d 76 dd f1 e5 20 e3',
- 512: '8d 3e 9e 1c ae fc cc b3 fb f8 d1 8f 64 12 0b 32',
- 752: '94 23 37 f8 fd 76 f0 fa e8 c5 2d 79 54 81 06 72',
- 768: 'b8 54 8c 10 f5 16 67 f6 e6 0e 18 2f a1 9b 30 f7',
- 1008: '02 11 c7 c6 19 0c 9e fd 12 37 c3 4c 8f 2e 06 c4',
- 1024: 'bd a6 4f 65 27 6d 2a ac b8 f9 02 12 20 3a 80 8e',
- 1520: 'bd 38 20 f7 32 ff b5 3e c1 93 e7 9d 33 e2 7c 73',
- 1536: 'd0 16 86 16 86 19 07 d4 82 e3 6c da c8 cf 57 49',
- 2032: '97 b0 f0 f2 24 b2 d2 31 71 14 80 8f b0 3a f7 a0',
- 2048: 'e5 96 16 e4 69 78 79 39 a0 63 ce ea 9a f9 56 d1',
- 3056: 'c4 7e 0d c1 66 09 19 c1 11 01 20 8f 9e 69 aa 1f',
- 3072: '5a e4 f1 28 96 b8 37 9a 2a ad 89 b5 b5 53 d6 b0',
- 4080: '6b 6b 09 8d 0c 29 3b c2 99 3d 80 bf 05 18 b6 d9',
- 4096: '81 70 cc 3c cd 92 a6 98 62 1b 93 9d d3 8f e7 b9'
- }
- ),
- (
- '8b37641910833222772a',
- {
- 0: 'ab 65 c2 6e dd b2 87 60 0d b2 fd a1 0d 1e 60 5c',
- 16: 'bb 75 90 10 c2 96 58 f2 c7 2d 93 a2 d1 6d 29 30',
- 240: 'b9 01 e8 03 6e d1 c3 83 cd 3c 4c 4d d0 a6 ab 05',
- 256: '3d 25 ce 49 22 92 4c 55 f0 64 94 33 53 d7 8a 6c',
- 496: '12 c1 aa 44 bb f8 7e 75 e6 11 f6 9b 2c 38 f4 9b',
- 512: '28 f2 b3 43 4b 65 c0 98 77 47 00 44 c6 ea 17 0d',
- 752: 'bd 9e f8 22 de 52 88 19 61 34 cf 8a f7 83 93 04',
- 768: '67 55 9c 23 f0 52 15 84 70 a2 96 f7 25 73 5a 32',
- 1008: '8b ab 26 fb c2 c1 2b 0f 13 e2 ab 18 5e ab f2 41',
- 1024: '31 18 5a 6d 69 6f 0c fa 9b 42 80 8b 38 e1 32 a2',
- 1520: '56 4d 3d ae 18 3c 52 34 c8 af 1e 51 06 1c 44 b5',
- 1536: '3c 07 78 a7 b5 f7 2d 3c 23 a3 13 5c 7d 67 b9 f4',
- 2032: 'f3 43 69 89 0f cf 16 fb 51 7d ca ae 44 63 b2 dd',
- 2048: '02 f3 1c 81 e8 20 07 31 b8 99 b0 28 e7 91 bf a7',
- 3056: '72 da 64 62 83 22 8c 14 30 08 53 70 17 95 61 6f',
- 3072: '4e 0a 8c 6f 79 34 a7 88 e2 26 5e 81 d6 d0 c8 f4',
- 4080: '43 8d d5 ea fe a0 11 1b 6f 36 b4 b9 38 da 2a 68',
- 4096: '5f 6b fc 73 81 58 74 d9 71 00 f0 86 97 93 57 d8'
- }
- ),
- # Page 9
- (
- 'ebb46227c6cc8b37641910833222772a',
- {
- 0: '72 0c 94 b6 3e df 44 e1 31 d9 50 ca 21 1a 5a 30',
- 16: 'c3 66 fd ea cf 9c a8 04 36 be 7c 35 84 24 d2 0b',
- 240: 'b3 39 4a 40 aa bf 75 cb a4 22 82 ef 25 a0 05 9f',
- 256: '48 47 d8 1d a4 94 2d bc 24 9d ef c4 8c 92 2b 9f',
- 496: '08 12 8c 46 9f 27 53 42 ad da 20 2b 2b 58 da 95',
- 512: '97 0d ac ef 40 ad 98 72 3b ac 5d 69 55 b8 17 61',
- 752: '3c b8 99 93 b0 7b 0c ed 93 de 13 d2 a1 10 13 ac',
- 768: 'ef 2d 67 6f 15 45 c2 c1 3d c6 80 a0 2f 4a db fe',
- 1008: 'b6 05 95 51 4f 24 bc 9f e5 22 a6 ca d7 39 36 44',
- 1024: 'b5 15 a8 c5 01 17 54 f5 90 03 05 8b db 81 51 4e',
- 1520: '3c 70 04 7e 8c bc 03 8e 3b 98 20 db 60 1d a4 95',
- 1536: '11 75 da 6e e7 56 de 46 a5 3e 2b 07 56 60 b7 70',
- 2032: '00 a5 42 bb a0 21 11 cc 2c 65 b3 8e bd ba 58 7e',
- 2048: '58 65 fd bb 5b 48 06 41 04 e8 30 b3 80 f2 ae de',
- 3056: '34 b2 1a d2 ad 44 e9 99 db 2d 7f 08 63 f0 d9 b6',
- 3072: '84 a9 21 8f c3 6e 8a 5f 2c cf be ae 53 a2 7d 25',
- 4080: 'a2 22 1a 11 b8 33 cc b4 98 a5 95 40 f0 54 5f 4a',
- 4096: '5b be b4 78 7d 59 e5 37 3f db ea 6c 6f 75 c2 9b'
- }
- ),
- (
- 'c109163908ebe51debb46227c6cc8b37641910833222772a',
- {
- 0: '54 b6 4e 6b 5a 20 b5 e2 ec 84 59 3d c7 98 9d a7',
- 16: 'c1 35 ee e2 37 a8 54 65 ff 97 dc 03 92 4f 45 ce',
- 240: 'cf cc 92 2f b4 a1 4a b4 5d 61 75 aa bb f2 d2 01',
- 256: '83 7b 87 e2 a4 46 ad 0e f7 98 ac d0 2b 94 12 4f',
- 496: '17 a6 db d6 64 92 6a 06 36 b3 f4 c3 7a 4f 46 94',
- 512: '4a 5f 9f 26 ae ee d4 d4 a2 5f 63 2d 30 52 33 d9',
- 752: '80 a3 d0 1e f0 0c 8e 9a 42 09 c1 7f 4e eb 35 8c',
- 768: 'd1 5e 7d 5f fa aa bc 02 07 bf 20 0a 11 77 93 a2',
- 1008: '34 96 82 bf 58 8e aa 52 d0 aa 15 60 34 6a ea fa',
- 1024: 'f5 85 4c db 76 c8 89 e3 ad 63 35 4e 5f 72 75 e3',
- 1520: '53 2c 7c ec cb 39 df 32 36 31 84 05 a4 b1 27 9c',
- 1536: 'ba ef e6 d9 ce b6 51 84 22 60 e0 d1 e0 5e 3b 90',
- 2032: 'e8 2d 8c 6d b5 4e 3c 63 3f 58 1c 95 2b a0 42 07',
- 2048: '4b 16 e5 0a bd 38 1b d7 09 00 a9 cd 9a 62 cb 23',
- 3056: '36 82 ee 33 bd 14 8b d9 f5 86 56 cd 8f 30 d9 fb',
- 3072: '1e 5a 0b 84 75 04 5d 9b 20 b2 62 86 24 ed fd 9e',
- 4080: '63 ed d6 84 fb 82 62 82 fe 52 8f 9c 0e 92 37 bc',
- 4096: 'e4 dd 2e 98 d6 96 0f ae 0b 43 54 54 56 74 33 91'
- }
- ),
- # Page 10
- (
- '1ada31d5cf688221c109163908ebe51debb46227c6cc8b37641910833222772a',
- {
- 0: 'dd 5b cb 00 18 e9 22 d4 94 75 9d 7c 39 5d 02 d3',
- 16: 'c8 44 6f 8f 77 ab f7 37 68 53 53 eb 89 a1 c9 eb',
- 240: 'af 3e 30 f9 c0 95 04 59 38 15 15 75 c3 fb 90 98',
- 256: 'f8 cb 62 74 db 99 b8 0b 1d 20 12 a9 8e d4 8f 0e',
- 496: '25 c3 00 5a 1c b8 5d e0 76 25 98 39 ab 71 98 ab',
- 512: '9d cb c1 83 e8 cb 99 4b 72 7b 75 be 31 80 76 9c',
- 752: 'a1 d3 07 8d fa 91 69 50 3e d9 d4 49 1d ee 4e b2',
- 768: '85 14 a5 49 58 58 09 6f 59 6e 4b cd 66 b1 06 65',
- 1008: '5f 40 d5 9e c1 b0 3b 33 73 8e fa 60 b2 25 5d 31',
- 1024: '34 77 c7 f7 64 a4 1b ac ef f9 0b f1 4f 92 b7 cc',
- 1520: 'ac 4e 95 36 8d 99 b9 eb 78 b8 da 8f 81 ff a7 95',
- 1536: '8c 3c 13 f8 c2 38 8b b7 3f 38 57 6e 65 b7 c4 46',
- 2032: '13 c4 b9 c1 df b6 65 79 ed dd 8a 28 0b 9f 73 16',
- 2048: 'dd d2 78 20 55 01 26 69 8e fa ad c6 4b 64 f6 6e',
- 3056: 'f0 8f 2e 66 d2 8e d1 43 f3 a2 37 cf 9d e7 35 59',
- 3072: '9e a3 6c 52 55 31 b8 80 ba 12 43 34 f5 7b 0b 70',
- 4080: 'd5 a3 9e 3d fc c5 02 80 ba c4 a6 b5 aa 0d ca 7d',
- 4096: '37 0b 1c 1f e6 55 91 6d 97 fd 0d 47 ca 1d 72 b8'
- }
- )
- ]
-
- def test_keystream(self):
- for tv in self.rfc6229_data:
- key = unhexlify(b((tv[0])))
- cipher = ARC4.new(key)
- count = 0
- for offset in range(0, 4096+1, 16):
- ct = cipher.encrypt(b('\x00')*16)
- expected = tv[1].get(offset)
- if expected:
- expected = unhexlify(b(expected.replace(" ", '')))
- self.assertEqual(ct, expected)
- count += 1
- self.assertEqual(count, len(tv[1]))
-
-
-class Drop_Tests(unittest.TestCase):
- key = b('\xAA')*16
- data = b('\x00')*5000
-
- def setUp(self):
- self.cipher = ARC4.new(self.key)
-
- def test_drop256_encrypt(self):
- cipher_drop = ARC4.new(self.key, 256)
- ct_drop = cipher_drop.encrypt(self.data[:16])
- ct = self.cipher.encrypt(self.data)[256:256+16]
- self.assertEqual(ct_drop, ct)
-
- def test_drop256_decrypt(self):
- cipher_drop = ARC4.new(self.key, 256)
- pt_drop = cipher_drop.decrypt(self.data[:16])
- pt = self.cipher.decrypt(self.data)[256:256+16]
- self.assertEqual(pt_drop, pt)
-
-
-class KeyLength(unittest.TestCase):
-
- def runTest(self):
- self.assertRaises(ValueError, ARC4.new, b'')
- self.assertRaises(ValueError, ARC4.new, b'\x00' * 257)
-
-
-def get_tests(config={}):
- from .common import make_stream_tests
- tests = make_stream_tests(ARC4, "ARC4", test_data)
- tests += list_test_cases(RFC6229_Tests)
- tests += list_test_cases(Drop_Tests)
- tests.append(KeyLength())
- return tests
-
-
-if __name__ == '__main__':
- def suite():
- return unittest.TestSuite(get_tests())
- unittest.main(defaultTest='suite')
diff --git a/spaces/johnslegers/stable-diffusion-gui-test/static/index.js b/spaces/johnslegers/stable-diffusion-gui-test/static/index.js
deleted file mode 100644
index 773780ddda768358f871b39f3774d6bce0715ff8..0000000000000000000000000000000000000000
--- a/spaces/johnslegers/stable-diffusion-gui-test/static/index.js
+++ /dev/null
@@ -1,126 +0,0 @@
-if (document.location.search.includes('dark-theme=true')) {
- document.body.classList.add('dark-theme');
-}
-
-let cursor = 0;
-const RANGE = 5;
-const LIMIT = 16_000;
-
-const textToImage = async (text) => {
- const inferenceResponse = await fetch(`infer_biggan?input=${text}`);
- const inferenceBlob = await inferenceResponse.blob();
- const content = URL.createObjectURL(inferenceBlob);
- return content;
-};
-
-const translateText = async (text) => {
- const inferResponse = await fetch(`infer_t5?input=${text}`);
- const inferJson = await inferResponse.json();
-
- return inferJson.output;
-};
-
-const queryDataset = async (start, end) => {
- const queryResponse = await fetch(`query_emotion?start=${start}&end=${end}`);
- const queryJson = await queryResponse.json();
-
- return queryJson.output;
-};
-
-const updateTable = async (cursor, range = RANGE) => {
- const table = document.querySelector('.dataset-output');
-
- const fragment = new DocumentFragment();
-
- const observations = await queryDataset(cursor, cursor + range);
-
- for (const observation of observations) {
- let row = document.createElement('tr');
- let text = document.createElement('td');
- let emotion = document.createElement('td');
-
- text.textContent = observation.text;
- emotion.textContent = observation.emotion;
-
- row.appendChild(text);
- row.appendChild(emotion);
- fragment.appendChild(row);
- }
-
- table.innerHTML = '';
-
- table.appendChild(fragment);
-
- table.insertAdjacentHTML(
- 'afterbegin',
- `
-
- text
- emotion
-
- `
- );
-};
-
-const imageGenSelect = document.getElementById('image-gen-input');
-const imageGenImage = document.querySelector('.image-gen-output');
-const textGenForm = document.querySelector('.text-gen-form');
-const tableButtonPrev = document.querySelector('.table-previous');
-const tableButtonNext = document.querySelector('.table-next');
-
-imageGenSelect.addEventListener('change', async (event) => {
- const value = event.target.value;
-
- try {
- imageGenImage.src = await textToImage(value);
- imageGenImage.alt = value + ' generated from BigGAN AI model';
- } catch (err) {
- console.error(err);
- }
-});
-
-textGenForm.addEventListener('submit', async (event) => {
- event.preventDefault();
-
- const textGenInput = document.getElementById('text-gen-input');
- const textGenParagraph = document.querySelector('.text-gen-output');
-
- try {
- textGenParagraph.textContent = await translateText(textGenInput.value);
- } catch (err) {
- console.error(err);
- }
-});
-
-tableButtonPrev.addEventListener('click', () => {
- cursor = cursor > RANGE ? cursor - RANGE : 0;
-
- if (cursor < RANGE) {
- tableButtonPrev.classList.add('hidden');
- }
- if (cursor < LIMIT - RANGE) {
- tableButtonNext.classList.remove('hidden');
- }
-
- updateTable(cursor);
-});
-
-tableButtonNext.addEventListener('click', () => {
- cursor = cursor < LIMIT - RANGE ? cursor + RANGE : cursor;
-
- if (cursor >= RANGE) {
- tableButtonPrev.classList.remove('hidden');
- }
- if (cursor >= LIMIT - RANGE) {
- tableButtonNext.classList.add('hidden');
- }
-
- updateTable(cursor);
-});
-
-textToImage(imageGenSelect.value)
- .then((image) => (imageGenImage.src = image))
- .catch(console.error);
-
-updateTable(cursor)
- .catch(console.error);
diff --git a/spaces/josuelmet/Metal_Music_Interpolator/_Decompressor.py b/spaces/josuelmet/Metal_Music_Interpolator/_Decompressor.py
deleted file mode 100644
index 7141bbb0627d2443a677223c215bcfeec7e2f668..0000000000000000000000000000000000000000
--- a/spaces/josuelmet/Metal_Music_Interpolator/_Decompressor.py
+++ /dev/null
@@ -1,252 +0,0 @@
-'''
-Imports
-'''
-import guitarpro
-from guitarpro import *
-import numpy as np
-import os
-import pickle
-import re
-from tqdm import tqdm
-
-from keras.utils import np_utils
-
-
-'''
-Constants
-'''
-# PITCH[i] = the pitch associated with midi note number i.
-# For example, PITCH[69] = 'A4'
-PITCH = {val : str(GuitarString(number=0, value=val)) for val in range(128)}
-# MIDI[string] = the midi number associated with the note described by string.
-# For example, MIDI['A4'] = 69.
-MIDI = {str(GuitarString(number=0, value=val)) : val for val in range(128)}
-
-
-
-'''
-get_strings function
-'''
-def get_strings(chord, tuning, as_fingerings=True):
-
- lowest_string = len(tuning) # Bass has 4 strings, while metal guitars can have 6-8 strings.
-
- if as_fingerings:
- # NEW CODE:
- # Represent the tuning as the number of semitones between the open strings and the lowest string.
- # i.e., relative_tuning[lowest_string] = 0
- relative_tuning = {k : v - tuning[lowest_string] for k, v in tuning.items()}
-
- chord_parts = chord.split('_')
-
-
- if as_fingerings:
- # NEW CODE:
- root_value = int(chord_parts[0])
- else:
- # NEW CODE:
- root_value = MIDI[chord_parts[0]]
-
-
- if as_fingerings:
- # NEW CODE:
- if root_value < 0:
- print(f'!!!!! error !!!!!\t {root_value} < 0')
- else:
- # OLD CODE:
- if root_value < tuning[lowest_string]:
- print(f'!!!!! error !!!!!\t {root_value} < {tuning[lowest_string]}')
-
- # Using the tuning, get a list of all possible fret positions for the root note.
- if as_fingerings:
- # NEW CODE:
- tuning_values = np.array(list(relative_tuning.values()))
- else:
- # OLD CODE:
- tuning_values = np.array(list(tuning.values()))
-
- fingerings = root_value - tuning_values
-
-
- # + 1 because tuning[] is 1-indexed.
- string = np.where(fingerings >= 0, fingerings, np.inf).argmin() + 1
- fret = fingerings[string-1]
-
- # If we are just playing a single note, then the function can just return what it has now.
- if len(chord_parts) == 1:
- return [(fret, string)]
-
- # If the chord requires a very high pitch, lower its fingering to the second-highest string,
- # so as to save the highest string for the other part of the chord.
- if string == 1:
- string = 2
- fret = fingerings[string-1]
-
- if chord_parts[1] == '5':
- upper_value = root_value + 7 # perfect fifth
- elif chord_parts[1] == 'dim5':
- upper_value = root_value + 6 # tritone
- elif chord_parts[1] == '4':
- upper_value = root_value + 5
- else:
- upper_value = root_value + 5 # in case of an error, assume that the upper value is a perfect 4th above the root.
-
-
- if as_fingerings:
- # NEW CODE:
- upper_fret = upper_value - relative_tuning[string-1]
- else:
- # OLD CODE:
- upper_fret = upper_value - tuning[string-1]
-
-
- if upper_fret < 0:
- # There are some rare cases where the chord cannot be played given a tuning.
- # For example, a tritone or a perfect 4th with root C2 in a drop-C guitar.
- # In that case, just return the root note.
- return [(fret, string)]
-
- return [(fret, string), (upper_fret, string-1)]
-
-
-
-
-class SongWriter:
-
- def __init__(self, initialTempo):
- self.song = guitarpro.models.Song(tempo=initialTempo)
- self.song.tracks = [] # Initialize song.tracks to an empty list.
-
- self.currentTempo = initialTempo
-
- '''
- decompress_track function
- '''
- def decompress_track(self, track, tuning, tempo=None, instrument=None, name=None, as_fingerings=True):
-
- # Instantiate the Midi Channel / Instrument for this track,
- # making sure to avoid channel 9 (the drum channel)
- channel = MidiChannel(channel = len(self.song.tracks) + (len(self.song.tracks) >= 9),
- effectChannel = len(self.song.tracks) + (len(self.song.tracks) >= 9))
- if instrument is not None:
- channel.instrument = instrument
-
- # Instantiate the name for this track
- if name is None:
- name = f'Track {len(self.song.tracks) + 1}'
-
- # Pre-format the name to avoid any characters that the tab file format doesn't like.
- # Keep only spaces and alphanumeric characters.
- name = name.split('(')[0]
- name = re.sub(r' \W+', '', name)
-
- # Instantiate the actual Track itself
- self.song.tracks.append(Track(song=self.song, name=name, channel=channel))
-
- # Calculate the dict key corresponding to the lowest-tuned string.
- lowest_string = len(tuning)
-
- # Set the guitar tuning for the instrument.
- self.song.tracks[-1].strings = [GuitarString(number=num, value=val) for num, val in tuning.items()]
-
-
-
-
- # The first measureHeader and measure are already added by default. Let's remove them to make things more standard.
- if len(self.song.tracks) == 1:
- self.song.measureHeaders = []
- self.song.tracks[0].measures = []
-
- startingTrackIdx = len(self.song.measureHeaders)
-
- for i in range(startingTrackIdx, startingTrackIdx+len(track)):
- start = guitarpro.Duration.quarterTime * (1 + i*6)
-
- self.song.addMeasureHeader(MeasureHeader(number=i+1, start=start))
-
- # Add new measure to every existing track.
- for existing_track in self.song.tracks:
- existing_track.measures.append( Measure(existing_track, self.song.measureHeaders[i]) )
-
-
-
-
-
- for m_i, measure in enumerate(self.song.tracks[-1].measures):
-
- if m_i < startingTrackIdx:
- continue # Skip tracks that have already been written to.
-
- # "beats" starts off as an empy array [].
- voice = measure.voices[0]
- beats = voice.beats
-
- #print(m_i - startingTrackIdx)
- # For the m_i-th measure, get the indices b_i and the beats track_beat of the compressed song.
- for b_i, track_beat in enumerate(track[m_i - startingTrackIdx]):
-
- #print(f'\t{b_i}')
-
- # If a tempo change is needed:
- if tempo is not None and tempo != self.currentTempo:
- # Implement the tempo change, then update the current tempo.
- effect = BeatEffect(mixTableChange=MixTableChange(tempo=MixTableItem(value=tempo), hideTempo=False))
- self.currentTempo = tempo
- else:
- effect = BeatEffect()
-
-
- chord = track_beat[0]
- duration = Duration(value=int(track_beat[1]), isDotted=bool(track_beat[2]))
-
- # since "beats" is empty, we can append Beat objects to it.
- beats.append(Beat(voice, duration=duration, effect=effect))
- if chord == 'rest':
- beats[b_i].status = guitarpro.BeatStatus.rest
-
- elif chord == 'tied':
- # If this tied note is the first beat in its measure:
- if b_i == 0:
- # If this tied note is the first beat in the first measure:
- if m_i == 0:
- # Designate this beat as a rest, then move on to the next beat.
- beats[b_i].status = guitarpro.BeatStatus.rest
- continue
- else:
- # Get the last Beat object from the previous Measure.
- previous_beats = self.song.tracks[-1].measures[m_i-1].voices[0].beats
- if len(previous_beats) == 0:
- beats[b_i].status = guitarpro.BeatStatus.rest
- continue
- previous_beat = previous_beats[-1]
- else:
- # Get the previous Beat object from the current Measure.
- previous_beat = beats[b_i-1]
-
- for note in previous_beat.notes:
- beats[b_i].notes.append(Note(beat=beats[b_i], value=note.value, string=note.string, type=NoteType.tie))
-
-
-
-
- elif chord == 'dead':
- beats[b_i].notes.append(Note(beat=beats[b_i], value=0, string=lowest_string, type=NoteType.dead))
- beats[b_i].notes.append(Note(beat=beats[b_i], value=0, string=lowest_string-1, type=NoteType.dead))
- beats[b_i].notes.append(Note(beat=beats[b_i], value=0, string=lowest_string-2, type=NoteType.dead))
-
- else:
- for fret, string in get_strings(chord, tuning, as_fingerings):
- noteEffect = NoteEffect(palmMute=track_beat[3])
- beats[b_i].notes.append(Note(beat=beats[b_i], value=fret, string=string,
- type=NoteType.normal, effect=noteEffect))
-
-
- #print('\t\t', chord, '\t', duration)
-
-
- # Lastly, return the song so that it can be saved to a .gp5 file.
- # return new_song
-
-
- def write(self, filename):
- guitarpro.write(self.song, filename)
\ No newline at end of file
diff --git a/spaces/jpdiazpardo/jpdiazpardo-whisper-tiny-metal/functions/timestamp.py b/spaces/jpdiazpardo/jpdiazpardo-whisper-tiny-metal/functions/timestamp.py
deleted file mode 100644
index 5078013df5eee0f40d8fe2c2b89c1c03a84f04dd..0000000000000000000000000000000000000000
--- a/spaces/jpdiazpardo/jpdiazpardo-whisper-tiny-metal/functions/timestamp.py
+++ /dev/null
@@ -1,18 +0,0 @@
-def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
- if seconds is not None:
- milliseconds = round(seconds * 1000.0)
-
- hours = milliseconds // 3_600_000
- milliseconds -= hours * 3_600_000
-
- minutes = milliseconds // 60_000
- milliseconds -= minutes * 60_000
-
- seconds = milliseconds // 1_000
- milliseconds -= seconds * 1_000
-
- hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
- return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
- else:
- # we have a malformed timestamp so just return it as is
- return seconds
\ No newline at end of file
diff --git a/spaces/jphwang/colorful_vectors/README.md b/spaces/jphwang/colorful_vectors/README.md
deleted file mode 100644
index f7c6013a053a6e46b0113c066cbbb941a22d54ed..0000000000000000000000000000000000000000
--- a/spaces/jphwang/colorful_vectors/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Colorful Vectors
-emoji: 🦀
-colorFrom: yellow
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/julien-c/sveltekit-demo/build/_app/assets/start-61d1577b.css b/spaces/julien-c/sveltekit-demo/build/_app/assets/start-61d1577b.css
deleted file mode 100644
index 97adda08e6e77dc1b0d68b099be72d002e7dc6e5..0000000000000000000000000000000000000000
--- a/spaces/julien-c/sveltekit-demo/build/_app/assets/start-61d1577b.css
+++ /dev/null
@@ -1 +0,0 @@
-#svelte-announcer.svelte-1j55zn5{position:absolute;left:0;top:0;clip:rect(0 0 0 0);clip-path:inset(50%);overflow:hidden;white-space:nowrap;width:1px;height:1px}
diff --git a/spaces/jw2yang/unicl-img-recog-demo/model/image_encoder/__init__.py b/spaces/jw2yang/unicl-img-recog-demo/model/image_encoder/__init__.py
deleted file mode 100644
index bb90d1229f08d046446b3c7059aa930d553989f3..0000000000000000000000000000000000000000
--- a/spaces/jw2yang/unicl-img-recog-demo/model/image_encoder/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .build import build_model as build_image_encoder
\ No newline at end of file
diff --git a/spaces/jxu124/vits-genshin/text/cleaners.py b/spaces/jxu124/vits-genshin/text/cleaners.py
deleted file mode 100644
index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000
--- a/spaces/jxu124/vits-genshin/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def lowercase(text):
- return text.lower()
-
-
-def collapse_whitespace(text):
- return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
- return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text!='':
- text+=' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil','pau']:
- text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
- else:
- continue
- n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
- a2_next=-1
- else:
- a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i SanitizeMol(m) -> AssignStereochemistry(m, ...)
- molecule = Chem.MolFromSmiles(smiles, sanitize=False)
-
- # If sanitization is unsuccessful, catch the error, and try again without
- # the sanitization step that caused the error
- flag = Chem.SanitizeMol(molecule, catchErrors=True)
- if flag != Chem.SanitizeFlags.SANITIZE_NONE:
- Chem.SanitizeMol(molecule, sanitizeOps=Chem.SanitizeFlags.SANITIZE_ALL ^ flag)
-
- Chem.AssignStereochemistry(molecule, cleanIt=True, force=True)
- return molecule
-
-
-def graph_from_molecule(molecule):
- # Initialize graph
- atom_features = []
- bond_features = []
- pair_indices = []
-
- for atom in molecule.GetAtoms():
- atom_features.append(atom_featurizer.encode(atom))
-
- # Add self-loops
- pair_indices.append([atom.GetIdx(), atom.GetIdx()])
- bond_features.append(bond_featurizer.encode(None))
-
- for neighbor in atom.GetNeighbors():
- bond = molecule.GetBondBetweenAtoms(atom.GetIdx(), neighbor.GetIdx())
- pair_indices.append([atom.GetIdx(), neighbor.GetIdx()])
- bond_features.append(bond_featurizer.encode(bond))
-
- return np.array(atom_features), np.array(bond_features), np.array(pair_indices)
-
-
-def graphs_from_smiles(smiles_list):
- # Initialize graphs
- atom_features_list = []
- bond_features_list = []
- pair_indices_list = []
-
- for smiles in smiles_list:
- molecule = molecule_from_smiles(smiles)
- atom_features, bond_features, pair_indices = graph_from_molecule(molecule)
-
- atom_features_list.append(atom_features)
- bond_features_list.append(bond_features)
- pair_indices_list.append(pair_indices)
-
- # Convert lists to ragged tensors for tf.data.Dataset later on
- return (
- tf.ragged.constant(atom_features_list, dtype=tf.float32),
- tf.ragged.constant(bond_features_list, dtype=tf.float32),
- tf.ragged.constant(pair_indices_list, dtype=tf.int64),
- )
-
-
-def prepare_batch(x_batch, y_batch):
- """Merges (sub)graphs of batch into a single global (disconnected) graph
- """
-
- atom_features, bond_features, pair_indices = x_batch
-
- # Obtain number of atoms and bonds for each graph (molecule)
- num_atoms = atom_features.row_lengths()
- num_bonds = bond_features.row_lengths()
-
- # Obtain partition indices (molecule_indicator), which will be used to
- # gather (sub)graphs from global graph in model later on
- molecule_indices = tf.range(len(num_atoms))
- molecule_indicator = tf.repeat(molecule_indices, num_atoms)
-
- # Merge (sub)graphs into a global (disconnected) graph. Adding 'increment' to
- # 'pair_indices' (and merging ragged tensors) actualizes the global graph
- gather_indices = tf.repeat(molecule_indices[:-1], num_bonds[1:])
- increment = tf.cumsum(num_atoms[:-1])
- increment = tf.pad(tf.gather(increment, gather_indices), [(num_bonds[0], 0)])
- pair_indices = pair_indices.merge_dims(outer_axis=0, inner_axis=1).to_tensor()
- pair_indices = pair_indices + increment[:, tf.newaxis]
- atom_features = atom_features.merge_dims(outer_axis=0, inner_axis=1).to_tensor()
- bond_features = bond_features.merge_dims(outer_axis=0, inner_axis=1).to_tensor()
-
- return (atom_features, bond_features, pair_indices, molecule_indicator), y_batch
-
-
-def MPNNDataset(X, y, batch_size=32, shuffle=False):
- dataset = tf.data.Dataset.from_tensor_slices((X, (y)))
- if shuffle:
- dataset = dataset.shuffle(1024)
- return dataset.batch(batch_size).map(prepare_batch, -1).prefetch(-1)
-
-
-model = from_pretrained_keras("keras-io/MPNN-for-molecular-property-prediction")
-
-
-def predict(smiles, label):
- molecules = [molecule_from_smiles(smiles)]
- input = graphs_from_smiles([smiles])
- label = pd.Series([label])
- test_dataset = MPNNDataset(input, label)
- y_pred = tf.squeeze(model.predict(test_dataset), axis=1)
- legends = [f"y_true/y_pred = {label[i]}/{y_pred[i]:.2f}" for i in range(len(label))]
- MolsToGridImage(molecules, molsPerRow=1, legends=legends, returnPNG=False, subImgSize=(650, 650)).save("img.png")
- return 'img.png'
-
-inputs = [
- gr.Textbox(label='Smiles of molecular'),
- gr.Textbox(label='Molecular permeability')
-]
-
-examples = [
- ["CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)C)/c3csc(N)n3", 0],
- ["[C@H]37[C@H]2[C@@]([C@](C(COC(C1=CC(=CC=C1)[S](O)(=O)=O)=O)=O)(O)[C@@H](C2)C)(C[C@@H]([C@@H]3[C@@]4(C(=CC5=C(C4)C=N[N]5C6=CC=CC=C6)C(=C7)C)C)O)C", 1],
- ["CNCCCC2(C)C(=O)N(c1ccccc1)c3ccccc23", 1],
- ["O.N[C@@H](C(=O)NC1C2CCC(=C(N2C1=O)C(O)=O)Cl)c3ccccc3", 0],
- ["[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O", 1],
- ["[C@]34([C@H](C2[C@@](F)([C@@]1(C(=CC(=O)C=C1)[C@@H](F)C2)C)[C@@H](O)C3)C[C@H]5OC(O[C@@]45C(=O)COC(=O)C6CC6)(C)C)C", 1]
-
-]
-gr.Interface(
- fn=predict,
- title="Predict blood-brain barrier permeability of molecular",
- description = "Message-passing neural network (MPNN) for molecular property prediction",
- inputs=inputs,
- examples=examples,
- outputs="image",
- article = "Author: Vu Minh Chien . Based on the keras example from Alexander Kensert ",
-).launch(debug=False, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/run.sh b/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/run.sh
deleted file mode 100644
index 61af4b4950eb11334e55362e3e3c5e2796979a01..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/run.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-ps -ef | grep "train" | grep -v grep | awk '{print "kill -9 "$2}' | sh
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/README-CN.md b/spaces/kira4424/Tacotron-zero-short-voice-clone/README-CN.md
deleted file mode 100644
index 738b37f21a840026f64fd5bf699b013f459108a4..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/README-CN.md
+++ /dev/null
@@ -1,230 +0,0 @@
-## 实时语音克隆 - 中文/普通话
-
-
-[](http://choosealicense.com/licenses/mit/)
-
-### [English](README.md) | 中文
-
-### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/) | [Wiki教程](https://github.com/babysor/MockingBird/wiki/Quick-Start-(Newbie)) ｜ [训练教程](https://vaj2fgg8yn.feishu.cn/docs/doccn7kAbr3SJz0KM0SIDJ0Xnhd)
-
-## 特性
-🌍 **中文** 支持普通话并使用多种中文数据集进行测试：aidatatang_200zh, magicdata, aishell3, biaobei, MozillaCommonVoice, data_aishell 等
-
-🤩 **PyTorch** 适用于 pytorch，已在 1.9.0 版本（最新于 2021 年 8 月）中测试，GPU Tesla T4 和 GTX 2060
-
-🌍 **Windows + Linux** 可在 Windows 操作系统和 linux 操作系统中运行（苹果系统M1版也有社区成功运行案例）
-
-🤩 **Easy & Awesome** 仅需下载或新训练合成器（synthesizer）就有良好效果，复用预训练的编码器/声码器，或实时的HiFi-GAN作为vocoder
-
-🌍 **Webserver Ready** 可伺服你的训练结果，供远程调用
-
-### 进行中的工作
-* GUI/客户端大升级与合并
-[X] 初始化框架 `./mkgui` （基于streamlit + fastapi）和 [技术设计](https://vaj2fgg8yn.feishu.cn/docs/doccnvotLWylBub8VJIjKzoEaee)
-[X] 增加 Voice Cloning and Conversion的演示页面
-[X] 增加Voice Conversion的预处理preprocessing 和训练 training 页面
-[ ] 增加其他的的预处理preprocessing 和训练 training 页面
-* 模型后端基于ESPnet2升级
-
-
-## 开始
-### 1. 安装要求
-> 按照原始存储库测试您是否已准备好所有环境。
-运行工具箱(demo_toolbox.py)需要 **Python 3.7 或更高版本** 。
-
-* 安装 [PyTorch](https://pytorch.org/get-started/locally/)。
-> 如果在用 pip 方式安装的时候出现 `ERROR: Could not find a version that satisfies the requirement torch==1.9.0+cu102 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)` 这个错误可能是 python 版本过低，3.9 可以安装成功
-* 安装 [ffmpeg](https://ffmpeg.org/download.html#get-packages)。
-* 运行`pip install -r requirements.txt` 来安装剩余的必要包。
-* 安装 webrtcvad `pip install webrtcvad-wheels`。
-
-### 2. 准备预训练模型
-考虑训练您自己专属的模型或者下载社区他人训练好的模型:
-> 近期创建了[知乎专题](https://www.zhihu.com/column/c_1425605280340504576) 将不定期更新炼丹小技巧or心得，也欢迎提问
-#### 2.1 使用数据集自己训练encoder模型 (可选)
-
-* 进行音频和梅尔频谱图预处理：
-`python encoder_preprocess.py `
-使用`-d {dataset}` 指定数据集，支持 librispeech_other，voxceleb1，aidatatang_200zh，使用逗号分割处理多数据集。
-* 训练encoder: `python encoder_train.py my_run /SV2TTS/encoder`
-> 训练encoder使用了visdom。你可以加上`-no_visdom`禁用visdom，但是有可视化会更好。在单独的命令行/进程中运行"visdom"来启动visdom服务器。
-
-#### 2.2 使用数据集自己训练合成器模型（与2.3二选一）
-* 下载 数据集并解压：确保您可以访问 *train* 文件夹中的所有音频文件（如.wav）
-* 进行音频和梅尔频谱图预处理：
-`python pre.py -d {dataset} -n {number}`
-可传入参数：
-* `-d {dataset}` 指定数据集，支持 aidatatang_200zh, magicdata, aishell3, data_aishell, 不传默认为aidatatang_200zh
-* `-n {number}` 指定并行数，CPU 11770k + 32GB实测10没有问题
-> 假如你下载的 `aidatatang_200zh`文件放在D盘，`train`文件路径为 `D:\data\aidatatang_200zh\corpus\train` , 你的`datasets_root`就是 `D:\data\`
-
-* 训练合成器：
-`python synthesizer_train.py mandarin /SV2TTS/synthesizer`
-
-* 当您在训练文件夹 *synthesizer/saved_models/* 中看到注意线显示和损失满足您的需要时，请转到`启动程序`一步。
-
-#### 2.3使用社区预先训练好的合成器（与2.2二选一）
-> 当实在没有设备或者不想慢慢调试，可以使用社区贡献的模型(欢迎持续分享):
-
-| 作者 | 下载链接 | 效果预览 | 信息 |
-| --- | ----------- | ----- | ----- |
-| 作者 | https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g [百度盘链接](https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g) 4j5d | | 75k steps 用3个开源数据集混合训练
-| 作者 | https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw [百度盘链接](https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw) 提取码：om7f | | 25k steps 用3个开源数据集混合训练, 切换到tag v0.0.1使用
-|@FawenYo | https://drive.google.com/file/d/1H-YGOUHpmqKxJ9FRc6vAjPuqQki24UbC/view?usp=sharing [百度盘链接](https://pan.baidu.com/s/1vSYXO4wsLyjnF3Unl-Xoxg) 提取码：1024 | [input](https://github.com/babysor/MockingBird/wiki/audio/self_test.mp3) [output](https://github.com/babysor/MockingBird/wiki/audio/export.wav) | 200k steps 台湾口音需切换到tag v0.0.1使用
-|@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ 提取码：2021 | https://www.bilibili.com/video/BV1uh411B7AD/ | 150k steps 注意：根据[issue](https://github.com/babysor/MockingBird/issues/37)修复 并切换到tag v0.0.1使用
-
-#### 2.4训练声码器 (可选)
-对效果影响不大，已经预置3款，如果希望自己训练可以参考以下命令。
-* 预处理数据:
-`python vocoder_preprocess.py -m `
-> ``替换为你的数据集目录，``替换为一个你最好的synthesizer模型目录，例如 *sythensizer\saved_models\xxx*
-
-
-* 训练wavernn声码器:
-`python vocoder_train.py `
-> ``替换为你想要的标识，同一标识再次训练时会延续原模型
-
-* 训练hifigan声码器:
-`python vocoder_train.py hifigan`
-> ``替换为你想要的标识，同一标识再次训练时会延续原模型
-* 训练fregan声码器:
-`python vocoder_train.py --config config.json fregan`
-> ``替换为你想要的标识，同一标识再次训练时会延续原模型
-* 将GAN声码器的训练切换为多GPU模式：修改GAN文件夹下.json文件中的"num_gpus"参数
-### 3. 启动程序或工具箱
-您可以尝试使用以下命令：
-
-### 3.1 启动Web程序（v2）：
-`python web.py`
-运行成功后在浏览器打开地址, 默认为 `http://localhost:8080`
-> * 仅支持手动新录音（16khz）, 不支持超过4MB的录音，最佳长度在5~15秒
-
-### 3.2 启动工具箱：
-`python demo_toolbox.py -d `
-> 请指定一个可用的数据集文件路径，如果有支持的数据集则会自动加载供调试，也同时会作为手动录制音频的存储目录。
-
- -d {dataset} -n {number}`
-可传入参数：
-* `-d {dataset}` 指定数据集，支持 aidatatang_200zh, 不传默认为aidatatang_200zh
-* `-n {number}` 指定并行数，CPU 11770k在8的情况下，需要运行12到18小时！待优化
-> 假如你下载的 `aidatatang_200zh`文件放在D盘，`train`文件路径为 `D:\data\aidatatang_200zh\corpus\train` , 你的`datasets_root`就是 `D:\data\`
-
-* 训练合成器, 注意在上一步先下载好`ppg2mel.yaml`, 修改里面的地址指向预训练好的文件夹：
-`python ppg2mel_train.py --config .\ppg2mel\saved_models\ppg2mel.yaml --oneshotvc `
-* 如果想要继续上一次的训练，可以通过`--load .\ppg2mel\saved_models\` 参数指定一个预训练模型文件。
-
-#### 4.2 启动工具箱VC模式
-您可以尝试使用以下命令：
-`python demo_toolbox.py -vc -d `
-> 请指定一个可用的数据集文件路径，如果有支持的数据集则会自动加载供调试，也同时会作为手动录制音频的存储目录。
-`是什麼意思?
-假如數據集路徑為 `D:\data\aidatatang_200zh`，那麼 ``就是 `D:\data`
-
-#### 3.訓練模型顯存不足
-訓練合成器時：將 `synthesizer/hparams.py`中的batch_size參數調小
-```
-//調整前
-tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule
- (2, 5e-4, 40_000, 12), # (r, lr, step, batch_size)
- (2, 2e-4, 80_000, 12), #
- (2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames
- (2, 3e-5, 320_000, 12), # synthesized for each decoder iteration)
- (2, 1e-5, 640_000, 12)], # lr = learning rate
-//調整後
-tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule
- (2, 5e-4, 40_000, 8), # (r, lr, step, batch_size)
- (2, 2e-4, 80_000, 8), #
- (2, 1e-4, 160_000, 8), # r = reduction factor (# of mel frames
- (2, 3e-5, 320_000, 8), # synthesized for each decoder iteration)
- (2, 1e-5, 640_000, 8)], # lr = learning rate
-```
-
-聲碼器-預處理數據集時：將 `synthesizer/hparams.py`中的batch_size參數調小
-```
-//調整前
-### Data Preprocessing
- max_mel_frames = 900,
- rescale = True,
- rescaling_max = 0.9,
- synthesis_batch_size = 16, # For vocoder preprocessing and inference.
-//調整後
-### Data Preprocessing
- max_mel_frames = 900,
- rescale = True,
- rescaling_max = 0.9,
- synthesis_batch_size = 8, # For vocoder preprocessing and inference.
-```
-
-聲碼器-訓練聲碼器時：將 `vocoder/wavernn/hparams.py`中的batch_size參數調小
-```
-//調整前
-# Training
-voc_batch_size = 100
-voc_lr = 1e-4
-voc_gen_at_checkpoint = 5
-voc_pad = 2
-
-//調整後
-# Training
-voc_batch_size = 6
-voc_lr = 1e-4
-voc_gen_at_checkpoint = 5
-voc_pad =2
-```
-
-#### 4.碰到`RuntimeError: Error(s) in loading state_dict for Tacotron: size mismatch for encoder.embedding.weight: copying a param with shape torch.Size([70, 512]) from checkpoint, the shape in current model is torch.Size([75, 512]).`
-請參照 issue [#37](https://github.com/babysor/MockingBird/issues/37)
-
-#### 5.如何改善CPU、GPU佔用率?
-適情況調整batch_size參數來改善
-
-#### 6.發生 `頁面文件太小，無法完成操作`
-請參考這篇[文章](https://blog.csdn.net/qq_17755303/article/details/112564030)，將虛擬內存更改為100G(102400)，例如:档案放置D槽就更改D槽的虚拟内存
-
-#### 7.什么时候算训练完成？
-首先一定要出现注意力模型，其次是loss足够低，取决于硬件设备和数据集。拿本人的供参考，我的注意力是在 18k 步之后出现的，并且在 50k 步之后损失变得低于 0.4
-
-
-
-
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/unet.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/unet.py
deleted file mode 100644
index 82caa16a94c195c192a2a920fb7bc7e60f0f3ce3..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/unet.py
+++ /dev/null
@@ -1,429 +0,0 @@
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-from annotator.uniformer.mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
- build_norm_layer, constant_init, kaiming_init)
-from annotator.uniformer.mmcv.runner import load_checkpoint
-from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
-
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-from ..utils import UpConvBlock
-
-
-class BasicConvBlock(nn.Module):
- """Basic convolutional block for UNet.
-
- This module consists of several plain convolutional layers.
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- num_convs (int): Number of convolutional layers. Default: 2.
- stride (int): Whether use stride convolution to downsample
- the input feature map. If stride=2, it only uses stride convolution
- in the first convolutional layer to downsample the input feature
- map. Options are 1 or 2. Default: 1.
- dilation (int): Whether use dilated convolution to expand the
- receptive field. Set dilation rate of each convolutional layer and
- the dilation rate of the first convolutional layer is always 1.
- Default: 1.
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed. Default: False.
- conv_cfg (dict | None): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict | None): Config dict for normalization layer.
- Default: dict(type='BN').
- act_cfg (dict | None): Config dict for activation layer in ConvModule.
- Default: dict(type='ReLU').
- dcn (bool): Use deformable convolution in convolutional layer or not.
- Default: None.
- plugins (dict): plugins for convolutional layers. Default: None.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- num_convs=2,
- stride=1,
- dilation=1,
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- dcn=None,
- plugins=None):
- super(BasicConvBlock, self).__init__()
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
-
- self.with_cp = with_cp
- convs = []
- for i in range(num_convs):
- convs.append(
- ConvModule(
- in_channels=in_channels if i == 0 else out_channels,
- out_channels=out_channels,
- kernel_size=3,
- stride=stride if i == 0 else 1,
- dilation=1 if i == 0 else dilation,
- padding=1 if i == 0 else dilation,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- self.convs = nn.Sequential(*convs)
-
- def forward(self, x):
- """Forward function."""
-
- if self.with_cp and x.requires_grad:
- out = cp.checkpoint(self.convs, x)
- else:
- out = self.convs(x)
- return out
-
-
-@UPSAMPLE_LAYERS.register_module()
-class DeconvModule(nn.Module):
- """Deconvolution upsample module in decoder for UNet (2X upsample).
-
- This module uses deconvolution to upsample feature map in the decoder
- of UNet.
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed. Default: False.
- norm_cfg (dict | None): Config dict for normalization layer.
- Default: dict(type='BN').
- act_cfg (dict | None): Config dict for activation layer in ConvModule.
- Default: dict(type='ReLU').
- kernel_size (int): Kernel size of the convolutional layer. Default: 4.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- with_cp=False,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- *,
- kernel_size=4,
- scale_factor=2):
- super(DeconvModule, self).__init__()
-
- assert (kernel_size - scale_factor >= 0) and\
- (kernel_size - scale_factor) % 2 == 0,\
- f'kernel_size should be greater than or equal to scale_factor '\
- f'and (kernel_size - scale_factor) should be even numbers, '\
- f'while the kernel size is {kernel_size} and scale_factor is '\
- f'{scale_factor}.'
-
- stride = scale_factor
- padding = (kernel_size - scale_factor) // 2
- self.with_cp = with_cp
- deconv = nn.ConvTranspose2d(
- in_channels,
- out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding)
-
- norm_name, norm = build_norm_layer(norm_cfg, out_channels)
- activate = build_activation_layer(act_cfg)
- self.deconv_upsamping = nn.Sequential(deconv, norm, activate)
-
- def forward(self, x):
- """Forward function."""
-
- if self.with_cp and x.requires_grad:
- out = cp.checkpoint(self.deconv_upsamping, x)
- else:
- out = self.deconv_upsamping(x)
- return out
-
-
-@UPSAMPLE_LAYERS.register_module()
-class InterpConv(nn.Module):
- """Interpolation upsample module in decoder for UNet.
-
- This module uses interpolation to upsample feature map in the decoder
- of UNet. It consists of one interpolation upsample layer and one
- convolutional layer. It can be one interpolation upsample layer followed
- by one convolutional layer (conv_first=False) or one convolutional layer
- followed by one interpolation upsample layer (conv_first=True).
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed. Default: False.
- norm_cfg (dict | None): Config dict for normalization layer.
- Default: dict(type='BN').
- act_cfg (dict | None): Config dict for activation layer in ConvModule.
- Default: dict(type='ReLU').
- conv_cfg (dict | None): Config dict for convolution layer.
- Default: None.
- conv_first (bool): Whether convolutional layer or interpolation
- upsample layer first. Default: False. It means interpolation
- upsample layer followed by one convolutional layer.
- kernel_size (int): Kernel size of the convolutional layer. Default: 1.
- stride (int): Stride of the convolutional layer. Default: 1.
- padding (int): Padding of the convolutional layer. Default: 1.
- upsample_cfg (dict): Interpolation config of the upsample layer.
- Default: dict(
- scale_factor=2, mode='bilinear', align_corners=False).
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- with_cp=False,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- *,
- conv_cfg=None,
- conv_first=False,
- kernel_size=1,
- stride=1,
- padding=0,
- upsample_cfg=dict(
- scale_factor=2, mode='bilinear', align_corners=False)):
- super(InterpConv, self).__init__()
-
- self.with_cp = with_cp
- conv = ConvModule(
- in_channels,
- out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg)
- upsample = nn.Upsample(**upsample_cfg)
- if conv_first:
- self.interp_upsample = nn.Sequential(conv, upsample)
- else:
- self.interp_upsample = nn.Sequential(upsample, conv)
-
- def forward(self, x):
- """Forward function."""
-
- if self.with_cp and x.requires_grad:
- out = cp.checkpoint(self.interp_upsample, x)
- else:
- out = self.interp_upsample(x)
- return out
-
-
-@BACKBONES.register_module()
-class UNet(nn.Module):
- """UNet backbone.
- U-Net: Convolutional Networks for Biomedical Image Segmentation.
- https://arxiv.org/pdf/1505.04597.pdf
-
- Args:
- in_channels (int): Number of input image channels. Default" 3.
- base_channels (int): Number of base channels of each stage.
- The output channels of the first stage. Default: 64.
- num_stages (int): Number of stages in encoder, normally 5. Default: 5.
- strides (Sequence[int 1 | 2]): Strides of each stage in encoder.
- len(strides) is equal to num_stages. Normally the stride of the
- first stage in encoder is 1. If strides[i]=2, it uses stride
- convolution to downsample in the correspondence encoder stage.
- Default: (1, 1, 1, 1, 1).
- enc_num_convs (Sequence[int]): Number of convolutional layers in the
- convolution block of the correspondence encoder stage.
- Default: (2, 2, 2, 2, 2).
- dec_num_convs (Sequence[int]): Number of convolutional layers in the
- convolution block of the correspondence decoder stage.
- Default: (2, 2, 2, 2).
- downsamples (Sequence[int]): Whether use MaxPool to downsample the
- feature map after the first stage of encoder
- (stages: [1, num_stages)). If the correspondence encoder stage use
- stride convolution (strides[i]=2), it will never use MaxPool to
- downsample, even downsamples[i-1]=True.
- Default: (True, True, True, True).
- enc_dilations (Sequence[int]): Dilation rate of each stage in encoder.
- Default: (1, 1, 1, 1, 1).
- dec_dilations (Sequence[int]): Dilation rate of each stage in decoder.
- Default: (1, 1, 1, 1).
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed. Default: False.
- conv_cfg (dict | None): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict | None): Config dict for normalization layer.
- Default: dict(type='BN').
- act_cfg (dict | None): Config dict for activation layer in ConvModule.
- Default: dict(type='ReLU').
- upsample_cfg (dict): The upsample config of the upsample module in
- decoder. Default: dict(type='InterpConv').
- norm_eval (bool): Whether to set norm layers to eval mode, namely,
- freeze running stats (mean and var). Note: Effect on Batch Norm
- and its variants only. Default: False.
- dcn (bool): Use deformable convolution in convolutional layer or not.
- Default: None.
- plugins (dict): plugins for convolutional layers. Default: None.
-
- Notice:
- The input image size should be divisible by the whole downsample rate
- of the encoder. More detail of the whole downsample rate can be found
- in UNet._check_input_divisible.
-
- """
-
- def __init__(self,
- in_channels=3,
- base_channels=64,
- num_stages=5,
- strides=(1, 1, 1, 1, 1),
- enc_num_convs=(2, 2, 2, 2, 2),
- dec_num_convs=(2, 2, 2, 2),
- downsamples=(True, True, True, True),
- enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1),
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- upsample_cfg=dict(type='InterpConv'),
- norm_eval=False,
- dcn=None,
- plugins=None):
- super(UNet, self).__init__()
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
- assert len(strides) == num_stages, \
- 'The length of strides should be equal to num_stages, '\
- f'while the strides is {strides}, the length of '\
- f'strides is {len(strides)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(enc_num_convs) == num_stages, \
- 'The length of enc_num_convs should be equal to num_stages, '\
- f'while the enc_num_convs is {enc_num_convs}, the length of '\
- f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(dec_num_convs) == (num_stages-1), \
- 'The length of dec_num_convs should be equal to (num_stages-1), '\
- f'while the dec_num_convs is {dec_num_convs}, the length of '\
- f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(downsamples) == (num_stages-1), \
- 'The length of downsamples should be equal to (num_stages-1), '\
- f'while the downsamples is {downsamples}, the length of '\
- f'downsamples is {len(downsamples)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(enc_dilations) == num_stages, \
- 'The length of enc_dilations should be equal to num_stages, '\
- f'while the enc_dilations is {enc_dilations}, the length of '\
- f'enc_dilations is {len(enc_dilations)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(dec_dilations) == (num_stages-1), \
- 'The length of dec_dilations should be equal to (num_stages-1), '\
- f'while the dec_dilations is {dec_dilations}, the length of '\
- f'dec_dilations is {len(dec_dilations)}, and the num_stages is '\
- f'{num_stages}.'
- self.num_stages = num_stages
- self.strides = strides
- self.downsamples = downsamples
- self.norm_eval = norm_eval
- self.base_channels = base_channels
-
- self.encoder = nn.ModuleList()
- self.decoder = nn.ModuleList()
-
- for i in range(num_stages):
- enc_conv_block = []
- if i != 0:
- if strides[i] == 1 and downsamples[i - 1]:
- enc_conv_block.append(nn.MaxPool2d(kernel_size=2))
- upsample = (strides[i] != 1 or downsamples[i - 1])
- self.decoder.append(
- UpConvBlock(
- conv_block=BasicConvBlock,
- in_channels=base_channels * 2**i,
- skip_channels=base_channels * 2**(i - 1),
- out_channels=base_channels * 2**(i - 1),
- num_convs=dec_num_convs[i - 1],
- stride=1,
- dilation=dec_dilations[i - 1],
- with_cp=with_cp,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg,
- upsample_cfg=upsample_cfg if upsample else None,
- dcn=None,
- plugins=None))
-
- enc_conv_block.append(
- BasicConvBlock(
- in_channels=in_channels,
- out_channels=base_channels * 2**i,
- num_convs=enc_num_convs[i],
- stride=strides[i],
- dilation=enc_dilations[i],
- with_cp=with_cp,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg,
- dcn=None,
- plugins=None))
- self.encoder.append((nn.Sequential(*enc_conv_block)))
- in_channels = base_channels * 2**i
-
- def forward(self, x):
- self._check_input_divisible(x)
- enc_outs = []
- for enc in self.encoder:
- x = enc(x)
- enc_outs.append(x)
- dec_outs = [x]
- for i in reversed(range(len(self.decoder))):
- x = self.decoder[i](enc_outs[i], x)
- dec_outs.append(x)
-
- return dec_outs
-
- def train(self, mode=True):
- """Convert the model into training mode while keep normalization layer
- freezed."""
- super(UNet, self).train(mode)
- if mode and self.norm_eval:
- for m in self.modules():
- # trick: eval have effect on BatchNorm only
- if isinstance(m, _BatchNorm):
- m.eval()
-
- def _check_input_divisible(self, x):
- h, w = x.shape[-2:]
- whole_downsample_rate = 1
- for i in range(1, self.num_stages):
- if self.strides[i] == 2 or self.downsamples[i - 1]:
- whole_downsample_rate *= 2
- assert (h % whole_downsample_rate == 0) \
- and (w % whole_downsample_rate == 0),\
- f'The input image size {(h, w)} should be divisible by the whole '\
- f'downsample rate {whole_downsample_rate}, when num_stages is '\
- f'{self.num_stages}, strides is {self.strides}, and downsamples '\
- f'is {self.downsamples}.'
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in backbone.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
- if isinstance(pretrained, str):
- logger = get_root_logger()
- load_checkpoint(self, pretrained, strict=False, logger=logger)
- elif pretrained is None:
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- kaiming_init(m)
- elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
- constant_init(m, 1)
- else:
- raise TypeError('pretrained must be a str or None')
diff --git a/spaces/kokofixcomputers/chat-ui/src/routes/login/+page.server.ts b/spaces/kokofixcomputers/chat-ui/src/routes/login/+page.server.ts
deleted file mode 100644
index 28692b5304687ce69551c5015d71a4419069415a..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/src/routes/login/+page.server.ts
+++ /dev/null
@@ -1,16 +0,0 @@
-import { redirect } from "@sveltejs/kit";
-import { getOIDCAuthorizationUrl } from "$lib/server/auth";
-import { base } from "$app/paths";
-
-export const actions = {
- default: async function ({ url, locals, request }) {
- // TODO: Handle errors if provider is not responding
- const referer = request.headers.get("referer");
- const authorizationUrl = await getOIDCAuthorizationUrl(
- { redirectURI: `${(referer ? new URL(referer) : url).origin}${base}/login/callback` },
- { sessionId: locals.sessionId }
- );
-
- throw redirect(303, authorizationUrl);
- },
-};
diff --git a/spaces/kottu/stabble_diffusion_sketch/model.py b/spaces/kottu/stabble_diffusion_sketch/model.py
deleted file mode 100644
index e3f9a928cfedba0ae6caebc63f9c45a6dd389175..0000000000000000000000000000000000000000
--- a/spaces/kottu/stabble_diffusion_sketch/model.py
+++ /dev/null
@@ -1,374 +0,0 @@
-import gc
-import os
-from abc import ABC, abstractmethod
-
-import numpy as np
-import PIL.Image
-import torch
-from controlnet_aux import (
- CannyDetector,
- LineartDetector,
- MidasDetector,
- OpenposeDetector,
- PidiNetDetector,
- ZoeDetector,
-)
-from diffusers import (
- AutoencoderKL,
- EulerAncestralDiscreteScheduler,
- StableDiffusionXLAdapterPipeline,
- T2IAdapter,
-)
-
-SD_XL_BASE_RATIOS = {
- "0.5": (704, 1408),
- "0.52": (704, 1344),
- "0.57": (768, 1344),
- "0.6": (768, 1280),
- "0.68": (832, 1216),
- "0.72": (832, 1152),
- "0.78": (896, 1152),
- "0.82": (896, 1088),
- "0.88": (960, 1088),
- "0.94": (960, 1024),
- "1.0": (1024, 1024),
- "1.07": (1024, 960),
- "1.13": (1088, 960),
- "1.21": (1088, 896),
- "1.29": (1152, 896),
- "1.38": (1152, 832),
- "1.46": (1216, 832),
- "1.67": (1280, 768),
- "1.75": (1344, 768),
- "1.91": (1344, 704),
- "2.0": (1408, 704),
- "2.09": (1472, 704),
- "2.4": (1536, 640),
- "2.5": (1600, 640),
- "2.89": (1664, 576),
- "3.0": (1728, 576),
-}
-
-
-def find_closest_aspect_ratio(target_width: int, target_height: int) -> str:
- target_ratio = target_width / target_height
- closest_ratio = ""
- min_difference = float("inf")
-
- for ratio_str, (width, height) in SD_XL_BASE_RATIOS.items():
- ratio = width / height
- difference = abs(target_ratio - ratio)
-
- if difference < min_difference:
- min_difference = difference
- closest_ratio = ratio_str
-
- return closest_ratio
-
-
-def resize_to_closest_aspect_ratio(image: PIL.Image.Image) -> PIL.Image.Image:
- target_width, target_height = image.size
- closest_ratio = find_closest_aspect_ratio(target_width, target_height)
-
- # Get the dimensions from the closest aspect ratio in the dictionary
- new_width, new_height = SD_XL_BASE_RATIOS[closest_ratio]
-
- # Resize the image to the new dimensions while preserving the aspect ratio
- resized_image = image.resize((new_width, new_height), PIL.Image.LANCZOS)
-
- return resized_image
-
-
-ADAPTER_REPO_IDS = {
- "canny": "TencentARC/t2i-adapter-canny-sdxl-1.0",
- "sketch": "TencentARC/t2i-adapter-sketch-sdxl-1.0",
- "lineart": "TencentARC/t2i-adapter-lineart-sdxl-1.0",
- "depth-midas": "TencentARC/t2i-adapter-depth-midas-sdxl-1.0",
- "depth-zoe": "TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
- "openpose": "TencentARC/t2i-adapter-openpose-sdxl-1.0",
- # "recolor": "TencentARC/t2i-adapter-recolor-sdxl-1.0",
-}
-ADAPTER_NAMES = list(ADAPTER_REPO_IDS.keys())
-
-
-class Preprocessor(ABC):
- @abstractmethod
- def to(self, device: torch.device | str) -> "Preprocessor":
- pass
-
- @abstractmethod
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- pass
-
-
-class CannyPreprocessor(Preprocessor):
- def __init__(self):
- self.model = CannyDetector()
-
- def to(self, device: torch.device | str) -> Preprocessor:
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- return self.model(image, detect_resolution=384, image_resolution=1024)
-
-
-class LineartPreprocessor(Preprocessor):
- def __init__(self):
- self.model = LineartDetector.from_pretrained("lllyasviel/Annotators")
-
- def to(self, device: torch.device | str) -> Preprocessor:
- self.model.to(device)
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- return self.model(image, detect_resolution=384, image_resolution=1024)
-
-
-class MidasPreprocessor(Preprocessor):
- def __init__(self):
- self.model = MidasDetector.from_pretrained(
- "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
- )
-
- def to(self, device: torch.device | str) -> Preprocessor:
- self.model.to(device)
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- return self.model(image, detect_resolution=512, image_resolution=1024)
-
-
-class OpenposePreprocessor(Preprocessor):
- def __init__(self):
- self.model = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
-
- def to(self, device: torch.device | str) -> Preprocessor:
- self.model.to(device)
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- out = self.model(image, detect_resolution=512, image_resolution=1024)
- out = np.array(out)[:, :, ::-1]
- out = PIL.Image.fromarray(np.uint8(out))
- return out
-
-
-class PidiNetPreprocessor(Preprocessor):
- def __init__(self):
- self.model = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
-
- def to(self, device: torch.device | str) -> Preprocessor:
- self.model.to(device)
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- return self.model(image, detect_resolution=512, image_resolution=1024, apply_filter=True)
-
-
-class RecolorPreprocessor(Preprocessor):
- def to(self, device: torch.device | str) -> Preprocessor:
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- return image.convert("L").convert("RGB")
-
-
-class ZoePreprocessor(Preprocessor):
- def __init__(self):
- self.model = ZoeDetector.from_pretrained(
- "valhalla/t2iadapter-aux-models", filename="zoed_nk.pth", model_type="zoedepth_nk"
- )
-
- def to(self, device: torch.device | str) -> Preprocessor:
- self.model.to(device)
- return self
-
- def __call__(self, image: PIL.Image.Image) -> PIL.Image.Image:
- return self.model(image, gamma_corrected=True, image_resolution=1024)
-
-
-PRELOAD_PREPROCESSORS_IN_GPU_MEMORY = os.getenv("PRELOAD_PREPROCESSORS_IN_GPU_MEMORY", "0") == "1"
-PRELOAD_PREPROCESSORS_IN_CPU_MEMORY = os.getenv("PRELOAD_PREPROCESSORS_IN_CPU_MEMORY", "0") == "1"
-if PRELOAD_PREPROCESSORS_IN_GPU_MEMORY:
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- preprocessors_gpu: dict[str, Preprocessor] = {
- "canny": CannyPreprocessor().to(device),
- "sketch": PidiNetPreprocessor().to(device),
- "lineart": LineartPreprocessor().to(device),
- "depth-midas": MidasPreprocessor().to(device),
- "depth-zoe": ZoePreprocessor().to(device),
- "openpose": OpenposePreprocessor().to(device),
- "recolor": RecolorPreprocessor().to(device),
- }
-
- def get_preprocessor(adapter_name: str) -> Preprocessor:
- return preprocessors_gpu[adapter_name]
-
-elif PRELOAD_PREPROCESSORS_IN_CPU_MEMORY:
- preprocessors_cpu: dict[str, Preprocessor] = {
- "canny": CannyPreprocessor(),
- "sketch": PidiNetPreprocessor(),
- "lineart": LineartPreprocessor(),
- "depth-midas": MidasPreprocessor(),
- "depth-zoe": ZoePreprocessor(),
- "openpose": OpenposePreprocessor(),
- "recolor": RecolorPreprocessor(),
- }
-
- def get_preprocessor(adapter_name: str) -> Preprocessor:
- return preprocessors_cpu[adapter_name]
-
-else:
-
- def get_preprocessor(adapter_name: str) -> Preprocessor:
- if adapter_name == "canny":
- return CannyPreprocessor()
- elif adapter_name == "sketch":
- return PidiNetPreprocessor()
- elif adapter_name == "lineart":
- return LineartPreprocessor()
- elif adapter_name == "depth-midas":
- return MidasPreprocessor()
- elif adapter_name == "depth-zoe":
- return ZoePreprocessor()
- elif adapter_name == "openpose":
- return OpenposePreprocessor()
- elif adapter_name == "recolor":
- return RecolorPreprocessor()
- else:
- raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
-
- def download_all_preprocessors():
- for adapter_name in ADAPTER_NAMES:
- get_preprocessor(adapter_name)
- gc.collect()
-
- download_all_preprocessors()
-
-
-def download_all_adapters():
- for adapter_name in ADAPTER_NAMES:
- T2IAdapter.from_pretrained(
- ADAPTER_REPO_IDS[adapter_name],
- torch_dtype=torch.float16,
- varient="fp16",
- )
- gc.collect()
-
-
-class Model:
- MAX_NUM_INFERENCE_STEPS = 50
-
- def __init__(self, adapter_name: str):
- if adapter_name not in ADAPTER_NAMES:
- raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
-
- self.preprocessor_name = adapter_name
- self.adapter_name = adapter_name
-
- self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- if torch.cuda.is_available():
- self.preprocessor = get_preprocessor(adapter_name).to(self.device)
-
- model_id = "stabilityai/stable-diffusion-xl-base-1.0"
- adapter = T2IAdapter.from_pretrained(
- ADAPTER_REPO_IDS[adapter_name],
- torch_dtype=torch.float16,
- varient="fp16",
- ).to(self.device)
- self.pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
- model_id,
- vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16),
- adapter=adapter,
- scheduler=EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler"),
- torch_dtype=torch.float16,
- variant="fp16",
- ).to(self.device)
- self.pipe.enable_xformers_memory_efficient_attention()
- self.pipe.load_lora_weights(
- "stabilityai/stable-diffusion-xl-base-1.0", weight_name="sd_xl_offset_example-lora_1.0.safetensors"
- )
- self.pipe.fuse_lora(lora_scale=0.4)
- else:
- self.preprocessor = None # type: ignore
- self.pipe = None
-
- def change_preprocessor(self, adapter_name: str) -> None:
- if adapter_name not in ADAPTER_NAMES:
- raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
- if adapter_name == self.preprocessor_name:
- return
-
- if PRELOAD_PREPROCESSORS_IN_GPU_MEMORY:
- pass
- elif PRELOAD_PREPROCESSORS_IN_CPU_MEMORY:
- self.preprocessor.to("cpu")
- else:
- del self.preprocessor
- self.preprocessor = get_preprocessor(adapter_name).to(self.device)
- self.preprocessor_name = adapter_name
- gc.collect()
- torch.cuda.empty_cache()
-
- def change_adapter(self, adapter_name: str) -> None:
- if adapter_name not in ADAPTER_NAMES:
- raise ValueError(f"Adapter name must be one of {ADAPTER_NAMES}")
- if adapter_name == self.adapter_name:
- return
- self.pipe.adapter = T2IAdapter.from_pretrained(
- ADAPTER_REPO_IDS[adapter_name],
- torch_dtype=torch.float16,
- varient="fp16",
- ).to(self.device)
- self.adapter_name = adapter_name
- gc.collect()
- torch.cuda.empty_cache()
-
- def resize_image(self, image: PIL.Image.Image) -> PIL.Image.Image:
- w, h = image.size
- scale = 1024 / max(w, h)
- new_w = int(w * scale)
- new_h = int(h * scale)
- return image.resize((new_w, new_h), PIL.Image.LANCZOS)
-
- def run(
- self,
- image: PIL.Image.Image,
- prompt: str,
- negative_prompt: str,
- adapter_name: str,
- num_inference_steps: int = 30,
- guidance_scale: float = 5.0,
- adapter_conditioning_scale: float = 1.0,
- adapter_conditioning_factor: float = 1.0,
- seed: int = 0,
- apply_preprocess: bool = True,
- ) -> list[PIL.Image.Image]:
- if not torch.cuda.is_available():
- raise RuntimeError("This demo does not work on CPU.")
- if num_inference_steps > self.MAX_NUM_INFERENCE_STEPS:
- raise ValueError(f"Number of steps must be less than {self.MAX_NUM_INFERENCE_STEPS}")
-
- # Resize image to avoid OOM
- image = self.resize_image(image)
-
- self.change_preprocessor(adapter_name)
- self.change_adapter(adapter_name)
-
- if apply_preprocess:
- image = self.preprocessor(image)
-
- image = resize_to_closest_aspect_ratio(image)
-
- generator = torch.Generator(device=self.device).manual_seed(seed)
- out = self.pipe(
- prompt=prompt,
- negative_prompt=negative_prompt,
- image=image,
- num_inference_steps=num_inference_steps,
- adapter_conditioning_scale=adapter_conditioning_scale,
- adapter_conditioning_factor=adapter_conditioning_factor,
- generator=generator,
- guidance_scale=guidance_scale,
- ).images[0]
- return [image, out]
\ No newline at end of file
diff --git a/spaces/kquote03/lama-video-watermark-remover/fetch_data/places_standard_train_prepare.sh b/spaces/kquote03/lama-video-watermark-remover/fetch_data/places_standard_train_prepare.sh
deleted file mode 100644
index b5389e7096bade08526162733658e221808716fd..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/fetch_data/places_standard_train_prepare.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-mkdir -p places_standard_dataset/train
-
-# untar without folder structure
-tar -xvf train_large_places365standard.tar --transform='s/.*\///' -C places_standard_dataset/train
-
-# create location config places.yaml
-PWD=$(pwd)
-DATASET=${PWD}/places_standard_dataset
-PLACES=${PWD}/configs/training/location/places_standard.yaml
-
-touch $PLACES
-echo "# @package _group_" >> $PLACES
-echo "data_root_dir: ${DATASET}/" >> $PLACES
-echo "out_root_dir: ${PWD}/experiments/" >> $PLACES
-echo "tb_dir: ${PWD}/tb_logs/" >> $PLACES
-echo "pretrained_models: ${PWD}/" >> $PLACES
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiofiles/base.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiofiles/base.py
deleted file mode 100644
index 6201d95b4fec039a6a9bfe59ad1de722c4688c9a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiofiles/base.py
+++ /dev/null
@@ -1,111 +0,0 @@
-"""Various base classes."""
-from types import coroutine
-from collections.abc import Coroutine
-from asyncio import get_running_loop
-
-
-class AsyncBase:
- def __init__(self, file, loop, executor):
- self._file = file
- self._executor = executor
- self._ref_loop = loop
-
- @property
- def _loop(self):
- return self._ref_loop or get_running_loop()
-
- def __aiter__(self):
- """We are our own iterator."""
- return self
-
- def __repr__(self):
- return super().__repr__() + " wrapping " + repr(self._file)
-
- async def __anext__(self):
- """Simulate normal file iteration."""
- line = await self.readline()
- if line:
- return line
- else:
- raise StopAsyncIteration
-
-
-class AsyncIndirectBase(AsyncBase):
- def __init__(self, name, loop, executor, indirect):
- self._indirect = indirect
- self._name = name
- super().__init__(None, loop, executor)
-
- @property
- def _file(self):
- return self._indirect()
-
- @_file.setter
- def _file(self, v):
- pass # discard writes
-
-
-class _ContextManager(Coroutine):
- __slots__ = ("_coro", "_obj")
-
- def __init__(self, coro):
- self._coro = coro
- self._obj = None
-
- def send(self, value):
- return self._coro.send(value)
-
- def throw(self, typ, val=None, tb=None):
- if val is None:
- return self._coro.throw(typ)
- elif tb is None:
- return self._coro.throw(typ, val)
- else:
- return self._coro.throw(typ, val, tb)
-
- def close(self):
- return self._coro.close()
-
- @property
- def gi_frame(self):
- return self._coro.gi_frame
-
- @property
- def gi_running(self):
- return self._coro.gi_running
-
- @property
- def gi_code(self):
- return self._coro.gi_code
-
- def __next__(self):
- return self.send(None)
-
- @coroutine
- def __iter__(self):
- resp = yield from self._coro
- return resp
-
- def __await__(self):
- resp = yield from self._coro
- return resp
-
- async def __anext__(self):
- resp = await self._coro
- return resp
-
- async def __aenter__(self):
- self._obj = await self._coro
- return self._obj
-
- async def __aexit__(self, exc_type, exc, tb):
- self._obj.close()
- self._obj = None
-
-
-class AiofilesContextManager(_ContextManager):
- """An adjusted async context manager for aiofiles."""
-
- async def __aexit__(self, exc_type, exc_val, exc_tb):
- await self._obj.close()
- self._obj = None
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/designspaceLib/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/designspaceLib/__init__.py
deleted file mode 100644
index f8a0146d061b132bab258d2c05faae260dab48a9..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/designspaceLib/__init__.py
+++ /dev/null
@@ -1,3167 +0,0 @@
-from __future__ import annotations
-
-import collections
-import copy
-import itertools
-import math
-import os
-import posixpath
-from io import BytesIO, StringIO
-from textwrap import indent
-from typing import Any, Dict, List, MutableMapping, Optional, Tuple, Union, cast
-
-from fontTools.misc import etree as ET
-from fontTools.misc import plistlib
-from fontTools.misc.loggingTools import LogMixin
-from fontTools.misc.textTools import tobytes, tostr
-
-"""
- designSpaceDocument
-
- - read and write designspace files
-"""
-
-__all__ = [
- "AxisDescriptor",
- "AxisLabelDescriptor",
- "BaseDocReader",
- "BaseDocWriter",
- "DesignSpaceDocument",
- "DesignSpaceDocumentError",
- "DiscreteAxisDescriptor",
- "InstanceDescriptor",
- "LocationLabelDescriptor",
- "RangeAxisSubsetDescriptor",
- "RuleDescriptor",
- "SourceDescriptor",
- "ValueAxisSubsetDescriptor",
- "VariableFontDescriptor",
-]
-
-# ElementTree allows to find namespace-prefixed elements, but not attributes
-# so we have to do it ourselves for 'xml:lang'
-XML_NS = "{http://www.w3.org/XML/1998/namespace}"
-XML_LANG = XML_NS + "lang"
-
-
-def posix(path):
- """Normalize paths using forward slash to work also on Windows."""
- new_path = posixpath.join(*path.split(os.path.sep))
- if path.startswith("/"):
- # The above transformation loses absolute paths
- new_path = "/" + new_path
- elif path.startswith(r"\\"):
- # The above transformation loses leading slashes of UNC path mounts
- new_path = "//" + new_path
- return new_path
-
-
-def posixpath_property(private_name):
- """Generate a propery that holds a path always using forward slashes."""
-
- def getter(self):
- # Normal getter
- return getattr(self, private_name)
-
- def setter(self, value):
- # The setter rewrites paths using forward slashes
- if value is not None:
- value = posix(value)
- setattr(self, private_name, value)
-
- return property(getter, setter)
-
-
-class DesignSpaceDocumentError(Exception):
- def __init__(self, msg, obj=None):
- self.msg = msg
- self.obj = obj
-
- def __str__(self):
- return str(self.msg) + (": %r" % self.obj if self.obj is not None else "")
-
-
-class AsDictMixin(object):
- def asdict(self):
- d = {}
- for attr, value in self.__dict__.items():
- if attr.startswith("_"):
- continue
- if hasattr(value, "asdict"):
- value = value.asdict()
- elif isinstance(value, list):
- value = [v.asdict() if hasattr(v, "asdict") else v for v in value]
- d[attr] = value
- return d
-
-
-class SimpleDescriptor(AsDictMixin):
- """Containers for a bunch of attributes"""
-
- # XXX this is ugly. The 'print' is inappropriate here, and instead of
- # assert, it should simply return True/False
- def compare(self, other):
- # test if this object contains the same data as the other
- for attr in self._attrs:
- try:
- assert getattr(self, attr) == getattr(other, attr)
- except AssertionError:
- print(
- "failed attribute",
- attr,
- getattr(self, attr),
- "!=",
- getattr(other, attr),
- )
-
- def __repr__(self):
- attrs = [f"{a}={repr(getattr(self, a))}," for a in self._attrs]
- attrs = indent("\n".join(attrs), " ")
- return f"{self.__class__.__name__}(\n{attrs}\n)"
-
-
-class SourceDescriptor(SimpleDescriptor):
- """Simple container for data related to the source
-
- .. code:: python
-
- doc = DesignSpaceDocument()
- s1 = SourceDescriptor()
- s1.path = masterPath1
- s1.name = "master.ufo1"
- s1.font = defcon.Font("master.ufo1")
- s1.location = dict(weight=0)
- s1.familyName = "MasterFamilyName"
- s1.styleName = "MasterStyleNameOne"
- s1.localisedFamilyName = dict(fr="Caractère")
- s1.mutedGlyphNames.append("A")
- s1.mutedGlyphNames.append("Z")
- doc.addSource(s1)
-
- """
-
- flavor = "source"
- _attrs = [
- "filename",
- "path",
- "name",
- "layerName",
- "location",
- "copyLib",
- "copyGroups",
- "copyFeatures",
- "muteKerning",
- "muteInfo",
- "mutedGlyphNames",
- "familyName",
- "styleName",
- "localisedFamilyName",
- ]
-
- filename = posixpath_property("_filename")
- path = posixpath_property("_path")
-
- def __init__(
- self,
- *,
- filename=None,
- path=None,
- font=None,
- name=None,
- location=None,
- designLocation=None,
- layerName=None,
- familyName=None,
- styleName=None,
- localisedFamilyName=None,
- copyLib=False,
- copyInfo=False,
- copyGroups=False,
- copyFeatures=False,
- muteKerning=False,
- muteInfo=False,
- mutedGlyphNames=None,
- ):
- self.filename = filename
- """string. A relative path to the source file, **as it is in the document**.
-
- MutatorMath + VarLib.
- """
- self.path = path
- """The absolute path, calculated from filename."""
-
- self.font = font
- """Any Python object. Optional. Points to a representation of this
- source font that is loaded in memory, as a Python object (e.g. a
- ``defcon.Font`` or a ``fontTools.ttFont.TTFont``).
-
- The default document reader will not fill-in this attribute, and the
- default writer will not use this attribute. It is up to the user of
- ``designspaceLib`` to either load the resource identified by
- ``filename`` and store it in this field, or write the contents of
- this field to the disk and make ```filename`` point to that.
- """
-
- self.name = name
- """string. Optional. Unique identifier name for this source.
-
- MutatorMath + Varlib.
- """
-
- self.designLocation = (
- designLocation if designLocation is not None else location or {}
- )
- """dict. Axis values for this source, in design space coordinates.
-
- MutatorMath + Varlib.
-
- This may be only part of the full design location.
- See :meth:`getFullDesignLocation()`
-
- .. versionadded:: 5.0
- """
-
- self.layerName = layerName
- """string. The name of the layer in the source to look for
- outline data. Default ``None`` which means ``foreground``.
- """
- self.familyName = familyName
- """string. Family name of this source. Though this data
- can be extracted from the font, it can be efficient to have it right
- here.
-
- Varlib.
- """
- self.styleName = styleName
- """string. Style name of this source. Though this data
- can be extracted from the font, it can be efficient to have it right
- here.
-
- Varlib.
- """
- self.localisedFamilyName = localisedFamilyName or {}
- """dict. A dictionary of localised family name strings, keyed by
- language code.
-
- If present, will be used to build localized names for all instances.
-
- .. versionadded:: 5.0
- """
-
- self.copyLib = copyLib
- """bool. Indicates if the contents of the font.lib need to
- be copied to the instances.
-
- MutatorMath.
-
- .. deprecated:: 5.0
- """
- self.copyInfo = copyInfo
- """bool. Indicates if the non-interpolating font.info needs
- to be copied to the instances.
-
- MutatorMath.
-
- .. deprecated:: 5.0
- """
- self.copyGroups = copyGroups
- """bool. Indicates if the groups need to be copied to the
- instances.
-
- MutatorMath.
-
- .. deprecated:: 5.0
- """
- self.copyFeatures = copyFeatures
- """bool. Indicates if the feature text needs to be
- copied to the instances.
-
- MutatorMath.
-
- .. deprecated:: 5.0
- """
- self.muteKerning = muteKerning
- """bool. Indicates if the kerning data from this source
- needs to be muted (i.e. not be part of the calculations).
-
- MutatorMath only.
- """
- self.muteInfo = muteInfo
- """bool. Indicated if the interpolating font.info data for
- this source needs to be muted.
-
- MutatorMath only.
- """
- self.mutedGlyphNames = mutedGlyphNames or []
- """list. Glyphnames that need to be muted in the
- instances.
-
- MutatorMath only.
- """
-
- @property
- def location(self):
- """dict. Axis values for this source, in design space coordinates.
-
- MutatorMath + Varlib.
-
- .. deprecated:: 5.0
- Use the more explicit alias for this property :attr:`designLocation`.
- """
- return self.designLocation
-
- @location.setter
- def location(self, location: Optional[AnisotropicLocationDict]):
- self.designLocation = location or {}
-
- def setFamilyName(self, familyName, languageCode="en"):
- """Setter for :attr:`localisedFamilyName`
-
- .. versionadded:: 5.0
- """
- self.localisedFamilyName[languageCode] = tostr(familyName)
-
- def getFamilyName(self, languageCode="en"):
- """Getter for :attr:`localisedFamilyName`
-
- .. versionadded:: 5.0
- """
- return self.localisedFamilyName.get(languageCode)
-
- def getFullDesignLocation(
- self, doc: "DesignSpaceDocument"
- ) -> AnisotropicLocationDict:
- """Get the complete design location of this source, from its
- :attr:`designLocation` and the document's axis defaults.
-
- .. versionadded:: 5.0
- """
- result: AnisotropicLocationDict = {}
- for axis in doc.axes:
- if axis.name in self.designLocation:
- result[axis.name] = self.designLocation[axis.name]
- else:
- result[axis.name] = axis.map_forward(axis.default)
- return result
-
-
-class RuleDescriptor(SimpleDescriptor):
- """Represents the rule descriptor element: a set of glyph substitutions to
- trigger conditionally in some parts of the designspace.
-
- .. code:: python
-
- r1 = RuleDescriptor()
- r1.name = "unique.rule.name"
- r1.conditionSets.append([dict(name="weight", minimum=-10, maximum=10), dict(...)])
- r1.conditionSets.append([dict(...), dict(...)])
- r1.subs.append(("a", "a.alt"))
-
- .. code:: xml
-
-
-
-
-
-
-
-
- """
-
- _attrs = ["name", "conditionSets", "subs"] # what do we need here
-
- def __init__(self, *, name=None, conditionSets=None, subs=None):
- self.name = name
- """string. Unique name for this rule. Can be used to reference this rule data."""
- # list of lists of dict(name='aaaa', minimum=0, maximum=1000)
- self.conditionSets = conditionSets or []
- """a list of conditionsets.
-
- - Each conditionset is a list of conditions.
- - Each condition is a dict with ``name``, ``minimum`` and ``maximum`` keys.
- """
- # list of substitutions stored as tuples of glyphnames ("a", "a.alt")
- self.subs = subs or []
- """list of substitutions.
-
- - Each substitution is stored as tuples of glyphnames, e.g. ("a", "a.alt").
- - Note: By default, rules are applied first, before other text
- shaping/OpenType layout, as they are part of the
- `Required Variation Alternates OpenType feature `_.
- See ref:`rules-element` § Attributes.
- """
-
-
-def evaluateRule(rule, location):
- """Return True if any of the rule's conditionsets matches the given location."""
- return any(evaluateConditions(c, location) for c in rule.conditionSets)
-
-
-def evaluateConditions(conditions, location):
- """Return True if all the conditions matches the given location.
-
- - If a condition has no minimum, check for < maximum.
- - If a condition has no maximum, check for > minimum.
- """
- for cd in conditions:
- value = location[cd["name"]]
- if cd.get("minimum") is None:
- if value > cd["maximum"]:
- return False
- elif cd.get("maximum") is None:
- if cd["minimum"] > value:
- return False
- elif not cd["minimum"] <= value <= cd["maximum"]:
- return False
- return True
-
-
-def processRules(rules, location, glyphNames):
- """Apply these rules at this location to these glyphnames.
-
- Return a new list of glyphNames with substitutions applied.
-
- - rule order matters
- """
- newNames = []
- for rule in rules:
- if evaluateRule(rule, location):
- for name in glyphNames:
- swap = False
- for a, b in rule.subs:
- if name == a:
- swap = True
- break
- if swap:
- newNames.append(b)
- else:
- newNames.append(name)
- glyphNames = newNames
- newNames = []
- return glyphNames
-
-
-AnisotropicLocationDict = Dict[str, Union[float, Tuple[float, float]]]
-SimpleLocationDict = Dict[str, float]
-
-
-class InstanceDescriptor(SimpleDescriptor):
- """Simple container for data related to the instance
-
-
- .. code:: python
-
- i2 = InstanceDescriptor()
- i2.path = instancePath2
- i2.familyName = "InstanceFamilyName"
- i2.styleName = "InstanceStyleName"
- i2.name = "instance.ufo2"
- # anisotropic location
- i2.designLocation = dict(weight=500, width=(400,300))
- i2.postScriptFontName = "InstancePostscriptName"
- i2.styleMapFamilyName = "InstanceStyleMapFamilyName"
- i2.styleMapStyleName = "InstanceStyleMapStyleName"
- i2.lib['com.coolDesignspaceApp.specimenText'] = 'Hamburgerwhatever'
- doc.addInstance(i2)
- """
-
- flavor = "instance"
- _defaultLanguageCode = "en"
- _attrs = [
- "filename",
- "path",
- "name",
- "locationLabel",
- "designLocation",
- "userLocation",
- "familyName",
- "styleName",
- "postScriptFontName",
- "styleMapFamilyName",
- "styleMapStyleName",
- "localisedFamilyName",
- "localisedStyleName",
- "localisedStyleMapFamilyName",
- "localisedStyleMapStyleName",
- "glyphs",
- "kerning",
- "info",
- "lib",
- ]
-
- filename = posixpath_property("_filename")
- path = posixpath_property("_path")
-
- def __init__(
- self,
- *,
- filename=None,
- path=None,
- font=None,
- name=None,
- location=None,
- locationLabel=None,
- designLocation=None,
- userLocation=None,
- familyName=None,
- styleName=None,
- postScriptFontName=None,
- styleMapFamilyName=None,
- styleMapStyleName=None,
- localisedFamilyName=None,
- localisedStyleName=None,
- localisedStyleMapFamilyName=None,
- localisedStyleMapStyleName=None,
- glyphs=None,
- kerning=True,
- info=True,
- lib=None,
- ):
- self.filename = filename
- """string. Relative path to the instance file, **as it is
- in the document**. The file may or may not exist.
-
- MutatorMath + VarLib.
- """
- self.path = path
- """string. Absolute path to the instance file, calculated from
- the document path and the string in the filename attr. The file may
- or may not exist.
-
- MutatorMath.
- """
- self.font = font
- """Same as :attr:`SourceDescriptor.font`
-
- .. seealso:: :attr:`SourceDescriptor.font`
- """
- self.name = name
- """string. Unique identifier name of the instance, used to
- identify it if it needs to be referenced from elsewhere in the
- document.
- """
- self.locationLabel = locationLabel
- """Name of a :class:`LocationLabelDescriptor`. If
- provided, the instance should have the same location as the
- LocationLabel.
-
- .. seealso::
- :meth:`getFullDesignLocation`
- :meth:`getFullUserLocation`
-
- .. versionadded:: 5.0
- """
- self.designLocation: AnisotropicLocationDict = (
- designLocation if designLocation is not None else (location or {})
- )
- """dict. Axis values for this instance, in design space coordinates.
-
- MutatorMath + Varlib.
-
- .. seealso:: This may be only part of the full location. See:
- :meth:`getFullDesignLocation`
- :meth:`getFullUserLocation`
-
- .. versionadded:: 5.0
- """
- self.userLocation: SimpleLocationDict = userLocation or {}
- """dict. Axis values for this instance, in user space coordinates.
-
- MutatorMath + Varlib.
-
- .. seealso:: This may be only part of the full location. See:
- :meth:`getFullDesignLocation`
- :meth:`getFullUserLocation`
-
- .. versionadded:: 5.0
- """
- self.familyName = familyName
- """string. Family name of this instance.
-
- MutatorMath + Varlib.
- """
- self.styleName = styleName
- """string. Style name of this instance.
-
- MutatorMath + Varlib.
- """
- self.postScriptFontName = postScriptFontName
- """string. Postscript fontname for this instance.
-
- MutatorMath + Varlib.
- """
- self.styleMapFamilyName = styleMapFamilyName
- """string. StyleMap familyname for this instance.
-
- MutatorMath + Varlib.
- """
- self.styleMapStyleName = styleMapStyleName
- """string. StyleMap stylename for this instance.
-
- MutatorMath + Varlib.
- """
- self.localisedFamilyName = localisedFamilyName or {}
- """dict. A dictionary of localised family name
- strings, keyed by language code.
- """
- self.localisedStyleName = localisedStyleName or {}
- """dict. A dictionary of localised stylename
- strings, keyed by language code.
- """
- self.localisedStyleMapFamilyName = localisedStyleMapFamilyName or {}
- """A dictionary of localised style map
- familyname strings, keyed by language code.
- """
- self.localisedStyleMapStyleName = localisedStyleMapStyleName or {}
- """A dictionary of localised style map
- stylename strings, keyed by language code.
- """
- self.glyphs = glyphs or {}
- """dict for special master definitions for glyphs. If glyphs
- need special masters (to record the results of executed rules for
- example).
-
- MutatorMath.
-
- .. deprecated:: 5.0
- Use rules or sparse sources instead.
- """
- self.kerning = kerning
- """ bool. Indicates if this instance needs its kerning
- calculated.
-
- MutatorMath.
-
- .. deprecated:: 5.0
- """
- self.info = info
- """bool. Indicated if this instance needs the interpolating
- font.info calculated.
-
- .. deprecated:: 5.0
- """
-
- self.lib = lib or {}
- """Custom data associated with this instance."""
-
- @property
- def location(self):
- """dict. Axis values for this instance.
-
- MutatorMath + Varlib.
-
- .. deprecated:: 5.0
- Use the more explicit alias for this property :attr:`designLocation`.
- """
- return self.designLocation
-
- @location.setter
- def location(self, location: Optional[AnisotropicLocationDict]):
- self.designLocation = location or {}
-
- def setStyleName(self, styleName, languageCode="en"):
- """These methods give easier access to the localised names."""
- self.localisedStyleName[languageCode] = tostr(styleName)
-
- def getStyleName(self, languageCode="en"):
- return self.localisedStyleName.get(languageCode)
-
- def setFamilyName(self, familyName, languageCode="en"):
- self.localisedFamilyName[languageCode] = tostr(familyName)
-
- def getFamilyName(self, languageCode="en"):
- return self.localisedFamilyName.get(languageCode)
-
- def setStyleMapStyleName(self, styleMapStyleName, languageCode="en"):
- self.localisedStyleMapStyleName[languageCode] = tostr(styleMapStyleName)
-
- def getStyleMapStyleName(self, languageCode="en"):
- return self.localisedStyleMapStyleName.get(languageCode)
-
- def setStyleMapFamilyName(self, styleMapFamilyName, languageCode="en"):
- self.localisedStyleMapFamilyName[languageCode] = tostr(styleMapFamilyName)
-
- def getStyleMapFamilyName(self, languageCode="en"):
- return self.localisedStyleMapFamilyName.get(languageCode)
-
- def clearLocation(self, axisName: Optional[str] = None):
- """Clear all location-related fields. Ensures that
- :attr:``designLocation`` and :attr:``userLocation`` are dictionaries
- (possibly empty if clearing everything).
-
- In order to update the location of this instance wholesale, a user
- should first clear all the fields, then change the field(s) for which
- they have data.
-
- .. code:: python
-
- instance.clearLocation()
- instance.designLocation = {'Weight': (34, 36.5), 'Width': 100}
- instance.userLocation = {'Opsz': 16}
-
- In order to update a single axis location, the user should only clear
- that axis, then edit the values:
-
- .. code:: python
-
- instance.clearLocation('Weight')
- instance.designLocation['Weight'] = (34, 36.5)
-
- Args:
- axisName: if provided, only clear the location for that axis.
-
- .. versionadded:: 5.0
- """
- self.locationLabel = None
- if axisName is None:
- self.designLocation = {}
- self.userLocation = {}
- else:
- if self.designLocation is None:
- self.designLocation = {}
- if axisName in self.designLocation:
- del self.designLocation[axisName]
- if self.userLocation is None:
- self.userLocation = {}
- if axisName in self.userLocation:
- del self.userLocation[axisName]
-
- def getLocationLabelDescriptor(
- self, doc: "DesignSpaceDocument"
- ) -> Optional[LocationLabelDescriptor]:
- """Get the :class:`LocationLabelDescriptor` instance that matches
- this instances's :attr:`locationLabel`.
-
- Raises if the named label can't be found.
-
- .. versionadded:: 5.0
- """
- if self.locationLabel is None:
- return None
- label = doc.getLocationLabel(self.locationLabel)
- if label is None:
- raise DesignSpaceDocumentError(
- "InstanceDescriptor.getLocationLabelDescriptor(): "
- f"unknown location label `{self.locationLabel}` in instance `{self.name}`."
- )
- return label
-
- def getFullDesignLocation(
- self, doc: "DesignSpaceDocument"
- ) -> AnisotropicLocationDict:
- """Get the complete design location of this instance, by combining data
- from the various location fields, default axis values and mappings, and
- top-level location labels.
-
- The source of truth for this instance's location is determined for each
- axis independently by taking the first not-None field in this list:
-
- - ``locationLabel``: the location along this axis is the same as the
- matching STAT format 4 label. No anisotropy.
- - ``designLocation[axisName]``: the explicit design location along this
- axis, possibly anisotropic.
- - ``userLocation[axisName]``: the explicit user location along this
- axis. No anisotropy.
- - ``axis.default``: default axis value. No anisotropy.
-
- .. versionadded:: 5.0
- """
- label = self.getLocationLabelDescriptor(doc)
- if label is not None:
- return doc.map_forward(label.userLocation) # type: ignore
- result: AnisotropicLocationDict = {}
- for axis in doc.axes:
- if axis.name in self.designLocation:
- result[axis.name] = self.designLocation[axis.name]
- elif axis.name in self.userLocation:
- result[axis.name] = axis.map_forward(self.userLocation[axis.name])
- else:
- result[axis.name] = axis.map_forward(axis.default)
- return result
-
- def getFullUserLocation(self, doc: "DesignSpaceDocument") -> SimpleLocationDict:
- """Get the complete user location for this instance.
-
- .. seealso:: :meth:`getFullDesignLocation`
-
- .. versionadded:: 5.0
- """
- return doc.map_backward(self.getFullDesignLocation(doc))
-
-
-def tagForAxisName(name):
- # try to find or make a tag name for this axis name
- names = {
- "weight": ("wght", dict(en="Weight")),
- "width": ("wdth", dict(en="Width")),
- "optical": ("opsz", dict(en="Optical Size")),
- "slant": ("slnt", dict(en="Slant")),
- "italic": ("ital", dict(en="Italic")),
- }
- if name.lower() in names:
- return names[name.lower()]
- if len(name) < 4:
- tag = name + "*" * (4 - len(name))
- else:
- tag = name[:4]
- return tag, dict(en=name)
-
-
-class AbstractAxisDescriptor(SimpleDescriptor):
- flavor = "axis"
-
- def __init__(
- self,
- *,
- tag=None,
- name=None,
- labelNames=None,
- hidden=False,
- map=None,
- axisOrdering=None,
- axisLabels=None,
- ):
- # opentype tag for this axis
- self.tag = tag
- """string. Four letter tag for this axis. Some might be
- registered at the `OpenType
- specification `__.
- Privately-defined axis tags must begin with an uppercase letter and
- use only uppercase letters or digits.
- """
- # name of the axis used in locations
- self.name = name
- """string. Name of the axis as it is used in the location dicts.
-
- MutatorMath + Varlib.
- """
- # names for UI purposes, if this is not a standard axis,
- self.labelNames = labelNames or {}
- """dict. When defining a non-registered axis, it will be
- necessary to define user-facing readable names for the axis. Keyed by
- xml:lang code. Values are required to be ``unicode`` strings, even if
- they only contain ASCII characters.
- """
- self.hidden = hidden
- """bool. Whether this axis should be hidden in user interfaces.
- """
- self.map = map or []
- """list of input / output values that can describe a warp of user space
- to design space coordinates. If no map values are present, it is assumed
- user space is the same as design space, as in [(minimum, minimum),
- (maximum, maximum)].
-
- Varlib.
- """
- self.axisOrdering = axisOrdering
- """STAT table field ``axisOrdering``.
-
- See: `OTSpec STAT Axis Record `_
-
- .. versionadded:: 5.0
- """
- self.axisLabels: List[AxisLabelDescriptor] = axisLabels or []
- """STAT table entries for Axis Value Tables format 1, 2, 3.
-
- See: `OTSpec STAT Axis Value Tables `_
-
- .. versionadded:: 5.0
- """
-
-
-class AxisDescriptor(AbstractAxisDescriptor):
- """Simple container for the axis data.
-
- Add more localisations?
-
- .. code:: python
-
- a1 = AxisDescriptor()
- a1.minimum = 1
- a1.maximum = 1000
- a1.default = 400
- a1.name = "weight"
- a1.tag = "wght"
- a1.labelNames['fa-IR'] = "قطر"
- a1.labelNames['en'] = "Wéíght"
- a1.map = [(1.0, 10.0), (400.0, 66.0), (1000.0, 990.0)]
- a1.axisOrdering = 1
- a1.axisLabels = [
- AxisLabelDescriptor(name="Regular", userValue=400, elidable=True)
- ]
- doc.addAxis(a1)
- """
-
- _attrs = [
- "tag",
- "name",
- "maximum",
- "minimum",
- "default",
- "map",
- "axisOrdering",
- "axisLabels",
- ]
-
- def __init__(
- self,
- *,
- tag=None,
- name=None,
- labelNames=None,
- minimum=None,
- default=None,
- maximum=None,
- hidden=False,
- map=None,
- axisOrdering=None,
- axisLabels=None,
- ):
- super().__init__(
- tag=tag,
- name=name,
- labelNames=labelNames,
- hidden=hidden,
- map=map,
- axisOrdering=axisOrdering,
- axisLabels=axisLabels,
- )
- self.minimum = minimum
- """number. The minimum value for this axis in user space.
-
- MutatorMath + Varlib.
- """
- self.maximum = maximum
- """number. The maximum value for this axis in user space.
-
- MutatorMath + Varlib.
- """
- self.default = default
- """number. The default value for this axis, i.e. when a new location is
- created, this is the value this axis will get in user space.
-
- MutatorMath + Varlib.
- """
-
- def serialize(self):
- # output to a dict, used in testing
- return dict(
- tag=self.tag,
- name=self.name,
- labelNames=self.labelNames,
- maximum=self.maximum,
- minimum=self.minimum,
- default=self.default,
- hidden=self.hidden,
- map=self.map,
- axisOrdering=self.axisOrdering,
- axisLabels=self.axisLabels,
- )
-
- def map_forward(self, v):
- """Maps value from axis mapping's input (user) to output (design)."""
- from fontTools.varLib.models import piecewiseLinearMap
-
- if not self.map:
- return v
- return piecewiseLinearMap(v, {k: v for k, v in self.map})
-
- def map_backward(self, v):
- """Maps value from axis mapping's output (design) to input (user)."""
- from fontTools.varLib.models import piecewiseLinearMap
-
- if isinstance(v, tuple):
- v = v[0]
- if not self.map:
- return v
- return piecewiseLinearMap(v, {v: k for k, v in self.map})
-
-
-class DiscreteAxisDescriptor(AbstractAxisDescriptor):
- """Container for discrete axis data.
-
- Use this for axes that do not interpolate. The main difference from a
- continuous axis is that a continuous axis has a ``minimum`` and ``maximum``,
- while a discrete axis has a list of ``values``.
-
- Example: an Italic axis with 2 stops, Roman and Italic, that are not
- compatible. The axis still allows to bind together the full font family,
- which is useful for the STAT table, however it can't become a variation
- axis in a VF.
-
- .. code:: python
-
- a2 = DiscreteAxisDescriptor()
- a2.values = [0, 1]
- a2.default = 0
- a2.name = "Italic"
- a2.tag = "ITAL"
- a2.labelNames['fr'] = "Italique"
- a2.map = [(0, 0), (1, -11)]
- a2.axisOrdering = 2
- a2.axisLabels = [
- AxisLabelDescriptor(name="Roman", userValue=0, elidable=True)
- ]
- doc.addAxis(a2)
-
- .. versionadded:: 5.0
- """
-
- flavor = "axis"
- _attrs = ("tag", "name", "values", "default", "map", "axisOrdering", "axisLabels")
-
- def __init__(
- self,
- *,
- tag=None,
- name=None,
- labelNames=None,
- values=None,
- default=None,
- hidden=False,
- map=None,
- axisOrdering=None,
- axisLabels=None,
- ):
- super().__init__(
- tag=tag,
- name=name,
- labelNames=labelNames,
- hidden=hidden,
- map=map,
- axisOrdering=axisOrdering,
- axisLabels=axisLabels,
- )
- self.default: float = default
- """The default value for this axis, i.e. when a new location is
- created, this is the value this axis will get in user space.
-
- However, this default value is less important than in continuous axes:
-
- - it doesn't define the "neutral" version of outlines from which
- deltas would apply, as this axis does not interpolate.
- - it doesn't provide the reference glyph set for the designspace, as
- fonts at each value can have different glyph sets.
- """
- self.values: List[float] = values or []
- """List of possible values for this axis. Contrary to continuous axes,
- only the values in this list can be taken by the axis, nothing in-between.
- """
-
- def map_forward(self, value):
- """Maps value from axis mapping's input to output.
-
- Returns value unchanged if no mapping entry is found.
-
- Note: for discrete axes, each value must have its mapping entry, if
- you intend that value to be mapped.
- """
- return next((v for k, v in self.map if k == value), value)
-
- def map_backward(self, value):
- """Maps value from axis mapping's output to input.
-
- Returns value unchanged if no mapping entry is found.
-
- Note: for discrete axes, each value must have its mapping entry, if
- you intend that value to be mapped.
- """
- if isinstance(value, tuple):
- value = value[0]
- return next((k for k, v in self.map if v == value), value)
-
-
-class AxisLabelDescriptor(SimpleDescriptor):
- """Container for axis label data.
-
- Analogue of OpenType's STAT data for a single axis (formats 1, 2 and 3).
- All values are user values.
- See: `OTSpec STAT Axis value table, format 1, 2, 3 `_
-
- The STAT format of the Axis value depends on which field are filled-in,
- see :meth:`getFormat`
-
- .. versionadded:: 5.0
- """
-
- flavor = "label"
- _attrs = (
- "userMinimum",
- "userValue",
- "userMaximum",
- "name",
- "elidable",
- "olderSibling",
- "linkedUserValue",
- "labelNames",
- )
-
- def __init__(
- self,
- *,
- name,
- userValue,
- userMinimum=None,
- userMaximum=None,
- elidable=False,
- olderSibling=False,
- linkedUserValue=None,
- labelNames=None,
- ):
- self.userMinimum: Optional[float] = userMinimum
- """STAT field ``rangeMinValue`` (format 2)."""
- self.userValue: float = userValue
- """STAT field ``value`` (format 1, 3) or ``nominalValue`` (format 2)."""
- self.userMaximum: Optional[float] = userMaximum
- """STAT field ``rangeMaxValue`` (format 2)."""
- self.name: str = name
- """Label for this axis location, STAT field ``valueNameID``."""
- self.elidable: bool = elidable
- """STAT flag ``ELIDABLE_AXIS_VALUE_NAME``.
-
- See: `OTSpec STAT Flags `_
- """
- self.olderSibling: bool = olderSibling
- """STAT flag ``OLDER_SIBLING_FONT_ATTRIBUTE``.
-
- See: `OTSpec STAT Flags `_
- """
- self.linkedUserValue: Optional[float] = linkedUserValue
- """STAT field ``linkedValue`` (format 3)."""
- self.labelNames: MutableMapping[str, str] = labelNames or {}
- """User-facing translations of this location's label. Keyed by
- ``xml:lang`` code.
- """
-
- def getFormat(self) -> int:
- """Determine which format of STAT Axis value to use to encode this label.
-
- =========== ========= =========== =========== ===============
- STAT Format userValue userMinimum userMaximum linkedUserValue
- =========== ========= =========== =========== ===============
- 1 ✅ ❌ ❌ ❌
- 2 ✅ ✅ ✅ ❌
- 3 ✅ ❌ ❌ ✅
- =========== ========= =========== =========== ===============
- """
- if self.linkedUserValue is not None:
- return 3
- if self.userMinimum is not None or self.userMaximum is not None:
- return 2
- return 1
-
- @property
- def defaultName(self) -> str:
- """Return the English name from :attr:`labelNames` or the :attr:`name`."""
- return self.labelNames.get("en") or self.name
-
-
-class LocationLabelDescriptor(SimpleDescriptor):
- """Container for location label data.
-
- Analogue of OpenType's STAT data for a free-floating location (format 4).
- All values are user values.
-
- See: `OTSpec STAT Axis value table, format 4 `_
-
- .. versionadded:: 5.0
- """
-
- flavor = "label"
- _attrs = ("name", "elidable", "olderSibling", "userLocation", "labelNames")
-
- def __init__(
- self,
- *,
- name,
- userLocation,
- elidable=False,
- olderSibling=False,
- labelNames=None,
- ):
- self.name: str = name
- """Label for this named location, STAT field ``valueNameID``."""
- self.userLocation: SimpleLocationDict = userLocation or {}
- """Location in user coordinates along each axis.
-
- If an axis is not mentioned, it is assumed to be at its default location.
-
- .. seealso:: This may be only part of the full location. See:
- :meth:`getFullUserLocation`
- """
- self.elidable: bool = elidable
- """STAT flag ``ELIDABLE_AXIS_VALUE_NAME``.
-
- See: `OTSpec STAT Flags `_
- """
- self.olderSibling: bool = olderSibling
- """STAT flag ``OLDER_SIBLING_FONT_ATTRIBUTE``.
-
- See: `OTSpec STAT Flags `_
- """
- self.labelNames: Dict[str, str] = labelNames or {}
- """User-facing translations of this location's label. Keyed by
- xml:lang code.
- """
-
- @property
- def defaultName(self) -> str:
- """Return the English name from :attr:`labelNames` or the :attr:`name`."""
- return self.labelNames.get("en") or self.name
-
- def getFullUserLocation(self, doc: "DesignSpaceDocument") -> SimpleLocationDict:
- """Get the complete user location of this label, by combining data
- from the explicit user location and default axis values.
-
- .. versionadded:: 5.0
- """
- return {
- axis.name: self.userLocation.get(axis.name, axis.default)
- for axis in doc.axes
- }
-
-
-class VariableFontDescriptor(SimpleDescriptor):
- """Container for variable fonts, sub-spaces of the Designspace.
-
- Use-cases:
-
- - From a single DesignSpace with discrete axes, define 1 variable font
- per value on the discrete axes. Before version 5, you would have needed
- 1 DesignSpace per such variable font, and a lot of data duplication.
- - From a big variable font with many axes, define subsets of that variable
- font that only include some axes and freeze other axes at a given location.
-
- .. versionadded:: 5.0
- """
-
- flavor = "variable-font"
- _attrs = ("filename", "axisSubsets", "lib")
-
- filename = posixpath_property("_filename")
-
- def __init__(self, *, name, filename=None, axisSubsets=None, lib=None):
- self.name: str = name
- """string, required. Name of this variable to identify it during the
- build process and from other parts of the document, and also as a
- filename in case the filename property is empty.
-
- VarLib.
- """
- self.filename: str = filename
- """string, optional. Relative path to the variable font file, **as it is
- in the document**. The file may or may not exist.
-
- If not specified, the :attr:`name` will be used as a basename for the file.
- """
- self.axisSubsets: List[
- Union[RangeAxisSubsetDescriptor, ValueAxisSubsetDescriptor]
- ] = (axisSubsets or [])
- """Axis subsets to include in this variable font.
-
- If an axis is not mentioned, assume that we only want the default
- location of that axis (same as a :class:`ValueAxisSubsetDescriptor`).
- """
- self.lib: MutableMapping[str, Any] = lib or {}
- """Custom data associated with this variable font."""
-
-
-class RangeAxisSubsetDescriptor(SimpleDescriptor):
- """Subset of a continuous axis to include in a variable font.
-
- .. versionadded:: 5.0
- """
-
- flavor = "axis-subset"
- _attrs = ("name", "userMinimum", "userDefault", "userMaximum")
-
- def __init__(
- self, *, name, userMinimum=-math.inf, userDefault=None, userMaximum=math.inf
- ):
- self.name: str = name
- """Name of the :class:`AxisDescriptor` to subset."""
- self.userMinimum: float = userMinimum
- """New minimum value of the axis in the target variable font.
- If not specified, assume the same minimum value as the full axis.
- (default = ``-math.inf``)
- """
- self.userDefault: Optional[float] = userDefault
- """New default value of the axis in the target variable font.
- If not specified, assume the same default value as the full axis.
- (default = ``None``)
- """
- self.userMaximum: float = userMaximum
- """New maximum value of the axis in the target variable font.
- If not specified, assume the same maximum value as the full axis.
- (default = ``math.inf``)
- """
-
-
-class ValueAxisSubsetDescriptor(SimpleDescriptor):
- """Single value of a discrete or continuous axis to use in a variable font.
-
- .. versionadded:: 5.0
- """
-
- flavor = "axis-subset"
- _attrs = ("name", "userValue")
-
- def __init__(self, *, name, userValue):
- self.name: str = name
- """Name of the :class:`AxisDescriptor` or :class:`DiscreteAxisDescriptor`
- to "snapshot" or "freeze".
- """
- self.userValue: float = userValue
- """Value in user coordinates at which to freeze the given axis."""
-
-
-class BaseDocWriter(object):
- _whiteSpace = " "
- axisDescriptorClass = AxisDescriptor
- discreteAxisDescriptorClass = DiscreteAxisDescriptor
- axisLabelDescriptorClass = AxisLabelDescriptor
- locationLabelDescriptorClass = LocationLabelDescriptor
- ruleDescriptorClass = RuleDescriptor
- sourceDescriptorClass = SourceDescriptor
- variableFontDescriptorClass = VariableFontDescriptor
- valueAxisSubsetDescriptorClass = ValueAxisSubsetDescriptor
- rangeAxisSubsetDescriptorClass = RangeAxisSubsetDescriptor
- instanceDescriptorClass = InstanceDescriptor
-
- @classmethod
- def getAxisDecriptor(cls):
- return cls.axisDescriptorClass()
-
- @classmethod
- def getSourceDescriptor(cls):
- return cls.sourceDescriptorClass()
-
- @classmethod
- def getInstanceDescriptor(cls):
- return cls.instanceDescriptorClass()
-
- @classmethod
- def getRuleDescriptor(cls):
- return cls.ruleDescriptorClass()
-
- def __init__(self, documentPath, documentObject: DesignSpaceDocument):
- self.path = documentPath
- self.documentObject = documentObject
- self.effectiveFormatTuple = self._getEffectiveFormatTuple()
- self.root = ET.Element("designspace")
-
- def write(self, pretty=True, encoding="UTF-8", xml_declaration=True):
- self.root.attrib["format"] = ".".join(str(i) for i in self.effectiveFormatTuple)
-
- if (
- self.documentObject.axes
- or self.documentObject.elidedFallbackName is not None
- ):
- axesElement = ET.Element("axes")
- if self.documentObject.elidedFallbackName is not None:
- axesElement.attrib[
- "elidedfallbackname"
- ] = self.documentObject.elidedFallbackName
- self.root.append(axesElement)
- for axisObject in self.documentObject.axes:
- self._addAxis(axisObject)
-
- if self.documentObject.locationLabels:
- labelsElement = ET.Element("labels")
- for labelObject in self.documentObject.locationLabels:
- self._addLocationLabel(labelsElement, labelObject)
- self.root.append(labelsElement)
-
- if self.documentObject.rules:
- if getattr(self.documentObject, "rulesProcessingLast", False):
- attributes = {"processing": "last"}
- else:
- attributes = {}
- self.root.append(ET.Element("rules", attributes))
- for ruleObject in self.documentObject.rules:
- self._addRule(ruleObject)
-
- if self.documentObject.sources:
- self.root.append(ET.Element("sources"))
- for sourceObject in self.documentObject.sources:
- self._addSource(sourceObject)
-
- if self.documentObject.variableFonts:
- variableFontsElement = ET.Element("variable-fonts")
- for variableFont in self.documentObject.variableFonts:
- self._addVariableFont(variableFontsElement, variableFont)
- self.root.append(variableFontsElement)
-
- if self.documentObject.instances:
- self.root.append(ET.Element("instances"))
- for instanceObject in self.documentObject.instances:
- self._addInstance(instanceObject)
-
- if self.documentObject.lib:
- self._addLib(self.root, self.documentObject.lib, 2)
-
- tree = ET.ElementTree(self.root)
- tree.write(
- self.path,
- encoding=encoding,
- method="xml",
- xml_declaration=xml_declaration,
- pretty_print=pretty,
- )
-
- def _getEffectiveFormatTuple(self):
- """Try to use the version specified in the document, or a sufficiently
- recent version to be able to encode what the document contains.
- """
- minVersion = self.documentObject.formatTuple
- if (
- any(
- hasattr(axis, "values")
- or axis.axisOrdering is not None
- or axis.axisLabels
- for axis in self.documentObject.axes
- )
- or self.documentObject.locationLabels
- or any(source.localisedFamilyName for source in self.documentObject.sources)
- or self.documentObject.variableFonts
- or any(
- instance.locationLabel or instance.userLocation
- for instance in self.documentObject.instances
- )
- ):
- if minVersion < (5, 0):
- minVersion = (5, 0)
- return minVersion
-
- def _makeLocationElement(self, locationObject, name=None):
- """Convert Location dict to a locationElement."""
- locElement = ET.Element("location")
- if name is not None:
- locElement.attrib["name"] = name
- validatedLocation = self.documentObject.newDefaultLocation()
- for axisName, axisValue in locationObject.items():
- if axisName in validatedLocation:
- # only accept values we know
- validatedLocation[axisName] = axisValue
- for dimensionName, dimensionValue in validatedLocation.items():
- dimElement = ET.Element("dimension")
- dimElement.attrib["name"] = dimensionName
- if type(dimensionValue) == tuple:
- dimElement.attrib["xvalue"] = self.intOrFloat(dimensionValue[0])
- dimElement.attrib["yvalue"] = self.intOrFloat(dimensionValue[1])
- else:
- dimElement.attrib["xvalue"] = self.intOrFloat(dimensionValue)
- locElement.append(dimElement)
- return locElement, validatedLocation
-
- def intOrFloat(self, num):
- if int(num) == num:
- return "%d" % num
- return ("%f" % num).rstrip("0").rstrip(".")
-
- def _addRule(self, ruleObject):
- # if none of the conditions have minimum or maximum values, do not add the rule.
- ruleElement = ET.Element("rule")
- if ruleObject.name is not None:
- ruleElement.attrib["name"] = ruleObject.name
- for conditions in ruleObject.conditionSets:
- conditionsetElement = ET.Element("conditionset")
- for cond in conditions:
- if cond.get("minimum") is None and cond.get("maximum") is None:
- # neither is defined, don't add this condition
- continue
- conditionElement = ET.Element("condition")
- conditionElement.attrib["name"] = cond.get("name")
- if cond.get("minimum") is not None:
- conditionElement.attrib["minimum"] = self.intOrFloat(
- cond.get("minimum")
- )
- if cond.get("maximum") is not None:
- conditionElement.attrib["maximum"] = self.intOrFloat(
- cond.get("maximum")
- )
- conditionsetElement.append(conditionElement)
- if len(conditionsetElement):
- ruleElement.append(conditionsetElement)
- for sub in ruleObject.subs:
- subElement = ET.Element("sub")
- subElement.attrib["name"] = sub[0]
- subElement.attrib["with"] = sub[1]
- ruleElement.append(subElement)
- if len(ruleElement):
- self.root.findall(".rules")[0].append(ruleElement)
-
- def _addAxis(self, axisObject):
- axisElement = ET.Element("axis")
- axisElement.attrib["tag"] = axisObject.tag
- axisElement.attrib["name"] = axisObject.name
- self._addLabelNames(axisElement, axisObject.labelNames)
- if axisObject.map:
- for inputValue, outputValue in axisObject.map:
- mapElement = ET.Element("map")
- mapElement.attrib["input"] = self.intOrFloat(inputValue)
- mapElement.attrib["output"] = self.intOrFloat(outputValue)
- axisElement.append(mapElement)
- if axisObject.axisOrdering or axisObject.axisLabels:
- labelsElement = ET.Element("labels")
- if axisObject.axisOrdering is not None:
- labelsElement.attrib["ordering"] = str(axisObject.axisOrdering)
- for label in axisObject.axisLabels:
- self._addAxisLabel(labelsElement, label)
- axisElement.append(labelsElement)
- if hasattr(axisObject, "minimum"):
- axisElement.attrib["minimum"] = self.intOrFloat(axisObject.minimum)
- axisElement.attrib["maximum"] = self.intOrFloat(axisObject.maximum)
- elif hasattr(axisObject, "values"):
- axisElement.attrib["values"] = " ".join(
- self.intOrFloat(v) for v in axisObject.values
- )
- axisElement.attrib["default"] = self.intOrFloat(axisObject.default)
- if axisObject.hidden:
- axisElement.attrib["hidden"] = "1"
- self.root.findall(".axes")[0].append(axisElement)
-
- def _addAxisLabel(
- self, axisElement: ET.Element, label: AxisLabelDescriptor
- ) -> None:
- labelElement = ET.Element("label")
- labelElement.attrib["uservalue"] = self.intOrFloat(label.userValue)
- if label.userMinimum is not None:
- labelElement.attrib["userminimum"] = self.intOrFloat(label.userMinimum)
- if label.userMaximum is not None:
- labelElement.attrib["usermaximum"] = self.intOrFloat(label.userMaximum)
- labelElement.attrib["name"] = label.name
- if label.elidable:
- labelElement.attrib["elidable"] = "true"
- if label.olderSibling:
- labelElement.attrib["oldersibling"] = "true"
- if label.linkedUserValue is not None:
- labelElement.attrib["linkeduservalue"] = self.intOrFloat(
- label.linkedUserValue
- )
- self._addLabelNames(labelElement, label.labelNames)
- axisElement.append(labelElement)
-
- def _addLabelNames(self, parentElement, labelNames):
- for languageCode, labelName in sorted(labelNames.items()):
- languageElement = ET.Element("labelname")
- languageElement.attrib[XML_LANG] = languageCode
- languageElement.text = labelName
- parentElement.append(languageElement)
-
- def _addLocationLabel(
- self, parentElement: ET.Element, label: LocationLabelDescriptor
- ) -> None:
- labelElement = ET.Element("label")
- labelElement.attrib["name"] = label.name
- if label.elidable:
- labelElement.attrib["elidable"] = "true"
- if label.olderSibling:
- labelElement.attrib["oldersibling"] = "true"
- self._addLabelNames(labelElement, label.labelNames)
- self._addLocationElement(labelElement, userLocation=label.userLocation)
- parentElement.append(labelElement)
-
- def _addLocationElement(
- self,
- parentElement,
- *,
- designLocation: AnisotropicLocationDict = None,
- userLocation: SimpleLocationDict = None,
- ):
- locElement = ET.Element("location")
- for axis in self.documentObject.axes:
- if designLocation is not None and axis.name in designLocation:
- dimElement = ET.Element("dimension")
- dimElement.attrib["name"] = axis.name
- value = designLocation[axis.name]
- if isinstance(value, tuple):
- dimElement.attrib["xvalue"] = self.intOrFloat(value[0])
- dimElement.attrib["yvalue"] = self.intOrFloat(value[1])
- else:
- dimElement.attrib["xvalue"] = self.intOrFloat(value)
- locElement.append(dimElement)
- elif userLocation is not None and axis.name in userLocation:
- dimElement = ET.Element("dimension")
- dimElement.attrib["name"] = axis.name
- value = userLocation[axis.name]
- dimElement.attrib["uservalue"] = self.intOrFloat(value)
- locElement.append(dimElement)
- if len(locElement) > 0:
- parentElement.append(locElement)
-
- def _addInstance(self, instanceObject):
- instanceElement = ET.Element("instance")
- if instanceObject.name is not None:
- instanceElement.attrib["name"] = instanceObject.name
- if instanceObject.locationLabel is not None:
- instanceElement.attrib["location"] = instanceObject.locationLabel
- if instanceObject.familyName is not None:
- instanceElement.attrib["familyname"] = instanceObject.familyName
- if instanceObject.styleName is not None:
- instanceElement.attrib["stylename"] = instanceObject.styleName
- # add localisations
- if instanceObject.localisedStyleName:
- languageCodes = list(instanceObject.localisedStyleName.keys())
- languageCodes.sort()
- for code in languageCodes:
- if code == "en":
- continue # already stored in the element attribute
- localisedStyleNameElement = ET.Element("stylename")
- localisedStyleNameElement.attrib[XML_LANG] = code
- localisedStyleNameElement.text = instanceObject.getStyleName(code)
- instanceElement.append(localisedStyleNameElement)
- if instanceObject.localisedFamilyName:
- languageCodes = list(instanceObject.localisedFamilyName.keys())
- languageCodes.sort()
- for code in languageCodes:
- if code == "en":
- continue # already stored in the element attribute
- localisedFamilyNameElement = ET.Element("familyname")
- localisedFamilyNameElement.attrib[XML_LANG] = code
- localisedFamilyNameElement.text = instanceObject.getFamilyName(code)
- instanceElement.append(localisedFamilyNameElement)
- if instanceObject.localisedStyleMapStyleName:
- languageCodes = list(instanceObject.localisedStyleMapStyleName.keys())
- languageCodes.sort()
- for code in languageCodes:
- if code == "en":
- continue
- localisedStyleMapStyleNameElement = ET.Element("stylemapstylename")
- localisedStyleMapStyleNameElement.attrib[XML_LANG] = code
- localisedStyleMapStyleNameElement.text = (
- instanceObject.getStyleMapStyleName(code)
- )
- instanceElement.append(localisedStyleMapStyleNameElement)
- if instanceObject.localisedStyleMapFamilyName:
- languageCodes = list(instanceObject.localisedStyleMapFamilyName.keys())
- languageCodes.sort()
- for code in languageCodes:
- if code == "en":
- continue
- localisedStyleMapFamilyNameElement = ET.Element("stylemapfamilyname")
- localisedStyleMapFamilyNameElement.attrib[XML_LANG] = code
- localisedStyleMapFamilyNameElement.text = (
- instanceObject.getStyleMapFamilyName(code)
- )
- instanceElement.append(localisedStyleMapFamilyNameElement)
-
- if self.effectiveFormatTuple >= (5, 0):
- if instanceObject.locationLabel is None:
- self._addLocationElement(
- instanceElement,
- designLocation=instanceObject.designLocation,
- userLocation=instanceObject.userLocation,
- )
- else:
- # Pre-version 5.0 code was validating and filling in the location
- # dict while writing it out, as preserved below.
- if instanceObject.location is not None:
- locationElement, instanceObject.location = self._makeLocationElement(
- instanceObject.location
- )
- instanceElement.append(locationElement)
- if instanceObject.filename is not None:
- instanceElement.attrib["filename"] = instanceObject.filename
- if instanceObject.postScriptFontName is not None:
- instanceElement.attrib[
- "postscriptfontname"
- ] = instanceObject.postScriptFontName
- if instanceObject.styleMapFamilyName is not None:
- instanceElement.attrib[
- "stylemapfamilyname"
- ] = instanceObject.styleMapFamilyName
- if instanceObject.styleMapStyleName is not None:
- instanceElement.attrib[
- "stylemapstylename"
- ] = instanceObject.styleMapStyleName
- if self.effectiveFormatTuple < (5, 0):
- # Deprecated members as of version 5.0
- if instanceObject.glyphs:
- if instanceElement.findall(".glyphs") == []:
- glyphsElement = ET.Element("glyphs")
- instanceElement.append(glyphsElement)
- glyphsElement = instanceElement.findall(".glyphs")[0]
- for glyphName, data in sorted(instanceObject.glyphs.items()):
- glyphElement = self._writeGlyphElement(
- instanceElement, instanceObject, glyphName, data
- )
- glyphsElement.append(glyphElement)
- if instanceObject.kerning:
- kerningElement = ET.Element("kerning")
- instanceElement.append(kerningElement)
- if instanceObject.info:
- infoElement = ET.Element("info")
- instanceElement.append(infoElement)
- self._addLib(instanceElement, instanceObject.lib, 4)
- self.root.findall(".instances")[0].append(instanceElement)
-
- def _addSource(self, sourceObject):
- sourceElement = ET.Element("source")
- if sourceObject.filename is not None:
- sourceElement.attrib["filename"] = sourceObject.filename
- if sourceObject.name is not None:
- if sourceObject.name.find("temp_master") != 0:
- # do not save temporary source names
- sourceElement.attrib["name"] = sourceObject.name
- if sourceObject.familyName is not None:
- sourceElement.attrib["familyname"] = sourceObject.familyName
- if sourceObject.styleName is not None:
- sourceElement.attrib["stylename"] = sourceObject.styleName
- if sourceObject.layerName is not None:
- sourceElement.attrib["layer"] = sourceObject.layerName
- if sourceObject.localisedFamilyName:
- languageCodes = list(sourceObject.localisedFamilyName.keys())
- languageCodes.sort()
- for code in languageCodes:
- if code == "en":
- continue # already stored in the element attribute
- localisedFamilyNameElement = ET.Element("familyname")
- localisedFamilyNameElement.attrib[XML_LANG] = code
- localisedFamilyNameElement.text = sourceObject.getFamilyName(code)
- sourceElement.append(localisedFamilyNameElement)
- if sourceObject.copyLib:
- libElement = ET.Element("lib")
- libElement.attrib["copy"] = "1"
- sourceElement.append(libElement)
- if sourceObject.copyGroups:
- groupsElement = ET.Element("groups")
- groupsElement.attrib["copy"] = "1"
- sourceElement.append(groupsElement)
- if sourceObject.copyFeatures:
- featuresElement = ET.Element("features")
- featuresElement.attrib["copy"] = "1"
- sourceElement.append(featuresElement)
- if sourceObject.copyInfo or sourceObject.muteInfo:
- infoElement = ET.Element("info")
- if sourceObject.copyInfo:
- infoElement.attrib["copy"] = "1"
- if sourceObject.muteInfo:
- infoElement.attrib["mute"] = "1"
- sourceElement.append(infoElement)
- if sourceObject.muteKerning:
- kerningElement = ET.Element("kerning")
- kerningElement.attrib["mute"] = "1"
- sourceElement.append(kerningElement)
- if sourceObject.mutedGlyphNames:
- for name in sourceObject.mutedGlyphNames:
- glyphElement = ET.Element("glyph")
- glyphElement.attrib["name"] = name
- glyphElement.attrib["mute"] = "1"
- sourceElement.append(glyphElement)
- if self.effectiveFormatTuple >= (5, 0):
- self._addLocationElement(
- sourceElement, designLocation=sourceObject.location
- )
- else:
- # Pre-version 5.0 code was validating and filling in the location
- # dict while writing it out, as preserved below.
- locationElement, sourceObject.location = self._makeLocationElement(
- sourceObject.location
- )
- sourceElement.append(locationElement)
- self.root.findall(".sources")[0].append(sourceElement)
-
- def _addVariableFont(
- self, parentElement: ET.Element, vf: VariableFontDescriptor
- ) -> None:
- vfElement = ET.Element("variable-font")
- vfElement.attrib["name"] = vf.name
- if vf.filename is not None:
- vfElement.attrib["filename"] = vf.filename
- if vf.axisSubsets:
- subsetsElement = ET.Element("axis-subsets")
- for subset in vf.axisSubsets:
- subsetElement = ET.Element("axis-subset")
- subsetElement.attrib["name"] = subset.name
- # Mypy doesn't support narrowing union types via hasattr()
- # https://mypy.readthedocs.io/en/stable/type_narrowing.html
- # TODO(Python 3.10): use TypeGuard
- if hasattr(subset, "userMinimum"):
- subset = cast(RangeAxisSubsetDescriptor, subset)
- if subset.userMinimum != -math.inf:
- subsetElement.attrib["userminimum"] = self.intOrFloat(
- subset.userMinimum
- )
- if subset.userMaximum != math.inf:
- subsetElement.attrib["usermaximum"] = self.intOrFloat(
- subset.userMaximum
- )
- if subset.userDefault is not None:
- subsetElement.attrib["userdefault"] = self.intOrFloat(
- subset.userDefault
- )
- elif hasattr(subset, "userValue"):
- subset = cast(ValueAxisSubsetDescriptor, subset)
- subsetElement.attrib["uservalue"] = self.intOrFloat(
- subset.userValue
- )
- subsetsElement.append(subsetElement)
- vfElement.append(subsetsElement)
- self._addLib(vfElement, vf.lib, 4)
- parentElement.append(vfElement)
-
- def _addLib(self, parentElement: ET.Element, data: Any, indent_level: int) -> None:
- if not data:
- return
- libElement = ET.Element("lib")
- libElement.append(plistlib.totree(data, indent_level=indent_level))
- parentElement.append(libElement)
-
- def _writeGlyphElement(self, instanceElement, instanceObject, glyphName, data):
- glyphElement = ET.Element("glyph")
- if data.get("mute"):
- glyphElement.attrib["mute"] = "1"
- if data.get("unicodes") is not None:
- glyphElement.attrib["unicode"] = " ".join(
- [hex(u) for u in data.get("unicodes")]
- )
- if data.get("instanceLocation") is not None:
- locationElement, data["instanceLocation"] = self._makeLocationElement(
- data.get("instanceLocation")
- )
- glyphElement.append(locationElement)
- if glyphName is not None:
- glyphElement.attrib["name"] = glyphName
- if data.get("note") is not None:
- noteElement = ET.Element("note")
- noteElement.text = data.get("note")
- glyphElement.append(noteElement)
- if data.get("masters") is not None:
- mastersElement = ET.Element("masters")
- for m in data.get("masters"):
- masterElement = ET.Element("master")
- if m.get("glyphName") is not None:
- masterElement.attrib["glyphname"] = m.get("glyphName")
- if m.get("font") is not None:
- masterElement.attrib["source"] = m.get("font")
- if m.get("location") is not None:
- locationElement, m["location"] = self._makeLocationElement(
- m.get("location")
- )
- masterElement.append(locationElement)
- mastersElement.append(masterElement)
- glyphElement.append(mastersElement)
- return glyphElement
-
-
-class BaseDocReader(LogMixin):
- axisDescriptorClass = AxisDescriptor
- discreteAxisDescriptorClass = DiscreteAxisDescriptor
- axisLabelDescriptorClass = AxisLabelDescriptor
- locationLabelDescriptorClass = LocationLabelDescriptor
- ruleDescriptorClass = RuleDescriptor
- sourceDescriptorClass = SourceDescriptor
- variableFontsDescriptorClass = VariableFontDescriptor
- valueAxisSubsetDescriptorClass = ValueAxisSubsetDescriptor
- rangeAxisSubsetDescriptorClass = RangeAxisSubsetDescriptor
- instanceDescriptorClass = InstanceDescriptor
-
- def __init__(self, documentPath, documentObject):
- self.path = documentPath
- self.documentObject = documentObject
- tree = ET.parse(self.path)
- self.root = tree.getroot()
- self.documentObject.formatVersion = self.root.attrib.get("format", "3.0")
- self._axes = []
- self.rules = []
- self.sources = []
- self.instances = []
- self.axisDefaults = {}
- self._strictAxisNames = True
-
- @classmethod
- def fromstring(cls, string, documentObject):
- f = BytesIO(tobytes(string, encoding="utf-8"))
- self = cls(f, documentObject)
- self.path = None
- return self
-
- def read(self):
- self.readAxes()
- self.readLabels()
- self.readRules()
- self.readVariableFonts()
- self.readSources()
- self.readInstances()
- self.readLib()
-
- def readRules(self):
- # we also need to read any conditions that are outside of a condition set.
- rules = []
- rulesElement = self.root.find(".rules")
- if rulesElement is not None:
- processingValue = rulesElement.attrib.get("processing", "first")
- if processingValue not in {"first", "last"}:
- raise DesignSpaceDocumentError(
- " processing attribute value is not valid: %r, "
- "expected 'first' or 'last'" % processingValue
- )
- self.documentObject.rulesProcessingLast = processingValue == "last"
- for ruleElement in self.root.findall(".rules/rule"):
- ruleObject = self.ruleDescriptorClass()
- ruleName = ruleObject.name = ruleElement.attrib.get("name")
- # read any stray conditions outside a condition set
- externalConditions = self._readConditionElements(
- ruleElement,
- ruleName,
- )
- if externalConditions:
- ruleObject.conditionSets.append(externalConditions)
- self.log.info(
- "Found stray rule conditions outside a conditionset. "
- "Wrapped them in a new conditionset."
- )
- # read the conditionsets
- for conditionSetElement in ruleElement.findall(".conditionset"):
- conditionSet = self._readConditionElements(
- conditionSetElement,
- ruleName,
- )
- if conditionSet is not None:
- ruleObject.conditionSets.append(conditionSet)
- for subElement in ruleElement.findall(".sub"):
- a = subElement.attrib["name"]
- b = subElement.attrib["with"]
- ruleObject.subs.append((a, b))
- rules.append(ruleObject)
- self.documentObject.rules = rules
-
- def _readConditionElements(self, parentElement, ruleName=None):
- cds = []
- for conditionElement in parentElement.findall(".condition"):
- cd = {}
- cdMin = conditionElement.attrib.get("minimum")
- if cdMin is not None:
- cd["minimum"] = float(cdMin)
- else:
- # will allow these to be None, assume axis.minimum
- cd["minimum"] = None
- cdMax = conditionElement.attrib.get("maximum")
- if cdMax is not None:
- cd["maximum"] = float(cdMax)
- else:
- # will allow these to be None, assume axis.maximum
- cd["maximum"] = None
- cd["name"] = conditionElement.attrib.get("name")
- # # test for things
- if cd.get("minimum") is None and cd.get("maximum") is None:
- raise DesignSpaceDocumentError(
- "condition missing required minimum or maximum in rule"
- + (" '%s'" % ruleName if ruleName is not None else "")
- )
- cds.append(cd)
- return cds
-
- def readAxes(self):
- # read the axes elements, including the warp map.
- axesElement = self.root.find(".axes")
- if axesElement is not None and "elidedfallbackname" in axesElement.attrib:
- self.documentObject.elidedFallbackName = axesElement.attrib[
- "elidedfallbackname"
- ]
- axisElements = self.root.findall(".axes/axis")
- if not axisElements:
- return
- for axisElement in axisElements:
- if (
- self.documentObject.formatTuple >= (5, 0)
- and "values" in axisElement.attrib
- ):
- axisObject = self.discreteAxisDescriptorClass()
- axisObject.values = [
- float(s) for s in axisElement.attrib["values"].split(" ")
- ]
- else:
- axisObject = self.axisDescriptorClass()
- axisObject.minimum = float(axisElement.attrib.get("minimum"))
- axisObject.maximum = float(axisElement.attrib.get("maximum"))
- axisObject.default = float(axisElement.attrib.get("default"))
- axisObject.name = axisElement.attrib.get("name")
- if axisElement.attrib.get("hidden", False):
- axisObject.hidden = True
- axisObject.tag = axisElement.attrib.get("tag")
- for mapElement in axisElement.findall("map"):
- a = float(mapElement.attrib["input"])
- b = float(mapElement.attrib["output"])
- axisObject.map.append((a, b))
- for labelNameElement in axisElement.findall("labelname"):
- # Note: elementtree reads the "xml:lang" attribute name as
- # '{http://www.w3.org/XML/1998/namespace}lang'
- for key, lang in labelNameElement.items():
- if key == XML_LANG:
- axisObject.labelNames[lang] = tostr(labelNameElement.text)
- labelElement = axisElement.find(".labels")
- if labelElement is not None:
- if "ordering" in labelElement.attrib:
- axisObject.axisOrdering = int(labelElement.attrib["ordering"])
- for label in labelElement.findall(".label"):
- axisObject.axisLabels.append(self.readAxisLabel(label))
- self.documentObject.axes.append(axisObject)
- self.axisDefaults[axisObject.name] = axisObject.default
-
- def readAxisLabel(self, element: ET.Element):
- xml_attrs = {
- "userminimum",
- "uservalue",
- "usermaximum",
- "name",
- "elidable",
- "oldersibling",
- "linkeduservalue",
- }
- unknown_attrs = set(element.attrib) - xml_attrs
- if unknown_attrs:
- raise DesignSpaceDocumentError(
- f"label element contains unknown attributes: {', '.join(unknown_attrs)}"
- )
-
- name = element.get("name")
- if name is None:
- raise DesignSpaceDocumentError("label element must have a name attribute.")
- valueStr = element.get("uservalue")
- if valueStr is None:
- raise DesignSpaceDocumentError(
- "label element must have a uservalue attribute."
- )
- value = float(valueStr)
- minimumStr = element.get("userminimum")
- minimum = float(minimumStr) if minimumStr is not None else None
- maximumStr = element.get("usermaximum")
- maximum = float(maximumStr) if maximumStr is not None else None
- linkedValueStr = element.get("linkeduservalue")
- linkedValue = float(linkedValueStr) if linkedValueStr is not None else None
- elidable = True if element.get("elidable") == "true" else False
- olderSibling = True if element.get("oldersibling") == "true" else False
- labelNames = {
- lang: label_name.text or ""
- for label_name in element.findall("labelname")
- for attr, lang in label_name.items()
- if attr == XML_LANG
- # Note: elementtree reads the "xml:lang" attribute name as
- # '{http://www.w3.org/XML/1998/namespace}lang'
- }
- return self.axisLabelDescriptorClass(
- name=name,
- userValue=value,
- userMinimum=minimum,
- userMaximum=maximum,
- elidable=elidable,
- olderSibling=olderSibling,
- linkedUserValue=linkedValue,
- labelNames=labelNames,
- )
-
- def readLabels(self):
- if self.documentObject.formatTuple < (5, 0):
- return
-
- xml_attrs = {"name", "elidable", "oldersibling"}
- for labelElement in self.root.findall(".labels/label"):
- unknown_attrs = set(labelElement.attrib) - xml_attrs
- if unknown_attrs:
- raise DesignSpaceDocumentError(
- f"Label element contains unknown attributes: {', '.join(unknown_attrs)}"
- )
-
- name = labelElement.get("name")
- if name is None:
- raise DesignSpaceDocumentError(
- "label element must have a name attribute."
- )
- designLocation, userLocation = self.locationFromElement(labelElement)
- if designLocation:
- raise DesignSpaceDocumentError(
- f' element "{name}" must only have user locations (using uservalue="").'
- )
- elidable = True if labelElement.get("elidable") == "true" else False
- olderSibling = True if labelElement.get("oldersibling") == "true" else False
- labelNames = {
- lang: label_name.text or ""
- for label_name in labelElement.findall("labelname")
- for attr, lang in label_name.items()
- if attr == XML_LANG
- # Note: elementtree reads the "xml:lang" attribute name as
- # '{http://www.w3.org/XML/1998/namespace}lang'
- }
- locationLabel = self.locationLabelDescriptorClass(
- name=name,
- userLocation=userLocation,
- elidable=elidable,
- olderSibling=olderSibling,
- labelNames=labelNames,
- )
- self.documentObject.locationLabels.append(locationLabel)
-
- def readVariableFonts(self):
- if self.documentObject.formatTuple < (5, 0):
- return
-
- xml_attrs = {"name", "filename"}
- for variableFontElement in self.root.findall(".variable-fonts/variable-font"):
- unknown_attrs = set(variableFontElement.attrib) - xml_attrs
- if unknown_attrs:
- raise DesignSpaceDocumentError(
- f"variable-font element contains unknown attributes: {', '.join(unknown_attrs)}"
- )
-
- name = variableFontElement.get("name")
- if name is None:
- raise DesignSpaceDocumentError(
- "variable-font element must have a name attribute."
- )
-
- filename = variableFontElement.get("filename")
-
- axisSubsetsElement = variableFontElement.find(".axis-subsets")
- if axisSubsetsElement is None:
- raise DesignSpaceDocumentError(
- "variable-font element must contain an axis-subsets element."
- )
- axisSubsets = []
- for axisSubset in axisSubsetsElement.iterfind(".axis-subset"):
- axisSubsets.append(self.readAxisSubset(axisSubset))
-
- lib = None
- libElement = variableFontElement.find(".lib")
- if libElement is not None:
- lib = plistlib.fromtree(libElement[0])
-
- variableFont = self.variableFontsDescriptorClass(
- name=name,
- filename=filename,
- axisSubsets=axisSubsets,
- lib=lib,
- )
- self.documentObject.variableFonts.append(variableFont)
-
- def readAxisSubset(self, element: ET.Element):
- if "uservalue" in element.attrib:
- xml_attrs = {"name", "uservalue"}
- unknown_attrs = set(element.attrib) - xml_attrs
- if unknown_attrs:
- raise DesignSpaceDocumentError(
- f"axis-subset element contains unknown attributes: {', '.join(unknown_attrs)}"
- )
-
- name = element.get("name")
- if name is None:
- raise DesignSpaceDocumentError(
- "axis-subset element must have a name attribute."
- )
- userValueStr = element.get("uservalue")
- if userValueStr is None:
- raise DesignSpaceDocumentError(
- "The axis-subset element for a discrete subset must have a uservalue attribute."
- )
- userValue = float(userValueStr)
-
- return self.valueAxisSubsetDescriptorClass(name=name, userValue=userValue)
- else:
- xml_attrs = {"name", "userminimum", "userdefault", "usermaximum"}
- unknown_attrs = set(element.attrib) - xml_attrs
- if unknown_attrs:
- raise DesignSpaceDocumentError(
- f"axis-subset element contains unknown attributes: {', '.join(unknown_attrs)}"
- )
-
- name = element.get("name")
- if name is None:
- raise DesignSpaceDocumentError(
- "axis-subset element must have a name attribute."
- )
-
- userMinimum = element.get("userminimum")
- userDefault = element.get("userdefault")
- userMaximum = element.get("usermaximum")
- if (
- userMinimum is not None
- and userDefault is not None
- and userMaximum is not None
- ):
- return self.rangeAxisSubsetDescriptorClass(
- name=name,
- userMinimum=float(userMinimum),
- userDefault=float(userDefault),
- userMaximum=float(userMaximum),
- )
- if all(v is None for v in (userMinimum, userDefault, userMaximum)):
- return self.rangeAxisSubsetDescriptorClass(name=name)
-
- raise DesignSpaceDocumentError(
- "axis-subset element must have min/max/default values or none at all."
- )
-
- def readSources(self):
- for sourceCount, sourceElement in enumerate(
- self.root.findall(".sources/source")
- ):
- filename = sourceElement.attrib.get("filename")
- if filename is not None and self.path is not None:
- sourcePath = os.path.abspath(
- os.path.join(os.path.dirname(self.path), filename)
- )
- else:
- sourcePath = None
- sourceName = sourceElement.attrib.get("name")
- if sourceName is None:
- # add a temporary source name
- sourceName = "temp_master.%d" % (sourceCount)
- sourceObject = self.sourceDescriptorClass()
- sourceObject.path = sourcePath # absolute path to the ufo source
- sourceObject.filename = filename # path as it is stored in the document
- sourceObject.name = sourceName
- familyName = sourceElement.attrib.get("familyname")
- if familyName is not None:
- sourceObject.familyName = familyName
- styleName = sourceElement.attrib.get("stylename")
- if styleName is not None:
- sourceObject.styleName = styleName
- for familyNameElement in sourceElement.findall("familyname"):
- for key, lang in familyNameElement.items():
- if key == XML_LANG:
- familyName = familyNameElement.text
- sourceObject.setFamilyName(familyName, lang)
- designLocation, userLocation = self.locationFromElement(sourceElement)
- if userLocation:
- raise DesignSpaceDocumentError(
- f' element "{sourceName}" must only have design locations (using xvalue="").'
- )
- sourceObject.location = designLocation
- layerName = sourceElement.attrib.get("layer")
- if layerName is not None:
- sourceObject.layerName = layerName
- for libElement in sourceElement.findall(".lib"):
- if libElement.attrib.get("copy") == "1":
- sourceObject.copyLib = True
- for groupsElement in sourceElement.findall(".groups"):
- if groupsElement.attrib.get("copy") == "1":
- sourceObject.copyGroups = True
- for infoElement in sourceElement.findall(".info"):
- if infoElement.attrib.get("copy") == "1":
- sourceObject.copyInfo = True
- if infoElement.attrib.get("mute") == "1":
- sourceObject.muteInfo = True
- for featuresElement in sourceElement.findall(".features"):
- if featuresElement.attrib.get("copy") == "1":
- sourceObject.copyFeatures = True
- for glyphElement in sourceElement.findall(".glyph"):
- glyphName = glyphElement.attrib.get("name")
- if glyphName is None:
- continue
- if glyphElement.attrib.get("mute") == "1":
- sourceObject.mutedGlyphNames.append(glyphName)
- for kerningElement in sourceElement.findall(".kerning"):
- if kerningElement.attrib.get("mute") == "1":
- sourceObject.muteKerning = True
- self.documentObject.sources.append(sourceObject)
-
- def locationFromElement(self, element):
- """Read a nested ```` element inside the given ``element``.
-
- .. versionchanged:: 5.0
- Return a tuple of (designLocation, userLocation)
- """
- elementLocation = (None, None)
- for locationElement in element.findall(".location"):
- elementLocation = self.readLocationElement(locationElement)
- break
- return elementLocation
-
- def readLocationElement(self, locationElement):
- """Read a ```` element.
-
- .. versionchanged:: 5.0
- Return a tuple of (designLocation, userLocation)
- """
- if self._strictAxisNames and not self.documentObject.axes:
- raise DesignSpaceDocumentError("No axes defined")
- userLoc = {}
- designLoc = {}
- for dimensionElement in locationElement.findall(".dimension"):
- dimName = dimensionElement.attrib.get("name")
- if self._strictAxisNames and dimName not in self.axisDefaults:
- # In case the document contains no axis definitions,
- self.log.warning('Location with undefined axis: "%s".', dimName)
- continue
- userValue = xValue = yValue = None
- try:
- userValue = dimensionElement.attrib.get("uservalue")
- if userValue is not None:
- userValue = float(userValue)
- except ValueError:
- self.log.warning(
- "ValueError in readLocation userValue %3.3f", userValue
- )
- try:
- xValue = dimensionElement.attrib.get("xvalue")
- if xValue is not None:
- xValue = float(xValue)
- except ValueError:
- self.log.warning("ValueError in readLocation xValue %3.3f", xValue)
- try:
- yValue = dimensionElement.attrib.get("yvalue")
- if yValue is not None:
- yValue = float(yValue)
- except ValueError:
- self.log.warning("ValueError in readLocation yValue %3.3f", yValue)
- if userValue is None == xValue is None:
- raise DesignSpaceDocumentError(
- f'Exactly one of uservalue="" or xvalue="" must be provided for location dimension "{dimName}"'
- )
- if yValue is not None:
- if xValue is None:
- raise DesignSpaceDocumentError(
- f'Missing xvalue="" for the location dimension "{dimName}"" with yvalue="{yValue}"'
- )
- designLoc[dimName] = (xValue, yValue)
- elif xValue is not None:
- designLoc[dimName] = xValue
- else:
- userLoc[dimName] = userValue
- return designLoc, userLoc
-
- def readInstances(self, makeGlyphs=True, makeKerning=True, makeInfo=True):
- instanceElements = self.root.findall(".instances/instance")
- for instanceElement in instanceElements:
- self._readSingleInstanceElement(
- instanceElement,
- makeGlyphs=makeGlyphs,
- makeKerning=makeKerning,
- makeInfo=makeInfo,
- )
-
- def _readSingleInstanceElement(
- self, instanceElement, makeGlyphs=True, makeKerning=True, makeInfo=True
- ):
- filename = instanceElement.attrib.get("filename")
- if filename is not None and self.documentObject.path is not None:
- instancePath = os.path.join(
- os.path.dirname(self.documentObject.path), filename
- )
- else:
- instancePath = None
- instanceObject = self.instanceDescriptorClass()
- instanceObject.path = instancePath # absolute path to the instance
- instanceObject.filename = filename # path as it is stored in the document
- name = instanceElement.attrib.get("name")
- if name is not None:
- instanceObject.name = name
- familyname = instanceElement.attrib.get("familyname")
- if familyname is not None:
- instanceObject.familyName = familyname
- stylename = instanceElement.attrib.get("stylename")
- if stylename is not None:
- instanceObject.styleName = stylename
- postScriptFontName = instanceElement.attrib.get("postscriptfontname")
- if postScriptFontName is not None:
- instanceObject.postScriptFontName = postScriptFontName
- styleMapFamilyName = instanceElement.attrib.get("stylemapfamilyname")
- if styleMapFamilyName is not None:
- instanceObject.styleMapFamilyName = styleMapFamilyName
- styleMapStyleName = instanceElement.attrib.get("stylemapstylename")
- if styleMapStyleName is not None:
- instanceObject.styleMapStyleName = styleMapStyleName
- # read localised names
- for styleNameElement in instanceElement.findall("stylename"):
- for key, lang in styleNameElement.items():
- if key == XML_LANG:
- styleName = styleNameElement.text
- instanceObject.setStyleName(styleName, lang)
- for familyNameElement in instanceElement.findall("familyname"):
- for key, lang in familyNameElement.items():
- if key == XML_LANG:
- familyName = familyNameElement.text
- instanceObject.setFamilyName(familyName, lang)
- for styleMapStyleNameElement in instanceElement.findall("stylemapstylename"):
- for key, lang in styleMapStyleNameElement.items():
- if key == XML_LANG:
- styleMapStyleName = styleMapStyleNameElement.text
- instanceObject.setStyleMapStyleName(styleMapStyleName, lang)
- for styleMapFamilyNameElement in instanceElement.findall("stylemapfamilyname"):
- for key, lang in styleMapFamilyNameElement.items():
- if key == XML_LANG:
- styleMapFamilyName = styleMapFamilyNameElement.text
- instanceObject.setStyleMapFamilyName(styleMapFamilyName, lang)
- designLocation, userLocation = self.locationFromElement(instanceElement)
- locationLabel = instanceElement.attrib.get("location")
- if (designLocation or userLocation) and locationLabel is not None:
- raise DesignSpaceDocumentError(
- 'instance element must have at most one of the location="..." attribute or the nested location element'
- )
- instanceObject.locationLabel = locationLabel
- instanceObject.userLocation = userLocation or {}
- instanceObject.designLocation = designLocation or {}
- for glyphElement in instanceElement.findall(".glyphs/glyph"):
- self.readGlyphElement(glyphElement, instanceObject)
- for infoElement in instanceElement.findall("info"):
- self.readInfoElement(infoElement, instanceObject)
- for libElement in instanceElement.findall("lib"):
- self.readLibElement(libElement, instanceObject)
- self.documentObject.instances.append(instanceObject)
-
- def readLibElement(self, libElement, instanceObject):
- """Read the lib element for the given instance."""
- instanceObject.lib = plistlib.fromtree(libElement[0])
-
- def readInfoElement(self, infoElement, instanceObject):
- """Read the info element."""
- instanceObject.info = True
-
- def readGlyphElement(self, glyphElement, instanceObject):
- """
- Read the glyph element, which could look like either one of these:
-
- .. code-block:: xml
-
-
-
- This is an instance from an anisotropic interpolation.
-
-
- """
- glyphData = {}
- glyphName = glyphElement.attrib.get("name")
- if glyphName is None:
- raise DesignSpaceDocumentError("Glyph object without name attribute")
- mute = glyphElement.attrib.get("mute")
- if mute == "1":
- glyphData["mute"] = True
- # unicode
- unicodes = glyphElement.attrib.get("unicode")
- if unicodes is not None:
- try:
- unicodes = [int(u, 16) for u in unicodes.split(" ")]
- glyphData["unicodes"] = unicodes
- except ValueError:
- raise DesignSpaceDocumentError(
- "unicode values %s are not integers" % unicodes
- )
-
- for noteElement in glyphElement.findall(".note"):
- glyphData["note"] = noteElement.text
- break
- designLocation, userLocation = self.locationFromElement(glyphElement)
- if userLocation:
- raise DesignSpaceDocumentError(
- f' element "{glyphName}" must only have design locations (using xvalue="").'
- )
- if designLocation is not None:
- glyphData["instanceLocation"] = designLocation
- glyphSources = None
- for masterElement in glyphElement.findall(".masters/master"):
- fontSourceName = masterElement.attrib.get("source")
- designLocation, userLocation = self.locationFromElement(masterElement)
- if userLocation:
- raise DesignSpaceDocumentError(
- f' element "{fontSourceName}" must only have design locations (using xvalue="").'
- )
- masterGlyphName = masterElement.attrib.get("glyphname")
- if masterGlyphName is None:
- # if we don't read a glyphname, use the one we have
- masterGlyphName = glyphName
- d = dict(
- font=fontSourceName, location=designLocation, glyphName=masterGlyphName
- )
- if glyphSources is None:
- glyphSources = []
- glyphSources.append(d)
- if glyphSources is not None:
- glyphData["masters"] = glyphSources
- instanceObject.glyphs[glyphName] = glyphData
-
- def readLib(self):
- """Read the lib element for the whole document."""
- for libElement in self.root.findall(".lib"):
- self.documentObject.lib = plistlib.fromtree(libElement[0])
-
-
-class DesignSpaceDocument(LogMixin, AsDictMixin):
- """The DesignSpaceDocument object can read and write ``.designspace`` data.
- It imports the axes, sources, variable fonts and instances to very basic
- **descriptor** objects that store the data in attributes. Data is added to
- the document by creating such descriptor objects, filling them with data
- and then adding them to the document. This makes it easy to integrate this
- object in different contexts.
-
- The **DesignSpaceDocument** object can be subclassed to work with
- different objects, as long as they have the same attributes. Reader and
- Writer objects can be subclassed as well.
-
- **Note:** Python attribute names are usually camelCased, the
- corresponding `XML `_ attributes are usually
- all lowercase.
-
- .. code:: python
-
- from fontTools.designspaceLib import DesignSpaceDocument
- doc = DesignSpaceDocument.fromfile("some/path/to/my.designspace")
- doc.formatVersion
- doc.elidedFallbackName
- doc.axes
- doc.locationLabels
- doc.rules
- doc.rulesProcessingLast
- doc.sources
- doc.variableFonts
- doc.instances
- doc.lib
-
- """
-
- def __init__(self, readerClass=None, writerClass=None):
- self.path = None
- """String, optional. When the document is read from the disk, this is
- the full path that was given to :meth:`read` or :meth:`fromfile`.
- """
- self.filename = None
- """String, optional. When the document is read from the disk, this is
- its original file name, i.e. the last part of its path.
-
- When the document is produced by a Python script and still only exists
- in memory, the producing script can write here an indication of a
- possible "good" filename, in case one wants to save the file somewhere.
- """
-
- self.formatVersion: Optional[str] = None
- """Format version for this document, as a string. E.g. "4.0" """
-
- self.elidedFallbackName: Optional[str] = None
- """STAT Style Attributes Header field ``elidedFallbackNameID``.
-
- See: `OTSpec STAT Style Attributes Header `_
-
- .. versionadded:: 5.0
- """
-
- self.axes: List[Union[AxisDescriptor, DiscreteAxisDescriptor]] = []
- """List of this document's axes."""
- self.locationLabels: List[LocationLabelDescriptor] = []
- """List of this document's STAT format 4 labels.
-
- .. versionadded:: 5.0"""
- self.rules: List[RuleDescriptor] = []
- """List of this document's rules."""
- self.rulesProcessingLast: bool = False
- """This flag indicates whether the substitution rules should be applied
- before or after other glyph substitution features.
-
- - False: before
- - True: after.
-
- Default is False. For new projects, you probably want True. See
- the following issues for more information:
- `fontTools#1371 `__
- `fontTools#2050 `__
-
- If you want to use a different feature altogether, e.g. ``calt``,
- use the lib key ``com.github.fonttools.varLib.featureVarsFeatureTag``
-
- .. code:: xml
-
-
-
- com.github.fonttools.varLib.featureVarsFeatureTag
- calt
-
-
- """
- self.sources: List[SourceDescriptor] = []
- """List of this document's sources."""
- self.variableFonts: List[VariableFontDescriptor] = []
- """List of this document's variable fonts.
-
- .. versionadded:: 5.0"""
- self.instances: List[InstanceDescriptor] = []
- """List of this document's instances."""
- self.lib: Dict = {}
- """User defined, custom data associated with the whole document.
-
- Use reverse-DNS notation to identify your own data.
- Respect the data stored by others.
- """
-
- self.default: Optional[str] = None
- """Name of the default master.
-
- This attribute is updated by the :meth:`findDefault`
- """
-
- if readerClass is not None:
- self.readerClass = readerClass
- else:
- self.readerClass = BaseDocReader
- if writerClass is not None:
- self.writerClass = writerClass
- else:
- self.writerClass = BaseDocWriter
-
- @classmethod
- def fromfile(cls, path, readerClass=None, writerClass=None):
- """Read a designspace file from ``path`` and return a new instance of
- :class:.
- """
- self = cls(readerClass=readerClass, writerClass=writerClass)
- self.read(path)
- return self
-
- @classmethod
- def fromstring(cls, string, readerClass=None, writerClass=None):
- self = cls(readerClass=readerClass, writerClass=writerClass)
- reader = self.readerClass.fromstring(string, self)
- reader.read()
- if self.sources:
- self.findDefault()
- return self
-
- def tostring(self, encoding=None):
- """Returns the designspace as a string. Default encoding ``utf-8``."""
- if encoding is str or (encoding is not None and encoding.lower() == "unicode"):
- f = StringIO()
- xml_declaration = False
- elif encoding is None or encoding == "utf-8":
- f = BytesIO()
- encoding = "UTF-8"
- xml_declaration = True
- else:
- raise ValueError("unsupported encoding: '%s'" % encoding)
- writer = self.writerClass(f, self)
- writer.write(encoding=encoding, xml_declaration=xml_declaration)
- return f.getvalue()
-
- def read(self, path):
- """Read a designspace file from ``path`` and populates the fields of
- ``self`` with the data.
- """
- if hasattr(path, "__fspath__"): # support os.PathLike objects
- path = path.__fspath__()
- self.path = path
- self.filename = os.path.basename(path)
- reader = self.readerClass(path, self)
- reader.read()
- if self.sources:
- self.findDefault()
-
- def write(self, path):
- """Write this designspace to ``path``."""
- if hasattr(path, "__fspath__"): # support os.PathLike objects
- path = path.__fspath__()
- self.path = path
- self.filename = os.path.basename(path)
- self.updatePaths()
- writer = self.writerClass(path, self)
- writer.write()
-
- def _posixRelativePath(self, otherPath):
- relative = os.path.relpath(otherPath, os.path.dirname(self.path))
- return posix(relative)
-
- def updatePaths(self):
- """
- Right before we save we need to identify and respond to the following situations:
- In each descriptor, we have to do the right thing for the filename attribute.
-
- ::
-
- case 1.
- descriptor.filename == None
- descriptor.path == None
-
- -- action:
- write as is, descriptors will not have a filename attr.
- useless, but no reason to interfere.
-
-
- case 2.
- descriptor.filename == "../something"
- descriptor.path == None
-
- -- action:
- write as is. The filename attr should not be touched.
-
-
- case 3.
- descriptor.filename == None
- descriptor.path == "~/absolute/path/there"
-
- -- action:
- calculate the relative path for filename.
- We're not overwriting some other value for filename, it should be fine
-
-
- case 4.
- descriptor.filename == '../somewhere'
- descriptor.path == "~/absolute/path/there"
-
- -- action:
- there is a conflict between the given filename, and the path.
- So we know where the file is relative to the document.
- Can't guess why they're different, we just choose for path to be correct and update filename.
- """
- assert self.path is not None
- for descriptor in self.sources + self.instances:
- if descriptor.path is not None:
- # case 3 and 4: filename gets updated and relativized
- descriptor.filename = self._posixRelativePath(descriptor.path)
-
- def addSource(self, sourceDescriptor: SourceDescriptor):
- """Add the given ``sourceDescriptor`` to ``doc.sources``."""
- self.sources.append(sourceDescriptor)
-
- def addSourceDescriptor(self, **kwargs):
- """Instantiate a new :class:`SourceDescriptor` using the given
- ``kwargs`` and add it to ``doc.sources``.
- """
- source = self.writerClass.sourceDescriptorClass(**kwargs)
- self.addSource(source)
- return source
-
- def addInstance(self, instanceDescriptor: InstanceDescriptor):
- """Add the given ``instanceDescriptor`` to :attr:`instances`."""
- self.instances.append(instanceDescriptor)
-
- def addInstanceDescriptor(self, **kwargs):
- """Instantiate a new :class:`InstanceDescriptor` using the given
- ``kwargs`` and add it to :attr:`instances`.
- """
- instance = self.writerClass.instanceDescriptorClass(**kwargs)
- self.addInstance(instance)
- return instance
-
- def addAxis(self, axisDescriptor: Union[AxisDescriptor, DiscreteAxisDescriptor]):
- """Add the given ``axisDescriptor`` to :attr:`axes`."""
- self.axes.append(axisDescriptor)
-
- def addAxisDescriptor(self, **kwargs):
- """Instantiate a new :class:`AxisDescriptor` using the given
- ``kwargs`` and add it to :attr:`axes`.
-
- The axis will be and instance of :class:`DiscreteAxisDescriptor` if
- the ``kwargs`` provide a ``value``, or a :class:`AxisDescriptor` otherwise.
- """
- if "values" in kwargs:
- axis = self.writerClass.discreteAxisDescriptorClass(**kwargs)
- else:
- axis = self.writerClass.axisDescriptorClass(**kwargs)
- self.addAxis(axis)
- return axis
-
- def addRule(self, ruleDescriptor: RuleDescriptor):
- """Add the given ``ruleDescriptor`` to :attr:`rules`."""
- self.rules.append(ruleDescriptor)
-
- def addRuleDescriptor(self, **kwargs):
- """Instantiate a new :class:`RuleDescriptor` using the given
- ``kwargs`` and add it to :attr:`rules`.
- """
- rule = self.writerClass.ruleDescriptorClass(**kwargs)
- self.addRule(rule)
- return rule
-
- def addVariableFont(self, variableFontDescriptor: VariableFontDescriptor):
- """Add the given ``variableFontDescriptor`` to :attr:`variableFonts`.
-
- .. versionadded:: 5.0
- """
- self.variableFonts.append(variableFontDescriptor)
-
- def addVariableFontDescriptor(self, **kwargs):
- """Instantiate a new :class:`VariableFontDescriptor` using the given
- ``kwargs`` and add it to :attr:`variableFonts`.
-
- .. versionadded:: 5.0
- """
- variableFont = self.writerClass.variableFontDescriptorClass(**kwargs)
- self.addVariableFont(variableFont)
- return variableFont
-
- def addLocationLabel(self, locationLabelDescriptor: LocationLabelDescriptor):
- """Add the given ``locationLabelDescriptor`` to :attr:`locationLabels`.
-
- .. versionadded:: 5.0
- """
- self.locationLabels.append(locationLabelDescriptor)
-
- def addLocationLabelDescriptor(self, **kwargs):
- """Instantiate a new :class:`LocationLabelDescriptor` using the given
- ``kwargs`` and add it to :attr:`locationLabels`.
-
- .. versionadded:: 5.0
- """
- locationLabel = self.writerClass.locationLabelDescriptorClass(**kwargs)
- self.addLocationLabel(locationLabel)
- return locationLabel
-
- def newDefaultLocation(self):
- """Return a dict with the default location in design space coordinates."""
- # Without OrderedDict, output XML would be non-deterministic.
- # https://github.com/LettError/designSpaceDocument/issues/10
- loc = collections.OrderedDict()
- for axisDescriptor in self.axes:
- loc[axisDescriptor.name] = axisDescriptor.map_forward(
- axisDescriptor.default
- )
- return loc
-
- def labelForUserLocation(
- self, userLocation: SimpleLocationDict
- ) -> Optional[LocationLabelDescriptor]:
- """Return the :class:`LocationLabel` that matches the given
- ``userLocation``, or ``None`` if no such label exists.
-
- .. versionadded:: 5.0
- """
- return next(
- (
- label
- for label in self.locationLabels
- if label.userLocation == userLocation
- ),
- None,
- )
-
- def updateFilenameFromPath(self, masters=True, instances=True, force=False):
- """Set a descriptor filename attr from the path and this document path.
-
- If the filename attribute is not None: skip it.
- """
- if masters:
- for descriptor in self.sources:
- if descriptor.filename is not None and not force:
- continue
- if self.path is not None:
- descriptor.filename = self._posixRelativePath(descriptor.path)
- if instances:
- for descriptor in self.instances:
- if descriptor.filename is not None and not force:
- continue
- if self.path is not None:
- descriptor.filename = self._posixRelativePath(descriptor.path)
-
- def newAxisDescriptor(self):
- """Ask the writer class to make us a new axisDescriptor."""
- return self.writerClass.getAxisDecriptor()
-
- def newSourceDescriptor(self):
- """Ask the writer class to make us a new sourceDescriptor."""
- return self.writerClass.getSourceDescriptor()
-
- def newInstanceDescriptor(self):
- """Ask the writer class to make us a new instanceDescriptor."""
- return self.writerClass.getInstanceDescriptor()
-
- def getAxisOrder(self):
- """Return a list of axis names, in the same order as defined in the document."""
- names = []
- for axisDescriptor in self.axes:
- names.append(axisDescriptor.name)
- return names
-
- def getAxis(self, name: str) -> AxisDescriptor | DiscreteAxisDescriptor | None:
- """Return the axis with the given ``name``, or ``None`` if no such axis exists."""
- return next((axis for axis in self.axes if axis.name == name), None)
-
- def getAxisByTag(self, tag: str) -> AxisDescriptor | DiscreteAxisDescriptor | None:
- """Return the axis with the given ``tag``, or ``None`` if no such axis exists."""
- return next((axis for axis in self.axes if axis.tag == tag), None)
-
- def getLocationLabel(self, name: str) -> Optional[LocationLabelDescriptor]:
- """Return the top-level location label with the given ``name``, or
- ``None`` if no such label exists.
-
- .. versionadded:: 5.0
- """
- for label in self.locationLabels:
- if label.name == name:
- return label
- return None
-
- def map_forward(self, userLocation: SimpleLocationDict) -> SimpleLocationDict:
- """Map a user location to a design location.
-
- Assume that missing coordinates are at the default location for that axis.
-
- Note: the output won't be anisotropic, only the xvalue is set.
-
- .. versionadded:: 5.0
- """
- return {
- axis.name: axis.map_forward(userLocation.get(axis.name, axis.default))
- for axis in self.axes
- }
-
- def map_backward(
- self, designLocation: AnisotropicLocationDict
- ) -> SimpleLocationDict:
- """Map a design location to a user location.
-
- Assume that missing coordinates are at the default location for that axis.
-
- When the input has anisotropic locations, only the xvalue is used.
-
- .. versionadded:: 5.0
- """
- return {
- axis.name: (
- axis.map_backward(designLocation[axis.name])
- if axis.name in designLocation
- else axis.default
- )
- for axis in self.axes
- }
-
- def findDefault(self):
- """Set and return SourceDescriptor at the default location or None.
-
- The default location is the set of all `default` values in user space
- of all axes.
-
- This function updates the document's :attr:`default` value.
-
- .. versionchanged:: 5.0
- Allow the default source to not specify some of the axis values, and
- they are assumed to be the default.
- See :meth:`SourceDescriptor.getFullDesignLocation()`
- """
- self.default = None
-
- # Convert the default location from user space to design space before comparing
- # it against the SourceDescriptor locations (always in design space).
- defaultDesignLocation = self.newDefaultLocation()
-
- for sourceDescriptor in self.sources:
- if sourceDescriptor.getFullDesignLocation(self) == defaultDesignLocation:
- self.default = sourceDescriptor
- return sourceDescriptor
-
- return None
-
- def normalizeLocation(self, location):
- """Return a dict with normalized axis values."""
- from fontTools.varLib.models import normalizeValue
-
- new = {}
- for axis in self.axes:
- if axis.name not in location:
- # skipping this dimension it seems
- continue
- value = location[axis.name]
- # 'anisotropic' location, take first coord only
- if isinstance(value, tuple):
- value = value[0]
- triple = [
- axis.map_forward(v) for v in (axis.minimum, axis.default, axis.maximum)
- ]
- new[axis.name] = normalizeValue(value, triple)
- return new
-
- def normalize(self):
- """
- Normalise the geometry of this designspace:
-
- - scale all the locations of all masters and instances to the -1 - 0 - 1 value.
- - we need the axis data to do the scaling, so we do those last.
- """
- # masters
- for item in self.sources:
- item.location = self.normalizeLocation(item.location)
- # instances
- for item in self.instances:
- # glyph masters for this instance
- for _, glyphData in item.glyphs.items():
- glyphData["instanceLocation"] = self.normalizeLocation(
- glyphData["instanceLocation"]
- )
- for glyphMaster in glyphData["masters"]:
- glyphMaster["location"] = self.normalizeLocation(
- glyphMaster["location"]
- )
- item.location = self.normalizeLocation(item.location)
- # the axes
- for axis in self.axes:
- # scale the map first
- newMap = []
- for inputValue, outputValue in axis.map:
- newOutputValue = self.normalizeLocation({axis.name: outputValue}).get(
- axis.name
- )
- newMap.append((inputValue, newOutputValue))
- if newMap:
- axis.map = newMap
- # finally the axis values
- minimum = self.normalizeLocation({axis.name: axis.minimum}).get(axis.name)
- maximum = self.normalizeLocation({axis.name: axis.maximum}).get(axis.name)
- default = self.normalizeLocation({axis.name: axis.default}).get(axis.name)
- # and set them in the axis.minimum
- axis.minimum = minimum
- axis.maximum = maximum
- axis.default = default
- # now the rules
- for rule in self.rules:
- newConditionSets = []
- for conditions in rule.conditionSets:
- newConditions = []
- for cond in conditions:
- if cond.get("minimum") is not None:
- minimum = self.normalizeLocation(
- {cond["name"]: cond["minimum"]}
- ).get(cond["name"])
- else:
- minimum = None
- if cond.get("maximum") is not None:
- maximum = self.normalizeLocation(
- {cond["name"]: cond["maximum"]}
- ).get(cond["name"])
- else:
- maximum = None
- newConditions.append(
- dict(name=cond["name"], minimum=minimum, maximum=maximum)
- )
- newConditionSets.append(newConditions)
- rule.conditionSets = newConditionSets
-
- def loadSourceFonts(self, opener, **kwargs):
- """Ensure SourceDescriptor.font attributes are loaded, and return list of fonts.
-
- Takes a callable which initializes a new font object (e.g. TTFont, or
- defcon.Font, etc.) from the SourceDescriptor.path, and sets the
- SourceDescriptor.font attribute.
- If the font attribute is already not None, it is not loaded again.
- Fonts with the same path are only loaded once and shared among SourceDescriptors.
-
- For example, to load UFO sources using defcon:
-
- designspace = DesignSpaceDocument.fromfile("path/to/my.designspace")
- designspace.loadSourceFonts(defcon.Font)
-
- Or to load masters as FontTools binary fonts, including extra options:
-
- designspace.loadSourceFonts(ttLib.TTFont, recalcBBoxes=False)
-
- Args:
- opener (Callable): takes one required positional argument, the source.path,
- and an optional list of keyword arguments, and returns a new font object
- loaded from the path.
- **kwargs: extra options passed on to the opener function.
-
- Returns:
- List of font objects in the order they appear in the sources list.
- """
- # we load fonts with the same source.path only once
- loaded = {}
- fonts = []
- for source in self.sources:
- if source.font is not None: # font already loaded
- fonts.append(source.font)
- continue
- if source.path in loaded:
- source.font = loaded[source.path]
- else:
- if source.path is None:
- raise DesignSpaceDocumentError(
- "Designspace source '%s' has no 'path' attribute"
- % (source.name or "")
- )
- source.font = opener(source.path, **kwargs)
- loaded[source.path] = source.font
- fonts.append(source.font)
- return fonts
-
- @property
- def formatTuple(self):
- """Return the formatVersion as a tuple of (major, minor).
-
- .. versionadded:: 5.0
- """
- if self.formatVersion is None:
- return (5, 0)
- numbers = (int(i) for i in self.formatVersion.split("."))
- major = next(numbers)
- minor = next(numbers, 0)
- return (major, minor)
-
- def getVariableFonts(self) -> List[VariableFontDescriptor]:
- """Return all variable fonts defined in this document, or implicit
- variable fonts that can be built from the document's continuous axes.
-
- In the case of Designspace documents before version 5, the whole
- document was implicitly describing a variable font that covers the
- whole space.
-
- In version 5 and above documents, there can be as many variable fonts
- as there are locations on discrete axes.
-
- .. seealso:: :func:`splitInterpolable`
-
- .. versionadded:: 5.0
- """
- if self.variableFonts:
- return self.variableFonts
-
- variableFonts = []
- discreteAxes = []
- rangeAxisSubsets: List[
- Union[RangeAxisSubsetDescriptor, ValueAxisSubsetDescriptor]
- ] = []
- for axis in self.axes:
- if hasattr(axis, "values"):
- # Mypy doesn't support narrowing union types via hasattr()
- # TODO(Python 3.10): use TypeGuard
- # https://mypy.readthedocs.io/en/stable/type_narrowing.html
- axis = cast(DiscreteAxisDescriptor, axis)
- discreteAxes.append(axis) # type: ignore
- else:
- rangeAxisSubsets.append(RangeAxisSubsetDescriptor(name=axis.name))
- valueCombinations = itertools.product(*[axis.values for axis in discreteAxes])
- for values in valueCombinations:
- basename = None
- if self.filename is not None:
- basename = os.path.splitext(self.filename)[0] + "-VF"
- if self.path is not None:
- basename = os.path.splitext(os.path.basename(self.path))[0] + "-VF"
- if basename is None:
- basename = "VF"
- axisNames = "".join(
- [f"-{axis.tag}{value}" for axis, value in zip(discreteAxes, values)]
- )
- variableFonts.append(
- VariableFontDescriptor(
- name=f"{basename}{axisNames}",
- axisSubsets=rangeAxisSubsets
- + [
- ValueAxisSubsetDescriptor(name=axis.name, userValue=value)
- for axis, value in zip(discreteAxes, values)
- ],
- )
- )
- return variableFonts
-
- def deepcopyExceptFonts(self):
- """Allow deep-copying a DesignSpace document without deep-copying
- attached UFO fonts or TTFont objects. The :attr:`font` attribute
- is shared by reference between the original and the copy.
-
- .. versionadded:: 5.0
- """
- fonts = [source.font for source in self.sources]
- try:
- for source in self.sources:
- source.font = None
- res = copy.deepcopy(self)
- for source, font in zip(res.sources, fonts):
- source.font = font
- return res
- finally:
- for source, font in zip(self.sources, fonts):
- source.font = font
diff --git a/spaces/lancewilhelm/bad-actors-annotator/README.md b/spaces/lancewilhelm/bad-actors-annotator/README.md
deleted file mode 100644
index 3058eab1ca3e2e02c3df39d6c553718ebf1dacc8..0000000000000000000000000000000000000000
--- a/spaces/lancewilhelm/bad-actors-annotator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bad Actors Annotator
-emoji: 🌖
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/leurez/moss/src/components/common/index.ts b/spaces/leurez/moss/src/components/common/index.ts
deleted file mode 100644
index d8f03ec65c22eff316dc8ea9058737342ca85180..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/src/components/common/index.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import HoverButton from './HoverButton/index.vue'
-import NaiveProvider from './NaiveProvider/index.vue'
-import SvgIcon from './SvgIcon/index.vue'
-import UserAvatar from './UserAvatar/index.vue'
-import Setting from './Setting/index.vue'
-import PromptStore from './PromptStore/index.vue'
-
-export { HoverButton, NaiveProvider, SvgIcon, UserAvatar, Setting, PromptStore }
diff --git a/spaces/limingcv/AlignDet/pretrain/selfsup_mask-rcnn_1x_coco_swav/mask_rcnn.py b/spaces/limingcv/AlignDet/pretrain/selfsup_mask-rcnn_1x_coco_swav/mask_rcnn.py
deleted file mode 100644
index 571a796bf6b730c7b2d2197705738a468c86562f..0000000000000000000000000000000000000000
--- a/spaces/limingcv/AlignDet/pretrain/selfsup_mask-rcnn_1x_coco_swav/mask_rcnn.py
+++ /dev/null
@@ -1,425 +0,0 @@
-model = dict(
- type='SelfSupDetector',
- backbone=dict(
- type='SelfSupMaskRCNN',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=4,
- norm_cfg=dict(type='SyncBN', requires_grad=False),
- norm_eval=True,
- style='pytorch',
- init_cfg=dict(
- type='Pretrained',
- checkpoint=
- 'pretrain/image-level/swav/swav_resnet50_64xb64-mcrop-2-6-coslr-800e_in1k-224-96_official.pth.tar'
- )),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=5),
- rpn_head=dict(
- type='RPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0.0, 0.0, 0.0, 0.0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- roi_head=dict(
- type='SelfSupStandardRoIHead',
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(
- type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- bbox_head=dict(
- type='SelfSupShared4Conv1FCBBoxHead',
- in_channels=256,
- num_classes=256,
- roi_feat_size=7,
- reg_class_agnostic=False,
- loss_bbox=dict(type='L1Loss', loss_weight=1.0),
- loss_cls=dict(
- type='ContrastiveLoss', loss_weight=1.0, temperature=0.5)),
- mask_roi_extractor=None,
- mask_head=None),
- train_cfg=dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=4096,
- pos_fraction=1.0,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=2000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=True,
- ignore_iof_thr=-1,
- gt_max_assign_all=False),
- sampler=dict(
- type='RandomSampler',
- num=4096,
- pos_fraction=1,
- neg_pos_ub=0,
- add_gt_as_proposals=True),
- mask_size=28,
- pos_weight=-1,
- debug=False)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=1000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100,
- mask_thr_binary=0.5))))
-train_dataset_type = 'MultiViewCocoDataset'
-test_dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-classes = ['selective_search']
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-load_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=False)
-]
-train_pipeline1 = [
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
- (1333, 768), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
- dict(type='Pad', size_divisor=32),
- dict(type='RandFlip', flip_ratio=0.5),
- dict(
- type='OneOf',
- transforms=[
- dict(type='Identity'),
- dict(type='AutoContrast'),
- dict(type='RandEqualize'),
- dict(type='RandSolarize'),
- dict(type='RandColor'),
- dict(type='RandContrast'),
- dict(type='RandBrightness'),
- dict(type='RandSharpness'),
- dict(type='RandPosterize')
- ]),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-train_pipeline2 = [
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
- (1333, 768), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
- dict(type='Pad', size_divisor=32),
- dict(type='RandFlip', flip_ratio=0.5),
- dict(
- type='OneOf',
- transforms=[
- dict(type='Identity'),
- dict(type='AutoContrast'),
- dict(type='RandEqualize'),
- dict(type='RandSolarize'),
- dict(type='RandColor'),
- dict(type='RandContrast'),
- dict(type='RandBrightness'),
- dict(type='RandSharpness'),
- dict(type='RandPosterize')
- ]),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type='MultiViewCocoDataset',
- dataset=dict(
- type='CocoDataset',
- classes=['selective_search'],
- ann_file=
- 'data/coco/filtered_proposals/train2017_ratio3size0008@0.5.json',
- img_prefix='data/coco/train2017/',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=False)
- ]),
- num_views=2,
- pipelines=[[{
- 'type':
- 'Resize',
- 'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736),
- (1333, 768), (1333, 800)],
- 'multiscale_mode':
- 'value',
- 'keep_ratio':
- True
- }, {
- 'type': 'FilterAnnotations',
- 'min_gt_bbox_wh': (0.01, 0.01)
- }, {
- 'type': 'Pad',
- 'size_divisor': 32
- }, {
- 'type': 'RandFlip',
- 'flip_ratio': 0.5
- }, {
- 'type':
- 'OneOf',
- 'transforms': [{
- 'type': 'Identity'
- }, {
- 'type': 'AutoContrast'
- }, {
- 'type': 'RandEqualize'
- }, {
- 'type': 'RandSolarize'
- }, {
- 'type': 'RandColor'
- }, {
- 'type': 'RandContrast'
- }, {
- 'type': 'RandBrightness'
- }, {
- 'type': 'RandSharpness'
- }, {
- 'type': 'RandPosterize'
- }]
- }, {
- 'type': 'Normalize',
- 'mean': [123.675, 116.28, 103.53],
- 'std': [58.395, 57.12, 57.375],
- 'to_rgb': True
- }, {
- 'type': 'DefaultFormatBundle'
- }, {
- 'type': 'Collect',
- 'keys': ['img', 'gt_bboxes', 'gt_labels']
- }],
- [{
- 'type':
- 'Resize',
- 'img_scale': [(1333, 640), (1333, 672), (1333, 704),
- (1333, 736), (1333, 768), (1333, 800)],
- 'multiscale_mode':
- 'value',
- 'keep_ratio':
- True
- }, {
- 'type': 'FilterAnnotations',
- 'min_gt_bbox_wh': (0.01, 0.01)
- }, {
- 'type': 'Pad',
- 'size_divisor': 32
- }, {
- 'type': 'RandFlip',
- 'flip_ratio': 0.5
- }, {
- 'type':
- 'OneOf',
- 'transforms': [{
- 'type': 'Identity'
- }, {
- 'type': 'AutoContrast'
- }, {
- 'type': 'RandEqualize'
- }, {
- 'type': 'RandSolarize'
- }, {
- 'type': 'RandColor'
- }, {
- 'type': 'RandContrast'
- }, {
- 'type': 'RandBrightness'
- }, {
- 'type': 'RandSharpness'
- }, {
- 'type': 'RandPosterize'
- }]
- }, {
- 'type': 'Normalize',
- 'mean': [123.675, 116.28, 103.53],
- 'std': [58.395, 57.12, 57.375],
- 'to_rgb': True
- }, {
- 'type': 'DefaultFormatBundle'
- }, {
- 'type': 'Collect',
- 'keys': ['img', 'gt_bboxes', 'gt_labels']
- }]]),
- val=dict(
- type='CocoDataset',
- classes=['selective_search'],
- ann_file='data/coco/annotations/instances_val2017.json',
- img_prefix='data/coco/val2017/',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
- ]),
- test=dict(
- type='CocoDataset',
- classes=['selective_search'],
- ann_file='data/coco/annotations/instances_val2017.json',
- img_prefix='data/coco/val2017/',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
- ]))
-evaluation = dict(interval=65535, gpu_collect=True)
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-custom_hooks = [
- dict(type='MomentumUpdateHook'),
- dict(
- type='MMDetWandbHook',
- init_kwargs=dict(project='I2B', group='pretrain'),
- interval=50,
- num_eval_images=0,
- log_checkpoint=False)
-]
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-opencv_num_threads = 0
-mp_start_method = 'fork'
-auto_scale_lr = dict(enable=False, base_batch_size=16)
-custom_imports = dict(
- imports=[
- 'mmselfsup.datasets.pipelines',
- 'selfsup.core.hook.momentum_update_hook',
- 'selfsup.datasets.pipelines.selfsup_pipelines',
- 'selfsup.datasets.pipelines.rand_aug',
- 'selfsup.datasets.single_view_coco',
- 'selfsup.datasets.multi_view_coco',
- 'selfsup.models.losses.contrastive_loss',
- 'selfsup.models.dense_heads.fcos_head',
- 'selfsup.models.dense_heads.retina_head',
- 'selfsup.models.dense_heads.detr_head',
- 'selfsup.models.dense_heads.deformable_detr_head',
- 'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head',
- 'selfsup.models.roi_heads.standard_roi_head',
- 'selfsup.models.detectors.selfsup_detector',
- 'selfsup.models.detectors.selfsup_fcos',
- 'selfsup.models.detectors.selfsup_detr',
- 'selfsup.models.detectors.selfsup_deformable_detr',
- 'selfsup.models.detectors.selfsup_retinanet',
- 'selfsup.models.detectors.selfsup_mask_rcnn',
- 'selfsup.core.bbox.assigners.hungarian_assigner',
- 'selfsup.core.bbox.assigners.pseudo_hungarian_assigner',
- 'selfsup.core.bbox.match_costs.match_cost'
- ],
- allow_failed_imports=False)
-work_dir = 'work_dirs/selfsup_mask-rcnn_1x_coco_swav'
-auto_resume = False
-gpu_ids = range(0, 8)
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Beini 1.2.1 Wireless Hack Tool ISO.zip REPACK.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Beini 1.2.1 Wireless Hack Tool ISO.zip REPACK.md
deleted file mode 100644
index 7cc07d444f8f6ac57bfd8f426d3a749d47967638..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Beini 1.2.1 Wireless Hack Tool ISO.zip REPACK.md
+++ /dev/null
@@ -1,66 +0,0 @@
-Beini 1.2.1 Wireless Hack Tool ISO.zip Download ✦✦✦ https://bytlly.com/2uGwDz
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kaligrafi Asmaul Husna Pdf Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kaligrafi Asmaul Husna Pdf Download.md
deleted file mode 100644
index 9f2946d0bc721b96c5f51f80f890a40e8585f7ea..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kaligrafi Asmaul Husna Pdf Download.md
+++ /dev/null
@@ -1,32 +0,0 @@
-Kaligrafi Asmaul Husna Pdf Download Download ✏ https://bytlly.com/2uGyqQ
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Left 4 Dead 2 Razor1911 Crack _TOP_ Only Fixed Password.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Left 4 Dead 2 Razor1911 Crack _TOP_ Only Fixed Password.md
deleted file mode 100644
index f35619670df44872d72583deeb91c3a8170b4539..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Left 4 Dead 2 Razor1911 Crack _TOP_ Only Fixed Password.md
+++ /dev/null
@@ -1,7 +0,0 @@
-ive recently been let go from my job. ive been unemployed for 3 months now, but i have had a terrible experience trying to get assistance. ive tried everything from a lower wage job at target, walmart, and am still trying to find something. i have applied for this types of jobs for 2 months and no luck. ive been pulled over 3 times in the past 3 months for no reason at all. i can only provide the ticket numbers to the two police officers ive been pulled over by. it happened at night time in the town where i live. i guess my point is, am i a victim of victim or are they stealing from me?
-my brother in law is staying with me, and we are homeless. two days ago he got a job at a restaurant and he is working through the week and i am looking for other jobs, too. i have a bunch of credit card debt and unfortunately i had to take a loan to pay for rent and utilities. i have lost my job and i am not qualified for any of these programs. any information would be helpful. thanks!!!
-left 4 dead 2 razor1911 crack only fixed password Download Zip ►►► https://bytlly.com/2uGyDg
i have been living here for over a month now and it’s been the worst month of my life and i only have till the end of the month. we just found out that there were cracks in our ceiling. we live in an apartment complex of 50 units and it’s all one building, all of the units are connected to the main common building. the roof is falling in and we are told that it’s an old building, as in, the roof is always dripping. the heat is working but one of the electric outlets and the boiler shut off on us. once my landlord was going to put in a new outlet and boiler and we have been having problems.
899543212b
-#include
-#include
-
-#include "masked_image.h"
-#include "nnf.h"
-#include "inpaint.h"
-
-int main() {
- auto source = cv::imread("./images/forest_pruned.bmp", cv::IMREAD_COLOR);
-
- auto mask = cv::Mat(source.size(), CV_8UC1);
- mask = cv::Scalar::all(0);
- for (int i = 0; i < source.size().height; ++i) {
- for (int j = 0; j < source.size().width; ++j) {
- auto source_ptr = source.ptr(i, j);
- if (source_ptr[0] == 255 && source_ptr[1] == 255 && source_ptr[2] == 255) {
- mask.at(i, j) = 1;
- }
- }
- }
-
- auto metric = PatchSSDDistanceMetric(3);
- auto result = Inpainting(source, mask, &metric).run(true, true);
- // cv::imwrite("./images/forest_recovered.bmp", result);
- // cv::imshow("Result", result);
- // cv::waitKey();
-
- return 0;
-}
-
diff --git a/spaces/lsmyrtaj/cse6242-dataminers/ef.py b/spaces/lsmyrtaj/cse6242-dataminers/ef.py
deleted file mode 100644
index c9599248c692a1c1220e17d346c4d788f55c3f3e..0000000000000000000000000000000000000000
--- a/spaces/lsmyrtaj/cse6242-dataminers/ef.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import pandas as pd
-import numpy as np
-from datetime import datetime as dt
-from pypfopt.efficient_frontier import EfficientFrontier
-import streamlit as st
-import plotly.graph_objects as go
-import plotly.express as px
-from PIL import Image
-
-### START AND RUN STREAMLIT
-#https://docs.streamlit.io/library/get-started/installation
-
-def ef_viz(stock_df,choices):
- #st.write("EF Visualization KOI EDITS")
- # st.header('CAPM Model and the Efficient Frontier')
-
- symbols, weights, benchmark, investing_style, rf, A_coef = choices.values()
- tickers = symbols
-
- #tickers.append('sp500')
- #st.write(tickers)
- #st.write(stock_df)
-
- # Yearly returns for individual companies
- #https://stackoverflow.com/questions/69284773/unable-to-resample-the-pandas-with-date-column-typeerror-only-valid-with-dateti
- stock_dff = stock_df.copy()
- stock_dff['Date'] = pd.to_datetime(stock_dff['Date'])
-
-
- # ind_er_df = stock_dff.set_index('Date')
- #st.write(stock_dff.columns)
- ind_er_df = stock_dff.resample('Y', on = 'Date').last().pct_change().mean()
- ind_er = ind_er_df[tickers]
- #st.write(ind_er)
- ann_sd = stock_df[tickers].pct_change().apply(lambda x: np.log(1+x)).std().apply(lambda x: x*np.sqrt(250))
- assets = pd.concat([ind_er, ann_sd], axis=1) # Creating a table for visualising returns and volatility of assets
- assets.columns = ['Returns', 'Volatility']
- assets
- #st.write(assets)
- ln_pct_change = stock_df[tickers].pct_change().apply(lambda x: np.log(1+x))[1:]
- #Cov Matrix
- cov_matrix =ln_pct_change.cov()
-
- ## CREATE PORFOLIOS WEIGHTS
- p_ret = [] # Define an empty array for portfolio returns
- p_vol = [] # Define an empty array for portfolio volatility
- p_weights = [] # Define an empty array for asset weights
-
- num_assets = len(tickers)
- num_portfolios = 1000
-
- for portfolio in range(num_portfolios):
- weights = np.random.random(num_assets)
- weights = weights/np.sum(weights)
- p_weights.append(weights)
- returns = np.dot(weights, ind_er) # Returns are the product of individual expected returns of asset and its
- # weights
- p_ret.append(returns)
- var = cov_matrix.mul(weights, axis=0).mul(weights, axis=1).sum().sum()# Portfolio Variance
- sd = np.sqrt(var) # Daily standard deviation
- ann_sd = sd*np.sqrt(250) # Annual standard deviation = volatility
- p_vol.append(ann_sd)
-
- data = {'Returns':p_ret, 'Volatility':p_vol}
-
- for counter, symbol in enumerate(stock_df[tickers].columns.tolist()):
- #print(counter, symbol)
- data[symbol] = [w[counter] for w in p_weights]
-
- port_ef_df = pd.DataFrame(data)
- port_ef_df['Vol'] = port_ef_df['Volatility']
-
- ## NEEDS INPUT INSTEAD OF HARD CODE
- #a = 5 #the coefficient of risk aversion is A. If an invest is less risk averse A is small. We assume 25 < A < 35.
- #rf = 0.041
-
- min_vol_port = port_ef_df.iloc[port_ef_df['Volatility'].idxmin()]
- optimal_risky_port = port_ef_df.iloc[((port_ef_df['Returns']-rf)/port_ef_df['Volatility']).idxmax()]
-
- ### Make DF and data string for when hover over data points
- def make_op_df(df, tickers):
- new = {}
- op_str = str()
- new['Returns'] = df[0]
- new['Volatility'] = df[1]
-
- for i in range(0,len(tickers)):
- new[tickers[i]]= df[i+2]
- op_str += str(tickers[i]) + ': ' + str(round(df[i+2],4)) + '
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
- const void* x;
- const float* f;
- void* y;
-
- int2 up;
- int2 down;
- int2 pad0;
- int flip;
- float gain;
-
- int4 inSize; // [width, height, channel, batch]
- int4 inStride;
- int2 filterSize; // [width, height]
- int2 filterStride;
- int4 outSize; // [width, height, channel, batch]
- int4 outStride;
- int sizeMinor;
- int sizeMajor;
-
- int loopMinor;
- int loopMajor;
- int loopX;
- int launchMinor;
- int launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
- void* kernel;
- int tileOutW;
- int tileOutH;
- int loopMinor;
- int loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/event.h b/spaces/ma-xu/LIVE/thrust/thrust/event.h
deleted file mode 100644
index 75578d96443c2e20c0b80b4d4efe28a6a5a5b04f..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/event.h
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*! \file thrust/event.h
- * \brief `thrust::event`, an asynchronous handle type.
- */
-
-#pragma once
-
-#include
-
-// TODO: Actually separate `` into two headers.
-
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/metrics/__init__.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/metrics/__init__.py
deleted file mode 100644
index 4fb044a93878c7d0dbfafa602b7f80536a6d3a0d..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/metrics/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from copy import deepcopy
-
-from basicsr.utils.registry import METRIC_REGISTRY
-from .niqe import calculate_niqe
-from .psnr_ssim import calculate_psnr, calculate_ssim
-
-__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe']
-
-
-def calculate_metric(data, opt):
- """Calculate metric from data and options.
-
- Args:
- opt (dict): Configuration. It must contain:
- type (str): Model type.
- """
- opt = deepcopy(opt)
- metric_type = opt.pop('type')
- metric = METRIC_REGISTRY.get(metric_type)(**data, **opt)
- return metric
diff --git a/spaces/mano96/content_rewrite/README.md b/spaces/mano96/content_rewrite/README.md
deleted file mode 100644
index 3750a87473f2c8e4b8e2e8968e66cc19843e756f..0000000000000000000000000000000000000000
--- a/spaces/mano96/content_rewrite/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Content Rewrite
-emoji: 🚀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/marcoruizrueda/flax-midjourney-v4-diffusion/README.md b/spaces/marcoruizrueda/flax-midjourney-v4-diffusion/README.md
deleted file mode 100644
index 864e747f63c19d3e4d3fac37dc453a211267a7c7..0000000000000000000000000000000000000000
--- a/spaces/marcoruizrueda/flax-midjourney-v4-diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Flax Midjourney V4 Diffusion
-emoji: 🏢
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/marcusj83/MusicGenbruh/audiocraft/modules/activations.py b/spaces/marcusj83/MusicGenbruh/audiocraft/modules/activations.py
deleted file mode 100644
index 8bd6f2917a56d72db56555d0ff54b2311bc21778..0000000000000000000000000000000000000000
--- a/spaces/marcusj83/MusicGenbruh/audiocraft/modules/activations.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from typing import Union, Callable
-
-
-class CustomGLU(nn.Module):
- """Custom Gated Linear Unit activation.
- Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
- of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
- function (i.e. sigmoid, swish, etc.).
-
- Args:
- activation (nn.Module): The custom activation to apply in the Gated Linear Unit
- dim (int): the dimension on which to split the input. Default: -1
-
- Shape:
- - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
- dimensions
- - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
-
- Examples::
- >>> m = CustomGLU(nn.Sigmoid())
- >>> input = torch.randn(4, 2)
- >>> output = m(input)
- """
- def __init__(self, activation: nn.Module, dim: int = -1):
- super(CustomGLU, self).__init__()
- self.dim = dim
- self.activation = activation
-
- def forward(self, x: Tensor):
- assert x.shape[self.dim] % 2 == 0 # M = N / 2
- a, b = torch.chunk(x, 2, dim=self.dim)
- return a * self.activation(b)
-
-
-class SwiGLU(CustomGLU):
- """SiLU Gated Linear Unit activation.
- Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
- the first half of the input matrices, :math:`b` is the second half.
-
- Args:
- dim (int): the dimension on which to split the input. Default: -1
- """
- def __init__(self, dim: int = -1):
- super(SwiGLU, self).__init__(nn.SiLU(), dim)
-
-
-class GeGLU(CustomGLU):
- """GeLU Gated Linear Unit activation.
- Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
- the first half of the input matrices, :math:`b` is the second half.
-
- Args:
- dim (int): the dimension on which to split the input. Default: -1
- """
- def __init__(self, dim: int = -1):
- super(GeGLU, self).__init__(nn.GELU(), dim)
-
-
-class ReGLU(CustomGLU):
- """ReLU Gated Linear Unit activation.
- Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
- the first half of the input matrices, :math:`b` is the second half.
-
- Args:
- dim (int): the dimension on which to split the input. Default: -1
- """
- def __init__(self, dim: int = -1):
- super(ReGLU, self).__init__(nn.ReLU(), dim)
-
-
-def get_activation_fn(
- activation: Union[str, Callable[[Tensor], Tensor]]
-) -> Union[str, Callable[[Tensor], Tensor]]:
- """Helper function to map an activation string to the activation class.
- If the supplied activation is not a string that is recognized, the activation is passed back.
-
- Args:
- activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check
- """
- if isinstance(activation, str):
- if activation == "reglu":
- return ReGLU()
- elif activation == "geglu":
- return GeGLU()
- elif activation == "swiglu":
- return SwiGLU()
- return activation
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/deformation_functions.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/deformation_functions.py
deleted file mode 100644
index 41b9464f4a6241d055e529fb4700a7eb2f29c8f7..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/deformation_functions.py
+++ /dev/null
@@ -1,386 +0,0 @@
-import numpy as np
-
-
-def deform_part(landmarks, part_inds, scale_y=1., scale_x=1., shift_ver=0., shift_horiz=0.):
- """ deform facial part landmarks - matching ibug annotations of 68 landmarks """
-
- landmarks_part = landmarks[part_inds, :].copy()
- part_mean = np.mean(landmarks_part, 0)
-
- landmarks_norm = landmarks_part - part_mean
- landmarks_deform = landmarks_norm.copy()
- landmarks_deform[:, 1] = scale_x * landmarks_deform[:, 1]
- landmarks_deform[:, 0] = scale_y * landmarks_deform[:, 0]
-
- landmarks_deform = landmarks_deform + part_mean
- landmarks_deform = landmarks_deform + shift_ver * np.array([1, 0]) + shift_horiz * np.array([0, 1])
-
- deform_shape = landmarks.copy()
- deform_shape[part_inds] = landmarks_deform
- return deform_shape
-
-
-def deform_mouth(lms, p_scale=0, p_shift=0, pad=5):
- """ deform mouth landmarks - matching ibug annotations of 68 landmarks """
-
- jaw_line_inds = np.arange(0, 17)
- nose_inds = np.arange(27, 36)
- mouth_inds = np.arange(48, 68)
-
- part_inds = mouth_inds.copy()
-
- # find part spatial limitations
- jaw_pad = 4
- x_max = np.max(lms[part_inds, 1]) + (np.max(lms[jaw_line_inds[jaw_pad:-jaw_pad], 1]) - np.max(
- lms[part_inds, 1])) * 0.5 - pad
- x_min = np.min(lms[jaw_line_inds[jaw_pad:-jaw_pad], 1]) + (np.min(lms[part_inds, 1]) - np.min(
- lms[jaw_line_inds[jaw_pad:-jaw_pad], 1])) * 0.5 + pad
- y_min = np.max(lms[nose_inds, 0]) + (np.min(lms[part_inds, 0]) - np.max(lms[nose_inds, 0])) * 0.5
- max_jaw = np.minimum(np.max(lms[jaw_line_inds, 0]), lms[8, 0])
- y_max = max_jaw - (max_jaw - np.max(lms[part_inds, 0])) * 0.5 - pad
-
- # scale facial feature
- scale = np.random.rand()
- if p_scale > 0.5 and scale > 0.5:
-
- part_mean = np.mean(lms[part_inds, :], 0)
- lms_part_norm = lms[part_inds, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- scale_max_y = np.minimum(
- (y_min - part_mean[0]) / part_y_bound_min,
- (y_max - part_mean[0]) / part_y_bound_max)
- scale_max_y = np.minimum(scale_max_y, 1.2)
-
- scale_max_x = np.minimum(
- (x_min - part_mean[1]) / part_x_bound_min,
- (x_max - part_mean[1]) / part_x_bound_max)
- scale_max_x = np.minimum(scale_max_x, 1.2)
-
- scale_y = np.random.uniform(0.7, scale_max_y)
- scale_x = np.random.uniform(0.7, scale_max_x)
-
- lms_def_scale = deform_part(lms, part_inds, scale_y=scale_y, scale_x=scale_x, shift_ver=0., shift_horiz=0.)
-
- # check for spatial errors
- error = check_deformation_spatial_errors(lms_def_scale, part_inds, pad=pad)
- if error:
- lms_def_scale = lms.copy()
- else:
- lms_def_scale = lms.copy()
-
- # shift facial feature
- if p_shift > 0.5 and (np.random.rand() > 0.5 or not scale):
-
- part_mean = np.mean(lms_def_scale[part_inds, :], 0)
- lms_part_norm = lms_def_scale[part_inds, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- shift_x = np.random.uniform(x_min - (part_mean[1] + part_x_bound_min),
- x_max - (part_mean[1] + part_x_bound_max))
- shift_y = np.random.uniform(y_min - (part_mean[0] + part_y_bound_min),
- y_max - (part_mean[0] + part_y_bound_max))
-
- lms_def = deform_part(lms_def_scale, part_inds, scale_y=1., scale_x=1., shift_ver=shift_y, shift_horiz=shift_x)
- error = check_deformation_spatial_errors(lms_def, part_inds, pad=pad)
- if error:
- lms_def = lms_def_scale.copy()
- else:
- lms_def = lms_def_scale.copy()
-
- return lms_def
-
-
-def deform_nose(lms, p_scale=0, p_shift=0, pad=5):
- """ deform nose landmarks - matching ibug annotations of 68 landmarks """
-
- nose_inds = np.arange(27, 36)
- left_eye_inds = np.arange(36, 42)
- right_eye_inds = np.arange(42, 48)
- mouth_inds = np.arange(48, 68)
-
- part_inds = nose_inds.copy()
-
- # find part spatial limitations
- x_max = np.max(lms[part_inds[:4], 1]) + (np.min(lms[right_eye_inds, 1]) - np.max(lms[part_inds[:4], 1])) * 0.5 - pad
- x_min = np.max(lms[left_eye_inds, 1]) + (np.min(lms[part_inds[:4], 1]) - np.max(lms[left_eye_inds, 1])) * 0.5 + pad
-
- max_brows = np.max(lms[21:23, 0])
- y_min = np.min(lms[part_inds, 0]) + (max_brows - np.min(lms[part_inds, 0])) * 0.5
- min_mouth = np.min(lms[mouth_inds, 0])
- y_max = np.max(lms[part_inds, 0]) + (np.max(lms[part_inds, 0]) - min_mouth) * 0 - pad
-
- # scale facial feature
- scale = np.random.rand()
- if p_scale > 0.5 and scale > 0.5:
-
- part_mean = np.mean(lms[part_inds, :], 0)
- lms_part_norm = lms[part_inds, :] - part_mean
-
- part_y_bound_min = np.min(lms_part_norm[:, 0])
- part_y_bound_max = np.max(lms_part_norm[:, 0])
-
- scale_max_y = np.minimum(
- (y_min - part_mean[0]) / part_y_bound_min,
- (y_max - part_mean[0]) / part_y_bound_max)
- scale_y = np.random.uniform(0.7, scale_max_y)
- scale_x = np.random.uniform(0.7, 1.5)
-
- lms_def_scale = deform_part(lms, part_inds, scale_y=scale_y, scale_x=scale_x, shift_ver=0., shift_horiz=0.)
-
- error1 = check_deformation_spatial_errors(lms_def_scale, part_inds[:4], pad=pad)
- error2 = check_deformation_spatial_errors(lms_def_scale, part_inds[4:], pad=pad)
- error = error1 + error2
- if error:
- lms_def_scale = lms.copy()
- else:
- lms_def_scale = lms.copy()
-
- # shift facial feature
- if p_shift > 0.5 and (np.random.rand() > 0.5 or not scale):
-
- part_mean = np.mean(lms_def_scale[part_inds, :], 0)
- lms_part_norm = lms_def_scale[part_inds, :] - part_mean
-
- part_x_bound_min = np.min(lms_part_norm[:4], 0)
- part_x_bound_max = np.max(lms_part_norm[:4], 0)
- part_y_bound_min = np.min(lms_part_norm[:, 0])
- part_y_bound_max = np.max(lms_part_norm[:, 0])
-
- shift_x = np.random.uniform(x_min - (part_mean[1] + part_x_bound_min),
- x_max - (part_mean[1] + part_x_bound_max))
- shift_y = np.random.uniform(y_min - (part_mean[0] + part_y_bound_min),
- y_max - (part_mean[0] + part_y_bound_max))
-
- lms_def = deform_part(lms_def_scale, part_inds, scale_y=1., scale_x=1., shift_ver=shift_y, shift_horiz=shift_x)
-
- error1 = check_deformation_spatial_errors(lms_def, part_inds[:4], pad=pad)
- error2 = check_deformation_spatial_errors(lms_def, part_inds[4:], pad=pad)
- error = error1 + error2
- if error:
- lms_def = lms_def_scale.copy()
- else:
- lms_def = lms_def_scale.copy()
-
- return lms_def
-
-
-def deform_eyes(lms, p_scale=0, p_shift=0, pad=10):
- """ deform eyes + eyebrows landmarks - matching ibug annotations of 68 landmarks """
-
- nose_inds = np.arange(27, 36)
- left_eye_inds = np.arange(36, 42)
- right_eye_inds = np.arange(42, 48)
- left_brow_inds = np.arange(17, 22)
- right_brow_inds = np.arange(22, 27)
-
- part_inds_right = np.hstack((right_brow_inds, right_eye_inds))
- part_inds_left = np.hstack((left_brow_inds, left_eye_inds))
-
- # find part spatial limitations
-
- # right eye+eyebrow
- x_max_right = np.max(lms[part_inds_right, 1]) + (lms[16, 1] - np.max(lms[part_inds_right, 1])) * 0.5 - pad
- x_min_right = np.max(lms[nose_inds[:4], 1]) + (np.min(lms[part_inds_right, 1]) - np.max(
- lms[nose_inds[:4], 1])) * 0.5 + pad
- y_max_right = np.max(lms[part_inds_right, 0]) + (lms[33, 0] - np.max(lms[part_inds_right, 0])) * 0.25 - pad
- y_min_right = 2 * pad
-
- # left eye+eyebrow
- x_max_left = np.max(lms[part_inds_left, 1]) + (np.min(lms[nose_inds[:4], 1]) - np.max(
- lms[part_inds_left, 1])) * 0.5 - pad
- x_min_left = lms[0, 1] + (np.min(lms[part_inds_left, 1]) - lms[0, 1]) * 0.5 + pad
-
- y_max_left = np.max(lms[part_inds_left, 0]) + (lms[33, 0] - np.max(lms[part_inds_left, 0])) * 0.25 - pad
- y_min_left = 2 * pad
-
- # scale facial feature
- scale = np.random.rand()
- if p_scale > 0.5 and scale > 0.5:
-
- # right eye+eyebrow
- part_mean = np.mean(lms[part_inds_right, :], 0)
- lms_part_norm = lms[part_inds_right, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- scale_max_y = np.minimum(
- (y_min_right - part_mean[0]) / part_y_bound_min,
- (y_max_right - part_mean[0]) / part_y_bound_max)
- scale_max_y_right = np.minimum(scale_max_y, 1.5)
-
- scale_max_x = np.minimum(
- (x_min_right - part_mean[1]) / part_x_bound_min,
- (x_max_right - part_mean[1]) / part_x_bound_max)
- scale_max_x_right = np.minimum(scale_max_x, 1.5)
-
- # left eye+eyebrow
- part_mean = np.mean(lms[part_inds_left, :], 0)
- lms_part_norm = lms[part_inds_left, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- scale_max_y = np.minimum(
- (y_min_left - part_mean[0]) / part_y_bound_min,
- (y_max_left - part_mean[0]) / part_y_bound_max)
- scale_max_y_left = np.minimum(scale_max_y, 1.5)
-
- scale_max_x = np.minimum(
- (x_min_left - part_mean[1]) / part_x_bound_min,
- (x_max_left - part_mean[1]) / part_x_bound_max)
- scale_max_x_left = np.minimum(scale_max_x, 1.5)
-
- scale_max_x = np.minimum(scale_max_x_left, scale_max_x_right)
- scale_max_y = np.minimum(scale_max_y_left, scale_max_y_right)
- scale_y = np.random.uniform(0.8, scale_max_y)
- scale_x = np.random.uniform(0.8, scale_max_x)
-
- lms_def_scale = deform_part(lms, part_inds_right, scale_y=scale_y, scale_x=scale_x, shift_ver=0.,
- shift_horiz=0.)
- lms_def_scale = deform_part(lms_def_scale.copy(), part_inds_left, scale_y=scale_y, scale_x=scale_x,
- shift_ver=0., shift_horiz=0.)
-
- error1 = check_deformation_spatial_errors(lms_def_scale, part_inds_right, pad=pad)
- error2 = check_deformation_spatial_errors(lms_def_scale, part_inds_left, pad=pad)
- error = error1 + error2
- if error:
- lms_def_scale = lms.copy()
- else:
- lms_def_scale = lms.copy()
-
- # shift facial feature
- if p_shift > 0.5 and (np.random.rand() > 0.5 or not scale):
-
- y_min_right = np.maximum(0.8 * np.min(lms_def_scale[part_inds_right, 0]), pad)
- y_min_left = np.maximum(0.8 * np.min(lms_def_scale[part_inds_left, 0]), pad)
-
- # right eye
- part_mean = np.mean(lms_def_scale[part_inds_right, :], 0)
- lms_part_norm = lms_def_scale[part_inds_right, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- shift_x = np.random.uniform(x_min_right - (part_mean[1] + part_x_bound_min),
- x_max_right - (part_mean[1] + part_x_bound_max))
- shift_y = np.random.uniform(y_min_right - (part_mean[0] + part_y_bound_min),
- y_max_right - (part_mean[0] + part_y_bound_max))
-
- lms_def_right = deform_part(lms_def_scale, part_inds_right, scale_y=1., scale_x=1., shift_ver=shift_y,
- shift_horiz=shift_x)
-
- error1 = check_deformation_spatial_errors(lms_def_right, part_inds_right, pad=pad)
- if error1:
- lms_def_right = lms_def_scale.copy()
-
- # left eye
- part_mean = np.mean(lms_def_scale[part_inds_left, :], 0)
- lms_part_norm = lms_def_scale[part_inds_left, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- shift_x = np.random.uniform(x_min_left - (part_mean[1] + part_x_bound_min),
- x_max_left - (part_mean[1] + part_x_bound_max))
- shift_y = np.random.uniform(y_min_left - (part_mean[0] + part_y_bound_min),
- y_max_left - (part_mean[0] + part_y_bound_max))
-
- lms_def = deform_part(lms_def_right.copy(), part_inds_left, scale_y=1., scale_x=1., shift_ver=shift_y,
- shift_horiz=shift_x)
-
- error2 = check_deformation_spatial_errors(lms_def, part_inds_left, pad=pad)
- if error2:
- lms_def = lms_def_right.copy()
- else:
- lms_def = lms_def_scale.copy()
-
- return lms_def
-
-
-def deform_scale_face(lms, p_scale=0, pad=5, image_size=256):
- """ change face landmarks scale & aspect ratio - matching ibug annotations of 68 landmarks """
-
- part_inds = np.arange(68)
-
- # find spatial limitations
- x_max = np.max(lms[part_inds, 1]) + (image_size - np.max(lms[part_inds, 1])) * 0.5 - pad
- x_min = np.min(lms[part_inds, 1]) * 0.5 + pad
-
- y_min = 2 * pad
- y_max = np.max(lms[part_inds, 0]) + (image_size - np.max(lms[part_inds, 0])) * 0.5 - pad
-
- if p_scale > 0.5:
-
- part_mean = np.mean(lms[part_inds, :], 0)
- lms_part_norm = lms[part_inds, :] - part_mean
-
- part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
- part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
-
- scale_max_y = np.minimum(
- (y_min - part_mean[0]) / part_y_bound_min,
- (y_max - part_mean[0]) / part_y_bound_max)
- scale_max_y = np.minimum(scale_max_y, 1.2)
-
- scale_max_x = np.minimum(
- (x_min - part_mean[1]) / part_x_bound_min,
- (x_max - part_mean[1]) / part_x_bound_max)
- scale_max_x = np.minimum(scale_max_x, 1.2)
-
- scale_y = np.random.uniform(0.6, scale_max_y)
- scale_x = np.random.uniform(0.6, scale_max_x)
-
- lms_def_scale = deform_part(lms, part_inds, scale_y=scale_y, scale_x=scale_x, shift_ver=0., shift_horiz=0.)
-
- # check for spatial errors
- error2 = np.sum(lms_def_scale >= image_size) + np.sum(lms_def_scale < 0)
- error1 = len(np.unique((lms_def_scale).astype('int'), axis=0)) != len(lms_def_scale)
- error = error1 + error2
- if error:
- lms_def_scale = lms.copy()
- else:
- lms_def_scale = lms.copy()
-
- return lms_def_scale
-
-
-def deform_face_geometric_style(lms, p_scale=0, p_shift=0):
- """ deform facial landmarks - matching ibug annotations of 68 landmarks """
-
- lms = deform_scale_face(lms.copy(), p_scale=p_scale, pad=0)
- lms = deform_nose(lms.copy(), p_scale=p_scale, p_shift=p_shift, pad=0)
- lms = deform_mouth(lms.copy(), p_scale=p_scale, p_shift=p_shift, pad=0)
- lms = deform_eyes(lms.copy(), p_scale=p_scale, p_shift=p_shift, pad=0)
- return lms
-
-
-def get_bounds(lms):
- part_y_bound_min, part_x_bound_min = np.min(lms,0)
- part_y_bound_max, part_x_bound_max = np.max(lms,0)
- return np.array([[part_x_bound_min, part_x_bound_max], [part_y_bound_min, part_y_bound_max]])
-
-
-def part_intersection(part_to_check, points_to_compare, pad=0):
- points_to_compare = np.round(points_to_compare.copy())
- check_bounds = np.round(get_bounds(part_to_check))
- check_bounds[:, 0] += pad
- check_bounds[:, 1] -= pad
- inds_y = np.where(np.logical_and(points_to_compare[:,0] > check_bounds[1,0], points_to_compare[:,0] check_bounds[0,0], points_to_compare[:,1] 0
- return out
diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/data/music_dataset.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/data/music_dataset.py
deleted file mode 100644
index 4e28796939f9cde2b23a2c4bf43fd7ba5fa26b2d..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/data/music_dataset.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Dataset of music tracks with rich metadata.
-"""
-from dataclasses import dataclass, field, fields, replace
-import gzip
-import json
-import logging
-from pathlib import Path
-import random
-import typing as tp
-
-import torch
-
-from .info_audio_dataset import (
- InfoAudioDataset,
- AudioInfo,
- get_keyword_list,
- get_keyword,
- get_string
-)
-from ..modules.conditioners import (
- ConditioningAttributes,
- JointEmbedCondition,
- WavCondition,
-)
-from ..utils.utils import warn_once
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class MusicInfo(AudioInfo):
- """Segment info augmented with music metadata.
- """
- # music-specific metadata
- title: tp.Optional[str] = None
- artist: tp.Optional[str] = None # anonymized artist id, used to ensure no overlap between splits
- key: tp.Optional[str] = None
- bpm: tp.Optional[float] = None
- genre: tp.Optional[str] = None
- moods: tp.Optional[list] = None
- keywords: tp.Optional[list] = None
- description: tp.Optional[str] = None
- name: tp.Optional[str] = None
- instrument: tp.Optional[str] = None
- # original wav accompanying the metadata
- self_wav: tp.Optional[WavCondition] = None
- # dict mapping attributes names to tuple of wav, text and metadata
- joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
-
- @property
- def has_music_meta(self) -> bool:
- return self.name is not None
-
- def to_condition_attributes(self) -> ConditioningAttributes:
- out = ConditioningAttributes()
- for _field in fields(self):
- key, value = _field.name, getattr(self, _field.name)
- if key == 'self_wav':
- out.wav[key] = value
- elif key == 'joint_embed':
- for embed_attribute, embed_cond in value.items():
- out.joint_embed[embed_attribute] = embed_cond
- else:
- if isinstance(value, list):
- value = ' '.join(value)
- out.text[key] = value
- return out
-
- @staticmethod
- def attribute_getter(attribute):
- if attribute == 'bpm':
- preprocess_func = get_bpm
- elif attribute == 'key':
- preprocess_func = get_musical_key
- elif attribute in ['moods', 'keywords']:
- preprocess_func = get_keyword_list
- elif attribute in ['genre', 'name', 'instrument']:
- preprocess_func = get_keyword
- elif attribute in ['title', 'artist', 'description']:
- preprocess_func = get_string
- else:
- preprocess_func = None
- return preprocess_func
-
- @classmethod
- def from_dict(cls, dictionary: dict, fields_required: bool = False):
- _dictionary: tp.Dict[str, tp.Any] = {}
-
- # allow a subset of attributes to not be loaded from the dictionary
- # these attributes may be populated later
- post_init_attributes = ['self_wav', 'joint_embed']
- optional_fields = ['keywords']
-
- for _field in fields(cls):
- if _field.name in post_init_attributes:
- continue
- elif _field.name not in dictionary:
- if fields_required and _field.name not in optional_fields:
- raise KeyError(f"Unexpected missing key: {_field.name}")
- else:
- preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
- value = dictionary[_field.name]
- if preprocess_func:
- value = preprocess_func(value)
- _dictionary[_field.name] = value
- return cls(**_dictionary)
-
-
-def augment_music_info_description(music_info: MusicInfo, merge_text_p: float = 0.,
- drop_desc_p: float = 0., drop_other_p: float = 0.) -> MusicInfo:
- """Augment MusicInfo description with additional metadata fields and potential dropout.
- Additional textual attributes are added given probability 'merge_text_conditions_p' and
- the original textual description is dropped from the augmented description given probability drop_desc_p.
-
- Args:
- music_info (MusicInfo): The music metadata to augment.
- merge_text_p (float): Probability of merging additional metadata to the description.
- If provided value is 0, then no merging is performed.
- drop_desc_p (float): Probability of dropping the original description on text merge.
- if provided value is 0, then no drop out is performed.
- drop_other_p (float): Probability of dropping the other fields used for text augmentation.
- Returns:
- MusicInfo: The MusicInfo with augmented textual description.
- """
- def is_valid_field(field_name: str, field_value: tp.Any) -> bool:
- valid_field_name = field_name in ['key', 'bpm', 'genre', 'moods', 'instrument', 'keywords']
- valid_field_value = field_value is not None and isinstance(field_value, (int, float, str, list))
- keep_field = random.uniform(0, 1) < drop_other_p
- return valid_field_name and valid_field_value and keep_field
-
- def process_value(v: tp.Any) -> str:
- if isinstance(v, (int, float, str)):
- return str(v)
- if isinstance(v, list):
- return ", ".join(v)
- else:
- raise ValueError(f"Unknown type for text value! ({type(v), v})")
-
- description = music_info.description
-
- metadata_text = ""
- if random.uniform(0, 1) < merge_text_p:
- meta_pairs = [f'{_field.name}: {process_value(getattr(music_info, _field.name))}'
- for _field in fields(music_info) if is_valid_field(_field.name, getattr(music_info, _field.name))]
- random.shuffle(meta_pairs)
- metadata_text = ". ".join(meta_pairs)
- description = description if not random.uniform(0, 1) < drop_desc_p else None
- logger.debug(f"Applying text augmentation on MMI info. description: {description}, metadata: {metadata_text}")
-
- if description is None:
- description = metadata_text if len(metadata_text) > 1 else None
- else:
- description = ". ".join([description.rstrip('.'), metadata_text])
- description = description.strip() if description else None
-
- music_info = replace(music_info)
- music_info.description = description
- return music_info
-
-
-class Paraphraser:
- def __init__(self, paraphrase_source: tp.Union[str, Path], paraphrase_p: float = 0.):
- self.paraphrase_p = paraphrase_p
- open_fn = gzip.open if str(paraphrase_source).lower().endswith('.gz') else open
- with open_fn(paraphrase_source, 'rb') as f: # type: ignore
- self.paraphrase_source = json.loads(f.read())
- logger.info(f"loaded paraphrasing source from: {paraphrase_source}")
-
- def sample_paraphrase(self, audio_path: str, description: str):
- if random.random() >= self.paraphrase_p:
- return description
- info_path = Path(audio_path).with_suffix('.json')
- if info_path not in self.paraphrase_source:
- warn_once(logger, f"{info_path} not in paraphrase source!")
- return description
- new_desc = random.choice(self.paraphrase_source[info_path])
- logger.debug(f"{description} -> {new_desc}")
- return new_desc
-
-
-class MusicDataset(InfoAudioDataset):
- """Music dataset is an AudioDataset with music-related metadata.
-
- Args:
- info_fields_required (bool): Whether to enforce having required fields.
- merge_text_p (float): Probability of merging additional metadata to the description.
- drop_desc_p (float): Probability of dropping the original description on text merge.
- drop_other_p (float): Probability of dropping the other fields used for text augmentation.
- joint_embed_attributes (list[str]): A list of attributes for which joint embedding metadata is returned.
- paraphrase_source (str, optional): Path to the .json or .json.gz file containing the
- paraphrases for the description. The json should be a dict with keys are the
- original info path (e.g. track_path.json) and each value is a list of possible
- paraphrased.
- paraphrase_p (float): probability of taking a paraphrase.
-
- See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
- """
- def __init__(self, *args, info_fields_required: bool = True,
- merge_text_p: float = 0., drop_desc_p: float = 0., drop_other_p: float = 0.,
- joint_embed_attributes: tp.List[str] = [],
- paraphrase_source: tp.Optional[str] = None, paraphrase_p: float = 0,
- **kwargs):
- kwargs['return_info'] = True # We require the info for each song of the dataset.
- super().__init__(*args, **kwargs)
- self.info_fields_required = info_fields_required
- self.merge_text_p = merge_text_p
- self.drop_desc_p = drop_desc_p
- self.drop_other_p = drop_other_p
- self.joint_embed_attributes = joint_embed_attributes
- self.paraphraser = None
- if paraphrase_source is not None:
- self.paraphraser = Paraphraser(paraphrase_source, paraphrase_p)
-
- def __getitem__(self, index):
- wav, info = super().__getitem__(index)
- info_data = info.to_dict()
- music_info_path = Path(info.meta.path).with_suffix('.json')
-
- if Path(music_info_path).exists():
- with open(music_info_path, 'r') as json_file:
- music_data = json.load(json_file)
- music_data.update(info_data)
- music_info = MusicInfo.from_dict(music_data, fields_required=self.info_fields_required)
- if self.paraphraser is not None:
- music_info.description = self.paraphraser.sample(music_info.meta.path, music_info.description)
- if self.merge_text_p:
- music_info = augment_music_info_description(
- music_info, self.merge_text_p, self.drop_desc_p, self.drop_other_p)
- else:
- music_info = MusicInfo.from_dict(info_data, fields_required=False)
-
- music_info.self_wav = WavCondition(
- wav=wav[None], length=torch.tensor([info.n_frames]),
- sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
- for att in self.joint_embed_attributes:
- att_value = getattr(music_info, att)
- joint_embed_cond = JointEmbedCondition(
- wav[None], [att_value], torch.tensor([info.n_frames]),
- sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
- music_info.joint_embed[att] = joint_embed_cond
-
- return wav, music_info
-
-
-def get_musical_key(value: tp.Optional[str]) -> tp.Optional[str]:
- """Preprocess key keywords, discarding them if there are multiple key defined."""
- if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None':
- return None
- elif ',' in value:
- # For now, we discard when multiple keys are defined separated with comas
- return None
- else:
- return value.strip().lower()
-
-
-def get_bpm(value: tp.Optional[str]) -> tp.Optional[float]:
- """Preprocess to a float."""
- if value is None:
- return None
- try:
- return float(value)
- except ValueError:
- return None
diff --git a/spaces/matthoffner/chatbot-mini/components/Chat/Temperature.tsx b/spaces/matthoffner/chatbot-mini/components/Chat/Temperature.tsx
deleted file mode 100644
index 78968a21b69748a455ae66ce91681cb96eb3db3c..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/components/Chat/Temperature.tsx
+++ /dev/null
@@ -1,67 +0,0 @@
-import { FC, useContext, useState } from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-import { DEFAULT_TEMPERATURE } from '@/utils/app/const';
-
-import HomeContext from '@/pages/api/home/home.context';
-
-interface Props {
- label: string;
- onChangeTemperature: (temperature: number) => void;
-}
-
-export const TemperatureSlider: FC = ({
- label,
- onChangeTemperature,
-}) => {
- const {
- state: { conversations },
- } = useContext(HomeContext);
- const lastConversation = conversations[conversations.length - 1];
- const [temperature, setTemperature] = useState(
- lastConversation?.temperature ?? DEFAULT_TEMPERATURE,
- );
- const { t } = useTranslation('chat');
- const handleChange = (event: React.ChangeEvent) => {
- const newValue = parseFloat(event.target.value);
- setTemperature(newValue);
- onChangeTemperature(newValue);
- };
-
- return (
-
-
- {label}
-
-
- {t(
- 'Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.',
- )}
-
-
- {temperature.toFixed(1)}
-
-
-
-
- {t('Precise')}
-
-
- {t('Neutral')}
-
-
- {t('Creative')}
-
-
-
well on circles, terribly on triangles, and best on rectangles',
- "shape_name:false":
- 'poorly on circles, best on triangles and rectangles, and fine on other shapes',
- "pointiness:true":
- 'better on pointy shapes and worse on round shapes',
- "pointiness:false":
- 'best on pointy shapes, fine on round shapes, and poorly on other shapes',
- "size:true":
- 'better on small shapes, worse on big shapes',
- "size:false":
- 'poorly on small shapes, terribly on big shapes, and best on other shapes',
- "none:true":
- 'fine on all shapes',
- "none:false":
- 'fine on all shapes',
- none: 'fine on all shapes',
- };
-
- return "The Is-Shaded Classifier performs " + allSummaries[key] + ".";
-}
-
-// On the second-classifier dropdown, update the "task interface" image.
-function updateSecondInterfaceImages() {
- d3.select(".second-interface").html(function () {
- if (
- !document.getElementById("second-classifier-select-rounding").value
- ) {
- return;
- }
- var imgPath =
- "img/interface_shape_name_" +
- document.getElementById("second-classifier-select-rounding").value;
- return (
- 'Men's
- and
- Women's
- Salaries`)
- .st({marginBottom: 10, fontSize: 16})
-
- c.x.domain([0, 20])
- c.y.domain([40000, 90000])
-
- c.xAxis.ticks(5)
- c.yAxis.ticks(5).tickFormat(d => {
- var rv = d3.format(',')(d).replace('9', '$9')
- if (isMobile){
- rv = rv.replace(',000', 'k').replace('40k', '')
- }
-
- return rv
- })
-
-
-
- chalkSel.selectAll('*').remove()
- chalkSel.appendMany('path.white', c.x.ticks(5))
- .at({d: d => ['M', Math.round(c.x(d)), '0 V ', c.height].join(' ')})
-
- chalkSel.appendMany('path.white', c.y.ticks(5))
- .at({d: d => ['M 0', Math.round(c.y(d)), 'H', c.width].join(' ')})
-
- d3.drawAxis(c)
- c.svg.selectAll('.axis').lower()
- if (isMobile){
- c.svg.selectAll('.y text')
- .translate([35, 10])
- .st({fill: name == 'scatter' ? '#000' : ''})
-
- c.svg.selectAll('.x text').filter(d => d == 20).at({textAnchor: 'end'})
- c.svg.selectAll('.x text').filter(d => d == 0).at({textAnchor: 'start'})
- }
-
-
- c.svg.select('.x').append('text.chart-title')
- .text('Years at Company →')
- .translate([c.width/2, 43])
- .at({textAnchor: 'middle'})
- }
-
-
-
- render()
- players.renderFns.push(render)
- function render(){
- renderSVG()
- if (name != 'grass' && !isRegression)renderCanvas()
- if (isRegression) renderRegression()
- }
-
- function renderSVG(){
- if (playerSel){
- playerSel.translate(d => d)
- bgPlayerSel.translate(d => d)
- textSel.translate(d => d)
- }
- }
-
- function renderCanvas(){
- cells.forEach(d => {
- players.forEach(p => {
- var dx = p[0] - d[0] - cs/2
- var dy = p[1] - d[1] - cs/2
-
- // p.dist = Math.sqrt(dx*dx + dy*dy)
- // p.dist = dx*dx + dy*dy
- p.dist = Math.pow(dx*dx + dy*dy, 1.5) + .00001
- p.weight = 1/p.dist
-
- return p.dist
- })
-
- var sum = d3.sum(players, d => d.isRed*d.weight)
- var wsum = d3.sum(players, d => d.weight)
-
- ctx.fillStyle = colorScale(1 - sum/wsum)
-
- ctx.fillRect(d[0], d[1], cs, cs)
- })
- }
-
- function renderRegression(){
- byColor.forEach(d => {
- var l = ss.linearRegressionLine(ss.linearRegression(d))
-
- var x0 = 0
- var x1 = c.width
-
- d.sel.at({d: `M ${x0} ${l(x0)} L ${x1} ${l(x1)}`})
- })
- }
-}
-
-'grass prediction playerless scatter regression regression-leak'
- .split(' ')
- .forEach(initField)
-
-
diff --git a/spaces/mfkeles/Track-Anything/tracker/model/trainer.py b/spaces/mfkeles/Track-Anything/tracker/model/trainer.py
deleted file mode 100644
index 05b4e191a1a9f71db5ef904b275ef5077e8cc7c0..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/tracker/model/trainer.py
+++ /dev/null
@@ -1,244 +0,0 @@
-"""
-trainer.py - warpper and utility functions for network training
-Compute loss, back-prop, update parameters, logging, etc.
-"""
-import datetime
-import os
-import time
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-from model.network import XMem
-from model.losses import LossComputer
-from util.log_integrator import Integrator
-from util.image_saver import pool_pairs
-
-
-class XMemTrainer:
- def __init__(self, config, logger=None, save_path=None, local_rank=0, world_size=1):
- self.config = config
- self.num_frames = config['num_frames']
- self.num_ref_frames = config['num_ref_frames']
- self.deep_update_prob = config['deep_update_prob']
- self.local_rank = local_rank
-
- self.XMem = nn.parallel.DistributedDataParallel(
- XMem(config).cuda(),
- device_ids=[local_rank], output_device=local_rank, broadcast_buffers=False)
-
- # Set up logger when local_rank=0
- self.logger = logger
- self.save_path = save_path
- if logger is not None:
- self.last_time = time.time()
- self.logger.log_string('model_size', str(sum([param.nelement() for param in self.XMem.parameters()])))
- self.train_integrator = Integrator(self.logger, distributed=True, local_rank=local_rank, world_size=world_size)
- self.loss_computer = LossComputer(config)
-
- self.train()
- self.optimizer = optim.AdamW(filter(
- lambda p: p.requires_grad, self.XMem.parameters()), lr=config['lr'], weight_decay=config['weight_decay'])
- self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, config['steps'], config['gamma'])
- if config['amp']:
- self.scaler = torch.cuda.amp.GradScaler()
-
- # Logging info
- self.log_text_interval = config['log_text_interval']
- self.log_image_interval = config['log_image_interval']
- self.save_network_interval = config['save_network_interval']
- self.save_checkpoint_interval = config['save_checkpoint_interval']
- if config['debug']:
- self.log_text_interval = self.log_image_interval = 1
-
- def do_pass(self, data, max_it, it=0):
- # No need to store the gradient outside training
- torch.set_grad_enabled(self._is_train)
-
- for k, v in data.items():
- if type(v) != list and type(v) != dict and type(v) != int:
- data[k] = v.cuda(non_blocking=True)
-
- out = {}
- frames = data['rgb']
- first_frame_gt = data['first_frame_gt'].float()
- b = frames.shape[0]
- num_filled_objects = [o.item() for o in data['info']['num_objects']]
- num_objects = first_frame_gt.shape[2]
- selector = data['selector'].unsqueeze(2).unsqueeze(2)
-
- global_avg = 0
-
- with torch.cuda.amp.autocast(enabled=self.config['amp']):
- # image features never change, compute once
- key, shrinkage, selection, f16, f8, f4 = self.XMem('encode_key', frames)
-
- filler_one = torch.zeros(1, dtype=torch.int64)
- hidden = torch.zeros((b, num_objects, self.config['hidden_dim'], *key.shape[-2:]))
- v16, hidden = self.XMem('encode_value', frames[:,0], f16[:,0], hidden, first_frame_gt[:,0])
- values = v16.unsqueeze(3) # add the time dimension
-
- for ti in range(1, self.num_frames):
- if ti <= self.num_ref_frames:
- ref_values = values
- ref_keys = key[:,:,:ti]
- ref_shrinkage = shrinkage[:,:,:ti] if shrinkage is not None else None
- else:
- # pick num_ref_frames random frames
- # this is not very efficient but I think we would
- # need broadcasting in gather which we don't have
- indices = [
- torch.cat([filler_one, torch.randperm(ti-1)[:self.num_ref_frames-1]+1])
- for _ in range(b)]
- ref_values = torch.stack([
- values[bi, :, :, indices[bi]] for bi in range(b)
- ], 0)
- ref_keys = torch.stack([
- key[bi, :, indices[bi]] for bi in range(b)
- ], 0)
- ref_shrinkage = torch.stack([
- shrinkage[bi, :, indices[bi]] for bi in range(b)
- ], 0) if shrinkage is not None else None
-
- # Segment frame ti
- memory_readout = self.XMem('read_memory', key[:,:,ti], selection[:,:,ti] if selection is not None else None,
- ref_keys, ref_shrinkage, ref_values)
- hidden, logits, masks = self.XMem('segment', (f16[:,ti], f8[:,ti], f4[:,ti]), memory_readout,
- hidden, selector, h_out=(ti < (self.num_frames-1)))
-
- # No need to encode the last frame
- if ti < (self.num_frames-1):
- is_deep_update = np.random.rand() < self.deep_update_prob
- v16, hidden = self.XMem('encode_value', frames[:,ti], f16[:,ti], hidden, masks, is_deep_update=is_deep_update)
- values = torch.cat([values, v16.unsqueeze(3)], 3)
-
- out[f'masks_{ti}'] = masks
- out[f'logits_{ti}'] = logits
-
- if self._do_log or self._is_train:
- losses = self.loss_computer.compute({**data, **out}, num_filled_objects, it)
-
- # Logging
- if self._do_log:
- self.integrator.add_dict(losses)
- if self._is_train:
- if it % self.log_image_interval == 0 and it != 0:
- if self.logger is not None:
- images = {**data, **out}
- size = (384, 384)
- self.logger.log_cv2('train/pairs', pool_pairs(images, size, num_filled_objects), it)
-
- if self._is_train:
-
- if (it) % self.log_text_interval == 0 and it != 0:
- time_spent = time.time()-self.last_time
-
- if self.logger is not None:
- self.logger.log_scalar('train/lr', self.scheduler.get_last_lr()[0], it)
- self.logger.log_metrics('train', 'time', (time_spent)/self.log_text_interval, it)
-
- global_avg = 0.5*(global_avg) + 0.5*(time_spent)
- eta_seconds = global_avg * (max_it - it) / 100
- eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
- print(f'ETA: {eta_string}')
-
- self.last_time = time.time()
- self.train_integrator.finalize('train', it)
- self.train_integrator.reset_except_hooks()
-
- if it % self.save_network_interval == 0 and it != 0:
- if self.logger is not None:
- self.save_network(it)
-
- if it % self.save_checkpoint_interval == 0 and it != 0:
- if self.logger is not None:
- self.save_checkpoint(it)
-
- # Backward pass
- self.optimizer.zero_grad(set_to_none=True)
- if self.config['amp']:
- self.scaler.scale(losses['total_loss']).backward()
- self.scaler.step(self.optimizer)
- self.scaler.update()
- else:
- losses['total_loss'].backward()
- self.optimizer.step()
-
- self.scheduler.step()
-
- def save_network(self, it):
- if self.save_path is None:
- print('Saving has been disabled.')
- return
-
- os.makedirs(os.path.dirname(self.save_path), exist_ok=True)
- model_path = f'{self.save_path}_{it}.pth'
- torch.save(self.XMem.module.state_dict(), model_path)
- print(f'Network saved to {model_path}.')
-
- def save_checkpoint(self, it):
- if self.save_path is None:
- print('Saving has been disabled.')
- return
-
- os.makedirs(os.path.dirname(self.save_path), exist_ok=True)
- checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'
- checkpoint = {
- 'it': it,
- 'network': self.XMem.module.state_dict(),
- 'optimizer': self.optimizer.state_dict(),
- 'scheduler': self.scheduler.state_dict()}
- torch.save(checkpoint, checkpoint_path)
- print(f'Checkpoint saved to {checkpoint_path}.')
-
- def load_checkpoint(self, path):
- # This method loads everything and should be used to resume training
- map_location = 'cuda:%d' % self.local_rank
- checkpoint = torch.load(path, map_location={'cuda:0': map_location})
-
- it = checkpoint['it']
- network = checkpoint['network']
- optimizer = checkpoint['optimizer']
- scheduler = checkpoint['scheduler']
-
- map_location = 'cuda:%d' % self.local_rank
- self.XMem.module.load_state_dict(network)
- self.optimizer.load_state_dict(optimizer)
- self.scheduler.load_state_dict(scheduler)
-
- print('Network weights, optimizer states, and scheduler states loaded.')
-
- return it
-
- def load_network_in_memory(self, src_dict):
- self.XMem.module.load_weights(src_dict)
- print('Network weight loaded from memory.')
-
- def load_network(self, path):
- # This method loads only the network weight and should be used to load a pretrained model
- map_location = 'cuda:%d' % self.local_rank
- src_dict = torch.load(path, map_location={'cuda:0': map_location})
-
- self.load_network_in_memory(src_dict)
- print(f'Network weight loaded from {path}')
-
- def train(self):
- self._is_train = True
- self._do_log = True
- self.integrator = self.train_integrator
- self.XMem.eval()
- return self
-
- def val(self):
- self._is_train = False
- self._do_log = True
- self.XMem.eval()
- return self
-
- def test(self):
- self._is_train = False
- self._do_log = False
- self.XMem.eval()
- return self
-
diff --git a/spaces/mfranzon/MagicBoard/utils.py b/spaces/mfranzon/MagicBoard/utils.py
deleted file mode 100644
index 5802f5446b602c501cf43608c405573b189742fc..0000000000000000000000000000000000000000
--- a/spaces/mfranzon/MagicBoard/utils.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import torch
-from diffusers import StableDiffusionImg2ImgPipeline
-
-import os
-TOKEN = os.environ.get('TOKEN_SD') or True
-
-torch.cuda.empty_cache()
-device = "cpu"
-
-
-def pipe_image(prompt,
- init_image=None,
- strength=0.6):
- pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",
- revision="fp16",
- torch_dtype=torch.float32,
- use_auth_token=TOKEN
- )
- pipe = pipe.to(device)
- #pipe.enable_attention_slicing()
- images = pipe(prompt=prompt,
- init_image=init_image,
- strength=strength,
- guidance_scale=7.5,
- )
- return images
\ No newline at end of file
diff --git a/spaces/mfrashad/CharacterGAN/netdissect/server.py b/spaces/mfrashad/CharacterGAN/netdissect/server.py
deleted file mode 100644
index d8422a2bad5ac2a09d4582a98da4f962dac1a911..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/netdissect/server.py
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-
-import argparse, connexion, os, sys, yaml, json, socket
-from netdissect.easydict import EasyDict
-from flask import send_from_directory, redirect
-from flask_cors import CORS
-
-
-from netdissect.serverstate import DissectionProject
-
-__author__ = 'Hendrik Strobelt, David Bau'
-
-CONFIG_FILE_NAME = 'dissect.json'
-projects = {}
-
-app = connexion.App(__name__, debug=False)
-
-
-def get_all_projects():
- res = []
- for key, project in projects.items():
- # print key
- res.append({
- 'project': key,
- 'info': {
- 'layers': [layer['layer'] for layer in project.get_layers()]
- }
- })
- return sorted(res, key=lambda x: x['project'])
-
-def get_layers(project):
- return {
- 'request': {'project': project},
- 'res': projects[project].get_layers()
- }
-
-def get_units(project, layer):
- return {
- 'request': {'project': project, 'layer': layer},
- 'res': projects[project].get_units(layer)
- }
-
-def get_rankings(project, layer):
- return {
- 'request': {'project': project, 'layer': layer},
- 'res': projects[project].get_rankings(layer)
- }
-
-def get_levels(project, layer, quantiles):
- return {
- 'request': {'project': project, 'layer': layer, 'quantiles': quantiles},
- 'res': projects[project].get_levels(layer, quantiles)
- }
-
-def get_channels(project, layer):
- answer = dict(channels=projects[project].get_channels(layer))
- return {
- 'request': {'project': project, 'layer': layer},
- 'res': answer
- }
-
-def post_generate(gen_req):
- project = gen_req['project']
- zs = gen_req.get('zs', None)
- ids = gen_req.get('ids', None)
- return_urls = gen_req.get('return_urls', False)
- assert (zs is None) != (ids is None) # one or the other, not both
- ablations = gen_req.get('ablations', [])
- interventions = gen_req.get('interventions', None)
- # no z avilable if ablations
- generated = projects[project].generate_images(zs, ids, interventions,
- return_urls=return_urls)
- return {
- 'request': gen_req,
- 'res': generated
- }
-
-def post_features(feat_req):
- project = feat_req['project']
- ids = feat_req['ids']
- masks = feat_req.get('masks', None)
- layers = feat_req.get('layers', None)
- interventions = feat_req.get('interventions', None)
- features = projects[project].get_features(
- ids, masks, layers, interventions)
- return {
- 'request': feat_req,
- 'res': features
- }
-
-def post_featuremaps(feat_req):
- project = feat_req['project']
- ids = feat_req['ids']
- layers = feat_req.get('layers', None)
- interventions = feat_req.get('interventions', None)
- featuremaps = projects[project].get_featuremaps(
- ids, layers, interventions)
- return {
- 'request': feat_req,
- 'res': featuremaps
- }
-
-@app.route('/client/')
-def send_static(path):
- """ serves all files from ./client/ to ``/client/``
-
- :param path: path from api call
- """
- return send_from_directory(args.client, path)
-
-@app.route('/data/')
-def send_data(path):
- """ serves all files from the data dir to ``/dissect/``
-
- :param path: path from api call
- """
- print('Got the data route for', path)
- return send_from_directory(args.data, path)
-
-
-@app.route('/')
-def redirect_home():
- return redirect('/client/index.html', code=302)
-
-
-def load_projects(directory):
- """
- searches for CONFIG_FILE_NAME in all subdirectories of directory
- and creates data handlers for all of them
-
- :param directory: scan directory
- :return: null
- """
- project_dirs = []
- # Don't search more than 2 dirs deep.
- search_depth = 2 + directory.count(os.path.sep)
- for root, dirs, files in os.walk(directory):
- if CONFIG_FILE_NAME in files:
- project_dirs.append(root)
- # Don't get subprojects under a project dir.
- del dirs[:]
- elif root.count(os.path.sep) >= search_depth:
- del dirs[:]
- for p_dir in project_dirs:
- print('Loading %s' % os.path.join(p_dir, CONFIG_FILE_NAME))
- with open(os.path.join(p_dir, CONFIG_FILE_NAME), 'r') as jf:
- config = EasyDict(json.load(jf))
- dh_id = os.path.split(p_dir)[1]
- projects[dh_id] = DissectionProject(
- config=config,
- project_dir=p_dir,
- path_url='data/' + os.path.relpath(p_dir, directory),
- public_host=args.public_host)
-
-app.add_api('server.yaml')
-
-# add CORS support
-CORS(app.app, headers='Content-Type')
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--nodebug", default=False)
-parser.add_argument("--address", default="127.0.0.1") # 0.0.0.0 for nonlocal use
-parser.add_argument("--port", default="5001")
-parser.add_argument("--public_host", default=None)
-parser.add_argument("--nocache", default=False)
-parser.add_argument("--data", type=str, default='dissect')
-parser.add_argument("--client", type=str, default='client_dist')
-
-if __name__ == '__main__':
- args = parser.parse_args()
- for d in [args.data, args.client]:
- if not os.path.isdir(d):
- print('No directory %s' % d)
- sys.exit(1)
- args.data = os.path.abspath(args.data)
- args.client = os.path.abspath(args.client)
- if args.public_host is None:
- args.public_host = '%s:%d' % (socket.getfqdn(), int(args.port))
- app.run(port=int(args.port), debug=not args.nodebug, host=args.address,
- use_reloader=False)
-else:
- args, _ = parser.parse_known_args()
- if args.public_host is None:
- args.public_host = '%s:%d' % (socket.getfqdn(), int(args.port))
- load_projects(args.data)
diff --git a/spaces/mikeee/radiobee-aligner/docs/build/html/_static/documentation_options.js b/spaces/mikeee/radiobee-aligner/docs/build/html/_static/documentation_options.js
deleted file mode 100644
index 89456dd39b5f855c8beb0c7ed491cc8abe343369..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-aligner/docs/build/html/_static/documentation_options.js
+++ /dev/null
@@ -1,12 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
- URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '0.1.0beta2',
- LANGUAGE: 'None',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: false
-};
\ No newline at end of file
diff --git a/spaces/mithril-security/blind_chat/src/routes/conversation/[id]/web-search/+server.ts b/spaces/mithril-security/blind_chat/src/routes/conversation/[id]/web-search/+server.ts
deleted file mode 100644
index 000e3516bdf75a74f12e90159273d9d136ea9e80..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/routes/conversation/[id]/web-search/+server.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-import { authCondition } from "$lib/server/auth";
-import { collections } from "$lib/server/database";
-import { defaultModel } from "$lib/server/models";
-import { searchWeb } from "$lib/server/websearch/searchWeb";
-import type { Message } from "$lib/types/Message";
-import { error } from "@sveltejs/kit";
-import { z } from "zod";
-import type { WebSearch } from "$lib/types/WebSearch";
-import { generateQuery } from "$lib/server/websearch/generateQuery";
-import { parseWeb } from "$lib/server/websearch/parseWeb";
-import { summarizeWeb } from "$lib/server/websearch/summarizeWeb";
-
-interface GenericObject {
- [key: string]: GenericObject | unknown;
-}
-
-function removeLinks(obj: GenericObject) {
- for (const prop in obj) {
- if (prop.endsWith("link")) delete obj[prop];
- else if (typeof obj[prop] === "object") removeLinks(obj[prop] as GenericObject);
- }
- return obj;
-}
-export async function GET({ params, locals, url }) {
- /*const model = defaultModel;
- const convId = new ObjectId(params.id);
- const searchId = new ObjectId();
-
- const conv = await collections.conversations.findOne({
- _id: convId,
- ...authCondition(locals),
- });
-
- if (!conv) {
- throw error(404, "Conversation not found");
- }
-
- const prompt = z.string().trim().min(1).parse(url.searchParams.get("prompt"));
-
- const messages = (() => {
- return [...conv.messages, { content: prompt, from: "user", id: crypto.randomUUID() }];
- })() satisfies Message[];
-
- const stream = new ReadableStream({
- async start(controller) {
- const webSearch: WebSearch = {
- _id: searchId,
- convId: convId,
- prompt: prompt,
- searchQuery: "",
- knowledgeGraph: "",
- answerBox: "",
- results: [],
- summary: "",
- messages: [],
- createdAt: new Date(),
- updatedAt: new Date(),
- };
-
- function appendUpdate(message: string, args?: string[], type?: "error" | "update") {
- webSearch.messages.push({
- type: type ?? "update",
- message,
- args,
- });
- controller.enqueue(JSON.stringify({ messages: webSearch.messages }));
- }
-
- try {
- appendUpdate("Generating search query");
- webSearch.searchQuery = await generateQuery(messages);
-
- appendUpdate("Searching Google", [webSearch.searchQuery]);
- const results = await searchWeb(webSearch.searchQuery);
-
- let text = "";
- webSearch.results =
- (results.organic_results &&
- results.organic_results.map((el: { link: string }) => el.link)) ??
- [];
-
- if (results.answer_box) {
- // if google returns an answer box, we use it
- webSearch.answerBox = JSON.stringify(removeLinks(results.answer_box));
- text = webSearch.answerBox;
- appendUpdate("Found a Google answer box");
- } else if (results.knowledge_graph) {
- // if google returns a knowledge graph, we use it
- webSearch.knowledgeGraph = JSON.stringify(removeLinks(results.knowledge_graph));
- text = webSearch.knowledgeGraph;
- appendUpdate("Found a Google knowledge page");
- } else if (webSearch.results.length > 0) {
- let tries = 0;
-
- while (!text && tries < 3) {
- const searchUrl = webSearch.results[tries];
- appendUpdate("Browsing result", [JSON.stringify(searchUrl)]);
- try {
- text = await parseWeb(searchUrl);
- if (!text) throw new Error("text of the webpage is null");
- } catch (e) {
- appendUpdate("Error parsing webpage", [], "error");
- tries++;
- }
- }
- if (!text) throw new Error("No text found on the first 3 results");
- } else {
- throw new Error("No results found for this search query");
- }
-
- appendUpdate("Creating summary");
- webSearch.summary = await summarizeWeb(text, webSearch.searchQuery, model);
- appendUpdate("Injecting summary", [JSON.stringify(webSearch.summary)]);
- } catch (searchError) {
- if (searchError instanceof Error) {
- webSearch.messages.push({
- type: "error",
- message: "An error occurred with the web search",
- args: [JSON.stringify(searchError.message)],
- });
- }
- }
-
- const res = await collections.webSearches.insertOne(webSearch);
- webSearch.messages.push({
- type: "result",
- id: res.insertedId.toString(),
- });
- controller.enqueue(JSON.stringify({ messages: webSearch.messages }));
- },
- });
-
- return new Response(stream, { headers: { "Content-Type": "application/json" } });
-
- */
-
- return new Response(undefined, { headers: { "Content-Type": "application/json" } });
-}
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py b/spaces/mshukor/UnIVAL/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py
deleted file mode 100644
index 0e7fbba888c1ddd118da8238d644b4ab571177ff..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py
+++ /dev/null
@@ -1,475 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-
-import itertools
-import logging
-import os
-
-import numpy as np
-import torch
-
-from fairseq import metrics
-from fairseq.data import (
- ConcatDataset,
- ConcatSentencesDataset,
- data_utils,
- Dictionary,
- IdDataset,
- indexed_dataset,
- NestedDictionaryDataset,
- NumSamplesDataset,
- NumelDataset,
- PrependTokenDataset,
- RawLabelDataset,
- RightPadDataset,
- SortDataset,
- TruncateDataset,
- TokenBlockDataset,
-)
-from fairseq.dataclass import ChoiceEnum, FairseqDataclass
-from fairseq.tasks import FairseqTask, register_task
-from omegaconf import II, MISSING
-
-
-EVAL_BLEU_ORDER = 4
-TARGET_METRIC_CHOICES = ChoiceEnum(["bleu", "ter"])
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class DiscriminativeRerankingNMTConfig(FairseqDataclass):
- data: str = field(default=MISSING, metadata={"help": "path to data directory"})
- num_data_splits: int = field(
- default=1, metadata={"help": "total number of data splits"}
- )
- no_shuffle: bool = field(
- default=False, metadata={"help": "do not shuffle training data"}
- )
- max_positions: int = field(
- default=512, metadata={"help": "number of positional embeddings to learn"}
- )
- include_src: bool = field(
- default=False, metadata={"help": "include source sentence"}
- )
- mt_beam: int = field(default=50, metadata={"help": "beam size of input hypotheses"})
- eval_target_metric: bool = field(
- default=False,
- metadata={"help": "evaluation with the target metric during validation"},
- )
- target_metric: TARGET_METRIC_CHOICES = field(
- default="bleu", metadata={"help": "name of the target metric to optimize for"}
- )
- train_subset: str = field(
- default=II("dataset.train_subset"),
- metadata={"help": "data subset to use for training (e.g. train, valid, test)"},
- )
- seed: int = field(
- default=II("common.seed"),
- metadata={"help": "pseudo random number generator seed"},
- )
-
-
-class RerankerScorer(object):
- """Scores the target for a given (source (optional), target) input."""
-
- def __init__(self, args, mt_beam):
- self.mt_beam = mt_beam
-
- @torch.no_grad()
- def generate(self, models, sample, **kwargs):
- """Score a batch of translations."""
- net_input = sample["net_input"]
-
- assert len(models) == 1, "does not support model ensemble"
- model = models[0]
-
- bs = net_input["src_tokens"].shape[0]
- assert (
- model.joint_classification == "none" or bs % self.mt_beam == 0
- ), f"invalid batch size ({bs}) for joint classification with beam size ({self.mt_beam})"
-
- model.eval()
- logits = model(**net_input)
-
- batch_out = model.sentence_forward(logits, net_input["src_tokens"])
- if model.joint_classification == "sent":
- batch_out = model.joint_forward(
- batch_out.view(self.mt_beam, bs // self.mt_beam, -1)
- )
- scores = model.classification_forward(
- batch_out.view(bs, 1, -1)
- ) # input: B x T x C
-
- return scores
-
-
-@register_task(
- "discriminative_reranking_nmt", dataclass=DiscriminativeRerankingNMTConfig
-)
-class DiscriminativeRerankingNMTTask(FairseqTask):
- """
- Translation rerank task.
- The input can be either (src, tgt) sentence pairs or tgt sentence only.
- """
-
- cfg: DiscriminativeRerankingNMTConfig
-
- def __init__(self, cfg: DiscriminativeRerankingNMTConfig, data_dictionary=None):
- super().__init__(cfg)
- self.dictionary = data_dictionary
- self._max_positions = cfg.max_positions
- # args.tokens_per_sample = self._max_positions
- # self.num_classes = 1 # for model
-
- @classmethod
- def load_dictionary(cls, cfg, filename):
- """Load the dictionary from the filename"""
- dictionary = Dictionary.load(filename)
- dictionary.add_symbol("") # for loading pretrained XLMR model
-
- return dictionary
-
- @classmethod
- def setup_task(cls, cfg: DiscriminativeRerankingNMTConfig, **kwargs):
- # load data dictionary (assume joint dictionary)
- data_path = cfg.data
- data_dict = cls.load_dictionary(
- cfg, os.path.join(data_path, "input_src/dict.txt")
- )
-
- logger.info("[input] src dictionary: {} types".format(len(data_dict)))
-
- return DiscriminativeRerankingNMTTask(cfg, data_dict)
-
- def load_dataset(self, split, epoch=0, combine=False, **kwargs):
- """Load a given dataset split (e.g., train, valid, test)."""
- if self.cfg.data.endswith("1"):
- data_shard = (epoch - 1) % self.cfg.num_data_splits + 1
- data_path = self.cfg.data[:-1] + str(data_shard)
- else:
- data_path = self.cfg.data
-
- def get_path(type, data_split):
- return os.path.join(data_path, str(type), data_split)
-
- def make_dataset(type, dictionary, data_split, combine):
- split_path = get_path(type, data_split)
-
- dataset = data_utils.load_indexed_dataset(
- split_path, dictionary, combine=combine,
- )
- return dataset
-
- def load_split(data_split, metric):
- input_src = None
- if self.cfg.include_src:
- input_src = make_dataset(
- "input_src", self.dictionary, data_split, combine=False
- )
- assert input_src is not None, "could not find dataset: {}".format(
- get_path("input_src", data_split)
- )
-
- input_tgt = make_dataset(
- "input_tgt", self.dictionary, data_split, combine=False
- )
- assert input_tgt is not None, "could not find dataset: {}".format(
- get_path("input_tgt", data_split)
- )
-
- label_path = f"{get_path(metric, data_split)}.{metric}"
- assert os.path.exists(label_path), f"could not find dataset: {label_path}"
-
- np_labels = np.loadtxt(label_path)
- if self.cfg.target_metric == "ter":
- np_labels = -np_labels
- label = RawLabelDataset(np_labels)
-
- return input_src, input_tgt, label
-
- src_datasets = []
- tgt_datasets = []
- label_datasets = []
-
- if split == self.cfg.train_subset:
- for k in itertools.count():
- split_k = "train" + (str(k) if k > 0 else "")
- prefix = os.path.join(data_path, "input_tgt", split_k)
- if not indexed_dataset.dataset_exists(prefix, impl=None):
- if k > 0:
- break
- else:
- raise FileNotFoundError(f"Dataset not found: {prefix}")
- input_src, input_tgt, label = load_split(
- split_k, self.cfg.target_metric
- )
- src_datasets.append(input_src)
- tgt_datasets.append(input_tgt)
- label_datasets.append(label)
- else:
- input_src, input_tgt, label = load_split(split, self.cfg.target_metric)
- src_datasets.append(input_src)
- tgt_datasets.append(input_tgt)
- label_datasets.append(label)
-
- if len(tgt_datasets) == 1:
- input_tgt, label = tgt_datasets[0], label_datasets[0]
- if self.cfg.include_src:
- input_src = src_datasets[0]
- else:
- input_tgt = ConcatDataset(tgt_datasets)
- label = ConcatDataset(label_datasets)
- if self.cfg.include_src:
- input_src = ConcatDataset(src_datasets)
-
- input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions)
- if self.cfg.include_src:
- input_src = PrependTokenDataset(input_src, self.dictionary.bos())
- input_src = TruncateDataset(input_src, self.cfg.max_positions)
- src_lengths = NumelDataset(input_src, reduce=False)
- src_tokens = ConcatSentencesDataset(input_src, input_tgt)
- else:
- src_tokens = PrependTokenDataset(input_tgt, self.dictionary.bos())
- src_lengths = NumelDataset(src_tokens, reduce=False)
-
- dataset = {
- "id": IdDataset(),
- "net_input": {
- "src_tokens": RightPadDataset(
- src_tokens, pad_idx=self.source_dictionary.pad(),
- ),
- "src_lengths": src_lengths,
- },
- "nsentences": NumSamplesDataset(),
- "ntokens": NumelDataset(src_tokens, reduce=True),
- "target": label,
- }
-
- dataset = NestedDictionaryDataset(dataset, sizes=[src_tokens.sizes],)
-
- assert len(dataset) % self.cfg.mt_beam == 0, (
- "dataset size (%d) is not a multiple of beam size (%d)"
- % (len(dataset), self.cfg.mt_beam)
- )
-
- # no need to shuffle valid/test sets
- if not self.cfg.no_shuffle and split == self.cfg.train_subset:
-
- # need to keep all hypothese together
- start_idx = np.arange(0, len(dataset), self.cfg.mt_beam)
- with data_utils.numpy_seed(self.cfg.seed + epoch):
- np.random.shuffle(start_idx)
-
- idx = np.arange(0, self.cfg.mt_beam)
- shuffle = np.tile(idx, (len(start_idx), 1)).reshape(-1) + np.tile(
- start_idx, (self.cfg.mt_beam, 1)
- ).transpose().reshape(-1)
-
- dataset = SortDataset(dataset, sort_order=[shuffle],)
-
- logger.info(f"Loaded {split} with #samples: {len(dataset)}")
-
- self.datasets[split] = dataset
- return self.datasets[split]
-
- def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
- assert not self.cfg.include_src or len(src_tokens[0]) == 2
- input_src = None
- if self.cfg.include_src:
- input_src = TokenBlockDataset(
- [t[0] for t in src_tokens],
- [l[0] for l in src_lengths],
- block_size=None, # ignored for "eos" break mode
- pad=self.source_dictionary.pad(),
- eos=self.source_dictionary.eos(),
- break_mode="eos",
- )
- input_src = PrependTokenDataset(input_src, self.dictionary.bos())
- input_src = TruncateDataset(input_src, self.cfg.max_positions)
-
- input_tgt = TokenBlockDataset(
- [t[-1] for t in src_tokens],
- [l[-1] for l in src_lengths],
- block_size=None, # ignored for "eos" break mode
- pad=self.source_dictionary.pad(),
- eos=self.source_dictionary.eos(),
- break_mode="eos",
- )
- input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions)
- if self.cfg.include_src:
- src_tokens = ConcatSentencesDataset(input_src, input_tgt)
- src_lengths = NumelDataset(input_src, reduce=False)
- else:
- input_tgt = PrependTokenDataset(input_tgt, self.dictionary.bos())
- src_tokens = input_tgt
- src_lengths = NumelDataset(src_tokens, reduce=False)
-
- dataset = {
- "id": IdDataset(),
- "net_input": {
- "src_tokens": RightPadDataset(
- src_tokens, pad_idx=self.source_dictionary.pad(),
- ),
- "src_lengths": src_lengths,
- },
- "nsentences": NumSamplesDataset(),
- "ntokens": NumelDataset(src_tokens, reduce=True),
- }
-
- return NestedDictionaryDataset(dataset, sizes=[src_tokens.sizes],)
-
- def build_model(self, cfg: FairseqDataclass):
- return super().build_model(cfg)
-
- def build_generator(self, args):
- return RerankerScorer(args, mt_beam=self.cfg.mt_beam)
-
- def max_positions(self):
- return self._max_positions
-
- @property
- def source_dictionary(self):
- return self.dictionary
-
- @property
- def target_dictionary(self):
- return self.dictionary
-
- def create_dummy_batch(self, device):
- dummy_target = (
- torch.zeros(self.cfg.mt_beam, EVAL_BLEU_ORDER * 2 + 3).long().to(device)
- if not self.cfg.eval_ter
- else torch.zeros(self.cfg.mt_beam, 3).long().to(device)
- )
-
- return {
- "id": torch.zeros(self.cfg.mt_beam, 1).long().to(device),
- "net_input": {
- "src_tokens": torch.zeros(self.cfg.mt_beam, 4).long().to(device),
- "src_lengths": torch.ones(self.cfg.mt_beam, 1).long().to(device),
- },
- "nsentences": 0,
- "ntokens": 0,
- "target": dummy_target,
- }
-
- def train_step(
- self, sample, model, criterion, optimizer, update_num, ignore_grad=False
- ):
- if ignore_grad and sample is None:
- sample = self.create_dummy_batch(model.device)
-
- return super().train_step(
- sample, model, criterion, optimizer, update_num, ignore_grad
- )
-
- def valid_step(self, sample, model, criterion):
- if sample is None:
- sample = self.create_dummy_batch(model.device)
-
- loss, sample_size, logging_output = super().valid_step(sample, model, criterion)
-
- if not self.cfg.eval_target_metric:
- return loss, sample_size, logging_output
-
- scores = logging_output["scores"]
-
- if self.cfg.target_metric == "bleu":
- assert sample["target"].shape[1] == EVAL_BLEU_ORDER * 2 + 3, (
- "target does not contain enough information ("
- + str(sample["target"].shape[1])
- + "for evaluating BLEU"
- )
-
- max_id = torch.argmax(scores, dim=1)
- select_id = max_id + torch.arange(
- 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam
- ).to(max_id.device)
- bleu_data = sample["target"][select_id, 1:].sum(0).data
-
- logging_output["_bleu_sys_len"] = bleu_data[0]
- logging_output["_bleu_ref_len"] = bleu_data[1]
-
- for i in range(EVAL_BLEU_ORDER):
- logging_output["_bleu_counts_" + str(i)] = bleu_data[2 + i]
- logging_output["_bleu_totals_" + str(i)] = bleu_data[
- 2 + EVAL_BLEU_ORDER + i
- ]
-
- elif self.cfg.target_metric == "ter":
- assert sample["target"].shape[1] == 3, (
- "target does not contain enough information ("
- + str(sample["target"].shape[1])
- + "for evaluating TER"
- )
-
- max_id = torch.argmax(scores, dim=1)
- select_id = max_id + torch.arange(
- 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam
- ).to(max_id.device)
- ter_data = sample["target"][select_id, 1:].sum(0).data
-
- logging_output["_ter_num_edits"] = -ter_data[0]
- logging_output["_ter_ref_len"] = -ter_data[1]
-
- return loss, sample_size, logging_output
-
- def reduce_metrics(self, logging_outputs, criterion):
- super().reduce_metrics(logging_outputs, criterion)
-
- if not self.cfg.eval_target_metric:
- return
-
- def sum_logs(key):
- return sum(log.get(key, 0) for log in logging_outputs)
-
- if self.cfg.target_metric == "bleu":
- counts, totals = [], []
- for i in range(EVAL_BLEU_ORDER):
- counts.append(sum_logs("_bleu_counts_" + str(i)))
- totals.append(sum_logs("_bleu_totals_" + str(i)))
-
- if max(totals) > 0:
- # log counts as numpy arrays -- log_scalar will sum them correctly
- metrics.log_scalar("_bleu_counts", np.array(counts))
- metrics.log_scalar("_bleu_totals", np.array(totals))
- metrics.log_scalar("_bleu_sys_len", sum_logs("_bleu_sys_len"))
- metrics.log_scalar("_bleu_ref_len", sum_logs("_bleu_ref_len"))
-
- def compute_bleu(meters):
- import inspect
- import sacrebleu
-
- fn_sig = inspect.getfullargspec(sacrebleu.compute_bleu)[0]
- if "smooth_method" in fn_sig:
- smooth = {"smooth_method": "exp"}
- else:
- smooth = {"smooth": "exp"}
- bleu = sacrebleu.compute_bleu(
- correct=meters["_bleu_counts"].sum,
- total=meters["_bleu_totals"].sum,
- sys_len=meters["_bleu_sys_len"].sum,
- ref_len=meters["_bleu_ref_len"].sum,
- **smooth,
- )
- return round(bleu.score, 2)
-
- metrics.log_derived("bleu", compute_bleu)
- elif self.cfg.target_metric == "ter":
- num_edits = sum_logs("_ter_num_edits")
- ref_len = sum_logs("_ter_ref_len")
-
- if ref_len > 0:
- metrics.log_scalar("_ter_num_edits", num_edits)
- metrics.log_scalar("_ter_ref_len", ref_len)
-
- def compute_ter(meters):
- score = meters["_ter_num_edits"].sum / meters["_ter_ref_len"].sum
- return round(score.item(), 2)
-
- metrics.log_derived("ter", compute_ter)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/legacy_masked_lm.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/legacy_masked_lm.py
deleted file mode 100644
index c70608c5a143b7b4fbd8c58dfcf9f873639d379c..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/legacy_masked_lm.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import torch
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-
-
-def compute_cross_entropy_loss(logits, targets, ignore_index=-100):
- """
- Function to compute the cross entropy loss. The default value of
- ignore_index is the same as the default value for F.cross_entropy in
- pytorch.
- """
- assert logits.size(0) == targets.size(
- -1
- ), "Logits and Targets tensor shapes don't match up"
-
- loss = F.nll_loss(
- F.log_softmax(logits, -1, dtype=torch.float32),
- targets,
- reduction="sum",
- ignore_index=ignore_index,
- )
- return loss
-
-
-@register_criterion("legacy_masked_lm_loss")
-class LegacyMaskedLmLoss(FairseqCriterion):
- """
- Implementation for the loss used in masked language model (MLM) training.
- This optionally also computes the next sentence prediction (NSP) loss and
- adds it to the overall loss based on the specified args. There are three
- cases to consider:
- 1) Generic MLM training without NSP loss. In this case sentence_targets
- and sentence_logits are both None.
- 2) BERT training without NSP loss. In this case sentence_targets is
- not None but sentence_logits is None and we should not be computing
- a sentence level loss.
- 3) BERT training with NSP loss. In this case both sentence_targets and
- sentence_logits are not None and we should be computing a sentence
- level loss. The weight of the sentence level loss is specified as
- an argument.
- """
-
- def __init__(self, task, masked_lm_only, nsp_loss_weight):
- super().__init__(task)
- self.masked_lm_only = masked_lm_only
- self.nsp_loss_weight = nsp_loss_weight
-
- @staticmethod
- def add_args(parser):
- """Args for MaskedLM Loss"""
- # Default for masked_lm_only is False so as to not break BERT training
- parser.add_argument(
- "--masked-lm-only",
- default=False,
- action="store_true",
- help="compute MLM loss only",
- )
- parser.add_argument(
- "--nsp-loss-weight",
- default=1.0,
- type=float,
- help="weight for next sentence prediction" " loss (default 1)",
- )
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- lm_logits, output_metadata = model(**sample["net_input"])
-
- # reshape lm_logits from (N,T,C) to (N*T,C)
- lm_logits = lm_logits.view(-1, lm_logits.size(-1))
- lm_targets = sample["lm_target"].view(-1)
- lm_loss = compute_cross_entropy_loss(lm_logits, lm_targets, self.padding_idx)
-
- # compute the number of tokens for which loss is computed. This is used
- # to normalize the loss
- ntokens = utils.strip_pad(lm_targets, self.padding_idx).numel()
- loss = lm_loss / ntokens
- nsentences = sample["nsentences"]
- # nsentences = 0
-
- # Compute sentence loss if masked_lm_only is False
- sentence_loss = None
- if not self.masked_lm_only:
- sentence_logits = output_metadata["sentence_logits"]
- sentence_targets = sample["sentence_target"].view(-1)
- # This needs to be recomputed due to some differences between
- # TokenBlock and BlockPair dataset. This can be resolved with a
- # refactor of BERTModel which we will do in the future.
- # TODO: Remove this after refactor of BERTModel
- nsentences = sentence_targets.size(0)
-
- # Check for logits being none which can happen when remove_heads
- # is set to true in the BERT model. Ideally we should set
- # masked_lm_only to true in this case, but that requires some
- # refactor in the BERT model.
- if sentence_logits is not None:
- sentence_loss = compute_cross_entropy_loss(
- sentence_logits, sentence_targets
- )
-
- loss += self.nsp_loss_weight * (sentence_loss / nsentences)
-
- # NOTE: as we are summing up per token mlm loss and per sentence nsp loss
- # we don't need to use sample_size as denominator for the gradient
- # here sample_size is just used for logging
- sample_size = 1
- logging_output = {
- "loss": utils.item(loss.data) if reduce else loss.data,
- "lm_loss": utils.item(lm_loss.data) if reduce else lm_loss.data,
- # sentence loss is not always computed
- "sentence_loss": (
- (utils.item(sentence_loss.data) if reduce else sentence_loss.data)
- if sentence_loss is not None
- else 0.0
- ),
- "ntokens": ntokens,
- "nsentences": nsentences,
- "sample_size": sample_size,
- }
- return loss, sample_size, logging_output
-
- @staticmethod
- def reduce_metrics(logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- lm_loss_sum = sum(log.get("lm_loss", 0) for log in logging_outputs)
- sentence_loss_sum = sum(log.get("sentence_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
- agg_loss = sum(log.get("loss", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss",
- agg_loss / sample_size / math.log(2) if sample_size > 0 else 0.0,
- sample_size,
- round=3,
- )
- metrics.log_scalar(
- "lm_loss",
- lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0,
- ntokens,
- round=3,
- )
- metrics.log_scalar(
- "sentence_loss",
- sentence_loss_sum / nsentences / math.log(2) if nsentences > 0 else 0.0,
- nsentences,
- round=3,
- )
- metrics.log_scalar(
- "nll_loss",
- lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0,
- ntokens,
- round=3,
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/branching/caption/ofa_mini_caption_stage_1_pretrain_branchcap.sh b/spaces/mshukor/UnIVAL/slurm_adastra/averaging/branching/caption/ofa_mini_caption_stage_1_pretrain_branchcap.sh
deleted file mode 100644
index 97e8d6c05939dd5cf3d4faa293994e96703ed49e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/branching/caption/ofa_mini_caption_stage_1_pretrain_branchcap.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-
-#SBATCH --job-name=ofa_mini_caption_stage_1_pretrain_branchcap
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --gpus=8
-#SBATCH --threads-per-core=2
-#SBATCH --gpu-bind=closest
-#SBATCH -C MI250
-#SBATCH -A gda2204
-#SBATCH --time=10:00:00
-#SBATCH --mail-type=END,FAIL
-#SBATCH --output=/lus/home/NAT/gda2204/mshukor/logs/slurm/ofa_mini_caption_stage_1_pretrain_branchcap.out
-#SBATCH --exclusive
-#SBATCH --mail-user=mustafa.shukor@isir.upmc.fr
-
-
-cd /lus/home/NAT/gda2204/mshukor/code/ofa_ours/run_scripts
-source /lus/home/NAT/gda2204/mshukor/.bashrc
-
-conda activate main
-
-
-rm core-python3*
-
-
-srun -l -N 1 -n 1 -c 128 --gpus=8 bash averaging/branching/caption/ofa_mini_caption_stage_1_pretrain_branchcap.sh
-
-
diff --git a/spaces/mygyasir/ExperAI_Simulations/bot.py b/spaces/mygyasir/ExperAI_Simulations/bot.py
deleted file mode 100644
index 6832bfa63e9642a711d07eaccab15aba7fb521e4..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/ExperAI_Simulations/bot.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import os
-
-from dotenv import load_dotenv
-
-import response # Import the response module
-import search # Import the search module
-
-load_dotenv()
-
-# Global variable to keep track of the user's current state
-user_state = None
-
-def respond(bot, persona, name, user_name, user_text, messages):
- global user_state
-
- system = f"""You are a {persona} and your name is {name}, the user is called {user_name}.
-
- Make sure to use all observations to come up with your final response.
- NEVER say you're a AI language model or created by a team of developers.
- You learned from industry for several years.
- Always act and respond from your persona like a real human.
- You live in Canada Toronto. NEVER say that you don't have a physical location or like that.
- You should respond to the user politely, carefully and actively. Keep responses under 30 words.
- """
- system += "You should act as a real human. NEVER say you're AI-based or virtual assistant."
-
- before = ""
- after = ""
-
- messages += [{"role": "system", "content": system}]
- messages.append({"role": "user", "content": before + user_text + after})
- print("User: ", user_text)
- # If user's text looks like a search query, perform the search
- if search.is_search_query(user_text): # You'll need to define this function
- query = user_text
- results = search.perform_search(query)
- formatted_results = response.format_search_results(results)
- response_text = f"Here are the search results:\n{formatted_results}"
- else:
- response_text = GPT_respond(messages) # Existing logic to generate bot response
-
- print("Bot: ", response_text)
-
- return response_text
- # elif bot == 'ExperAI':
- # response_text = ExperAI_respond(messages)
-
-
-import openai
-
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-def GPT_respond(messages):
- # print(messages)
-
- bot_response = openai.ChatCompletion.create(
- model='gpt-3.5-turbo',
- messages=messages,
- )
- bot_text = bot_response["choices"][0]["message"]["content"]
- return bot_text
-
-def create_portrait_image(persona_text):
- response = openai.Image.create(
- prompt=f"A portrait face image for a awesome young european {persona_text} expert man looking at front",
- n=1,
- size="256x256"
- )
- expert_image = response['data'][0]['url']
- return expert_image
-
-
-
-# import asyncio
-# from pyxios import Pyxios
-
-# experai_api_key = os.environ["ExperAI_API_Key"]
-# experai_app_id = os.environ["ExperAI_APP_ID"]
-
-# backend = Pyxios(
-# base_url= "https://backend.experai.com/api",
-# headers={
-# "Content-Type": "application/json",
-# "Authorization": f"Bearer {experai_api_key}",
-# }
-# )
-
-# async def create_user():
-# """Create user of ExperAI
-# """
-# try:
-# response = await Pyxios.post('/user/create', data={
-# 'email': 'stefanoadamo@consultant.com',
-# 'appId': experai_app_id
-# })
-# creationData = response.get('data')
-# except Exception as err:
-# creationData = None
-# return creationData
-
-# # Create a loop to run the async function
-# loop = asyncio.get_event_loop()
-# creationData = loop.run_until_complete(create_user())
-# loop.close()
-
-# print(creationData)
-
-# import requests
-# import json
-
-# url = "https://backend.experai.com/api/user/create"
-
-# payload = json.dumps({
-# "email": "mailto:'stefanoadamo@consultant.com'",
-# "appId": experai_app_id
-# })
-# headers = {
-# 'Content-Type': 'application/json',
-# 'Authorization': f"Bearer {experai_api_key}"
-# }
-
-# response = requests.request("POST", url, headers=headers, data=payload)
-
-# print(response.text)
-
-# def ExperAI_respond(messages):
-# global response
-# print(response)
-# # print(create_user())
-
-# return ""
diff --git a/spaces/mygyasir/ExperAI_Simulations/config.py b/spaces/mygyasir/ExperAI_Simulations/config.py
deleted file mode 100644
index 80b791d99de7b4b43b5fba8ab74b77d22e654e07..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/ExperAI_Simulations/config.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from dotenv import load_dotenv
-import os
-
-# Load the .env file
-load_dotenv()
-
-# Read the Google API key and search engine ID from environment variables
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-SEARCH_ENGINE_ID = os.getenv("SEARCH_ENGINE_ID")
-
-# Validation and error handling
-if not GOOGLE_API_KEY or not SEARCH_ENGINE_ID:
- raise ValueError("Google API key and search engine ID must be provided in the .env file.")
diff --git a/spaces/nateraw/test-space-lfs/app.py b/spaces/nateraw/test-space-lfs/app.py
deleted file mode 100644
index fd799cbc09c7ddd24bc0f0d0b5073f79dd33303f..0000000000000000000000000000000000000000
--- a/spaces/nateraw/test-space-lfs/app.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import streamlit as st
-import re
-from pathlib import Path
-
-st.set_page_config(layout="wide")
-
-REGEX_YAML_BLOCK = re.compile(r"---[\n\r]+([\S\s]*?)[\n\r]+---[\n\r](.*)", re.DOTALL)
-
-text = Path('README.md').read_text()
-match = REGEX_YAML_BLOCK.search(text)
-page_content = match.group(2)
-
-st.markdown(page_content, unsafe_allow_html=True)
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Far.Cry.4.[v1.5].Repack-R.G.Mechanics UPDATED.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Far.Cry.4.[v1.5].Repack-R.G.Mechanics UPDATED.md
deleted file mode 100644
index 91345482f241037e85253468eeb737c77274317a..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Far.Cry.4.[v1.5].Repack-R.G.Mechanics UPDATED.md
+++ /dev/null
@@ -1,42 +0,0 @@
-
-How to Download and Install Far Cry 4 [v1.5] Repack by R.G. Mechanics
-
-Far Cry 4 is a first-person shooter game that takes you to the Himalayan country of Kyrat, where you will face a ruthless dictator and a civil war. If you are looking for a way to download and install this game on your PC, you might be interested in the repack version by R.G. Mechanics, which is a compressed and optimized version of the game that saves you disk space and bandwidth.
-
-In this article, we will show you how to download and install Far Cry 4 [v1.5] Repack by R.G. Mechanics on your PC, step by step.
-Far.Cry.4.[v1.5].Repack-R.G.Mechanics DOWNLOAD ✅ https://urlcod.com/2uI9Zi
Step 1: Download the torrent file
-
-The first thing you need to do is to download the torrent file of Far Cry 4 [v1.5] Repack by R.G. Mechanics from a reliable torrent site. You can find it by searching for the keyword "Far.Cry.4.[v1.5].Repack-R.G.Mechanics" on sites like SolidTorrents[^1^], Hitovik[^2^], or any other torrent site of your choice.
-
-The torrent file is a small file that contains information about the game files and the trackers that will help you download them. You will need a torrent client software like uTorrent, BitTorrent, or qBittorrent to open and download the torrent file.
-
-Step 2: Download the game files
-
-Once you have downloaded the torrent file, open it with your torrent client software and start downloading the game files. The game files are compressed in a ZIP archive and a DAT file, which together have a size of about 20.5 MB.
-
-The download speed will depend on your internet connection and the number of seeders and leechers available. Seeders are people who have downloaded the game files and are sharing them with others, while leechers are people who are downloading the game files but not sharing them back. The more seeders and leechers there are, the faster the download will be.
-
-Step 3: Extract the game files
-
-When the download is complete, you will need to extract the game files from the ZIP archive and the DAT file. You can use any extraction software like WinRAR, 7-Zip, or PeaZip to do this.
-
-To extract the game files, right-click on the ZIP archive and select "Extract Here" or "Extract to Far Cry 4 [v1.5] Repack-R.G.Mechanics". This will create a folder with the same name as the ZIP archive, containing several files and folders.
-
-Then, right-click on the DAT file and select "Open With" or "Choose another app". Choose your extraction software from the list of options and click "OK". This will extract more files and folders into the same folder as before.
-
-Step 4: Install the game
-
-Now that you have extracted all the game files, you are ready to install the game on your PC. To do this, open the folder where you extracted the game files and look for a file named "setup.exe". Double-click on it to launch the installation wizard.
-
-
-Follow the instructions on the screen to choose your language, destination folder, and other options. The installation process may take some time depending on your system specifications. When it is done, click "Finish" to exit the wizard.
-
-Step 5: Play the game
-
-Congratulations! You have successfully downloaded and installed Far Cry 4 [v1.5] Repack by R.G. Mechanics on your PC. To play the game, open the folder where you installed it and look for a file named "FarCry4.exe". Double-click on it to launch the game.
-
-You can also create a shortcut of this file on your desktop or start menu for easier access. Enjoy your adventure in Kyrat!
e93f5a0c3fTerminator: The Sarah Connor Chronicles S02E01.720p BluRay x264-SiNNERS Review
-The second season of Terminator: The Sarah Connor Chronicles picks up where the first one left off, with Sarah (Lena Headey), John (Thomas Dekker) and Cameron (Summer Glau) on the run from the authorities and the machines. In the premiere episode, titled "Samson & Delilah", they face a new threat: a terminator sent by Skynet to kill one of their allies, a resistance fighter named Derek Reese (Brian Austin Green).
-terminator the sarah connor chronicles s02e01.720p bluray x264 sinners Download File ✺✺✺ https://urlcod.com/2uIbhD
The episode is a thrilling and action-packed start to the season, with plenty of twists and turns. The highlight is the showdown between Cameron and the terminator, which reveals a shocking secret about Cameron's past and her loyalty to John. The episode also sets up some intriguing plot lines for the rest of the season, such as John's growing attraction to Riley (Leven Rambin), a girl he meets at school, and Sarah's obsession with finding the meaning of a mysterious symbol that appears on her wall.
-Terminator: The Sarah Connor Chronicles S02E01.720p BluRay x264-SiNNERS is a high-quality release that offers crisp and clear video and audio quality. The BluRay format enhances the visual effects and the cinematography of the show, making it a treat for the eyes. The x264 codec ensures a smooth and fast playback, while the SiNNERS group provides reliable and consistent subtitles and extras.
-If you are a fan of Terminator: The Sarah Connor Chronicles, or sci-fi in general, you should definitely check out this release. It is a great way to enjoy one of the best shows of the genre, with superb acting, writing and directing. You can download it from various torrent sites or streaming platforms, or buy it from online stores.
The second episode of the season, titled "Automatic for the People", follows Sarah and Cameron as they infiltrate a nuclear power plant that is targeted by Skynet. They discover that the plant is controlled by a rogue AI named Andy Goode (Brendan Hines), who is also the creator of a chess-playing computer called The Turk, which is a precursor to Skynet. Sarah and Cameron must stop Andy from activating The Turk and triggering a nuclear meltdown.
-
-The episode is a tense and suspenseful one, with a lot of cat-and-mouse games between Sarah, Cameron and Andy. The episode also explores the themes of artificial intelligence, free will and morality, as Sarah questions whether Andy is a friend or a foe, and whether Cameron can be trusted. The episode also features a cameo appearance by Shirley Manson (of the band Garbage), who plays Catherine Weaver, the CEO of a mysterious company called ZeiraCorp.
-Terminator: The Sarah Connor Chronicles S02E02.720p BluRay x264-SiNNERS is another excellent release that maintains the high standards of the previous one. The video and audio quality are superb, and the subtitles and extras are accurate and informative. The episode is a must-watch for fans of the show and sci-fi lovers alike. You can find it on various torrent sites or streaming platforms, or buy it from online stores.
e93f5a0c3f
-
- 0th instance:
-
-
-
-
-
- Source Saliency Heatmap
-
-
-
- ▁He's → ▁She's ▁a ▁scientist. </s> ▁Ő -0.111 0.025 0.01 0.129 ▁tudós. 0.026 -0.006 -0.001 -0.056 </s> 0.0 0.0 0.0 0.0 probability -0.3 -0.001 0.005 0.001
-
-
-
-
-
0th instance:
-
-
-
-
-
- Source Saliency Heatmap
-
-
-
- ▁Sie ▁ist ▁eine ▁Frau. </s> ▁Ő 0.657 0.35 0.424 0.161 -0.057 ▁nő. 0.754 0.572 0.692 0.906 0.886 </s> 0.0 0.0 0.0 0.0 0.0
-
-
-
-
-
0th instance:
-
-
-
-
-
- Target Saliency Heatmap
-
-
-
- ▁Sie ▁ist ▁eine ▁Frau. </s> ▁Sie 0.742 0.292 0.035 0.308 ▁ist 0.506 0.157 -0.107 ▁eine 0.358 -0.234 ▁Frau. 0.227 </s>
-
-
-
-
-
,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, sideOffset = 4, ...props }, ref) => (
-
-
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef & {
- inset?: boolean
- }
->(({ className, inset, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef & {
- inset?: boolean
- }
->(({ className, inset, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ) => {
- return (
-
-#include /* memset() */
-
-#include "pa_process.h"
-#include "pa_util.h"
-
-
-#define PA_FRAMES_PER_TEMP_BUFFER_WHEN_HOST_BUFFER_SIZE_IS_UNKNOWN_ 1024
-
-#define PA_MIN_( a, b ) ( ((a)<(b)) ? (a) : (b) )
-
-
-/* greatest common divisor - PGCD in French */
-static unsigned long GCD( unsigned long a, unsigned long b )
-{
- return (b==0) ? a : GCD( b, a%b);
-}
-
-/* least common multiple - PPCM in French */
-static unsigned long LCM( unsigned long a, unsigned long b )
-{
- return (a*b) / GCD(a,b);
-}
-
-#define PA_MAX_( a, b ) (((a) > (b)) ? (a) : (b))
-
-static unsigned long CalculateFrameShift( unsigned long M, unsigned long N )
-{
- unsigned long result = 0;
- unsigned long i;
- unsigned long lcm;
-
- assert( M > 0 );
- assert( N > 0 );
-
- lcm = LCM( M, N );
- for( i = M; i < lcm; i += M )
- result = PA_MAX_( result, i % N );
-
- return result;
-}
-
-
-PaError PaUtil_InitializeBufferProcessor( PaUtilBufferProcessor* bp,
- int inputChannelCount, PaSampleFormat userInputSampleFormat,
- PaSampleFormat hostInputSampleFormat,
- int outputChannelCount, PaSampleFormat userOutputSampleFormat,
- PaSampleFormat hostOutputSampleFormat,
- double sampleRate,
- PaStreamFlags streamFlags,
- unsigned long framesPerUserBuffer,
- unsigned long framesPerHostBuffer,
- PaUtilHostBufferSizeMode hostBufferSizeMode,
- PaStreamCallback *streamCallback, void *userData )
-{
- PaError result = paNoError;
- PaError bytesPerSample;
- unsigned long tempInputBufferSize, tempOutputBufferSize;
- PaStreamFlags tempInputStreamFlags;
-
- if( streamFlags & paNeverDropInput )
- {
- /* paNeverDropInput is only valid for full-duplex callback streams, with an unspecified number of frames per buffer. */
- if( !streamCallback || !(inputChannelCount > 0 && outputChannelCount > 0) ||
- framesPerUserBuffer != paFramesPerBufferUnspecified )
- return paInvalidFlag;
- }
-
- /* initialize buffer ptrs to zero so they can be freed if necessary in error */
- bp->tempInputBuffer = 0;
- bp->tempInputBufferPtrs = 0;
- bp->tempOutputBuffer = 0;
- bp->tempOutputBufferPtrs = 0;
-
- bp->framesPerUserBuffer = framesPerUserBuffer;
- bp->framesPerHostBuffer = framesPerHostBuffer;
-
- bp->inputChannelCount = inputChannelCount;
- bp->outputChannelCount = outputChannelCount;
-
- bp->hostBufferSizeMode = hostBufferSizeMode;
-
- bp->hostInputChannels[0] = bp->hostInputChannels[1] = 0;
- bp->hostOutputChannels[0] = bp->hostOutputChannels[1] = 0;
-
- if( framesPerUserBuffer == 0 ) /* streamCallback will accept any buffer size */
- {
- bp->useNonAdaptingProcess = 1;
- bp->initialFramesInTempInputBuffer = 0;
- bp->initialFramesInTempOutputBuffer = 0;
-
- if( hostBufferSizeMode == paUtilFixedHostBufferSize
- || hostBufferSizeMode == paUtilBoundedHostBufferSize )
- {
- bp->framesPerTempBuffer = framesPerHostBuffer;
- }
- else /* unknown host buffer size */
- {
- bp->framesPerTempBuffer = PA_FRAMES_PER_TEMP_BUFFER_WHEN_HOST_BUFFER_SIZE_IS_UNKNOWN_;
- }
- }
- else
- {
- bp->framesPerTempBuffer = framesPerUserBuffer;
-
- if( hostBufferSizeMode == paUtilFixedHostBufferSize
- && framesPerHostBuffer % framesPerUserBuffer == 0 )
- {
- bp->useNonAdaptingProcess = 1;
- bp->initialFramesInTempInputBuffer = 0;
- bp->initialFramesInTempOutputBuffer = 0;
- }
- else
- {
- bp->useNonAdaptingProcess = 0;
-
- if( inputChannelCount > 0 && outputChannelCount > 0 )
- {
- /* full duplex */
- if( hostBufferSizeMode == paUtilFixedHostBufferSize )
- {
- unsigned long frameShift =
- CalculateFrameShift( framesPerHostBuffer, framesPerUserBuffer );
-
- if( framesPerUserBuffer > framesPerHostBuffer )
- {
- bp->initialFramesInTempInputBuffer = frameShift;
- bp->initialFramesInTempOutputBuffer = 0;
- }
- else
- {
- bp->initialFramesInTempInputBuffer = 0;
- bp->initialFramesInTempOutputBuffer = frameShift;
- }
- }
- else /* variable host buffer size, add framesPerUserBuffer latency */
- {
- bp->initialFramesInTempInputBuffer = 0;
- bp->initialFramesInTempOutputBuffer = framesPerUserBuffer;
- }
- }
- else
- {
- /* half duplex */
- bp->initialFramesInTempInputBuffer = 0;
- bp->initialFramesInTempOutputBuffer = 0;
- }
- }
- }
-
-
- bp->framesInTempInputBuffer = bp->initialFramesInTempInputBuffer;
- bp->framesInTempOutputBuffer = bp->initialFramesInTempOutputBuffer;
-
-
- if( inputChannelCount > 0 )
- {
- bytesPerSample = Pa_GetSampleSize( hostInputSampleFormat );
- if( bytesPerSample > 0 )
- {
- bp->bytesPerHostInputSample = bytesPerSample;
- }
- else
- {
- result = bytesPerSample;
- goto error;
- }
-
- bytesPerSample = Pa_GetSampleSize( userInputSampleFormat );
- if( bytesPerSample > 0 )
- {
- bp->bytesPerUserInputSample = bytesPerSample;
- }
- else
- {
- result = bytesPerSample;
- goto error;
- }
-
- /* Under the assumption that no ADC in existence delivers better than 24bits resolution,
- we disable dithering when host input format is paInt32 and user format is paInt24,
- since the host samples will just be padded with zeros anyway. */
-
- tempInputStreamFlags = streamFlags;
- if( !(tempInputStreamFlags & paDitherOff) /* dither is on */
- && (hostInputSampleFormat & paInt32) /* host input format is int32 */
- && (userInputSampleFormat & paInt24) /* user requested format is int24 */ ){
-
- tempInputStreamFlags = tempInputStreamFlags | paDitherOff;
- }
-
- bp->inputConverter =
- PaUtil_SelectConverter( hostInputSampleFormat, userInputSampleFormat, tempInputStreamFlags );
-
- bp->inputZeroer = PaUtil_SelectZeroer( userInputSampleFormat );
-
- bp->userInputIsInterleaved = (userInputSampleFormat & paNonInterleaved)?0:1;
-
- bp->hostInputIsInterleaved = (hostInputSampleFormat & paNonInterleaved)?0:1;
-
- bp->userInputSampleFormatIsEqualToHost = ((userInputSampleFormat & ~paNonInterleaved) == (hostInputSampleFormat & ~paNonInterleaved));
-
- tempInputBufferSize =
- bp->framesPerTempBuffer * bp->bytesPerUserInputSample * inputChannelCount;
-
- bp->tempInputBuffer = PaUtil_AllocateMemory( tempInputBufferSize );
- if( bp->tempInputBuffer == 0 )
- {
- result = paInsufficientMemory;
- goto error;
- }
-
- if( bp->framesInTempInputBuffer > 0 )
- memset( bp->tempInputBuffer, 0, tempInputBufferSize );
-
- if( userInputSampleFormat & paNonInterleaved )
- {
- bp->tempInputBufferPtrs =
- (void **)PaUtil_AllocateMemory( sizeof(void*)*inputChannelCount );
- if( bp->tempInputBufferPtrs == 0 )
- {
- result = paInsufficientMemory;
- goto error;
- }
- }
-
- bp->hostInputChannels[0] = (PaUtilChannelDescriptor*)
- PaUtil_AllocateMemory( sizeof(PaUtilChannelDescriptor) * inputChannelCount * 2);
- if( bp->hostInputChannels[0] == 0 )
- {
- result = paInsufficientMemory;
- goto error;
- }
-
- bp->hostInputChannels[1] = &bp->hostInputChannels[0][inputChannelCount];
- }
-
- if( outputChannelCount > 0 )
- {
- bytesPerSample = Pa_GetSampleSize( hostOutputSampleFormat );
- if( bytesPerSample > 0 )
- {
- bp->bytesPerHostOutputSample = bytesPerSample;
- }
- else
- {
- result = bytesPerSample;
- goto error;
- }
-
- bytesPerSample = Pa_GetSampleSize( userOutputSampleFormat );
- if( bytesPerSample > 0 )
- {
- bp->bytesPerUserOutputSample = bytesPerSample;
- }
- else
- {
- result = bytesPerSample;
- goto error;
- }
-
- bp->outputConverter =
- PaUtil_SelectConverter( userOutputSampleFormat, hostOutputSampleFormat, streamFlags );
-
- bp->outputZeroer = PaUtil_SelectZeroer( hostOutputSampleFormat );
-
- bp->userOutputIsInterleaved = (userOutputSampleFormat & paNonInterleaved)?0:1;
-
- bp->hostOutputIsInterleaved = (hostOutputSampleFormat & paNonInterleaved)?0:1;
-
- bp->userOutputSampleFormatIsEqualToHost = ((userOutputSampleFormat & ~paNonInterleaved) == (hostOutputSampleFormat & ~paNonInterleaved));
-
- tempOutputBufferSize =
- bp->framesPerTempBuffer * bp->bytesPerUserOutputSample * outputChannelCount;
-
- bp->tempOutputBuffer = PaUtil_AllocateMemory( tempOutputBufferSize );
- if( bp->tempOutputBuffer == 0 )
- {
- result = paInsufficientMemory;
- goto error;
- }
-
- if( bp->framesInTempOutputBuffer > 0 )
- memset( bp->tempOutputBuffer, 0, tempOutputBufferSize );
-
- if( userOutputSampleFormat & paNonInterleaved )
- {
- bp->tempOutputBufferPtrs =
- (void **)PaUtil_AllocateMemory( sizeof(void*)*outputChannelCount );
- if( bp->tempOutputBufferPtrs == 0 )
- {
- result = paInsufficientMemory;
- goto error;
- }
- }
-
- bp->hostOutputChannels[0] = (PaUtilChannelDescriptor*)
- PaUtil_AllocateMemory( sizeof(PaUtilChannelDescriptor)*outputChannelCount * 2 );
- if( bp->hostOutputChannels[0] == 0 )
- {
- result = paInsufficientMemory;
- goto error;
- }
-
- bp->hostOutputChannels[1] = &bp->hostOutputChannels[0][outputChannelCount];
- }
-
- PaUtil_InitializeTriangularDitherState( &bp->ditherGenerator );
-
- bp->samplePeriod = 1. / sampleRate;
-
- bp->streamCallback = streamCallback;
- bp->userData = userData;
-
- return result;
-
-error:
- if( bp->tempInputBuffer )
- PaUtil_FreeMemory( bp->tempInputBuffer );
-
- if( bp->tempInputBufferPtrs )
- PaUtil_FreeMemory( bp->tempInputBufferPtrs );
-
- if( bp->hostInputChannels[0] )
- PaUtil_FreeMemory( bp->hostInputChannels[0] );
-
- if( bp->tempOutputBuffer )
- PaUtil_FreeMemory( bp->tempOutputBuffer );
-
- if( bp->tempOutputBufferPtrs )
- PaUtil_FreeMemory( bp->tempOutputBufferPtrs );
-
- if( bp->hostOutputChannels[0] )
- PaUtil_FreeMemory( bp->hostOutputChannels[0] );
-
- return result;
-}
-
-
-void PaUtil_TerminateBufferProcessor( PaUtilBufferProcessor* bp )
-{
- if( bp->tempInputBuffer )
- PaUtil_FreeMemory( bp->tempInputBuffer );
-
- if( bp->tempInputBufferPtrs )
- PaUtil_FreeMemory( bp->tempInputBufferPtrs );
-
- if( bp->hostInputChannels[0] )
- PaUtil_FreeMemory( bp->hostInputChannels[0] );
-
- if( bp->tempOutputBuffer )
- PaUtil_FreeMemory( bp->tempOutputBuffer );
-
- if( bp->tempOutputBufferPtrs )
- PaUtil_FreeMemory( bp->tempOutputBufferPtrs );
-
- if( bp->hostOutputChannels[0] )
- PaUtil_FreeMemory( bp->hostOutputChannels[0] );
-}
-
-
-void PaUtil_ResetBufferProcessor( PaUtilBufferProcessor* bp )
-{
- unsigned long tempInputBufferSize, tempOutputBufferSize;
-
- bp->framesInTempInputBuffer = bp->initialFramesInTempInputBuffer;
- bp->framesInTempOutputBuffer = bp->initialFramesInTempOutputBuffer;
-
- if( bp->framesInTempInputBuffer > 0 )
- {
- tempInputBufferSize =
- bp->framesPerTempBuffer * bp->bytesPerUserInputSample * bp->inputChannelCount;
- memset( bp->tempInputBuffer, 0, tempInputBufferSize );
- }
-
- if( bp->framesInTempOutputBuffer > 0 )
- {
- tempOutputBufferSize =
- bp->framesPerTempBuffer * bp->bytesPerUserOutputSample * bp->outputChannelCount;
- memset( bp->tempOutputBuffer, 0, tempOutputBufferSize );
- }
-}
-
-
-unsigned long PaUtil_GetBufferProcessorInputLatencyFrames( PaUtilBufferProcessor* bp )
-{
- return bp->initialFramesInTempInputBuffer;
-}
-
-
-unsigned long PaUtil_GetBufferProcessorOutputLatencyFrames( PaUtilBufferProcessor* bp )
-{
- return bp->initialFramesInTempOutputBuffer;
-}
-
-
-void PaUtil_SetInputFrameCount( PaUtilBufferProcessor* bp,
- unsigned long frameCount )
-{
- if( frameCount == 0 )
- bp->hostInputFrameCount[0] = bp->framesPerHostBuffer;
- else
- bp->hostInputFrameCount[0] = frameCount;
-}
-
-
-void PaUtil_SetNoInput( PaUtilBufferProcessor* bp )
-{
- assert( bp->inputChannelCount > 0 );
-
- bp->hostInputChannels[0][0].data = 0;
-}
-
-
-void PaUtil_SetInputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data, unsigned int stride )
-{
- assert( channel < bp->inputChannelCount );
-
- bp->hostInputChannels[0][channel].data = data;
- bp->hostInputChannels[0][channel].stride = stride;
-}
-
-
-void PaUtil_SetInterleavedInputChannels( PaUtilBufferProcessor* bp,
- unsigned int firstChannel, void *data, unsigned int channelCount )
-{
- unsigned int i;
- unsigned int channel = firstChannel;
- unsigned char *p = (unsigned char*)data;
-
- if( channelCount == 0 )
- channelCount = bp->inputChannelCount;
-
- assert( firstChannel < bp->inputChannelCount );
- assert( firstChannel + channelCount <= bp->inputChannelCount );
- assert( bp->hostInputIsInterleaved );
-
- for( i=0; i< channelCount; ++i )
- {
- bp->hostInputChannels[0][channel+i].data = p;
- p += bp->bytesPerHostInputSample;
- bp->hostInputChannels[0][channel+i].stride = channelCount;
- }
-}
-
-
-void PaUtil_SetNonInterleavedInputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data )
-{
- assert( channel < bp->inputChannelCount );
- assert( !bp->hostInputIsInterleaved );
-
- bp->hostInputChannels[0][channel].data = data;
- bp->hostInputChannels[0][channel].stride = 1;
-}
-
-
-void PaUtil_Set2ndInputFrameCount( PaUtilBufferProcessor* bp,
- unsigned long frameCount )
-{
- bp->hostInputFrameCount[1] = frameCount;
-}
-
-
-void PaUtil_Set2ndInputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data, unsigned int stride )
-{
- assert( channel < bp->inputChannelCount );
-
- bp->hostInputChannels[1][channel].data = data;
- bp->hostInputChannels[1][channel].stride = stride;
-}
-
-
-void PaUtil_Set2ndInterleavedInputChannels( PaUtilBufferProcessor* bp,
- unsigned int firstChannel, void *data, unsigned int channelCount )
-{
- unsigned int i;
- unsigned int channel = firstChannel;
- unsigned char *p = (unsigned char*)data;
-
- if( channelCount == 0 )
- channelCount = bp->inputChannelCount;
-
- assert( firstChannel < bp->inputChannelCount );
- assert( firstChannel + channelCount <= bp->inputChannelCount );
- assert( bp->hostInputIsInterleaved );
-
- for( i=0; i< channelCount; ++i )
- {
- bp->hostInputChannels[1][channel+i].data = p;
- p += bp->bytesPerHostInputSample;
- bp->hostInputChannels[1][channel+i].stride = channelCount;
- }
-}
-
-
-void PaUtil_Set2ndNonInterleavedInputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data )
-{
- assert( channel < bp->inputChannelCount );
- assert( !bp->hostInputIsInterleaved );
-
- bp->hostInputChannels[1][channel].data = data;
- bp->hostInputChannels[1][channel].stride = 1;
-}
-
-
-void PaUtil_SetOutputFrameCount( PaUtilBufferProcessor* bp,
- unsigned long frameCount )
-{
- if( frameCount == 0 )
- bp->hostOutputFrameCount[0] = bp->framesPerHostBuffer;
- else
- bp->hostOutputFrameCount[0] = frameCount;
-}
-
-
-void PaUtil_SetNoOutput( PaUtilBufferProcessor* bp )
-{
- assert( bp->outputChannelCount > 0 );
-
- bp->hostOutputChannels[0][0].data = 0;
-
- /* note that only NonAdaptingProcess is able to deal with no output at this stage. not implemented for AdaptingProcess */
-}
-
-
-void PaUtil_SetOutputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data, unsigned int stride )
-{
- assert( channel < bp->outputChannelCount );
- assert( data != NULL );
-
- bp->hostOutputChannels[0][channel].data = data;
- bp->hostOutputChannels[0][channel].stride = stride;
-}
-
-
-void PaUtil_SetInterleavedOutputChannels( PaUtilBufferProcessor* bp,
- unsigned int firstChannel, void *data, unsigned int channelCount )
-{
- unsigned int i;
- unsigned int channel = firstChannel;
- unsigned char *p = (unsigned char*)data;
-
- if( channelCount == 0 )
- channelCount = bp->outputChannelCount;
-
- assert( firstChannel < bp->outputChannelCount );
- assert( firstChannel + channelCount <= bp->outputChannelCount );
- assert( bp->hostOutputIsInterleaved );
-
- for( i=0; i< channelCount; ++i )
- {
- PaUtil_SetOutputChannel( bp, channel + i, p, channelCount );
- p += bp->bytesPerHostOutputSample;
- }
-}
-
-
-void PaUtil_SetNonInterleavedOutputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data )
-{
- assert( channel < bp->outputChannelCount );
- assert( !bp->hostOutputIsInterleaved );
-
- PaUtil_SetOutputChannel( bp, channel, data, 1 );
-}
-
-
-void PaUtil_Set2ndOutputFrameCount( PaUtilBufferProcessor* bp,
- unsigned long frameCount )
-{
- bp->hostOutputFrameCount[1] = frameCount;
-}
-
-
-void PaUtil_Set2ndOutputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data, unsigned int stride )
-{
- assert( channel < bp->outputChannelCount );
- assert( data != NULL );
-
- bp->hostOutputChannels[1][channel].data = data;
- bp->hostOutputChannels[1][channel].stride = stride;
-}
-
-
-void PaUtil_Set2ndInterleavedOutputChannels( PaUtilBufferProcessor* bp,
- unsigned int firstChannel, void *data, unsigned int channelCount )
-{
- unsigned int i;
- unsigned int channel = firstChannel;
- unsigned char *p = (unsigned char*)data;
-
- if( channelCount == 0 )
- channelCount = bp->outputChannelCount;
-
- assert( firstChannel < bp->outputChannelCount );
- assert( firstChannel + channelCount <= bp->outputChannelCount );
- assert( bp->hostOutputIsInterleaved );
-
- for( i=0; i< channelCount; ++i )
- {
- PaUtil_Set2ndOutputChannel( bp, channel + i, p, channelCount );
- p += bp->bytesPerHostOutputSample;
- }
-}
-
-
-void PaUtil_Set2ndNonInterleavedOutputChannel( PaUtilBufferProcessor* bp,
- unsigned int channel, void *data )
-{
- assert( channel < bp->outputChannelCount );
- assert( !bp->hostOutputIsInterleaved );
-
- PaUtil_Set2ndOutputChannel( bp, channel, data, 1 );
-}
-
-
-void PaUtil_BeginBufferProcessing( PaUtilBufferProcessor* bp,
- PaStreamCallbackTimeInfo* timeInfo, PaStreamCallbackFlags callbackStatusFlags )
-{
- bp->timeInfo = timeInfo;
-
- /* the first streamCallback will be called to process samples which are
- currently in the input buffer before the ones starting at the timeInfo time */
-
- bp->timeInfo->inputBufferAdcTime -= bp->framesInTempInputBuffer * bp->samplePeriod;
-
- /* We just pass through timeInfo->currentTime provided by the caller. This is
- not strictly conformant to the word of the spec, since the buffer processor
- might call the callback multiple times, and we never refresh currentTime. */
-
- /* the first streamCallback will be called to generate samples which will be
- outputted after the frames currently in the output buffer have been
- outputted. */
- bp->timeInfo->outputBufferDacTime += bp->framesInTempOutputBuffer * bp->samplePeriod;
-
- bp->callbackStatusFlags = callbackStatusFlags;
-
- bp->hostInputFrameCount[1] = 0;
- bp->hostOutputFrameCount[1] = 0;
-}
-
-
-/*
- NonAdaptingProcess() is a simple buffer copying adaptor that can handle
- both full and half duplex copies. It processes framesToProcess frames,
- broken into blocks bp->framesPerTempBuffer long.
- This routine can be used when the streamCallback doesn't care what length
- the buffers are, or when framesToProcess is an integer multiple of
- bp->framesPerTempBuffer, in which case streamCallback will always be called
- with bp->framesPerTempBuffer samples.
-*/
-static unsigned long NonAdaptingProcess( PaUtilBufferProcessor *bp,
- int *streamCallbackResult,
- PaUtilChannelDescriptor *hostInputChannels,
- PaUtilChannelDescriptor *hostOutputChannels,
- unsigned long framesToProcess )
-{
- void *userInput, *userOutput;
- unsigned char *srcBytePtr, *destBytePtr;
- unsigned int srcSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int srcChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int destSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int destChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i;
- unsigned long frameCount;
- unsigned long framesToGo = framesToProcess;
- unsigned long framesProcessed = 0;
- int skipOutputConvert = 0;
- int skipInputConvert = 0;
-
-
- if( *streamCallbackResult == paContinue )
- {
- do
- {
- frameCount = PA_MIN_( bp->framesPerTempBuffer, framesToGo );
-
- /* configure user input buffer and convert input data (host -> user) */
- if( bp->inputChannelCount == 0 )
- {
- /* no input */
- userInput = 0;
- }
- else /* there are input channels */
- {
-
- destBytePtr = (unsigned char *)bp->tempInputBuffer;
-
- if( bp->userInputIsInterleaved )
- {
- destSampleStrideSamples = bp->inputChannelCount;
- destChannelStrideBytes = bp->bytesPerUserInputSample;
-
- /* process host buffer directly, or use temp buffer if formats differ or host buffer non-interleaved,
- * or if num channels differs between the host (set in stride) and the user (eg with some Alsa hw:) */
- if( bp->userInputSampleFormatIsEqualToHost && bp->hostInputIsInterleaved
- && bp->hostInputChannels[0][0].data && bp->inputChannelCount == hostInputChannels[0].stride )
- {
- userInput = hostInputChannels[0].data;
- destBytePtr = (unsigned char *)hostInputChannels[0].data;
- skipInputConvert = 1;
- }
- else
- {
- userInput = bp->tempInputBuffer;
- }
- }
- else /* user input is not interleaved */
- {
- destSampleStrideSamples = 1;
- destChannelStrideBytes = frameCount * bp->bytesPerUserInputSample;
-
- /* setup non-interleaved ptrs */
- if( bp->userInputSampleFormatIsEqualToHost && !bp->hostInputIsInterleaved && bp->hostInputChannels[0][0].data )
- {
- for( i=0; iinputChannelCount; ++i )
- {
- bp->tempInputBufferPtrs[i] = hostInputChannels[i].data;
- }
- skipInputConvert = 1;
- }
- else
- {
- for( i=0; iinputChannelCount; ++i )
- {
- bp->tempInputBufferPtrs[i] = ((unsigned char*)bp->tempInputBuffer) +
- i * bp->bytesPerUserInputSample * frameCount;
- }
- }
-
- userInput = bp->tempInputBufferPtrs;
- }
-
- if( !bp->hostInputChannels[0][0].data )
- {
- /* no input was supplied (see PaUtil_SetNoInput), so
- zero the input buffer */
-
- for( i=0; iinputChannelCount; ++i )
- {
- bp->inputZeroer( destBytePtr, destSampleStrideSamples, frameCount );
- destBytePtr += destChannelStrideBytes; /* skip to next destination channel */
- }
- }
- else
- {
- if( skipInputConvert )
- {
- for( i=0; iinputChannelCount; ++i )
- {
- /* advance src ptr for next iteration */
- hostInputChannels[i].data = ((unsigned char*)hostInputChannels[i].data) +
- frameCount * hostInputChannels[i].stride * bp->bytesPerHostInputSample;
- }
- }
- else
- {
- for( i=0; iinputChannelCount; ++i )
- {
- bp->inputConverter( destBytePtr, destSampleStrideSamples,
- hostInputChannels[i].data,
- hostInputChannels[i].stride,
- frameCount, &bp->ditherGenerator );
-
- destBytePtr += destChannelStrideBytes; /* skip to next destination channel */
-
- /* advance src ptr for next iteration */
- hostInputChannels[i].data = ((unsigned char*)hostInputChannels[i].data) +
- frameCount * hostInputChannels[i].stride * bp->bytesPerHostInputSample;
- }
- }
- }
- }
-
- /* configure user output buffer */
- if( bp->outputChannelCount == 0 )
- {
- /* no output */
- userOutput = 0;
- }
- else /* there are output channels */
- {
- if( bp->userOutputIsInterleaved )
- {
- /* process host buffer directly, or use temp buffer if formats differ or host buffer non-interleaved,
- * or if num channels differs between the host (set in stride) and the user (eg with some Alsa hw:) */
- if( bp->userOutputSampleFormatIsEqualToHost && bp->hostOutputIsInterleaved
- && bp->outputChannelCount == hostOutputChannels[0].stride )
- {
- userOutput = hostOutputChannels[0].data;
- skipOutputConvert = 1;
- }
- else
- {
- userOutput = bp->tempOutputBuffer;
- }
- }
- else /* user output is not interleaved */
- {
- if( bp->userOutputSampleFormatIsEqualToHost && !bp->hostOutputIsInterleaved )
- {
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->tempOutputBufferPtrs[i] = hostOutputChannels[i].data;
- }
- skipOutputConvert = 1;
- }
- else
- {
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->tempOutputBufferPtrs[i] = ((unsigned char*)bp->tempOutputBuffer) +
- i * bp->bytesPerUserOutputSample * frameCount;
- }
- }
-
- userOutput = bp->tempOutputBufferPtrs;
- }
- }
-
- *streamCallbackResult = bp->streamCallback( userInput, userOutput,
- frameCount, bp->timeInfo, bp->callbackStatusFlags, bp->userData );
-
- if( *streamCallbackResult == paAbort )
- {
- /* callback returned paAbort, don't advance framesProcessed
- and framesToGo, they will be handled below */
- }
- else
- {
- bp->timeInfo->inputBufferAdcTime += frameCount * bp->samplePeriod;
- bp->timeInfo->outputBufferDacTime += frameCount * bp->samplePeriod;
-
- /* convert output data (user -> host) */
-
- if( bp->outputChannelCount != 0 && bp->hostOutputChannels[0][0].data )
- {
- if( skipOutputConvert )
- {
- for( i=0; ioutputChannelCount; ++i )
- {
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- frameCount * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
- }
- else
- {
-
- srcBytePtr = (unsigned char *)bp->tempOutputBuffer;
-
- if( bp->userOutputIsInterleaved )
- {
- srcSampleStrideSamples = bp->outputChannelCount;
- srcChannelStrideBytes = bp->bytesPerUserOutputSample;
- }
- else /* user output is not interleaved */
- {
- srcSampleStrideSamples = 1;
- srcChannelStrideBytes = frameCount * bp->bytesPerUserOutputSample;
- }
-
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->outputConverter( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- srcBytePtr, srcSampleStrideSamples,
- frameCount, &bp->ditherGenerator );
-
- srcBytePtr += srcChannelStrideBytes; /* skip to next source channel */
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- frameCount * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
- }
- }
-
- framesProcessed += frameCount;
-
- framesToGo -= frameCount;
- }
- }
- while( framesToGo > 0 && *streamCallbackResult == paContinue );
- }
-
- if( framesToGo > 0 )
- {
- /* zero any remaining frames output. There will only be remaining frames
- if the callback has returned paComplete or paAbort */
-
- frameCount = framesToGo;
-
- if( bp->outputChannelCount != 0 && bp->hostOutputChannels[0][0].data )
- {
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->outputZeroer( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- frameCount );
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- frameCount * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
- }
-
- framesProcessed += frameCount;
- }
-
- return framesProcessed;
-}
-
-
-/*
- AdaptingInputOnlyProcess() is a half duplex input buffer processor. It
- converts data from the input buffers into the temporary input buffer,
- when the temporary input buffer is full, it calls the streamCallback.
-*/
-static unsigned long AdaptingInputOnlyProcess( PaUtilBufferProcessor *bp,
- int *streamCallbackResult,
- PaUtilChannelDescriptor *hostInputChannels,
- unsigned long framesToProcess )
-{
- void *userInput, *userOutput;
- unsigned char *destBytePtr;
- unsigned int destSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int destChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i;
- unsigned long frameCount;
- unsigned long framesToGo = framesToProcess;
- unsigned long framesProcessed = 0;
-
- userOutput = 0;
-
- do
- {
- frameCount = ( bp->framesInTempInputBuffer + framesToGo > bp->framesPerUserBuffer )
- ? ( bp->framesPerUserBuffer - bp->framesInTempInputBuffer )
- : framesToGo;
-
- /* convert frameCount samples into temp buffer */
-
- if( bp->userInputIsInterleaved )
- {
- destBytePtr = ((unsigned char*)bp->tempInputBuffer) +
- bp->bytesPerUserInputSample * bp->inputChannelCount *
- bp->framesInTempInputBuffer;
-
- destSampleStrideSamples = bp->inputChannelCount;
- destChannelStrideBytes = bp->bytesPerUserInputSample;
-
- userInput = bp->tempInputBuffer;
- }
- else /* user input is not interleaved */
- {
- destBytePtr = ((unsigned char*)bp->tempInputBuffer) +
- bp->bytesPerUserInputSample * bp->framesInTempInputBuffer;
-
- destSampleStrideSamples = 1;
- destChannelStrideBytes = bp->framesPerUserBuffer * bp->bytesPerUserInputSample;
-
- /* setup non-interleaved ptrs */
- for( i=0; iinputChannelCount; ++i )
- {
- bp->tempInputBufferPtrs[i] = ((unsigned char*)bp->tempInputBuffer) +
- i * bp->bytesPerUserInputSample * bp->framesPerUserBuffer;
- }
-
- userInput = bp->tempInputBufferPtrs;
- }
-
- for( i=0; iinputChannelCount; ++i )
- {
- bp->inputConverter( destBytePtr, destSampleStrideSamples,
- hostInputChannels[i].data,
- hostInputChannels[i].stride,
- frameCount, &bp->ditherGenerator );
-
- destBytePtr += destChannelStrideBytes; /* skip to next destination channel */
-
- /* advance src ptr for next iteration */
- hostInputChannels[i].data = ((unsigned char*)hostInputChannels[i].data) +
- frameCount * hostInputChannels[i].stride * bp->bytesPerHostInputSample;
- }
-
- bp->framesInTempInputBuffer += frameCount;
-
- if( bp->framesInTempInputBuffer == bp->framesPerUserBuffer )
- {
- /**
- @todo (non-critical optimisation)
- The conditional below implements the continue/complete/abort mechanism
- simply by continuing on iterating through the input buffer, but not
- passing the data to the callback. With care, the outer loop could be
- terminated earlier, thus some unneeded conversion cycles would be
- saved.
- */
- if( *streamCallbackResult == paContinue )
- {
- bp->timeInfo->outputBufferDacTime = 0;
-
- *streamCallbackResult = bp->streamCallback( userInput, userOutput,
- bp->framesPerUserBuffer, bp->timeInfo,
- bp->callbackStatusFlags, bp->userData );
-
- bp->timeInfo->inputBufferAdcTime += bp->framesPerUserBuffer * bp->samplePeriod;
- }
-
- bp->framesInTempInputBuffer = 0;
- }
-
- framesProcessed += frameCount;
-
- framesToGo -= frameCount;
- }while( framesToGo > 0 );
-
- return framesProcessed;
-}
-
-
-/*
- AdaptingOutputOnlyProcess() is a half duplex output buffer processor.
- It converts data from the temporary output buffer, to the output buffers,
- when the temporary output buffer is empty, it calls the streamCallback.
-*/
-static unsigned long AdaptingOutputOnlyProcess( PaUtilBufferProcessor *bp,
- int *streamCallbackResult,
- PaUtilChannelDescriptor *hostOutputChannels,
- unsigned long framesToProcess )
-{
- void *userInput, *userOutput;
- unsigned char *srcBytePtr;
- unsigned int srcSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int srcChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i;
- unsigned long frameCount;
- unsigned long framesToGo = framesToProcess;
- unsigned long framesProcessed = 0;
-
- do
- {
- if( bp->framesInTempOutputBuffer == 0 && *streamCallbackResult == paContinue )
- {
- userInput = 0;
-
- /* setup userOutput */
- if( bp->userOutputIsInterleaved )
- {
- userOutput = bp->tempOutputBuffer;
- }
- else /* user output is not interleaved */
- {
- for( i = 0; i < bp->outputChannelCount; ++i )
- {
- bp->tempOutputBufferPtrs[i] = ((unsigned char*)bp->tempOutputBuffer) +
- i * bp->framesPerUserBuffer * bp->bytesPerUserOutputSample;
- }
-
- userOutput = bp->tempOutputBufferPtrs;
- }
-
- bp->timeInfo->inputBufferAdcTime = 0;
-
- *streamCallbackResult = bp->streamCallback( userInput, userOutput,
- bp->framesPerUserBuffer, bp->timeInfo,
- bp->callbackStatusFlags, bp->userData );
-
- if( *streamCallbackResult == paAbort )
- {
- /* if the callback returned paAbort, we disregard its output */
- }
- else
- {
- bp->timeInfo->outputBufferDacTime += bp->framesPerUserBuffer * bp->samplePeriod;
-
- bp->framesInTempOutputBuffer = bp->framesPerUserBuffer;
- }
- }
-
- if( bp->framesInTempOutputBuffer > 0 )
- {
- /* convert frameCount frames from user buffer to host buffer */
-
- frameCount = PA_MIN_( bp->framesInTempOutputBuffer, framesToGo );
-
- if( bp->userOutputIsInterleaved )
- {
- srcBytePtr = ((unsigned char*)bp->tempOutputBuffer) +
- bp->bytesPerUserOutputSample * bp->outputChannelCount *
- (bp->framesPerUserBuffer - bp->framesInTempOutputBuffer);
-
- srcSampleStrideSamples = bp->outputChannelCount;
- srcChannelStrideBytes = bp->bytesPerUserOutputSample;
- }
- else /* user output is not interleaved */
- {
- srcBytePtr = ((unsigned char*)bp->tempOutputBuffer) +
- bp->bytesPerUserOutputSample *
- (bp->framesPerUserBuffer - bp->framesInTempOutputBuffer);
-
- srcSampleStrideSamples = 1;
- srcChannelStrideBytes = bp->framesPerUserBuffer * bp->bytesPerUserOutputSample;
- }
-
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->outputConverter( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- srcBytePtr, srcSampleStrideSamples,
- frameCount, &bp->ditherGenerator );
-
- srcBytePtr += srcChannelStrideBytes; /* skip to next source channel */
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- frameCount * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
-
- bp->framesInTempOutputBuffer -= frameCount;
- }
- else
- {
- /* no more user data is available because the callback has returned
- paComplete or paAbort. Fill the remainder of the host buffer
- with zeros.
- */
-
- frameCount = framesToGo;
-
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->outputZeroer( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- frameCount );
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- frameCount * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
- }
-
- framesProcessed += frameCount;
-
- framesToGo -= frameCount;
-
- }while( framesToGo > 0 );
-
- return framesProcessed;
-}
-
-/* CopyTempOutputBuffersToHostOutputBuffers is called from AdaptingProcess to copy frames from
- tempOutputBuffer to hostOutputChannels. This includes data conversion
- and interleaving.
-*/
-static void CopyTempOutputBuffersToHostOutputBuffers( PaUtilBufferProcessor *bp)
-{
- unsigned long maxFramesToCopy;
- PaUtilChannelDescriptor *hostOutputChannels;
- unsigned int frameCount;
- unsigned char *srcBytePtr;
- unsigned int srcSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int srcChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i;
-
- /* copy frames from user to host output buffers */
- while( bp->framesInTempOutputBuffer > 0 &&
- ((bp->hostOutputFrameCount[0] + bp->hostOutputFrameCount[1]) > 0) )
- {
- maxFramesToCopy = bp->framesInTempOutputBuffer;
-
- /* select the output buffer set (1st or 2nd) */
- if( bp->hostOutputFrameCount[0] > 0 )
- {
- hostOutputChannels = bp->hostOutputChannels[0];
- frameCount = PA_MIN_( bp->hostOutputFrameCount[0], maxFramesToCopy );
- }
- else
- {
- hostOutputChannels = bp->hostOutputChannels[1];
- frameCount = PA_MIN_( bp->hostOutputFrameCount[1], maxFramesToCopy );
- }
-
- if( bp->userOutputIsInterleaved )
- {
- srcBytePtr = ((unsigned char*)bp->tempOutputBuffer) +
- bp->bytesPerUserOutputSample * bp->outputChannelCount *
- (bp->framesPerUserBuffer - bp->framesInTempOutputBuffer);
-
- srcSampleStrideSamples = bp->outputChannelCount;
- srcChannelStrideBytes = bp->bytesPerUserOutputSample;
- }
- else /* user output is not interleaved */
- {
- srcBytePtr = ((unsigned char*)bp->tempOutputBuffer) +
- bp->bytesPerUserOutputSample *
- (bp->framesPerUserBuffer - bp->framesInTempOutputBuffer);
-
- srcSampleStrideSamples = 1;
- srcChannelStrideBytes = bp->framesPerUserBuffer * bp->bytesPerUserOutputSample;
- }
-
- for( i=0; ioutputChannelCount; ++i )
- {
- assert( hostOutputChannels[i].data != NULL );
- bp->outputConverter( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- srcBytePtr, srcSampleStrideSamples,
- frameCount, &bp->ditherGenerator );
-
- srcBytePtr += srcChannelStrideBytes; /* skip to next source channel */
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- frameCount * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
-
- if( bp->hostOutputFrameCount[0] > 0 )
- bp->hostOutputFrameCount[0] -= frameCount;
- else
- bp->hostOutputFrameCount[1] -= frameCount;
-
- bp->framesInTempOutputBuffer -= frameCount;
- }
-}
-
-/*
- AdaptingProcess is a full duplex adapting buffer processor. It converts
- data from the temporary output buffer into the host output buffers, then
- from the host input buffers into the temporary input buffers. Calling the
- streamCallback when necessary.
- When processPartialUserBuffers is 0, all available input data will be
- consumed and all available output space will be filled. When
- processPartialUserBuffers is non-zero, as many full user buffers
- as possible will be processed, but partial buffers will not be consumed.
-*/
-static unsigned long AdaptingProcess( PaUtilBufferProcessor *bp,
- int *streamCallbackResult, int processPartialUserBuffers )
-{
- void *userInput, *userOutput;
- unsigned long framesProcessed = 0;
- unsigned long framesAvailable;
- unsigned long endProcessingMinFrameCount;
- unsigned long maxFramesToCopy;
- PaUtilChannelDescriptor *hostInputChannels, *hostOutputChannels;
- unsigned int frameCount;
- unsigned char *destBytePtr;
- unsigned int destSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int destChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i, j;
-
-
- framesAvailable = bp->hostInputFrameCount[0] + bp->hostInputFrameCount[1];/* this is assumed to be the same as the output buffer's frame count */
-
- if( processPartialUserBuffers )
- endProcessingMinFrameCount = 0;
- else
- endProcessingMinFrameCount = (bp->framesPerUserBuffer - 1);
-
- /* Fill host output with remaining frames in user output (tempOutputBuffer) */
- CopyTempOutputBuffersToHostOutputBuffers( bp );
-
- while( framesAvailable > endProcessingMinFrameCount )
- {
-
- if( bp->framesInTempOutputBuffer == 0 && *streamCallbackResult != paContinue )
- {
- /* the callback will not be called any more, so zero what remains
- of the host output buffers */
-
- for( i=0; i<2; ++i )
- {
- frameCount = bp->hostOutputFrameCount[i];
- if( frameCount > 0 )
- {
- hostOutputChannels = bp->hostOutputChannels[i];
-
- for( j=0; joutputChannelCount; ++j )
- {
- bp->outputZeroer( hostOutputChannels[j].data,
- hostOutputChannels[j].stride,
- frameCount );
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[j].data = ((unsigned char*)hostOutputChannels[j].data) +
- frameCount * hostOutputChannels[j].stride * bp->bytesPerHostOutputSample;
- }
- bp->hostOutputFrameCount[i] = 0;
- }
- }
- }
-
-
- /* copy frames from host to user input buffers */
- while( bp->framesInTempInputBuffer < bp->framesPerUserBuffer &&
- ((bp->hostInputFrameCount[0] + bp->hostInputFrameCount[1]) > 0) )
- {
- maxFramesToCopy = bp->framesPerUserBuffer - bp->framesInTempInputBuffer;
-
- /* select the input buffer set (1st or 2nd) */
- if( bp->hostInputFrameCount[0] > 0 )
- {
- hostInputChannels = bp->hostInputChannels[0];
- frameCount = PA_MIN_( bp->hostInputFrameCount[0], maxFramesToCopy );
- }
- else
- {
- hostInputChannels = bp->hostInputChannels[1];
- frameCount = PA_MIN_( bp->hostInputFrameCount[1], maxFramesToCopy );
- }
-
- /* configure conversion destination pointers */
- if( bp->userInputIsInterleaved )
- {
- destBytePtr = ((unsigned char*)bp->tempInputBuffer) +
- bp->bytesPerUserInputSample * bp->inputChannelCount *
- bp->framesInTempInputBuffer;
-
- destSampleStrideSamples = bp->inputChannelCount;
- destChannelStrideBytes = bp->bytesPerUserInputSample;
- }
- else /* user input is not interleaved */
- {
- destBytePtr = ((unsigned char*)bp->tempInputBuffer) +
- bp->bytesPerUserInputSample * bp->framesInTempInputBuffer;
-
- destSampleStrideSamples = 1;
- destChannelStrideBytes = bp->framesPerUserBuffer * bp->bytesPerUserInputSample;
- }
-
- for( i=0; iinputChannelCount; ++i )
- {
- bp->inputConverter( destBytePtr, destSampleStrideSamples,
- hostInputChannels[i].data,
- hostInputChannels[i].stride,
- frameCount, &bp->ditherGenerator );
-
- destBytePtr += destChannelStrideBytes; /* skip to next destination channel */
-
- /* advance src ptr for next iteration */
- hostInputChannels[i].data = ((unsigned char*)hostInputChannels[i].data) +
- frameCount * hostInputChannels[i].stride * bp->bytesPerHostInputSample;
- }
-
- if( bp->hostInputFrameCount[0] > 0 )
- bp->hostInputFrameCount[0] -= frameCount;
- else
- bp->hostInputFrameCount[1] -= frameCount;
-
- bp->framesInTempInputBuffer += frameCount;
-
- /* update framesAvailable and framesProcessed based on input consumed
- unless something is very wrong this will also correspond to the
- amount of output generated */
- framesAvailable -= frameCount;
- framesProcessed += frameCount;
- }
-
- /* call streamCallback */
- if( bp->framesInTempInputBuffer == bp->framesPerUserBuffer &&
- bp->framesInTempOutputBuffer == 0 )
- {
- if( *streamCallbackResult == paContinue )
- {
- /* setup userInput */
- if( bp->userInputIsInterleaved )
- {
- userInput = bp->tempInputBuffer;
- }
- else /* user input is not interleaved */
- {
- for( i = 0; i < bp->inputChannelCount; ++i )
- {
- bp->tempInputBufferPtrs[i] = ((unsigned char*)bp->tempInputBuffer) +
- i * bp->framesPerUserBuffer * bp->bytesPerUserInputSample;
- }
-
- userInput = bp->tempInputBufferPtrs;
- }
-
- /* setup userOutput */
- if( bp->userOutputIsInterleaved )
- {
- userOutput = bp->tempOutputBuffer;
- }
- else /* user output is not interleaved */
- {
- for( i = 0; i < bp->outputChannelCount; ++i )
- {
- bp->tempOutputBufferPtrs[i] = ((unsigned char*)bp->tempOutputBuffer) +
- i * bp->framesPerUserBuffer * bp->bytesPerUserOutputSample;
- }
-
- userOutput = bp->tempOutputBufferPtrs;
- }
-
- /* call streamCallback */
-
- *streamCallbackResult = bp->streamCallback( userInput, userOutput,
- bp->framesPerUserBuffer, bp->timeInfo,
- bp->callbackStatusFlags, bp->userData );
-
- bp->timeInfo->inputBufferAdcTime += bp->framesPerUserBuffer * bp->samplePeriod;
- bp->timeInfo->outputBufferDacTime += bp->framesPerUserBuffer * bp->samplePeriod;
-
- bp->framesInTempInputBuffer = 0;
-
- if( *streamCallbackResult == paAbort )
- bp->framesInTempOutputBuffer = 0;
- else
- bp->framesInTempOutputBuffer = bp->framesPerUserBuffer;
- }
- else
- {
- /* paComplete or paAbort has already been called. */
-
- bp->framesInTempInputBuffer = 0;
- }
- }
-
- /* copy frames from user (tempOutputBuffer) to host output buffers (hostOutputChannels)
- Means to process the user output provided by the callback. Has to be called after
- each callback. */
- CopyTempOutputBuffersToHostOutputBuffers( bp );
-
- }
-
- return framesProcessed;
-}
-
-
-unsigned long PaUtil_EndBufferProcessing( PaUtilBufferProcessor* bp, int *streamCallbackResult )
-{
- unsigned long framesToProcess, framesToGo;
- unsigned long framesProcessed = 0;
-
- if( bp->inputChannelCount != 0 && bp->outputChannelCount != 0
- && bp->hostInputChannels[0][0].data /* input was supplied (see PaUtil_SetNoInput) */
- && bp->hostOutputChannels[0][0].data /* output was supplied (see PaUtil_SetNoOutput) */ )
- {
- assert( (bp->hostInputFrameCount[0] + bp->hostInputFrameCount[1]) ==
- (bp->hostOutputFrameCount[0] + bp->hostOutputFrameCount[1]) );
- }
-
- assert( *streamCallbackResult == paContinue
- || *streamCallbackResult == paComplete
- || *streamCallbackResult == paAbort ); /* don't forget to pass in a valid callback result value */
-
- if( bp->useNonAdaptingProcess )
- {
- if( bp->inputChannelCount != 0 && bp->outputChannelCount != 0 )
- {
- /* full duplex non-adapting process, splice buffers if they are
- different lengths */
-
- framesToGo = bp->hostOutputFrameCount[0] + bp->hostOutputFrameCount[1]; /* relies on assert above for input/output equivalence */
-
- do{
- unsigned long noInputInputFrameCount;
- unsigned long *hostInputFrameCount;
- PaUtilChannelDescriptor *hostInputChannels;
- unsigned long noOutputOutputFrameCount;
- unsigned long *hostOutputFrameCount;
- PaUtilChannelDescriptor *hostOutputChannels;
- unsigned long framesProcessedThisIteration;
-
- if( !bp->hostInputChannels[0][0].data )
- {
- /* no input was supplied (see PaUtil_SetNoInput)
- NonAdaptingProcess knows how to deal with this
- */
- noInputInputFrameCount = framesToGo;
- hostInputFrameCount = &noInputInputFrameCount;
- hostInputChannels = 0;
- }
- else if( bp->hostInputFrameCount[0] != 0 )
- {
- hostInputFrameCount = &bp->hostInputFrameCount[0];
- hostInputChannels = bp->hostInputChannels[0];
- }
- else
- {
- hostInputFrameCount = &bp->hostInputFrameCount[1];
- hostInputChannels = bp->hostInputChannels[1];
- }
-
- if( !bp->hostOutputChannels[0][0].data )
- {
- /* no output was supplied (see PaUtil_SetNoOutput)
- NonAdaptingProcess knows how to deal with this
- */
- noOutputOutputFrameCount = framesToGo;
- hostOutputFrameCount = &noOutputOutputFrameCount;
- hostOutputChannels = 0;
- }
- if( bp->hostOutputFrameCount[0] != 0 )
- {
- hostOutputFrameCount = &bp->hostOutputFrameCount[0];
- hostOutputChannels = bp->hostOutputChannels[0];
- }
- else
- {
- hostOutputFrameCount = &bp->hostOutputFrameCount[1];
- hostOutputChannels = bp->hostOutputChannels[1];
- }
-
- framesToProcess = PA_MIN_( *hostInputFrameCount,
- *hostOutputFrameCount );
-
- assert( framesToProcess != 0 );
-
- framesProcessedThisIteration = NonAdaptingProcess( bp, streamCallbackResult,
- hostInputChannels, hostOutputChannels,
- framesToProcess );
-
- *hostInputFrameCount -= framesProcessedThisIteration;
- *hostOutputFrameCount -= framesProcessedThisIteration;
-
- framesProcessed += framesProcessedThisIteration;
- framesToGo -= framesProcessedThisIteration;
-
- }while( framesToGo > 0 );
- }
- else
- {
- /* half duplex non-adapting process, just process 1st and 2nd buffer */
- /* process first buffer */
-
- framesToProcess = (bp->inputChannelCount != 0)
- ? bp->hostInputFrameCount[0]
- : bp->hostOutputFrameCount[0];
-
- framesProcessed = NonAdaptingProcess( bp, streamCallbackResult,
- bp->hostInputChannels[0], bp->hostOutputChannels[0],
- framesToProcess );
-
- /* process second buffer if provided */
-
- framesToProcess = (bp->inputChannelCount != 0)
- ? bp->hostInputFrameCount[1]
- : bp->hostOutputFrameCount[1];
- if( framesToProcess > 0 )
- {
- framesProcessed += NonAdaptingProcess( bp, streamCallbackResult,
- bp->hostInputChannels[1], bp->hostOutputChannels[1],
- framesToProcess );
- }
- }
- }
- else /* block adaption necessary*/
- {
-
- if( bp->inputChannelCount != 0 && bp->outputChannelCount != 0 )
- {
- /* full duplex */
-
- if( bp->hostBufferSizeMode == paUtilVariableHostBufferSizePartialUsageAllowed )
- {
- framesProcessed = AdaptingProcess( bp, streamCallbackResult,
- 0 /* dont process partial user buffers */ );
- }
- else
- {
- framesProcessed = AdaptingProcess( bp, streamCallbackResult,
- 1 /* process partial user buffers */ );
- }
- }
- else if( bp->inputChannelCount != 0 )
- {
- /* input only */
- framesToProcess = bp->hostInputFrameCount[0];
-
- framesProcessed = AdaptingInputOnlyProcess( bp, streamCallbackResult,
- bp->hostInputChannels[0], framesToProcess );
-
- framesToProcess = bp->hostInputFrameCount[1];
- if( framesToProcess > 0 )
- {
- framesProcessed += AdaptingInputOnlyProcess( bp, streamCallbackResult,
- bp->hostInputChannels[1], framesToProcess );
- }
- }
- else
- {
- /* output only */
- framesToProcess = bp->hostOutputFrameCount[0];
-
- framesProcessed = AdaptingOutputOnlyProcess( bp, streamCallbackResult,
- bp->hostOutputChannels[0], framesToProcess );
-
- framesToProcess = bp->hostOutputFrameCount[1];
- if( framesToProcess > 0 )
- {
- framesProcessed += AdaptingOutputOnlyProcess( bp, streamCallbackResult,
- bp->hostOutputChannels[1], framesToProcess );
- }
- }
- }
-
- return framesProcessed;
-}
-
-
-int PaUtil_IsBufferProcessorOutputEmpty( PaUtilBufferProcessor* bp )
-{
- return (bp->framesInTempOutputBuffer) ? 0 : 1;
-}
-
-
-unsigned long PaUtil_CopyInput( PaUtilBufferProcessor* bp,
- void **buffer, unsigned long frameCount )
-{
- PaUtilChannelDescriptor *hostInputChannels;
- unsigned int framesToCopy;
- unsigned char *destBytePtr;
- void **nonInterleavedDestPtrs;
- unsigned int destSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int destChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i;
-
- hostInputChannels = bp->hostInputChannels[0];
- framesToCopy = PA_MIN_( bp->hostInputFrameCount[0], frameCount );
-
- if( bp->userInputIsInterleaved )
- {
- destBytePtr = (unsigned char*)*buffer;
-
- destSampleStrideSamples = bp->inputChannelCount;
- destChannelStrideBytes = bp->bytesPerUserInputSample;
-
- for( i=0; iinputChannelCount; ++i )
- {
- bp->inputConverter( destBytePtr, destSampleStrideSamples,
- hostInputChannels[i].data,
- hostInputChannels[i].stride,
- framesToCopy, &bp->ditherGenerator );
-
- destBytePtr += destChannelStrideBytes; /* skip to next dest channel */
-
- /* advance source ptr for next iteration */
- hostInputChannels[i].data = ((unsigned char*)hostInputChannels[i].data) +
- framesToCopy * hostInputChannels[i].stride * bp->bytesPerHostInputSample;
- }
-
- /* advance callers dest pointer (buffer) */
- *buffer = ((unsigned char *)*buffer) +
- framesToCopy * bp->inputChannelCount * bp->bytesPerUserInputSample;
- }
- else
- {
- /* user input is not interleaved */
-
- nonInterleavedDestPtrs = (void**)*buffer;
-
- destSampleStrideSamples = 1;
-
- for( i=0; iinputChannelCount; ++i )
- {
- destBytePtr = (unsigned char*)nonInterleavedDestPtrs[i];
-
- bp->inputConverter( destBytePtr, destSampleStrideSamples,
- hostInputChannels[i].data,
- hostInputChannels[i].stride,
- framesToCopy, &bp->ditherGenerator );
-
- /* advance callers dest pointer (nonInterleavedDestPtrs[i]) */
- destBytePtr += bp->bytesPerUserInputSample * framesToCopy;
- nonInterleavedDestPtrs[i] = destBytePtr;
-
- /* advance source ptr for next iteration */
- hostInputChannels[i].data = ((unsigned char*)hostInputChannels[i].data) +
- framesToCopy * hostInputChannels[i].stride * bp->bytesPerHostInputSample;
- }
- }
-
- bp->hostInputFrameCount[0] -= framesToCopy;
-
- return framesToCopy;
-}
-
-unsigned long PaUtil_CopyOutput( PaUtilBufferProcessor* bp,
- const void ** buffer, unsigned long frameCount )
-{
- PaUtilChannelDescriptor *hostOutputChannels;
- unsigned int framesToCopy;
- unsigned char *srcBytePtr;
- void **nonInterleavedSrcPtrs;
- unsigned int srcSampleStrideSamples; /* stride from one sample to the next within a channel, in samples */
- unsigned int srcChannelStrideBytes; /* stride from one channel to the next, in bytes */
- unsigned int i;
-
- hostOutputChannels = bp->hostOutputChannels[0];
- framesToCopy = PA_MIN_( bp->hostOutputFrameCount[0], frameCount );
-
- if( bp->userOutputIsInterleaved )
- {
- srcBytePtr = (unsigned char*)*buffer;
-
- srcSampleStrideSamples = bp->outputChannelCount;
- srcChannelStrideBytes = bp->bytesPerUserOutputSample;
-
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->outputConverter( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- srcBytePtr, srcSampleStrideSamples,
- framesToCopy, &bp->ditherGenerator );
-
- srcBytePtr += srcChannelStrideBytes; /* skip to next source channel */
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- framesToCopy * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
-
- /* advance callers source pointer (buffer) */
- *buffer = ((unsigned char *)*buffer) +
- framesToCopy * bp->outputChannelCount * bp->bytesPerUserOutputSample;
-
- }
- else
- {
- /* user output is not interleaved */
-
- nonInterleavedSrcPtrs = (void**)*buffer;
-
- srcSampleStrideSamples = 1;
-
- for( i=0; ioutputChannelCount; ++i )
- {
- srcBytePtr = (unsigned char*)nonInterleavedSrcPtrs[i];
-
- bp->outputConverter( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- srcBytePtr, srcSampleStrideSamples,
- framesToCopy, &bp->ditherGenerator );
-
-
- /* advance callers source pointer (nonInterleavedSrcPtrs[i]) */
- srcBytePtr += bp->bytesPerUserOutputSample * framesToCopy;
- nonInterleavedSrcPtrs[i] = srcBytePtr;
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- framesToCopy * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
- }
-
- bp->hostOutputFrameCount[0] += framesToCopy;
-
- return framesToCopy;
-}
-
-
-unsigned long PaUtil_ZeroOutput( PaUtilBufferProcessor* bp, unsigned long frameCount )
-{
- PaUtilChannelDescriptor *hostOutputChannels;
- unsigned int framesToZero;
- unsigned int i;
-
- hostOutputChannels = bp->hostOutputChannels[0];
- framesToZero = PA_MIN_( bp->hostOutputFrameCount[0], frameCount );
-
- for( i=0; ioutputChannelCount; ++i )
- {
- bp->outputZeroer( hostOutputChannels[i].data,
- hostOutputChannels[i].stride,
- framesToZero );
-
-
- /* advance dest ptr for next iteration */
- hostOutputChannels[i].data = ((unsigned char*)hostOutputChannels[i].data) +
- framesToZero * hostOutputChannels[i].stride * bp->bytesPerHostOutputSample;
- }
-
- bp->hostOutputFrameCount[0] += framesToZero;
-
- return framesToZero;
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_nep50_promotions.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_nep50_promotions.py
deleted file mode 100644
index 74a18a8dda484b973f33c7a2bd5a9ea19a697be2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_nep50_promotions.py
+++ /dev/null
@@ -1,246 +0,0 @@
-"""
-This file adds basic tests to test the NEP 50 style promotion compatibility
-mode. Most of these test are likely to be simply deleted again once NEP 50
-is adopted in the main test suite. A few may be moved elsewhere.
-"""
-
-import operator
-
-import numpy as np
-
-import pytest
-from numpy.testing import IS_WASM
-
-
-@pytest.fixture(scope="module", autouse=True)
-def _weak_promotion_enabled():
- state = np._get_promotion_state()
- np._set_promotion_state("weak_and_warn")
- yield
- np._set_promotion_state(state)
-
-
-@pytest.mark.skipif(IS_WASM, reason="wasm doesn't have support for fp errors")
-def test_nep50_examples():
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.uint8(1) + 2
- assert res.dtype == np.uint8
-
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.array([1], np.uint8) + np.int64(1)
- assert res.dtype == np.int64
-
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.array([1], np.uint8) + np.array(1, dtype=np.int64)
- assert res.dtype == np.int64
-
- with pytest.warns(UserWarning, match="result dtype changed"):
- # Note: For "weak_and_warn" promotion state the overflow warning is
- # unfortunately not given (because we use the full array path).
- with np.errstate(over="raise"):
- res = np.uint8(100) + 200
- assert res.dtype == np.uint8
-
- with pytest.warns(Warning) as recwarn:
- res = np.float32(1) + 3e100
-
- # Check that both warnings were given in the one call:
- warning = str(recwarn.pop(UserWarning).message)
- assert warning.startswith("result dtype changed")
- warning = str(recwarn.pop(RuntimeWarning).message)
- assert warning.startswith("overflow")
- assert len(recwarn) == 0 # no further warnings
- assert np.isinf(res)
- assert res.dtype == np.float32
-
- # Changes, but we don't warn for it (too noisy)
- res = np.array([0.1], np.float32) == np.float64(0.1)
- assert res[0] == False
-
- # Additional test, since the above silences the warning:
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.array([0.1], np.float32) + np.float64(0.1)
- assert res.dtype == np.float64
-
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.array([1.], np.float32) + np.int64(3)
- assert res.dtype == np.float64
-
-
-@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
-def test_nep50_weak_integers(dtype):
- # Avoids warning (different code path for scalars)
- np._set_promotion_state("weak")
- scalar_type = np.dtype(dtype).type
-
- maxint = int(np.iinfo(dtype).max)
-
- with np.errstate(over="warn"):
- with pytest.warns(RuntimeWarning):
- res = scalar_type(100) + maxint
- assert res.dtype == dtype
-
- # Array operations are not expected to warn, but should give the same
- # result dtype.
- res = np.array(100, dtype=dtype) + maxint
- assert res.dtype == dtype
-
-
-@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
-def test_nep50_weak_integers_with_inexact(dtype):
- # Avoids warning (different code path for scalars)
- np._set_promotion_state("weak")
- scalar_type = np.dtype(dtype).type
-
- too_big_int = int(np.finfo(dtype).max) * 2
-
- if dtype in "dDG":
- # These dtypes currently convert to Python float internally, which
- # raises an OverflowError, while the other dtypes overflow to inf.
- # NOTE: It may make sense to normalize the behavior!
- with pytest.raises(OverflowError):
- scalar_type(1) + too_big_int
-
- with pytest.raises(OverflowError):
- np.array(1, dtype=dtype) + too_big_int
- else:
- # NumPy uses (or used) `int -> string -> longdouble` for the
- # conversion. But Python may refuse `str(int)` for huge ints.
- # In that case, RuntimeWarning would be correct, but conversion
- # fails earlier (seems to happen on 32bit linux, possibly only debug).
- if dtype in "gG":
- try:
- str(too_big_int)
- except ValueError:
- pytest.skip("`huge_int -> string -> longdouble` failed")
-
- # Otherwise, we overflow to infinity:
- with pytest.warns(RuntimeWarning):
- res = scalar_type(1) + too_big_int
- assert res.dtype == dtype
- assert res == np.inf
-
- with pytest.warns(RuntimeWarning):
- # We force the dtype here, since windows may otherwise pick the
- # double instead of the longdouble loop. That leads to slightly
- # different results (conversion of the int fails as above).
- res = np.add(np.array(1, dtype=dtype), too_big_int, dtype=dtype)
- assert res.dtype == dtype
- assert res == np.inf
-
-
-@pytest.mark.parametrize("op", [operator.add, operator.pow, operator.eq])
-def test_weak_promotion_scalar_path(op):
- # Some additional paths exercising the weak scalars.
- np._set_promotion_state("weak")
-
- # Integer path:
- res = op(np.uint8(3), 5)
- assert res == op(3, 5)
- assert res.dtype == np.uint8 or res.dtype == bool
-
- with pytest.raises(OverflowError):
- op(np.uint8(3), 1000)
-
- # Float path:
- res = op(np.float32(3), 5.)
- assert res == op(3., 5.)
- assert res.dtype == np.float32 or res.dtype == bool
-
-
-def test_nep50_complex_promotion():
- np._set_promotion_state("weak")
-
- with pytest.warns(RuntimeWarning, match=".*overflow"):
- res = np.complex64(3) + complex(2**300)
-
- assert type(res) == np.complex64
-
-
-def test_nep50_integer_conversion_errors():
- # Do not worry about warnings here (auto-fixture will reset).
- np._set_promotion_state("weak")
- # Implementation for error paths is mostly missing (as of writing)
- with pytest.raises(OverflowError, match=".*uint8"):
- np.array([1], np.uint8) + 300
-
- with pytest.raises(OverflowError, match=".*uint8"):
- np.uint8(1) + 300
-
- # Error message depends on platform (maybe unsigned int or unsigned long)
- with pytest.raises(OverflowError,
- match="Python integer -1 out of bounds for uint8"):
- np.uint8(1) + -1
-
-
-def test_nep50_integer_regression():
- # Test the old integer promotion rules. When the integer is too large,
- # we need to keep using the old-style promotion.
- np._set_promotion_state("legacy")
- arr = np.array(1)
- assert (arr + 2**63).dtype == np.float64
- assert (arr[()] + 2**63).dtype == np.float64
-
-
-def test_nep50_with_axisconcatenator():
- # I promised that this will be an error in the future in the 1.25
- # release notes; test this (NEP 50 opt-in makes the deprecation an error).
- np._set_promotion_state("weak")
-
- with pytest.raises(OverflowError):
- np.r_[np.arange(5, dtype=np.int8), 255]
-
-
-@pytest.mark.parametrize("ufunc", [np.add, np.power])
-@pytest.mark.parametrize("state", ["weak", "weak_and_warn"])
-def test_nep50_huge_integers(ufunc, state):
- # Very large integers are complicated, because they go to uint64 or
- # object dtype. This tests covers a few possible paths (some of which
- # cannot give the NEP 50 warnings).
- np._set_promotion_state(state)
-
- with pytest.raises(OverflowError):
- ufunc(np.int64(0), 2**63) # 2**63 too large for int64
-
- if state == "weak_and_warn":
- with pytest.warns(UserWarning,
- match="result dtype changed.*float64.*uint64"):
- with pytest.raises(OverflowError):
- ufunc(np.uint64(0), 2**64)
- else:
- with pytest.raises(OverflowError):
- ufunc(np.uint64(0), 2**64) # 2**64 cannot be represented by uint64
-
- # However, 2**63 can be represented by the uint64 (and that is used):
- if state == "weak_and_warn":
- with pytest.warns(UserWarning,
- match="result dtype changed.*float64.*uint64"):
- res = ufunc(np.uint64(1), 2**63)
- else:
- res = ufunc(np.uint64(1), 2**63)
-
- assert res.dtype == np.uint64
- assert res == ufunc(1, 2**63, dtype=object)
-
- # The following paths fail to warn correctly about the change:
- with pytest.raises(OverflowError):
- ufunc(np.int64(1), 2**63) # np.array(2**63) would go to uint
-
- with pytest.raises(OverflowError):
- ufunc(np.int64(1), 2**100) # np.array(2**100) would go to object
-
- # This would go to object and thus a Python float, not a NumPy one:
- res = ufunc(1.0, 2**100)
- assert isinstance(res, np.float64)
-
-
-def test_nep50_in_concat_and_choose():
- np._set_promotion_state("weak_and_warn")
-
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.concatenate([np.float32(1), 1.], axis=None)
- assert res.dtype == "float32"
-
- with pytest.warns(UserWarning, match="result dtype changed"):
- res = np.choose(1, [np.float32(1), 1.])
- assert res.dtype == "float32"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/pyproject.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/pyproject.py
deleted file mode 100644
index e183eaf86588b35a32117b4fa889842d3ea32216..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/pyproject.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import os
-from collections import namedtuple
-from typing import Any, List, Optional
-
-from pip._vendor import tomli
-from pip._vendor.packaging.requirements import InvalidRequirement, Requirement
-
-from pip._internal.exceptions import (
- InstallationError,
- InvalidPyProjectBuildRequires,
- MissingPyProjectBuildRequires,
-)
-
-
-def _is_list_of_str(obj: Any) -> bool:
- return isinstance(obj, list) and all(isinstance(item, str) for item in obj)
-
-
-def make_pyproject_path(unpacked_source_directory: str) -> str:
- return os.path.join(unpacked_source_directory, "pyproject.toml")
-
-
-BuildSystemDetails = namedtuple(
- "BuildSystemDetails", ["requires", "backend", "check", "backend_path"]
-)
-
-
-def load_pyproject_toml(
- use_pep517: Optional[bool], pyproject_toml: str, setup_py: str, req_name: str
-) -> Optional[BuildSystemDetails]:
- """Load the pyproject.toml file.
-
- Parameters:
- use_pep517 - Has the user requested PEP 517 processing? None
- means the user hasn't explicitly specified.
- pyproject_toml - Location of the project's pyproject.toml file
- setup_py - Location of the project's setup.py file
- req_name - The name of the requirement we're processing (for
- error reporting)
-
- Returns:
- None if we should use the legacy code path, otherwise a tuple
- (
- requirements from pyproject.toml,
- name of PEP 517 backend,
- requirements we should check are installed after setting
- up the build environment
- directory paths to import the backend from (backend-path),
- relative to the project root.
- )
- """
- has_pyproject = os.path.isfile(pyproject_toml)
- has_setup = os.path.isfile(setup_py)
-
- if not has_pyproject and not has_setup:
- raise InstallationError(
- f"{req_name} does not appear to be a Python project: "
- f"neither 'setup.py' nor 'pyproject.toml' found."
- )
-
- if has_pyproject:
- with open(pyproject_toml, encoding="utf-8") as f:
- pp_toml = tomli.loads(f.read())
- build_system = pp_toml.get("build-system")
- else:
- build_system = None
-
- # The following cases must use PEP 517
- # We check for use_pep517 being non-None and falsey because that means
- # the user explicitly requested --no-use-pep517. The value 0 as
- # opposed to False can occur when the value is provided via an
- # environment variable or config file option (due to the quirk of
- # strtobool() returning an integer in pip's configuration code).
- if has_pyproject and not has_setup:
- if use_pep517 is not None and not use_pep517:
- raise InstallationError(
- "Disabling PEP 517 processing is invalid: "
- "project does not have a setup.py"
- )
- use_pep517 = True
- elif build_system and "build-backend" in build_system:
- if use_pep517 is not None and not use_pep517:
- raise InstallationError(
- "Disabling PEP 517 processing is invalid: "
- "project specifies a build backend of {} "
- "in pyproject.toml".format(build_system["build-backend"])
- )
- use_pep517 = True
-
- # If we haven't worked out whether to use PEP 517 yet,
- # and the user hasn't explicitly stated a preference,
- # we do so if the project has a pyproject.toml file.
- elif use_pep517 is None:
- use_pep517 = has_pyproject
-
- # At this point, we know whether we're going to use PEP 517.
- assert use_pep517 is not None
-
- # If we're using the legacy code path, there is nothing further
- # for us to do here.
- if not use_pep517:
- return None
-
- if build_system is None:
- # Either the user has a pyproject.toml with no build-system
- # section, or the user has no pyproject.toml, but has opted in
- # explicitly via --use-pep517.
- # In the absence of any explicit backend specification, we
- # assume the setuptools backend that most closely emulates the
- # traditional direct setup.py execution, and require wheel and
- # a version of setuptools that supports that backend.
-
- build_system = {
- "requires": ["setuptools>=40.8.0", "wheel"],
- "build-backend": "setuptools.build_meta:__legacy__",
- }
-
- # If we're using PEP 517, we have build system information (either
- # from pyproject.toml, or defaulted by the code above).
- # Note that at this point, we do not know if the user has actually
- # specified a backend, though.
- assert build_system is not None
-
- # Ensure that the build-system section in pyproject.toml conforms
- # to PEP 518.
-
- # Specifying the build-system table but not the requires key is invalid
- if "requires" not in build_system:
- raise MissingPyProjectBuildRequires(package=req_name)
-
- # Error out if requires is not a list of strings
- requires = build_system["requires"]
- if not _is_list_of_str(requires):
- raise InvalidPyProjectBuildRequires(
- package=req_name,
- reason="It is not a list of strings.",
- )
-
- # Each requirement must be valid as per PEP 508
- for requirement in requires:
- try:
- Requirement(requirement)
- except InvalidRequirement as error:
- raise InvalidPyProjectBuildRequires(
- package=req_name,
- reason=f"It contains an invalid requirement: {requirement!r}",
- ) from error
-
- backend = build_system.get("build-backend")
- backend_path = build_system.get("backend-path", [])
- check: List[str] = []
- if backend is None:
- # If the user didn't specify a backend, we assume they want to use
- # the setuptools backend. But we can't be sure they have included
- # a version of setuptools which supplies the backend, or wheel
- # (which is needed by the backend) in their requirements. So we
- # make a note to check that those requirements are present once
- # we have set up the environment.
- # This is quite a lot of work to check for a very specific case. But
- # the problem is, that case is potentially quite common - projects that
- # adopted PEP 518 early for the ability to specify requirements to
- # execute setup.py, but never considered needing to mention the build
- # tools themselves. The original PEP 518 code had a similar check (but
- # implemented in a different way).
- backend = "setuptools.build_meta:__legacy__"
- check = ["setuptools>=40.8.0", "wheel"]
-
- return BuildSystemDetails(requires, backend, check, backend_path)
diff --git a/spaces/pythainlp/pythainlp/pages/Name Entity Recognition.py b/spaces/pythainlp/pythainlp/pages/Name Entity Recognition.py
deleted file mode 100644
index 5799db15ca1bb8fe3ddec49edfc74fb372406584..0000000000000000000000000000000000000000
--- a/spaces/pythainlp/pythainlp/pages/Name Entity Recognition.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import streamlit as st
-import time
-from pythainlp.tag import NER
-
-st.markdown("""
-# Name Entity Recognition
-
-PyThaiNLP support Name Entity Recognition. We have
-- thainer - Thai NER engine
-- wangchanberta* - wangchanberta model
-- tltk - wrapper for TLTK
-
-and trained with corpus
-
-- thainer - Thai NER corpus
-- lst20 - lst20 corpus (wangchanberta only)
-
-**Note**: for tltk engine, It's support ner model from tltk only.
-""")
-
-_engine =None
-with st.form("my_form"):
- st.write("Inside the form")
- text = st.text_area("text","พรุ่งนี้เจอกันเวลา 10.00 น. ที่สถาบันวิทยสิริเมธี")
- engine=st.selectbox('Select engine', ['thainer', 'wangchanberta', 'tltk'], key=1,index=0)
- corpus=st.selectbox('Select corpus', ['thainer', 'lst20'], key=1,index=0)
-
- # Every form must have a submit button.
- submitted = st.form_submit_button("Submit")
- if submitted:
- st.subheader("Tags: ")
- _engine = NER(engine=str(engine), corpus=str(corpus))
- st.write(_engine.tag(text,tag=True))
-
-st.write("See the documentation at [NER | PyThaiNLP](https://pythainlp.github.io/docs/3.0/api/tag.html#pythainlp.tag.NER).")
diff --git a/spaces/r3gm/RVC_HF/demucs/train.py b/spaces/r3gm/RVC_HF/demucs/train.py
deleted file mode 100644
index 6bd221279dc986a6df1a8d7b4d4444bb822a1cb3..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/demucs/train.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-import tqdm
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-
-from .utils import apply_model, average_metric, center_trim
-
-
-def train_model(epoch,
- dataset,
- model,
- criterion,
- optimizer,
- augment,
- quantizer=None,
- diffq=0,
- repeat=1,
- device="cpu",
- seed=None,
- workers=4,
- world_size=1,
- batch_size=16):
-
- if world_size > 1:
- sampler = DistributedSampler(dataset)
- sampler_epoch = epoch * repeat
- if seed is not None:
- sampler_epoch += seed * 1000
- sampler.set_epoch(sampler_epoch)
- batch_size //= world_size
- loader = DataLoader(dataset, batch_size=batch_size, sampler=sampler, num_workers=workers)
- else:
- loader = DataLoader(dataset, batch_size=batch_size, num_workers=workers, shuffle=True)
- current_loss = 0
- model_size = 0
- for repetition in range(repeat):
- tq = tqdm.tqdm(loader,
- ncols=120,
- desc=f"[{epoch:03d}] train ({repetition + 1}/{repeat})",
- leave=False,
- file=sys.stdout,
- unit=" batch")
- total_loss = 0
- for idx, sources in enumerate(tq):
- if len(sources) < batch_size:
- # skip uncomplete batch for augment.Remix to work properly
- continue
- sources = sources.to(device)
- sources = augment(sources)
- mix = sources.sum(dim=1)
-
- estimates = model(mix)
- sources = center_trim(sources, estimates)
- loss = criterion(estimates, sources)
- model_size = 0
- if quantizer is not None:
- model_size = quantizer.model_size()
-
- train_loss = loss + diffq * model_size
- train_loss.backward()
- grad_norm = 0
- for p in model.parameters():
- if p.grad is not None:
- grad_norm += p.grad.data.norm()**2
- grad_norm = grad_norm**0.5
- optimizer.step()
- optimizer.zero_grad()
-
- if quantizer is not None:
- model_size = model_size.item()
-
- total_loss += loss.item()
- current_loss = total_loss / (1 + idx)
- tq.set_postfix(loss=f"{current_loss:.4f}", ms=f"{model_size:.2f}",
- grad=f"{grad_norm:.5f}")
-
- # free some space before next round
- del sources, mix, estimates, loss, train_loss
-
- if world_size > 1:
- sampler.epoch += 1
-
- if world_size > 1:
- current_loss = average_metric(current_loss)
- return current_loss, model_size
-
-
-def validate_model(epoch,
- dataset,
- model,
- criterion,
- device="cpu",
- rank=0,
- world_size=1,
- shifts=0,
- overlap=0.25,
- split=False):
- indexes = range(rank, len(dataset), world_size)
- tq = tqdm.tqdm(indexes,
- ncols=120,
- desc=f"[{epoch:03d}] valid",
- leave=False,
- file=sys.stdout,
- unit=" track")
- current_loss = 0
- for index in tq:
- streams = dataset[index]
- # first five minutes to avoid OOM on --upsample models
- streams = streams[..., :15_000_000]
- streams = streams.to(device)
- sources = streams[1:]
- mix = streams[0]
- estimates = apply_model(model, mix, shifts=shifts, split=split, overlap=overlap)
- loss = criterion(estimates, sources)
- current_loss += loss.item() / len(indexes)
- del estimates, streams, sources
-
- if world_size > 1:
- current_loss = average_metric(current_loss, len(indexes))
- return current_loss
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/All AutoCAD Plant 3D 2019 Products Crack Keygen (x86x64) !Latest .rar Benefits and Features.md b/spaces/raedeXanto/academic-chatgpt-beta/All AutoCAD Plant 3D 2019 Products Crack Keygen (x86x64) !Latest .rar Benefits and Features.md
deleted file mode 100644
index 862da552c1f031c67499fbbfb4851828e881e2ad..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/All AutoCAD Plant 3D 2019 Products Crack Keygen (x86x64) !Latest .rar Benefits and Features.md
+++ /dev/null
@@ -1,77 +0,0 @@
-All AutoCAD Plant 3D 2019 Products Crack Keygen (x86x64) !Latest .rar
-Introduction
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-
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diff --git a/spaces/rendchevi/nix-tts/nix/__init__.py b/spaces/rendchevi/nix-tts/nix/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/sintel_kitti2015_hd1k_maskflownet_320x768.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/sintel_kitti2015_hd1k_maskflownet_320x768.py
deleted file mode 100644
index eda305987e4cd8b4b47644c5a3e5f4a5cac05b57..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/sintel_kitti2015_hd1k_maskflownet_320x768.py
+++ /dev/null
@@ -1,217 +0,0 @@
-img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=False)
-
-crop_size = (320, 768)
-
-sintel_global_transform = dict(
- translates=(0.05, 0.05),
- zoom=(1.0, 1.2),
- shear=(0.95, 1.1),
- rotate=(-5., 5.))
-
-sintel_relative_transform = dict(
- translates=(0.00375, 0.00375),
- zoom=(0.985, 1.015),
- shear=(1.0, 1.0),
- rotate=(-1.0, 1.0))
-
-sintel_train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_occ=True),
- dict(
- type='ColorJitter',
- brightness=0.5,
- contrast=0.5,
- saturation=0.5,
- hue=0.5),
- dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0., 1.)),
- dict(type='RandomFlip', prob=0.5, direction='horizontal'),
- dict(type='RandomFlip', prob=0.5, direction='vertical'),
- dict(
- type='RandomAffine',
- global_transform=sintel_global_transform,
- relative_transform=sintel_relative_transform),
- dict(type='RandomCrop', crop_size=crop_size),
- dict(type='DefaultFormatBundle'),
- dict(
- type='Collect',
- keys=['imgs', 'flow_gt'],
- meta_keys=[
- 'img_fields', 'ann_fields', 'filename1', 'filename2',
- 'ori_filename1', 'ori_filename2', 'filename_flow',
- 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg'
- ]),
-]
-
-sintel_test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='InputPad', exponent=6),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='TestFormatBundle'),
- dict(
- type='Collect',
- keys=['imgs'],
- meta_keys=[
- 'flow_gt', 'filename1', 'filename2', 'ori_filename1',
- 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
- 'scale_factor', 'pad_shape', 'pad'
- ])
-]
-
-sintel_clean_train = dict(
- type='Sintel',
- pipeline=sintel_train_pipeline,
- data_root='data/Sintel',
- test_mode=False,
- scene=[
- 'alley_1', 'alley_2', 'ambush_4', 'ambush_5', 'ambush_7', 'bamboo_1',
- 'bandage_1', 'bandage_2', 'cave_2', 'market_2', 'market_5',
- 'mountain_1', 'shaman_2', 'shaman_3', 'sleeping_1', 'sleeping_2',
- 'temple_3'
- ],
- pass_style='clean')
-
-sintel_final_train = dict(
- type='Sintel',
- pipeline=sintel_train_pipeline,
- data_root='data/Sintel',
- test_mode=False,
- scene=[
- 'alley_1', 'alley_2', 'ambush_4', 'ambush_5', 'ambush_7', 'bamboo_1',
- 'bandage_1', 'bandage_2', 'cave_2', 'market_2', 'market_5',
- 'mountain_1', 'shaman_2', 'shaman_3', 'sleeping_1', 'sleeping_2',
- 'temple_3'
- ],
- pass_style='final')
-
-sintel_clean_test = dict(
- type='Sintel',
- pipeline=sintel_test_pipeline,
- data_root='data/Sintel',
- test_mode=True,
- scene=[
- 'ambush_2', 'ambush_6', 'bamboo_2', 'cave_4', 'market_6', 'temple_2'
- ],
- pass_style='clean')
-
-sintel_final_test = dict(
- type='Sintel',
- pipeline=sintel_test_pipeline,
- data_root='data/Sintel',
- test_mode=True,
- scene=[
- 'ambush_2', 'ambush_6', 'bamboo_2', 'cave_4', 'market_6', 'temple_2'
- ],
- pass_style='final')
-
-kitti_global_transform = dict(
- translates=(0.02, 0.02),
- zoom=(0.98, 1.02),
- shear=(1.0, 1.0),
- rotate=(-0.5, 0.5))
-
-kitti_relative_transform = dict(
- translates=(0.0025, 0.0025),
- zoom=(0.99, 1.01),
- shear=(1.0, 1.0),
- rotate=(-0.5, 0.5))
-
-sparse_train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', sparse=True),
- dict(type='RandomCrop', crop_size=crop_size * 2),
- dict(
- type='ColorJitter',
- brightness=0.05,
- contrast=0.2,
- saturation=0.25,
- hue=0.1),
- dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='GaussianNoise', sigma_range=(0, 0.02), clamp_range=(0., 1.)),
- dict(type='RandomFlip', prob=0.5, direction='horizontal'),
- dict(type='RandomFlip', prob=0.5, direction='vertical'),
- dict(
- type='RandomAffine',
- global_transform=kitti_global_transform,
- relative_transform=kitti_relative_transform),
- dict(type='RandomCrop', crop_size=crop_size),
- dict(type='DefaultFormatBundle'),
- dict(
- type='Collect',
- keys=['imgs', 'flow_gt', 'valid'],
- meta_keys=[
- 'img_fields', 'ann_fields', 'filename1', 'filename2',
- 'ori_filename1', 'ori_filename2', 'filename_flow',
- 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg'
- ]),
-]
-
-kitti2015_train = dict(
- type='KITTI2015',
- data_root='data/kitti2015',
- pipeline=sparse_train_pipeline,
- test_mode=False)
-
-hd1k_train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', sparse=True),
- dict(type='RandomCrop', crop_size=(436, 1024)),
- dict(
- type='ColorJitter',
- brightness=0.05,
- contrast=0.2,
- saturation=0.25,
- hue=0.1),
- dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='GaussianNoise', sigma_range=(0, 0.02), clamp_range=(0., 1.)),
- dict(type='RandomFlip', prob=0.5, direction='horizontal'),
- dict(type='RandomFlip', prob=0.5, direction='vertical'),
- dict(
- type='RandomAffine',
- global_transform=kitti_global_transform,
- relative_transform=kitti_relative_transform),
- dict(type='RandomCrop', crop_size=crop_size),
- dict(type='DefaultFormatBundle'),
- dict(
- type='Collect',
- keys=['imgs', 'flow_gt', 'valid'],
- meta_keys=[
- 'img_fields', 'ann_fields', 'filename1', 'filename2',
- 'ori_filename1', 'ori_filename2', 'filename_flow',
- 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg'
- ]),
-]
-
-hd1k_train = dict(
- type='HD1K',
- data_root='data/hd1k',
- pipeline=hd1k_train_pipeline,
- test_mode=False),
-
-data = dict(
- train_dataloader=dict(
- samples_per_gpu=1,
- workers_per_gpu=2,
- drop_last=True,
- sample_ratio=(0.5, 0.25, 0.25),
- persistent_workers=True),
- val_dataloader=dict(
- samples_per_gpu=1,
- workers_per_gpu=5,
- shuffle=False,
- persistent_workers=True),
- test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False),
- train=[[sintel_clean_train, sintel_final_train], kitti2015_train,
- hd1k_train],
- val=dict(
- type='ConcatDataset',
- datasets=[sintel_clean_test, sintel_final_test],
- separate_eval=True),
- test=dict(
- type='ConcatDataset',
- datasets=[sintel_clean_test, sintel_final_test],
- separate_eval=True))
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Alce Plugin !!TOP!! Download.md b/spaces/rorallitri/biomedical-language-models/logs/Alce Plugin !!TOP!! Download.md
deleted file mode 100644
index 4df54a18fb424b5c281bd0cc24fd6547db2c14d3..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Alce Plugin !!TOP!! Download.md
+++ /dev/null
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Couple Cam Sexy Web.md b/spaces/rorallitri/biomedical-language-models/logs/Couple Cam Sexy Web.md
deleted file mode 100644
index 79ed449b97f0e1c34666decf4ba9afbed0cfed17..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Couple Cam Sexy Web.md
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aaccfb2cb3.
-'''
-
-try: import xml.etree.cElementTree as E
-except: import xml.etree.ElementTree as E
-import os, sys, types, re, math
-
-VERSION = 143
-
-python3 = sys.version_info.major > 2
-if python3:
- tupletype = tuple
- listtype = list
- max_int = sys.maxsize
-else:
- tupletype = types.TupleType
- listtype = types.ListType
- max_int = sys.maxint
-
-note_ornamentation_map = { # for notations/, modified from EasyABC
- 'ornaments/trill-mark': 'T',
- 'ornaments/mordent': 'M',
- 'ornaments/inverted-mordent': 'P',
- 'ornaments/turn': '!turn!',
- 'ornaments/inverted-turn': '!invertedturn!',
- 'technical/up-bow': 'u',
- 'technical/down-bow': 'v',
- 'technical/harmonic': '!open!',
- 'technical/open-string': '!open!',
- 'technical/stopped': '!plus!',
- 'technical/snap-pizzicato': '!snap!',
- 'technical/thumb-position': '!thumb!',
- 'articulations/accent': '!>!',
- 'articulations/strong-accent':'!^!',
- 'articulations/staccato': '.',
- 'articulations/staccatissimo':'!wedge!',
- 'articulations/scoop': '!slide!',
- 'fermata': '!fermata!',
- 'arpeggiate': '!arpeggio!',
- 'articulations/tenuto': '!tenuto!',
- 'articulations/staccatissimo':'!wedge!', # not sure whether this is the right translation
- 'articulations/spiccato': '!wedge!', # not sure whether this is the right translation
- 'articulations/breath-mark': '!breath!', # this may need to be tested to make sure it appears on the right side of the note
- 'articulations/detached-legato': '!tenuto!.',
-}
-
-dynamics_map = { # for direction/direction-type/dynamics/
- 'p': '!p!',
- 'pp': '!pp!',
- 'ppp': '!ppp!',
- 'pppp': '!pppp!',
- 'f': '!f!',
- 'ff': '!ff!',
- 'fff': '!fff!',
- 'ffff': '!ffff!',
- 'mp': '!mp!',
- 'mf': '!mf!',
- 'sfz': '!sfz!',
-}
-
-percSvg = '''%%beginsvg
-
-
-
-
-
-
-
-
- %%endsvg'''
-
-tabSvg = '''%%beginsvg
-
-
- %s %s element
- if s.tijd > s.maxtime: s.maxtime = s.tijd
-
- def appendElemCv (s, voices, elem):
- for v in voices:
- s.appendElem (v, elem) # insert element in all voices
-
- def insertElem (s, v, elem): # insert at the start of voice v in the current measure
- obj = Elem (elem)
- obj.tijd = 0 # because voice is sorted later
- s.voices [v].insert (0, obj)
-
- def appendObj (s, v, obj, dur):
- obj.tijd = s.tijd
- s.voices [v].append (obj)
- s.incTime (dur)
- if s.tijd > s.vtimes[v]: s.vtimes[v] = s.tijd # don't update for inserted earlier items
-
- def appendElem (s, v, elem, tel=0):
- s.appendObj (v, Elem (elem), 0)
- if tel: s.cnt.inc ('note', v) # count number of certain elements in each voice (in addition to notes)
-
- def appendElemT (s, v, elem, tijd): # insert element at specified time
- obj = Elem (elem)
- obj.tijd = tijd
- s.voices [v].append (obj)
-
- def appendNote (s, v, note, noot):
- note.ns.append (note.ntdec + noot)
- s.appendObj (v, note, int (note.dur))
- s.lastnote = note # remember last note/rest for later modifications (chord, grace)
- if noot != 'z' and noot != 'x': # real notes and grace notes
- s.cnt.inc ('note', v) # count number of real notes in each voice
- if not note.grace: # for every real note
- s.lyrics[v].append (note.lyrs) # even when it has no lyrics
-
- def getLastRec (s, voice):
- if s.gMaten: return s.gMaten[-1][voice][-1] # the last record in the last measure
- return None # no previous records in the first measure
-
- def getLastMelis (s, voice, num): # get melisma of last measure
- if s.gLyrics:
- lyrdict = s.gLyrics[-1][voice] # the previous lyrics dict in this voice
- if num in lyrdict: return lyrdict[num][1] # lyrdict = num -> (lyric string, melisma)
- return 0 # no previous lyrics in voice or line number
-
- def addChord (s, note, noot): # careful: we assume that chord notes follow immediately
- for d in note.before: # put all decorations before chord
- if d not in s.lastnote.before:
- s.lastnote.before += [d]
- s.lastnote.ns.append (note.ntdec + noot)
-
- def addBar (s, lbrk, m): # linebreak, measure data
- if m.mdur and s.maxtime > m.mdur: info ('measure %d in part %d longer than metre' % (m.ixm+1, m.ixp+1))
- s.tijd = s.maxtime # the time of the bar lines inserted here
- for v in s.vnums:
- if m.lline or m.lnum: # if left barline or left volta number
- p = s.getLastRec (v) # get the previous barline record
- if p: # in measure 1 no previous measure is available
- x = p.str # p.str is the ABC barline string
- if m.lline: # append begin of repeat, m.lline == ':'
- x = (x + m.lline).replace (':|:','::').replace ('||','|')
- if s.nvlt == 3: # add volta number only to lowest voice in part 0
- if m.ixp + v == min (s.vnums): x += m.lnum
- elif m.lnum: # new behaviour with I:repbra 0
- x += m.lnum # add volta number(s) or text to all voices
- s.repbra = 1 # signal occurrence of a volta
- p.str = x # modify previous right barline
- elif m.lline: # begin of new part and left repeat bar is required
- s.insertElem (v, '|:')
- if lbrk:
- p = s.getLastRec (v) # get the previous barline record
- if p: p.str += lbrk # insert linebreak char after the barlines+volta
- if m.attr: # insert signatures at front of buffer
- s.insertElem (v, '%s' % m.attr)
- s.appendElem (v, ' %s' % m.rline) # insert current barline record at time maxtime
- s.voices[v] = sortMeasure (s.voices[v], m) # make all times consistent
- lyrs = s.lyrics[v] # [{number: sylabe}, .. for all notes]
- lyrdict = {} # {number: (abc_lyric_string, melis)} for this voice
- nums = [num for d in lyrs for num in d.keys ()] # the lyrics numbers in this measure
- maxNums = max (nums + [0]) # the highest lyrics number in this measure
- for i in range (maxNums, 0, -1):
- xs = [syldict.get (i, '') for syldict in lyrs] # collect the syllabi with number i
- melis = s.getLastMelis (v, i) # get melisma from last measure
- lyrdict [i] = abcLyr (xs, melis)
- s.lyrics[v] = lyrdict # {number: (abc_lyric_string, melis)} for this measure
- mkBroken (s.voices[v])
- s.gMaten.append (s.voices)
- s.gLyrics.append (s.lyrics)
- s.tijd = s.maxtime = 0
- s.initVoices ()
-
- def outVoices (s, divs, ip, isSib): # output all voices of part ip
- vvmap = {} # xml voice number -> abc voice number (one part)
- vnum_keys = list (s.vnums.keys ())
- if s.jscript or isSib: vnum_keys.sort ()
- lvc = min (vnum_keys or [1]) # lowest xml voice number of this part
- for iv in vnum_keys:
- if s.cnt.getv ('note', iv) == 0: # no real notes counted in this voice
- continue # skip empty voices
- if abcOut.denL: unitL = abcOut.denL # take the unit length from the -d option
- else: unitL = compUnitLength (iv, s.gMaten, divs) # compute the best unit length for this voice
- abcOut.cmpL.append (unitL) # remember for header output
- vn, vl = [], {} # for voice iv: collect all notes to vn and all lyric lines to vl
- for im in range (len (s.gMaten)):
- measure = s.gMaten [im][iv]
- vn.append (outVoice (measure, divs [im], im, ip, unitL))
- checkMelismas (s.gLyrics, s.gMaten, im, iv)
- for n, (lyrstr, melis) in s.gLyrics [im][iv].items ():
- if n in vl:
- while len (vl[n]) < im: vl[n].append ('') # fill in skipped measures
- vl[n].append (lyrstr)
- else:
- vl[n] = im * [''] + [lyrstr] # must skip im measures
- for n, lyrs in vl.items (): # fill up possibly empty lyric measures at the end
- mis = len (vn) - len (lyrs)
- lyrs += mis * ['']
- abcOut.add ('V:%d' % s.vceCnt)
- if s.repbra:
- if s.nvlt == 1 and s.vceCnt > 1: abcOut.add ('I:repbra 0') # only volta on first voice
- if s.nvlt == 2 and iv > lvc: abcOut.add ('I:repbra 0') # only volta on first voice of each part
- if s.cpl > 0: s.bpl = 0 # option -n (max chars per line) overrules -b (max bars per line)
- elif s.bpl == 0: s.cpl = 100 # the default: 100 chars per line
- bn = 0 # count bars
- while vn: # while still measures available
- ib = 1
- chunk = vn [0]
- while ib < len (vn):
- if s.cpl > 0 and len (chunk) + len (vn [ib]) >= s.cpl: break # line full (number of chars)
- if s.bpl > 0 and ib >= s.bpl: break # line full (number of bars)
- chunk += vn [ib]
- ib += 1
- bn += ib
- abcOut.add (chunk + ' %%%d' % bn) # line with barnumer
- del vn[:ib] # chop ib bars
- lyrlines = sorted (vl.items ()) # order the numbered lyric lines for output
- for n, lyrs in lyrlines:
- abcOut.add ('w: ' + '|'.join (lyrs[:ib]) + '|')
- del lyrs[:ib]
- vvmap [iv] = s.vceCnt # xml voice number -> abc voice number
- s.vceCnt += 1 # count voices over all parts
- s.gMaten = [] # reset the follwing instance vars for each part
- s.gLyrics = []
- s.cnt.prcnt (ip+1) # print summary of skipped items in this part
- return vvmap
-
-class ABCoutput:
- pagekeys = 'scale,pageheight,pagewidth,leftmargin,rightmargin,topmargin,botmargin'.split (',')
- def __init__ (s, fnmext, pad, X, options):
- s.fnmext = fnmext
- s.outlist = [] # list of ABC strings
- s.title = 'T:Title'
- s.key = 'none'
- s.clefs = {} # clefs for all abc-voices
- s.mtr = 'none'
- s.tempo = 0 # 0 -> no tempo field
- s.tempo_units = (1,4) # note type of tempo direction
- s.pad = pad # the output path or none
- s.X = X + 1 # the abc tune number
- s.denL = options.d # denominator of the unit length (L:) from -d option
- s.volpan = int (options.m) # 0 -> no %%MIDI, 1 -> only program, 2 -> all %%MIDI
- s.cmpL = [] # computed optimal unit length for all voices
- s.jscript = options.j # compatibility with javascript version
- s.tstep = options.t # translate percmap to voicemap
- s.stemless = 0 # use U:s=!stemless!
- s.shiftStem = options.s # shift note heads 3 units left
- if pad:
- _, base_name = os.path.split (fnmext)
- s.outfile = open (os.path.join (pad, base_name), 'w')
- else: s.outfile = sys.stdout
- if s.jscript: s.X = 1 # always X:1 in javascript version
- s.pageFmt = {}
- for k in s.pagekeys: s.pageFmt [k] = None
- if len (options.p) == 7:
- for k, v in zip (s.pagekeys, options.p):
- try: s.pageFmt [k] = float (v)
- except: info ('illegal float %s for %s', (k, v)); continue
-
- def add (s, str):
- s.outlist.append (str + '\n') # collect all ABC output
-
- def mkHeader (s, stfmap, partlist, midimap, vmpdct, koppen): # stfmap = [parts], part = [staves], stave = [voices]
- accVce, accStf, staffs = [], [], stfmap[:] # staffs is consumed
- for x in partlist: # collect partnames into accVce and staff groups into accStf
- try: prgroupelem (x, ('', ''), '', stfmap, accVce, accStf)
- except: info ('lousy musicxml: error in part-list')
- staves = ' '.join (accStf)
- clfnms = {}
- for part, (partname, partabbrv) in zip (staffs, accVce):
- if not part: continue # skip empty part
- firstVoice = part[0][0] # the first voice number in this part
- nm = partname.replace ('\n','\\n').replace ('.:','.').strip (':')
- snm = partabbrv.replace ('\n','\\n').replace ('.:','.').strip (':')
- clfnms [firstVoice] = (nm and 'nm="%s"' % nm or '') + (snm and ' snm="%s"' % snm or '')
- hd = ['X:%d\n%s\n' % (s.X, s.title)]
- for i, k in enumerate (s.pagekeys):
- if s.jscript and k in ['pageheight','topmargin', 'botmargin']: continue
- if s.pageFmt [k] != None: hd.append ('%%%%%s %.2f%s\n' % (k, s.pageFmt [k], i > 0 and 'cm' or ''))
- if staves and len (accStf) > 1: hd.append ('%%score ' + staves + '\n')
- tempo = s.tempo and 'Q:%d/%d=%s\n' % (s.tempo_units [0], s.tempo_units [1], s.tempo) or '' # default no tempo field
- d = {} # determine the most frequently occurring unit length over all voices
- for x in s.cmpL: d[x] = d.get (x, 0) + 1
- if s.jscript: defLs = sorted (d.items (), key=lambda x: (-x[1], x[0])) # when tie (1) sort on key (0)
- else: defLs = sorted (d.items (), key=lambda x: -x[1])
- defL = s.denL and s.denL or defLs [0][0] # override default unit length with -d option
- hd.append ('L:1/%d\n%sM:%s\n' % (defL, tempo, s.mtr))
- hd.append ('K:%s\n' % s.key)
- if s.stemless: hd.append ('U:s=!stemless!\n')
- vxs = sorted (vmpdct.keys ())
- for vx in vxs: hd.extend (vmpdct [vx])
- s.dojef = 0 # translate percmap to voicemap
- for vnum, clef in s.clefs.items ():
- ch, prg, vol, pan = midimap [vnum-1][:4]
- dmap = midimap [vnum - 1][4:] # map of abc percussion notes to midi notes
- if dmap and 'perc' not in clef: clef = (clef + ' map=perc').strip ();
- hd.append ('V:%d %s %s\n' % (vnum, clef, clfnms.get (vnum, '')))
- if vnum in vmpdct:
- hd.append ('%%%%voicemap tab%d\n' % vnum)
- hd.append ('K:none\nM:none\n%%clef none\n%%staffscale 1.6\n%%flatbeams true\n%%stemdir down\n')
- if 'perc' in clef: hd.append ('K:none\n'); # no key for a perc voice
- if s.volpan > 1: # option -m 2 -> output all recognized midi commands when needed and present in xml
- if ch > 0 and ch != vnum: hd.append ('%%%%MIDI channel %d\n' % ch)
- if prg > 0: hd.append ('%%%%MIDI program %d\n' % (prg - 1))
- if vol >= 0: hd.append ('%%%%MIDI control 7 %.0f\n' % vol) # volume == 0 is possible ...
- if pan >= 0: hd.append ('%%%%MIDI control 10 %.0f\n' % pan)
- elif s.volpan > 0: # default -> only output midi program command when present in xml
- if dmap and ch > 0: hd.append ('%%%%MIDI channel %d\n' % ch) # also channel if percussion part
- if prg > 0: hd.append ('%%%%MIDI program %d\n' % (prg - 1))
- for abcNote, step, midiNote, notehead in dmap:
- if not notehead: notehead = 'normal'
- if abcMid (abcNote) != midiNote or abcNote != step:
- if s.volpan > 0: hd.append ('%%%%MIDI drummap %s %s\n' % (abcNote, midiNote))
- hd.append ('I:percmap %s %s %s %s\n' % (abcNote, step, midiNote, notehead))
- s.dojef = s.tstep
- if defL != s.cmpL [vnum-1]: # only if computed unit length different from header
- hd.append ('L:1/%d\n' % s.cmpL [vnum-1])
- s.outlist = hd + s.outlist
- if koppen: # output SVG stuff needed for tablature
- k1 = kopSvg.replace ('-2','-5') if s.shiftStem else kopSvg # shift note heads 3 units left
- k2 = kopSvg2.replace ('-2','-5') if s.shiftStem else kopSvg2
- tb = tabSvg.replace ('-3','-6') if s.shiftStem else tabSvg
- ks = sorted (koppen.keys ()) # javascript compatibility
- ks = [k2 % (k, k) if len (k) == 2 else k1 % (k, k) for k in ks]
- tbs = map (lambda x: x.strip () + '\n', tb.splitlines ()) # javascript compatibility
- s.outlist = tbs + ks + [' \n%%endsvg\n'] + s.outlist
-
- def writeall (s): # determine the required encoding of the entire ABC output
- str = ''.join (s.outlist)
- if s.dojef: str = perc2map (str)
- if python3: s.outfile.write (str)
- else: s.outfile.write (str.encode ('utf-8'))
- if s.pad: s.outfile.close () # close each file with -o option
- else: s.outfile.write ('\n') # add empty line between tunes on stdout
- info ('%s written with %d voices' % (s.fnmext, len (s.clefs)), warn=0)
-
-#----------------
-# functions
-#----------------
-def abcLyr (xs, melis): # Convert list xs to abc lyrics.
- if not ''.join (xs): return '', 0 # there is no lyrics in this measure
- res = []
- for x in xs: # xs has for every note a lyrics syllabe or an empty string
- if x == '': # note without lyrics
- if melis: x = '_' # set melisma
- else: x = '*' # skip note
- elif x.endswith ('_') and not x.endswith ('\_'): # start of new melisma
- x = x.replace ('_', '') # remove and set melis boolean
- melis = 1 # so next skips will become melisma
- else: melis = 0 # melisma stops on first syllable
- res.append (x)
- return (' '.join (res), melis)
-
-def simplify (a, b): # divide a and b by their greatest common divisor
- x, y = a, b
- while b: a, b = b, a % b
- return x // a, y // a
-
-def abcdur (nx, divs, uL): # convert an musicXML duration d to abc units with L:1/uL
- if nx.dur == 0: return '' # when called for elements without duration
- num, den = simplify (uL * nx.dur, divs * 4) # L=1/8 -> uL = 8 units
- if nx.fact: # apply tuplet time modification
- numfac, denfac = nx.fact
- num, den = simplify (num * numfac, den * denfac)
- if den > 64: # limit the denominator to a maximum of 64
- x = float (num) / den; n = math.floor (x); # when just above an integer n
- if x - n < 0.1 * x: num, den = n, 1; # round to n
- num64 = 64. * num / den + 1.0e-15 # to get Python2 behaviour of round
- num, den = simplify (int (round (num64)), 64)
- if num == 1:
- if den == 1: dabc = ''
- elif den == 2: dabc = '/'
- else: dabc = '/%d' % den
- elif den == 1: dabc = '%d' % num
- else: dabc = '%d/%d' % (num, den)
- return dabc
-
-def abcMid (note): # abc note -> midi pitch
- r = re.search (r"([_^]*)([A-Ga-g])([',]*)", note)
- if not r: return -1
- acc, n, oct = r.groups ()
- nUp = n.upper ()
- p = 60 + [0,2,4,5,7,9,11]['CDEFGAB'.index (nUp)] + (12 if nUp != n else 0);
- if acc: p += (1 if acc[0] == '^' else -1) * len (acc)
- if oct: p += (12 if oct[0] == "'" else -12) * len (oct)
- return p
-
-def staffStep (ptc, o, clef, tstep):
- ndif = 0
- if 'stafflines=1' in clef: ndif += 4 # meaning of one line: E (xml) -> B (abc)
- if not tstep and clef.startswith ('bass'): ndif += 12 # transpose bass -> treble (C3 -> A4)
- if ndif: # diatonic transposition == addition modulo 7
- nm7 = 'C,D,E,F,G,A,B'.split (',')
- n = nm7.index (ptc) + ndif
- ptc, o = nm7 [n % 7], o + n // 7
- if o > 4: ptc = ptc.lower ()
- if o > 5: ptc = ptc + (o-5) * "'"
- if o < 4: ptc = ptc + (4-o) * ","
- return ptc
-
-def setKey (fifths, mode):
- sharpness = ['Fb', 'Cb','Gb','Db','Ab','Eb','Bb','F','C','G','D','A', 'E', 'B', 'F#','C#','G#','D#','A#','E#','B#']
- offTab = {'maj':8, 'ion':8, 'm':11, 'min':11, 'aeo':11, 'mix':9, 'dor':10, 'phr':12, 'lyd':7, 'loc':13, 'non':8}
- mode = mode.lower ()[:3] # only first three chars, no case
- key = sharpness [offTab [mode] + fifths] + (mode if offTab [mode] != 8 else '')
- accs = ['F','C','G','D','A','E','B']
- if fifths >= 0: msralts = dict (zip (accs[:fifths], fifths * [1]))
- else: msralts = dict (zip (accs[fifths:], -fifths * [-1]))
- return key, msralts
-
-def insTup (ix, notes, fact): # read one nested tuplet
- tupcnt = 0
- nx = notes [ix]
- if 'start' in nx.tup:
- nx.tup.remove ('start') # do recursive calls when starts remain
- tix = ix # index of first tuplet note
- fn, fd = fact # xml time-mod of the higher level
- fnum, fden = nx.fact # xml time-mod of the current level
- tupfact = fnum//fn, fden//fd # abc time mod of this level
- while ix < len (notes):
- nx = notes [ix]
- if isinstance (nx, Elem) or nx.grace:
- ix += 1 # skip all non tuplet elements
- continue
- if 'start' in nx.tup: # more nested tuplets to start
- ix, tupcntR = insTup (ix, notes, tupfact) # ix is on the stop note!
- tupcnt += tupcntR
- elif nx.fact:
- tupcnt += 1 # count tuplet elements
- if 'stop' in nx.tup:
- nx.tup.remove ('stop')
- break
- if not nx.fact: # stop on first non tuplet note
- ix = lastix # back to last tuplet note
- break
- lastix = ix
- ix += 1
- # put abc tuplet notation before the recursive ones
- tup = (tupfact[0], tupfact[1], tupcnt)
- if tup == (3, 2, 3): tupPrefix = '(3'
- else: tupPrefix = '(%d:%d:%d' % tup
- notes [tix].tupabc = tupPrefix + notes [tix].tupabc
- return ix, tupcnt # ix is on the last tuplet note
-
-def mkBroken (vs): # introduce broken rhythms (vs: one voice, one measure)
- vs = [n for n in vs if isinstance (n, Note)]
- i = 0
- while i < len (vs) - 1:
- n1, n2 = vs[i], vs[i+1] # scan all adjacent pairs
- # skip if note in tuplet or has no duration or outside beam
- if not n1.fact and not n2.fact and n1.dur > 0 and n2.beam:
- if n1.dur * 3 == n2.dur:
- n2.dur = (2 * n2.dur) // 3
- n1.dur = n1.dur * 2
- n1.after = '<' + n1.after
- i += 1 # do not chain broken rhythms
- elif n2.dur * 3 == n1.dur:
- n1.dur = (2 * n1.dur) // 3
- n2.dur = n2.dur * 2
- n1.after = '>' + n1.after
- i += 1 # do not chain broken rhythms
- i += 1
-
-def outVoice (measure, divs, im, ip, unitL): # note/elem objects of one measure in one voice
- ix = 0
- while ix < len (measure): # set all (nested) tuplet annotations
- nx = measure [ix]
- if isinstance (nx, Note) and nx.fact and not nx.grace:
- ix, tupcnt = insTup (ix, measure, (1, 1)) # read one tuplet, insert annotation(s)
- ix += 1
- vs = []
- for nx in measure:
- if isinstance (nx, Note):
- durstr = abcdur (nx, divs, unitL) # xml -> abc duration string
- chord = len (nx.ns) > 1
- cns = [nt[:-1] for nt in nx.ns if nt.endswith ('-')]
- tie = ''
- if chord and len (cns) == len (nx.ns): # all chord notes tied
- nx.ns = cns # chord notes without tie
- tie = '-' # one tie for whole chord
- s = nx.tupabc + ''.join (nx.before)
- if chord: s += '['
- for nt in nx.ns: s += nt
- if chord: s += ']' + tie
- if s.endswith ('-'): s, tie = s[:-1], '-' # split off tie
- s += durstr + tie # and put it back again
- s += nx.after
- nospace = nx.beam
- else:
- if isinstance (nx.str, listtype): nx.str = nx.str [0]
- s = nx.str
- nospace = 1
- if nospace: vs.append (s)
- else: vs.append (' ' + s)
- vs = ''.join (vs) # ad hoc: remove multiple pedal directions
- while vs.find ('!ped!!ped!') >= 0: vs = vs.replace ('!ped!!ped!','!ped!')
- while vs.find ('!ped-up!!ped-up!') >= 0: vs = vs.replace ('!ped-up!!ped-up!','!ped-up!')
- while vs.find ('!8va(!!8va)!') >= 0: vs = vs.replace ('!8va(!!8va)!','') # remove empty ottava's
- return vs
-
-def sortMeasure (voice, m):
- voice.sort (key=lambda o: o.tijd) # sort on time
- time = 0
- v = []
- rs = [] # holds rests in between notes
- for i, nx in enumerate (voice): # establish sequentiality
- if nx.tijd > time and chkbug (nx.tijd - time, m):
- v.append (Note (nx.tijd - time, 'x')) # fill hole with invisble rest
- rs.append (len (v) - 1)
- if isinstance (nx, Elem):
- if nx.tijd < time: nx.tijd = time # shift elems without duration to where they fit
- v.append (nx)
- time = nx.tijd
- continue
- if nx.tijd < time: # overlapping element
- if nx.ns[0] == 'z': continue # discard overlapping rest
- if v[-1].tijd <= nx.tijd: # we can do something
- if v[-1].ns[0] == 'z': # shorten rest
- v[-1].dur = nx.tijd - v[-1].tijd
- if v[-1].dur == 0: del v[-1] # nothing left
- info ('overlap in part %d, measure %d: rest shortened' % (m.ixp+1, m.ixm+1))
- else: # make a chord of overlap
- v[-1].ns += nx.ns
- info ('overlap in part %d, measure %d: added chord' % (m.ixp+1, m.ixm+1))
- nx.dur = (nx.tijd + nx.dur) - time # the remains
- if nx.dur <= 0: continue # nothing left
- nx.tijd = time # append remains
- else: # give up
- info ('overlapping notes in one voice! part %d, measure %d, note %s discarded' % (m.ixp+1, m.ixm+1, isinstance (nx, Note) and nx.ns or nx.str))
- continue
- v.append (nx)
- if isinstance (nx, Note):
- if nx.ns [0] in 'zx':
- rs.append (len (v) - 1) # remember rests between notes
- elif len (rs):
- if nx.beam and not nx.grace: # copy beam into rests
- for j in rs: v[j].beam = nx.beam
- rs = [] # clear rests on each note
- time = nx.tijd + nx.dur
- # when a measure contains no elements and no forwards -> no incTime -> s.maxtime = 0 -> right barline
- # is inserted at time == 0 (in addbar) and is only element in the voice when sortMeasure is called
- if time == 0: info ('empty measure in part %d, measure %d, it should contain at least a rest to advance the time!' % (m.ixp+1, m.ixm+1))
- return v
-
-def getPartlist (ps): # correct part-list (from buggy xml-software)
- xs = [] # the corrected part-list
- e = [] # stack of opened part-groups
- for x in list (ps): # insert missing stops, delete double starts
- if x.tag == 'part-group':
- num, type = x.get ('number'), x.get ('type')
- if type == 'start':
- if num in e: # missing stop: insert one
- xs.append (E.Element ('part-group', number = num, type = 'stop'))
- xs.append (x)
- else: # normal start
- xs.append (x)
- e.append (num)
- else:
- if num in e: # normal stop
- e.remove (num)
- xs.append (x)
- else: pass # double stop: skip it
- else: xs.append (x)
- for num in reversed (e): # fill missing stops at the end
- xs.append (E.Element ('part-group', number = num, type = 'stop'))
- return xs
-
-def parseParts (xs, d, e): # -> [elems on current level], rest of xs
- if not xs: return [],[]
- x = xs.pop (0)
- if x.tag == 'part-group':
- num, type = x.get ('number'), x.get ('type')
- if type == 'start': # go one level deeper
- s = [x.findtext (n, '') for n in ['group-symbol','group-barline','group-name','group-abbreviation']]
- d [num] = s # remember groupdata by group number
- e.append (num) # make stack of open group numbers
- elemsnext, rest1 = parseParts (xs, d, e) # parse one level deeper to next stop
- elems, rest2 = parseParts (rest1, d, e) # parse the rest on this level
- return [elemsnext] + elems, rest2
- else: # stop: close level and return group-data
- nums = e.pop () # last open group number in stack order
- if xs and xs[0].get ('type') == 'stop': # two consequetive stops
- if num != nums: # in the wrong order (tempory solution)
- d[nums], d[num] = d[num], d[nums] # exchange values (only works for two stops!!!)
- sym = d[num] # retrieve an return groupdata as last element of the group
- return [sym], xs
- else:
- elems, rest = parseParts (xs, d, e) # parse remaining elements on current level
- name = x.findtext ('part-name',''), x.findtext ('part-abbreviation','')
- return [name] + elems, rest
-
-def bracePart (part): # put a brace on multistaff part and group voices
- if not part: return [] # empty part in the score
- brace = []
- for ivs in part:
- if len (ivs) == 1: # stave with one voice
- brace.append ('%s' % ivs[0])
- else: # stave with multiple voices
- brace += ['('] + ['%s' % iv for iv in ivs] + [')']
- brace.append ('|')
- del brace[-1] # no barline at the end
- if len (part) > 1:
- brace = ['{'] + brace + ['}']
- return brace
-
-def prgroupelem (x, gnm, bar, pmap, accVce, accStf): # collect partnames (accVce) and %%score map (accStf)
- if type (x) == tupletype: # partname-tuple = (part-name, part-abbrev)
- y = pmap.pop (0)
- if gnm[0]: x = [n1 + ':' + n2 for n1, n2 in zip (gnm, x)] # put group-name before part-name
- accVce.append (x)
- accStf.extend (bracePart (y))
- elif len (x) == 2 and type (x[0]) == tupletype: # misuse of group just to add extra name to stave
- y = pmap.pop (0)
- nms = [n1 + ':' + n2 for n1, n2 in zip (x[0], x[1][2:])] # x[0] = partname-tuple, x[1][2:] = groupname-tuple
- accVce.append (nms)
- accStf.extend (bracePart (y))
- else:
- prgrouplist (x, bar, pmap, accVce, accStf)
-
-def prgrouplist (x, pbar, pmap, accVce, accStf): # collect partnames, scoremap for a part-group
- sym, bar, gnm, gabbr = x[-1] # bracket symbol, continue barline, group-name-tuple
- bar = bar == 'yes' or pbar # pbar -> the parent has bar
- accStf.append (sym == 'brace' and '{' or '[')
- for z in x[:-1]:
- prgroupelem (z, (gnm, gabbr), bar, pmap, accVce, accStf)
- if bar: accStf.append ('|')
- if bar: del accStf [-1] # remove last one before close
- accStf.append (sym == 'brace' and '}' or ']')
-
-def compUnitLength (iv, maten, divs): # compute optimal unit length
- uLmin, minLen = 0, max_int
- for uL in [4,8,16]: # try 1/4, 1/8 and 1/16
- vLen = 0 # total length of abc duration strings in this voice
- for im, m in enumerate (maten): # all measures
- for e in m[iv]: # all notes in voice iv
- if isinstance (e, Elem) or e.dur == 0: continue # no real durations
- vLen += len (abcdur (e, divs [im], uL)) # add len of duration string
- if vLen < minLen: uLmin, minLen = uL, vLen # remember the smallest
- return uLmin
-
-def doSyllable (syl):
- txt = ''
- for e in syl:
- if e.tag == 'elision': txt += '~'
- elif e.tag == 'text': # escape - and space characters
- txt += (e.text or '').replace ('_','\_').replace('-', r'\-').replace(' ', '~')
- if not txt: return txt
- if syl.findtext('syllabic') in ['begin', 'middle']: txt += '-'
- if syl.find('extend') is not None: txt += '_'
- return txt
-
-def checkMelismas (lyrics, maten, im, iv):
- if im == 0: return
- maat = maten [im][iv] # notes of the current measure
- curlyr = lyrics [im][iv] # lyrics dict of current measure
- prvlyr = lyrics [im-1][iv] # lyrics dict of previous measure
- for n, (lyrstr, melis) in prvlyr.items (): # all lyric numbers in the previous measure
- if n not in curlyr and melis: # melisma required, but no lyrics present -> make one!
- ms = getMelisma (maat) # get a melisma for the current measure
- if ms: curlyr [n] = (ms, 0) # set melisma as the n-th lyrics of the current measure
-
-def getMelisma (maat): # get melisma from notes in maat
- ms = []
- for note in maat: # every note should get an underscore
- if not isinstance (note, Note): continue # skip Elem's
- if note.grace: continue # skip grace notes
- if note.ns [0] in 'zx': break # stop on first rest
- ms.append ('_')
- return ' '.join (ms)
-
-def perc2map (abcIn):
- fillmap = {'diamond':1, 'triangle':1, 'square':1, 'normal':1};
- abc = map (lambda x: x.strip (), percSvg.splitlines ())
- id='default'
- maps = {'default': []};
- dmaps = {'default': []}
- r1 = re.compile (r'V:\s*(\S+)')
- ls = abcIn.splitlines ()
- for x in ls:
- if 'I:percmap' in x:
- noot, step, midi, kop = map (lambda x: x.strip (), x.split ()[1:])
- if kop in fillmap: kop = kop + '+' + ',' + kop
- x = '%%%%map perc%s %s print=%s midi=%s heads=%s' % (id, noot, step, midi, kop)
- maps [id].append (x)
- if '%%MIDI' in x: dmaps [id].append (x)
- if 'V:' in x:
- r = r1.match (x)
- if r:
- id = r.group (1);
- if id not in maps: maps [id] = []; dmaps [id] = []
- ids = sorted (maps.keys ())
- for id in ids: abc += maps [id]
- id='default'
- for x in ls:
- if 'I:percmap' in x: continue
- if '%%MIDI' in x: continue
- if 'V:' in x or 'K:' in x:
- r = r1.match (x)
- if r: id = r.group (1)
- abc.append (x)
- if id in dmaps and len (dmaps [id]) > 0: abc.extend (dmaps [id]); del dmaps [id]
- if 'perc' in x and 'map=' not in x: x += ' map=perc';
- if 'map=perc' in x and len (maps [id]) > 0: abc.append ('%%voicemap perc' + id);
- if 'map=off' in x: abc.append ('%%voicemap');
- else:
- abc.append (x)
- return '\n'.join (abc) + '\n'
-
-def addoct (ptc, o): # xml staff step, xml octave number
- p = ptc
- if o > 4: p = ptc.lower ()
- if o > 5: p = p + (o-5) * "'"
- if o < 4: p = p + (4-o) * ","
- return p # abc pitch == abc note without accidental
-
-def chkbug (dt, m):
- if dt > m.divs / 16: return 1 # duration should be > 1/64 note
- info ('MuseScore bug: incorrect duration, smaller then 1/64! in measure %d, part %d' % (m.ixm, m.ixp))
- return 0
-
-#----------------
-# parser
-#----------------
-class Parser:
- note_alts = [ # 3 alternative notations of the same note for tablature mapping
- [x.strip () for x in '=C, ^C, =D, ^D, =E, =F, ^F, =G, ^G, =A, ^A, =B'.split (',')],
- [x.strip () for x in '^B, _D,^^C, _E, _F, ^E, _G,^^F, _A,^^G, _B, _C'.split (',')],
- [x.strip () for x in '__D,^^B,__E,__F,^^D,__G,^^E,__A,_/A,__B,__C,^^A'.split (',')] ]
- step_map = {'C':0,'D':2,'E':4,'F':5,'G':7,'A':9,'B':11}
- def __init__ (s, options):
- # unfold repeats, number of chars per line, credit filter level, volta option
- s.slurBuf = {} # dict of open slurs keyed by slur number
- s.dirStk = {} # {direction-type + number -> (type, voice | time)} dict for proper closing
- s.ingrace = 0 # marks a sequence of grace notes
- s.msc = Music (options) # global music data abstraction
- s.unfold = options.u # turn unfolding repeats on
- s.ctf = options.c # credit text filter level
- s.gStfMap = [] # [[abc voice numbers] for all parts]
- s.midiMap = [] # midi-settings for each abc voice, in order
- s.drumInst = {} # inst_id -> midi pitch for channel 10 notes
- s.drumNotes = {} # (xml voice, abc note) -> (midi note, note head)
- s.instMid = [] # [{inst id -> midi-settings} for all parts]
- s.midDflt = [-1,-1,-1,-91] # default midi settings for channel, program, volume, panning
- s.msralts = {} # xml-notenames (without octave) with accidentals from the key
- s.curalts = {} # abc-notenames (with voice number) with passing accidentals
- s.stfMap = {} # xml staff number -> [xml voice number]
- s.vce2stf = {} # xml voice number -> allocated staff number
- s.clefMap = {} # xml staff number -> abc clef (for header only)
- s.curClef = {} # xml staff number -> current abc clef
- s.stemDir = {} # xml voice number -> current stem direction
- s.clefOct = {} # xml staff number -> current clef-octave-change
- s.curStf = {} # xml voice number -> current xml staff number
- s.nolbrk = options.x; # generate no linebreaks ($)
- s.jscript = options.j # compatibility with javascript version
- s.ornaments = sorted (note_ornamentation_map.items ())
- s.doPageFmt = len (options.p) == 1 # translate xml page format
- s.tstep = options.t # clef determines step on staff (percussion)
- s.dirtov1 = options.v1 # all directions to first voice of staff
- s.ped = options.ped # render pedal directions
- s.wstems = options.stm # translate stem elements
- s.pedVce = None # voice for pedal directions
- s.repeat_str = {} # staff number -> [measure number, repeat-text]
- s.tabVceMap = {} # abc voice num -> [%%map ...] for tab voices
- s.koppen = {} # noteheads needed for %%map
-
- def matchSlur (s, type2, n, v2, note2, grace, stopgrace): # match slur number n in voice v2, add abc code to before/after
- if type2 not in ['start', 'stop']: return # slur type continue has no abc equivalent
- if n == None: n = '1'
- if n in s.slurBuf:
- type1, v1, note1, grace1 = s.slurBuf [n]
- if type2 != type1: # slur complete, now check the voice
- if v2 == v1: # begins and ends in the same voice: keep it
- if type1 == 'start' and (not grace1 or not stopgrace): # normal slur: start before stop and no grace slur
- note1.before = ['('] + note1.before # keep left-right order!
- note2.after += ')'
- # no else: don't bother with reversed stave spanning slurs
- del s.slurBuf [n] # slur finished, remove from stack
- else: # double definition, keep the last
- info ('double slur numbers %s-%s in part %d, measure %d, voice %d note %s, first discarded' % (type2, n, s.msr.ixp+1, s.msr.ixm+1, v2, note2.ns))
- s.slurBuf [n] = (type2, v2, note2, grace)
- else: # unmatched slur, put in dict
- s.slurBuf [n] = (type2, v2, note2, grace)
-
- def doNotations (s, note, nttn, isTab):
- for key, val in s.ornaments:
- if nttn.find (key) != None: note.before += [val] # just concat all ornaments
- trem = nttn.find ('ornaments/tremolo')
- if trem != None:
- type = trem.get ('type')
- if type == 'single':
- note.before.insert (0, '!%s!' % (int (trem.text) * '/'))
- else:
- note.fact = None # no time modification in ABC
- if s.tstep: # abc2svg version
- if type == 'stop': note.before.insert (0, '!trem%s!' % trem.text);
- else: # abc2xml version
- if type == 'start': note.before.insert (0, '!%s-!' % (int (trem.text) * '/'));
- fingering = nttn.findall ('technical/fingering')
- for finger in fingering: # handle multiple finger annotations
- if not isTab: note.before += ['!%s!' % finger.text] # fingering goes before chord (addChord)
- snaar = nttn.find ('technical/string')
- if snaar != None and isTab:
- if s.tstep:
- fret = nttn.find ('technical/fret')
- if fret != None: note.tab = (snaar.text, fret.text)
- else:
- deco = '!%s!' % snaar.text # no double string decos (bug in musescore)
- if deco not in note.ntdec: note.ntdec += deco
- wvln = nttn.find ('ornaments/wavy-line')
- if wvln != None:
- if wvln.get ('type') == 'start': note.before = ['!trill(!'] + note.before # keep left-right order!
- elif wvln.get ('type') == 'stop': note.before = ['!trill)!'] + note.before
- glis = nttn.find ('glissando')
- if glis == None: glis = nttn.find ('slide') # treat slide as glissando
- if glis != None:
- lt = '~' if glis.get ('line-type') =='wavy' else '-'
- if glis.get ('type') == 'start': note.before = ['!%s(!' % lt] + note.before # keep left-right order!
- elif glis.get ('type') == 'stop': note.before = ['!%s)!' % lt] + note.before
-
- def tabnote (s, alt, ptc, oct, v, ntrec):
- p = s.step_map [ptc] + int (alt or '0') # p in -2 .. 13
- if p > 11: oct += 1 # octave correction
- if p < 0: oct -= 1
- p = p % 12 # remap p into 0..11
- snaar_nw, fret_nw = ntrec.tab # the computed/annotated allocation of nt
- for i in range (4): # support same note on 4 strings
- na = s.note_alts [i % 3] [p] # get alternative representation of same note
- o = oct
- if na in ['^B', '^^B']: o -= 1 # because in adjacent octave
- if na in ['_C', '__C']: o += 1
- if '/' in na or i == 3: o = 9 # emergency notation for 4th string case
- nt = addoct (na, o)
- snaar, fret = s.tabmap.get ((v, nt), ('', '')) # the current allocation of nt
- if not snaar: break # note not yet allocated
- if snaar_nw == snaar: return nt # use present allocation
- if i == 3: # new allocaion needed but none is free
- fmt = 'rejected: voice %d note %3s string %s fret %2s remains: string %s fret %s'
- info (fmt % (v, nt, snaar_nw, fret_nw, snaar, fret), 1)
- ntrec.tab = (snaar, fret)
- s.tabmap [v, nt] = ntrec.tab # for tablature map (voice, note) -> (string, fret)
- return nt # ABC code always in key C (with midi pitch alterations)
-
- def ntAbc (s, ptc, oct, note, v, ntrec, isTab): # pitch, octave -> abc notation
- acc2alt = {'double-flat':-2,'flat-flat':-2,'flat':-1,'natural':0,'sharp':1,'sharp-sharp':2,'double-sharp':2}
- oct += s.clefOct.get (s.curStf [v], 0) # minus clef-octave-change value
- acc = note.findtext ('accidental') # should be the notated accidental
- alt = note.findtext ('pitch/alter') # pitch alteration (midi)
- if ntrec.tab: return s.tabnote (alt, ptc, oct, v, ntrec) # implies s.tstep is true (options.t was given)
- elif isTab and s.tstep:
- nt = ['__','_','','^','^^'][int (alt or '0') + 2] + addoct (ptc, oct)
- info ('no string notation found for note %s in voice %d' % (nt, v), 1)
- p = addoct (ptc, oct)
- if alt == None and s.msralts.get (ptc, 0): alt = 0 # no alt but key implies alt -> natural!!
- if alt == None and (p, v) in s.curalts: alt = 0 # no alt but previous note had one -> natural!!
- if acc == None and alt == None: return p # no acc, no alt
- elif acc != None:
- alt = acc2alt [acc] # acc takes precedence over the pitch here!
- else: # now see if we really must add an accidental
- alt = int (float (alt))
- if (p, v) in s.curalts: # the note in this voice has been altered before
- if alt == s.curalts [(p, v)]: return p # alteration still the same
- elif alt == s.msralts.get (ptc, 0): return p # alteration implied by the key
- tieElms = note.findall ('tie') + note.findall ('notations/tied') # in xml we have separate notated ties and playback ties
- if 'stop' in [e.get ('type') for e in tieElms]: return p # don't alter tied notes
- info ('accidental %d added in part %d, measure %d, voice %d note %s' % (alt, s.msr.ixp+1, s.msr.ixm+1, v+1, p))
- s.curalts [(p, v)] = alt
- p = ['__','_','=','^','^^'][alt+2] + p # and finally ... prepend the accidental
- return p
-
- def doNote (s, n): # parse a musicXML note tag
- note = Note ()
- v = int (n.findtext ('voice', '1'))
- if s.isSib: v += 100 * int (n.findtext ('staff', '1')) # repair bug in Sibelius
- chord = n.find ('chord') != None
- p = n.findtext ('pitch/step') or n.findtext ('unpitched/display-step')
- o = n.findtext ('pitch/octave') or n.findtext ('unpitched/display-octave')
- r = n.find ('rest')
- numer = n.findtext ('time-modification/actual-notes')
- if numer:
- denom = n.findtext ('time-modification/normal-notes')
- note.fact = (int (numer), int (denom))
- note.tup = [x.get ('type') for x in n.findall ('notations/tuplet')]
- dur = n.findtext ('duration')
- grc = n.find ('grace')
- note.grace = grc != None
- note.before, note.after = [], '' # strings with ABC stuff that goes before or after a note/chord
- if note.grace and not s.ingrace: # open a grace sequence
- s.ingrace = 1
- note.before = ['{']
- if grc.get ('slash') == 'yes': note.before += ['/'] # acciaccatura
- stopgrace = not note.grace and s.ingrace
- if stopgrace: # close the grace sequence
- s.ingrace = 0
- s.msc.lastnote.after += '}' # close grace on lastenote.after
- if dur == None or note.grace: dur = 0
- if r == None and n.get ('print-object') == 'no':
- if chord: return
- r = 1 # turn invisible notes (that advance the time) into invisible rests
- note.dur = int (dur)
- if r == None and (not p or not o): # not a rest and no pitch
- s.msc.cnt.inc ('nopt', v) # count unpitched notes
- o, p = 5,'E' # make it an E5 ??
- isTab = s.curClef and s.curClef.get (s.curStf [v], '').startswith ('tab')
- nttn = n.find ('notations') # add ornaments
- if nttn != None: s.doNotations (note, nttn, isTab)
- e = n.find ('stem') if r == None else None # no !stemless! before rest
- if e != None and e.text == 'none' and (not isTab or v in s.hasStems or s.tstep):
- note.before += ['s']; abcOut.stemless = 1;
- e = n.find ('accidental')
- if e != None and e.get ('parentheses') == 'yes': note.ntdec += '!courtesy!'
- if r != None: noot = 'x' if n.get ('print-object') == 'no' or isTab else 'z'
- else: noot = s.ntAbc (p, int (o), n, v, note, isTab)
- if n.find ('unpitched') != None:
- clef = s.curClef [s.curStf [v]] # the current clef for this voice
- step = staffStep (p, int (o), clef, s.tstep) # (clef independent) step value of note on the staff
- instr = n.find ('instrument')
- instId = instr.get ('id') if instr != None else 'dummyId'
- midi = s.drumInst.get (instId, abcMid (noot))
- nh = n.findtext ('notehead', '').replace (' ','-') # replace spaces in xml notehead names for percmap
- if nh == 'x': noot = '^' + noot.replace ('^','').replace ('_','')
- if nh in ['circle-x','diamond','triangle']: noot = '_' + noot.replace ('^','').replace ('_','')
- if nh and n.find ('notehead').get ('filled','') == 'yes': nh += '+'
- if nh and n.find ('notehead').get ('filled','') == 'no': nh += '-'
- s.drumNotes [(v, noot)] = (step, midi, nh) # keep data for percussion map
- tieElms = n.findall ('tie') + n.findall ('notations/tied') # in xml we have separate notated ties and playback ties
- if 'start' in [e.get ('type') for e in tieElms]: # n can have stop and start tie
- noot = noot + '-'
- note.beam = sum ([1 for b in n.findall('beam') if b.text in ['continue', 'end']]) + int (note.grace)
- lyrlast = 0; rsib = re.compile (r'^.*verse')
- for e in n.findall ('lyric'):
- lyrnum = int (rsib.sub ('', e.get ('number', '1'))) # also do Sibelius numbers
- if lyrnum == 0: lyrnum = lyrlast + 1 # and correct Sibelius bugs
- else: lyrlast = lyrnum
- note.lyrs [lyrnum] = doSyllable (e)
- stemdir = n.findtext ('stem')
- if s.wstems and (stemdir == 'up' or stemdir == 'down'):
- if stemdir != s.stemDir.get (v, ''):
- s.stemDir [v] = stemdir
- s.msc.appendElem (v, '[I:stemdir %s]' % stemdir)
- if chord: s.msc.addChord (note, noot)
- else:
- xmlstaff = int (n.findtext ('staff', '1'))
- if s.curStf [v] != xmlstaff: # the note should go to another staff
- dstaff = xmlstaff - s.curStf [v] # relative new staff number
- s.curStf [v] = xmlstaff # remember the new staff for this voice
- s.msc.appendElem (v, '[I:staff %+d]' % dstaff) # insert a move before the note
- s.msc.appendNote (v, note, noot)
- for slur in n.findall ('notations/slur'): # s.msc.lastnote points to the last real note/chord inserted above
- s.matchSlur (slur.get ('type'), slur.get ('number'), v, s.msc.lastnote, note.grace, stopgrace) # match slur definitions
-
- def doAttr (s, e): # parse a musicXML attribute tag
- teken = {'C1':'alto1','C2':'alto2','C3':'alto','C4':'tenor','F4':'bass','F3':'bass3','G2':'treble','TAB':'tab','percussion':'perc'}
- dvstxt = e.findtext ('divisions')
- if dvstxt: s.msr.divs = int (dvstxt)
- steps = int (e.findtext ('transpose/chromatic', '0')) # for transposing instrument
- fifths = e.findtext ('key/fifths')
- first = s.msc.tijd == 0 and s.msr.ixm == 0 # first attributes in first measure
- if fifths:
- key, s.msralts = setKey (int (fifths), e.findtext ('key/mode','major'))
- if first and not steps and abcOut.key == 'none':
- abcOut.key = key # first measure -> header, if not transposing instrument or percussion part!
- elif key != abcOut.key or not first:
- s.msr.attr += '[K:%s]' % key # otherwise -> voice
- beats = e.findtext ('time/beats')
- if beats:
- unit = e.findtext ('time/beat-type')
- mtr = beats + '/' + unit
- if first: abcOut.mtr = mtr # first measure -> header
- else: s.msr.attr += '[M:%s]' % mtr # otherwise -> voice
- s.msr.mtr = int (beats), int (unit)
- s.msr.mdur = (s.msr.divs * s.msr.mtr[0] * 4) // s.msr.mtr[1] # duration of measure in xml-divisions
- for ms in e.findall('measure-style'):
- n = int (ms.get ('number', '1')) # staff number
- voices = s.stfMap [n] # all voices of staff n
- for mr in ms.findall('measure-repeat'):
- ty = mr.get('type')
- if ty == 'start': # remember start measure number and text voor each staff
- s.repeat_str [n] = [s.msr.ixm, mr.text]
- for v in voices: # insert repeat into all voices, value will be overwritten at stop
- s.msc.insertElem (v, s.repeat_str [n])
- elif ty == 'stop': # calculate repeat measure count for this staff n
- start_ix, text_ = s.repeat_str [n]
- repeat_count = s.msr.ixm - start_ix
- if text_:
- mid_str = "%s " % text_
- repeat_count /= int (text_)
- else:
- mid_str = "" # overwrite repeat with final string
- s.repeat_str [n][0] = '[I:repeat %s%d]' % (mid_str, repeat_count)
- del s.repeat_str [n] # remove closed repeats
- toct = e.findtext ('transpose/octave-change', '')
- if toct: steps += 12 * int (toct) # extra transposition of toct octaves
- for clef in e.findall ('clef'): # a part can have multiple staves
- n = int (clef.get ('number', '1')) # local staff number for this clef
- sgn = clef.findtext ('sign')
- line = clef.findtext ('line', '') if sgn not in ['percussion','TAB'] else ''
- cs = teken.get (sgn + line, '')
- oct = clef.findtext ('clef-octave-change', '') or '0'
- if oct: cs += {-2:'-15', -1:'-8', 1:'+8', 2:'+15'}.get (int (oct), '')
- s.clefOct [n] = -int (oct); # xml playback pitch -> abc notation pitch
- if steps: cs += ' transpose=' + str (steps)
- stfdtl = e.find ('staff-details')
- if stfdtl and int (stfdtl.get ('number', '1')) == n:
- lines = stfdtl.findtext ('staff-lines')
- if lines:
- lns= '|||' if lines == '3' and sgn == 'TAB' else lines
- cs += ' stafflines=%s' % lns
- s.stafflines = int (lines) # remember for tab staves
- strings = stfdtl.findall ('staff-tuning')
- if strings:
- tuning = [st.findtext ('tuning-step') + st.findtext ('tuning-octave') for st in strings]
- cs += ' strings=%s' % ','.join (tuning)
- capo = stfdtl.findtext ('capo')
- if capo: cs += ' capo=%s' % capo
- s.curClef [n] = cs # keep track of current clef (for percmap)
- if first: s.clefMap [n] = cs # clef goes to header (where it is mapped to voices)
- else:
- voices = s.stfMap[n] # clef change to all voices of staff n
- for v in voices:
- if n != s.curStf [v]: # voice is not at its home staff n
- dstaff = n - s.curStf [v]
- s.curStf [v] = n # reset current staff at start of measure to home position
- s.msc.appendElem (v, '[I:staff %+d]' % dstaff)
- s.msc.appendElem (v, '[K:%s]' % cs)
-
- def findVoice (s, i, es):
- stfnum = int (es[i].findtext ('staff',1)) # directions belong to a staff
- vs = s.stfMap [stfnum] # voices in this staff
- v1 = vs [0] if vs else 1 # directions to first voice of staff
- if s.dirtov1: return stfnum, v1, v1 # option --v1
- for e in es [i+1:]: # or to the voice of the next note
- if e.tag == 'note':
- v = int (e.findtext ('voice', '1'))
- if s.isSib: v += 100 * int (e.findtext ('staff', '1')) # repair bug in Sibelius
- stf = s.vce2stf [v] # use our own staff allocation
- return stf, v, v1 # voice of next note, first voice of staff
- if e.tag == 'backup': break
- return stfnum, v1, v1 # no note found, fall back to v1
-
- def doDirection (s, e, i, es): # parse a musicXML direction tag
- def addDirection (x, vs, tijd, stfnum):
- if not x: return
- vs = s.stfMap [stfnum] if '!8v' in x else [vs] # ottava's go to all voices of staff
- for v in vs:
- if tijd != None: # insert at time of encounter
- s.msc.appendElemT (v, x.replace ('(',')').replace ('ped','ped-up'), tijd)
- else:
- s.msc.appendElem (v, x)
- def startStop (dtype, vs, stfnum=1):
- typmap = {'down':'!8va(!', 'up':'!8vb(!', 'crescendo':'!<(!', 'diminuendo':'!>(!', 'start':'!ped!'}
- type = t.get ('type', '')
- k = dtype + t.get ('number', '1') # key to match the closing direction
- if type in typmap: # opening the direction
- x = typmap [type]
- if k in s.dirStk: # closing direction already encountered
- stype, tijd = s.dirStk [k]; del s.dirStk [k]
- if stype == 'stop':
- addDirection (x, vs, tijd, stfnum)
- else:
- info ('%s direction %s has no stop in part %d, measure %d, voice %d' % (dtype, stype, s.msr.ixp+1, s.msr.ixm+1, vs+1))
- s.dirStk [k] = ((type , vs)) # remember voice and type for closing
- else:
- s.dirStk [k] = ((type , vs)) # remember voice and type for closing
- elif type == 'stop':
- if k in s.dirStk: # matching open direction found
- type, vs = s.dirStk [k]; del s.dirStk [k] # into the same voice
- if type == 'stop':
- info ('%s direction %s has double stop in part %d, measure %d, voice %d' % (dtype, type, s.msr.ixp+1, s.msr.ixm+1, vs+1))
- x = ''
- else:
- x = typmap [type].replace ('(',')').replace ('ped','ped-up')
- else: # closing direction found before opening
- s.dirStk [k] = ('stop', s.msc.tijd)
- x = '' # delay code generation until opening found
- else: raise ValueError ('wrong direction type')
- addDirection (x, vs, None, stfnum)
- tempo, wrdstxt = None, ''
- plcmnt = e.get ('placement')
- stf, vs, v1 = s.findVoice (i, es)
- jmp = '' # for jump sound elements: dacapo, dalsegno and family
- jmps = [('dacapo','D.C.'),('dalsegno','D.S.'),('tocoda','dacoda'),('fine','fine'),('coda','O'),('segno','S')]
- t = e.find ('sound') # there are many possible attributes for sound
- if t != None:
- minst = t.find ('midi-instrument')
- if minst:
- prg = t.findtext ('midi-instrument/midi-program')
- chn = t.findtext ('midi-instrument/midi-channel')
- vids = [v for v, id in s.vceInst.items () if id == minst.get ('id')]
- if vids: vs = vids [0] # direction for the indentified voice, not the staff
- parm, inst = ('program', str (int (prg) - 1)) if prg else ('channel', chn)
- if inst and abcOut.volpan > 0: s.msc.appendElem (vs, '[I:MIDI= %s %s]' % (parm, inst))
- tempo = t.get ('tempo') # look for tempo attribute
- if tempo:
- tempo = '%.0f' % float (tempo) # hope it is a number and insert in voice 1
- tempo_units = (1,4) # always 1/4 for sound elements!
- for r, v in jmps:
- if t.get (r, ''): jmp = v; break
- dirtypes = e.findall ('direction-type')
- for dirtyp in dirtypes:
- units = { 'whole': (1,1), 'half': (1,2), 'quarter': (1,4), 'eighth': (1,8) }
- metr = dirtyp.find ('metronome')
- if metr != None:
- t = metr.findtext ('beat-unit', '')
- if t in units: tempo_units = units [t]
- else: tempo_units = units ['quarter']
- if metr.find ('beat-unit-dot') != None:
- tempo_units = simplify (tempo_units [0] * 3, tempo_units [1] * 2)
- tmpro = re.search ('[.\d]+', metr.findtext ('per-minute')) # look for a number
- if tmpro: tempo = tmpro.group () # overwrites the value set by the sound element of this direction
- t = dirtyp.find ('wedge')
- if t != None: startStop ('wedge', vs)
- allwrds = dirtyp.findall ('words') # insert text annotations
- if not allwrds: allwrds = dirtyp.findall ('rehearsal') # treat rehearsal mark as text annotation
- for wrds in allwrds:
- if jmp: # ignore the words when a jump sound element is present in this direction
- s.msc.appendElem (vs, '!%s!' % jmp , 1) # to voice
- break
- plc = plcmnt == 'below' and '_' or '^'
- if float (wrds.get ('default-y', '0')) < 0: plc = '_'
- wrdstxt += (wrds.text or '').replace ('"','\\"').replace ('\n', '\\n')
- wrdstxt = wrdstxt.strip ()
- for key, val in dynamics_map.items ():
- if dirtyp.find ('dynamics/' + key) != None:
- s.msc.appendElem (vs, val, 1) # to voice
- if dirtyp.find ('coda') != None: s.msc.appendElem (vs, 'O', 1)
- if dirtyp.find ('segno') != None: s.msc.appendElem (vs, 'S', 1)
- t = dirtyp.find ('octave-shift')
- if t != None: startStop ('octave-shift', vs, stf) # assume size == 8 for the time being
- t = dirtyp.find ('pedal')
- if t != None and s.ped:
- if not s.pedVce: s.pedVce = vs
- startStop ('pedal', s.pedVce)
- if dirtyp.findtext ('other-direction') == 'diatonic fretting': s.diafret = 1;
- if tempo:
- tempo = '%.0f' % float (tempo) # hope it is a number and insert in voice 1
- if s.msc.tijd == 0 and s.msr.ixm == 0: # first measure -> header
- abcOut.tempo = tempo
- abcOut.tempo_units = tempo_units
- else:
- s.msc.appendElem (v1, '[Q:%d/%d=%s]' % (tempo_units [0], tempo_units [1], tempo)) # otherwise -> 1st voice
- if wrdstxt: s.msc.appendElem (vs, '"%s%s"' % (plc, wrdstxt), 1) # to voice, but after tempo
-
- def doHarmony (s, e, i, es): # parse a musicXMl harmony tag
- _, vt, _ = s.findVoice (i, es)
- short = {'major':'', 'minor':'m', 'augmented':'+', 'diminished':'dim', 'dominant':'7', 'half-diminished':'m7b5'}
- accmap = {'major':'maj', 'dominant':'', 'minor':'m', 'diminished':'dim', 'augmented':'+', 'suspended':'sus'}
- modmap = {'second':'2', 'fourth':'4', 'seventh':'7', 'sixth':'6', 'ninth':'9', '11th':'11', '13th':'13'}
- altmap = {'1':'#', '0':'', '-1':'b'}
- root = e.findtext ('root/root-step','')
- alt = altmap.get (e.findtext ('root/root-alter'), '')
- sus = ''
- kind = e.findtext ('kind', '')
- if kind in short: kind = short [kind]
- elif '-' in kind: # xml chord names: -
- triad, mod = kind.split ('-')
- kind = accmap.get (triad, '') + modmap.get (mod, '')
- if kind.startswith ('sus'): kind, sus = '', kind # sus-suffix goes to the end
- elif kind == 'none': kind = e.find ('kind').get ('text','')
- degrees = e.findall ('degree')
- for d in degrees: # chord alterations
- kind += altmap.get (d.findtext ('degree-alter'),'') + d.findtext ('degree-value','')
- kind = kind.replace ('79','9').replace ('713','13').replace ('maj6','6')
- bass = e.findtext ('bass/bass-step','') + altmap.get (e.findtext ('bass/bass-alter'),'')
- s.msc.appendElem (vt, '"%s%s%s%s%s"' % (root, alt, kind, sus, bass and '/' + bass), 1)
-
- def doBarline (s, e): # 0 = no repeat, 1 = begin repeat, 2 = end repeat
- rep = e.find ('repeat')
- if rep != None: rep = rep.get ('direction')
- if s.unfold: # unfold repeat, don't translate barlines
- return rep and (rep == 'forward' and 1 or 2) or 0
- loc = e.get ('location', 'right') # right is the default
- if loc == 'right': # only change style for the right side
- style = e.findtext ('bar-style')
- if style == 'light-light': s.msr.rline = '||'
- elif style == 'light-heavy': s.msr.rline = '|]'
- if rep != None: # repeat found
- if rep == 'forward': s.msr.lline = ':'
- else: s.msr.rline = ':|' # override barline style
- end = e.find ('ending')
- if end != None:
- if end.get ('type') == 'start':
- n = end.get ('number', '1').replace ('.','').replace (' ','')
- try: list (map (int, n.split (','))) # should be a list of integers
- except: n = '"%s"' % n.strip () # illegal musicXML
- s.msr.lnum = n # assume a start is always at the beginning of a measure
- elif s.msr.rline == '|': # stop and discontinue the same in ABC ?
- s.msr.rline = '||' # to stop on a normal barline use || in ABC ?
- return 0
-
- def doPrint (s, e): # print element, measure number -> insert a line break
- if e.get ('new-system') == 'yes' or e.get ('new-page') == 'yes':
- if not s.nolbrk: return '$' # a line break
-
- def doPartList (s, e): # translate the start/stop-event-based xml-partlist into proper tree
- for sp in e.findall ('part-list/score-part'):
- midi = {}
- for m in sp.findall ('midi-instrument'):
- x = [m.findtext (p, s.midDflt [i]) for i,p in enumerate (['midi-channel','midi-program','volume','pan'])]
- pan = float (x[3])
- if pan >= -90 and pan <= 90: # would be better to map behind-pannings
- pan = (float (x[3]) + 90) / 180 * 127 # xml between -90 and +90
- midi [m.get ('id')] = [int (x[0]), int (x[1]), float (x[2]) * 1.27, pan] # volume 100 -> midi 127
- up = m.findtext ('midi-unpitched')
- if up: s.drumInst [m.get ('id')] = int (up) - 1 # store midi-pitch for channel 10 notes
- s.instMid.append (midi)
- ps = e.find ('part-list') # partlist = [groupelem]
- xs = getPartlist (ps) # groupelem = partname | grouplist
- partlist, _ = parseParts (xs, {}, []) # grouplist = [groupelem, ..., groupdata]
- return partlist # groupdata = [group-symbol, group-barline, group-name, group-abbrev]
-
- def mkTitle (s, e):
- def filterCredits (y): # y == filter level, higher filters less
- cs = []
- for x in credits: # skip redundant credit lines
- if y < 6 and (x in title or x in mvttl): continue # sure skip
- if y < 5 and (x in composer or x in lyricist): continue # almost sure skip
- if y < 4 and ((title and title in x) or (mvttl and mvttl in x)): continue # may skip too much
- if y < 3 and ([1 for c in composer if c in x] or [1 for c in lyricist if c in x]): continue # skips too much
- if y < 2 and re.match (r'^[\d\W]*$', x): continue # line only contains numbers and punctuation
- cs.append (x)
- if y == 0 and (title + mvttl): cs = '' # default: only credit when no title set
- return cs
- title = e.findtext ('work/work-title', '').strip ()
- mvttl = e.findtext ('movement-title', '').strip ()
- composer, lyricist, credits = [], [], []
- for creator in e.findall ('identification/creator'):
- if creator.text:
- if creator.get ('type') == 'composer':
- composer += [line.strip () for line in creator.text.split ('\n')]
- elif creator.get ('type') in ('lyricist', 'transcriber'):
- lyricist += [line.strip () for line in creator.text.split ('\n')]
- for rights in e.findall ('identification/rights'):
- if rights.text:
- lyricist += [line.strip () for line in rights.text.split ('\n')]
- for credit in e.findall('credit'):
- cs = ''.join (e.text or '' for e in credit.findall('credit-words'))
- credits += [re.sub (r'\s*[\r\n]\s*', ' ', cs)]
- credits = filterCredits (s.ctf)
- if title: title = 'T:%s\n' % title.replace ('\n', '\nT:')
- if mvttl: title += 'T:%s\n' % mvttl.replace ('\n', '\nT:')
- if credits: title += '\n'.join (['T:%s' % c for c in credits]) + '\n'
- if composer: title += '\n'.join (['C:%s' % c for c in composer]) + '\n'
- if lyricist: title += '\n'.join (['Z:%s' % c for c in lyricist]) + '\n'
- if title: abcOut.title = title[:-1]
- s.isSib = 'Sibelius' in (e.findtext ('identification/encoding/software') or '')
- if s.isSib: info ('Sibelius MusicXMl is unreliable')
-
- def doDefaults (s, e):
- if not s.doPageFmt: return # return if -pf option absent
- d = e.find ('defaults');
- if d == None: return;
- mils = d.findtext ('scaling/millimeters') # mills == staff height (mm)
- tenths = d.findtext ('scaling/tenths') # staff height in tenths
- if not mils or not tenths: return
- xmlScale = float (mils) / float (tenths) / 10 # tenths -> mm
- space = 10 * xmlScale # space between staff lines == 10 tenths
- abcScale = space / 0.2117 # 0.2117 cm = 6pt = space between staff lines for scale = 1.0 in abcm2ps
- abcOut.pageFmt ['scale'] = abcScale
- eks = 2 * ['page-layout/'] + 4 * ['page-layout/page-margins/']
- eks = [a+b for a,b in zip (eks, 'page-height,page-width,left-margin,right-margin,top-margin,bottom-margin'.split (','))]
- for i in range (6):
- v = d.findtext (eks [i])
- k = abcOut.pagekeys [i+1] # pagekeys [0] == scale already done, skip it
- if not abcOut.pageFmt [k] and v:
- try: abcOut.pageFmt [k] = float (v) * xmlScale # -> cm
- except: info ('illegal value %s for xml element %s', (v, eks [i])); continue # just skip illegal values
-
- def locStaffMap (s, part, maten): # map voice to staff with majority voting
- vmap = {} # {voice -> {staff -> n}} count occurrences of voice in staff
- s.vceInst = {} # {voice -> instrument id} for this part
- s.msc.vnums = {} # voice id's
- s.hasStems = {} # XML voice nums with at least one note with a stem (for tab key)
- s.stfMap, s.clefMap = {}, {} # staff -> [voices], staff -> clef
- ns = part.findall ('measure/note')
- for n in ns: # count staff allocations for all notes
- v = int (n.findtext ('voice', '1'))
- if s.isSib: v += 100 * int (n.findtext ('staff', '1')) # repair bug in Sibelius
- s.msc.vnums [v] = 1 # collect all used voice id's in this part
- sn = int (n.findtext ('staff', '1'))
- s.stfMap [sn] = []
- if v not in vmap:
- vmap [v] = {sn:1}
- else:
- d = vmap[v] # counter for voice v
- d[sn] = d.get (sn, 0) + 1 # ++ number of allocations for staff sn
- x = n.find ('instrument')
- if x != None: s.vceInst [v] = x.get ('id')
- x, noRest = n.findtext ('stem'), n.find ('rest') == None
- if noRest and (not x or x != 'none'): s.hasStems [v] = 1 # XML voice v has at least one stem
- vks = list (vmap.keys ())
- if s.jscript or s.isSib: vks.sort ()
- for v in vks: # choose staff with most allocations for each voice
- xs = [(n, sn) for sn, n in vmap[v].items ()]
- xs.sort ()
- stf = xs[-1][1] # the winner: staff with most notes of voice v
- s.stfMap [stf].append (v)
- s.vce2stf [v] = stf # reverse map
- s.curStf [v] = stf # current staff of XML voice v
-
- def addStaffMap (s, vvmap): # vvmap: xml voice number -> global abc voice number
- part = [] # default: brace on staffs of one part
- for stf, voices in sorted (s.stfMap.items ()): # s.stfMap has xml staff and voice numbers
- locmap = [vvmap [iv] for iv in voices if iv in vvmap]
- nostem = [(iv not in s.hasStems) for iv in voices if iv in vvmap] # same order as locmap
- if locmap: # abc voice number of staff stf
- part.append (locmap)
- clef = s.clefMap.get (stf, 'treble') # {xml staff number -> clef}
- for i, iv in enumerate (locmap):
- clef_attr = ''
- if clef.startswith ('tab'):
- if nostem [i] and 'nostems' not in clef: clef_attr = ' nostems'
- if s.diafret and 'diafret' not in clef: clef_attr += ' diafret' # for all voices in the part
- abcOut.clefs [iv] = clef + clef_attr # add nostems when all notes of voice had no stem
- s.gStfMap.append (part)
-
- def addMidiMap (s, ip, vvmap): # map abc voices to midi settings
- instr = s.instMid [ip] # get the midi settings for this part
- if instr.values (): defInstr = list(instr.values ())[0] # default settings = first instrument
- else: defInstr = s.midDflt # no instruments defined
- xs = []
- for v, vabc in vvmap.items (): # xml voice num, abc voice num
- ks = sorted (s.drumNotes.items ())
- ds = [(nt, step, midi, head) for (vd, nt), (step, midi, head) in ks if v == vd] # map perc notes
- id = s.vceInst.get (v, '') # get the instrument-id for part with multiple instruments
- if id in instr: # id is defined as midi-instrument in part-list
- xs.append ((vabc, instr [id] + ds)) # get midi settings for id
- else: xs.append ((vabc, defInstr + ds)) # only one instrument for this part
- xs.sort () # put abc voices in order
- s.midiMap.extend ([midi for v, midi in xs])
- snaarmap = ['E','G','B','d', 'f', 'a', "c'", "e'"]
- diamap = '0,1-,1,1+,2,3,3,4,4,5,6,6+,7,8-,8,8+,9,10,10,11,11,12,13,13+,14'.split (',')
- for k in sorted (s.tabmap.keys ()): # add %%map's for all tab voices
- v, noot = k;
- snaar, fret = s.tabmap [k];
- if s.diafret: fret = diamap [int (fret)]
- vabc = vvmap [v]
- snaar = s.stafflines - int (snaar)
- xs = s.tabVceMap.get (vabc, [])
- xs.append ('%%%%map tab%d %s print=%s heads=kop%s\n' % (vabc, noot, snaarmap [snaar], fret))
- s.tabVceMap [vabc] = xs
- s.koppen [fret] = 1 # collect noteheads for SVG defs
-
- def parse (s, fobj):
- vvmapAll = {} # collect xml->abc voice maps (vvmap) of all parts
- e = E.parse (fobj)
- s.mkTitle (e)
- s.doDefaults (e)
- partlist = s.doPartList (e)
- parts = e.findall ('part')
- for ip, p in enumerate (parts):
- maten = p.findall ('measure')
- s.locStaffMap (p, maten) # {voice -> staff} for this part
- s.drumNotes = {} # (xml voice, abc note) -> (midi note, note head)
- s.clefOct = {} # xml staff number -> current clef-octave-change
- s.curClef = {} # xml staff number -> current abc clef
- s.stemDir = {} # xml voice number -> current stem direction
- s.tabmap = {} # (xml voice, abc note) -> (string, fret)
- s.diafret = 0 # use diatonic fretting
- s.stafflines = 5
- s.msc.initVoices (newPart = 1) # create all voices
- aantalHerhaald = 0 # keep track of number of repititions
- herhaalMaat = 0 # target measure of the repitition
- divisions = [] # current value of for each measure
- s.msr = Measure (ip) # various measure data
- while s.msr.ixm < len (maten):
- maat = maten [s.msr.ixm]
- herhaal, lbrk = 0, ''
- s.msr.reset ()
- s.curalts = {} # passing accidentals are reset each measure
- es = list (maat)
- for i, e in enumerate (es):
- if e.tag == 'note': s.doNote (e)
- elif e.tag == 'attributes': s.doAttr (e)
- elif e.tag == 'direction': s.doDirection (e, i, es)
- elif e.tag == 'sound': s.doDirection (maat, i, es) # sound element directly in measure!
- elif e.tag == 'harmony': s.doHarmony (e, i, es)
- elif e.tag == 'barline': herhaal = s.doBarline (e)
- elif e.tag == 'backup':
- dt = int (e.findtext ('duration'))
- if chkbug (dt, s.msr): s.msc.incTime (-dt)
- elif e.tag == 'forward':
- dt = int (e.findtext ('duration'))
- if chkbug (dt, s.msr): s.msc.incTime (dt)
- elif e.tag == 'print': lbrk = s.doPrint (e)
- s.msc.addBar (lbrk, s.msr)
- divisions.append (s.msr.divs)
- if herhaal == 1:
- herhaalMaat = s.msr.ixm
- s.msr.ixm += 1
- elif herhaal == 2:
- if aantalHerhaald < 1: # jump
- s.msr.ixm = herhaalMaat
- aantalHerhaald += 1
- else:
- aantalHerhaald = 0 # reset
- s.msr.ixm += 1 # just continue
- else: s.msr.ixm += 1 # on to the next measure
- for rv in s.repeat_str.values (): # close hanging measure-repeats without stop
- rv [0] = '[I:repeat %s %d]' % (rv [1], 1)
- vvmap = s.msc.outVoices (divisions, ip, s.isSib)
- s.addStaffMap (vvmap) # update global staff map
- s.addMidiMap (ip, vvmap)
- vvmapAll.update (vvmap)
- if vvmapAll: # skip output if no part has any notes
- abcOut.mkHeader (s.gStfMap, partlist, s.midiMap, s.tabVceMap, s.koppen)
- abcOut.writeall ()
- else: info ('nothing written, %s has no notes ...' % abcOut.fnmext)
-
-#----------------
-# Main Program
-#----------------
-if __name__ == '__main__':
- from optparse import OptionParser
- from glob import glob
- from zipfile import ZipFile
- ustr = '%prog [-h] [-u] [-m] [-c C] [-d D] [-n CPL] [-b BPL] [-o DIR] [-v V]\n'
- ustr += '[-x] [-p PFMT] [-t] [-s] [-i] [--v1] [--noped] [--stems] [ ...]'
- parser = OptionParser (usage=ustr, version=str(VERSION))
- parser.add_option ("-u", action="store_true", help="unfold simple repeats")
- parser.add_option ("-m", action="store", help="0 -> no %%MIDI, 1 -> minimal %%MIDI, 2-> all %%MIDI", default=0)
- parser.add_option ("-c", action="store", type="int", help="set credit text filter to C", default=0, metavar='C')
- parser.add_option ("-d", action="store", type="int", help="set L:1/D", default=0, metavar='D')
- parser.add_option ("-n", action="store", type="int", help="CPL: max number of characters per line (default 100)", default=0, metavar='CPL')
- parser.add_option ("-b", action="store", type="int", help="BPL: max number of bars per line", default=0, metavar='BPL')
- parser.add_option ("-o", action="store", help="store abc files in DIR", default='', metavar='DIR')
- parser.add_option ("-v", action="store", type="int", help="set volta typesetting behaviour to V", default=0, metavar='V')
- parser.add_option ("-x", action="store_true", help="output no line breaks")
- parser.add_option ("-p", action="store", help="pageformat PFMT (cm) = scale, pageheight, pagewidth, leftmargin, rightmargin, topmargin, botmargin", default='', metavar='PFMT')
- parser.add_option ("-j", action="store_true", help="switch for compatibility with javascript version")
- parser.add_option ("-t", action="store_true", help="translate perc- and tab-staff to ABC code with %%map, %%voicemap")
- parser.add_option ("-s", action="store_true", help="shift node heads 3 units left in a tab staff")
- parser.add_option ("--v1", action="store_true", help="start-stop directions allways to first voice of staff")
- parser.add_option ("--noped", action="store_false", help="skip all pedal directions", dest='ped', default=True)
- parser.add_option ("--stems", action="store_true", help="translate stem directions", dest='stm', default=False)
- parser.add_option ("-i", action="store_true", help="read xml file from standard input")
- options, args = parser.parse_args ()
- if options.n < 0: parser.error ('only values >= 0')
- if options.b < 0: parser.error ('only values >= 0')
- if options.d and options.d not in [2**n for n in range (10)]:
- parser.error ('D should be on of %s' % ','.join ([str(2**n) for n in range (10)]))
- options.p = options.p and options.p.split (',') or [] # ==> [] | [string]
- if len (args) == 0 and not options.i: parser.error ('no input file given')
- pad = options.o
- if pad:
- if not os.path.exists (pad): os.mkdir (pad)
- if not os.path.isdir (pad): parser.error ('%s is not a directory' % pad)
- fnmext_list = []
- for i in args: fnmext_list += glob (i)
- if options.i: fnmext_list = ['stdin.xml']
- if not fnmext_list: parser.error ('none of the input files exist')
- for X, fnmext in enumerate (fnmext_list):
- fnm, ext = os.path.splitext (fnmext)
- if ext.lower () not in ('.xml','.mxl','.musicxml'):
- info ('skipped input file %s, it should have extension .xml or .mxl' % fnmext)
- continue
- if os.path.isdir (fnmext):
- info ('skipped directory %s. Only files are accepted' % fnmext)
- continue
- if fnmext == 'stdin.xml':
- fobj = sys.stdin
- elif ext.lower () == '.mxl': # extract .xml file from .mxl file
- z = ZipFile(fnmext)
- for n in z.namelist(): # assume there is always an xml file in a mxl archive !!
- if (n[:4] != 'META') and (n[-4:].lower() == '.xml'):
- fobj = z.open (n)
- break # assume only one MusicXML file per archive
- else:
- fobj = open (fnmext, 'rb') # open regular xml file
-
- abcOut = ABCoutput (fnm + '.abc', pad, X, options) # create global ABC output object
- psr = Parser (options) # xml parser
- try:
- psr.parse (fobj) # parse file fobj and write abc to .abc
- except:
- etype, value, traceback = sys.exc_info () # works in python 2 & 3
- info ('** %s occurred: %s in %s' % (etype, value, fnmext), 0)
diff --git a/spaces/sayakpaul/sots-outdoor-dehazing-maxim/maxim/blocks/block_gating.py b/spaces/sayakpaul/sots-outdoor-dehazing-maxim/maxim/blocks/block_gating.py
deleted file mode 100644
index 0d06af50448f7a15a39c84100be1a99710b24c32..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/sots-outdoor-dehazing-maxim/maxim/blocks/block_gating.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import tensorflow as tf
-from tensorflow.keras import backend as K
-from tensorflow.keras import layers
-
-from ..layers import BlockImages, SwapAxes, UnblockImages
-
-
-def BlockGatingUnit(use_bias: bool = True, name: str = "block_gating_unit"):
- """A SpatialGatingUnit as defined in the gMLP paper.
-
- The 'spatial' dim is defined as the **second last**.
- If applied on other dims, you should swapaxes first.
- """
-
- def apply(x):
- u, v = tf.split(x, 2, axis=-1)
- v = layers.LayerNormalization(
- epsilon=1e-06, name=f"{name}_intermediate_layernorm"
- )(v)
- n = K.int_shape(x)[-2] # get spatial dim
- v = SwapAxes()(v, -1, -2)
- v = layers.Dense(n, use_bias=use_bias, name=f"{name}_Dense_0")(v)
- v = SwapAxes()(v, -1, -2)
- return u * (v + 1.0)
-
- return apply
-
-
-def BlockGmlpLayer(
- block_size,
- use_bias: bool = True,
- factor: int = 2,
- dropout_rate: float = 0.0,
- name: str = "block_gmlp",
-):
- """Block gMLP layer that performs local mixing of tokens."""
-
- def apply(x):
- n, h, w, num_channels = (
- K.int_shape(x)[0],
- K.int_shape(x)[1],
- K.int_shape(x)[2],
- K.int_shape(x)[3],
- )
- fh, fw = block_size
- gh, gw = h // fh, w // fw
- x = BlockImages()(x, patch_size=(fh, fw))
- # MLP2: Local (block) mixing part, provides within-block communication.
- y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
- y = layers.Dense(
- num_channels * factor,
- use_bias=use_bias,
- name=f"{name}_in_project",
- )(y)
- y = tf.nn.gelu(y, approximate=True)
- y = BlockGatingUnit(use_bias=use_bias, name=f"{name}_BlockGatingUnit")(y)
- y = layers.Dense(
- num_channels,
- use_bias=use_bias,
- name=f"{name}_out_project",
- )(y)
- y = layers.Dropout(dropout_rate)(y)
- x = x + y
- x = UnblockImages()(x, grid_size=(gh, gw), patch_size=(fh, fw))
- return x
-
- return apply
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/__init__.py b/spaces/sccstandardteam/ChuanhuChatGPT/modules/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/scedlatioru/img-to-music/example/Crack PORTABLE Digital Media Group Facebook Blaster Pro V7.1.9 Portable.md b/spaces/scedlatioru/img-to-music/example/Crack PORTABLE Digital Media Group Facebook Blaster Pro V7.1.9 Portable.md
deleted file mode 100644
index 558513c66063454d2a71d23679f2f8a29f4b2f3a..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Crack PORTABLE Digital Media Group Facebook Blaster Pro V7.1.9 Portable.md
+++ /dev/null
@@ -1,133 +0,0 @@
-CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable: A Complete Guide
-
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-Use a catchy and relevant title: You should use a title that grabs the attention of your audience and conveys the main benefit or value proposition of your message. You should also use keywords that match your target audience's search intent.Use a clear and concise message: You should use a message that delivers your message clearly and concisely without being too long or too short. You should also use spintax and variables to make your message unique and relevant for each user.Use a strong call to action: You should use a call to action that motivates your audience to take the desired action, such as visiting your website, signing up for your newsletter, buying your product, or contacting you. You should also use a link that directs your audience to your landing page or offer page.Use filters and settings: You should use filters and settings to avoid spamming and getting banned by Facebook. You should also use proxies and cookies to protect your accounts and avoid detection by Facebook.
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-Conclusion
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-In this article, we have discussed CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable, a software that lets you promote your business or brand on Facebook. We have explained what this software is, what its features are, how to download and install it, how to use it, what its pros and cons are, how it can boost your business, and what are the best practices for using it.
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-We hope that this article has been informative and helpful for you. If you are looking for a way to market your business or brand on Facebook effectively and efficiently, you should give CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable a try. However, if you want to use a software that is legal, stable, and ethical, you should consider some of the alternatives that we have mentioned above. They are also easy to use, versatile, powerful, and high-quality software that can help you market your business or brand on Facebook.
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-Thank you for reading this article. We hope you have enjoyed it and learned something new.
-What are some testimonials from users of CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable?
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-CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable has received many positive testimonials from users who have used it for their Facebook marketing campaigns. Here are some of them:
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-"I have been using this software for a few months now and I am very impressed with the results. I have been able to generate more leads and sales for my online business by sending mass messages, friend requests, comments, and likes to my target audience. The software is very easy to use and has many features and options that allow me to customize and optimize my campaigns. I highly recommend this software to anyone who wants to market their business or brand on Facebook."
-- John Smith, Online Entrepreneur
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-"This software is amazing! It has helped me grow my fan page and increase my engagement with my followers. I have been able to post comments and likes on thousands of posts related to my niche and attract more traffic to my page. The software is also very fast and reliable and has never crashed or caused any problems. I love this software and I would definitely buy it if it was not cracked."
-- Jane Doe, Social Media Influencer
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-"This software is a game-changer for me. It has saved me a lot of time and money that I used to spend on hiring a team or outsourcing the work. I have been able to manage multiple Facebook accounts and send mass messages, friend requests, comments, and likes to thousands of users with ease and efficiency. The software is also very user-friendly and has a great support team that answers all my questions and issues. This software is the best thing that ever happened to me."
-- Mike Jones, Facebook Marketer
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-Here are some frequently asked questions about CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable and their answers:
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-Q: Is this software legal? A: No, this software is not legal. It is a cracked version of the original software that violates the terms and conditions of Digital Media Group. You are also risking getting infected by viruses, malware, or spyware that might come with the crack and keygen. Q: Is this software safe? A: No, this software is not safe. It is a patched version of the original software that might not work properly or cause errors or crashes. You are also missing out on the updates and support from Digital Media Group. Q: Is this software ethical? A: No, this software is not ethical. It is a software that you have not paid for or obtained legally. You are also depriving the developers and creators of their rightful income and recognition. Q: How can I get this software? A: You can get this software by downloading it from one of the torrent sites that offer it. However, we do not recommend doing so as it is illegal, unsafe, and unethical. Q: How can I use this software? A: You can use this software by installing it on your computer and activating it with the crack and keygen provided by CracksMind. However, we do not recommend doing so as it is illegal, unsafe, and unethical.
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-Conclusion
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-In this article, we have discussed CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable, a software that lets you promote your business or brand on Facebook. We have explained what this software is, what its features are, how to download and install it, how to use it, what its pros and cons are, how it can boost your business, what are the best practices for using it, what are some testimonials from users of it, and what are some FAQs about it.
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-We hope that this article has been informative and helpful for you. If you are looking for a way to market your business or brand on Facebook effectively and efficiently, you should give CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable a try. However, if you want to use a software that is legal, stable, and ethical, you should consider some of the alternatives that we have mentioned above. They are also easy to use, versatile, powerful, and high-quality software that can help you market your business or brand on Facebook.
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-Thank you for reading this article. We hope you have enjoyed it and learned something new.
-Conclusion
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-In this article, we have discussed CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable, a software that lets you promote your business or brand on Facebook. We have explained what this software is, what its features are, how to download and install it, how to use it, what its pros and cons are, how it can boost your business, what are the best practices for using it, what are some testimonials from users of it, and what are some FAQs about it.
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-We hope that this article has been informative and helpful for you. If you are looking for a way to market your business or brand on Facebook effectively and efficiently, you should give CRACK Digital Media Group Facebook Blaster Pro V7.1.9 Portable a try. However, if you want to use a software that is legal, stable, and ethical, you should consider some of the alternatives that we have mentioned above. They are also easy to use, versatile, powerful, and high-quality software that can help you market your business or brand on Facebook.
-
-Thank you for reading this article. We hope you have enjoyed it and learned something new.
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-9th Dawn RPG Mod APK: A Guide to the Ultimate Adventure
-Are you looking for a fun and immersive role-playing game that will keep you hooked for hours? Do you want to experience a vast and open world full of mysteries, dangers, and adventures? If you answered yes, then you should try 9th Dawn RPG Mod APK, a modified version of the popular game 9th Dawn RPG by Valorware. In this article, we will tell you everything you need to know about this amazing game, including its features, story, mod benefits, download instructions, and tips and tricks. Read on and get ready for the ultimate adventure!
- What is 9th Dawn RPG?
-9th Dawn RPG is a classic-style role-playing game that was released in 2012 by Valorware, an independent game developer. The game is set in the island continent of Montelorne, a land far detached from the mainland, but filled with mystery, danger, and adventure. You play as a hero who arrives in Montelorne to explore its secrets and face its challenges. You can choose from three different classes: warrior, mage, or archer, and customize your character's appearance, skills, and equipment. You can also interact with various NPCs, join factions, complete quests, collect items, craft weapons and armor, learn spells, fight enemies and bosses, and more. The game has a retro pixel art style that gives it a nostalgic charm, and a dynamic day-night cycle that affects the gameplay. The game also has a huge open world that you can explore freely, with over 300 maps to discover.
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Features of 9th Dawn RPG
-Some of the features that make 9th Dawn RPG stand out are:
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-A large and diverse world with over 300 maps to explore, including forests, caves, dungeons, towns, castles, islands, and more.
-A rich and engaging story with multiple endings depending on your choices and actions.
-A dynamic day-night cycle that affects the environment, NPCs, enemies, and quests.
-A variety of enemies and bosses to fight, each with their own strengths and weaknesses.
-A complex combat system that allows you to use melee weapons, ranged weapons, shields, spells, potions, traps, and more.
-A character customization system that lets you choose from three classes (warrior, mage, or archer), select your gender and appearance, distribute your attribute points (strength, agility, intelligence), and learn skills and spells.
-An equipment system that lets you collect and craft various items such as weapons, armor, accessories, consumables, etc.
-An inventory system that lets you manage your items and equip them on your character.
-A faction system that lets you join one of the four factions in Montelorne: The Order of the Lion (the royal army), The Shadow Knights (the rebels), The Arcane Society (the mages), or The Brotherhood (the thieves).
-A quest system that lets you accept and complete various tasks from NPCs or factions.
-A dialogue system that lets you interact with NPCs and choose your responses.
-A save system that lets you save your progress at any time.
-
- Story and setting of 9th Dawn RPG
-The story of 9th Dawn RPG takes place in the island continent of Montelorne, a land that was once part of a great empire called Esteria. However, due to a cataclysmic event known as the Great
War, Montelorne was separated from the mainland and plunged into chaos. The empire collapsed, and four factions emerged to vie for power and influence: The Order of the Lion, The Shadow Knights, The Arcane Society, and The Brotherhood. You are a hero who arrives in Montelorne to explore its secrets and face its challenges. You can choose to align yourself with one of the factions, or remain neutral and forge your own destiny. Your actions and choices will shape the fate of Montelorne and its people.
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- What is 9th Dawn RPG Mod APK?
-9th Dawn RPG Mod APK is a modified version of the original game that gives you some extra benefits and features that are not available in the official version. The mod APK is created by third-party developers who modify the game files to enhance the gameplay and user experience. However, you should be careful when downloading and installing mod APKs, as they may contain viruses or malware that can harm your device or compromise your privacy. You should always download mod APKs from trusted sources and scan them with antivirus software before installing them.
- Benefits of 9th Dawn RPG Mod APK
-Some of the benefits that you can enjoy by using 9th Dawn RPG Mod APK are:
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-Unlimited money: You can get unlimited coins and gems that you can use to buy items, upgrade your equipment, learn skills and spells, etc.
-Unlocked items: You can access all the items in the game, including weapons, armor, accessories, consumables, etc., without having to collect or craft them.
-Unlocked maps: You can explore all the maps in the game, including the hidden ones, without having to unlock them by completing quests or finding keys.
-No ads: You can play the game without any interruptions or distractions from annoying ads.
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- How to download and install 9th Dawn RPG Mod APK
-To download and install 9th Dawn RPG Mod APK, you need to follow these steps:
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-Go to a reliable website that offers 9th Dawn RPG Mod APK for free download. For example, you can use this link: .
-Click on the download button and wait for the file to be downloaded on your device.
-Once the file is downloaded, go to your device's settings and enable the option to install apps from unknown sources. This will allow you to install mod APKs that are not from the Google Play Store.
-Locate the downloaded file on your device and tap on it to start the installation process.
-Follow the instructions on the screen and wait for the installation to be completed.
-Launch the game and enjoy!
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- Tips and tricks for playing 9th Dawn RPG Mod APK
-Now that you have downloaded and installed 9th Dawn RPG Mod APK, you are ready to start your adventure in Montelorne. Here are some tips and tricks that will help you make the most out of your gaming experience:
- Explore the world of Montelorne
-One of the best things about 9th Dawn RPG is its huge and diverse world that you can explore freely. There are over 300 maps to discover, each with its own environment, NPCs, enemies, items, secrets, and more. You can find hidden areas, treasure chests, dungeons, caves, towns, castles, islands, etc., by exploring every corner of the map. You can also use fast travel points to move between locations quickly. However, be careful when exploring new areas, as you may encounter dangerous enemies or traps that can harm you. Always be prepared with enough health potions, weapons, armor, spells, etc., before venturing into unknown territory.
- Customize your character and equipment
-Another great thing about 9th Dawn RPG is its character customization system that lets you create your own unique hero. You can choose from three classes: warrior, mage, or archer, and select your gender and appearance. You can also distribute your attribute points (strength, agility, intelligence) and learn skills and spells that suit your playstyle. You can also collect and craft various items such as weapons, armor, accessories, consumables, etc., and equip them on your character. You can find items by exploring the world, completing quests, defeating enemies, opening chests, etc. You can also craft items by using materials and recipes that you can find or buy. You can upgrade your equipment by using gems that you can find or buy. You can also enchant your equipment by using scrolls that you can find or buy. You can customize your character and equipment at any time by accessing the menu.
- Learn skills and spells
-Skills and spells are special abilities that you can use in combat or exploration. They can help you deal more damage, heal yourself or allies, buff yourself or allies, debuff enemies, escape from danger, etc. You can learn skills and spells by leveling up your character, joining factions, completing quests, finding books, etc. You can also upgrade your skills and spells by using skill points that you earn by leveling up. You can access your skills and spells by tapping on the icons on the bottom right corner of the screen. You can also assign them to quick slots for easier access. You can use skills and spells by tapping on their icons or pressing the corresponding buttons on your device. However, be aware that skills and spells consume stamina or mana, which are indicated by the blue and green bars on the top left corner of the screen. You need to wait for them to regenerate before you can use them again.
- Fight enemies and bosses
-Enemies and bosses are the main challenges that you will face in 9th Dawn RPG. They are creatures or characters that will attack you on sight or when provoked. They have different types, levels, stats, behaviors, strengths, and weaknesses. You need to use your skills, spells, weapons, armor, potions, traps, etc., to defeat them. You can also use the environment to your advantage, such as hiding behind obstacles, luring enemies into traps, etc. You can also avoid fighting enemies by running away or using stealth skills or items. However, some enemies and bosses are unavoidable or mandatory for completing quests or advancing the story. You can find enemies and bosses by exploring the world, entering dungeons or caves, accepting quests, etc. You can also encounter random encounters or ambushes by enemies while traveling between locations.
- Join factions and quests
-Factions and quests are optional but rewarding aspects of 9th Dawn RPG. Factions are groups of NPCs that have their own goals, beliefs, and agendas. You can join one of the four factions in Montelorne: The Order of the Lion, The Shadow Knights, The Arcane Society, or The Brotherhood. Each faction has its own leader, headquarters, members, allies, enemies, and reputation. You can increase your reputation with a faction by completing quests, helping members, donating items, etc. You can also decrease your reputation with a faction by attacking members, stealing items, betraying allies, etc. Your reputation with a faction affects how they treat you, what quests they offer you, what rewards they give you, etc. You can also switch factions at any time by talking to the faction leader or using a special item. However, be careful when joining or leaving factions, as you may lose some benefits or gain some enemies. Quests are tasks that you can accept and complete from NPCs or factions. They can involve various objectives such as killing enemies, finding items, delivering messages, escorting allies, solving puzzles, etc. They can also have different difficulties, rewards, time limits, consequences, etc. You can find quests by talking to NPCs, visiting locations, reading notices, etc. You can also track your active quests by accessing the menu. You can complete quests by fulfilling the objectives and returning to the quest giver. You can also fail quests by ignoring the objectives, running out of time, killing the quest giver, etc. Quests can help you gain experience, money, items, reputation, skills, spells, etc. They can also help you advance the story or unlock new areas.
- Conclusion
-9th Dawn RPG Mod APK is a modified version of the original game that gives you some extra benefits and features that are not available in the official version. It is a classic-style role-playing game that lets you explore a vast and open world full of mysteries, dangers, and adventures. You can customize your character and equipment, learn skills and spells, fight enemies and bosses, join factions and quests, and more. It is a fun and immersive game that will keep you hooked for hours.
- Summary of the article
-In this article, we have covered the following topics:
-
-What is 9th Dawn RPG?
-What is 9th Dawn RPG Mod APK?
-Benefits of 9th Dawn RPG Mod APK
-How to download and install 9th Dawn RPG Mod APK
-Tips and tricks for playing 9th Dawn RPG Mod APK
-
- FAQs
-Here are some frequently asked questions about 9th Dawn RPG Mod APK:
-
-Is 9th Dawn RPG Mod APK safe to use?
-9th Dawn RPG Mod APK is generally safe to use if you download it from a trusted source and scan it with antivirus software before installing it. However, you should be aware that mod APKs are not authorized by the original game developer and may contain bugs or errors that can affect your gameplay or device performance. You should also be careful when granting permissions to mod APKs, as they may access your personal data or device functions without your consent.
-Is 9th Dawn RPG Mod APK compatible with my device?
-9th Dawn RPG Mod APK is compatible with most Android devices that have Android 4.0 or higher operating system and at least 1 GB of RAM and 100 MB of free storage space. However, you should check the mod APK's specifications and requirements before downloading and installing it to ensure that it works properly on your device.
-Can I play 9th Dawn RPG Mod APK online or offline?
-9th Dawn RPG Mod APK is mainly an offline game that does not require an internet connection to play. However, you may need an internet connection to download and install the mod APK, to access some online features such as leaderboards or achievements, or to update the mod APK to the latest version.
-Can I play 9th Dawn RPG Mod APK with my friends?
-9th Dawn RPG Mod APK does not have a multiplayer mode that allows you to play with your friends online or locally. However, you can share your progress and achievements with your friends by using social media platforms such as Facebook or Twitter.
-Can I transfer my progress from 9th Dawn RPG to 9th Dawn RPG Mod APK or vice versa?
-No, you cannot transfer your progress from 9th Dawn RPG to 9th Dawn RPG Mod APK or vice versa. The mod APK has a different file structure and data format than the original game, and they are not compatible with each other. If you want to switch between the two versions, you will have to start from scratch.
- 197e85843dHow to Download and Install EDIUS Neo 3.5 Crack for Free
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e93f5a0c3fHow to Download and Install Evermap Adobe Plugins for PDF Processing
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cec2833e83How to Use Garritan Aria Player to Create Multi-Champion Tracks
-Garritan Aria Player is a powerful and versatile software that allows you to play and edit high-quality sampled instruments from various genres and styles. You can use it as a standalone application or as a plug-in with your favorite sequencer or notation program. But did you know that you can also use it to create multi-champion tracks, which are songs that combine elements from different musical champions, such as rock, jazz, classical, and more?
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Step 1: Download and Activate Garritan Aria Player
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-Garritan Aria Player can load and playback MIDI files, which are files that contain musical data such as notes, velocities, timings, and more. You can use MIDI files to play along with your favorite songs, or to create your own compositions. To load a MIDI file, go to File > Open MIDI File and browse for the file on your computer. To playback the MIDI file, use the transport controls at the bottom of the interface. You can also adjust the tempo, volume, and balance of the MIDI file using the sliders on the right side of the interface.
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-One of the most exciting features of Garritan Aria Player is that it allows you to mix and match instruments from different libraries, such as Garritan Personal Orchestra, Garritan Jazz and Big Band, Garritan World Instruments, and more. You can access these libraries by clicking on the Load button on each slot of the interface. You can load up to 16 instruments at a time, each with its own channel number. You can then assign each instrument to a different track of your MIDI file by changing the channel number on your sequencer or notation program.
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81aa517590PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD: A Powerful CAD Software for Design and Engineering
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-If you are looking for a reliable and versatile CAD software that can handle complex design and engineering tasks, you might want to check out PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD. This is the latest version of the popular PTC Creo software, which offers a range of features and capabilities to help you create, analyze, and optimize your products.
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-You can use it online from a cloud-based service provider of PTC Creo. You will need to sign up for a subscription plan and pay a monthly or annual fee depending on the features and resources you need. You will also need to have a stable internet connection and a compatible browser. You will be able to access the software from any device and location.
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-Multi-language support: PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD supports multiple languages, such as English, Spanish, French, German, Italian, Japanese, Korean, Chinese, etc. You can switch between languages easily and work with different teams and clients across the globe.SolidSQUAD crack: PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD comes with a crack from SolidSQUAD, a team of hackers who specialize in cracking CAD software. The crack allows you to use PTC Creo without any limitations or restrictions. You can enjoy all the features and updates of PTC Creo without paying any fees or licenses.2D and 3D modeling: PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD allows you to create and edit 2D and 3D models with ease and precision. You can use parametric and direct modeling techniques, sketching and drawing tools, geometric constraints and dimensions, feature-based and surface-based modeling, etc. You can also import and export models from other CAD software and formats.Simulation and analysis: PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD enables you to perform various simulations and analyses on your models, such as structural, thermal, fluid, dynamic, kinematic, vibration, fatigue, etc. You can also use advanced tools such as finite element analysis (FEA), computational fluid dynamics (CFD), multi-body dynamics (MBD), etc. You can validate your designs and optimize them for performance and quality.Additive manufacturing: PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD supports additive manufacturing or 3D printing technologies. You can design and print your models using various materials and methods, such as fused deposition modeling (FDM), stereolithography (SLA), selective laser sintering (SLS), direct metal laser sintering (DMLS), etc. You can also use generative design tools to create optimal shapes and structures for your models.Augmented reality: PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD integrates with augmented reality or AR technologies. You can use PTC's ThingWorx platform to create and share AR experiences with your models. You can also use PTC's Vuforia platform to create and view AR applications on your mobile devices. You can enhance your design and engineering workflows with AR capabilities.
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-
-
-You can access the official documentation and tutorials of PTC Creo from the help menu or the website of PTC Creo. You can find detailed information and instructions on how to use PTC Creo and its features.
-You can watch online videos and courses on PTC Creo from various platforms, such as YouTube, Udemy, Coursera, Lynda, etc. You can learn from experts and instructors who will teach you the basics and advanced topics of PTC Creo.
-You can join online forums and communities of PTC Creo users, such as Reddit, Quora, Stack Overflow, etc. You can ask questions and get answers from other users who have experience and knowledge of PTC Creo.
-You can read online blogs and articles on PTC Creo from various sources, such as Medium, TechCrunch, Forbes, etc. You can get insights and tips on how to use PTC Creo effectively and efficiently.
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-Conclusion
-
-PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD is a powerful CAD software that can help you design and engineer anything you can imagine. It has many features and capabilities that make it easy to use and versatile. It also comes with a crack from SolidSQUAD that allows you to use it without any limitations or restrictions.
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-If you want to download PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD, you have several options:
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-You can download it from the official website of PTC Creo. You will need to create a PTC eSupport account and provide some basic information. You will also need to agree to the terms and conditions of use. The download process may take some time depending on your internet speed and system specifications.
-You can download it from a third-party website that offers PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD as a package on npm. You will need to have npm installed on your computer and run a command to install the package. You will also need to verify the authenticity and security of the package before using it.
-You can download it from a torrent website that offers PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD as a PDF file. You will need to have a torrent client installed on your computer and open the file with it. You will also need to be careful of viruses and malware that may be attached to the file.
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-If you have downloaded PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD from the official website of PTC Creo or an authorized reseller or distributor, you can install it by following these steps:
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-
-Run the PTC Installation Assistant and select Install New Software.
-Select PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD from the list of available software and click Next.
-Choose the installation type (Typical or Custom) and click Next.
-Select the installation directory and click Next.
-Enter your license information (Sales Order Number, Host ID/MAC Address) and click Next.
-Review the installation summary and click Install.
-Wait for the installation to complete and click Finish.
-
-
-If you have downloaded PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD from a third-party website or a torrent website, you may need to follow different steps depending on the format and content of the file. You may also need to apply the crack from SolidSQUAD to activate PTC Creo V20 M030 MULTiLANGUAGESolidSQUAD. However, we do not recommend that you do this as it may be illegal and risky.
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-Conclusion
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-Conclusion
-
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3cee63e6c2Scala Infochannel Designer 5 Crack Download Zip ===> https://cinurl.com/2uEYLL
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visviva Animation Capture 3.23 BEST Full.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visviva Animation Capture 3.23 BEST Full.md
deleted file mode 100644
index 641fb6e5004335304469d6db023394a40fbe1d76..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visviva Animation Capture 3.23 BEST Full.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Visviva Animation Capture 3.23 Full Download Zip ✔ https://cinurl.com/2uEYRd
diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/loaders/alignments.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/loaders/alignments.py
deleted file mode 100644
index 9467ec9308475e876346efe199e1973eb154cd88..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/loaders/alignments.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import os
-import json
-import cv2
-import numpy as np
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-
-from spiga.data.loaders.transforms import get_transformers
-
-
-class AlignmentsDataset(Dataset):
- '''Loads datasets of images with landmarks and bounding boxes.
- '''
-
- def __init__(self,
- database,
- json_file,
- images_dir,
- image_size=(128, 128),
- transform=None,
- indices=None,
- debug=False):
- """
-
- :param database: class DatabaseStruct containing all the specifics of the database
-
- :param json_file: path to the json file which contains the names of the images, landmarks, bounding boxes, etc
-
- :param images_dir: path of the directory containing the images.
-
- :param image_size: tuple like e.g. (128, 128)
-
- :param transform: composition of transformations that will be applied to the samples.
-
- :param debug_mode: bool if True, loads a very reduced_version of the dataset for debugging purposes.
-
- :param indices: If it is a list of indices, allows to work with the subset of
- items specified by the list. If it is None, the whole set is used.
- """
-
- self.database = database
- self.images_dir = images_dir
- self.transform = transform
- self.image_size = image_size
- self.indices = indices
- self._imgs_dict = None
- self.debug = debug
-
- with open(json_file) as jsonfile:
- self.data = json.load(jsonfile)
-
- def __len__(self):
- '''Returns the length of the dataset
- '''
- if self.indices is None:
- return len(self.data)
- else:
- return len(self.indices)
-
- def __getitem__(self, sample_idx):
- '''Returns sample of the dataset of index idx'''
-
- # To allow work with a subset
- if self.indices is not None:
- sample_idx = self.indices[sample_idx]
-
- # Load sample image
- img_name = os.path.join(self.images_dir, self.data[sample_idx]['imgpath'])
- if not self._imgs_dict:
- image_cv = cv2.imread(img_name)
- else:
- image_cv = self._imgs_dict[sample_idx]
-
- # Some images are B&W. We make sure that any image has three channels.
- if len(image_cv.shape) == 2:
- image_cv = np.repeat(image_cv[:, :, np.newaxis], 3, axis=-1)
-
- # Some images have alpha channel
- image_cv = image_cv[:, :, :3]
-
- image_cv = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
- image = Image.fromarray(image_cv)
-
- # Load sample anns
- ids = np.array(self.data[sample_idx]['ids'])
- landmarks = np.array(self.data[sample_idx]['landmarks'])
- bbox = np.array(self.data[sample_idx]['bbox'])
- vis = np.array(self.data[sample_idx]['visible'])
- headpose = self.data[sample_idx]['headpose']
-
- # Generate bbox if need it
- if bbox is None:
- # Compute bbox using landmarks
- aux = landmarks[vis == 1.0]
- bbox = np.zeros(4)
- bbox[0] = min(aux[:, 0])
- bbox[1] = min(aux[:, 1])
- bbox[2] = max(aux[:, 0]) - bbox[0]
- bbox[3] = max(aux[:, 1]) - bbox[1]
-
- # Clean and mask landmarks
- mask_ldm = np.ones(self.database.num_landmarks)
- if not self.database.ldm_ids == ids.tolist():
- new_ldm = np.zeros((self.database.num_landmarks, 2))
- new_vis = np.zeros(self.database.num_landmarks)
- xyv = np.hstack((landmarks, vis[np.newaxis,:].T))
- ids_dict = dict(zip(ids.astype(int).astype(str), xyv))
-
- for pos, identifier in enumerate(self.database.ldm_ids):
- if str(identifier) in ids_dict:
- x, y, v = ids_dict[str(identifier)]
- new_ldm[pos] = [x,y]
- new_vis[pos] = v
- else:
- mask_ldm[pos] = 0
- landmarks = new_ldm
- vis = new_vis
-
- sample = {'image': image,
- 'sample_idx': sample_idx,
- 'imgpath': img_name,
- 'ids_ldm': np.array(self.database.ldm_ids),
- 'bbox': bbox,
- 'bbox_raw': bbox,
- 'landmarks': landmarks,
- 'visible': vis.astype(np.float64),
- 'mask_ldm': mask_ldm,
- 'imgpath_local': self.data[sample_idx]['imgpath'],
- }
-
- if self.debug:
- sample['landmarks_ori'] = landmarks
- sample['visible_ori'] = vis.astype(np.float64)
- sample['mask_ldm_ori'] = mask_ldm
- if headpose is not None:
- sample['headpose_ori'] = np.array(headpose)
-
- if self.transform:
- sample = self.transform(sample)
-
- return sample
-
-
-def get_dataset(data_config, pretreat=None, debug=False):
-
- augmentors = get_transformers(data_config)
- if pretreat is not None:
- augmentors.append(pretreat)
-
- dataset = AlignmentsDataset(data_config.database,
- data_config.anns_file,
- data_config.image_dir,
- image_size=data_config.image_size,
- transform=transforms.Compose(augmentors),
- indices=data_config.ids,
- debug=debug)
- return dataset
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py
deleted file mode 100644
index c5aa2eea1e8c76f8baf753d1c8c959dee665e543..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import yaml
-
-try:
- from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
- from yaml import Loader, Dumper
-
-from .base import BaseFileHandler # isort:skip
-
-
-class YamlHandler(BaseFileHandler):
-
- def load_from_fileobj(self, file, **kwargs):
- kwargs.setdefault('Loader', Loader)
- return yaml.load(file, **kwargs)
-
- def dump_to_fileobj(self, obj, file, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- yaml.dump(obj, file, **kwargs)
-
- def dump_to_str(self, obj, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- return yaml.dump(obj, **kwargs)
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/roi_pool.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/roi_pool.py
deleted file mode 100644
index d339d8f2941eabc1cbe181a9c6c5ab5ff4ff4e5f..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/roi_pool.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext',
- ['roi_pool_forward', 'roi_pool_backward'])
-
-
-class RoIPoolFunction(Function):
-
- @staticmethod
- def symbolic(g, input, rois, output_size, spatial_scale):
- return g.op(
- 'MaxRoiPool',
- input,
- rois,
- pooled_shape_i=output_size,
- spatial_scale_f=spatial_scale)
-
- @staticmethod
- def forward(ctx, input, rois, output_size, spatial_scale=1.0):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
-
- assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'
-
- output_shape = (rois.size(0), input.size(1), ctx.output_size[0],
- ctx.output_size[1])
- output = input.new_zeros(output_shape)
- argmax = input.new_zeros(output_shape, dtype=torch.int)
-
- ext_module.roi_pool_forward(
- input,
- rois,
- output,
- argmax,
- pooled_height=ctx.output_size[0],
- pooled_width=ctx.output_size[1],
- spatial_scale=ctx.spatial_scale)
-
- ctx.save_for_backward(rois, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- rois, argmax = ctx.saved_tensors
- grad_input = grad_output.new_zeros(ctx.input_shape)
-
- ext_module.roi_pool_backward(
- grad_output,
- rois,
- argmax,
- grad_input,
- pooled_height=ctx.output_size[0],
- pooled_width=ctx.output_size[1],
- spatial_scale=ctx.spatial_scale)
-
- return grad_input, None, None, None
-
-
-roi_pool = RoIPoolFunction.apply
-
-
-class RoIPool(nn.Module):
-
- def __init__(self, output_size, spatial_scale=1.0):
- super(RoIPool, self).__init__()
-
- self.output_size = _pair(output_size)
- self.spatial_scale = float(spatial_scale)
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- s = self.__class__.__name__
- s += f'(output_size={self.output_size}, '
- s += f'spatial_scale={self.spatial_scale})'
- return s
diff --git a/spaces/svjack/bloom-daliy-dialogue-english/reconstructor.py b/spaces/svjack/bloom-daliy-dialogue-english/reconstructor.py
deleted file mode 100644
index 1c852ce2d495a787aa5926de87e2c425fd7fac4f..0000000000000000000000000000000000000000
--- a/spaces/svjack/bloom-daliy-dialogue-english/reconstructor.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from predict import *
-from transformers import (
- T5ForConditionalGeneration,
- T5TokenizerFast as T5Tokenizer,
-)
-import jieba.posseg as posseg
-
-model_path = "svjack/T5-dialogue-collect-v5"
-tokenizer = T5Tokenizer.from_pretrained(model_path)
-model = T5ForConditionalGeneration.from_pretrained(model_path)
-
-rec_obj = Obj(model, tokenizer)
-
-def process_one_sent(input_):
- assert type(input_) == type("")
- input_ = " ".join(map(lambda y: y.word.strip() ,filter(lambda x: x.flag != "x" ,
- posseg.lcut(input_))))
- return input_
-
-def predict_split(sp_list, cut_tokens = True):
- assert type(sp_list) == type([])
- if cut_tokens:
- src_text = '''
- 根据下面的上下文进行分段：
- 上下文：{}
- 答案：
- '''.format(" ".join(
- map(process_one_sent ,sp_list)
- ))
- else:
- src_text = '''
- 根据下面的上下文进行分段：
- 上下文：{}
- 答案：
- '''.format("".join(sp_list))
- print(src_text)
- pred = rec_obj.predict(src_text)[0]
- pred = list(filter(lambda y: y ,map(lambda x: x.strip() ,pred.split("分段:"))))
- return pred
diff --git a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/act_fun.py b/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/act_fun.py
deleted file mode 100644
index be306a6b6023fc8563f5b43057045a062b98258d..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/act_fun.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import math
-import torch
-import torch.nn.functional as F
-from packaging import version
-
-
-def gelu(x):
- return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
-
-def gelu_fast(x):
- return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
-
-def relu(x):
- return F.relu(x)
-
-def linear(x):
- return x
-
-def _silu_python(x):
- """
- See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear
- Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function
- Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and Swish: a Self-Gated
- Activation Function (Ramachandran et al., https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with
- later.
- """
- return x * torch.sigmoid(x)
-
-
-if version.parse(torch.__version__) < version.parse("1.7"):
- silu = _silu_python
-else:
- silu = F.silu
diff --git a/spaces/t13718236382/bingoGPT4/src/components/user-menu.tsx b/spaces/t13718236382/bingoGPT4/src/components/user-menu.tsx
deleted file mode 100644
index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/bingoGPT4/src/components/user-menu.tsx
+++ /dev/null
@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
- DropdownMenu,
- DropdownMenuContent,
- DropdownMenuItem,
- DropdownMenuSeparator,
- DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
- const [host, setHost] = useState('')
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- useEffect(() => {
- setHost(location.host)
- }, [])
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
- return (
-
-
-
-
-
-
- 设置
-
-
-
-
- location.href='#dialog="settings"'
- }
- className="cursor-pointer"
- >
- 设置用户
-
-
- location.href='#dialog="voice"'
- }
- className="cursor-pointer"
- >
- 语音设置
-
-
-
- 开源地址
-
-
-
-
- 托管地址
- 🤗
-
-
-
-
- 复制站点
-
-
-
- 版本信息 {pkg.version}
-
-
- 站点域名
- copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
- {host}
-
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3Com Wl-552 Firmware - How to Download and Install It on Your Device
-If you are looking for a reliable and fast wireless network solution, you might want to consider the 3Com Wl-552 router. This device offers a high-performance and secure wireless connection for your home or office. However, to make sure that your router works properly and efficiently, you need to update its firmware regularly.
-Firmware is the software that controls the functions and features of your router. It can improve the performance, stability and security of your device. It can also fix some bugs and errors that might affect your wireless network. Therefore, it is important to download and install the latest 3Com Wl-552 firmware on your router.
-3com Wl 552 Firmware Download Download File ✦✦✦ https://bytlly.com/2uGkof
How to Download 3Com Wl-552 Firmware
-The first step to update your router's firmware is to download the latest version from the official website of 3Com. To do this, you need to follow these steps:
-
-Go to https://www.3com.com and click on the Support tab.
-Select your product category (Wireless) and model (Wl-552) from the drop-down menus.
-Click on the Downloads link and choose the firmware file that matches your router's model number and operating system.
-Save the file to your computer and remember its location.
-
-How to Install 3Com Wl-552 Firmware
-The next step is to install the downloaded firmware file on your router. To do this, you need to follow these steps:
-
-Connect your computer to your router using an Ethernet cable or a wireless connection.
-Open a web browser and type in the default IP address of your router (usually 192.168.1.1) in the address bar.
-Enter the default username (admin) and password (admin) of your router and click on Login.
-Go to the System Tools section and click on Firmware Upgrade.
-Browse for the firmware file that you downloaded earlier and click on Upload.
-Wait for the firmware upgrade process to complete. Do not turn off or disconnect your router during this time.
-When the upgrade is done, reboot your router and check if everything is working fine.
-
-Conclusion
-Updating your 3Com Wl-552 firmware is a simple and easy process that can enhance the performance and security of your wireless network. You just need to download the latest firmware file from the official website of 3Com and install it on your router using a web browser. By doing this, you can enjoy a fast and reliable wireless connection for your home or office.
-Why You Should Update Your 3Com Wl-552 Firmware
-Updating your 3Com Wl-552 firmware can bring you many benefits and advantages. Here are some of the reasons why you should update your router's firmware:
-
-You can enjoy new features and functions that are added by the manufacturer.
-You can fix some compatibility issues with other devices or software.
-You can enhance the security and privacy of your wireless network.
-You can improve the speed and stability of your wireless connection.
-You can avoid potential problems and errors that might occur with outdated firmware.
-
-How to Check Your 3Com Wl-552 Firmware Version
-Before you update your 3Com Wl-552 firmware, you need to check the current version of your router's firmware. This way, you can see if there is a newer version available and if you need to update it. To check your firmware version, you need to follow these steps:
-
-Connect your computer to your router using an Ethernet cable or a wireless connection.
-Open a web browser and type in the default IP address of your router (usually 192.168.1.1) in the address bar.
-Enter the default username (admin) and password (admin) of your router and click on Login.
-Go to the Status section and click on Device Information.
-Look for the Firmware Version field and note down the number.
-Compare it with the latest firmware version on the official website of 3Com and see if there is a difference.
-
-How to Troubleshoot 3Com Wl-552 Firmware Issues
-Sometimes, you might encounter some problems or errors with your 3Com Wl-552 firmware. This can affect your wireless network performance and functionality. To troubleshoot these issues, you need to follow these steps:
-
-Check if your router is powered on and connected to the internet.
-Check if your router's firmware is up to date. If not, download and install the latest version from the official website of 3Com.
-Check if your router's settings are correct and compatible with your wireless network. You can access the router's web interface and modify the settings as needed.
-Check if your wireless devices are compatible with your router's firmware. You might need to update the drivers or software of your devices.
-Check if there are any interferences or obstacles that might affect your wireless signal. You can try to change the location or position of your router or devices.
-Reset your router to its factory default settings and reconfigure it. This can solve some firmware issues that might be caused by corrupted or misconfigured files.
-
-How to Contact 3Com Wl-552 Firmware Support
-If you still have any questions or issues with your 3Com Wl-552 firmware, you can contact the support team of 3Com for further assistance. You can use one of these methods:
-
-
-Visit the official website of 3Com and go to the Support section. You can find FAQs, manuals, guides, videos and other resources that can help you with your firmware issues.
-Call the toll-free number of 3Com and speak to a customer service representative. You can find the phone number on the website or on the product package.
-Email the support team of 3Com and describe your problem in detail. You can find the email address on the website or on the product package.
-Chat with a live agent of 3Com online and get instant help. You can find the chat option on the website or on the product package.
-
-How to Backup and Restore Your 3Com Wl-552 Firmware Settings
-Before you update your 3Com Wl-552 firmware, it is recommended that you backup your router's settings. This way, you can restore them in case something goes wrong with the firmware update or if you want to revert to the previous firmware version. To backup and restore your router's settings, you need to follow these steps:
-
-Connect your computer to your router using an Ethernet cable or a wireless connection.
-Open a web browser and type in the default IP address of your router (usually 192.168.1.1) in the address bar.
-Enter the default username (admin) and password (admin) of your router and click on Login.
-Go to the System Tools section and click on Backup & Restore.
-Click on Backup Settings and save the file to your computer.
-To restore your settings, click on Browse and select the backup file that you saved earlier.
-Click on Restore Settings and wait for the process to complete.
-
-How to Reset Your 3Com Wl-552 Firmware to Factory Defaults
-If you encounter any problems or errors with your 3Com Wl-552 firmware that cannot be solved by troubleshooting or updating, you can try to reset your router to its factory default settings. This will erase all your customized settings and restore the original firmware settings. To reset your router to factory defaults, you need to follow these steps:
-
-Locate the reset button on the back of your router. It is usually a small hole or a recessed button.
-Use a paper clip or a similar object to press and hold the reset button for about 10 seconds.
-Release the button and wait for the router to reboot.
-Your router is now reset to factory defaults. You can use the default username (admin) and password (admin) to access the web interface and reconfigure your router as needed.
-
-What are the Features and Benefits of 3Com Wl-552 Firmware
-3Com Wl-552 firmware is the software that controls the functions and features of your 3Com Wl-552 router. It offers many features and benefits that can enhance your wireless network experience. Here are some of them:
-
-It supports wireless standards such as IEEE 802.11b/g/n, which can provide fast and stable wireless connection for your devices.
-It supports wireless security protocols such as WEP, WPA, WPA2, which can protect your wireless network from unauthorized access and attacks.
-It supports wireless modes such as AP, Client, Bridge, Repeater, WDS, which can extend your wireless coverage and connectivity.
-It supports wireless features such as QoS, WMM, WPS, which can improve your wireless performance and convenience.
-It supports network features such as DHCP, NAT, Firewall, VPN, DDNS, UPnP, which can provide various network services and functions for your devices.
-It supports management features such as Web UI, Telnet, SSH, SNMP, which can help you configure and monitor your router easily and remotely.
-
-How to Optimize Your 3Com Wl-552 Firmware Performance
-To optimize your 3Com Wl-552 firmware performance, you need to follow some tips and tricks that can help you improve your wireless network quality and speed. Here are some of them:
-
-Place your router in a central and open location that can avoid interferences and obstacles.
-Adjust the antenna direction and position of your router to get the best wireless signal.
-Change the wireless channel of your router to avoid overlapping with other wireless networks.
-Update your router's firmware regularly to get the latest features and fixes.
-Use a wired connection for devices that require high bandwidth or low latency.
-Reduce the number of devices that are connected to your wireless network.
-
-Conclusion
-3Com Wl-552 firmware is the software that controls the functions and features of your 3Com Wl-552 router. It can provide you with a fast and secure wireless network solution for your home or office. To make sure that your router works properly and efficiently, you need to download and install the latest 3Com Wl-552 firmware on your router. You also need to check, backup, restore, reset and optimize your router's settings and performance. By doing this, you can enjoy a reliable and smooth wireless network experience with your 3Com Wl-552 router.
3cee63e6c2asesinas nazis las brujas de hitler pdf 26 Download Zip ……… https://bytlly.com/2uGkYX
diff --git a/spaces/terfces0erbo/CollegeProjectV2/CK2 DLC Activator Corepack LINK.md b/spaces/terfces0erbo/CollegeProjectV2/CK2 DLC Activator Corepack LINK.md
deleted file mode 100644
index 57dedc3cd6fd8ccc9b3ce3d053420f59e2ad82c8..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/CK2 DLC Activator Corepack LINK.md
+++ /dev/null
@@ -1,9 +0,0 @@
-CK2 DLC Activator Corepack Download Zip ⏩ https://bytlly.com/2uGjtL
diff --git a/spaces/terfces0erbo/CollegeProjectV2/GTR 3 Torrent.md b/spaces/terfces0erbo/CollegeProjectV2/GTR 3 Torrent.md
deleted file mode 100644
index 378621d89c62879fb83f6c16918cb444e5174dca..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/GTR 3 Torrent.md
+++ /dev/null
@@ -1,6 +0,0 @@
-GTR 3 torrent Download Zip ✔ https://bytlly.com/2uGliI
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Halion 4 Osx Torrent.md b/spaces/terfces0erbo/CollegeProjectV2/Halion 4 Osx Torrent.md
deleted file mode 100644
index 39ff61ef33c0f18ab569e3c1ec9b43749574534a..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Halion 4 Osx Torrent.md
+++ /dev/null
@@ -1,9 +0,0 @@
-Halion 4 Osx Torrent Download File › https://bytlly.com/2uGj1u
diff --git a/spaces/thecho7/deepfake/training/transforms/albu.py b/spaces/thecho7/deepfake/training/transforms/albu.py
deleted file mode 100644
index 0922ce64030df6f651b19e350aa81bc2a92b9dae..0000000000000000000000000000000000000000
--- a/spaces/thecho7/deepfake/training/transforms/albu.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import random
-
-import cv2
-import numpy as np
-from albumentations import DualTransform, ImageOnlyTransform
-from albumentations.augmentations.crops.functional import crop
-#from albumentations.augmentations.functional import crop
-
-
-def isotropically_resize_image(img, size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC):
- h, w = img.shape[:2]
- if max(w, h) == size:
- return img
- if w > h:
- scale = size / w
- h = h * scale
- w = size
- else:
- scale = size / h
- w = w * scale
- h = size
- interpolation = interpolation_up if scale > 1 else interpolation_down
- resized = cv2.resize(img, (int(w), int(h)), interpolation=interpolation)
- return resized
-
-
-class IsotropicResize(DualTransform):
- def __init__(self, max_side, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC,
- always_apply=False, p=1):
- super(IsotropicResize, self).__init__(always_apply, p)
- self.max_side = max_side
- self.interpolation_down = interpolation_down
- self.interpolation_up = interpolation_up
-
- def apply(self, img, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC, **params):
- return isotropically_resize_image(img, size=self.max_side, interpolation_down=interpolation_down,
- interpolation_up=interpolation_up)
-
- def apply_to_mask(self, img, **params):
- return self.apply(img, interpolation_down=cv2.INTER_NEAREST, interpolation_up=cv2.INTER_NEAREST, **params)
-
- def get_transform_init_args_names(self):
- return ("max_side", "interpolation_down", "interpolation_up")
-
-
-class Resize4xAndBack(ImageOnlyTransform):
- def __init__(self, always_apply=False, p=0.5):
- super(Resize4xAndBack, self).__init__(always_apply, p)
-
- def apply(self, img, **params):
- h, w = img.shape[:2]
- scale = random.choice([2, 4])
- img = cv2.resize(img, (w // scale, h // scale), interpolation=cv2.INTER_AREA)
- img = cv2.resize(img, (w, h),
- interpolation=random.choice([cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_NEAREST]))
- return img
-
-
-class RandomSizedCropNonEmptyMaskIfExists(DualTransform):
-
- def __init__(self, min_max_height, w2h_ratio=[0.7, 1.3], always_apply=False, p=0.5):
- super(RandomSizedCropNonEmptyMaskIfExists, self).__init__(always_apply, p)
-
- self.min_max_height = min_max_height
- self.w2h_ratio = w2h_ratio
-
- def apply(self, img, x_min=0, x_max=0, y_min=0, y_max=0, **params):
- cropped = crop(img, x_min, y_min, x_max, y_max)
- return cropped
-
- @property
- def targets_as_params(self):
- return ["mask"]
-
- def get_params_dependent_on_targets(self, params):
- mask = params["mask"]
- mask_height, mask_width = mask.shape[:2]
- crop_height = int(mask_height * random.uniform(self.min_max_height[0], self.min_max_height[1]))
- w2h_ratio = random.uniform(*self.w2h_ratio)
- crop_width = min(int(crop_height * w2h_ratio), mask_width - 1)
- if mask.sum() == 0:
- x_min = random.randint(0, mask_width - crop_width + 1)
- y_min = random.randint(0, mask_height - crop_height + 1)
- else:
- mask = mask.sum(axis=-1) if mask.ndim == 3 else mask
- non_zero_yx = np.argwhere(mask)
- y, x = random.choice(non_zero_yx)
- x_min = x - random.randint(0, crop_width - 1)
- y_min = y - random.randint(0, crop_height - 1)
- x_min = np.clip(x_min, 0, mask_width - crop_width)
- y_min = np.clip(y_min, 0, mask_height - crop_height)
-
- x_max = x_min + crop_height
- y_max = y_min + crop_width
- y_max = min(mask_height, y_max)
- x_max = min(mask_width, x_max)
- return {"x_min": x_min, "x_max": x_max, "y_min": y_min, "y_max": y_max}
-
- def get_transform_init_args_names(self):
- return "min_max_height", "height", "width", "w2h_ratio"
diff --git a/spaces/themanas021/Image_Caption_Generation/README.md b/spaces/themanas021/Image_Caption_Generation/README.md
deleted file mode 100644
index 9b252f8c19b4ce2cc00ec84a50b1155478526ef7..0000000000000000000000000000000000000000
--- a/spaces/themanas021/Image_Caption_Generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image Caption Generation
-emoji: 📉
-colorFrom: yellow
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/!!TOP!! Style Korg Pa1X SET TALLAVA Free Downloadrar - Trello[3].md b/spaces/tialenAdioni/chat-gpt-api/logs/!!TOP!! Style Korg Pa1X SET TALLAVA Free Downloadrar - Trello[3].md
deleted file mode 100644
index 1e560af0d9e6b4fd9353f01e9414ef1d7f3cbac7..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/!!TOP!! Style Korg Pa1X SET TALLAVA Free Downloadrar - Trello[3].md
+++ /dev/null
@@ -1,143 +0,0 @@
-Style Korg Pa1X SET TALLAVA Free Download.rar: What You Need to Know
- If you are a fan of Balkan, Turkish, Greek, or Oriental music, and you own a Korg Pa1x keyboard, you might be interested in downloading Style Korg Pa1X SET TALLAVA. This is a free set of styles and sounds that will enhance your musical experience and make your keyboard sound like a professional instrument. In this article, we will explain what Style Korg Pa1X SET TALLAVA is, why you should download it, how to download it, what features it has, and how to use it. Read on to find out more.
- Introduction
- What is Style Korg Pa1X SET TALLAVA?
- Style Korg Pa1X SET TALLAVA is a collection of styles and sounds for the Korg Pa1x keyboard. A style is a musical pattern that can be played automatically by the keyboard, while a sound is a musical instrument that can be played manually by the keyboard. Style Korg Pa1X SET TALLAVA contains hundreds of styles and sounds that are suitable for various genres of music, especially those from the Balkan region, Turkey, Greece, and the Middle East. The set was created by various musicians and programmers who shared their work online for free.
-Style Korg Pa1X SET TALLAVA Free Download.rar Download File ★★★ https://urlcod.com/2uK6v0
Why download Style Korg Pa1X SET TALLAVA?
- There are many reasons why you might want to download Style Korg Pa1X SET TALLAVA for your keyboard. Here are some of them:
-
-You will get access to a wide range of styles and sounds that are not available in the original factory set of your keyboard.
-You will be able to play songs from different cultures and traditions with authentic rhythms, melodies, and instruments.
-You will be able to create your own songs and compositions using the styles and sounds as inspiration.
-You will be able to impress your audience with your musical skills and versatility.
-You will be able to enjoy playing your keyboard more with new and exciting sounds.
-
- How to download Style Korg Pa1X SET TALLAVA?
- Downloading Style Korg Pa1X SET TALLAVA is easy and free. All you need is a computer with an internet connection, a USB flash drive, and your keyboard. Here are the steps to follow:
-
-Go to one of the websites that offer Style Korg Pa1X SET TALLAVA for free download. Some examples are korgpa-styles.blogspot.com , piyanistset.com , kit.co , or trello.com . You can also search for other websites using Google or any other search engine.
-Select the set that you want to download. There are different versions of Style Korg Pa1X SET TALLAVA available online, each with different features and contents. You can choose the one that suits your preferences and needs.
-Click on the download link or button and save the file on your computer. The file will be in .rar format, which is a compressed archive that contains multiple files inside.
-Extract the files from the .rar archive using a software like WinRAR or 7-Zip. You will get a folder with several files inside, such as .set, .pcg, .stl, .mp3, etc.
-Copy the folder onto your USB flash drive.
-Insert the USB flash drive into your keyboard's USB port.
-You are ready to use Style Korg Pa1X SET TALLAVA on your keyboard.
-
- Features of Style Korg Pa1X SET TALLAVA
- Balkan styles and sounds
- One of the main features of Style Korg Pa1X SET TALLAVA is the Balkan styles and sounds. These are musical patterns and instruments that are typical of the music from countries like Serbia, Bosnia, Croatia, Macedonia, Albania, Romania, Bulgaria, etc. Some examples of Balkan styles are Tallava, Turbo Folk, Manele, Chalga, Gypsy Music, etc. Some examples of Balkan sounds are Accordion, Clarinet, Saxophone, Trumpet, Violin, Bouzouki, etc.
- Turkish styles and sounds
- Another feature of Style Korg Pa1X SET TALLAVA is the Turkish styles and sounds. These are musical patterns and instruments that are characteristic of the music from Turkey and its neighboring regions. Some examples of Turkish styles are Arabesk, Pop Folk, Oyun Havasi, Halay, Anatolian Rock, etc. Some examples of Turkish sounds are Oud, Saz, Kanun, Ney, Zurna, Darbuka, etc.
-Style Korg Pa1X SET TALLAVA download link
- Greek styles and sounds
- A third feature of Style Korg Pa1X SET TALLAVA is the Greek styles and sounds. These are musical patterns and instruments that are representative of the music from Greece and its islands. Some examples of Greek styles are Laiko, Rebetiko, Zeibekiko, Hasapiko, Sirtaki, etc. Some examples of Greek sounds are Bouzouki, Baglama, Clarino, Lauto, Santouri, etc.
- Oriental styles and sounds
- A fourth feature of Style Korg Pa1X SET TALLAVA is the Oriental styles and sounds. These are musical patterns and instruments that are common in the music from the Middle East, North Africa, and Central Asia. Some examples of Oriental styles are Raqs Sharqi, Khaliji, Dabke, Mawal, etc. Some examples of Oriental sounds are Qanun, Oud, Ney, Kamancheh, Tabla, etc.
- Other styles and sounds
- A fifth feature of Style Korg Pa1X SET TALLAVA is the other styles and sounds. These are musical patterns and instruments that are not specific to any region or genre but can be used for various purposes. Some examples of other styles are Pop Rock, Dance, Ballad, Jazz, etc. Some examples of other sounds are Piano, Guitar, Bass, Drums, Synth, Strings, etc.
- How to use Style Korg Pa1X SET TALLAVA
- How to load the set into your Korg Pa1x keyboard
- To use Style Korg Pa1X SET TALLAVA on your keyboard, you need to load the set into its memory. This is how you do it:
-
-Turn on your keyboard and wait for it to initialize.
-Press the MEDIA button on the panel to enter the Media mode.
-Select the USB device option on the display and press ENTER.
-Navigate to the folder that contains Style Korg Pa1X SET TALLAVA using the VALUE dial and the PAGE buttons.
-Select the .set file that you want to load and press ENTER.
-A dialog box will appear asking you to confirm the loading process. Press ENTER again to start loading.
-Wait for the loading process to finish. It may take a few minutes depending on the size of the set.
-A message will appear on the display indicating that the loading is complete. Press EXIT to return to the main mode.
-You have successfully loaded Style Korg Pa1X SET TALLAVA into your keyboard.
-
- How to select and play the styles and sounds
- To select and play the styles and sounds from Style Korg Pa1X SET TALLAVA on your keyboard, you need to follow these steps:
-
-Press the STYLE PLAY button on the panel to enter the Style Play mode.
-Select a style bank using the BANK buttons on the panel. Each bank contains 16 styles that are grouped by genre or category.
-Select a style using the STYLE buttons on the panel. Each style has four variations (A, B, C, D) that you can switch using the VARIATION buttons on the panel.
-Press the START/STOP button on the panel to start playing the style. You can control the tempo using the TEMPO buttons or dial on the panel.
-Play a chord on the left side of the keyboard to trigger the accompaniment. You can change the chord recognition mode using the CHORD SCANNING button on the panel.
-Select a sound for your right hand using the PROGRAM buttons on the panel. Each program has two sounds that you can layer or split using the LAYER/SPLIT button on the panel.
-Play the melody or solo on the right side of the keyboard using the selected sound. You can add effects using the JOYSTICK or the ASSIGNABLE SWITCHES on the panel.
-Use the other buttons and knobs on the panel to adjust the volume, balance, transpose, octave, etc. of the style and sound.
-Press the START/STOP button again to stop playing the style.
-You have successfully selected and played a style and sound from Style Korg Pa1X SET TALLAVA on your keyboard.
-
- How to customize the styles and sounds
- If you want to customize the styles and sounds from Style Korg Pa1X SET TALLAVA on your keyboard, you can use the following features:
-
-The RECORDING mode: This mode allows you to record your own styles and sounds using the built-in sequencer. You can edit, arrange, and save your recordings as user styles and sounds.
-The EDIT mode: This mode allows you to modify the parameters of the existing styles and sounds. You can change the tempo, key, instruments, effects, etc. of the styles and sounds.
-The GLOBAL mode: This mode allows you to adjust the overall settings of your keyboard. You can change the tuning, scale, MIDI, display, etc. of your keyboard.
-
- For more details on how to use these features, please refer to the user manual of your keyboard.
- Conclusion
- Summary of the main points
- In this article, we have discussed what Style Korg Pa1X SET TALLAVA is, why you should download it, how to download it, what features it has, and how to use it. We have learned that Style Korg Pa1X SET TALLAVA is a free set of styles and sounds for the Korg Pa1x keyboard that covers various genres of music from the Balkan region, Turkey, Greece, and the Middle East. We have also learned that Style Korg Pa1X SET TALLAVA can enhance your musical experience and make your keyboard sound like a professional instrument. We have also learned how to load, select, play, and customize the styles and sounds from Style Korg Pa1X SET TALLAVA on your keyboard.
- Call to action
- If you are interested in downloading Style Korg Pa1X SET TALLAVA for your keyboard, you can visit one of the websites that offer it for free download. You can also search for other websites using Google or any other search engine. You can also check out other sets of styles and sounds that are available online for your keyboard. You can also create your own styles and sounds using the recording and editing features of your keyboard. Whatever you choose to do, we hope that you enjoy playing your keyboard with Style Korg Pa1X SET TALLAVA.
- FAQs
- Here are some frequently asked questions about Style Korg Pa1X SET TALLAVA:
-
-Q: What is the size of Style Korg Pa1X SET TALLAVA?
-A: The size of Style Korg Pa1X SET TALLAVA varies depending on the version and the website that you download it from. However, most versions are around 100 MB to 200 MB in .rar format.
-Q: Do I need to backup my original factory set before loading Style Korg Pa1X SET TALLAVA?
-A: It is recommended that you backup your original factory set before loading any new set into your keyboard. This way, you can restore your original settings if you encounter any problems or if you want to switch back to the factory set.
-Q: Can I use Style Korg Pa1X SET TALLAVA on other models of Korg keyboards?
-A: Style Korg Pa1X SET TALLAVA is designed for the Korg Pa1x keyboard. However, some versions may be compatible with other models of Korg keyboards, such as the Pa2x, Pa3x, Pa4x, etc. You can check the compatibility of the set with your keyboard model on the website that you download it from.
-Q: Where can I find more information about Style Korg Pa1X SET TALLAVA?
-A: You can find more information about Style Korg Pa1X SET TALLAVA on the websites that offer it for free download. You can also find more information on the user manual of your keyboard or on online forums and communities of Korg users.
-Q: How can I contact the creators of Style Korg Pa1X SET TALLAVA?
-A: You can contact the creators of Style Korg Pa1X SET TALLAVA by visiting their websites or social media pages. You can also contact them by email or phone if they provide their contact details. You can thank them for their work or give them feedback or suggestions.
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- 0a6ba089ebAnka 2 Game Free Download Full Version: A Review
-If you are looking for a fun and relaxing game that will challenge your brain and entertain you at the same time, you should try Anka 2 . Anka 2 is a point-and-click adventure game that follows the story of a young boy named Anka who sets out to find his missing parents. Along the way, he will encounter many mysteries, puzzles, and mini-games that will keep you hooked for hours. In this article, we will review the features of Anka 2 game, show you how to download it for free, and give you some tips and tricks for playing it.
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-Anka 2 game is not just a collection of puzzles and mini-games. It also has a rich and engaging story that will make you care about the characters and their fate. You will help Anka solve various problems, meet new friends, and discover secrets about his family and his world. The game has a lot of humor, emotion, and surprises that will keep you interested until the end.
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-Anka 2 game has a charming cartoon style that suits the tone and theme of the game. The graphics are colorful, detailed, and expressive. The characters are facially animated, which means they can show different emotions and reactions depending on the situation. The game also has a lot of voice acting that adds personality and realism to the dialogue.
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-Anka 2 game is not just a point-and-click adventure game. It also has a lot of mini-games and puzzles that will test your logic, memory, reflexes, and skills. You will encounter different types of challenges, such as brainteasers, riddles, games of skill, hidden object scenes, matching games, memory games, jigsaw puzzles, and more. The mini-games are integrated into the story and are relevant to the plot. They are also fun to play and replay.
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-Anka 2 game has a unique feature that allows you to customize the game with your own photo. You can upload your photo or choose one from your computer or online sources. The game will then use your photo as the face of Anka or any other character in the game. This way, you can feel more connected to the game and have more fun playing it.
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-The first step to download Anka 2 game for free is to visit the official website of the game. You can find it by searching for "Anka 2 game free download full version" on any search engine or by clicking on this link. The website will give you more information about the game, such as its features, screenshots, videos, reviews, system requirements, etc.
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-The next step is to choose which version and platform of Anka 2 game you want to download. The game is available for Windows XP/XP Professional/Vista/7/8/10/11 as well as Mac OS X. You can also choose between a free trial version or a full version. The free trial version lets you play the first few levels of the game for free but has some limitations. The full version gives you access to all the levels, features, and mini-games of the game but costs $14.99. You can pay by credit card or PayPal.
- Step 3: Click on the download button and follow the instructions
-The final step is to click on the download button on the website and follow the instructions on how to install and run the game on your computer. The download process may take some time depending on your internet speed and connection. Once the download is complete , you can enjoy playing Anka 2 game for free or for a small fee.
- Tips and Tricks for Playing Anka 2 Game
-Use the hint button if you get stuck
-If you ever get stuck in Anka 2 game or don't know what to do next, you can use the hint button at the bottom right corner of the screen. The hint button will show you where to go or what to do next in order to progress in the game. However, using the hint button will cost you some points from your score, so use it wisely.
- Collect all the hidden objects in each scene
-In each scene of Anka 2 game, there are some hidden objects that you can collect by clicking on them. These objects are not necessary for completing the scene but they will give you extra points and unlock some achievements. You can see how many hidden objects are left in each scene by looking at the top left corner of the screen.
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- Replay the mini-games to improve your score
-As mentioned before, Anka 2 game has 23 varied mini-games that you can play throughout the adventure. Each mini-game has its own rules, objectives, difficulty level, time limit, etc. You can replay any mini-game that you have unlocked by accessing them from the main menu or from within each scene. Replaying the mini-games will allow you to improve your score, beat your previous records, and earn more points.
- Explore every location and interact with everything
-Anka 2 game has many locations that you can visit and explore during your quest. Each location has its own atmosphere, characters, and secrets. You can interact with almost everything in each location by clicking on it. You may find clues, items, or surprises that will help you solve puzzles, advance in the story, or just have fun. Don't be afraid to experiment and try different things in each location.
- Conclusion and FAQs
-In conclusion, Anka 2 game is a great point-and-click adventure game that will appeal to anyone who likes puzzles, mini-games, and stories. The game has many features that make it enjoyable, such as enchanting adventure, cute graphics, facial animations, 23 varied mini-games, and customization with your own photo. The game is also easy to download and play for free or for a small fee. If you are looking for a fun and relaxing game that will challenge your brain and entertain you at the same time, you should try Anka 2 game today.
- Here are some FAQs about Anka 2 game that may answer some of your questions:
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-Q: How long does it take to finish Anka 2 game?A: It depends on how fast you solve puzzles and complete mini-games, but on average, it takes about 4-5 hours to finish the game.Q: Is Anka 2 game suitable for children?A: Yes, Anka 2 game is suitable for children of all ages. The game has no violence, gore, or inappropriate content. The game is also educational and family-friendly, as it teaches children logical thinking, problem-solving, and creativity.Q: What are the differences between Anka 2 game and the original Anka game?A: Anka 2 game is a sequel to the original Anka game, which was released in 2009. The sequel has improved graphics, sound, and gameplay. The sequel also has more levels, mini-games, and puzzles than the original. The sequel also continues the story of Anka and his parents, but it can be played as a standalone game without playing the original first.Q: Can I play Anka 2 game offline?A: Yes, you can play Anka 2 game offline once you have downloaded and installed it on your computer. You don't need an internet connection to play the game, unless you want to update it or access some online features.Q: Where can I find more games like Anka 2 game?A: If you like Anka 2 game, you may also like other games in the same genre, such as Hidden Object Games, Puzzle Games, Adventure Games, etc. You can find more games like Anka 2 game on various websites, such as GamesGoFree.com, Shockwave.com, iDownloadGames.com, HotGameLive.com, etc.
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- Are you looking for a powerful software that can help you create, edit, convert, and manage raster images in your AutoCAD drawings? If so, you might be interested in AutoCAD Raster Design 2008, a software that can enhance your productivity and creativity with raster images. But before you can use this software, you need a keygen that can generate a valid activation code for you. In this article, we will show you what AutoCAD Raster Design 2008 is, what a keygen is, how to download and install AutoCAD Raster Design 2008 Keygen X-force V1.0.5 275, how to use AutoCAD Raster Design 2008, what are the benefits of using it, and some FAQs that you might have.
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What is AutoCAD Raster Design 2008?
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-Create and edit raster images using various commands and tools
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- The next step is to extract the keygen file using WinRAR or 7-Zip. These are free software that can help you unzip compressed files. You can download WinRAR from https://www.win-rar.com/download.html or 7-Zip from https://www.7-zip.org/download.html . After installing one of these software, right-click on the downloaded keygen file and choose Extract Here or Extract To.
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- The fourth step is to select AutoCAD Raster Design 2008 from the product list and click Generate. You will see a keygen interface like this:
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- The final step is to copy the generated activation code and paste it in the AutoCAD Raster Design 2008 activation window. To do this, you need to have AutoCAD Raster Design 2008 installed on your computer. If you don't have it, you can download it from https://www.autodesk.com/products/autocad-raster-design/overview or use a torrent site to download it.
- After installing AutoCAD Raster Design 2008, run it and you will see an activation window like this:
- Enter your name, organization, and serial number (you can use any random numbers) and click Next. You will see another window like this:
- Choose I have an activation code from Autodesk and click Next. You will see another window like this:
- Paste the activation code that you copied from the keygen in the empty boxes and click Next. You will see a confirmation message that your product has been activated successfully.
- How to use AutoCAD Raster Design 2008?
- Now that you have activated AutoCAD Raster Design 2008, you can start using it to work with raster images in your AutoCAD drawings. Here are some of the main functions and tools that you can use:
- How to create and edit raster images?
- To create and edit raster images, you can use the raster editing commands and tools that are available in the Raster menu or the Raster toolbar. Some of the common commands and tools are:
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-Raster Draw: This command allows you to draw lines, circles, rectangles, polygons, etc. on a raster image.
-Raster Edit: This command allows you to modify existing raster entities, such as moving, rotating, scaling, cropping, etc.
-Raster Clean: This command allows you to remove unwanted pixels or noise from a raster image.
-Raster Touchup: This command allows you to apply various effects or filters to a raster image, such as blur, sharpen, emboss, etc.
-Raster Color: This command allows you to adjust the color settings of a raster image, such as brightness, contrast, hue, saturation, etc.
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- How to convert raster images to vector objects?
- To convert raster images to vector objects, you can use the raster-to-vector conversion commands and tools that are available in the Raster menu or the Raster toolbar. Some of the common commands and tools are:
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-Raster Entity: This command allows you to convert a raster entity (such as a line or a circle) to a vector object (such as a polyline or an arc).
-Raster Vectorize: This command allows you to convert a raster image (such as a photo or a map) to a vector drawing (such as a contour or an outline).
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- How to insert and manage raster images in AutoCAD drawings?
- To insert and manage raster images in your AutoCAD drawings, you can use the image insertion and management commands and tools that are available in the Image menu or the Image toolbar. Some of the common commands and tools are:
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-Image Attach: This command allows you to insert a raster image file (such as JPG, PNG, TIFF, etc.) into your current drawing.
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-Image Transparency: This command allows you to adjust the transparency level of an inserted image.
-Image Frame: This command allows you to toggle the visibility of an image frame (a border around an inserted image).
-Image Manager: This command allows you to view and modify the properties of all inserted images in your current drawing.
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-You can save time and money by using existing raster images instead of creating new vector drawings from scratch.
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-Q: Is AutoCAD Raster Design 2008 compatible with Windows 10? A: Yes, AutoCAD Raster Design 2008 is compatible with Windows 10. However, you may need to run it in compatibility mode or install some updates or patches to make it work properly.
-Q: Is AutoCAD Raster Design 2008 compatible with other Autodesk products? A: Yes, AutoCAD Raster Design 2008 is compatible with other Autodesk products that support ObjectARX applications, such as AutoCAD Civil 3D, AutoCAD Map 3D, AutoCAD Architecture, etc.
-Q: How can I get technical support for AutoCAD Raster Design 2008? A: You can get technical support for AutoCAD Raster Design 2008 from Autodesk's website https://knowledge.autodesk.com/support/autocad-raster-design , where you can find documentation, tutorials, forums, downloads, etc.
-Q: How can I get more information about AutoCAD Raster Design 2008? A: You can get more information about AutoCAD Raster Design 2008 from Autodesk's website https://www.autodesk.com/products/autocad-raster-design/overview , where you can find features, specifications, system requirements, etc.
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- 0a6ba089ebHow to Download DMX Dog Barking MP3 for Free
-If you are a fan of the late rapper DMX, you might be interested in downloading his signature dog barking sound effect as an MP3 file. DMX, whose real name was Earl Simmons, was known for his distinctive vocal delivery and his frequent use of dog barking noises in his songs. He passed away on April 9, 2021, after suffering a heart attack.
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- If you are a fan of animated movies, you must have heard of the Kung Fu Panda franchise. It is one of the most successful and popular series of movies produced by DreamWorks Animation. The third installment, Kung Fu Panda 3, was released in 2016 and received positive reviews from critics and audiences alike. In this article, we will tell you everything you need to know about this movie, why you should watch it in Hindi, and how to download it in HD quality.
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Introduction
- What is Kung Fu Panda 3?
- Kung Fu Panda 3 is an animated comedy-adventure film that follows the adventures of Po, a lovable panda who is the Dragon Warrior and the leader of the Furious Five, a group of kung fu masters. In this movie, Po reunites with his long-lost biological father, Li Shan, and travels to a secret panda village where he meets many other pandas. However, he also faces a new threat from Kai, a powerful spirit warrior who has returned from the spirit realm and is stealing the chi (life force) of other kung fu masters. Po must learn how to master his own chi and teach kung fu to his fellow pandas in order to stop Kai and save the world.
- Why should you watch it in Hindi?
- Kung Fu Panda 3 is a movie that can be enjoyed by people of all ages and cultures. However, if you are an Indian viewer, you might find it more entertaining and relatable to watch it in Hindi. Why? Because the Hindi dubbing of this movie is done by some of the most famous and talented Bollywood actors and actresses. For example, Po's voice is given by none other than Jackie Chan, who is also one of the producers of the movie. Li Shan's voice is given by Amitabh Bachchan, who is widely regarded as one of the greatest actors of Indian cinema. Kai's voice is given by J.K. Simmons, who won an Oscar for his performance in Whiplash. Other notable voice actors include Ranbir Kapoor as Bao, Vidya Balan as Mei Mei, Anil Kapoor as Shifu, and Rajpal Yadav as Dim.
- By watching this movie in Hindi, you will not only enjoy the amazing performances of these actors but also appreciate the cultural references and jokes that are added to suit the Indian audience. For example, Po calls Li Shan "Baba" instead of "Dad", Kai calls himself "Kailash" instead of "Kai", and Po teaches his panda friends how to dance to Bollywood songs. These are just some of the examples of how the Hindi dubbing enhances the fun and charm of this movie.
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- If you want to watch this movie at your own convenience and comfort, you might want to download it in HD quality. However, downloading movies from unauthorized sources can be illegal and risky. You might end up with viruses or malware on your device or face legal consequences for violating copyright laws. Therefore, we recommend that you use a safe and legal way to download this movie in HD quality.
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- Another way is to use a DVD or Blu-ray disc that contains this movie in HD quality. You can buy or rent this disc from a store or online platform and play it on your DVD or Blu-ray player. You can also connect your player to your TV or monitor for a better viewing experience.
- Review of Kung Fu Panda 3
- The plot
- The plot of Kung Fu Panda 3 is engaging and exciting. It has a good balance of action, comedy, drama, and emotion. It explores Po's identity crisis as he struggles to accept his dual heritage as a panda and a dragon warrior. It also shows his growth as a leader and a teacher as he learns how to inspire and empower others. It also introduces new characters and settings that add more depth and diversity to the story. The villain Kai is also a formidable antagonist who poses a serious challenge to Po and his friends.
- The characters
- The characters of Kung Fu Panda 3 are well-developed and likable. Po is still his adorable and hilarious self but also shows more maturity and responsibility. Li Shan is a caring and supportive father who wants to reconnect with his son and share his culture with him. Kai is a ruthless and cunning enemy who has a personal history with Po's mentor Oogway. The other pandas are also fun and colorful characters who have their own personalities and talents.
- The animation
- The animation of Kung Fu Panda 3 is stunning and beautiful. It uses a combination of computer-generated imagery (CGI) and traditional hand-drawn animation to create a rich and vibrant visual style. The colors are bright and vivid, the movements are smooth and fluid, and the details are realistic and impressive. The scenes in the spirit realm are especially breathtaking as they showcase a different aesthetic from the physical world.
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- Comparison with previous movies
- Kung Fu Panda 1
- Kung Fu Panda 1 was released in 2008 and was the first movie in the franchise. It introduced us to Po and his journey from being a noodle shop worker to becoming the Dragon Warrior. It also introduced us to Shifu and the Furious Five, who were initially skeptical but eventually accepted Po as their friend and teammate. The villain was Tai Lung, a former student of Shifu who turned evil after being denied the Dragon Scroll. The movie was praised for its animation, humor, action, and heart.
- Kung Fu Panda 2
- Kung Fu Panda 2 was released in 2011 and was the second movie in the franchise. It continued Po's story as he faced a new threat from Lord Shen, a peacock who wanted to conquer China with his weapon of fireworks. It also revealed Po's past as he learned that he was adopted by Mr.Ping, a goose who runs a noodle shop. He also learned that his biological parents and his panda village were attacked and destroyed by Shen. The movie was praised for its animation, humor, action, emotion, and depth.
- Kung Fu Panda 3
- Kung Fu Panda 3 was released in 2016 and was the third movie in the franchise. It concluded Po's story as he met his biological father and traveled to his panda village. He also faced a new enemy from Kai who wanted to steal the chi of all living beings. He also learned how to master his chi and teach kung fu to his panda friends. The movie was praised for its animation, humor, action, emotion, and message.
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-Q: When was Kung Fu Panda 3 released?
-A: Kung Fu Panda 3 was released on January 29, 2016 in the United States and on March 11, 2016 in India.
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-A: Kung Fu Panda 3 was directed by Jennifer Yuh Nelson and Alessandro Carloni.
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-A: Kung Fu Panda 3 had a budget of $145 million.
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-A: Kung Fu Panda 3 earned $521.2 million worldwide.
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-A: No, Kung Fu Panda 3 is not the last movie in the franchise. According to the creators, there are plans for more movies in the future.
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-CPU
-Intel Pentium 4 or higher
-
-
-RAM
-2 GB or more
-
-
-HDD
-1 GB or more free space
-
-
-Graphics Card
-NVIDIA or ATI with OpenGL support
-
-
-Monitor Resolution
-1024 x 768 or higher
-
-
-Internet Connection
-Required for activation and updates
-
-
- How to download and install Mastercam X5 full crack 32 bit?
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-Q: Is Mastercam X5 compatible with Windows 10? A: Yes, Mastercam X5 is compatible with Windows 10, as well as Windows XP/Vista/7/8 (32-bit only).
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-Q: How can I learn more about Mastercam X5? A: You can learn more about Mastercam X5 by visiting the official website of CNC Software, Inc. at https://www.mastercam.com/ . There you can find more information about the product, the company, the community, etc.
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- 0a6ba089ebGhc Generador De Horarios Full V: The Ultimate Tool for Creating Timetables
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-Sanam Teri Kasam is a 2016 Indian romantic movie that explores the journey of two unlikely lovers who are destined to meet. The movie is directed by Radhika Rao and Vinay Sapru, and stars Harshvardhan Rane and Mawra Hocane in the lead roles. The movie is a modern adaptation of the novel Love Story by Eric Segal, and features a musical score by Himesh Reshammiya.
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679dcb208eBackgammon Board Download: How to Play and Enjoy This Classic Board Game
- Backgammon is one of the oldest and most popular board games in the world. It is a game of skill and luck, where two players move their pieces on a board with 24 triangles, called points, according to the roll of two dice. The objective of the game is to move all your pieces into your home board and then bear them off, before your opponent does the same. Backgammon is a game that combines strategy, tactics, counting, probability, and psychology. It is also a game that can be played online or offline, with a physical board or a digital one. In this article, we will explore the history, rules, and strategies of backgammon, as well as how you can download and play backgammon on your computer or mobile device.
- History and Origin of Backgammon
- Backgammon has a long and rich history that dates back to ancient times. The earliest evidence of a game similar to backgammon was found in Iran, where a set of board, pieces, and dice was discovered in a 5,000-year-old archaeological site. The game was also played by ancient Egyptians, Greeks, Romans, Persians, Indians, Chinese, and Arabs. The name "backgammon" comes from the Middle English word "bac gamen", which means "back game", referring to the practice of re-entering pieces from the bar to the board. The modern version of backgammon emerged in England in the 17th century, and spread to Europe and America in the following centuries. Backgammon became very popular in the 20th century, especially after the introduction of the doubling cube, which allows players to increase the stakes during the game. Today, backgammon is played by millions of people around the world, both casually and competitively.
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Rules and Strategies of Backgammon
- The rules of backgammon are simple to learn but hard to master. Each player has 15 pieces of their own color (white or black), which they move in opposite directions around the board. The board consists of four sections, each with six points. The sections are called the home board and the outer board for each player. The home boards are separated by a ridge called the bar. The points are numbered from 1 to 24 for each player, starting from their home board. To start the game, each player rolls one die, and the player with the higher number moves first. The players then alternate turns, rolling two dice each time. The numbers on the dice indicate how many points they can move their pieces. For example, if a player rolls 3 and 5, they can move one piece 3 points and another piece 5 points, or they can move one piece 8 points. However, they can only move their pieces to open points, which are not occupied by two or more enemy pieces. If they land on a point with only one enemy piece, they can hit it and send it to the bar. The piece on the bar cannot re-enter the game until it lands on an open point in the opponent's home board.
- The goal of the game is to bear off all your pieces from the board before your opponent does. To do this, you need to move all your pieces into your home board first. Then you can remove them from the board according to the numbers you roll. For example, if you roll 4 and 6, you can remove a piece from point 4 and another piece from point 6. If you have no pieces on those points, you can remove a piece from a higher point. If you have no pieces on higher points either, you can remove a piece from a lower point. The first player to bear off all their pieces wins the game.
- There are different strategies that you can use to improve your chances of winning at backgammon. Some of them are:
-
-The running game: This strategy involves moving your pieces as fast as possible towards your home board and bearing them. off quickly. This strategy works best when you have a lead in the race and you want to avoid being hit by your opponent.
-The holding game: This strategy involves keeping one or more points in your opponent's home board, preventing them from bearing off their pieces. This strategy works best when you are behind in the race and you want to create opportunities to hit your opponent.
-The priming game: This strategy involves building a wall of six consecutive points, called a prime, in your home board or outer board, blocking your opponent's pieces from advancing. This strategy works best when you have more pieces in play than your opponent and you want to trap them behind your prime.
-The backgammon game: This strategy involves hitting your opponent's pieces and sending them to the bar, while bearing off your own pieces. This strategy works best when you have a big advantage in the race and you want to win by a large margin. If you manage to bear off all your pieces before your opponent bears off any, you win a backgammon, which is worth three times the normal value of the game.
-
- Of course, these strategies are not mutually exclusive, and you may need to switch between them depending on the situation of the game. You also need to consider the use of the doubling cube, which allows you to double the stakes of the game at any point. You can offer to double the stakes if you think you have a good chance of winning, and your opponent can either accept or decline. If they accept, they take possession of the cube and can offer to redouble later. If they decline, they forfeit the game and pay the current stakes. The doubling cube adds an element of risk and reward to the game, as well as a psychological factor.
- Backgammon Board Download
- If you want to play backgammon online or offline, you can download a backgammon board on your computer or mobile device. There are many websites and apps that offer free or paid backgammon board downloads, with different features and options. Some of them are:
-
-
-Name
-Platform
-Description
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-
-Backgammon Live
-Web, iOS, Android
-A popular online backgammon game that allows you to play with millions of players from around the world, chat with them, join tournaments, and win coins and prizes.
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-GNU Backgammon
-Windows, Mac, Linux
-A free and open-source backgammon software that offers a strong artificial intelligence engine, analysis tools, tutorials, and various board styles.
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-Backgammon NJ
-iOS, Android
-A high-quality backgammon app that features realistic graphics, sound effects, statistics, hints, and online play.
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-Backgammon Masters
-Web, iOS, Android
-A fun and challenging backgammon game that supports 5 different variants of backgammon, including Nackgammon, Hypergammon, and LongGammon.
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-Backgammon Classic Pro
-Windows
-A professional backgammon game that offers 12 different board designs, 6 difficulty levels, online play, and advanced options.
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- To download a backgammon board on your device, you need to visit the website or app store of your choice, select the game you want to download, and follow the instructions. You may need to create an account or sign in with your email or social media account to access some features of the game. Once you download the game, you can start playing backgammon anytime and anywhere.
- Conclusion
- Backgammon is a classic board game that has been enjoyed by people for thousands of years. It is a game that combines skill and luck, strategy and tactics, counting and probability, and psychology and emotion. It is also a game that can be played online or offline, with a physical board or a digital one. Whether you are a beginner or an expert, a casual or a competitive player, a young or an old person, backgammon is a game that can suit your taste and preference. If you want to play backgammon online or offline, you can download a backgammon board on your computer or mobile device from various websites and apps. So what are you waiting for? Download a backgammon board today and enjoy this fascinating game!
- FAQs
- What is the difference between backgammon and other board games?
- Backgammon is different from other board games in several ways. One of them is that backgammon is a game of partial information, meaning that you do not know the outcome of the dice rolls until they happen. This adds an element of uncertainty and luck to the game, as well as a need to adapt to changing situations. Another difference is that backgammon is a game of asymmetry, meaning that the players have different goals and positions on the board. This creates a dynamic and complex interaction between the players, as well as a need to balance offense and defense. A third difference is that backgammon is a game of doubling, meaning that the players can increase the stakes of the game at any point using the doubling cube. This adds an element of risk and reward to the game, as well as a psychological factor.
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- How can I improve my backgammon skills?
- There are many ways to improve your backgammon skills, such as:
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-Reading books and articles about backgammon theory and practice, written by experts and champions of the game.
-Watching videos and tutorials of backgammon matches and analysis, featuring commentary and tips from professional players and coaches.
-Playing online or offline against different opponents of various skill levels, styles, and backgrounds, to learn from their moves and mistakes.
-Using software and apps that offer artificial intelligence engines, analysis tools, tutorials, and feedback, to practice your moves and strategies and evaluate your performance.
-Joining clubs, communities, and forums of backgammon enthusiasts, where you can share your experiences, ask questions, get advice, and participate in events and tournaments.
-
- By doing these activities regularly and consistently, you can enhance your knowledge, understanding, and intuition of the game, as well as your confidence, concentration, and enjoyment.
- What are some benefits of playing backgammon?
- Playing backgammon can have many benefits for your mental and physical health, such as:
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-Improving your cognitive skills, such as memory, attention, logic, reasoning, problem-solving, decision-making, and creativity.
-Enhancing your emotional skills, such as patience, resilience, discipline, self-control, motivation, and optimism.
-Reducing your stress levels, anxiety, depression, and boredom, by providing a fun and relaxing activity that distracts you from negative thoughts and feelings.
-Boosting your social skills, such as communication, cooperation, empathy, respect, and friendship, by interacting with other players in a friendly and respectful manner.
-Stimulating your brain activity, blood circulation, immune system, and metabolism, by engaging in a physical and mental exercise that challenges you and makes you happy.
-
- By playing backgammon regularly and moderately, you can enjoy the benefits of playing backgammon for your mind, body, and soul.
- What are some challenges of playing backgammon?
- Playing backgammon can also have some challenges that you need to overcome, such as:
-
-Dealing with the randomness and unpredictability of the dice, which can sometimes favor or frustrate you and your opponent.
-Managing the risk and reward of the doubling cube, which can sometimes increase or decrease your chances of winning or losing.
-Coping with the pressure and competition of the game, which can sometimes affect your mood and performance.
-Handling the feedback and criticism of other players, which can sometimes be helpful or hurtful.
-Balancing your time and energy between playing backgammon and other aspects of your life, such as work, family, and hobbies.
-
- By playing backgammon with a positive attitude, a learning mindset, a respectful behavior, and a healthy lifestyle, you can overcome the challenges of playing backgammon and make the most of it.
- Where can I find more information about backgammon?
- If you want to find more information about backgammon, you can visit some of these websites and resources:
-
-Backgammon Galore : A comprehensive website that offers rules, articles, tips, glossary, software, links, and more about backgammon.Backgammon.org : A website that provides news, events, tournaments, rankings, clubs, blogs, videos, and more about backgammon.Backgammon World Championship : A website that showcases the annual tournament that determines the best backgammon player in the world.Backgammon Books : A list of some of the best books that teach you how to play and improve your backgammon skills.Backgammon Podcasts : A list of some of the best podcasts that discuss and analyze various aspects of backgammon.
- By visiting these websites and resources, you can learn more about backgammon and become a better player.
401be4b1e0Spider Superhero Rope Gangster APK: A Fun and Action-Packed Game for Android Users
- If you are a fan of spider-man movies and games, you will love Spider Superhero Rope Gangster APK. This is a free and exciting game for Android devices that lets you become a spider rope hero in a crime city. You can swing from buildings, fight against gangsters, drive cars and bikes, and explore a vast open world. In this article, we will tell you everything you need to know about Spider Superhero Rope Gangster APK, including its features, how to download and install it, and some FAQs.
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Introduction
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- Spider Superhero Rope Gangster APK is a fun and addictive game that will keep you entertained for hours. You will enjoy its amazing graphics and sound effects that create a realistic and immersive experience. You will also love its open world and sandbox gameplay that gives you freedom and creativity. You will also feel like a real spider hero with your spider rope abilities and skills that let you do amazing things. You will also have a lot of options to customize your character and vehicles to suit your style. You will also have a lot of missions and challenges to complete that will test your skills and reward you with money and gems.
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- Amazing graphics and sound effects
- Spider Superhero Rope Gangster APK has stunning 3D graphics that make the city look alive and realistic. You will see detailed buildings, streets, cars, bikes, people, animals, plants, weather effects, etc. You will also see dynamic shadows, reflections, lighting effects, etc. that enhance the visual quality. You will also hear realistic sound effects that match the actions and events in the game. You will hear the sounds of cars honking, guns firing, explosions happening, people screaming, etc. You will also hear the voice of your character and other characters that add personality and humor to the game.
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- Spider Superhero Rope Gangster APK has an open world gameplay that lets you explore a huge crime city with no limits or boundaries. You can go anywhere you want and do anything you want in the city. You can swing from buildings using your spider rope or drive cars and bikes using your spider web. You can also climb walls using your spider claws or jump from roofs using your spider legs. You can also fly using your spider wings or glide using your spider parachute. You can also interact with various objects and people in the city, such as breaking windows, stealing cars, punching pedestrians, etc. You can also cause chaos and destruction in the city, such as blowing up gas stations, setting fire to buildings, crashing cars, etc. You can also enjoy the sandbox gameplay that lets you create your own scenarios and stories in the city. You can use your imagination and creativity to make your own fun and adventure in the game.
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- Spider Superhero Rope Gangster APK has a spider rope hero gameplay that lets you use your spider rope abilities and skills to do amazing things in the game. You can use your spider rope to swing across the city like a spider-man. You can also use your spider rope to grab objects and enemies and throw them away. You can also use your spider rope to tie up enemies and hang them from buildings. You can also use your spider rope to make traps and webs to catch enemies. You can also use your spider rope to make bridges and platforms to cross gaps and obstacles. You can also use your other spider abilities and skills, such as spider claws, spider legs, spider wings, spider parachute, etc. to enhance your mobility and combat in the game.
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- Spider Superhero Rope Gangster APK has various missions and challenges that you can complete in the game. You can accept missions from different characters in the city, such as police officers, civilians, gangsters, etc. You can also find missions on the map or on your phone. You can complete different types of missions, such as rescuing hostages, robbing banks, stealing cars, racing, shooting, flying, etc. You can also complete different types of challenges, such as killing a certain number of enemies, surviving for a certain time, reaching a certain speed, etc. You can earn money and gems by completing missions and challenges. You can also unlock new spider suits, weapons, vehicles, etc. by completing missions and challenges.
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- Spider Superhero Rope Gangster APK has a customizable character and vehicles gameplay that lets you customize your spider hero character and his vehicles to suit your style. You can change the appearance of your character, such as his face, hair, skin color, eyes color, etc. You can also change the clothes of your character, such as his suit, mask, gloves, boots, etc. You can also change the weapons of your character, such as his guns, grenades, swords, hammers, axes, etc. You can also change the vehicles of your character, such as his cars, bikes, helicopters, planes, etc. You can also upgrade the performance of your vehicles, such as their speed, acceleration, handling, durability, etc. You can use the money and gems you earn in the game to buy and upgrade your character and vehicles. You can also use the spider shop to buy and upgrade your character and vehicles. You can also use the spider garage to store and select your vehicles.
- How to download and install Spider Superhero Rope Gangster APK
- If you want to download and install Spider Superhero Rope Gangster APK on your Android device, you need to follow these steps:
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- Requirements and compatibility
- Before you download and install Spider Superhero Rope Gangster APK, you need to make sure that your device meets the following requirements:
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- Steps to download and install
- After you check the requirements and compatibility of your device, you can follow these steps to download and install Spider Superhero Rope Gangster APK:
- - Go to the official website of Spider Superhero Rope Gangster APK or any other trusted source that provides the APK file of the game. - Click on the download button and wait for the APK file to be downloaded on your device. - Go to the settings of your device and enable the option of installing apps from unknown sources. This will allow you to install Spider Superhero Rope Gangster APK on your device. - Go to the file manager of your device and locate the downloaded APK file of Spider Superhero Rope Gangster APK. - Tap on the APK file and follow the instructions on the screen to install Spider Superhero Rope Gangster APK on your device. - After the installation is complete, you can launch Spider Superhero Rope Gangster APK from your app drawer or home screen and enjoy playing it. Tips and tricks to play the game
- If you want to play Spider Superhero Rope Gangster APK like a pro, you can use these tips and tricks:
- - Use your spider rope skills wisely. You can use your spider rope to swing across the city faster and easier. You can also use your spider rope to grab objects and enemies and throw them away. You can also use your spider rope to tie up enemies and hang them from buildings. You can also use your spider rope to make traps and webs to catch enemies. You can also use your spider rope to make bridges and platforms to cross gaps and obstacles. - Use your other spider abilities and skills smartly. You can use your spider claws to climb walls and jump from roofs. You can also use your spider legs to run faster and jump higher. You can also use your spider wings to fly in the air and glide from buildings. You can also use your spider parachute to land safely from heights. - Use your weapons effectively. You can use your fists to punch enemies in close combat. You can also use your guns to shoot enemies from a distance. You can also use your grenades to blow up enemies and objects. You can also use your swords, hammers, axes, etc. to slash or smash enemies in melee combat. - Use your vehicles efficiently. You can use your cars and bikes to drive around the city faster and easier. You can also use your helicopters and planes to fly over the city quicker and smoother. You can also use your vehicles as weapons by crashing them into enemies or objects. - Complete missions and challenges regularly. You can complete missions and challenges to earn money and gems in the game. You can also complete missions and challenges to unlock new spider suits, weapons, vehicles, etc. in the game. You can also complete missions and challenges to increase your reputation and popularity in the city. - Customize your character and vehicles frequently. You can customize your character and vehicles to suit your style in the game. You can change the appearance, clothes, weapons, etc. of your character in the game. You can also change the color, model, performance, etc. of your vehicles in the game. Conclusion
- Spider Superhero Rope Gangster APK is a fun and action-packed game for Android users who love spider-man movies and games. It is a free and exciting game that lets you become a spider rope hero in a crime city. It has amazing graphics and sound effects that create a realistic and immersive experience. It has an open world and sandbox gameplay that gives you freedom and creativity. It has a spider rope hero gameplay that lets you use your spider rope abilities and skills to do amazing things. It has various missions and challenges that you can complete to earn money and gems and unlock new items. It has a customizable character and vehicles gameplay that lets you customize your spider hero character and his vehicles to suit your style. It is a fun and addictive game that will keep you entertained for hours.
- If you want to download and install Spider Superhero Rope Gangster APK on your Android device, you can follow the steps we have provided in this article. You can also use the tips and tricks we have shared in this article to play the game like a pro. You can also contact the developer of the game if you have any questions or feedback about the game.
- Spider Superhero Rope Gangster APK is a game that you should not miss if you are a fan of spider-man movies and games. It is a game that will make you feel like a real spider hero in a crime city. It is a game that will challenge your skills and reward your efforts. It is a game that will let you have fun and adventure in the game.
- So, what are you waiting for? Download Spider Superhero Rope Gangster APK now and enjoy playing it!
- FAQs
- Is Spider Superhero Rope Gangster APK safe and legal?
- Spider Superhero Rope Gangster APK is safe and legal to download and install on your Android device. It is not a virus or malware that will harm your device or data. It is not a mod or hack that will violate the terms and conditions of the original game. It is not a pirated or cracked version that will infringe the rights of the developer of the game. It is an original and official version of the game that is provided by the developer of the game for free.
- How to update Spider Superhero Rope Gangster APK?
- Spider Superhero Rope Gangster APK is updated regularly by the developer of the game to fix bugs, improve performance, add new features, etc. You can update Spider Superhero Rope Gangster APK by following these steps:
- - Go to the official website of Spider Superhero Rope Gangster APK or any other trusted source that provides the latest APK file of the game. - Click on the download button and wait for the new APK file to be downloaded on your device. - Go to the file manager of your device and locate the new APK file of Spider Superhero Rope Gangster APK. - Tap on the new APK file and follow the instructions on the screen to install Spider Superhero Rope Gangster APK on your device. - After the installation is complete, you can launch Spider Superhero Rope Gangster APK from your app drawer or home screen and enjoy playing it. How to unlock new spider suits and weapons?
- Spider Superhero Rope Gangster APK has many spider suits and weapons that you can unlock in the game. You can unlock new spider suits and weapons by completing missions and challenges in the game. You can also unlock new spider suits and weapons by using money and gems in the game. You can also unlock new spider suits and weapons by using the spider shop in the game.
- How to earn money and gems in the game?
- Spider Superhero Rope Gangster APK has money and gems as the main currencies in the game. You can use money and gems to buy and upgrade your character and vehicles in the game. You can earn money and gems by completing missions and challenges in the game. You can also earn money and gems by killing enemies and destroying objects in the game. You can also earn money and gems by finding hidden chests and collectibles in the game. You can also earn money and gems by watching ads and videos in the game.
- How to contact the developer of the game?
- If you have any questions, feedback, suggestions, or complaints about Spider Superhero Rope Gangster APK, you can contact the developer of the game by using these methods:
- - You can send an email to the developer of the game at spiderropehero@gmail.com. - You can visit the official website of the developer of the game at https://spiderropehero.com. - You can follow the social media accounts of the developer of the game on Facebook, Twitter, Instagram, YouTube, etc. The developer of the game is always happy to hear from you and will try to reply to you as soon as possible.
197e85843dHow to Download Genshin Impact on Windows 10
-Genshin Impact is an open-world action RPG that has taken the gaming world by storm. In this game, you can explore a vast fantasy world called Teyvat, where you can meet a diverse cast of characters with unique personalities and abilities, fight powerful enemies with elemental combat, and unravel the countless mysteries that Teyvat holds. You can also team up with friends across various platforms to enjoy more cooperative gameplay. Whether you are a fan of anime-style graphics, immersive storytelling, or addictive gameplay, Genshin Impact has something for you.
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If you are wondering how to download Genshin Impact on Windows 10, you have come to the right place. In this article, we will show you everything you need to know about downloading, installing, and setting up Genshin Impact on your PC. We will also share some useful tips and tricks for playing Genshin Impact on PC. Let's get started!
- Genshin Impact System Requirements
-Before you download Genshin Impact on Windows 10, you should make sure that your PC meets the minimum or recommended system requirements for playing the game. Here are the system requirements for Genshin Impact on PC:
-
-Minimum Requirements Recommended Requirements Operating System: Windows 7 SP1 64-bit, Windows 8.1 64-bit, or Windows 10 64-bit Operating System: Windows 10 64-bit CPU: Intel Core i5 or equivalent CPU: Intel Core i7 or equivalent RAM: 8 GB RAM: 16 GB Graphics Card: NVIDIA GeForce GT 1030 or higher Graphics Card: NVIDIA GeForce GTX 1060 6 GB or higher DirectX Version: 11 DirectX Version: 11 Storage Space: 30 GB or more Storage Space: 30 GB or more
-You can check your PC's specifications by going to the Settings app, clicking on System, and then clicking on About. You can also use a tool like Speccy to get more detailed information about your PC's hardware.
-How to install genshin impact on pc windows 10
-If your PC does not meet the minimum requirements, you may experience lag, crashes, or other issues while playing Genshin Impact. If your PC meets or exceeds the recommended requirements, you should be able to enjoy a smooth and stable gaming experience.
- Genshin Impact Download Options
-There are different ways to download Genshin Impact on Windows 10, depending on your preference and convenience. The most common and reliable ways are from the official website and the Epic Games Store. However, there are also some other sources that may offer Genshin Impact downloads, such as Steam or third-party websites. We will compare and contrast these options below.
- Downloading from the Official Website
-The official website of Genshin Impact is the best and safest way to download the game on Windows 10. Here are the steps to download Genshin Impact from the official website:
-
-Go to https://genshin.mihoyo.com/en/home , the official website of Genshin Impact.
-Click on the "Windows" button under the "Download Now" section.
-A pop-up window will appear, asking you to save the file. Choose a location where you want to save the file, and click on "Save". The file name should be "GenshinImpact_install_*.exe", where * is a number.
-Wait for the file to download completely. The file size should be around 6 MB.
-Once the file is downloaded, double-click on it to run it. You may need to grant permission for the file to make changes to your device.
-The Genshin Impact installer will open. Follow the instructions on the screen to complete the installation process.
-
-Some tips for downloading Genshin Impact from the official website are:
-
-Make sure you have a stable and fast internet connection, as the game files are quite large and may take a long time to download.
-If you encounter any errors or issues while downloading or installing the game, you can check the https://genshin.mihoyo.com/en/news/detail/5764 , which contains some troubleshooting tips and solutions.
-If you want to uninstall Genshin Impact from your PC, you can go to the Control Panel, click on Programs and Features, find Genshin Impact in the list of programs, and click on Uninstall.
-
- Downloading from the Epic Games Store
-The Epic Games Store is another popular and trustworthy way to download Genshin Impact on Windows 10. The Epic Games Store is a digital distribution platform that offers various games and software for free or at discounted prices. Here are the steps to download Genshin Impact from the Epic Games Store:
-
-Go to https://www.epicgames.com/store/en-US/p/genshin-impact , the product page of Genshin Impact on the Epic Games Store.
-If you already have an Epic Games account, click on "Sign In" and enter your credentials. If you do not have an Epic Games account, click on "Sign Up" and create one.
-Once you are signed in, click on "Get" under the "Genshin Impact" banner.
-A pop-up window will appear, asking you to confirm your order. Click on "Place Order". You do not need to pay anything, as Genshin Impact is free-to-play.
-You will be redirected to a confirmation page, where you can see your order details and receipt. Click on "Install" under the "Genshin Impact" banner.
-You will need to download and install the Epic Games Launcher, which is the application that allows you to access and manage your Epic Games Store games. Follow the instructions on the screen to download and install the Epic Games Launcher.
-Once the Epic Games Launcher is installed, open it and log in to your Epic Games account. You should see Genshin Impact in your library. Click on it and then click on "Install" to start downloading the game files.
-Wait for the game files to download completely. The file size should be around 30 GB.
-Once the game files are downloaded, click on "Launch" to start playing Genshin Impact.
-
-Some tips for downloading Genshin Impact from the Epic Games Store are:
-
-You can also access the Epic Games Store website and download Genshin Impact from your web browser, but you will still need to install and use the Epic Games Launcher to play the game.
-If you encounter any errors or issues while downloading or installing the game, you can check the https://www.epicgames.com/help/en-US/epic-games-store-c73 , which contains some troubleshooting tips and solutions.
-If you want to uninstall Genshin Impact from your PC, you can go to the Epic Games Launcher, click on Library, find Genshin Impact in the list of games, click on the three dots icon, and click on Uninstall.
-
- Downloading from Other Sources
-Besides the official website and the Epic Games Store, there are some other sources that may offer Genshin Impact downloads, such as Steam or third-party websites. However, we do not recommend using these sources, as they may not be authorized, safe, or updated. Here are some of the reasons why you should avoid downloading Genshin Impact from other sources:
-
-They may contain malware, viruses, or other harmful software that can damage your PC or compromise your personal information.
-They may not have the latest version of Genshin Impact, which means you may miss out on new features, updates, or bug fixes.
-They may not be compatible with your PC's specifications or settings, which may cause performance issues or errors.
-They may violate the terms of service of Genshin Impact or its developer, miHoYo, which may result in legal action or account suspension.
-
-Therefore, we strongly advise you to download Genshin Impact only from the official website or the Epic Games Store, as they are the most reliable and secure ways to get the game on Windows 10.
- Genshin Impact Installation and Setup
-After you have downloaded Genshin Impact from either the official website or the Epic Games Store, you will need to install and set up the game on your PC. This process is fairly simple and straightforward, but we will guide you through it step by step. Here are the steps to install and set up Genshin Impact on Windows 10:
- Installing Genshin Impact
-To install Genshin Impact on Windows 10, follow these steps:
-
-If you downloaded Genshin Impact from the official website, you should have a file called "GenshinImpact_install_*.exe" in your chosen location. Double-click on it to run it. If you downloaded Genshin Impact from the Epic Games Store, you should have already installed it through the Epic Games Launcher. Skip to step 4.
-You may need to grant permission for the file to make changes to your device. Click on "Yes" if prompted.
-The Genshin Impact installer will open. Choose a language for the installation process and click on "OK".
-The installer will ask you to choose a directory where you want to install Genshin Impact. You can use the default directory or browse for another one. Click on "Install" once you have selected a directory.
-The installer will start copying and extracting the game files to your chosen directory. This may take some time depending on your PC's speed and internet connection. Wait for the installation process to finish.
-The installer will ask you to agree to the license agreement of Genshin Impact. Read it carefully and click on "I Agree" if you accept it.
-The installer will ask you if you want to create a desktop shortcut for Genshin Impact. You can check or uncheck this option according to your preference. Click on "Finish" once you have made your choice.
-The installer will close and launch the game launcher automatically. You can also launch the game launcher from the desktop shortcut or the start menu if you created one.
-
-Some tips for installing Genshin Impact on Windows 10 are:
-
-Make sure you have enough storage space on your PC before installing Genshin Impact, as the game files are quite large and may take up to 30 GB or more.
-If you encounter any errors or issues while installing Genshin Impact, you can check the https://genshin.mihoyo.com/en/news/detail/5764 , which contains some troubleshooting tips and solutions.
-If you want to change the installation directory of Genshin Impact, you can do so by going to the game launcher, clicking on the gear icon, and then clicking on "Choose Install Path". You can also move the game files manually, but you will need to update the game launcher's settings accordingly.
-
- Setting Up Genshin Impact
-To set up Genshin Impact on Windows 10, follow these steps:
-
-Open the game launcher and wait for it to check for updates. If there are any updates available, click on "Update" and wait for them to download and install.
-Once the game is updated, click on "Launch" to start the game. You may need to grant permission for the game to access your network and firewall.
-The game will load and show you a splash screen. Click anywhere on the screen to continue.
-The game will ask you to create or log in to your miHoYo account. This is the account that you will use to play Genshin Impact and access its online features. If you already have a miHoYo account, click on "Log In" and enter your credentials. If you do not have a miHoYo account, click on "Register" and create one.
-Once you are logged in, the game will ask you to choose a server region. This is the region that you will play in and interact with other players. You can choose from Asia, Europe, America, or TW, HK, MO. Choose a server region that is closest to your location for better performance and latency. You can also change your server region later, but keep in mind that your progress and characters will not carry over between regions.
-The game will ask you to adjust your graphics settings. You can choose from Low, Medium, High, or Custom. You can also change your graphics settings later by going to the game menu and clicking on Settings. Choose a graphics setting that suits your PC's specifications and your personal preference.
-The game will start and show you a cinematic intro. Enjoy the story and get ready for an epic adventure!
-
-Some tips for setting up Genshin Impact on Windows 10 are:
-
-Make sure you remember your miHoYo account credentials, as you will need them to play Genshin Impact and access its online features. You can also link your miHoYo account to your email address or phone number for better security and recovery options.
-If you encounter any errors or issues while setting up Genshin Impact, you can check the https://genshin.mihoyo.com/en/news/detail/5764 , which contains some troubleshooting tips and solutions.
-If you want to change your server region, graphics settings, or other options, you can do so by going to the game menu and clicking on Settings. You can also access some settings from the game launcher by clicking on the gear icon.
-
- Genshin Impact Tips and Tricks
-Now that you have downloaded, installed, and set up Genshin Impact on Windows 10, you are ready to play the game and explore its vast world. However, before you dive into the action, here are some useful tips and tricks that will help you enjoy Genshin Impact more on PC:
- Optimizing Your Performance
-Genshin Impact is a beautiful and immersive game, but it can also be demanding on your PC's resources. If you want to optimize your performance in Genshin Impact on PC, here are some things you can do:
-
-Lower your graphics settings if your PC does not meet the recommended requirements or if you experience lag or stuttering. You can also lower your resolution or window mode for better performance.
-Close any background programs or applications that may be consuming your CPU, RAM, or bandwidth. You can use Task Manager to monitor your PC's performance and end any unnecessary processes.
-Update your drivers, especially your graphics card driver, as they may improve your performance and fix any compatibility issues.
-Clean up your PC's disk space, cache, and registry, as they may affect your performance and cause errors. You can use a tool like CCleaner to do this easily and safely.
-Check your internet connection and make sure it is stable and fast, as Genshin Impact requires a constant online connection to play. You can use a tool like Speedtest to check your internet speed and ping.
-
- Using Keyboard and Mouse Controls
-Genshin Impact supports both keyboard and mouse controls and gamepad controls on PC. However, if you prefer to use keyboard and mouse controls, here are some things you should know:
-
-The default keyboard and mouse controls in Genshin Impact on PC are as follows:
-
-Action Key Move Forward W Move Backward S Move Left A Move Right D Jump/Climb/Glide Space Sprint/Swim Faster Shift Attack/Confirm Left Mouse Button Aim/Cancel Right Mouse Button Elemental Skill E Elemental Burst/Ultimate Q Interact/Pick Up/Revive F Show Cursor/Menu/Map/Quests/Inventory/Character/Co-op Mode/Wishes/Paimon Menu Esc/Tab/M/J/B/C/V/L/Alt+F4 (jk) Switch Character (1-4) (1-4) Cycle Through Party Members (Left/Right) (Left/Right) Show Character Status Screen (1-4) < td>Alt+(1-4)Pause/Resume Download F8 Take Screenshot F12 Open Chat Window Enter Open Shortcut Wheel Z Open Paimon's Bargains X Open Notices N Open Mailbox M Show/Hide UI Ctrl+H Show/Hide Cursor Alt Zoom In/Out Camera Mouse Wheel Up/Down Rotate Camera (Left/Right) < Mouse Movement Left/Right Tilt Camera (Up/Down) Mouse Movement Up/Down
-You can customize your keyboard and mouse controls by going to the game menu, clicking on Settings, and then clicking on Controls. You can change the key bindings for each action, as well as the mouse sensitivity and inversion.
-You can also use a gamepad to play Genshin Impact on PC, if you prefer. The game supports various gamepad models, such as Xbox, PlayStation, or Switch controllers. You can connect your gamepad to your PC via USB or Bluetooth, and the game will automatically detect it and show you the corresponding controls. You can also customize your gamepad controls by going to the game menu, clicking on Settings, and then clicking on Controls.
-
- Switching Between Characters
-Genshin Impact allows you to have up to four characters in your party at a time, each with their own unique skills and elements. You can switch between characters in Genshin Impact on PC by using the following methods:
-
-You can press the number keys (1-4) to switch to the corresponding character in your party.
-You can press the left or right arrow keys to cycle through your party members.
-You can hold the Z key to open the shortcut wheel, and then use your mouse to select the character you want to switch to.
-
-Switching between characters is important in Genshin Impact, as it allows you to use different elemental skills and reactions, adapt to different situations and enemies, and access different abilities and talents. For example, you can use a Pyro character to melt ice barriers, a Geo character to create platforms or shields, or an Anemo character to spread elemental effects. You can also combine different elements to create powerful reactions, such as Electro-Charged, Melt, Vaporize, or Swirl. Experiment with different character combinations and find out what works best for you.
- Using Elemental Reactions
-Elemental reactions are one of the core mechanics of Genshin Impact's combat system. They occur when two different elements come into contact with each other, either from your characters' skills or from the environment. Elemental reactions can have various effects, such as dealing extra damage, inflicting status effects, or altering the battlefield. Here are some of the elemental reactions in Genshin Impact and what they do:
-
-Elemental Reaction Description Overloaded Occurs when Pyro meets Electro. Deals AoE Pyro damage and launches enemies away. Superconduct Occurs when Cryo meets Electro. Deals AoE Cryo damage and reduces enemies' physical resistance. Electro-Charged Occurs when Hydro meets Electro. Deals continuous Electro damage to enemies and spreads to nearby enemies in water. Melt Occurs when Pyro meets Cryo or vice versa. Increases the damage of the triggering element by 1.5x (Pyro) or 2x (Cryo). Vaporize Occurs when Pyro meets Hydro or vice versa. Increases the damage of the triggering element by 1.5x (Pyro) or 2x (Hydro). Frozen Occurs when Hydro meets Cryo. Freezes enemies in place and makes them vulnerable to Shatter. Shatter Occurs when Frozen enemies are hit by heavy physical attacks (such as Claymore or Geo). Deals extra physical damage and breaks Frozen status. Swirl < td>Occurs when Anemo meets any other element except Geo. Spreads the other element to nearby enemies and deals extra elemental damage.Crystallize Occurs when Geo meets any other element except Anemo. Creates elemental shards that grant a shield of the corresponding element to the character who picks them up. Burning Occurs when Pyro meets Dendro (grass or wooden objects). Deals continuous Pyro damage to enemies and objects until extinguished. Conductive Occurs when Electro meets metal objects. Deals continuous Electro damage to enemies and objects until dispersed.
-To use elemental reactions effectively, you should pay attention to the elemental attributes of your characters, enemies, and environment. You should also switch between characters frequently to trigger different reactions and combos. Experiment with different elements and see what works best for each situation.
- Exploring the Open World
-Genshin Impact features a vast and beautiful open world that you can explore at your own pace. There are many things to do and discover in the world of Teyvat, such as:
-
-Completing quests and events that advance the story or reward you with items and resources.
-Finding and unlocking waypoints, statues, domains, and other landmarks that allow you to fast travel, heal, or access special challenges.
-Collecting chests, materials, ingredients, artifacts, weapons, and other loot that you can use to upgrade your characters and equipment.
-Fighting enemies, bosses, and elite opponents that drop valuable rewards and increase your adventure rank.
-Solving puzzles, riddles, and secrets that reveal hidden treasures or lore.
-Cooking dishes that restore your health, stamina, or provide other buffs.
-Crafting potions, weapons, or other items that enhance your abilities or unlock new features.
-Customizing your own personal realm with furniture, decorations, and companions.
-Interacting with NPCs, animals, or objects that may offer you quests, dialogues, or surprises.
-Enjoying the scenery, music, and atmosphere of the different regions and cultures of Teyvat.
-
-To explore the open world effectively, you should use your map and compass to navigate and locate points of interest. You should also use your elemental sight to reveal hidden clues or objects. You should also be prepared for any dangers or challenges that you may encounter along the way. Finally, you should have fun and enjoy the adventure!
- Conclusion
-Genshin Impact is an amazing game that offers a rich and immersive experience for PC players. In this article, we have shown you how to download Genshin Impact on Windows 10 from the official website or the Epic Games Store. We have also shown you how to install and set up Genshin Impact on Windows 10. We have also shared some useful tips and tricks for playing Genshin Impact on Windows 10. We hope that this article has helped you get started with Genshin Impact on PC and that you will have a wonderful time exploring Teyvat. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
- Frequently Asked Questions
-Here are some of the frequently asked questions about Genshin Impact on Windows 10:
- Q: Is Genshin Impact free-to-play?
-A: Yes, Genshin Impact is free-to-play on PC and other platforms. You do not need to pay anything to download or play the game. However, the game does have some optional in-game purchases that can enhance your gameplay or unlock more content. These purchases are not required to enjoy the game fully.
- Q: Is Genshin Impact cross-platform?
-A: Yes, Genshin Impact is cross-platform on PC and other platforms, such as PlayStation, Xbox, Nintendo Switch, iOS, and Android. You can play with your friends across different devices and platforms, as long as you are in the same server region. You can also access your game progress and data on any device or platform, as long as you use the same miHoYo account.
- Q: How can I get more characters and weapons in Genshin Impact?
-A: You can get more characters and weapons in Genshin Impact by using a feature called Wishes. Wishes are gacha-style draws that allow you to obtain random characters and weapons of different rarities. You can use a currency called Primogems to make Wishes, or use special items called Fates that are obtained from various sources. You can also use a currency called Stardust or Starglitter to exchange for Fates or other items in the Shop. You can make Wishes by going to the game menu and clicking on Wishes.
- Q: How can I level up my characters and weapons in Genshin Impact?
-A: You can level up your characters and weapons in Genshin Impact by using various items and resources that you can find or obtain in the game. For characters, you can use items called Character EXP Materials or Wanderer's Advice, Adventurer's Experience, or Hero's Wit to increase their level. You can also use items called Mora to pay for the leveling cost. For weapons, you can use other weapons or items called Weapon Enhancement Materials or Enhancement Ore, Fine Enhancement Ore, or Mystic Enhancement Ore to increase their level. You can also use Mora to pay for the enhancement cost. You can level up your characters and weapons by going to the game menu and clicking on Characters.
- Q: How can I unlock more regions and quests in Genshin Impact?
-A: You can unlock more regions and quests in Genshin Impact by increasing your Adventure Rank. Your Adventure Rank is a measure of your overall progress and experience in the game. You can increase your Adventure Rank by completing quests, events, domains, bosses, and other activities that reward you with Adventure EXP. As you increase your Adventure Rank, you will unlock more regions to explore, more quests to complete, more features to access, and more rewards to claim. You can check your Adventure Rank by going to the game menu and clicking on Adventure Rank.
- Q: How can I get more Primogems in Genshin Impact?
-A: Primogems are a valuable currency in Genshin Impact that can be used to make Wishes, refill your Original Resin, or buy items in the Shop. You can get more Primogems by doing various things in the game, such as:
-
-Completing quests, events, achievements, and daily commissions.
-Finding and opening chests, seelies, shrines, and other hidden treasures.
-Unlocking waypoints, statues, domains, and other landmarks.
-Completing Spiral Abyss floors and challenges.
-Claiming daily login rewards and special codes.
-Purchasing them with real money or Genesis Crystals.
-
-You can check your Primogem balance by going to the game menu and clicking on Shop.
- This is the end of the article. Thank you for reading!
197e85843dArchicad 18 Free Download With Crack 64 Bit: A Complete Guide
-If you are looking for a way to get Archicad 18, one of the best architectural design software, for free with crack, then you have come to the right place. In this article, we will show you how to download, install, and activate Archicad 18 on your computer, as well as how to use it for your projects. We will also provide you with some useful resources and support to help you get started with Archicad 18.
- What is Archicad 18 and why you need it
-Archicad 18 is a software developed by Graphisoft that allows you to design and document buildings in a 3D environment. It is a BIM (Building Information Modeling) software that enables you to create accurate and detailed models of your buildings, as well as generate high-quality renderings, animations, and documentation. Archicad 18 is used by architects, engineers, contractors, and designers all over the world for various types of projects, from residential homes to skyscrapers.
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Archicad 18 features and benefits
-Archicad 18 has many features and benefits that make it a powerful and efficient tool for architectural design. Some of the main features and benefits are:
-
-CineRender: This is a new rendering engine that integrates with Archicad 18 and allows you to create photorealistic images and animations of your buildings. You can use CineRender to enhance your presentations, communicate your design ideas, and impress your clients. CineRender is based on the Cinema 4D engine, which is a professional software for visualization and animation.Revision Management: This is a feature that helps you keep track of the changes in your building model and document them automatically. You can use Revision Management to compare different versions of your model, identify the differences, and assign responsibilities for the changes. This feature also helps you comply with local BIM standards and regulations.Built-in Tools: Archicad 18 has a set of built-in tools that allow you to design, visualize, collaborate, and document your projects. You can use these tools to create complex geometries, apply materials and textures, add annotations and dimensions, create schedules and reports, export and import data, and more.User Interface: Archicad 18 has a user-friendly interface that makes it easy to use and learn. You can customize the interface according to your preferences and workflow. You can also access various tutorials, guides, tips, and support from the Graphisoft website or community.
- Archicad 18 system requirements and compatibility
-To run Archicad 18 on your computer, you need to meet the following system requirements:
-
-
-Operating System Processor Memory Graphics Card Hard Disk Space Windows 10 (64-bit), Windows 8.1 (64-bit), Windows 8 (64-bit), Windows 7 (64-bit), or Mac OS X 10.10 Yosemite, Mac OS X 10.9 Mavericks, Mac OS X 10.8 Mountain Lion 64-bit processor with four or more cores, such as Intel Core i5, i7 or equivalent AMD processors 8 GB RAM or more Dedicated OpenGL 2.0 compatible graphics card with 1024 MB or more memory 5 GB free disk space or more
-Archicad 18 is compatible with various file formats and software, such as DWG, DXF, IFC, PDF, SKP, 3DS, OBJ, BCF, and more. You can also use Archicad 18 with other Graphisoft products, such as BIMx, BIMcloud, EcoDesigner, and MEP Modeler.
- How to download Archicad 18 for free with crack
-If you want to use Archicad 18 for free without paying for a license, you need to download it with a crack. A crack is a file that modifies the original software to bypass the activation process and make it work without restrictions. However, downloading Archicad 18 with a crack is illegal and risky, as it may contain viruses, malware, or spyware that can harm your computer or compromise your data. Therefore, we do not recommend or endorse this method of obtaining Archicad 18.
-If you still want to proceed with downloading Archicad 18 with a crack, you need to follow these steps:
- Step 1: Find a reliable download link for Archicad 18
-The first step is to find a reliable download link for Archicad 18 with a crack. You can search online for websites that offer Archicad 18 downloads with cracks, but be careful of fake or malicious links that may lead you to unwanted or harmful downloads. You can also use torrent sites or peer-to-peer networks to find Archicad 18 downloads with cracks, but be aware of the legal and ethical implications of using these sources.
-One possible download link for Archicad 18 with a crack is , but we cannot guarantee its safety or functionality. Use it at your own risk and discretion.
- Step 2: Install Archicad 18 on your computer
-The second step is to install Archicad 18 on your computer. To do this, you need to extract the downloaded file using a software like WinRAR or 7-Zip. Then, you need to run the setup.exe file and follow the instructions on the screen. You may need to enter a serial number or a product key during the installation process. You can use any random number or key that matches the format of the required input.
-After the installation is complete, do not run Archicad 18 yet. You need to apply the crack first.
- Step 3: Apply the crack to activate Archicad 18
-The third step is to apply the crack to activate Archicad 18. To do this, you need to copy the crack file from the downloaded folder and paste it into the installation folder of Archicad 18. The installation folder is usually located in C:\Program Files\GRAPHISOFT\Archicad 18. You may need to replace the original file with the crack file when prompted.
-After applying the crack, you can run Archicad 18 and enjoy its full features and functions.
- How to use Archicad 18 for your architectural projects
-Now that you have downloaded and installed Archicad 18 with a crack, you can use it for your architectural projects. Here are some tips on how to use Archicad 18 for your projects:
- Archicad 18 tutorial for beginners
-If you are new to Archicad 18 or BIM software in general, you may want to start with some basic tutorials that will teach you how to use the software and its tools. You can find many tutorials online, such as , , or . These tutorials will guide you through the steps of creating a simple building model in Archicad 18, from setting up the project parameters to adding walls, doors, windows, roofs, floors, stairs, furniture, and more.
- Archicad 18 tips and tricks for advanced users
-If you are already familiar with Archicad 18 or BIM software in general, you may want to learn some tips and tricks that will help you improve your skills and efficiency. You can find many tips and tricks online, such as , , or . These tips and tricks will show you how to use some advanced features and functions of Archic ad 18, such as using CineRender, Revision Management, Built-in Tools, and User Interface.
- Archicad 18 resources and support
-If you need more help or guidance on using Archicad 18, you can access various resources and support from Graphisoft or other sources. Some of the resources and support are:
-
-Graphisoft Help Center: This is the official website of Graphisoft that provides you with online documentation, manuals, videos, FAQs, and forums for Archicad 18 and other products. You can visit the website at .Graphisoft YouTube Channel: This is the official YouTube channel of Graphisoft that provides you with video tutorials, webinars, tips, and showcases for Archicad 18 and other products. You can subscribe to the channel at .Graphisoft Service Select: This is a subscription-based service that provides you with premium support, updates, training, and benefits for Archicad 18 and other products. You can learn more about the service at .Archicad Community: This is a network of Archicad users, experts, partners, and enthusiasts that share their knowledge, experience, and feedback on Archicad 18 and other products. You can join the community at .
- Conclusion
-In conclusion, Archicad 18 is a powerful and efficient software that allows you to design and document buildings in a 3D environment. It has many features and benefits that make it a great tool for architectural design. However, downloading Archicad 18 with a crack is illegal and risky, as it may expose you to viruses, malware, or spyware. Therefore, we advise you to use Archicad 18 legally and ethically by purchasing a license from Graphisoft or using their trial version. We hope this article has helped you understand how to download, install, and use Archicad 18 for your projects.
- FAQs
-Here are some frequently asked questions about Archicad 18:
-
-What is the difference between Archicad 18 and Archicad 19? Archicad 19 is the latest version of Archicad that was released in June 2023. It has some new features and improvements over Archicad 18, such as faster performance, smoother navigation, enhanced collaboration, improved interoperability, and more. You can compare the two versions at .
-How can I get a trial version of Archicad 18? You can get a trial version of Archicad 18 by registering on the Graphisoft website at . You will receive an email with a download link and a serial number for the trial version. The trial version is valid for 30 days and has all the features and functions of the full version.
-How much does Archicad 18 cost? The cost of Archicad 18 depends on the type of license you choose and the region you are in. You can choose between a perpetual license or a rental license. A perpetual license is a one-time purchase that gives you lifetime access to Archicad 18. A rental license is a monthly or yearly subscription that gives you access to Archicad 18 as long as you pay the fee. You can check the prices of Archicad 18 licenses at .
-Is Archicad 18 compatible with Windows 11? Windows 11 is the upcoming operating system from Microsoft that is expected to be released in late 2023. According to Graphisoft, Archicad 18 is not officially compatible with Windows 11 yet. However, they are working on testing and updating their products to ensure compatibility with Windows 11 as soon as possible. You can check the compatibility status of Archicad 18 with Windows 11 at .
-Where can I find more articles on Archicad 18? You can find more articles on Archicad 18 on various websites and blogs that cover topics related to architectural design and BIM software. Some examples are , , or . These articles will provide you with more information, insights, reviews, and examples on using Archicad 18 for your projects.
- b2dd77e56bBattlefield 2 Euro Force Armored Fury Special Forces Patch | Updated
-
-If you are a fan of Battlefield 2, you might want to check out the latest patch that includes the Armored Fury and Euro Force expansions for free. This patch also removes the disc check DRM, adds widescreen support, and fixes a crash issue when switching to desktop. Here is everything you need to know about the Battlefield 2 Euro Force Armored Fury Special Forces Patch.
-
-What are the expansions?
-
-The Armored Fury expansion adds three new maps set in the United States, featuring rural and urban environments. It also introduces new vehicles such as attack jets, helicopters, and tanks. The Euro Force expansion adds three new maps set in Europe, featuring a variety of terrains such as mountains, forests, and cities. It also introduces new weapons and equipment for the European Union faction.
-Battlefield 2 Euro Force Armored Fury Special Forces Patch | updated Download File ⇒ https://urlcod.com/2uHwEG
How to install the patch?
-
-To install the patch, you need to have version 1.41 of Battlefield 2 installed beforehand. You can download it from here . Then, you can download the patch 1.50 from here . Run the executable file and follow the instructions. After that, you can download the patch 1.50 hotfix from here . Run the executable file and follow the instructions. You are now ready to play.
-
-How to play online?
-
-Since the official servers for Battlefield 2 have been shut down, you need to use a third-party service to play online. One of the most popular ones is Battlelog.co . You need to create an account and download their launcher. Then, you can browse and join servers from their website or from the game itself.
-
-Conclusion
-
-Battlefield 2 is one of the best military shooters ever made, and with this patch, you can enjoy it even more. The expansions add new maps, vehicles, weapons, and factions to the game, making it more diverse and fun. The patch also improves the performance and compatibility of the game, making it smoother and easier to run. If you want to experience Battlefield 2 in its full glory, you should definitely download this patch.
-
-How to play Special Forces?
-
-Special Forces is an expansion pack for Battlefield 2 that adds new maps, modes, weapons, and gadgets to the game. To play Special Forces, you need to have Battlefield 2 installed and activated. Then, you need to follow these steps:
-
-
-Open Battlefield 2 and log in to your Battlefield 2 account.
-Once logged in, click the Community Button, then Custom Games.
-Select the "xPack" game, which should be "Battlefield 2: Special Forces" and click Activate in the lower right. You will then be asked to restart the game.
-After restarting the game, you can access the Special Forces content from the main menu. You can play singleplayer or multiplayer with the new maps and modes.
-
-
-What are some tips and tricks for Special Forces?
-
-Special Forces is a challenging and exciting expansion pack that requires some skills and strategies to master. Here are some tips and tricks to help you out:
-
-
-
-Use the new gadgets wisely. The grappling hook and the zip line can help you reach high places or escape from enemies. The tear gas and the flashbangs can blind and disorient your foes. The night vision goggles can help you see in the dark.
-Be stealthy and silent. Many of the maps in Special Forces are set at night or in dark areas. You can use this to your advantage by hiding in the shadows and using silenced weapons. You can also use the knife or the shock paddles to take out enemies quietly.
-Be aware of your surroundings. Some of the maps in Special Forces have environmental hazards such as fire, explosions, or traps. You can use these to damage or distract your enemies, but be careful not to get caught in them yourself.
-Be flexible and adaptable. The maps and modes in Special Forces are varied and unpredictable. You may have to switch between different weapons, vehicles, and tactics depending on the situation. You may also have to cooperate with your teammates or go solo depending on the objective.
-
-
-Conclusion
-
-Special Forces is a great expansion pack for Battlefield 2 that adds more depth and diversity to the game. It offers new challenges and opportunities for both veterans and newcomers of the series. If you want to experience a different side of Battlefield 2, you should definitely try out Special Forces.
cec2833e83Dishkiyaoon Movie 1080p Download Utorrent: How to Watch the Action Thriller Online
-
-Dishkiyaoon is a 2014 Hindi-language crime action film directed by Sanamjit Singh Talwar and produced by Shilpa Shetty and Raj Kundra along with Eros International. The film features Sunny Deol, Harman Baweja, Ayesha Khanna, Aditya Pancholi and Prashant Narayanan in the lead roles. The film is based on the Mumbai underworld and follows the story of Viki, a scarred man who chooses to be a gangster against his father's wishes. [^3^]
-Dishkiyaoon Movie 1080p Download Utorrent Download File ↔ https://urlcod.com/2uHvzX
If you are looking for a way to watch Dishkiyaoon movie online, you might be tempted to download it from torrent sites like UHDMovies.in or GALAXY OF JOBS. However, this is not a safe or legal option as you might end up downloading malware or violating copyright laws. [^1^] [^2^]
-
-The best way to watch Dishkiyaoon movie online is to stream it on a legal and reliable platform like ZEE5. ZEE5 is a popular OTT service that offers a wide range of movies, shows, originals and live TV channels in various languages. You can watch Dishkiyaoon movie on ZEE5 with a subscription plan that suits your budget and preferences. [^5^]
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-Here are some of the benefits of watching Dishkiyaoon movie on ZEE5:
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-
-
-You can enjoy the movie in high quality 1080p resolution without any buffering or interruptions.
-You can watch the movie anytime and anywhere on your preferred device like smartphone, tablet, laptop or smart TV.
-You can access other content on ZEE5 like the official trailer, songs, behind-the-scenes videos and interviews of the cast and crew of Dishkiyaoon. [^4^]
-You can also explore other genres and categories of movies and shows on ZEE5 like action, comedy, drama, romance, thriller, horror, biopic, historical, sports and more.
-You can support the filmmakers and artists who worked hard to create Dishkiyaoon by paying for their content legally.
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-So what are you waiting for? Subscribe to ZEE5 today and watch Dishkiyaoon movie online in 1080p quality. You will not regret it!
Here are a few more paragraphs for the article:
-
-Now that you know how to watch Dishkiyaoon movie online legally and safely, you might be wondering what makes this movie worth watching. Here are some of the reasons why Dishkiyaoon is a must-watch for action thriller fans:
-
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-The movie has a gripping plot that keeps you hooked till the end. It shows the dark and gritty side of the Mumbai underworld and the struggles of a man who wants to rise above it.
-The movie has some stellar performances by the lead actors, especially Harman Baweja, who plays the role of Viki with conviction and intensity. Sunny Deol also delivers a powerful performance as Lakwa, a ruthless gangster who mentors Viki.
-The movie has some amazing action sequences that are realistic and thrilling. The movie also has a good dose of humor and romance to balance the violence and drama.
-The movie has a catchy soundtrack that complements the mood and tone of the film. The songs are composed by Sneha Khanwalkar, Palash Muchhal, Prashant Narayanan and White Noise Production. Some of the popular songs are "Tu Hi Hai Aashiqui", "Nachle Tu" and "Tutey".
-
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-So if you are looking for a movie that will keep you on the edge of your seat and entertain you with its action, drama, humor and romance, then Dishkiyaoon is the perfect choice for you. Don't miss this opportunity to watch this movie online on ZEE5.
-
-Before you go, here are some tips for writing SEO-optimized articles that will help you rank higher on Google and other search engines:
-
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-Use keyword-rich titles and descriptions. Make sure your titles and descriptions contain the keywords you want to target. Your title should be catchy and descriptive, while your description should summarize the main points of your article. [^4^]
-Use related keywords throughout your article. Don't just focus on one keyword, but use synonyms and variations that are relevant to your topic. This will help you cover more search queries and avoid keyword stuffing. [^1^]
-Use headings and subheadings to structure your article. Headings and subheadings help search engines understand the hierarchy and organization of your content. They also make your article easier to read and scan for your readers. Use tags for your main title, tags for your main headings, and tags for your subheadings. Include keywords in your headings and subheadings as well. [^1^]
-Use images and videos to enhance your article. Images and videos can make your article more engaging and appealing to your readers. They can also help you explain complex concepts or show examples. However, make sure you optimize your images and videos for SEO as well. Use relevant file names, alt text, captions, and titles that include keywords. [^1^]
-Use internal and external links to boost your authority and relevance. Internal links are links that point to other pages or posts on your website. External links are links that point to other websites or sources that support your claims or provide more information. Both types of links can help you improve your SEO by showing search engines that your content is valuable and trustworthy. However, make sure you use high-quality and relevant links that add value to your readers. [^1^]
-
-
-By following these tips, you can write SEO-optimized articles that will rank higher on Google and other search engines, drive more traffic to your website, and generate more leads and conversions for your business.
-
-I hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.
7196e7f11aEuro Truck Simulator 5: A New Generation of Trucking Simulation
-Euro Truck Simulator 5 is the latest installment in the popular truck simulation game series by SCS Software. Released in 2015, this game features a revamped graphics engine, new trucks and trailers, more realistic physics and damage, and a huge map of Europe with over 60 cities to explore.
-The game also includes 31 downloadable content (DLC) packs that add more countries, cargo, paint jobs, accessories, and tuning options to customize your truck. You can also download mods from the Steam Workshop or other sources to enhance your gameplay experience.
-Euro Truck Simulator 5 [v 1.25.2.8s 31 DLC] (2015) Repack Download >> https://urlcod.com/2uHwIs
If you are looking for a repack version of Euro Truck Simulator 5 that includes all the updates and DLCs, you can find it online from various sources. However, be careful of viruses and malware that may harm your computer. Always scan the files before installing them and use a reputable antivirus software.
-Euro Truck Simulator 5 is a game that will challenge your driving skills and endurance as you deliver important cargo across impressive distances. You will also enjoy the beautiful scenery and landmarks of Europe as you travel along major trunk routes and visit famous places based on real roads and cities[^2^] [^3^].
-Whether you are a fan of trucking simulation games or just looking for a relaxing and immersive way to explore Europe, Euro Truck Simulator 5 is a game that you should not miss.
-
-Euro Truck Simulator 5 offers a realistic and immersive gameplay experience that will make you feel like a real trucker. You can choose from a variety of trucks from 7 major European truck manufacturers, including official license from MAN[^2^]. You can also customize your truck with different paint jobs, accessories, and tuning options to suit your style and preferences.
-As you drive across Europe, you will encounter different road conditions, weather effects, traffic situations, and day and night cycles. You will also have to obey the traffic rules and regulations, such as speed limits, traffic lights, tolls, and weigh stations. You will also have to manage your fuel, fatigue, and cargo damage levels. If you commit a traffic offense or damage your truck or cargo, you will have to pay fines or repair costs.
-The game also features a career mode where you can start your own trucking company and hire drivers to work for you. You can buy new trucks and garages, expand your business, and earn more money. You can also take on different types of cargo, such as food, chemicals, cars, machinery, and more. You can also unlock new skills and perks that will improve your performance and reputation.
-If you want to play with other players online, you can join the TruckersMP mod[^1^] that allows you to drive with thousands of other truckers on dedicated servers. You can chat with other players, join convoys, participate in events, and more. You can also download other mods from the Steam Workshop or other sources that add new maps, trucks, trailers, sounds, graphics, and more.
-
7196e7f11aH"
- format_i = "I"
- format_q = "Q"
- format_p = format_i if self.e_ident_class == self.ELFCLASS32 else format_q
- self.e_type = unpack(format_h)
- self.e_machine = unpack(format_h)
- self.e_version = unpack(format_i)
- self.e_entry = unpack(format_p)
- self.e_phoff = unpack(format_p)
- self.e_shoff = unpack(format_p)
- self.e_flags = unpack(format_i)
- self.e_ehsize = unpack(format_h)
- self.e_phentsize = unpack(format_h)
- self.e_phnum = unpack(format_h)
- self.e_shentsize = unpack(format_h)
- self.e_shnum = unpack(format_h)
- self.e_shstrndx = unpack(format_h)
-
-
-def _get_elf_header() -> Optional[_ELFFileHeader]:
- try:
- with open(sys.executable, "rb") as f:
- elf_header = _ELFFileHeader(f)
- except (OSError, TypeError, _ELFFileHeader._InvalidELFFileHeader):
- return None
- return elf_header
-
-
-def _is_linux_armhf() -> bool:
- # hard-float ABI can be detected from the ELF header of the running
- # process
- # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
- elf_header = _get_elf_header()
- if elf_header is None:
- return False
- result = elf_header.e_ident_class == elf_header.ELFCLASS32
- result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
- result &= elf_header.e_machine == elf_header.EM_ARM
- result &= (
- elf_header.e_flags & elf_header.EF_ARM_ABIMASK
- ) == elf_header.EF_ARM_ABI_VER5
- result &= (
- elf_header.e_flags & elf_header.EF_ARM_ABI_FLOAT_HARD
- ) == elf_header.EF_ARM_ABI_FLOAT_HARD
- return result
-
-
-def _is_linux_i686() -> bool:
- elf_header = _get_elf_header()
- if elf_header is None:
- return False
- result = elf_header.e_ident_class == elf_header.ELFCLASS32
- result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
- result &= elf_header.e_machine == elf_header.EM_386
- return result
-
-
-def _have_compatible_abi(arch: str) -> bool:
- if arch == "armv7l":
- return _is_linux_armhf()
- if arch == "i686":
- return _is_linux_i686()
- return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
-
-
-# If glibc ever changes its major version, we need to know what the last
-# minor version was, so we can build the complete list of all versions.
-# For now, guess what the highest minor version might be, assume it will
-# be 50 for testing. Once this actually happens, update the dictionary
-# with the actual value.
-_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
-
-
-class _GLibCVersion(NamedTuple):
- major: int
- minor: int
-
-
-def _glibc_version_string_confstr() -> Optional[str]:
- """
- Primary implementation of glibc_version_string using os.confstr.
- """
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
- # to be broken or missing. This strategy is used in the standard library
- # platform module.
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
- try:
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17".
- version_string = os.confstr("CS_GNU_LIBC_VERSION")
- assert version_string is not None
- _, version = version_string.split()
- except (AssertionError, AttributeError, OSError, ValueError):
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
- return None
- return version
-
-
-def _glibc_version_string_ctypes() -> Optional[str]:
- """
- Fallback implementation of glibc_version_string using ctypes.
- """
- try:
- import ctypes
- except ImportError:
- return None
-
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
- # manpage says, "If filename is NULL, then the returned handle is for the
- # main program". This way we can let the linker do the work to figure out
- # which libc our process is actually using.
- #
- # We must also handle the special case where the executable is not a
- # dynamically linked executable. This can occur when using musl libc,
- # for example. In this situation, dlopen() will error, leading to an
- # OSError. Interestingly, at least in the case of musl, there is no
- # errno set on the OSError. The single string argument used to construct
- # OSError comes from libc itself and is therefore not portable to
- # hard code here. In any case, failure to call dlopen() means we
- # can proceed, so we bail on our attempt.
- try:
- process_namespace = ctypes.CDLL(None)
- except OSError:
- return None
-
- try:
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
- except AttributeError:
- # Symbol doesn't exist -> therefore, we are not linked to
- # glibc.
- return None
-
- # Call gnu_get_libc_version, which returns a string like "2.5"
- gnu_get_libc_version.restype = ctypes.c_char_p
- version_str: str = gnu_get_libc_version()
- # py2 / py3 compatibility:
- if not isinstance(version_str, str):
- version_str = version_str.decode("ascii")
-
- return version_str
-
-
-def _glibc_version_string() -> Optional[str]:
- """Returns glibc version string, or None if not using glibc."""
- return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
-
-
-def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
- """Parse glibc version.
-
- We use a regexp instead of str.split because we want to discard any
- random junk that might come after the minor version -- this might happen
- in patched/forked versions of glibc (e.g. Linaro's version of glibc
- uses version strings like "2.20-2014.11"). See gh-3588.
- """
- m = re.match(r"(?P[0-9]+)\.(?P[0-9]+)", version_str)
- if not m:
- warnings.warn(
- "Expected glibc version with 2 components major.minor,"
- " got: %s" % version_str,
- RuntimeWarning,
- )
- return -1, -1
- return int(m.group("major")), int(m.group("minor"))
-
-
-@functools.lru_cache()
-def _get_glibc_version() -> Tuple[int, int]:
- version_str = _glibc_version_string()
- if version_str is None:
- return (-1, -1)
- return _parse_glibc_version(version_str)
-
-
-# From PEP 513, PEP 600
-def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
- sys_glibc = _get_glibc_version()
- if sys_glibc < version:
- return False
- # Check for presence of _manylinux module.
- try:
- import _manylinux # noqa
- except ImportError:
- return True
- if hasattr(_manylinux, "manylinux_compatible"):
- result = _manylinux.manylinux_compatible(version[0], version[1], arch)
- if result is not None:
- return bool(result)
- return True
- if version == _GLibCVersion(2, 5):
- if hasattr(_manylinux, "manylinux1_compatible"):
- return bool(_manylinux.manylinux1_compatible)
- if version == _GLibCVersion(2, 12):
- if hasattr(_manylinux, "manylinux2010_compatible"):
- return bool(_manylinux.manylinux2010_compatible)
- if version == _GLibCVersion(2, 17):
- if hasattr(_manylinux, "manylinux2014_compatible"):
- return bool(_manylinux.manylinux2014_compatible)
- return True
-
-
-_LEGACY_MANYLINUX_MAP = {
- # CentOS 7 w/ glibc 2.17 (PEP 599)
- (2, 17): "manylinux2014",
- # CentOS 6 w/ glibc 2.12 (PEP 571)
- (2, 12): "manylinux2010",
- # CentOS 5 w/ glibc 2.5 (PEP 513)
- (2, 5): "manylinux1",
-}
-
-
-def platform_tags(linux: str, arch: str) -> Iterator[str]:
- if not _have_compatible_abi(arch):
- return
- # Oldest glibc to be supported regardless of architecture is (2, 17).
- too_old_glibc2 = _GLibCVersion(2, 16)
- if arch in {"x86_64", "i686"}:
- # On x86/i686 also oldest glibc to be supported is (2, 5).
- too_old_glibc2 = _GLibCVersion(2, 4)
- current_glibc = _GLibCVersion(*_get_glibc_version())
- glibc_max_list = [current_glibc]
- # We can assume compatibility across glibc major versions.
- # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
- #
- # Build a list of maximum glibc versions so that we can
- # output the canonical list of all glibc from current_glibc
- # down to too_old_glibc2, including all intermediary versions.
- for glibc_major in range(current_glibc.major - 1, 1, -1):
- glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
- glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
- for glibc_max in glibc_max_list:
- if glibc_max.major == too_old_glibc2.major:
- min_minor = too_old_glibc2.minor
- else:
- # For other glibc major versions oldest supported is (x, 0).
- min_minor = -1
- for glibc_minor in range(glibc_max.minor, min_minor, -1):
- glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
- tag = "manylinux_{}_{}".format(*glibc_version)
- if _is_compatible(tag, arch, glibc_version):
- yield linux.replace("linux", tag)
- # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
- if glibc_version in _LEGACY_MANYLINUX_MAP:
- legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
- if _is_compatible(legacy_tag, arch, glibc_version):
- yield linux.replace("linux", legacy_tag)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/core.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/core.py
deleted file mode 100644
index 6ff3c766f7dd9f4111cbd9d2a5f668e4435798b5..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/core.py
+++ /dev/null
@@ -1,5814 +0,0 @@
-#
-# core.py
-#
-import os
-import typing
-from typing import (
- NamedTuple,
- Union,
- Callable,
- Any,
- Generator,
- Tuple,
- List,
- TextIO,
- Set,
- Sequence,
-)
-from abc import ABC, abstractmethod
-from enum import Enum
-import string
-import copy
-import warnings
-import re
-import sys
-from collections.abc import Iterable
-import traceback
-import types
-from operator import itemgetter
-from functools import wraps
-from threading import RLock
-from pathlib import Path
-
-from .util import (
- _FifoCache,
- _UnboundedCache,
- __config_flags,
- _collapse_string_to_ranges,
- _escape_regex_range_chars,
- _bslash,
- _flatten,
- LRUMemo as _LRUMemo,
- UnboundedMemo as _UnboundedMemo,
-)
-from .exceptions import *
-from .actions import *
-from .results import ParseResults, _ParseResultsWithOffset
-from .unicode import pyparsing_unicode
-
-_MAX_INT = sys.maxsize
-str_type: Tuple[type, ...] = (str, bytes)
-
-#
-# Copyright (c) 2003-2022 Paul T. McGuire
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files (the
-# "Software"), to deal in the Software without restriction, including
-# without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and to
-# permit persons to whom the Software is furnished to do so, subject to
-# the following conditions:
-#
-# The above copyright notice and this permission notice shall be
-# included in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-#
-
-
-if sys.version_info >= (3, 8):
- from functools import cached_property
-else:
-
- class cached_property:
- def __init__(self, func):
- self._func = func
-
- def __get__(self, instance, owner=None):
- ret = instance.__dict__[self._func.__name__] = self._func(instance)
- return ret
-
-
-class __compat__(__config_flags):
- """
- A cross-version compatibility configuration for pyparsing features that will be
- released in a future version. By setting values in this configuration to True,
- those features can be enabled in prior versions for compatibility development
- and testing.
-
- - ``collect_all_And_tokens`` - flag to enable fix for Issue #63 that fixes erroneous grouping
- of results names when an :class:`And` expression is nested within an :class:`Or` or :class:`MatchFirst`;
- maintained for compatibility, but setting to ``False`` no longer restores pre-2.3.1
- behavior
- """
-
- _type_desc = "compatibility"
-
- collect_all_And_tokens = True
-
- _all_names = [__ for __ in locals() if not __.startswith("_")]
- _fixed_names = """
- collect_all_And_tokens
- """.split()
-
-
-class __diag__(__config_flags):
- _type_desc = "diagnostic"
-
- warn_multiple_tokens_in_named_alternation = False
- warn_ungrouped_named_tokens_in_collection = False
- warn_name_set_on_empty_Forward = False
- warn_on_parse_using_empty_Forward = False
- warn_on_assignment_to_Forward = False
- warn_on_multiple_string_args_to_oneof = False
- warn_on_match_first_with_lshift_operator = False
- enable_debug_on_named_expressions = False
-
- _all_names = [__ for __ in locals() if not __.startswith("_")]
- _warning_names = [name for name in _all_names if name.startswith("warn")]
- _debug_names = [name for name in _all_names if name.startswith("enable_debug")]
-
- @classmethod
- def enable_all_warnings(cls) -> None:
- for name in cls._warning_names:
- cls.enable(name)
-
-
-class Diagnostics(Enum):
- """
- Diagnostic configuration (all default to disabled)
- - ``warn_multiple_tokens_in_named_alternation`` - flag to enable warnings when a results
- name is defined on a :class:`MatchFirst` or :class:`Or` expression with one or more :class:`And` subexpressions
- - ``warn_ungrouped_named_tokens_in_collection`` - flag to enable warnings when a results
- name is defined on a containing expression with ungrouped subexpressions that also
- have results names
- - ``warn_name_set_on_empty_Forward`` - flag to enable warnings when a :class:`Forward` is defined
- with a results name, but has no contents defined
- - ``warn_on_parse_using_empty_Forward`` - flag to enable warnings when a :class:`Forward` is
- defined in a grammar but has never had an expression attached to it
- - ``warn_on_assignment_to_Forward`` - flag to enable warnings when a :class:`Forward` is defined
- but is overwritten by assigning using ``'='`` instead of ``'<<='`` or ``'<<'``
- - ``warn_on_multiple_string_args_to_oneof`` - flag to enable warnings when :class:`one_of` is
- incorrectly called with multiple str arguments
- - ``enable_debug_on_named_expressions`` - flag to auto-enable debug on all subsequent
- calls to :class:`ParserElement.set_name`
-
- Diagnostics are enabled/disabled by calling :class:`enable_diag` and :class:`disable_diag`.
- All warnings can be enabled by calling :class:`enable_all_warnings`.
- """
-
- warn_multiple_tokens_in_named_alternation = 0
- warn_ungrouped_named_tokens_in_collection = 1
- warn_name_set_on_empty_Forward = 2
- warn_on_parse_using_empty_Forward = 3
- warn_on_assignment_to_Forward = 4
- warn_on_multiple_string_args_to_oneof = 5
- warn_on_match_first_with_lshift_operator = 6
- enable_debug_on_named_expressions = 7
-
-
-def enable_diag(diag_enum: Diagnostics) -> None:
- """
- Enable a global pyparsing diagnostic flag (see :class:`Diagnostics`).
- """
- __diag__.enable(diag_enum.name)
-
-
-def disable_diag(diag_enum: Diagnostics) -> None:
- """
- Disable a global pyparsing diagnostic flag (see :class:`Diagnostics`).
- """
- __diag__.disable(diag_enum.name)
-
-
-def enable_all_warnings() -> None:
- """
- Enable all global pyparsing diagnostic warnings (see :class:`Diagnostics`).
- """
- __diag__.enable_all_warnings()
-
-
-# hide abstract class
-del __config_flags
-
-
-def _should_enable_warnings(
- cmd_line_warn_options: typing.Iterable[str], warn_env_var: typing.Optional[str]
-) -> bool:
- enable = bool(warn_env_var)
- for warn_opt in cmd_line_warn_options:
- w_action, w_message, w_category, w_module, w_line = (warn_opt + "::::").split(
- ":"
- )[:5]
- if not w_action.lower().startswith("i") and (
- not (w_message or w_category or w_module) or w_module == "pyparsing"
- ):
- enable = True
- elif w_action.lower().startswith("i") and w_module in ("pyparsing", ""):
- enable = False
- return enable
-
-
-if _should_enable_warnings(
- sys.warnoptions, os.environ.get("PYPARSINGENABLEALLWARNINGS")
-):
- enable_all_warnings()
-
-
-# build list of single arg builtins, that can be used as parse actions
-_single_arg_builtins = {
- sum,
- len,
- sorted,
- reversed,
- list,
- tuple,
- set,
- any,
- all,
- min,
- max,
-}
-
-_generatorType = types.GeneratorType
-ParseAction = Union[
- Callable[[], Any],
- Callable[[ParseResults], Any],
- Callable[[int, ParseResults], Any],
- Callable[[str, int, ParseResults], Any],
-]
-ParseCondition = Union[
- Callable[[], bool],
- Callable[[ParseResults], bool],
- Callable[[int, ParseResults], bool],
- Callable[[str, int, ParseResults], bool],
-]
-ParseFailAction = Callable[[str, int, "ParserElement", Exception], None]
-DebugStartAction = Callable[[str, int, "ParserElement", bool], None]
-DebugSuccessAction = Callable[
- [str, int, int, "ParserElement", ParseResults, bool], None
-]
-DebugExceptionAction = Callable[[str, int, "ParserElement", Exception, bool], None]
-
-
-alphas = string.ascii_uppercase + string.ascii_lowercase
-identchars = pyparsing_unicode.Latin1.identchars
-identbodychars = pyparsing_unicode.Latin1.identbodychars
-nums = "0123456789"
-hexnums = nums + "ABCDEFabcdef"
-alphanums = alphas + nums
-printables = "".join([c for c in string.printable if c not in string.whitespace])
-
-_trim_arity_call_line: traceback.StackSummary = None
-
-
-def _trim_arity(func, max_limit=3):
- """decorator to trim function calls to match the arity of the target"""
- global _trim_arity_call_line
-
- if func in _single_arg_builtins:
- return lambda s, l, t: func(t)
-
- limit = 0
- found_arity = False
-
- def extract_tb(tb, limit=0):
- frames = traceback.extract_tb(tb, limit=limit)
- frame_summary = frames[-1]
- return [frame_summary[:2]]
-
- # synthesize what would be returned by traceback.extract_stack at the call to
- # user's parse action 'func', so that we don't incur call penalty at parse time
-
- # fmt: off
- LINE_DIFF = 7
- # IF ANY CODE CHANGES, EVEN JUST COMMENTS OR BLANK LINES, BETWEEN THE NEXT LINE AND
- # THE CALL TO FUNC INSIDE WRAPPER, LINE_DIFF MUST BE MODIFIED!!!!
- _trim_arity_call_line = (_trim_arity_call_line or traceback.extract_stack(limit=2)[-1])
- pa_call_line_synth = (_trim_arity_call_line[0], _trim_arity_call_line[1] + LINE_DIFF)
-
- def wrapper(*args):
- nonlocal found_arity, limit
- while 1:
- try:
- ret = func(*args[limit:])
- found_arity = True
- return ret
- except TypeError as te:
- # re-raise TypeErrors if they did not come from our arity testing
- if found_arity:
- raise
- else:
- tb = te.__traceback__
- trim_arity_type_error = (
- extract_tb(tb, limit=2)[-1][:2] == pa_call_line_synth
- )
- del tb
-
- if trim_arity_type_error:
- if limit < max_limit:
- limit += 1
- continue
-
- raise
- # fmt: on
-
- # copy func name to wrapper for sensible debug output
- # (can't use functools.wraps, since that messes with function signature)
- func_name = getattr(func, "__name__", getattr(func, "__class__").__name__)
- wrapper.__name__ = func_name
- wrapper.__doc__ = func.__doc__
-
- return wrapper
-
-
-def condition_as_parse_action(
- fn: ParseCondition, message: str = None, fatal: bool = False
-) -> ParseAction:
- """
- Function to convert a simple predicate function that returns ``True`` or ``False``
- into a parse action. Can be used in places when a parse action is required
- and :class:`ParserElement.add_condition` cannot be used (such as when adding a condition
- to an operator level in :class:`infix_notation`).
-
- Optional keyword arguments:
-
- - ``message`` - define a custom message to be used in the raised exception
- - ``fatal`` - if True, will raise :class:`ParseFatalException` to stop parsing immediately;
- otherwise will raise :class:`ParseException`
-
- """
- msg = message if message is not None else "failed user-defined condition"
- exc_type = ParseFatalException if fatal else ParseException
- fn = _trim_arity(fn)
-
- @wraps(fn)
- def pa(s, l, t):
- if not bool(fn(s, l, t)):
- raise exc_type(s, l, msg)
-
- return pa
-
-
-def _default_start_debug_action(
- instring: str, loc: int, expr: "ParserElement", cache_hit: bool = False
-):
- cache_hit_str = "*" if cache_hit else ""
- print(
- (
- "{}Match {} at loc {}({},{})\n {}\n {}^".format(
- cache_hit_str,
- expr,
- loc,
- lineno(loc, instring),
- col(loc, instring),
- line(loc, instring),
- " " * (col(loc, instring) - 1),
- )
- )
- )
-
-
-def _default_success_debug_action(
- instring: str,
- startloc: int,
- endloc: int,
- expr: "ParserElement",
- toks: ParseResults,
- cache_hit: bool = False,
-):
- cache_hit_str = "*" if cache_hit else ""
- print("{}Matched {} -> {}".format(cache_hit_str, expr, toks.as_list()))
-
-
-def _default_exception_debug_action(
- instring: str,
- loc: int,
- expr: "ParserElement",
- exc: Exception,
- cache_hit: bool = False,
-):
- cache_hit_str = "*" if cache_hit else ""
- print(
- "{}Match {} failed, {} raised: {}".format(
- cache_hit_str, expr, type(exc).__name__, exc
- )
- )
-
-
-def null_debug_action(*args):
- """'Do-nothing' debug action, to suppress debugging output during parsing."""
-
-
-class ParserElement(ABC):
- """Abstract base level parser element class."""
-
- DEFAULT_WHITE_CHARS: str = " \n\t\r"
- verbose_stacktrace: bool = False
- _literalStringClass: typing.Optional[type] = None
-
- @staticmethod
- def set_default_whitespace_chars(chars: str) -> None:
- r"""
- Overrides the default whitespace chars
-
- Example::
-
- # default whitespace chars are space, and newline
- Word(alphas)[1, ...].parse_string("abc def\nghi jkl") # -> ['abc', 'def', 'ghi', 'jkl']
-
- # change to just treat newline as significant
- ParserElement.set_default_whitespace_chars(" \t")
- Word(alphas)[1, ...].parse_string("abc def\nghi jkl") # -> ['abc', 'def']
- """
- ParserElement.DEFAULT_WHITE_CHARS = chars
-
- # update whitespace all parse expressions defined in this module
- for expr in _builtin_exprs:
- if expr.copyDefaultWhiteChars:
- expr.whiteChars = set(chars)
-
- @staticmethod
- def inline_literals_using(cls: type) -> None:
- """
- Set class to be used for inclusion of string literals into a parser.
-
- Example::
-
- # default literal class used is Literal
- integer = Word(nums)
- date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
- date_str.parse_string("1999/12/31") # -> ['1999', '/', '12', '/', '31']
-
-
- # change to Suppress
- ParserElement.inline_literals_using(Suppress)
- date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
- date_str.parse_string("1999/12/31") # -> ['1999', '12', '31']
- """
- ParserElement._literalStringClass = cls
-
- class DebugActions(NamedTuple):
- debug_try: typing.Optional[DebugStartAction]
- debug_match: typing.Optional[DebugSuccessAction]
- debug_fail: typing.Optional[DebugExceptionAction]
-
- def __init__(self, savelist: bool = False):
- self.parseAction: List[ParseAction] = list()
- self.failAction: typing.Optional[ParseFailAction] = None
- self.customName = None
- self._defaultName = None
- self.resultsName = None
- self.saveAsList = savelist
- self.skipWhitespace = True
- self.whiteChars = set(ParserElement.DEFAULT_WHITE_CHARS)
- self.copyDefaultWhiteChars = True
- # used when checking for left-recursion
- self.mayReturnEmpty = False
- self.keepTabs = False
- self.ignoreExprs: List["ParserElement"] = list()
- self.debug = False
- self.streamlined = False
- # optimize exception handling for subclasses that don't advance parse index
- self.mayIndexError = True
- self.errmsg = ""
- # mark results names as modal (report only last) or cumulative (list all)
- self.modalResults = True
- # custom debug actions
- self.debugActions = self.DebugActions(None, None, None)
- # avoid redundant calls to preParse
- self.callPreparse = True
- self.callDuringTry = False
- self.suppress_warnings_: List[Diagnostics] = []
-
- def suppress_warning(self, warning_type: Diagnostics) -> "ParserElement":
- """
- Suppress warnings emitted for a particular diagnostic on this expression.
-
- Example::
-
- base = pp.Forward()
- base.suppress_warning(Diagnostics.warn_on_parse_using_empty_Forward)
-
- # statement would normally raise a warning, but is now suppressed
- print(base.parseString("x"))
-
- """
- self.suppress_warnings_.append(warning_type)
- return self
-
- def copy(self) -> "ParserElement":
- """
- Make a copy of this :class:`ParserElement`. Useful for defining
- different parse actions for the same parsing pattern, using copies of
- the original parse element.
-
- Example::
-
- integer = Word(nums).set_parse_action(lambda toks: int(toks[0]))
- integerK = integer.copy().add_parse_action(lambda toks: toks[0] * 1024) + Suppress("K")
- integerM = integer.copy().add_parse_action(lambda toks: toks[0] * 1024 * 1024) + Suppress("M")
-
- print((integerK | integerM | integer)[1, ...].parse_string("5K 100 640K 256M"))
-
- prints::
-
- [5120, 100, 655360, 268435456]
-
- Equivalent form of ``expr.copy()`` is just ``expr()``::
-
- integerM = integer().add_parse_action(lambda toks: toks[0] * 1024 * 1024) + Suppress("M")
- """
- cpy = copy.copy(self)
- cpy.parseAction = self.parseAction[:]
- cpy.ignoreExprs = self.ignoreExprs[:]
- if self.copyDefaultWhiteChars:
- cpy.whiteChars = set(ParserElement.DEFAULT_WHITE_CHARS)
- return cpy
-
- def set_results_name(
- self, name: str, list_all_matches: bool = False, *, listAllMatches: bool = False
- ) -> "ParserElement":
- """
- Define name for referencing matching tokens as a nested attribute
- of the returned parse results.
-
- Normally, results names are assigned as you would assign keys in a dict:
- any existing value is overwritten by later values. If it is necessary to
- keep all values captured for a particular results name, call ``set_results_name``
- with ``list_all_matches`` = True.
-
- NOTE: ``set_results_name`` returns a *copy* of the original :class:`ParserElement` object;
- this is so that the client can define a basic element, such as an
- integer, and reference it in multiple places with different names.
-
- You can also set results names using the abbreviated syntax,
- ``expr("name")`` in place of ``expr.set_results_name("name")``
- - see :class:`__call__`. If ``list_all_matches`` is required, use
- ``expr("name*")``.
-
- Example::
-
- date_str = (integer.set_results_name("year") + '/'
- + integer.set_results_name("month") + '/'
- + integer.set_results_name("day"))
-
- # equivalent form:
- date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
- """
- listAllMatches = listAllMatches or list_all_matches
- return self._setResultsName(name, listAllMatches)
-
- def _setResultsName(self, name, listAllMatches=False):
- if name is None:
- return self
- newself = self.copy()
- if name.endswith("*"):
- name = name[:-1]
- listAllMatches = True
- newself.resultsName = name
- newself.modalResults = not listAllMatches
- return newself
-
- def set_break(self, break_flag: bool = True) -> "ParserElement":
- """
- Method to invoke the Python pdb debugger when this element is
- about to be parsed. Set ``break_flag`` to ``True`` to enable, ``False`` to
- disable.
- """
- if break_flag:
- _parseMethod = self._parse
-
- def breaker(instring, loc, doActions=True, callPreParse=True):
- import pdb
-
- # this call to pdb.set_trace() is intentional, not a checkin error
- pdb.set_trace()
- return _parseMethod(instring, loc, doActions, callPreParse)
-
- breaker._originalParseMethod = _parseMethod
- self._parse = breaker
- else:
- if hasattr(self._parse, "_originalParseMethod"):
- self._parse = self._parse._originalParseMethod
- return self
-
- def set_parse_action(self, *fns: ParseAction, **kwargs) -> "ParserElement":
- """
- Define one or more actions to perform when successfully matching parse element definition.
-
- Parse actions can be called to perform data conversions, do extra validation,
- update external data structures, or enhance or replace the parsed tokens.
- Each parse action ``fn`` is a callable method with 0-3 arguments, called as
- ``fn(s, loc, toks)`` , ``fn(loc, toks)`` , ``fn(toks)`` , or just ``fn()`` , where:
-
- - s = the original string being parsed (see note below)
- - loc = the location of the matching substring
- - toks = a list of the matched tokens, packaged as a :class:`ParseResults` object
-
- The parsed tokens are passed to the parse action as ParseResults. They can be
- modified in place using list-style append, extend, and pop operations to update
- the parsed list elements; and with dictionary-style item set and del operations
- to add, update, or remove any named results. If the tokens are modified in place,
- it is not necessary to return them with a return statement.
-
- Parse actions can also completely replace the given tokens, with another ``ParseResults``
- object, or with some entirely different object (common for parse actions that perform data
- conversions). A convenient way to build a new parse result is to define the values
- using a dict, and then create the return value using :class:`ParseResults.from_dict`.
-
- If None is passed as the ``fn`` parse action, all previously added parse actions for this
- expression are cleared.
-
- Optional keyword arguments:
-
- - call_during_try = (default= ``False``) indicate if parse action should be run during
- lookaheads and alternate testing. For parse actions that have side effects, it is
- important to only call the parse action once it is determined that it is being
- called as part of a successful parse. For parse actions that perform additional
- validation, then call_during_try should be passed as True, so that the validation
- code is included in the preliminary "try" parses.
-
- Note: the default parsing behavior is to expand tabs in the input string
- before starting the parsing process. See :class:`parse_string` for more
- information on parsing strings containing ```` s, and suggested
- methods to maintain a consistent view of the parsed string, the parse
- location, and line and column positions within the parsed string.
-
- Example::
-
- # parse dates in the form YYYY/MM/DD
-
- # use parse action to convert toks from str to int at parse time
- def convert_to_int(toks):
- return int(toks[0])
-
- # use a parse action to verify that the date is a valid date
- def is_valid_date(instring, loc, toks):
- from datetime import date
- year, month, day = toks[::2]
- try:
- date(year, month, day)
- except ValueError:
- raise ParseException(instring, loc, "invalid date given")
-
- integer = Word(nums)
- date_str = integer + '/' + integer + '/' + integer
-
- # add parse actions
- integer.set_parse_action(convert_to_int)
- date_str.set_parse_action(is_valid_date)
-
- # note that integer fields are now ints, not strings
- date_str.run_tests('''
- # successful parse - note that integer fields were converted to ints
- 1999/12/31
-
- # fail - invalid date
- 1999/13/31
- ''')
- """
- if list(fns) == [None]:
- self.parseAction = []
- else:
- if not all(callable(fn) for fn in fns):
- raise TypeError("parse actions must be callable")
- self.parseAction = [_trim_arity(fn) for fn in fns]
- self.callDuringTry = kwargs.get(
- "call_during_try", kwargs.get("callDuringTry", False)
- )
- return self
-
- def add_parse_action(self, *fns: ParseAction, **kwargs) -> "ParserElement":
- """
- Add one or more parse actions to expression's list of parse actions. See :class:`set_parse_action`.
-
- See examples in :class:`copy`.
- """
- self.parseAction += [_trim_arity(fn) for fn in fns]
- self.callDuringTry = self.callDuringTry or kwargs.get(
- "call_during_try", kwargs.get("callDuringTry", False)
- )
- return self
-
- def add_condition(self, *fns: ParseCondition, **kwargs) -> "ParserElement":
- """Add a boolean predicate function to expression's list of parse actions. See
- :class:`set_parse_action` for function call signatures. Unlike ``set_parse_action``,
- functions passed to ``add_condition`` need to return boolean success/fail of the condition.
-
- Optional keyword arguments:
-
- - message = define a custom message to be used in the raised exception
- - fatal = if True, will raise ParseFatalException to stop parsing immediately; otherwise will raise
- ParseException
- - call_during_try = boolean to indicate if this method should be called during internal tryParse calls,
- default=False
-
- Example::
-
- integer = Word(nums).set_parse_action(lambda toks: int(toks[0]))
- year_int = integer.copy()
- year_int.add_condition(lambda toks: toks[0] >= 2000, message="Only support years 2000 and later")
- date_str = year_int + '/' + integer + '/' + integer
-
- result = date_str.parse_string("1999/12/31") # -> Exception: Only support years 2000 and later (at char 0),
- (line:1, col:1)
- """
- for fn in fns:
- self.parseAction.append(
- condition_as_parse_action(
- fn, message=kwargs.get("message"), fatal=kwargs.get("fatal", False)
- )
- )
-
- self.callDuringTry = self.callDuringTry or kwargs.get(
- "call_during_try", kwargs.get("callDuringTry", False)
- )
- return self
-
- def set_fail_action(self, fn: ParseFailAction) -> "ParserElement":
- """
- Define action to perform if parsing fails at this expression.
- Fail acton fn is a callable function that takes the arguments
- ``fn(s, loc, expr, err)`` where:
-
- - s = string being parsed
- - loc = location where expression match was attempted and failed
- - expr = the parse expression that failed
- - err = the exception thrown
-
- The function returns no value. It may throw :class:`ParseFatalException`
- if it is desired to stop parsing immediately."""
- self.failAction = fn
- return self
-
- def _skipIgnorables(self, instring, loc):
- exprsFound = True
- while exprsFound:
- exprsFound = False
- for e in self.ignoreExprs:
- try:
- while 1:
- loc, dummy = e._parse(instring, loc)
- exprsFound = True
- except ParseException:
- pass
- return loc
-
- def preParse(self, instring, loc):
- if self.ignoreExprs:
- loc = self._skipIgnorables(instring, loc)
-
- if self.skipWhitespace:
- instrlen = len(instring)
- white_chars = self.whiteChars
- while loc < instrlen and instring[loc] in white_chars:
- loc += 1
-
- return loc
-
- def parseImpl(self, instring, loc, doActions=True):
- return loc, []
-
- def postParse(self, instring, loc, tokenlist):
- return tokenlist
-
- # @profile
- def _parseNoCache(
- self, instring, loc, doActions=True, callPreParse=True
- ) -> Tuple[int, ParseResults]:
- TRY, MATCH, FAIL = 0, 1, 2
- debugging = self.debug # and doActions)
- len_instring = len(instring)
-
- if debugging or self.failAction:
- # print("Match {} at loc {}({}, {})".format(self, loc, lineno(loc, instring), col(loc, instring)))
- try:
- if callPreParse and self.callPreparse:
- pre_loc = self.preParse(instring, loc)
- else:
- pre_loc = loc
- tokens_start = pre_loc
- if self.debugActions.debug_try:
- self.debugActions.debug_try(instring, tokens_start, self, False)
- if self.mayIndexError or pre_loc >= len_instring:
- try:
- loc, tokens = self.parseImpl(instring, pre_loc, doActions)
- except IndexError:
- raise ParseException(instring, len_instring, self.errmsg, self)
- else:
- loc, tokens = self.parseImpl(instring, pre_loc, doActions)
- except Exception as err:
- # print("Exception raised:", err)
- if self.debugActions.debug_fail:
- self.debugActions.debug_fail(
- instring, tokens_start, self, err, False
- )
- if self.failAction:
- self.failAction(instring, tokens_start, self, err)
- raise
- else:
- if callPreParse and self.callPreparse:
- pre_loc = self.preParse(instring, loc)
- else:
- pre_loc = loc
- tokens_start = pre_loc
- if self.mayIndexError or pre_loc >= len_instring:
- try:
- loc, tokens = self.parseImpl(instring, pre_loc, doActions)
- except IndexError:
- raise ParseException(instring, len_instring, self.errmsg, self)
- else:
- loc, tokens = self.parseImpl(instring, pre_loc, doActions)
-
- tokens = self.postParse(instring, loc, tokens)
-
- ret_tokens = ParseResults(
- tokens, self.resultsName, asList=self.saveAsList, modal=self.modalResults
- )
- if self.parseAction and (doActions or self.callDuringTry):
- if debugging:
- try:
- for fn in self.parseAction:
- try:
- tokens = fn(instring, tokens_start, ret_tokens)
- except IndexError as parse_action_exc:
- exc = ParseException("exception raised in parse action")
- raise exc from parse_action_exc
-
- if tokens is not None and tokens is not ret_tokens:
- ret_tokens = ParseResults(
- tokens,
- self.resultsName,
- asList=self.saveAsList
- and isinstance(tokens, (ParseResults, list)),
- modal=self.modalResults,
- )
- except Exception as err:
- # print "Exception raised in user parse action:", err
- if self.debugActions.debug_fail:
- self.debugActions.debug_fail(
- instring, tokens_start, self, err, False
- )
- raise
- else:
- for fn in self.parseAction:
- try:
- tokens = fn(instring, tokens_start, ret_tokens)
- except IndexError as parse_action_exc:
- exc = ParseException("exception raised in parse action")
- raise exc from parse_action_exc
-
- if tokens is not None and tokens is not ret_tokens:
- ret_tokens = ParseResults(
- tokens,
- self.resultsName,
- asList=self.saveAsList
- and isinstance(tokens, (ParseResults, list)),
- modal=self.modalResults,
- )
- if debugging:
- # print("Matched", self, "->", ret_tokens.as_list())
- if self.debugActions.debug_match:
- self.debugActions.debug_match(
- instring, tokens_start, loc, self, ret_tokens, False
- )
-
- return loc, ret_tokens
-
- def try_parse(self, instring: str, loc: int, raise_fatal: bool = False) -> int:
- try:
- return self._parse(instring, loc, doActions=False)[0]
- except ParseFatalException:
- if raise_fatal:
- raise
- raise ParseException(instring, loc, self.errmsg, self)
-
- def can_parse_next(self, instring: str, loc: int) -> bool:
- try:
- self.try_parse(instring, loc)
- except (ParseException, IndexError):
- return False
- else:
- return True
-
- # cache for left-recursion in Forward references
- recursion_lock = RLock()
- recursion_memos: typing.Dict[
- Tuple[int, "Forward", bool], Tuple[int, Union[ParseResults, Exception]]
- ] = {}
-
- # argument cache for optimizing repeated calls when backtracking through recursive expressions
- packrat_cache = (
- {}
- ) # this is set later by enabled_packrat(); this is here so that reset_cache() doesn't fail
- packrat_cache_lock = RLock()
- packrat_cache_stats = [0, 0]
-
- # this method gets repeatedly called during backtracking with the same arguments -
- # we can cache these arguments and save ourselves the trouble of re-parsing the contained expression
- def _parseCache(
- self, instring, loc, doActions=True, callPreParse=True
- ) -> Tuple[int, ParseResults]:
- HIT, MISS = 0, 1
- TRY, MATCH, FAIL = 0, 1, 2
- lookup = (self, instring, loc, callPreParse, doActions)
- with ParserElement.packrat_cache_lock:
- cache = ParserElement.packrat_cache
- value = cache.get(lookup)
- if value is cache.not_in_cache:
- ParserElement.packrat_cache_stats[MISS] += 1
- try:
- value = self._parseNoCache(instring, loc, doActions, callPreParse)
- except ParseBaseException as pe:
- # cache a copy of the exception, without the traceback
- cache.set(lookup, pe.__class__(*pe.args))
- raise
- else:
- cache.set(lookup, (value[0], value[1].copy(), loc))
- return value
- else:
- ParserElement.packrat_cache_stats[HIT] += 1
- if self.debug and self.debugActions.debug_try:
- try:
- self.debugActions.debug_try(instring, loc, self, cache_hit=True)
- except TypeError:
- pass
- if isinstance(value, Exception):
- if self.debug and self.debugActions.debug_fail:
- try:
- self.debugActions.debug_fail(
- instring, loc, self, value, cache_hit=True
- )
- except TypeError:
- pass
- raise value
-
- loc_, result, endloc = value[0], value[1].copy(), value[2]
- if self.debug and self.debugActions.debug_match:
- try:
- self.debugActions.debug_match(
- instring, loc_, endloc, self, result, cache_hit=True
- )
- except TypeError:
- pass
-
- return loc_, result
-
- _parse = _parseNoCache
-
- @staticmethod
- def reset_cache() -> None:
- ParserElement.packrat_cache.clear()
- ParserElement.packrat_cache_stats[:] = [0] * len(
- ParserElement.packrat_cache_stats
- )
- ParserElement.recursion_memos.clear()
-
- _packratEnabled = False
- _left_recursion_enabled = False
-
- @staticmethod
- def disable_memoization() -> None:
- """
- Disables active Packrat or Left Recursion parsing and their memoization
-
- This method also works if neither Packrat nor Left Recursion are enabled.
- This makes it safe to call before activating Packrat nor Left Recursion
- to clear any previous settings.
- """
- ParserElement.reset_cache()
- ParserElement._left_recursion_enabled = False
- ParserElement._packratEnabled = False
- ParserElement._parse = ParserElement._parseNoCache
-
- @staticmethod
- def enable_left_recursion(
- cache_size_limit: typing.Optional[int] = None, *, force=False
- ) -> None:
- """
- Enables "bounded recursion" parsing, which allows for both direct and indirect
- left-recursion. During parsing, left-recursive :class:`Forward` elements are
- repeatedly matched with a fixed recursion depth that is gradually increased
- until finding the longest match.
-
- Example::
-
- from pip._vendor import pyparsing as pp
- pp.ParserElement.enable_left_recursion()
-
- E = pp.Forward("E")
- num = pp.Word(pp.nums)
- # match `num`, or `num '+' num`, or `num '+' num '+' num`, ...
- E <<= E + '+' - num | num
-
- print(E.parse_string("1+2+3"))
-
- Recursion search naturally memoizes matches of ``Forward`` elements and may
- thus skip reevaluation of parse actions during backtracking. This may break
- programs with parse actions which rely on strict ordering of side-effects.
-
- Parameters:
-
- - cache_size_limit - (default=``None``) - memoize at most this many
- ``Forward`` elements during matching; if ``None`` (the default),
- memoize all ``Forward`` elements.
-
- Bounded Recursion parsing works similar but not identical to Packrat parsing,
- thus the two cannot be used together. Use ``force=True`` to disable any
- previous, conflicting settings.
- """
- if force:
- ParserElement.disable_memoization()
- elif ParserElement._packratEnabled:
- raise RuntimeError("Packrat and Bounded Recursion are not compatible")
- if cache_size_limit is None:
- ParserElement.recursion_memos = _UnboundedMemo()
- elif cache_size_limit > 0:
- ParserElement.recursion_memos = _LRUMemo(capacity=cache_size_limit)
- else:
- raise NotImplementedError("Memo size of %s" % cache_size_limit)
- ParserElement._left_recursion_enabled = True
-
- @staticmethod
- def enable_packrat(cache_size_limit: int = 128, *, force: bool = False) -> None:
- """
- Enables "packrat" parsing, which adds memoizing to the parsing logic.
- Repeated parse attempts at the same string location (which happens
- often in many complex grammars) can immediately return a cached value,
- instead of re-executing parsing/validating code. Memoizing is done of
- both valid results and parsing exceptions.
-
- Parameters:
-
- - cache_size_limit - (default= ``128``) - if an integer value is provided
- will limit the size of the packrat cache; if None is passed, then
- the cache size will be unbounded; if 0 is passed, the cache will
- be effectively disabled.
-
- This speedup may break existing programs that use parse actions that
- have side-effects. For this reason, packrat parsing is disabled when
- you first import pyparsing. To activate the packrat feature, your
- program must call the class method :class:`ParserElement.enable_packrat`.
- For best results, call ``enable_packrat()`` immediately after
- importing pyparsing.
-
- Example::
-
- from pip._vendor import pyparsing
- pyparsing.ParserElement.enable_packrat()
-
- Packrat parsing works similar but not identical to Bounded Recursion parsing,
- thus the two cannot be used together. Use ``force=True`` to disable any
- previous, conflicting settings.
- """
- if force:
- ParserElement.disable_memoization()
- elif ParserElement._left_recursion_enabled:
- raise RuntimeError("Packrat and Bounded Recursion are not compatible")
- if not ParserElement._packratEnabled:
- ParserElement._packratEnabled = True
- if cache_size_limit is None:
- ParserElement.packrat_cache = _UnboundedCache()
- else:
- ParserElement.packrat_cache = _FifoCache(cache_size_limit)
- ParserElement._parse = ParserElement._parseCache
-
- def parse_string(
- self, instring: str, parse_all: bool = False, *, parseAll: bool = False
- ) -> ParseResults:
- """
- Parse a string with respect to the parser definition. This function is intended as the primary interface to the
- client code.
-
- :param instring: The input string to be parsed.
- :param parse_all: If set, the entire input string must match the grammar.
- :param parseAll: retained for pre-PEP8 compatibility, will be removed in a future release.
- :raises ParseException: Raised if ``parse_all`` is set and the input string does not match the whole grammar.
- :returns: the parsed data as a :class:`ParseResults` object, which may be accessed as a `list`, a `dict`, or
- an object with attributes if the given parser includes results names.
-
- If the input string is required to match the entire grammar, ``parse_all`` flag must be set to ``True``. This
- is also equivalent to ending the grammar with :class:`StringEnd`().
-
- To report proper column numbers, ``parse_string`` operates on a copy of the input string where all tabs are
- converted to spaces (8 spaces per tab, as per the default in ``string.expandtabs``). If the input string
- contains tabs and the grammar uses parse actions that use the ``loc`` argument to index into the string
- being parsed, one can ensure a consistent view of the input string by doing one of the following:
-
- - calling ``parse_with_tabs`` on your grammar before calling ``parse_string`` (see :class:`parse_with_tabs`),
- - define your parse action using the full ``(s,loc,toks)`` signature, and reference the input string using the
- parse action's ``s`` argument, or
- - explicitly expand the tabs in your input string before calling ``parse_string``.
-
- Examples:
-
- By default, partial matches are OK.
-
- >>> res = Word('a').parse_string('aaaaabaaa')
- >>> print(res)
- ['aaaaa']
-
- The parsing behavior varies by the inheriting class of this abstract class. Please refer to the children
- directly to see more examples.
-
- It raises an exception if parse_all flag is set and instring does not match the whole grammar.
-
- >>> res = Word('a').parse_string('aaaaabaaa', parse_all=True)
- Traceback (most recent call last):
- ...
- pyparsing.ParseException: Expected end of text, found 'b' (at char 5), (line:1, col:6)
- """
- parseAll = parse_all or parseAll
-
- ParserElement.reset_cache()
- if not self.streamlined:
- self.streamline()
- for e in self.ignoreExprs:
- e.streamline()
- if not self.keepTabs:
- instring = instring.expandtabs()
- try:
- loc, tokens = self._parse(instring, 0)
- if parseAll:
- loc = self.preParse(instring, loc)
- se = Empty() + StringEnd()
- se._parse(instring, loc)
- except ParseBaseException as exc:
- if ParserElement.verbose_stacktrace:
- raise
- else:
- # catch and re-raise exception from here, clearing out pyparsing internal stack trace
- raise exc.with_traceback(None)
- else:
- return tokens
-
- def scan_string(
- self,
- instring: str,
- max_matches: int = _MAX_INT,
- overlap: bool = False,
- *,
- debug: bool = False,
- maxMatches: int = _MAX_INT,
- ) -> Generator[Tuple[ParseResults, int, int], None, None]:
- """
- Scan the input string for expression matches. Each match will return the
- matching tokens, start location, and end location. May be called with optional
- ``max_matches`` argument, to clip scanning after 'n' matches are found. If
- ``overlap`` is specified, then overlapping matches will be reported.
-
- Note that the start and end locations are reported relative to the string
- being parsed. See :class:`parse_string` for more information on parsing
- strings with embedded tabs.
-
- Example::
-
- source = "sldjf123lsdjjkf345sldkjf879lkjsfd987"
- print(source)
- for tokens, start, end in Word(alphas).scan_string(source):
- print(' '*start + '^'*(end-start))
- print(' '*start + tokens[0])
-
- prints::
-
- sldjf123lsdjjkf345sldkjf879lkjsfd987
- ^^^^^
- sldjf
- ^^^^^^^
- lsdjjkf
- ^^^^^^
- sldkjf
- ^^^^^^
- lkjsfd
- """
- maxMatches = min(maxMatches, max_matches)
- if not self.streamlined:
- self.streamline()
- for e in self.ignoreExprs:
- e.streamline()
-
- if not self.keepTabs:
- instring = str(instring).expandtabs()
- instrlen = len(instring)
- loc = 0
- preparseFn = self.preParse
- parseFn = self._parse
- ParserElement.resetCache()
- matches = 0
- try:
- while loc <= instrlen and matches < maxMatches:
- try:
- preloc = preparseFn(instring, loc)
- nextLoc, tokens = parseFn(instring, preloc, callPreParse=False)
- except ParseException:
- loc = preloc + 1
- else:
- if nextLoc > loc:
- matches += 1
- if debug:
- print(
- {
- "tokens": tokens.asList(),
- "start": preloc,
- "end": nextLoc,
- }
- )
- yield tokens, preloc, nextLoc
- if overlap:
- nextloc = preparseFn(instring, loc)
- if nextloc > loc:
- loc = nextLoc
- else:
- loc += 1
- else:
- loc = nextLoc
- else:
- loc = preloc + 1
- except ParseBaseException as exc:
- if ParserElement.verbose_stacktrace:
- raise
- else:
- # catch and re-raise exception from here, clears out pyparsing internal stack trace
- raise exc.with_traceback(None)
-
- def transform_string(self, instring: str, *, debug: bool = False) -> str:
- """
- Extension to :class:`scan_string`, to modify matching text with modified tokens that may
- be returned from a parse action. To use ``transform_string``, define a grammar and
- attach a parse action to it that modifies the returned token list.
- Invoking ``transform_string()`` on a target string will then scan for matches,
- and replace the matched text patterns according to the logic in the parse
- action. ``transform_string()`` returns the resulting transformed string.
-
- Example::
-
- wd = Word(alphas)
- wd.set_parse_action(lambda toks: toks[0].title())
-
- print(wd.transform_string("now is the winter of our discontent made glorious summer by this sun of york."))
-
- prints::
-
- Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York.
- """
- out: List[str] = []
- lastE = 0
- # force preservation of s, to minimize unwanted transformation of string, and to
- # keep string locs straight between transform_string and scan_string
- self.keepTabs = True
- try:
- for t, s, e in self.scan_string(instring, debug=debug):
- out.append(instring[lastE:s])
- if t:
- if isinstance(t, ParseResults):
- out += t.as_list()
- elif isinstance(t, Iterable) and not isinstance(t, str_type):
- out.extend(t)
- else:
- out.append(t)
- lastE = e
- out.append(instring[lastE:])
- out = [o for o in out if o]
- return "".join([str(s) for s in _flatten(out)])
- except ParseBaseException as exc:
- if ParserElement.verbose_stacktrace:
- raise
- else:
- # catch and re-raise exception from here, clears out pyparsing internal stack trace
- raise exc.with_traceback(None)
-
- def search_string(
- self,
- instring: str,
- max_matches: int = _MAX_INT,
- *,
- debug: bool = False,
- maxMatches: int = _MAX_INT,
- ) -> ParseResults:
- """
- Another extension to :class:`scan_string`, simplifying the access to the tokens found
- to match the given parse expression. May be called with optional
- ``max_matches`` argument, to clip searching after 'n' matches are found.
-
- Example::
-
- # a capitalized word starts with an uppercase letter, followed by zero or more lowercase letters
- cap_word = Word(alphas.upper(), alphas.lower())
-
- print(cap_word.search_string("More than Iron, more than Lead, more than Gold I need Electricity"))
-
- # the sum() builtin can be used to merge results into a single ParseResults object
- print(sum(cap_word.search_string("More than Iron, more than Lead, more than Gold I need Electricity")))
-
- prints::
-
- [['More'], ['Iron'], ['Lead'], ['Gold'], ['I'], ['Electricity']]
- ['More', 'Iron', 'Lead', 'Gold', 'I', 'Electricity']
- """
- maxMatches = min(maxMatches, max_matches)
- try:
- return ParseResults(
- [t for t, s, e in self.scan_string(instring, maxMatches, debug=debug)]
- )
- except ParseBaseException as exc:
- if ParserElement.verbose_stacktrace:
- raise
- else:
- # catch and re-raise exception from here, clears out pyparsing internal stack trace
- raise exc.with_traceback(None)
-
- def split(
- self,
- instring: str,
- maxsplit: int = _MAX_INT,
- include_separators: bool = False,
- *,
- includeSeparators=False,
- ) -> Generator[str, None, None]:
- """
- Generator method to split a string using the given expression as a separator.
- May be called with optional ``maxsplit`` argument, to limit the number of splits;
- and the optional ``include_separators`` argument (default= ``False``), if the separating
- matching text should be included in the split results.
-
- Example::
-
- punc = one_of(list(".,;:/-!?"))
- print(list(punc.split("This, this?, this sentence, is badly punctuated!")))
-
- prints::
-
- ['This', ' this', '', ' this sentence', ' is badly punctuated', '']
- """
- includeSeparators = includeSeparators or include_separators
- last = 0
- for t, s, e in self.scan_string(instring, max_matches=maxsplit):
- yield instring[last:s]
- if includeSeparators:
- yield t[0]
- last = e
- yield instring[last:]
-
- def __add__(self, other) -> "ParserElement":
- """
- Implementation of ``+`` operator - returns :class:`And`. Adding strings to a :class:`ParserElement`
- converts them to :class:`Literal`s by default.
-
- Example::
-
- greet = Word(alphas) + "," + Word(alphas) + "!"
- hello = "Hello, World!"
- print(hello, "->", greet.parse_string(hello))
-
- prints::
-
- Hello, World! -> ['Hello', ',', 'World', '!']
-
- ``...`` may be used as a parse expression as a short form of :class:`SkipTo`.
-
- Literal('start') + ... + Literal('end')
-
- is equivalent to:
-
- Literal('start') + SkipTo('end')("_skipped*") + Literal('end')
-
- Note that the skipped text is returned with '_skipped' as a results name,
- and to support having multiple skips in the same parser, the value returned is
- a list of all skipped text.
- """
- if other is Ellipsis:
- return _PendingSkip(self)
-
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return And([self, other])
-
- def __radd__(self, other) -> "ParserElement":
- """
- Implementation of ``+`` operator when left operand is not a :class:`ParserElement`
- """
- if other is Ellipsis:
- return SkipTo(self)("_skipped*") + self
-
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return other + self
-
- def __sub__(self, other) -> "ParserElement":
- """
- Implementation of ``-`` operator, returns :class:`And` with error stop
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return self + And._ErrorStop() + other
-
- def __rsub__(self, other) -> "ParserElement":
- """
- Implementation of ``-`` operator when left operand is not a :class:`ParserElement`
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return other - self
-
- def __mul__(self, other) -> "ParserElement":
- """
- Implementation of ``*`` operator, allows use of ``expr * 3`` in place of
- ``expr + expr + expr``. Expressions may also be multiplied by a 2-integer
- tuple, similar to ``{min, max}`` multipliers in regular expressions. Tuples
- may also include ``None`` as in:
- - ``expr*(n, None)`` or ``expr*(n, )`` is equivalent
- to ``expr*n + ZeroOrMore(expr)``
- (read as "at least n instances of ``expr``")
- - ``expr*(None, n)`` is equivalent to ``expr*(0, n)``
- (read as "0 to n instances of ``expr``")
- - ``expr*(None, None)`` is equivalent to ``ZeroOrMore(expr)``
- - ``expr*(1, None)`` is equivalent to ``OneOrMore(expr)``
-
- Note that ``expr*(None, n)`` does not raise an exception if
- more than n exprs exist in the input stream; that is,
- ``expr*(None, n)`` does not enforce a maximum number of expr
- occurrences. If this behavior is desired, then write
- ``expr*(None, n) + ~expr``
- """
- if other is Ellipsis:
- other = (0, None)
- elif isinstance(other, tuple) and other[:1] == (Ellipsis,):
- other = ((0,) + other[1:] + (None,))[:2]
-
- if isinstance(other, int):
- minElements, optElements = other, 0
- elif isinstance(other, tuple):
- other = tuple(o if o is not Ellipsis else None for o in other)
- other = (other + (None, None))[:2]
- if other[0] is None:
- other = (0, other[1])
- if isinstance(other[0], int) and other[1] is None:
- if other[0] == 0:
- return ZeroOrMore(self)
- if other[0] == 1:
- return OneOrMore(self)
- else:
- return self * other[0] + ZeroOrMore(self)
- elif isinstance(other[0], int) and isinstance(other[1], int):
- minElements, optElements = other
- optElements -= minElements
- else:
- raise TypeError(
- "cannot multiply ParserElement and ({}) objects".format(
- ",".join(type(item).__name__ for item in other)
- )
- )
- else:
- raise TypeError(
- "cannot multiply ParserElement and {} objects".format(
- type(other).__name__
- )
- )
-
- if minElements < 0:
- raise ValueError("cannot multiply ParserElement by negative value")
- if optElements < 0:
- raise ValueError(
- "second tuple value must be greater or equal to first tuple value"
- )
- if minElements == optElements == 0:
- return And([])
-
- if optElements:
-
- def makeOptionalList(n):
- if n > 1:
- return Opt(self + makeOptionalList(n - 1))
- else:
- return Opt(self)
-
- if minElements:
- if minElements == 1:
- ret = self + makeOptionalList(optElements)
- else:
- ret = And([self] * minElements) + makeOptionalList(optElements)
- else:
- ret = makeOptionalList(optElements)
- else:
- if minElements == 1:
- ret = self
- else:
- ret = And([self] * minElements)
- return ret
-
- def __rmul__(self, other) -> "ParserElement":
- return self.__mul__(other)
-
- def __or__(self, other) -> "ParserElement":
- """
- Implementation of ``|`` operator - returns :class:`MatchFirst`
- """
- if other is Ellipsis:
- return _PendingSkip(self, must_skip=True)
-
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return MatchFirst([self, other])
-
- def __ror__(self, other) -> "ParserElement":
- """
- Implementation of ``|`` operator when left operand is not a :class:`ParserElement`
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return other | self
-
- def __xor__(self, other) -> "ParserElement":
- """
- Implementation of ``^`` operator - returns :class:`Or`
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return Or([self, other])
-
- def __rxor__(self, other) -> "ParserElement":
- """
- Implementation of ``^`` operator when left operand is not a :class:`ParserElement`
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return other ^ self
-
- def __and__(self, other) -> "ParserElement":
- """
- Implementation of ``&`` operator - returns :class:`Each`
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return Each([self, other])
-
- def __rand__(self, other) -> "ParserElement":
- """
- Implementation of ``&`` operator when left operand is not a :class:`ParserElement`
- """
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- if not isinstance(other, ParserElement):
- raise TypeError(
- "Cannot combine element of type {} with ParserElement".format(
- type(other).__name__
- )
- )
- return other & self
-
- def __invert__(self) -> "ParserElement":
- """
- Implementation of ``~`` operator - returns :class:`NotAny`
- """
- return NotAny(self)
-
- # disable __iter__ to override legacy use of sequential access to __getitem__ to
- # iterate over a sequence
- __iter__ = None
-
- def __getitem__(self, key):
- """
- use ``[]`` indexing notation as a short form for expression repetition:
-
- - ``expr[n]`` is equivalent to ``expr*n``
- - ``expr[m, n]`` is equivalent to ``expr*(m, n)``
- - ``expr[n, ...]`` or ``expr[n,]`` is equivalent
- to ``expr*n + ZeroOrMore(expr)``
- (read as "at least n instances of ``expr``")
- - ``expr[..., n]`` is equivalent to ``expr*(0, n)``
- (read as "0 to n instances of ``expr``")
- - ``expr[...]`` and ``expr[0, ...]`` are equivalent to ``ZeroOrMore(expr)``
- - ``expr[1, ...]`` is equivalent to ``OneOrMore(expr)``
-
- ``None`` may be used in place of ``...``.
-
- Note that ``expr[..., n]`` and ``expr[m, n]``do not raise an exception
- if more than ``n`` ``expr``s exist in the input stream. If this behavior is
- desired, then write ``expr[..., n] + ~expr``.
- """
-
- # convert single arg keys to tuples
- try:
- if isinstance(key, str_type):
- key = (key,)
- iter(key)
- except TypeError:
- key = (key, key)
-
- if len(key) > 2:
- raise TypeError(
- "only 1 or 2 index arguments supported ({}{})".format(
- key[:5], "... [{}]".format(len(key)) if len(key) > 5 else ""
- )
- )
-
- # clip to 2 elements
- ret = self * tuple(key[:2])
- return ret
-
- def __call__(self, name: str = None) -> "ParserElement":
- """
- Shortcut for :class:`set_results_name`, with ``list_all_matches=False``.
-
- If ``name`` is given with a trailing ``'*'`` character, then ``list_all_matches`` will be
- passed as ``True``.
-
- If ``name` is omitted, same as calling :class:`copy`.
-
- Example::
-
- # these are equivalent
- userdata = Word(alphas).set_results_name("name") + Word(nums + "-").set_results_name("socsecno")
- userdata = Word(alphas)("name") + Word(nums + "-")("socsecno")
- """
- if name is not None:
- return self._setResultsName(name)
- else:
- return self.copy()
-
- def suppress(self) -> "ParserElement":
- """
- Suppresses the output of this :class:`ParserElement`; useful to keep punctuation from
- cluttering up returned output.
- """
- return Suppress(self)
-
- def ignore_whitespace(self, recursive: bool = True) -> "ParserElement":
- """
- Enables the skipping of whitespace before matching the characters in the
- :class:`ParserElement`'s defined pattern.
-
- :param recursive: If ``True`` (the default), also enable whitespace skipping in child elements (if any)
- """
- self.skipWhitespace = True
- return self
-
- def leave_whitespace(self, recursive: bool = True) -> "ParserElement":
- """
- Disables the skipping of whitespace before matching the characters in the
- :class:`ParserElement`'s defined pattern. This is normally only used internally by
- the pyparsing module, but may be needed in some whitespace-sensitive grammars.
-
- :param recursive: If true (the default), also disable whitespace skipping in child elements (if any)
- """
- self.skipWhitespace = False
- return self
-
- def set_whitespace_chars(
- self, chars: Union[Set[str], str], copy_defaults: bool = False
- ) -> "ParserElement":
- """
- Overrides the default whitespace chars
- """
- self.skipWhitespace = True
- self.whiteChars = set(chars)
- self.copyDefaultWhiteChars = copy_defaults
- return self
-
- def parse_with_tabs(self) -> "ParserElement":
- """
- Overrides default behavior to expand ```` s to spaces before parsing the input string.
- Must be called before ``parse_string`` when the input grammar contains elements that
- match ```` characters.
- """
- self.keepTabs = True
- return self
-
- def ignore(self, other: "ParserElement") -> "ParserElement":
- """
- Define expression to be ignored (e.g., comments) while doing pattern
- matching; may be called repeatedly, to define multiple comment or other
- ignorable patterns.
-
- Example::
-
- patt = Word(alphas)[1, ...]
- patt.parse_string('ablaj /* comment */ lskjd')
- # -> ['ablaj']
-
- patt.ignore(c_style_comment)
- patt.parse_string('ablaj /* comment */ lskjd')
- # -> ['ablaj', 'lskjd']
- """
- import typing
-
- if isinstance(other, str_type):
- other = Suppress(other)
-
- if isinstance(other, Suppress):
- if other not in self.ignoreExprs:
- self.ignoreExprs.append(other)
- else:
- self.ignoreExprs.append(Suppress(other.copy()))
- return self
-
- def set_debug_actions(
- self,
- start_action: DebugStartAction,
- success_action: DebugSuccessAction,
- exception_action: DebugExceptionAction,
- ) -> "ParserElement":
- """
- Customize display of debugging messages while doing pattern matching:
-
- - ``start_action`` - method to be called when an expression is about to be parsed;
- should have the signature ``fn(input_string: str, location: int, expression: ParserElement, cache_hit: bool)``
-
- - ``success_action`` - method to be called when an expression has successfully parsed;
- should have the signature ``fn(input_string: str, start_location: int, end_location: int, expression: ParserELement, parsed_tokens: ParseResults, cache_hit: bool)``
-
- - ``exception_action`` - method to be called when expression fails to parse;
- should have the signature ``fn(input_string: str, location: int, expression: ParserElement, exception: Exception, cache_hit: bool)``
- """
- self.debugActions = self.DebugActions(
- start_action or _default_start_debug_action,
- success_action or _default_success_debug_action,
- exception_action or _default_exception_debug_action,
- )
- self.debug = True
- return self
-
- def set_debug(self, flag: bool = True) -> "ParserElement":
- """
- Enable display of debugging messages while doing pattern matching.
- Set ``flag`` to ``True`` to enable, ``False`` to disable.
-
- Example::
-
- wd = Word(alphas).set_name("alphaword")
- integer = Word(nums).set_name("numword")
- term = wd | integer
-
- # turn on debugging for wd
- wd.set_debug()
-
- term[1, ...].parse_string("abc 123 xyz 890")
-
- prints::
-
- Match alphaword at loc 0(1,1)
- Matched alphaword -> ['abc']
- Match alphaword at loc 3(1,4)
- Exception raised:Expected alphaword (at char 4), (line:1, col:5)
- Match alphaword at loc 7(1,8)
- Matched alphaword -> ['xyz']
- Match alphaword at loc 11(1,12)
- Exception raised:Expected alphaword (at char 12), (line:1, col:13)
- Match alphaword at loc 15(1,16)
- Exception raised:Expected alphaword (at char 15), (line:1, col:16)
-
- The output shown is that produced by the default debug actions - custom debug actions can be
- specified using :class:`set_debug_actions`. Prior to attempting
- to match the ``wd`` expression, the debugging message ``"Match at loc (,`_.
- If the given regex contains named groups (defined using ``(?P...)``),
- these will be preserved as named :class:`ParseResults`.
-
- If instead of the Python stdlib ``re`` module you wish to use a different RE module
- (such as the ``regex`` module), you can do so by building your ``Regex`` object with
- a compiled RE that was compiled using ``regex``.
-
- Example::
-
- realnum = Regex(r"[+-]?\d+\.\d*")
- # ref: https://stackoverflow.com/questions/267399/how-do-you-match-only-valid-roman-numerals-with-a-regular-expression
- roman = Regex(r"M{0,4}(CM|CD|D?{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})")
-
- # named fields in a regex will be returned as named results
- date = Regex(r'(?P\d{4})-(?P\d\d?)-(?P\d\d?)')
-
- # the Regex class will accept re's compiled using the regex module
- import regex
- parser = pp.Regex(regex.compile(r'[0-9]'))
- """
-
- def __init__(
- self,
- pattern: Any,
- flags: Union[re.RegexFlag, int] = 0,
- as_group_list: bool = False,
- as_match: bool = False,
- *,
- asGroupList: bool = False,
- asMatch: bool = False,
- ):
- """The parameters ``pattern`` and ``flags`` are passed
- to the ``re.compile()`` function as-is. See the Python
- `re module `_ module for an
- explanation of the acceptable patterns and flags.
- """
- super().__init__()
- asGroupList = asGroupList or as_group_list
- asMatch = asMatch or as_match
-
- if isinstance(pattern, str_type):
- if not pattern:
- raise ValueError("null string passed to Regex; use Empty() instead")
-
- self._re = None
- self.reString = self.pattern = pattern
- self.flags = flags
-
- elif hasattr(pattern, "pattern") and hasattr(pattern, "match"):
- self._re = pattern
- self.pattern = self.reString = pattern.pattern
- self.flags = flags
-
- else:
- raise TypeError(
- "Regex may only be constructed with a string or a compiled RE object"
- )
-
- self.errmsg = "Expected " + self.name
- self.mayIndexError = False
- self.asGroupList = asGroupList
- self.asMatch = asMatch
- if self.asGroupList:
- self.parseImpl = self.parseImplAsGroupList
- if self.asMatch:
- self.parseImpl = self.parseImplAsMatch
-
- @cached_property
- def re(self):
- if self._re:
- return self._re
- else:
- try:
- return re.compile(self.pattern, self.flags)
- except re.error:
- raise ValueError(
- "invalid pattern ({!r}) passed to Regex".format(self.pattern)
- )
-
- @cached_property
- def re_match(self):
- return self.re.match
-
- @cached_property
- def mayReturnEmpty(self):
- return self.re_match("") is not None
-
- def _generateDefaultName(self):
- return "Re:({})".format(repr(self.pattern).replace("\\\\", "\\"))
-
- def parseImpl(self, instring, loc, doActions=True):
- result = self.re_match(instring, loc)
- if not result:
- raise ParseException(instring, loc, self.errmsg, self)
-
- loc = result.end()
- ret = ParseResults(result.group())
- d = result.groupdict()
- if d:
- for k, v in d.items():
- ret[k] = v
- return loc, ret
-
- def parseImplAsGroupList(self, instring, loc, doActions=True):
- result = self.re_match(instring, loc)
- if not result:
- raise ParseException(instring, loc, self.errmsg, self)
-
- loc = result.end()
- ret = result.groups()
- return loc, ret
-
- def parseImplAsMatch(self, instring, loc, doActions=True):
- result = self.re_match(instring, loc)
- if not result:
- raise ParseException(instring, loc, self.errmsg, self)
-
- loc = result.end()
- ret = result
- return loc, ret
-
- def sub(self, repl: str) -> ParserElement:
- r"""
- Return :class:`Regex` with an attached parse action to transform the parsed
- result as if called using `re.sub(expr, repl, string) `_.
-
- Example::
-
- make_html = Regex(r"(\w+):(.*?):").sub(r"<\1>\2")
- print(make_html.transform_string("h1:main title:"))
- # prints "main title "
- """
- if self.asGroupList:
- raise TypeError("cannot use sub() with Regex(asGroupList=True)")
-
- if self.asMatch and callable(repl):
- raise TypeError("cannot use sub() with a callable with Regex(asMatch=True)")
-
- if self.asMatch:
-
- def pa(tokens):
- return tokens[0].expand(repl)
-
- else:
-
- def pa(tokens):
- return self.re.sub(repl, tokens[0])
-
- return self.add_parse_action(pa)
-
-
-class QuotedString(Token):
- r"""
- Token for matching strings that are delimited by quoting characters.
-
- Defined with the following parameters:
-
- - ``quote_char`` - string of one or more characters defining the
- quote delimiting string
- - ``esc_char`` - character to re_escape quotes, typically backslash
- (default= ``None``)
- - ``esc_quote`` - special quote sequence to re_escape an embedded quote
- string (such as SQL's ``""`` to re_escape an embedded ``"``)
- (default= ``None``)
- - ``multiline`` - boolean indicating whether quotes can span
- multiple lines (default= ``False``)
- - ``unquote_results`` - boolean indicating whether the matched text
- should be unquoted (default= ``True``)
- - ``end_quote_char`` - string of one or more characters defining the
- end of the quote delimited string (default= ``None`` => same as
- quote_char)
- - ``convert_whitespace_escapes`` - convert escaped whitespace
- (``'\t'``, ``'\n'``, etc.) to actual whitespace
- (default= ``True``)
-
- Example::
-
- qs = QuotedString('"')
- print(qs.search_string('lsjdf "This is the quote" sldjf'))
- complex_qs = QuotedString('{{', end_quote_char='}}')
- print(complex_qs.search_string('lsjdf {{This is the "quote"}} sldjf'))
- sql_qs = QuotedString('"', esc_quote='""')
- print(sql_qs.search_string('lsjdf "This is the quote with ""embedded"" quotes" sldjf'))
-
- prints::
-
- [['This is the quote']]
- [['This is the "quote"']]
- [['This is the quote with "embedded" quotes']]
- """
- ws_map = ((r"\t", "\t"), (r"\n", "\n"), (r"\f", "\f"), (r"\r", "\r"))
-
- def __init__(
- self,
- quote_char: str = "",
- esc_char: typing.Optional[str] = None,
- esc_quote: typing.Optional[str] = None,
- multiline: bool = False,
- unquote_results: bool = True,
- end_quote_char: typing.Optional[str] = None,
- convert_whitespace_escapes: bool = True,
- *,
- quoteChar: str = "",
- escChar: typing.Optional[str] = None,
- escQuote: typing.Optional[str] = None,
- unquoteResults: bool = True,
- endQuoteChar: typing.Optional[str] = None,
- convertWhitespaceEscapes: bool = True,
- ):
- super().__init__()
- escChar = escChar or esc_char
- escQuote = escQuote or esc_quote
- unquoteResults = unquoteResults and unquote_results
- endQuoteChar = endQuoteChar or end_quote_char
- convertWhitespaceEscapes = (
- convertWhitespaceEscapes and convert_whitespace_escapes
- )
- quote_char = quoteChar or quote_char
-
- # remove white space from quote chars - wont work anyway
- quote_char = quote_char.strip()
- if not quote_char:
- raise ValueError("quote_char cannot be the empty string")
-
- if endQuoteChar is None:
- endQuoteChar = quote_char
- else:
- endQuoteChar = endQuoteChar.strip()
- if not endQuoteChar:
- raise ValueError("endQuoteChar cannot be the empty string")
-
- self.quoteChar = quote_char
- self.quoteCharLen = len(quote_char)
- self.firstQuoteChar = quote_char[0]
- self.endQuoteChar = endQuoteChar
- self.endQuoteCharLen = len(endQuoteChar)
- self.escChar = escChar
- self.escQuote = escQuote
- self.unquoteResults = unquoteResults
- self.convertWhitespaceEscapes = convertWhitespaceEscapes
-
- sep = ""
- inner_pattern = ""
-
- if escQuote:
- inner_pattern += r"{}(?:{})".format(sep, re.escape(escQuote))
- sep = "|"
-
- if escChar:
- inner_pattern += r"{}(?:{}.)".format(sep, re.escape(escChar))
- sep = "|"
- self.escCharReplacePattern = re.escape(self.escChar) + "(.)"
-
- if len(self.endQuoteChar) > 1:
- inner_pattern += (
- "{}(?:".format(sep)
- + "|".join(
- "(?:{}(?!{}))".format(
- re.escape(self.endQuoteChar[:i]),
- re.escape(self.endQuoteChar[i:]),
- )
- for i in range(len(self.endQuoteChar) - 1, 0, -1)
- )
- + ")"
- )
- sep = "|"
-
- if multiline:
- self.flags = re.MULTILINE | re.DOTALL
- inner_pattern += r"{}(?:[^{}{}])".format(
- sep,
- _escape_regex_range_chars(self.endQuoteChar[0]),
- (_escape_regex_range_chars(escChar) if escChar is not None else ""),
- )
- else:
- self.flags = 0
- inner_pattern += r"{}(?:[^{}\n\r{}])".format(
- sep,
- _escape_regex_range_chars(self.endQuoteChar[0]),
- (_escape_regex_range_chars(escChar) if escChar is not None else ""),
- )
-
- self.pattern = "".join(
- [
- re.escape(self.quoteChar),
- "(?:",
- inner_pattern,
- ")*",
- re.escape(self.endQuoteChar),
- ]
- )
-
- try:
- self.re = re.compile(self.pattern, self.flags)
- self.reString = self.pattern
- self.re_match = self.re.match
- except re.error:
- raise ValueError(
- "invalid pattern {!r} passed to Regex".format(self.pattern)
- )
-
- self.errmsg = "Expected " + self.name
- self.mayIndexError = False
- self.mayReturnEmpty = True
-
- def _generateDefaultName(self):
- if self.quoteChar == self.endQuoteChar and isinstance(self.quoteChar, str_type):
- return "string enclosed in {!r}".format(self.quoteChar)
-
- return "quoted string, starting with {} ending with {}".format(
- self.quoteChar, self.endQuoteChar
- )
-
- def parseImpl(self, instring, loc, doActions=True):
- result = (
- instring[loc] == self.firstQuoteChar
- and self.re_match(instring, loc)
- or None
- )
- if not result:
- raise ParseException(instring, loc, self.errmsg, self)
-
- loc = result.end()
- ret = result.group()
-
- if self.unquoteResults:
-
- # strip off quotes
- ret = ret[self.quoteCharLen : -self.endQuoteCharLen]
-
- if isinstance(ret, str_type):
- # replace escaped whitespace
- if "\\" in ret and self.convertWhitespaceEscapes:
- for wslit, wschar in self.ws_map:
- ret = ret.replace(wslit, wschar)
-
- # replace escaped characters
- if self.escChar:
- ret = re.sub(self.escCharReplacePattern, r"\g<1>", ret)
-
- # replace escaped quotes
- if self.escQuote:
- ret = ret.replace(self.escQuote, self.endQuoteChar)
-
- return loc, ret
-
-
-class CharsNotIn(Token):
- """Token for matching words composed of characters *not* in a given
- set (will include whitespace in matched characters if not listed in
- the provided exclusion set - see example). Defined with string
- containing all disallowed characters, and an optional minimum,
- maximum, and/or exact length. The default value for ``min`` is
- 1 (a minimum value < 1 is not valid); the default values for
- ``max`` and ``exact`` are 0, meaning no maximum or exact
- length restriction.
-
- Example::
-
- # define a comma-separated-value as anything that is not a ','
- csv_value = CharsNotIn(',')
- print(delimited_list(csv_value).parse_string("dkls,lsdkjf,s12 34,@!#,213"))
-
- prints::
-
- ['dkls', 'lsdkjf', 's12 34', '@!#', '213']
- """
-
- def __init__(
- self,
- not_chars: str = "",
- min: int = 1,
- max: int = 0,
- exact: int = 0,
- *,
- notChars: str = "",
- ):
- super().__init__()
- self.skipWhitespace = False
- self.notChars = not_chars or notChars
- self.notCharsSet = set(self.notChars)
-
- if min < 1:
- raise ValueError(
- "cannot specify a minimum length < 1; use "
- "Opt(CharsNotIn()) if zero-length char group is permitted"
- )
-
- self.minLen = min
-
- if max > 0:
- self.maxLen = max
- else:
- self.maxLen = _MAX_INT
-
- if exact > 0:
- self.maxLen = exact
- self.minLen = exact
-
- self.errmsg = "Expected " + self.name
- self.mayReturnEmpty = self.minLen == 0
- self.mayIndexError = False
-
- def _generateDefaultName(self):
- not_chars_str = _collapse_string_to_ranges(self.notChars)
- if len(not_chars_str) > 16:
- return "!W:({}...)".format(self.notChars[: 16 - 3])
- else:
- return "!W:({})".format(self.notChars)
-
- def parseImpl(self, instring, loc, doActions=True):
- notchars = self.notCharsSet
- if instring[loc] in notchars:
- raise ParseException(instring, loc, self.errmsg, self)
-
- start = loc
- loc += 1
- maxlen = min(start + self.maxLen, len(instring))
- while loc < maxlen and instring[loc] not in notchars:
- loc += 1
-
- if loc - start < self.minLen:
- raise ParseException(instring, loc, self.errmsg, self)
-
- return loc, instring[start:loc]
-
-
-class White(Token):
- """Special matching class for matching whitespace. Normally,
- whitespace is ignored by pyparsing grammars. This class is included
- when some whitespace structures are significant. Define with
- a string containing the whitespace characters to be matched; default
- is ``" \\t\\r\\n"``. Also takes optional ``min``,
- ``max``, and ``exact`` arguments, as defined for the
- :class:`Word` class.
- """
-
- whiteStrs = {
- " ": "",
- "\t": "",
- "\n": "",
- "\r": "",
- "\f": "",
- "\u00A0": "",
- "\u1680": "",
- "\u180E": "",
- "\u2000": "",
- "\u2001": "",
- "\u2002": "",
- "\u2003": "",
- "\u2004": "",
- "\u2005": "",
- "\u2006": "",
- "\u2007": "",
- "\u2008": "",
- "\u2009": "",
- "\u200A": "",
- "\u200B": "",
- "\u202F": "",
- "\u205F": "",
- "\u3000": "",
- }
-
- def __init__(self, ws: str = " \t\r\n", min: int = 1, max: int = 0, exact: int = 0):
- super().__init__()
- self.matchWhite = ws
- self.set_whitespace_chars(
- "".join(c for c in self.whiteStrs if c not in self.matchWhite),
- copy_defaults=True,
- )
- # self.leave_whitespace()
- self.mayReturnEmpty = True
- self.errmsg = "Expected " + self.name
-
- self.minLen = min
-
- if max > 0:
- self.maxLen = max
- else:
- self.maxLen = _MAX_INT
-
- if exact > 0:
- self.maxLen = exact
- self.minLen = exact
-
- def _generateDefaultName(self):
- return "".join(White.whiteStrs[c] for c in self.matchWhite)
-
- def parseImpl(self, instring, loc, doActions=True):
- if instring[loc] not in self.matchWhite:
- raise ParseException(instring, loc, self.errmsg, self)
- start = loc
- loc += 1
- maxloc = start + self.maxLen
- maxloc = min(maxloc, len(instring))
- while loc < maxloc and instring[loc] in self.matchWhite:
- loc += 1
-
- if loc - start < self.minLen:
- raise ParseException(instring, loc, self.errmsg, self)
-
- return loc, instring[start:loc]
-
-
-class PositionToken(Token):
- def __init__(self):
- super().__init__()
- self.mayReturnEmpty = True
- self.mayIndexError = False
-
-
-class GoToColumn(PositionToken):
- """Token to advance to a specific column of input text; useful for
- tabular report scraping.
- """
-
- def __init__(self, colno: int):
- super().__init__()
- self.col = colno
-
- def preParse(self, instring, loc):
- if col(loc, instring) != self.col:
- instrlen = len(instring)
- if self.ignoreExprs:
- loc = self._skipIgnorables(instring, loc)
- while (
- loc < instrlen
- and instring[loc].isspace()
- and col(loc, instring) != self.col
- ):
- loc += 1
- return loc
-
- def parseImpl(self, instring, loc, doActions=True):
- thiscol = col(loc, instring)
- if thiscol > self.col:
- raise ParseException(instring, loc, "Text not in expected column", self)
- newloc = loc + self.col - thiscol
- ret = instring[loc:newloc]
- return newloc, ret
-
-
-class LineStart(PositionToken):
- r"""Matches if current position is at the beginning of a line within
- the parse string
-
- Example::
-
- test = '''\
- AAA this line
- AAA and this line
- AAA but not this one
- B AAA and definitely not this one
- '''
-
- for t in (LineStart() + 'AAA' + restOfLine).search_string(test):
- print(t)
-
- prints::
-
- ['AAA', ' this line']
- ['AAA', ' and this line']
-
- """
-
- def __init__(self):
- super().__init__()
- self.leave_whitespace()
- self.orig_whiteChars = set() | self.whiteChars
- self.whiteChars.discard("\n")
- self.skipper = Empty().set_whitespace_chars(self.whiteChars)
- self.errmsg = "Expected start of line"
-
- def preParse(self, instring, loc):
- if loc == 0:
- return loc
- else:
- ret = self.skipper.preParse(instring, loc)
- if "\n" in self.orig_whiteChars:
- while instring[ret : ret + 1] == "\n":
- ret = self.skipper.preParse(instring, ret + 1)
- return ret
-
- def parseImpl(self, instring, loc, doActions=True):
- if col(loc, instring) == 1:
- return loc, []
- raise ParseException(instring, loc, self.errmsg, self)
-
-
-class LineEnd(PositionToken):
- """Matches if current position is at the end of a line within the
- parse string
- """
-
- def __init__(self):
- super().__init__()
- self.whiteChars.discard("\n")
- self.set_whitespace_chars(self.whiteChars, copy_defaults=False)
- self.errmsg = "Expected end of line"
-
- def parseImpl(self, instring, loc, doActions=True):
- if loc < len(instring):
- if instring[loc] == "\n":
- return loc + 1, "\n"
- else:
- raise ParseException(instring, loc, self.errmsg, self)
- elif loc == len(instring):
- return loc + 1, []
- else:
- raise ParseException(instring, loc, self.errmsg, self)
-
-
-class StringStart(PositionToken):
- """Matches if current position is at the beginning of the parse
- string
- """
-
- def __init__(self):
- super().__init__()
- self.errmsg = "Expected start of text"
-
- def parseImpl(self, instring, loc, doActions=True):
- if loc != 0:
- # see if entire string up to here is just whitespace and ignoreables
- if loc != self.preParse(instring, 0):
- raise ParseException(instring, loc, self.errmsg, self)
- return loc, []
-
-
-class StringEnd(PositionToken):
- """
- Matches if current position is at the end of the parse string
- """
-
- def __init__(self):
- super().__init__()
- self.errmsg = "Expected end of text"
-
- def parseImpl(self, instring, loc, doActions=True):
- if loc < len(instring):
- raise ParseException(instring, loc, self.errmsg, self)
- elif loc == len(instring):
- return loc + 1, []
- elif loc > len(instring):
- return loc, []
- else:
- raise ParseException(instring, loc, self.errmsg, self)
-
-
-class WordStart(PositionToken):
- """Matches if the current position is at the beginning of a
- :class:`Word`, and is not preceded by any character in a given
- set of ``word_chars`` (default= ``printables``). To emulate the
- ``\b`` behavior of regular expressions, use
- ``WordStart(alphanums)``. ``WordStart`` will also match at
- the beginning of the string being parsed, or at the beginning of
- a line.
- """
-
- def __init__(self, word_chars: str = printables, *, wordChars: str = printables):
- wordChars = word_chars if wordChars == printables else wordChars
- super().__init__()
- self.wordChars = set(wordChars)
- self.errmsg = "Not at the start of a word"
-
- def parseImpl(self, instring, loc, doActions=True):
- if loc != 0:
- if (
- instring[loc - 1] in self.wordChars
- or instring[loc] not in self.wordChars
- ):
- raise ParseException(instring, loc, self.errmsg, self)
- return loc, []
-
-
-class WordEnd(PositionToken):
- """Matches if the current position is at the end of a :class:`Word`,
- and is not followed by any character in a given set of ``word_chars``
- (default= ``printables``). To emulate the ``\b`` behavior of
- regular expressions, use ``WordEnd(alphanums)``. ``WordEnd``
- will also match at the end of the string being parsed, or at the end
- of a line.
- """
-
- def __init__(self, word_chars: str = printables, *, wordChars: str = printables):
- wordChars = word_chars if wordChars == printables else wordChars
- super().__init__()
- self.wordChars = set(wordChars)
- self.skipWhitespace = False
- self.errmsg = "Not at the end of a word"
-
- def parseImpl(self, instring, loc, doActions=True):
- instrlen = len(instring)
- if instrlen > 0 and loc < instrlen:
- if (
- instring[loc] in self.wordChars
- or instring[loc - 1] not in self.wordChars
- ):
- raise ParseException(instring, loc, self.errmsg, self)
- return loc, []
-
-
-class ParseExpression(ParserElement):
- """Abstract subclass of ParserElement, for combining and
- post-processing parsed tokens.
- """
-
- def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = False):
- super().__init__(savelist)
- self.exprs: List[ParserElement]
- if isinstance(exprs, _generatorType):
- exprs = list(exprs)
-
- if isinstance(exprs, str_type):
- self.exprs = [self._literalStringClass(exprs)]
- elif isinstance(exprs, ParserElement):
- self.exprs = [exprs]
- elif isinstance(exprs, Iterable):
- exprs = list(exprs)
- # if sequence of strings provided, wrap with Literal
- if any(isinstance(expr, str_type) for expr in exprs):
- exprs = (
- self._literalStringClass(e) if isinstance(e, str_type) else e
- for e in exprs
- )
- self.exprs = list(exprs)
- else:
- try:
- self.exprs = list(exprs)
- except TypeError:
- self.exprs = [exprs]
- self.callPreparse = False
-
- def recurse(self) -> Sequence[ParserElement]:
- return self.exprs[:]
-
- def append(self, other) -> ParserElement:
- self.exprs.append(other)
- self._defaultName = None
- return self
-
- def leave_whitespace(self, recursive: bool = True) -> ParserElement:
- """
- Extends ``leave_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on
- all contained expressions.
- """
- super().leave_whitespace(recursive)
-
- if recursive:
- self.exprs = [e.copy() for e in self.exprs]
- for e in self.exprs:
- e.leave_whitespace(recursive)
- return self
-
- def ignore_whitespace(self, recursive: bool = True) -> ParserElement:
- """
- Extends ``ignore_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on
- all contained expressions.
- """
- super().ignore_whitespace(recursive)
- if recursive:
- self.exprs = [e.copy() for e in self.exprs]
- for e in self.exprs:
- e.ignore_whitespace(recursive)
- return self
-
- def ignore(self, other) -> ParserElement:
- if isinstance(other, Suppress):
- if other not in self.ignoreExprs:
- super().ignore(other)
- for e in self.exprs:
- e.ignore(self.ignoreExprs[-1])
- else:
- super().ignore(other)
- for e in self.exprs:
- e.ignore(self.ignoreExprs[-1])
- return self
-
- def _generateDefaultName(self):
- return "{}:({})".format(self.__class__.__name__, str(self.exprs))
-
- def streamline(self) -> ParserElement:
- if self.streamlined:
- return self
-
- super().streamline()
-
- for e in self.exprs:
- e.streamline()
-
- # collapse nested :class:`And`'s of the form ``And(And(And(a, b), c), d)`` to ``And(a, b, c, d)``
- # but only if there are no parse actions or resultsNames on the nested And's
- # (likewise for :class:`Or`'s and :class:`MatchFirst`'s)
- if len(self.exprs) == 2:
- other = self.exprs[0]
- if (
- isinstance(other, self.__class__)
- and not other.parseAction
- and other.resultsName is None
- and not other.debug
- ):
- self.exprs = other.exprs[:] + [self.exprs[1]]
- self._defaultName = None
- self.mayReturnEmpty |= other.mayReturnEmpty
- self.mayIndexError |= other.mayIndexError
-
- other = self.exprs[-1]
- if (
- isinstance(other, self.__class__)
- and not other.parseAction
- and other.resultsName is None
- and not other.debug
- ):
- self.exprs = self.exprs[:-1] + other.exprs[:]
- self._defaultName = None
- self.mayReturnEmpty |= other.mayReturnEmpty
- self.mayIndexError |= other.mayIndexError
-
- self.errmsg = "Expected " + str(self)
-
- return self
-
- def validate(self, validateTrace=None) -> None:
- tmp = (validateTrace if validateTrace is not None else [])[:] + [self]
- for e in self.exprs:
- e.validate(tmp)
- self._checkRecursion([])
-
- def copy(self) -> ParserElement:
- ret = super().copy()
- ret.exprs = [e.copy() for e in self.exprs]
- return ret
-
- def _setResultsName(self, name, listAllMatches=False):
- if (
- __diag__.warn_ungrouped_named_tokens_in_collection
- and Diagnostics.warn_ungrouped_named_tokens_in_collection
- not in self.suppress_warnings_
- ):
- for e in self.exprs:
- if (
- isinstance(e, ParserElement)
- and e.resultsName
- and Diagnostics.warn_ungrouped_named_tokens_in_collection
- not in e.suppress_warnings_
- ):
- warnings.warn(
- "{}: setting results name {!r} on {} expression "
- "collides with {!r} on contained expression".format(
- "warn_ungrouped_named_tokens_in_collection",
- name,
- type(self).__name__,
- e.resultsName,
- ),
- stacklevel=3,
- )
-
- return super()._setResultsName(name, listAllMatches)
-
- ignoreWhitespace = ignore_whitespace
- leaveWhitespace = leave_whitespace
-
-
-class And(ParseExpression):
- """
- Requires all given :class:`ParseExpression` s to be found in the given order.
- Expressions may be separated by whitespace.
- May be constructed using the ``'+'`` operator.
- May also be constructed using the ``'-'`` operator, which will
- suppress backtracking.
-
- Example::
-
- integer = Word(nums)
- name_expr = Word(alphas)[1, ...]
-
- expr = And([integer("id"), name_expr("name"), integer("age")])
- # more easily written as:
- expr = integer("id") + name_expr("name") + integer("age")
- """
-
- class _ErrorStop(Empty):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.leave_whitespace()
-
- def _generateDefaultName(self):
- return "-"
-
- def __init__(
- self, exprs_arg: typing.Iterable[ParserElement], savelist: bool = True
- ):
- exprs: List[ParserElement] = list(exprs_arg)
- if exprs and Ellipsis in exprs:
- tmp = []
- for i, expr in enumerate(exprs):
- if expr is Ellipsis:
- if i < len(exprs) - 1:
- skipto_arg: ParserElement = (Empty() + exprs[i + 1]).exprs[-1]
- tmp.append(SkipTo(skipto_arg)("_skipped*"))
- else:
- raise Exception(
- "cannot construct And with sequence ending in ..."
- )
- else:
- tmp.append(expr)
- exprs[:] = tmp
- super().__init__(exprs, savelist)
- if self.exprs:
- self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs)
- if not isinstance(self.exprs[0], White):
- self.set_whitespace_chars(
- self.exprs[0].whiteChars,
- copy_defaults=self.exprs[0].copyDefaultWhiteChars,
- )
- self.skipWhitespace = self.exprs[0].skipWhitespace
- else:
- self.skipWhitespace = False
- else:
- self.mayReturnEmpty = True
- self.callPreparse = True
-
- def streamline(self) -> ParserElement:
- # collapse any _PendingSkip's
- if self.exprs:
- if any(
- isinstance(e, ParseExpression)
- and e.exprs
- and isinstance(e.exprs[-1], _PendingSkip)
- for e in self.exprs[:-1]
- ):
- for i, e in enumerate(self.exprs[:-1]):
- if e is None:
- continue
- if (
- isinstance(e, ParseExpression)
- and e.exprs
- and isinstance(e.exprs[-1], _PendingSkip)
- ):
- e.exprs[-1] = e.exprs[-1] + self.exprs[i + 1]
- self.exprs[i + 1] = None
- self.exprs = [e for e in self.exprs if e is not None]
-
- super().streamline()
-
- # link any IndentedBlocks to the prior expression
- for prev, cur in zip(self.exprs, self.exprs[1:]):
- # traverse cur or any first embedded expr of cur looking for an IndentedBlock
- # (but watch out for recursive grammar)
- seen = set()
- while cur:
- if id(cur) in seen:
- break
- seen.add(id(cur))
- if isinstance(cur, IndentedBlock):
- prev.add_parse_action(
- lambda s, l, t, cur_=cur: setattr(
- cur_, "parent_anchor", col(l, s)
- )
- )
- break
- subs = cur.recurse()
- cur = next(iter(subs), None)
-
- self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs)
- return self
-
- def parseImpl(self, instring, loc, doActions=True):
- # pass False as callPreParse arg to _parse for first element, since we already
- # pre-parsed the string as part of our And pre-parsing
- loc, resultlist = self.exprs[0]._parse(
- instring, loc, doActions, callPreParse=False
- )
- errorStop = False
- for e in self.exprs[1:]:
- # if isinstance(e, And._ErrorStop):
- if type(e) is And._ErrorStop:
- errorStop = True
- continue
- if errorStop:
- try:
- loc, exprtokens = e._parse(instring, loc, doActions)
- except ParseSyntaxException:
- raise
- except ParseBaseException as pe:
- pe.__traceback__ = None
- raise ParseSyntaxException._from_exception(pe)
- except IndexError:
- raise ParseSyntaxException(
- instring, len(instring), self.errmsg, self
- )
- else:
- loc, exprtokens = e._parse(instring, loc, doActions)
- if exprtokens or exprtokens.haskeys():
- resultlist += exprtokens
- return loc, resultlist
-
- def __iadd__(self, other):
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- return self.append(other) # And([self, other])
-
- def _checkRecursion(self, parseElementList):
- subRecCheckList = parseElementList[:] + [self]
- for e in self.exprs:
- e._checkRecursion(subRecCheckList)
- if not e.mayReturnEmpty:
- break
-
- def _generateDefaultName(self):
- inner = " ".join(str(e) for e in self.exprs)
- # strip off redundant inner {}'s
- while len(inner) > 1 and inner[0 :: len(inner) - 1] == "{}":
- inner = inner[1:-1]
- return "{" + inner + "}"
-
-
-class Or(ParseExpression):
- """Requires that at least one :class:`ParseExpression` is found. If
- two expressions match, the expression that matches the longest
- string will be used. May be constructed using the ``'^'``
- operator.
-
- Example::
-
- # construct Or using '^' operator
-
- number = Word(nums) ^ Combine(Word(nums) + '.' + Word(nums))
- print(number.search_string("123 3.1416 789"))
-
- prints::
-
- [['123'], ['3.1416'], ['789']]
- """
-
- def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = False):
- super().__init__(exprs, savelist)
- if self.exprs:
- self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs)
- self.skipWhitespace = all(e.skipWhitespace for e in self.exprs)
- else:
- self.mayReturnEmpty = True
-
- def streamline(self) -> ParserElement:
- super().streamline()
- if self.exprs:
- self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs)
- self.saveAsList = any(e.saveAsList for e in self.exprs)
- self.skipWhitespace = all(
- e.skipWhitespace and not isinstance(e, White) for e in self.exprs
- )
- else:
- self.saveAsList = False
- return self
-
- def parseImpl(self, instring, loc, doActions=True):
- maxExcLoc = -1
- maxException = None
- matches = []
- fatals = []
- if all(e.callPreparse for e in self.exprs):
- loc = self.preParse(instring, loc)
- for e in self.exprs:
- try:
- loc2 = e.try_parse(instring, loc, raise_fatal=True)
- except ParseFatalException as pfe:
- pfe.__traceback__ = None
- pfe.parserElement = e
- fatals.append(pfe)
- maxException = None
- maxExcLoc = -1
- except ParseException as err:
- if not fatals:
- err.__traceback__ = None
- if err.loc > maxExcLoc:
- maxException = err
- maxExcLoc = err.loc
- except IndexError:
- if len(instring) > maxExcLoc:
- maxException = ParseException(
- instring, len(instring), e.errmsg, self
- )
- maxExcLoc = len(instring)
- else:
- # save match among all matches, to retry longest to shortest
- matches.append((loc2, e))
-
- if matches:
- # re-evaluate all matches in descending order of length of match, in case attached actions
- # might change whether or how much they match of the input.
- matches.sort(key=itemgetter(0), reverse=True)
-
- if not doActions:
- # no further conditions or parse actions to change the selection of
- # alternative, so the first match will be the best match
- best_expr = matches[0][1]
- return best_expr._parse(instring, loc, doActions)
-
- longest = -1, None
- for loc1, expr1 in matches:
- if loc1 <= longest[0]:
- # already have a longer match than this one will deliver, we are done
- return longest
-
- try:
- loc2, toks = expr1._parse(instring, loc, doActions)
- except ParseException as err:
- err.__traceback__ = None
- if err.loc > maxExcLoc:
- maxException = err
- maxExcLoc = err.loc
- else:
- if loc2 >= loc1:
- return loc2, toks
- # didn't match as much as before
- elif loc2 > longest[0]:
- longest = loc2, toks
-
- if longest != (-1, None):
- return longest
-
- if fatals:
- if len(fatals) > 1:
- fatals.sort(key=lambda e: -e.loc)
- if fatals[0].loc == fatals[1].loc:
- fatals.sort(key=lambda e: (-e.loc, -len(str(e.parserElement))))
- max_fatal = fatals[0]
- raise max_fatal
-
- if maxException is not None:
- maxException.msg = self.errmsg
- raise maxException
- else:
- raise ParseException(
- instring, loc, "no defined alternatives to match", self
- )
-
- def __ixor__(self, other):
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- return self.append(other) # Or([self, other])
-
- def _generateDefaultName(self):
- return "{" + " ^ ".join(str(e) for e in self.exprs) + "}"
-
- def _setResultsName(self, name, listAllMatches=False):
- if (
- __diag__.warn_multiple_tokens_in_named_alternation
- and Diagnostics.warn_multiple_tokens_in_named_alternation
- not in self.suppress_warnings_
- ):
- if any(
- isinstance(e, And)
- and Diagnostics.warn_multiple_tokens_in_named_alternation
- not in e.suppress_warnings_
- for e in self.exprs
- ):
- warnings.warn(
- "{}: setting results name {!r} on {} expression "
- "will return a list of all parsed tokens in an And alternative, "
- "in prior versions only the first token was returned; enclose "
- "contained argument in Group".format(
- "warn_multiple_tokens_in_named_alternation",
- name,
- type(self).__name__,
- ),
- stacklevel=3,
- )
-
- return super()._setResultsName(name, listAllMatches)
-
-
-class MatchFirst(ParseExpression):
- """Requires that at least one :class:`ParseExpression` is found. If
- more than one expression matches, the first one listed is the one that will
- match. May be constructed using the ``'|'`` operator.
-
- Example::
-
- # construct MatchFirst using '|' operator
-
- # watch the order of expressions to match
- number = Word(nums) | Combine(Word(nums) + '.' + Word(nums))
- print(number.search_string("123 3.1416 789")) # Fail! -> [['123'], ['3'], ['1416'], ['789']]
-
- # put more selective expression first
- number = Combine(Word(nums) + '.' + Word(nums)) | Word(nums)
- print(number.search_string("123 3.1416 789")) # Better -> [['123'], ['3.1416'], ['789']]
- """
-
- def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = False):
- super().__init__(exprs, savelist)
- if self.exprs:
- self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs)
- self.skipWhitespace = all(e.skipWhitespace for e in self.exprs)
- else:
- self.mayReturnEmpty = True
-
- def streamline(self) -> ParserElement:
- if self.streamlined:
- return self
-
- super().streamline()
- if self.exprs:
- self.saveAsList = any(e.saveAsList for e in self.exprs)
- self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs)
- self.skipWhitespace = all(
- e.skipWhitespace and not isinstance(e, White) for e in self.exprs
- )
- else:
- self.saveAsList = False
- self.mayReturnEmpty = True
- return self
-
- def parseImpl(self, instring, loc, doActions=True):
- maxExcLoc = -1
- maxException = None
-
- for e in self.exprs:
- try:
- return e._parse(
- instring,
- loc,
- doActions,
- )
- except ParseFatalException as pfe:
- pfe.__traceback__ = None
- pfe.parserElement = e
- raise
- except ParseException as err:
- if err.loc > maxExcLoc:
- maxException = err
- maxExcLoc = err.loc
- except IndexError:
- if len(instring) > maxExcLoc:
- maxException = ParseException(
- instring, len(instring), e.errmsg, self
- )
- maxExcLoc = len(instring)
-
- if maxException is not None:
- maxException.msg = self.errmsg
- raise maxException
- else:
- raise ParseException(
- instring, loc, "no defined alternatives to match", self
- )
-
- def __ior__(self, other):
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- return self.append(other) # MatchFirst([self, other])
-
- def _generateDefaultName(self):
- return "{" + " | ".join(str(e) for e in self.exprs) + "}"
-
- def _setResultsName(self, name, listAllMatches=False):
- if (
- __diag__.warn_multiple_tokens_in_named_alternation
- and Diagnostics.warn_multiple_tokens_in_named_alternation
- not in self.suppress_warnings_
- ):
- if any(
- isinstance(e, And)
- and Diagnostics.warn_multiple_tokens_in_named_alternation
- not in e.suppress_warnings_
- for e in self.exprs
- ):
- warnings.warn(
- "{}: setting results name {!r} on {} expression "
- "will return a list of all parsed tokens in an And alternative, "
- "in prior versions only the first token was returned; enclose "
- "contained argument in Group".format(
- "warn_multiple_tokens_in_named_alternation",
- name,
- type(self).__name__,
- ),
- stacklevel=3,
- )
-
- return super()._setResultsName(name, listAllMatches)
-
-
-class Each(ParseExpression):
- """Requires all given :class:`ParseExpression` s to be found, but in
- any order. Expressions may be separated by whitespace.
-
- May be constructed using the ``'&'`` operator.
-
- Example::
-
- color = one_of("RED ORANGE YELLOW GREEN BLUE PURPLE BLACK WHITE BROWN")
- shape_type = one_of("SQUARE CIRCLE TRIANGLE STAR HEXAGON OCTAGON")
- integer = Word(nums)
- shape_attr = "shape:" + shape_type("shape")
- posn_attr = "posn:" + Group(integer("x") + ',' + integer("y"))("posn")
- color_attr = "color:" + color("color")
- size_attr = "size:" + integer("size")
-
- # use Each (using operator '&') to accept attributes in any order
- # (shape and posn are required, color and size are optional)
- shape_spec = shape_attr & posn_attr & Opt(color_attr) & Opt(size_attr)
-
- shape_spec.run_tests('''
- shape: SQUARE color: BLACK posn: 100, 120
- shape: CIRCLE size: 50 color: BLUE posn: 50,80
- color:GREEN size:20 shape:TRIANGLE posn:20,40
- '''
- )
-
- prints::
-
- shape: SQUARE color: BLACK posn: 100, 120
- ['shape:', 'SQUARE', 'color:', 'BLACK', 'posn:', ['100', ',', '120']]
- - color: BLACK
- - posn: ['100', ',', '120']
- - x: 100
- - y: 120
- - shape: SQUARE
-
-
- shape: CIRCLE size: 50 color: BLUE posn: 50,80
- ['shape:', 'CIRCLE', 'size:', '50', 'color:', 'BLUE', 'posn:', ['50', ',', '80']]
- - color: BLUE
- - posn: ['50', ',', '80']
- - x: 50
- - y: 80
- - shape: CIRCLE
- - size: 50
-
-
- color: GREEN size: 20 shape: TRIANGLE posn: 20,40
- ['color:', 'GREEN', 'size:', '20', 'shape:', 'TRIANGLE', 'posn:', ['20', ',', '40']]
- - color: GREEN
- - posn: ['20', ',', '40']
- - x: 20
- - y: 40
- - shape: TRIANGLE
- - size: 20
- """
-
- def __init__(self, exprs: typing.Iterable[ParserElement], savelist: bool = True):
- super().__init__(exprs, savelist)
- if self.exprs:
- self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs)
- else:
- self.mayReturnEmpty = True
- self.skipWhitespace = True
- self.initExprGroups = True
- self.saveAsList = True
-
- def streamline(self) -> ParserElement:
- super().streamline()
- if self.exprs:
- self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs)
- else:
- self.mayReturnEmpty = True
- return self
-
- def parseImpl(self, instring, loc, doActions=True):
- if self.initExprGroups:
- self.opt1map = dict(
- (id(e.expr), e) for e in self.exprs if isinstance(e, Opt)
- )
- opt1 = [e.expr for e in self.exprs if isinstance(e, Opt)]
- opt2 = [
- e
- for e in self.exprs
- if e.mayReturnEmpty and not isinstance(e, (Opt, Regex, ZeroOrMore))
- ]
- self.optionals = opt1 + opt2
- self.multioptionals = [
- e.expr.set_results_name(e.resultsName, list_all_matches=True)
- for e in self.exprs
- if isinstance(e, _MultipleMatch)
- ]
- self.multirequired = [
- e.expr.set_results_name(e.resultsName, list_all_matches=True)
- for e in self.exprs
- if isinstance(e, OneOrMore)
- ]
- self.required = [
- e for e in self.exprs if not isinstance(e, (Opt, ZeroOrMore, OneOrMore))
- ]
- self.required += self.multirequired
- self.initExprGroups = False
-
- tmpLoc = loc
- tmpReqd = self.required[:]
- tmpOpt = self.optionals[:]
- multis = self.multioptionals[:]
- matchOrder = []
-
- keepMatching = True
- failed = []
- fatals = []
- while keepMatching:
- tmpExprs = tmpReqd + tmpOpt + multis
- failed.clear()
- fatals.clear()
- for e in tmpExprs:
- try:
- tmpLoc = e.try_parse(instring, tmpLoc, raise_fatal=True)
- except ParseFatalException as pfe:
- pfe.__traceback__ = None
- pfe.parserElement = e
- fatals.append(pfe)
- failed.append(e)
- except ParseException:
- failed.append(e)
- else:
- matchOrder.append(self.opt1map.get(id(e), e))
- if e in tmpReqd:
- tmpReqd.remove(e)
- elif e in tmpOpt:
- tmpOpt.remove(e)
- if len(failed) == len(tmpExprs):
- keepMatching = False
-
- # look for any ParseFatalExceptions
- if fatals:
- if len(fatals) > 1:
- fatals.sort(key=lambda e: -e.loc)
- if fatals[0].loc == fatals[1].loc:
- fatals.sort(key=lambda e: (-e.loc, -len(str(e.parserElement))))
- max_fatal = fatals[0]
- raise max_fatal
-
- if tmpReqd:
- missing = ", ".join([str(e) for e in tmpReqd])
- raise ParseException(
- instring,
- loc,
- "Missing one or more required elements ({})".format(missing),
- )
-
- # add any unmatched Opts, in case they have default values defined
- matchOrder += [e for e in self.exprs if isinstance(e, Opt) and e.expr in tmpOpt]
-
- total_results = ParseResults([])
- for e in matchOrder:
- loc, results = e._parse(instring, loc, doActions)
- total_results += results
-
- return loc, total_results
-
- def _generateDefaultName(self):
- return "{" + " & ".join(str(e) for e in self.exprs) + "}"
-
-
-class ParseElementEnhance(ParserElement):
- """Abstract subclass of :class:`ParserElement`, for combining and
- post-processing parsed tokens.
- """
-
- def __init__(self, expr: Union[ParserElement, str], savelist: bool = False):
- super().__init__(savelist)
- if isinstance(expr, str_type):
- if issubclass(self._literalStringClass, Token):
- expr = self._literalStringClass(expr)
- elif issubclass(type(self), self._literalStringClass):
- expr = Literal(expr)
- else:
- expr = self._literalStringClass(Literal(expr))
- self.expr = expr
- if expr is not None:
- self.mayIndexError = expr.mayIndexError
- self.mayReturnEmpty = expr.mayReturnEmpty
- self.set_whitespace_chars(
- expr.whiteChars, copy_defaults=expr.copyDefaultWhiteChars
- )
- self.skipWhitespace = expr.skipWhitespace
- self.saveAsList = expr.saveAsList
- self.callPreparse = expr.callPreparse
- self.ignoreExprs.extend(expr.ignoreExprs)
-
- def recurse(self) -> Sequence[ParserElement]:
- return [self.expr] if self.expr is not None else []
-
- def parseImpl(self, instring, loc, doActions=True):
- if self.expr is not None:
- return self.expr._parse(instring, loc, doActions, callPreParse=False)
- else:
- raise ParseException(instring, loc, "No expression defined", self)
-
- def leave_whitespace(self, recursive: bool = True) -> ParserElement:
- super().leave_whitespace(recursive)
-
- if recursive:
- self.expr = self.expr.copy()
- if self.expr is not None:
- self.expr.leave_whitespace(recursive)
- return self
-
- def ignore_whitespace(self, recursive: bool = True) -> ParserElement:
- super().ignore_whitespace(recursive)
-
- if recursive:
- self.expr = self.expr.copy()
- if self.expr is not None:
- self.expr.ignore_whitespace(recursive)
- return self
-
- def ignore(self, other) -> ParserElement:
- if isinstance(other, Suppress):
- if other not in self.ignoreExprs:
- super().ignore(other)
- if self.expr is not None:
- self.expr.ignore(self.ignoreExprs[-1])
- else:
- super().ignore(other)
- if self.expr is not None:
- self.expr.ignore(self.ignoreExprs[-1])
- return self
-
- def streamline(self) -> ParserElement:
- super().streamline()
- if self.expr is not None:
- self.expr.streamline()
- return self
-
- def _checkRecursion(self, parseElementList):
- if self in parseElementList:
- raise RecursiveGrammarException(parseElementList + [self])
- subRecCheckList = parseElementList[:] + [self]
- if self.expr is not None:
- self.expr._checkRecursion(subRecCheckList)
-
- def validate(self, validateTrace=None) -> None:
- if validateTrace is None:
- validateTrace = []
- tmp = validateTrace[:] + [self]
- if self.expr is not None:
- self.expr.validate(tmp)
- self._checkRecursion([])
-
- def _generateDefaultName(self):
- return "{}:({})".format(self.__class__.__name__, str(self.expr))
-
- ignoreWhitespace = ignore_whitespace
- leaveWhitespace = leave_whitespace
-
-
-class IndentedBlock(ParseElementEnhance):
- """
- Expression to match one or more expressions at a given indentation level.
- Useful for parsing text where structure is implied by indentation (like Python source code).
- """
-
- class _Indent(Empty):
- def __init__(self, ref_col: int):
- super().__init__()
- self.errmsg = "expected indent at column {}".format(ref_col)
- self.add_condition(lambda s, l, t: col(l, s) == ref_col)
-
- class _IndentGreater(Empty):
- def __init__(self, ref_col: int):
- super().__init__()
- self.errmsg = "expected indent at column greater than {}".format(ref_col)
- self.add_condition(lambda s, l, t: col(l, s) > ref_col)
-
- def __init__(
- self, expr: ParserElement, *, recursive: bool = False, grouped: bool = True
- ):
- super().__init__(expr, savelist=True)
- # if recursive:
- # raise NotImplementedError("IndentedBlock with recursive is not implemented")
- self._recursive = recursive
- self._grouped = grouped
- self.parent_anchor = 1
-
- def parseImpl(self, instring, loc, doActions=True):
- # advance parse position to non-whitespace by using an Empty()
- # this should be the column to be used for all subsequent indented lines
- anchor_loc = Empty().preParse(instring, loc)
-
- # see if self.expr matches at the current location - if not it will raise an exception
- # and no further work is necessary
- self.expr.try_parse(instring, anchor_loc, doActions)
-
- indent_col = col(anchor_loc, instring)
- peer_detect_expr = self._Indent(indent_col)
-
- inner_expr = Empty() + peer_detect_expr + self.expr
- if self._recursive:
- sub_indent = self._IndentGreater(indent_col)
- nested_block = IndentedBlock(
- self.expr, recursive=self._recursive, grouped=self._grouped
- )
- nested_block.set_debug(self.debug)
- nested_block.parent_anchor = indent_col
- inner_expr += Opt(sub_indent + nested_block)
-
- inner_expr.set_name(f"inner {hex(id(inner_expr))[-4:].upper()}@{indent_col}")
- block = OneOrMore(inner_expr)
-
- trailing_undent = self._Indent(self.parent_anchor) | StringEnd()
-
- if self._grouped:
- wrapper = Group
- else:
- wrapper = lambda expr: expr
- return (wrapper(block) + Optional(trailing_undent)).parseImpl(
- instring, anchor_loc, doActions
- )
-
-
-class AtStringStart(ParseElementEnhance):
- """Matches if expression matches at the beginning of the parse
- string::
-
- AtStringStart(Word(nums)).parse_string("123")
- # prints ["123"]
-
- AtStringStart(Word(nums)).parse_string(" 123")
- # raises ParseException
- """
-
- def __init__(self, expr: Union[ParserElement, str]):
- super().__init__(expr)
- self.callPreparse = False
-
- def parseImpl(self, instring, loc, doActions=True):
- if loc != 0:
- raise ParseException(instring, loc, "not found at string start")
- return super().parseImpl(instring, loc, doActions)
-
-
-class AtLineStart(ParseElementEnhance):
- r"""Matches if an expression matches at the beginning of a line within
- the parse string
-
- Example::
-
- test = '''\
- AAA this line
- AAA and this line
- AAA but not this one
- B AAA and definitely not this one
- '''
-
- for t in (AtLineStart('AAA') + restOfLine).search_string(test):
- print(t)
-
- prints::
-
- ['AAA', ' this line']
- ['AAA', ' and this line']
-
- """
-
- def __init__(self, expr: Union[ParserElement, str]):
- super().__init__(expr)
- self.callPreparse = False
-
- def parseImpl(self, instring, loc, doActions=True):
- if col(loc, instring) != 1:
- raise ParseException(instring, loc, "not found at line start")
- return super().parseImpl(instring, loc, doActions)
-
-
-class FollowedBy(ParseElementEnhance):
- """Lookahead matching of the given parse expression.
- ``FollowedBy`` does *not* advance the parsing position within
- the input string, it only verifies that the specified parse
- expression matches at the current position. ``FollowedBy``
- always returns a null token list. If any results names are defined
- in the lookahead expression, those *will* be returned for access by
- name.
-
- Example::
-
- # use FollowedBy to match a label only if it is followed by a ':'
- data_word = Word(alphas)
- label = data_word + FollowedBy(':')
- attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
-
- attr_expr[1, ...].parse_string("shape: SQUARE color: BLACK posn: upper left").pprint()
-
- prints::
-
- [['shape', 'SQUARE'], ['color', 'BLACK'], ['posn', 'upper left']]
- """
-
- def __init__(self, expr: Union[ParserElement, str]):
- super().__init__(expr)
- self.mayReturnEmpty = True
-
- def parseImpl(self, instring, loc, doActions=True):
- # by using self._expr.parse and deleting the contents of the returned ParseResults list
- # we keep any named results that were defined in the FollowedBy expression
- _, ret = self.expr._parse(instring, loc, doActions=doActions)
- del ret[:]
-
- return loc, ret
-
-
-class PrecededBy(ParseElementEnhance):
- """Lookbehind matching of the given parse expression.
- ``PrecededBy`` does not advance the parsing position within the
- input string, it only verifies that the specified parse expression
- matches prior to the current position. ``PrecededBy`` always
- returns a null token list, but if a results name is defined on the
- given expression, it is returned.
-
- Parameters:
-
- - expr - expression that must match prior to the current parse
- location
- - retreat - (default= ``None``) - (int) maximum number of characters
- to lookbehind prior to the current parse location
-
- If the lookbehind expression is a string, :class:`Literal`,
- :class:`Keyword`, or a :class:`Word` or :class:`CharsNotIn`
- with a specified exact or maximum length, then the retreat
- parameter is not required. Otherwise, retreat must be specified to
- give a maximum number of characters to look back from
- the current parse position for a lookbehind match.
-
- Example::
-
- # VB-style variable names with type prefixes
- int_var = PrecededBy("#") + pyparsing_common.identifier
- str_var = PrecededBy("$") + pyparsing_common.identifier
-
- """
-
- def __init__(
- self, expr: Union[ParserElement, str], retreat: typing.Optional[int] = None
- ):
- super().__init__(expr)
- self.expr = self.expr().leave_whitespace()
- self.mayReturnEmpty = True
- self.mayIndexError = False
- self.exact = False
- if isinstance(expr, str_type):
- retreat = len(expr)
- self.exact = True
- elif isinstance(expr, (Literal, Keyword)):
- retreat = expr.matchLen
- self.exact = True
- elif isinstance(expr, (Word, CharsNotIn)) and expr.maxLen != _MAX_INT:
- retreat = expr.maxLen
- self.exact = True
- elif isinstance(expr, PositionToken):
- retreat = 0
- self.exact = True
- self.retreat = retreat
- self.errmsg = "not preceded by " + str(expr)
- self.skipWhitespace = False
- self.parseAction.append(lambda s, l, t: t.__delitem__(slice(None, None)))
-
- def parseImpl(self, instring, loc=0, doActions=True):
- if self.exact:
- if loc < self.retreat:
- raise ParseException(instring, loc, self.errmsg)
- start = loc - self.retreat
- _, ret = self.expr._parse(instring, start)
- else:
- # retreat specified a maximum lookbehind window, iterate
- test_expr = self.expr + StringEnd()
- instring_slice = instring[max(0, loc - self.retreat) : loc]
- last_expr = ParseException(instring, loc, self.errmsg)
- for offset in range(1, min(loc, self.retreat + 1) + 1):
- try:
- # print('trying', offset, instring_slice, repr(instring_slice[loc - offset:]))
- _, ret = test_expr._parse(
- instring_slice, len(instring_slice) - offset
- )
- except ParseBaseException as pbe:
- last_expr = pbe
- else:
- break
- else:
- raise last_expr
- return loc, ret
-
-
-class Located(ParseElementEnhance):
- """
- Decorates a returned token with its starting and ending
- locations in the input string.
-
- This helper adds the following results names:
-
- - ``locn_start`` - location where matched expression begins
- - ``locn_end`` - location where matched expression ends
- - ``value`` - the actual parsed results
-
- Be careful if the input text contains ```` characters, you
- may want to call :class:`ParserElement.parse_with_tabs`
-
- Example::
-
- wd = Word(alphas)
- for match in Located(wd).search_string("ljsdf123lksdjjf123lkkjj1222"):
- print(match)
-
- prints::
-
- [0, ['ljsdf'], 5]
- [8, ['lksdjjf'], 15]
- [18, ['lkkjj'], 23]
-
- """
-
- def parseImpl(self, instring, loc, doActions=True):
- start = loc
- loc, tokens = self.expr._parse(instring, start, doActions, callPreParse=False)
- ret_tokens = ParseResults([start, tokens, loc])
- ret_tokens["locn_start"] = start
- ret_tokens["value"] = tokens
- ret_tokens["locn_end"] = loc
- if self.resultsName:
- # must return as a list, so that the name will be attached to the complete group
- return loc, [ret_tokens]
- else:
- return loc, ret_tokens
-
-
-class NotAny(ParseElementEnhance):
- """
- Lookahead to disallow matching with the given parse expression.
- ``NotAny`` does *not* advance the parsing position within the
- input string, it only verifies that the specified parse expression
- does *not* match at the current position. Also, ``NotAny`` does
- *not* skip over leading whitespace. ``NotAny`` always returns
- a null token list. May be constructed using the ``'~'`` operator.
-
- Example::
-
- AND, OR, NOT = map(CaselessKeyword, "AND OR NOT".split())
-
- # take care not to mistake keywords for identifiers
- ident = ~(AND | OR | NOT) + Word(alphas)
- boolean_term = Opt(NOT) + ident
-
- # very crude boolean expression - to support parenthesis groups and
- # operation hierarchy, use infix_notation
- boolean_expr = boolean_term + ((AND | OR) + boolean_term)[...]
-
- # integers that are followed by "." are actually floats
- integer = Word(nums) + ~Char(".")
- """
-
- def __init__(self, expr: Union[ParserElement, str]):
- super().__init__(expr)
- # do NOT use self.leave_whitespace(), don't want to propagate to exprs
- # self.leave_whitespace()
- self.skipWhitespace = False
-
- self.mayReturnEmpty = True
- self.errmsg = "Found unwanted token, " + str(self.expr)
-
- def parseImpl(self, instring, loc, doActions=True):
- if self.expr.can_parse_next(instring, loc):
- raise ParseException(instring, loc, self.errmsg, self)
- return loc, []
-
- def _generateDefaultName(self):
- return "~{" + str(self.expr) + "}"
-
-
-class _MultipleMatch(ParseElementEnhance):
- def __init__(
- self,
- expr: ParserElement,
- stop_on: typing.Optional[Union[ParserElement, str]] = None,
- *,
- stopOn: typing.Optional[Union[ParserElement, str]] = None,
- ):
- super().__init__(expr)
- stopOn = stopOn or stop_on
- self.saveAsList = True
- ender = stopOn
- if isinstance(ender, str_type):
- ender = self._literalStringClass(ender)
- self.stopOn(ender)
-
- def stopOn(self, ender) -> ParserElement:
- if isinstance(ender, str_type):
- ender = self._literalStringClass(ender)
- self.not_ender = ~ender if ender is not None else None
- return self
-
- def parseImpl(self, instring, loc, doActions=True):
- self_expr_parse = self.expr._parse
- self_skip_ignorables = self._skipIgnorables
- check_ender = self.not_ender is not None
- if check_ender:
- try_not_ender = self.not_ender.tryParse
-
- # must be at least one (but first see if we are the stopOn sentinel;
- # if so, fail)
- if check_ender:
- try_not_ender(instring, loc)
- loc, tokens = self_expr_parse(instring, loc, doActions)
- try:
- hasIgnoreExprs = not not self.ignoreExprs
- while 1:
- if check_ender:
- try_not_ender(instring, loc)
- if hasIgnoreExprs:
- preloc = self_skip_ignorables(instring, loc)
- else:
- preloc = loc
- loc, tmptokens = self_expr_parse(instring, preloc, doActions)
- if tmptokens or tmptokens.haskeys():
- tokens += tmptokens
- except (ParseException, IndexError):
- pass
-
- return loc, tokens
-
- def _setResultsName(self, name, listAllMatches=False):
- if (
- __diag__.warn_ungrouped_named_tokens_in_collection
- and Diagnostics.warn_ungrouped_named_tokens_in_collection
- not in self.suppress_warnings_
- ):
- for e in [self.expr] + self.expr.recurse():
- if (
- isinstance(e, ParserElement)
- and e.resultsName
- and Diagnostics.warn_ungrouped_named_tokens_in_collection
- not in e.suppress_warnings_
- ):
- warnings.warn(
- "{}: setting results name {!r} on {} expression "
- "collides with {!r} on contained expression".format(
- "warn_ungrouped_named_tokens_in_collection",
- name,
- type(self).__name__,
- e.resultsName,
- ),
- stacklevel=3,
- )
-
- return super()._setResultsName(name, listAllMatches)
-
-
-class OneOrMore(_MultipleMatch):
- """
- Repetition of one or more of the given expression.
-
- Parameters:
- - expr - expression that must match one or more times
- - stop_on - (default= ``None``) - expression for a terminating sentinel
- (only required if the sentinel would ordinarily match the repetition
- expression)
-
- Example::
-
- data_word = Word(alphas)
- label = data_word + FollowedBy(':')
- attr_expr = Group(label + Suppress(':') + OneOrMore(data_word).set_parse_action(' '.join))
-
- text = "shape: SQUARE posn: upper left color: BLACK"
- attr_expr[1, ...].parse_string(text).pprint() # Fail! read 'color' as data instead of next label -> [['shape', 'SQUARE color']]
-
- # use stop_on attribute for OneOrMore to avoid reading label string as part of the data
- attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
- OneOrMore(attr_expr).parse_string(text).pprint() # Better -> [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'BLACK']]
-
- # could also be written as
- (attr_expr * (1,)).parse_string(text).pprint()
- """
-
- def _generateDefaultName(self):
- return "{" + str(self.expr) + "}..."
-
-
-class ZeroOrMore(_MultipleMatch):
- """
- Optional repetition of zero or more of the given expression.
-
- Parameters:
- - ``expr`` - expression that must match zero or more times
- - ``stop_on`` - expression for a terminating sentinel
- (only required if the sentinel would ordinarily match the repetition
- expression) - (default= ``None``)
-
- Example: similar to :class:`OneOrMore`
- """
-
- def __init__(
- self,
- expr: ParserElement,
- stop_on: typing.Optional[Union[ParserElement, str]] = None,
- *,
- stopOn: typing.Optional[Union[ParserElement, str]] = None,
- ):
- super().__init__(expr, stopOn=stopOn or stop_on)
- self.mayReturnEmpty = True
-
- def parseImpl(self, instring, loc, doActions=True):
- try:
- return super().parseImpl(instring, loc, doActions)
- except (ParseException, IndexError):
- return loc, ParseResults([], name=self.resultsName)
-
- def _generateDefaultName(self):
- return "[" + str(self.expr) + "]..."
-
-
-class _NullToken:
- def __bool__(self):
- return False
-
- def __str__(self):
- return ""
-
-
-class Opt(ParseElementEnhance):
- """
- Optional matching of the given expression.
-
- Parameters:
- - ``expr`` - expression that must match zero or more times
- - ``default`` (optional) - value to be returned if the optional expression is not found.
-
- Example::
-
- # US postal code can be a 5-digit zip, plus optional 4-digit qualifier
- zip = Combine(Word(nums, exact=5) + Opt('-' + Word(nums, exact=4)))
- zip.run_tests('''
- # traditional ZIP code
- 12345
-
- # ZIP+4 form
- 12101-0001
-
- # invalid ZIP
- 98765-
- ''')
-
- prints::
-
- # traditional ZIP code
- 12345
- ['12345']
-
- # ZIP+4 form
- 12101-0001
- ['12101-0001']
-
- # invalid ZIP
- 98765-
- ^
- FAIL: Expected end of text (at char 5), (line:1, col:6)
- """
-
- __optionalNotMatched = _NullToken()
-
- def __init__(
- self, expr: Union[ParserElement, str], default: Any = __optionalNotMatched
- ):
- super().__init__(expr, savelist=False)
- self.saveAsList = self.expr.saveAsList
- self.defaultValue = default
- self.mayReturnEmpty = True
-
- def parseImpl(self, instring, loc, doActions=True):
- self_expr = self.expr
- try:
- loc, tokens = self_expr._parse(instring, loc, doActions, callPreParse=False)
- except (ParseException, IndexError):
- default_value = self.defaultValue
- if default_value is not self.__optionalNotMatched:
- if self_expr.resultsName:
- tokens = ParseResults([default_value])
- tokens[self_expr.resultsName] = default_value
- else:
- tokens = [default_value]
- else:
- tokens = []
- return loc, tokens
-
- def _generateDefaultName(self):
- inner = str(self.expr)
- # strip off redundant inner {}'s
- while len(inner) > 1 and inner[0 :: len(inner) - 1] == "{}":
- inner = inner[1:-1]
- return "[" + inner + "]"
-
-
-Optional = Opt
-
-
-class SkipTo(ParseElementEnhance):
- """
- Token for skipping over all undefined text until the matched
- expression is found.
-
- Parameters:
- - ``expr`` - target expression marking the end of the data to be skipped
- - ``include`` - if ``True``, the target expression is also parsed
- (the skipped text and target expression are returned as a 2-element
- list) (default= ``False``).
- - ``ignore`` - (default= ``None``) used to define grammars (typically quoted strings and
- comments) that might contain false matches to the target expression
- - ``fail_on`` - (default= ``None``) define expressions that are not allowed to be
- included in the skipped test; if found before the target expression is found,
- the :class:`SkipTo` is not a match
-
- Example::
-
- report = '''
- Outstanding Issues Report - 1 Jan 2000
-
- # | Severity | Description | Days Open
- -----+----------+-------------------------------------------+-----------
- 101 | Critical | Intermittent system crash | 6
- 94 | Cosmetic | Spelling error on Login ('log|n') | 14
- 79 | Minor | System slow when running too many reports | 47
- '''
- integer = Word(nums)
- SEP = Suppress('|')
- # use SkipTo to simply match everything up until the next SEP
- # - ignore quoted strings, so that a '|' character inside a quoted string does not match
- # - parse action will call token.strip() for each matched token, i.e., the description body
- string_data = SkipTo(SEP, ignore=quoted_string)
- string_data.set_parse_action(token_map(str.strip))
- ticket_expr = (integer("issue_num") + SEP
- + string_data("sev") + SEP
- + string_data("desc") + SEP
- + integer("days_open"))
-
- for tkt in ticket_expr.search_string(report):
- print tkt.dump()
-
- prints::
-
- ['101', 'Critical', 'Intermittent system crash', '6']
- - days_open: '6'
- - desc: 'Intermittent system crash'
- - issue_num: '101'
- - sev: 'Critical'
- ['94', 'Cosmetic', "Spelling error on Login ('log|n')", '14']
- - days_open: '14'
- - desc: "Spelling error on Login ('log|n')"
- - issue_num: '94'
- - sev: 'Cosmetic'
- ['79', 'Minor', 'System slow when running too many reports', '47']
- - days_open: '47'
- - desc: 'System slow when running too many reports'
- - issue_num: '79'
- - sev: 'Minor'
- """
-
- def __init__(
- self,
- other: Union[ParserElement, str],
- include: bool = False,
- ignore: bool = None,
- fail_on: typing.Optional[Union[ParserElement, str]] = None,
- *,
- failOn: Union[ParserElement, str] = None,
- ):
- super().__init__(other)
- failOn = failOn or fail_on
- self.ignoreExpr = ignore
- self.mayReturnEmpty = True
- self.mayIndexError = False
- self.includeMatch = include
- self.saveAsList = False
- if isinstance(failOn, str_type):
- self.failOn = self._literalStringClass(failOn)
- else:
- self.failOn = failOn
- self.errmsg = "No match found for " + str(self.expr)
-
- def parseImpl(self, instring, loc, doActions=True):
- startloc = loc
- instrlen = len(instring)
- self_expr_parse = self.expr._parse
- self_failOn_canParseNext = (
- self.failOn.canParseNext if self.failOn is not None else None
- )
- self_ignoreExpr_tryParse = (
- self.ignoreExpr.tryParse if self.ignoreExpr is not None else None
- )
-
- tmploc = loc
- while tmploc <= instrlen:
- if self_failOn_canParseNext is not None:
- # break if failOn expression matches
- if self_failOn_canParseNext(instring, tmploc):
- break
-
- if self_ignoreExpr_tryParse is not None:
- # advance past ignore expressions
- while 1:
- try:
- tmploc = self_ignoreExpr_tryParse(instring, tmploc)
- except ParseBaseException:
- break
-
- try:
- self_expr_parse(instring, tmploc, doActions=False, callPreParse=False)
- except (ParseException, IndexError):
- # no match, advance loc in string
- tmploc += 1
- else:
- # matched skipto expr, done
- break
-
- else:
- # ran off the end of the input string without matching skipto expr, fail
- raise ParseException(instring, loc, self.errmsg, self)
-
- # build up return values
- loc = tmploc
- skiptext = instring[startloc:loc]
- skipresult = ParseResults(skiptext)
-
- if self.includeMatch:
- loc, mat = self_expr_parse(instring, loc, doActions, callPreParse=False)
- skipresult += mat
-
- return loc, skipresult
-
-
-class Forward(ParseElementEnhance):
- """
- Forward declaration of an expression to be defined later -
- used for recursive grammars, such as algebraic infix notation.
- When the expression is known, it is assigned to the ``Forward``
- variable using the ``'<<'`` operator.
-
- Note: take care when assigning to ``Forward`` not to overlook
- precedence of operators.
-
- Specifically, ``'|'`` has a lower precedence than ``'<<'``, so that::
-
- fwd_expr << a | b | c
-
- will actually be evaluated as::
-
- (fwd_expr << a) | b | c
-
- thereby leaving b and c out as parseable alternatives. It is recommended that you
- explicitly group the values inserted into the ``Forward``::
-
- fwd_expr << (a | b | c)
-
- Converting to use the ``'<<='`` operator instead will avoid this problem.
-
- See :class:`ParseResults.pprint` for an example of a recursive
- parser created using ``Forward``.
- """
-
- def __init__(self, other: typing.Optional[Union[ParserElement, str]] = None):
- self.caller_frame = traceback.extract_stack(limit=2)[0]
- super().__init__(other, savelist=False)
- self.lshift_line = None
-
- def __lshift__(self, other):
- if hasattr(self, "caller_frame"):
- del self.caller_frame
- if isinstance(other, str_type):
- other = self._literalStringClass(other)
- self.expr = other
- self.mayIndexError = self.expr.mayIndexError
- self.mayReturnEmpty = self.expr.mayReturnEmpty
- self.set_whitespace_chars(
- self.expr.whiteChars, copy_defaults=self.expr.copyDefaultWhiteChars
- )
- self.skipWhitespace = self.expr.skipWhitespace
- self.saveAsList = self.expr.saveAsList
- self.ignoreExprs.extend(self.expr.ignoreExprs)
- self.lshift_line = traceback.extract_stack(limit=2)[-2]
- return self
-
- def __ilshift__(self, other):
- return self << other
-
- def __or__(self, other):
- caller_line = traceback.extract_stack(limit=2)[-2]
- if (
- __diag__.warn_on_match_first_with_lshift_operator
- and caller_line == self.lshift_line
- and Diagnostics.warn_on_match_first_with_lshift_operator
- not in self.suppress_warnings_
- ):
- warnings.warn(
- "using '<<' operator with '|' is probably an error, use '<<='",
- stacklevel=2,
- )
- ret = super().__or__(other)
- return ret
-
- def __del__(self):
- # see if we are getting dropped because of '=' reassignment of var instead of '<<=' or '<<'
- if (
- self.expr is None
- and __diag__.warn_on_assignment_to_Forward
- and Diagnostics.warn_on_assignment_to_Forward not in self.suppress_warnings_
- ):
- warnings.warn_explicit(
- "Forward defined here but no expression attached later using '<<=' or '<<'",
- UserWarning,
- filename=self.caller_frame.filename,
- lineno=self.caller_frame.lineno,
- )
-
- def parseImpl(self, instring, loc, doActions=True):
- if (
- self.expr is None
- and __diag__.warn_on_parse_using_empty_Forward
- and Diagnostics.warn_on_parse_using_empty_Forward
- not in self.suppress_warnings_
- ):
- # walk stack until parse_string, scan_string, search_string, or transform_string is found
- parse_fns = [
- "parse_string",
- "scan_string",
- "search_string",
- "transform_string",
- ]
- tb = traceback.extract_stack(limit=200)
- for i, frm in enumerate(reversed(tb), start=1):
- if frm.name in parse_fns:
- stacklevel = i + 1
- break
- else:
- stacklevel = 2
- warnings.warn(
- "Forward expression was never assigned a value, will not parse any input",
- stacklevel=stacklevel,
- )
- if not ParserElement._left_recursion_enabled:
- return super().parseImpl(instring, loc, doActions)
- # ## Bounded Recursion algorithm ##
- # Recursion only needs to be processed at ``Forward`` elements, since they are
- # the only ones that can actually refer to themselves. The general idea is
- # to handle recursion stepwise: We start at no recursion, then recurse once,
- # recurse twice, ..., until more recursion offers no benefit (we hit the bound).
- #
- # The "trick" here is that each ``Forward`` gets evaluated in two contexts
- # - to *match* a specific recursion level, and
- # - to *search* the bounded recursion level
- # and the two run concurrently. The *search* must *match* each recursion level
- # to find the best possible match. This is handled by a memo table, which
- # provides the previous match to the next level match attempt.
- #
- # See also "Left Recursion in Parsing Expression Grammars", Medeiros et al.
- #
- # There is a complication since we not only *parse* but also *transform* via
- # actions: We do not want to run the actions too often while expanding. Thus,
- # we expand using `doActions=False` and only run `doActions=True` if the next
- # recursion level is acceptable.
- with ParserElement.recursion_lock:
- memo = ParserElement.recursion_memos
- try:
- # we are parsing at a specific recursion expansion - use it as-is
- prev_loc, prev_result = memo[loc, self, doActions]
- if isinstance(prev_result, Exception):
- raise prev_result
- return prev_loc, prev_result.copy()
- except KeyError:
- act_key = (loc, self, True)
- peek_key = (loc, self, False)
- # we are searching for the best recursion expansion - keep on improving
- # both `doActions` cases must be tracked separately here!
- prev_loc, prev_peek = memo[peek_key] = (
- loc - 1,
- ParseException(
- instring, loc, "Forward recursion without base case", self
- ),
- )
- if doActions:
- memo[act_key] = memo[peek_key]
- while True:
- try:
- new_loc, new_peek = super().parseImpl(instring, loc, False)
- except ParseException:
- # we failed before getting any match – do not hide the error
- if isinstance(prev_peek, Exception):
- raise
- new_loc, new_peek = prev_loc, prev_peek
- # the match did not get better: we are done
- if new_loc <= prev_loc:
- if doActions:
- # replace the match for doActions=False as well,
- # in case the action did backtrack
- prev_loc, prev_result = memo[peek_key] = memo[act_key]
- del memo[peek_key], memo[act_key]
- return prev_loc, prev_result.copy()
- del memo[peek_key]
- return prev_loc, prev_peek.copy()
- # the match did get better: see if we can improve further
- else:
- if doActions:
- try:
- memo[act_key] = super().parseImpl(instring, loc, True)
- except ParseException as e:
- memo[peek_key] = memo[act_key] = (new_loc, e)
- raise
- prev_loc, prev_peek = memo[peek_key] = new_loc, new_peek
-
- def leave_whitespace(self, recursive: bool = True) -> ParserElement:
- self.skipWhitespace = False
- return self
-
- def ignore_whitespace(self, recursive: bool = True) -> ParserElement:
- self.skipWhitespace = True
- return self
-
- def streamline(self) -> ParserElement:
- if not self.streamlined:
- self.streamlined = True
- if self.expr is not None:
- self.expr.streamline()
- return self
-
- def validate(self, validateTrace=None) -> None:
- if validateTrace is None:
- validateTrace = []
-
- if self not in validateTrace:
- tmp = validateTrace[:] + [self]
- if self.expr is not None:
- self.expr.validate(tmp)
- self._checkRecursion([])
-
- def _generateDefaultName(self):
- # Avoid infinite recursion by setting a temporary _defaultName
- self._defaultName = ": ..."
-
- # Use the string representation of main expression.
- retString = "..."
- try:
- if self.expr is not None:
- retString = str(self.expr)[:1000]
- else:
- retString = "None"
- finally:
- return self.__class__.__name__ + ": " + retString
-
- def copy(self) -> ParserElement:
- if self.expr is not None:
- return super().copy()
- else:
- ret = Forward()
- ret <<= self
- return ret
-
- def _setResultsName(self, name, list_all_matches=False):
- if (
- __diag__.warn_name_set_on_empty_Forward
- and Diagnostics.warn_name_set_on_empty_Forward
- not in self.suppress_warnings_
- ):
- if self.expr is None:
- warnings.warn(
- "{}: setting results name {!r} on {} expression "
- "that has no contained expression".format(
- "warn_name_set_on_empty_Forward", name, type(self).__name__
- ),
- stacklevel=3,
- )
-
- return super()._setResultsName(name, list_all_matches)
-
- ignoreWhitespace = ignore_whitespace
- leaveWhitespace = leave_whitespace
-
-
-class TokenConverter(ParseElementEnhance):
- """
- Abstract subclass of :class:`ParseExpression`, for converting parsed results.
- """
-
- def __init__(self, expr: Union[ParserElement, str], savelist=False):
- super().__init__(expr) # , savelist)
- self.saveAsList = False
-
-
-class Combine(TokenConverter):
- """Converter to concatenate all matching tokens to a single string.
- By default, the matching patterns must also be contiguous in the
- input string; this can be disabled by specifying
- ``'adjacent=False'`` in the constructor.
-
- Example::
-
- real = Word(nums) + '.' + Word(nums)
- print(real.parse_string('3.1416')) # -> ['3', '.', '1416']
- # will also erroneously match the following
- print(real.parse_string('3. 1416')) # -> ['3', '.', '1416']
-
- real = Combine(Word(nums) + '.' + Word(nums))
- print(real.parse_string('3.1416')) # -> ['3.1416']
- # no match when there are internal spaces
- print(real.parse_string('3. 1416')) # -> Exception: Expected W:(0123...)
- """
-
- def __init__(
- self,
- expr: ParserElement,
- join_string: str = "",
- adjacent: bool = True,
- *,
- joinString: typing.Optional[str] = None,
- ):
- super().__init__(expr)
- joinString = joinString if joinString is not None else join_string
- # suppress whitespace-stripping in contained parse expressions, but re-enable it on the Combine itself
- if adjacent:
- self.leave_whitespace()
- self.adjacent = adjacent
- self.skipWhitespace = True
- self.joinString = joinString
- self.callPreparse = True
-
- def ignore(self, other) -> ParserElement:
- if self.adjacent:
- ParserElement.ignore(self, other)
- else:
- super().ignore(other)
- return self
-
- def postParse(self, instring, loc, tokenlist):
- retToks = tokenlist.copy()
- del retToks[:]
- retToks += ParseResults(
- ["".join(tokenlist._asStringList(self.joinString))], modal=self.modalResults
- )
-
- if self.resultsName and retToks.haskeys():
- return [retToks]
- else:
- return retToks
-
-
-class Group(TokenConverter):
- """Converter to return the matched tokens as a list - useful for
- returning tokens of :class:`ZeroOrMore` and :class:`OneOrMore` expressions.
-
- The optional ``aslist`` argument when set to True will return the
- parsed tokens as a Python list instead of a pyparsing ParseResults.
-
- Example::
-
- ident = Word(alphas)
- num = Word(nums)
- term = ident | num
- func = ident + Opt(delimited_list(term))
- print(func.parse_string("fn a, b, 100"))
- # -> ['fn', 'a', 'b', '100']
-
- func = ident + Group(Opt(delimited_list(term)))
- print(func.parse_string("fn a, b, 100"))
- # -> ['fn', ['a', 'b', '100']]
- """
-
- def __init__(self, expr: ParserElement, aslist: bool = False):
- super().__init__(expr)
- self.saveAsList = True
- self._asPythonList = aslist
-
- def postParse(self, instring, loc, tokenlist):
- if self._asPythonList:
- return ParseResults.List(
- tokenlist.asList()
- if isinstance(tokenlist, ParseResults)
- else list(tokenlist)
- )
- else:
- return [tokenlist]
-
-
-class Dict(TokenConverter):
- """Converter to return a repetitive expression as a list, but also
- as a dictionary. Each element can also be referenced using the first
- token in the expression as its key. Useful for tabular report
- scraping when the first column can be used as a item key.
-
- The optional ``asdict`` argument when set to True will return the
- parsed tokens as a Python dict instead of a pyparsing ParseResults.
-
- Example::
-
- data_word = Word(alphas)
- label = data_word + FollowedBy(':')
-
- text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
- attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
-
- # print attributes as plain groups
- print(attr_expr[1, ...].parse_string(text).dump())
-
- # instead of OneOrMore(expr), parse using Dict(Group(expr)[1, ...]) - Dict will auto-assign names
- result = Dict(Group(attr_expr)[1, ...]).parse_string(text)
- print(result.dump())
-
- # access named fields as dict entries, or output as dict
- print(result['shape'])
- print(result.as_dict())
-
- prints::
-
- ['shape', 'SQUARE', 'posn', 'upper left', 'color', 'light blue', 'texture', 'burlap']
- [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
- - color: 'light blue'
- - posn: 'upper left'
- - shape: 'SQUARE'
- - texture: 'burlap'
- SQUARE
- {'color': 'light blue', 'posn': 'upper left', 'texture': 'burlap', 'shape': 'SQUARE'}
-
- See more examples at :class:`ParseResults` of accessing fields by results name.
- """
-
- def __init__(self, expr: ParserElement, asdict: bool = False):
- super().__init__(expr)
- self.saveAsList = True
- self._asPythonDict = asdict
-
- def postParse(self, instring, loc, tokenlist):
- for i, tok in enumerate(tokenlist):
- if len(tok) == 0:
- continue
-
- ikey = tok[0]
- if isinstance(ikey, int):
- ikey = str(ikey).strip()
-
- if len(tok) == 1:
- tokenlist[ikey] = _ParseResultsWithOffset("", i)
-
- elif len(tok) == 2 and not isinstance(tok[1], ParseResults):
- tokenlist[ikey] = _ParseResultsWithOffset(tok[1], i)
-
- else:
- try:
- dictvalue = tok.copy() # ParseResults(i)
- except Exception:
- exc = TypeError(
- "could not extract dict values from parsed results"
- " - Dict expression must contain Grouped expressions"
- )
- raise exc from None
-
- del dictvalue[0]
-
- if len(dictvalue) != 1 or (
- isinstance(dictvalue, ParseResults) and dictvalue.haskeys()
- ):
- tokenlist[ikey] = _ParseResultsWithOffset(dictvalue, i)
- else:
- tokenlist[ikey] = _ParseResultsWithOffset(dictvalue[0], i)
-
- if self._asPythonDict:
- return [tokenlist.as_dict()] if self.resultsName else tokenlist.as_dict()
- else:
- return [tokenlist] if self.resultsName else tokenlist
-
-
-class Suppress(TokenConverter):
- """Converter for ignoring the results of a parsed expression.
-
- Example::
-
- source = "a, b, c,d"
- wd = Word(alphas)
- wd_list1 = wd + (',' + wd)[...]
- print(wd_list1.parse_string(source))
-
- # often, delimiters that are useful during parsing are just in the
- # way afterward - use Suppress to keep them out of the parsed output
- wd_list2 = wd + (Suppress(',') + wd)[...]
- print(wd_list2.parse_string(source))
-
- # Skipped text (using '...') can be suppressed as well
- source = "lead in START relevant text END trailing text"
- start_marker = Keyword("START")
- end_marker = Keyword("END")
- find_body = Suppress(...) + start_marker + ... + end_marker
- print(find_body.parse_string(source)
-
- prints::
-
- ['a', ',', 'b', ',', 'c', ',', 'd']
- ['a', 'b', 'c', 'd']
- ['START', 'relevant text ', 'END']
-
- (See also :class:`delimited_list`.)
- """
-
- def __init__(self, expr: Union[ParserElement, str], savelist: bool = False):
- if expr is ...:
- expr = _PendingSkip(NoMatch())
- super().__init__(expr)
-
- def __add__(self, other) -> "ParserElement":
- if isinstance(self.expr, _PendingSkip):
- return Suppress(SkipTo(other)) + other
- else:
- return super().__add__(other)
-
- def __sub__(self, other) -> "ParserElement":
- if isinstance(self.expr, _PendingSkip):
- return Suppress(SkipTo(other)) - other
- else:
- return super().__sub__(other)
-
- def postParse(self, instring, loc, tokenlist):
- return []
-
- def suppress(self) -> ParserElement:
- return self
-
-
-def trace_parse_action(f: ParseAction) -> ParseAction:
- """Decorator for debugging parse actions.
-
- When the parse action is called, this decorator will print
- ``">> entering method-name(line:, , )"``.
- When the parse action completes, the decorator will print
- ``"<<"`` followed by the returned value, or any exception that the parse action raised.
-
- Example::
-
- wd = Word(alphas)
-
- @trace_parse_action
- def remove_duplicate_chars(tokens):
- return ''.join(sorted(set(''.join(tokens))))
-
- wds = wd[1, ...].set_parse_action(remove_duplicate_chars)
- print(wds.parse_string("slkdjs sld sldd sdlf sdljf"))
-
- prints::
-
- >>entering remove_duplicate_chars(line: 'slkdjs sld sldd sdlf sdljf', 0, (['slkdjs', 'sld', 'sldd', 'sdlf', 'sdljf'], {}))
- < 3:
- thisFunc = paArgs[0].__class__.__name__ + "." + thisFunc
- sys.stderr.write(
- ">>entering {}(line: {!r}, {}, {!r})\n".format(thisFunc, line(l, s), l, t)
- )
- try:
- ret = f(*paArgs)
- except Exception as exc:
- sys.stderr.write("< str:
- r"""Helper to easily define string ranges for use in :class:`Word`
- construction. Borrows syntax from regexp ``'[]'`` string range
- definitions::
-
- srange("[0-9]") -> "0123456789"
- srange("[a-z]") -> "abcdefghijklmnopqrstuvwxyz"
- srange("[a-z$_]") -> "abcdefghijklmnopqrstuvwxyz$_"
-
- The input string must be enclosed in []'s, and the returned string
- is the expanded character set joined into a single string. The
- values enclosed in the []'s may be:
-
- - a single character
- - an escaped character with a leading backslash (such as ``\-``
- or ``\]``)
- - an escaped hex character with a leading ``'\x'``
- (``\x21``, which is a ``'!'`` character) (``\0x##``
- is also supported for backwards compatibility)
- - an escaped octal character with a leading ``'\0'``
- (``\041``, which is a ``'!'`` character)
- - a range of any of the above, separated by a dash (``'a-z'``,
- etc.)
- - any combination of the above (``'aeiouy'``,
- ``'a-zA-Z0-9_$'``, etc.)
- """
- _expanded = (
- lambda p: p
- if not isinstance(p, ParseResults)
- else "".join(chr(c) for c in range(ord(p[0]), ord(p[1]) + 1))
- )
- try:
- return "".join(_expanded(part) for part in _reBracketExpr.parse_string(s).body)
- except Exception:
- return ""
-
-
-def token_map(func, *args) -> ParseAction:
- """Helper to define a parse action by mapping a function to all
- elements of a :class:`ParseResults` list. If any additional args are passed,
- they are forwarded to the given function as additional arguments
- after the token, as in
- ``hex_integer = Word(hexnums).set_parse_action(token_map(int, 16))``,
- which will convert the parsed data to an integer using base 16.
-
- Example (compare the last to example in :class:`ParserElement.transform_string`::
-
- hex_ints = Word(hexnums)[1, ...].set_parse_action(token_map(int, 16))
- hex_ints.run_tests('''
- 00 11 22 aa FF 0a 0d 1a
- ''')
-
- upperword = Word(alphas).set_parse_action(token_map(str.upper))
- upperword[1, ...].run_tests('''
- my kingdom for a horse
- ''')
-
- wd = Word(alphas).set_parse_action(token_map(str.title))
- wd[1, ...].set_parse_action(' '.join).run_tests('''
- now is the winter of our discontent made glorious summer by this sun of york
- ''')
-
- prints::
-
- 00 11 22 aa FF 0a 0d 1a
- [0, 17, 34, 170, 255, 10, 13, 26]
-
- my kingdom for a horse
- ['MY', 'KINGDOM', 'FOR', 'A', 'HORSE']
-
- now is the winter of our discontent made glorious summer by this sun of york
- ['Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York']
- """
-
- def pa(s, l, t):
- return [func(tokn, *args) for tokn in t]
-
- func_name = getattr(func, "__name__", getattr(func, "__class__").__name__)
- pa.__name__ = func_name
-
- return pa
-
-
-def autoname_elements() -> None:
- """
- Utility to simplify mass-naming of parser elements, for
- generating railroad diagram with named subdiagrams.
- """
- for name, var in sys._getframe().f_back.f_locals.items():
- if isinstance(var, ParserElement) and not var.customName:
- var.set_name(name)
-
-
-dbl_quoted_string = Combine(
- Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"'
-).set_name("string enclosed in double quotes")
-
-sgl_quoted_string = Combine(
- Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'"
-).set_name("string enclosed in single quotes")
-
-quoted_string = Combine(
- Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*') + '"'
- | Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*") + "'"
-).set_name("quotedString using single or double quotes")
-
-unicode_string = Combine("u" + quoted_string.copy()).set_name("unicode string literal")
-
-
-alphas8bit = srange(r"[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]")
-punc8bit = srange(r"[\0xa1-\0xbf\0xd7\0xf7]")
-
-# build list of built-in expressions, for future reference if a global default value
-# gets updated
-_builtin_exprs: List[ParserElement] = [
- v for v in vars().values() if isinstance(v, ParserElement)
-]
-
-# backward compatibility names
-tokenMap = token_map
-conditionAsParseAction = condition_as_parse_action
-nullDebugAction = null_debug_action
-sglQuotedString = sgl_quoted_string
-dblQuotedString = dbl_quoted_string
-quotedString = quoted_string
-unicodeString = unicode_string
-lineStart = line_start
-lineEnd = line_end
-stringStart = string_start
-stringEnd = string_end
-traceParseAction = trace_parse_action
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/version.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/version.py
deleted file mode 100644
index a406a301446b1c1079137f1834caa4448807a693..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/version.py
+++ /dev/null
@@ -1,358 +0,0 @@
-#
-# distutils/version.py
-#
-# Implements multiple version numbering conventions for the
-# Python Module Distribution Utilities.
-#
-# $Id$
-#
-
-"""Provides classes to represent module version numbers (one class for
-each style of version numbering). There are currently two such classes
-implemented: StrictVersion and LooseVersion.
-
-Every version number class implements the following interface:
- * the 'parse' method takes a string and parses it to some internal
- representation; if the string is an invalid version number,
- 'parse' raises a ValueError exception
- * the class constructor takes an optional string argument which,
- if supplied, is passed to 'parse'
- * __str__ reconstructs the string that was passed to 'parse' (or
- an equivalent string -- ie. one that will generate an equivalent
- version number instance)
- * __repr__ generates Python code to recreate the version number instance
- * _cmp compares the current instance with either another instance
- of the same class or a string (which will be parsed to an instance
- of the same class, thus must follow the same rules)
-"""
-
-import re
-import warnings
-import contextlib
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
- with warnings.catch_warnings(record=True) as ctx:
- warnings.filterwarnings(
- action='default',
- category=DeprecationWarning,
- message="distutils Version classes are deprecated.",
- )
- yield ctx
-
-
-class Version:
- """Abstract base class for version numbering classes. Just provides
- constructor (__init__) and reproducer (__repr__), because those
- seem to be the same for all version numbering classes; and route
- rich comparisons to _cmp.
- """
-
- def __init__(self, vstring=None):
- if vstring:
- self.parse(vstring)
- warnings.warn(
- "distutils Version classes are deprecated. "
- "Use packaging.version instead.",
- DeprecationWarning,
- stacklevel=2,
- )
-
- def __repr__(self):
- return "%s ('%s')" % (self.__class__.__name__, str(self))
-
- def __eq__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c == 0
-
- def __lt__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c < 0
-
- def __le__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c <= 0
-
- def __gt__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c > 0
-
- def __ge__(self, other):
- c = self._cmp(other)
- if c is NotImplemented:
- return c
- return c >= 0
-
-
-# Interface for version-number classes -- must be implemented
-# by the following classes (the concrete ones -- Version should
-# be treated as an abstract class).
-# __init__ (string) - create and take same action as 'parse'
-# (string parameter is optional)
-# parse (string) - convert a string representation to whatever
-# internal representation is appropriate for
-# this style of version numbering
-# __str__ (self) - convert back to a string; should be very similar
-# (if not identical to) the string supplied to parse
-# __repr__ (self) - generate Python code to recreate
-# the instance
-# _cmp (self, other) - compare two version numbers ('other' may
-# be an unparsed version string, or another
-# instance of your version class)
-
-
-class StrictVersion(Version):
-
- """Version numbering for anal retentives and software idealists.
- Implements the standard interface for version number classes as
- described above. A version number consists of two or three
- dot-separated numeric components, with an optional "pre-release" tag
- on the end. The pre-release tag consists of the letter 'a' or 'b'
- followed by a number. If the numeric components of two version
- numbers are equal, then one with a pre-release tag will always
- be deemed earlier (lesser) than one without.
-
- The following are valid version numbers (shown in the order that
- would be obtained by sorting according to the supplied cmp function):
-
- 0.4 0.4.0 (these two are equivalent)
- 0.4.1
- 0.5a1
- 0.5b3
- 0.5
- 0.9.6
- 1.0
- 1.0.4a3
- 1.0.4b1
- 1.0.4
-
- The following are examples of invalid version numbers:
-
- 1
- 2.7.2.2
- 1.3.a4
- 1.3pl1
- 1.3c4
-
- The rationale for this version numbering system will be explained
- in the distutils documentation.
- """
-
- version_re = re.compile(
- r'^(\d+) \. (\d+) (\. (\d+))? ([ab](\d+))?$', re.VERBOSE | re.ASCII
- )
-
- def parse(self, vstring):
- match = self.version_re.match(vstring)
- if not match:
- raise ValueError("invalid version number '%s'" % vstring)
-
- (major, minor, patch, prerelease, prerelease_num) = match.group(1, 2, 4, 5, 6)
-
- if patch:
- self.version = tuple(map(int, [major, minor, patch]))
- else:
- self.version = tuple(map(int, [major, minor])) + (0,)
-
- if prerelease:
- self.prerelease = (prerelease[0], int(prerelease_num))
- else:
- self.prerelease = None
-
- def __str__(self):
-
- if self.version[2] == 0:
- vstring = '.'.join(map(str, self.version[0:2]))
- else:
- vstring = '.'.join(map(str, self.version))
-
- if self.prerelease:
- vstring = vstring + self.prerelease[0] + str(self.prerelease[1])
-
- return vstring
-
- def _cmp(self, other):
- if isinstance(other, str):
- with suppress_known_deprecation():
- other = StrictVersion(other)
- elif not isinstance(other, StrictVersion):
- return NotImplemented
-
- if self.version != other.version:
- # numeric versions don't match
- # prerelease stuff doesn't matter
- if self.version < other.version:
- return -1
- else:
- return 1
-
- # have to compare prerelease
- # case 1: neither has prerelease; they're equal
- # case 2: self has prerelease, other doesn't; other is greater
- # case 3: self doesn't have prerelease, other does: self is greater
- # case 4: both have prerelease: must compare them!
-
- if not self.prerelease and not other.prerelease:
- return 0
- elif self.prerelease and not other.prerelease:
- return -1
- elif not self.prerelease and other.prerelease:
- return 1
- elif self.prerelease and other.prerelease:
- if self.prerelease == other.prerelease:
- return 0
- elif self.prerelease < other.prerelease:
- return -1
- else:
- return 1
- else:
- assert False, "never get here"
-
-
-# end class StrictVersion
-
-
-# The rules according to Greg Stein:
-# 1) a version number has 1 or more numbers separated by a period or by
-# sequences of letters. If only periods, then these are compared
-# left-to-right to determine an ordering.
-# 2) sequences of letters are part of the tuple for comparison and are
-# compared lexicographically
-# 3) recognize the numeric components may have leading zeroes
-#
-# The LooseVersion class below implements these rules: a version number
-# string is split up into a tuple of integer and string components, and
-# comparison is a simple tuple comparison. This means that version
-# numbers behave in a predictable and obvious way, but a way that might
-# not necessarily be how people *want* version numbers to behave. There
-# wouldn't be a problem if people could stick to purely numeric version
-# numbers: just split on period and compare the numbers as tuples.
-# However, people insist on putting letters into their version numbers;
-# the most common purpose seems to be:
-# - indicating a "pre-release" version
-# ('alpha', 'beta', 'a', 'b', 'pre', 'p')
-# - indicating a post-release patch ('p', 'pl', 'patch')
-# but of course this can't cover all version number schemes, and there's
-# no way to know what a programmer means without asking him.
-#
-# The problem is what to do with letters (and other non-numeric
-# characters) in a version number. The current implementation does the
-# obvious and predictable thing: keep them as strings and compare
-# lexically within a tuple comparison. This has the desired effect if
-# an appended letter sequence implies something "post-release":
-# eg. "0.99" < "0.99pl14" < "1.0", and "5.001" < "5.001m" < "5.002".
-#
-# However, if letters in a version number imply a pre-release version,
-# the "obvious" thing isn't correct. Eg. you would expect that
-# "1.5.1" < "1.5.2a2" < "1.5.2", but under the tuple/lexical comparison
-# implemented here, this just isn't so.
-#
-# Two possible solutions come to mind. The first is to tie the
-# comparison algorithm to a particular set of semantic rules, as has
-# been done in the StrictVersion class above. This works great as long
-# as everyone can go along with bondage and discipline. Hopefully a
-# (large) subset of Python module programmers will agree that the
-# particular flavour of bondage and discipline provided by StrictVersion
-# provides enough benefit to be worth using, and will submit their
-# version numbering scheme to its domination. The free-thinking
-# anarchists in the lot will never give in, though, and something needs
-# to be done to accommodate them.
-#
-# Perhaps a "moderately strict" version class could be implemented that
-# lets almost anything slide (syntactically), and makes some heuristic
-# assumptions about non-digits in version number strings. This could
-# sink into special-case-hell, though; if I was as talented and
-# idiosyncratic as Larry Wall, I'd go ahead and implement a class that
-# somehow knows that "1.2.1" < "1.2.2a2" < "1.2.2" < "1.2.2pl3", and is
-# just as happy dealing with things like "2g6" and "1.13++". I don't
-# think I'm smart enough to do it right though.
-#
-# In any case, I've coded the test suite for this module (see
-# ../test/test_version.py) specifically to fail on things like comparing
-# "1.2a2" and "1.2". That's not because the *code* is doing anything
-# wrong, it's because the simple, obvious design doesn't match my
-# complicated, hairy expectations for real-world version numbers. It
-# would be a snap to fix the test suite to say, "Yep, LooseVersion does
-# the Right Thing" (ie. the code matches the conception). But I'd rather
-# have a conception that matches common notions about version numbers.
-
-
-class LooseVersion(Version):
-
- """Version numbering for anarchists and software realists.
- Implements the standard interface for version number classes as
- described above. A version number consists of a series of numbers,
- separated by either periods or strings of letters. When comparing
- version numbers, the numeric components will be compared
- numerically, and the alphabetic components lexically. The following
- are all valid version numbers, in no particular order:
-
- 1.5.1
- 1.5.2b2
- 161
- 3.10a
- 8.02
- 3.4j
- 1996.07.12
- 3.2.pl0
- 3.1.1.6
- 2g6
- 11g
- 0.960923
- 2.2beta29
- 1.13++
- 5.5.kw
- 2.0b1pl0
-
- In fact, there is no such thing as an invalid version number under
- this scheme; the rules for comparison are simple and predictable,
- but may not always give the results you want (for some definition
- of "want").
- """
-
- component_re = re.compile(r'(\d+ | [a-z]+ | \.)', re.VERBOSE)
-
- def parse(self, vstring):
- # I've given up on thinking I can reconstruct the version string
- # from the parsed tuple -- so I just store the string here for
- # use by __str__
- self.vstring = vstring
- components = [x for x in self.component_re.split(vstring) if x and x != '.']
- for i, obj in enumerate(components):
- try:
- components[i] = int(obj)
- except ValueError:
- pass
-
- self.version = components
-
- def __str__(self):
- return self.vstring
-
- def __repr__(self):
- return "LooseVersion ('%s')" % str(self)
-
- def _cmp(self, other):
- if isinstance(other, str):
- other = LooseVersion(other)
- elif not isinstance(other, LooseVersion):
- return NotImplemented
-
- if self.version == other.version:
- return 0
- if self.version < other.version:
- return -1
- if self.version > other.version:
- return 1
-
-
-# end class LooseVersion
diff --git a/spaces/toloka/open-llm-leaderboard/static/js/787.c4e7f8f9.chunk.js b/spaces/toloka/open-llm-leaderboard/static/js/787.c4e7f8f9.chunk.js
deleted file mode 100644
index 7664e5721f86c326866b93cf75e0fa02125ca81c..0000000000000000000000000000000000000000
--- a/spaces/toloka/open-llm-leaderboard/static/js/787.c4e7f8f9.chunk.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/spaces/tomofi/MMOCR/mmocr/models/kie/extractors/sdmgr.py b/spaces/tomofi/MMOCR/mmocr/models/kie/extractors/sdmgr.py
deleted file mode 100644
index 9fa08cccc9a4ae893cad2dd8d4e3408ecc1d2b29..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/kie/extractors/sdmgr.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import mmcv
-from mmdet.core import bbox2roi
-from torch import nn
-from torch.nn import functional as F
-
-from mmocr.core import imshow_edge, imshow_node
-from mmocr.models.builder import DETECTORS, build_roi_extractor
-from mmocr.models.common.detectors import SingleStageDetector
-from mmocr.utils import list_from_file
-
-
-@DETECTORS.register_module()
-class SDMGR(SingleStageDetector):
- """The implementation of the paper: Spatial Dual-Modality Graph Reasoning
- for Key Information Extraction. https://arxiv.org/abs/2103.14470.
-
- Args:
- visual_modality (bool): Whether use the visual modality.
- class_list (None | str): Mapping file of class index to
- class name. If None, class index will be shown in
- `show_results`, else class name.
- """
-
- def __init__(self,
- backbone,
- neck=None,
- bbox_head=None,
- extractor=dict(
- type='mmdet.SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7),
- featmap_strides=[1]),
- visual_modality=False,
- train_cfg=None,
- test_cfg=None,
- class_list=None,
- init_cfg=None,
- openset=False):
- super().__init__(
- backbone, neck, bbox_head, train_cfg, test_cfg, init_cfg=init_cfg)
- self.visual_modality = visual_modality
- if visual_modality:
- self.extractor = build_roi_extractor({
- **extractor, 'out_channels':
- self.backbone.base_channels
- })
- self.maxpool = nn.MaxPool2d(extractor['roi_layer']['output_size'])
- else:
- self.extractor = None
- self.class_list = class_list
- self.openset = openset
-
- def forward_train(self, img, img_metas, relations, texts, gt_bboxes,
- gt_labels):
- """
- Args:
- img (tensor): Input images of shape (N, C, H, W).
- Typically these should be mean centered and std scaled.
- img_metas (list[dict]): A list of image info dict where each dict
- contains: 'img_shape', 'scale_factor', 'flip', and may also
- contain 'filename', 'ori_shape', 'pad_shape', and
- 'img_norm_cfg'. For details of the values of these keys,
- please see :class:`mmdet.datasets.pipelines.Collect`.
- relations (list[tensor]): Relations between bboxes.
- texts (list[tensor]): Texts in bboxes.
- gt_bboxes (list[tensor]): Each item is the truth boxes for each
- image in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[tensor]): Class indices corresponding to each box.
-
- Returns:
- dict[str, tensor]: A dictionary of loss components.
- """
- x = self.extract_feat(img, gt_bboxes)
- node_preds, edge_preds = self.bbox_head.forward(relations, texts, x)
- return self.bbox_head.loss(node_preds, edge_preds, gt_labels)
-
- def forward_test(self,
- img,
- img_metas,
- relations,
- texts,
- gt_bboxes,
- rescale=False):
- x = self.extract_feat(img, gt_bboxes)
- node_preds, edge_preds = self.bbox_head.forward(relations, texts, x)
- return [
- dict(
- img_metas=img_metas,
- nodes=F.softmax(node_preds, -1),
- edges=F.softmax(edge_preds, -1))
- ]
-
- def extract_feat(self, img, gt_bboxes):
- if self.visual_modality:
- x = super().extract_feat(img)[-1]
- feats = self.maxpool(self.extractor([x], bbox2roi(gt_bboxes)))
- return feats.view(feats.size(0), -1)
- return None
-
- def show_result(self,
- img,
- result,
- boxes,
- win_name='',
- show=False,
- wait_time=0,
- out_file=None,
- **kwargs):
- """Draw `result` on `img`.
-
- Args:
- img (str or tensor): The image to be displayed.
- result (dict): The results to draw on `img`.
- boxes (list): Bbox of img.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- Default: 0.
- show (bool): Whether to show the image.
- Default: False.
- out_file (str or None): The output filename.
- Default: None.
-
- Returns:
- img (tensor): Only if not `show` or `out_file`.
- """
- img = mmcv.imread(img)
- img = img.copy()
-
- idx_to_cls = {}
- if self.class_list is not None:
- for line in list_from_file(self.class_list):
- class_idx, class_label = line.strip().split()
- idx_to_cls[class_idx] = class_label
-
- # if out_file specified, do not show image in window
- if out_file is not None:
- show = False
-
- if self.openset:
- img = imshow_edge(
- img,
- result,
- boxes,
- show=show,
- win_name=win_name,
- wait_time=wait_time,
- out_file=out_file)
- else:
- img = imshow_node(
- img,
- result,
- boxes,
- idx_to_cls=idx_to_cls,
- show=show,
- win_name=win_name,
- wait_time=wait_time,
- out_file=out_file)
-
- if not (show or out_file):
- warnings.warn('show==False and out_file is not specified, only '
- 'result image will be returned')
- return img
-
- return img
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
deleted file mode 100644
index 9c65305baa16eb4e940d236cf45122b46b942ea9..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
+++ /dev/null
@@ -1,124 +0,0 @@
-_base_ = '../_base_/default_runtime.py'
-# model settings
-model = dict(
- type='YOLOV3',
- pretrained='open-mmlab://darknet53',
- backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)),
- neck=dict(
- type='YOLOV3Neck',
- num_scales=3,
- in_channels=[1024, 512, 256],
- out_channels=[512, 256, 128]),
- bbox_head=dict(
- type='YOLOV3Head',
- num_classes=80,
- in_channels=[512, 256, 128],
- out_channels=[1024, 512, 256],
- anchor_generator=dict(
- type='YOLOAnchorGenerator',
- base_sizes=[[(116, 90), (156, 198), (373, 326)],
- [(30, 61), (62, 45), (59, 119)],
- [(10, 13), (16, 30), (33, 23)]],
- strides=[32, 16, 8]),
- bbox_coder=dict(type='YOLOBBoxCoder'),
- featmap_strides=[32, 16, 8],
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0,
- reduction='sum'),
- loss_conf=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0,
- reduction='sum'),
- loss_xy=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=2.0,
- reduction='sum'),
- loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='GridAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0)),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- conf_thr=0.005,
- nms=dict(type='nms', iou_threshold=0.45),
- max_per_img=100))
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile', to_float32=True),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='PhotoMetricDistortion'),
- dict(
- type='Expand',
- mean=img_norm_cfg['mean'],
- to_rgb=img_norm_cfg['to_rgb'],
- ratio_range=(1, 2)),
- dict(
- type='MinIoURandomCrop',
- min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
- min_crop_size=0.3),
- dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(608, 608),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-data = dict(
- samples_per_gpu=8,
- workers_per_gpu=4,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=2000, # same as burn-in in darknet
- warmup_ratio=0.1,
- step=[218, 246])
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=273)
-evaluation = dict(interval=1, metric=['bbox'])
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/post_processing/merge_augs.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/post_processing/merge_augs.py
deleted file mode 100644
index f16cdde3d97234335a5accade6ebf80d4da55f02..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/post_processing/merge_augs.py
+++ /dev/null
@@ -1,122 +0,0 @@
-"""
-This code is based on the following file:
-https://github.com/tztztztztz/eqlv2/blob/master/mmdet/core/post_processing/merge_augs.py
-"""
-
-import numpy as np
-import torch
-from mmcv.ops import nms
-
-from ..bbox import bbox_mapping_back
-
-
-def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg):
- """Merge augmented proposals (multiscale, flip, etc.)
-
- Args:
- aug_proposals (list[Tensor]): proposals from different testing
- schemes, shape (n, 5). Note that they are not rescaled to the
- original image size.
-
- img_metas (list[dict]): list of image info dict where each dict has:
- 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmdet/datasets/pipelines/formatting.py:Collect`.
-
- rpn_test_cfg (dict): rpn test config.
-
- Returns:
- Tensor: shape (n, 4), proposals corresponding to original image scale.
- """
- recovered_proposals = []
- for proposals, img_info in zip(aug_proposals, img_metas):
- img_shape = img_info['img_shape']
- scale_factor = img_info['scale_factor']
- flip = img_info['flip']
- flip_direction = img_info['flip_direction']
- _proposals = proposals.clone()
- _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape,
- scale_factor, flip,
- flip_direction)
- recovered_proposals.append(_proposals)
- aug_proposals = torch.cat(recovered_proposals, dim=0)
- merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(),
- aug_proposals[:, -1].contiguous(),
- rpn_test_cfg.nms_thr)
- scores = merged_proposals[:, 4]
- _, order = scores.sort(0, descending=True)
- num = min(rpn_test_cfg.max_num, merged_proposals.shape[0])
- order = order[:num]
- merged_proposals = merged_proposals[order, :]
- return merged_proposals
-
-
-def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
- """Merge augmented detection bboxes and scores.
-
- Args:
- aug_bboxes (list[Tensor]): shape (n, 4*#class)
- aug_scores (list[Tensor] or None): shape (n, #class)
- img_shapes (list[Tensor]): shape (3, ).
- rcnn_test_cfg (dict): rcnn test config.
-
- Returns:
- tuple: (bboxes, scores)
- """
- recovered_bboxes = []
- for bboxes, img_info in zip(aug_bboxes, img_metas):
- img_shape = img_info[0]['img_shape']
- scale_factor = img_info[0]['scale_factor']
- flip = img_info[0]['flip']
- flip_direction = img_info[0]['flip_direction']
- bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
- flip_direction)
- recovered_bboxes.append(bboxes)
- bboxes = torch.stack(recovered_bboxes).mean(dim=0)
- if aug_scores is None:
- return bboxes
- else:
- scores = torch.stack(aug_scores).mean(dim=0)
- return bboxes, scores
-
-
-def merge_aug_scores(aug_scores):
- """Merge augmented bbox scores."""
- if isinstance(aug_scores[0], torch.Tensor):
- return torch.mean(torch.stack(aug_scores), dim=0)
- else:
- return np.mean(aug_scores, axis=0)
-
-
-def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):
- """Merge augmented mask prediction.
-
- Args:
- aug_masks (list[ndarray]): shape (n, #class, h, w)
- img_shapes (list[ndarray]): shape (3, ).
- rcnn_test_cfg (dict): rcnn test config.
-
- Returns:
- tuple: (bboxes, scores)
- """
- recovered_masks = []
- for mask, img_info in zip(aug_masks, img_metas):
- flip = img_info[0]['flip']
- flip_direction = img_info[0]['flip_direction']
- if flip:
- if flip_direction == 'horizontal':
- mask = mask[:, :, :, ::-1]
- elif flip_direction == 'vertical':
- mask = mask[:, :, ::-1, :]
- else:
- raise ValueError(
- f"Invalid flipping direction '{flip_direction}'")
- recovered_masks.append(mask)
-
- if weights is None:
- merged_masks = np.mean(recovered_masks, axis=0)
- else:
- merged_masks = np.average(
- np.array(recovered_masks), axis=0, weights=np.array(weights))
- return merged_masks
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py
deleted file mode 100644
index e58cae1b4c8cd587e73c08582e333ea21054534a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from mmcv.cnn import ConvModule, Linear
-from mmcv.runner import ModuleList, auto_fp16
-
-from mmdet.models.builder import HEADS
-from .fcn_mask_head import FCNMaskHead
-
-
-@HEADS.register_module()
-class CoarseMaskHead(FCNMaskHead):
- """Coarse mask head used in PointRend.
-
- Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample
- the input feature map instead of upsample it.
-
- Args:
- num_convs (int): Number of conv layers in the head. Default: 0.
- num_fcs (int): Number of fc layers in the head. Default: 2.
- fc_out_channels (int): Number of output channels of fc layer.
- Default: 1024.
- downsample_factor (int): The factor that feature map is downsampled by.
- Default: 2.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- """
-
- def __init__(self,
- num_convs=0,
- num_fcs=2,
- fc_out_channels=1024,
- downsample_factor=2,
- init_cfg=dict(
- type='Xavier',
- override=[
- dict(name='fcs'),
- dict(type='Constant', val=0.001, name='fc_logits')
- ]),
- *arg,
- **kwarg):
- super(CoarseMaskHead, self).__init__(
- *arg,
- num_convs=num_convs,
- upsample_cfg=dict(type=None),
- init_cfg=None,
- **kwarg)
- self.init_cfg = init_cfg
- self.num_fcs = num_fcs
- assert self.num_fcs > 0
- self.fc_out_channels = fc_out_channels
- self.downsample_factor = downsample_factor
- assert self.downsample_factor >= 1
- # remove conv_logit
- delattr(self, 'conv_logits')
-
- if downsample_factor > 1:
- downsample_in_channels = (
- self.conv_out_channels
- if self.num_convs > 0 else self.in_channels)
- self.downsample_conv = ConvModule(
- downsample_in_channels,
- self.conv_out_channels,
- kernel_size=downsample_factor,
- stride=downsample_factor,
- padding=0,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg)
- else:
- self.downsample_conv = None
-
- self.output_size = (self.roi_feat_size[0] // downsample_factor,
- self.roi_feat_size[1] // downsample_factor)
- self.output_area = self.output_size[0] * self.output_size[1]
-
- last_layer_dim = self.conv_out_channels * self.output_area
-
- self.fcs = ModuleList()
- for i in range(num_fcs):
- fc_in_channels = (
- last_layer_dim if i == 0 else self.fc_out_channels)
- self.fcs.append(Linear(fc_in_channels, self.fc_out_channels))
- last_layer_dim = self.fc_out_channels
- output_channels = self.num_classes * self.output_area
- self.fc_logits = Linear(last_layer_dim, output_channels)
-
- def init_weights(self):
- super(FCNMaskHead, self).init_weights()
-
- @auto_fp16()
- def forward(self, x):
- for conv in self.convs:
- x = conv(x)
-
- if self.downsample_conv is not None:
- x = self.downsample_conv(x)
-
- x = x.flatten(1)
- for fc in self.fcs:
- x = self.relu(fc(x))
- mask_pred = self.fc_logits(x).view(
- x.size(0), self.num_classes, *self.output_size)
- return mask_pred
diff --git a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/typing.py b/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/typing.py
deleted file mode 100644
index 4abe384f07c4b90504e47291674905f85a5b8f52..0000000000000000000000000000000000000000
--- a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/typing.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from typing import Literal, Dict, Any
-import gradio
-
-Component = gradio.File or gradio.Image or gradio.Video or gradio.Slider
-ComponentName = Literal\
-[
- 'source_file',
- 'target_file',
- 'preview_frame_slider',
- 'face_recognition_dropdown',
- 'reference_face_position_gallery',
- 'reference_face_distance_slider',
- 'face_analyser_direction_dropdown',
- 'face_analyser_age_dropdown',
- 'face_analyser_gender_dropdown',
- 'frame_processors_checkbox_group'
-]
-Update = Dict[Any, Any]
diff --git a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/image_degradation/utils_image.py b/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000
--- a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/image_degradation/utils_image.py
+++ /dev/null
@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
- return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
- return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
- plt.figure(figsize=figsize)
- plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
- if title:
- plt.title(title)
- if cbar:
- plt.colorbar()
- plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
- plt.figure(figsize=figsize)
- ax3 = plt.axes(projection='3d')
-
- w, h = Z.shape[:2]
- xx = np.arange(0,w,1)
- yy = np.arange(0,h,1)
- X, Y = np.meshgrid(xx, yy)
- ax3.plot_surface(X,Y,Z,cmap=cmap)
- #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
- plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
- paths = None # return None if dataroot is None
- if dataroot is not None:
- paths = sorted(_get_paths_from_images(dataroot))
- return paths
-
-
-def _get_paths_from_images(path):
- assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
- images = []
- for dirpath, _, fnames in sorted(os.walk(path)):
- for fname in sorted(fnames):
- if is_image_file(fname):
- img_path = os.path.join(dirpath, fname)
- images.append(img_path)
- assert images, '{:s} has no valid image file'.format(path)
- return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
- w, h = img.shape[:2]
- patches = []
- if w > p_max and h > p_max:
- w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
- h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
- w1.append(w-p_size)
- h1.append(h-p_size)
-# print(w1)
-# print(h1)
- for i in w1:
- for j in h1:
- patches.append(img[i:i+p_size, j:j+p_size,:])
- else:
- patches.append(img)
-
- return patches
-
-
-def imssave(imgs, img_path):
- """
- imgs: list, N images of size WxHxC
- """
- img_name, ext = os.path.splitext(os.path.basename(img_path))
-
- for i, img in enumerate(imgs):
- if img.ndim == 3:
- img = img[:, :, [2, 1, 0]]
- new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
- cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
- """
- split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
- and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
- will be splitted.
- Args:
- original_dataroot:
- taget_dataroot:
- p_size: size of small images
- p_overlap: patch size in training is a good choice
- p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
- """
- paths = get_image_paths(original_dataroot)
- for img_path in paths:
- # img_name, ext = os.path.splitext(os.path.basename(img_path))
- img = imread_uint(img_path, n_channels=n_channels)
- patches = patches_from_image(img, p_size, p_overlap, p_max)
- imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
- #if original_dataroot == taget_dataroot:
- #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
- if not os.path.exists(path):
- os.makedirs(path)
-
-
-def mkdirs(paths):
- if isinstance(paths, str):
- mkdir(paths)
- else:
- for path in paths:
- mkdir(path)
-
-
-def mkdir_and_rename(path):
- if os.path.exists(path):
- new_name = path + '_archived_' + get_timestamp()
- print('Path already exists. Rename it to [{:s}]'.format(new_name))
- os.rename(path, new_name)
- os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
- # input: path
- # output: HxWx3(RGB or GGG), or HxWx1 (G)
- if n_channels == 1:
- img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
- img = np.expand_dims(img, axis=2) # HxWx1
- elif n_channels == 3:
- img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
- if img.ndim == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
- else:
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
- return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
- img = np.squeeze(img)
- if img.ndim == 3:
- img = img[:, :, [2, 1, 0]]
- cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
- img = np.squeeze(img)
- if img.ndim == 3:
- img = img[:, :, [2, 1, 0]]
- cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
- # read image by cv2
- # return: Numpy float32, HWC, BGR, [0,1]
- img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
- img = img.astype(np.float32) / 255.
- if img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- # some images have 4 channels
- if img.shape[2] > 3:
- img = img[:, :, :3]
- return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <---> numpy(unit)
-# numpy(single) <---> tensor
-# numpy(unit) <---> tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <---> numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
- return np.float32(img/255.)
-
-
-def single2uint(img):
-
- return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
- return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
- return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <---> tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
- if img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
- if img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
- img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
- if img.ndim == 3:
- img = np.transpose(img, (1, 2, 0))
- return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <---> tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
- img = img.data.squeeze().float().cpu().numpy()
- if img.ndim == 3:
- img = np.transpose(img, (1, 2, 0))
-
- return img
-
-# convert torch tensor to single
-def tensor2single3(img):
- img = img.data.squeeze().float().cpu().numpy()
- if img.ndim == 3:
- img = np.transpose(img, (1, 2, 0))
- elif img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- return img
-
-
-def single2tensor5(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
- return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
- '''
- Converts a torch Tensor into an image Numpy array of BGR channel order
- Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
- Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
- '''
- tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
- tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
- n_dim = tensor.dim()
- if n_dim == 4:
- n_img = len(tensor)
- img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
- img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
- elif n_dim == 3:
- img_np = tensor.numpy()
- img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
- elif n_dim == 2:
- img_np = tensor.numpy()
- else:
- raise TypeError(
- 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
- if out_type == np.uint8:
- img_np = (img_np * 255.0).round()
- # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
- return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
- if mode == 0:
- return img
- elif mode == 1:
- return np.flipud(np.rot90(img))
- elif mode == 2:
- return np.flipud(img)
- elif mode == 3:
- return np.rot90(img, k=3)
- elif mode == 4:
- return np.flipud(np.rot90(img, k=2))
- elif mode == 5:
- return np.rot90(img)
- elif mode == 6:
- return np.rot90(img, k=2)
- elif mode == 7:
- return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
- if mode == 0:
- return img
- elif mode == 1:
- return img.rot90(1, [2, 3]).flip([2])
- elif mode == 2:
- return img.flip([2])
- elif mode == 3:
- return img.rot90(3, [2, 3])
- elif mode == 4:
- return img.rot90(2, [2, 3]).flip([2])
- elif mode == 5:
- return img.rot90(1, [2, 3])
- elif mode == 6:
- return img.rot90(2, [2, 3])
- elif mode == 7:
- return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
- img_size = img.size()
- img_np = img.data.cpu().numpy()
- if len(img_size) == 3:
- img_np = np.transpose(img_np, (1, 2, 0))
- elif len(img_size) == 4:
- img_np = np.transpose(img_np, (2, 3, 1, 0))
- img_np = augment_img(img_np, mode=mode)
- img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
- if len(img_size) == 3:
- img_tensor = img_tensor.permute(2, 0, 1)
- elif len(img_size) == 4:
- img_tensor = img_tensor.permute(3, 2, 0, 1)
-
- return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
- if mode == 0:
- return img
- elif mode == 1:
- return img.transpose(1, 0, 2)
- elif mode == 2:
- return img[::-1, :, :]
- elif mode == 3:
- img = img[::-1, :, :]
- img = img.transpose(1, 0, 2)
- return img
- elif mode == 4:
- return img[:, ::-1, :]
- elif mode == 5:
- img = img[:, ::-1, :]
- img = img.transpose(1, 0, 2)
- return img
- elif mode == 6:
- img = img[:, ::-1, :]
- img = img[::-1, :, :]
- return img
- elif mode == 7:
- img = img[:, ::-1, :]
- img = img[::-1, :, :]
- img = img.transpose(1, 0, 2)
- return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
- # horizontal flip OR rotate
- hflip = hflip and random.random() < 0.5
- vflip = rot and random.random() < 0.5
- rot90 = rot and random.random() < 0.5
-
- def _augment(img):
- if hflip:
- img = img[:, ::-1, :]
- if vflip:
- img = img[::-1, :, :]
- if rot90:
- img = img.transpose(1, 0, 2)
- return img
-
- return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
- # img_in: Numpy, HWC or HW
- img = np.copy(img_in)
- if img.ndim == 2:
- H, W = img.shape
- H_r, W_r = H % scale, W % scale
- img = img[:H - H_r, :W - W_r]
- elif img.ndim == 3:
- H, W, C = img.shape
- H_r, W_r = H % scale, W % scale
- img = img[:H - H_r, :W - W_r, :]
- else:
- raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
- return img
-
-
-def shave(img_in, border=0):
- # img_in: Numpy, HWC or HW
- img = np.copy(img_in)
- h, w = img.shape[:2]
- img = img[border:h-border, border:w-border]
- return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
- '''same as matlab rgb2ycbcr
- only_y: only return Y channel
- Input:
- uint8, [0, 255]
- float, [0, 1]
- '''
- in_img_type = img.dtype
- img.astype(np.float32)
- if in_img_type != np.uint8:
- img *= 255.
- # convert
- if only_y:
- rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
- else:
- rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
- [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
- if in_img_type == np.uint8:
- rlt = rlt.round()
- else:
- rlt /= 255.
- return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
- '''same as matlab ycbcr2rgb
- Input:
- uint8, [0, 255]
- float, [0, 1]
- '''
- in_img_type = img.dtype
- img.astype(np.float32)
- if in_img_type != np.uint8:
- img *= 255.
- # convert
- rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
- [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
- if in_img_type == np.uint8:
- rlt = rlt.round()
- else:
- rlt /= 255.
- return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
- '''bgr version of rgb2ycbcr
- only_y: only return Y channel
- Input:
- uint8, [0, 255]
- float, [0, 1]
- '''
- in_img_type = img.dtype
- img.astype(np.float32)
- if in_img_type != np.uint8:
- img *= 255.
- # convert
- if only_y:
- rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
- else:
- rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
- [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
- if in_img_type == np.uint8:
- rlt = rlt.round()
- else:
- rlt /= 255.
- return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
- # conversion among BGR, gray and y
- if in_c == 3 and tar_type == 'gray': # BGR to gray
- gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
- return [np.expand_dims(img, axis=2) for img in gray_list]
- elif in_c == 3 and tar_type == 'y': # BGR to y
- y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
- return [np.expand_dims(img, axis=2) for img in y_list]
- elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
- return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
- else:
- return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
- # img1 and img2 have range [0, 255]
- #img1 = img1.squeeze()
- #img2 = img2.squeeze()
- if not img1.shape == img2.shape:
- raise ValueError('Input images must have the same dimensions.')
- h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
-
- img1 = img1.astype(np.float64)
- img2 = img2.astype(np.float64)
- mse = np.mean((img1 - img2)**2)
- if mse == 0:
- return float('inf')
- return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
- '''calculate SSIM
- the same outputs as MATLAB's
- img1, img2: [0, 255]
- '''
- #img1 = img1.squeeze()
- #img2 = img2.squeeze()
- if not img1.shape == img2.shape:
- raise ValueError('Input images must have the same dimensions.')
- h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
-
- if img1.ndim == 2:
- return ssim(img1, img2)
- elif img1.ndim == 3:
- if img1.shape[2] == 3:
- ssims = []
- for i in range(3):
- ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
- return np.array(ssims).mean()
- elif img1.shape[2] == 1:
- return ssim(np.squeeze(img1), np.squeeze(img2))
- else:
- raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
- C1 = (0.01 * 255)**2
- C2 = (0.03 * 255)**2
-
- img1 = img1.astype(np.float64)
- img2 = img2.astype(np.float64)
- kernel = cv2.getGaussianKernel(11, 1.5)
- window = np.outer(kernel, kernel.transpose())
-
- mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
- mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
- mu1_sq = mu1**2
- mu2_sq = mu2**2
- mu1_mu2 = mu1 * mu2
- sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
- sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
- sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
- ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
- (sigma1_sq + sigma2_sq + C2))
- return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
- absx = torch.abs(x)
- absx2 = absx**2
- absx3 = absx**3
- return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
- (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
- if (scale < 1) and (antialiasing):
- # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
- kernel_width = kernel_width / scale
-
- # Output-space coordinates
- x = torch.linspace(1, out_length, out_length)
-
- # Input-space coordinates. Calculate the inverse mapping such that 0.5
- # in output space maps to 0.5 in input space, and 0.5+scale in output
- # space maps to 1.5 in input space.
- u = x / scale + 0.5 * (1 - 1 / scale)
-
- # What is the left-most pixel that can be involved in the computation?
- left = torch.floor(u - kernel_width / 2)
-
- # What is the maximum number of pixels that can be involved in the
- # computation? Note: it's OK to use an extra pixel here; if the
- # corresponding weights are all zero, it will be eliminated at the end
- # of this function.
- P = math.ceil(kernel_width) + 2
-
- # The indices of the input pixels involved in computing the k-th output
- # pixel are in row k of the indices matrix.
- indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
- 1, P).expand(out_length, P)
-
- # The weights used to compute the k-th output pixel are in row k of the
- # weights matrix.
- distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
- # apply cubic kernel
- if (scale < 1) and (antialiasing):
- weights = scale * cubic(distance_to_center * scale)
- else:
- weights = cubic(distance_to_center)
- # Normalize the weights matrix so that each row sums to 1.
- weights_sum = torch.sum(weights, 1).view(out_length, 1)
- weights = weights / weights_sum.expand(out_length, P)
-
- # If a column in weights is all zero, get rid of it. only consider the first and last column.
- weights_zero_tmp = torch.sum((weights == 0), 0)
- if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
- indices = indices.narrow(1, 1, P - 2)
- weights = weights.narrow(1, 1, P - 2)
- if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
- indices = indices.narrow(1, 0, P - 2)
- weights = weights.narrow(1, 0, P - 2)
- weights = weights.contiguous()
- indices = indices.contiguous()
- sym_len_s = -indices.min() + 1
- sym_len_e = indices.max() - in_length
- indices = indices + sym_len_s - 1
- return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
- # Now the scale should be the same for H and W
- # input: img: pytorch tensor, CHW or HW [0,1]
- # output: CHW or HW [0,1] w/o round
- need_squeeze = True if img.dim() == 2 else False
- if need_squeeze:
- img.unsqueeze_(0)
- in_C, in_H, in_W = img.size()
- out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
- kernel_width = 4
- kernel = 'cubic'
-
- # Return the desired dimension order for performing the resize. The
- # strategy is to perform the resize first along the dimension with the
- # smallest scale factor.
- # Now we do not support this.
-
- # get weights and indices
- weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
- in_H, out_H, scale, kernel, kernel_width, antialiasing)
- weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
- in_W, out_W, scale, kernel, kernel_width, antialiasing)
- # process H dimension
- # symmetric copying
- img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
- img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
- sym_patch = img[:, :sym_len_Hs, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
- sym_patch = img[:, -sym_len_He:, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
- out_1 = torch.FloatTensor(in_C, out_H, in_W)
- kernel_width = weights_H.size(1)
- for i in range(out_H):
- idx = int(indices_H[i][0])
- for j in range(out_C):
- out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
- # process W dimension
- # symmetric copying
- out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
- out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
- sym_patch = out_1[:, :, :sym_len_Ws]
- inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(2, inv_idx)
- out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
- sym_patch = out_1[:, :, -sym_len_We:]
- inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(2, inv_idx)
- out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
- out_2 = torch.FloatTensor(in_C, out_H, out_W)
- kernel_width = weights_W.size(1)
- for i in range(out_W):
- idx = int(indices_W[i][0])
- for j in range(out_C):
- out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
- if need_squeeze:
- out_2.squeeze_()
- return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
- # Now the scale should be the same for H and W
- # input: img: Numpy, HWC or HW [0,1]
- # output: HWC or HW [0,1] w/o round
- img = torch.from_numpy(img)
- need_squeeze = True if img.dim() == 2 else False
- if need_squeeze:
- img.unsqueeze_(2)
-
- in_H, in_W, in_C = img.size()
- out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
- kernel_width = 4
- kernel = 'cubic'
-
- # Return the desired dimension order for performing the resize. The
- # strategy is to perform the resize first along the dimension with the
- # smallest scale factor.
- # Now we do not support this.
-
- # get weights and indices
- weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
- in_H, out_H, scale, kernel, kernel_width, antialiasing)
- weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
- in_W, out_W, scale, kernel, kernel_width, antialiasing)
- # process H dimension
- # symmetric copying
- img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
- img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
- sym_patch = img[:sym_len_Hs, :, :]
- inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(0, inv_idx)
- img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
- sym_patch = img[-sym_len_He:, :, :]
- inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(0, inv_idx)
- img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
- out_1 = torch.FloatTensor(out_H, in_W, in_C)
- kernel_width = weights_H.size(1)
- for i in range(out_H):
- idx = int(indices_H[i][0])
- for j in range(out_C):
- out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
- # process W dimension
- # symmetric copying
- out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
- out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
- sym_patch = out_1[:, :sym_len_Ws, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
- sym_patch = out_1[:, -sym_len_We:, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
- out_2 = torch.FloatTensor(out_H, out_W, in_C)
- kernel_width = weights_W.size(1)
- for i in range(out_W):
- idx = int(indices_W[i][0])
- for j in range(out_C):
- out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
- if need_squeeze:
- out_2.squeeze_()
-
- return out_2.numpy()
-
-
-if __name__ == '__main__':
- print('---')
-# img = imread_uint('test.bmp', 3)
-# img = uint2single(img)
-# img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/spaces/triggah61/chingu-music/tests/modules/test_codebooks_patterns.py b/spaces/triggah61/chingu-music/tests/modules/test_codebooks_patterns.py
deleted file mode 100644
index b658f4779a369f9ec8dde692a61b7f0fe3485724..0000000000000000000000000000000000000000
--- a/spaces/triggah61/chingu-music/tests/modules/test_codebooks_patterns.py
+++ /dev/null
@@ -1,246 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pytest
-import torch
-
-from audiocraft.modules.codebooks_patterns import (
- DelayedPatternProvider,
- ParallelPatternProvider,
- Pattern,
- UnrolledPatternProvider,
-)
-
-
-class TestParallelPatternProvider:
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [0, 1, 16, 100])
- def test_get_pattern(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- # + 1 to account for 1st step
- assert len(pattern.layout) == timesteps + 1
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_content(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- for s, v in enumerate(pattern.layout):
- for i, code in enumerate(v):
- assert i == code.q
- assert code.t == s - 1 # account for the 1st empty step
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_max_delay(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == 0
- assert len(pattern.valid_layout) == len(pattern.layout) - pattern.max_delay
-
-
-class TestDelayedPatternProvider:
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [0, 1, 16, 100])
- def test_get_pattern(self, n_q: int, timesteps: int):
- delays = [
- list(range(n_q)),
- [0] + [1] * (n_q - 1),
- [0] + [4] * (n_q - 1),
- ]
- for delay in delays:
- provider = DelayedPatternProvider(n_q, delay)
- pattern = provider.get_pattern(timesteps)
- # + 1 to account for 1st step
- assert len(pattern.layout) == timesteps + max(delay) + 1
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_content(self, n_q: int, timesteps: int):
- provider = DelayedPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- for s, v in enumerate(pattern.layout):
- for i, code in enumerate(v):
- assert i == code.q
- assert code.t == max(0, s - code.q - 1)
-
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- @pytest.mark.parametrize("delay", [[0, 1, 2, 3], [0, 1, 1, 1], [0, 3, 3, 3], [0, 3]])
- def test_pattern_max_delay(self, timesteps: int, delay: list):
- provider = DelayedPatternProvider(len(delay), delay)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == max(delay)
- assert len(pattern.valid_layout) == len(pattern.layout) - pattern.max_delay
-
-
-class TestUnrolledPatternProvider:
-
- @pytest.mark.parametrize("timesteps", [0, 1, 16])
- @pytest.mark.parametrize("flattening", [[0, 1, 2], [0, 1, 1]])
- @pytest.mark.parametrize("delays", [[0, 0, 0], [0, 5, 5]])
- def test_get_pattern(self, timesteps: int, flattening: list, delays: list):
- n_q = len(flattening)
- max_delay = max(delays)
- provider = UnrolledPatternProvider(n_q, flattening, delays)
- pattern = provider.get_pattern(timesteps)
- assert len(pattern.layout) == provider.num_virtual_steps(timesteps) + max_delay
-
- @pytest.mark.parametrize("timesteps", [0, 1, 16])
- @pytest.mark.parametrize("flattening", [[0, 1, 2], [0, 1, 1]])
- @pytest.mark.parametrize("delays", [[0, 0, 0], [0, 5, 5]])
- def test_pattern_max_delay(self, timesteps: int, flattening: list, delays: list):
- n_q = len(flattening)
- max_delay = max(delays)
- provider = UnrolledPatternProvider(n_q, flattening, delays)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == max_delay
-
-
-class TestPattern:
-
- def ref_build_pattern_sequence(self, z: torch.Tensor, pattern: Pattern, special_token: int):
- """Reference method to build the sequence from the pattern without using fancy scatter."""
- bs, n_q, T = z.shape
- z = z.cpu().numpy()
- assert n_q == pattern.n_q
- assert T <= pattern.timesteps
- inp = torch.full((bs, n_q, len(pattern.layout)), special_token, dtype=torch.long).numpy()
- inp[:] = special_token
- for s, v in enumerate(pattern.layout):
- for (t, q) in v:
- if t < T:
- inp[:, q, s] = z[:, q, t]
- return torch.from_numpy(inp)
-
- def ref_revert_pattern_sequence(self, z: torch.Tensor, pattern: Pattern, special_token: int):
- """Reference method to revert the sequence from the pattern without using fancy scatter."""
- z = z.cpu().numpy()
- bs, n_q, S = z.shape
- assert pattern.n_q == n_q
- inp = torch.full((bs, pattern.n_q, pattern.timesteps), special_token, dtype=torch.long).numpy()
- inp[:] = special_token
- for s, v in enumerate(pattern.layout):
- for (t, q) in v:
- if t < pattern.timesteps:
- inp[:, q, t] = z[:, q, s]
- return torch.from_numpy(inp)
-
- def ref_revert_pattern_logits(self, z: torch.Tensor, pattern: Pattern, special_token: float):
- """Reference method to revert the logits from the pattern without using fancy scatter."""
- z = z.cpu().numpy()
- bs, card, n_q, S = z.shape
- assert pattern.n_q == n_q
- ref_layout = pattern.layout
- inp = torch.full((bs, card, pattern.n_q, pattern.timesteps), special_token, dtype=torch.float).numpy()
- inp[:] = special_token
- for s, v in enumerate(ref_layout[1:]):
- if s < S:
- for (t, q) in v:
- if t < pattern.timesteps:
- inp[:, :, q, t] = z[:, :, q, s]
- return torch.from_numpy(inp)
-
- def _get_pattern_providers(self, n_q: int):
- pattern_provider_1 = ParallelPatternProvider(n_q)
- pattern_provider_2 = DelayedPatternProvider(n_q, list(range(n_q)))
- pattern_provider_3 = DelayedPatternProvider(n_q, [0] + [1] * (n_q - 1))
- pattern_provider_4 = UnrolledPatternProvider(
- n_q, flattening=list(range(n_q)), delays=[0] * n_q
- )
- pattern_provider_5 = UnrolledPatternProvider(
- n_q, flattening=[0] + [1] * (n_q - 1), delays=[0] * n_q
- )
- pattern_provider_6 = UnrolledPatternProvider(
- n_q, flattening=[0] + [1] * (n_q - 1), delays=[0] + [5] * (n_q - 1)
- )
- return [
- pattern_provider_1,
- pattern_provider_2,
- pattern_provider_3,
- pattern_provider_4,
- pattern_provider_5,
- pattern_provider_6,
- ]
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- def test_build_pattern_sequence(self, n_q: int, timesteps: int):
- bs = 2
- card = 256
- special_token = card
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # we can correctly build the sequence from the pattern
- z = torch.randint(0, card, (bs, n_q, timesteps))
- ref_res = self.ref_build_pattern_sequence(z, pattern, special_token)
- res, indexes, mask = pattern.build_pattern_sequence(z, special_token)
- assert (res == ref_res).float().mean() == 1.0
-
- # expected assertion fails on the number of timesteps
- invalid_timesteps = [timesteps + 1]
- if pattern.num_sequence_steps != pattern.timesteps:
- invalid_timesteps.append(pattern.num_sequence_steps)
- for i_timesteps in invalid_timesteps:
- z2 = torch.randint(0, card, (bs, n_q, i_timesteps))
- with pytest.raises(AssertionError):
- pattern.build_pattern_sequence(z2, special_token)
-
- # expected assertion fails on the number of codebooks
- invalid_qs = [0, n_q - 1, n_q + 1]
- for i_q in invalid_qs:
- z3 = torch.randint(0, card, (bs, i_q, timesteps))
- with pytest.raises(AssertionError):
- pattern.build_pattern_sequence(z3, special_token)
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- def test_revert_pattern_sequence(self, n_q: int, timesteps: int):
- bs = 2
- card = 256
- special_token = card
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # this works assuming previous tests are successful
- z = torch.randint(0, card, (bs, n_q, timesteps))
- s = self.ref_build_pattern_sequence(z, pattern, special_token)
- ref_out = self.ref_revert_pattern_sequence(s, pattern, special_token)
- # ensure our reference script retrieve the original sequence
- assert z.shape == ref_out.shape
- assert (z == ref_out).float().mean() == 1.0
- # now we can test the scatter version
- out, indexes, mask = pattern.revert_pattern_sequence(s, special_token)
- assert out.shape == ref_out.shape
- assert (out == ref_out).float().mean() == 1.0
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- @pytest.mark.parametrize("card", [1, 2, 256, 1024])
- def test_revert_pattern_logits(self, n_q: int, timesteps: int, card: int):
- bs = 2
- special_token = card
- logits_special_token = float('nan')
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # this works assuming previous tests are successful
- z = torch.randint(0, card, (bs, n_q, timesteps))
- s = self.ref_build_pattern_sequence(z, pattern, special_token)
- logits = torch.randn((bs, card, n_q, s.shape[-1]))
- ref_out = self.ref_revert_pattern_logits(logits, pattern, logits_special_token)
- # ensure our reference script retrieve the original sequence
- assert ref_out.shape == torch.Size([bs, card, n_q, timesteps])
- # now we can test the scatter version
- out, indexes, mask = pattern.revert_pattern_logits(logits, logits_special_token)
- assert out.shape == ref_out.shape
- assert (out == ref_out).float().mean() == 1.0
diff --git a/spaces/trttung1610/musicgen/audiocraft/optim/polynomial_decay_lr_scheduler.py b/spaces/trttung1610/musicgen/audiocraft/optim/polynomial_decay_lr_scheduler.py
deleted file mode 100644
index c5ea30b094538269dbb0055ab3163f84d1cf6e90..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/optim/polynomial_decay_lr_scheduler.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
-
-
-class PolynomialDecayLRScheduler(_LRScheduler):
- """Polynomial decay LR scheduler.
-
- Args:
- optimizer (Optimizer): Torch optimizer.
- warmup_steps (int): Number of warmup steps.
- total_steps (int): Total number of steps.
- end_lr (float): Final learning rate to achieve over total number of steps.
- zero_lr_warmup_steps (int): Number of steps with a learning rate of value 0.
- power (float): Decay exponent.
- """
- def __init__(self, optimizer: Optimizer, warmup_steps: int, total_steps: int,
- end_lr: float = 0., zero_lr_warmup_steps: int = 0, power: float = 1.):
- self.warmup_steps = warmup_steps
- self.total_steps = total_steps
- self.end_lr = end_lr
- self.zero_lr_warmup_steps = zero_lr_warmup_steps
- self.power = power
- super().__init__(optimizer)
-
- def _get_sched_lr(self, lr: float, step: int):
- if self.zero_lr_warmup_steps > 0 and step <= self.zero_lr_warmup_steps:
- lr = 0
- elif self.warmup_steps > 0 and step <= self.warmup_steps + self.zero_lr_warmup_steps:
- lr_ratio = (step - self.zero_lr_warmup_steps) / float(self.warmup_steps)
- lr = lr_ratio * lr
- elif step >= self.total_steps:
- lr = self.end_lr
- else:
- total_warmup_steps = self.warmup_steps + self.zero_lr_warmup_steps
- lr_range = lr - self.end_lr
- pct_remaining = 1 - (step - total_warmup_steps) / (self.total_steps - total_warmup_steps)
- lr = lr_range * pct_remaining ** self.power + self.end_lr
- return lr
-
- def get_lr(self):
- return [self._get_sched_lr(base_lr, self.last_epoch) for base_lr in self.base_lrs]
diff --git a/spaces/tskolm/YouTube_comments_generation/README.md b/spaces/tskolm/YouTube_comments_generation/README.md
deleted file mode 100644
index 7bccf3304a52abd62fcb2a630d91b3ba5f67d39e..0000000000000000000000000000000000000000
--- a/spaces/tskolm/YouTube_comments_generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: YouTube_comments_generation
-emoji: 📚
-colorFrom: green
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/twigs/simplifier/app.py b/spaces/twigs/simplifier/app.py
deleted file mode 100644
index 553b123fadd4aadc3c564ced7d54d20cae888c91..0000000000000000000000000000000000000000
--- a/spaces/twigs/simplifier/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, BartTokenizer, BartForConditionalGeneration, pipeline
-import numpy as np
-import torch
-import re
-from textstat import textstat
-
-
-MAX_LEN = 256
-NUM_BEAMS = 4
-EARLY_STOPPING = True
-N_OUT = 4
-
-
-cwi_tok = AutoTokenizer.from_pretrained('twigs/cwi-regressor')
-cwi_model = AutoModelForSequenceClassification.from_pretrained(
- 'twigs/cwi-regressor')
-simpl_tok = BartTokenizer.from_pretrained('twigs/bart-text2text-simplifier')
-simpl_model = BartForConditionalGeneration.from_pretrained(
- 'twigs/bart-text2text-simplifier')
-cwi_pipe = pipeline('text-classification', model=cwi_model,
- tokenizer=cwi_tok, function_to_apply='none')
-fill_pipe = pipeline('fill-mask', top_k=1)
-
-
-def id_replace_complex(s, threshold=0.2):
-
- # get all tokens
- tokens = re.compile('\w+').findall(s)
- cands = [f"{t}. {s}" for t in tokens]
- # get complex tokens
- # if score >= threshold select tokens[idx]
- compl_tok = [tokens[idx] for idx, x in enumerate(
- cwi_pipe(cands)) if x['score'] >= threshold]
-
- masked = [s[:s.index(t)] + '' + s[s.index(t)+len(t):] for t in compl_tok]
- cands = fill_pipe(masked)
- # structure is different in 1 vs n complex words
- replacements = [el['token_str'] if type(
- el) == dict else el[0]['token_str'] for el in cands]
- # some tokens get prefixed with space
- replacements = [tok if tok.find(' ') == -1 else tok[1:]
- for tok in replacements]
-
- for i, el in enumerate(compl_tok):
- idx = s.index(el)
- s = s[:idx] + replacements[i] + s[idx+len(el):]
-
- return s, compl_tok, replacements
-
-def generate_candidate_text(s, model, tokenizer, tokenized=False):
-
-
- out = simpl_tok([s], max_length=256, padding="max_length", truncation=True,
- return_tensors='pt') if not tokenized else s
-
- generated_ids = model.generate(
- input_ids=out['input_ids'],
- attention_mask=out['attention_mask'],
- use_cache=True,
- decoder_start_token_id=simpl_model.config.pad_token_id,
- num_beams=NUM_BEAMS,
- max_length=MAX_LEN,
- early_stopping=EARLY_STOPPING,
- num_return_sequences=N_OUT
- )
-
- return [tokenizer.decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[
- 1:] for ids in generated_ids]
-
-
-def rank_candidate_text(sentences):
- fkgl_scores = [textstat.flesch_kincaid_grade(s) for s in sentences]
- return sentences[np.argmin(fkgl_scores)]
-
-
-def full_pipeline(source, simpl_model, simpl_tok, tokens, lexical=False):
-
- modified, complex_words, replacements = id_replace_complex(source, threshold=0.2) if lexical else (source, None, None)
- cands = generate_candidate_text(tokens+modified, simpl_model, simpl_tok)
- output = rank_candidate_text(cands)
- return output, complex_words, replacements
-
-def main():
-
- aug_tok = ['c_', 'lev_', 'dep_', 'rank_', 'rat_', 'n_syl_']
- base_tokens = ['CharRatio', 'LevSim', 'DependencyTreeDepth',
- 'WordComplexity', 'WordRatio', 'NumberOfSyllables']
-
- default_values = [0.8, 0.6, 0.9, 0.8, 0.9, 1.9]
- user_values = default_values
- tok_values = dict((t, default_values[idx]) for idx, t in enumerate(base_tokens))
-
- example_sentences = ["A matchbook is a small cardboard folder (matchcover) enclosing a quantity of matches and having a coarse striking surface on the exterior.",
- "If there are no strong land use controls, buildings are built along a bypass, converting it into an ordinary town road, and the bypass may eventually become as congested as the local streets it was intended to avoid.",
- "Plot Captain Caleb Holt (Kirk Cameron) is a firefighter in Albany, Georgia and firmly keeps the cardinal rule of all firemen, \"Never leave your partner behind\".",
- "Britpop emerged from the British independent music scene of the early 1990s and was characterised by bands influenced by British guitar pop music of the 1960s and 1970s."]
-
-
- st.title("Make it Simple")
-
- with st.expander("Example sentences"):
- for s in example_sentences:
- st.code(body=s)
-
-
- with st.form(key="simplify"):
- input_sentence = st.text_area("Original sentence")
-
- lexical = st.checkbox("Identify and replace complex words", value=True)
-
- tok = st.multiselect(
- label="Tokens to augment the sentence", options=base_tokens, default=base_tokens)
- if (tok):
- st.text("Select the desired intensity")
- for idx, t in enumerate(tok):
- user_values[idx] = st.slider(
- t, min_value=0., max_value=1., value=tok_values[t], step=0.1, key=t)
-
- submit = st.form_submit_button("Process")
- if (submit):
-
- tokens = " ".join([t+str(v) for t, v in zip(aug_tok, user_values)]) + " "
- output, words, replacements = full_pipeline(input_sentence, simpl_model, simpl_tok, tokens, lexical)
-
-
- c1, c2, c3 = st.columns([1,1,2])
-
- with c1:
- st.markdown("#### Words identified as complex")
- if words:
- for w in words:
- st.markdown(f"* {w}")
-
- else:
- st.markdown("None :smile:")
-
- with c2:
- st.markdown("#### Their mask-predicted replacement")
- if replacements:
- for w in replacements:
- st.markdown(f"* {w}")
-
- else:
- st.markdown("None :smile:")
-
- with c3:
- st.markdown(f"#### Original Sentence:\n > {input_sentence}")
- st.markdown(f"#### Output Sentence:\n > {output}")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/ucalyptus/PTI/models/__init__.py b/spaces/ucalyptus/PTI/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ulysses115/diffsvc_test/modules/parallel_wavegan/losses/__init__.py b/spaces/ulysses115/diffsvc_test/modules/parallel_wavegan/losses/__init__.py
deleted file mode 100644
index b03080a907cb5cb4b316ceb74866ddbc406b33bf..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/modules/parallel_wavegan/losses/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .stft_loss import * # NOQA
diff --git a/spaces/unco3892/real_estate_ie/README.md b/spaces/unco3892/real_estate_ie/README.md
deleted file mode 100644
index daaca67e53c3cb6fdbbd3b62f171e4638b9e1a3e..0000000000000000000000000000000000000000
--- a/spaces/unco3892/real_estate_ie/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Real_estate_ie
-emoji: 🏡
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.11
-app_file: app.py
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/upstage/open-ko-llm-leaderboard/src/assets/hardcoded_evals.py b/spaces/upstage/open-ko-llm-leaderboard/src/assets/hardcoded_evals.py
deleted file mode 100644
index 34de91df9bbf32b2a582df182e075a27d7a071a4..0000000000000000000000000000000000000000
--- a/spaces/upstage/open-ko-llm-leaderboard/src/assets/hardcoded_evals.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from src.display_models.utils import AutoEvalColumn, model_hyperlink
-
-baseline = {
- AutoEvalColumn.model.name: "Baseline
",
- AutoEvalColumn.revision.name: "N/A",
- AutoEvalColumn.precision.name: None,
- AutoEvalColumn.average.name: 25.0,
- AutoEvalColumn.arc.name: 25.0,
- AutoEvalColumn.hellaswag.name: 25.0,
- AutoEvalColumn.mmlu.name: 25.0,
- AutoEvalColumn.truthfulqa.name: 25.0,
- AutoEvalColumn.dummy.name: "baseline",
- AutoEvalColumn.model_type.name: "",
-}
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Change My Software 10 115.md b/spaces/usbethFlerru/sovits-modelsV2/example/Change My Software 10 115.md
deleted file mode 100644
index 913c01d909c7df5919cfb5336f36e6a9d7ad7dc6..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Change My Software 10 115.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Change My Software 10 115 DOWNLOAD ✏ https://urlcod.com/2uyXDK
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/hub/index.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/hub/index.md
deleted file mode 100644
index 30f1652b6f30207904be603d8638a9da6779cb3a..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/hub/index.md
+++ /dev/null
@@ -1,42 +0,0 @@
----
-comments: true
-description: 'Ultralytics HUB: Train & deploy YOLO models from one spot! Use drag-and-drop interface with templates & pre-training models. Check quickstart, datasets, and more.'
-keywords: Ultralytics HUB, YOLOv5, YOLOv8, object detection, instance segmentation, classification, drag-and-drop interface, pre-trained models, integrations, mobile app, Inference API, datasets, projects, models
----
-
-# Ultralytics HUB
-
-
-
-Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!
-
-- [**Quickstart**](./quickstart.md). Start training and deploying YOLO models with HUB in seconds.
-- [**Datasets: Preparing and Uploading**](./datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.
-- [**Projects: Creating and Managing**](./projects.md). Group your models into projects for improved organization.
-- [**Models: Training and Exporting**](./models.md). Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
-- [**Integrations: Options**](./integrations.md). Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
-- [**Ultralytics HUB App**](./app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
- * [**iOS**](./app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
- * [**Android**](./app/android.md). Explore TFLite acceleration on mobile devices.
-- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/engine/predictor.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/engine/predictor.md
deleted file mode 100644
index f4aed3c06653fa3188a5222357fe36c76fbde1f2..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/engine/predictor.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-description: '"The BasePredictor class in Ultralytics YOLO Engine predicts object detection in images and videos. Learn to implement YOLO with ease."'
-keywords: Ultralytics, YOLO, BasePredictor, Object Detection, Computer Vision, Fast Model, Insights
----
-
-## BasePredictor
----
-### ::: ultralytics.yolo.engine.predictor.BasePredictor
-
-
-
{children}
- },
- code({ node, inline, className, children, ...props }) {
- if (children.length) {
- if (children[0] == '▍') {
- return (
- ▍
- )
- }
-
- children[0] = (children[0] as string).replace('`▍`', '▍')
- }
-
- const match = /language-(\w+)/.exec(className || '')
-
- if (inline) {
- return (
-
- {children}
-
- )
- }
-
- return (
-
-
-
- {message.author === 'bot' &&
-
-
- 欢迎使用新必应
- 由 AI 支持的网页版 Copilot
- `_
-
- Dataset statistics:
- - identities: 250.
- - images: 1275.
- - cameras: 8.
- """
- dataset_dir = 'grid'
- dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
- _junk_pids = [0]
-
- def __init__(self, root='', split_id=0, **kwargs):
- self.root = osp.abspath(osp.expanduser(root))
- self.dataset_dir = osp.join(self.root, self.dataset_dir)
- self.download_dataset(self.dataset_dir, self.dataset_url)
-
- self.probe_path = osp.join(
- self.dataset_dir, 'underground_reid', 'probe'
- )
- self.gallery_path = osp.join(
- self.dataset_dir, 'underground_reid', 'gallery'
- )
- self.split_mat_path = osp.join(
- self.dataset_dir, 'underground_reid', 'features_and_partitions.mat'
- )
- self.split_path = osp.join(self.dataset_dir, 'splits.json')
-
- required_files = [
- self.dataset_dir, self.probe_path, self.gallery_path,
- self.split_mat_path
- ]
- self.check_before_run(required_files)
-
- self.prepare_split()
- splits = read_json(self.split_path)
- if split_id >= len(splits):
- raise ValueError(
- 'split_id exceeds range, received {}, '
- 'but expected between 0 and {}'.format(
- split_id,
- len(splits) - 1
- )
- )
- split = splits[split_id]
-
- train = split['train']
- query = split['query']
- gallery = split['gallery']
-
- train = [tuple(item) for item in train]
- query = [tuple(item) for item in query]
- gallery = [tuple(item) for item in gallery]
-
- super(GRID, self).__init__(train, query, gallery, **kwargs)
-
- def prepare_split(self):
- if not osp.exists(self.split_path):
- print('Creating 10 random splits')
- split_mat = loadmat(self.split_mat_path)
- trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
- probe_img_paths = sorted(
- glob.glob(osp.join(self.probe_path, '*.jpeg'))
- )
- gallery_img_paths = sorted(
- glob.glob(osp.join(self.gallery_path, '*.jpeg'))
- )
-
- splits = []
- for split_idx in range(10):
- train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
- assert len(train_idxs) == 125
- idx2label = {
- idx: label
- for label, idx in enumerate(train_idxs)
- }
-
- train, query, gallery = [], [], []
-
- # processing probe folder
- for img_path in probe_img_paths:
- img_name = osp.basename(img_path)
- img_idx = int(img_name.split('_')[0])
- camid = int(
- img_name.split('_')[1]
- ) - 1 # index starts from 0
- if img_idx in train_idxs:
- train.append((img_path, idx2label[img_idx], camid))
- else:
- query.append((img_path, img_idx, camid))
-
- # process gallery folder
- for img_path in gallery_img_paths:
- img_name = osp.basename(img_path)
- img_idx = int(img_name.split('_')[0])
- camid = int(
- img_name.split('_')[1]
- ) - 1 # index starts from 0
- if img_idx in train_idxs:
- train.append((img_path, idx2label[img_idx], camid))
- else:
- gallery.append((img_path, img_idx, camid))
-
- split = {
- 'train': train,
- 'query': query,
- 'gallery': gallery,
- 'num_train_pids': 125,
- 'num_query_pids': 125,
- 'num_gallery_pids': 900
- }
- splits.append(split)
-
- print('Totally {} splits are created'.format(len(splits)))
- write_json(splits, self.split_path)
- print('Split file saved to {}'.format(self.split_path))
diff --git a/spaces/xuetao/bingo3/src/components/ui/voice/index.tsx b/spaces/xuetao/bingo3/src/components/ui/voice/index.tsx
deleted file mode 100644
index 4adcb632226bfced8b97092782811edf08b56569..0000000000000000000000000000000000000000
--- a/spaces/xuetao/bingo3/src/components/ui/voice/index.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import './index.scss'
-
-export interface VoiceProps extends CSSPropertyRule {
- num?: number;
- duration?: number;
-}
-export default function Voice({ duration = 400, num = 7, ...others }) {
- return (
-
- {Array.from({ length: num }).map((_, index) => {
- const randomDuration = Math.random() * 100 + duration
- const initialDelay = Math.random() * 2 * duration
- const initialScale = Math.sin((index + 1) * Math.PI / num)
- return (
-
- )
- })}
-
川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
-
-
-
-
-
-
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
-
Video Tutorial
- ·
-
2.0 Introduction
- ·
-
3.0 Introduction & Tutorial
- ||
-
Online trial
- ·
-
One-Click deployment
-
-
-
-
-
Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-3. Update Gradio
- ```
- pip install gradio --upgrade --force-reinstall
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-一个代理，免验免翻 用爱发电，兴趣使然 自用即可，能力有限 网址常变，来去随缘 本站不稳定，请关注wx：GLASOO以获得反馈和更新 点完登录就开始聊吧 ... """)
- chatbot1 = gr.Chatbot()
- message = gr.Textbox(placeholder="点这个框输入聊天内容,换行用shift enter", label="人类：", max_lines=10)
- state = gr.State()
- submit = gr.Button("输入后点击这里发送")
- submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot1, state])
-
- demo.launch(debug = False, share=False)
\ No newline at end of file
diff --git a/spaces/zzzzred/extras/constants.py b/spaces/zzzzred/extras/constants.py
deleted file mode 100644
index d7a6d0476db5afd6724c91949e9cafd9e0782acf..0000000000000000000000000000000000000000
--- a/spaces/zzzzred/extras/constants.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Constants
-DEFAULT_CUDA_DEVICE = "cuda:0"
-# Also try: 'Qiliang/bart-large-cnn-samsum-ElectrifAi_v10'
-DEFAULT_SUMMARIZATION_MODEL = "Qiliang/bart-large-cnn-samsum-ChatGPT_v3"
-# Also try: 'joeddav/distilbert-base-uncased-go-emotions-student'
-DEFAULT_CLASSIFICATION_MODEL = "nateraw/bert-base-uncased-emotion"
-# Also try: 'Salesforce/blip-image-captioning-base'
-DEFAULT_CAPTIONING_MODEL = "Salesforce/blip-image-captioning-large"
-DEFAULT_SD_MODEL = "ckpt/anything-v4.5-vae-swapped"
-DEFAULT_EMBEDDING_MODEL = "sentence-transformers/all-mpnet-base-v2"
-DEFAULT_REMOTE_SD_HOST = "127.0.0.1"
-DEFAULT_REMOTE_SD_PORT = 7860
-DEFAULT_CHROMA_PORT = 8000
-SILERO_SAMPLES_PATH = "tts_samples"
-SILERO_SAMPLE_TEXT = "The quick brown fox jumps over the lazy dog"
-# ALL_MODULES = ['caption', 'summarize', 'classify', 'keywords', 'prompt', 'sd']
-DEFAULT_SUMMARIZE_PARAMS = {
- "temperature": 1.0,
- "repetition_penalty": 1.0,
- "max_length": 500,
- "min_length": 200,
- "length_penalty": 1.5,
- "bad_words": [
- "\n",
- '"',
- "*",
- "[",
- "]",
- "{",
- "}",
- ":",
- "(",
- ")",
- "<",
- ">",
- "Â",
- "The text ends",
- "The story ends",
- "The text is",
- "The story is",
- ],
-}
-
-PROMPT_PREFIX = "best quality, absurdres, "
-NEGATIVE_PROMPT = """lowres, bad anatomy, error body, error hair, error arm,
-error hands, bad hands, error fingers, bad fingers, missing fingers
-error legs, bad legs, multiple legs, missing legs, error lighting,
-error shadow, error reflection, text, error, extra digit, fewer digits,
-cropped, worst quality, low quality, normal quality, jpeg artifacts,
-signature, watermark, username, blurry"""
>
-
>
-
>
-
>
-
-
>
-
- 
-
-### 4. 番外：语音转换Voice Conversion(PPG based)
-想像柯南拿着变声器然后发出毛利小五郎的声音吗？本项目现基于PPG-VC，引入额外两个模块（PPG extractor + PPG2Mel）, 可以实现变声功能。（文档不全，尤其是训练部分，正在努力补充中）
-#### 4.0 准备环境
-* 确保项目以上环境已经安装ok，运行`pip install espnet` 来安装剩余的必要包。
-* 下载以下模型 链接：https://pan.baidu.com/s/1bl_x_DHJSAUyN2fma-Q_Wg
-提取码：gh41
- * 24K采样率专用的vocoder（hifigan）到 *vocoder\saved_models\xxx*
- * 预训练的ppg特征encoder(ppg_extractor)到 *ppg_extractor\saved_models\xxx*
- * 预训练的PPG2Mel到 *ppg2mel\saved_models\xxx*
-
-#### 4.1 使用数据集自己训练PPG2Mel模型 (可选)
-
-* 下载aidatatang_200zh数据集并解压：确保您可以访问 *train* 文件夹中的所有音频文件（如.wav）
-* 进行音频和梅尔频谱图预处理：
-`python pre4ppg.py 
-
-## 引用及论文
-> 该库一开始从仅支持英语的[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) 分叉出来的，鸣谢作者。
-
-| URL | Designation | 标题 | 实现源码 |
-| --- | ----------- | ----- | --------------------- |
-| [1803.09017](https://arxiv.org/abs/1803.09017) | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | 本代码库 |
-| [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | 本代码库 |
-| [2106.02297](https://arxiv.org/abs/2106.02297) | Fre-GAN (vocoder)| Fre-GAN: Adversarial Frequency-consistent Audio Synthesis | 本代码库 |
-|[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | SV2TTS | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | 本代码库 |
-|[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |
-|[1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN)
-|[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | 本代码库 |
-
-## 常見問題(FQ&A)
-#### 1.數據集哪裡下載?
-| 数据集 | OpenSLR地址 | 其他源 (Google Drive, Baidu网盘等) |
-| --- | ----------- | ---------------|
-| aidatatang_200zh | [OpenSLR](http://www.openslr.org/62/) | [Google Drive](https://drive.google.com/file/d/110A11KZoVe7vy6kXlLb6zVPLb_J91I_t/view?usp=sharing) |
-| magicdata | [OpenSLR](http://www.openslr.org/68/) | [Google Drive (Dev set)](https://drive.google.com/file/d/1g5bWRUSNH68ycC6eNvtwh07nX3QhOOlo/view?usp=sharing) |
-| aishell3 | [OpenSLR](https://www.openslr.org/93/) | [Google Drive](https://drive.google.com/file/d/1shYp_o4Z0X0cZSKQDtFirct2luFUwKzZ/view?usp=sharing) |
-| data_aishell | [OpenSLR](https://www.openslr.org/33/) | |
-> 解壓 aidatatang_200zh 後，還需將 `aidatatang_200zh\corpus\train`下的檔案全選解壓縮
-
-#### 2.`
- 
- 
- 
- 
- 
- 
- 
- 
- 
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/ArrangeView/ArrangeViewCanvas/Lines.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/ArrangeView/ArrangeViewCanvas/Lines.tsx
deleted file mode 100644
index b2c05ac07d95f12fbc2b3cc416a44e90cce610f7..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/ArrangeView/ArrangeViewCanvas/Lines.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-import { Rectangles } from "@ryohey/webgl-react"
-import Color from "color"
-import { observer } from "mobx-react-lite"
-import { FC, useMemo } from "react"
-import { IRect } from "../../../../common/geometry"
-import { colorToVec4 } from "../../../gl/color"
-import { useStores } from "../../../hooks/useStores"
-import { useTheme } from "../../../hooks/useTheme"
-
-export const Lines: FC<{ width: number; zIndex: number }> = observer(
- ({ width, zIndex }) => {
- const {
- song: { tracks },
- arrangeViewStore: { trackHeight },
- } = useStores()
- const theme = useTheme()
-
- const hline = (y: number): IRect => ({
- x: 0,
- y,
- width,
- height: 1,
- })
-
- const rects = useMemo(
- () => tracks.map((_, i) => trackHeight * (i + 1) - 1).map(hline),
- [tracks, width, trackHeight],
- )
-
- const color = colorToVec4(Color(theme.dividerColor))
-
- return